Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes
Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes
复制标题
模型对温室气体强迫响应的约束以及次网格尺度过程的作用
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
M. Allen
中科院分区：
文献类型：
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作者：
B. Sanderson;R. Knutti;T. Aina;C. Christensen;N. Faull;D. Frame;W. Ingram;C. Piani;D. Stainforth;D. Stone;M. Allen
使用神经网络技术开发了一个气候模型模拟器，并用来自气候预测网的数千名成员的扰动物理GCM集合的数据进行了训练。该方法重建了模型参数之间的非线性相互作用，允许对更大的集合进行模拟，从而更充分地探索模型参数空间。模拟的集合被用来在对温室气体强迫的广泛平衡响应范围内搜索与观测最接近的模型。这些模型与观测值的相对差异可以用来限制气候敏感性。使用大气层顶部辐射通量的年平均或季节差异作为观测误差度量，可以得到最明确定义的最小误差作为灵敏度的函数，在使用地表温度或总降水量的季节周期时，结果是一致的，但不太明确。为了达到不同的气候敏感值，同时最小化与观测的差异，还考虑了模型参数的必要变化，并与以前的研究进行了比较。这些信息被用来为未来的集成提出更有效的参数采样策略。
A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully. The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation. The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.