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Estimates of Future Sea-Level

Changes for Norway

March 26, 2012

Matthew Simpson, Kristian Breili, Halfdan Pascal Kierulf,

Dagny Lysaker, Mohammed Ouassou and Even Haug

Please reference this report as Simpson, M., Breili, K., Kierulf, H. P., Lysaker, D., Ouassou, M. and Haug, E. (2012). Estimates of Future Sea-Level Changes for Norway. Technical Report of the Norwegian Mapping Authority.

2 Estimates of Future Sea-Level Changes for Norway

Abstract

In this work we establish a framework for estimating future regional sea-level changes for

Norway. We consider how different physical processes drive non-uniform sea-level changes

by accounting for spatial variations in (1) ocean density and circulation (2) ice and ocean

mass changes and associated gravitational effects on sea level and (3) vertical land motion

arising from past surface loading change and associated gravitational effects on sea level.

An important component of past and present sea-level change in Norway is glacial isostatic

adjustment. Central to our study, therefore, is a reassessment of vertical land motion using a

far larger set of new observations from a permanent GPS network. We find that uplift rates

along the Norwegian coast vary between 1 and 5 mm/yr. We also examine extreme sea

levels and trends in sea-level changes using tide gauge records. If we assume that observed

rates for the last 30 years continue unchanged, sea-level changes in Norway will vary

between -6.5 and 6 cm for the period 2000 to 2030.

Our 21st century sea level estimates are split into two parts. Firstly, following Slangen et al.

[2011] we show regional projections largely based on findings from the 4th IPCC assessment

and dependent on the emission scenarios A2, A1B and B1. These indicate that 21st century

regional sea-level changes in Norway will vary between -20 to 30 cm. Secondly, we explore

the high-end scenario presented by Katsman et al. [2011], in which a global atmospheric

temperature rise of up to 6 °C and emerging collapse for some areas of the Antarctic ice

sheets is assumed. Using this approach we tentatively estimate the upper bound of 21st

century relative sea-level changes in Norway as between 70 and 130 cm. We attach no

likelihood to any of our projections owing to the lack of understanding of some of the

processes that cause sea-level change.


Contents

Abstract .............................................................................................................................................................. 2

Contents ............................................................................................................................................................. 3

1. Introduction .......................................................................................................................................... 6

1.1. Determining Sea-Level Changes ................................................................................................... 7

2. Previous Work .................................................................................................................................... 9

2.1. The IPCC’s Fourth Assessment Report (AR4) ......................................................................... 9

2.1.1. Observed Sea-Level Changes ................................................................................................. 9

2.1.2. Projected Sea-level Changes .................................................................................................. 9

2.2. Progress Following the IPCC’s 4th Assessment ................................................................... 10

2.3. National Efforts to Estimate Future Sea-Level Changes ................................................... 12

2.3.1. The United Kingdom’s Climate Projections Report ................................................... 12

2.3.2. The Delta Commission .......................................................................................................... 12

2.4. Past Studies for Norway ............................................................................................................... 13

3. Present-Day Vertical Land Motion in Norway ...................................................... 15

3.1. Observed Vertical Land Motion in Norway ........................................................................... 15

3.1.1. The GPS network ..................................................................................................................... 16

3.1.2. GPS Analysis-Strategies and Determining Vertical Velocities ............................... 17

3.1.3. Time-Series Analysis .............................................................................................................. 17

3.2. Defining a Vertical Velocity Field for Norway ...................................................................... 18

3.2.1. Statistical Interpolation ........................................................................................................ 19

3.2.2. Glacial Isostatic Adjustment Modeling ........................................................................... 20

3.3. Discussion .......................................................................................................................................... 24

3.3.1. Comparison of GIA Modeling and Previous Work ...................................................... 25

3.3.2. Reference Frame Issues ........................................................................................................ 27

3.3.3. Uncertainties in the Crustal Velocity Field Solution .................................................. 28

3.4. Conclusions ........................................................................................................................................ 29


4. Observed Sea-level Changes for Norway ................................................................... 30

4.1. Tide Gauge Records ........................................................................................................................ 30

4.1.1. Global Sea-Level Changes From Tide Gauge Records ............................................... 30

4.1.2. The Norwegian Tide Gauge Records ............................................................................... 31

4.2. Satellite Altimetry ........................................................................................................................... 37

4.2.1. Altimetry Measurements for Norway ............................................................................. 38

4.3. Discussion .......................................................................................................................................... 39

4.4. Conclusions ........................................................................................................................................ 40

5. Projected 21st Century Sea-level Changes for Norway .................................. 41

5.1. Data and Model Descriptions ...................................................................................................... 42

5.1.1. Model of Vertical Land Motion and Associated Gravity Changes ......................... 42

5.1.2. The Climate Models (AOGCMs) .......................................................................................... 43

5.1.3. Future Ocean Density and Circulation Changes .......................................................... 43

5.1.4. Future Ocean Mass Changes ............................................................................................... 45

5.1.4. Future Non-Uniform Sea-Level Changes due to Land Ice Changes...................... 46

5.2. Analysis ............................................................................................................................................... 46

5.2.1. Global Mean Sea-Level Changes ........................................................................................ 46

5.2.2. Regional Sea-Level Changes ................................................................................................ 48

5.3. Regional 21st Century Sea-Level Projections for Norway ................................................ 55

5.4. Discussion .......................................................................................................................................... 58

5.5. A High-End Scenario of Sea-level Change for Norway ...................................................... 59

5.5.1. High-End Global Mean Sea-Level Changes .................................................................... 60

5.5.2. High-End Regional Sea-Level Changes ........................................................................... 63

5.5.3. High-End Regional 21st Century Sea-Level Projections for Norway ................... 66

5.6. Conclusions ........................................................................................................................................ 68

6. Extreme Sea Levels ....................................................................................................................... 69

6.1. Observed Extreme Sea Levels..................................................................................................... 69

6.1.1. Statistical Methods ................................................................................................................. 69

6.1.2. Estimated Return Levels ...................................................................................................... 70

6.1.3. Changes in Observed Extreme Sea Levels ..................................................................... 71

6.2. Future Changes in Extreme Sea Levels ................................................................................... 71

6.3. Conclusions ........................................................................................................................................ 72


7. Summary ............................................................................................................................................... 73

7.1. Comparisons to the DSB-Report ................................................................................................ 75

Acknowledgements ................................................................................................................................. 77

8. Appendix I: Projected 21st Century Sea Levels ..................................................... 78

8.1. Projected 21st Century GIA Effects and Vertical Land Motion ....................................... 79

8.2. Projected 21st Century Sea Surface Changes ........................................................................ 90

8.3. Projected High-End 21st Century Sea Surface Changes .................................................... 91

9. Appendix II: Notes on the Reference Levels ........................................................... 92

9.1. The National Vertical Reference Systems of Norway ........................................................ 92

9.2. The Connection Between Mean Sea Level and the Vertical Reference System ....... 93

9.3. Which Reference Level Should be Used? ................................................................................ 95

10. References ........................................................................................................................................ 96


1. Introduction

There are potentially serious socio-economic consequences of continued global sea-level rise

through the 21st Century [Stern, 2007]. Higher sea-levels can have a variety of impacts, for

example: the inundation of coastal areas, increased risk of flooding, erosion of the coastline

and the salinization of ground waters. There is, however, large uncertainty associated with

projections of future sea-levels. This uncertainty is, in part, due to our lack of understanding

of some of the processes that drive sea-level changes. In particularly, the potential ice-

dynamic contributions of the large ice sheets [e.g. Alley et al., 2005].

In a recent review by Pfeffer [2011], it was underscored that for planners and coastal

engineers, the people that make decisions based on projections from scientists, information

on future sea levels is best presented in a probabilistic form. That is, for a specific future

date, an assessment is made of the probability of a certain sea level occurring. Given our

current limited understanding of the causes of sea-level change, however, the scientific

community has not yet been able to deliver such a product to decision makers. Although,

efforts are now underway to improve projections of the contributions of ice mass changes to

future sea-level rise (e.g. www.ice2sea.eu). It is also important to note that there has been

significant progress in other areas of sea level research [e.g. Cazenave et al., 2009].

Following the publication of the Fourth Assessment Report from the Intergovernmental

Panel for Climate Change (hereafter IPCC AR4), there has been increased interest in regional

and/or local projections of sea-level change [e.g. Katsman et al., 2008, 2011; Slangen et al.,

2011]. Observations show that past sea-level changes have been spatially variable (or non-

uniform), so we expect that future changes will also be of this nature [Milne et al., 2009].

Thus, identifying the processes causing sea-level changes at regional scales [e.g. Landerer et

al., 2007] and improving our future projections of the spatial variability of sea-level change

[e.g. Gomez et al., 2010] have become an important focus for scientific researchers.

http://www.ice2sea.eu/


The move towards regional projections has also led to several countries commissioning

national reports into future sea-level changes. For example, the Delta Commissie [2008]

report for the Netherlands and the United Kingdom’s climate projections [see Lowe et al.,

2009]. In this vein, we present here a new assessment of future sea-level change for Norway.

The main aim of this report is, therefore, to demonstrate that a methodology used to

determine local or regional sea-level projections can successfully be applied to the

Norwegian coast. The secondary aims of the work, which assist in the evaluation of our

future projections, are: (1) to compare observed and modeled land motion in Norway, (2)

analyze present and recent past observed patterns of local sea-level changes and (3)

examine data on regional extreme sea levels.

The report is structured as follows: Chapter 1, this chapter, outlines the motivation of the

work and defines how we determine sea-level changes. The previous work is described in

Chapter 2, this includes the main findings of the IPCC AR4 and progress since then, it also

discusses the past sea level studies for Norway and the approach taken in other national

reports. Chapter 3 examines vertical land motion in Norway, an important component of

present sea-level change in Scandinavia, which we constrain using new GPS measurements.

In Chapter 4 we analyze observed sea-level changes in Norway from tide gauge records and

satellite altimetry data. Our projections of 21st century sea-level change for Norway are

given in Chapter 5. These are largely based on the results of the IPCC AR4 which, closely

following the methodology of Slangen et al. [2011], are used to determine future regional

sea-level changes. Chapter 6 analyses extreme sea-levels for the Norwegian coast and

Chapter 7 summarizes our results.

1.1. Determining Sea-Level Changes

Determining sea-level changes can be thought about in two separate ways: relative sea-level

change which is measured with respect to the ocean bottom. Whereas, absolute sea-level

change is measured with respect to the Earth’s centre of mass. As the focus of this report is

regional sea-level changes for the Norwegian coast, we concentrate on data and projections

that show relative sea-level changes.


Fig. 1.1. The processes that cause sea-level change. Taken from Milne et al. [2009].

There are a number of different processes which drive sea-level changes (see Fig. 1.1). Over

the 20th century, changes in (1) ocean mass; the exchange of mass between the oceans, land

based ice and terrestrial water storage (2) ocean density; changes to the temperature and

salinity of sea water (also called steric changes), dominated mean global sea-level changes

[see Lemke et al., 2007]. This will also be the case over the 21st century.

The reader should note that all the physical processes shown in Figure 1.1 produce a non-

uniform sea-level change. Thus, on a regional scale, it is important to take account of the

spatial variability of these separate contributions. Effects other than ocean mass and ocean

density changes can also play an important role in determining local sea-level changes.

Indeed, for the case of Norway, it is clear that the process of Glacial Isostatic Adjustment

(GIA) produces a significant and ongoing regional pattern of Earth response [e.g. Milne et al.,

2001]. The influence of GIA on relative sea-level changes is, therefore, carefully considered

in this analysis.


2. Previous Work

2.1. The IPCC’s Fourth Assessment Report (AR4)

The task of the IPCC’s Working Group 1 is to critically review and assess the most recent

scientific work pertaining to climate change. The issue of sea-level change is addressed in a

number of chapters of the AR4, however, we only examine the findings from Chapters 5 and

10. Chapter 5 covers observed sea-level changes [Bindoff et al., 2007] while Chapter 10

presents projections of future sea-level changes [Meehl et al., 2007]. In the below we briefly

summarize the relevant findings of the IPCC AR4, beginning with observed sea-level changes.

2.1.1. Observed Sea-Level Changes

Observations from the global tide gauge network and satellite altimetry provide information

on 20th century sea-level changes. Using tide gauge records, the IPCC assesses the rate of

global mean sea-level rise as 1.8 ± 0.5 mm/yr for 1961 to 2003 and 1.7 ± 0.5 mm/yr for the

20th century [e.g. Holgate and Woodworth, 2004; Leuliette et al., 2004; Church and White,

2006]. For the period 1993 to 2003, satellite altimetry observations indicate the current rate

of sea-level rise as 3.1 ± 0.7 mm/yr [see Cazenave and Nerem, 2004]. It is not yet apparent if

this increased rate of rise is short-term variation or a change in the long-term trend.

2.1.2. Projected Sea-level Changes

The AR4 gives global average sea-level projections for the 21st century which include

contributions from thermal expansion and land based ice (salinity changes are only

important on regional scales). The results are based on projections from 17 different

Atmosphere Ocean General Circulation Models (AOGCMs) using 6 different future

greenhouse gas emission scenarios (the methods of the IPCC are explained in more detail in

Chapter 5). For the period 2090 to 2099 relative to 1980 to 1999, future global sea-level

change is projected to range from 18 to 59 cm [Meehl et al., 2007]. A significant issue with

these results, however, was that no likelihood was attached to the AR4 projections. As

briefly discussed in Chapter 1, it is currently not possible to assess the probability of future

sea-level changes owing to our (the scientific community’s) lack of understanding of some of


the processes that drive sea-level change. For this reason, the AR4 does not give a best

estimate or upper bound for future sea-level changes.

Fig. 2.1. Local sea-level changes for the period 2090 to 2099 relative 1980 to 1999. Sea-level changes are shown as deviations from the global mean. Projections are forced using the A1B emissions scenario and generated from 16 AOGCMs. The shaded areas indicate a value of 1 or greater for the multi-model mean divided by the multi-model standard deviation. Taken from Meehl et al. [2007].

The IPCC also briefly addresses geographical patterns of future sea-level change. As shown in

the AR4, projected local sea-level changes driven by ocean density and circulation changes

are given in Figure 2.1. The results generated from the AOGCMs generally show poor

agreement; there are few areas where the mean of the model projections exceeds the

standard deviation.

2.2. Progress Following the IPCC’s 4th Assessment

Following the publication of the AR4, significant progress has been made in sea level

research. In a review by Milne et al. [2009], the authors highlight two main advances: (1) the

correction of biases discovered in the ocean temperature measurements [Willis et al., 2007]

and (2) our ability to combine different observation systems (namely GRACE, altimetry and

Argo) to better constrain the contributions to sea-level change.


As mentioned, there is large uncertainty associated with current projections of sea-level

change and, in particular, the potential ice-dynamic contributions of the large ice sheets [e.g.

Alley et al., 2005] (the limitations of the current generation of ice sheet models is briefly

discussed in Chapter 5). Work since the AR4 has suggested that larger future contributions

from Greenland and Antarctica are plausible [e.g. Pfeffer et al., 2008]. New observations,

using two independent methods, indicate an increased (and accelerating) contribution from

the ice sheets [Rignot et al., 2011]. If these observed trends continue then the IPCC AR4 sea-

level projections for the 21st century will be exceeded. It is not clear, however, if these

changes are the beginning of a sustained response to recent warming.

Other studies have examined the relationship between observed sea-level change and global

averaged temperature changes or changes in radiative forcing, they are generally known as

semi-empirical models [e.g. Rahmstorf, 2007; Vermeer and Rahmstorf, 2009]. Semi-empirical

models give higher sea-level projections when compared to more complex physical models

(e.g. as used in the IPCC AR4) and have suggested that global sea-level rise by the end of the

21st century could approach 2 m. They offer an interesting alternative to the way in which

we project or assess the risk of future sea-level changes, but their results should be used

with caution. Most importantly, it remains uncertain whether a simple historical relationship

between sea-level change and, for example, global temperature change will hold in the

future. This is a major limitation of semi-empirical models and should be kept in mind when

interpreting their results. The reader should also be aware that the use of such models for

future projections has faced other criticisms [Holgate et al., 2007; Schmith et al., 2007;

Rahmstorf, 2008; von Storch et al., 2008]; a summary of the main outstanding issues is

detailed by Church et al. [2011]. Given these concerns, therefore, we decide not to adopt

projections from semi-empirical models in this report. Moreover, the focus of this work is on

regional sea-level projections and current semi-empirical models only give global sea-level

rise estimates.


2.3. National Efforts to Estimate Future Sea-Level Changes

Several countries have begun working towards regional projections of sea-level change.

Owing to their vulnerability to sea-level rise, significant efforts to better constrain future

sea-level changes have been made in both the Netherlands and U.K.

2.3.1. The United Kingdom’s Climate Projections Report

The United Kingdom’s climate projection report provides information on many aspects of

future climate (see http://ukclimateprojections.defra.gov.uk/). Chapter 3 deals with changes

to mean sea level [Lowe et al., 2009].

Relative sea-level changes for the 21st century (2090–2099 relative to 1980–1999) are

reported as 0.21 to 0.68 m for London and 0.07 to 0.54 m for Edinburgh (5th to 95th

percentile for a medium emissions scenario). For a fossil fuel intensive scenario, the 95th

percentile for relative sea-level change over the same period in London is 0.83 m and

Edinburgh 0.7 m. The report also discusses a high-end scenario and, based on geological

observations [Rohling et al., 2008], indicates a range of 0.93 to 1.9 m for the 21st century.

The authors consider the occurrence of high-end changes to be very unlikely, however, no

formal assessment of their probability can be made.

2.3.2. The Delta Commission

The 2nd Delta commission was organized to come up with recommendations on how the

Dutch coast can be protected against the consequences of climate change. In 2008 the

commission published the Delta-report “Working together with water” *Delta Commissie,

2008] (see http://www.deltacommissie.com/en/advies), in which projections of future sea

level and increased river discharge are presented.

The sea level projections given in the Delta-report are based on the work of Vellinga et al.

[2008] (see also Katsman et al. [2011]). Regional 21st century sea-level changes were

computed for a high-end scenario, corresponding to a global temperature increase of 2 to 6

°C, and a warm scenario as detailed in KNMI [2006]. For these two scenarios, the resulting

regional sea-level changes for the Dutch coast are 0.55 to 1.20 m and 0.40 to 0.80 m,

respectively. In addition to the 21st century projection, the Delta-report indicates a possible

http://ukclimateprojections.defra.gov.uk/

http://www.deltacommissie.com/en/advies


sea level rise of 2 to 4 m along the Dutch coast within 2200. It should be noted that the final

sea level projections given in the Delta-report do not include the effect of vertical land

motion or gravitational effects on sea level due to land ice mass changes [e.g. Mitrovica et

al., 2001].

2.4. Past Studies for Norway

Official projections for the 21st century sea level change along the Norwegian coast are

found in the report “Havnivåstigning” *Vasskog et al., 2009] based on a study by Drange et

al. [2007]. The report was prepared mainly by the Bjerknes Centre for Climate Research and

the work was organized by the Directorate for Civil Protection and Emergency Planning

(DSB). Hereafter, the report will be denoted the DSB-report. In the summary, Chapter 7, we

compare the findings of the DSB-report to our estimates of future sea-level changes for

Norway.

The projections found in the DSB-report are the ones presently recommended for land use

planning in Norway. The report presents storm-surge heights and sea-level changes for 2050

and 2100 with respect to the level in 2000. Generally speaking, sea level is expected to rise

by 70 cm along the southern and western part of the Norwegian coast, by 60 cm in the north

of Norway, and by 40 cm in the inner part of Trondheims- and Oslofjorden over the 21st

century. Spatial variation in sea-level change is only due to variations in vertical land motion;

ranging from 1 to 5 mm/yr along the Norwegian coast.

The authors of the DSB-report recognize that IPCC AR4 does not provide a likelihood nor an

upper bound for future sea-level change. They conclude, therefore, that IPCC AR4 should not

be used for future land use planning in coastal areas. Instead, a projection based on the

semi-empirical approach of Rahmstorf [2007] was adopted. Using this study, the DSB-report

indicates the most likely sea-level change as 31 (-8 to +14) cm for 2050 and 80 (-20 to +35)

cm for 2100. For 2100, a regional correction of 10 cm, taken from Fig. 10.32 IPCC AR4, was

added to the semi-empirical estimate. This figure indicates sea-level changes due to ocean

density and circulation changes larger than the global average along the Norwegian coast.

After applying this correction, the total absolute sea level change is 90 cm for 2100. Finally,


relative sea-level changes were computed for 279 coastal municipals by subtracting the

effect of vertical land motion. The land uplift rates were computed from the rates presented

in Vestøl [2006]. These rates are relative rates, i.e. uplift with respect to local ocean surface

change. Apparent uplift rates in the DSB-report are transformed into absolute uplift rates by

adding 1.4 mm/yr (see also Section 3.3.1).

The report also discusses extreme sea levels for the Norwegian coast due to storm-surges.

Extreme sea level in the DSB-report is defined as that with a return period of 100 years or

over. This level was computed from time series of tide gauges along the Norwegian coast.

Based on a study by Lowe and Gregory [2005], storm-surge heights are expected to increase

by 5 ± 2.5 cm for 2050 and 10 ± 5 cm for 2100. Hence, these corrections were added to the

computed storm-surge heights. Storm-surge heights were then added to the highest

observed astronomical tides and referred to the national geodetic height datum of Norway

(NN1954, see Appendix II for additional details about the vertical reference systems of

Norway).


3. Present-Day Vertical Land Motion in Norway

Observations of Glacial Isostatic Adjustment (GIA) in Fennoscandia1 have traditionally been

used to infer details of Earth’s viscosity structure and the region’s ice history [e.g. Fjeldskaar,

1994; Lambeck et al., 1998a; Milne et al., 2001]. They also inform us on vertical land motion,

which is an important component of present-day relative sea-level change for Norway. The

development of GPS, in particular, has enabled us to image crustal deformation to a high

degree of precision. These observations show that present-day vertical Earth deformation

across Fennoscandia is dominated by the ongoing relaxation of the Earth in response to past

ice mass loss [e.g. Milne et al., 2001].

In this Chapter we investigate present-day vertical land motion in Norway using new GPS

observations [Kierulf et al., in prep.]. We focus on the vertical component of motion as it is

this, rather than horizontal movements, that is important for estimating present and future

sea-level changes. Using the new GPS observations we use two different techniques to

determine a crustal velocity field for Norway by (1) a method of interpolation called kriging

and (2) a forward model of GIA. In our analysis, we compare these two methods and

examine how our results differ to the land uplift model of Vestøl [2006], which has been

applied in the previous DSB-report of sea-level changes in Norway.

3.1. Observed Vertical Land Motion in Norway

In a landmark project named BIFROST (Baseline Inferences for Fennoscandian Rebound

Observations Sea Level and Tectonics), a network of GPS observations from across

Fennoscandia was used to investigate regional present-day crustal motion [Milne et al.,

2001; Johansson et al., 2002]. These studies included measurements from the GPS networks

in Sweden and Finland but data from only 1 GPS site in Norway. Results from the BIFROST

analyses show a pattern of present-day Earth response with highest rates of uplift

corresponding to areas of thickest ice during the last glacial period ( 21,000 years ago).

Since the early 2000s, members of the BIFROST project have continued to update their

1 Fennoscandia is defined here as the geographic regions of Norway, Finland and Sweden.


results and incorporate new GPS observations into their analyses [Lidberg et al., 2007, 2010].

In addition to these efforts, a model of land uplift based on a collocation method was

proposed by Vestøl [2006]. He used observations from leveling, tide gauges and GPS data

from Fennoscandia and the nearby areas of continental Europe (the GPS velocities used in

his analysis are the same as in Lidberg et al. [2007]). The model of Vestøl [2006], as

mentioned above, is the land motion component that has been used in earlier analyses of

sea-level change for Norway.

3.1.1. The GPS network

Today 140 permanent GPS stations exist in Norway, although, only around half have been

operating for sufficiently long that reliable velocity estimates can be determined from the

data (see Section 3.1.2). The land motion model of Vestøl [2006] includes velocity estimates

from 6 of the Norwegian GPS sites. In the following section, we outline the analysis of Kierulf

et al. [in prep.] in which all the existing Norwegian GPS stations are examined and, from

which, they determine a new vertical crustal velocity field for Norway.

Fig. 3.1. Locations of the 139 permanent GPS stations on the Norwegian mainland. Symbols correspond to length of time-series relative to the start of 2011. Stars indicate more than 10 years of data, diamonds more than 5 years and circles more than 3 years. Dots mark stations with less than 3 years of data; these observations are not included in this report.


3.1.2. GPS Analysis-Strategies and Determining Vertical Velocities

Three different approaches (software packages) are used in the analysis of the GPS data,

these are: (1) the GPS Analysis Software of MIT (GAMIT) [King and Bock, 2003] (2) the GPS

Inferred Positioning System/Orbit Analysis and Simulation Software (GIPSY/OASIS-II)

[Zumberge et al., 1997] and (3) Bernese [Hugentobler et al., 2001]. All solutions are given in

the ITRF2008 reference frame. Vertical velocities are determined using a least squares

analysis and maximum likelihood estimation [e.g. Williams et al., 2004].

3.1.3. Time-Series Analysis

In a number of tests, Kierulf et al. [in prep.] assess the stability, uncertainty and consistency

of the velocity estimates determined using the GAMIT, GIPSY and Bernese software. For the

stability test, velocities estimated from shorter time-series are compared to the velocity

determined from a 10 year observation period. This provides a guide as to what length time-

series is required to obtain results similar to the 10 year estimate (i.e. it tells us how quickly

velocities converge on our 10 year velocity result). The RMS of the differences between the

velocities determined from the shorter time-series and the 10 year period is given as:

Eq. 3.1.

Where is the station specific rate for a chosen time-series length and is the station

specific rate for the 10 year period. The total number of GPS stations is given by . For the

Norwegian GPS stations Kierulf et al. [in prep.] find that the GAMIT solution is generally

slightly more stable than results from the other analysis strategies. In Figure 3.2 we show

calculated using the vertical velocities from the GAMIT, GIPSY and Bernese

software. For the GAMIT solution, velocities based on 3 years of data agree to within 0.5

mm/yr of the 10 year estimate. Velocities based on 5 years of data agree to within 0.2

mm/yr.


Fig. 3.2. The RMS of the differences in calculated velocities for a time-series of a given length and the 10 year estimate (see Eq. 3.1). Lines show solutions from GAMIT (green), GIPSY (blue) and Bernese (yellow). The length of the time-series is plotted relative to 2001.

The aim of this work is to determine a crustal velocity field for Norway. We want to include

as much of the available GPS data as possible but, clearly, our solution also has to be stable

and reliable. In this case, we opt to use velocities calculated from the GAMIT software and

using data from GPS stations that have been operating for 3 years or more (and are

therefore within 0.5 mm/yr of our 10 year long-term estimates). Of the 139 stations

considered, 66 have been operating for 3 years or longer. This means we lack GPS

observations for the middle of Norway (the area north of Trondheim and south of Bodø) and

the central mountainous areas of the south (see Fig. 3.1).

3.2. Defining a Vertical Velocity Field for Norway

To establish a continuous crustal velocity field in areas where we have (1) no GPS receivers

or (2) the observation period is too short to obtain reliable results, either interpolation or

modeling is required. In the first part of this Section (3.2.1) we show results from a statistical

interpolation method called kriging. Section 3.2.2 presents results from a GIA forward model

constrained by the GPS data. The observed vertical velocities used here are based on the

GAMIT solution using GPS time-series of 3 years or longer.


3.2.1. Statistical Interpolation

We make use of a spatial interpolation theory called kriging [Cressie, 1993]. The methods

used are described in Kierulf et al. [in prep.].

In Figure 3.3 we show predicted values for the 277 coastal municipalities and locations of the

66 GPS stations used in the kriging solution. Vertical velocities for the coastal municipalities

are predicted to vary between 2 and 5 mm/yr. In their analysis, Kierulf et al. [in prep.] find

that the kriging performs very well for locations inside of the GPS network (i.e. points with

GPS observations available in several different directions like southern Norway). Outside of

the GPS network, however, then the solution is less reliable. Thus, for parts of the far north

and the area north of Trondheim and south of Bodø, we have little confidence in the

predicted values.

Fig. 3.3. Predicted vertical velocities (mm/yr) for the 277 coastal municipalities using the kriging statistical interpolation method. Black triangles mark the 66 GPS observations used in our solution.


3.2.2. Glacial Isostatic Adjustment Modeling

3.2.2.1. Description of the GIA Model

The GIA model employed is composed of three components: a model of grounded past ice

evolution (for Fennoscandia and other ice covered areas), a sea level model to compute the

redistribution of ocean mass for a given ice and Earth model, and an Earth model to

compute the solid Earth deformation associated with the ice‐ocean loading history. The GIA

model used here, and the method used to calculate present-day land motion, is the same as

applied by Milne et al. [2001] except that the sea level component of the model was

improved as discussed in Mitrovica and Milne [2003] and Kendall et al. [2005].

Note that the global ice model used in the analysis of Milne et al. [2001] is made up of two

parts: The Fennoscandian and Barents Sea ice sheets are represented by the model of

Lambeck et al. [1998a], which has been shown to provide good fit to paleo sea level data

from the region. For other areas of the globe, they use the ICE-3G ice sheet reconstruction of

Tushingham and Peltier [1991].

3.2.2.2. Earth Model Sensitivity Test and Determining a Best-Fit Model

Past GIA modeling studies have used both paleo sea level data [e.g. Lambeck et al., 1998a]

and/or GPS observations [e.g. Milne et al., 2001; 2004] to help constrain Earth model

parameters. These investigations have shown that it is not yet possible to uniquely constrain

Earth’s viscosity structure for the Fennoscandian region. Such studies, however, are able to

provide us with a range of Earth parameter values that satisfy the various GIA observables.

Given our limited knowledge of Earth’s viscosity structure, we generate predictions of

present-day vertical land motion using a suite of 297 Earth viscosity models. The range of

values we explore is similar to those in Milne et al. [2001; 2004], namely; lithospheric

thickness is varied from 71 to 120 km, upper mantle viscosity from 0.05 x 1021 to 5 x 1021 Pas

and lower mantle viscosity from 1021 to 50 x 1021 Pas.

To determine an optimal Earth model (i.e. the model which gives best-fit to the GPS data) we

conduct a simple statistical test. We compute vertical velocities at all 66 GPS stations


considered for each of the 297 Earth models introduced above and quantify the goodness of

fit for each Earth model using the criterion:

Eq. 3.2.

The value indicates the difference between the predicted ( ) and observed vertical

velocity ( ) for a specified observational error ( ) and given GPS station ( ). A value of 1

or less indicates fit to the data.

Fig. 3.4. The results for 297 different Earth viscosity models (see text for details). Each frame is based on a fixed value for lithospheric thickness. The 95% confidence level is marked by the white dashed line.

Figure 3.4 shows how goodness of fit to the GPS observations varies with Earth model

parameters. Encouragingly, we find similar results to Milne et al. [2001; 2004], namely that

the vertical velocities favor an Earth model with a relatively stiff upper mantle. Differences

between values for the various lithospheric thicknesses are small. Results from a more

comprehensive investigation, however, suggest a preference for a lithosphere of 100 km or

thicker [Milne et al., 2004]. For the models with a 120 km lithospheric thickness, an upper

mantle viscosity of 3 x 1021 Pas and lower mantle viscosity of 5 x 1021 Pas gives best-fit to the

GPS data. In the remainder of this analysis, we only use predictions from this model

(hereafter referred to as our best-fit GIA model).


As discussed above, the vertical component of motion is most important when considering

sea-level changes. The intent of the GIA modeling work performed here is, therefore, to find

a land motion model that best fits the observed vertical velocities, rather than as an

investigation of Earth viscosity structure. We note that other studies have inferred Earth

viscosity values differing to ours and indicate significant lateral variations of Earth structure

across Fennoscandia [see Steffen and Wu, 2011].

3.2.2.3. Modeled Vertical Velocity Field and Residuals

Predicted vertical velocities generated using our best-fit GIA model (Fig. 3.5) show a pattern

of land motion similar to previous work [Milne et al., 2001]. All of mainland Norway is

predicted to be uplifting, rates along the Norwegian coast vary between 1 and 5 mm/yr.

Fig. 3.5. Predicted vertical velocities (mm/yr) for Fennoscandia using our best-fit GIA model. White triangles mark the 66 GPS observations used to constrain our model. Residuals between the best-fit GIA model and GPS data show that the model tends to

slightly over predict rates of uplift in the middle of Norway, around 64° N, and under predict

towards the south (Fig. 3.6). However, no clear pattern of misfit is apparent. At 39 of the 66

GPS stations examined, differences between the modeled and observed vertical velocities

are less than the uncertainty on the observed value. In other words, at these positions is

less than 1 and the model provides a good fit to the GPS data.


Fig. 3.6. Residuals; observed vertical velocities from the GAMIT solution minus our best-fit GIA model prediction for the 66 GPS stations examined (units are mm/yr). Circles with a horizontal line through have a residual value less than the uncertainty of the observed velocity (i.e. a value of 1 or less).

3.2.2.4. Modeled Sea Surface Changes Associated With GIA

The GIA model can also be used to predict geoid (i.e. ocean surface) changes associated with

ongoing land motion and the movement of mantle material. Previous studies have suggested

that these ocean surface changes are 6 % of the vertical land motion signal in

Fennoscandia [e.g. Ekman and Mäkinen, 1996; Vestøl, 2006]. This means that at the centre

of uplift where vertical velocities are around 10 mm/yr, we would expect a sea-level rise due

to GIA of 0.6 mm/yr. This is a significant effect and, therefore, is included in our future sea-

level projections for Norway. Note that ocean surface changes associated with GIA also need

to be carefully considered when analyzing tide gauge records (see also Section 3.3.1 and

4.1.2.2).


Fig. 3.7. Predicted geoid changes (mm/yr) for Fennoscandia using our best-fit GIA model. White triangles mark the 66 GPS observations used to constrain our model. Predicted geoid rates generated using our best-fit GIA model (Fig. 3.7) show a similar pattern

of change to our predicted vertical velocities. Maximum rates at the centre of uplift are

around 0.6 mm/yr, this is slightly larger than the 0.4 ± 0.1 mm/yr found by Milne et al.

[2001] although there are differences in our model setup. (Using a similar range of Earth

model parameters as we explore here, Milne et al. [2001] show the sensitivity of the geoid

rates to changes in Earth viscosity structure is no larger than ± 0.1 mm/yr). Geoid rates along

the Norwegian coast vary between 0.2 to 0.5 mm/yr.

3.3. Discussion

We have shown two different approaches to predicting vertical crustal velocities for the

coastal municipalities; statistical interpolation (kriging) and GIA modeling. It is of clear

interest to determine which method best describes land motion for coastal Norway and,

therefore, which is preferable when calculating future sea-level changes. In a simple test of

the two methods, we first remove the 10 longest time-series from the original 66 GPS

stations examined. The kriging and modeling analysis is then repeated based on this reduced

dataset of 56 time-series. For the GIA modeling Earth sensitivity analysis, we arrive at a slight


different result; a best-fit model with a 120 km lithosphere, upper mantle viscosity of 5 x

1021 Pas and lower mantle viscosity of 3 x 1021 Pas. Note that this model is within the 95 %

confidence value of our earlier results (see Fig. 3.4). Kriging and GIA model predictions are

then generated for the 10 sites that have been removed from the dataset. This allows us to

test how well the predicted velocity fields fit to observations which have not been used to

constrain our solutions.

Observed kriging (mm/yr)

Observed GIA model (mm/yr)

RMS difference 0.96 0.69

Table 3.1. RMS differences between the 10 longest time-series of observed vertical velocities and those calculated from kriging and GIA modeling (with a best-fit model of 120 km lithosphere, upper mantle viscosity of 5 x 1021 Pas and lower mantle viscosity of 3 x 1021 Pas). See above text for details.

We find that the RMS error value is lower for the GIA model than for the kriging solution

(Table 3.1). For this reason, we opt to use vertical velocities generated from the GIA model in

the remainder of the report. An RMS error of 0.69 gives us reasonable confidence in the

ability of the model to predict vertical velocities in areas where currently we have no

observations (it is not so different to the observed errors which are typically 0.5 mm/yr). In

comparison to the GIA model, kriging is generally more sensitive to outliers and,

unsurprisingly, is not able to give as reliable predictions where there are limited GPS

observations [Kierulf et al., in prep.]. As more GPS observations become available, eventually

covering all of mainland Norway, then the reliability of the kriging solution will improve.

3.3.1. Comparison of GIA Modeling and Previous Work

As discussed above, it is the land motion model of Vestøl [2006] which was used in the

previous DSB-report of future sea-level change for Norway. The model is based on

observations from leveling, tide gauges and GPS data from across Fennoscandia and the

nearby areas of continental Europe. These separate observations measure different things;

tide gauge and leveling data give information on relative sea-level and land height changes,

whereas, the GPS data provide absolute height changes.


Differences between rates calculated from these separate measurement techniques can be

used to investigate sea-level changes. For example, Milne et al. [2001] show the following

relation between tide gauge and GPS observations:

Eq. 3.3.

Where spatially varying relative sea-level changes ( ) can be considered as being made

up of varying vertical crustal velocities ( ), varying geoid (or ocean surface) changes

( ) and a uniform ocean surface change ( ). The non-uniform processes are a function

of ( ) latitude and ( ) longitude.

In his analysis, Vestøl [2006] determines (1) to be a scale factor (5.7 %) of the vertical

crustal velocity term and (2) as a uniform regional ocean surface change of 1.32 mm/yr.

Vertical crustal velocities are presented as an apparent uplift model (values are given

relative to ocean surface height changes). It is important to note, however, that the accurate

determination of absolute land motion cannot be achieved by correcting the apparent uplift

rates presented by Vestøl [2006] using the above numbers (as done in the DSB-report).

Doing this means that regional variations in sea surface height will, to some extent, be

included in your solution (e.g. see Fig. 5 of Milne et al. [2001] and Marcos and Tsimplis

[2007]). We caution against the use of land motion models partly based on relative sea-level

observations for the detailed study of sea-level changes or, indeed, future estimates of sea-

level change.


Fig. 3.8. The land motion model of Vestøl [2006] minus the velocity field predicted from our best-fit GIA model (note that to make this comparison the model of Vestøl [2006] was transformed to absolute values). The left panel shows the spatial pattern across Fennoscandia. The right panel shows the differences for the 277 coastal municipalities, black triangles mark the 66 GPS stations used to constrain our model. Units are in mm/yr.

In comparison with earlier GIA modeling work [Lambeck et al., 1998b], the Vestøl [2006]

model generally indicates smaller rates of uplift over Norway. A comparison to our best-fit

GIA model shows a similar pattern of results (Fig. 3.8). Differences over the rest of

Fennoscandia should be interpreted with care as our model is only constrained by the

Norwegian GPS observations. For the coastal municipalities we find some notable

differences in our results; in some locations GIA model rates of uplift are up to 2 mm/yr

higher than the absolute rates calculated from Vestøl [2006]. This corresponds to a 20 cm

difference in land height by 2100.

3.3.2. Reference Frame Issues

In Table 3.2 we show estimated vertical velocities obtained from four different GPS analyses,

each study has employed different analysis strategies. Some of the differences between the

separate estimates will be due to different reference frame realizations. For example, Kierulf

et al. [2009] reports differences of 1 mm/yr in the vertical component between the


ITRF2000 and ITRF2005 realizations over Fennoscandia. (ITRF2008 shows negligible

differences to ITRF2005).

The Vestøl [2006] uplift model makes use of vertical velocities presented by Lidberg [2007]

which are in ITRF2000. Whereas, our best-fit GIA model solution is constrained by results

from Kierulf et al. [in prep.] which are in ITRF2008. Differences shown in Figure 3.8 will,

therefore, largely reflect differences between ITRF2000 and ITRF2008. Comparisons of the

two different realizations indicate ITRF2008 to be the far more precise solution [Altamimi et

al., 2011]. Thus, we have more confidence in the vertical velocities presented by Kierulf et al.

[in prep.] but note that ITRF2008 will still contain uncertainties.

Oslo Stavanger Trondheim Tromsø Vardø

Johanssen et al. [2002] — — — 4 — Lidberg et al. [2007] ITRF2000 5.8 1.2 3.8 2.3 1.9 Lidberg et al. [2010] ITRF2005 6.5 2.9 6.2 4.6 5.7 Kierulf et al. [in prep.] ITRF2008 5.1 1.5 4.3 2.9 2.7

Table. 3.2. Estimated vertical velocities (mm/yr) obtained in different analyses for 5 of the Norwegian GPS stations.

3.3.3. Uncertainties in the Crustal Velocity Field Solution

We estimate the uncertainty of the best-fit GIA model and kriging vertical crustal velocity

solutions as 0.5 mm/yr (1-sigma). This is the RMS of the differences between velocities from

the model/kriging and all the GPS observations but with some outliers removed [see Kierulf

et al., in prep.]. We note that our vertical velocity solutions are constrained by observations

in the ITRF2008 reference frame, which also has uncertainties [Altamimi et al., 2011; Wu et

al., 2011]. The uncertainties in geocenter motion and scale of the reference frame are

important for the vertical velocity estimates and, consequently, our regional sea-level

projections. Reference frame uncertainties are hard to quantify due to lack of independent

measurements. Recent work by Wu et al. [2011], however, presents an estimate of these

uncertainties by combining data from GRACE, ocean bottom pressure measurements and

ITRF2008 results. They find the geocenter of ITRF2008 is consistent with the center of mass

of the Earth at 0.5 mm/yr and that the accuracy of the scale of the reference frame is 0.2


mm/yr. We, therefore, estimate the total uncertainty of our crustal velocity field as

mm/yr (1-sigma).

3.4. Conclusions

Vertical velocities are calculated for the current 140 permanent GPS stations in

Norway, the vast majority of which have not been analyzed previously. Around half

of these stations have been operating for a sufficient length of time for their results

to be considered reliable.

Based on the new GPS data we compute a vertical crustal velocity field using

statistical interpolation and preliminary GIA modeling. We note that the Earth model

that gives best fit to the observed vertical velocities is similar to that determined by

Milne et al. [2001]. Our best-fit GIA model shows good fit to the majority of the GPS

data but with noticeable misfits in some areas.

Given the existing geographical ‘gaps’ in the GPS network, we consider the GIA model

more suitable than the statistical interpolation approach for the analysis of regional

sea-level changes. This may change as more GPS observations become available.

Differences between our GIA model results and the uplift model of Vestøl [2006] are

likely due to differences in the methods applied, reference frame issues and/or the

use of different types of data (paleo sea level, GPS, tidegauges and leveling) to

constrain the solutions.


4. Observed Sea-level Changes for Norway

Observations of present-day sea-level changes are available from the global tide gauge

network and satellite altimetry. Tide gauge records provide measurements along the

coastlines of the continents and at some islands. Satellite altimetry measures sea surface

heights primarily in the open ocean. In the following Chapter, we first discuss global sea-level

changes observed using tide gauges (Section 4.1.1) and go on to complete our own analysis

of sea-level changes observed by the Norwegian tide gauges (Section 4.1.2). In the second

part of the Chapter (Section 4.2) we discuss satellite altimetry and, in particular, the

challenges of using this method to measure regional sea-level changes along the Norwegian

coast.

4.1. Tide Gauge Records

Records from the global tide gauge network provide a useful tool for understanding 20th

century sea-level changes and variations in sea level over multi-decade to century time

scales. Tide gauges are coupled to the solid Earth, which means that they measure relative

sea-level changes (i.e. both deflections of the Earth’s surface and the ocean surface). Thus,

to arrive at an estimate of absolute sea-level change, the tide gauge data first needs to be

corrected for land motion. For Norway, vertical land motion due to GIA is an important

component of contemporary sea-level change. The land motion signal can be separated

from the tide gauge records using GIA modeling and/or observations from permanent GPS

stations (see Chapter 3). In addition, it is worth remembering that vertical land motion also

affects the Earth’s gravity field and, therefore, acts to perturb the ocean surface. This effect

needs to be taken in account if the tide gauge data are to be fully “GIA corrected” and to

help us understand the separate contributions to sea-level change (see Tamisiea and

Mitrovica [2011] and Section 3.3.1).

4.1.1. Global Sea-Level Changes From Tide Gauge Records

As discussed in Chapter 2, the IPCC AR4 concluded that the global sea level rate was 1.8 ± 0.5

mm/yr for 1961 to 2003 and 1.7 ± 0.5 mm/yr over the whole 20th century. Other tide gauge

studies have investigated the presence of nonlinear trends in global sea-level changes. For


example, Jevrejeva et al. [2006] determine a trend of 2.4 ± 1 mm/yr for the period 1993 to

2000. The authors find that this recent trend is similar to the observed trend between 1920

and 1945. There is also evidence of accelerations in the tide gauge records, Jevrejeva et al.

[2008] reconstruct global sea level 300 years back in time from tide gauge records. The time

series indicates that global sea-level change has accelerated by 0.01 mm/yr2, starting at the

end of the 18th century.

In Church and White [2006], altimetry data and tide gauge records are combined to

reconstruct global sea level back to 1870. They find a sea-level rise of 1.7 ± 0.3 mm/yr over

the 20th century and reported an acceleration of 0.013 ± 0.006 mm/yr2 for the same period.

If this acceleration continues over the 21st century, it corresponds to a sea level rise of 0.28

to 0.34 m. An updated analysis using five additional years with altimetry data comes to a

similar conclusion; a sea-level rise of 1.7 ± 0.2 mm/yr for 1900 to 2009 and an acceleration of

0.009 ± 0.003 mm/yr2 over the same period [Church and White, 2011].

4.1.2. The Norwegian Tide Gauge Records

The Norwegian Mapping Authority presently operates 22 tide gauges along the coast of

Norway (Fig. 4.1). To the best of our knowledge, there are few comprehensive studies of the

records from the Norwegian network but detailed analyses are forthcoming [Richter et al., in

revision]. Some investigations, however, have included Norwegian stations in wider regional

analyses [e.g. Douglas, 1991; Vestøl, 2006; Marcos and Tsimplis, 2007]. In a study of tide

gauge records surrounding the North Sea, a trend of 1.6 ± 0.9 mm/yr (corrected for vertical

land motion) was found for the period 1960 to 2000 [Marcos and Tsimplis, 2007]. As

discussed in Chapter 3, Vestøl [2006] finds averaged regional sea-level change over

Fennoscandia of 1.32 mm/yr (corrected for vertical land motion) for 1891 to 1990.


Fig. 4.1. Locations of the 18 Norwegian tide gauges used in this report. For reasons explained below, the records from Trondheim, Viker, Andøya and Vardø were not included in our analysis.

4.1.2.1. Analysis of the Norwegian Tide Gauges

In order to better quantify observed sea-level changes along the Norwegian coast, we

conduct our own analysis of tide gauge records. We use data from the Permanent Service for

Mean Sea Level [Woodworth and Player, 2003] and follow their recommendation of only

using the revised local reference datasets. These datasets are reduced to a common datum

by making use of the tide gauge datum history provided by the supplying authority; this

means that shifts in the records are eliminated. In this study, we chose to use the monthly

datasets which appear to be more complete when compared to the annual records.

The length of tide gauge records available from the Norwegian stations varies. The longest

are from Bergen, Oslo, and Stavanger, having records beginning in 1883, 1885, and 1919,

respectively. Other stations like Honningsvåg and Rørvik provide data from only 1970 to

present. In a quality control of the data, we use the most complete datasets and avoid parts

of the time-series that contain significant gaps. For example, in Oslo data before 1914 is

removed from our analysis as the record prior to that time is not continuous. For the same

reason, data before 1915 is excluded from the Bergen record. We also chose not to include

the tide gauges at Trondheim, Viker, Andøya, and Vardø. In Trondheim the tide gauge was


moved in 1991, the time-series from Viker starts in 1992 and is too short for estimating a sea

level trend and the time-series from both Andøya and Vardø suffer from significant data

gaps. To determine long-term trends from the observed relative sea-level changes we

conduct a least squares adjustment for each tide gauge (Eq. 4.1).

Eq. 4.1

Here z is the observation at the epoch t, a is the intersect of the model, b is the rate of sea

level change, and A, φ and f are the amplitude, phase and frequency of the annual periodic

variation in the time-series. The periodic term was included because visual inspection of the

monthly datasets revealed significant annual variation. If not captured by the model, the

annual variation increases the standard deviation of the estimated rate of sea-level change.

We compute two sets of rates. The first set makes use of all reliable data available from each

tide gauge. The second set uses data from only the past 30 years (1980 to 2010) and

represents present-day sea-level change along the Norwegian coast. As well as observed

relative sea-level changes we also present rates that have been fully GIA-corrected. That is,

the tide gauge records are adjusted for both vertical land motion and geoid changes using

predictions generated from our best-fit GIA model (Chapter 3).

4.1.2.2. Results From the Norwegian Tide Gauges

Our estimated relative and GIA-corrected rates are listed in Table 4.1 and illustrated in Fig.

4.2. We estimate the uncertainties (1-sigma) of the rates to be between 0.1 and 0.3 mm/yr

when using all the available reliable data. Whereas, rates determined for the period 1980 to

2010 have an uncertainty of 0.5 to 0.7 mm/yr (see Fig. 4.2). The uncertainties for the shorter

time-series are therefore somewhat larger.


First Year

RSLR (mm/yr) →2010

GIA-corrected SLR (mm/yr)

→2010

RSLR (mm/yr)

1980–2010

GIA-corrected

SLR (mm/yr)

1980–2010

RSLC (cm)

2000→2030

Oslo 1914 -3.7 1.4 -1.7 3.4 -5.2

Oscarsborg 1954 -1.8 2.9 -2.2 2.6 -6.5

Helgeroa 1981 - 2.9 -0.7 2.9 -2.1

Tregde 1928 0.2 1.7 1.2 2.6 3.6

Stavanger 1919 0.4 1.9 1.8 3.3 5.4

Bergen 1915 -0.1 2.0 1.1 3.1 3.2

Måløy 1944 0.6 2.4 1.5 3.3 4.5

Ålesund 1951 0.9 3.3 1.6 4.0 4.9

Kristiansund 1952 -1.0 2.1 0.2 3.3 0.7

Heimsjø 1928 -1.5 2.5 0.0 4.0 -0.1

Rørvik 1970 -0.8 3.7 -0.9 3.6 -2.6

Bodø 1950 -1.3 2.8 0.4 4.4 1.1

Kabelvåg 1948 -1.2 1.6 -1.4 1.4 -4.1

Narvik 1932 -2.6 1.5 -0.3 3.7 -1.0

Harstad 1952 -1.0 1.9 0.7 3.6 2.0

Tromsø 1952 0.0 2.4 0.8 3.2 2.4

Hammerfest 1957 0.9 2.6 2.0 3.7 6.0

Honningsvåg 1970 1.6 2.9 1.4 2.7 4.2

Table 4.1. Observed relative sea-level rates (RSLR) for 18 of the Norwegian tide gauges. Projected total relative sea-level change (RSLC) for 2000 to 2030 is calculated on the assumption that rates observed over 1980 to 2010 continue over the next 20 years unchanged. To determine the GIA-corrected sea-level rates we adjusted the RSLR using vertical crustal velocities and geoid changes generated using our best-fit GIA model (see Chapter 3).

The majority of relative sea-level rates computed from the entire time-series of reliable data

are less than zero, i.e. at most sites the sea level has fallen during the 20th century (Fig. 4.2

top panel). For most locations, therefore, 20th century relative sea-level change is dominated

by vertical land motion. We note that the lowest rates are found in Oslo and in the middle

part of Norway. The highest rates are found along the west coast of Norway and at the two

northernmost sites at Honningsvåg and Hammerfest. After correcting for GIA, all rates are

positive and are in the range 1.4 to 3.7 mm/yr. They are generally larger than global


estimates of 20th century sea-level rise [e.g. Church and White, 2011] but detailed

comparisons are difficult and not made here. We note that the GIA-corrected rates vary

considerably, which can partly be explained by the different lengths of the tide gauges

records (varying between 29 and 96 years). Rates determined from the shorter time-series

tend to be larger and this suggests an increased rate of sea-level rise in the past few

decades.

Indeed, if we then determine sea-level rates for the period 1980 to 2010 we find that rates

for the majority of the stations (excluding Oscarsborg, Helgeroa, Rørvik, Narvik, and

Honningsvåg) have increased (see bottom panel Fig. 4.2). Most of the relative rates are

positive but range between -2.2 and 2 mm/yr. After correcting for GIA, the rates vary

between 2.6 and 4.4 mm/yr excluding Kabelvåg. Here the rate is 1.4 mm/yr, this is

remarkably low when compared to GIA-corrected rates determined for the nearby stations

of Bodø, Narvik and Harstad. The cause of the low rate at Kabelvåg is not known. If we

ignore this station then our rates for the period 1980 to 2010 are more uniform (i.e. there is

less variation between locations) when compared to rates computed from the entire time-

series. Variations in the GIA-corrected rates along the Norwegian coast could be due to

several different factors. Given that the GIA model is poorly constrained in some areas of

Norway, however, we do not attempt to interpret this pattern.

Over short time-scales, the extrapolation of present-day observations can be used as an

alternative method to modeling studies. Here we assume that observed relative sea-level

rates over 1980 to 2010 will continue over the next 20 years unchanged (see also Section 4.4

of Flæte et al. [2010]). Thus, relative to the 2000-level, sea level in 2030 will range between -

6.5 and 6 cm.


Figure 4.2. Relative (blue), adjusted for vertical motion (red) and fully GIA-corrected (open red) sea level rates estimated from tide gauge observations along the Norwegian coast. The top panel shows the rates computed from the entire time-series (which varies in length from 29 to 96 years) and the bottom panel shows rates computed for the period 1980 to 2010.


4.2. Satellite Altimetry

Over the past 20 years, satellite altimetry has been the main observation techniques for

mapping sea surface topography and measuring sea-level changes. The working principle of

the technique is to transmit short pulses of microwave radiation which interact with the sea

surface and are partly reflected back to the satellite. From the two-way travel time of the

pulses, the distance between the satellite and the sea surface can be estimated. The sea

surface height is computed by subtracting this distance from the height of the satellite

determined in a global reference frame. Satellite altimetry observations have been used in,

for example; sea level change studies, mapping of ocean currents, mean sea surface

determination, gravity field determination, lake level monitoring, river discharge studies,

development of ocean tide models, and ENSO studies [see e.g. Beckley et al., 2007; Lysaker

et al., 2009; Andersen et al., 1998; Hwang et al., 2005; Kouraev et al., 2004; Smith et al.,

2000; Andersen et al., 2006].

Accurate sea level monitoring requires precise range measurements, precise satellite orbits

(satellite positions) as well as a precise and stable reference frame. The most precise range

measurements are today obtained by dual frequency radar transmitters, which directly

observe the ionospheric delay, combined with microwave radiometers which measure the

atmospheric water vapor delay. This allows the ranges to be determined with a precision of

3 cm. Precise orbits are determined by utilizing satellite tracking systems like GNSS (Global

navigation satellite systems), DORIS (Doppler Orbitography and Radio positioning Integrated

by Satellite), and SLR (Satellite Laser Ranging). The orbits of the latest altimetry missions are

determined using these techniques with accuracy better than 2 cm. Averaging the sea

surface height measurements over larger regions or over the whole Earth allows the sea

level to be determined with a precision of some tenths of a millimeter per year. In a study by

Ablain et al. [2009], the error budget of global sea level rates was assessed. The authors

found a total uncertainty of 0.6 mm/yr (90 % confidence interval) for the global sea level

rate estimated by combining data from the TOPEX altimeters and Jason-1 over 1993 to 2008.

However, this error budget did not include systematic errors which may arise due to

reference frame instabilities over time. This effect is poorly constrained, but Minster et al.


[2010] conclude that the current version of the International Terrestrial Reference Frame

does not meet the requirements for 1 mm/yr sea level monitoring.

4.2.1. Altimetry Measurements for Norway

Sea level can be monitored by combining the data from several succeeding altimetry

missions, e.g. Topex/Poseidon, Jason-1, and Jason-2. Together, these three satellites provide

nearly 20 years of observations starting in 1992. These observations indicate a global sea

level rise of approximately 3 mm/yr in this interval. Satellite altimetry may also provide the

local sea level change. Figure 3 in Cazenave and Llovel [2010] shows that the local sea level

trend varies from -10 to +20 mm/yr. The largest rates are found in the Indian Ocean and in

the Western Pacific while the lowest rates are found along the west coast of the United

States. In the Norwegian Sea the rate is between 2 and 5 mm/yr, i.e. close to the global

average and close to the rates estimated from the Norwegian tide gauges. This is only a

rough comparison and, unlike the tide gauge records, the altimetry data is not corrected for

geoid changes associated with GIA. It should also be noted that precise orbit determination

is especially important for local applications. Many orbital errors have a periodic pattern

which average close to zero for global applications. However, for regional applications such

errors have a direct influence on the observed sea-level rate. This was demonstrated by e.g.

Beckley et al. [2007] who computed sea level rates by using satellite orbits in different

reference frames. The study indicates that the sea level rates may be biased by up to 1.5

mm/yr along the Norwegian coast due to errors in the orbits. This is half the size of the

global rate and illustrates the importance of precise satellite orbits for regional sea level

measurements.

Applications of satellite altimetry in coastal areas (closer than 50 km to the land) are

especially demanding tasks. This is due to returned radar waveforms and range corrections

(troposphere and ocean tide) contaminated by the land areas. Sea surface heights from

coastal areas can still be extracted by applying appropriate waveform retracking techniques

and by using corrections tailored to coastal areas.

The angle between the satellite orbit and the equatorial plane of the Earth (the orbit’s

inclination) controls the area observed by the satellite. The orbit of Topex/Poseidon, Jason-1


and Jason-2 has an inclination which allows the ocean areas between ±66° latitude to be

observed while the European satellites ERS-1, ERS-2, and Envisat observe the ocean areas

between ±81.5° latitude. From this, it is clear that the Norwegian Sea is better covered by

the European satellites. Data from the ERS-1 and ERS-2 satellites, however, suffer from

weakly determined orbits. This is due to the fact that ERS-1 and ERS-2 only have SLR as a

technique for precise orbit determination. Hence, precise sea level monitoring at high

latitude starts with the Envisat data from 2002. Table 4.2 shows available data from the main

altimetry missions and also the latitudinal boundary of the datasets.

Satellite Latitudinal boundary Start of mission Mission completed

ERS-1 ±81.5° July 1991 June 1996

Topex/Poseidon ±66° 10 August 1992 October 2005

ERS-2 ±81.5° April 1995 2004

Jason-1 ±66° 7 December 2001 In orbit

Envisat ±81.5° March 2002 In orbit

Jason-2 ±66° 20 June 2008 In orbit

Cryosat-2 ±88° 8 April 2010 In orbit

SARAL/Altika ±81.5° April 2012

Sentinel-3 ±81.5° 2013

Jason-3 ±66° 2014

Table 4.2. Overview of the latest and some future satellite altimetry missions.

4.3. Discussion

Tide gauges and satellite altimetry are complementary techniques for measuring sea-level

changes. Observations from tide gauges and from the altimetry satellites indicate present-

day sea-level changes along the Norwegian coast are similar to the observed global mean.

Using temperature and salinity profiles (from hydrological stations and drifters in the Argo

network) could aid our interpretation of the tide gauges and altimetry observations. By

combining these datasets, it may be possible to separate and quantify the contributions

from ocean density and mass changes along the Norwegian coast [e.g. Richter et al., in

revision].


There is also a need for regional analyses of altimetry data for Norway. Such a study would

require combined use of several altimetry satellites, e.g. the Jason-satellites (below 66°N

latitude), Envisat, and the future Sentinel-3. If we are to reliably resolve regional sea-level

changes then, for all these satellites, it requires the computation of internal biases and the

optimization of precise orbits for the Norwegian territories.

4.4. Conclusions

Our analysis of 18 tide gauge records reveals relative sea-level rates for 1980 to 2010

range from -2.2 and 2 mm/yr along the Norwegian coast. If we assume this rate

continues unchanged for the next 20 years, total sea-level change for the period

2000 to 2030 will vary between -6.5 and 6 cm.

After correcting the tide gauge data for vertical land motion and associated gravity

changes, we find that GIA-corrected sea-level changes over the period 1980 to 2010

are between 2.6 and 4.4 mm/yr (if we exclude the anomalous rate at Kabelvåg).

Analysis of data prior to 1980 suggests that the rate of absolute sea-level rise has

increased in the past few decades.

Estimating regional sea-level changes for Norway from satellite altimetry

measurements is challenging; largely because some satellite missions do not make

observations above 66°N. In a brief review of the literature, we estimate absolute

sea-level changes in the Norwegian Sea from 1992 to present as 2 to 5 mm/yr. This

is similar to the rates determined in our tide gauges analysis and is not dissimilar to

the observed rate of global sea level rise ( 3 mm/yr).


5. Projected 21st Century Sea-level Changes for

Norway

In this Chapter we present regional sea-level projections for Norway for the 21st century. As

discussed above, recent analyses have worked towards estimating regional and/or local

projections of sea-level change [e.g. Katsman et al., 2008, 2011; Slangen et al., 2011]. In our

first analysis we opt to closely follow the methodology presented by Slangen et al. [2011],

which builds on the approach and results of the IPCC AR4. Slangen et al. [2011] consider how

different physical processes cause non-uniform sea-level changes by accounting for spatial

variations in (1) ocean density (steric changes) and circulation [e.g. Landerer et al., 2007] (2)

ice and ocean mass changes and associated gravitational effects on sea level [e.g. Mitrovica

et al., 2001] and (3) vertical land motion arising from past surface loading change and

associated gravitational effects on sea level (see Chapter 3).

This Chapter is structured as follows: First, we describe the data and models applied (Section

5.1) and outline our analysis (Section 5.2). Our 21st century regional sea-level projections are

summarized in Section 5.3 and in Appendix I. Note that here we only consider mean local

sea-level changes, possible changes in extreme sea-levels are discussed in Chapter 6. As

discussed above, no likelihood was attached to the IPCC AR4 sea-level estimates and, for this

reason, the AR4 does not give a best estimate or upper bound for future sea-level change.

This is mainly due to the large uncertainty associated with the potential ice-dynamic

contributions of the large ice sheets [e.g. Alley et al., 2005]. For the period 2090 to 2099

relative to 1980 to 1999, future global sea-level change is reported in AR4 to range from 18

to 59 cm [Meehl et al., 2007]. It may be, however, that the AR4 sea-level projections for the

21st century will be exceeded (see Chapter 2). In addition to our projections which follow the

methodology of Slangen et al. [2011] and are based on the AR4 results, therefore, we also

present a second analysis largely based on the high-end scenario of Katsman et al. [2011]

(Section 5.5).


5.1. Data and Model Descriptions

To obtain regional sea-level estimates for the Norwegian municipalities we closely follow the

methodology of Slangen et al. [2011] (Fig. 5.1). In the below Sections we describe the

different contributions used in our regional sea-level analysis.

Fig. 5.1. The methodology followed to calculate our regional sea-level change projections. Adapted from Slangen et al. [2011].

5.1.1. Model of Vertical Land Motion and Associated Gravity Changes

Predictions of vertical land motion and associated gravitational effects on sea level are taken

from our best-fit GIA model (see Chapter 3). The model is constrained by observations from

66 permanent GPS stations on the Norwegian mainland (those with 3 or more years of data)

analyzed using GAMIT [King and Bock, 2003]. For comparison, we also include vertical land

motion predictions from kriging for areas where we believe the spatial interpolation solution

to be reliable. To obtain cumulative land height and geoid changes for the period 2090–2099

relative to 1980–1999 we multiply our velocities (mm/yr) by 105. Note that our approach

differs to Slangen et al. [2011] who, in the main part of their analysis, make use of the ICE-

5G(VM2) model [Peltier, 2004].


5.1.2. The Climate Models (AOGCMs)

We make use of results from Atmosphere Ocean General Circulation Models (AOGCMs)

which are available in the Coupled Model Intercomparison Project phase 3 (CMIP3)

database. As in Slangen et al. [2011], we examine output from models forced by the IPCC

SRES scenarios A2, A1B and B1 [Nakícenović and Swart, 2000]. These scenarios represent

varying development pathways for society and have different greenhouse gas emission

forcings. Note that the IPCC does not assign a likelihood to the different emission scenarios.

To calculate regional sea-level projections requires several model outputs, this information is

not available for all of the AOGCMs in the CMIP3 database (see Sections 5.1.3 and 5.1.4

below)

5.1.3. Future Ocean Density and Circulation Changes

To calculate regional future ocean density and circulation changes requires (1) the projected

global mean, which can be approximated as the global mean thermal expansion as global

salinity changes are so small and (2) the local deviation with respect to the global mean,

which is called the dynamic sea level (DSL). It is related to ocean circulation, 3D density

structure, and mass distribution of the ocean, i.e. it has a mass component and a steric

(temperature and salinity sea water changes) component. Non-uniform sea-level changes

owing to density and circulation changes ( ) are given as (adapted from Eq. 5 in

Yin et al. [2010]):

Eq. 5.1.

The contributions to local sea-level change are the global mean thermal expansion ( )

and the local sea level deviation from the global mean which is also called the dynamic sea

level ( ). Projections are a function of ( ) latitude and ( ) longitude and ( )

time. As in Slangen et al. [2011], the global mean thermal expansion component has been

corrected for the near linear trend found in some of the AOGCMs control runs. Table 5.1 lists

the models for which we calculate projected regional sea-level changes driven by ocean

density and circulation changes.


Model Center Scenarios Reference

BCCR BCM 2.0 Bjerknes Centre for Climate Research, Norway

A1B, A2, B1

Furevik et al. [2003]

CCCMA CGCM 3.1

Canadian Centre for Climate Modeling and Analysis, Canada

A1B, A2, B1

Flato [2005]

GFDL CM 2.0 NOAA Geophysical Fluid Dynamics Laboratory, USA

A1B, A2 Delworth et al. [2006]

GFDL CM 2.1 NOAA Geophysical Fluid Dynamics Laboratory, USA

A1B, A2, B1

Delworth et al. [2006]

GISS AOM NASA/Goddard Institute for Space Studies, USA

A1B, B1 Lucarini and Russell [2002]

GISS MODEL EH NASA/Goddard Institute for Space Studies, USA

A1B Schmidt et al. [2006]

GISS MODEL ER NASA/Goddard Institute for Space Studies, USA

A1B, A2, B1,

Schmidt et al. [2006]

IAP FGOALS 1.0g

Insitute of Atmospheric Physics, China

A1B, B1 Yongqiang et al. [2002]

MIROC 3.2 (hires)

Center for Climate System Research, Japan National Institute for Environmental Studies, Japan Frontier Research Center for Global Change, Japan

A1B, B1 Hasumi and Emori [2004]

MIROC 3.2 (medres)

Center for Climate System Research, Japan National Institute for Environmental Studies, Japan Frontier Research Center for Global Change, Japan

A2, B1 Hasumi and Emori [2004]

MIUB ECHO-g University of Bonn, Germany A1B, A2, B1

Min et al. [2005]

MPI ECHAM5 Max Planck Institute for Meteorology, Germany

A1B, A2, B1

Jungclaus et al. [2006]

MRI CGCM 2.3.2a

Meteorological Research Institute, Japan

A1B, A2, B1

Yukimoto et al. [2001]

NCAR CCSM 3.0 National Center for Atmospheric Research, USA

A1B, A2, B1

Collins et al. [2006]

NCAR PCM 1 National Center for Atmospheric Research, USA

A1B, A2, B1

Washington et al. [2000]

UKMO HADCM 3

Met Office, UK A1B, A2, B1

Gordon et al. [2000]

UKMO HADGEM Met Office, UK A1B, A2 Johns et al. [2006]

Table 5.1. The 17 AOGCMs used to calculate regional ocean density and circulation changes.


5.1.4. Future Ocean Mass Changes

Temperature and precipitation fields from the AOGCMs are used to calculate future land ice

mass changes, which can be split into the contributions from glaciers and ice caps (Section

5.1.4.1) and from the ice sheets (Section 5.1.4.2). All ice mass changes were provided by A.

Slangen (personal communication) and are based on scenarios A2, A1B and B1 and results

from around 12 of the AOGCMs available from the CMIP3 database (see Slangen et al.

[2011] for details).

5.1.4.1. Contributions From Glaciers and Ice Caps

Slangen et al. [2011] employ a glacier model based on the volume-area scaling approach.

Following this method, temperature and precipitation fields from the AOCGMs are used to

calculate glacier area changes. Glacier volume ( ) is then related to glacier area ( ) using a

power law [e.g. Bahr et al., 1997]:

Eq. 5.2.

Where the other values ( and ) are scaling parameters. The glacier inventory used is

divided into 19 regions [Radić and Hock, 2010] and, therefore, we have separate ice mass

projections for each region. Note that as no complete glacier inventory exists, upscaling was

performed in 10 of the 19 regions [see Radić and Hock, 2010].

5.1.4.2. Contributions From the Ice Sheets

The method employed by Slangen et al. [2011] to determine future ice mass changes from

the ice sheets (Greenland and Antarctica) is the same as in IPCC AR4. Projected surface mass

balance changes are calculated following Gregory and Huybrechts [2006]. (Note that

modeled changes in ice sheet flow are also taken into account by modifying the sea level

contribution due to surface mass balance changes). In addition, we opt to use the so called

scaled-up values for future ice-dynamic changes (see Meehl et al. [2007]) - where the

present-day ice sheet imbalance (0.32 mm/yr for the period 1993 to 2003) scales linearly

with the projected average atmospheric temperature change. Projected ice mass changes

are confined to the areas of southwest Greenland and the Antarctic Peninsula.


5.1.4. Future Non-Uniform Sea-Level Changes due to Land Ice Changes

Predictions of future sea-level changes are generated using the GIA model described in

Chapter 3 except that (1) the ice model input is the projected ice sheet and glacier mass

changes as detailed above and (2) the setup of the GIA model is altered so that, instead of

performing past ice age calculations, it is used to predict future sea-level changes.

These predictions give relative sea-level changes, which can be considered separately as

perturbations to the solid Earth surface and to the ocean surface. For the Earth response, we

assume that deformations over the next century will be purely elastic. Projected ice mass

loss, therefore, will lead to a relatively localized elastic rebound of the Earth’s surface. As

both the elastic Earth response and ocean surface perturbation scales linearly with the

surface loading change, non-uniform sea-level changes can be normalized by the ice mass

loss [e.g. Mitrovica et al., 2001]:

Eq. 5.3.

Equation 5.3, modified from Mitrovica et al. [2001], describes how the total projected sea-

level change ( ) is the sum of the normalized sea-level change ( ) from

Antarctica, Greenland and the 19 glacier regions considered. Predictions of sea-level change

are non-uniform being a function of ( ) latitude and ( ) longitude. Total sea-level changes

are found by multiplying the normalized pattern of sea-level change by the individual

projections of ice mass changes for the glaciers and ice sheets (see above).

5.2. Analysis

Our analysis is divided into projected global sea-level changes (Section 5.2.1) and regional

sea level estimates (Section 5.2.2).

5.2.1. Global Mean Sea-Level Changes

Global mean sea-level changes are, except for the ocean density contribution where we

make use of a different set of AOGCMs, the same as those presented in Slangen et al.


[2011]. As mentioned above, the contribution from ocean density changes can be

approximated as the global mean thermal expansion because global salinity changes are so

small. Fig. 5.2 shows the projected multi-model average and range of calculated

thermosteric sea-level changes for the scenarios A2, A1B and B1.

Fig. 5.2. 21st century global mean thermosteric sea-level change computed for the A2, A1B and B1 scenarios. For the AOGCMs considered, squares mark the average and error bars indicate the multi-model range. The projections show that the multi-model range overlaps between the scenarios (i.e. there

is little difference between projections from A2, A1B and B1). Given this, we opt to use the

multi-model average across all scenarios as our central value for global thermosteric sea-

level change. We computed this, and the corresponding uncertainty (1-sigma), for 2030 as

0.05 ± 0.017 m, for 2050 as 0.09 ± 0.025 m, and for 2100 as 0.22 ± 0.063 m (changes are

relative to the period 1980 to 1999). The uncertainties (1-sigma) indicate that the variation

between the models increases later in the 21st century. It is important to note that the

standard deviations only quantify the variability of the AOGCMs and not the uncertainty of

the estimates. The uncertainty of the projected thermosteric sea-level change is difficult to

assess because the uncertainty of each model and the probability of each scenario are not

known.


Table 5.2 is adapted from Slangen et al. [2011], shows the contributions to projected global

mean sea-level changes. We do not include the effect of GIA on ocean basin volume changes

but this is predicted to be very small (less than 0.01 mm/yr). The sum of mean sea-level

changes across scenarios A2, A1B and B1 is 0.47 m, this is useful to know as we can then see

how different our regional projections are when compared to the global mean (see Table

8.18). Note that projected global mean sea level is split almost equally between thermal

expansion and ice mass loss.

Contribution to global mean sea-level change (cm)

Steric 22 ± 6 (47%)

Glaciers* 17 ± 4 (36%) Greenland* 7 ± 2 (15%) Antarctica* 1 ± 2 (2%)

Sum 47 ± 8

Table. 5.2. Contributions to projected 21st century (2090–2099 relative to 1980–1999) global mean sea-level change across scenarios A2, A1B and B1. Uncertainties are 1-sigma and contributions are also expressed as percentages of the global mean. *based on numbers from Slangen et al. [2011].

5.2.2. Regional Sea-Level Changes

5.2.2.1. Projected Ocean Density and Circulation Changes

Before computing local sea-level changes due to ocean density and circulation changes for

the 21st century, we perform a regional assessment of the AOGCMs. In this test, we examine

the ability of the AOGCMs to replicate present-day observed dynamic sea-level (DSL)

changes. If the models are able to adequately reproduce present-day regional patterns of

DSL change, then it gives us increased confidence in their suitability for projecting 21st

century sea-level changes for the Norwegian coast [see Yin et al., 2010; Slangen et al., 2011].

We follow a similar methodology as described by Yin et al. [2010]. Observed DSL changes,

obtained from altimetry and drifting buoys, are available from 1992 to 2002 [Maximenko et

al., 2009; Niiler et al., 2003]. In order to make a comparison, modeled DSL changes were

averaged over the same period. We select two rectangular windows to make our regional

assessment; the areas 0 – 14° E, 56 – 66° N and 0 – 34° E, 66 – 73° N. Note that this study


area excludes all data in the Gulf of Bothnia. Differences between models ( ) and

observations ( ) were calculated by computing the RMS error [Yin et al., 2010]:

Eq. 5.4.

Where the weight of the grid-point ( ) is set equal to the area of the corresponding grid-cell

and the sum of the weights ( ) corresponds to the total ocean area covered. Observed and

modeled regional DSL for the period 1992 to 2002 are shown in Figure 5.3, visual inspection

indicates generally good agreement between models and to the observed DSL. Some

notable differences are present in the north of our study area.


Fig. 5.3. (below). Modeled present day (1992 to 2002) DSL from 17 AOGCMs and the observed DSL from altimetry measurements and drifting buoys [Maximenko et al., 2009; Niiler et al., 2003] (upper left panel).


RMS differences between observed DSL and the ensemble of 17 AOGCMs vary between 0.08

and 0.51 m (Fig. 5.4). We opt to use only models with a RMS error of less than 0.3 m [Yin et

al., 2010]. This threshold eliminates the four models CCCMA CGCM 3.1, GISS ER, MIUB ECHO

G, and MRI CGCM 2.3.2 from further analysis. We also omit the models GISS AOM and GISS-

ER because they include a contribution from land ice which cannot be separated from the

steric signal [Katsman et al., 2008]. This leaves 11 AOGCMs for the calculation of future

ocean density and circulation changes for Norway.

Fig. 5.4. The root mean square error between the modeled and observed DSL from 1992 to 2002. All models with a RMS error larger than the 0.3 m threshold (dashed line) were not used for computing the DSL change for the coastal municipalities. We calculate DSL fields for 2030, 2050 and 2100 for our chosen 11 AOGCMs. (Note that in

some models the global mean of the DSL is non-zero, in which case we subtract the global

epoch-average of the DSL from each grid point at each epoch). The time-series of DSL change

are noisy so, in a simple approach, we average the results over multi-year intervals. For

example, for 2100 we examine average DSL for the period 2090 to 2100 relative to 1981 to


2000 (i.e. essentially the same intervals as in IPCC AR4). As with the global thermosteric

change, we conclude that the AOGCMs do not give a precise description of the DSL along the

Norwegian coast. Still, most models point towards a sea-level change higher than the global

mean. We take the ensemble average as a best guess for the regional DSL change. This gives

values of 0.04 ± 0.04 m, 0.06 ± 0.04 m, and 0.09 ± 0.08 m for 2030, 2050 and 2100,

respectively (Fig. 5.5). Again, it should be stressed that the standard deviations only quantify

the variability of the models and not the estimated uncertainty.

Fig. 5.5. 21st century regional dynamic sea-level change (the areas 0 – 14° E, 56 – 66° N and 0 – 34° E, 66 – 73° N) computed for the A2, A1B and B1 scenarios. For the AOGCMs considered, squares mark the average and error bars indicate the multi-model range. Spatial patterns of the local DSL change using 11 AOGCMs and for the A1B scenario are

shown in Fig. 5.6. Visual inspection of the fields indicates that differences within our study

area are no larger than a few centimeters. Thus, given the somewhat larger range between

the AOGCMs (Fig. 5.5), local variations along the Norwegian coast are not taken into

account.


Fig. 5.6. Projected 21st century (2090–2100 relative to 1981–2000) dynamic sea-level changes for the A1B scenario. The 11 AOGCMs shown are those which are able to adequately reproduce observed present-day regional patterns of DSL change (see Fig. 5.4). We note that, in relation to the other AOGCMs, the MIROC3.2 (medres) and NCAR CCSM 3.0 models project significantly larger DSL changes. It is beyond the scope of this study to explain differences between the models.


5.2.2.2. Projected Non-Uniform Sea-Level Changes due to Land Ice Changes

Non-uniform sea-level variations due to projected 21st century ice sheet and glacier ice mass

changes for the A1B scenario are shown in Fig. 5.7. The spatial patterns compare well to the

projections shown by Slangen et al. [2011] (see Figs. 2 and 3), which gives us confidence in

our results. Uncertainties between sea-level predictions for the different AOGCMs (1-sigma)

are less than ± 5 cm over the majority of the globe, including the Norwegian coastline.

Fig. 5.7. Projected 21st century (2090–2099 relative to 1980–1999) multi-model mean sea-level changes for the A1B scenario from Greenland and Antarctica (top left) and the 19 glaciated regions (top right). Locations of ice mass changes are colored white. Corresponding uncertainties between the models (1-sigma) are shown in the bottom panels. Units are in meters.

In addition, we compute non-uniform sea-level variations due to projected 21st century ice

sheet and glacier ice mass changes for the scenarios A2 and B1 (figures not show). For the

Norwegian coastal municipalities, we find only small differences between the scenarios. This

is not surprising as differences in projected land ice mass changes between the scenarios are

small (Table 5.2). Thus, as with our ocean density and circulation projections, we opt to take

the multi-model average across all scenarios. For the coastal municipalities, non-uniform


sea-level variations due to projected 21st century ice sheet and glacier ice mass changes are

shown in Fig. 5.8. The local uncertainty (1-sigma), which is not shown, varies between ± 3.5

and 4 cm. The projections show significant regional variations; sea-level changes in the south

are approximately twice as large as those in northern Norway. This pattern can be attributed

to mass loss from glaciated areas to the north of Norway. We find that the contribution from

nearby Scandinavian glaciers is around -1 cm and, therefore, relatively unimportant.

Projected mass loss in Svalbard affects sea levels along the Norwegian coast by between -1

and -3 cm.

Fig. 5.8. Projected 21st century (2090–2099 relative to 1980–1999) multi-model mean sea-level changes due to land ice changes across scenarios A2, A1B and B1. Units are in meters.

5.3. Regional 21st Century Sea-Level Projections for Norway

Here we give regional sea-level projections for the end of the 21st century. Our sea level

estimates take account of (1) ocean density (steric) and circulation changes, (2) ice and

ocean mass changes and associated gravitational effects on sea-level and (3) vertical land

motion arising from past surface loading change and associated gravitational effects on sea

level.


Ocean density and circulation changes can be considered separately as global thermal

expansion (Section 5.2.1) and dynamic sea-level changes (Section 5.2.2.1). As shown above,

we estimate global thermal expansion as 0.22 ± 0.063 m and regional dynamic sea-level

change as 0.09 ± 0.08 m for the end of the 21st century. Thus, total ocean density and

circulation changes are projected to be 0.31 ± 0.1 m (1-sigma) for the period 2090 to 2100

relative to 1981 to 2000. Non-uniform sea-level changes due to land ice mass changes are

given in Section 5.2.2.2. Our regional sea surface projections (both those driven by ocean

mass and density changes) show that differences between the models are larger than

differences between the scenarios. In other words, there is significant overlap between

projections from scenarios A2, A1B and B2. Given this, we opt to use the multi-model

average across all scenarios as our central value. Vertical land motion and associated

gravitational effects on sea level are taken from the results of our best-fit GIA model in

Chapter 3. Regional 21st century projected relative sea-level changes for the Norwegian

coastal municipalities are shown in Fig. 5.9.

Projected 21st century relative sea-level change varies between -0.2 and 0.3 m (Fig. 5.9).

The pattern of sea-level change largely reflects land motion due to glacial Isostatic

adjustment, this process dominates over ocean density and mass changes in areas where

sea-level change is projected to be negative. A summary of the projected relative sea-level

changes for key locations is given in Table 5.3.


Fig. 5.9. Projected 21st century (2090–2099 relative to 1980–1999) relative sea-level changes. Our regional estimates take account of (1) ocean density and circulation changes, (2) ice and ocean mass changes and associated gravitational effects on sea-level and (3) vertical land motion and associated gravitational effects arising from past surface loading change. Units are in meters.

Location Projected ocean density and circulation

changes (1σ ± 10 cm)

Projected non-uniform sea-level change from land ice (1σ ± 4 cm)

GIA effects of land motion and gravity

changes (1σ ± 7 cm)

Total relative sea-level

change (1σ ± 13 cm)

Oslo 31 13 -54 -10 Stavanger 31 13 -16 28 Bergen 31 12 -22 21 Trondheim 31 11 -52 -10 Tromsø 31 8 -24 11 Table 5.3. Projected 21st century (2090–2099 relative to 1980–1999) sea-level changes at key locations in Norway. For comparison, global mean sea level is projected to be 47 ± 8 cm (1-sigma).


5.4. Discussion

Our projections of 21st century sea-level change for Norway show a pattern of complex

interplay between glacial isostatic adjustment and non-uniform ocean surface changes. This

underlines the importance of working towards regional and/or local projections of sea-level

change and understanding the processes that drive them. By adopting an approach similar to

Slangen et al. [2011], we have established a framework that can be used for regional sea-

level projections along the Norwegian coast. This methodology should be viewed as a

starting point, which can be improved upon and updated as the research community is able

to better constrain future contributions to sea-level change. In addition, it is important to

remember that our projections are (1) not assigned a likelihood or upper bound due to the

lack of understanding of some of the processes that affect sea level (mainly due to the large

uncertainty associated with the potential contributions of the ice sheets) and (2) are

dependent on the IPCC SRES scenarios A2, A1B and B1.

Reducing the uncertainty of our projections depends on how we can address the following

main issues (this is not an exhaustive list): Most importantly, and as briefly discussed, the

largest uncertainty in future estimates of global sea-level change is the potential

contributions of the large ice sheets [e.g. Alley et al., 2005]. Our future ice mass estimates

for Greenland and Antarctica are based on ice modeling results using the same methods as

the IPCC AR4 [Meehl et al., 2007]. There are, however, issues with the current generation of

ice sheet models as processes such as ice stream dynamics, basal sliding and ice-ocean

interactions are either poorly represented or absent from the model setup. The models,

therefore, cannot provide a bound on sea-level projections and, as the above physical

processes are not included, then we have reason to believe that current model projections

might be biased low. In an attempt to account for this problem, the IPCC AR4 includes an

estimate of the present-day ice sheet imbalance due to recent ice flow acceleration (0.32

mm/yr for the period 1993 to 2003). The ice sheet imbalance is scaled linearly with

projected average atmospheric temperature change to give the so called scaled-up

contribution. The reader should note that, while the IPCC chose not to include the scaled-up

contribution in their main sea-level projections, it is included here (the 5 to 95% range for

scenarios A2, A1B and B1 is between -1 and 13 cm as presented in Table 10.7 of Meehl et al.


[2007]). This is relatively conservative estimate of how processes affecting ice flow may

contribute to sea-level change over the 21st century. As mentioned, progress towards

constraining the potential contributions of the ice sheets is at a preliminary stage (e.g.

www.ice2sea.eu). There have been some recent encouraging advances [e.g. Price et al.,

2011] but many unanswered questions remain [Pfeffer, 2011]. More reliable sea-level

projections from the next generation of ice sheet models are some years away. Given the

large uncertainty associated with the potential contribution of the ice sheets, therefore, we

examine how our projections might be influenced by substantial changes in Greenland and

Antarctica in Section 5.5 below.

As well as issues concerning the uncertainty of the ice sheet contributions, recent works

have shown how regional sea-level projections are also sensitive to the pattern of ice mass

change [e.g. Gomez et al., 2010; Tamisiea and Mitrovica, 2011]. In particular, our assumption

of uniform ice mass loss in southwest Greenland should be investigated in future work. As

Norway is located in the ‘far field’ of the Antarctic ice sheets, it is relatively insensitive to the

pattern of ice mass change that occurs there [Tamisiea and Mitrovica, 2011]. For ocean

density and circulation changes, we know the AOGCMs generally show poor agreement

(Section 2.1.2). We try to improve on that here by testing the ability of the models to

reproduce observed present-day sea-level change (see above). In addition, one aspect of

steric sea-level change that could be important is the contribution of the deep ocean to

global thermal expansion [see Purkey and Johnson, 2010]. Other effects on sea-level change

that we do not take into account here are, among others; local subsidence, changes in

terrestrial water storage, the affect of GIA on ocean basin volume and the impact of ice melt

on ocean freshening. Of these, subsidence is likely the most important for determining local

sea-level changes in Norway.

5.5. A High-End Scenario of Sea-level Change for Norway

Here we explore a high-end scenario in an attempt to define an upper bound on 21st Century

regional sea-level change for Norway. There are two motivations for examining a high-end

scenario: Firstly, thus far in the report we have computed regional sea-level projections

using the IPCC SRES scenarios A2, A1B and B1. For these scenarios, maximum global

http://www.ice2sea.eu/


atmospheric temperature rise over the 21st century is projected to be not much larger than

4 °C (see Fig. 10.26 in Meehl et al. [2007]). Hence, none of these scenarios are considered

to be high-end scenarios of climate change. High-end outputs from the AOGCMs are

unfortunately not available from the CMIP3 database and, consequently, it is necessary to

use another approach for computing sea-level change for high-end climate change scenarios.

For ocean density and circulation changes, therefore, we use a methodology based on the

assumption of a relationship between global atmospheric temperature and sea level to

explore the scenario of up to a 6 °C warming [see Katsman et al., 2008; 2011].

The second reason for exploring a high-end scenario is to examine the glaciers and large ice

sheets which, as discussed above, have large uncertainties associated with their potential

contributions. Ocean mass changes are estimated using the assumption that observed

present-trends of ice loss (accelerations) continue and/or using expert judgment [Meier et

al., 2007; Katsman et al., 2011]. Note that the work of Katsman et al. [2011] forms part of

the Netherland’s Delta-report. Owing to the lack of understanding of some of the processes

that drive sea-level change, we do not assign a liklihood to our high-end estimates. The

analysis of the high-end scenario is structured as before, that is, we begin with global mean

sea-level changes and then go on to examine regional projections.

5.5.1. High-End Global Mean Sea-Level Changes

For the global thermosteric sea-level change, we make use of results from Katsman et al.

[2011]. The authors use two different approaches to extrapolate beyond the temperature

range covered in the AOGCMs: (1) they establish a relationship between the change in global

atmospheric temperature and thermosteric sea-level change and (2) the change in rates

between these two datasets is examined. The latter of these two methods is similar to the

semi-empirical method of Rahmstorf [2007] except that here it is used in a more restricted

way as it is only the thermosteric sea-level change that is considered. For a temperature

change of 6 °C, the two different approaches give a global thermosteric sea-level change of

0.12 to 0.49 m (with a central value of 0.31 m).


Projected high-end ocean mass changes can be split into the contributions from (1) glaciers

and ice caps and (2) the large ice sheets. For glaciers and ice caps we take the estimates

from Meier et al. [2007]. They examine two scenarios, firstly assuming that that the

observed present-day imbalance will remain constant over the 21st century, which results in

a 0.1 m contribution for 2006 to 2100. Secondly, assuming present accelerations continue,

this leads to a total sea-level change of 0.24 m for the same period. Extending the time

interval back to 1990 would only make a small difference ( 0.01 m) to our results, the IPCC

assess the contribution of glaciers and ice caps as 0.77 ± 0.22 mm/yr for 1993 to 2003

[Lemke et al., 2007]. The range for glacier changes is, therefore, between 0.1 and 0.24 m

(central value 0.17 m). Note that this is at the top end of the estimates presented by Slangen

et al. [2011].

For the ice sheets, we take the severe scenario from Katsman et al. [2011]. The authors use

similar estimates for surface mass balance changes as in the IPCC AR4. Ice-dynamic changes

are based on expert judgment, which is aided using recent geodetic observations of ice mass

changes. This approach allows us to roughly estimate possible high-end contributions from

the ice sheets but, clearly, it does not tell us how these changes will relate to future climate

or temperature change. Katsman et al. [2011] estimate a global mean contribution of 0.13 to

0.22 m (central value 0.18 m) from Greenland for the period 1990 to 2100. These values are

not dissimilar to those obtained in a detailed assessment by Dahl-Jensen et al. [2009] in

which the authors take ice flux estimates from Pfeffer et al. [2008] to place an upper bound

of 0.2 m from Greenland by 2100. The latest modeling work, however, suggests that the

potential ice-dynamic contribution from Greenland over the next century may be somewhat

less than this [Price et al., 2011]. For the Antarctica, the severe scenario of Katsman et al.

[2011] is based on the case of an emerging collapse for some areas of the ice sheets. In this

situation the region of the Amundsen Sea Embayment and marine terminating glaciers in

East Antarctica are assumed to increase their ice loss to eight times the balance value. The

northern part of the Antarctic Peninsula is assumed to lose 50% of existing ice mass by the

end of the century. In total, this gives a global mean contribution of -0.01 to 0.41 m (central

value 0.2 m) from Antarctica for the period 1990 to 2100.


Contribution to global mean sea-level change; central value and

range (cm)

Central value as a percentage of the central global mean

Steric 31 (12 to 49) 36%

Glaciers 17 (10 to 24) 20% Greenland 18 (13 to 22) 23% Antarctica 20 (-1 to 41) 21%

Sum 85 (56 to 114)

Table. 5.4. Contributions to high-end projected 21st century (approximately 2090–2099 relative to 1980–1999) global mean sea-level change. Thermosteric changes are calculated using the methods of Katsman et al. [2008; 2011]. Glacier changes are based on the work of Meier et al. [2007] and ice sheet contributions taken from Katsman et al. [2011]. Contributions are also expressed as percentages of the global mean.

Following Katsman et al. [2011] the separate contributions to global sea level are added

together using their median values and their uncertainties are summed quadratically (Table

5.4). If we examine the central estimates, we find that contributions to the global mean are

35% from thermal expansion and 65% from ice mass loss. The upper bound of 21st

century sea-level change is, for the scenario of a global atmospheric temperature rise of 6 °C

and assuming the emerging collapse of some areas of the Antarctic ice sheets, around 1.15

m (see also Katsman et al. [2011]). It is of interest to see how this estimate of the upper

bound of 21st century global sea-level change compares with other approaches. In their

assessment, Katsman et al. [2011] examine evidence of paleo sea-level changes to help put

these numbers into context. Observations from the last interglacial (125,000 years ago) are

relevant because the configuration of the ice sheets was similar to present but global

temperatures were warmer [e.g. Otto-Bliesner et al., 2006+. This period of Earth’s history,

therefore, can be a useful analogue of what sea-level changes we might expect in a warming

climate. Evidence from the last interglacial indicates that rates of sea-level change were as

high as 1 to 2.4 m/100 yr [Rohling et al., 2008]. As a rough estimate, therefore, Katsman et

al. [2011] ascribe a maximum global sea-level rise of 1.9 m for the period 2000 to 2100

based on the paleo data. To what extent these records can be used as a guideline for 21st

century sea-level changes is unclear. They show, however, that high rates of sea-level

change have occurred in the past and might be physically attainable in the future.


5.5.2. High-End Regional Sea-Level Changes

5.5.2.1. High-End Projected Ocean Density and Circulation Changes

As before, dynamic sea-level changes are examined using a regional analysis for the

Norwegian coast (the areas 0 – 14° E, 56 – 66° N and 0 – 34° E, 66 – 73° N). We aim to

compare projected global temperature changes to the regional DSL changes [e.g. Katsman et

al., 2008; 2011]. This requires that we first compute a time-series of projected global

atmospheric temperature changes from the CMIP3 database (note that these were

corrected for any linear drifts found in the preindustrial control run). Secondly, we take the

DSL changes computed in Section 5.2.2.1. The projected global temperature and DSL

changes are averaged over 5-year periods between 2000 and 2100 (Fig. 5.10). Our results

show that both the DSL change and variation increases with increasing temperature. To

estimate the trend of the data we use the method outlined by Katsman et al. [2008] and, in

doing so, adopt a central value as the trend of the dataset and use the 10 % and 90 %

quintiles to indicate the variability of the data for increasing temperature change. For the

Norwegian coast, the resulting dynamic sea-level change for a global warming of 6 °C is -0.06

to 0.57 m with a central value of 0.22 m. We note that this is significantly larger than the

range of -0.05 to 0.2 m reported for the Netherlands [Katsman et al., 2011]. Our study area

is smaller than the area used for the Netherlands and, therefore, we redid the computations

for an enlarged area covering -20 – 14° E, 55 – 66° N and -20 – 40° E, 66 – 75° N, i.e. the most

of the Nordic Seas and the Barents Sea. For this area, the dynamic sea-level change for 6 °C

global warming ranges from -0.11 to 0.50 m with a central value of 0.18 m. These numbers

are similar to our original results. This suggests that the larger dynamic sea-level change

computed for the Norwegian coast is not a coastal effect of the AOGCMs, but something

that is particular to the Nordic Seas.


Fig. 5.10. Projected regional dynamic sea-level change (the areas 0 – 14° E, 56 – 66° N and 0 – 34° E, 66 – 73° N) versus global atmospheric temperature change. Each point represents a result from one AOGCM using a 5-year average from the period 2000 to 2100 (relative to 1981–2000). The solid and dashed lines represent trends of the central and lower/upper boundaries of the data.

Finally, the central value ( ) of the total sea level-change due to density and ocean

circulation changes is found by adding the central values of the high-end projection for

thermosteric sea level-change and DSL change (0.31 + 0.22 = 0.53 m). Then, the upper and

lower bounds are found by Eq. 5.5.

Eq. 5.5.

Where is the upper uncertainty band, the upper boundary value for the contributor

and is the central value. A similar equation also applies to the lower uncertainty band

( ). The range for the total projection is given by Eq. 5.6.

Eq. 5.6.


For a global atmospheric temperature rise of 6 °C, total sea-level change due to ocean

density and circulation changes is 0.19 to 0.93 m (central value 0.53 m) for the Norwegian

coast.

5.5.2.2. High-End Projected Non-Uniform Sea-Level Changes due to Land Ice

Changes

Estimates of high-end future ice mass changes are taken from Section 5.5.1. For glaciers and

ice caps, our values based on the work of Meier et al. [2007] give a global mean contribution

ranging between 0.1 and 0.24 m for the 21st century. We assume that this sea-level change

has a corresponding non-uniform pattern calculated for glacier changes using the same

multi-model average of scenarios A2, A1B and B2 (Section 5.2.2.2). Thus, for the Norwegian

coastal municipalities, this leads to a sea-level change of between 0.04 and 0.18 m (see

Table 8.19).

For the ice sheets we use the values given by Katsman et al. [2011], that is, a global mean

contribution of between 0.13 and 0.22 m from Greenland and -0.01 to 0.41 m from

Antarctica for the period 2000 to 2100 (see Section 5.5.1). The corresponding non-uniform

sea level response is found using Eq. 5.3 and, as before, we assume an even ice mass loss in

southwest Greenland and on the Antarctic Peninsula. This approach is not strictly correct as

Katsman et al. [2011] propose that future ice mass changes will occur across several

different regions of the ice sheets. As discussed, our assumed pattern of ice mass change is

potentially important for understanding the non-uniform sea level response to changes in

Greenland but less so for Antarctica (see also Tamisiea and Mitrovica [2011]). Using the

range 0.13 to 0.22 m for the global mean Greenland contribution, we find a corresponding

sea-level change for Norway of between -0.01 and 0.02 m (Table 5.5 and 8.19). This shows

that the Norwegian coastline is relatively insensitive to ice mass change in Greenland. On the

other hand, a global contribution from Antarctica of -0.01 to 0.41 m leads to a regional sea-

level in Norway of between -0.01 and 0.5 m. Sea-level rise in Norway due to ice mass loss in

Antarctica, therefore, is larger than the global mean. This is not a surprising result, the

pattern of non-uniform sea level response owing to rapid ice sheet changes is well

established [e.g. Farrell and Clark, 1976; Mitrovica et al., 2001]. It is interesting, however, to


examine the sensitivity of our regional sea-level projections to ice sheet changes which are

substantially larger than the estimates of the IPCC AR4.

5.5.3. High-End Regional 21st Century Sea-Level Projections for Norway

Here we show the high-end scenario of regional sea-level projections for the end of the 21st

century. Our sea level estimates take account of (1) ocean density (steric) and circulation

changes, (2) ice and ocean mass changes and associated gravitational effects on sea-level

and (3) vertical land motion arising from past surface loading change and associated

gravitational effects on sea level.

Ocean density and circulation changes can be considered separately as global thermal

expansion (Section 5.5.1) and dynamic sea-level changes (Section 5.5.2.1). As shown, we

estimate global thermal expansion as 0.12 to 0.49 m and regional dynamic sea-level change

as between -0.06 and 0.57 m for the end of the 21st century. Total sea-level change due to

ocean density and circulation changes is calculated as 0.19 to 0.93 m, which reflects the

large variation between the AOGCMs with increasing temperature. Non-uniform sea-level

changes due to land ice changes are as follows: As shown in our earlier analysis (Section

5.2.2.2) glaciers changes appear most important for the spatial variation of the projected

changes, being approximately twice as large in the south of Norway when compared to the

north. Changes to the Greenland ice sheet are unimportant for the Norwegian coast (-0.01

to 0.02 m) whereas the contribution from the Antarctic ice sheets ranges widely from -0.01

m to 0.5 m. Vertical land motion and associated gravitational effects on sea level are taken

from the results of our best-fit GIA model in Chapter 3. Following Katsman et al. [2011] the

separate contributions to global sea level are added together using their median values and

their uncertainties are summed quadratically. We note that there are some small

inconsistencies between the time periods we consider for our high-end contributions but we

believe this will not greatly influence our final estimates.


Fig. 5.11. The upper bound of high-end 21st century (approximately 2090–2099 relative to 1980–1999) projected relative sea-level changes. Our regional estimates take account of (1) ocean density and circulation changes, (2) ice and ocean mass changes and associated gravitational effects on sea-level, and (3) vertical land motion and associated gravitational effects arising from past surface loading change. Units are in meters.

Figure 5.11 shows high-end regional 21st century projected relative sea-level changes for the

Norwegian coastal municipalities. We show maximum projected changes as we are primarily

interested in defining an upper bound on future sea-level change, which we find varies

between 0.7 and 1.3 m along the Norwegian coast (Fig 5.11). For the top end of our high-

end scenario, therefore, future ocean density and mass changes dominate the land uplift

signal across all of Norway. The pattern of sea-level change, however, still largely reflects

land motion due to glacial Isostatic adjustment. A summary of the projected relative sea-

level changes for key locations is given in Table 5.5.


Location Global thermal

expansion (cm)

Local ocean

density changes

(cm)

Non-uniform glaciers

(cm)

Non-uniform Green-

land (cm)

Non-uniform Antarc-

tica (cm)

GIA effect (± 7 cm)

Relative sea-level change

(cm)

Oslo 12 to 49 -6 to 57 7 to 17 0 to 1 -1 to 49 -54 -6 to 83 Stavanger 12 to 49 -6 to 57 7 to 17 0 -1 to 50 -16 32 to 121

Bergen 12 to 49 -6 to 57 7 to 17 0 -1 to 49 -22 25 to 114 Trondheim 12 to 49 -6 to 57 6 to 14 0 -1 to 48 -52 -7 to 82

Tromsø 12 to 49 -6 to 57 4 to 10 0 -1 to 46 -24 18 to 106 Table 5.5. Projected high-end 21st century (approximately 2090–2099 relative to 1980–1999) sea-level changes at key locations in Norway. The projected ocean density and circulation changes are calculated using the methods of Katsman et al. [2008; 2011]. The land ice signal includes an estimation of glacier changes based on the work of Meier et al. [2007] and an ice sheet contribution from Katsman et al. [2011]. For comparison, global mean sea level is projected to be between 56 and 114 cm (central value 85 cm).

5.6. Conclusions

We calculate 21st century regional sea-level changes for Norway by closely following

the methodology presented by Slangen et al. [2011] and using work from IPCC AR4.

Our projections, which are dependent on the SRES scenarios A2, A1B and B1, indicate

relative sea-level changes for the Norwegian coastal municipalities to be around -0.2

to 0.3 m for the period 2090–2099 relative to 1980–1999. Given the large

uncertainties associated with the future contributions of the ice sheets, we do not

attach a probability to our projections.

Our sea-level projections show a pattern of complex interplay between glacial

isostatic adjustment and non-uniform ocean surface changes. By isolating the

individual processes that drive sea-level changes, we are able to better constrain (or

at least test the sensitivity of) their contributions.

We investigate a high-end scenario based on the approach of Katsman et al. [2008,

2011]. As a tentative upper bound, relative sea-level changes for the Norwegian

coastal municipalities are between 0.7 and 1.3 m for the period 2090–2099 relative

to 1980–1999. Owing to the lack of understanding of some of the processes that

drive sea-level change, we do not formally assign a probability to our high-end

estimates. However, we consider the upper bound of our high-end scenario as a very

unlikely future.


6. Extreme Sea Levels

Extreme sea-level events such as storm surges can have large impacts. Quantifying the

amplitude and frequency of extreme sea-levels, therefore, is needed for the effective

management of coastal zones. In the first part of this Chapter, we present a statistical

analysis of observed extreme sea levels along the Norwegian coast. The analysis provides

estimates of extreme sea levels for a selection of return periods. We go on to briefly

consider potential future changes in extreme sea levels.

6.1. Observed Extreme Sea Levels

Our observations of extreme sea levels come from the Norwegian tide gauge network which

is operated by the Norwegian Mapping Authority. The methodology and analysis presented

here is based on the work of Haug et al. [2011].

6.1.1. Statistical Methods

In our analysis of extreme sea levels, we have used the recently developed Average

Conditional Exceedance Rate (ACER) method. Here, we will only give a brief description of

the ACER method, the reader is referred to Naess and Gaidai [2009] for details. The ACER

method differs from older methods (see Coles [2001] for descriptions) by focusing on the

exceedance rates of sea levels, rather than the actual observations, and by also trying to

capture the sub-asymptotic behavior of the relevant process. By extrapolating the fitted

curve to levels where there are few or no data, one is able to estimate return levels of

extreme sea levels, without invoking the strict assumption of asymptotic behavior. Also, by

taking sub-asymptotic behavior into consideration, the ACER method may be able to take

advantage of more data than the older methods, which only use a small fraction of the

available observations. The ACER method may thus be able to give more robust and more

precise estimations of return levels, especially when only a few years of observations are

available, as is the case at several of the tide gauges in Norway.


Due to the presence of long-term relative sea-level changes, recent sea level observations

are not directly comparable with older ones. Thus, the time-series of observations is first

detrended before we perform our analysis using the ACER method.

6.1.2. Estimated Return Levels

Below, we present the return levels estimated using the ACER method. Note that a return

period of 10 years mean that such an event will occur, on average, once every 10 years.

There is also 10% chance every year that this level will be exceeded. The complete results of

the analysis (including 95% confidence intervals) are given in Haug et al. [2011], where, for

comparison, return levels obtained using the traditional methods for extreme value analysis

are included.

5 yrs 10 yrs 20 yrs 50 yrs 100 yrs

200 yrs

500 yrs 1000 yrs

Viker 122 133 142 155 163 172 183 191 Oslo 125 136 147 161 171 180 193 203 Oscarsborg 121 131 141 152 161 169 180 188 Helgeroa 108 117 125 136 143 150 160 167 Tregde 86 92 98 105 111 116 122 127 Stavanger 94 99 104 110 115 119 125 129 Bergen 122 127 132 138 142 146 150 154 Måløy 147 152 158 164 168 172 177 181 Ålesund 161 169 176 184 190 196 204 209 Kristiansund 167 174 180 188 193 198 204 209 Heimsjø 180 187 193 201 206 211 217 222 Trondheim 202 209 216 224 230 235 242 247 Rørvik 190 198 206 216 222 229 237 243 Bodø 208 217 225 234 241 248 256 262 Kabelvåg 222 232 241 252 260 267 277 283 Narvik 236 246 255 267 275 283 293 300 Andenes 169 178 185 195 201 208 216 222 Harstad 163 170 176 184 190 195 202 207 Tromsø 191 198 204 212 218 223 230 235 Hammerfest 191 199 205 214 219 225 232 237 Honningsvåg 190 198 205 214 220 226 233 238 Vardø 208 216 223 231 237 243 250 255

Table 6.1. Estimated storm-surge return levels at the tide gauges on the Norwegian mainland for a range of return periods. Storm-surge heights (cms) are given relative to mean sea level.


6.1.3. Changes in Observed Extreme Sea Levels

There is little observational evidence to suggest that extreme sea levels change at a different

rate to mean sea-level changes. A study by Woodworth and Blackman [2004] using data

from 141 tide gauge stations from around the globe showed that, in some cases, extreme

sea levels have increased since 1975. However, for the majority of records examined,

including the Norwegian tide gauges included in their analyses, changes in extreme sea

levels are not significantly different from mean sea-level changes. We believe it is reasonable

to expect that this will continue to be the case for the Norwegian coast in the near future

(the next 20 to 30 years).

6.2. Future Changes in Extreme Sea Levels

As discussed above, observational evidence suggests that extreme sea-level changes for

Norway over the next few decades will not deviate significantly from mean sea-level

changes. Towards the end of the 21st century, however, that picture could change. Modeling

studies for the North Sea (i.e. south of Norway) indicate that storm-surge heights could

increase significantly along the continental coast towards 2100 [e.g. Woth et al., 2005;

2006]. Projections for the North Atlantic and Norwegian coast also suggest changes in

extreme sea levels will occur, but these results are often not statistically significant and

highly dependent on the model and emission forcing scenario applied [e.g. Wang et al.,

2004; Debernard and Røed, 2008]. In a recent study by Lowe and Gregory [2005], the

authors highlight the difficulties in quantifying uncertainty in projections of future storm-

surge heights.

Our brief review of the literature indicates that (1) observations from the Norwegian tide

gauge network show that changes in extreme sea levels are not significantly different from

mean sea-level changes [Woodworth and Blackman, 2004] and (2) projections for Norway

are not robust (statistically significant). Given this, we do not attempt to include an estimate

of future extreme sea-level changes in this report.


6.3. Conclusions

We present a new analysis of extreme sea levels for Norway using the recently

developed Average Conditional Exceedance Rate method. Estimated storm-surge

heights are given for the locations of the Norwegian tide gauges and for a range of

return periods.

Findings from our brief review of the literature suggest that in the short-term (the

next 20 to 30 years) changes in extreme sea levels will not be significantly different

from mean sea-level changes. Some modeling studies suggest that changes in

extreme sea levels will occur towards 2100.


7. Summary

Estimating future sea-level changes is a challenging task as it requires a sound understanding

of many different aspects of the Earth-climate system. Due to our limited understanding of

some of the processes that drive sea-level changes, there are large uncertainties associated

with projections of future sea levels. In particular, the future contributions of the large ice

sheets are presently difficult to constrain [e.g. Alley et al., 2005]. These difficulties should be

kept in mind when interpreting our results. In essence, the work in this report can be

summarized in two main points:

1) We have established a framework for estimating future regional sea-level changes.

This is an important first step towards improving sea-level projections for Norway.

2) By isolating the individual processes that drive sea-level changes we are able to

better constrain their contributions to future sea-level changes along the Norwegian

coast. For example, our reassessment of vertical crustal velocities based on the new

GPS data.

A summary of our findings for mean sea-level changes is as follows (see Table 7.1 for key

locations): Observations from the Norwegian tide gauges indicate that over the period 1980

to 2010 (the past 30 years) relative sea-level change has been positive in some areas and

negative in others. These differences largely reflect the ongoing vertical motion of the solid

Earth due to the process of glacial isostatic adjustment. If we assume the observed rates

continue unchanged for the next 20 years, relative sea-level changes at the tide gauge sites

will vary between -6.5 and 6 cm for the period 2000 to 2030. It is reasonable to expect that

sea-level changes over such short time-scales will not deviate significantly from our

observations. If we assume that the observed rates remain unchanged over the 21st century

(i.e. over the 105 year period 2090–2099 relative to 1980–1999) then this leads to a

corresponding relative sea level variation of between -23 and 21 cm. This is rather

speculative but gives us a yardstick to which we can compare our projections.


Observed sea level extrapolated for 2000 → 2030 (cm)

Observed sea level

extrapolated for 2100 (cm)

Projected sea level for 2100 (1σ ± 13 cm)

Projected upper bound for 2100 (cm)

Oslo -5 -18 -10 83 Stavanger 5 19 28 121 Bergen 3 12 21 114 Trondheim - - -10 82 Tromsø 2 8 11 106 Table 7.1. Extrapolated and projected sea-level changes for key locations in Norway. Column 2 is taken from Table 4.1 and assumes that observed relative sea-level rates for 1980 to 2010 continue unchanged for the next 20 years. Column 3 assumes that the observed rates remain unchanged over the 21st century (2090–2099 relative to 1980–1999). Column 4 gives 21st century projections which are based on the method of Slangen et al. [2011] and dependent on the IPCC SRES scenarios A2, A1B and B1. Column 5 gives our upper bound on 21st century projections based on the approach of Katsman et al. [2011].

Following IPCC AR4, we attach no likelihood to any of our projections owing to the lack of

understanding of some of the processes that cause sea-level change. Our projections based

on the method of Slangen et al. [2011] and which are dependent on the IPCC SRES scenarios

A2, A1B and B1 indicate that 21st century regional sea-level changes in Norway will vary

between -20 to 30 cm. It is interesting to see that, at least for key locations, our projected

values are similar to the extrapolated rates from the tide gauge observations (compare

columns 3 and 4). This is not entirely surprising, both observations and projections are

strongly influenced by the land motion signal, but the interpretation of such comparisons

should be made with care. For the high-end scenario, our 21st century projections suggest

regional sea-level changes in Norway could be as large as between 70 and 130 cm. These

results are for a global atmospheric temperature rise of up to 6 °C and assuming the scenario

of an emerging collapse for some areas of the Antarctic ice sheets (note that the ice-dynamic

estimates used here are not temperature dependent but based on expert judgment)

[Katsman et al., 2011]. This high-end estimate can tentatively be considered as the upper

bound of 21st century sea-level change for Norway.


7.1. Comparisons to the DSB-Report

As discussed, official projections for the 21st century sea level change along the Norwegian

coast are found in the DSB-report “Havnivåstigning” *Vasskog et al., 2009] based on a study

by Drange et al. [2007]. The projections found in the DSB-report are the ones presently

recommended for land use planning in Norway and are based on the semi-empirical

approach of Rahmstorf [2007]. The main improvement on the DSB-report, therefore, is that

we analyze the separate components contributing to sea-level change to arrive at a truly

regional prognosis.

In Figure 7.1 we show global mean and local sea-level projections for Oslo from both the

DSB-report and our analysis. If we first examine projected global mean changes, the DSB-

report gives the most likely sea-level change as 80 (ranging 60 to 115) cm for 2000 to 2100.

In contrast, we find a range of between 40 cm (the lower bound following the methods of

Slangen et al. [2011] and findings of the IPCC AR4) and 115 cm (the upper limit largely

based on the approach of Katsman et al. [2011]) for the period 1980–1999 to 2090–2099.

Our analysis, therefore, suggests a larger uncertainty and reduced lower bound when

compared to the DSB-report.

Fig. 7.1. Projected 21st century global mean and relative sea-level changes for Oslo. Dark blue rectangles mark results based on the method of Slangen et al. [2011] and light blue are those from the high-end projections based on the approach of Katsman et al. [2011]. Results from the DSB-report are in red and the horizontal black line shows their most likely future sea level estimate.


Comparisons between our findings and those of the DSB-report are, of course, not

straightforward. For example, the separate reports consider different emission scenarios

(global temperature changes) and slightly different time periods. By focusing on the semi-

empirical model of Rahmstorf [2007], however, we believe that the DSB-report gives a rather

narrow view of current scientific understanding and the uncertainties involved. It is not yet

clear why semi-empirical models give higher sea-level projections when compared to more

complex physical models (e.g. as used in IPCC AR4). On one hand we aware that complex

physical models are limited and, in particular, the absence of ice-dynamic processes and ice-

ocean interactions in the current generation of ice sheet models remains an issue. On the

other hand, it is not known if the simple historical relationship between sea-level change and

global temperature change, which is the basis of semi-empirical models [e.g. Rahmstorf,

2007], will hold in the future. There are also a number of other concerns about semi-

empirical models, not all of these have been addressed [e.g. Church et al., 2011]. To

summarize, there is currently no scientific consensus about why there are differences

between these two approaches to global mean sea-level projections or, indeed, which is the

more reliable. These issues are being investigated by the sea level community. Adopting

projections based on semi-empirical models implies a substantial contribution from land ice

to 21st century global mean changes. Whereas, in our analysis, we consider both a small and

large land ice contribution, which we believe gives a more complete picture of the state of

sea level science today.

While global mean changes are clearly relevant, the main objective of our analysis here has

been to arrive at regional sea-level projections by examining the individual processes that

cause sea-level changes. We note that discrepancies between the DSB-report and our

findings are larger for local sea-level projections than for estimates of the global mean (see

Fig. 7.1). There are several reasons for this. Firstly, we take a different approach to

calculating glacial isostatic adjustment. Our estimates of vertical land motion are constrained

using new GPS observations which generally indicate higher rates of uplift than shown in the

DSB-report. Secondly, and more importantly, we are able to analyze how sea surface

changes (those driven by ocean density and ice mass changes) will deviate from the global

mean. The DSB-report indicates that Norway will experience an absolute sea-level change 10

cm above the global mean (or 10% larger). Our findings, however, would suggest that


future absolute sea-level changes along the Norwegian coast could, depending on the

different scenarios considered, vary by around -20 to +20% in relation to the global mean

(see Tables 8.18 and 8.19).

These discrepancies underline the importance of working towards truly regional sea-level

projections and trying to better understand the individual processes contributing to local

sea-level changes. For example, a key difference between our analysis and that of the DSB-

report is that we attempt to account for the non-uniform sea-level response to projected ice

mass changes. This shows that the Norwegian coast is relatively insensitive to substantial

changes in Greenland, but will experience above average sea-level rise if large changes in

Antarctica occur. Finally, we note that although it is mean sea-level changes that we have

focused on this report, it is the combined impact of these changes alongside extreme sea

level events that will shape public perception of climate change in years to come.

Acknowledgements

First and foremost we thank Aimée Slangen for making the ice sheet and glacier ice mass

change estimates available to us. This work has benefited from useful feedback and

discussions, for which we thank Glenn Milne, Pippa Whitehouse, Jon Ove Hagen, Leanne

Wake, Aimée Slangen, Willy Fjeldskaar, Helge Drange, Jens Debernard, Lars Petter Røed, Jack

Kohler, Jon Inge Svendsen and Olav Vestøl.


8. Appendix I: Projected 21st Century Sea Levels

Projected 21st century relative sea-level changes for the Norwegian coastal municipalities

can be calculated using the below tables. Estimates of vertical land motion and geoid

changes (Chapter 3) are listed in Section 8.1. Kriging values are only given for areas where

we have sufficient GPS observations and believe the interpolation results to be reliable.

Projections of sea surface changes (Chapter 5) are given in Section 8.2 and 8.3. Note that

projected sea surface changes are only given for the counties (e.g. Finnmark); spatial

variations in sea surface heights were found to be less than ±1 cm for each county. Below we

give an example of how to calculate projected 21st century relative sea-level changes for

Mandal in Vest-Agder.

Thus, for our projections based on Slangen et al. [2011] and results of IPCC AR4:

And for our estimation of the upper bound of sea-level changes based largely on Katsman et

al. [2011]:


8.1. Projected 21st Century GIA Effects and Vertical Land

Motion

8.1.1. Finnmark

Municipality Municipality no.

Projection site

GIA-land (1σ ± 7 cm)

GIA-geoid (1σ <± 1 cm)

Kriging (1σ ± 7 cm)

Sør-Varanger 2030 Kirkenes -36 4 - Nesseby 2027 Nesseby -30 4 - Vadsø 2003 Vadsø -28 4 - Vardø 2002 Vardø -24 4 - Båtsfjord 2028 Båtsfjord -21 4 - Berlevåg 2024 Berlevåg -20 3 -

Tana 2025 Smalfjord -28 4 - Gamvik 2023 Gamvik -17 3 - Lebesby 2022 Lebesby -23 4 - Nordkapp 2019 Honningsvåg -18 3 - Porsanger 2020 Lakselv -30 4 - Måsøy 2018 Havøysund -17 3 - Kvalsund 2017 Kvalsund -25 4 - Hammerfest 2004 Hammerfest -19 3 - Hasvik 2015 Breivikbotn -20 3 - Alta 2012 Alta -32 4 - Loppa 2014 Øksfjord -22 4 -

Table. 8.1. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Finnmark. Units are in centimeters.

8.1.2 Troms


Projection site GIA-land (1σ ± 7 cm)



Kvænangen 1943 Burfjord -33 4 -33 Nordreisa 1942 Sørkjosen -37 4 -30 Skjervøy 1941 Skjervøy -24 4 -27 Kåfjord 1940 Olderdalen -37 4 -31

Storfjord 1939 Skibotn -41 4 -31 Lyngen 1938 Lyngseidet -30 4 -30 Karlsøy 1936 Karlsøy -21 4 -30 Tromsø 1902 Tromsø -26 4 -32 Balsfjord 1933 Storsteinnes -36 4 -30 Målselv 1924 Målsnes -43 4 -30 Lenvik 1931 Finnsnes -29 4 -35


Berg 1929 Skaland -26 4 -32

Torsken 1928 Gryllefjord -26 4 -30 Tranøy 1927 Vangsvik -32 4 -33 Sørreisa 1925 Sørreisa -36 4 -39 Dyrøy 1926 Brøstadbotn -35 4 -35 Salangen 1923 Sjøvegan -39 4 -36 Lavangen 1920 Tennevoll -42 4 -36 Gratangen 1919 Årstein -42 4 -34 Ibestad 1917 Hamnvik -37 4 -32 Skånland 1913 Evenskjer -40 4 -32 Bjarkøy 1915 Nergårshamn -28 4 -28 Harstad 1901 Harstad -34 4 -30 Kvæfjord 1911 Borkenes -33 4 -30

Table. 8.2. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Troms. Units are in centimeters.

8.1.3. Nordland



GIA-geoid (1σ <± 1

cm)


Andøy 1871 Andenes -25 4 - Øksnes 1868 Myre -24 4 - Sortland 1870 Sortland -29 4 - Bø 1867 Straume -26 4 - Hadsel 1866 Stokmarknes -30 4 - Vågan 1865 Svolvær -32 4 - Vestvågøy 1860 Leknes -28 4 - Flakstad 1859 Ramberg -26 4 - Moskenes 1874 Reine -29 4 - Værøy 1857 Sørland -30 4 - Røst 1856 Røstlandet -28 4 - Lødingen 1851 Lødingen -37 4 - Tjeldsund 1852 Nedre Fjeldal -39 4 - Evenes 1853 Bogen -42 4 - Narvik 1805 Narvik -49 5 - Ballangen 1854 Ballangen -45 4 - Tysfjord 1850 Kjøpsvik -49 5 - Hamarøy 1849 Oppeid -47 5 - Steigen 1848 Leinesfjorden -41 4 - Sørfold 1845 Straumen -53 5 - Bodø

1804 Bodø Skjerstadfjorden

-43 4 -


Fauske 1841 Fauske -59 5 - Saltdal 1840 Rognan -61 5 - Beiarn

1839 Moldjord (Leirvika)

-55 5 -

Gildeskål 1838 Inndyr -47 5 - Meløy 1837 Ørnes -47 5 - Rødøy 1836 Våga -43 5 - Rana 1833 Mo i Rana -62 5 - Træna 1835 Selvær -40 4 - Lurøy 1834 Lurøy -46 5 - Nesna 1828 Nesna -51 5 - Leirfjord 1822 Leland -53 5 - Hemnes 1832 Bjerka -64 5 - Vefsn 1824 Mosjøen -58 5 - Dønna 1827 Solfjellsjøen -46 5 - Herøy 1818 Silvalen -43 4 - Alstahaug 1820 Sandnessjøen -51 5 - Vega 1815 Holand -47 5 - Vevelstad 1816 Forvik -55 5 - Brønnøy 1813 Brønnøysund -51 5 - Sømna 1812 Vik (Sørvika) -54 5 - Bindal 1811 Terråk -61 5 -

Table. 8.3. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Nordland. Units are in centimeters.

8.1.4. Nord-Trøndelag




cm)


Leka 1755 Sør-Gutvika -50 5 -

Nærøy 1751 Kolvereid -57 5 - Høylandet 1743 Kongsmoen -63 5 - Vikna 1750 Rørvik -48 5 - Fosnes 1748 Salsnes -58 5 - Namsos 1703 Namsos -57 5 -

Flatanger 1749 Lauvsnes -50 5 - Namdalseid 1725 Sjøåsen -58 5 - Verran 1724 Malm -58 5 - Steinkjer 1702 Steinkjer -66 5 - Inderøy 1729 Straumen -62 5 - Leksvik 1718 Leksvik -57 5 - Mosvik 1723 Saltvikhamna -59 5 -


Verdal 1721 Verdal -68 5 -

Levanger 1719 Levanger -64 5 - Frosta 1717 Sørgrenda -59 5 - Stjørdal 1714 Stjørdalshalsen -64 5 -

Table. 8.4. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Nord-Trøndelag. Units are in centimeters.

8.1.5. Sør-Trøndelag


Projection site

GIA model (1σ ± 7 cm)

GIA model (1σ <± 1

cm)


Osen 1633 Osen -47 5 - Roan 1632 Roan -49 4 - Åfjord 1630 Årnes -52 5 - Bjugn 1627 Botngård -47 4 - Frøya 1620 Sistranda -39 4 - Ørland 1621 Brekstad -48 4 - Rissa 1624 Rissa -53 5 - Hitra 1617 Fillan -42 4 - Snillfjord 1613 Krogstadøra -49 4 - Agdenes 1622 Lensvik -50 4 - Hemne 1612 Kyrksæterøra -47 4 -

Orkdal 1638 Orkanger -53 5 - Skaun 1657 Børsa -56 5 - Melhus 1653 Gran -59 5 - Trondheim 1601 Trondheim -57 5 - Malvik 1663 Hommelvik -63 5 -

Table. 8.5. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Sør-Trøndelag. Units are in centimeters.


8.1.6. Møre and Romsdal




cm)

Kriging (1σ ± 7

cm)

Smøla 1573 Hopen -36 4 32 Aure 1576 Aure -42 4 34 Halsa 1571 Vågland -44 4 36 Surnadal 1566 Surnadalsøra -48 4 39 Kristiansund 1505 Kristiansund -34 4 33 Tingvoll 1560 Tingvoll -42 4 36 Sunndal 1563 Sunndalsøra -51 4 43 Averøy 1554 Kårvåg -35 4 33 Gjemnes 1557 Batnfjordsøra -39 4 36

Nesset 1543 Eidsvåg -44 4 39 Eide 1551 Eide -35 4 34 Fræna 1548 Elnesvågen -32 4 33

Molde 1502 Molde -37 4 35 Rauma 1539 Åndalsnes -41 4 38 Aukra 1547 Aukrasanden -31 4 33 Sandøy 1546 Steinshamn -28 3 33 Midsund 1545 Midsund -31 4 34 Vestnes 1535 Helland -35 4 35 Haram 1534 Brattvåg -30 4 33 Skodje 1529 Skodje -32 4 34 Ørskog 1523 Sjøholt -34 4 35 Stordal 1526 Stordal -37 4 36

Norddal 1524 Sylte -40 4 38 Giske 1532 Valderhaugstranda -26 3 32 Ålesund 1504 Ålesund -29 3 33 Sykkylven 1528 Aure -33 4 34 Stranda 1525 Stranda -38 4 37 Ulstein 1516 Ulsteinvik -26 3 32 Hareid 1517 Hareid -28 3 33 Sula 1531 Langevågen -29 4 33 Ørsta 1520 Ørsta -31 4 33 Herøy 1515 Fosnavåg -23 3 31 Volda 1519 Volda -30 3 33 Sande 1514 Larsnes -24 3 31

Vanylven 1511 Fiskå -26 3 32

Table. 8.6. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Møre and Romsdal. Units are in centimeters.


8.1.7. Sogn and Fjordane




cm)


Selje 1441 Selje -22 3 30 Vågsøy 1439 Måløy -21 3 30 Eid 1443 Nordfjordeid -30 3 33 Stryn 1449 Stryn -38 4 36 Bremanger 1438 Svelgen -23 3 30 Gloppen 1445 Sandane -31 4 33 Flora 1401 Florø -23 3 30 Naustdal 1433 Naustdal -30 3 32 Luster 1426 Gaupne -41 4 38

Askvoll 1428 Askvoll -23 3 29 Førde 1432 Førde -32 3 32 Fjaler 1429 Dale -27 3 30

Gaular 1430 Bygstad -32 3 32 Balestrand 1418 Balestrand -36 4 35 Leikanger 1419 Leikanger -39 4 36 Sogndal 1420 Sogndal -41 4 38 Årdal 1424 Årdalstangen -48 4 43 Solund 1412 Hardbakke -22 3 28 Hyllestad 1413 Hyllestad -25 3 29 Høyanger 1416 Høyanger -30 3 31 Vik 1417 Vik -37 4 35 Aurland 1421 Aurlandsvangen -42 4 38

Lærdal 1422 Lærdalsøyri -47 4 42 Gulen 1411 Eivindvik -25 3 28

Table. 8.7. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Sogn and Fjordane. Units are in centimeters.


8.1.8. Hordaland


Projection site GIA-land (1σ ± 7

cm)


cm)


Fedje 1265 Fedje -21 3 27 Austrheim 1264 Fonnes -22 3 27 Masfjorden 1266 Solheim -27 3 29 Modalen 1252 Mo -32 3 31 Radøy 1260 Manger -23 3 27 Lindås 1263 Knarvik -26 3 28 Vaksdal 1251 Vaksdal -30 3 30 Voss 1235 Bolstadøyri -35 4 33 Øygarden 1259 Tjeldstø -22 3 27

Meland 1256 Frekhaug -24 3 27 Osterøy 1253 Lonevåg -27 3 29 Fjell 1246 Straume -22 3 27

Askøy 1247 Kleppestø -24 3 27 Bergen 1201 Bergen -25 3 28 Samnanger 1242 Tysse -29 3 29 Kvam 1238 Norheimsund -31 3 30 Granvin 1234 Eide -37 3 34 Ulvik 1233 Ulvik -40 4 37 Sund 1245 Tælavåg -23 3 27 Austevoll 1244 Storebø -22 3 26 Os 1243 Osøyro -25 3 27 Fusa 1241 Eikelandsosen -28 3 29

Jondal 1227 Jondal -32 3 31 Ullensvang 1231 Kinsarvik -36 3 33 Eidfjord 1232 Eidfjord -41 4 37 Tysnes 1223 Uggdalseidet -25 3 27 Bømlo 1219 Svortland -20 3 26 Fitjar 1222 Fitjar -22 3 26 Stord 1221 Leirvik -24 3 27 Kvinnherad 1224 Rosendal -29 3 29 Odda 1228 Odda -35 3 32 Sveio 1216 Mølstrevåg -21 3 26 Etne 1211 Etne -27 3 28

Table. 8.8. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Hordaland. Units are in centimeters.


8.1.9. Rogaland



cm)


Kriging (1σ ± 7

cm)

Haugesund 1106 Haugesund -19 3 25 Vindafjord 1160 Ølen -24 3 27 Sauda 1135 Sauda -30 3 30 Utsira 1151 Nordvik -16 3 25 Karmøy 1149 Kopervik -18 3 25 Tysvær 1146 Hervik -21 3 26 Suldal 1134 Sand -29 3 29 Bokn 1145 Føresvik -19 3 25 Finnøy 1141 Judaberg -21 3 26

Hjelmeland 1133 Hjelmeland -25 3 27 Kvitsøy 1144 Ystabøhamn -18 3 25 Rennesøy 1142 Vikevåg -19 3 25

Randaberg 1127 Bøvika -18 3 25 Stavanger 1103 Stavanger -19 3 25 Strand 1130 Jørpeland -21 3 26 Sola 1124 Solavika -17 3 25 Sandnes 1102 Sandnes -18 3 25 Forsand 1129 Forsand -24 3 27 Klepp 1120 Revtangen -16 3 25 Gjesdal 1122 Frafjord -21 3 26 Hå 1119 Sirevåg -15 3 25 Eigersund 1101 Eigersund -16 3 25

Sokndal 1111 Sogndalsstranda -15 3 25

Table. 8.9. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Rogaland. Units are in centimeters.


8.1.10. Vest-Agder


Projection site

GIA-land (1σ ± 7

cm)



Flekkefjord 1004 Flekkefjord -18 3 25 Kvinesdal 1037 Øye -20 3 25 Farsund 1003 Farsund -15 3 24 Lyngdal 1032 Lyngdal -18 3 24 Lindesnes 1029 Åvik -18 3 24 Mandal 1002 Mandal -18 3 24 Søgne 1018 Høllen -19 3 24 Kristiansand 1001 Kristiansand -21 3 25

Table. 8.10. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Vest-Agder. Units are in centimeters.

8.1.11. Aust-Agder


Projection site

GIA-land (1σ ± 7

cm)



Lillesand 926 Lillesand -23 3 26 Grimstad 904 Grimstad -26 3 27 Arendal 906 Arendal -29 3 29

Tvedestrand 914 Tvedestrand -32 4 32 Risør 901 Risør -35 4 33

Table. 8.11. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Aust-Agder. Units are in centimeters.

8.1.12. Telemark


Projection site




Kragerø 815 Kragerø -38 4 36

Bamble 814 Langesund -40 4 39 Porsgrunn 805 Porsgrunn -43 4 41

Table. 8.12. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Telemark. Units are in centimeters.


8.1.13. Vestfold


Projection site

GIA-land (1σ ± 7

cm)


cm)


Larvik 709 Larvik -43 4 41 Sandefjord 706 Sandefjord -44 4 43 Tjøme 723 Verdens Ende -46 4 44 Stokke 720 Melsomvik -47 4 45 Nøtterøy 722 Årøysund -47 4 45 Tønsberg 704 Tønsberg -49 4 47 Horten 701 Horten -50 4 49 Re 716 Mulodden -49 4 47 Holmestrand 702 Holmestrand -50 4 48

Sande 713 Selvik -52 4 50 Svelvik 711 Svelvik -53 4 51

Table. 8.13. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Vestfold. Units are in centimeters.

8.1.14. Buskerud



cm)


cm)


Drammen 602 Drammen (Tangen) -53 4 -51 Lier 626 Linnesstranda -55 4 -53

Røyken 627 Nærsnes -55 5 -52 Hurum 628 Tofte -53 5 -51

Table. 8.14. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Buskerud. Units are in centimeters.

8.1.15. Oslo


Projection site




Oslo 301 Oslo -59 5

-57

Table. 8.15. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Oslo. Units are in centimeters.


8.1.16. Akershus



cm)


cm)


Asker 220 Konglungen -56 5 54 Bærum 219 Sandvika -58 5 55 Nesodden 216 Nesoddtangen -57 5 55 Oppegård 217 Svartskog -57 5 55 Frogn 215 Drøbak -55 4 53 Ås 214 Nesset -56 4 54 Vestby 211 Son -54 5 52

Table. 8.16. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Akershus. Units are in centimeters.

8.1.17. Østfold


Projection site

GIA-land (1σ ± 7

cm)



Moss 104 Moss -52 4 50 Rygge 136 Rørvik -51 4 49 Råde 135 Saltnes -50 4 49 Fredrikstad 106 Fredrikstad -49 4 48 Sarpsborg 105 Høysand -51 4 50 Hvaler 111 Skjærhollen -47 4 45

Halden 101 Halden -50 4 48

Table. 8.17. Projected 21st century (2090–2099 relative to 1980–1999) vertical land motion and geoid changes for the coastal municipalities within the county of Østfold. Units are in centimeters.


8.2. Projected 21st Century Sea Surface Changes

County Projected ocean density and

circulation changes (1σ ± 10 cm)

Projected land ice mass change (1σ ± 4 cm)

Total sea surface change

(1σ ± 11 cm)

Finnmark 31 8 39 (83%) Troms 31 8 39 (83%) Nordland 31 9 40 (85%) Nord-Trøndelag 31 11 42 (89%) Sør-Trøndelag 31 11 42 (89%) Møre og Romsdal 31 11 42 (89%) Sogn og Fjordane 31 12 43 (91%)

Hordaland 31 12 43 (91%) Rogaland 31 13 44 (94%) Vest-Agder 31 14 45 (96%) Aust-Agder 31 14 45 (96%) Telemark 31 14 45 (96%) Vestfold 31 14 45 (96%) Buskerud 31 13 44 (94%) Oslo 31 13 44 (94%) Akershus 31 14 45 (96%) Østfold 31 14 45 (96%)

Table 8.18. Projected 21st century (2090–2099 relative to 1980–1999) sea surface changes based the method of Slangen et al. [2011] and dependent on scenarios A2, A1B and B1 (see Chapters 5 and 7 for details). It is important to note that, following IPCC AR4, we attach no likelihood to the projections owing to our lack of understanding of some of the processes that cause sea-level change. The brackets in column 4 give local sea surface changes as a percentage of the global mean (47 ± 8 cm).


8.3. Projected High-End 21st Century Sea Surface Changes

County Global

thermal expansion

(cm)

Local density changes

(cm)

Non-uniform glaciers

(cm)

Non-uniform Green-

land (cm)

Non-uniform Antarc-

tica (cm)

Relative sea-level change

(cm)

Finnmark 12 to 49 -6 to 57 4 to 9 0 to 1 -1 to 46 42 to 129

(76 to 113%)

Troms 12 to 49 -6 to 57 4 to 10 0 to 0 -1 to 46 42 to 130

(75 to 114%)

Nordland 12 to 49 -6 to 57 5 to 12 0 to 0 -1 to 47 43 to 131

(77 to 115%)

Nord-Trøndelag

12 to 49 -6 to 57 6 to 14 0 to 0 -1 to 48 45 to 133

(80 to 117%)

Sør-Trøndelag

12 to 49 -6 to 57 6 to 14 0 to 0 -1 to 48 45 to 134

(81 to 117%)

Møre og Romsdal

12 to 49 -6 to 57 6 to 15 0 to -1 -1 to 49 46 to 134

(81 to 118%) Sogn og Fjordane

12 to 49 -6 to 57 7 to 16 0 to -1 -1 to 49 46 to 135

(82 to 119%)

Hordaland 12 to 49 -6 to 57 7 to 17 0 to 0 -1 to 49 47 to 136

(84 to 120%)

Rogaland 12 to 49 -6 to 57 7 to 17 0 to 0 -1 to 50 48 to 137

(86 to 121%)

Vest-Agder 12 to 49 -6 to 57 8 to 18 0 to 1 -1 to 50 49 to 139

(88 to 122%)

Aust-Agder 12 to 49 -6 to 57 7 to 18 0 to 2 -1 to 49 49 to 138

(87 to 121%)

Telemark 12 to 49 -6 to 57 7 to 17 0 to 1 -1 to 49 49 to 138

(87 to 121%)

Vestfold 12 to 49 -6 to 57 7 to 17 1 to 2 -1 to 49 49 to 138

(87 to 121%)

Buskerud 12 to 49 -6 to 57 7 to 17 0 to 1 -1 to 49 48 to 137

(86 to 120%)

Oslo 12 to 49 -6 to 57 7 to 17 0 to 1 -1 to 49 48 to 137

(86 to 120%)

Akershus 12 to 49 -6 to 57 7 to 17 0 to 1 -1 to 49 48 to 137

(86 to 120%)

Østfold 12 to 49 -6 to 57 7 to 18 1 to 2 -1 to 49 49 to 138

(88 to 121%) Table 8.19. Projected high-end 21st century sea surface changes (see Chapter 5 for details). The glacier contribution is based on the work of Meier et al. [2007]. The ice sheet contribution and ocean density and circulations changes are calculated using methods and numbers from Katsman et al. [2008, 2011]. We attach no likelihood to the projections. The brackets in column 7 give local sea surface changes as a percentage of global sea level (56 to 114 cm).


9. Appendix II: Notes on the Reference Levels

In this report, all sea-level changes and extreme sea levels are given relative to mean sea

level. For example, our sea-level projections are calculated for 2090–2099 relative to mean

sea-level changes over 1980–1999. For land use planning and mapping, however, it is

necessary to compute sea level with respect to the national vertical reference system. In

order to do so, the height of mean sea level in the national system must be known. The

vertical reference system of all geographical data and maps in Norway is NN1954 or NN2000

(see Lysaker et al. [2006] for a description of the former).

9.1. The National Vertical Reference Systems of Norway

A height is defined as the distance above a mathematically or physically defined reference

surface (see http://www.statkart.no/filestore/Geovekstforum_-

_Ekstranett/Referaterinnkallinger/-/standard_hoydesystemer.pdf). The reference surface, or

the zero-level, is theoretically a potential surface in the Earth's gravity field. We are

interested in the potential surface which corresponds to mean sea level. This enables us to

define heights with respect to mean sea level, these heights are commonly referred to as

“meters above sea level”.

Normalnull 1954 (NN1954) and Normalnull 2000 (NN2000) are the names of the two official

vertical reference systems currently in use in Norway. NN1954 is the oldest system which

will be replaced by NN2000 from 2011. Both systems will be used in parallel for a few more

years after this. The difference between NN1954 and NN2000 varies between –17 cm and 16

cm at the tide gauges along the Norwegian coast (see Fig. 9.1). The largest differences are

found around Vestfjorden in the north of Norway. Across the fjord from Kabelvåg in Lofoten

to Narvik, the zero-level changes from -10 cm to 8 cm relative to MSL for NN1954, i.e. a

difference of 18 cm. For NN2000 the change is from 8 cm to 9 cm relative to MSL, i.e. a

difference of only 1 cm. These differences are partly due to the ongoing glacial isostatic

adjustment of Fennoscandia and partly due to changes (developments) in the methods used

to determine the zero-level. The discrepancies between the two systems are significant and

http://www.statkart.no/filestore/Geovekstforum_-_Ekstranett/Referaterinnkallinger/-/standard_hoydesystemer.pdf

http://www.statkart.no/filestore/Geovekstforum_-_Ekstranett/Referaterinnkallinger/-/standard_hoydesystemer.pdf


must be taken into account when considering future sea levels or storm surges for land use

planning.

9.2. The Connection Between Mean Sea Level and the Vertical

Reference System

Each tide gauge is connected to a leveled bench mark on solid rock. The tide gauge bench

marks are parts of the precise leveling network used to realize the Norwegian vertical

reference system over land. All leveled benchmarks in solid rock have a height value in the

Norwegian height system NN2000 with accuracy of a few millimeters. Hence, by measuring

the height difference between the benchmark and the tide gauge, it is possible to determine

the height of local mean sea level in the national vertical reference system.

Local mean sea level is established by averaging tide gauge readings over a period of 19

years. In Norway, mean sea level is currently computed from observations between 1979

and 1997. Local mean sea level is connected to the tide gauge bench mark through the zero-

level of the tide gauge. The height of zero-level in the national vertical reference system is

controlled every third year by leveling between the tide gauge bench mark and the contact

point (reference stick) on the tide gauge. The contact point has a defined height value above

the zero-level. If the control leveling detects a change in the height difference between the

contact point and the tide gauge bench mark of more than 1 to 2 mm/yr, the contact point is

moved. This procedure ensures that the height of the zero-level is always the same relative

to the tide gauge bench mark.


Fig. 9.1. The height of NN1954 and NN2000 with respect to mean sea level (over the interval 1979 to 1997) at the Norwegian tide gauges.

The height of the mean sea level in NN1954 and NN2000 for all Norwegian tide gauges is

listed in Table 9.1. With this value known, the height of the sea level can be determined in

the preferred height system by Eq. 9.1.

Eq. 9.1. ,

where is the height of the sea level in the national reference system, is the height

of the sea level relative to mean sea level, and is the height of mean sea level in

the national reference system.


9.3. Which Reference Level Should be Used?

It is important to compute the sea level in the same reference system as used for land use

planning or mapping applications. However, the old NN1954 is known to include errors. We

recommend, therefore, that the new national reference system NN2000 be used for future

mapping and planning.

Tide gauge Municipality Height of MSL in NN1954 (m)

Height of MSL in NN2000 (m)

Viker Hvaler -0.149 -0.038

Oslo Oslo -0.146 0.012

Oscarsborg Frogn -0.112 0.016

Helgeroa Larvik -0.064 -0.036

Tregde Mandal -0.004 -0.119

Stavanger Stavanger -0.034 -0.107

Bergen Bergen -0.015 -0.077

Måløy Vågsøy -0.034 -0.081

Ålesund Ålesund -0.044 -0.069

Kristiansund Kristiansund -0.070 -0.057

Heimsjø Hemne -0.074 -0.062

Trondheim Trondheim -0.092 0.032

Rørvik Vikna -0.170 -0.085

Bodø Bodø -0.159 -0.113

Kabelvåg Vågan 0.099 -0.074

Narvik Narvik -0.082 -0.090

Harstad Harstad -0.072 -0.171

Andenes Andøy -0.055 -0.166

Tromsø Tromsø -0.060 -0.172

Hammerfest Hammerfest -0.098 -0.205

Honningsvåg Nordkapp -0.134 -0.225

Vardø Vardø -0.143 -0.241

Table 9.1. The heights of local mean sea level at the tide gauges along the Norwegian coast. Mean sea level was computed for the period 1979 to 1997.


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Estimates of Future Sea-Level Changes for Norway › globalassets › kunnskap › ... · current limited understanding of the causes of sea-level change, however, the scientific