Attribution of Land‐Use/Land‐Cover Change Induced Surface Temperature Anomaly: How Accurate Is the First‐Order Taylor Series Expansion?
Attribution of Land‐Use/Land‐Cover Change Induced Surface Temperature Anomaly: How Accurate Is the First‐Order Taylor Series Expansion?
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DOI：
10.1029/2020jg005787
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发表时间：
2020-09
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影响因子：
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通讯作者：
Chi Chen;Liang Wang;R. Myneni;Dan Li
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作者：
Chi Chen;Liang Wang;R. Myneni;Dan Li
地表温度(LST)是对土地利用/土地覆盖变化(LULCC)的响应，它改变了控制地表能量平衡(SEB)的地表属性。对LULCC引起的个体扰动引起的LST变化进行量化是一个归因问题。大多数归因方法都是基于线性化的SEB方程的一阶泰勒级数展开(FOTSE)。这些方法的准确性受到FOTSE在两个地方的使用的影响。第一步是将SEB方程线性化，得到LST的解析解(LST模型)；第二步是将LST变化表示为多因素同时变化的线性和(归因模型)。在这项研究中，我们使用二阶泰勒级数展开(SOTSE)系统地评估了在这些线性化过程中丢失的非线性效应的重要性。结果表明，尽管SOTSE LST模型的表现优于FOTSE LST模型，但LST模型中泰勒级数展开的阶数并不显著影响LST变化的归因。然而，SOTSE归因模型比FOTSE归因模型要准确得多，特别是在扰动幅度很大的情况下。结果表明，高阶项和交叉阶项在归因模型中的贡献率可高达50%。敏感性分析进一步表明，对于具有较大扰动(例如，毁林和城市化)的LULCC情景，与地表阻力变化相关的非线性效应特别强。总之，我们建议使用FOTSE LST模型和SOTSE归因模型。
Land surface temperature (LST) responds to land‐use/land‐cover change (LULCC), which modifies surface properties that control the surface energy balance (SEB). Quantifying changes in LST due to individual perturbations caused by LULCC is an attribution problem. Most attribution methods are based on the first‐order Taylor series expansion (FOTSE) of a linearized SEB equation. The accuracy of these methods is affected by the use of FOTSE at two places. The first is to linearize the SEB equation and to obtain an analytical solution for LST (the LST model), and the second is to obtain LST changes as the linear sum of concurrent changes in multiple factors (the attribution model). In this study, we systematically assess the importance of non‐linear effects lost in these linearization processes using the second‐order Taylor series expansion (SOTSE). Results show that while the SOTSE LST model outperforms the FOTSE LST model, the order of Taylor series expansion in the LST model does not significantly influence the attribution of LST changes. However, the SOTSE attribution model is considerably more accurate than the FOTSE attribution model, especially when the magnitude of perturbations is large. Results suggest that contributions from higher‐order and cross‐order terms in the attribution model can be as large as 50%. Sensitivity analysis further shows that non‐linear effects associated with changing surface resistance for LULCC scenarios with large perturbations (e.g., deforestation and urbanization) are particularly strong. In conclusion, we recommend using the FOTSE LST model and the SOTSE attribution model.