SST Observations of large scale ocean processesenvisat.esa.int/Ocean2006/course_material/Thu_AATSR/...SST Observations of large scale ocean processes ESA Training Course: Ocean2006

ESA Oceans2006, Hamburg © I. S. Robinson (2006)

SST : Large scale ocean processes 1

Ian RobinsonIan Robinson

2525--29 Sep, Hamburg29 Sep, Hamburg

ESA Training Course: Ocean2006 ESA Training Course: Ocean2006

National Oceanography Centre Southampton

Professor of Oceanography from Space, University of Southampton.Co-head of the Ocean Observations and Climate Research Group, NOCS

SST Observations of large scale ocean processes

SST Observations of large scale ocean processes

SST: Large scale processes 225-29 Sep, HamburgESA Training Course: Ocean2006

Outline of lectureOutline of lectureOutline of lecture

Why use satellites to observe large scale ocean processes?

How global sea surface temperature (SST) distribution varies through the year

Processing global data to reveal new features

Some large scale ocean phenomena that can be measured from satellites




Satellite data provide the best means to observe large scale ocean processes

Satellite data provide the best means to Satellite data provide the best means to observe large scale ocean processesobserve large scale ocean processes

Applications to large scale oceanography exploit the unique benefits of satellite data

Global coverage with good spatial detailRepeated coverage over many yearsA consistent view from the same sensorOpportunity to combine data from different sensor types

Special data analysis methods for time series of large scale images

Creating climatologiesProducing anomaliesHovmöller plots

A new look at large scale ocean phenomena


Large scale ocean processes from SpaceLarge scale ocean processes from SpaceLarge scale ocean processes from Space

Climatology of the oceansObserve the whole world ocean at onceIsolate the seasonal cycleIdentify long term fluctuations or trendsLook for characteristic patterns of ocean behaviour

Large scale propagating featuresRossby wavesTropical instability waves

El Niño Detection from multiple data typesPossibility of prediction

Monsoons etc




1996 : 011996 : 021996 : 031996 : 041996 : 051996 : 061996 : 071996 : 081996 : 091996 : 101996 : 111996 : 12

Time series of monthly SST composites; 1996

Time series of monthly SST Time series of monthly SST composites; 1996composites; 1996

Annual Mean 1996

Monthly mean SST data from the NASA “Pathfinder” analysis for 1996. The spatial resolution is 9 km.

-2 2 6 10 14 18 22 28 30 34 C


Monthly mean Satellite SST: 1997Monthly mean Satellite SST: 1997Monthly mean Satellite SST: 1997

Monthly mean SST data from the NASA “Pathfinder” analysis for 1997. The spatial resolution is 9 km.

-2 2 6 10 14 18 22 28 30 34 C

1997 : 011997 : 021997 : 031997 : 041997 : 051997 : 061997 : 071997 : 081997 : 091997 : 101997 : 111997 : 12Annual Mean 1997




month B

month B

Year N, month B

Average over N years for month B

Seasonal climatologies from image time seriesSeasonal climatologies from image time seriesSeasonal climatologies from image time series

Jan

Dec

Jan

Dec

Jan

Dec

Average over N years for month A

Instead of using monthly averages, we can use any interval, e.g. 3 days, 8 days, as long as the same calendar interval is used from each year

NOTE – Ideally the climatology should also map the standard deviation

Year 1

Year n

Year N

month A

month A

month A

yr2, A

yr3, A

yr n+1, A


Generating Anomaly Datasets for SST or other variables

Generating Anomaly Datasets for SST or Generating Anomaly Datasets for SST or other variablesother variables

Day 001

Day 365

Composite SST map for a specific period e.g. days 172-176 in 2005 Select the data for days 172-176

from the N-year climatology(Note that the period must match the way the climatology is constructed.).

Anomaly for days 172-176 in 2005

Subtract relevant clim-atology from SST map.

Climatology




SST Anomaly map from AVHRRSST Anomaly map from AVHRRSST Anomaly map from AVHRR

0 1 2 3 4 5-1-2-3-4-5Degrees C relative to climatological mean

Data processed by Al Strong and provided by the Products System Branch of NOAA/NESDIS


Anomaly differences over 3-4 daysAnomaly differences over 3Anomaly differences over 3--4 days4 days

5th April – 19 April, 2005 From NOAA website5th April – 19 April, 2005 From NOAA website




Anomaly differences over a yearAnomaly differences over a yearAnomaly differences over a year

20 April, 2004 – 19 April, 2005 From NOAA website


Analysing time sequences of global datasetsAnalysing time sequences of global datasetsAnalysing time sequences of global datasets

Time series of 2-D mapped data stack up as “cubes” of an ocean variableThis is height, but could be SSTSlice the cube in different planes

Latitu

deLongitude

Time




Analysing a 3-dimensional dataset Analysing a 3Analysing a 3--dimensional dataset dimensional dataset The challenge of extracting the information distributed in latitude, longitude and time

Longitude

Tim

eLa

titude

Tim

eLongitude

Hovmüller Diagram


Applications of space-time section plots Applications of spaceApplications of space--time section plots time section plots To emphasise a process in which a property changes its distribution quite slowly with time

Section across the Arctic to show the ice edge advance / retreat.A North-South (Meridional) section to show the seasonal migration of temperature contours at a particular longitude.Compare response of different variables, and at different longitudes

Any wavelike motionWaves on the Antarctic Circumpolar CurrentRossby WavesTropical Instability Waves

Compare SST, altimetry and wind sections to reveal atmosphere-ocean interactions.

El Niño




Rossby waves are revealed in Hovmöller plotsRossby waves are revealed in Hovmöller plotsRossby waves are revealed in Hovmöller plots


Estimating the speed of Rossby WavesEstimating the speed of Rossby WavesEstimating the speed of Rossby Waves

Proportional to slope of linesMust be measured objectively for consistencyNeeds to be automated for global coverageUse Radon Transform AnalysisCan detect several waves of different speeds if present

(From Cipollini, Cromwell, Quartly and Challenor, 2000)




Comparison with theoretical Rossby wave models

Comparison with theoretical Rossby wave Comparison with theoretical Rossby wave modelsmodels

Altimetry gave the first chance to test the theoretical models

Discrepancies discoveredLed to refinement of theory (Killworth & Blundell)

Improved understanding

Importance of Rossby wave measurements

Slow east-west information feedbackPotential role in climate processes


Westward propagating waves detected in the SST anomaly field

Westward propagating waves detected Westward propagating waves detected in the SST anomaly fieldin the SST anomaly field




Rossby Wave speedsRossby Wave speedsRossby Wave speedsDetected independently by height, SST and colour signature

and compared with theoretical models





Rossby Wave speed v. latitudeRossby Wave speed v. latitudeRossby Wave speed v. latitude

from Hill, Robinson & Cipollini, JGR, 2000


Tropical Instability WavesTropical Instability WavesTropical Instability WavesTMI (microwave)-derived SST, 17-19 Sep, 2001

From Caltabiano PhD thesis, NOCS, 2005




TMI (microwave)-derived SST, (3-day maps ~ Sep)

From Caltabiano PhD thesis, SOC, 2005


Hovmöller Plots from TMI From Caltabiano PhD thesis, SOC, 2005

Tim

e →

Deg C




Filtered Hovmöller Plots from TMI

TropicalInstability

Waves (TIW)


El NiñoEl NiñoEl NiñoA change in the ocean-atmosphere pattern over the Pacific ocean – recurs after 3 – 7 yearsClear example of atmosphere-ocean feedback

Interesting scientificallyIt is not in itself an effect of global warming - though it may be affected by it

Has large, global human and economic consequences

1982-3 event had estimated insurance costs in $ billions due to changes in global weather patternsLeads to excessive rainfall or droughts (and drought-related wildfires) in parts of globe with consequent human impactsAffects marine living resources




The El Niño process (1) Normal tropical Pacific conditions

The El Niño process The El Niño process (1) Normal tropical Pacific conditions(1) Normal tropical Pacific conditions

Westward blowing winds (easterlies) along equator generate a current that results in warmer water at the western side (near Indonesia; so-called “warm pool”)

Temperature gradient east-west of ~6-7˚ (~23˚→ ~29˚)Sea surface higher in west than eastThermocline deeper to west, shallower to east → upwelling in east → supply of nutrients → growth of plankton → fish“warm pool” leads to increased atmospheric convection and precipitation in the west (Note: precipitation and evaporation affect sea surface salinity)

West East


The El Niño process (2) El Niño conditionsThe El Niño process (2) El Niño conditionsThe El Niño process (2) El Niño conditionsWestward blowing winds weaken along equator allowing warm water to propagate from west to east (equatorial Kelvin wave) → a positive feed back effect

Temperature gradient east-west is almost non-existentSea surface higher in east than westThermocline shallows in west, deepens in east → suppresses upwelling in east → less nutrients → decreased plankton → less fish“warm pool” moves east leading to increased atmospheric convection and precipitation in the mid-tropical Pacific




The El Niño process (3) La Niña conditionsThe El Niño process (3) La Niña conditionsThe El Niño process (3) La Niña conditions

Westward blowing winds intensify along equator pushing warm water to back to west from eastTemperature gradient east-west re-established, but cool “tongue” extends further into central tropical PacificSea surface now lower across central tropical PacificThermocline deepens in west,shallows in east →increased upwelling in east → more nutrients →increased plankton → more fish“warm pool” moves west leading to increased atmospheric convection and precipitation beyond Indonesia (overshoot)


ATSR SSTsEvidence of El

Niño in Bi-monthly ATSR SST record




Evidence of El Niño in SST AnomaliesEvidence of El Niño in SST AnomaliesEvidence of El Niño in SST Anomalies

1997-011997-021997-031997-041997-051997-061997-071997-081997-091997-101997-111997-12 1998-011998-021998-031998-041998-051998-061998-071998-081998-091998-101998-111998-12

0 1 2 3 4 5-1-2-3-4-5Degrees C relative to climatological mean


ConclusionConclusionConclusionGlobal maps of SST reveal a variety of large scale ocean features

Sometimes special processing is needed to make the phenomena stand out clearly

El Niño has a strong SSTanomaly signature

The big challenge is to use the information available from satellites to constrain short-term climate forecasting models

There is plenty of scope for more research

Question: How should we set about detecting global warming in the global SST data ?

Documents

SST Observations of large scale ocean processesenvisat.esa.int/Ocean2006/course_material/Thu_AATSR/...SST Observations of large scale ocean processes ESA Training Course: Ocean2006