scholarly journals Supplementary material to "Climatology and Interannual Variability of Dynamic Variables in Multiple Reanalyses Evaluated by the SPARC Reanalysis Intercomparison Project (S-RIP)"

Author(s):  
Craig S. Long ◽  
Masatomo Fujiwara ◽  
Sean Davis ◽  
Daniel M. Mitchell ◽  
Corwin J. Wright
2017 ◽  
Vol 17 (23) ◽  
pp. 14593-14629 ◽  
Author(s):  
Craig S. Long ◽  
Masatomo Fujiwara ◽  
Sean Davis ◽  
Daniel M. Mitchell ◽  
Corwin J. Wright

Abstract. Two of the most basic parameters generated from a reanalysis are temperature and winds. Temperatures in the reanalyses are derived from conventional (surface and balloon), aircraft, and satellite observations. Winds are observed by conventional systems, cloud tracked, and derived from height fields, which are in turn derived from the vertical temperature structure. In this paper we evaluate as part of the SPARC Reanalysis Intercomparison Project (S-RIP) the temperature and wind structure of all the recent and past reanalyses. This evaluation is mainly among the reanalyses themselves, but comparisons against independent observations, such as HIRDLS and COSMIC temperatures, are also presented. This evaluation uses monthly mean and 2.5° zonal mean data sets and spans the satellite era from 1979–2014. There is very good agreement in temperature seasonally and latitudinally among the more recent reanalyses (CFSR, MERRA, ERA-Interim, JRA-55, and MERRA-2) between the surface and 10 hPa. At lower pressures there is increased variance among these reanalyses that changes with season and latitude. This variance also changes during the time span of these reanalyses with greater variance during the TOVS period (1979–1998) and less variance afterward in the ATOVS period (1999–2014). There is a distinct change in the temperature structure in the middle and upper stratosphere during this transition from TOVS to ATOVS systems. Zonal winds are in greater agreement than temperatures and this agreement extends to lower pressures than the temperatures. Older reanalyses (NCEP/NCAR, NCEP/DOE, ERA-40, JRA-25) have larger temperature and zonal wind disagreement from the more recent reanalyses. All reanalyses to date have issues analysing the quasi-biennial oscillation (QBO) winds. Comparisons with Singapore QBO winds show disagreement in the amplitude of the westerly and easterly anomalies. The disagreement with Singapore winds improves with the transition from TOVS to ATOVS observations. Temperature bias characteristics determined via comparisons with a reanalysis ensemble mean (MERRA, ERA-Interim, JRA-55) are similarly observed when compared with Aura HIRDLS and Aura MLS observations. There is good agreement among the NOAA TLS, SSU1, and SSU2 Climate Data Records and layer mean temperatures from the more recent reanalyses. Caution is advised for using reanalysis temperatures for trend detection and anomalies from a long climatology period as the quality and character of reanalyses may have changed over time.


2017 ◽  
Vol 30 (15) ◽  
pp. 5871-5884 ◽  
Author(s):  
Andrew Manaster ◽  
Christopher W. O’Dell ◽  
Gregory Elsaesser

In this study, observed cloud liquid water path (LWP) trends from the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) dataset (1988–2014) are compared to trends computed from the temporally coincident records of 16 global climate models (GCMs) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). For many regions, observed trend magnitudes are several times larger than the corresponding model mean trend magnitudes. Muted model mean trends are thought to be the result of cancellation effects arising from differing interannual variability characteristics and differences in model physics–dynamics. In most regions, the majority of modeled trends were statistically consistent with the observed trends. This was thought to be because of large estimated errors in both the observations and the models due to interannual variability. Over the southern oceans (south of 40°S latitude), general agreement between the observed trend and virtually all GCM trends is also found (about 1–2 g m−2 decade−1). Observed trends are also compared to those from the Atmospheric Model Intercomparison Project (AMIP). Like the CMIP5 models, the majority of modeled AMIP trends were statistically consistent with the observed trends. It was also found that, in regions where the AMIP model mean time series better captures observed interannual variability, it tends to better capture the magnitude of the observed trends.


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