A Regional Climate Model Evaluation System Facilitating the

A Regional Climate Model Evaluation System: Facilitating the Use of Contemporary Satellite and Other Observations for Evaluating Regional Climate Model Fidelity D. E. 1 Jet 1, 2 Waliser , 2 Kim , J. C. 1, 3 Mattmann , C. 1 Goodale , A. 1 Hart , P. 1 Zimdars and P. 1 Lean Propulsion Laboratory, California Institute of Technology; 2 JIFRESSE, UCLA; 3 University of Southern California For more information, please email duane. waliser@jpl. nasa. gov Background: Why model evaluation? • Climate model projections play a crucial role in developing plans to mitigate and adapt to climate variations and change for sustainable developments. • Assessing model performance is an important step in linking climate simulation quality to projection uncertainty and then to climate change impacts assessments. • Uncertainties propagate according to model hierarchy • Bias correction is based on model evaluation • Determination of the weights in multi-model ensemble • Model evaluation is also a fundamental part of model development and improvement (Figure 1). Evaluation of the Simulated Cold Season Hydrology in California WRF; Oct 2008 – Mar 2009; NCEP Final Analysis forcing RCMES High-level technical architecture user choice RCM data Seasonal-mean 2 -m Air Temperature URL Extract OBS data Metadata TRMM Data Table MODIS Regridder Put the OBS & RCM data on the same grid for comparison Data Table My. SQL AIRS Data extractor (Fortran binary) Data Table Extractor SWE Metrics Calculator Data Table Calculate comparison metrics Data Table Soil moisture Data extractor (Fortran binary) Data Table Bias (K): WRF-AIRS User’s own codes for ANAL and VIS. Plot the metrics Efficient architecture Raw Data: Various Formats, Resolutions, Coverage AIRS T 2 (K): Ascending passes (1: 30 PM) Visualizer Common Format, Native grid, ETC WRF T 2 (K): 00 UTC Extract RCM data RCMED RCMET (Regional Climate Model Evaluation Database) (Regional Climate Model Evaluation Toolkit) A large scalable database to store data in a common format A library of codes for extracting data from RCMED and model and for calculating evaluation metrics Season-total Precipitation (mm): Multiple Reference Data TRMM WRF RCMES overview: Bias (mm): WRF-TRMM • Large database (My. SQL + Apache Hadoop): • Multiple reference datasets from: • Satellite remote sensing Issues: • Remote sensing data: • TRMM (1998 -2010) • AIRS (2002 -2010) • MODIS Cloudiness CPC • Multiple REF data: • Differences between REF datasets • Reference data intercomparison • Observational uncertainty • Analysis • CPC precipitation, CRU precipitation, 2 -m air temperatures • Assimilation Figure 1. The role of model evaluation in the model development process and uncertainty estimations. Regional Climate Model Evaluation System (RCMES): • 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 with cutting-edge observations and diagnostics to evaluate and improve their models. • Help end-users understand the uncertainties in climate projections for the regions of interest. • Efficient: Fast access to reference data and toolkit • User Friendly: Intuitive and transferrable GUI • Flexible: Cloud-based architecture • Expandable: • Easy to add new data/analysis tool • Scalable storage solution • SWR (SNODAS; JPL&U. Colorado) • Reanalysis • Extractors: • Process data from various data formats into a common database schema. • 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). Select data period Next > Select Reference Dataset Select Data Timestep TRMM AIRS level III gridded ERA-Interim URD SNODAS Daily Monthly Seasonal Annual Map Time series Process > Select Spatial Grid Select model data Reference Data Model Next > Select Plots Next > Mean bias RMSE Pattern correlation PDF Similarity score Coeff. of Efficiency Precipitation Annual Cycle in 6 Regions using Portrait diagram RMSE Correlation Overland mean (mm/day) ENS Select Metrics 2 -m temperature Precipitation OLR (TOA) Cloud fraction 10 m wind speed Jet Propulsion Laboratory California Institute of Technology Pasadena, California Copyright 2010. All rights reserved. Spatial Variability of the Precipitation Climatology using Taylor diagram Sample Graphical User interface National Aeronautics and Space Administration www. nasa. gov Evaluation of the CORDEX-Africa Multi-Model Ensemble Preliminary 20 -year runs; 1989 – 2008 • ERA-Interim (e. g. U(p), V(p), q(p), T(p), SLP) Select Variable Bias (mm): WRF-CPC • Satellite fly-over timing • Sensor footprints Intuitive presentation schema can facilitate intercomparison of multiple models RMSE (mm/day) Future works: 1. 2. 3. 4. 5. Add additional reference datasets (e. g. , other reanalysis, satellite data, in-situ) Examine remote sensing data for evaluating fine-scale (<10 km) regional climate data. Additional metrics calculations and visualizations Improve GUI Use the system to evaluate regional/global climate models associated with National Climate Assessment (NCA), NARCCAP, CMIP 5 and CORDEX (Africa and Asia). Reference Hart, A. F. , C. E. Goodale, C. A. Mattmann, P. Zimdars, D. Crichton, P. Lean, J. Kim, and D. E. Waliser, 2011: A cloud-enabled regional climate evaluation system. SECLOUD’ 11, May 22, 2011, Waikiki, Honolulu, HI, USA.