Transport from the Northern Hemisphere (NH) midlatitudes to the Arctic plays a crucial role in determining the abundance of trace gases and aerosols that are important to Arctic climate via impacts on radiation and chemistry. Here we examine this transport using an idealized tracer with a fixed lifetime and predominantly midlatitude land-based sources in models participating in the Chemistry Climate Model Initiative (CCMI). We show that there is a 25 %–45 % difference in the Arctic concentrations of this tracer among the models. This spread is correlated with the spread in the location of the Pacific jet, as well as the spread in the location of the Hadley Cell (HC) edge, which varies consistently with jet latitude. Our results suggest that it is likely that the HC-related zonal-mean meridional transport rather than the jet-related eddy mixing is the major contributor to the inter-model spread in the transport of land-based tracers into the Arctic. Specifically, in models with a more northern jet, the HC generally extends further north and the tracer source region is mostly covered by surface southward flow associated with the lower branch of the HC, resulting in less efficient transport poleward to the Arctic. During boreal summer, there are poleward biases in jet location in free-running models, and these models likely underestimate the rate of transport into the Arctic. Models using specified dynamics do not have biases in the jet location, but do have biases in the surface meridional flow, which may result in differences in transport into the Arctic. In addition to the land-based tracer, the midlatitude-to-Arctic transport is further examined by another idealized tracer with zonally uniform sources. With equal sources from both land and ocean, the inter-model spread of this zonally uniform tracer is more related to variations in parameterized convection over oceans rather than variations in HC extent, particularly during boreal winter. This suggests that transport of land-based and oceanic tracers or aerosols towards the Arctic differs in pathways and therefore their corresponding inter-model variabilities result from different physical processes.
The Arctic is characterized by the largest climate sensitivity with surface
temperatures increasing much more rapidly than the global average in recent
decades
A limitation of the study of
Here we revisit the issue of transport into the Arctic within the CCMI
models, considering an idealized “CO5O” tracer. This tracer has realistic,
zonally varying emissions corresponding to anthropogenic carbon monoxide
(CO) emissions but with an idealized, fixed decay time of 50 d. We examine
the transport of CO50, and also the “NH50” tracer (the same 50 d lifetime
but with a zonally uniform boundary condition) considered by
Section
This study analyzes simulation results from models
participating in the Chemistry Climate Model Initiative (CCMI) phase 1
Simulations analyzed in this study
Tracers and dynamical/thermodynamic variables of models
analyzed in the study.
Fields analyzed in this study are listed in Table
To quantify the large-scale transport from the NH
midlatitude land sources to the middle-troposphere Arctic, we examine the
idealized CO50 tracer. The CO50 tracer has a flux boundary condition,
corresponding to the annual mean value of anthropogenic emissions of CO for
2000, from the Hemispheric Transport of Air Pollution (HTAP) REanalysis of
the TROpospheric chemical composition (RETRO)
Ensemble mean of the horizontal distribution of CO50 concentration
(shades, units: ppbv) at levels of
We also compare the simulated CO50 to the NH50 idealized tracer. The NH50
also has a spatially uniform 50 d loss, but with a different boundary
condition. The concentration of NH50 (
Last, to examine how well CO50 can represent real tracers with land sources,
we compare it with carbon monoxide (CO) that undergoes the full chemistry
(spatially and temporally varying) in the models. CO is removed from the
troposphere primarily by reacting with the hydroxyl radical (OH) that yields
a global mean annual mean lifetime of
For all the above tracers, we are particularly interested in their
concentrations over the polar region in the middle and lower troposphere
(70–90
As previous studies have indicated the importance of the midlatitude jet
streams and associated storm tracks for tracer transport into the Arctic
For the midlatitude jet, we focus on the zonal wind
For the HC we examine the 800–950 hPa averaged zonal-mean meridional wind
We first examine the multi-model mean (i.e., C1 and C1SD simulations
combined) distributions of CO50, and then examine the spread among the models
with a focus on distributions in the Arctic. The CCMI multi-model mean
horizontal and vertical distributions of CO50 are shown in
Fig.
The meridional and vertical distribution of zonal-mean CO50 varies with
season. During boreal winter, CO50 features a much stronger meridional
transport near the surface in both the poleward and equatorward directions.
The distribution of CO50 also generally follows the slope of isentropic
surfaces, exhibiting stronger vertical transport north of the midlatitude
CO50 source region and suppressed vertical transport in the south. During
summer,
The spatial distribution of zonal-mean CO50 for each model is similar to that
for the multi-model mean distribution discussed above. This is illustrated in
Fig.
We will focus here on model differences in CO50 concentrations over the
Arctic, and the poleward transport from NH midlatitudes. The differences in
Arctic CO50 concentrations among the models peak around 400 hPa during
winter and remain at a similar maximum for all levels below 400 hPa during
summer. In the middle and lower troposphere, the range of
The difference between pairs of simulations (and hence the ordering of
simulations) is generally the same at all altitudes. For example,
The large spread in CO50 concentration among the models is consistent with
the wide spread reported by
Having shown a large model spread in the Arctic concentrations of CO50, we
now examine possible causes for these differences.
Correlation between Arctic CO50 concentration
The absence of a strong correlation between Arctic CO50 and midlatitude
convection may be largely due to the zonally asymmetric boundary condition of
CO50, particularly in winter. With primary sources over land, CO50 tends to
be less impacted by the variability of convection that maximizes over the
oceans during winter. In summer, despite midlatitude convection being the
strongest and also having the largest model spread over the land-based
emission regions, the poleward transport of CO50 along isentropic surfaces is
much weaker than that in winter (comparing Fig.
Figure
Multi-model spread of latitudinal profile of zonal wind
However, in summer, there is a much larger variation among the models, not
only in the magnitude and location of peak winds but also the latitudinal
structure.
Maps of 500–800 hPa averaged CO50 distribution (shades, units:
ppbv) and the corresponding 500–800 hPa averaged
The summertime distribution of the 500–800 hPa average CO50 concentration
is also shown in Fig.
Similar to Fig.
Repeating the above analysis for winter, we find the wintertime tracer
transport from NH midlatitudes into the Arctic is also sensitive to the jet
location, with negative correlations between
While there is a negative
We now explore the underlying mechanisms for the above connection between the
Pacific jet and transport into the Arctic. A strong jet with rapid zonal flow
at its center can act as a barrier to meridional transport
Schematics of mechanisms illustrating dynamic influences on the NH
midlatitude-to-Arctic transport for the midlatitude jet situated more
southern in
Tracer flux diagnostics showing the total flux
One approach to examine whether transport caused by RWB is the cause of
differences in the transport into the Arctic is to decompose the tracer flux
into zonal-mean and eddy components, i.e.,
A substantial contribution of the eddy flux comes from synoptic eddies, and
to calculate this flux requires
Approximated zonal-mean flux using monthly output:
The flux diagnostics for CO50 meridional transport in the two GEOS
simulations are shown in Fig.
The above analysis of tracer fluxes in GEOS-C1 and GEOS-C1SD contradicts our
original speculation that the difference in Arctic
The results for the approximated zonal-mean flux in each simulation are shown
in Fig.
There are a few simulations deviating from this positive correlation between the zonal-mean flux and CO50 concentrations over the Arctic. For example, the zonal-mean flux in CMAM-C1 is larger than that in CMAM-C1SD but the Arctic CO50 concentrations are similar between the two simulations. Further analysis is required to determine what are the other processes responsible for the variations in the Arctic CO50 concentrations among these simulations. Also, the positive correlation is higher among C1 simulations than C1SD simulations, especially during summer (also inferred from Table S1). Last, the positive mean flux–CO50 relationship does not hold for interannual variations in most simulations, which will be discussed in detail below.
The above results suggest an important role of mean meridional circulations
in separating the meridional transport of tracers among the CCMI models, with
larger poleward transport when the jet is located more equatorward. A
possible reason for this connection between jet location and transport by the
mean meridional circulation could be the well-known link between the jet
latitude and the edge of the HC
Similar to Fig.
To examine this possible relationship, we use
Putting those complications aside, the
The
As noted above, previous studies have shown a connection between the
latitudinal extent of the HC and the latitude of the midlatitude jet. We
verify this connection among the CCMI models by showing a positive
correlation between
In summary, we have proposed two mechanisms, illustrated in
Fig.
The above analysis suggests that variations in the near-surface extent of the
HC (latitude where
The multi-model mean distribution of NH50 features a stronger transport along
isentropic surfaces so that Arctic
As in Fig.
To explore the relative role of changes in CMF, latitude of the Pacific jet
(
Unlike CO50, NH50 exhibits only a moderate or weak correlation with
In summary, despite NH50 having a zonally uniform boundary condition, the
multi-model spread of Arctic NH50 seems to be much less impacted by
differences in the HC extent and associated zonal-mean transport among the
models. Instead, NH50 shows a stronger correlation with low-level
midlatitude convection, especially during boreal winter, as shown by
It is also of interest to examine whether the above conclusions based on
idealized tracers apply to more realistic tracers with interactive chemistry.
We therefore examine whether the spread, and relationship with the HC extent
(i.e.,
The comparison of CO50 and CO from CCMI results shows a positive relationship
between the Arctic concentration of CO50 and CO in winter, but no
relationship in summer; see Fig.
Similar to Fig.
In addition to chemistry, differences in emissions between CO and CO50 are also likely to result in their different sensitivities to variations in the HC extent among the models. In particular, CO features an additional summertime emission source from biomass burning over Siberia, which is in close proximity to the Arctic and hence tends to have a strong influence on the Arctic CO concentration. However, this emission region is distant from the HC edge over the NH midlatitudes, and tracer transport from this higher-latitude region is less likely to be impacted by variations in the HC extent.
In this study, we examine long-range transport into the Arctic using an idealized CO5O tracer with predominantly midlatitude Asian emissions in simulations from a suite of CCMs. There is a wide spread (25 %–45 %) of the Arctic concentrations of CO50 among the simulations, indicating a large inter-model variability in the simulated NH midlatitude-to-Arctic transport. Further, this spread is found to be correlated with the variation in the location of the Pacific jet among the models, with lower Arctic tracer concentrations for a more northern Pacific jet. While the inter-model spread in transport to the Arctic is associated with the latitude of the jet, our analysis indicates that this may be an indirect relationship, with difference in the mean meridional flow (that is correlated with the jet latitude) being the cause of differences in the poleward transport of tracers. Specifically, in models with a more northern jet, the Hadley Cell (HC) generally extends further north and the tracer's source region is mostly covered by the lower branch of the HC with southward surface flow, resulting in less poleward transport. Differences in midlatitude convection among the models appear to play a secondary role.
While the inter-model spread in Arctic CO50 concentrations is largely
determined by the HC-related mean meridional transport, this is not the case
for the NH50 tracer that features zonally uniform midlatitude sources, which
shows a larger correlation with midlatitude convection over the Pacific Ocean
during winter, as shown for NH5 by
The free-running model C1 simulations have a jet on average further poleward
than observed during summer (a common bias in climate models;
The specified dynamics simulations (C1SD), which use the same (or very
similar) specified meteorological fields, do not have bias in jet location,
but, surprisingly, there is a spread in the latitude where
The results presented here suggest that differences in the HC extent and associated mean meridional transport are a major factor in causing the large spread in Arctic CO50 among the models. However, the rather small number of models available and the wide range of differences among these models limits how strong conclusions can be made about the relative importance of different processes. To be more definitive, studies are required where individual aspects of a model (or models) are varied to isolate the role of this process in the transport into the Arctic. Such experiments are planned for the future.
Most of data from CCMI-1 used in this study can be obtained
through the British Atmosphere Data Centre (BADC) archive
(
The supplement related to this article is available online at:
HY performed the analysis and wrote the paper; DWW supervised the project and participated in paper editing. CO helped in collecting model output, and performed GEOS simulations with daily output as well as paper editing. GZ, OM, DEK, JFL, ST, DAP, PJ, SES, KAS, and RS all helped in providing model outputs and suggestions for paper editing.
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 would like 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. Olaf Morgenstern and Guang Zeng
acknowledge the UK Met Office for use of the MetUM. Their research was
supported by the New Zealand 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 (
This paper was edited by Peter Hess and reviewed by three anonymous referees.