Published January 17, 2024 | Version v2
Dataset Open

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

  • 1. Pacific Northwest National Laboratory

Description

Summary: 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for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets.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 1k 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 LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.


Format: NetCDF.
Institution: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory
Contacts: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)

Description: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.
Spatial resolution: ~1 km, corresponding to 1/120 degree.
Temporal resolution: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.


Reference: Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.

It includes four categories of parameters, Please refer to the readme file for details:
1. LULC: land use and land cover parameters
2. VEGE: vegetation paramertes
3. SOIL: soil parameters
4. TOPO: topography parameters

Due to storage limitations, the LAI and SAI files are stored in the following repositories:

1) LAI 2001-2005:  https://zenodo.org/records/10815637; 2) LAI 2006-2010: https://zenodo.org/records/10815649; 3) LAI 2011-2015: https://zenodo.org/records/10815658; 4) LAI 2016-2020: https://zenodo.org/records/10815662;

5) SAI 2001-2005: https://zenodo.org/records/10815623; 6) SAI 2006-2010: https://zenodo.org/records/10815629; 7) SAI 2011-2015: https://zenodo.org/records/10790724; 8) SAI 2016-2020: https://zenodo.org/records/10790758

Files

LULC.zip

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Additional details

Identifiers

Related works

Is metadata for
Dataset: 10.5194/essd-2023-242 (DOI)

Dates

Available
2024-01-17

References

  • Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, in review, 2023.