Accuracy and precision of lower stratospheric polar reanalysis
temperatures evaluated from A-train CALIOP and MLS, COSMIC GPS RO, and the equilibrium thermodynamics of supercooled ternary solutions and ice clouds
Authors: Lambert Alyn , Santee Michelle L. 
Published: August 2017

Abstract. We investigate the accuracy and precision of polar lower stratospheric temperatures (100–10 hPa during 2008–2013) reported in several contemporary reanalysis data sets comprising two versions of the Modern-Era Retrospective analysis for Research and Applications (MERRA and MERRA-2), the Japanese 55-year Reanalysis (JRA-55), the European Centre for Medium-range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (NCEP-CFSR). We also include the Goddard Earth Observing System Model version 5.9.1 near real-time analysis (GEOS-5.9.1). Comparisons of these datasets are made with respect to retrieved temperatures from the Aura Microwave Limb Sounder (MLS), Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Positioning System (GPS) Radio Occultation (RO) temperatures, and independent absolute temperature references defined by the equilibrium thermodynamics of supercooled ternary solutions (STS) and ice clouds. Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations of polar stratospheric clouds are used to determine the cloud particle types within the Aura MLS geometric field of view. The thermodynamic calculations for STS and the ice frost point use the colocated MLS gas-phase measurements of HNO3 and H2O. The estimated accuracy and precision for the STS temperature reference, over the 68 to 21 hPa pressure range, is 0.6–1.5 K and 0.3–0.6 K, respectively; for the ice temperature reference they are 0.4 K and 0.3 K, respectively. These uncertainties are smaller than those estimated for the retrieved MLS temperatures and also comparable to GPS RO uncertainties (accuracy  0.7 K) in the same pressure range. We examine a case study of the time-varying temperature structure associated with layered ice clouds formed by orographic gravity waves forced by flow over the Palmer peninsula, and compare how the wave amplitudes are reproduced by each reanalysis data set. We find that the spatial and temporal distribution of temperatures below the ice frost point, and hence the potential to form ice PSCs in model studies driven by the reanalyses, varies significantly because of the underlying differences in the representation of mountain wave activity. We have therefore used temperature variances, calculated from the COSMIC GPS RO temperature data (80–20 hPa), and imposed a variance threshold to selectively remove profiles with suspected enhanced wave activity. We examine the resulting improvement to the fidelity of the reanalysis temperatures. High accuracy COSMIC temperatures are used as a common reference to intercompare the reanalysis temperatures and, although the COSMIC data are routinely assimilated by each reanalysis scheme except for MERRA, we find that there are significant departures from uniformity in the structure of the meridional and altitude temperature differences. Over the 68–21 hPa pressure range, the biases of the reanalyses with respect to COSMIC temperatures for both polar regions fall within the narrow range of −0.6 K to +0.5 K. The corresponding standard deviations of the differences are ~ 0.8 K at 100 hPa and increase exponentially with altitude, as expected because of the gradually worsening GPS RO measurement precision. GEOS-5.9.1, MERRA, MERRA-2 and JRA-55 have predominantly cold biases, whereas ERA-I has a predominantly warm bias. NCEP-CFSR has a warm bias in the Arctic, but becomes substantially colder in the Antarctic. For the comparisons of the reanalysis temperatures with the thermodynamically calculated temperature references, we use the concept of an instrument field of view (FOV) fill-fraction. The distribution of CALIOP PSC types within the MLS geometric FOV are used to mitigate the effects of disparate types of PSCs occurring in the much larger MLS sample volume. This removes the speckle effect that arises either because of uncertainty in the detection classification from random noise in the CALIOP signals or from the microphysical effects of small-scale temperature fluctuations on the formation of PSC particles. We select viewing scenes with the requirement that 75 % or more of the MLS geometric FOV is filled with CALIOP PSC detections of the same PSC type classification. Scenes satisfying this requirement for CALIOP STS detections we denote as LIQ, and for CALIOP ice detections we denote as ICE. Over the 68–21 hPa pressure range, the reanalysis temperature biases are in the range −1.6 K to −0.3 K with standard deviations ~ 0.6 K for the LIQ reference, and in the range −0.9 K to +0.1 K with standard deviations ~ 0.7 K for the ICE reference. Comparisons of MLS temperatures with the LIQ and ICE reference temperatures reveal vertical oscillations in the MLS temperatures, and a significant low bias in MLS temperatures of up to 3 K.