Coupled high-resolution land-atmosphere modeling for hydroclimate and terrestrial hydrology in Alaska and the Yukon River Basin (1990-2021)

Coupled high-resolution land-atmosphere modeling for hydroclimate and terrestrial hydrology in Alaska and the Yukon River Basin (1990-2021)

Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high-resolution re-simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic Systems Model (RASM) and conducted coupled land-atmosphere modeling for Alaska and Yukon River Basin at 4-km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and re-optimization of CTSM. A multi-decadal climate dataset (1990-2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational datasets, this dataset well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation-to-precipitation ratios

水文气候和陆地水文学对阿拉斯加和育空河流域的当地社区、生态系统以及经济产生重大影响。对阿拉斯加历史气候的高分辨率再模拟能够为气候变化研究提供重要的基准。在本研究中，我们利用区域北极系统模型（RASM），并以4千米的网格间距对阿拉斯加和育空河流域进行了陆 - 气耦合模拟。在RASM中，鉴于社区陆地系统模型（CTSM）对寒冷地区有全面的过程描述，陆地模型被其取代。对天气研究与预报（WRF）大气模型中的微物理方案进行了手动调整，以实现最佳模型性能。本研究旨在使水文气候和陆地水文学，尤其是径流（在耦合模型中很少被优先考虑）都能保持良好的模型性能。因此，我们实施了对CTSM进行迭代测试和重新优化的策略。利用具有优化陆地参数和手动调整的WRF微物理方案的RASM生成了一个长达数十年的气候数据集（1990 - 2021年）。当与多个观测数据集进行对比评估时，该数据集很好地捕捉了五个关键天气变量和水文通量（包括降水、气温、积雪比例、蒸发 - 降水比）的气候统计数据和空间分布。