School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China.
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China; Qinghai Provincial Key Laboratory of Plateau Climate Change and Corresponding Ecological and Environmental Effects, Qinghai Institute of Technology, Xining, China; School of Geographical Sciences, Qinghai Normal University, Xining, China.
Sci Total Environ. 2024 Aug 10;937:173432. doi: 10.1016/j.scitotenv.2024.173432. Epub 2024 May 24.
The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015-2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051-2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %-53.9 % (30.5 %-50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %-45.7 % (4.6 %-34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.
旱地东亚（DEA）是最大的内陆干旱地区之一，植被对气候变化非常敏感。DEA 复杂的环境和建模结构的缺陷使得模拟和预测植被结构和生产力的变化变得困难。在这里，我们使用新兴约束（EC）方法来约束最新的第六次耦合模式比较计划（CMIP6）模型集合中四个情景下 DEA 未来的年际叶面积指数（LAI）和总初级生产力（GPP）趋势。在短期内（2015-2050 年），所有情景下的 LAI 和 GPP 都会增加，而在长期（2051-2100 年）中，SSP370 和 SSP585 会继续增长，SSP126 和 SSP245 则会停滞。然而，在建立有效的 EC 关系之后，与原始集合的不确定性相比，受约束的 LAI（GPP）增长趋势减少了 43.5%-53.9%（30.5%-50.0%），减少了 10.0%-45.7%（4.6%-34.3%）。我们还将 EC 扩展到移动窗口和网格单元中，进一步增强了约束的稳健性，特别是通过说明这些新兴关系的空间来源。LAI 和 GPP 趋势的高估表明，当前的 CMIP6 模型可能不足以捕捉气候变化和 DEA 植被动态之间的复杂关系；然而，这些模型可以根据已建立的新兴关系进行调整。
