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The Brewer-Dobson Circulation (BDC) transports chemical tracers from the well-mixed tropical troposphere to the polar stratosphere, with many important implications for climate, chemistry, ozone distribution and recovery. Since the photochemical losses of nitrous oxide (N[sub 2]O) are well-known, model differences in its rate of change are due to transport processes that can be separated in the mean residual advection and the isentropic mixing terms in the Transformed Eulerian Mean (TEM) framework. Here the climatological impact of the stratospheric BDC on the long-lived tracer N2O is evaluated through a comparison of its TEM budget in the Whole Atmosphere Community Climate Model (WACCM), a chemical reanalysis of the Aura Microwave Limb Sounder version 2 (BRAM2) and in a Chemistry-Transport Model (CTM) driven by four modern reanalyses (ERA-Interim, JRA-55, MERRA and MERRA2). The effects of stratospheric transport on the N[sub 2]O rate of change, as depicted in this study, have not been compared across this variety of datasets and never investigated in a chemical reanalysis. We focus 10 on the seasonal means and climatological annual cycles of the two main contributions to the N2O TEM budget: the vertical residual advection and the horizontal mixing terms. The N2O mixing ratio in the CTM experiments has a spread of approximately ~ 20% in the middle stratosphere, reflecting the large diversity in the mean Age of Air obtained with the same experiments. In all datasets the TEM budget is well-closed and the agreement between the vertical advection terms is qualitatively very good in the Northern Hemisphere, and good in the Southern Hemisphere except above the Antarctic region. The datasets do not agree as well with respect to the horizontal mixing term, especially in the Northern Hemisphere where horizontal mixing has a smaller contribution in WACCM than in the reanalyses. WACCM is investigated through three model realizations and a sensitivity test where gravity waves are forced differently in the Southern Hemisphere. The internal variability of the horizontal mixing in WACCM is large in the polar regions, and comparable to the differences between the dynamical reanalyses. The sensitivity test has a relatively small impact on the horizontal mixing term, but significantly changes the vertical advection term and produces a less realistic N2O annual cycle above the Antarctic. In this region, all reanalyses show a large wintertime N2O decrease, which is mainly due to horizontal mixing. This is not seen with WACCM, where the horizontal mixing term barely contributes to the TEM budget. While we must use caution in the interpretation of the differences in this region, where the reanalyses show large residuals of the TEM budget, they could be due to the fact that the polar jet is stronger and not tilted equatorward in WACCM compared 25 with the reanalyses. We also compare the inter-annual variability in the horizontal mixing and the vertical advection terms. As expected, the horizontal mixing term presents a large variability during austral fall and boreal winter in the polar regions . In the Tropics, the inter-annual variability of the vertical advection term is much smaller in WACCM and JRA-55 than in the other experiments. The large residual in the reanalyses and the disagreement between WACCM and the reanalyses in the Antarctic region highlight 30 the need for further investigations on the modeling of transport in this region of the stratosphere. [ABSTRACT FROM AUTHOR]

This paper presents a reanalysis of the atmospheric chemical composition from the upper troposphere to the lower mesosphere from August 2004 to December 2017. This reanalysis is produced by the Belgian Assimilation System for Chemical ObsErvations (BASCOE) constrained by the chemical observations from the Microwave Limb Sounder (MLS) onboard the Aura satellite. BASCOE is based on the Ensemble Kalman Filter (EnKF) method and includes a chemical transport model driven by the winds and temperature from the ERA-Interim meteorological reanalysis. The model resolution is 3.75° in longitude, 2.5° in latitude and 37 vertical levels from the surface to 0.1 hPa with 25 levels above 100 hPa. The outputs are provided every 6 hours. This reanalysis is called BRAM2 for BASCOE Reanalysis of Aura MLS, version 2. Vertical profiles of eight species from MLS version 4 are assimilated and are evaluated in this paper: ozone (O3), water vapour (H2O), nitrous oxide (N2O), nitric acid (HNO3), hydrogen chloride (HCl), chlorine oxide (ClO), methyl chloride (CH3Cl) and carbon monoxide (CO). They are evaluated using independent observations from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACEFTS), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES), N2O observations from another MLS radiometer than the one used to deliver the standard product and ozonesondes. The evaluation is done in four regions of interest where only selected species are evaluated. These regions are (1) the lower stratospheric polar vortex where O3, H2O, N2O, HNO3, HCl and ClO are evaluated, (2) the upper stratospheric lower mesospheric polar vortex where H2O, N2O, HNO3 and CO are evaluated, (3) the tropical tropopause layer (TTL) where O3, H2O, CO and CH3Cl are evaluated and (4) the middle stratosphere where O3, H2O, N2O, HNO3, HCl, ClO and CH3Cl are evaluated. In general BRAM2 reproduces MLS observations within their uncertainties and agrees well with independent observations, with several limitations discussed in this paper (see the summary in Sect. 5.5). In particular, ozone is not assimilated at altitudes above (i.e. pressures lower than) 4 hPa due to a model bias that cannot be corrected by the assimilation. MLS ozone profiles display unphysical oscillations in the TTL which are corrected by the assimilation, allowing a good agreement with ozonesondes. Moreover, in the upper troposphere, comparison of BRAM2 with MLS and independent observations suggests a positive bias in MLS O3 and a negative bias in MLS H2O. The reanalysis also reveals a drift in MLS N2O against independent observations which highlights the potential use of BRAM2 to estimate biases between instruments. BRAM2 is publicly available and will be extended to assimilate MLS observations post 2017. [ABSTRACT FROM AUTHOR]

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 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 differ in the worst case by one year in the tropical lower stratosphere and more than two years in the upper stratosphere. At all latitudes and altitudes, MERRA-2 and MERRA provide the oldest values (~ 5–6 years in mid-stratosphere at mid-latitudes) 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 mid-latitudes 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 general hierarchy of reanalyses delivering older AoA (MERRA, MERRA-2) and younger AoA (JRA-55, CFSR) holds during the whole 1989–2015 period, with AoA derived from ERA-I keeping intermediate values. The spread between the five simulations in the northern mid-latitudes is as large as the observational uncertainties in a multidecadal time series of balloon observations, i.e., approximately two years. No global impact of the Pinatubo eruption can be found in our simulations of AoA, contrarily 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-Biennal Oscillation and the linear trends over four time periods. The amplitudes of AoA seasonal variations in the lower stratosphere are significantly larger using MERRA and MERRA-2 than with the other reanalyses (up to twice as large at the 50 hPa pressure level). 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 mid-stratosphere and negative in the southern mid-stratosphere) also matches trends derived from satellite observations of SF6. Yet the linear trends vary considerably depending on the considered period. Over 2002–2015 the ERA-I results still show a dipole structure but it is much less pronounced, with positive trends in the northern hemisphere remaining significant only in the polar lower stratosphere (where they reach 0.2 years per decade). 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 years per decade. 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 these large disagreements, 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. [ABSTRACT FROM AUTHOR]