We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high‐dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). The relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.

中文翻译：

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E3SM土地模型中参数不确定性对生物地球化学的影响

我们对能源百亿亿地球系统模型（E3SM）和土地模型（ELM）进行了全局敏感性分析（GSA），以计算五个关键碳循环输出对68个模型参数的敏感性。通过首先通过新的加权迭代贝叶斯压缩感知（WIBCS）算法构造一个多项式混沌（PC）替代物来进行自适应基础增长，从而导致稀疏的高维PC替代物（具有3,000个模型评估）来进行此GSA。PC代理可以有效提取GSA信息，从而进一步降低尺寸。GSA在96个FLUXNET站点执行，涵盖多种植物功能类型（PFT）和气候条件。大约20个模型参数被确定为敏感参数，其余参数在所有输出和PFT中相对不敏感。这些敏感性取决于PFT，并且在同一PFT中的站点之间相对一致。这五个模型输出具有大多数共同的高度敏感参数。PFT之间还共享敏感参数的公共子集，但某些参数特定于某些类型（例如，落叶物候）。这些参数的相对重要性在PFT之间以及气候变量（例如年平均温度）之间发生了显着变化。