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Downscaling Techniques and Climate Scenarios for Impacts Assessments PRECIS Workshop, MMD, KL, November 2012 © Crown copyright Met Office Objectives of the session • 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 © Crown copyright Met Office Outline Part 1: 1. Downscaling techniques - Statistical methods - Dynamical methods 2. Suitability of downscaling techniques 3. Use of regional climate models © Crown copyright Met What are downscaling techniques? © Crown copyright Met Climate downscaling • 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 © Crown copyright Met Office Going from global to local climate © Crown copyright Met Office Classification • Statistical • Transfer functions • Weather generators • Weather typing • Dynamical • High resolution and variable resolution AGCMs • Regional Climate Models © Crown copyright Met Office Statistical or empirical techniques 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) © Crown copyright Met Office 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 NewZealand). © Crown copyright Met Office Regional climate models Courtesy of H. von Storch Regional atmospheric modelling: nesting into a global state © Crown copyright Met Office Suitability of regionalisation techniques © Crown copyright Met Office Suitability of regionalisation techniques Method Strengths Weaknesses Statistical High resolution Computationally cheap • Dependent on empirical relationship High (very high) resolution Can represent extremes Physically based Many variables RCM: easily relocatable • Dependent on surface boundary conditions from coupled model • Computationally expensive • Have to parameterize across scales Stretched grid AGCMs Regional models derived from present-day climate • Dependent on long time-series and good quality historical data • Few variable available • Not easily relocatable • Dependent on driving model and surface boundary conditions • Possible lack of two-way nesting • Have to parameterize across scales ( scales ) © Crown copyright Met Office Boundary conditions © Crown copyright Met Office 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 © Crown copyright Met Office 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 LBC variables • Lateral boundary condition variables • Temperature • Water • Pressure • Aerosols © Crown copyright Met Office LBC variables • Wind LBC variables 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 © Crown copyright Met Office Added value of RCMs © Crown copyright Met Office RCMs simulate current climate more realistically Patterns of present-day winter precipitation over Great Britain © Crown copyright Met Office Represent smaller islands Projected changes in summer surface air temperature between present day and the end of the 21st century. © Crown copyright Met Office Simulate and predict changes in extremes more realistically Frequency of winter days over the Alps with different daily rainfall thresholds. © Crown copyright Met Office Simulate cyclones and hurricanes A tropical cyclone is evident in the RCM (right) but not in the GCM © Crown copyright Met Office 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 © Crown copyright Met Office 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. © Crown copyright Met Types of climate scenarios • Incremental scenarios for sensitivity studies • Analogue scenarios • Scenarios based on outputs from Climate Models © Crown Crown copyright copyright Met MetOffice Office 1. Incremental scenarios • 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 © Crown Crown copyright copyright Met MetOffice Office 2. Analogue scenarios • 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 © Crown Crown copyright copyright Met MetOffice Office 3. Scenarios based on outputs from Climate Models • 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 © Crown Crown copyright copyright Met MetOffice Office Stages required to develop climate change scenarios © Crown Crown copyright copyright Met MetOffice Office More adverse than beneficial impacts on ecological and socioeconomic systems are projected © Crown Crown copyright copyright Met MetOffice Office Impacts Assessment • 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 © Crown Crown copyright copyright Met MetOffice Office 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 © Crown Crown copyright copyright Met MetOffice Office Xiong et al, 2007, Climate change and critical thresholds in China’s food security, Climatic Change, 81:205-221 Example: modelling climate change impacts on Hydrology 2020s • Change in water stress in the Ganges-Brahmaputra-Meghna Basin derived using the Global Water Availability Assessment (GWAVA) model 2050s (CLASIC project – work with CEGIS) © Crown Crown copyright copyright Met MetOffice Office 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 © Crown Crown copyright copyright Met MetOffice Office Scenarios based on outputs from Climate Models 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 © Crown copyright Met Office Using climate model scenarios with impact models 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. © Crown Crown copyright copyright Met MetOffice Office 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 © Crown Crown copyright copyright Met MetOffice Office 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 of 3.75° Apply mean change factor of 3.75° Future scenario has same variability as observations The PRECIS modelling system: An impacts case-study © Crown copyright Met Office © Crown copyright Met 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) © Crown Crown copyright copyright Met MetOffice Office 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 © Crown Crown copyright copyright Met MetOffice Office Some different methods for CCS construction Constructing the baseline and future timeseries of data required by the runoff model. For example: BASELINE FUTURE CLIMATE SCENARIO Mean Variance Mean Variance Observed Observed Observed + Observed model difference Simulated Simulated Simulated Simulated …these are just some of the possibilities © Crown Crown copyright copyright Met MetOffice Office Mean temperature and rainfall Average annual rainfall is systematically overestimated by the model © Crown Crown copyright copyright Met MetOffice Office Rainfall variability is accurately represented by the model © Crown Crown copyright copyright Met MetOffice Office The best method CCS construction in this case? BASELINE Mean Observed Simulated © Crown Crown copyright copyright Met MetOffice Office FUTURE CLIMATE SCENARIO Variance Mean Variance Observed Observed + Observed model difference Simulated Simulated Simulated Does the choice of scenario construction technique affect result of impact study? YES! © Crown Crown copyright copyright Met MetOffice Office 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 © Crown Crown copyright copyright Met MetOffice Office Questions © Crown copyright Met Office
