Preprints
https://doi.org/10.5194/essd-2023-242
https://doi.org/10.5194/essd-2023-242
19 Jul 2023
 | 19 Jul 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling

Lingcheng Li, Gautam Bisht, Dalei Hao, and 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for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets.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).

Lingcheng Li, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-242', Anonymous Referee #1, 27 Aug 2023
  • RC2: 'Comment on essd-2023-242', Anonymous Referee #2, 07 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-242', Anonymous Referee #1, 27 Aug 2023
  • RC2: 'Comment on essd-2023-242', Anonymous Referee #2, 07 Oct 2023
Lingcheng Li, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung

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, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung

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Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth System Modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
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