Climate Change Impact Assessments: Uncertainty at its Finest. Josh Cowden SFI Colloquium July 18, 2007

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1 Climate Change Impact Assessments: Uncertainty at its Finest Josh Cowden SFI Colloquium July 18, 27

2 Global Climate Modeling

3 Emission Scenarios (SRES) A1 very rapid economic growth global population that peaks in mid-century and declines thereafter rapid introduction of new and more efficient technologies convergence among regions fossil-intensive (A1FI), non-fossil energy sources (A1T), balance across all sources (A1B) A2 self-reliance and preservation of local identities continuously increasing population economic development is primarily regionally oriented slow and fragmented per capita economic growth and technological change

4 Emission Scenarios (SRES) B1 rapid economic growth towards a service and information economy global population that peaks in mid-century and declines thereafter reductions in material intensity and introduction of clean and resourceefficient technologies emphasis is on global solutions to economic, social and environmental sustainability, including improved equity B2 emphasis is on local solutions to economic, social and environmental sustainability continuously increasing population intermediate levels of economic development less rapid and more diverse technological change

5 Emission Scenarios (SRES)

6 Emission Scenarios (SRES)

7 Radiative Forcing

8 AOGCMs

9 AOGCMs

10 IPCC Uncertainty

11 IPCC Uncertainty

12 IPCC Results

13 IPCC Results

14 IPCC Results

15 GCM Downscaling GCM grid scale Variable Aggregation Regional Grid Scale Model Downscaling Precipitation Topography Hydrological Grid Scale Vegetation Soils Wate rshed (adapted from Wilby and Dawson, 24)

16 GCM Downscaling Statistical downscaling Dynamical downscaling Strengths Weaknesses Station-scale climate information from GCMscale output Cheap, computationally undemanding and readily transferable Ensembles of climate scenarios permit risk/uncertainty analyses Applicable to exotic predictands such as air quality and wave heights Dependent on the realism of GCM boundary forcing Choice of domain size and location affects results Requires high quality data for model calibration Predictor-predictand relationships are often nonstationary Choice of predictor variables affects results Choice of empirical transfer scheme affects results Low-frequency climate variability problematic Always applied off-line, therefore, results do not feed back into the host GCM 1-5 km resolution climate information from GCM-scale output Respond in physically consistent ways to different external forcings Resolve atmospheric processes such as orographic precipitation Consistency with GCM Dependent on the realism of GCM boundary forcing Choice of domain size and location affects results Requires significant computing resources Ensembles of climate scenarios seldom produced Initial boundary conditions affect results Choice of cloud/convection scheme affects (precipitation) results Not readily transferred to new regions or domains Typically applied off-line, therefore, results do not always feed back into the host GCM

17 Statistical Downscaling R = F X T t t ( ) for T R t is the predictand at time t, F is the method to quantify the relationship, and X t is the predictor set of current or past atmospheric variables SD Assumptions predictors are relevant to the predictand and can be realistically modeled by the GCM the quantitative relationship or transfer function between the predictor and predictand does not change in future altered climates chosen predictors fully represent the climate change signal

18 Statistical Downscaling Method Regression (e.g. linear regression, neural networks, canonical correlation analysis, kriging) Weather Classification (e.g. analogue methods, hybrid approaches, fuzzy classification, self organizing maps, Monte Carlo methods) Weather Generators (e.g. Markov chains, stochastic models, spell-length methods, storm arrival times, mixture modeling) Strengths Relatively straightforward to apply Employs full range of available predictor variables Off-the-shelf solutions and software available Yields physically interpretable linkages to surface climate Versatile (e.g. can be applied to surface climate, air quality, flooding, erosion, etc.) Compositing for analysis of extreme events Production of large ensembles for uncertainty analysis or long simulations fore extremes Spatial interpolation of model parameters using landscape Can generate sub-daily information Weaknesses Poor representation of observed variance May assume linearity and/or normality of data Poor representation of extreme events Requires additional task of weather classification Circulation-based schemes can be insensitive to future climate forcing May not capture intra-type variations in surface climate Arbitrary adjustments of parameters for future climate Unanticipated effects to secondary variables of changing precipitation parameters

19 (a) seasonal amount (b) seasonal amount (c) seasonal amount Statistical Downscaling bias (mean) in % 5 5 bias (std) in % 1 5 RMSE (in mm) (d) occurence (e) occurence 12 (f) occurence Uncertainty and variability associated with method choice bias (mean) in % bias (std) in % (g) dry spell length 1 (h) dry spell length RMSE (in days) (i) dry spell length bias (mean) in % bias (std) in % 5 5 RMSE (in days) Moron et al., 26 bias (mean) in % (j) wet spell length bias (std) in % 1 5 (k) wet spell length RMSE (in days) (l) wet spell length

20 Climate Change Impact on Rainwater Harvesting Gauge# Gauge# Reliability (%) Reliability (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec observed observed Gauge# Gauge# Reliability (%) Reliability (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec observed observed

21 Conclusions Global Climate Modeling emission scenarios, GHG concentrations, radiative forcing, climate response Downscaling method choice, predictor/predictand choice, policy application Compounded Uncertainty recognition and transparency not predictions, rather what if s

22 Thank You Questions?

23 IPCC Results

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