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Stephen R. Shaffer, Mohamed Moustaoui, Alex Mahalov, and Benjamin L. Ruddell

Abstract

A method of representing grid-scale heterogeneous development density for urban climate models from probability density functions of subgrid-resolution observed data is proposed. Derived values are evaluated in relation to normalized Shannon entropy to provide guidance in assessing model input data. Urban fraction for dominant-class and mosaic urban contributions is estimated by combining analysis of 30-m-resolution National Land Cover Database 2006 data products for continuous impervious surface area and categorical land cover. The aim of the method is to reduce model error through improvement of urban parameterization and representation of observations employed as input data. The multiscale variation of parameter values is demonstrated for several methods of utilizing input. This approach provides multiscale and spatial guidance for determining where parameterization schemes may be misrepresenting heterogeneity of input data, along with motivation for employing mosaic techniques that are based upon assessment of input data. The proposed method has wider potential for geographic application and complements data products that focus on characterizing central business districts. It utilizes observations to obtain a parameterization of urban fraction that is dependent upon resolution and class-partition scheme, thus providing one means of influencing simulation prediction at various aggregated grid scales.

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

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.

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