Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

中文翻译：

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用于公里级地球系统建模的全球 1 公里陆地表面参数

摘要。地球系统模型（ESM）正在逐步向千米尺度（“k尺度”）迈进。然而，以 k 尺度运行的 ESM 内的陆地表面模型 (LSM) 的表面参数通常源自粗分辨率和过时的数据集。本研究旨在利用最新、最准确的可用数据集，开发一套新的 2001 年至 2020 年多年分辨率为 1 公里的全球地表参数。具体来说，数据集包含与土地利用和土地覆盖、植被、土壤和地形相关的参数。新开发的1公里陆地表面参数与常规参数之间的差异强调了它们由于结合了最先进和最新的数据源而具有更高精度的潜力。为了证明这些新参数的功能，我们使用 E3SM 土地模型版本 2 (ELM2) 在美国本土进行了 1 公里分辨率模拟。我们的结果表明，地表参数在土壤湿度、潜热、发射的长波辐射和吸收的短波辐射的 ELM2 模拟中导致显着的空间异质性。平均而言，将 1 公里 ELM2 模拟放大到 12 公里分辨率会丢失约 31% 至 54% 的空间信息。利用可解释机器学习（XML）方法，识别了驱动ELM2模拟的空间变异和空间信息丢失的影响因素，突出了各种地表参数以及平均气候条件的空间变异和信息丢失的实质性影响。与四个基准数据集的比较表明，ELM 在模拟土壤湿度和表面能量通量方面通常表现良好。新的地表参数是为了满足 k 尺度 LSM 和 ESM 建模的新兴需求而定制的，这对于增进我们对全球变化下的水、碳和能源循环的理解具有重大意义。 1 公里陆地表面参数可在 https://doi.org/10.5281/zenodo.10815170 上公开获取（Li et al., 2024）。