CLIMATE SCIENTISTS’ BIG CHALLENGE: REPRODUCIBILITY USING BIG DATA Kyo Lee, Chris Mattmann, and RCMES team Jet Propulsion Laboratory (JPL), Caltech Reproducibility issues in climate science • Lots of published papers and reports do not include a computational description which is sufficiently detailed to reproduce the results. • Even with detailed description, it is practically impossible to reproduce others’ climate simulation results. • How many readers of the IPCC report can draw this plot? (from the latest IPCC report) Climate Science is Big Data Science • Data sets are massive and stored in distributed systems over many physical locations. • Coupled Model Intercomparison Project Phase 5 (CMIP5) for IPCC assessment: 110 different experiments, 24 modeling centers, 45 models, 3.3 petabytes of data. • By 2020 each experiment will generate an exabyte of data. • Use massive observational data sets to: • Formulate hypotheses from observed empirical relationships. • Simulate current and past conditions under those hypotheses using climate models. • Test hypotheses by comparing simulations to observations. Our unique challenges : data change quickly over time • Community Earth System Model (CESM) developed at National Center for Atmospheric Research CESM 1.0 (June 2010) minor updates and branch versions CESM 1.0.3 (June 2011) numerous ways to configure a simulation CESM 1.0.6 (May 2014) • Options: discretization methods, sub-grid scale physics, coupling with ocean, and so on. • CESM is open source, but it is practically impossible to reproduce others’ simulation results. Regional Climate Model Evaluation System (RCMES, http://rcmes.jpl.nasa.gov/) • RCMES is an open source software package developed by NASA’s JPL and UCLA to facilitate the evaluation of climate models. Now Open Climate Workbench (OCW) is one of top-level projects at the Apache Software Foundation. • Make observational datasets, with some emphasis on NASA satellite data, more accessible to the climate modeling community for climate model evaluation. • Provide researchers more time to spend on analyzing results and less time coding and worrying about file formats, data transfers, etc. • Provide guidance to further improve models by visualizing collective evaluation results of models. • Make some basic model evaluation for climate models reproducible. Regional Climate Model Evaluation System powered by Apache Software Foundation Other Data Centers (ESG, DAAC, ExArch Network) URL Metadata TRMM MODIS AIRS CERES Soil moisture ETC Data Table Extractor for various data formats Data Table Data Table Data Table Data Table User input Model data Extract OBS data Extract model data Regridder (Put the OBS & model data on the same time/space grid) Data extractor (Binary or netCDF) Metrics Calculator (Calculate evaluation metrics) Data Table Common Format, Native grid, Efficient architecture Use the regridded data for user’s own analyses and VIS. Visualizer (Plot the metrics) Raw Data: Various sources, formats, Resolutions, Coverage RCMED RCMET (Regional Climate Model Evaluation Database) A large scalable database to store data from variety of sources in a common format (Regional Climate Model Evaluation Tool) A library of codes for extracting data from RCMED and model and for calculating evaluation metrics Ingest obs/models, re-gridding, calculate metrics (e.g., bias, RMSE, correlation, significance, PDFs), and visualize results (e.g., contour, time series, Taylor). Replication of Kim et al. (2013) using RCMES How to make climate studies more reproducible? • Different programming languages (Fortran, Matlab, R, Python, IDL, NCL, GrADS, ….): the workflow system could facilitate replication of other studies. • Difficulties in reproducing others’ simulation results: Earth System Grid Federation (ESGF) provides software infrastructure to facilitate model intercomparison projects using observational data. • Climate scientists need more open source software similar to RCMES that can facilitate their analyses of observational and model data.