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

Abstract

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.

Benefits of mitigation for future heat extremes under RCP4.5 compared to RCP8.5

“…Note that the first two indices, TXx and TNx, respectively, are part of the suite identified by the Expert Team on Climate Change Detection Indices, ETCCDI (Zhang et al 2011;Alexander et al 2006) as indicators of a changing climate, and are regularly monitored in observations and calculated from model output in a standardized fashion, making a substantial part of our study comparable to multimodel studies in the literature (Donat et al 2013(Donat et al , 2014. The availability of these indices computed from observations allows us to perform a basic validation exercise of the model output over the period 1951-2003 (common to the observations).…”

“…Much work has been devoted to the characterization of changes in the statistics of such extremes as observed (Donat et al 2013(Donat et al , 2014 or as simulated by climate models (Sillmann et al 2013a, b;Kharin et al 2013), and about the portion of an individual event's severity that can be attributed to anthropogenic interference in our climate and. As the climate warms it has been shown that the tail behavior of temperature distributions is often the canary in the coal mine, in the sense of revealing statistically significant changes even before changes in mean quantities emerge from the noise of internal variability (Zhang et al 2011).…”

Can Temperature Extremes in East Antarctica be Replicated from ERA Interim Reanalysis?

“…Kharin et al (2007) showed some substantial differences between extreme values derived from models versus observations. Donat et al (2014) found a high level of consistency between various interpolated observations of temperature extremes over the past 60 years, with most reanalysis reproducing observed changes and spatial patterns reasonably well for the post-1979 period, when satellite data were available for assimilation. In Europe, Cornes and Jones (2013) demonstrated a good agreement between gridded observations and the reanalysis data over the past 30 years for extreme temperatures.…”

“…Few studies in Antarctica have focused on maximum temperatures (T max ) or minimum temperatures (T min ), which are more sensitive to climate change than their mean values (IPCC, 2012). Therefore, given the limited coverage of purely in situ observations, it is necessary to determine whether reanalysis can fill the gaps in these databases (Donat et al, 2014). Xie et al (2014) suggested that ERA Interim performs best in East Antarctica, after comparing these data with the other reanalysis for T mean .…”

Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods

“…These differences arise because near surface temperature and precipitation extremes are calculated from variables that are relatively poorly constrained by observations in reanalyses. Additionally, nonstationarity exists in some reanalysis products because they amalgamate observational data sets from different sources over time (Donat et al, 2014). In the context of historical validation of downscaling methods, statistical downscaling methods may perform poorly simply because reanalysis outputs are not stationary over the calibration and validation periods .…”