The seasonal and interannual variability of transport times from the northern
midlatitude surface into the Southern Hemisphere is examined using
simulations of three idealized “age” tracers: an ideal age tracer
that yields the mean transit time from northern midlatitudes
and two tracers with uniform 50- and 5-day decay. For all tracers the largest
seasonal and interannual variability occurs near the surface within the
tropics and is generally closely coupled to movement of the Intertropical
Convergence Zone (ITCZ). There are, however, notable differences in
variability between the different tracers. The largest seasonal and
interannual variability in the mean age is generally confined to latitudes
spanning the ITCZ, with very weak variability in the southern
extratropics. In contrast, for tracers subject to spatially uniform exponential loss the peak
variability tends to be south of the ITCZ, and there is a smaller contrast
between tropical and extratropical variability. These differences in
variability occur because the distribution of transit times from northern
midlatitudes is very broad and tracers with more rapid loss are more sensitive to
changes in fast transit times than the mean age tracer. These simulations
suggest that the seasonal–interannual variability in the southern extratropics
of trace gases with predominantly NH midlatitude sources may differ
depending on the gases' chemical lifetimes.
Introduction
Interhemispheric transport is important for understanding the global
distribution of tropospheric trace gases. In particular, it is important to
quantify the pathways and timescales for transport from Northern Hemisphere
(NH) middle latitudes into the Southern Hemisphere (SH) as anthropogenic
emissions of tropospheric ozone precursors, major greenhouse gases, aerosols,
and ozone-depleting substances occur primarily in the NH.
Most previous studies that have examined interhemispheric transport
have used a simple two-box framework to quantify a single interhemispheric
exchange time, calculated in terms of the temporal change in the difference
between the southern and northern hemispherically integrated tracer mass
e.g.,.
This metric is useful as it collapses all the transport into a single parameter
that can be used for model–observation or inter-model comparisons. However, it is only
a gross measure of interhemispheric transport, with no information of spatial
variations in transport times. In particular, it does not distinguish between
transport into the southern tropics and transport into the southern
extratropics. Tracer observations and simulations support
the existence of a strong tropical–extratropical transport barrier
e.g.,. In
fact, suggest that it may be more appropriate to use a three-box model (with northern extratropical, tropical, and southern extratropical
boxes) to quantify tropospheric transport (see also ).
Alternatively, recent studies have used observed and simulated SF6 or
simulated idealized mean age tracers to estimate the mean transport time from
the NH surface to locations throughout the troposphere . This approach provides a more complete description of
interhemispheric transport, quantifying not only differences in transport into
the tropics versus southern extratropics but also differences in transport
between the lower and upper troposphere. However, these tracers (and two-box or
three-box exchange times) only provide information about the mean transport
time, whereas observations and models show there is a wide range of times and
paths for transport from the NH surface. More precisely, both observational-based
estimates and numerical simulations
of the distributions of transit times from the
NH midlatitude surface show very broad distributions in the SH, characterized by young modes
and long tails. As a result, the mean transit time to SH locations, which
controls the distributions of long-lived trace gases, is much larger than the
modal transit time, which is associated with the fast transport pathways that
play a much more important role in controlling the distributions of chemical
tracers with lifetimes of days to months.
Most of the focus in the above studies has been on the climatological mean
transport, with only limited analysis of seasonal and interannual variability.
However, understanding the temporal variability of the transport is important for understanding and interpreting the observed temporal
variations in tracer concentrations and determining the relative role of changes in transport, emissions, sinks, and chemistry for
different species. For example, observations of methyl chloroform, or other species with reaction with OH as their primary sink,
can be used to infer the abundance of OH e.g.,, and knowledge of the seasonal
and interannual variability of the transport is required to isolate similar variability in the OH abundance. Similarly, knowledge of
the interannual variability of transport from the NH is required for estimates of the variability in emissions or sinks (ocean uptake)
of CO2 from measurements of CO2 in the SH e.g.,.
Here we examine the seasonality and interannual variability of transport from
the NH surface into the SH, considering not only the mean transit
times but also faster transport pathways and zonal variations in the
transport. The approach taken is to examine simulations of several tracers with the same NH
source region but different time dependences (loss rates)
e.g.,. This approach does not enable the same
detailed analysis of pulse release simulations (unless a large number of
tracers are simulated), but does enable detailed analysis of seasonal and
interannual variations (see next section for more discussion). Here we examine
30-year simulations of three idealized “age” tracers requested as part of
IGAC/SPARC Chemistry-Climate Model Initiative (CCMI) . One of
the tracers (the NH clock or ideal age tracer) yields the mean transit time from
the NH source region, while the other two tracers have 50- and 5-day loss rates
and provide information on shorter transit times (that is more useful for
understanding the distributions of short-lived trace gases). The long
simulations enable an examination of interannual, as well as seasonal,
variations of transport into the SH.
The tracers and simulations examined are described in the next section, and the
climatological distribution of the tracers is presented in Sect. 3. Then the
seasonal and interannual variations are examined in Sects. 4 and 5,
respectively, with concluding remarks in Sect. 6.
MethodsTracers
We examine interhemispheric transport using simulations of three
idealized age tracers: an ideal age tracer that yields the mean transit time from
the NH source region and two tracers with uniform decay time of 50 or
5 days.
The governing equation for the ideal age tracer Γ(x,t) is ∂Γ∂t+LΓ=Θ(t),
where L is the linear transport operator and Θ(t) is
the Heaviside function (0 for t<0 and 1 for t>0). The boundary
condition is Γ(Ω,t)=0, where Ω is the source region,
and Γ(x,0)=0 initially. In other words, the tracer is
initially set to a value of zero throughout the atmosphere, is held to
be zero over Ω, and is subject to a constant aging of 1 year per
year in the rest of the model surface layer and throughout the
atmosphere. Here Ω is the surface layer between 30
and 50∘ N, and the ideal age tracer yields the mean transport
time from this region. The ideal age tracer Γ is referred to as
the age of air from the Northern Hemisphere (AOA_NH) in CCMI
.
The two decay tracers have fixed concentration over Ω and undergo spatially uniform exponential loss, i.e.,
∂χT∂t+LχT=-1TχT,
where T is the constant decay time, χT is the concentration of
tracer with decay time T, and χT(Ω,t)=χΩ is a
constant. We consider tracers with T= 5 and 50 days, which we
referred to as the 5- and 50-day loss tracers (the tracers
correspond to NH_5 and NH_50 in CCMI).
In our analysis we express the concentration of the loss tracers as an age
τT(r,t)=-TlnχT(r,t)χΩ.
This approach is common in oceanography
e.g.,, and enables easier comparison with Γ. The basis for the
age definition Eq. (3) can be seen by considering the idealized case of steady,
advective flow with no mixing (i.e., L(χ)=u∂χ/∂r, with u a
constant). The tracer concentration satisfying Eq. (2) is then given by χT(r,t)=χΩexp(-tadv/T), where tadv=r/u is the advective time
from the source region to the interior location, and Eq. (3) reduces to
τT=tadv, i.e., for purely advective flow the tracer age Eq. (3) equals the
advective time.
In the simple advective flow case the tracer age is independent of the tracer
decay time T, and tracers with different decay rates yield the same age.
However, this is not the case for more realistic flows with mixing, where the
tracer age depends on the flow and the tracer decay T. For a steady flow with
mixing the tracer age is τT(r)=-Tln∫0∞G(r,t′)e-t′/Tdt′,
where G(r,t) is the distribution of transit times, or the
elapsed times, (t-t′) since the air at (r,t) was last at the
source region at time t′, and is referred
to as the transit time distribution (TTD) or age spectrum. Because of the exponential term inside the
convolution integral in Eq. (4), tracers with different T yield
different τT. This is illustrated by considering
a loss tracer with decay time T much larger than the width of
the TTD, Δ. In this case Eq. (4) reduces to τT≈Γ-Δ2/T.
From this we can see that tracers with smaller T have a younger
τT, and that for tracers with very slow decay the tracer age is
close to the mean age (τT→Γ as T→∞).
While the dependence of the tracer ages on the decay time T means that the ages cannot be interpreted directly
as a transport timescale, it does mean examination of tracers with
different decay times highlight different aspects of the distribution
of transit times (i.e., analysis of multiple tracers provides
information on the characteristics of the TTD). Specifically, the age
of a tracer is sensitive to the fraction of transit times less than
the decay time of the tracer, but insensitive to transit time much
longer than the decay time, as these long transit times carry very
little tracer mass.
Model and analysis
We focus primarily on tracer fields from a simulation with the 4th
version of the Community Atmospheric Model with
troposphere–stratosphere chemistry (CAM4-chem) run in “specified dynamics” mode using meteorology from
Modern-Era Retrospective Analysis for Research and Application
(MERRA) . This corresponds to the CAM4-REFC1SD simulation in
and the CAM-C1SD simulation in the recent CCMI model intercomparison of
. Here we use the latter notation. As the CAM-C1SD simulation uses meteorology from reanalyses it has the advantage
over free-running simulations (in which the meteorology is generated internally) in that the tracer distributions can then be directly
compared with observations for the same period. However, have recently shown there is large uncertainty in specified
dynamics simulations due to the transport by parameterized convection.
The CAM-C1SD simulation has a horizontal resolution of 1.9∘
latitude by 2.5∘ longitude and 56 hybrid vertical levels from the surface
to 1.87 hPa. For our analysis we interpolate from the hybrid levels to a
standard set of isobaric levels spanning 1000 to 10 hPa. The simulation
examined was run from 1979 to 2010, after being “spun up” by running 5 years
with 1979 meteorology. Here, we examine the monthly averaged fields from January
1980 to December 2009.
We examine the climatological seasonal mean of the tracer ages (i.e., 30-year
average for each month), as well as the seasonal and interannual
variability. The seasonal variability is quantified by calculating the standard
deviation of the climatological 12-month annual cycle and is referred to as
στseas (with τ=Γ, τ50, or τ5). The interannual variability
is similarly quantified by calculating the standard deviation over 30 years. To minimize the impact of seasonality, the interannual variability is
calculated for each season; i.e., τ is averaged over every 3 months and
the standard deviation is calculated of these seasonal means. We focus here on
interannual variability for December to February (DJF) and June to August (JJA),
which we refer to as στDJF and στJJA. (For both seasonal and
interannual variability, the calculations of the standard deviation are performed
at individual locations, and any zonal averaging is done after these calculations.)
Latitude–pressure distribution of the climatological
seasonal-mean zonal-mean (a, b)Γ, (c, d)τ50, and (e, f)τ5, for boreal winter (DJF) and boreal summer (JJA). Arrows show
the meridional circulation, thin contours show isentropes (contours
every 20 K), and the thick
contour shows the tropopause.
Climatological distributions
We first examine the climatological seasonal-mean distributions of the tracer ages, and the connection of these distributions with
the general circulation. Figure shows the zonally averaged Γ, τ50, and τ5 for northern winter
(DJF) and summer (JJA). There is a similar distribution for the different tracer ages, with the smallest values in northern midlatitudes
(close to the source region), oldest surface values at the South Pole, weak meridional gradients in northern extratropics, largest meridional
gradients in tropics, and relative weak vertical gradients at all latitudes (with slightly positive vertical gradients in the NH and slightly negative gradients in the SH). The spatial distribution of the tracer age shown
in Fig. is similar to the distribution of idealized or realistic long-lived tracers shown in previous studies
e.g., and can be related to the general circulation (e.g.,
Hadley cells and Intertropical Convergence Zone, ITCZ). There is rapid transport from the NH midlatitude surface into the NH
extratropical troposphere, through a combination of along-isentropic and convective mixing, and as a consequence there are
weak age gradients in the NH extratropics. There is also rapid low-level transport from NH midlatitudes into the tropics, but
the transport into the SH is “slowed” by convection and rapid vertical mixing associated with the ITCZ, resulting in large
surface meridional age gradients near the ITCZ. The rapid vertical mixing within tropical convection results in very weak
vertical tracer gradients within the tropics, and the strong meridional gradients in the tropics persist into the middle troposphere.
In the tropical upper troposphere there is increased meridional transport due to the upper branch of the Hadley cell, and this
results in weaker meridional tracer gradients.
While there is qualitative agreement in the spatial distributions of
the different tracer ages, there are substantial quantitative
differences. First, there are large differences in the magnitude of
the ages, especially in the SH where Γ≫τ50≫τ5
(consistent with Eq. 5). Second, there are differences in the
meridional gradients: the meridional gradients of Γ in the tropics
are much larger than those in the SH (where Γ is nearly constant),
whereas the meridional gradients of τ5 are similar in the tropics
and SH. These differences are illustrated in Fig. a and b,
which show the latitudinal variation of the tracer ages at 900 hPa
for DJF and JJA.
These quantitative differences among the tracer ages occur because the
TTDs in the tropics and SH are very broad , and the tracers are sensitive to different aspects of the
TTDs. As discussed above, τ5 is most sensitive to the shorter
transit times whereas Γ is the mean of the TTD and is dependent on
the long tail of old transit times. The differences in meridional
gradients of the two ages are related to changes in the shape of the
TTD with latitude. showed there is a transition in
shape of the TTD from north of the ITCZ to south of the ITCZ, which
they attributed to a change in the relative contribution of rapid
advective pathways from northern midlatitudes and slow eddy-diffusive
recirculation of “old” air into the tropics from the SH. The latter
has a much larger impact on Γ than on τ5, resulting in a much
larger increase in Γ across the ITCZ but relatively constant
values in the southern extratropics. By comparison, τ5 is most
sensitive to very short transit times as it is determined more by rapid
advective pathways, resulting in roughly constant meridional gradients
of τ5 throughout the SH.
Climatological mean Γ and τ5 at 900 hPa
for (a, c) DJF and (b, d) JJA. Arrows show horizontal velocity, black
contours convergence at 900 hPa (contours at (-3, -2, -1) ×106 s-1, with -2 ×106 s-1 bold),
and blue and pink curves show the approximate location of the ITCZ.
The latitudinal gradients in the tracers are much larger than zonal gradients, but there are still some zonal variations. This is
illustrated in Fig. , which shows the 900 hPa distribution of the climatological Γ and τ5 for (a, c)
DJF and (b, d) JJA. There are weak zonal variations in the extratropics for both tracers, but noticeable zonal variations within the
tropics. For example, in DJF the mean age over the equatorial Indian Ocean is smaller than over the Equator of other oceans, whereas
in JJA the mean over the northern tropical Indian Ocean is larger than over the Pacific or Atlantic oceans. These variations in the
tracers can again be related to variations in meteorology. In particular, the large seasonal variation over the tropical Indian
Ocean is related to seasonal changes in convection and wind direction associated with the South Asian monsoon (i.e., there is
deep convection over the equatorial Indian Ocean and northerly surface winds in DJF), whereas the deep convection is over the
northern subtropics and there are southerly winds in JJA.
Latitudinal variation of (a) DJF climatological mean, (b) JJA
climatological mean, (c) seasonal standard deviation, (d) DJF interannual
standard deviation, and (e) JJA interannual
standard deviation for Γ, τ50, and τ5 at 900 hPa. For all panels the quantity for τ50 is multiplied by 3
and that for τ5 multiplied by 8.
Seasonal variability
We now examine the seasonality of the tracer ages in more detail. Previous studies have linked seasonal differences in the distributions
of tracer to the seasonally varying Hadley circulation e.g.,, and we examine this connection for the
tracer ages.
Comparison of the left and right panels of Fig. shows seasonal
differences in all three tracer ages: the location of the largest surface
meridional gradients are south of the Equator during DJF but north of the
Equator during JJA, and the near-surface tracer ages at the Equator and in the
southern tropics are younger in DJF than in JJA (see also Fig. a, b). There are
also seasonal differences away from the surface, with older ages in DJF
than in JJA in both northern and southern subtropical middle–upper
troposphere.
As expected, these seasonal differences in the tracer ages are linked to the
seasonal variations is the Hadley circulation and location of the ITCZ.
The largest surface age gradients occur at the ITCZ, with young ages north of
the ITCZ and older ages south. The latitude of the ITCZ moves with season and
there is a corresponding north–south shift in the latitude of large meridional
age gradients; i.e., largest surface meridional gradients are south of the
Equator during DJF but north of the Equator during JJA
(Figs. , a, b). This results, as will be shown below, in a large seasonality at locations within
the seasonal range of the ITCZ, with older ages when the ITCZ is north of the
location and younger ages when it is to the south.
The seasonality of the Hadley circulation can also explain the
seasonality in the tracers in the northern subtropical middle
troposphere and southern tropical upper troposphere. During DJF the
northern cell is strongest (see arrows in Fig. a) and
transports “older” ages from the equatorial upper troposphere into the
northern subtropical middle troposphere (resulting in older ages in
DJF than JJA), whereas during JJA the stronger southern cell
(Fig. b) increases the transport of “young” air into
the southern subtropical upper troposphere (again resulting in older
ages in DJF than JJA).
As with the climatological distributions, there are quantitative
differences in the seasonality (DJF–JJA differences) of the different
tracer ages. In particular, the seasonality of near-surface Γ
south of 20∘ S is much smaller than at the Equator, whereas for
near-surface τ5 there is a smaller decrease in the seasonality
from the Equator to southern midlatitudes. This can be seen clearly
in Fig. c, which shows the latitudinal variation in the
seasonal standard deviation στseas.
As mentioned in the previous section, there are zonal variations
in the tracer ages within the tropics that vary with season. Again, these
zonal variations in the ages are consistent with variations in the
ITCZ (see surface winds (arrows) and convergence (contours) in
Fig. ). The ITCZ is close to the Equator in both DJF and
JJA over the Pacific, whereas there is a large seasonal variation of
ITCZ over the Indian Ocean: it is well north of the Equator during JJA
but south of the Equator in DJF. Similar variations occur for the
regions of largest meridional age gradients. Associated with the
seasonal movement of the ITCZ, there is a change in direction of the
surface winds, with the largest changes again occurring in the Indian
Ocean sector. In particular, in the tropical western Indian Ocean
there is a strong southward flow during DJF, but a strong northward
flow in JJA. This seasonality in wind direction results in a large
seasonality in the age.
The spatial variation of the seasonality, and differences between
Γ and τ5, can be seen clearly in Figs. and , which show maps of surface and vertical
cross sections, respectively, of the seasonal standard deviation
στseas. Consistent with the above discussion, the largest values of
both σΓseas and στ5seas are within the tropics. However, while the
peak σΓseas and στ5seas occur at similar latitudes over the Pacific
and Atlantic oceans, the peak στ5seas is south of the peak σΓseas in
the Indian Ocean sector. Also, σΓseas in the southern extratropics
is much smaller than in the tropics (σΓseas is as high as 180 days in
the tropics but only around 10 days in the southern extratropics), whereas
στ5seas is comparable in the tropics and southern extratropics (5–10 days). Figure also shows that large seasonality is
generally only near the surface (pressures above 800 hPa). However,
there is larger seasonality in the northern subtropical
mid-troposphere, southern subtropical upper troposphere over the
Indian Ocean, and near the tropopause (especially for τ5).
Maps of seasonal variability of (a)Γ and (b)τ5
at 900 hPa. Thin contours show climatological mean tracer ages (in days), and
blue and pink curves show approximate location of the ITCZ in DJF and
JJA, respectively.
The seasonal movement of convergence zones can explain much of the seasonality
in the tracer ages. In particular, the north–south movement of the
ITCZ results in a similar movement of the region of high meridional
age gradients and large seasonality of tracer age for tropical
locations; i.e., when the ITCZ is displaced south from its climatological location there will be a decrease in the tracer age, and
vice versa for a northward
shift . The seasonal migration of the ITCZ varies with longitude, with
a much larger variation over the Indian Ocean than over the eastern
Pacific e.g.,. This is shown by
contours in Figs. and ; it results in
a wider range of latitudes that the ITCZ crosses during the annual
cycle and hence larger seasonality in tracer ages over the Indian
Ocean than over the eastern Pacific (Fig. ).
Seasonality in surface convergence also contributed to the region of enhanced seasonal variability in the subtropical western south Pacific.
The South Pacific Convergence Zone (SPCZ) lies within this region, and the orientation and intensity of the SPCZ vary on synoptic through
to interannual timescales e.g.,. This variability in the SPCZ then results in variability in tracer
ages; e.g., when the SPCZ is shifted to the northeast from its climatological there is less rapid transport from the NH and more from SH
middle latitudes, resulting in older ages.
Latitude–pressure variation of (a, b)Γ and
(c, d)τ5, for eastern Pacific (150–120∘ W) and Indian Ocean
(60–90∘ E) sections. Contours show climatological mean
distributions of (a, b)Γ and
(c, d)τ5 (in days).
Relationship between the latitude of maximum surface
convergence and tracer age at 900 Pa for locations in the (a, e) Indian, (b, f) western Pacific, (c, g) eastern Pacific, and (d, h) Atlantic oceans. Top
row shows Γ and bottom row τ5. Coordinates of the locations
are shown above (a)–(d). Width of the horizontal or
vertical bars are twice the interannual standard deviation, and
different colors represent different seasons (see a). Black line shows linear fit, and
correlation coefficient is given within each
plot.
To quantify the age–ITCZ relationship we compare the latitudinal
movement of the ITCZ with the age at a fixed location.
Figure shows the relationship between the simulated 900 hPa Γ or τ5 and the ITCZ latitude (calculated as the latitude
of maximum convergence at 900 hPa between 15∘ S and 30∘ N for each
longitude) for four different longitudes (corresponding to the Indian,
western Pacific, eastern Pacific or Atlantic oceans). For both tracer
ages and all locations there is a positive correlation, i.e., older age
for a more northern location of the ITCZ. There are some differences
in the age–ITCZ relationships between the ocean basins, with a more
linear relationship over the Atlantic and eastern Pacific
than other regions. Over the Indian Ocean the age–ITCZ relationship is
nonlinear, especially for Γ, with a more rapid change of age with
latitude of the ITCZ when the ITCZ is south of 10∘ N than north.
Observational evidence for the above relationship between the
seasonality of tracer ages and latitude of the ITCZ is found in the
measurements of SF6 from surface stations.
showed that a “SF6 age”, which is an approximation of the ideal age, can be estimated from measurements
of SF6. There are large annual cycles of SF6
age derived from measurements in the tropical Indian (Mahé Island,
Seychelles; 4.7∘ S, 55.5∘ E) and eastern Pacific (Christmas Island; 1.7∘ N,
157.1∘ W) oceans, and the variation of the SF6 age with latitude of
ITCZ at these stations (Fig. ) is similar to those for the simulated
Γ (Fig. a, c), including the linear
relationship for the eastern Pacific station but nonlinear
relationship for the Indian Ocean station. Unfortunately, there are no stations within the tropical western Pacific or Atlantic
to test the simulated seasonality in these regions.
As in Fig. a and c but for the relationship
between observed SF6 age and the latitude of the ITCZ, for
measurements from (a) Seychilles and (b) Christmas
Island.
Interannual variability
We now examine the interannual variability of the tracers, first for
northern winter (DJF) and then northern summer (JJA).
Northern winter
Given the above relationship between seasonal variability of tropical convergence zones and tracer ages, we expect the interannual
variations of the tracer ages to be largest near these convergence zones. As shown in Fig. , this is indeed the case
for DJF, with largest variance generally around or south of the ITCZ (contours) and SPCZ (not marked). The interannual variability
is weaker than the seasonality, e.g., the maximum σΓDJF is
around 50 days compared to 180 days for σΓseas, and there
are also differences in the locations of peak seasonal and interannual
variability. For example, the peak σΓDJF over the Indian sector is in the
central equatorial Indian Ocean, whereas the peak σΓseas is north of the
Equator (with two local maxima). A similar difference in locations of peak
values occurs between στ5DJF and στ5seas, and the tropical–extratropical
difference in στ5DJF is much smaller than that for στ5seas (see also
Fig. ). Again consistent with seasonal variability, the
interannual variability is largest near the surface and generally small in the
upper troposphere (not shown).
As in Fig. but for DJF interannual standard deviation.
The El Niño–Southern Oscillation (ENSO) is the major cause of interannual variability in low-latitude meteorology, and
previous studies have shown that variability in interhemispheric
transport is linked to ENSO
e.g,. We
examine this relationship here by calculating the correlation r and regression m coefficients between the
30-year times series of DJF Γ at each location and the Oceanic Niño
Index (ONI).
Relationships between tracer ages and ENSO. (a) Regression coefficients (shading) and correlation coefficients
(contours) between Γ at 900 hPa and ONI for DJF.
Correlations with absolute value larger than 0.361 are significant
at 95 % confidence level. (b) As in (a) but for τ5 at 900 hPa.
(c, d)Γ (shading), 900 hPa horizonal winds
(arrows) and precipitation (contours; (4, 6, 8) mm day-1, with the 6
mm day-1 bold) for composites of (c) El Niño winters and (d) La Niña winters.
(e, f) As in
panels (c, d) but showing the anomaly from
climatological DJF fields. In panel (e) black contours show precipitation
anomalies of (4, 6, 8) mm day-1, and gray contours show precipitation
anomalies of (-8, -6, -4) mm day-1 while in panel (f) black contours show
precipitation anomalies of (2, 3, 4) mm day-1, and gray contours show
precipitation anomalies of (-4, -3, -2) mm day-1.
Figure a show maps of the regression coefficients (shading) and correlation coefficients (contours) for the Γ–ENSO
relationship. There are coherent regions
with large positive or negative correlations in both hemispheres, with correlations north of the Equator generally the
opposite sign to those south of the Equator at the same longitude. There is large region with
positive correlation in the southern subtropical central Pacific (near
170∘ E), but negative correlations are found in southern
subtropical eastern Pacific and Indian oceans. Thus, during an El Niño
year there tends to be older ages over the southern subtropical
central Pacific but younger ages over the southern subtropical eastern
Pacific and Indian oceans, and the reverse for La Niña years. The
Γ–ENSO correlations at and north of the Equator are generally the
opposite sign to those south of the Equator at the same longitude;
i.e., there are negative correlations in northern tropical central
Pacific. A similar pattern of correlations with ENSO is also found
for τ5, with, consistent with the above analysis, the region of largest correlations
slightly south of that for correlation with Γ (Fig. b).
The above Γ–ENSO correlations are example further by considering
composites of Γ and meteorological fields for El Niño years (ONI
greater than 1) and La Niña years (ONI less than -1).
Figure b–e show maps of DJF Γ, CAM-C1SD precipitation (as
a proxy for the intensity of tropical convection), and surface winds
for the El Niño and La Niña composites; in panels b and c the full
fields are shown whereas in panels d and e the anomalies from the 30-year
climatology are shown. These maps show a difference in the location
of the region of high precipitation (ITCZ) over the tropical Pacific
between ENSO phases: during El Niño years the ITCZ is south of its
location during La Niña years (around 5∘ S compared to around
∼ 10∘ N). This results in differences in transport to the
equatorial western-central Pacific. During El Niño years there is
rapid, direct low-level transport to the Equator, whereas for La Niña
years the convection around 10∘ N reduces this direct
transport and the Γ is older in the same region (consistent with
the negative correlation shown in Fig. a). The reverse
Γ–ENSO correlation occurs in the southern tropical Pacific because
of interannual variations in the SPCZ. During most winters the SPCZ is
orientated diagonally in the northwest to southeast direction, but
during some strong El Niño events the SPCZ is shifted north and is
more zonally orientated . During these El Niño
years there is less rapid transport of younger air from the NH and older air from the SH high latitudes, and hence older tracer
ages, in the southwestern tropical Pacific.
The signs of the age–ENSO correlations over the eastern Pacific, Atlantic and
Indian oceans are opposite to that over the western-central Pacific;
i.e., there is positive correlation in the northern tropics over eastern Pacific
but a negative correlation over the western Pacific
(Fig. a). The cause of this is not clear, although it likely
due to El Niño–La Niña differences in the subtropical surface flow
over these regions. For example, during El Niño years the
equatorial winds over the equatorial Indian Ocean have a stronger than
average northward component, which transport more old, southern hemispheric air across the Equator, resulting in older age in the northern
tropical Indian Ocean.
Some caution is needed with the simulated age–ENSO relationship as it
is based only on a 30-year simulation. However, analysis of the
Γ–ENSO relationship in two free-running CAM4-chem simulations yields very similar regression patterns,
including high negative and positive
correlations either side of the Equator in western-central Pacific and
the opposite signed correlations over the eastern Pacific and Indian
oceans; see Fig. . (In the “REFC1” simulation CAM4-chem is constrained by
observed sea surface temperatures and sea ice concentrations, whereas “REFC2” is a simulation where the atmosphere is
coupled to dynamic ocean and sea ice models.)
Observational support for the above age–ENSO correlation is found in
trace gas measurements at American Samoa (14∘ S,
170∘ W). Measurements of methyl chloroform and CFCs from this
station show lower concentrations (indicating slower transport from NH
sources) during El Niño years e.g,.
As American Samoa lies just inside the region of positive age–ONI
correlation, this is consistent with the above simulated
variability. The simulations indicate that the observed result of
slower transport to the SH during El Niño years may hold only in the
western-central Pacific, and there could be faster transport to the
eastern Pacific or Indian subtropical oceans. Unfortunately similar
multi-year trace gas measurements are not available from these
locations to test this.
As in Fig. a but for CAM4Chem (a) REFC1 and (b) REFC2 simulations.
Same as Fig. but for JJA interannual standard deviation.
Although ENSO explains much of the variability in the Pacific this is
not the case for other basins. In particular, the largest interannual
variability of Γ and τ5 occurs over the southern tropical Indian Ocean, but
variability here is only weakly correlated with ENSO. The interannual
of the tracers is still related to changes in location of surface
convergence and direction of surface winds, but this variability
surface flow is not correlated with ENSO or with the Indian Ocean
Dipole (not shown). Further analysis is required to determine the causes of the
interannual variability of the flow and transport in the Indian Ocean.
Northern summer
The general characteristics of the interannual variability during northern
summer (JJA) are similar to that in winter; i.e., the largest variability is in
the tropics and there is small variability in the SH (especially for Γ) (see
Fig. a and b). However, the location of the peak interannual variability at
the surface varies between seasons, with the peak in σΓJJA generally north of
that for στDJF. This is consistent with the more northern location of the ITCZ
in JJA; i.e., the peak standard deviation for each season is close to the
climatological location of the ITCZ for that season. As in DJF, the largest
στ5JJA is located south of peak σΓJJA. This is especially true in the Indian
Ocean sector, where σΓJJA is largest around 20∘ N but στ5JJA is largest
around 5∘ S. This difference between the tracer ages is again consistent with
the differences in their meridional gradients; e.g., there are weak Γ
gradients in the tropical Indian Ocean but large τ5 gradients in southern
tropics.
Some of the interannual variability in JJA tracer ages is correlated
with ENSO, with older ages over the northern central tropical Pacific
and southern subtropical eastern Pacific and younger ages over
southeastern Asia during the warm phase (El Niño); see
Fig. . However, as in DJF, this applies mainly to the
Pacific ocean and ENSO is not the dominant source of variability over
the Indian and Atlantic oceans.
As in Fig. a and b but for JJA.
Same as Fig. but for 650 hPa.
While the variability of the tracer ages generally decreases with height from
the surface, this is not the case for σΓJJA over the western tropical Indian
Ocean. Here there is very little interannual variability near the surface for
this region, but as shown in Fig. there is a region of high
interannual variability at 650 hPa that extends from tropical Africa over the
Indian Ocean. Near the surface the largest σΓJJA occurs around 30∘ N, but
around 650 hPa the largest variability is around 5∘ N. The large σΓJJA near
the surface can be attributed to the variations of ITCZ (surface convergence),
but variability in the ITCZ does not account for the large variability near 650 hPa. A possible cause of the large σΓJJA at 650 hPa is variability in the
ascent in the lower–middle troposphere over this region. During JJA there is a
narrow region of strong ascent in the lower–middle troposphere over tropical Africa
and the Indian Ocean, between the African easterly jet and tropical easterly jet, that
does not extend down to the surface but does produce large precipitation in a
“tropical rain belt” south of the surface ITCZ (e.g., ).
This region of strong ascent likely impacts meridional tracer transport, and the
large σΓJJA at 650 hPa could be connected to variability in ascent. This
possibility requires further examination.
Conclusions
The seasonal and interannual variability of transport times from the NH midlatitude surface into the tropics and SH has
been examined using simulations of idealized “age” tracers. For all tracers
the largest seasonal and interannual variability occurs near the surface within
the tropics and is generally closely coupled to variability in the tropical
convergence zones (ITCZ, SPCZ). The seasonal migration of the ITCZ is
responsible for the majority of seasonality in the tracer ages (with
younger ages when the ITCZ is further south), while a large amount of the
interannual variability during DJF is due to ENSO-related variations in surface
convergence and convection, especially over the Pacific Ocean. Trace gas observations from surface stations provide support
for these model results: the “SF6 age” derived from tropical measurements varies seasonally with the latitude of the ITCZ
in a similar manner to the simulated ideal age, and lower concentrations of tracers with NH sources are observed at the
American
Samoa station during El Niño years (consistent with slower transport).
There are, however, notable differences in the variability of tracers
with different time dependencies. The largest variability in the mean
age (Γ) is confined to the tropics, generally close to the location of
the ITCZ (or SPCZ), and there is very weak seasonal or interannual
variability in the southern extratropics (e.g., the interannual
standard deviation of Γ in the southern extratropics is less than
1 % of the climatological mean value). In contrast, for the 5- and
50-day loss tracers the peak variability of the tracer age tends to
be south of the ITCZ, and there is a smaller contrast between tropical
and extratropical variability. For example, the DJF interannual
standard deviation of the age of the 5-day loss tracer (τ5) is around
30–40 % of the mean in both the tropics and southern midlatitudes.
These differences in temporal variability of the tracers occur because
the tracers are sensitive to different aspects of the TTD (e.g.,
τ5 is more sensitive to changes in the fast transit times than
Γ), and this results in differing meridional age gradients.
noted that fast (advective) transport pathways make
only a very small contribution to the TTD south of the ITCZ, and the
TTD is dominated by slow (eddy-diffusive) pathways. Changes in these
fast transport pathways south of the ITCZ can cause substantial
variations in tracers with rapid loss (e.g., τ5) as these tracers
are sensitive to changes in the fast timescales (and insensitive to
changes in transit times much longer than a month as these carry
little tracer), but have much weaker impact of the mean transit time
(which is more strongly influenced by the tail of the TTD).
The differing seasonal and interannual variability of the idealized
tracers suggests that the seasonal–interannual variability in the southern
extratropics of trace gases, with predominantly NH sources, may differ depending
on the chemical lifetimes of the gases. For tracers with very long lifetimes (e.g.,
SF6 and CFCs) we may expect very weak temporal variability due to transport,
whereas for tracers with shorter lifetimes (e.g., non-methane hydrocarbons) there
may be noticeable transport-induced seasonal or interannual variability.
Conversely, our study also suggests that combinations of tracers with different
lifetimes may be used to constrain the TTD from observations. This
possibility requires further examination.
The CAM4chem output can be downloaded from
https://www.earthsystemgrid.org/search.html?freeText=ccmi1 (last access: January 2017) and
the ONI data from http://www.cpc.ncep.noaa.gov/ (last access: August 2016).
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.
Acknowledgements
This work was supported by NSF Grant AGS-1403676 and NASA Grant
NNX14AP58G.
Edited by: Peter Hess
Reviewed by: two anonymous referees
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