Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model.
Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model.
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DOI：
10.1038/s41467-018-03379-6
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发表时间：
2018-03-07
影响因子：
16.6
通讯作者：
Nakajima T
中科院分区：
文献类型：
--
作者：
Sato Y;Goto D;Michibata T;Suzuki K;Takemura T;Tomita H;Nakajima T
气溶胶通过其作为云凝结核或冰核的作用改变云的性质来影响气候，称为气溶胶-云相互作用。在大多数的全球气候模式中，气溶胶-云相互作用是用经验参数化来表示的，其中云液态水的质量（LWP）被假定为随着气溶胶含量的增加而单调增加。然而，最近的卫星观测却得出了相互矛盾的结果：LWP会随着气溶胶载量的增加而降低。这种差异表明，GCM高估了气溶胶的影响，但差异的原因并不明显。在这里，我们重现卫星观测的LWP响应使用全球模拟云微物理的明确表示，而不是参数化。我们的分析表明，LWP的减少源于蒸发和凝结过程的气溶胶扰动，这是不代表在GCM的响应。在全球尺度模拟中云微物理的明确表示减少了气候预测的不确定性。大多数全球气候模式高估了气溶胶对云特性的影响，但原因尚不清楚。在这里，作者表明，在全球尺度建模中使用云微物理的显式表示，而不是参数化，可以减少高估。
Aerosols affect climate by modifying cloud properties through their role as cloud condensation nuclei or ice nuclei, called aerosol–cloud interactions. In most global climate models (GCMs), the aerosol–cloud interactions are represented by empirical parameterisations, in which the mass of cloud liquid water (LWP) is assumed to increase monotonically with increasing aerosol loading. Recent satellite observations, however, have yielded contradictory results: LWP can decrease with increasing aerosol loading. This difference implies that GCMs overestimate the aerosol effect, but the reasons for the difference are not obvious. Here, we reproduce satellite-observed LWP responses using a global simulation with explicit representations of cloud microphysics, instead of the parameterisations. Our analyses reveal that the decrease in LWP originates from the response of evaporation and condensation processes to aerosol perturbations, which are not represented in GCMs. The explicit representation of cloud microphysics in global scale modelling reduces the uncertainty of climate prediction. Most global climate models overestimate the aerosol effect on cloud properties, but the reason for this is unclear. Here the authors show that using explicit representation of cloud microphysics, in global scale modelling, rather than parameterisations, reduces the overestimation.
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影响因子：
4.6
作者：
Sato Y;Miura H;Yashiro H;Goto D;Takemura T;Tomita H;Nakajima T
通讯作者：
Nakajima T
影响因子：
6.3
作者：
Janssens-Maenhout, G.;Crippa, M.;Li, M.
通讯作者：
Li, M.
影响因子：
56.9
作者：
ALBRECHT, BA
通讯作者：
ALBRECHT, BA
影响因子：
4.9
作者：
Gettelman, A.;Morrison, H.;Caldwell, P. M.
通讯作者：
Caldwell, P. M.
影响因子：
3.1
作者：
Kodama, Chihiro;Yamada, Yohei;Sugi, Masato
通讯作者：
Sugi, Masato