Data from: Separating the impact of individual land surface properties on the terrestrial surface energy budget in both the coupled and uncoupled land–atmosphere system
Data files
Dec 14, 2023 version files 175.36 GB
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global_a1_cv2_hc0.1_rs100_cheyenne_atm.tar
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global_a1_cv2_hc0.1_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc0.01_rs100_cheyenne_atm.tar
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global_a2_cv2_hc0.01_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc0.05_rs100_cheyenne_atm.tar
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global_a2_cv2_hc0.05_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc0.1_rs100_cheyenne_atm.tar
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global_a2_cv2_hc0.1_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc0.1_rs200_cheyenne_atm.tar
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global_a2_cv2_hc0.1_rs200_cheyenne_lnd.tar
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global_a2_cv2_hc0.1_rs30_cheyenne_atm.tar
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global_a2_cv2_hc0.1_rs30_cheyenne_lnd.tar
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global_a2_cv2_hc0.5_rs100_cheyenne_atm.tar
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global_a2_cv2_hc0.5_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc1.0_rs100_cheyenne_atm.tar
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global_a2_cv2_hc1.0_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc10.0_rs100_cheyenne_atm.tar
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global_a2_cv2_hc10.0_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc2.0_rs100_cheyenne_atm.tar
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global_a2_cv2_hc2.0_rs100_cheyenne_lnd.tar
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global_a2_cv2_hc20.0_rs100_cheyenne_atm.tar
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global_a2_cv2_hc5.0_rs100_cheyenne_atm.tar
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global_a2_cv2_hc5.0_rs100_cheyenne_lnd.tar
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global_a3_cv2_hc0.1_rs100_cheyenne_atm.tar
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global_a3_cv2_hc0.1_rs100_cheyenne_lnd.tar
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README.md
Abstract
Changes in the land surface can drive large responses in the atmosphere on local, regional, and global scales. Surface properties control the partitioning of energy within the surface energy budget to fluxes of shortwave and longwave radiation, sensible and latent heat, and ground heat storage. Changes in surface energy fluxes can impact the atmosphere across scales through changes in temperature, cloud cover, and large-scale atmospheric circulation. We test the sensitivity of the atmosphere to global changes in three land surface properties: albedo, evaporative resistance, and surface roughness. We show the impact of changing these surface properties differs drastically between simulations run with an offline land model, compared to coupled land–atmosphere simulations that allow for atmospheric feedbacks associated with land–atmosphere coupling. Atmospheric feedbacks play a critical role in defining the temperature response to changes in albedo and evaporative resistance, particularly in the extratropics. More than 50% of the surface temperature response to changing albedo comes from atmospheric feedbacks in over 80% of land areas. In some regions, cloud feedbacks in response to increased evaporative resistance result in nearly 1 K of additional surface warming. In contrast, the magnitude of surface temperature responses to changes in vegetation height are comparable between offline and coupled simulations. We improve our fundamental understanding of how and why changes in vegetation cover drive responses in the atmosphere, and develop understanding of the role of individual land surface properties in controlling climate across spatial scales—critical to understanding the effects of land-use change on Earth’s climate.
README: Separating the impact of individual land surface properties on the terrestrial surface energy budget in both the coupled and uncoupled land–atmosphere system
https://doi.org/10.5061/dryad.18931zd41
This dataset contains climate model simulations using the CESM-SLIM model as described in Laguë et al. 2019.
Description of the data and file structure
There are 13 experiments with different combinations of uniform land surface properties. For each experiment there is one tarball for land output, and one tarball for atmospheric output. Within each tarball are netcdf files which each contain one variable. The name and unit for each variable are listed in the metadata of the netcdf files.
The naming convention for the experiments is as follows:
a1 = albedo of 0.1
a2 = albedo of 0.2
a3 = albedo of 0.3
hc represents the effective vegetation height for aerodynamic roughness, with experiments for values of 0.01, 0.05, 0.1, 0.5, 1.0, 2.0, 5.0, 10, and 20.0
rs represents surface resistance to evaporation, with experiments for values of 30, 100, 200
The central baseline experiment is global_a2_hc0.1_rs100_cheyenne
There is some data missing from global_a2_cv2_hc0.1_rs200_cheyenne and global_a2_cv2_hc20.0_rs100_cheyenne, so there are fewer time series variables available for those simulations.
Full list of experiment names:
global_a1_cv2_hc0.1_rs100_cheyenne
global_a2_cv2_hc0.01_rs100_cheyenne
global_a2_cv2_hc0.05_rs100_cheyenne
global_a2_cv2_hc0.1_rs100_cheyenne
global_a2_cv2_hc0.1_rs200_cheyenne
global_a2_cv2_hc0.1_rs30_cheyenne
global_a2_cv2_hc0.5_rs100_cheyenne
global_a2_cv2_hc1.0_rs100_cheyenne
global_a2_cv2_hc10.0_rs100_cheyenne
global_a2_cv2_hc2.0_rs100_cheyenne
global_a2_cv2_hc20.0_rs100_cheyenne
global_a2_cv2_hc5.0_rs100_cheyenne
global_a3_cv2_hc0.1_rs100_cheyenne
Code/Software
The CESM model is described here (https://www.cesm.ucar.edu), see further specifics in the paper. The SLIM land submodel described in this paper will soon be available as a pre-defined compset.
Methods
Simulations with the CESM model coupled to a simple land model as described in Laguë et al. 2019 linked here.