1. Figure 3. Overview of system architecture for RCMES. A Regional Climate Model Evaluation System based on Satellite and other Observations Peter Lean 1 and J. Kim 1,2, D. E. Waliser 1, A. Hall 2, C. Mattmann 1,3, S. L. Granger 1, K. Case 1, C. Goodale 1, A. Hart 1, B. Guan 1, N. Molotch 1, and S. Kaki 1 National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California www.nasa.gov Copyright 2010. All rights reserved. National Aeronautics and Space Administration Background Ongoing global climate can significantly impact both infrastructure and society. Governments need to understand the likely impacts at regional scales in order to make adaptation plans. Climate models contribute to the IPCC Assessment Reports and are used to drive impact models utilized by governments and infrastructure planners. The coarse resolution offered by current global climate models is inadequate to resolve many important local details. Regional climate models (RCMs) downscale the predictions and provide a better representation of spatial details (Figure 1). The need for an evaluation system The IPCC’s upcoming 5 th Assessment Report will emphasize decadal predictions at regional scales to help decision makers. Evaluation of model errors is crucial to understand the uncertainties in climate projections. In addition, evaluation of both the global and regional climate models is an essential part of the model development process (Figure 2). Recent satellite observations are particularly useful evaluation as they provide a wealth of information on the climate system at the fine resolutions (O[10km]) required for RCM evaluation. The difficulty in handling very large datasets (often in different file formats) and the ad-hoc basis on which many previous evaluation studies have been undertaken are the major difficulties in evaluation studies. Recognizing this problem, JPL and UCLA are developing a comprehensive and flexible model evaluation system to help make satellite observations, in conjunction with in-situ, assimilated, and reanalysis datasets, more readily accessible to the modeling community. Background Ongoing global climate can significantly impact both infrastructure and society. Governments need to understand the likely impacts at regional scales in order to make adaptation plans. Climate models contribute to the IPCC Assessment Reports and are used to drive impact models utilized by governments and infrastructure planners. The coarse resolution offered by current global climate models is inadequate to resolve many important local details. Regional climate models (RCMs) downscale the predictions and provide a better representation of spatial details (Figure 1). The need for an evaluation system The IPCC’s upcoming 5 th Assessment Report will emphasize decadal predictions at regional scales to help decision makers. Evaluation of model errors is crucial to understand the uncertainties in climate projections. In addition, evaluation of both the global and regional climate models is an essential part of the model development process (Figure 2). Recent satellite observations are particularly useful evaluation as they provide a wealth of information on the climate system at the fine resolutions (O[10km]) required for RCM evaluation. The difficulty in handling very large datasets (often in different file formats) and the ad-hoc basis on which many previous evaluation studies have been undertaken are the major difficulties in evaluation studies. Recognizing this problem, JPL and UCLA are developing a comprehensive and flexible model evaluation system to help make satellite observations, in conjunction with in-situ, assimilated, and reanalysis datasets, more readily accessible to the modeling community. Figure 1. Regional climate models are used to downscale global climate predictions to local scales more relevant to governments and regional planners. Image reproduced from IPCC Understand uncertainty in projections Figure 2. The role of model evaluation in the model development process. End user experience 1.Load required model data files quickly and efficiently. 2.System automatically retrieves observational data for region and time of simulation. 3.Select choice of pre-coded statistical metrics from list. 4.System outputs evaluation plots and data. 5. Ease of use gives researcher more time for scientific analysis of model performance and a more efficient model development process. End user experience 1.Load required model data files quickly and efficiently. 2.System automatically retrieves observational data for region and time of simulation. 3.Select choice of pre-coded statistical metrics from list. 4.System outputs evaluation plots and data. 5. Ease of use gives researcher more time for scientific analysis of model performance and a more efficient model development process. Regional Climate Model Evaluation System (RCMES) Aims: Provide a fast, flexible, comprehensive system to allow easy comparison of climate models with observations. Enable researchers to handle a large volume of data and reduce time taken for model evaluation studies from weeks to hours. Help model developers in model diagnostics to improve the models. Help end-users understand the uncertainties in climate projections for the regions of interests Regional Climate Model Evaluation System (RCMES) Aims: Provide a fast, flexible, comprehensive system to allow easy comparison of climate models with observations. Enable researchers to handle a large volume of data and reduce time taken for model evaluation studies from weeks to hours. Help model developers in model diagnostics to improve the models. Help end-users understand the uncertainties in climate projections for the regions of interests Future work 1.Add additional observational datasets, including mesoscale data. 2.Develop metrics calculations to utilize mesoscale data, especially handling discontinuous in temporal and spatial coverage. 3.Use the system to evaluate regional climate models associated with CMIP5/CORDEX. Future work 1.Add additional observational datasets, including mesoscale data. 2.Develop metrics calculations to utilize mesoscale data, especially handling discontinuous in temporal and spatial coverage. 3.Use the system to evaluate regional climate models associated with CMIP5/CORDEX. RCMES system overview Large database (MySQL + Apache Hadoop): multiple observational atmospheric & surface data from, currently,: Extractors: process data from various data formats into a common database schema (Figure 3). Library of statistical metrics: Python routines with plug-ins in other languages (Fortran, c, idl) to calculate and plot standard metrics of model performance. (e.g. Bias, RMS error, Anomaly Correlation, Probability Distribution Functions). Flexible and expandable: designed to grow with additional datasets and statistical metrics calculations in the future. RCMES system overview Large database (MySQL + Apache Hadoop): multiple observational atmospheric & surface data from, currently,: Extractors: process data from various data formats into a common database schema (Figure 3). Library of statistical metrics: Python routines with plug-ins in other languages (Fortran, c, idl) to calculate and plot standard metrics of model performance. (e.g. Bias, RMS error, Anomaly Correlation, Probability Distribution Functions). Flexible and expandable: designed to grow with additional datasets and statistical metrics calculations in the future. TRMM (satellite precipitation): [1998– 2010] ‏ AIRS (satellite surface + profile retrievals) [2002 – 2010] ERA-Interim (reanalysis): [1989 – 2010] NCEP CPC gridded precipitation data: [1948 – 2010] Snow Water Equivalent (U of Colorado + JPL): [2000- 2009]‏ continue to add more data. GC41B-0909 1 Jet Propulsion Laboratory, California Institute of Technology 2 UCLA JIFRESSE 3 University of Southern California