Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic

• Published in: Journal of climate Vol. 27; no. 7; pp. 2588 - 2606
• Main Authors: Lindsay, R., Wensnahan, M., Schweiger, A., Zhang, J.
• Format: Journal Article
• Language: English
• Published: Boston, MA American Meteorological Society 01-04-2014
• Subjects: Agreements; Arctic zone; Atmosphere; Atmospheric models; Atmospheric research; Atmospherics; Bias; Climate; Climate models; Climate system; Climatology. Bioclimatology. Climate change; Clouds; Correlation; Data assimilation; Data collection; Datasets; Earth, ocean, space; Exact sciences and technology; External geophysics; Fluxes; Geophysics. Techniques, methods, instrumentation and models; Global warming; Humidity; Ice; Ice cover; Ice environments; Ice thickness; Ice volume; Meteorology; Modelling; Ocean models; Oceans; Precipitation; Radiation; Sea ice; Sea level; Seasonal variation; Studies; Surface fluxes; Surface temperature; Temperature; Thickness measurement; Time series; Trends; Variables; Weather; Wind speed; Wind velocity; polar regions; Arctic region; PN, Arctic; Japan; Coupled model; atmospheric precipitation; Radiation flux; Time series; Wind velocity; Surface temperature; Sea ice ocean model; Atmospheric temperature; 1990-2000; 2000-2010; Reanalysis; Polar region; Forcing; Comparative study; 1980-1990
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/jcli-d-13-00014.1

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice–ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R= 0.82). The trend in the total ice volume in September is greatest with MERRA (−4.1 × 10³ km³ decade−1) and least with CFSR (−2.7 × 10³ km³ decade−1).