A modeling study of effective radiative forcing and climate response due to increased methane concentration Available online at www sciencedirect com + MODEL ScienceDirect Advances in Climate Change R[.]
Trang 1A modeling study of effective radiative forcing and climate response due to
increased methane concentration
XIE Binga,b, ZHANG Huaa,b,* , YANG Dong-Dongc, WANG Zhi-Lid a
Laboratory for Climate Studies of China Meteorological Administration, National Climate Center, China Meteorological Administration, Beijing 100081, China
b
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing
210044, China
c College of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
d State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081,
China Received 27 July 2016; accepted 8 December 2016
Abstract
An atmospheric general circulation model BCC_AGCM2.0 and observation data from ARIS were used to calculate the effective radiative forcing (ERF) due to increased methane concentration since pre-industrial times and its impacts on climate The ERF of methane from 1750 to
2011 was 0.46 W m2by taking it as a well-mixed greenhouse gas, and the inhomogeneity of methane increased its ERF by about 0.02 W m2 The change of methane concentration since pre-industrial led to an increase of 0.31C in global mean surface air temperature and 0.02 mm d1
in global mean precipitation The warming was prominent over the middle and high latitudes of the Northern Hemisphere (with a maximum increase exceeding 1.4C) The precipitation notably increased (maximum increase of 1.8 mm d1) over the ocean between 10N and 20N and significantly decreased (maximum decrease>e0.6 mm d1) between 10S and 10N These changes caused a northward movement of pre-cipitation cell in the Intertropical Convergence Zone (ITCZ) Cloud cover significantly increased (by approximately 4%) in the high latitudes in both hemispheres, and sharply decreased (by approximately 3%) in tropical areas
Keywords: Methane; Effective radiative forcing; Climate change
1 Introduction
Global surface air temperatures have increased due to
in-creases in emissions of anthropogenic greenhouse gases
Zhang et al., 2014b) Methane (CH4), with a relatively short lifetime (about 12 years), is the most important anthropogenic
1803 109(IPCC, 2013) CH
Then, after a decade of stabilization or slightly decreasing
well-* Corresponding author Laboratory for Climate Studies of China
Meteoro-logical Administration, National Climate Center, China MeteoroMeteoro-logical
Administration, Beijing 100081, China.
E-mail address: huazhang@cma.gov.cn (ZHANG H.).
Peer review under responsibility of National Climate Center (China
Meteorological Administration).
Production and Hosting by Elsevier on behalf of KeAi
ScienceDirect
Advances in Climate Change Research xx (2016) 1 e6
www.keaipublishing.com/en/journals/accr/
http://dx.doi.org/10.1016/j.accre.2016.12.001
1674-9278/Copyright © 2017, National Climate Center (China Meteorological Administration) Production and hosting by Elsevier B.V on behalf of KeAi This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).
Trang 2defined increase again in 2007 (Dlugokencky et al., 2003;
Rigby et al., 2008), measured using ground-based
Dlugokencky et al., 2009) and aircraft profiles (Wecht et al.,
2012; Worden et al., 2012) The IPCC Fifth Assessment
Myhre et al (1998) CH4is well mixed in the atmosphere, but
its concentrations vary with latitude and altitude, contributing
In this study, we estimated the effective radiative forcing
(ERF) and climate response due to changes in atmospheric
methane concentration from 1750 to 2011 using the general
circulation model BCC_AGCM2.0 from the National Climate
Center of China
2 Descriptions of model and method
2.1 Model
We used the general circulation model BCC_AGCM2.0
developed by the National Climate Center of China The
a rigid lid at 2.9 hPa This model was based on the Community
Atmosphere Model Version 3 (CAM3.0) from the National
Center for Atmospheric Research, U.S BCC_AGCM2.0
BCC_AGCM2.0 uses the radiation scheme of BCC_RAD
(Beijing Climate Center Radiative Transfer Model), developed
byZhang et al (2003, 2006a, 2006b), and the cloud overlap
scheme of the Monte Carlo independent column
accu-racy of the sub-grid cloud structure and its radiative transfer
process Further details on BCC_AGCM2.0 can be found in
Wu et al (2010) The model has been used to study the RFs
and the subsequent effects on climate due to aerosols (e.g.,
Zhang et al., 2012; Wang et al., 2013a, 2013b, 2014, 2015;
Zhao et al., 2015) and tropospheric ozone (Xie et al., 2016)
2.2 Satellite data
launched by the National Aeronautic and Space
Administra-tion (NASA) in May 2002 An overview of the AIRS
channels, which cover from 649 to 1136, 1217 to 1613, and
AIRS dataset used in this study is on a global spatial
1000 hPa to 1 hPa (divided into 24 levels) More information
about characterization and validation of methane products
et al (2014c)
2.3 Experimental design Our aim was to calculate the ERF and climate response due
simulations (EXP1, EXP2, EXP3, EXP4, and EXP5) were conducted EXP1, EXP2 and EXP3 were used to calculate the
et al., 2008) The same model settings were used in these
(Table 1 shows the details) The differences between EXP1 and EXP2 (EXP2 minus EXP1) were regarded as the ERF of
(2005)reported that, after a period of adjustment (generally 5 years for the model with prescribed SST and 30 years for the model with a coupled slab ocean model), the global mean surface air temperature reached equilibrium Therefore, the results from the last 10 years of the 15-year simulations of EXP1, EXP2 and EXP3 were used to calculate the ERF, as follows:
ERF¼ DFEXP2 or EXP3 DFEXP1 ð1Þ
incoming and outgoing shortwave and longwave radiative flux) at the top of the atmosphere (TOA)
EXP4 and EXP5 were used to calculate the climate
ra-tios in EXP4 and EXP5 as in EXP1 and EXP2, respectively
forcing, a slab ocean model was coupled with BCC_AGCM2.0
in the simulations instead of using the fixed SST The two simulations were run for 70 years, and we used the results from the last 40 years to discuss the climate response due to
3 Simulation results and analysis 3.1 ERF
Human activities have increased anthropogenic emissions
atmospheric concentration and, subsequently, changes in its
Table 1 Experimental design.
Number CH 4 data Sea temperature Running time EXP1 WMGHG 1750 a Prescribed SST 15 years EXP2 WMGHG 2011b Prescribed SST 15 years EXP3 AIRS 2011c Prescribed SST 15 years EXP4 WMGHG 1750 Slab ocean model 70 years EXP5 WMGHG 2011 Slab ocean model 70 years
a
CH 4 concentration in 1750, as well mixed greenhouse gas, from IPCC AR5.
b
CH 4 concentration in 2011, as well mixed greenhouse gas, from IPCC AR5.
c
CH 4 concentration in 2011, observed by AIRS.
Trang 3ERF.Fig 1shows the simulated ERF (the difference between
greenhouse gas (WMGHG) A well-defined positive ERF was
in western Siberia, southern Africa, Greenland, and most parts
over southern Africa The negative ERF in those areas might
be explained by an increase in low cloud cover The simulated
concentrations vary with latitude and sharply decrease above
the tropopause In lower troposphere, the concentration of
methane was mainly in zonal division, and asymmetry on the
Northern and Southern Hemispheres The volume mixing ratio
in-crease of altitude In the stratosphere, the volume mixing ratio
was symmetrically distributed in both hemispheres, and it got
less with the increasing latitude These spatial variations of
3.2 Surface air temperature and cloud cover
surface The difference between EXP5 and EXP4 showed that
air temperature increased over the globe except for the small decreases in several high-latitude areas in both hemispheres Warming over the middle latitudes of the Northern Hemi-sphere was prominent, with the maximum temperature
response of the surface net radiation flux (SNRF) due to the
consistent with that of surface temperature over the ocean There were significant increases in SNRF over the high lati-tudes in both hemispheres For example, the SNRF over the
the surface air temperature also increased significantly Changes in cloud cover and heat transportation can also affect surface air temperature Although the SNRF showed a well-defined decrease over the Indian Ocean, South Pacific, and the high latitudes of the Southern Hemisphere, the surface air temperature in the same regions did not change accordingly
Fig 3c and d shows the changes in low-level (below
680 hPa) and high-level (above 440 hPa) cloud cover Changes
in cloud cover directly affect SNRF, thereby influencing sur-face air temperature Increases in low-level cloud result in decreases in SNRF and a cooling effect at the surface, whereas increases in high-level cloud cause increases in surface air
clearly negative in the western and southern regions of South America, and low-level cloud cover in these areas increased by
temperature due to the scattering effect of low-level clouds to solar radiation The increase in temperature observed over the eastern Japan Sea and Mediterranean regions might be due to
Fig 3e and f shows the zonally averaged distributions of the changes in cloud cover and relative humidity There is a high level of correlation between the two variables The relative humidity showed significant increases in most of the
lower troposphere in the Southern Hemisphere, in the higher troposphere in tropical areas, and in most of the troposphere
in these regions These increases led to decreases in the SNRF (Fig 3b) In contrast, the relative humidity and cloud cover
near the equator and in most of stratosphere, resulting in
Fig 1 Distribution of the effective radiative forcing (ERF) of well mixed
atmospheric methane from 1750 to 2011 (units: W m2) Shaded area
repre-sents the value at 0.05 significance level.
Fig 2 Zonal distribution of the volume mixing ratio of CH 4 in 2011 ( 10 6 ),
observed by AIRS.
Trang 43.3 Precipitation and surface water flux
effect in the atmosphere and at the surface due to positive ERF
distributions of the changes in SWF and SNRF were similar
(Figs 2b and 3b) The SWF dramatically increased (by
the northern Pacific, western Atlantic, and equatorial Pacific
In contrast, the SWF showed well-defined decreases due to the
decreased SNRF in most areas In particular, the SWF
America and central Africa
Fig 4a shows the changes in precipitation due to CH4, which were notable in the Intertropical Convergence Zone
corre-lation between changes in precipitation over the tropics in each hemisphere, with precipitation increased in the Northern Hemisphere and decreased in the Southern Hemisphere
C
o
Wm-2
(c)
%
(d)
%
(e)
(f)
Fig 3 Climate responses due to changes in atmospheric CH 4 concentration since pre-industrial times Distribution of (a) surface air temperature, (b) surface net radiation flux, (c) low cloud, and (d) high cloud Zonal average distribution of (e) cloud and (f) relative humidity Shaded area represents the values at 0.05 significance level.
Trang 54 Conclusions
The ERF and climate responses due to the change in
to 2011 were investigated using the atmospheric general
volume mixture ratios from IPCC AR5 The global mean ERF
precipitation, respectively Warming was significant in the
middle and high latitudes, especially in the Northern
global distribution of change in precipitation was in line with
that of changes in cloud cover, especially near the equator The
Hemisphere and sharply decreased (maximum decrease
led the precipitation cell in ITCZ to move northward In the
most of high latitudes in both hemispheres, cloud cover was
significantly increased (by approximately 4%) and decreased
(by approximately 3%) in tropical areas
Acknowledgments
This work was supported by the National Natural Science
Foundation of China (41575002, 91644211)
References
Aumann, H., Chahine, M.T., Gautier, C., et al., 2003 AIRS/AMSU/HSB on
the Aqua mission: design, science objectives, data products, and
process-ing systems IEEE Trans Geosci Remote Sens 41, 253e264
Cunnold, D.M., Steele, L.P., Fraser, P.J., et al., 2002 In situ measurements of
atmospheric methane at GAGE/AGAGE sites during 1985e2000 and
resulting source inferences J Geophys Res Atmos 107 ACH20-1, Cite
ID 4225
Dlugokencky, E., Houweling, S., Bruhwiler, L., et al., 2003 Atmospheric
methane levels off: temporary pause or a new steady-state Geophys Res.
Lett 30 (19), 1992 http://dx.doi.org/10.1029/2003GL018126
Dlugokencky, E., Bruhwiler, L., White, J.W.C., et al., 2009 Observational constraints on recent increases in the atmospheric CH 4 burden J Geophys Res Lett 36, L18803 http://dx.doi.org/10.1029/2009GL039780
Freckleton, R., Highwood, E., Shine, K., et al., 1998 Greenhouse gas radiative forcing: effects of averaging and inhomogeneities in trace gas distribution.
Q J R Meteorol Soc 124, 2099 e2127
Hurrell, J.W., Hack, J.J., Shea, D., et al., 2008 A new sea surface temperature and sea ice boundary dataset for the community atmosphere modal J Clim 21, 5145 e5153
IPCC Climate Change, 2013 The Physical Science Basis Contribution of Working Group I to the Fifth Assessment Report of the IPCC Cambridge University Press, Cambridge and New York
Kristjansson, J.E., Iversen, T., Kirkevåg, A., et al., 2005 Response of the climate system to aerosol direct and indirect forcing: role of cloud feed-backs J Geophys Res 110, D24206 http://dx.doi.org/10.1029/ 2005JD006299
Langenfelds, R.L., Francey, R.J., Pak, B.C., et al., 2002 Interannual growth rate variations of atmospheric CO 2 and its d13C, H 2 , CH 4 , and CO between
1992 and 1999 linked to biomass burning Glob Biogeochem Cycles 16 (3), 21e22
Myhre, G., Highwood, E.J., Shine, K.P., et al., 1998 New estimates of radi-ative forcing due to well mixed greenhouse gases Geophys Res Lett 25,
2715 e2718
Renaud, de R., Caillol, S., 2011 Fighting global warming: the potential of photocatalysis against CO 2 , CH 4 , N 2 O, CFCs, tropospheric O 3 , BC and other major contributors to climate change J Photochem Photobiol C 12,
1 e19 Rigby, M., Prinn, R.G., Fraser, P.J., et al., 2008 Renewed growth of atmo-spheric methane Geophys Res Lett 35, L22805 http://dx.doi.org/ 10.1029/2008GL036037
Wang, Z.L., Zhang, H., Lu, P., 2014 Improvement of cloud microphysics in the aerosol-climate model BCC_AGCM2.0.1_CUACE/Aero, evaluation against observations, and updated aerosol indirect effect J Geophys Res.
119 (13), 8400 e8417
Wang, Z.L., Zhang, H., Zhang, X.Y., 2015 Simultaneous reductions in emissions of black carbon and co-emitted species will weaken the aerosol net cooling effect Atmos Chem Phys 15 (7), 3671e3685
Wang, Z.L., Zhang, H., Jing, X.W., et al., 2013a Effect of non-spherical dust aerosol on its direct radiative forcing Atmos Res 120, 112e126
Wang, Z.L., Zhang, H., Li, J., et al., 2013b Radiative forcing and climate response due to the presence of black carbon in cloud droplets J Geophys Res Atmos 118, 3662 e3675
Wecht, K.J., Jacob, D.J., Wofsy, S.C., et al., 2012 Validation of TES methane with HIPPO aircraft observations: implications for inverse modeling of methane sources Atmos Chem Phys 12, 1823 e1832
Worden, J., Kulawik, S., Frankenberg, C., et al., 2012 Profiles of CH 4 , HDO,
H 2 O, and N 2 O with improved lower tropospheric vertical resolution from Aura TES radiances Atmos Meas Techn 5, 397 e411
Fig 4 Climate responses due to changes in atmospheric CH 4 concentration since pre-industrial times Distribution of (a) precipitation and (b) surface water flux Shaded area represents the values at 0.05 significance level.
Trang 6Wu, T., Yu, R.C., Zhang, F., et al., 2010 The Beijing Climate Center
atmo-spheric general circulation model: description and its performance for the
present-day Clim Dyn 34, 123 e147
http://dx.doi.org/10.1007/s00382-009-0594-8
Xie, B., Zhang, H., Wang, Z., et al., 2016 A modeling study of effective
radiative forcing and climate response due to tropospheric ozone Adv.
Atmos Sci 33 (7), 819 e828 http://dx.doi.org/10.1007/s00376-016-5193-0
Xiong, X., Barnet, C., Maddy, E., et al., 2008 Characterization and validation
of methane products from the Atmospheric Infrared Sounder (AIRS) J.
Geophys Res 113, G00A01 http://dx.doi.org/10.1029/2007JG000500
Yashiro, Y., Kadir, W.R., Okuda, T., et al., 2008 The effects of logging on soil
greenhouse gas (CO 2 , CH 4 , and N 2 O) flux in a tropical rain forest,
Peninsular Malaysia Agric For Meteorol 148, 799 e806
Zhang, H., Jing, X.W., Li, J., 2014a Application and evaluation of a new
radiation code under McICA scheme in BCC_AGCM_2.0.1 Geosci.
Model Dev 7, 737e754
Zhang, H., Nakajima, T., Shi, G.-Y., et al., 2003 An optimal approach to
overlapping bands with correlated k distribution method and its application
to radiative calculations J Geophys Res 108 (D20), 4641 http://
dx.doi.org/10.1029/2002JD003358
Zhang, H., Shi, G., Nakajima, T., et al., 2006a The effects of the choice of the
‘K ’-interval number on radiative calculations J Quant Spectrosc Radiat Transf 98, 31 e43
Zhang, H., Suzuki, T., Nakajima, T., et al., 2006b Effects of band division on radiative calculations Opt Eng 45, 016002, 1 e10
Zhang, H., Wang, Z.L., Wang, Z., et al., 2012 Simulation of direct radi-ative forcing of aerosols and their effects on East Asian climate using
an interactive AGCM-aerosol coupled system Clim Dyn 38,
1675 e1693
Zhang, H., Xie, B., Zhao, S.Y., et al., 2014b PM2.5 and tropospheric O 3 in China and an analysis of the impact of pollutant emission control Adv Clim Change Res 5 (3), 136 e141
Zhang, Y., Xiong, X.Z., Tao, J.H., et al., 2014c Methane retrieval from At-mospheric Infrared Sounder using EOF-based regression algorithm and its validation Chin Sci Bull 59 (14), 1508e1518
Zhao, S.Y., Zhang, H., Feng, S., et al., 2015 Simulating direct effects of dust aerosol on arid and semi-arid regions using an aerosol-climate model system Int J Climatol 35 (8), 1858 e1866 http://dx.doi.org/10.1002/ joc.4093