Comparison of mean age of air in five reanalyses using the BASCOE transport model
Authors: Chabrillat Simon , Vigouroux Corinne , Christophe Yves , Engel Andreas , Errera Quentin , Minganti Daniele , Monge-Sanz Beatriz M. , Segers Arjo , Mahieu Emmanuel 
Published: October 2018

Abstract. 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. 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 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. 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 yr 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 hPa varying between approximately −0.4 and 0.4 yr dec−1.
Using ERA-I and CFSR, the 2002–2015 trends are negative above 10 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. 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.