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Tackling Reproducibility Challenges in Climate Science: Insights from JPL and RCMES Team

nicole-finch avatar
nicole-finch
8 diapositives · 213 Views · Nov 12, 2014

Climate science faces significant reproducibility challenges, as many published papers lack the computational descriptions needed for results to be replicated. This is particularly evident in large data projects such as CMIP5, which comprises vast datasets and varied climate models. The Jet Propulsion Laboratory’s RCMES team has developed open-source tools to enhance the accessibility of observational datasets and streamline model evaluation. These innovations aim to improve data transparency, support hypothesis testing, and facilitate climate research by making evaluation processes more reproducible.

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Tackling Reproducibility Challenges in Climate Science: Insights from JPL and RCMES Team
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  1. Climate scientists’ big challenge: reproducibility using big data Kyo Lee, Chris Mattmann, and RCMES team Jet Propulsion Laboratory (JPL), Caltech

  2. 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)

  3. 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.

  4. Our unique challenges :data change quickly over time • Community Earth System Model (CESM) developed at National Center for Atmospheric Research • 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. CESM 1.0.3 (June 2011) CESM 1.0.6 (May 2014) CESM 1.0 (June 2010) numerous ways to configure a simulation minor updates and branch versions

  5. 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 modelingcommunity 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.

  6. Regional Climate Model Evaluation System powered by Apache Software Foundation Other Data Centers (ESG, DAAC, ExArch Network) User input Model data URL Extract OBS data Extract model data TRMM Metadata MODIS Regridder (Put the OBS & model data on the same time/space grid) Data Table Use the re-gridded data for user’s own analyses and VIS. Data Table Extractor for various data formats AIRS Data Table • PostgreSQL Data extractor (Binary or netCDF) CERES Data Table Metrics Calculator (Calculate evaluation metrics) Data Table Soil moisture Data Table Common Format, Native grid, Efficient architecture Visualizer (Plot the metrics) ETC Raw Data: Various sources, formats, Resolutions, Coverage RCMED (Regional Climate Model Evaluation Database) A large scalable database to store data from variety of sources in a common format RCMET (Regional Climate Model Evaluation Tool) A library of codes for extracting data from RCMED and model and for calculating evaluation metrics RCMESHigh-level technical architecture RCMESHigh-level technical architecture Ingest obs/models, re-gridding, calculate metrics (e.g., bias, RMSE, correlation, significance, PDFs), and visualize results (e.g., contour, time series, Taylor). RCM data RCM data user choice user choice URL URL TRMM TRMM Extract OBS data Extract OBS data Extract RCM data Extract RCM data Metadata Metadata User’s own codes for ANAL and VIS. User’s own codes for ANAL and VIS. Data Table Data Table MODIS MODIS Regridder Put the OBS & RCM data on the same grid for comparison Regridder Put the OBS & RCM data on the same grid for comparison Data Table Data Table Extractor Extractor AIRS AIRS MySQL MySQL Data extractor (Fortran binary) Data extractor (Fortran binary) Data Table Data Table Metrics Calculator Calculate comparison metrics Metrics Calculator Calculate comparison metrics SWE SWE Data Table Data Table Data Table Data Table Data extractor (Fortran binary) Data extractor (Fortran binary) Soil moisture Soil moisture Data Table Data Table Visualizer Plot the metrics Visualizer Plot the metrics Common Format, Native grid, Efficient architecture Common Format, Native grid, Efficient architecture ETC ETC Raw Data: Various Formats, Resolutions, Coverage Raw Data: Various Formats, Resolutions, Coverage RCMET (Regional Climate Model Evaluation Toolkit) A library of codes for extracting data from RCMED and model and for calculating evaluation metrics RCMET (Regional Climate Model Evaluation Toolkit) A library of codes for extracting data from RCMED and model and for calculating evaluation metrics RCMED (Regional Climate Model Evaluation Database) A large scalable database to store data in a common format RCMED (Regional Climate Model Evaluation Database) A large scalable database to store data in a common format

  7. Replication of Kim et al. (2013) using RCMES

  8. 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.