Considerations in Using Climate Change Information in Hydrologic Models and Water Resources Assessments JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering University of Washington November, 2003 Alan F. Hamlet Dennis P. Lettenmaier Philip W. Mote

Climate Change Scenarios Incorporating Climate Change in Critical Period Planning Long term planning for climate change may include a stronger emphasis on drought contingency planning, testing of preferred planning alternatives for robustness under various climate change scenarios, and increased flexibility and adaptation to climate and streamflow uncertainty. Observed Streamflows Planning Models Altered Streamflows Climate Change Scenarios System Drivers

Global climate models predict temperature more accurately than precipitation at the global scale. 1 BMRC 2 CCC 3 CCSR 4 CSIRO 5 ECHAM3 6 ECHAM4 7 GFDL 8 HadCM2 9 IAP 10 MRI 11 CERFACS 12 PCM 13 GISS 14 HadCM3 15 LMD 16 CSM Lower Predictive Skill For Precipitation For each of 15 climate models with CMIP runs, plus NCEP and ECMWF reanalyses, this figure shows comparisons with observations as correlation (azimuth) and ratio of simulated to observed variance. Model 3 is CCSR, 6 ECHAM4, 8 HadCM2, 10 MRI, 12 PCM, 14 HadCM3, 15 LMD, 16 NCAR Higher Predictive Skill For Temperature

Bias Correction of GCM simulations is required for hydrologic studies. ACPI: PCM-climate change scenarios, historic simulation v air temperature observations

ACPI: PCM-climate change scenarios, historic simulation v precipitation observations

Downscaling of GCM simulations is required for hydrologic studies. ~ Five grid cells over CA Small scale topography is not represented.

Overview of GCM Downscaling Techniques

“Delta” Method Monthly changes in temperature and precipitation from the GCM simulation are applied uniformly to an observed temperature and precipitation record. Advantages: Relatively simple to interpret Simultaneously removes GCM bias and downscales Minimizes the effects of time series and spatial uncertainties in free running GCMs Disadvantages: Assumes time series and variability of monthly values like those in the historic record

Statistical Bias Correction and Downscaling Advantages: Computationally efficient in comparison with dynamic downscaling and has been shown to have comparable performance at large spatial scales when the spatial variability is principally controlled by topography. Includes potentially significant temporal, spatial or topographic variations in temperature and precipitation (or other variables) unlike those in the historic record. (Most sophisticated methods may include variation in number of sunny days, daily precipitation variability, etc.) Disadvantages: Inherits additional uncertainties from the large scale GCM forcing simulation (e.g. including spatial variability from the GCM also introduces uncertainty about the accuracy of the spatial variability in the simulations.)

Dynamic Downscaling Using Meso-Scale Climate Models Advantages: Includes potentially significant temporal, spatial or topographic variations in temperature and precipitation (or other variables) unlike those in the historic record. Spatial downscaling not required in some cases (depends on scale of interest) Produces physically-based simulations at small spatial scales (e.g. changes to topographically driven micro-climates may be more realistically simulated). Disadvantages: Computationally intensive Meso-scale simulations inherit uncertainties from the large scale GCM forcing Adds another stage of modeling with associated increases in cumulative bias

Hybrid Methods (e.g. traditional stochastic hydrology with altered probability distributions derived from GCM simulations) Advantages: Can potentially exploit the strengths of different methods while avoiding some pitfalls. Disadvantages: Objective rationale for mixing and matching results from different sources is not straight-forward to produce.

Conclusions Currently one of the largest sources of uncertainty in climate change hydrologic assessments is contained in the GCM simulations themselves and in the linkages between GCMs and hydrologic models. Bias must be removed from GCM and Meso-Scale climate simulations to produce useful hydrologic assessments, and spatial and temporal downscaling is also frequently required. More sophisticated downscaling techniques can include more information from GCM simulations, but also introduce additional uncertainties. No downscaling scheme is free of problems, and the techniques used to interpret GCM simulations should be chosen based on the goals of the study.