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Evaluation of CESM1 (WACCM) free-running and specified dynamics atmospheric composition simulations using global multispecies satellite data records

Abstract. We have analyzed near-global stratospheric data (and mesospheric data as well
for H2O) in terms of absolute abundances, variability, and trends for
O3, H2O, HCl, N2O, and HNO3, based on Aura
Microwave Limb Sounder (MLS) data, as well as longer-term series from the
Global OZone Chemistry And Related trace gas Data records for the
Stratosphere (GOZCARDS). While we emphasize the evaluation of stratospheric
models via data comparisons through 2014 to free-running (FR-WACCM) and
specified dynamics (SD-WACCM) versions of the Community Earth System Model
version 1 (CESM1) Whole Atmosphere Community Climate Model (WACCM), we also
highlight observed stratospheric changes, using the most recent data from
MLS. Regarding highlights from the satellite data, we have used multiple linear
regression to derive trends based on zonal mean time series from Aura MLS
data alone, between 60∘ S and 60∘ N. In the upper
stratosphere, MLS O3 shows increases over 2005–2018 at ∼0.1–0.3 % yr−1 (depending on altitude and latitude) with 2σ errors of
∼0.2 % yr−1. For the lower stratosphere (LS), GOZCARDS O3
data for 1998–2014 point to small decreases between 60∘ S and
60∘ N, but the trends are more positive if the starting year is
2005. Southern midlatitudes (30–60∘ S) exhibit
near-zero or slightly positive LS trends for 1998–2018. The LS O3 trends
based on 2005–2018 MLS data are most positive (0.1–0.2 % yr−1) at these
southern midlatitudes, although marginally statistically significant, in
contrast to slightly negative or near-zero trends for 2005–2014. Given the high
variability in LS O3, and the high sensitivity of trends to the choice
of years used, especially for short periods, further studies are required for
a robust longer-term LS trend result. For H2O, upper-stratospheric
and mesospheric trends from GOZCARDS 1992–2010 data are near zero (within
∼0.2 % yr−1) and significantly smaller than trends (within
∼0.4–0.7 % yr−1) from MLS for 2005–2014 or 2005–2018. The
latter short-term positive H2O trends are larger than expected from
changes resulting from long-term increases in methane. We note that the very
shallow solar flux maximum of solar cycle 24 has contributed to fairly large
short-term mesospheric and upper-stratospheric H2O trends since 2005.
However, given known drifts in the MLS H2O time series, MLS
H2O trend results, especially after 2010, should be viewed as upper
limits. The MLS data also show regions and periods of small HCl increases in
the lower stratosphere, within the context of the longer-term stratospheric
decrease in HCl, as well as interhemispheric–latitudinal differences in
short-term HCl tendencies. We observe similarities in such short-term
tendencies, and interhemispheric asymmetries therein, for lower-stratospheric
HCl and HNO3, while N2O trend profiles exhibit anti-correlated
patterns. In terms of the model evaluation, climatological averages for 2005–2014 from
both FR-WACCM and SD-WACCM for O3, H2O, HCl, N2O, and
HNO3 compare favorably with Aura MLS data averages over this period.
However, the models at mid- to high latitudes overestimate mean MLS LS
O3 values and seasonal amplitudes by as much as 50 %–60 %;
such differences appear to implicate, in part, a transport-related model
issue. At lower-stratospheric high southern latitudes, variations in polar
winter and spring composition observed by MLS are well matched by SD-WACCM, with the main
exception being for the early winter rate of decrease in HCl, which is too
slow in the model. In general, we find that the latitude–pressure
distributions of annual and semiannual oscillation amplitudes derived from
MLS data are properly captured by the model amplitudes. In terms of closeness
of fit diagnostics for model–data anomaly series, not surprisingly, SD-WACCM
(driven by realistic dynamics) generally matches the observations better than
FR-WACCM does. We also use root mean square variability as a more valuable
metric to evaluate model–data differences. We find, most notably, that
FR-WACCM underestimates observed interannual variability for H2O;
this has implications for the time period needed to detect small trends,
based on model predictions. The WACCM O3 trends generally agree (within 2σ uncertainties)
with the MLS data trends, although LS trends are typically not statistically
different from zero. The MLS O3 trend dependence on latitude and
pressure is matched quite well by the SD-WACCM results. For H2O, MLS
and SD-WACCM positive trends agree fairly well, but FR-WACCM shows
significantly smaller increases; this discrepancy for FR-WACCM is even more
pronounced for longer-term GOZCARDS H2O records. The larger
discrepancies for FR-WACCM likely arise from its poorer correlations with
cold point temperatures and with quasi-biennial oscillation (QBO) variability. For HCl, while some
expected decreases in the global LS are seen in the observations, there are
interhemispheric differences in the trends, and increasing tendencies are
suggested in tropical MLS data at 68 hPa, where there is only a slight
positive trend in SD-WACCM. Although the vertical gradients in MLS HCl trends
are well duplicated by SD-WACCM, the model trends are always somewhat more
negative; this deserves further investigation. The original MLS N2O
product time series yield small positive LS tropical trends (2005–2012),
consistent with models and with rates of increase in tropospheric N2O.
However, longer-term series from the more current MLS N2O standard
product are affected by instrument-related drifts that have also impacted MLS
H2O. The LS short-term trend profiles from MLS N2O and
HNO3 at midlatitudes in the two hemispheres have different signs;
these patterns are well matched by SD-WACCM trends for these species. These model–data comparisons provide a reminder that the QBO and other
dynamical factors affect decadal variability in a major way, notably in the
lower stratosphere, and can thus significantly hinder the goals of robustly
extracting (and explaining) small underlying long-term trends. The data sets
and tools discussed here for model evaluation could be expanded to
comparisons of species or regions not included here, as well as to
comparisons between a variety of chemistry–climate models.
