Comparison of mean age of air in five reanalyses using the BASCOE transport model

• Main Authors: S. Chabrillat, C. Vigouroux, Y. Christophe, A. Engel, Q. Errera, D. Minganti, B. M. Monge-Sanz, A. Segers, E. Mahieu
• Format: Article
• Language: English
• Published: Copernicus Publications 2018-10-01
• Series: Atmospheric Chemistry and Physics
• Online Access: https://www.atmos-chem-phys.net/18/14715/2018/acp-18-14715-2018.pdf

<p>We present a consistent intercomparison of the mean age of air (AoA) according to five modern reanalyses: the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), the Japanese Meteorological Agency's Japanese 55-year Reanalysis (JRA-55), the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR) and the National Aeronautics and Space Administration's Modern Era Retrospective analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2). The modeling tool is a kinematic transport model driven only by the surface pressure and wind fields. It is validated for ERA-I through a comparison with the AoA computed by another transport model.</p><p>The five reanalyses deliver AoA which differs in the worst case by 1 year in the tropical lower stratosphere and more than 2 years in the upper stratosphere. At all latitudes and altitudes, MERRA-2 and MERRA provide the oldest values ( ∼ 5–6 years in midstratosphere at midlatitudes), while JRA-55 and CFSR provide the youngest values ( ∼ 4 years) and ERA-I delivers intermediate results. The spread of AoA at 50&thinsp;hPa is as large as the spread obtained in a comparison of chemistry–climate models. The differences between tropical and midlatitude AoA are in better agreement except for MERRA-2. Compared with in situ observations, they indicate that the upwelling is too fast in the tropical lower stratosphere. The spread between the five simulations in the northern midlatitudes is as large as the observational uncertainties in a multidecadal time series of balloon observations, i.e., approximately 2 years. No global impact of the Pinatubo eruption can be found in our simulations of AoA, contrary to a recent study which used a diabatic transport model driven by ERA-I and JRA-55 winds and heating rates.</p><p>The time variations are also analyzed through multiple linear regression analyses taking into account the seasonal cycles, the quasi-biennial oscillation and the linear trends over four time periods. The amplitudes of AoA seasonal variations in the lower stratosphere are significantly larger when using MERRA and MERRA-2 than with the other reanalyses. The linear trends of AoA using ERA-I confirm those found by earlier model studies, especially for the period 2002–2012, where the dipole structure of the latitude–height distribution (positive in the northern midstratosphere and negative in the southern midstratosphere) also matches trends derived from satellite observations of SF₆. Yet the linear trends vary substantially depending on the considered period. Over 2002–2015, the ERA-I results still show a dipole structure with positive trends in the Northern Hemisphere reaching up to 0.3&thinsp;yr&thinsp;dec⁻¹. No reanalysis other than ERA-I finds any dipole structure of AoA trends. The signs of the trends depend strongly on the input reanalysis and on the considered period, with values above 10&thinsp;hPa varying between approximately −0.4 and 0.4&thinsp;yr&thinsp;dec⁻¹. Using ERA-I and CFSR, the 2002–2015 trends are negative above 10&thinsp;hPa, but using the three other reanalyses these trends are positive. Over the whole period (1989–2015) each reanalysis delivers opposite trends; i.e., AoA is mostly increasing with CFSR and ERA-I but mostly decreasing with MERRA, JRA-55 and MERRA-2.</p><p>In view of this large disagreement, we urge great caution for studies aiming to assess AoA trends derived only from reanalysis winds. We briefly discuss some possible causes for the dependency of AoA on the input reanalysis and highlight the need for complementary intercomparisons using diabatic transport models.</p>