In response to increasing greenhouse gases, the
subtropical edges of Earth's Hadley circulation shift poleward in global
climate models. Recent studies have found that reanalysis trends in the
Hadley cell edge over the past 30–40 years are within the range of trends
simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) models
and have documented seasonal and hemispheric asymmetries in these trends. In
this study, we evaluate whether these conclusions hold for the newest
generation of models (CMIP6). Overall, we find similar characteristics of
Hadley cell expansion in CMIP5 and CMIP6 models. In both CMIP5 and CMIP6
models, the poleward shift of the Hadley cell edge in response to increasing
greenhouse gases is 2–3 times larger in the Southern Hemisphere (SH),
except during September–November. The trends from CMIP5 and CMIP6 models
agree well with reanalyses, although prescribing observed coupled
atmosphere–ocean variability allows the models to better capture reanalysis
trends in the Northern Hemisphere (NH). We find two notable differences
between CMIP5 and CMIP6 models. First, while both CMIP5 and CMIP6 models
contract the NH summertime Hadley circulation equatorward (particularly over
the Pacific sector), this contraction is larger in CMIP6 models due to their
higher average climate sensitivity. Second, in recent decades, the poleward
shift of the NH annual-mean Hadley cell edge is slightly larger in CMIP6
models. Increasing greenhouse gases drive similar trends in CMIP5 and CMIP6
models, so the larger recent NH trends in CMIP6 models point to the role of
other forcings, such as aerosols.
Introduction
The poleward expansion of the Hadley circulation is one of the most robust
aspects of the atmospheric general circulation's response to a warming
climate in global climate models. This response is seen in models of varying
complexity, ranging from idealized aquaplanet simulations (Frierson et al.,
2007; Levine and Schneider, 2011; Tandon et al., 2013) to comprehensive
general circulation model experiments (Hu et al., 2013; Lu et al., 2007; Tao
et al., 2016), such as those from phases 3 and 5 of the Coupled Model
Intercomparison Project (CMIP). The poleward expansion of the Hadley
circulation is anticipated to have a number of regional climate impacts in
the subtropics, potentially shifting dry regions (Feng and Fu, 2013; Scheff
and Frierson, 2012; Schmidt and Grise, 2017), altering zones of ocean
upwelling (Cook and Vizy, 2018; Rykaczewski et al., 2015), and modifying
hurricane tracks (Kossin et al., 2014; Sharmila and Walsh, 2018; Studholme
and Gulev, 2018).
A decade ago, a number of studies began estimating rates of Hadley cell
expansion using various observational data sets (Fu et al., 2006; Hu and Fu,
2007; Seidel and Randel, 2007; Seidel et al., 2008). These rates varied
widely by study, ranging from 0.2 to 3∘ latitude per
decade over the period from 1979 until the mid-2000s (Birner et al.,
2014; Davis and Rosenlof, 2012; Lucas et al., 2014). The largest observed
trends were an order of magnitude larger than those projected by climate
models over the same period (Hu et al., 2013; Johanson and Fu, 2009),
calling into question whether the observed trends were biased high and/or
whether the models were deficient in simulating circulation trends.
Additionally, studies disagreed on the cause of the observed trends. Some
studies identified an important role for anthropogenic forcing, including
increasing greenhouse gases (Hu et al., 2013; Nguyen et al., 2015; Tao et
al., 2016), stratospheric ozone depletion (Kang et al., 2011; McLandress et
al., 2011; Min and Son, 2013; Polvani et al., 2011; Son et al., 2010), and
changes in anthropogenic aerosols (Allen et al., 2012; Allen and Ajoku,
2016; Kovilakam and Mahajan, 2015). However, other studies concluded that
the observed trends strongly reflected natural climate variability (Allen
and Kovilakam, 2017; Amaya et al., 2018; Mantsis et al., 2017).
Recent efforts by the US CLIVAR Working Group on the Changing Width of the
Tropical Belt and the International Space Science Institute (ISSI) Tropical
Width Diagnostics Intercomparison Project have addressed many of these
discrepancies in the previous literature. For example, the large observed
rates of expansion documented by some earlier studies have been attributed
to methodological issues. Traditionally, the edge of the Hadley circulation
has been defined using the poleward boundary of the zonal-mean meridional
mass streamfunction in the mid-troposphere, but departures from mass
conservation in reanalyses (particularly in older-generation reanalyses) can
lead to large spurious trends in the location of the Hadley cell edge
defined using the mass streamfunction (Davis and Davis, 2018). Consequently,
many studies have sought to estimate trends in the location of the Hadley
cell edge using other metrics, including the transition from zonal-mean
surface easterlies to zonal-mean surface westerlies (Grise et al., 2018,
hereafter G18; Grise et al., 2019, hereafter G19), the subtropical sea level
pressure maximum (Choi et al., 2014), the latitude of the subtropical jet
(Maher et al., 2020), the altitude break in tropopause height in the
subtropics (Seidel and Randel, 2007; Lucas et al., 2012), thresholds in
outgoing longwave radiation (Hu and Fu, 2007; Mantsis et al., 2017), and
total column ozone (Hudson et al., 2006). Some of the largest trends in
recent decades arise from the metrics derived from tropopause height and
outgoing longwave radiation, but it appears that these metrics are measuring
changes unrelated to the poleward expansion of the Hadley circulation. While
all of the metrics listed above co-locate climatologically with the poleward
boundary of the mass streamfunction, only the surface wind and sea level
pressure metrics covary interannually with the streamfunction boundary
(Davis and Birner, 2017; Davis et al., 2018; Solomon et al., 2016; Waugh et
al., 2018), at least in reanalyses and models. Accounting for these issues,
estimates of the recent expansion of the Hadley circulation have been
narrowed to be ≤0.5∘ latitude per decade and within
the range of trends indicated by global climate models over the historical
period (G18; Staten et al., 2018).
Additionally, in terms of the attribution of the recent trends, G19
concluded that the recent poleward expansion of the Southern Hemisphere (SH)
Hadley cell edge was driven in part by anthropogenic forcing (increasing
greenhouse gases and stratospheric ozone depletion) and in part by natural
variability, whereas the recent poleward expansion of the Northern
Hemisphere (NH) Hadley cell edge was predominantly driven by natural
variability. While the observed rates of expansion are approximately
comparable in the two hemispheres, models indicate that anthropogenic
forcing alone should drive a 3–4 times larger expansion in the SH (cf. Fig. 2
of G19). Over the historical period, stratospheric ozone depletion plays a
key role in this hemispheric asymmetry, especially during austral summer
(December–January–February, DJF). However, even in models forced only by increasing greenhouse gases,
the poleward shift of the SH Hadley cell edge is substantially larger than
that in the NH (Davis et al., 2016; Grise and Polvani, 2016; Watt-Meyer et
al., 2019); only during the SON (September–October–November) season are expansion rates comparable
between the two hemispheres. G19 concluded that the role of aerosols in the
observed Hadley cell expansion appears to be small based on CMIP5 models
but remains very uncertain due to the diverse treatment of aerosols in
models.
Most of the conclusions discussed above were formulated using CMIP5 model
output, and as CMIP represents an “ensemble of opportunity”, it is quite
possible that some of the relationships established from CMIP5 models may
have been unique to that model generation. The goal of this study is to
re-evaluate key conclusions about Hadley cell expansion in a new generation
of global climate models (CMIP6) and to assess their robustness across model generation. CMIP6 includes output from updated versions of CMIP5 models
(many of which have different treatments of clouds and aerosols, among other
factors) as well as new models that did not participate in CMIP5. Overall,
we find that the characteristics of Hadley cell expansion are very similar
in CMIP5 and CMIP6 models, but we find several notable exceptions, which we
detail below.
The paper is organized as follows. Section 2 details the data and methods.
Section 3 examines the response of the Hadley cell edge to an idealized
4×CO2 forcing in CMIP6 models and compares the results to CMIP5 models.
Section 4 then examines the trends from the historical runs of CMIP6 models
and contrasts them with reanalyses and CMIP5 models. Section 5 briefly
compares the 21st-century trends in CMIP5 and CMIP6 models. Section 6
provides a summary and concluding thoughts.
Data and methodsData
The primary data used in this study are output from the 24 CMIP5 (Taylor et
al., 2012) and 20 CMIP6 (Eyring et al., 2016) models listed in Table 1.
These models were selected because they had data available from all of the
following runs at the time of the writing of this paper:
pre-industrial control: fully coupled runs simulating 200+ years of unforced variability
historical: fully coupled runs forced with observed radiative forcings over the
period 1850–2005 for CMIP5 and 1850–2014 for CMIP6
AMIP (Atmospheric Model Intercomparison Project): atmosphere-only runs forced with observed radiative forcings, sea
surface temperatures, and sea ice concentrations over the period 1979–2008
for CMIP5 and 1979–2014 for CMIP6
abrupt 4×CO2: fully coupled runs in which atmospheric CO2 concentrations
are abruptly quadrupled from pre-industrial levels and held fixed for 150 years.
Additionally, to examine a high-emissions scenario for the 21st
century, we use the Representative Concentration Pathway (RCP) 8.5 runs
(2006–2100) for CMIP5 models and the Shared Socioeconomic Pathway (SSP)
5–8.5 runs (2015–2100) for CMIP6 models. All 24 CMIP5 models have data
available for the RCP 8.5 scenario, but only 14 of the 20 CMIP6 models have
data available for the SSP 5–8.5 scenario (see CMIP6 models marked with ∗
symbol in Table 1).
Global climate models used in this study. Resolution
indicates the horizontal resolution at which the data are provided. Bolded
models denote those models with output available from the
amip4×CO2, amipFuture/amip-future4K, and
amip4K/amip-p4K runs. The asterisks denote the CMIP6 models with
output available from the SSP 5–8.5 run. The first ensemble member is used
for each model, except in two cases when it is unavailable. In those cases,
the “r8i1p1f1” ensemble member is used for the abrupt
4×CO2 run of EC-Earth3, and the “r2i1p1f1” ensemble
member is used for the amip-p4K run of MIROC6.
CMIP5 modelResolution (∘ long ×∘ lat)CMIP6 modelResolution (∘ long ×∘ lat)ACCESS1.01.875∘×1.25∘BCC-CSM2-MR∗1.125∘×1.1215∘ACCESS1.31.875∘×1.25∘BCC-ESM12.8125∘×2.7906∘BCC-CSM1.12.8125∘×2.7906∘CAMS-CSM1-0∗1.125∘×1.1215∘BCC-CSM1.1(m)1.125∘×1.1215∘CanESM5∗2.8125∘×2.7906∘BNU-ESM2.8125∘×2.7906∘CESM2∗1.25∘×0.9424∘CanESM2 (CanAM4)2.8125∘×2.7906∘CESM2-WACCM∗1.25∘×0.9424∘CCSM41.25∘×0.9424∘CNRM-CM6-1∗1.40625∘×1.4008∘CNRM-CM51.40625∘×1.4008∘CNRM-ESM2-1∗1.40625∘×1.4008∘CSIRO Mk3.6.01.875∘×1.8653∘E3SM-1-01.0∘×1.0∘EC-EARTH1.125∘×1.1215∘EC-Earth3∗0.7031∘×0.7018∘FGOALS-g22.8125∘×2.7906∘EC-Earth3-Veg∗0.7031∘×0.7018∘GFDL CM32.5∘×2.0∘GISS-E2-1-G2.5∘×2.0∘GISS-E2-R2.5∘×2.0∘HadGEM3-GC31-LL1.875∘×1.25∘HadGEM2-ES (HadGEM2-A)1.875∘×1.25∘IPSL-CM6A-LR∗2.5∘×1.2676∘INM-CM4.02.0∘×1.5∘MIROC6∗1.40625∘×1.4008∘IPSL-CM5A-LR3.75∘×1.8947∘MRI-ESM2-0∗1.125∘×1.1215∘IPSL-CM5A-MR2.5∘×1.2676∘NESM3∗1.875∘×1.8653∘IPSL-CM5B-LR3.75∘×1.8947∘NorESM2-LM2.5∘×1.8947∘MIROC51.40625∘×1.4008∘SAM0-UNICON1.25∘×0.9424∘MIROC-ESM2.8125∘×2.7906∘UKESM1-0-LL∗1.875∘×1.25∘MPI-ESM-LR1.875∘×1.8653∘MPI-ESM-MR1.875∘×1.8653∘MRI-CGCM31.125∘×1.1215∘NorESM1-M2.5∘×1.8947∘
For a subset of the models in Table 1, we use three additional runs, which
are useful in the attribution of Hadley cell expansion. Following Grise and
Polvani (2014), we use the amip4×CO2 and amipFuture (called
“amip-future4K” for CMIP6) runs to partition the circulation response to
increased atmospheric CO2 into components associated with the direct
radiative forcing of CO2 (amip4×CO2 – AMIP) and sea surface
temperature (SST) warming (amipFuture – AMIP). The amip4×CO2 runs are
atmosphere-only runs with the same SSTs and sea ice as the AMIP runs, but
with quadrupled atmospheric CO2 concentrations; the amipFuture runs add
a patterned SST anomaly (normalized to a global-mean value of 4 K) to the
AMIP SSTs but retain the same CO2 and sea ice concentrations as the
AMIP runs (Webb et al., 2017). To determine whether the results are
sensitive to the patterned SST anomaly used in the amipFuture runs, we also
examine the amip4K (called “amip-p4K” for CMIP6) runs, which add a uniform
SST anomaly of 4 K to the AMIP SSTs but retain the same CO2 and sea ice
concentrations as the AMIP runs (Webb et al., 2017). Overall, 10 CMIP5 models and 7
CMIP6 models have output available for the amip4×CO2, amipFuture, and amip4K
runs (see bolded models in Table 1).
Over the historical period (1850–2005 for CMIP5, 1850–2014 for CMIP6),
single forcing runs are also examined from available models (see Table S1
for CMIP5 and Table S2 for CMIP6 in the Supplement). These runs are identical to the
historical runs, except that they only prescribe one forcing over the
historical period: well-mixed greenhouse gases, natural (solar and volcanic) forcing, anthropogenic aerosols, and ozone. Note that, in CMIP5 models,
the ozone-only runs include changes in both stratospheric and tropospheric
ozone concentrations, whereas the ozone-only runs in CMIP6 models are only
forced by changes in stratospheric ozone concentrations. Furthermore, some
CMIP5 models included ozone changes in their greenhouse-gas-only runs
(Gillett et al., 2016), and following G19, we exclude those models here to
more clearly separate the influences of stratospheric ozone depletion and
increasing greenhouse gases on the circulation response.
To compare the historical circulation trends in models with observations, we
make use of the five modern reanalysis data sets listed in Table 2. Because
the CFSR reanalysis ends in 2010, we extend it through 2014 using CFSv2. We
do not examine the NCEP-NCAR or NCEP-DOE reanalyses here, as they contain
substantial departures from mass conservation over the historical period
(Davis and Davis, 2018).
Reanalysis data sets used in this study.
ReanalysisResolution (∘ long ×∘ lat)Time periodCitationERA-50.25∘×0.25∘1979–2014Hersbach et al. (2019)ERA-Interim0.75∘×0.75∘1979–2014Dee et al. (2011)JRA-551.25∘×1.25∘1979–2014Kobayashi et al. (2015)NASA MERRA-20.625∘×0.5∘1980–2014Gelaro et al. (2017)NCEP CFSR0.5∘×0.5∘1979–2010Saha et al. (2010a)NCEP CFSv20.5∘×0.5∘2011–2014Saha et al. (2014)Methods
To locate the edges of the Hadley circulation, we make use of two metrics:
PSI500 and USFC. PSI500 is defined as the subtropical latitude where the
zonal-mean meridional mass streamfunction at 500 hPa switches sign from
thermally direct (Hadley circulation) to thermally indirect (Ferrel
circulation). USFC is defined as the subtropical latitude where the
zonal-mean zonal wind at the surface switches sign from tropical easterlies
to midlatitude westerlies. The metrics are calculated using the
Tropical-width Diagnostics code package (TropD; Adam et al., 2018a). Before
calculating these metrics, the wind fields are zonally and time averaged
(i.e., annual-mean, zonal-mean or seasonal-mean, zonal-mean wind fields are
used). We note that the NH summertime Hadley circulation is very weak,
making it challenging to define the PSI500 metric during some years. We only
consider the PSI500 metric from years in which there is a clear crossing of
the 500 hPa streamfunction field from positive to negative in the NH
subtropics. We consider the PSI500 metric to be undefined if no zero
crossing in the streamfunction field occurs or if multiple zero crossings
from positive to negative occur within a 20∘ latitude band
(“Lat_Uncertainty =20” in TropD).
In this paper, we focus on results for the PSI500 metric, as it is the most
widely used metric of Hadley cell width in the previous literature. Key
results for the USFC metric are shown in the Supplement.
However, when comparing the Hadley cell expansion in models with
observations, we show results from both metrics because of potential biases
in the PSI500 metric in reanalyses (Davis and Davis, 2018; G19). We also
make brief use of the USFC metric to examine longitudinal asymmetries in the
circulation response, as the PSI500 metric can only strictly be defined in
the zonal mean. Some recent studies have attempted to generalize the
zonal-mean Hadley cell edge (as defined by the PSI500 metric) to individual
longitudes by isolating regional meridional overturning cells (Schwendike et
al., 2014; Staten et al., 2019). However, interpreting these regional
overturning circulations is challenging and remains an area of active
research, and thus we do not examine these local overturning cells here.
We evaluate whether the multi-model means of CMIP5 and CMIP6 models are
statistically different from one another using a two-tailed Student's
t test. When comparing values from CMIP5 and CMIP6 models, we use large
asterisks in the figures to denote where the multi-model means of CMIP5 and
CMIP6 models are statistically different at the 95 % confidence level. For
the significance testing, we treat each model as an independent sample.
However, because many climate models are closely related to one another
(e.g., Knutti et al., 2013), the actual value of significance is likely to
be much lower.
Dynamical sensitivity of CMIP6 models
Before examining Hadley cell expansion over the historical period, we first
compare and contrast the dynamical sensitivity of CMIP5 and CMIP6 models.
Following Grise and Polvani (2016, hereafter GP16), we define dynamical
sensitivity as the response of the circulation to 4×CO2 forcing, which
is calculated here as the difference in the Hadley cell edge latitude
between its mean position during the last 50 years (years 101–150) of the
abrupt 4×CO2 run and its mean position in the pre-industrial control
run. Examining the dynamical sensitivity is important, as it directly allows
us to compare generations of models to a common forcing. The abrupt
4×CO2 experiment is chosen for this purpose, as it is a standard
experiment planned to be included in all future phases of CMIP (Eyring et
al., 2016). In contrast, the forcings used in the historical and future
scenario runs of CMIP models change across model generations, making it
difficult to verify whether differences between model generations are
because of model improvements or changes in forcings.
Figure 1 shows the response of the NH and SH Hadley cell edge latitudes (as
measured by the PSI500 metric) to 4×CO2 forcing. Qualitatively similar
results for the USFC metric are shown in the Supplement (Fig. S1). In the SH, both CMIP5 and CMIP6 models show ∼2∘ of Hadley cell expansion in response to 4×CO2 forcing. The
SH expansion has little variation across the seasonal cycle, with slightly
larger poleward shifts of the Hadley cell edge in MAM (March–April–May) and SON (see also
GP16). On average, the poleward expansion seen in CMIP6 models is only
slightly larger than that in CMIP5 models, with the difference between CMIP5
and CMIP6 models only being statistically significant in JJA (June–July–August).
Response of (a) NH and (b) SH Hadley cell edge latitude (as
measured by PSI500 metric) to 4×CO2 forcing for (black) CMIP5 and (red)
CMIP6 models. Here, the response is defined as the difference in the Hadley
cell edge latitude between its mean position during the last 50 years (years
101–150) of the abrupt 4×CO2 run and its mean position in the
pre-industrial control run. The response of each model is shown with a small
“x”, and the multi-model mean response is shown as a large dot. Asterisks
denote where multi-model means of CMIP5 and CMIP6 models are statistically
different at the 95 % confidence level via Student's t test.
In the NH, the response of the Hadley cell edge to 4×CO2 has a more
dramatic seasonal variation. In the annual mean, the multi-model mean Hadley
cell expansion is ∼0.75∘ latitude, roughly 40 %
of the multi-model mean response in the SH. The smaller poleward shift of
the NH Hadley cell edge in the annual mean reflects a compensation between a
large poleward shift of the NH Hadley cell edge in SON (and to a lesser
extent in DJF) and a large equatorward shift of the NH Hadley cell edge in
JJA. This seasonality is consistent with previously published results based
on CMIP5 models (GP16; Watt-Meyer et al., 2019). The differences between
CMIP5 and CMIP6 models are small in all seasons except JJA. However, in JJA,
the equatorward contraction of the circulation is notably larger in CMIP6
models. As a result, three CMIP6 models (CESM2, CESM2-WACCM, and
SAM0-UNICON) actually contract the annual-mean NH Hadley cell edge
equatorward, a result not seen in CMIP5 models (at least as measured by the
PSI500 metric).
The differences between CMIP5 and CMIP6 models in Fig. 1 may be because the
CMIP6 models, on average, have a higher climate sensitivity (Forster et al.,
2020; Zelinka et al., 2020). To check this, in Table 3, we show correlations
between the annual-mean global-mean surface temperature response to
4×CO2 forcing and the Hadley cell edge response across the inter-model
spread of both CMIP5 and CMIP6 models. The results support the conclusions
of GP16 based upon CMIP5 models. In the SH, the magnitude of the poleward
shift in the Hadley cell edge is strongly correlated with the global-mean
surface temperature response throughout the year, with the largest and most
significant correlations in MAM and JJA (cf. Fig. 4 of GP16). In other
words, models that warm more in response to 4×CO2 forcing tend to shift
the SH Hadley cell edge further poleward. In contrast, in the NH, the
magnitude of the shift in the Hadley cell edge is very poorly correlated
with the global-mean surface temperature response in the annual mean. This
largely reflects a compensation between a significant positive correlation
in DJF and a significant negative correlation in JJA. That is, models that
warm more in response to 4×CO2 forcing tend to shift the NH Hadley cell
edge further poleward in DJF but also further equatorward in JJA. The fact
that the only significant differences between CMIP5 and CMIP6 models in Fig. 1 occur in the JJA season in both hemispheres is consistent with Table 3, as
JJA is the season with the largest magnitude correlation between the
dynamical sensitivity and the global-mean surface temperature response in
both hemispheres.
Correlations between the poleward shift of the Hadley cell edge
latitude in response to 4×CO2 forcing (as measured by
PSI500 metric) with the annual-mean global-mean surface temperature response to
4×CO2 forcing. Positive correlations imply that
models that warm more shift the Hadley cell edge further poleward.
Correlations for CMIP5 and CMIP6 models are shown in the top and bottom rows
of each cell, respectively. Correlations that are statistically significant
at the 95 % confidence level via Student's t test are bolded.
In Fig. 2, we further examine the largest difference between CMIP5 and CMIP6
models identified in Fig. 1: the response of the NH JJA Hadley cell edge to
4×CO2 forcing. Figure 2a shows the scatterplot between the responses
of the global-mean surface temperature and the NH JJA Hadley cell edge
latitude to 4×CO2 forcing. As documented in Table 3, the strong
anticorrelation between the NH JJA Hadley cell edge shift and the
global-mean surface temperature response is clearly visible. Because CMIP6
models have on average 1 K greater warming in response to 4×CO2 forcing
(6.1 K for CMIP6, compared to 5.1 K for CMIP5), the NH JJA Hadley cell edge
shifts significantly further equatorward (∼4∘
latitude for CMIP6, compared to 1.5∘ latitude for CMIP5).
(a) Scatterplot of NH JJA Hadley cell edge response to
4×CO2 forcing (as measured by PSI500 metric) versus
annual-mean global-mean surface temperature response for (black) CMIP5 and
(red) CMIP6 models. (b) Time series of NH JJA Hadley cell edge response to
abrupt 4×CO2 forcing for (black) CMIP5 and (red)
CMIP6 multi-model mean. (c) Response of NH JJA Hadley cell edge to (first
column) quadrupled atmospheric CO2 concentrations
with fixed sea surface temperatures (amip4×CO2 –
AMIP), (second column) patterned sea surface temperature increase with fixed
atmospheric CO2 concentrations (amipFuture – AMIP),
and (third column) uniform 4 K sea surface temperature increase with fixed
atmospheric CO2 concentrations (amip4K – AMIP).
The time series of the response of the NH JJA Hadley cell edge latitude to an
abrupt quadrupling of atmospheric CO2 yields further insight into the
processes involved (Fig. 2b). Initially, in both CMIP5 and CMIP6 models, the
Hadley cell edge shifts slightly poleward in the first decade after CO2
quadrupling, but then retreats equatorward for the remainder of the 150-year
run. Consistent with Figs. 1 and 2a, the equatorward retreat of the NH JJA
Hadley cell edge is substantially larger in CMIP6 models.
Following Grise and Polvani (2014) and Shaw and Voigt (2015), we can examine
the roles of the direct radiative effects of CO2 and SST warming in
this circulation response (see Sect. 2.1). In response to a
quadrupling of atmospheric CO2 concentrations (but no change in SSTs),
both CMIP5 and CMIP6 models show a ∼0.6∘ latitude
poleward expansion of the NH JJA Hadley circulation (Fig. 2c), consistent
with the immediate circulation response in Fig. 2b after abrupt CO2
quadrupling. In contrast, both CMIP5 and CMIP6 models show a ∼1.0∘ latitude equatorward contraction of the NH JJA Hadley
circulation in response to a patterned 4 K SST warming (with no change in
atmospheric CO2 concentrations). NH summer is the season when
circulation changes driven by the direct radiative effects of CO2 most
clearly oppose those driven by SST warming (Grise and Polvani, 2014). As
argued by Shaw and Voigt (2015), the direct radiative effects of CO2
enhance the land–sea temperature contrast and act to shift the circulation
poleward, whereas the SST warming reduces the land–sea temperature contrast and
acts to shift the circulation equatorward. Because the SST warming is larger
in CMIP6 models on average (due to their higher climate sensitivity), the
SST-driven component of the circulation response would be expected to be
larger in CMIP6 models, resulting in a larger net equatorward contraction of
the NH Hadley circulation during JJA than in CMIP5 models. However, as
pointed out by Zhou et al. (2019), the exact pattern of SST warming is
critical for capturing the equatorward contraction of the NH JJA Hadley cell
edge seen in the abrupt 4×CO2 runs. A uniform 4 K SST warming would
instead result in a poleward expansion of the NH JJA Hadley circulation
(Fig. 2c).
One may question the meaningfulness of looking at the NH summertime Hadley
circulation, which is generally very weak (Dima and Wallace, 2003) and
largely reflects regional overturning circulations in the Indian Ocean–west
Pacific sector (Hoskins et al., 2020). So, to aid in the interpretation of
the results in Figs. 1–2, we also examine the regional structure of the NH
circulation response during JJA. Figure 3a shows the multi-model mean
surface zonal wind response to 4×CO2 forcing for the JJA season for
CMIP6 models. From this figure, it is clear that the equatorward contraction
of the NH summertime circulation arises largely from the Pacific sector,
consistent with findings from CMIP5 models (Shaw and Voigt, 2015; GP16).
There is little net shift in the subtropical surface wind field over the
Atlantic sector during JJA (see also Fig. 3c).
(a) Multi-model mean response of JJA surface zonal wind
field to 4×CO2 forcing for CMIP6 models. The thick
dotted and solid lines indicate the 0 and 2.5 m s-1 wind contours from the pre-industrial control
climatology, respectively. Stippling indicates where the response is
statistically significant at the 95 % confidence level via Student's
t test. (b, c) As in Fig. 1, but for the USFC metric calculated over the
North Pacific (135∘ E–125∘ W) and North Atlantic
(60∘ W–0∘ E) sectors, respectively.
The latitude of the transition between tropical surface easterlies and
midlatitude surface westerlies over the North Pacific shifts poleward in
most seasons but shifts equatorward in summer (Fig. 3b), similar to the
zonal-mean Hadley circulation (Fig. 1). In CMIP6 models, the winter and fall
circulation shifts further poleward over the Pacific sector than in the
CMIP5 models, but the summer circulation shifts further equatorward. As a
result, there is little difference in annual-mean circulation shifts between
CMIP5 and CMIP6 models over either the North Pacific or North Atlantic
sectors. As noted above for the zonal-mean Hadley circulation (Fig. 2), the
equatorward contraction of the Pacific circulation during JJA results from
the competing effects of the direct radiative effects of CO2 and SST
warming on the circulation (see Fig. S2). The equatorward contraction of the
Pacific circulation is larger on average in CMIP6 models (Fig. 3b), as the
effect of the warming SSTs overpowers any poleward expansion driven by the
direct radiative effects of CO2.
In summary, in this section, we compared and contrasted the responses of the
NH and SH Hadley cell edges to abrupt 4×CO2 forcing. The magnitudes and
seasonality of the Hadley cell expansion in CMIP6 models are very similar to
those in CMIP5 models (Fig. 1). The most notable differences occur in the
JJA season, particularly in the NH where CMIP6 models show a substantially
larger equatorward contraction of the circulation than CMIP5 models. During
this season, the response of the NH Hadley cell edge to 4×CO2 forcing
is significantly anticorrelated with the global-mean surface temperature
response (Table 3; Fig. 2a), and because the average climate sensitivity of
CMIP6 models is larger, the circulation contracts further equatorward in
CMIP6 models. This equatorward contraction of the NH Hadley cell during
summer largely reflects an equatorward shift of the circulation over the
Pacific sector (Fig. 3), where there is a competition between the direct
radiative effects of CO2 (which act to expand the circulation poleward)
and SST warming (which acts to contract the circulation equatorward).
Because the CO2 forcing is the same but the SST warming is larger in
CMIP6 models, the net equatorward contraction of the NH summertime
circulation is notably larger in CMIP6 models.
The 1979–2008 trends in annual-mean Hadley cell edge
latitude, as measured by the (a, c) PSI500 and (b, d) USFC
metrics. Reanalysis trends (OBS, blue symbols) are taken from the
ERA-Interim (ERAI), MERRA-2, JRA-55, CFSR, and ERA5 reanalyses. Because
MERRA-2 begins in 1980, trends for MERRA-2 are shown for 1980–2008. Control
trends (PIC) show the 2.5th–97.5th percentile of trends over 30-year
periods from the pre-industrial control runs of (black) CMIP5 and (red)
CMIP6 models. Historical trends (HIST) and AMIP trends are calculated from
the first ensemble members of (black) CMIP5 and (red) CMIP6 models, where
the response of each model is shown with a small “x” and the multi-model
mean response is shown as a large dot. Because the historical runs of CMIP5
models end in 2005, they are extended with 3 years of the RCP 8.5 run
until 2008. Asterisks denote where multi-model means of CMIP5 and CMIP6
models are statistically different at the 95 % confidence level via
Student's t test.
Hadley cell expansion over the historical period
Having compared the models' Hadley cell edge response to a common forcing,
we now use this knowledge to compare the models' behavior over the
historical period. Figure 4 shows the trends in the annual-mean Hadley cell
edge latitude (as measured by both the PSI500 and USFC metrics) over the
period 1979–2008 from five reanalyses, CMIP5 models, and CMIP6 models. We
examine this 30-year period as it represents the common period covered by
the AMIP runs of both CMIP5 and CMIP6 models. Because CMIP5 models'
historical runs end in 2005, we have extended these runs with 3 years of
the RCP 8.5 runs until 2008. Qualitatively similar results are found if
slightly different end dates are used instead of 2008. For reference, in
Fig. 5, we plot the reanalysis and multi-model mean time series from which
the trends in Fig. 4 are calculated.
The 1979–2014 time series of annual-mean Hadley cell edge
latitude, as measured by the (a, c) PSI500 and (b, d) USFC
metrics. All time series are plotted with respect to their 1980–1990
average. The top set of time series in each panel shows five reanalyses
(ERA-Interim, MERRA-2, JRA-55, CFSR, and ERA5), along with the
multi-reanalysis mean (thick blue line). The bottom set of time series in
each panel shows the multi-reanalysis mean (thick blue line, reproduced from
the plot above) as well as the multi-model mean from (black) CMIP5
historical runs (extended with RCP 8.5 until 2014), (red) CMIP6 historical
runs, (orange) CMIP6 historical greenhouse-gas-only runs (using all
available ensemble members; see Table S2), (black dashed) CMIP5 AMIP runs,
and (red dashed) CMIP6 AMIP runs. The reanalysis-mean and multi-model mean
time series are smoothed with a 10-year running mean to better visualize
the low-frequency variability in each time series. Note that the scale is
different for the top and bottom sets of time series in each panel.
Figure 4 shows that the observed trends for the USFC metric (as estimated by
reanalyses) are relatively modest (≤0.2∘ latitude per
decade in each hemisphere) and within the bounds of the 30-year
trends from the control runs of the models (see also G18, G19). In the NH,
the reanalysis trends lie at the upper range of trends from the models'
historical runs and fall near the multi-model mean trend from the models'
AMIP runs, suggesting an important role for SST variability in driving the
recent poleward shift in the NH Hadley cell edge (Allen et al., 2014; Allen
and Kovilakam, 2017; G19). In the SH, the reanalysis trends compare well
with the multi-model mean trends from the historical runs of CMIP5 and CMIP6
models and the multi-model mean trend from the AMIP runs of CMIP5 models.
The multi-model mean trend from the AMIP runs of CMIP6 models compares well
with the trend from the ERA-5 reanalysis but exceeds the trends from the
other reanalyses.
For the PSI500 metric (Fig. 4a, c), trends from the ERA-Interim,
MERRA-2, and JRA-55 reanalyses in the NH and from the ERA-Interim reanalysis
in the SH are substantially larger than the trends from the models' control
runs and greatly exceed the trends from the historical and AMIP runs of most
models (see also G18, G19). As discussed by G19, the PSI500 metric is
subject to considerable uncertainty in reanalyses (see spread in reanalysis
time series in Fig. 5) because of inconsistencies in assimilated satellite
radiances across reanalyses (Fujiwara et al., 2017) and departures from mass
conservation (Davis and Davis, 2018). By contrast, at least some of the
surface pressure and marine surface wind observations are shared among
reanalysis centers (Fujiwara et al., 2017), resulting in stronger agreement
among the reanalysis time series for the USFC metric (Fig. 5b, d).
Over the 1979–2008 period, the trends from the historical and AMIP runs of
CMIP5 and CMIP6 models are very similar, with two key exceptions. First, as
noted above, for the USFC metric, the trends in the SH Hadley cell edge are
significantly larger in the AMIP runs of CMIP6 models than in the AMIP runs
of CMIP5 models (Fig. 4d), but this result is metric dependent and does not
hold for the PSI500 metric (Fig. 4c). Second, for both the PSI500 and USFC
metrics, the trends in the NH Hadley cell edge are significantly larger in
the historical runs of CMIP6 models than in the historical runs of CMIP5
models (Fig. 4a–b). This can also clearly be seen in the time series in
Fig. 5 and is not unique to the 1979–2008 period highlighted in Fig. 4. The
discrepancy between the historical trends in CMIP5 and CMIP6 models in Fig. 4 is unexpected, as increased CO2 results in very similar trends in the
NH annual-mean Hadley cell edge in CMIP5 and CMIP6 models (Fig. 1). Indeed,
CMIP6 models forced only with increasing greenhouse gases over the
historical period (Fig. 5, orange lines) compare very favorably with the
historical runs of CMIP5 models (Fig. 5, solid black lines). This evidence
suggests that other forcings (solar/volcanic, aerosol, ozone) could be
contributing to the larger NH circulation trends in recent decades in the
historical runs of CMIP6 models.
To address the role of different forcings in contributing to trends in the
models' historical runs, we examine trends in the Hadley cell edge latitude
from all available ensemble members of the historical single forcing runs of
CMIP5 and CMIP6 models, updating the results of G19 to include CMIP6 models
(see their Fig. 2). Results for the NH Hadley cell edge latitude are shown
in Fig. 6, and results for the SH Hadley cell edge latitude are shown in
Fig. 7. Recall that these single forcing runs are only available from a
small subset of the models (eight CMIP5 models and nine CMIP6 models, as listed in
Tables S1 and S2 in the Supplement). Following G19, results are shown for two time periods:
1950–2005 and 1979–2005, where 1950 is the start year of the single
forcing runs in some CMIP5 models and 2005 is the end year of the single
forcing runs in CMIP5 models.
In the NH, CMIP5 and CMIP6 models agree that increasing greenhouse gases
were the dominant forcing contributing to a poleward shift of the
annual-mean Hadley cell edge over the second half of the 20th century
(Fig. 6). However, the poleward trends in the Hadley cell edge latitude in
the NH associated with increasing greenhouse gases are ∼2–3
times smaller than those in the SH, consistent with the results from the
abrupt 4×CO2 runs shown in Fig. 1. The roles of the remaining forcings
(solar/volcanic, aerosol, ozone) are smaller and are of inconsistent sign
between CMIP5 and CMIP6 models. Natural (solar/volcanic) forcing contributes
to a poleward shift of the NH Hadley cell edge over the 1979–2005 period in
CMIP5 models (Allen et al., 2014), but an equatorward shift of the NH Hadley
cell edge over the same period in CMIP6 models. Anthropogenic aerosol
forcing contributes to a statistically significant equatorward shift of the
NH Hadley cell edge over the 1950–2005 period in CMIP5 models (Allen and
Ajoku, 2016), but this influence has weakened in CMIP6 models (particularly
for the USFC metric). Finally, the ozone single forcing run is associated
with a poleward shift of the NH Hadley cell edge in CMIP5 models (Allen et
al., 2014) but not in CMIP6 models. Here, a large difference between CMIP5
and CMIP6 models is expected, as the ozone single forcing runs are driven by
both tropospheric and stratospheric ozone forcing in CMIP5 models but only
by stratospheric ozone forcing in CMIP6 models (which is well known to have
a much larger effect on the circulation in the SH).
Trends in annual-mean NH Hadley cell edge latitude over
(a, c) 1950–2005 and (b, d) 1979–2005 for (black) CMIP5 and
(red) CMIP6 models. Trends are shown for all available ensemble members of
the following runs: (HIST) historical, (GHG) greenhouse gas only, (NAT)
solar and volcanic only, (AER) anthropogenic aerosol only, and (OZ) ozone
only. Multi-model mean trends are shown as thick horizontal lines. Trends
are also shown for all independent time periods of equivalent length from
the pre-industrial control (PIC) runs. Large black dots denote forcings with
trends statistically different from zero in both CMIP5 and CMIP6 models.
Asterisks denote where multi-model means of CMIP5 and CMIP6 models are
statistically different at the 95 % confidence level via Student's
t test.
Unfortunately, for this subset of models with single forcing runs, the
difference in the historical trends in the NH Hadley cell edge latitude
between CMIP5 and CMIP6 models (Fig. 6) is smaller than for the entire
ensemble of models shown in Fig. 4. Consequently, it is difficult to use
these runs to fully understand the discrepancies in the models' historical
runs shown in Figs. 4–5. For the USFC metric, the historical trends from
the nine CMIP6 models with single forcing runs are larger than those from the eight CMIP5 models with single forcing runs (Fig. 6c–d), consistent with Fig. 4b. Over the 1950–2005 period, the trends in the historical runs of CMIP5
models reflect a compensation between a poleward shift of the Hadley cell
edge due to greenhouse gas forcing and an equatorward shift of the Hadley
cell edge due to anthropogenic aerosol forcing (Fig. 6c). In CMIP6 models,
the aerosol influence on the circulation is weaker, allowing the greenhouse
gas forcing to dominate. A similar but weaker pattern in the trends is seen
over the 1979–2005 period for the USFC metric (Fig. 6d) but not for the
PSI500 metric (Fig. 6b). Therefore, while Fig. 6 provides some limited
evidence that aerosol forcing may play a role in the discrepancy in the NH
historical circulation trends between CMIP5 and CMIP6 models (Figs. 4–5),
it is difficult to generalize these conclusions based on a small subset of
models to the entire multi-model ensemble. What is clear is that the larger
historical trends in CMIP6 models over the last several decades appear
inconsistent with forcing by increasing greenhouse gases alone (compare
orange, black, and red lines in Fig. 5a–b).
Figure 7 shows the trends in the SH Hadley cell edge from the historical
single forcing runs of CMIP5 and CMIP6 models for the PSI500 metric for both
the annual mean and the DJF season. Results for the USFC metric are shown in
Fig. S3. The results in Fig. 7 largely support the results from Fig. 2 of
G19 based on CMIP5 models alone. Over the second half of the 20th
century, the models indicate that increasing greenhouse gases and
stratospheric ozone depletion (particularly during DJF) were the dominant
forcings contributing to a poleward shift of the SH Hadley cell edge. There
is also some suggestion that anthropogenic aerosols contributed to a slight
equatorward contraction of the SH Hadley cell edge, particularly over the
1950–2005 period (see also Choi et al., 2019). The one notable difference
in the SH historical trends between CMIP5 and CMIP6 models is that the
circulation trends associated with the ozone forcing appear to be
significantly weaker in CMIP6 models. However, only a small number of models
conducted the historical ozone forcing runs, and unfortunately none of the
same modeling centers conducted the runs for both CMIP5 and CMIP6.
Therefore, inter-model differences in the circulation response to ozone
forcing likely play a role in the discrepancy between CMIP5 and CMIP6 models
seen in Fig. 7, particularly because the magnitude of the austral spring
polar lower-stratospheric cooling in response to stratospheric ozone
depletion is similar in CMIP5 and CMIP6 models (not shown). The inclusion of
tropospheric ozone forcing in the CMIP5 single forcing runs may also be a
factor.
As in Fig. 6, but for (a, b) annual-mean SH Hadley cell
edge latitude and (c, d) DJF-mean SH Hadley cell edge latitude (as
measured by PSI500 metric).
Finally, we explore the seasonality of the recent trends in the NH and SH
Hadley cell edge latitudes. Time series of the reanalysis and multi-model
mean Hadley cell edge latitudes for all four seasons are shown in Fig. 8.
For reference, we also plot the 1979–2008 trends from individual reanalyses
and models in Fig. S4. Given the confounding issues with the PSI500 metric
in reanalyses discussed above, we focus on the USFC metric in these figures.
As in Fig. 5, but for time series of 1979–2014
seasonal-mean Hadley cell edge latitudes (as measured by USFC metric) in
both hemispheres.
In the NH, the reanalysis time series show near-zero to slightly equatorward
trends in the Hadley cell edge during MAM and JJA and fall close to the
multi-model mean of the CMIP historical runs during these seasons (see also
G18). However, during DJF and SON, the reanalysis time series show sizable
(∼0.3–0.4∘ latitude per decade)
poleward trends in the Hadley cell edge. During these seasons, the magnitude
of the reanalysis trends is larger than the trends from the historical and
AMIP runs of most models (Fig. S4; see also G18). In DJF, the AMIP runs of
CMIP5 and CMIP6 models approximate the reanalysis trends better than the
historical runs (Fig. 8), suggesting the importance of recent SST
variability in driving the observed NH circulation trends during this
season. In SON, the multi-model mean trends from CMIP6 models' historical
runs and AMIP runs are larger than those from CMIP5 models and are in better
agreement with the reanalysis trends (Fig. 8, compare red and black lines).
Hence, the larger trends in the NH Hadley cell edge in CMIP6 models noted
above in the annual mean most clearly manifest themselves during SON
(compare Fig. 5b with Fig. 8).
In the SH, the reanalysis time series show consistent poleward trends in the
Hadley cell edge (∼0.2–0.3∘ latitude
per decade in all seasons but JJA), falling close to the multi-model
mean trends from the CMIP5 and CMIP6 historical runs during all seasons (see
also G18). During all seasons but DJF, the time series of the SH Hadley cell
edge from reanalyses also closely parallels the time series from the CMIP6
runs forced only by increasing greenhouse gases (compare orange and thick
blue lines in the right column of Fig. 8). However, during DJF, the
historical greenhouse-gas-only runs substantially underestimate the trends
in reanalyses, pointing to the importance of stratospheric ozone depletion
in driving SH circulation trends during this season (Fig. 7c–d), as
documented by numerous previous studies (Garfinkel et al., 2015; McLandress
et al., 2011; Min and Son, 2013; Polvani et al., 2011; Son et al., 2010;
Waugh et al., 2015).
In summary, in this section, we examined the trends in the latitudes of the
NH and SH Hadley cell edges over the late 20th century and early
21st century in CMIP5 and CMIP6 models and compared them to trends from
five reanalyses. Our conclusions largely support the conclusions of recent
studies documenting Hadley cell expansion in CMIP5 models (e.g., Allen and
Kovilakam, 2017; G18; G19). However, we find that the historical trends in
the annual-mean NH Hadley cell edge latitude are significantly larger over
the 1979–2008 period in CMIP6 models (Fig. 4). One might be tempted to
attribute the larger trends in CMIP6 models to their higher average climate
sensitivity, but as shown in Sect. 3, the larger historical circulation
trends in CMIP6 models are actually inconsistent with greenhouse gas
forcing, which drives comparable magnitude shifts in the NH annual-mean
Hadley cell edge in CMIP5 and CMIP6 models (Figs. 1 and 6). We instead
conclude that some other forcing (possibly aerosol forcing; see Fig. 6) must
be contributing to the larger historical circulation trends in CMIP6 models.
Projected Hadley cell expansion over the 21st century
Finally, we briefly compare the 21st-century trends from the RCP 8.5
runs of CMIP5 models with those from the SSP 5–8.5 runs of CMIP6 models.
Figure 9 shows the time series of the annual-mean NH and SH Hadley cell edge
latitudes over the period 1920–2100 based on the PSI500 metric. The time
series show the multi-model mean of the historical runs extended through the
21st century with the RCP 8.5 runs for CMIP5 models and the SSP 5–8.5
runs for CMIP6 models. The multi-model mean 20th- and 21st-century
time series for CMIP5 and CMIP6 models are virtually identical. For
reference, we provide a scatterplot of the 2015–2100 trends from
individual models (as well as the trends by season) in Fig. S5. Given that
the RCP 8.5 and SSP 5–8.5 runs are dominated by greenhouse gas forcing, the
results in Fig. S5 are very similar to those shown for the 4×CO2
forcing in Fig. 1, but with slightly weaker magnitude.
1920–2100 time series of annual-mean Hadley cell edge
latitude (as measured by PSI500 metric) from CMIP5 historical +
RCP 8.5 and CMIP6 historical + SSP 5-8.5 runs. The multi-model
mean (smoothed with 10-year running mean) for CMIP5 (CMIP6) models is
plotted as a solid black (red) line. One standard deviation range across the
inter-model spread of CMIP6 models is shown as red shading. Two standard
deviation range across the inter-model spread of CMIP5 (CMIP6) models is
plotted as black (red) dashed lines. One and two standard deviation ranges
about the pre-industrial control latitude of CMIP6 models are shown as gray
shading and gray dashed lines, respectively. All latitudes are plotted with
respect to the multi-model mean pre-industrial control latitude.
Following Hawkins and Sutton (2012) and G19, we define a “timescale of
emergence” as the time at which the multi-model mean forced circulation
response surpasses a given threshold of natural variability (as defined from
the models' control runs). For the SH, both the CMIP5 and CMIP6 multi-model
mean Hadley cell edge latitudes surpass the 1 standard deviation threshold
of variability in the models' control runs (Fig. 9, gray shading) around the
year 2000 (Fig. 9b), suggesting that the circulation response to
anthropogenic forcing may have already emerged from natural variability (at
least by this measure). This early emergence arises principally from the DJF
season (G19; Solomon and Polvani, 2016; Thomas et al., 2015), due in large
part to the added influence of stratospheric ozone depletion on the
circulation during this season. In this high-emissions scenario, the SH
annual-mean Hadley cell edge would surpass the 2 standard deviation
threshold of variability in the models' control runs (Fig. 9, gray dashed
lines) around the year 2045. This timescale is slightly faster than the
timescale of emergence (2060) derived from the Community Earth System Model
(CESM) Large Ensemble (G19; Quan et al., 2018).
In the NH, as noted by G19, the circulation response would take much longer
to emerge from natural variability. In this high-emissions scenario, the NH
annual-mean Hadley cell edge would surpass the 1 standard deviation
threshold of variability in the models' control runs between 2060–2070 and
would not surpass the 2 standard deviation threshold of variability in the
21st century (Fig. 9a). Again, this timescale is faster than that noted
for the CESM Large Ensemble by G19, who did not find the poleward shift of
the NH Hadley cell edge to be large enough to emerge from natural
variability in the 21st century in that model. Regardless, the NH
circulation response will take much longer to emerge from natural
variability than the SH circulation response. This is for two reasons: (1) the larger magnitude response of the Hadley cell edge to increasing
greenhouse gases in the SH (Fig. 1) and (2) the slightly larger range of
natural variability in the Hadley cell edge latitude in the NH (compare gray
shading in Fig. 9a and b). Note that, during the SON season, the poleward
shift of the NH Hadley cell edge may emerge from natural variability as
early as 2040 (not shown), due to the larger NH circulation response to
greenhouse gas forcing during that season (Fig. 1).
Summary and conclusions
In response to increasing greenhouse gases, global climate models show a
robust poleward expansion of the Hadley circulation (Davis et al., 2016;
GP16; Watt-Meyer et al., 2019), and numerous lines of observational evidence
suggest that the Hadley circulation has already expanded over the last
30–40 years (Birner et al., 2014; Davis and Rosenlof, 2012; Seidel et al.,
2008; Staten et al., 2018). Within the past 5 years, studies have used
output from CMIP5 global climate models to better understand the causes of
the observed expansion (Allen et al., 2014; Allen and Kovilakam, 2017; G19)
and to predict its possible evolution over the 21st century (Hu et al.,
2013; Tao et al., 2016). In this paper, we assess whether these conclusions
are robust across model generations by examining output from CMIP6 models.
We find strong agreement in the trends in the latitudes of the NH and SH
Hadley cell edges from CMIP5 and CMIP6 models in response to abrupt
4×CO2 (Fig. 1), historical (Fig. 4), and 21st-century (Fig. 9)
forcings. Specifically, we find a number of features to be robust across model generation.
Models that warm more in response to CO2 forcing (i.e., models with a
higher climate sensitivity) generally shift the SH Hadley cell edge further
poleward during all seasons, shift the NH Hadley cell edge further poleward
during DJF, but contract the NH Hadley cell edge further equatorward during
JJA (Table 3; GP16). The equatorward contraction of the NH circulation
during summer arises from the Pacific sector (Fig. 3; Grise and Polvani,
2014; Shaw and Voigt, 2015).
In response to CO2 forcing, models shift the annual-mean Hadley cell
edge 2–3 times further poleward in the SH than in the NH (Fig. 1; GP16;
Watt-Meyer et al., 2019). Only during the SON season is the Hadley
circulation expansion comparable in the two hemispheres. This implies that,
with continued increases in greenhouse gases, the circulation response will
emerge from natural variability in the 21st century much sooner in the
SH than in the NH (Fig. 9; G19).
Over the last 30–40 years, the magnitude of the Hadley cell expansion
indicated by reanalyses using the USFC metric is within the range of trends
simulated by CMIP models' historical and AMIP runs (Fig. 4; G19). Large
discrepancies between reanalysis and model trends primarily result from
examining trends in the PSI500 metric, which has known biases in reanalyses
(Davis and Davis, 2018; G19).
Observed coupled atmosphere–ocean variability has likely played an important
role in recent trends, particularly in the NH (Fig. 4; Allen and Kovilakam,
2017). Increasing greenhouse gases and stratospheric ozone depletion have
likely played an important role in recent trends in the SH (Fig. 7).
There are, however, several notable differences in CMIP6 models. First, the
equatorward contraction of the NH summertime circulation is stronger in
CMIP6 models, apparently as a result of their higher average climate
sensitivity (Fig. 2). Second, over the last 30–40 years, the annual average
trends in the NH Hadley cell edge in CMIP6 models' historical runs are
slightly larger than those in CMIP5 models' historical runs. This
discrepancy is not associated with differences in climate sensitivity, as
trends in greenhouse-gas-only runs over this time period agree well between
CMIP5 and CMIP6 models (Figs. 5–6). The biggest discrepancies in historical
circulation trends between CMIP5 and CMIP6 models appear to arise from other
forcings (solar/volcanic, anthropogenic aerosol, ozone), which contribute to
substantial variance in circulation trends across model generations (Figs. 6–7).
Overall, there is good agreement on the characteristics of Hadley
circulation expansion in CMIP5 and CMIP6 models, yet several outstanding
issues remain that require further understanding. First, the consistency of
the hemispheric and seasonal asymmetries of the circulation trends across model generation attests to their robustness, emphasizing a greater need to
better understand the physical mechanisms responsible for these asymmetries
(see discussion in Watt-Meyer et al., 2019). Second, a better understanding
is needed of the roles of non-greenhouse-gas forcings on historical
circulation trends and why these trends diverge significantly across model
generation. Finally, we have focused almost entirely on zonal-mean
circulation trends in this paper. We plan to examine the regional
manifestations of these circulation trends in future work.
Code and data availability
Code to calculate the PSI500 and USFC metrics is freely available from the
TropD package (10.5281/zenodo.1157043, Adam et al., 2018b). CMIP5 and CMIP6
model output is freely available from the Lawrence Livermore National
Laboratory (https://esgf-node.llnl.gov/search/cmip5/,
World Climate Research Programme (WCRP), 2011;
https://esgf-node.llnl.gov/search/cmip6/,
World Climate Research Programme (WCRP), 2019). ERA-Interim and ERA-5 reanalysis
data are freely available from the European Centre for Medium-Range Weather
Forecasts (https://apps.ecmwf.int/datasets/data/interim-full-moda/, European Centre for Medium-Range Weather Forecasts (ECMWF), 2009;
https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset, Copernicus Climate Change Service (C3S), 2017).
MERRA-2 reanalysis data are freely available from NASA
(10.5067/AP1B0BA5PD2K, Global Modeling and Assimilation Office (GMAO), 2015a; 10.5067/2E096JV59PK7, Global Modeling and Assimilation Office (GMAO), 2015b). JRA-55, CFSR, and CFSv2 reanalysis data are freely
available from the National Center for Atmospheric Research Computational
and Information Systems Laboratory Research Data Archive
(10.5065/D60G3H5B, Japan Meteorological Agency, 2013;
10.5065/D6DN438J, Saha et al., 2010b;
10.5065/D69021ZF, Saha et al., 2012).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-5249-2020-supplement.
Author contributions
KG and SD designed the project, KG performed the formal analysis, and KG
and SD prepared the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Penelope Maher, Karen Rosenlof, and two anonymous reviewers for
helpful comments. We acknowledge the World Climate Research Programme,
which, through its Working Group on Coupled Modelling, coordinated and
promoted CMIP6. We thank the climate modeling groups for producing and
making available their model output, the Earth System Grid Federation (ESGF)
for archiving the data and providing access, and the multiple funding
agencies who support CMIP6 and ESGF.
Review statement
This paper was edited by Peter Haynes and reviewed by Penelope Maher and two anonymous referees.
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