Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition

Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition
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DOI:
10.5194/essd-2019-251
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发表时间:
2020-01
影响因子:
11.4
通讯作者:
N. Bellouin;W. Davies;K. Shine;J. Quaas;J. Mülmenstädt;P. Forster;Chris Smith;Lindsay A. Lee;L. Regayre;G. Brasseur;Natalia Sudarchikova;I. Bouarar;O. Boucher;G. Myhre
N. Bellouin;W. Davies;K. Shine;J. Quaas;J. Mülmenstädt;P. Forster;Chris Smith;Lindsay A. Lee;L. Regayre;G. Brasseur;Natalia Sudarchikova;I. Bouarar;O. Boucher;G. Myhre
中科院分区:
地球科学1区
文献类型:
--
作者:
N. Bellouin;W. Davies;K. Shine;J. Quaas;J. Mülmenstädt;P. Forster;Chris Smith;Lindsay A. Lee;L. Regayre;G. Brasseur;Natalia Sudarchikova;I. Bouarar;O. Boucher;G. Myhre

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抽象的。辐射强迫是理解和预测全球气候变化的重要基础,但其量化历来是针对不同的强迫因子独立进行的,在不同程度上涉及观测,研究并不总是包括对不确定性的详细分析。哥白尼大气监测服务再分析是大气成分建模和观测的最佳组合。它提供了一个独特的机会,依靠观测来量化六个最大的强迫因子:二氧化碳,甲烷,对流层臭氧,平流层臭氧,气溶胶-辐射相互作用和气溶胶-云相互作用的辐射强迫的每月和空间分辨的全球分布一致。这些辐射强迫估计值考虑了平流层温度的调整,但没有考虑对流层的快速调整。在2003-2017年的全球平均水平上,平流层调整的二氧化碳辐射强迫相对于1750年平均为+1.89 W m−2(5%-95%置信区间:1.50至2.29 W m−2),并以每十年18%的速度增长。甲烷的相应值为+0.46(0.36至0.56)W m−2,每十年增加4%,但自2007年以来明显加速。臭氧辐射强迫的平均值为+0.32(0 ~ 0.64)W m−2,几乎全部由对流层臭氧贡献,因为平流层臭氧辐射强迫只有+0.003 W m−2。气溶胶辐射强迫平均值为-1.25(-1.98至-0.52)W m−2,其中气溶胶-辐射相互作用贡献-0.56 W m−2,气溶胶-云相互作用贡献-0.69 W m−2。自2003年以来,两者都相对稳定。综合考虑这六种强迫因子,没有迹象表明在此期间人为辐射强迫的增长率持续减缓或加速。这些正在进行的辐射强迫估计将监测将地表温度变暖限制在巴黎协定温度目标所需的净零排放大幅减少对地球能源预算的影响。事实上,这种影响在温度记录中明确之前,应该在辐射强迫中清楚地表现出来。此外,这一辐射强迫数据集可以提供参与气候变化监测、探测和归因、年际至十年预测和综合评估建模的研究人员所需的输入分布。通过这项工作产生的数据可在https://doi.org/10.24380/ads.1hj3y896获得(Bellouin等人,2020年b)。
Abstract. Radiative forcing provides an important basis for understanding and predicting global climate changes, but its quantification has historically been done independently for different forcing agents, has involved observations to varying degrees, and studies have not always included a detailed analysis of uncertainties. The Copernicus Atmosphere Monitoring Service reanalysis is an optimal combination of modelling and observations of atmospheric composition. It provides a unique opportunity to rely on observations to quantify the monthly and spatially resolved global distributions of radiative forcing consistently for six of the largest forcing agents: carbon dioxide, methane, tropospheric ozone, stratospheric ozone, aerosol–radiation interactions, and aerosol–cloud interactions. These radiative-forcing estimates account for adjustments in stratospheric temperatures but do not account for rapid adjustments in the troposphere. On a global average and over the period 2003–2017, stratospherically adjusted radiative forcing of carbon dioxide has averaged +1.89 W m−2 (5 %–95 % confidence interval: 1.50 to 2.29 W m−2) relative to 1750 and increased at a rate of 18 % per decade. The corresponding values for methane are +0.46 (0.36 to 0.56) W m−2 and 4 % per decade but with a clear acceleration since 2007. Ozone radiative-forcing averages +0.32 (0 to 0.64) W m−2, almost entirely contributed by tropospheric ozone since stratospheric ozone radiative forcing is only +0.003 W m−2. Aerosol radiative-forcing averages −1.25 (−1.98 to −0.52) W m−2, with aerosol–radiation interactions contributing −0.56 W m−2 and aerosol–cloud interactions contributing −0.69 W m−2 to the global average. Both have been relatively stable since 2003. Taking the six forcing agents together, there is no indication of a sustained slowdown or acceleration in the rate of increase in anthropogenic radiative forcing over the period. These ongoing radiative-forcing estimates will monitor the impact on the Earth's energy budget of the dramatic emission reductions towards net-zero that are needed to limit surface temperature warming to the Paris Agreement temperature targets. Indeed, such impacts should be clearly manifested in radiative forcing before being clear in the temperature record. In addition, this radiative-forcing dataset can provide the input distributions needed by researchers involved in monitoring of climate change, detection and attribution, interannual to decadal prediction, and integrated assessment modelling. The data generated by this work are available at https://doi.org/10.24380/ads.1hj3y896 (Bellouin et al., 2020b).