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© Crown copyright Met Office Main Sources of Uncertainty Socio- Economic Uncertainty Uncertainty in the model representation of physical processes Natural annual- decadal variability (‘Internal variability’)
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© Crown copyright Met Office
Uncertainties in the Development of Climate ScenariosClimate Data Analysis for Crop Modelling workshop
Kasetsart University, February 2013
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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
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Main Sources of Uncertainty
Socio- Economic Uncertainty
Uncertainty in the model representation of physical processesNatural annual-
decadal variability (‘Internal variability’)
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Uncertainties
• Emissions
• Concentration
• GCMs
• Regional modelling
• Climate scenario construction
• ImpactsStages required to provide climate scenarios
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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)
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SRES Emissions Scenarios
1. Socio-economic scenarios
2. Emissions scenarios
3. Atmospheric concentrations
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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.
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Representative Concentration Pathways (RCPs)
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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
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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
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Impact of perturbations on global mean temperature.
Relative impact of uncertainties in the terrestrial carbon cycle (green) and atmospheric feedbacks (blue)
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Main Sources of Uncertainty
Socio- Economic Uncertainty
Uncertainty in the model representation of physical processesNatural annual-
decadal variability (‘Internal variability’)
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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
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Climate model formulation
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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
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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
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Change (%) in South Asian monsoon rainfall: A1B, 2090s, CMIP3 ensemble
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Temperature and precipitation changesAfrica, A1B, 2090s, CMIP3 ensemble
Figure 11.2
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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
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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
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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
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Main Sources of Uncertainty
Socio- Economic Uncertainty
Uncertainty in the model representation of physical processesNatural annual-
decadal variability (‘Internal variability’)
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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
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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!
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Questions
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