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Cécile B. Ménard, Jaakko Ikonen, Kimmo Rautiainen, Mika Aurela, Ali Nadir Arslan, and Jouni Pulliainen

Abstract

A single-model 16-member ensemble is used to investigate how external model factors can affect model performance. Ensemble members are constructed with the land surface model (LSM) Joint UK Land Environment Simulator (JULES), with different choices of meteorological forcing [in situ, NCEP Climate Forecast System Reanalysis (CFSR)/CFSv2, or Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] and ancillary datasets (in situ or remotely sensed), and with four time step modes. Effects of temporal averaging are investigated by comparing the hourly, daily, monthly, and seasonal ensemble performance against snow depth and water equivalent, soil temperature and moisture, and latent and sensible heat fluxes from one forest site and one clearing in the boreal ecozone of Finnish Lapland. Results show that meteorological data are the largest source of uncertainty; differences in ancillary data have little effect on model results. Although generally informative and representative, aggregated performance metrics fail to identify “right results for the wrong reasons”; to do so, scrutinizing of time series and of interactions between variables is necessary. Temporal averaging over longer intervals improves metrics—with the notable exception of bias, which increases—by reducing the effects of internal data and model variability on model response. Model evaluation during shoulder seasons (fall minus spring) identifies weaknesses in the reanalyses datasets that conventional seasonal performance (winter minus summer) neglects. In view of the importance of snow on the range of results obtained with the same model, let alone identical simulations using different temporal averaging, it is recommended that systematic evaluation, quantification of errors, and uncertainties in snow-covered regions be incorporated in future efforts to standardize evaluation methods of LSMs.

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Stefan Brönnimann, Rob Allan, Christopher Atkinson, Roberto Buizza, Olga Bulygina, Per Dahlgren, Dick Dee, Robert Dunn, Pedro Gomes, Viju O. John, Sylvie Jourdain, Leopold Haimberger, Hans Hersbach, John Kennedy, Paul Poli, Jouni Pulliainen, Nick Rayner, Roger Saunders, Jörg Schulz, Alexander Sterin, Alexander Stickler, Holly Titchner, Maria Antonia Valente, Clara Ventura, and Clive Wilkinson

Abstract

Global dynamical reanalyses of the atmosphere and ocean fundamentally rely on observations, not just for the assimilation (i.e., for the definition of the state of the Earth system components) but also in many other steps along the production chain. Observations are used to constrain the model boundary conditions, for the calibration or uncertainty determination of other observations, and for the evaluation of data products. This requires major efforts, including data rescue (for historical observations), data management (including metadatabases), compilation and quality control, and error estimation. The work on observations ideally occurs one cycle ahead of the generation cycle of reanalyses, allowing the reanalyses to make full use of it. In this paper we describe the activities within ERA-CLIM2, which range from surface, upper-air, and Southern Ocean data rescue to satellite data recalibration and from the generation of snow-cover products to the development of a global station data metadatabase. The project has not produced new data collections. Rather, the data generated has fed into global repositories and will serve future reanalysis projects. The continuation of this effort is first contingent upon the organization of data rescue and also upon a series of targeted research activities to address newly identified in situ and satellite records.

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