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 m−2 by taking it as a well-mixed greenhouse gas, and the inhomogeneity of methane increased its ERF by about 0.02 W m−2 . The change of methane concentration since pre-industrial led to an increase of 0.31 °C in global mean surface air temperature and 0.02 mm d−1 in global mean precipitation. The warming was prominent over the middle and high latitudes of the Northern Hemisphere (with a maximum increase exceeding 1.4 °C). The precipitation notably increased (maximum increase of 1.8 mm d−1 ) over the ocean between 10°N and 20°N and significantly decreased (maximum decrease >–0.6 mm d−1 ) between 10°S and 10°N. These changes caused a northward movement of precipitation 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 increases in emissions of anthropogenic greenhouse gases (GHGs) since the start of the industrial era (IPCC. Climate Change, 2013  ;  Zhang et al., 2014b ). Methane (CH4 ), with a relatively short lifetime (about 12 years), is the most important anthropogenic GHG besides CO2 (IPCC, 2013 ). Although the burden of CH4 in the atmosphere is significantly smaller than that of CO2 , the radiative efficiency (RE) of CH4 is 26.5 times as much as that of CO2 (Yashiro et al., 2008  ;  Renaud and Caillol, 2011 ). The absorption of CH4 on radiative flux influence the temperature, especially near the Earths surface.

The atmospheric CH4 concentration almost doubled from 1750 to 2011, the volume mixing ratio from 722 × 10−9 to 1803 × 10−9 (IPCC, 2013 ). CH4 increased rapidly until 2000. Then, after a decade of stabilization or slightly decreasing concentrations, the global CH4 concentration showed a well-defined increase again in 2007 (Dlugokencky et al., 2003  ;  Rigby et al., 2008 ), measured using ground-based observations (Cunnold et al., 2002 ; Langenfelds et al., 2002  ;  Dlugokencky et al., 2009 ) and aircraft profiles (Wecht et al., 2012  ;  Worden et al., 2012 ). The IPCC Fifth Assessment Report (IPCC, 2013 ) showed that the radiative forcing (RF) of CH4 was 0.48 ± 0.05 W m−2 from 1750 to 2011 based on Myhre et al. (1998) . CH4 is well mixed in the atmosphere, but its concentrations vary with latitude and altitude, contributing to 2% of the uncertainty in its RF (Freckleton et al., 1998 ).

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 horizontal resolution of the model is approximately 2.8° × 2.8°, and the vertical direction includes 26 layers, with 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 contains several enhancements in the physics: BCC_AGCM2.0 uses the radiation scheme of BCC_RAD (Beijing Climate Center Radiative Transfer Model), developed by Zhang et al., 2003 ; Zhang et al., 2006a  ;  Zhang et al., 2006b , and the cloud overlap scheme of the Monte Carlo independent column approximation (Zhang et al., 2014a ). These schemes increase the accuracy 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 ; Wang et al., 2013b ; Wang et al., 2014 ; Wang et al., 2015  ;  Zhao et al., 2015 ) and tropospheric ozone (Xie et al., 2016 ).

2.2. Satellite data

CH4 profile data observed by the Atmospheric Infrared Sounder (AIRS) (URL: http://www.nasa.gov/mission_pages/aqua ) was used. AIRS was onboard the Aqua spacecraft and launched by the National Aeronautic and Space Administration (NASA) in May 2002. An overview of the AIRS instrument is given by Aumann et al. (2003) . AIRS has 2378 channels, which cover from 649 to 1136, 1217 to 1613, and 2169 to 2674 cm−1 with high spectral resolution (λ/Δλ = 1200), and the absorption band of CH4 is included. The AIRS dataset used in this study is on a global spatial resolution of 1° × 1°, and on vertical pressure levels from 1000 hPa to 1 hPa (divided into 24 levels). More information about characterization and validation of methane products from AIRS can be found in Xiong et al. (2008) and Zhang et al. (2014c) .

2.3. Experimental design

Our aim was to calculate the ERF and climate response due to changes in atmospheric CH4 concentration. To this end, five simulations (EXP1, EXP2, EXP3, EXP4, and EXP5) were conducted. EXP1, EXP2 and EXP3 were used to calculate the ERF of CH4 at fixed sea surface temperature (SST) (Hurrell et al., 2008 ). The same model settings were used in these three simulations, only the CH4 concentrations were different (Table 1 shows the details). The differences between EXP1 and EXP2 (EXP2 minus EXP1) were regarded as the ERF of well mixed CH4 , and differences between EXP1 and EXP3 (EXP3 minus EXP1) were the ERF of CH4 with spatial variation. Each simulation was run for 15 years. Kristjánsson et al. (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:

( 1)

where ΔF is the net radiation flux (the difference between incoming and outgoing shortwave and longwave radiative flux) at the top of the atmosphere (TOA).

Table 1. Experimental design.
Number CH4 data Sea temperature Running time
EXP1 WMGHG 1750a 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. CH4 concentration in 1750, as well mixed greenhouse gas, from IPCC AR5.

b. CH4 concentration in 2011, as well mixed greenhouse gas, from IPCC AR5.

c. CH4 concentration in 2011, observed by AIRS.

EXP4 and EXP5 were used to calculate the climate response of CH4 . We used the same CH4 volume mixture ratios in EXP4 and EXP5 as in EXP1 and EXP2, respectively. However, to consider the feedback of the oceans to CH4 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 changes in CH4 concentration.

3. Simulation results and analysis

3.1. ERF

Human activities have increased anthropogenic emissions of CH4 since pre-industrial times, leading to increases in its atmospheric concentration and, subsequently, changes in its ERF. Fig. 1 shows the simulated ERF (the difference between EXP2 and EXP1) due to change in CH4 as well mixed greenhouse gas (WMGHG). A well-defined positive ERF was generally observed near 60° in both hemispheres, with the maximum value of 7 W m−2 . However, the ERF was negative in western Siberia, southern Africa, Greenland, and most parts of South America, with a value of approximately −6 W m−2 over southern Africa. The negative ERF in those areas might be explained by an increase in low cloud cover. The simulated global mean CH4 ERF was 0.46 W m−2 , which is consistent with the value reported in IPCC AR5. As Fig. 2 shown, CH4 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 of CH4 was reduced from north to south. The asymmetry of CH4 concentration was becoming less distinct with the increase 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 CH4 had little impact on ERF (less than 0.02 W m−2 ).


Fig. 1


Fig. 1.

Distribution of the effective radiative forcing (ERF) of well mixed atmospheric methane from 1750 to 2011 (units: W m−2 ). Shaded area represents the value at 0.05 significance level.


Fig. 2


Fig. 2.

Zonal distribution of the volume mixing ratio of CH4 in 2011 (×10−6 ), observed by AIRS.

3.2. Surface air temperature and cloud cover

CH4 is a key long-lived GHG that strongly absorbs longwave radiation. The ERF of CH4 is generally positive, leading to a warming effect on the Earths climate system and thus the surface. The difference between EXP5 and EXP4 showed that an increase in the atmospheric CH4 concentration since pre-industrial times caused an increase of 0.31 °C in global mean surface air temperature. As shown in Fig. 3 a, the surface 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 Hemisphere was prominent, with the maximum temperature increase exceeding 1.4 °C. There was also significant warming (approximately 1.0 °C) in the Antarctic area. Fig. 3 b shows the response of the surface net radiation flux (SNRF) due to the change in CH4 . The distribution of change in SNRF was consistent with that of surface temperature over the ocean. There were significant increases in SNRF over the high latitudes in both hemispheres. For example, the SNRF over the North Pacific Ocean increased by more than 6.0 W m−2 , and 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. 3


Fig. 3.

Climate responses due to changes in atmospheric CH4 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.

Fig. 3 c 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 surface 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 temperature due to high-level clouds warming effect on the Earths climate system. As shown in Fig. 1 , the ERF was clearly negative in the western and southern regions of South America, and low-level cloud cover in these areas increased by about 20% (Fig. 3 c), resulting in marked decreases in surface 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 increase in high-level cloud cover (Fig. 3 d).

Fig. 3 e 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 troposphere near 70°N and between 10°N and 20°N in the lower troposphere in the Southern Hemisphere, in the higher troposphere in tropical areas, and in most of the troposphere over the Antarctic, and the cloud cover increased by 0.2%–1% in these regions. These increases led to decreases in the SNRF (Fig. 3 b). In contrast, the relative humidity and cloud cover clearly decreased in the most of troposphere near 60°S and between 30°N and 40°N in the middle to upper troposphere near the equator and in most of stratosphere, resulting in increases in the SNRF (Fig. 3 b) in some areas.

3.3. Precipitation and surface water flux

The increase in CH4 concentration resulted in a warming effect in the atmosphere and at the surface due to positive ERF at the TOA, which caused an increase of 0.02 kg m−2  d−1 in global mean surface water flux (SWF) (Fig. 4 b). The spatial distributions of the changes in SWF and SNRF were similar (Fig. 2  ;  Fig. 3 b). The SWF dramatically increased (by >0.12 kg m−2  d−1 ) over most areas of the ocean, especially in 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 decreased by approximately 0.14 kg m−2  d−1 in eastern South America and central Africa.


Fig. 4


Fig. 4.

Climate responses due to changes in atmospheric CH4 concentration since pre-industrial times. Distribution of (a) precipitation and (b) surface water flux. Shaded area represents the values at 0.05 significance level.

Fig. 4 a shows the changes in precipitation due to CH4 , which were notable in the Intertropical Convergence Zone. Precipitation significantly increased (by >0.5 mm d−1 , with a maximum increase of 1.8 mm d−1 ) over the ocean between 10°N and 20°N. However, precipitation significantly decreased (maximum decrease >0.6 mm d−1 ) over the ocean between 10°S and 10°N. Hence, there was a negative correlation between changes in precipitation over the tropics in each hemisphere, with precipitation increased in the Northern Hemisphere and decreased in the Southern Hemisphere.

4. Conclusions

The ERF and climate responses due to the change in atmospheric CH4 concentration from pre-industrial times (1750) to 2011 were investigated using the atmospheric general circulation model BCC AGCM2.0, in combination with CH4 volume mixture ratios from IPCC AR5. The global mean ERF for CH4 as WMGHG was 0.46 W m−2 , and the spatial variation of methane influenced the ERF by 0.02 W m−2 . The increase in atmospheric CH4 led to an increase of 0.31 °C and 0.02 mm d−1 in global mean surface air temperature and precipitation, respectively. Warming was significant in the middle and high latitudes, especially in the Northern Hemisphere, with the maximum warming exceeding 1.4 °C. The global distribution of change in precipitation was in line with that of changes in cloud cover, especially near the equator. The precipitation notably increased (maximum increase of 1.8 mm d−1 ) over the tropical regions of the Northern Hemisphere and sharply decreased (maximum decrease >−0.6 mm d−1 ) between 10°S and 10°N, and these changes 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

  1. Aumann et al., 2003 H. Aumann, M.T. Chahine, C. Gautier, et al.; AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems; IEEE Trans. Geosci. Remote Sens., 41 (2003), pp. 253–264
  2. Cunnold et al., 2002 D.M. Cunnold, L.P. Steele, P.J. Fraser, et al.; In situ measurements of atmospheric methane at GAGE/AGAGE sites during 1985–2000 and resulting source inferences; J. Geophys. Res. Atmos., 107 (2002) ACH20-1, Cite ID 4225
  3. Dlugokencky et al., 2003 E. Dlugokencky, S. Houweling, L. Bruhwiler, et al.; Atmospheric methane levels off: temporary pause or a new steady-state; Geophys. Res. Lett., 30 (19) (2003), p. 1992 http://dx.doi.org/10.1029/2003GL018126
  4. Dlugokencky et al., 2009 E. Dlugokencky, L. Bruhwiler, J.W.C. White, et al.; Observational constraints on recent increases in the atmospheric CH4 burden  ; J. Geophys. Res. Lett., 36 (2009), p. L18803 http://dx.doi.org/10.1029/2009GL039780
  5. Freckleton et al., 1998 R. Freckleton, E. Highwood, K. Shine, et al.; Greenhouse gas radiative forcing: effects of averaging and inhomogeneities in trace gas distribution; Q. J. R. Meteorol. Soc., 124 (1998), pp. 2099–2127
  6. Hurrell et al., 2008 J.W. Hurrell, J.J. Hack, D. Shea, et al.; A new sea surface temperature and sea ice boundary dataset for the community atmosphere modal; J. Clim., 21 (2008), pp. 5145–5153
  7. IPCC. Climate Change, 2013 IPCC. Climate Change; The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the IPCC; Cambridge University Press, Cambridge and New York (2013)
  8. Kristjánsson et al., 2005 J.E. Kristjánsson, T. Iversen, A. Kirkevåg, et al.; Response of the climate system to aerosol direct and indirect forcing: role of cloud feedbacks; J. Geophys. Res., 110 (2005), p. D24206 http://dx.doi.org/10.1029/2005JD006299
  9. Langenfelds et al., 2002 R.L. Langenfelds, R.J. Francey, B.C. Pak, et al.; Interannual growth rate variations of atmospheric CO2 and its δ13C, H2 , CH4 , and CO between 1992 and 1999 linked to biomass burning  ; Glob. Biogeochem. Cycles, 16 (3) (2002), pp. 21–22
  10. Myhre et al., 1998 G. Myhre, E.J. Highwood, K.P. Shine, et al.; New estimates of radiative forcing due to well mixed greenhouse gases; Geophys. Res. Lett., 25 (1998), pp. 2715–2718
  11. Renaud and Caillol, 2011 de R. Renaud, S. Caillol; Fighting global warming: the potential of photocatalysis against CO2 , CH4 , N2 O, CFCs, tropospheric O3 , BC and other major contributors to climate change  ; J. Photochem. Photobiol. C, 12 (2011), pp. 1–19
  12. Rigby et al., 2008 M. Rigby, R.G. Prinn, P.J. Fraser, et al.; Renewed growth of atmospheric methane; Geophys. Res. Lett., 35 (2008), p. L22805 http://dx.doi.org/10.1029/2008GL036037
  13. Wang et al., 2014 Z.L. Wang, H. Zhang, P. Lu; 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) (2014), pp. 8400–8417
  14. Wang et al., 2015 Z.L. Wang, H. Zhang, X.Y. Zhang; Simultaneous reductions in emissions of black carbon and co-emitted species will weaken the aerosol net cooling effect; Atmos. Chem. Phys., 15 (7) (2015), pp. 3671–3685
  15. Wang et al., 2013a Z.L. Wang, H. Zhang, X.W. Jing, et al.; Effect of non-spherical dust aerosol on its direct radiative forcing; Atmos. Res., 120 (2013), pp. 112–126
  16. Wang et al., 2013b Z.L. Wang, H. Zhang, J. Li, et al.; Radiative forcing and climate response due to the presence of black carbon in cloud droplets; J. Geophys. Res. Atmos., 118 (2013), pp. 3662–3675
  17. Wecht et al., 2012 K.J. Wecht, D.J. Jacob, S.C. Wofsy, et al.; Validation of TES methane with HIPPO aircraft observations: implications for inverse modeling of methane sources; Atmos. Chem. Phys., 12 (2012), pp. 1823–1832
  18. Worden et al., 2012 J. Worden, S. Kulawik, C. Frankenberg, et al.; Profiles of CH4 , HDO, H2 O, and N2 O with improved lower tropospheric vertical resolution from Aura TES radiances  ; Atmos. Meas. Techn., 5 (2012), pp. 397–411
  19. Wu et al., 2010 T. Wu, R.C. Yu, F. Zhang, et al.; The Beijing Climate Center atmospheric general circulation model: description and its performance for the present-day; Clim. Dyn., 34 (2010), pp. 123–147 http://dx.doi.org/10.1007/s00382-009-0594-8
  20. Xie et al., 2016 B. Xie, H. Zhang, Z. Wang, et al.; A modeling study of effective radiative forcing and climate response due to tropospheric ozone; Adv. Atmos. Sci., 33 (7) (2016), pp. 819–828 http://dx.doi.org/10.1007/s00376-016-5193-0
  21. Xiong et al., 2008 X. Xiong, C. Barnet, E. Maddy, et al.; Characterization and validation of methane products from the Atmospheric Infrared Sounder (AIRS); J. Geophys. Res., 113 (2008), p. G00A01 http://dx.doi.org/10.1029/2007JG000500
  22. Yashiro et al., 2008 Y. Yashiro, W.R. Kadir, T. Okuda, et al.; The effects of logging on soil greenhouse gas (CO2 , CH4 , and N2 O) flux in a tropical rain forest, Peninsular Malaysia  ; Agric. For. Meteorol., 148 (2008), pp. 799–806
  23. Zhang et al., 2014a H. Zhang, X.W. Jing, J. Li; Application and evaluation of a new radiation code under McICA scheme in BCC_AGCM_2.0.1; Geosci. Model Dev., 7 (2014), pp. 737–754
  24. Zhang et al., 2003 H. Zhang, T. Nakajima, G.-Y. Shi, et al.; An optimal approach to overlapping bands with correlated k distribution method and its application to radiative calculations; J. Geophys. Res., 108 (D20) (2003), p. 4641 http://dx.doi.org/10.1029/2002JD003358
  25. Zhang et al., 2006a H. Zhang, G. Shi, T. Nakajima, et al.; The effects of the choice of the ‘K’-interval number on radiative calculations; J. Quant. Spectrosc. Radiat. Transf., 98 (2006), pp. 31–43
  26. Zhang et al., 2006b H. Zhang, T. Suzuki, T. Nakajima, et al.; Effects of band division on radiative calculations; Opt. Eng., 45 (2006) 016002, 1–10
  27. Zhang et al., 2012 H. Zhang, Z.L. Wang, Z. Wang, et al.; Simulation of direct radiative forcing of aerosols and their effects on East Asian climate using an interactive AGCM-aerosol coupled system; Clim. Dyn., 38 (2012), pp. 1675–1693
  28. Zhang et al., 2014b H. Zhang, B. Xie, S.Y. Zhao, et al.; PM2.5 and tropospheric O3 in China and an analysis of the impact of pollutant emission control  ; Adv. Clim. Change Res., 5 (3) (2014), pp. 136–141
  29. Zhang et al., 2014c Y. Zhang, X.Z. Xiong, J.H. Tao, et al.; Methane retrieval from Atmospheric Infrared Sounder using EOF-based regression algorithm and its validation; Chin. Sci. Bull., 59 (14) (2014), pp. 1508–1518
  30. Zhao et al., 2015 S.Y. Zhao, H. Zhang, S. Feng, et al.; Simulating direct effects of dust aerosol on arid and semi-arid regions using an aerosol-climate model system; Int. J. Climatol., 35 (8) (2015), pp. 1858–1866 http://dx.doi.org/10.1002/joc.4093
Back to Top

Document information

Published on 15/05/17
Submitted on 15/05/17

Licence: Other

Document Score

0

Views 6
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?