Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation
Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation
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DOI：
10.5194/gmd-12-3017-2019
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发表时间：
2019-07
影响因子：
5.1
通讯作者：
Sihan Li;D. Rupp;L. Hawkins;P. Mote;D. McNeall;S. Sparrow;D. Wallom;R. Betts;J. J. Wettstein-J.
中科院分区：
文献类型：
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作者：
Sihan Li;D. Rupp;L. Hawkins;P. Mote;D. McNeall;S. Sparrow;D. Wallom;R. Betts;J. J. Wettstein-J.
抽象的。了解气候变化带来的挑战依赖于气候模型，其中许多模型对北方半球大陆中纬度地区夏季温暖和干燥存在很大的偏差。这项工作的例子中使用的天气@家庭分布式气候模型框架的更新版本的模型，显示了通过多阶段参数细化方法，特别是在美国西北部（NWUS）改善气候模型模拟的潜力。每个阶段包括：（1）用耦合的全球-区域大气模式创建扰动参数集合，（2）建立统计模拟器，估计气候指标作为参数值的函数，（3）并使用模拟器进一步改进参数空间。细化过程包括敏感性分析，以确定对各种模型输出指标最有影响的参数;然后使用结果来挑选影响较小的参数。在结果被认为是令人满意的之前，进行了这个迭代过程的三个阶段;也就是说，确定了满足可接受的偏倚降低标准的少数参数集。结果不仅表明，74%的NWUS区域温暖的偏差，可以通过细化控制对流和水凝物输送的全球大气参数，以及影响植物光合作用，蒸腾作用和蒸发的陆面参数，但也表明，这种迭代方法扰动参数在物理参数化的演变中发挥了重要作用。
Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global–regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.