Reanalysis intercomparisons of stratospheric polar processing diagnostics
Authors: Lawrence Zachary D. , Manney Gloria L. , Wargan Krzysztof 
Published: September 2018

Abstract. We compare herein polar processing diagnostics derived from the four most
recent “full-input” reanalysis datasets: the National Centers for Environmental
Prediction Climate Forecast System Reanalysis/Climate Forecast System,
version 2 (CFSR/CFSv2), the European Centre for Medium-Range Weather
Forecasts Interim (ERA-Interim) reanalysis, the Japanese Meteorological
Agency's 55-year (JRA-55) reanalysis, and the National Aeronautics and Space
Administration (NASA) Modern-Era Retrospective analysis for Research and
Applications, version 2 (MERRA-2). We focus on diagnostics based on
temperatures and potential vorticity (PV) in the lower-to-middle stratosphere
that are related to formation of polar stratospheric clouds (PSCs), chlorine
activation, and the strength, size, and longevity of the stratospheric polar
vortex. Polar minimum temperatures (Tmin) and the area of regions having
temperatures below PSC formation thresholds (APSC) show large
persistent differences between the reanalyses, especially in the Southern
Hemisphere (SH), for years prior to 1999. Average absolute differences of the
reanalyses from the reanalysis ensemble mean (REM) in Tmin are as large
as 3 K at some levels in the SH (1.5 K in the Northern Hemisphere – NH), and absolute differences of
reanalysis APSC from the REM up to 1.5 % of a hemisphere (0.75 %
of a hemisphere in the NH). After 1999, the reanalyses converge toward better
agreement in both hemispheres, dramatically so in the SH: average Tmin
differences from the REM are generally less than 1 K in both hemispheres, and
average APSC differences less than 0.3 % of a hemisphere. The comparisons of diagnostics based on isentropic PV for assessing polar vortex
characteristics, including maximum PV gradients (MPVGs) and the area of the vortex
in sunlight (or sunlit vortex area, SVA), show more complex behavior: SH MPVGs showed
convergence toward better agreement with the REM after 1999, while NH MPVGs differences
remained largely constant over time; differences in SVA remained relatively constant in
both hemispheres. While the average differences from the REM are generally small for
these vortex diagnostics, understanding such differences among the reanalyses is complicated
by the need to use different methods to obtain vertically resolved PV for the different
reanalyses. We also evaluated other winter season summary diagnostics, including the winter mean
volume of air below PSC thresholds, and vortex decay dates. For the volume of air
below PSC thresholds, the reanalyses generally agree best in the SH, where relatively
small interannual variability has led to many winter seasons with similar
polar processing potential and duration, and thus low sensitivity to differences
in meteorological conditions among the reanalyses. In contrast, the large
interannual variability of NH winters has given rise to many seasons with
marginal conditions that are more sensitive to reanalysis differences. For vortex
decay dates, larger differences are seen in the SH than in the NH; in
general, the differences in decay dates among the reanalyses follow from persistent
differences in their vortex areas. Our results indicate that the transition from the reanalyses assimilating
Tiros Operational Vertical Sounder (TOVS) data to advanced TOVS and other
data around 1998–2000 resulted in a profound improvement in the agreement of
the temperature diagnostics presented (especially in the SH) and to a lesser
extent the agreement of the vortex diagnostics. We present several recommendations
for using reanalyses in polar processing studies, particularly related to
the sensitivity to changes in data inputs and assimilation. Because of these
sensitivities, we urge great caution for studies aiming to assess trends
derived from reanalysis temperatures. We also argue that one of the best
ways to assess the sensitivity of scientific results on polar processing
is to use multiple reanalysis datasets.