Benchmarking and process diagnostics of land models

Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, Christa Peters-Lidard

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

Original languageEnglish (US)
Pages (from-to)1835-1852
Number of pages18
JournalJournal of Hydrometeorology
Volume19
Issue number11
DOIs
StatePublished - Nov 1 2018

Keywords

  • Land surface model
  • Model comparison
  • Model errors
  • Model evaluation/performance
  • Neural networks

ASJC Scopus subject areas

  • Atmospheric Science

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