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Edited by John H. Seinfeld, California Institute of Technology, Pasadena, CA, and approved June 27, 2014 (received for review May 23, 2014)
Significance
The fact that water vapor is the
most dominant greenhouse gas underscores the need for an accurate
understanding of the changes
in its distribution over space and time.
Although satellite observations have revealed a moistening trend in the
upper troposphere,
it has been unclear whether the observed
moistening is a facet of natural variability or a direct result of human
activities.
Here, we use a set of coordinated model
experiments to confirm that the satellite-observed increase in
upper-tropospheric
water vapor over the last three decades is
primarily attributable to human activities. This attribution has
significant implications
for climate sciences because it
corroborates the presence of the largest positive feedback in the
climate system.
Abstract
Water vapor in the upper
troposphere strongly regulates the strength of water-vapor feedback,
which is the primary process
for amplifying the response of the climate
system to external radiative forcings. Monitoring changes in
upper-tropospheric
water vapor and scrutinizing the causes of
such changes are therefore of great importance for establishing the
credibility
of model projections of past and future
climates. Here, we use coupled ocean–atmosphere model simulations under
different
climate-forcing scenarios to investigate
satellite-observed changes in global-mean upper-tropospheric water
vapor. Our analysis
demonstrates that the upper-tropospheric
moistening observed over the period 1979–2005 cannot be explained by
natural causes
and results principally from an
anthropogenic warming of the climate. By attributing the observed
increase directly to human
activities, this study verifies the
presence of the largest known feedback mechanism for amplifying
anthropogenic climate
change.
Because water vapor is the principal greenhouse gas, variations in its concentration strongly influence the climate’s response
to both anthropogenic and natural forcings (1). Changes in the amount of water vapor in the upper troposphere play a particularly important role because the trapping of
outgoing terrestrial radiation is proportional to the logarithm of water-vapor concentration (1, 2), and climate models predict enhanced moistening in the upper troposphere compared with the boundary layer (3).
Although short-term fluctuations of upper-tropospheric water vapor are
consistent among reanalysis datasets, decadal variations
show substantial discrepancies even in sign (4, 5). Hence, long-term monitoring of upper-tropospheric water-vapor changes, and understanding causes responsible for such changes
are essential for enhancing confidence in the prediction of future climate change (4, 6).
Changes in upper-tropospheric water
vapor have been examined based on satellite-observed radiances of 6.7-μm
water-vapor channels
(3, 7, 8), which are closely related to the layer–mean relative humidity in the upper troposphere (9).
Decadal trends in upper-tropospheric relative humidity exhibits
distinct regional patterns associated with changes in the
atmospheric circulation, but the decadal
trends over larger domains are small due to opposing changes at regional
scales (8).
Analyzing the global-scale changes in 6.7-μm water-vapor radiances
reveals little change over the past three decades. However,
when the 6.7-μm radiances are examined
relative to microwave radiance emissions from oxygen, a distinct
radiative signature
of upper-tropospheric moistening can be
revealed (3).
Although the presence of a moistening trend has been detected in the satellite record, the cause of this moistening has not been determined. Thus, it remains unclear whether the observed moistening could result from natural fluctuations in the climate system, or whether human activities have significantly contributed to the trend. Because climate feedbacks can behave differently in response to natural climate variations compared with anthropogenic warming (10), fully validating the presence and strength of this feedback ultimately requires the detection of a change in upper-tropospheric water vapor that is directly attributable to human activities. Given the importance of upper-tropospheric water vapor, a direct verification of its feedback is critical to establishing the credibility of model projections of anthropogenic climate change.
A new set of coordinated climate change experiments have been conducted for the fifth phase of the Coupled Model Intercomparison Project (CMIP5; ref. 11). One of the climate change scenarios included in the CMIP5 is a historical experiment in which coupled ocean–atmosphere models are integrated with historical changes in forcing agents over the period 1850–2005. Climate variability produced from the historical experiment can then be analyzed in more detail in combination with two related experiments: one integrated with only anthropogenic forcings from well-mixed greenhouse gases, and the other integrated with only natural forcings from volcanoes and changes in solar activity. These two experiments can help identify the causes for recent changes in climate, provided the historical experiment with all forcings is capable of reproducing the observed trends. In this study, we use the historical climate change experiments from CMIP5 to demonstrate that the satellite-observed changes in upper-tropospheric water vapor are inconsistent with naturally forced variability and can only be explained by anthropogenic forcing.
Temporal Variations and Trends of Upper-Tropospheric Water Vapor
The National
Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting
satellites have been taking measurements
of the 6.7-μm water-vapor channel (channel
12) radiances from High-Resolution Infrared Radiation Sounder (HIRS)
version 2
(HIRS/2) since November 1978. Because
climate monitoring was not the primary purpose of the HIRS mission,
various attempts
have been made to correct for biases, and
to minimize intersatellite discrepancies, to make the HIRS record more
suitable
for climate study (8, 12). The bias-corrected, intercalibrated HIRS water-vapor channel radiance dataset (13)
is used to examine the decadal timescale variability of
upper-tropospheric water vapor. Unfortunately, the continuity of
the 6.7-μm water-vapor record ends in 2005
due to the shift of central wavelength from 6.7 μm (HIRS/2) to 6.5 μm
(HIRS/3),
which also coincides with the end of the
CMIP5 historical experiment. We therefore limit our observational
analysis to the
27-y period 1979–2005.
A time series of global, monthly mean brightness temperature anomalies of HIRS channel 12 (T12) is given in Fig. 1A
(red line). Brightness temperature anomalies are computed relative to
the mean seasonal cycle over the period 1980–2004.
For the period 1979–2005, the brightness
temperature anomalies vary within ±0.4 K, with only a very small
positive trend over
this period.
Fig. 1.
Time series of global-mean brightness temperature anomaly from satellite observations and CMIP5 historical experiment simulations for: (A) T12, (B) T2, and (C) T2 – T12. Error bars in light blue represent ±2 SD of the multimodel ensemble mean. The corresponding histograms of decadal trend from CMIP5 historical experiment simulations are given on Right with dashed lines in blue denoting a decadal trend of multimodel ensemble mean (T12: 0.05 ± 0.01 K decade−1, T2: 0.20 ± 0.10 K decade−1, T2 – T12: 0.15 ± 0.04 K decade−1). The bin size of histograms is 0.02 K decade−1. The decadal trend of multimodel ensemble mean for which model-simulated changes in T2 – T12 were computed under a constant water-vapor scenario is represented by a green dashed line (−0.08 ± 0.10 K decade−1). Vertical lines in red represent a decadal trend from satellite observations (T12: 0.04 ± 0.04 K decade−1, T2: 0.17 ± 0.06 K decade−1, T2 – T12: 0.13 ± 0.04 K decade−1). Horizontal error bars denote ±2 SE of the linear trend (±2 SE of the linear trend are computed using the method in ref. 20).
Time series of global-mean brightness temperature anomaly from satellite observations and CMIP5 historical experiment simulations for: (A) T12, (B) T2, and (C) T2 – T12. Error bars in light blue represent ±2 SD of the multimodel ensemble mean. The corresponding histograms of decadal trend from CMIP5 historical experiment simulations are given on Right with dashed lines in blue denoting a decadal trend of multimodel ensemble mean (T12: 0.05 ± 0.01 K decade−1, T2: 0.20 ± 0.10 K decade−1, T2 – T12: 0.15 ± 0.04 K decade−1). The bin size of histograms is 0.02 K decade−1. The decadal trend of multimodel ensemble mean for which model-simulated changes in T2 – T12 were computed under a constant water-vapor scenario is represented by a green dashed line (−0.08 ± 0.10 K decade−1). Vertical lines in red represent a decadal trend from satellite observations (T12: 0.04 ± 0.04 K decade−1, T2: 0.17 ± 0.06 K decade−1, T2 – T12: 0.13 ± 0.04 K decade−1). Horizontal error bars denote ±2 SE of the linear trend (±2 SE of the linear trend are computed using the method in ref. 20).
The time series of HIRS channel 12 brightness temperature anomalies simulated from the CMIP5 historical experiment of 20 coupled ocean–atmosphere models (Materials and Methods) is also presented in Fig. 1A. The multimodel ensemble mean is shown by the blue line, with vertical bars denoting the intermodel spread. Note that multimodel averaging dampens the amplitude of the monthly variability compared with that of the satellite observations. Nevertheless, the CMIP5 models capture the observed decadal variability despite substantial biases in climatological mean distribution (14). The observed linear trend in T12 (for more details about uncertainties in estimated linear trends, see SI Materials and Methods) is similar to that computed from the multimodel mean and lies near the center of the distribution of trends from the individual models (Fig. 1, Right). The small magnitude of the trend shown in both the satellite observations and the model ensemble confirms that global-mean upper-tropospheric relative humidity remained nearly constant on decadal timescales (3).
In addition to HIRS instruments, the NOAA operational polar-orbiting satellites are equipped with a microwave sounding unit (MSU) that provides weighted-average temperature information for deep atmospheric layers between the surface and the stratosphere, by way of four channels located in the 60-GHz oxygen absorption band. The remote sensing systems (RSS) reprocessed the brightness temperatures from the MSU and its follow-on, advanced MSU (AMSU), to construct a bias-corrected, intercalibrated MSU/AMSU dataset (15).
We use the MSU/AMSU channel 2 brightness temperatures (T2) in which the stratospheric contribution is removed using a combination of different viewing angles (16, 17). The time series of the observed T2 anomalies indicates sporadic warming and cooling associated with El Niño–La Niña events with a distinct warming trend over this period. Although the amplitude of this interannual variability is not captured in the multimodel mean (blue line in Fig. 1B) because El Niño–La Niña events do not occur simultaneously among the models, the multimodel ensemble mean of the historical experiment does show decadal-scale warming that is consistent with the MSU/AMSU observations. The observed trend in T2 (Fig. 1, Right) is slightly smaller than that predicted by the multimodel ensemble mean although it lies well within the distribution of individual model trends and is consistent with previous studies (18, 19).
The water-vapor channel radiances are influenced not only by variations in water-vapor concentration, which alter the atmospheric opacity, but also by atmospheric temperature variations, which alter the Plank emission. Although spatiotemporal variations in water vapor are significant, changes in atmospheric oxygen concentrations, which determine T2 emissions, are negligible (3). Thus, the difference T2 – T12 measures the divergence in emission levels between upper-tropospheric water vapor and oxygen. This divergence provides a direct measure of the extent of upper-tropospheric moistening; i.e., the increased concentration of water vapor elevates the emission level for T12 and offsets the warming evident in T2, which experiences no change in emission level (3).
Based on these properties, a time series of the brightness temperature difference, T2 –T12, is constructed to quantify the global-scale changes in upper-tropospheric water vapor for both satellite observations and CMIP5 historical simulations (Fig. 1C). El Niño–La Niña events dominate subdecadal-scale variations in the satellite observations but are not evident in the CMIP5 ensemble mean due to multimodel averaging. However, on decadal timescales both the satellite observations and the coupled ocean–atmosphere model simulations exhibit a distinct increase in global-mean upper-tropospheric water vapor. Moreover, the observed linear trend in T2 – T12 is very similar to that predicted by the multimodel mean and lies near the center of the distribution of individual model trends.
To demonstrate that the difference T2 – T12 is a measure of the concentration of upper-tropospheric water vapor, the model-simulated changes in T2 – T12 are computed by holding the concentration of water vapor constant over time. The resulting trend (green line in Fig. 1C) is near zero and lies well outside both the model simulations with changing water vapor and the observed trend. Both calculations use the same sets of temperature profiles, indicating that the increase in T2 –T12 is due to the increased concentration of water vapor in the upper troposphere and not due to changes in temperature.
Detection and Attribution of the Moistening Trend
To examine whether internally generated variability could produce the moistening trend, we analyze output of the corresponding
CMIP5 preindustrial control run (11),
which contains only unforced, internal climate variability. In contrast
to the historical experiment, none of the simulated
brightness temperature records (T12, T2,
or T2 – T12) show a significant trend over a 27-y period (Fig. 2).
The histograms presented on the right further demonstrate that decadal
trends with a magnitude equal to that observed do
not occur in any of the unforced
experiments. A constant water-vapor scenario results in near-zero trends
in T2 – T12. These
results suggest that the
upper-tropospheric moistening observed during the satellite era does not
result from internal variability
but from a combination of historical
changes in anthropogenic and natural forcings.
Fig. 2.
Time series of global-mean brightness temperature anomaly of (A) T12, (B) T2, and (C) T2 – T12, simulated from CMIP5 preindustrial control experiment for a 27-y period. Each line denotes an individual coupled ocean–atmosphere model. The corresponding histograms of decadal trend are given on Right with the bin size of 0.02 K decade−1. The decadal trend of multimodel ensemble mean for which model-simulated changes in T2 – T12 were computed under a constant water-vapor scenario is represented by a green dashed line (0.00 ± 0.01 K decade−1) with a horizontal error bar denoting ±2 SE of the linear trend.
Vertical lines in red represent decadal trends from satellite observations over the period 1979–2005.
Time series of global-mean brightness temperature anomaly of (A) T12, (B) T2, and (C) T2 – T12, simulated from CMIP5 preindustrial control experiment for a 27-y period. Each line denotes an individual coupled ocean–atmosphere model. The corresponding histograms of decadal trend are given on Right with the bin size of 0.02 K decade−1. The decadal trend of multimodel ensemble mean for which model-simulated changes in T2 – T12 were computed under a constant water-vapor scenario is represented by a green dashed line (0.00 ± 0.01 K decade−1) with a horizontal error bar denoting ±2 SE of the linear trend.
Vertical lines in red represent decadal trends from satellite observations over the period 1979–2005.
To examine different forcing
contributions, we assess the relative contribution of anthropogenic
greenhouse gases to historical
changes in the upper-tropospheric water
vapor by analyzing two additional CMIP5 experiments linked to the
historical experiment.
In these experiments, the coupled
ocean–atmosphere models are integrated with anthropogenic greenhouse
gases (i.e., historicalGHG),
and with natural forcing sources (i.e.,
historicalNat), respectively. For 12 out of 20 models in which output is
available
for all three experiments, the decadal
trends are computed for the five 30-y periods. Fig. 3 compares decadal trends for the multimodel ensemble mean with horizontal error bars denoting ±2 SE of the linear trend (±2
SE of the linear trend are computed using the method in ref. 20).
Fig. 3.
Decadal trends of multimodel ensemble mean brightness temperature simulated from CMIP5 historical experiment (red circles), HistoricalNat (blue triangles), and HistoricalGHG (green triangles) for five 30-y periods: (A) T12, (B) T2, and (C) T2 – T12. Error bars denote ±2 SE of the linear trend.
Decadal trends of multimodel ensemble mean brightness temperature simulated from CMIP5 historical experiment (red circles), HistoricalNat (blue triangles), and HistoricalGHG (green triangles) for five 30-y periods: (A) T12, (B) T2, and (C) T2 – T12. Error bars denote ±2 SE of the linear trend.
Decadal trends of the model-simulated T12 show both positive and negative values for the historicalNat experiment, but signs are predominantly positive for the historicalGHG experiment. Although the influences of changes in aerosols and land use cannot be ruled out, an increase in anthropogenic greenhouse gases seems to be responsible for the decadal trend over the satellite era, because trends from the historical and historicalNat experiments lie clearly outside each other’s range.
For decadal trends of T2, the range of estimated decadal trends is generally wider for historicalNat than historicalGHG (Fig. 3B), indicating that subdecadal variability could be more significant in the former. The increase of anthropogenic greenhouse gases consistently leads to a warming trend for all periods. Although the impact of natural forcing sources can negate greenhouse-gas-induced warming signals (e.g., for the period 1946–1975; refs. 21⇓–23), it is mostly weaker and more variable. Given these characteristics, the warming trend over the satellite era is primarily attributable to the increase of anthropogenic greenhouse gases.
For the T2 – T12 (Fig. 3C), decadal trends for historicalGHG and historicalNat fall within each other’s range for the first two periods, but become significantly different from each other in later periods. The magnitude of decadal trend due to natural forcings is generally small, whereas the contribution of anthropogenic greenhouse gases is always positive, and is amplified throughout the whole period. Comparisons with the historical experiment indicate that decadal trends for the historical experiment are affected by changes in natural forcing sources, as well as anthropogenic greenhouse gases. For example, a negative (thus drying) trend for the historical experiment over the period 1946–1975 is mainly induced by natural forcing sources, because increases in anthropogenic greenhouse gases induce a significantly positive (moistening) trend. Concerning the satellite-derived moistening trend in recent decades, the relations of trend and associated range among three experiments lead to the conclusion that an increase in anthropogenic greenhouse gases is the main cause of increased moistening in the upper troposphere.
Discussion and Conclusions
To illustrate the importance of the observed upper-tropospheric moistening in amplifying the climate sensitivity, radiative
kernels (24⇓–26) are used to quantify the strength of the water-vapor feedback from all levels with that obtained for the upper troposphere
alone (SI Materials and Methods). The histogram in Fig. 4
compares the distribution of model-simulated water-vapor feedback
during the historical scenario with and without historical
forcings. Simulations with
anthropogenically induced warming simulate large positive feedbacks from
water vapor and are distinctly
different from generated from natural
forcing alone (blue dashed line). To highlight the importance of the
upper troposphere,
the feedback calculations are repeated
using only water-vapor changes in the troposphere above 600 hPa from the
historical
simulation (green dashed line in Fig. 4).
Approximately 80% of the total water-vapor feedback results from water
vapor in the upper troposphere. Although the absolute
increase in water vapor is small at these
levels, the absorptivity scales with the fractional changes in water
vapor, which
are typically 2–3 times larger in the
upper troposphere compared with the surface (SI Materials and Methods). Note that the observational estimate for the period 2000–2010 (27) lies within the distribution of model simulations only when anthropogenic forcing is included, further indicating that the
observed changes in upper-tropospheric water vapor are a direct result of anthropogenic warming.
Fig. 4.
The histogram shows a distribution of the water-vapor feedback strength computed using a radiative kernel for two 10-y periods (i.e., 1979–1988 and 1989–1998) of the historical scenario with a red line denoting a multimodel mean (1.92 ± 0.99 W m−2 K−1). The bin size of the histogram is 0.2 W m−2 K−1. A blue dashed line indicates a multimodel mean of the water-vapor feedback strength for which water vapor in the troposphere would change under natural forcing alone (i.e., HistNat), and the case that the evolution of upper-tropospheric water vapor was not modified by HistNat is represented by a green dashed line (i.e., Hist UTWV-only). The multimodel mean values for the HistNat and Hist UTWV-only are 0.08 ± 0.99 W m−2 K−1 and 1.53 ± 0.87 W m−2 K−1, respectively. Horizontal error bars represent ±2 intermodel SD. A violet dashed line denotes the observational estimate of the water vapor feedback for the period 2000–2010 (∼1.2 W m−2 K−1) (27).
The histogram shows a distribution of the water-vapor feedback strength computed using a radiative kernel for two 10-y periods (i.e., 1979–1988 and 1989–1998) of the historical scenario with a red line denoting a multimodel mean (1.92 ± 0.99 W m−2 K−1). The bin size of the histogram is 0.2 W m−2 K−1. A blue dashed line indicates a multimodel mean of the water-vapor feedback strength for which water vapor in the troposphere would change under natural forcing alone (i.e., HistNat), and the case that the evolution of upper-tropospheric water vapor was not modified by HistNat is represented by a green dashed line (i.e., Hist UTWV-only). The multimodel mean values for the HistNat and Hist UTWV-only are 0.08 ± 0.99 W m−2 K−1 and 1.53 ± 0.87 W m−2 K−1, respectively. Horizontal error bars represent ±2 intermodel SD. A violet dashed line denotes the observational estimate of the water vapor feedback for the period 2000–2010 (∼1.2 W m−2 K−1) (27).
Bias-corrected, intercalibrated satellite observations produce a radiative signature, suggesting that moisture in the upper troposphere has increased over the past ∼30 y (3). When integrated with historical changes in forcing agents, coupled ocean–atmosphere models are found to produce decadal trends consistent with satellite observations. In contrast, coupled ocean–atmosphere models fail to capture observed trends in the preindustrial control experiment, suggesting that upper-tropospheric moistening over the satellite era is not an internally generated variability. Two additional model experiments, integrated with anthropogenic greenhouse gases and natural forcing sources separately, further indicate that the observed moistening trend is mainly induced by an increase in anthropogenic greenhouse gases. As a result, it is expected that the influence of a projected increase in anthropogenic greenhouse gases will amplify upper-tropospheric moistening, and is thus likely to amplify global warming via enhanced water-vapor feedback.
Materials and Methods
Decadal trends
of upper-tropospheric water vapor determined from the satellite
observations are compared with those simulated
from CMIP5 coupled ocean–atmosphere
climate models, to ascertain whether the satellite-determined
decadal-scale variations
are due to anthropogenic forcing agents.
In doing so, the historical experiment output from 20 climate models
(ACCESS1-0,
BNU-ESM, CCSM4, CNRM-CM5, GFDL-CM3,
GDFL-ESM2G, GFDL-ESM2M, GISS-E2-R, HadGEM2-ES, INMCM4, IPSL-CM5A-LR,
IPSL-CM5A-MR, MIROC-ESM-CHEM,
MIROC-ESM, MIROC5, MPI-ESM-LR, MPI-ESM-P,
MRI-CGCM3, NorESM1-M, and NorESM1-ME; see Table 1
for information about climate models) is contrasted with the
corresponding preindustrial control run results (i.e., piControl)
that represent an unforced climate
variability. The output of ocean–atmosphere coupling experiments is
analyzed, as suppressing
the ocean–atmosphere interactions could
inhibit the internally generated variability that might not be in phase
with externally
forced variability (28, 29).
Forcing agents included in the historical experiment are: well-mixed
greenhouse gases, tropospheric and stratospheric ozone,
land use, volcanoes, solar forcing,
sulfate, black carbon, organic carbon, dust, and sea salt, and their
detailed prescriptions
may vary depending on models. The CMIP5
includes two additional experiments designed to investigate the response
of the climate
system to changes in anthropogenic sources
(i.e., historicalGHG), and natural sources (historicalNat). Ref. 11
provides detailed information on the CMIP5 experiments. To avoid
uncertainties inherent to the inversion processes of satellite-observed
radiances, atmospheric profiles of
temperature and specific humidity produced from the CMIP5 experiments
are inserted into
a fast radiative transfer model (30) to compute synthetic brightness temperatures that would be observed by satellites for given atmospheric conditions.
Table 1.
A list of CMIP5 climate models used in this study
A list of CMIP5 climate models used in this study
Acknowledgments
We thank two anonymous reviewers
and the editor for their constructive and valuable comments, which led
to an improved version
of the manuscript. We acknowledge the
World Climate Research Programme’s Working Group on Coupled Modeling,
which is responsible
for CMIP, and we thank the climate
modelling groups (listed in Materials and Methods)
for producing and making available their model output. For CMIP, the US
Department of Energy's Program for Climate Model
Diagnosis and Intercomparison provides
coordinating support and led development of software infrastructure in
partnership
with the Global Organization for Earth
System Science Portals. This research was supported by grants from the
National Aeronautics
and Space Administration and the National
Oceanic and Atmospheric Administration Climate Program Office. B.J.S.
was supported
by the Korea Meteorological Administration
Research and Development Program under Grant CATER 2012–2061.
