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TRANSCRIPT
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Downscaling Techniques andClimate Scenarios for Impacts Assessments
PRECIS Workshop, MMD, KL, November 2012
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• Part 1: To review the different methods of obtaining fine-scale climate information from global climate models (GCMs), with an emphasis on regional climate models (RCMs)
• Part 2: To consider how we apply the climate projections as 'climate scenarios' in impacts assessments
Objectives of the session
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Outline
Part 1:
1. Downscaling techniques
- Statistical methods
- Dynamical methods
2. Suitability of downscaling techniques
3. Use of regional climate models
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What are downscaling techniques?
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• Techniques which allow fine scale information to be derived from GCM output.
• Smaller scale climate results from an interaction between global climate and local physiographic details
• Impact assessors need regional detail to assess vulnerability and possible adaptation strategies
• AOGCM projections lack that regional detail due to coarse spatial resolution
Climate downscaling
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Going from global to local climate
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Classification
• Statistical
• Transfer functions
• Weather generators
• Weather typing
• Dynamical
• High resolution and variable resolution AGCMs
• Regional Climate Models
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1) Find a function ƒ, derived from statistical relationships between observations of fine-scale and large scale variables:
fine scale value = ƒ (large-scale variables)
2) Use ƒ to find future fine-scale values from future large-scale variables:
future fine-scale value = ƒ(GCM large-scale variables)
Statistical or empirical techniques
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Stretched grid AGCMs
The spatial resolution here is equivalent to a grid mesh of approximately 30 km.
The spatial resolution is progressively relaxed towards the antipode (near New-Zealand).
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Regional atmospheric modelling: nesting into a global state
Regional climate models
Courtesy of H. von Storch
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Suitability of regionalisation techniques
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Suitability of regionalisation techniques
Method Strengths Weaknesses
Statistical High resolution Computationally
cheap
• Dependent on empirical relationship derived from present-day climate• Dependent on long time-series and good quality historical data• Few variable available• Not easily relocatable
Stretched gridAGCMs
• Dependent on surface boundary conditions from coupled model• Computationally expensive• Have to parameterize across scales
Regionalmodels
High (very high)resolution
Can representextremes
Physically based Many variables RCM: easily
relocatable
• Dependent on driving model and surface boundary conditions• Possible lack of two-way nesting• Have to parameterize across scales (scales )
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Boundary conditions
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One way nesting methodology
• A RCM is a limited area model (LAM), similar to those used in numerical weather prediction (NWP), i.e. short term weather forecasting
• LAMs are driven at the boundaries by GCM or observed data
• Lateral (side) and bottom (sea surface)
• LAMs are highly dependent on their boundary conditions and can not exist without them
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Lateral boundary conditions
• LBCs = Meteorological boundary conditions at the lateral (side) boundaries of the RCM domain
• They constrain the RCM throughout its simulation
• Provide the information the RCM needs from outside its domain
• Data come from a GCM or observations
• Lateral boundary condition variables
• Wind
• Temperature
• Water
• Pressure
• Aerosols
LBC
variables
LBC variables
LBC variables
LBC
variables
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Sea surface boundary conditions
• Two methods of supplying SST and sea ice:
• Using outputs from a coupled GCM
• Need good quality simulation of SST and sea ice in model
• Necessary for future simulations
• Using observed values
• Useful for the present-day simulation.
• For future climate need add changes in SST and ice from a coupled GCM to the observed values – complicated
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Added value of RCMs
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RCMs simulate current climate more realistically
Patterns of present-day winter precipitation over Great Britain
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Represent smaller islands
Projected changes in summer surface air temperature between present day and the end of the 21st century.
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Simulate and predict changes in extremes more realistically
Frequency of winter days over the Alps with different daily rainfall thresholds.
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Simulate cyclones and hurricanes
A tropical cyclone is evident in the RCM (right) but not in the GCM
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Part 1 Summary
• Downscaling techniques are used to add fine scale details to a GCM projection
• Several methods are available with their own strengths and weaknesses
• PRECIS is a physically-based and computationally accessible regional climate model for downscaling GCM projections
Contents part 2: Climate Scenarios for Impacts Assessments
• Types of Climate Scenarios
• Examples of Impacts studies that have used PRECIS
• An impacts case study – why the method for constructing a scenario is important.
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Types of climate scenarios
• Incremental scenarios for sensitivity studies
• Analogue scenarios
• Scenarios based on outputs from Climate Models
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• Particular climatic elements are changed incrementally by plausible though arbitrary amounts.
• Use for testing system sensitivity
• Use for identifying critical thresholds or discontinuities in climate
• Potentially leads to unrealistic scenarios
• Not related to anthropogenic emissions
1. Incremental scenarios
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• Identify recorded climate regimes which may resemble the future climate in a given region.
• Spatial analogues
• Temporal analogues
• Palaeoclimatic
• Instrumental
• Not related to anthropogenic emissions
• Often physically implausible
2. Analogue scenarios
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• Coupled Atmosphere-Ocean Global Climate Models (AOGCMs)
• Coarse resolution, and often have large biases
• Based on physics
• Internally consistent
• Dynamically downscaled AOGCMs
• High resolution GCMS (e.g. PRECIS)
• Require large computer resources
• Can inherit biases from AOGCM
• Statistically downscaled AOGCMs
• Statistical methods are based on current climate and trained on short-term variability
• Difficult to develop internally consistent climate variables
3. Scenarios based on outputs from Climate Models
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Stages required to develop climate change scenarios
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More adverse than beneficial impacts on ecological and socioeconomic systems
are projected
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• Evaluation of the detrimental and beneficial consequences of climate change on natural and human systems.
• Impacts models require climate scenarios as inputs.
• The impact of the climate change is determined by contrasting the effect of the observed/baseline climate with that of the future climate (scenario) on the exposure unit
Impacts Assessment
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Example: Modelling Impacts of climate change on agriculture
Use of PRECIS and the crop model CERES to simulate yield changes per hectare of three grain crops (rice, wheat, maize) in China when applying one future climate scenario and a representation of CO2 fertilization
Xiong et al, 2007, Climate change and critical thresholds in China’s food security, Climatic Change, 81:205-221
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Example: modelling climate change impacts on Hydrology
• Change in water stress in the Ganges-Brahmaputra-Meghna Basin derived using the Global Water Availability Assessment (GWAVA) model
(CLASIC project – work with CEGIS)
2020s
2050s
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Example: Modelling Storm Surge under climate scenarios
Simulated tropical cyclone and resulting storm surge. Produced using PRECIS and POLCOM storm surge
model
SLR projections from GCM
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Why are Regional Model simulations Useful for Impacts Assessments?
High resolution provides detail required by many impacts models (e.g. PRECIS)
Outputs are spatially and temporally ‘complete’, and variables are physically consistent with one another
What are the limitations of Using Model data for impacts assessments?
Model projections may not be available, and require large computer resources to generate
Often have biases
Scenarios based on outputs from Climate Models
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Using climate model scenarios with impact models
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Climate change impact = System under future climate – system under current climate (baseline)
Baseline Future Appropriate?
Observations Observations and climate model change factor
If we have sufficient obs. data to drive our impact model, yes. If we see only systematic biases in our model simulations, yes.
Climate model baseline Climate model future If our climate model baseline is realistic, yes.
Observations Climate model future Not normally. Only if model baseline is very similar to observed –otherwise the result is a combination of climate model error and climate change impact.
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One approach to combining climate observations and simulations
• If, as a result of systematic biases in the GCM/RCM simulations, the impact baseline is unrealistic then a simple approach is to apply the model change factor rather than the model output directly
• Model change factor = Model future – Model baseline: or
• Model change factor =(model future / model baseline) *100
• We can then add the change factor to an observed record to get a future scenario with the bias seen in the baseline removed
• Future climate scenario = Observed + model change factor: or
• Future climate scenario = Observed * model change factor (%)
• This approach may provide impact results which are more reasonable but the simple change factor applied does not account for changes in variability and may result in inconsistent future climates
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Bias Correct Model projection
Identify bias of 2°
Future scenario has variability as determined by the model
Apply Change factor to Observed data
Mean change of3.75°
Apply mean change factor of 3.75°
Future scenario has same variability as observations
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The PRECIS modelling system:
An impacts case-study
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Climate change scenarios from a recent climate model: estimating change in runoff in southern Africa
• Nigel Arnell
• (Dept. of Geography, University of Southampton, U.K.)
• Debbie Hudson and Richard Jones
• (Hadley Centre for Climate Prediction and Research)
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Methods
• Runoff: calculated from water balance
• runoff = precipitation – evaporation – absorption by soil
• Two sets of models - a climate model and a runoff model
• Baseline climate → Run-off model → Baseline Run-off
• Future climate → Run-off model → Future Run-off
• Compares different methods of constructing future climate scenario
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Constructing the baseline and future timeseries of data required by the runoff model. For example:
…these are just some of the possibilities
Some different methods for CCS construction
BASELINE FUTURE CLIMATE SCENARIO
Mean Variance Mean Variance
Observed Observed Observed + model
difference
Observed
Simulated Simulated Simulated Simulated
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Mean temperature and rainfall
Average annual rainfall is systematically over-estimated by the model
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Rainfall variability is accurately represented by the model
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The best method CCS construction in this case?
BASELINE FUTURE CLIMATE SCENARIO
Mean Variance Mean Variance
Observed Observed Observed + model
difference
Observed
Simulated Simulated Simulated Simulated
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Does the choice of scenario construction technique affect result of impact study? YES!
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Summary
• There are several techniques for producing future climate information
• Only climate model based climate change predictions can be used for providing climate scenarios which are plausible and self consistent
• Even when using a single climate model (or family of models) there are many different ways to provide climate change information for impacts studies
• The method of climate scenario construction adds a further uncertainty in assessing impacts of climate change
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Questions