1. Introduction
Soil background albedo serves as the lower boundary condition of vegetation radiative transfer schemes
in Earth system models (ESMs). In most ESMs, soil albedo is treated as fixed values in two spectral broad-
bands, namely, the photosynthetically active radiation band (PAR, 400–700 nm) and the near infra-red band
(NIR, 700–2,500 nm). These two broadbands are resolved by efficient radiative transfer schemes in the land
component of current ESMs, the so-called two-stream scheme (Braghiere et al.,
2019
; Sellers,
1985
). However,
recent advances in imaging spectroscopy have allowed for better modeling of hyperspectral reflectance of the
canopy-soil system (Braghiere et al.,
2021a
; Jiang & Fang,
2019
). In addition, particular wavelengths within the
PAR spectrum have varying quantum yields, impacting photosynthesis and transpiration differently (Cernusak
& Kauwe,
2022
; Liu & van Iersel,
2021
). Hyperspectral data can also be used to map different vegetation prop-
erties, such as canopy water content, leaf nitrogen and phosphorus compositions (Knyazikhin et al.,
2013
), and
a range of traits related to photosynthesis, respiration, and decomposition of plant material (Butler et al.,
2017
;
Cawse-Nicholson et al.,
2021
). However, current ESMs usually cannot calculate radiative transfer at a high spec-
tral resolution (∼10 nm) (Poulter et al.,
2023
), which limits their ability to utilize the additional information
provided by hyperspectral measurements for model calibration (Braghiere et al.,
2021a
).
Recent developments have moved away from the broadband approach, allowing for direct inversion of ecosys-
tem properties from high spectral resolution remotely sensed data (Dutta et al.,
2019
) and reducing uncer
-
tainty in surface albedo (Majasalmi & Bright,
2019
). However, these advances require explicit information of
Abstract
Earth 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 CO
2
concentrations.
Plain Language Summary
Due to computational and observational constraints, scientists must
make approximations when modeling the climate system. One simplification is to reduce soil background
albedos to two broad spectral bands, which can cause biases in climate models by not fully accounting for the
changing color of sunlight throughout the day. The limitations of the broadband approximation also affect
predictions of the global carbon and water cycles due to differences in radiation absorbed by vegetation.
BRAGHIERE ET AL.
© 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.
The Importance of Hyperspectral Soil Albedo Information for
Improving Earth System Model Projections
R. K. Braghiere
1,2
, Y. Wang
1
, A. Gagné-Landmann
1
, P. G. Brodrick
2
, A. A. Bloom
2
,
A. J. Norton
2
, S. Ma
2,3
, P. Levine
2
, M. Longo
4
, K. Deck
1
, P. Gentine
5
, J. R. Worden
2
,
C. Frankenberg
1,2
, and T. Schneider
1,2
1
California Institute of Technology, Pasadena, CA, USA,
2
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA,
3
Joint Institute for Regional Earth System Science and Engineering, University of California at Los
Angeles, Los Angeles, CA, USA,
4
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA, USA,
5
Department of Earth of Environmental Engineering, Columbia University, New York, NY, USA
Key Points:
•
Soil albedo differences between
hyperspectral and broadband
representations over desert areas can
reach ±20%
•
Spectrally integrated radiative forcing
biases can reach up to 30 W m
−2
over
desert areas
•
Climate simulations show impacts
on global energy fluxes and
photosynthesis due to hyperspectral
versus broadband soil albedo
differences
Supporting Information:
Supporting Information may be found in
the online version of this article.
Correspondence to:
R. K. Braghiere,
renato.k.braghiere@jpl.nasa.gov
Citation:
Braghiere, R. K., Wang, Y.,
Gagné-Landmann, A., Brodrick, P. G.,
Bloom, A. A., Norton, A. J., et al.
(2023). The importance of hyperspectral
soil albedo information for improving
Earth system model projections.
AGU
Advances
,
4
, e2023AV000910.
https://doi.
org/10.1029/2023AV000910
Received 1 MAR 2023
Accepted 27 MAY 2023
Author Contributions:
Conceptualization:
R. K. Braghiere,
Y. Wang, A. Gagné-Landmann, A. A.
Bloom, C. Frankenberg
Formal analysis:
R. K. Braghiere,
Y. Wang, A. Gagné-Landmann, P. G.
Brodrick
Funding acquisition:
A. A. Bloom, J. R.
Worden, C. Frankenberg, T. Schneider
10.1029/2023AV000910
Peer Review
The peer review history for
this article is available as a PDF in the
Supporting Information.
RESEARCH ARTICLE
1 of 14
AGU Advances
BRAGHIERE ET AL.
10.1029/2023AV000910
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hyperspectral soil reflectance globally and at spatial resolutions as high as 50 km. Currently, ESMs determine
soil background albedo through optimization methods (“soil colors”) to replicate remote sensing observations of
snow-free surface albedo at local noon (P. J. Lawrence & Chase,
2007
), which may conceal compensating errors.
However, broadband radiative transfer schemes are used to calculate the model equivalent surface albedo based
on climatological monthly soil moisture along with vegetation parameters of plant functional types, leaf area
index (LAI), and stem area index (SAI), generating highly parameterized global maps of soil albedo with two
associated fixed values (PAR and NIR reflectances), with a strong spectral discontinuity at 700 nm (Figures
1a
and
1b
).
Global data sets of soil spectroscopy (Viscarra Rossel et al.,
2016
) and new hyperspectral soil algorithms (Jiang
& Fang,
2019
) allow the development of continuous soil reflectance curves across the shortwave radiation spec-
trum (400–2,500 nm). This capability enables global calculations using hyperspectral radiative transfer schemes
in ESMs. Moreover, differences in soil albedo between the highly resolved and more coarsely resolved spectral
curves highlight systematic divergences in surface albedo and radiative forcing. These divergences in surface
reflectance propagate into other radiative partitioning terms, such as absorptance and transmittance, impacting
sun-induced fluorescence (Braghiere et al.,
2021a
), photosynthesis (Braghiere et al.,
2020a
), and evapotranspi-
ration (Viskari et al.,
2019
).
We aim to demonstrate sensitivity and quantify biases in surface albedo and following divergences in radiative
forcing globally, by focusing on soils in a desert scheme without vegetation. We also investigate how the addition
of vegetation on top of soils worldwide affects canopy reflectance and the resulting radiative forcing at the top of
the atmosphere (TOA). To address these questions, we compare a global hyperspectral land model, CliMA-Land
(Braghiere et al.,
2021a
; Wang et al.,
2021
), using a broadband representation of soil albedo to one that is hyper
-
spectrally resolved. Our analyses involve a few different scenarios, including a global desert scheme with barren
soil, an actual simulation with vegetation on top of the soil, and the photosynthetic response of the land surface,
which is driven by differences in the amount of absorbed radiation by the vegetation.
Furthermore, we perform coupled atmosphere-land simulations with an ESM to evaluate the impacts of blue
versus red light on surface fluxes and climatological variables in the present climate and under the influence of
elevated CO
2
. Particles in the atmosphere smaller than the incident electromagnetic radiation induce Rayleigh
scattering, where short wavelengths (blues) are scattered more efficiently than long wavelengths (reds); this is
the effect that gives the sky its blue hue. At large solar zenith angles (>60°), light must pass through more atmos-
pheric mass, leading to blue light being scattered out and making the sky appear red. Rayleigh scattering effects
mean that longer wavelengths make up a higher portion of incident PAR (Kravitz et al.,
2012
), the implications of
which cannot be estimated by ESMs with only two broad spectral bands. Diurnal variations in the red/blue ratio
of incoming PAR are usually accounted for by atmospheric radiative transfer models, but not by the broadband
land surface radiative transfer model, which might cause diurnal shortwave forcing divergences.
The aim of this study is to show how incorporating a more continuous, hyperspectral representation of shortwave
soil albedo affects the surface radiative forcing and simulations of the carbon and water cycles in ESMs. The
objectives of the study are to estimate soil albedo biases between hyperspectral and broadband representations,
to identify the spectrally integrated radiative forcing divergences between both cases, and to analyze the impacts
of these differences on global energy, water and carbon fluxes. By achieving these objectives, the study enables
the incorporation of hyperspectral soil background albedo information in ESMs, which will reduce uncertainty
in surface reflectance.
2. Methods
2.1.
Global Map of Hyperspectral Albedo
Soil albedo depends on soil intrinsic characteristics (e.g., mineral composition, color, organic matter, soil rough-
ness) and extrinsic characteristics, such as volumetric water content. The soil color and water dependencies are
usually modeled empirically in land surface models in two broadbands. In this study, we represent the effects
of soil color and water content globally at high spectral resolution using a combination of the Community
Land Model version 5 (CLM5) soil color scheme (P. J. Lawrence & Chase,
2007
; D. M. Lawrence et al.,
2019
)
and the General Spectral Vector (GSV) soil albedo model (Jiang & Fang,
2019
) based on previous work by
Condit (
1972
) and Price (
1990
). The GSV model simulates hyperspectral soil reflectance from multispectral
Investigation:
R. K. Braghiere, Y. Wang,
A. Gagné-Landmann, P. G. Brodrick
Methodology:
R. K. Braghiere, Y. Wang,
A. Gagné-Landmann, P. G. Brodrick, A.
A. Bloom, A. J. Norton, M. Longo, K.
Deck, C. Frankenberg, T. Schneider
Writing – original draft:
R. K.
Braghiere, Y. Wang, A. Gagné-
Landmann, P. G. Brodrick, A. A. Bloom,
A. J. Norton, S. Ma, P. Levine, M. Longo,
K. Deck, P. Gentine, J. R. Worden, C.
Frankenberg, T. Schneider
2576604x, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023AV000910 by California Inst of Technology, Wiley Online Library on [18/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
AGU Advances
BRAGHIERE ET AL.
10.1029/2023AV000910
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soil reflectance by fitting a discrete number of coefficients associated with known soil albedo values at differ
-
ent wavelengths to a linear combination of known spectral vectors. The spectral vectors were derived from dry
and humid observed soil reflectance data, which included 23,871 soil spectra from 400 to 2,500 nm, using a
common matrix decomposition method. Four vectors were provided, three of which are from databases for
completely dry soils (
υ
dryi), and one vector from databases for soils at different humidity levels (
υ
wet). Soil
albedo was modeled as a linear combination of
i
= 1, ..., 4 spectral vectors and their associated coefficients
c
dryi
and
c
wet
.
The broadband spectral curve needed to fit the GSV coefficients was obtained from the global soil color map in
CLM5 (see Supporting Information
S1
). The soil moisture function was modified as follows:
훼훼
band
=
훼훼
band
,
dry
⋅
(1−
휃휃
)+
훼훼
band
,
wet
⋅
(
휃휃
)
(1)
where band corresponds to PAR or NIR, either dry or wet (saturated) in Table S1 in Supporting Information
S1
,
and θ is the volumetric soil water content (m
3
m
−3
) at the top of the soil (0–7 cm, the surface is at 0 cm) from
ERA5 (Hersbach et al.,
2020
).
The fitting method minimizes the sum of the square error of: (a) two GSV vectors fitting points (2P); (b) two GSV
vectors fitting curves (2C); (c) two GSV vectors, fit-ting one point and one curve, or hybrid method (2H); (d) four
GSV vectors fitting two curves (4C); and (e) four GSV vectors fitting a point for PAR and a curve for NIR (4H).
“Point” refers to when the whole spectral window (either PAR or NIR) are considered single points (the average),
“curve” refers to when the whole spectral window (either PAR or NIR) are considered flat lines (on the average),
and “hybrid” means the PAR albedo was considered a point and the NIR albedo was considered a flat line. The
associated numbers indicate how many GSV vectors were used in the fitting method. A detailed description of
the method is given in Note S1 in Supporting Information
S1
.
All four GSV vectors were used to upscale two broadbands into a hyperspectral curve using a hybrid methodol-
ogy, where the PAR albedo was considered a point and the NIR albedo was considered a flat line. See Supporting
Information
S1
for a diagram of the fitting method.
Figure 1.
(a) Global average broadband soil albedo following the “soil color” scheme presented in P. J. Lawrence and Chase (
2007
) at 1° spatial resolution and the
equivalent hyperspectral soil albedo calculated following the hybrid method (see Section
2
) based on all four spectral vectors of the General Spectral Vector model
(Jiang & Fang,
2019
). (b) Difference between the continuous hyperspectral soil albedo and the discontinuous broadband albedo (∆
α
s
). Global spatial deviations between
the hyperspectral and the broadband cases in (c) blue (404–504), (d) red (624–697), and (e) far-red (702–747 nm) spectral regions, respectively.
2576604x, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023AV000910 by California Inst of Technology, Wiley Online Library on [18/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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2.2.
Reanalysis Data
The total surface downward shortwave radiation flux (SW), the direct (clear-sky) surface downward shortwave
radiation flux (SW
dir
), and the incoming solar radiation flux at TOA were extracted from the fifth generation
ECMWF global reanalysis, ERA5 (Hersbach et al.,
2020
) for 2020. The diffuse surface downward radiation flux
was calculated as the difference between the total and the direct fluxes at the surface. The spectral atmospheric
transmissivity was calculated as the ratio between SW and the downward shortwave radiation flux at TOA (
퐴퐴퐴퐴
↓
TOA
). The volumetric soil water content (m
3
m
−3
) at the top of the soil from ERA5 was used to calculate changes
in soil albedo due to soil moisture following Equation
1
.
2.3.
Field Sites
Hyperspectral soil reflectance was acquired with a GER 3700 spectroradiometer (Geophysical Environ. Res.
Corp., Millbrook, NY) over the 400–2,500 nm wavelength region at 1.5 nm intervals in the 400–1,050 nm region
and at 9 nm intervals in the region >1,050 nm taken from Lobell and Asner (
2002
). The samples were illuminated
by two 300 W quartz-halogen lamps mounted on the arms of a camera copy stand 50 cm above the sample at a
45° illumination zenith angle. The spectroradiometer was positioned 40 cm from the sample surface at a 0° view
zenith angle. With the 3° optics on the spectroradiometer, the diameter of the field of view at the sample was
2.1 cm. The illumination and view angles were chosen to minimize shadowing and emphasize the fundamental
spectral properties of the soils.
Five topsoil samples were used in this study and provided a range of colors and textures (Table S2 in Supporting
Information
S1
). Spectral data were acquired at nine evenly spaced locations on each sample. After acquiring the
spectral reflectance data from the oven-dried soils, the soils in the trays were saturated with water. The relative
water content was calculated as the water content divided by the maximum water content of each sample. For
further details on data acquisition refer to Daughtry (
2001
).
2.4.
AVIRIS and SMAP Data
As part of Western Diversity Time Series, the Airborne Visible Infrared Imaging Spectrometer “Classic”
(AVIRIS-C) was flown on the ER-2 high-altitude research aircraft in Niland, Southern California (33.2°N,
115.1°W) on 25 June 2018 around 18:00 UTC. AVIRIS-C is a whiskbroom spectrometer that measures radi-
ance from 380 to 2,500 nm with a spectral sampling of approximately 10 nm, for a total of 224 contiguous
bands (Green et al.,
1998
). The hemispherical-directional reflectance factor was estimated using an open-source
implementation of optimal estimation, Isofit v2.9.2, accelerated with radiative transfer and local atmospheric
emulation (Brodrick et al.,
2021
; Thompson et al.,
2018
,
2020
). Ten flight lines were mosaicked together, and
three sites were selected in areas with relatively low vegetation cover (less than 5%), as determined by spectral
mixture analysis.
Unlike the laboratory spectra, AVIRIS data were acquired under natural conditions, which will have included the
effects of soil surface crusts/seals and soil roughness and shadows. These factors can significantly influence the
reflectance spectra of soils, especially in the shortwave infrared region (Boardman et al.,
1995
; Goetz et al.,
1985
;
Roberts et al.,
1998
). Therefore, the AVIRIS reflectance spectra are expected to be more realistic than the labo-
ratory spectra and more representative of the actual soil conditions in the study area.
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture product provides global,
3-hourly, 9-km resolution estimates of top soil (0–5 cm) soil moisture assimilating SMAP L-band (1.4 GHz) daily
microwave brightness temperature into the NASA Catchment land surface model (Ducharne et al.,
2000
; Koster
et al.,
2000
). Reflectance estimates were averaged from AVIRIS-C data to match the resolution of corresponding
SMAP footprints (Figure
2c
).
2.5.
CliMA-Land Model
The CliMA-Land model includes a hyperspectral canopy radiative transfer, soil water movement, plant water
transport, stomatal regulation, and simulates water, carbon, and energy fluxes in a modular manner being part of
a new ESM developed by the Climate Modeling Alliance (CliMA). The CliMA-Land radiative transfer scheme
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is based on the vertically heterogeneous mSCOPE (Yang et al.,
2017
), which makes use of Fluspect (Vilfan
et al.,
2016
) to simulate leaf reflectance, transmittance, and fluorescence at leaf level, and a SAIL based formu-
lation (Verhoef,
1984
) to compute spectrally resolved radiative transfer, as well as emitted fluorescence (van
der Tol et al.,
2016
). However, some important changes were incorporated into the new CliMA-Land radiative
transfer scheme including: (a) accounting for carotenoid light absorption as part of absorbed photosynthetically
active radiation (Wang & Frankenberg,
2022
; Wang et al.,
2021
,
2023
) and (b) accounting for horizontal canopy
structure with the inclusion of a clumping index (Braghiere et al.,
2021a
).
We used the gridded Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product at 0.5° spatial
resolution and 8-day temporal resolution (Yuan et al.,
2011
), weekly mean leaf chlorophyll content to represent
seasonality of canopy greenness (Croft et al.,
2020
), and assumed leaf carotenoid content being 1/7 of the chlo-
rophyll content, specific leaf area as the inverse of leaf mass per area (Butler et al.,
2017
), leaf photosynthetic
capacity represented by the maximum carboxylation rate at a reference temperature of 25°C (Vcmax25) from a
machine learning based product (Luo et al.,
2021
), the maximum electron transport rate at a reference tempera-
ture of 25°C (Jmax25), and respiration rate at a reference temperature of 25°C (Rd25) scaled from Vcmax25 as
Jmax25 = 1.67.Vcmax25 and Rd25 = 0.015.Vcmax25, a canopy height map was used to initialize plant hydraulic
architecture within each simulation (Simard et al.,
2011
), and MODIS clumping index was used to represent
canopy horizontal structure (Braghiere et al.,
2019
; He et al.,
2012
). Gridding Machine (Wang et al.,
2022
) is a
tool developed for CliMA-Land that simplifies the replacement of plant trait maps. These maps contain impor
-
tant information about vegetation characteristics and are crucial for accurately representing vegetation processes
in the model. With Gridding Machine, researchers can easily incorporate new or improved plant trait maps into
Figure 2.
(a) Soil color map at 0.23° resolution over the continental USA from the Community Land Model version 5 (CML5). Locations of soil samples and
AVIRIS + Soil Moisture Active Passive (SMAP) data are indicated (see Methodology for a description of sites and data). (b) Scatter plot of photosynthetically active
radiation band and near infra-red band soil albedos for the default soil moisture scheme in CLM5 (left) versus the updated soil moisture scheme version (right).
Different colors indicate different sites, while shading indicates different levels of soil moisture. (c) Diagram showing the soil color map resolution from CLM5 in
two-band with the AVIRIS + SMAP data coverage indicated (left). The observed shortwave hyperspectral soil albedo is shown (black dotted line) with the two-band
albedo from CLM5 for soil color 15 (blue line), and the fitted curve using Four Bands Fitting Hybrid method (4H, brown line). See methodology for a complete
description of the fitting methods. (d) RMSE of soil albedo for the CLM5 two-band method and all the other fitting methods (2P: Two Bands Fitting Point; 2C: Two
Bands Fitting Curve; 2H: Two Bands Fitting Hybrid; and 4C: Four Bands Fitting Curve). All the other experiments carried out the 4H method due to smaller associated
errors.
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AGU Advances
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CliMA-Land, allowing for the integration of the latest scientific advancements and data sources. This flexibility
enhances the model's capabilities in capturing the complexities of vegetation processes and improves predictions
of ecosystem dynamics.
2.6.
Climate Simulation
We ran simulations using National Center for Atmospheric Research (NCAR) Community Atmosphere Model
(CAM) 6, coupled with CLM5 and prescribed surface ocean temperatures, a river transport model (MOSART)
and the Los Alamos Sea Ice Model (CICE). Simulations were run at a 30-min time step with a resolution of 1.9°
by 2.5° for 50 years. We specifically ran the model using compset F_2000_SP, which uses the models described
above. We ran the model with no dynamic vegetation response; atmospheric CO
2
was held constant at 367 ppm
for one set of runs and 700 ppm for the other set. Present-day prescribed surface ocean temperatures were used
in both CO
2
scenarios.
We simulate global climate for scenarios where the background soil albedo behaves as if the diffuse incident
PAR was completely blue (Figure
1c
), and the incident direct PAR was completely red (Figure
1d
), but only
for sun zenith angles over 60°. The justification for the general representation of the extreme sun zenith angles
(>60°) used in these climate simulations comes from radiative transfer model results under clear sky conditions
that indicate most of the direct photosynthetically active irradiance is skewed to red, while most of the diffuse
photosynthetically active irradiance is skewed to blue (Kravitz et al.,
2012
; Mayer & Kylling,
2005
). Supple-
mentary runs were performed assuming the background soil albedo acted as: (a) global PAR radiation was blue,
(b) global PAR radiation was red, and (c) global NIR radiation was far-red. These large changes to soil albedo
were chosen to clearly demonstrate the extremes of change. Similarly, analysis considering all sun zenith angles
is presented in Supporting Information (Table S3 in Supporting Information
S1
). The canopy albedo in CLM
was calculated using the two-stream radiative transfer scheme, which is a function of LAI and SAI, leaf albedo
and transmittance, soil albedo, and the cosine of the zenith angle of the incident beam radiation, among other
parameters (Sellers,
1985
). We averaged the final 30 years of the following variables (collected monthly) from
the modified CLM5 with new soil background albedos: net shortwave radiation flux at TOA (W m
−2
); latent heat
flux (W m
−2
); maximum daily 2 m air temperature (K); photosynthesis (μmol m
−2
s
−1
); precipitation (mm day
−1
);
and cloud cover (%) from CAM6.
3. Results
In this study, we represent the effects of soil color and soil water volumetric content globally using a combination
of the CLM5 soil color scheme (P. J. Lawrence & Chase,
2007
; D. M. Lawrence et al.,
2019
) and the GSV soil
albedo model (Jiang & Fang,
2019
). First, we updated the impact of top layer (0–5 cm) soil moisture content in
CLM5 by linearly averaging the soil albedos for saturated and dry soil color classes (Equation
1
). Figure
2
shows
a validation of the updated method for calculating the impact of soil moisture on PAR and NIR soil background
albedos.
Figure
2a
shows the CLM5 soil color map over the continental USA with five locations where soil samples
were collected (Lobell & Asner,
2002
) and one location in California where hyperspectral data was collected
with AVIRIS-C (hereafter “AVIRIS”) (Green et al.,
1998
,
2020
). Soil color was combined with measurements
of soil moisture collected with NASA's SMAP mission (Reichle et al.,
2019
) and validated with AVIRIS data
(Figure
2c
). The updated soil moisture scheme increased the predictive skill of top soil layer impact on soil albedo
(from
R
2 = 0.29 to
R
2 = 0.54). The data points show in Figure
2a
are independent spectral soil measurements,
either from ground data or a combination of AVIRIS and SMAP, and therefore, they were not used in the training
processes of the GSV model. Likewise, in Figures
2b
and
2d
, the updated CLM5 soil moisture scheme is vali-
dated with independent data, as well as the fitting methods.
The two-band soil albedo in CLM5 with the updated soil moisture scheme was then used with the GSV vectors to
upscale the broadband version into a hyperspectral curve using the different methodologies described in Note S1
in Supporting Information
S1
. The different fitting methodologies minimize the sum of the square error between
the average values for each part of the spectrum separately. A complete description of the method can be found
in Methods, and a visual diagram can be found in Figure S3 in Supporting Information
S1
. Figure
2d
shows the
RMSE of soil background albedo for the CLM5 two-band method and all the other fitting methods. The method
4H presented the lowest associated RMSE and it was used in all the experiments that followed.
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BRAGHIERE ET AL.
10.1029/2023AV000910
7 of 14
Figure
1
shows the global average broadband soil albedo following the “soil color” scheme at 1° resolution
(P. J. Lawrence & Chase,
2007
) and the hyperspectral soil albedo calculated using the GSV model (Jiang &
Fang,
2019
). Averaged across the shortwave radiation spectrum (400–2,500 nm), the difference between the
hyperspectral curve and the broadband curve are negative and less than 1% (∆
α
s
= −0.007 ± 0.005), indicat-
ing that overall, the solar spectrum reflectance is slightly greater when assuming a broadband soil albedo. In
some places on Earth, especially over deserts with sandy soils, these averaged spectral differences can reach
|∆
α
s
| > 0.02 (Figure S1 in Supporting Information
S1
). However, for particular wavelengths (e.g., blue and
far-red), the differences in spectral albedo can be substantial (|∆
α
s
| > 0.2). The largest differences between the
hyperspectral and broadband soil background albedos are found in three distinct bands of the shortwave radiation
spectrum, that is, far-red (702–747 nm with average ∆
α
s
= −0.046 ± 0.033, blue (404–504 nm) with average
∆
α
s
= −0.038 ± 0.033, and red (624–697 nm) with average ∆
α
s
= 0.035 ± 0.029 (Figures
1b–1d
).
The net shortwave surface radiation budget and thus surface temperature are directly affected by changes in
surface albedo. The shortwave radiative forcing has been used to estimate the global impact of regional changes
in surface albedo (Kramer et al.,
2021
; Loew et al.,
2014
). In this study, the shortwave radiative forcing is inter
-
preted as a disturbance of the reflected radiances caused by variations in soil and surface albedos. Therefore, the
soil albedo-induced radiative forcing as presented here should not be interpreted as estimates of radiative forcing
from different climate forcing agents (Bright & Lund,
2021
), but rather as a proxy of the surface shortwave energy
imbalance caused by assuming a broadband background soil albedo representation versus a hyperspectral one.
The albedo-induced radiative forcing (
퐴퐴
RF
훼훼
푠푠
) is given by
RF
∼
=−
푅푅
↓
TOA
Γ
↕
훼훼
Δ
훼훼
푠푠
=
∫
2500nm
400nm
−
푅푅
↓
TOA
(
휆휆
)Γ
↕
푎푎
(
휆휆
)Δ
훼훼
푠푠
(
휆휆
)
푑푑휆휆
(2)
where
퐴퐴
R
↓
TOA
(
λ
) (mW m
−2
nm
−1
) is the TOA spectral incoming solar radiation flux following Kurucz (Kurucz,
1992
)
(Figure S2 in Supporting Information
S1
),
퐴퐴
Γ
↕
a
(
λ
) is the two-way spectral atmospheric transmissivity that is given
as the product of the downward and upward spectral atmospheric transmissivities, assumed to be equal to one
another, and ∆
α
s
(
λ
) is the difference in spectral soil albedo between the hyperspectral and the broadband cases.
We calculate radiative forcing for barren soils and for soils with vegetation using the CliMA-Land model
(Figure
3a
). The mean yearly climatological values for 2020 from ERA5 of: (a) direct and diffuse incident short-
wave irradiance, (b) volumetric soil moisture at the first soil layer, and (c) incident shortwave radiation at the
TOA were used. Yearly means of all the canopy related variables (LAI, clumping index, chlorophyll content, soil
colors) were also used in these calculations. For barren soils globally, the absolute soil albedo-induced radiative
forcing is greater than the case when vegetation canopies and soils are considered together. Over areas with dense
vegetation (i.e., tropical and boreal forests), the difference between a hyperspectral soil representation and a
Figure 3.
(a) Global average spectral radiative forcing (mW m
−2
nm
−1
) caused by the difference between a hyperspectral
and broadband background soil albedo over soil only (continuous line) and for soil + canopy (dashed line) from CliMA-Land
using yearly climatological values for 2020 (see Methodology). (b) The spectrally integrated radiative forcing between the
continuous hyperspectral soil albedo and the discontinuous broadband albedo for soil + vegetation canopy from CliMA-Land.
The global mean radiative forcing with standard deviation is also shown.
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AGU Advances
BRAGHIERE ET AL.
10.1029/2023AV000910
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broadband one is attenuated because solar radiation interacts less with the background soil in the presence of an
optically active vegetation (Figure
3b
).
The spectral discontinuity in 700 nm, also known as the “red edge,” is more applicable to vegetated surfaces, as
it reflects the transition from PAR to NIR. This feature is often used to determine the two-band spectral curve of
soil background albedo in ESMs, but for barren soils or areas with low vegetation cover, the spectral discontinuity
becomes less relevant in determining surface reflectance. This can lead to inaccuracies in shortwave radiation
simulations in ESMs that assume a spectral discontinuity in the soil background albedo at 700 nm. Additionally,
the high variability in LAI throughout the year can lead to significant seasonal biases in shortwave radiation
simulations based on this assumption.
Over desert areas, the integrated radiative forcing can be as high as 30 W m
−2
, mostly due to blue and far-red
wavelengths. In the PAR spectral region, although the blue radiative forcing and the red one have opposite
signs, the blue radiative forcing is at least 50% higher than the red one, causing a positive bias over the entire
PAR region. In the NIR spectral region, most of the radiative forcing is positive and associated with the spectral
region 700–1,000 nm. The global-mean integrated radiative forcing bias including soil and vegetation canopy is
therefore positive and it is equal to 3.55 W m
−2
. We conducted an additional analysis to compute the global-mean
radiative forcing bias including ocean points set to zero, to further our initial analysis that used only land-based
reference values. The results show a smaller value (1.20 W m
−2
) but still positive (see Figure S6 in Supporting
Information
S1
).
This difference in soil albedo-induced global radiative forcing propagates into other ESM components, present-
ing further impacts on climate simulations. For instance, at CO
2
concentrations for the year 2000 (367 ppm),
accounting for the different spectral characteristics of direct (red) versus diffuse (blue) light for sun zenith angles
over 60° decreases average latent heat fluxes (Figure
4b
and Table
1
), cloud cover (Figure
4g
), and rainfall
(Figure
4d
). At current and future CO
2
concentrations, maximum daily temperature (Figure
4c
) increases when
considering the spectral effect of direct red versus diffuse blue light on soil reflectance. Photosynthesis decreases
for current CO
2
concentrations (Figure
4e
) but increases in the future relative to biases in blue/red ratio. The same
analysis is presented in Supporting Information
S1
considering all sun zenith angles (Table S3 in Supporting
Information
S1
).
The maps (Figure
4
) show a significant decrease in net solar flux at TOA over Alaska and northern/western
Canada, as well as parts of central Siberia. By contrast, over Europe and eastern Asia, we find an increase in
net solar flux at TOA. Over most tropical forests, there is an increase in net solar flux at TOA, although parts
of northern Amazon, eastern Africa, and southern/central Australia present a decrease in net solar flux at TOA.
Spatial patterns of maximum daily temperature tend to follow changes in net solar flux at TOA, with cloud cover
and rainfall patterns showing the opposite behavior.
A validation with observation-based products is shown in Figures S4 and S5 in Supporting Information
S1
.
The bias, RMSE, correlation coefficient, and overall score (the weighted average between bias and RMSE,
given double weight to RMSE) for the control global run of CLM 5.0 coupled to CAM 6.0 (control), the modi-
fied version of the model considering diffuse light as blue and direct light as red for sun zenith angles larger
than 60° (color) and observation products for precipitation in mm d
−1
from Willmott-Matsuura (Willmott &
Matsuura,
2018
), all-sky albedo from MODIS (2001–2003) (Schaaf et al.,
2002
), latent heat in W m
−2
and photo-
synthesis in gC m
−2
d
−1
from FLUXNET (1982–2008) (Jung et al.,
2011
) are shown in Table S4 in Supporting
Information
S1
. A validation for atmospheric variables is shown Table S5 in Supporting Information
S1
, includ-
ing the Clouds and Earth's Radiant Energy Systems (CERES) Energy Balanced and Filled (EBAF) shortwave
Cloud Forcing product at 1° resolution globally from March 2000 to February 2017 (Kato et al.,
2018
) showing
a slight improvement in the space-time correlation coefficient between the hyperspectral version of the model
and observations.
4. Discussion
ESMs can propagate errors into several parts of the climate system by making simplistic assumptions about the
hyperspectral nature of the shortwave radiation. Understanding how global changes affect the composition of
solar radiation reaching the Earth's surface is integral to accurate modeling of the global carbon cycle (Schneider,
Lan, et al.,
2017
). For instance, global changes in cloudiness and pollution may be affecting the sunlight received
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