Future climate projections of NYC watershed: GCM selection and downscaling

1. Water Quality New York City Department of Environmental Protection Bureau of Water Supply Future climate projections of NYC watershed: GCM selection and downscaling 1 CUNY Institute for Sustainable Cities/Hunter College, CUNY, NY. 2 Bureau of Water Supply, New York City Department of Environmental Protection 3 Upstate Freshwater Institute, Syracuse, NY. A.Anandhi 1, A. Frei 1, D.C. Pierson 2, H. Markensten 3, D. Lounsbury 2, M.S. Zion 2, A.H. Matonse 1, and E.M. Schneiderman2. Watershed Science & Technical Conference 2009

2. Outline of the presentation • Overview of Climate Models and Emission Scenarios • GCM selection : • Phase I • Phase II • Downscaling : • Phase I • Phase II • Conclusions • Future direction WSTC 2009

3. Overview of Climate Models and Emission Scenarios WSTC 2009

4. Introduction • Nature of climate system is chaotic and its exact state cannot be predicted years ahead. • Global climate models (GCM) simulate the climate by representing the climate system mathematically using equations and parameters. • Scenarios are defined to represent future climate, which are alternative images of how the future climate might unfold. • Multiple scenarios allow uncertainty in the possible future climate to be estimated. • A set of Global climate models (GCM) provide our best information to future realizations (time series) of climate. • There are multiple GCM models available that are developed by independent teams, and which use different modeling algorithms and approachs to simulate basic climate processes • Uncertainty in future climate simulations can be evaluated by using different GCM models to simulate the same future period WSTC 2009

5. Overview of a GCM Solar radiation Atmosphere Cryosphere Biosphere Hydrosphere Lithosphere The GCMs are models that represent the climate system mathematically by using the ....... Using basic laws of physics, fluid motion & chemistry etc; ……parameterization of the processes that are not explicitly taken into account. Components their interactions Responses to forcings Processes Earth’s shape, rotation, revolution Image source: IPCC http://ipcc-wg1.ucar.edu/wg1/FAQ/wg1_faq-1.2.html • The physical processes can have different approximations and simplifications because they are • (1) too complex to include in the model and still have the model run fast on a computer, or • (2) because our understanding of those processes is still too poor to accurately model them with equations. • Lack of consensus as to which approximations are most important to modeling results, is also another reason for different modeling approaches to be developed. WSTC 2009

6. SRES - Emission scenarios • IPCC’s (Intergovernmental Panel for Climate Change) Special Report on Emissions Scenarios (SRES) have become the standard scenarios. Describe different future climate • using 4 storylines (SRES A1, A2, B1, B2) • In terms of Greenhouse gases (GHGs) emitted in future based on how • world population, • economy, • new technologies, • energy resources, • land use changes & • political structure may evolve over the next few decades. http://www.grida.no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/ WSTC 2009

7. Scenarios in SRES Medium emissions Highest emissions Low emissions http://www.grida.no/climate/ipcc/emission/images/spm1.gif WSTC 2009

8. GCM Selection WSTC 2009

9. Phase I Climate Change Simulations • We used the future climate projections from GCM models pertaining to Intergovernmental Panel for climate change (IPCC’s) AR4 report • Four GCMs (NCAR, ECHAM, GISS & CGCM3) • All the three SRES emission scenarios that are available in the report were used (A1B, A2 & B1 representing medium, high and low emissions in future) WSTC 2009

10. Phase II Climate Change Simulations - need for GCM selection • Future climate projections in NYC watersheds are subjectedto uncertainty due to : • different GCMs & • climate scenarios • One way to represent this uncertainty is use a larger variety of GCMs & scenarios to represent the various future projections • However, with the increase in the number of future climate projections, the watershed and reservoir runs for impact studies increases exponentially with each addition. • Hence there is a need to select the GCMs & scenarios for impact assessments by evaluating the GCM simulations WSTC 2009

11. Many methods divide the world into 22 regions for evaluation WSTC 2009

12. The evaluation of GCMs for Eastern North America (ENA) WSTC 2009

13. Challenges in evaluation of climate models • There are many aspects of model behavior that can be compared with the real world • Morphology of climate – by comparing statistics such as means, variances, covariance etc • Budgets, balances & cycles – e.g energy budget, water balance, carbon cycle • Process studies of climate e.g comparing monsoons, convective processes • Evaluation can be limited by errors and/or lack of measured data • To some extent models are tuned to reproduce the observed climate • E.g Models to a certain degree may appear realistic in some respects, but this may be a result of compensating errors • There is no generally accepted metric for measuring model performance as a whole (Gleckler et al 2008) • Wide variety of variables are of interest • Observational uncertainties are often substantial but poorly estimated • Some aspects of climate model simulations are deterministic, while others are not, making quantitative verification more complex • Different models show varying strengths & weakness WSTC 2009

14. Evaluation/Reliability of climate models – Along 3 main lines Model Performance How well a model simulates the observed climate record (Girogi and Mearns, 2002; Johnson and Sharma, 2009) or The skill of models in simulating present-day climate (Raisanen 2007) Model Convergence How consistent the predictions are from a range of models in time & space (Girogi and Mearns, 2002; Johnson and Sharma, 2009) or Inter-model agreement on future climate changes (Raisanen 2007) climate changes = future climate –simulated present day climate Ability of models to simulate observed large scale changes (Raisanen 2007) • E.g. Decrease in Arctic Ocean ice cover • E.g. Changes in water temperature within the top 700m of the worlds oceans since 1960 WSTC 2009

15. Methods of evaluation in climate models Numerical methods & physical parameterizations in GCMs are compared the outputs from GCM compared with observed values Component level System level Ensembles Individual Models Methods Presently Under Evaluation at DEP • Simple Ensemble Average (Lambert & Boer, 2001) • Simple Ensemble Median • Ensemble variance • Their combinations e.g mean of median etc • Mean, • Median, • Variance WSTC 2009

16. Data Used Daily precipitation, temperature (av., max, min), wind speed & solar radiation data from 23 GCMs are obtained from IPCC’s data archive GCM Scenarios:Baseline : 20C3MFuture : A1B A2 B1 Time slice:Baseline : 1981-2000Future : 2045-2065 : 2081-2100 WSTC 2009

17. Methodology followed in the study to obtain future projections of climate variables for NYC watershed Select climate variables to be evaluated Download data for these variables from different GCM runs in IPCC AR4 Regrid the data from different GCMs to a common grid Select one or more methods of evaluation Evaluate the GCM models for the region of interest Downscaling WSTC 2009

18. Need for regridding? Different GCMs have different grid resolutionsRegridding brings the values of variables to a common grid for their comparison with other GCMs WSTC 2009

19. Results – GCM selection Euclidian distance WSTC 2009

20. Results – GCM Selection WSTC 2009

21. Results – GCM selection …contd WSTC 2009

22. Results – Model selection GCM mean Observed mean Euclidian Distance Ranking Criteria :Euclidian Distance of Individual model mean from observed mean. Preliminary rankings Ranking Criteria :Euclidian Distance of Individual model variance from observed variance. WSTC 2009

23. GCM Downscaling WSTC 2009

24. Sea Vegetation Marshes The future climate projections at watershed scale • The future climate projections from GCMs may not be directly used for NYC watershed because they are at coarser resolution & scale GCM scale http://www.windows.ucar.edu/physical_science/basic_tools/images/ipcc_ar4_wg1_ch1_fig_1_4_big.gif Rock Watershed scale beach WSTC 2009

25. Need for downscaling GCM scale Watershed scale Type of downscaling Change factor WSTC 2009

26. Phase I Downscaling • Change Factor Methodology • Additive factors (CFadd) used to adjust historical air temperature record • Multiplicative factors (CFmul) used to adjust historical precipitation records and other variables • Factors calculated on a monthly basis and applied to observed values • Change Factor Methodology • Additive factors (CFadd) used to adjust historical air temperature record • Multiplicative factors (CFmul) used to adjust historical precipitation record • Factors calculated on a monthly basis and applied to observed values WSTC 2009

27. Downscaling: Phase 1- Change Factor MethodExample of Calculating a Single Factor Estimating Change factor GCM future –A1B GCM baseline –20C3M Source: Anandhi et al 2009 Measured data WSTC 2009

28. Baseline Future +65yr Application of Climate Change Delta Method – Air Temp Average GCM-Projected Air Temperature by Month for Control and Future Periods 40 Daily Air Temperature Input Data for Model Simulations 20 0 Degrees C -20 (Future = Measured + Factor) -40 Jan Feb Mar 2000 -> WSTC 2009

29. Example of Phase I downscaled data Minimum Daily Air Temperature Cannonsville Watershed Baseline Historical Data 2080 – 2081 ECAM A2 Future Climate Simulation WSTC 2009

30. Downscaling : Phase II - Improved CFM type of downscaling Multiple Change Factors Calculated over Portions of Frequency Distribution Source: Anandhi et al 2009 WSTC 2009

31. Example of Phase II downscaled data Similar studies were carried out for precipitation, Av. & max. temperature, wind speed & solar radiation WSTC 2009

32. Conclusions • GCM Selection Phase I • Used GCMs readily available to DEP – Columbia GISS project • GCM Selection Phase II • 11 individual GCM models were evaluated based on mean, median & variance • The mean, median & variance of the ensemble of 11 models were also estimated • Models are ranked based on their Euclidian distance from the observed values • The rest of the available GCM models ( from the total 23) will be evaluated • Downscaling Phase I • Change factors applied on a monthly basis • Downscaling Phase II • Multiple change factors applied by frequency distribution • Other method will be investigated • Statistical downscaling • Dynamic downscaling ie Regional Climate Models WSTC 2009

33. Future Direction • Evaluate the GCMs using different evaluation techniques • Uncertainty Analysis in GCM evaluation • Use different downscaling techniques • Uncertainty Analysis in downscaling • Analysis of extreme values in a changed climate • Similar studies on other meteorological variables such as average, maximum & minimum temperatures, wind speed & solar radiation WSTC 2009

34. Questions ?

35. Thank You