ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-8031-2017Contribution of different processes to changes in tropical lower-stratospheric water vapor in chemistry–climate modelsSmalleyKevin M.https://orcid.org/0000-0002-0575-7286DesslerAndrew E.https://orcid.org/0000-0003-3939-4820BekkiSlimanehttps://orcid.org/0000-0002-5538-0800DeushiMakotoMarchandMarionMorgensternOlafhttps://orcid.org/0000-0002-9967-9740PlummerDavid A.https://orcid.org/0000-0001-8087-3976ShibataKiyotakaYamashitaYousukehttps://orcid.org/0000-0002-6813-4668ZengGuanghttps://orcid.org/0000-0002-9356-5021Department of Atmospheric Sciences,
Texas A&M, College Station, Texas, USALATMOS, Institut Pierre Simon Laplace (IPSL), Paris, FranceMeteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JapanNational Institute of Water and Atmospheric Research (NIWA), Wellington, New ZealandCanadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Montreal, CanadaSchool of Environmental Science and Engineering, Kochi University of Technology, Kami, JapanNational Institute for Environmental Studies (NIES), Tsukuba, Japannow at: Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, JapanAndrew Dessler (adessler@tamu.edu)4July201717138031804428October20168November201615May201729May2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/8031/2017/acp-17-8031-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/8031/2017/acp-17-8031-2017.pdf
Variations in tropical lower-stratospheric humidity influence both the
chemistry and climate of the atmosphere. We analyze tropical lower-stratospheric water vapor in 21st century simulations from 12
state-of-the-art chemistry–climate models (CCMs), using a linear regression
model to determine the factors driving the trends and variability. Within
CCMs, warming of the troposphere primarily drives the long-term trend in
stratospheric humidity. This is partially offset in most CCMs by an increase
in the strength of the Brewer–Dobson circulation, which tends to cool the
tropical tropopause layer (TTL). We also apply the regression model to
individual decades from the 21st century CCM runs and compare them to a
regression of a decade of observations. Many of the CCMs, but not all,
compare well with these observations, lending credibility to their
predictions. One notable deficiency is that most CCMs underestimate the
impact of the quasi-biennial oscillation on lower-stratospheric water vapor.
Our analysis provides a new and potentially superior way to evaluate model
trends in lower-stratospheric humidity.
Introduction
Stratospheric water vapor is well known to be a greenhouse gas
e.g.,. Because of this,
understanding the processes that control the humidity of air entering the
tropical lower stratosphere (hereafter [H2O]entry) has been a high
priority of the scientific community since first described
stratospheric circulation.
It is now well established that the fundamental control over
[H2O]entry comes from the cold temperatures found in the tropical
tropopause layer (TTL) , and that variability in these
temperatures translates into variability in [H2O]entry. The most
well-known example of this is the so-called “tape recorder”, in which the
seasonal cycle in TTL temperatures is imprinted on tropical stratospheric
water vapor .
Chemistry–climate models (CCMs) used in this analysis. The resolution is listed as (lat × long × number
of pressure levels). Thirty-one vertical levels indicates CCM data is given on isobaric levels,
while CCMs simulating data on > 31 levels are given on sigma (hybrid-pressure) levels.
Abbreviations are as follows: quasi-biennial oscillation (QBO); Center for Climate System Research/National Institute for Environmental Studies (CCSR/NIES);
Model for Interdisciplinary Research on Climate (MIROC); Canadian Middle Atmosphere Model (CMAM); Chemistry-Climate Model Initiative;
Centre National de Recherches Météorologiques (CNRM); Goddard Earth Observing System Chemistry-Climate Model
(GEOSCCM); Laboratorie de Meteorologie Dynamique Zoom-REPROBUS (LMDZrepro); Meteorological Research Institute
(MRI); National Institute of Water and Atmospheric Research (NIWA); United Kingdom Chemistry and Aerosols (UKCA); Whole Atmosphere Community Climate Model (WACCM); Chemistry-Climate Model Initiative (CCMI).
On interannual timescales, variability in [H2O]entry
originates from variability in the Brewer–Dobson circulation (BDC) and the
quasi-biennial oscillation (QBO; ; ; ; ; ; ;
;
; ; ).
suggest that the temperature of the troposphere
also exerts an influence on [H2O]entry based primarily
on an analysis of satellite measurements of [H2O]entry.
This is mainly caused by radiative heating of the TTL from increased
upwelling radiation from a warming troposphere . In addition to
this mechanism, demonstrated in two CCMs that a warming
climate also increases the amount of water directly injected into the
stratosphere via deep convection, providing another mechanism for
tropospheric temperature to affect [H2O]entry.
Putting these factors together, demonstrated that
observed [H2O]entry anomalies could be accurately reproduced with a
simple linear model:
[H2O]entry=β0+βΔTΔT+βBDCBDC+βQBOQBO+ϵ,
where ΔT is the temperature of the troposphere, BDC is the strength
of the Brewer–Dobson circulation, QBO represents the phase of the QBO, and
ϵ is the residual. analyzed the 21st century
trend in one chemistry–climate model (hereafter, CCM; similar to
general circulation models, but with a more realistic stratosphere and higher
vertical resolution in the TTL) and found that the regression model worked
well in reproducing the CCM's [H2O]entry trend over the
21st
century. They concluded that the increase in [H2O]entry was driven by
the increase in tropospheric temperatures, which was partially offset by a
strengthening BDC.
The regression method provides a novel way to examine the
regulation of [H2O]entry in CCMs and compare it to observations. The
purpose of this paper is to see whether this linear decomposition of
[H2O]entry variability holds in most CCMs and whether the same
factors dominate.
Bars show trended (light grey) and detrended (dark grey)
adjusted R2 values for annual-averaged data. The circles represent the
ensemble mean, with error bars indicating ± 1 standard deviation of
the CCM ensemble.
Models
We analyze model output from six CCMs participating in Phase 2 of the
Chemistry–Climate Model Validation Project (CCMVal-2; ) and output from six CCMs participating in Phase 1
of the Chemistry-Climate Model Initiative (CCMI-1; ).
Table lists the model specifics and documentation.
We use simulations from the REF-B2 scenario of CCMVal-2. In this scenario,
greenhouse gas concentrations during the 21st century come from the A1B
scenario, which lies in the middle of the Special Report on Emissions Scenarios (SRES; ).
Ozone-depleting substances come from the halogen emission scenario A1
. Specifics on CCMVal-2 can be found in and
. We use the refC2 scenario of the CCMI-1. In this
scenario, greenhouse gas concentrations come from the RCP6.0 scenario
and ozone-depleting substances come from the halogen
emission scenario A1 . CCMI-1 model specifics can be found in
. In order to maintain a consistent reference period
between models, our analysis covers 2000–2097, which we will hereafter refer
to as “the 21st century”.
For each model, we fit CCM [H2O]entry using the multivariate linear
regression (MLR) model described above. We use tropical average 80 hPa water
vapor volume mixing ratio as a proxy for [H2O]entry (all tropical
averages in this paper are averages over 30∘ N–30∘ S).
For our BDC index, we use 80 hPa diabatic heating rate (see
, for details). Within models, studies have shown that
the strength of the BDC increases throughout the 21st century, primarily
resulting from increasing greenhouse gases
e.g.,. Observations generally confirm that
tropical upwelling into the lower stratosphere has strengthened
. However, the BDC is not a directly
observable circulation, and different variables including trace gas
abundances, residual velocity, mean age of the air, and diabatic heating have
been used . Thus, depending
on the variable used, the strength of the connection between the BDC term and
[H2O]entry may change.
The tropospheric temperature index is the 500 hPa tropical average
temperature. For the few CCMI-1 simulations that only produce variables on
hybrid pressure levels (CMAM-CCMI, CCSR/NIES-MIROC3.2, and MRI-ESM1r1), we choose a
hybrid pressure level close to the 500 hPa pressure surface (See Table ). For the QBO index, we take the standardized anomaly of
equatorial 50 hPa zonal winds (anomalies in this paper are calculated by
subtracting the mean seasonal cycle). By examining 21st century 50 hPa
zonal winds (shown in the figures in the Supplement), we find that only 5 of the 12
models simulate a QBO (Table ). As a result, we do not expect
the QBO to significantly impact [H2O]entry in many of the models.
All of these choices are similar to those used by
. The MLR returns the coefficients for each
regressor in Eq. (), along with an uncertainty
for each coefficient. Unless otherwise noted, we use 95 % confidence
intervals in this paper. Autocorrelation in the residuals is accounted for in
the uncertainties following . Finally, we will illustrate
results with the MRI model; figures showing results derived from the other
models can be found in the Supplement.
Time series of annual-averaged anomalies of
[H2O]entry from the MRI (black), and its reconstruction
using a multivariate linear regression (brown). The red, green, and blue
lines are the ΔT, BDC, and QBO terms from the regression,
respectively.
21st century analysis
We first analyze the long-term trend in [H2O]entry over the
21st
century. To do this, we calculate annual average values of [H2O]entry
and perform an MLR against annual averages of the indices for BDC, QBO, and
ΔT. For consistency, all annual average time series have had the
2000–2010 mean subtracted out. Most models simulate [H2O]entry
increasing during the 21st century . However,
recent observational studies have concluded that no significant historical
trend in water vapor entering the lower stratosphere exists
.
Figure shows that the fits to most of the models
generate adjusted R2 values greater than 0.8. The NIWA-UKCA 21st century MLR
has the lowest adjusted R2, with a value of approximately 0.6. Overall,
this result confirms the result of that the regression
model does a good job reproducing the CCMs' [H2O]entry. Because
we have left long-term trends in the time series, we will refer to this as
the “trended analysis”.
Detrended 21st century
One concern with the trended analysis is that the [H2O]entry, BDC,
and ΔT time series are all dominated by long-term trends. In such a
case, an MLR may produce a high adjusted R2 even if there is no actual
relationship between the variables. To eliminate the influence of long-term
trends on adjusted R2, we detrend each variable using a Fourier transform
filter to remove long-term variability (> 10 years). We
then use the MLR on the detrended [H2O]entry and the detrended
indices. Detrending by removing the long-term linear trend yields similar
results.
Figure shows the adjusted R2 for the detrended
calculation. For most of the models, the adjusted R2 for the detrended MLR
is moderately smaller than that for the trended one. This confirms that the
long-term trends in the data tend to inflate the adjusted R2, at least somewhat. But we also confirm that the models' detrended [H2O]entry is
also well represented by the same linear model (Eq. ). Large differences do exist for some CCMs. For instance, the
CCSR/NIES trended century MLR captures approximately 90 % of the variance in
[H2O]entry, while the detrended 21st century MLR only explains about
40% of detrended variance; CNRM-CM5-3, NIWA-UKCA, and WACCM show
something similar.
Physical process effects
The coefficients from the trended and detrended calculations are listed in
Tables and , respectively. The
product of the regression coefficient and its index quantifies the impact of
the process on [H2O]entry. As an example, MRI
[H2O]entry increases by about 1.2 ppmv during the 21st century
(Fig. ). The regression shows that this is the result of
a large increase in [H2O]entry due to ΔT increases
(∼ 1.5 ppmv) that is offset by a strengthening BDC, which reduces [H2O]entry
by approximately 0.3 ppmv. The regression finds virtually no change in
[H2O]entry in response to the QBO.
Figure shows that [H2O]entry
increases as ΔT increases in all models and that the ΔT
regression coefficients are similar for both trended and detrended MLRs. The
coefficient for individual models ranges from 0.1 to 0.6 ppmv K-1, with
an average of 0.32 ppmv K-1 and a standard deviation of 0.15 ppmv K-1. It is worth pointing out that the models can get the right answer
for the wrong reason. For example, spurious diffusion of water vapor through
the tropopause has been shown to be an issue in models
e.g.,. This may impact the relationship
between [H2O]entry and tropospheric warming, thereby biasing our
results. However, was able to accurately simulate the
stratospheric trend in two CCMs using a diffusion-free trajectory model,
showing that, in some models at least, this is not an issue.
This figure also shows that the BDC coefficient is generally negative,
meaning that a strengthening BDC reduces [H2O]entry. This
relationship
arises from well established physics that a strengthening BDC should cool the
tropopause, reducing water vapor entering the stratosphere
e.g.,. This anticorrelation between BDC strength and TTL
temperatures has been observed e.g.,, and this has
been identified as the cause of the stratospheric tape recorder
. This anticorrelation has also been identified as the cause of
the large drop in [H2O]entry around 2000
e.g.,. The coefficient for individual models
ranges from -11.8 to +4.3 ppmv (K/day)-1, with an average of -3.55 ppmv
(K/day)-1 and a standard deviation of 4.45 ppmv (K/day)-1. Two
models (CNRM-CM5-3 and NIWA-UKCA) yield positive BDC coefficients, indicating
potential problems with these models. And the magnitude of the MRI BDC
coefficients are about 2 times larger than those produced by MRI-ESM1r1.
This could explain why the detrended adjusted R2 value for MRI-ESM1r1 is
so much smaller than that of MRI.
Circles show detrended (light grey) and trended (dark grey)
coefficients for each model; error bars correspond to 95th percentile
confidence interval bounding each regression coefficient. An asterisk
indicates models simulating a QBO. The ensemble mean corresponds to the
average of all model coefficients. The ensemble mean coefficients are also
represented by a circle, with associated error bars corresponding to ± 1
standard deviation of the ensemble. The units of
βΔt, βBDC, and βQBO
are ppmv K-1, ppmv (K/day)-1, and ppmv, respectively.
Figure shows that all QBO regression
coefficients are small, generally within ±0.04 ppmv, with even the
sign of the effect in doubt. Interestingly, one of the CCMs not simulating a
QBO, CMAM-CCMI, produces the largest QBO regression coefficients of 0.082 ± 0.04 and 0.077±0.04 ppmv for the trended and detrended
calculations, respectively. Among CCMs that do simulate a QBO, the ensemble
average QBO regression coefficient does not differ much from the same
quantity (approximately 0 ppmv) for the other models. We will discuss this
further in the next section.
As can be seen in the plots for individual models in the Supplement, the
variability in [H2O]entry in a few models comes almost entirely from
the variability in BDC, with almost no variability in the ΔT time
series (other than the long-term trend). That means that the ΔT
term, which is almost a pure trend, will fit whatever is left after matching
the interannual variability and trend of the QBO time series.
We have also calculated the long-term linear trend of [H2O]entry for
each model as well as the trend in each component of [H2O]entry, as
determined by the multivariate fit (e.g., the trend in the components plotted
in Fig. 2). We find that ΔT makes the largest contribution to the
trend in [H2O]entry, with a smaller negative effect from the a
strengthening BDC on [H2O]entry, and a trend of close to zero for the
QBO (Fig. 4).
To provide additional information about the relative contribution from the
individual terms in Eq. (1), we have also calculated the regression coefficient
using standardized variables. To do this, we take each regression coefficient
and multiply it by the standard deviation of the associated regressor index.
The values are listed in Tables and
and they confirm that, in the trended calculations, ΔT is the
dominant cause of the trend in [H2O]entry. The BDC acts to reduce the
trend, but its overall impact is much smaller than ΔT.
In the detrended calculations, the standardized ΔT regression
coefficients are smaller than those from the trended calculations, while the
magnitude of the BDC coefficients remains relatively constant. This results
in the BDC being more important than ΔT for short-term variability.
In all of our calculations, we find that the QBO has little impact on
[H2O]entry.
Coefficients (βs) from regressions of trended [H2O]entry
time series, and the change in [H2O]entry resulting from each process (βSTD()), where STD() is the standard deviation of each trended process.
The units of ΔT, BDC, and QBO are ppmv K-1, ppmv (K/day)-1, and ppmv,
while the units of βΔTSTD(ΔT), βBDCSTD(BDC), and βQBOSTD(QBO) are all ppmv.The uncertainty is the 95 % confidence
interval.
Coefficients (βs) from regressions of detrended [H2O]entry time series,
and the change in [H2O]entry resulting from each process (βSTD()), where STD()
is the standard deviation of each detrended process.
The units of ΔT, BDC, and QBO are ppmv K-1, ppmv (K/day)-1, and ppmv, while the units
of βΔTSTD(ΔT), βBDCSTD(BDC), and βQBOSTD(QBO) are all ppmv.The uncertainty is the 95 % confidence
interval.
Decadal analysis
Ideally, we would compare the results of the last section to observations.
Unfortunately, we do not have 100 years of observations to test the models
against. Instead, we will compare regressions of 10-year segments from the
CCMs to regressions of 10 years of observations. This will help us evaluate
how good the models are and provide us with an indication of how
representative a single decade is.
To do this, we split the 21st century of each CCM run into 10 decades
(2000–2010, 2010–2020, 2020–2030, 2040–2050, etc.) and fit each individual
decade using the regression model (Eq. ). The
regression calculation used on each 10-year segment is identical to the
century analysis, except monthly averaged anomalies of all quantities are
used instead of annual mean anomalies. Following , decadal
regression terms are lagged in order to maximize MLR fit: we lag ΔT
by 3 months, the BDC by 1 month, and the QBO by 3 months. These lags reflect
the time between changes in each index and the impact on [H2O]entry.
Figure shows the median ± 1 standard deviation
of the 10 decadal adjusted R2 values generated by each CCM. The ensemble
average is 0.61±0.25, with some spread among the models. Also plotted
are the adjusted R2 values from two regressions of the tropical average Aura
Microwave Limb Sounder (MLS) 82 hPa water vapor mixing ratio observations
from 2004–2014 . One regression uses Modern-Era
Retrospective Analysis for Research and Applications reanalysis (MERRA)
and the other uses European Centre for Medium-Range Weather
Forecasts interim reanalysis (ERAI) for the ΔT and BDC
indices; the QBO index in both regressions are from observations, as
calculated in .
Trends in [H2O]entry (white) resulting from ΔT
(yellow), BDC (red), and QBO (blue) predictor time series assuming the other
predictors are held constant. Error bars represent 95 % uncertainty. For
many models, the contribution of the QBO is too small to be seen.
Circles represent the median of the adjusted R2 value of the
decadal fits. Errors correspond to the ± 1 standard deviation of the
adjusted R2 values. The CCM ensemble average is also plotted, along with
error bars corresponding to ± 1 standard deviation of the ensemble set of
decadal adjusted R2 values. The lines are adjusted R2 values from
observations combined with reanalysis (ERAI (dotted) and MERRA (dashed)) from
.
Many of the models have a range of adjusted R2 values that overlap with
the observational regression. However, the models producing the smallest
decadal adjusted R2 values, CCSR/NIES, CNRM-CM5-3, and NIWA-UKCA, are also
the models that produced the poorest fits to long-term detrended
[H2O]entry. This provides some evidence that analysis of just a
decade of [H2O]entry can provide insight into the long-term behavior
of that quantity.
Figure shows the median and 1 standard
deviation of each coefficient (values are listed in Table ), along with the coefficients from the regression of the
MLS data (taken from Table 1 of ). We find that the CCMs
agree unanimously that increases in ΔT are associated with increased
[H2O]entry, though the CCM ensemble tends to underestimate the
observational estimate. The only models that do not fall within both
observational ranges are CCSR/NIES, CMAM-CCMI, and CNRM-CM5-3.
In addition, the spread between the different decades for a single model
tends to be small. The coefficient for individual models ranges from 0.01 to
0.4 ppmv K-1, with an average of 0.15 ppmv K-1 and a standard
deviation of 0.11 ppmv K-1. This provides additional confidence that the
comparison between the CCMs and 1 decade of observations is meaningful.
Figure shows that there exists significant spread
in the CCMs' decadal BDC regression coefficients. The coefficient for
individual models ranges from -8.4 to +2.9 ppmv (K/day)-1, with an
average of -3.55 ppmv (K/day)-1 and a standard deviation of 3.58 ppmv
(K/day)-1. On all timescales, we expect a strengthening BDC to cool
the TTL and reduce [H2O]entry, so the coefficient should be negative.
We see that the median is indeed negative for all CCMs except for the
CNRM-CM5-3 and NIWA-UKCA (these models also generated positive BDC
coefficients for the century analysis).
When comparing with observations, we find that the model ensemble does well. The
CCSR/NIES, CCSR/NIES-MIROC-3.2, CMAM, CMAM-CCMI, LMDZrepro, MRI-ESM1r1, and
WACCM decadal BDC regression coefficients fall within 95 % confidence of
MERRA, and the CCSR/NIES-MIROC-3.2, LMDZrepro, and WACCM fall within 95 %
confidence interval of ERAI. As with the ΔT coefficient, the spread
between the different decades for a single model tends to be small.
Median coefficients from the decadal regressions of [H2O]entry monthly
anomalies, and the change in [H2O]entry resulting from each process (βSTD()), where STD() is the standard deviation of each decadal process.
The units of ΔT, BDC, and QBO are ppmv K-1, ppmv (K/day)-1, and ppmv, while
the units of βΔTSTD(ΔT), βBDCSTD(BDC), and βQBOSTD(QBO) are all
ppmv.The uncertainty represents the variability (1 standard deviation) in the set of coefficients
produced by each CCM. For observations, the error bars represent 95 %
confidence.
Circles represent the median decadal regression coefficient from
each CCM, and error bars correspond to ± 1 standard deviation. An
asterisk indicates that the model simulates a QBO. The ensemble mean
corresponds to an average of all model coefficients. The ensemble mean
coefficients are also represented by a circle, with associated error bars
correspond to ± 1 standard deviation of the ensemble set of
coefficients. Estimates from observations combined with reanalysis
are shown, along with 95th percentile confidence interval.
The units of βΔt, βBDC, and βQBO
are ppmv K-1, ppmv/(K/day)-1, and ppmv, respectively.
(a) Scatter plots of trended ΔT regression
coefficients (ppmv K-1) vs. median decadal ΔT regression
coefficients (ppmv K-1) from each CCM. (b) Same as (a), but for
BDC coefficients. (c) Same as (a) and (b), but for QBO
coefficient. Black lines in all plots correspond to a best-fit line between
the trended and decadal coefficients, and the observational coefficients ERAI
(square) and MERRA (diamond) are fitted to each line (from
).
Figure shows that, for all CCMs, the ensemble
average decadal QBO coefficient is approximately 0 ppmv. For those CCMs that
do simulate a QBO, the ensemble average coefficient is 0.02±0.03 ppmv.
This is significantly smaller than the response to the QBO in the
observations. Only CCSR/NIES-MIROC3.2 and CMAM-CCMI decadal regressions
produce QBO coefficients approaching those from both observational
regressions. Again, CMAM-CCMI does not simulate a QBO, and it is not clear to
us why the model does so well in this aspect of our analysis.
Previous studies found that the QBO significantly influences TTL temperatures
and subsequently [H2O]entry, so the
lack of response in the model ensemble appears to be a problem in the models.
Previous studies have investigated this issue, finding that a higher vertical
resolution within the stratosphere can help resolve the QBO's impact on the
lower stratosphere . Clearly, this needs to be
investigated further.
Similar to both the trended and detrended regression analysis, we calculated
the regression coefficients using standardized variables of the decadal
analysis, and the values are listed in Table 4. Within most models, we see
that the BDC, on decadal timescales, has the largest impact on
[H2O]entry, with ΔT having a smaller impact.
Century and decadal regression coefficient comparison
One interesting question is whether or not the regression coefficients from the
decadal analyses are related to regression coefficients from century
regressions. To answer this, Fig. shows the coefficients
from the trended century regressions of each CCM plotted against the median
of the decadal regressions from the same CCM. Also shown is a linear
least-squares fit to the points. For the ΔT coefficient, the best-fit line is
β(ΔT,century)=1.21±0.44β(ΔT,decade)+0.13±0.08.
All uncertainties are 95 % confidence intervals. Thus, the ΔT
coefficients from the trended MLRs are slightly larger than those from the
decadal MLRs. Using values of β(ΔT,decade) from MLS
observations and this fit, we predict β(ΔT,century) of 0.50±0.06 and 0.55±0.08 ppmv K-1 for MERRA and ERAI
regressions, respectively.
For the BDC coefficient, the best-fit line is
β(BDC,century)=1.16±0.32β(BDC,decade)+0.56±1.56.
The BDC coefficients from the trended MLRs also have a slightly larger
magnitude than those from the decadal MLRs. By fitting the observed values of
β(BDC,decade) through Eq. (), we predict
β(BDC,century) values of β(BDC,century) of -3.45±1.09
and -2.34±1.09 ppmv (K/day)-1 for MERRA and ERAI regressions,
respectively.
For the QBO coefficient, the best-fit line is
β(QBO,century)=0.75±0.40β(QBO,decade)+0.004±0.01.
The QBO coefficients from the trended MLRs are slightly smaller than those
from the decadal MLRs. Again, using Eq. (), we predict
β(QBO,century) values of 0.09±0.03 and 0.09±0.02 ppmv
for MERRA and ERAI regressions, respectively.
Conclusions
Climate models predict that tropical lower-stratospheric humidity
([H2O]entry) will increase as the climate warms, with important
implications for the chemistry and climate of the atmosphere. We demonstrate
in this paper that the regression used by can be
used to quantify the physical processes underlying these model trends and
variability in an ensemble of CCMs. Our method is based on regressing CCM
[H2O]entry time series against three processes that have been shown
to be important to [H2O]entry: tropospheric temperature
(ΔT), the strength of the Brewer–Dobson circulation (BDC), and the
phase of the QBO. Our approach provides insight into model processes not
available by simply comparing [H2O]entry to TTL temperatures.
We do this on two separate timescales: (1) the 21st century and (2) on
decadal timescales. Considering all of our analyses, we find that long-term
increase in [H2O]entry, in the CCMs, is primarily driven by warming
of the troposphere. This is partially offset in most CCMs by an increase in
the strength of the Brewer–Dobson circulation, which tends to cool the
tropical tropopause layer (TTL) . For
shorter-term internal variability, we find variability in the Brewer–Dobson
circulation is of greater importance to the variability of
[H2O]entry, consistent with and . The
models show little impact from the QBO.
The coefficients from regressions of individual decades in the CCMs can be
compared to coefficients from regressions of observations covering a decade.
Overall, the CCM ensemble reproduces [H2O]entry observations well,
except for the fact that the CCMs simulate little response to the QBO, in
disagreement with the observations
;
this appears to be a deficiency in the models.
That said, the good agreement of the ensemble average hides some spread among
the models, particularly in the response to the BDC. Of particular note, the
CNRM-CM5-3 and NIWA-UKCA regressions generate positive BDC regression
coefficients, contrary to the other models and contrary to our expectations.
Our overall conclusions are encouraging – the models appear to respond to
the factors that control [H2O]entry in realistic ways, providing some
confidence in their simulations of [H2O]entry. Nevertheless, our work
has pointed out issues that should be resolved. Some models have clear
problems, e.g., the models that predict [H2O]entry will increase with
a strengthening BDC. In addition, nearly the entire ensemble does not
reproduce the observed variations of [H2O]entry with the phase of the
QBO. This analysis should help the modeling groups refine their models'
simulations of the 21st century.
Both the CCMVal-2 (last accessed on 5 May 2017 from URL:
http://browse.ceda.ac.uk/browse/badc/ccmval/data/CCMVal-2) and CCMI-1
(http://catalogue.ceda.ac.uk/uuid/9cc6b94df0f4469d8066d69b5df879d5; Hegglin and Lamarque, 2015) data used in this study can be obtained through
the British Atmospheric Data Centre (BADC) archive (BADC, 2017).
KS and AD performed this analysis and wrote most of this manuscript.
The other authors contributed information pertaining to their individual models and helped revise this paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by NASA grant NNX14AF15G to Texas A&M University.
We acknowledge the British Atmospheric Data Centre (BADC) for collecting and
archiving the CCMVal and CCMI model output. We would like to thank the WACCM
group at NCAR and the CNRM-CM5-3 group for model development and making their
simulations available to us. Additionally, we would like to thank those
involved in GEOSCCM model development, the NASA MAP program, and the
high-performance computing resources provided by the NASA Center for Climate
Simulation (NCCS). Olaf Morgenstern acknowledges funding by the New Zealand Royal Society
Marsden Fund (grant no. 12-NIW-006). Olaf Morgenstern and Guang Zeng wish to acknowledge the
contribution of NeSI high-performance computing facilities to the results of
this research. Olaf Morgenstern and Guang Zeng were also supported by the NZ Government's Strategic
Science Investment Fund (SSIF) through the NIWA programme CACV. New Zealand's national
facilities are provided by the NZ eScience Infrastructure and funded jointly
by NeSI's collaborator institutions and through the Ministry of Business,
Innovation & Employment's Research Infrastructure programme
(https://www.nesi.org.nz). Hideharu Akiyoshi acknowledges the Environment Research and
Technology Development Fund, Ministry of Environment, Japan (2-1303), and
NEC-SX9/A(ECO) computers at CGER, NIES. The LMDZ-REPRO contribution was
supported by the European Project StratoClim (7th Framework Programme, grant
agreement 603557) and the SOLSPEC grant from the Centre d'Etude
Spatiale (CNES).
Edited by: Paul Young
Reviewed by: two anonymous referees
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