the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
Gautam Bisht
Dalei Hao
Lai-Yung Ruby Leung
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for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets.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. 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 new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs 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/10.25584/PNNLDH/1986308 (Li et al., 2023).
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Lingcheng Li et al.
Status: open (extended)
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RC1: 'Comment on essd-2023-242', Anonymous Referee #1, 27 Aug 2023
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This research presents a new set of global land surface parameters with a resolution of 1 km for multiple years from 2003 to 2020. This manuscript is well-structured. The figures are well produced. The English is good. The results are clearly presented. However, major issues should be addressed before this manuscript may be reconsidered for publication in the esteemed ESSD.
For a majority of data description papers in ESSD, a solid verification of the new data based on ground truth data and the comparison between the new data set and the existing mainstreaming data set are necessary and always included. Without such information, readers cannot fully understand whether the new data set is reliable and how much this data set has been improved compared with existing data sets. Consequently, the significance of this research cannot be highlighted. Therefore, I strongly recommend the authors to present a quantitative comparison between the new data set and mainstream data sets (e.g. CLM5 and K2012 datasets) based on already existing reference data or manually collected reference data.
Another important issues is about the citation in the text.
The citation should be thoroughly revised. For instance, the list of more than 10 references in a line can provide readers no accurate information and a clear relation between the reference and the mentioned information.
e.g. L49-L50 ... and biogeochemical cycles, as well as land and atmosphere coupling (Giorgi and Avissar, 1997; Chaney et al., 2018; Zhou et al., 2019; Liu et al., 2017; Bou-Zeid et al., 2020; Chen et al., 2020; Nitta et al., 2020; Vrese et al., 2016)…
These references should be clearly cited and explained. Personally, I do not suggest a list of more than 3 references in a line. The citations in the text are poor and all citations throughout the manuscript should be double-checked and revised to the right form.
Citation: https://doi.org/10.5194/essd-2023-242-RC1
Lingcheng Li et al.
Data sets
Global 1km Land Surface Parameters for Kilometer Scale Earth System Modeling Lingcheng Li, Gautam Bisht, Dalai Hao, L. Ruby Leung https://doi.org/10.25584/PNNLDH/1986308
Lingcheng Li et al.
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