The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections
AbstractEarth system models (ESMs) typically simplify the representation of land surface spectral albedo to two values, which correspond to the photosynthetically active radiation (PAR, 400–700 nm) and the near infrared (NIR, 700–2,500 nm) spectral bands. However, the availability of hyperspectral observations now allows for a more direct retrieval of ecological parameters and reduction of uncertainty in surface reflectance. To investigate sensitivity and quantify biases of incorporating hyperspectral albedo information into ESMs, we examine how shortwave soil albedo affects surface radiative forcing and simulations of the carbon and water cycles. Results reveal that the use of two broadband values to represent soil albedo can introduce systematic radiative‐forcing differences compared to a hyperspectral representation. Specifically, we estimate soil albedo biases of ±0.2 over desert areas, which can result in spectrally integrated radiative forcing divergences of up to 30 W m⁻², primarily due to discrepancies in the blue (404–504 nm) and far‐red (702–747 nm) regions. Furthermore, coupled land‐atmosphere simulations indicate a significant difference in net solar flux at the top of the atmosphere (>3.3 W m⁻²), which can impact global energy fluxes, rainfall, temperature, and photosynthesis. Finally, simulations show that considering the hyperspectrally resolved soil reflectance leads to increased maximum daily temperatures under current and future CO₂ concentrations.
© 2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. Copyright 2023. All rights reserved. Part of this research was funded by Eric and Wendy Schmidt by recommendation of the Schmidt Futures program and by the Hopewell Fund. We acknowledge high-performance computing support from Cheyenne provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. A portion of this work was supported by the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission. This work was supported in part by Resnick Sustainability Institute. Data Availability Statement. The scripts to generate the global hyperspectral soil albedo map used in this study can be found in soil.jl (https://doi.org/10.5281/zenodo.7662527). Code and documentation of the in-development CliMA-Land model are publicly available at https://github.com/CliMA/Land and Zenodo (https://doi.org/10.5281/zenodo.7662527). Simulations using NCAR's Community Atmosphere Model (CAM-6.0, http://www.cesm.ucar.edu/models/cam), coupled with the Community Land Model (CLM 5.0, http://www.cgd.ucar.edu/tss/clm/) with prescribed surface ocean temperatures, a river transport model (MOSART, https://www.cesm.ucar.edu/models/cesm2/river) and the Los Alamos Sea Ice Model (CICE, https://www.cesm.ucar.edu/models/cice). The global reflectance maps and diagnostics plots of CAM and CLM are available in https://doi.org/10.5281/zenodo.7996096. The authors declare no conflicts of interest relevant to this study.
Published - AGU_Advances_-_2023_-_Braghiere.pdf
Supplemental Material - 2023av000910-sup-0001-supporting_information_si-s01.docx