Abstract
Abstract. Global reanalyses from data assimilation systems are among the most widely used datasets in weather and climate studies, and potential vorticity (PV) from reanalyses is invaluable for many studies of dynamical and transport processes. We assess how consistently modern reanalyses represent potential vorticity (PV) among each other, focusing not only on PV but also on process-oriented dynamical diagnostics including equivalent latitude calculated from PV and PV-based tropopause and stratospheric polar vortex characterization. In particular we assess the National Centers for Environmental Prediction Climate Forecast System Reanalysis/Climate Forecast System, version 2 (CFSR/CFSv2) reanalysis, the European Centre for Medium-Range Weather Forecasts Interim (ERA-Interim) reanalysis, the Japanese Meteorological Agency's 55-year (JRA-55) reanalysis, and the NASA Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). Overall, PV from all reanalyses agrees well with the reanalysis ensemble mean, providing some confidence that all of these recent reanalyses are suitable for most studies using PV-based diagnostics. Specific diagnostics where some larger differences are seen include PV-based tropopause locations in regions that have strong tropopause gradients (such as around the subtropical jets) or are sparse in high-resolution data (such as over Antarctica), and the stratospheric polar vortices during fall vortex formation and (especially) spring vortex breakup; studies of sensitive situations or regions such as these should examine PV from multiple reanalyses.
Highlights
Global reanalyses provide gridded high-resolution meteorological fields over several decades based on an optimized combination of general circulation models and observational data
The most pronounced differences are for CFSR/CFSv2 during each hemisphere’s winter, with the magnitude of sPV biased low by up to 1 × 10−4 s−1 in each hemisphere (i.e., up to around a 40 % bias at 2500 K (∼ 0.3 hPa))
– In the zonal mean, sPV from all reanalyses agrees with the reanalysis ensemble mean (REM) within 0.1 × 10−4 s−1
Summary
Global reanalyses provide gridded high-resolution meteorological fields over several decades based on an optimized combination of general circulation models and observational data. Data assimilation methods ingest observations to constrain the general circulation models and provide spatially and temporally consistent atmospheric states, offering a wide range of variables, such as temperature, humidity, winds, and vorticity. They are among the most widely used datasets in the study of weather and climate. Can PV be viewed as a tracer for many purposes, and the complete flow structure (balanced winds and temperature) can be determined from the spatial distribution of PV itself (a property referred to as the invertibility principle, e.g., Hoskins et al, 1985) These properties of PV make it useful for identifying features of the atmosphere.
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