© Crown copyright Met Office Uncertainties in the Development of Climate Scenarios Climate Data Analysis for Crop Modelling workshop Kasetsart University,

© Crown copyright Met Office

Uncertainties in the Development of Climate ScenariosClimate Data Analysis for Crop Modelling workshop

Kasetsart University, February 2013


Uncertainties in Climate Scenarios

• Goal of this session:

• understanding the cascade of uncertainties

• provide detail on the uncertainties in emissions scenarios

• provide detail on the uncertainties in regional climate change predictions


Main Sources of Uncertainty

Socio- Economic Uncertainty

Uncertainty in the model representation of physical processesNatural annual-

decadal variability (‘Internal variability’)


Uncertainties

• Emissions

• Concentration

• GCMs

• Regional modelling

• Climate scenario construction

• ImpactsStages required to provide climate scenarios


Uncertainties: Emission Scenarios

• Uncertainties in the key assumptions and relationship about future population, socio-economic development and technical changes.

• The consequent uncertainties are unquantifiable as IPCC does not assign probabilities to any of choices of the key assumptions involved

• We are currently working with 2 sets of scenarios: SRES (used for CMIP3/IPCC AR4) and RCPs (used for CMIP5/AR5)


SRES Emissions Scenarios

1. Socio-economic scenarios

2. Emissions scenarios

3. Atmospheric concentrations


ImpactsClimate

scenariosAtmospheric

concentrations

Emissions scenarios

Socio-economic scenarios

SRES: Sequential approach to developing climate scenarios

• Climate modellers await results from socio-economic modellers

• The first emissions scenarios produced for the IPCC first assessment report (i.e. Is92a) were restrictive.. E.g. There was no exploration of deliberate mitigation strategies, and it was difficult to explore uncertainties in carbon cycle feedbacks.


Representative Concentration Pathways (RCPs)


Uncertainties: Concentration Scenarios

• Uncertainties in the understanding of the processes and physics in the carbon cycle and chemistry models

• Models currently use a single set of concentrations derived from carbon cycle/chemistry models

• Experiments to date indicate the uncertainties may be large

• Coupling a carbon-cycle model into one AOGCM shows a large positive feedback

• Coupling an atmospheric chemistry model into one AOGCM shows a small negative feedback


Carbon cycle model

Coupled to standard HadCM3 atmosphere, ocean and interactive sulphur cycle.

Moses 2.1/ Triffid

land surface scheme:

Dynamic Vegetation

newHadOCC:

Ocean biology/carbon cycle model

Prescribe CO2 emissions

(not atmospheric concentration)

Photosynthesis

Respiration


Impact of perturbations on global mean temperature.

Relative impact of uncertainties in the terrestrial carbon cycle (green) and atmospheric feedbacks (blue)







Uncertainties: Climate models

• Incorrect, incomplete or missing description of key processes and feedbacks in the climate system e.g.

• Representation of clouds

• Complexity of sea-ice model

• Feedback from land-use change

• Internal (natural) variability of the climate system

• Decadal variability means that 30-year samples of a climate state may differ substantially


Climate model formulation


Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Land surfaceLand surfaceLand surfaceLand surfaceLand surface

Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice

Sulphateaerosol

Sulphateaerosol

Sulphateaerosol

Non-sulphateaerosol

Non-sulphateaerosol

Carbon cycle Carbon cycle

Atmosphericchemistry

Ocean & sea-icemodel

Sulphurcycle model

Non-sulphateaerosols

Carboncycle model

Land carboncycle model

Ocean carboncycle model

Atmosphericchemistry

Atmospheric

chemistry

Off-linemodeldevelopment

Strengthening coloursdenote improvementsin models

1985 1992 1997

HADLEY CENTRE EARTH SYSTEM MODEL


Uncertainties in climate model

Large Scale CloudIce fall speed

Critical relative humidity for formation

Cloud droplet to rain: conversion rate and threshold

Cloud fraction calculation

ConvectionEntrainment rate

Intensity of mass flux

Shape of cloud (anvils) (*)

Cloud water seen by radiation (*)

Radiation Ice particle size/shape

Cloud overlap assumptions

Water vapour continuum absorption (*)

Boundary layerTurbulent mixing coefficients: stability-dependence, neutral mixing length

Roughness length over sea: Charnock constant, free convective value

DynamicsDiffusion: order and e-folding time

Gravity wave drag: surface and trapped lee wave constants

Gravity wave drag start level

Land surface processes Root depths

Forest roughness lengths

Surface-canopy coupling

CO2 dependence of stomatal conductance (*)

Sea ice Albedo dependence on temperature

Ocean-ice heat transfer


Change (%) in South Asian monsoon rainfall: A1B, 2090s, CMIP3 ensemble


Temperature and precipitation changesAfrica, A1B, 2090s, CMIP3 ensemble

Figure 11.2


Perturbed physics approach

• The perturbed physics approach allows uncertainties in various components of the model to be systematically explored.

• This is done by:

• Identifying parameters in the model which are both uncertain and important for the model response

• Using an ensemble of models to explore the implications of these parameter uncertainties


Uncertainties: Climate change scenarios and impacts

• Climate change scenarios for impacts studies can be derived by:

• Combining climate model and observed data

• Using climate model data directly

• Choices are often required when considering:

• How to provide information at fine scales

• How to apply changes in the mean climate or climate variability

• As with climate modelling, the physical processes involved in studying climate impacts are often not well understood or well-simulated


Source of uncertainties

Source of Uncertainty Represented in Climate Scenarios?

Ways to address it

Alternative emission scenarios Yes Scale GCM patterns by the ratio of the radiative forcing

Emissions to concentrations Beginning Use GCMs that include interactive chemistry

Modelling the climate response Different responses by different

GCMs for the same forcing. Yes Use a range of GCMs

Signal (response)/noise (internal climate variability)

Not normally Use ensemble simulations

Providing regional climate scenarios

Baseline and future climates Yes Use observed or model baseline and different methods for changes

Adding high resolution detail Yes Use of a range of dynamical and statistical techniques







Q: Which are the most important sources of uncertainty?

A: That depends on the timescale that we are looking at…

Natural variability most important on timescales 0-20 years, small by 100 years

Emissions scenario important on timescales 40 years +

Model uncertainty important at all timescales


To summarise

• There are many uncertainties which need to be taken into account when assessing climate change (and its impact) over a region

• Some account may currently be taken for most (BUT NOT ALL) uncertainties

• Even those uncertainties that can be accounted for are currently not well described

• There is a lot more work for us all to do!


Questions

Documents

© Crown copyright Met Office Uncertainties in the Development of Climate Scenarios Climate Data Analysis for Crop Modelling workshop Kasetsart University,