Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Analytical Modeling to Identify the Mechanisms for CMIP Ensemble Error Spread
Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Analytical Modeling to Identify the Mechanisms for CMIP Ensemble Error Spread
复制标题
了解并减少美国中部温暖干燥的夏季偏差：通过分析模型确定 CMIP 集合误差传播机制
DOI：
10.1175/jcli-d-22-0255.1
复制
发表时间：
2023
影响因子：
4.9
通讯作者：
Liang, Xin-Zhong
中科院分区：
文献类型：
--
作者：
Sun, Chao;Liang, Xin-Zhong
耦合模式相互比较项目（CMIP 6）第6阶段的大多数气候模式在美国中部（CUS）仍然遭受明显的温暖和干燥的夏季偏差，即使在高分辨率模拟。我们发现，积云参数化中的云底定义是决定模型之间偏差传播的主导因素，并且在提升凝结层（LCL）定义云底的模型表现最好。为了确定潜在的机制，我们开发了一个物理为基础的分析偏差模式（ABM），以捕捉陆-气耦合的关键反馈过程。ABM具有显着的解释能力，捕获80%的方差的温度和降水偏差之间的所有模式。通过反事实实验的ABM分析表明，这种偏差主要由地面下沉流长波辐射误差引起，其次是地面净短波辐射误差，前者大2-5倍。这两个错误的有效辐射强迫加权其相对贡献引起失控的温度和降水反馈，这合作造成CUS夏季温暖和干燥的偏见。LCL积云通过两个关键机制减少偏差：它产生更多的云和更少的可降水量，这减少了用于地表加热和蒸散的辐射能量输入，导致土壤更冷更湿;它产生更多的降雨和更湿的土壤条件，这抑制了正的蒸散-降水反馈，以抑制温暖和干燥的偏差耦合。大多数使用非LCL方案的模型都低估了降水量和云量，这放大了正反馈，导致了显著的偏差。
Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases.