Understanding and modeling the large-scale transport of trace gases and aerosols is important for interpreting past (and projecting future) changes in atmospheric composition. Here we show that there are large differences in the global-scale atmospheric transport properties among the models participating in the IGAC SPARC Chemistry–Climate Model Initiative (CCMI). Specifically, we find up to 40 % differences in the transport timescales connecting the Northern Hemisphere (NH) midlatitude surface to the Arctic and to Southern Hemisphere high latitudes, where the mean age ranges between 1.7 and 2.6 years. We show that these differences are related to large differences in vertical transport among the simulations, in particular to differences in parameterized convection over the oceans. While stronger convection over NH midlatitudes is associated with slower transport to the Arctic, stronger convection in the tropics and subtropics is associated with faster interhemispheric transport. We also show that the differences among simulations constrained with fields derived from the same reanalysis products are as large as (and in some cases larger than) the differences among free-running simulations, most likely due to larger differences in parameterized convection. Our results indicate that care must be taken when using simulations constrained with analyzed winds to interpret the influence of meteorology on tropospheric composition.
The distributions of greenhouse gases (GHGs) and ozone-depleting substances
(ODSs) are strongly influenced by large-scale atmospheric transport. In the
extratropics the midlatitude jet stream influences the long-range transport
of pollutants and water vapor into the Arctic
There are large uncertainties in our understanding of how large-scale
atmospheric transport influences tropospheric composition. This is largely
because transport is difficult to constrain directly from observations and
because global-scale tropospheric transport properties differ widely among
models. For example,
One approach to reducing this uncertainty has been to use models constrained
with analysis fields, although comparisons of the transport properties among
these simulations also reveal large differences. For example,
More recently,
The results in
As in
Our analysis uses the models participating in CCMI, which builds upon previous
chemistry–climate model intercomparisons, including the SPARC Report on the
Evaluation of Chemistry–Climate Models
We focus only on those CCMI model simulations that output the idealized tracers (Tables 1 and 2). We present results from the pair of hindcast REF-C1 (simply C1) and REF-C1SD (or C1SD) simulations, which were constrained with observed SSTs and SICs. For each model, we analyze the first ensemble member “r1i1p1” from the REF-C1 and REF-C1SD simulations. Whereas the REF-C1 experiment simulates the recent past (1960–2010) using internally generated meteorological fields, the REF-C1SD or C1 “specified dynamics” simulation is constrained with (re)analysis meteorological fields and correspondingly only spans the years 1980–2010. Note that both online nudged simulations and offline CTMs are used, as indicated in the simulation name. Furthermore, while we have also examined tracer output from the REF-C2 simulation, which used SSTs from a coupled atmosphere–ocean model simulation, we find that the differences in the idealized tracers between the REF-C2 and REF-C1 simulations are significantly smaller than among the hindcast (C1 versus C1SD) simulations. For that reason, from here on we exclude the REF-C2 results from our discussions.
Details of the model integrations in which columns 3–6 correspond
to the horizontal resolution, the number of vertical levels and model top,
the source of meteorological fields and reference for the model's convective
parameterizations. T21 and T42 correspond to quadratic grids of approximately
Table of idealized tracers,
The simulations presented in
List of the model simulations for which the idealized tracers
(
In addition to differences among the REF-C1 and REF-C1SD experiments, the
models differ widely in terms of their horizontal resolution, which ranges
from
Climatological mean December–January–February
(DJF)
Finally, we complement our analysis of the idealized tracers with comparisons
of the models' convective mass fluxes, horizontal and vertical winds, and
temperature fields (when available; Table 3). All tracer and dynamical
variables were available as monthly mean output on native model levels.
Therefore, we interpolated all output to a standard pressure vector with 4 pressure levels in the stratosphere (10, 30, 50 and 80 hPa) and 19 pressure
levels in the troposphere spaced every 50 hPa between 100 and 1000 hPa.
Note that values for pressure levels below the surface topography are treated
as missing (NaN) values for all simulations. To construct all of the
multi-model means (denoted in the figures using solid grey lines) we first
interpolated all model output to the same 1
Several of the idealized tracers examined in this study (Table 2) were
discussed in
Three of the tracers' boundary conditions are zonally uniform and are defined
over the same NH surface region over midlatitudes,
The third NH midlatitude tracer,
Meridional profiles of the 400–700 hPa zonally averaged
DJF
In addition to the NH midlatitude source tracers, we also examine two other
tracers with global sources. The first tracer,
Meridional profiles of
Same as Fig. 2, except for the stratospheric and surface global
source tracers, e90
Interestingly, the differences in the concentrations of
Zonal profiles of the climatological mean 5-day idealized loss
tracer
Comparisons of the global source tracer e90 also reveal large differences
among the simulations (Fig. 3). The spread in e90 mixing ratios is similar
in magnitude to the spread in the concentrations of the idealized loss
tracers, which is consistent with the fact that they all have prescribed surface
mixing ratios. At the same time, the relationship between e90 and the
midlatitude-sourced tracers is complicated and depends sensitively on
latitude. In particular, over the southern edge of the NH midlatitude source
region we find that e90 and
Maps of the 700–900 hPa averaged multi-model mean convective mass
flux
The spread in
Among the other simulations, by comparison, the differences in
Zonal profiles of
Vertical profiles of the convective mass flux evaluated over regions
of strong midlatitude convection (black boxes in Fig. 6) during
DJF
One approach to interpreting the large differences in poleward transport
among the CCMI simulations is to compare the (parameterized) convection and
horizontal flow fields over northern midlatitudes (Fig. 6). During winter
the multi-model mean convective mass fluxes
(
By comparison, during boreal summer the (parameterized) convective mass
fluxes are generally weaker over midlatitudes and shift from the oceans
toward land, coincident with weaker and zonally shifted storm tracks.
Seasonal changes in the thermal structure of the extratropics also indicate
that the Arctic is isentropically isolated from the northern midlatitude
surface during summer compared to winter
Comparisons of the vertical profiles of the convective mass fluxes (CMFs)
over northern midlatitudes (black boxed regions in Fig. 6) reveal large
differences in (parameterized) convection among the models during both boreal
winter and summer (Fig. 7). Among the “weak midlatitude convection”
simulations (i.e., NASA and NCAR), the strength of
Scatterplots showing negative correlations between the strength of
parameterized convection in the midlatitude lower troposphere, represented by
the 800–950 hPa averaged convective mass flux
(
Closer inspection of the loss tracer profiles at 30
This is illustrated more clearly in Fig. 8, which shows strong negative
correlations during boreal winter between lower tropospheric (800–950 hPa)
convection (
We also find evidence of a relationship between midlatitude convection and
the loss tracer concentrations over the Arctic during boreal summer, although
this relationship is relatively weaker (Fig. 8d–f). This most likely
reflects the fact that the Arctic is isentropically isolated from the
northern midlatitude surface during boreal summer compared to during winter
Meridional profiles of the annual mean 5-day loss and 50-day loss
tracer ages,
We now compare different measures of interhemispheric transport among the
models. As in
Meridional profiles of the annually averaged
Consistent with the results in
Comparisons of the upper tropospheric meridional wind
While
A possible source of differences in interhemispheric transport among the C1SD
simulations are differences in the analysis fields themselves, which can
differ significantly among reanalysis products
Scatterplots showing negative correlations between the strength of
parameterized convection in the tropics, represented by the 700–900 hPa
averaged convective mass flux (
Rather,
Among the CCMI ensemble we find strong correlations between annually averaged
lower tropospheric (700–900 mb) convection over the tropical oceans and
Southern Hemisphere tracer ages averaged poleward of 60
Comparisons of idealized tracers among the CCMI hindcast simulations reveal
large differences in their global-scale tropospheric transport properties, in
particular the following.
There are large (30–40 %) differences in the efficiency of transport from the
Northern Hemisphere midlatitude surface into the Arctic. To first order,
these differences reflect differences in (parameterized) convection over the
northern midlatitude oceans, particularly during boreal winter. There are large differences in interhemispheric transport from northern
midlatitudes to southern high latitudes, where the mean age
The large-scale transport differences among simulations constrained with
analyzed winds are as large as the differences among simulations using
internally generated meteorological fields, which is consistent with the findings in
Our findings suggest that differences in parameterized convection over the oceans are the primary drivers of transport differences among the CCMI simulations. By comparison, the differences related to how the large-scale flow is specified (e.g., CTM vs. nudging or source of analysis fields) appear to be relatively smaller. Therefore, our results indicate that caution should be taken when using the C1SD simulations to interpret the influence of meteorology on tropospheric composition. In the future more attention will need to be paid to understanding both how the large-scale flow is specified and the behavior of convective parameterizations in simulations constrained with analyzed winds, both in offline (CTM) and online (nudged) frameworks.
At this point it is not clear why the convection differences among the C1SD simulations are in certain cases larger than among free-running simulations using the same models. One possibility is that these differences arise due to inconsistencies (e.g., in resolution or unbalanced dynamics) between the driving large-scale flow fields and the convective mass fluxes, which are recalculated online in all of the nudged simulations and in the MOCAGE-CTM or interpolated directly from analysis fields (e.g., GEOS-CTM). The analysis in this study has been limited by the small number of C1SD simulations that output all of the idealized tracers and convective mass fluxes (Table 3). Experiments using multiple sources of analysis fields and different convective parameterizations will need to be performed in order to examine this problem more carefully. A review of the CCMI C1SD simulations, with details on how these simulations were constrained, is also currently in preparation and may provide further insight.
One important caveat in this study is that our focus has been on tracers with zonally uniform boundary conditions. The implications of our findings will therefore vary among different species, depending on where they are emitted over the Earth's surface. In particular, our results highlight the differences in transport that arise due to large differences in (parameterized) oceanic convection among the simulations. We anticipate, therefore, that our results will primarily apply to species with oceanic sources, including marine-sourced volatile organic compounds and short-lived ozone-depleting halogenated species. By comparison, species with primarily land emissions (e.g., short-lived species) are expected to be more sensitive to other aspects of transport. To this end, a study is currently in preparation that addresses the implications of biases in the latitude of the midlatitude jet on carbon monoxide distributions over the Arctic among the CCMI models. We reserve further discussions for that study.
Finally, while we have shown that there are large differences in transport
among the models, we have not made comparisons with observations. As
mentioned in Sect. 3.2.1, estimates of
All data from CCMI-1 used in this study can be obtained
through the British Atmospheric Data Centre (BADC) archive
(
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
This article is part of the special issue “Chemistry–Climate Modelling Initiative (CCMI) (ACP/AMT/ESSD/GMD inter-journal SI)”. It is not associated with a conference.
We thank the Centre for Environmental Data Analysis (CEDA) for hosting the
CCMI data archive. We acknowledge the modeling groups for making their
simulations available for this analysis and the joint WCRP SPARC/IGAC
Chemistry–Climate Model Initiative (CCMI) for organizing and coordinating
this model data analysis activity. In addition, Clara Orbe and Luke D. Oman want to
acknowledge
the high-performance computing resources provided by the NASA Center for
Climate Simulation (NCCS) and support from the NASA Modeling, Analysis
and Prediction (MAP) program. Hideharu Akiyoshi acknowledges the Environment Research and
Technology Development Fund of the Environmental Restoration and Conservation
Agency, Japan (2-1709) and NECSX9/A(ECO) computers at CGER, NIES. Olaf Morgenstern and
Guang Zeng acknowledge the UK Met Office for use of the MetUM. Their research was
supported by the NZ government's Strategic Science Investment Fund (SSIF)
through the NIWA program CACV. Olaf Morgenstern acknowledges funding by the New Zealand
Royal Society Marsden Fund (grant 12-NIW-006) and by the Deep South National
Science Challenge (