We investigate the response of stratospheric water vapor
(SWV) to different forcing agents within the Precipitation Driver and
Response Model Intercomparison Project (PDRMIP) framework. For each model
and forcing agent, we break down the SWV response into a slow response, which is
coupled to surface temperature changes, and a fast response, which is the
response to external forcing but before the sea surface temperatures have
responded. Our results show that, for most climate perturbations, the slow
SWV response dominates the fast response. The slow SWV response exhibits a
similar sensitivity to surface temperature across all climate perturbations.
Specifically, the sensitivity is 0.35 ppmv K-1 in the tropical lower
stratosphere (TLS), 2.1 ppmv K-1 in the northern hemispheric lowermost
stratosphere (LMS), and 0.97 ppmv K-1 in the southern hemispheric LMS.
In the TLS, the fast SWV response only dominates the slow SWV response when
the forcing agent radiatively heats the cold-point region – for example,
black carbon, which directly heats the atmosphere by absorbing solar
radiation. The fast SWV response in the TLS is primarily controlled by the
fast adjustment of cold-point temperature across all climate perturbations.
This control becomes weaker at higher altitudes in the tropics and altitudes
below 150 hPa in the LMS.
Introduction
Stratospheric water vapor (SWV) plays an important role in global climate change.
It is an important greenhouse gas (GHG), which affects the Earth's radiative
budget (Forster and Shine, 2002; Solomon
et al., 2010), and it also plays an important role in stratospheric ozone
chemistry (Solomon et al., 1986; Dvortsov and Solomon,
2001).
SWV in the overworld (above the 380 K isentropic surface)
(e.g., Hoskins, 1991) and SWV in the extratropical
lowermost stratosphere (LMS, between the extratropical tropopause and the
380 K isentropic surface) (e.g., Holton et al., 1995)
are distinguished according to different mechanisms that control them.
Overworld SWV is primarily controlled by the temperatures in the tropical
tropopause layer (TTL) as air is transported through it
(e.g., Mote et al., 1996; Fueglistaler et al.,
2009) and by production from oxidation of methane (e.g.,
Brasseur and Solomon, 2005). The LMS SWV is controlled by three major
sources, including the transport of overworld air by the downward branch of the
Brewer–Dobson circulation, adiabatic quasi-horizontal transport from the
tropical upper troposphere, and diabatic cross-tropopause transport due to
deep convection
(Dessler et al.,
1995; Holton et al., 1995; Plumb, 2002; Gettelman et al., 2011).
The response of SWV to climate change can be partitioned into two
components: the fast response and slow response. The addition of a
radiatively active constituent to the atmosphere can influence the
atmosphere even before the surface temperature changes, leading to changes
in SWV. This is often referred to as an “adjustment” to the forcing and
is generally considered part of the external forcing
(e.g., Sherwood et al., 2015). We will refer to this
as the “fast response” of SWV to the forcing. The slow response is the
component in the SWV change that is coupled to changes in the surface
temperature, which occur on longer timescales. This slow response means
that SWV could be an important positive feedback to global warming
(Forster and Shine, 2002; Dessler et
al., 2013; Huang et al., 2016; Banerjee et al., 2019).
Banerjee et al. (2019) have shown that, when CO2 is
abruptly quadrupled, the change in SWV mainly consists of the slow response
and that the fast response is less important.
Previous studies have shown that climate models, which are able to
accurately reproduce observed interannual variations in SWV
(Dessler et al., 2013; Smalley et al.,
2017), robustly project a positive long-term trend in overworld SWV at entry
level with a warming climate due to increasing GHGs (Gettelman
et al., 2010; Dessler et al., 2013; Smalley et al., 2017). This is mainly
due to a warmer tropopause (Thuburn
and Craig, 2002; Gettelman et al., 2010; Lin et al., 2017; Smalley et al.,
2017; Xia et al., 2019), which is controlled, to some extent at least, by
the warming surface (Gettelman
et al., 2010; Shu et al., 2011; Dessler et al., 2013; Huang et al., 2016;
Revell et al., 2016; Lin et al., 2017; Smalley et al., 2017; Banerjee et
al., 2019). Dessler et al. (2016) suggested
that increases in convective injection into the stratosphere due to a
warming climate may also be contributing to the trend in entry SWV. In the
LMS, climate models show larger increases in SWV
(Dessler et al., 2013; Huang et al., 2016;
Banerjee et al., 2019). It is not known how SWV responds to different
forcing agents. Hodnebrog et al. (2019) investigated
the response of global integrated water vapor to different forcing agents
but focused on the troposphere.
The goal of this study is to investigate the response of both overworld and
LMS SWV to forcing agents with different physical properties. We will
explicitly investigate the fast and slow responses in SWV and compare them.
We will also investigate how SWV responds to surface temperature change when
the climate is forced by different forcing agents.
MethodThe PDRMIP setup
In this paper, we analyze nine models from the Precipitation Driver and
Response Model Intercomparison Project (PDRMIP) (Samset
et al., 2016; Myhre et al., 2017; Tang et al., 2018, 2019). These are
Coupled Model Intercomparison Project phase 5 (CMIP5) era models (Table 1),
and each performed a baseline and multiple climate perturbation experiments
(Table 1). This subset of the CMIP5 ensemble has a multi-model mean
equilibrium climate system (ECS) of 3.6 K, close to the ensemble average ECS
of the entire CMIP5 ensemble (3.3 K) (Zelinka et al. 2020).
ModelVersionResolutionOcean setupAerosol setupKey referencesPerturbation experimentsSecond Generation CanadianEarth System Model (CanESM2)20102.8∘× 2.8∘, 35 levelsCoupled oceanEmissionsArora et al. (2011)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4Community Earth SystemModel, version 1 (CommunityAtmosphere Model, version 4)[CESM1(CAM4)]1.0.32.5∘× 1.9∘, 26 levelsSlab oceanFixed concentrationsNeale et al. (2010),Gent et al. (2011)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4, 10×CFC-12,3×N2O, 10×BCSLTCESM1 CAM51.1.22.5∘× 1.9∘, 30 levelsCoupled oceanEmissionsHurrell et al. (2013),Kay et al. (2015), Otto-Bliesner et al. (2016)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4, 10×CFC-12Goddard Institute for Space Studies Model E2, coupled with theRussell ocean model (GISS-E2-R)E2-R2∘× 2.5∘, 40 levelsCoupled oceanFixed concentrationsSchmidt et al. (2014)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4, 10×CFC-12, 10×BCSLTHadley Centre Global Environment Model, version 2 – EarthSystem (includes Carbon Cycleconfiguration with chemistry) (HadGEM2-ES)6.6.31.875∘× 1.25∘, 38 levelsCoupled oceanEmissionsCollins et al. (2011),The HadGEM2 Development Team et al.(2011)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4, 10×CFC-12, 10×CFC-11, 3×N2OHadGEM3Global Atmosphere 4.01.875∘× 1.25∘, 85 levelsCoupled oceanFixed concentrationsBellouin et al. (2011),Walters et al. (2014)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4, 10×CFC-12L'Institut Pierre Simon LaplaceCoupled Model, version 5A (IPSL-CM5A)CMIP53.75∘× 1.875∘, 39 levelsCoupled oceanFixedconcentrationsDufresne et al. (2013)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4Max Planck Institute Earth System Model (MPI-ESM)1.1.00p2T63, 47 levelsCoupled oceanClimatology, year 2000Giorgetta et al. (2013)2×CO2, 3×CH4, 2%SolarModel for InterdisciplinaryResearch on Climate–SpectralRadiation Transport Modelfor Aerosol Species (MIROC-SPRINTARS)5.9.0T85 (approx.1.4∘× 1.4∘), 40 levelsCoupled oceanHemispheric Transport AirPollution, phase 2EmissionsTakemura et al. (2005),Takemura et al. (2009), Watanabe et al. (2010)2×CO2, 3×CH4, 2%Solar, 10×BC, 5×SO4, 10×CFC-12, 10×CFC-11, 3×N2O, 5×O3
In the perturbation experiments, perturbations on a global scale are applied
abruptly at the beginning of the model simulation. The five core experiments
include a doubling of CO2 concentration (2×CO2), a tripling of
CH4 concentration (3×CH4), a 2 % increase in solar irradiance
(2%Solar), an increase in present-day black carbon concentration or
emission by a factor of 10 (10×BC), and an increase in present-day SO4
concentration or emission by a factor of 5 (5×SO4). In addition to the
five core experiments, a subset of models also performed additional
perturbation experiments: an increase in CFC-11 concentration from 535 ppt
to 5 ppb (hereafter, 10×CFC-11), an increase in CFC-12 concentration from
653.45 ppt to 5 ppb (hereafter, 10×CFC-12), an increase in N2O
concentration from 316 ppb to 1 ppm (hereafter, 3×N2O), an increase
in tropospheric O3 concentration used in
MacIntosh et al. (2016) by a factor of 5
(5×O3), and an increase in present-day black carbon with a shorter
lifetime by a factor of 10 (10×BCSLT). We note that indirect chemical effects
are not included in the 3×CH4 experiment. Table 1 provides details
about the models and the perturbations each one simulated.
The perturbations in GHGs and solar irradiance are relative to the models'
baseline simulations, in which the concentrations of GHGs and solar
irradiance are either at present-day levels or preindustrial levels. The
perturbations in the aerosols depend on whether it is possible to prescribe
aerosol concentrations in the models. For models that are able to prescribe
aerosol concentrations, the aerosol perturbations are based on a multi-model
mean baseline aerosol concentration in 2000 obtained from the AeroCom Phase
II initiative (Myhre
et al., 2013a). For those that are only able to produce aerosols through
emissions, the perturbation is applied by increasing the emissions by the
factors listed above. The 10×BCSLT experiment is performed only by models
that are able to prescribe aerosol concentrations.
Each perturbation experiment is performed in two configurations: a fixed sea
surface temperatures simulation (“fixed SST”) and a fully coupled (slab
ocean for CAM4 only) simulation. The fixed SST simulations use the SST
climatology at either the present-day or preindustrial level. The fixed
SST simulations are at least 15 years, and the coupled simulations are at
least 100 years.
Fast response and slow response
When available, the SWV mixing ratio is obtained directly from the specific
humidity output by each model simulation. For the models that do not output
specific humidity (CAM5, GISS-E2-R, and MIROC-SPRINTARS), we calculate
specific humidity by multiplying the models' relative humidity by the
saturation mixing ratio with respect to ice calculated using model
temperature and pressure. Responses of specific humidity and relative
humidity in the PDRMIP have been investigated by
Hodnebrog et al. (2019), but they focused on water
vapor in the troposphere.
We define ΔSWV, the change in the SWV mixing ratio in response to a
particular perturbation, to be the difference between SWV in the perturbed
coupled run and that in the baseline coupled run. As discussed above, the
ΔSWV can then be broken down into the two components: the fast
response (ΔSWVfast) and slow response (ΔSWVslow).
We compute results in the tropical lower stratosphere (70 hPa, 30∘ N–30∘ S, hereafter, TLS), in the northern hemispheric (NH)
lowermost stratosphere (50–90∘ N at 200 hPa,
hereafter, NH LMS), and in the southern hemispheric (SH) lowermost
stratosphere (50–90∘ S at 200 hPa, hereafter, SH
LMS). Most previous studies have focused on the response of water vapor in the
TLS (e.g.,
Gettelman et al., 2010; Shu et al., 2011; Smalley et al., 2017). But recent
studies report that the climate is most sensitive to changes in water vapor
in the LMS (Solomon et al., 2010;
Dessler et al., 2013; Banerjee et al., 2019), so we also investigate that
region.
We use the fixed SST simulations to get ΔSWVfast, the rapid
adjustment in SWV before sea surface temperature changes. ΔSWVfast is the difference between the SWV mixing ratio averaged over
the last 10 years in the fixed SST run with the forcing perturbation and the
SWV mixing ratio averaged over the last 10 years in the fixed SST baseline
simulation. The fixed SST runs have some warming of the land surface,
meaning that our fast response includes a contribution from a warming
land surface. We expect this will have a small impact on our results, but it
remains one of the uncertainties in our analysis.
We calculate ΔSWVslow as ΔSWV minus ΔSWVfast. To estimate the time series of ΔSWVslow, we use annual mean
ΔSWV time series over the entire coupled run period (at least 100 years) minus the 10-year average ΔSWVfast. To estimate
equilibrium ΔSWVslow, we use a regression method similar to the
methodology introduced by Gregory et al. (2004). The basic
concept is that we regress the annual mean global average net downward
radiative flux (R) at the top of the atmosphere (TOA) against the annual mean
ΔSWV averaged at TLS, NH LMS, or SH LMS. The equilibrium ΔSWV is where the linear fit intercepts at R=0. Then we simply subtract
ΔSWVfast from the equilibrium ΔSWV to estimate
equilibrium ΔSWVslow.
These regressions can be very noisy and yield highly uncertain parameters,
particularly for perturbations with relatively small amounts of radiative
forcing and warming. To account for this, we first fit the R and ΔSWV time series using an exponential function (y(t)=b+a1⋅e-t/τ1+a2⋅e-t/τ2) and then do the regression using
the fitted time series. For fully coupled models, we constrain τ1 to
be within the range of 4±2 years and τ2 to be within the range of
250±70 years; for CAM4, in which the atmosphere is coupled to a slab
ocean, we constrain τ1 to be within the range of 4±2 years. We
then compute the best fit of all parameters. The ranges for the time
constants are based on previous estimations of climate system timescales
(Geoffroy et al., 2013). We
estimate the ΔSWV intercept at R=0 by regressing the fitted R and
ΔSWV data over the last 30 years, since the relation between R and
ΔSWV is not necessarily linear over the entire 100-year period. The
slow and fast responses of other variables, such as global average surface
temperatures and cold-point temperatures, are computed using the same method.
We tested this method in a climate model that nearly reaches the equilibrium
climate state. We analyzed runs of the fully coupled Max Planck Institute
Earth System Model version 1.1 (MPI-ESM1.1) (Maher et al.,
2019), which has a transient climate response and an effective climate
sensitivity near the middle of the CMIP5 ensemble range
(Adams and Dessler, 2019; Dessler,
2020). It includes a 2000-year preindustrial control run and a 2614-year
abruptly quadrupled CO2 run. The values of ΔSWV averaged over
the last 30 years of the 4×CO2 run relative to the control run are 4.60 ppmv in the TLS, 22.40 ppmv in the NH LMS, and 9.69 ppmv in the SH LMS. We
expect this to be close to equilibrium ΔSWV because the trend in
global average surface temperature over the last 500 years of the 4×CO2
run is 0.02 K per century. We use the regression method to estimate the
equilibrium ΔSWV using MPI-ESM1.1 water vapor mixing ratio time
series over the first 100 years and obtain estimates of 4.38 ppmv in the
TLS, 20.01 ppmv in the NH LMS, and 9.07 ppmv in the SH LMS; these yield
differences of 0.22 ppmv in the TLS, 2.39 ppmv in the NH LMS, and 0.62 ppmv
in the SH LMS. Thus, our method underestimates the true equilibrium value by
5 % in the TLS, 11 % in the NH LMS, and 6 % in the SH LMS.
Uncertainty for slow and fast responses of different quantities shown in
this paper is obtained from Monte Carlo samples as follows: for each
perturbation, we randomly sample with replacement 100 000 times for each
model that performed that perturbation and from these samples compute the
2.5–97.5 percentiles.
ResultsThe slow stratospheric water vapor response
We show equilibrium ΔSWVslow and its percentage contribution to
the total equilibrium ΔSWV in Fig. 1. We show results in the TLS
(Fig. 1a and d), in the NH LMS (Fig. 1b and e), and the SH LMS (Fig. 1c
and f). In evaluating the absolute magnitude of ΔSWVslow in
the first column of Fig. 1, we normalize the equilibrium ΔSWVslow using effective radiative forcing (ERF) so that differences
in the magnitude of the forcing do not confound our results.
(a–c) Equilibrium ΔSWVslow normalized by
ERF (ppmv (W m-2)-1) in TLS (70 hPa, 30∘ N–30∘ S), NH LMS (200 hPa, 50–90∘ N), and
SH LMS (200 hPa, 50–90∘ S). (d–f) Contribution (%) of equilibrium ΔSWVslow to total
equilibrium ΔSWV. (g–i)ΔSWVfast normalized
by ERF (ppmv (W m-2)-1). (j–l) Contribution (%)
of ΔSWVfast to total equilibrium ΔSWV. The marker
shapes indicate results from different models. For perturbations that are
performed by more than three models, the solid circles and error bars for
each perturbation plotted in solid black are the multi-model mean and
2.5–97.5 percentiles of the model samples. Note that in the second
and fourth columns, we took out models with extremely small ΔSWV
magnitudes that yield extremely large ΔSWVslow/ΔSWV and
ΔSWVfast/ΔSWV ratios.
ERF values used in the construction of Fig. 1 are plotted in Fig. 2a; they are
calculated as the difference in net radiation at the top of the atmosphere (TOA)
averaged over the last 10 years between the fixed SST perturbed and baseline
simulation. Previous studies have computed the ERF in the PDRMIP using
various methods (Richardson
et al., 2019; Tang et al., 2019). The ERF calculation in our paper uses the same method as the ERFsst in Richardson et al. (2019). We directly compared our ERF with the ERFsst listed in Richardson et al. (2019), which shows good agreement and can be found in the Supplement (Table S3). The
equilibrium global averaged surface temperature changes
(ΔTs),
estimated using the regression method described in Sect. 2.2 and
normalized by ERF, are plotted in Fig. 2b. The multi-model mean ΔTs/ ERF shows general agreement across different perturbations. This
quantity is the inverse of the feedback parameter λ
(e.g., Dessler and Zelinka, 2015), so Fig. 2b implies
that the climate sensitivity to these different perturbations is similar,
which also agrees with Richardson et al. (2019). We
list the ERF and ΔTs quantities for each model and perturbation in
Table S1.
(a) Global average ERF (W m-2) at the top of
the atmosphere. (b) Global averaged surface temperature change per unit
ERF (K (W m-2)-1). The marker shapes indicate results from
different models. For perturbations that are performed by more than three
models, the solid circles and error bars for each perturbation plotted in
solid black are the multi-model mean and 2.5–97.5 percentiles of the
model samples.
In each region, the magnitude of multi-model mean ΔSWVslow/ ERF
shows general agreement for different perturbations. The magnitudes of
ΔSWVslow/ ERF in the LMS are larger than those in the TLS (Fig. 1b–c). This is consistent with previous studies, which showed that the
long-term trend in SWV over the century in climate models is largest near
the LMS tropopause (Dessler et al., 2013; Huang
et al., 2016; Banerjee et al., 2019). This reflects different transport
pathways into the LMS, including downward transport by the Brewer–Dobson
circulation, quasi-horizontal isentropic mixing from the tropical troposphere,
and convective influence (Dessler et al.,
1995; Holton et al., 1995; Plumb, 2002; Gettelman et al., 2011).
In the LMS, the multi-model mean ΔSWVslow/ΔSWV ratio is
close to 100 % for many perturbations (Fig. 1e–f). The latitude band
(50–90∘) we choose is somewhat arbitrary,
so in the Supplement (Fig. S1), we also show ΔSWVslow/ ERF and
ΔSWVslow/ΔSWV ratios for water vapor averaged at 200 hPa
between 30∘ and 50∘ latitudes in the Northern Hemisphere and Southern Hemisphere, respectively, which
also show that the ΔSWVslow plays a dominant role and
contributes to close to 100 % of the total ΔSWV for most
perturbations. In the TLS, the multi-model mean ΔSWVslow/ΔSWV ratio is generally above 50 %, with a few
exceptions. We will discuss this in detail in Sect. 3.3.
We note that inter-model variability in ΔSWVslow/ ERF and
ΔSWVslow is generally consistent for different perturbations.
For example, HadGEM3 produces larger responses than the rest of the models
for most perturbations (Fig. 1a–c, Table S1). GISS-E2-R and MIROC-SPRINTARS
have ΔSWVslow/ ERF and ΔSWVslow values generally
below the rest of the models (Fig. 1a–c, Table S1). We have not further
investigated the causes of these differences among models; this clearly
warrants further investigation.
We also note that CAM5, CanESM2, and MIROC-SPRINTARS produce negative TLS
ΔSWVslow/ ERF for 10×BC. These negative values are partly
contributed by artifacts of the method we use to estimate equilibrium
ΔSWVslow, which is the residual of the total equilibrium
ΔSWV minus ΔSWVfast. When differencing two numbers with
similar magnitudes, the residual may be quite uncertain. So, the
negative values here do not necessarily mean that a BC-induced surface
warming results in a negative SWV slow response. The direct regression between
ΔSWVslow and surface temperature change described in the next
section more accurately describes the relationship for these cases.
The slow stratospheric water vapor response and the surface temperature change
Our results show that, in most climate perturbations analyzed in this study,
the equilibrium response of water vapor in both the TLS and the LMS is
dominated by ΔSWVslow, which is the component mediated by sea
surface temperature change. To directly quantify how SWV responds to surface
temperature across a range of different climate change mechanisms, we
linearly regress the time series of annual mean ΔSWVslow over
the entire period of the coupled simulations (at least 100 years) against
the time series of annual mean global averaged surface temperature change
(ΔTs). We do this regression for each model and perturbation
separately. This is similar to the analysis of Banerjee et al. (2019), who
did this for quadrupled CO2 perturbation, but we do this for multiple
perturbations.
The scatter plot for each perturbation and model is shown in the Supplement
(Figs. S3–S5). For most perturbations and models, the ΔSWVslow
time series in both the TLS and the LMS is positively correlated with the
ΔTs time series, supporting the hypothesis that the surface
temperature change contributes to the long-term trend in SWV for most cases.
Figure 3 shows the slopes of the regression for all perturbations and
models. The corresponding slope values are listed in Table S4. We also list
slopes in the unit of percent per Kelvin (% K-1) in Table S5. The uncertainty of the slopes is
obtained from Monte Carlo samples: for each model and perturbation, we first
randomly sample the slope 100 000 times, assuming a Gaussian distribution.
Then, for each perturbation, we sample from the slope distributions with
replacement 100 000 times for each model that performed that perturbation
and from these samples compute the 2.5–97.5
percentiles.
Slopes (ppmv K-1) from the linear regression between annual
mean ΔSWVslow time series and annual mean ΔTs time
series. The marker shapes indicate results from different models. For
perturbations that are performed by more than three models, the solid
circles and error bars for each perturbation plotted in solid black are
the multi-model mean and 2.5–97.5 percentiles of the model samples. The
horizontal dashed line is the multi-model mean of all slopes, and the
horizontal dotted lines are 2.5–97.5 percentiles of the model
samples.
In both the TLS and LMS, the slopes from different perturbations show
general agreement (Fig. 3); this is also true for water vapor averaged at
200 hPa between 30∘ and 50∘ latitudes in the Northern Hemisphere and Southern Hemisphere (Fig. S2). In
the TLS, the multi-model and multi-perturbation average slope is 0.35 ppmv K-1, with a 95 % confidence interval of 0.28–0.44 ppmv K-1 (Fig. 3a). The LMS ΔSWVslow time series has stronger correlations
with the ΔTs time series (Figs. S3–S5) and produces larger
sensitivities (Fig. 3b–c). Specifically, the multi-model and
multi-perturbation mean slope is 2.1 ppmv K-1 in the Northern Hemisphere and 0.97 ppmv K-1 in the Southern Hemisphere, with 95 % confidence intervals of 1.82–2.39 ppmv K-1 and 0.79–1.15 ppmv K-1, respectively. Our results are similar
to those of Dessler et al. (2013) and Smalley et al. (2017) despite the fact that
they used 500 hPa temperature as their regressor.
We show that the relation between ΔSWVslow and ΔTs time
series can be extended to the entire stratosphere (Fig. 4a). We regridded
the zonal mean ΔSWVslow from all models and perturbations onto
the same pressure–latitude grid (10 hPa above 100 hPa and 50 hPa below 100 hPa, 4∘ latitude) and regressed the ΔSWVslow time series
at each grid point against global average ΔTs time series. The
multi-model and multi-perturbation average slope of the linear fit at each
grid point is shown in Fig. 4a (figures for each individual perturbation are
shown in Fig. S6). Since the vertical gradient of water vapor is large, we
plot the percentage change in the mixing ratio per Kelvin relative to the
baseline. Lapse rate tropopause, the lowest level where the lapse rate
decreases to 2 K km-1, also plotted, is obtained using the atmospheric
temperatures from the baseline coupled run and multi-model mean.
(a) Multi-model and multi-perturbation mean slope (% K-1) from the regression between annual mean time series of ΔSWVslow (at each latitude grid point and pressure level) and annual mean
time series of global average ΔTs. (b) Slope (% K-1)
from the regression between ΔSWVfast (ppmv) (at each latitude
grid point and pressure level) and ΔTCPfast (K). The solid cyan
line is the multi-model mean lapse rate tropopause derived from the baseline
simulations.
We clearly see the larger sensitivity of ΔSWVslow to ΔTs in the LMS than in the overworld. In the LMS, the slope has a
hemispheric asymmetry, with larger values in the Northern Hemisphere. This is consistent with
previous studies, which showed that isentropic transport brings more
tropospheric water vapor to the NH than the SH (Pan et al., 1997, 2000;
Dethof et al., 1999, 2000; Ploeger et al., 2013). In addition, convective
moistening may be more important to the NH due to more land in the Northern Hemisphere and,
consequently, more convection (Dessler and Sherwood, 2004;
Smith et al., 2017; Ueyama et al., 2018; Wang et al., 2019). We also see
large responses in the tropical upper troposphere, which is the main part of
the tropospheric water vapor feedback. The sensitivity declines as one
ascends through the TTL. Once above the TTL, the sensitivity in the
overworld is relatively uniform with altitude.
The fast stratospheric water vapor response
Figure 1 also shows the ΔSWVfast normalized by the ERF (Fig. 1g–i) and its contribution to total equilibrium ΔSWV (Fig. 1j–l). As discussed previously, ΔSWVfast is the rapid
adjustment in SWV before the sea surface temperatures respond. For most
perturbations, especially in the LMS, ΔSWVfast/ ERF is smaller
than ΔSWVslow/ ERF, with a magnitude of a few tenths of a part per million by volume per watt per square meter
(ppmv (W m-2)-1).
For 2×CO2, the near-zero TLS ΔSWVfast/ ERF is the result of
cancellation between cooling by a strengthening Brewer–Dobson circulation
and increased local radiative heating (Lin et al., 2017).
Some other GHG forcing agents, however, produce larger TLS ΔSWVfast/ ERF and contributions in the TLS. For both 10×CFC-12 and 10×CFC-11 the multi-model mean ΔSWVfast contributes about half of the
total ΔSWV (Fig. 1j). This is a consequence of
halocarbons producing more TTL warming per watt per square meter (W m-2) by efficiently
absorbing upwelling longwave radiation from the troposphere in the
atmospheric window (Forster et al.,
1997; Jain et al., 2000; Forster and Joshi, 2005). Figure 5 shows the fast
temperature response per unit ERF due to different perturbations, and it
shows heating in the TTL for both 10×CFC-12 and 10×CFC-11.
Profiles of fast temperature response normalized by ERF (K (W m-2)-1) between 200 and 40 hPa, averaged over 30∘ N–30∘ S. The color coding indicates results from different
perturbations. Each profile is the multi-model mean.
The 3×CH4 also includes some models that produce large TLS ΔSWVfast/ ERF magnitudes. This is likely due to TTL heating (Fig. 5) by
CH4 shortwave absorption, which is explicitly treated in some models,
including CAM5, CanESM2, MPI-ESM, and MIROC-SPRINTARS
(Smith et al., 2018). These
models are also the ones that produce the largest TLS ΔSWVfast
contributions (Fig. 1g and j).
Increases in tropospheric O3 (in the 5×O3 experiment) reduce the
upwelling longwave radiation, which cools the stratosphere
(Ramaswamy and Bowen, 1994;
Berntsen et al., 1997; Forster et al., 1997). The longwave radiation
absorbed heats the TTL region (Fig. 5), resulting in larger TLS ΔSWVfast/ ERF magnitude than ΔSWVslow/ ERF and larger
contributions to total equilibrium ΔSWV (77 %) (Fig. 1g and j).
There is also heating in the LMS, resulting in larger LMS ΔSWVfast/ ERF magnitude than ΔSWVslow/ ERF (Fig. 1h–i and k–l). We
note that our conclusion on 5×O3 is based on only one model,
MIROC-SPRINTARS.
ΔSWVfast from 10×BC dominates total equilibrium ΔSWV in
the TLS, with a multi-model mean contribution of 84 %. The magnitude of the
multi-model mean ΔSWVfast/ ERF from 10×BC is also larger than
any other perturbations in each region. This occurs because the 10×BC
strongly absorbs shortwave radiation, causing large heating of the
tropopause region in both the tropics and extratropics. Figure 5 shows that the
10×BC gives by far the most warming per unit ERF, which is consistent with
the vertical profile of fast temperature response shown in
Stjern et al. (2017).
The 10×BC ΔSWVfast/ ERF in the NH and SH LMS contributes
about 50 % of the total equilibrium ΔSWV, with smaller magnitudes
in the Southern Hemisphere (Fig. 1h–i and k–l). This is because the total amount of black
carbon is smaller in the Southern Hemisphere (Myhre et al., 2017), since
black carbon is a combustion product and is predominantly emitted over the
NH continents (Ramanathan and Carmichael, 2008). The 10×BCSLT
ΔSWVfast also contributes about 50 % of the total 10×BCSLT
ΔSWV. The 10×BCSLT does not produce as strong a ΔSWVfast/ ERF as 10×BC, since the reduction in BC lifetime leads to less
BC in the TTL and therefore less heating per unit ERF.
We quantify control of TLS ΔSWVfast by the fast TTL temperature
adjustments across a range of different climate perturbations by regressing
the TLS ΔSWVfast against the fast response of the cold-point
temperature (ΔTCPfast). To estimate ΔTCPfast in
the models, we first find the minimum temperature in the profile at each
grid point in the fixed SST runs (no interpolation is done; we simply find
the minimum temperature on the output model levels). These minimum
temperatures are then averaged between 30∘ N and 30∘ S to
yield TCPfast in each run. ΔTCPfast is the difference
between TCPfast in the perturbed model run and that in the baseline
runs.
We find that TLS ΔSWVfast is strongly correlated with ΔTCPfast across all perturbations and models (Fig. 6a), with a slope of
0.52 ppmv K-1 and a 95 % confidence interval of 0.43 to 0.61 ppmv K-1. Randel and Park (2019) pointed out that the slope
from the Clausius–Clapeyron relationship evaluated near the tropical
tropopause is close to this value, about 0.5 ppmv K-1. We also tested
the relationship between TLS ΔSWVslow and the slow response of the
cold-point temperature (ΔTCPslow) across all perturbations and
models, yielding a slope of 0.72 ppmv K-1. However, for the slow
response, correlation does not necessarily prove causality, since
Dessler et al. (2016) showed that, in two
climate models at least, a significant fraction of the long-term trend was
due to increases in convective moistening, which bypasses the TTL cool trap.
Therefore, this relationship for the slow response could arise from either
TCP control, a process that correlates with it, such as deep convective
injection of ice, or some combination.
(a–c) Linear regression between ΔSWVfast
(ppmv) and ΔTCPfast (K) from all models and perturbations. The
color coding indicates different perturbations, while the marker shapes
indicate results from different models. The black solid line is the linear
fit of the regression. The black dotted lines indicate the linear fits
within the 95 % confidence interval, estimated using a t test.
(d–f) Slopes and their 95 % confidence intervals (for perturbations
that are performed by more than three models) obtained from linear
regression between ΔSWVfast (ppmv) and ΔTCPfast
(K) for each individual perturbation. The black dashed lines and dotted
lines are the slopes and their 95 % confidence intervals of the regressions in (a)–(c).
We also separately plot the slopes between ΔSWVfast and ΔTCPfast for each perturbation (Fig. 6d–f). For the perturbations that
have more than five participating models, including 2×CO2, 3×CH4,
2%Solar, 10×BC, 5×SO4, and 10×CFC-12, we calculate the linear
regression between ΔSWVfast and ΔTCPfast from the
models and show the slopes and 95 % confidence intervals. For the
perturbations that have fewer participating models, including 10×CFC11,
3×N2O, 5×O3, and 10×BCSLT, we plot the ratio ΔSWVfast/ΔTCPfast and show only the multi-model mean. The
slopes produced by different perturbations show general agreement (Fig. 6d).
The larger uncertainty in the slopes produced by 2%Solar and 10×CFC-12
occurs because both the ΔTCPfast and ΔSWVfast
produced by different models are similar, and therefore the slope of the
linear regression is uncertain. Overall, we find that the fast response of
TTL temperature is a good predictor for the TLS ΔSWVfast across
a range of different climate mechanisms and across multiple models.
For the LMS ΔSWVfast, the ΔTCPfast does not show a
control as strong as that in the TLS (Fig. 6b–c) due to the fact that TTL
temperatures are only one factor that influences the LMS. In addition, the
regression between ΔSWVfast and ΔTCPfast across
all perturbations at each grid point in the pressure–latitude domain shows
that the slope (% K-1) follows the transport pattern of the Brewer–Dobson circulation (BDC)
(Fig. 4b). The slope is large in the tropical overworld stratosphere and
becomes weaker as one moves poleward and downward in the extratropics below
150 hPa. The value is lower in the LMS, again consistent with the fact that
water vapor in the LMS is controlled by several processes, not just TTL
cold-point temperature. Clearly, more work on this is warranted.
Historical changes in SWV
Given the importance of SWV change, we now ask whether our results can help
us understand historical variations in TLS ΔSWV over 1980–2010
(Fig. 7). To do this, we estimate historical values of ΔSWVslow and ΔSWVfast based on the PDRMIP results,
historical surface temperature change, and historical radiative forcing. For
the slow component (blue in Fig. 7a), we multiply 0.35 ppmv K-1, the
multi-model multi-perturbation mean sensitivity of the PDRMIP TLS ΔSWVslow to ΔTs, by the historical surface temperature change
over 1980–2010. For the fast component (orange in Fig. 7a), we multiply the
multi-model mean PDRMIP TLS ΔSWVfast/ ERF value for each
perturbation by the corresponding historical radiative forcing and then sum
it up. We also show the fast component of the historical ΔSWV
contributed by each historical forcing agent in Fig. 7b. This is similar to
the analysis done by Hodnebrog et al. (2019) in their
Fig. 6, where they used this method to estimate the historical water vapor
lifetime change based on the PDRMIP results.
(a) TLS (30∘ S–30∘ N, 70 hPa) SWV change over
1980–2010 estimated using PDRMIP results. Blue indicates the component
contributed by the slow response, while orange indicates the component
contributed by the fast response. (b) The fast component of the
PDRMIP-estimated 1980–2010 SWV change contributed by each historical forcing
agent. The solid circles are the multi-model mean. The error bars are
2.5–97.5 percentiles of the model samples; in (b) they are
shown for perturbations that are performed by more than three models.
The historical surface temperature change and radiative forcing data used in
this analysis are listed in Table 2. The historical radiative forcing we use
here is defined as the change in net downward radiative flux at the
tropopause after adjustments in the stratospheric temperatures, while the
surface and troposphere are held unperturbed (Myhre et al., 2013b). This is
different from the ERF we use in the PDRMIP calculations, which introduces
uncertainties in the fast component of the historical ΔSWV we
estimate based on PDRMIP.
Historical global average surface temperature change and radiative
forcing (RF) by greenhouse gases (GHGs) and halocarbons over 1980–2010. The
SWV change over 1980–2010 estimated using PDRMIP results is also listed,
including the total SWV change, the slow component, and the fast component.
For the fast component of SWV change contributed by each forcing agent,
multi-model mean results are listed. The uncertainties are 2.5–97.5
percentiles of the model samples.
a We used NOAA Merged Land Ocean Global Surface Temperature
Analysis V5 (Zhang et al., 2020) to compute the global surface temperature
change. We use values averaged over 2005–2015 minus those averaged over
1975–1985.
b,c,e,f,g We compute the RFs using the formulae listed in
Table 3 of Myhre et al. (1998). These formulae were also
used to compute RFs of CO2, CH4, and N2O in IPCC reports
(Myhre et al., 2013b).
b,c,g Concentrations of GHGs were used to compute RFs.
CO2 and CH4 are samples collected in glass flasks at Cold Bay,
Alaska, United States (CBA), from the ERSL GML website (Dlugokencky et al.,
2020a, b). N2O is from the combined nitrous oxide data from the NOAA/ESRL
Global Monitoring Division. For CO2, concentrations averaged over
2005–2015 and averaged over 1978–1985 are used. For CH4, concentrations
averaged over 2005–2015 and averaged over 1983–1985 are used. For N2O,
concentrations averaged over 2005–2015 and averaged over 1977–1985 are used.
e–f Concentrations of CFC-12 and CFC-11 were used to compute RFs.
We use CFC-12 and CFC-11 data from combined stations from the NOAA/ESRL
Global Monitoring Division. Concentrations averaged over 2005–2015 and
averaged over 1977–1985 are used.
d We use 0.4 W m-2, the BC RF between 1750 and 2011 reported in
IPCC AR5, minus 0.1 W m-2, the BC RF between 1750 and 1993 reported in the 1995
IPCC report (see Table 8.4 of Myhre et al., 2013b).
Figure 7a shows our estimate that climate change over 1980–2010 has
increased TLS SWV by 0.51±0.16 ppmv (Fig. 7a); 36 % is due to the
slow component, although this is probably an overestimate because our
sensitivity value estimated using the PDRMIP results is for the long term. We
find that the rest of the ΔSWV, 64 %, is due to the fast component,
mainly from black carbon. We have also calculated the SWV sensitivity and
SWV fast response over 35–45∘ N between 100 and 80 hPa to
estimate the historical 1980–2010 ΔSWV using the same method,
which is 0.65±0.20 ppmv. This value shows reasonable agreement with
the SWV increase measured by Hurst et al. (2011) of
0.71±0.26 ppmv over Boulder between 16 and 18 km over 1980–2010.
Dessler et al. (2014) and Hegglin et al. (2014) argue that there
is not a detectible trend over this period. Such a conclusion is not
inconsistent with ours because any actual trend estimate has to contend with
short-term interannual variability (i.e., like that from the
quasi-biennial oscillation and
Brewer–Dobson circulation variability), which can mask a small trend. Our
estimate of the trend is based on sensitivity estimated from 100-year run,
and therefore short-term interannual variability has a small impact. Given
a continuous, reliable, long-term SWV observation record in the future, one will
be able to better test the model-predicted values.
For the fast component of the estimated historical ΔSWV, radiative
forcing by BC plays the dominant role (Fig. 7b). Uncertainties exist in the
historical BC radiative forcing we use in this analysis, which is shown in
the IPCC AR5 (Myhre et al., 2013b). In addition, Allen et
al. (2019) pointed out that the radiative effect by BC in the PDRMIP is
different from that shown in models using observationally constrained
aerosol forcing, which may overestimate the heating in the upper troposphere–lower stratosphere region.
However, Allen et al. (2019) also noted that
uncertainties exist in their observationally constrained aerosol forcing.
The uncertainties in the impact of BC forcing on SWV clearly merit more
analysis in the future.
Conclusions
It is of great interest for the climate community to understand how SWV
changes when the climate changes since SWV plays an important role in the
Earth's radiative budget and stratospheric ozone chemistry
(Solomon et al., 1986, 2010;
Dvortsov and Solomon, 2001; Forster and Shine, 2002). In this study, we
investigate the response of stratospheric water vapor (SWV) to a range of
different climate forcing mechanisms using a multi-model and multiple-forcing-agent framework. We use output from nine CMIP5 models participating in
the PDRMIP. Each model performs a baseline and up to 10 climate perturbation
experiments, including 2×CO2, 3×CH4, 2%Solar, 10×BC,
5×SO4, 10×CFC-11, 10×CFC-12, 3×N2O, 5×O3, and 10×BCSLT (Table 1). Each perturbation is performed in two configurations, including fixed
SST simulations (at least 15 years) and fully coupled simulations (at least
100 years).
To better understand the SWV response (ΔSWV), we partition it into
two parts: the slow response (ΔSWVslow) and the fast response
(ΔSWVfast). The ΔSWVfast is the change in response
to a perturbation on short timescales before the surface temperature has
responded. ΔSWVslow occurs on longer timescales and is coupled
to the surface temperature change. Our results show that, for most
perturbations, ΔSWV in the tropical lower stratosphere (TLS) and in
the lowermost stratosphere (LMS) (200 hPa, 50–90∘ N
and 50–90∘ S) is dominated by ΔSWVslow
(Fig. 1).
Analysis of ΔSWVslow shows that a warming surface increases SWV
(Figs. S3–S5). Furthermore, the response of SWV to the surface temperature
change has a similar sensitivity across different climate perturbations in
both the overworld stratosphere and the lowermost stratosphere (Figs. 3 and
4a). Specifically, the multi-model and multi-perturbation mean slope is 0.35 ppmv K-1 in the TLS, 2.1 ppmv K-1 in the northern hemispheric (NH)
LMS, and 0.97 ppmv K-1 in the southern hemispheric (SH) LMS (Fig. 3).
ΔSWVslow in the LMS is more sensitive to ΔTs than the
tropical overworld, reflecting different transport pathways into the LMS
compared to the overworld (Dessler et al.,
1995; Holton et al., 1995; Plumb, 2002; Gettelman et al., 2011). The ΔSWVslow in the NH LMS is more sensitive than the SH LMS, consistent
with hemispheric asymmetries in the isentropic transport and convective
moistening reported by previous studies (Pan
et al., 1997, 2000; Dethof et al., 1999, 2000; Dessler and Sherwood, 2004;
Ploeger et al., 2013; Smith et al., 2017; Ueyama et al., 2018; Wang et al.,
2019).
The fast response of SWV from most perturbations is weak compared to the
slow response and therefore plays a smaller role in ΔSWV (Fig. 1).
In the TLS, for forcing agents that directly heat tropopause levels (Fig. 5), ΔSWVfast makes a larger contribution to ΔSWV. In
particular, when the climate is perturbed by 10×BC, the ΔSWVfast
dominates the ΔSWVslow and has a larger magnitude than any other
perturbed simulations. This occurs because black carbon absorbs shortwave
radiation in the atmosphere and directly heats the temperatures at
tropopause levels. Other forcing agents also heat the tropopause levels and
increase ΔSWVfast through absorption of shortwave radiation or
longwave radiation at the atmospheric window range (3×CH4, 5×O3,
10×BCSLT, 10×CFC-12, 10×CFC-11), but these are not as strong as 10×BC.
The TLS ΔSWVfast is controlled by the fast response of the cold-point temperature across different climate change mechanisms (Fig. 6), with
a slope of 0.52 ppmv K-1, which is consistent with the
Clausius–Clapeyron relationship evaluated near the tropical tropopause
(Randel and Park, 2019). Control of the cold-point temperature
fast response over ΔSWVfast is stronger in the tropical
overworld and becomes weaker at higher latitudes and altitudes below 150 hPa
in the LMS (Fig. 4b).
Data availability
The PDRMIP data are publicly freely available (Samset et al., 2016; Myhre et al., 2017); the data can be accessed at http://cicero.uio.no/en/PDRMIP (CICERO, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-13267-2020-supplement.
Author contributions
XW performed analyses and wrote the paper. AED provided
the conceptualization, guidance, and editing.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was also supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the National Science Foundation. We would also like to acknowledge the
PDRMIP modeling groups and helpful discussions with Andrew Gettelman and
William Randel.
Financial support
This research has been supported by NASA (grant nos. 80NSSC18K0134 and 80NSSC19K0757).
Review statement
This paper was edited by Mathias Palm and reviewed by two anonymous referees.
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