Consistency of Temperature and Precipitation Extremes across Various Global Gridded In Situ and Reanalysis Datasets

• DOI: 10.1175/jcli-d-13-00405.1
• Publication Date: 2014-04-11T22:39:11Z
• Authors: Markus G. Donat; Jana Sillmann; Simon Wild; Lisa V. Alexander; Tanya Lippmann; Francis W. Zwiers
• Keywords: 13 Climate Action; 550; anzsrc-for: 0405 Oceanography; anzsrc-for: 3702 Climate Change Science; 0207 environmental engineering; 37 Earth Sciences; anzsrc-for: 37 Earth Sciences; 02 engineering and technology; anzsrc-for: 3708 Oceanography; 551; 3702 Climate Change Science; anzsrc-for: 0401 Atmospheric Sciences; 13. Climate action; 3701 Atmospheric Sciences; anzsrc-for: 3701 Atmospheric Sciences; anzsrc-for: 0909 Geomatic Engineering

Changes in climate extremes are often monitored using global gridded datasets of climate extremes based on in situ observations or reanalysis data. This study assesses the consistency of temperature and precipitation extremes between these datasets. Both the temporal evolution and spatial patterns of annual extremes of daily values are compared across multiple global gridded datasets of in situ observations and reanalyses to make inferences on the robustness of the obtained results. While normalized time series generally compare well, the actual values of annual extremes of daily data differ systematically across the different datasets. This is partly related to different computational approaches when calculating the gridded fields of climate extremes. There is strong agreement between extreme temperatures in the different in situ–based datasets. Larger differences are found for temperature extremes from the reanalyses, particularly during the presatellite era, indicating that reanalyses are most consistent with purely observational-based analyses of changes in climate extremes for the three most recent decades. In terms of both temporal and spatial correlations, the ECMWF reanalyses tend to show greater agreement with the gridded in situ–based datasets than the NCEP reanalyses and Japanese 25-year Reanalysis Project (JRA-25). Extreme precipitation is characterized by higher temporal and spatial variability than extreme temperatures, and there is less agreement between different datasets than for temperature. However, reasonable agreement between the gridded observational precipitation datasets remains. Extreme precipitation patterns and time series from reanalyses show lower agreement but generally still correlate significantly.