BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-15-4731-2018Evaluating and improving the Community Land Model's sensitivity to land coverEvaluating and improving the Community Land Model's sensitivityMeierRonnyronny.meier@env.ethz.chhttps://orcid.org/0000-0003-0200-6150DavinEdouard L.edouard.davin@env.ethz.chhttps://orcid.org/0000-0003-3322-9330LejeuneQuentinhttps://orcid.org/0000-0001-9152-3197HauserMathiashttps://orcid.org/0000-0002-0057-4878LiYanhttps://orcid.org/0000-0002-6336-0981MartensBrechthttps://orcid.org/0000-0002-7368-7953SchultzNatalie M.https://orcid.org/0000-0001-6269-2194SterlingShannonhttps://orcid.org/0000-0002-7253-4074ThieryWimhttps://orcid.org/0000-0002-5183-6145ETH Zurich, Institute for Atmospheric and Climate Science, Universitaetstrasse 16, 8092 Zurich, SwitzerlandUniversity of Illinois at Urbana-Champaign, Department of Natural Resources and Environmental Sciences, 1102 South Goodwin
Avenue, Urbana, IL 61801, USAGhent University, Laboratory of Hydrology and Water Management, Coupure links 653, 9000 Ghent, BelgiumYale University, School of Forestry and Environmental Studies, 195 Prospect Street, New Haven, CT 06511, USADalhousie University, Department of Earth Sciences, 1459 Oxford Street, Halifax NS B3H 4R2, CanadaVrije Universiteit Brussel, Department of Hydrology and Hydraulic Engineering, Pleinlaan 2, 1050 Brussels, Belgiumnow at: Climate Analytics, Ritterstrasse 3, 10969 Berlin, GermanyRonny Meier (ronny.meier@env.ethz.ch) and Edouard L. Davin (edouard.davin@env.ethz.ch)8August201815154731475721November20179January201814June20183July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://bg.copernicus.org/articles/15/4731/2018/bg-15-4731-2018.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/15/4731/2018/bg-15-4731-2018.pdf
Modeling studies have shown the importance of biogeophysical effects of
deforestation on local climate conditions but have also highlighted the lack
of agreement across different models. Recently, remote-sensing observations
have been used to assess the contrast in albedo, evapotranspiration (ET), and
land surface temperature (LST) between forest and nearby open land on a
global scale. These observations provide an unprecedented opportunity to
evaluate the ability of land surface models to simulate the biogeophysical
effects of forests. Here, we evaluate the representation of the difference of
forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST
in the Community Land Model version 4.5 (CLM4.5) using various remote-sensing
and in situ data sources. To extract the local sensitivity to land cover, we
analyze plant functional type level output from global CLM4.5 simulations,
using a model configuration that attributes a separate soil column to each
plant functional type. Using the separated soil column configuration, CLM4.5
is able to realistically reproduce the biogeophysical contrast between forest
and open land in terms of albedo, daily mean LST, and daily maximum LST,
while the effect on daily minimum LST is not well captured by the model.
Furthermore, we identify that the ET contrast between forests and open land
is underestimated in CLM4.5 compared to observation-based products and even
reversed in sign for some regions, even when considering uncertainties in
these products. We then show that these biases can be partly alleviated by
modifying several model parameters, such as the root distribution, the
formulation of plant water uptake, the light limitation of photosynthesis,
and the maximum rate of carboxylation. Furthermore, the ET contrast between
forest and open land needs to be better constrained by observations
to foster convergence amongst different land surface models on the
biogeophysical effects of forests. Overall, this study demonstrates the
potential of comparing subgrid model output to local observations to improve
current land surface models' ability to simulate land cover change effects,
which is a promising approach to reduce uncertainties in future assessments
of land use impacts on climate.
Introduction
While the forested area has stabilized or is even increasing over
Europe and North America, deforestation is still ongoing at a fast pace in
some areas of South America, Africa, and southeast Asia (;
; ; ). In addition, carbon sequestration by re- or
afforestation has been proposed as a strategy to mitigate anthropogenic
climate change (; ; Seneviratne et al., 2018), making forest loss or gain likely
an essential component of future climate change. Changes in forest coverage
impact climate by altering both the carbon cycle and
various biogeophysical properties of the land surface such as albedo,
evaporative fraction, and roughness length (; ; ; Akkermans et al., 2014;
). However, there exist considerable discrepancies in the representation
of biogeophysical effects amongst land surface models, thus generating a need
for a thorough evaluation of the representation of these effects in
individual models.
Model simulations indicate that the biogeophysical effects of historical
deforestation have been rather small on a global scale (; ; ;
; ). However, they have likely been
significant on regional and local scales, especially over areas which
experienced intense deforestation rates . Similarly, present-day
observational data, either based on in situ or remote-sensing measurements , show that biogeophysical effects of forests can strongly
influence local climate conditions. Among the different biophysical effects,
the increased surface albedo (cooling effect), the alteration of the
evaporative fraction (warming or cooling effect, depending on the region and
season), and the lower surface roughness causing a reduction of the turbulent
heat fluxes (warming effect) have been identified as the three main drivers
of the climate impact of deforestation . However, some of these biogeophysical processes are not well
represented in current land surface models. The model intercomparison
projects LUCID (Land-Use and Climate, IDentification of robust impacts) and
CMIP5 (Coupled Model Intercomparison Project Phase 5) exposed the lack of
model agreement concerning the biogeophysical impacts of historical land use
and land cover change (LULCC), especially regarding the impact on
evapotranspiration (ET) and temperature during the warm season over the
midlatitudes of the Northern Hemisphere . In addition, distinct discrepancies between present-day
temperature observations and the simulated historical effects of LULCC over
North America were identified . This highlights the need for
systematic evaluation and improvement of the representation of biogeophysical
processes in land surface models.
Observing the local climatic impact of LULCC is not straightforward. When
temporally comparing observational data over an area undergoing LULCC, it is
difficult to disentangle the effect of the LULCC forcing from other climatic
forcings (e.g., greenhouse gas forcing). To overcome this difficulty,
observational studies often spatially compare nearby sites of differing land
cover, assuming that they receive the same atmospheric forcing
e.g.,. Hence, the sensitivity of land surface
models to land cover can be evaluated best with observational data by
spatially comparing different land cover types in models. Recently,
employed a new approach to assess the local impacts of
LULCC in land surface models by comparing climate variables over tiles
corresponding to different plant functional types (PFTs) located within the
same grid cell. Since PFT tiles within the same grid cell experience exactly
the same atmospheric forcing, the resulting subgrid land cover signal
extracted by this method achieves good comparability to local observations
which contrast neighboring forest and open land sites .
Here, we aim to evaluate and improve the sensitivity of the Community Land
Model version 4.5 (CLM4.5) to land cover, using observational data of the local
contrast between forest and open land (i.e., grassland and cropland). In
Sect. of this study, we systematically analyze the
representation of the local difference of forest minus open land in albedo,
ET, and land surface temperature (LST) in CLM4.5 against the newly released
observational remote-sensing-based products of . The forest
signal in CLM4.5 is extracted by comparing tiles corresponding to forest and
open land, similar to . Given the uncertainties in
observation-based ET estimates, we further extend our evaluation by including
data from the Global Land Evaporation Amsterdam Model (GLEAM) version 3.1a
and the Global ET Assembly (GETA) 2.0
, which are based on remote-sensing and in situ
observations, respectively. Finally, a sensitivity experiment is presented in
Sect. , which explores the possibilities to better
represent the ET impact of forests in CLM4.5. This configuration of CLM4.5
incorporates modifications in root distribution, plant water uptake, light
limitation of photosynthesis, and maximum rates of carboxylation.
Methods and dataModel description and setup
CLM is the land surface component of the Community Earth System Model (CESM),
a state-of-the-art Earth system model widely applied in the climate science
community . CLM represents the interaction of the
terrestrial ecosystem with the atmosphere by simulating fluxes of energy,
water, and a number of chemical species at the interface between the land and
the atmosphere. The represented biogeophysical processes include absorption
and reflection of both diffuse and direct solar radiation by the vegetation
and soil surface, emission and absorption of longwave radiation, latent and
sensible heat fluxes from the soil and canopy, and heat transfer into the
snow and soil. Subgrid heterogeneity is taken into account in CLM by the
subdivision of each land grid cell in five land units (glacier, wetland,
vegetated, lake, and urban). The vegetated land unit is further divided into
16 tiles representing different PFTs (including bare soil). We run CLM
version 4.5 at 0.5∘ resolution for the period 1997–2010. A 5-year
(1997–2001) spin-up period is excluded from the analysis to minimize the
impact of the model initialization. The analysis of CLM4.5 therefore covers
the period of 2002 to 2010 which matches well with the observation period of
2002 to 2012 of . Assuming that the feedback of the land surface
to the atmosphere is of minor importance for the subgrid contrast between
forest and open land tiles, simulations are performed in offline mode using
atmospheric forcing from the CRUNCEP v4 reanalysis product . The land cover map and vegetation state data are prescribed based
on MODIS observations Fig. . The land
cover map from the year 2000 is kept static during the entire simulation
period, since no land cover change is required to retrieve a spatial contrast
between forest and open land. The optional carbon and nitrogen module of
CLM4.5 as well as the crop and irrigation modules are kept inactive in our
simulations.
By default, all PFTs within a grid cell in CLM4.5 share a single
soil column , implying that all PFTs experience the same soil
temperature and soil moisture (SM). Further, the surface energy balance at
PFT level is closed using the ground heat flux (GHF; i.e., GHF is calculated
as the residual of the other energy fluxes). Hence, the soil warms in the case of
an energy excess at the land surface, and vice versa. Warmer (cooler) soil in
turn will result in increased (decreased) sensible and latent heat fluxes
away from the ground and/or increased (decreased) emitted longwave radiation,
thereby counteracting the initial energy imbalance. Consequently, this model
architecture eventually results in near-zero daily mean GHF, once the soil
temperature has adjusted to an equilibrium state with a near-zero energy
imbalance. On shared soil columns (ShSCs), however, GHFs can reach
unrealistically high values for individual PFTs (Fig. a and c),
because a common soil temperature is artificially maintained for all PFTs,
which differs from their individual equilibrium states. This assumption leads
to a net GHF into the soil over open land PFTs and out of the soil over
forest PFTs for the majority of the locations across the globe, implying a
lateral subsurface heat transport from open land towards forests
. To resolve this issue, proposed a
modification of CLM4.5 which attributes a separate soil column (SeSC) to each
PFT. This modification allows the soil of individual PFTs to equilibrate to a
different temperature (Fig. ) and suppresses these
unrealistically high (lateral) GHFs (Fig. b and d). Here, we
present results from a simulation on SeSCs, called CLM-BASE, unless
stated otherwise (Table ). We also performed a simulation on
ShSCs named CLM-DFLT.
Further, we present a sensitivity experiment, named
CLM-PLUS in Sect. , in which we try to alleviate detected
biases in ET. Besides the SeSCs, four aspects in the parameterization of
vegetation transpiration (VTR) are modified in this sensitivity experiment:
The first aspect is shallower root distribution for grass- and cropland PFTs. CLM4.5 accounts for SM stress on transpiration through a stress
function βt, which ranges from 0 (when soil moisture limitation completely suppresses VTR) to 1 (corresponding to no
soil moisture limitation of VTR). Forests for the most part experience higher SM stress than open land in CLM-DFLT except in the
northern high-latitude winter (Fig. ), partly caused by the similar root distribution for all PFTs but evergreen
broadleaf trees (Fig. ). In reality, observed maximum rooting depths are considerably higher for forests than for
grassland and cropland . Likewise, in situ observations in the tropics show that grassland ET decreases
during dry periods, because grasses have only limited access to water reservoirs located below a depth of 2 m.
Hence, we aim to increase SM stress of open land PFTs and reduce their ability to extract water from the lower part of the soil,
by introducing a shallower root distribution for these PFTs (Fig. ). This root distribution was not fitted to a particular
observed root distribution. However, the new root distribution agrees better with the average rooting depth of annual grass reported by .
The second aspect is dynamic plant water uptake. Tropical forests are often observed to exhibit increased ET during dry periods, due to increased
incoming shortwave radiation . That is, despite the upper soil being dry, tropical trees still
have sufficient access to water from deeper soil layers . We aim to allow a similar behavior in CLM4.5
by introducing a dynamic plant water uptake, where plants only extract water from the 10 % of the roots with best access to SM
(example in Fig. ).
The third aspect is light limitation reduction for all C3 PFTs and enhancement for C4 PFTs. In CLM-BASE, ET of boreal PFTs is underestimated
compared to GETA 2.0 (Fig. f). Since VTR of these PFTs is only weakly affected by SM stress, light limitation for C3
plants is reduced. On the other hand, C4 grass shows a considerable positive bias in ET, which we try to alleviate by increasing
the light limitation of this PFT.
The last aspect is modified maximum rates of carboxylation (Vcmax; Table ). This PFT-specific parameter is suitable to tune VTR,
since it is not well constrained from observations and VTR in models is highly sensitive to this parameter . The new
values were chosen with the aim to alleviate biases relative to GETA 2.0 (Fig. f) and still lie well within the range of
observations collected in the TRY plant trait database . Additionally, the minimum stomatal conductance of C4
plants, which is by default 4 times larger than that of C3 plants, is reduced.
A technical description of these modifications as well as a discussion of the
effect on ET by each individual modification is provided in
Appendix .
Observational data
The data published in are used to evaluate the effects of
forests on local climate variables in CLM4.5. This data set was created by
applying a window-searching algorithm to remote-sensing LST, albedo, and ET
products from the MODerate resolution Imaging Spectroradiometer (MODIS) to
systematically compare these variables over forest and open land on
a global scale. The data of this study, hereafter referred to as MODIS, cover
the period of 2002 to 2012 and were aggregated from the initial window size
of 0.45∘×0.25∘ to 0.5∘×0.5∘
spatial resolution. Hence, the similar spatial scale of the MODIS data and
the CLM4.5 simulations allows for good comparability between these two data
sources.
We also use two additional observation-based data sets of ET to consider
uncertainties in present-day ET estimates. Various global ET
products are available which, however, exhibit substantial discrepancies
. In particular,
the algorithm from used to retrieve the MODIS ET product was
found to systematically underestimate ET compared to in situ and
catchment-scale observations . In addition,
algorithms used to infer ET from remote-sensing observations make assumptions
on how the land cover type influences ET, preventing an independent
identification of the influence of LULCCs on ET. We therefore complement our
evaluation of the ET impact of forest in CLM4.5 with two additional data
sets: GLEAM version 3.1a and GETA 2.0.
GLEAM was introduced in 2011 and revised twice, resulting
in the current version (3.1; ). It provides estimates of
potential ET for tall canopy, bare soil, and low vegetation after
. Potential ET of vegetated land surfaces is converted
into actual ET using vegetation-dependent parameterizations of evaporative
stress. Canopy interception evaporation is calculated separately using the
parameterization of . GLEAM uses surface radiation,
near-surface air temperature, surface SM, precipitation, snow water
equivalent, and vegetation optical depth observations to estimate ET globally
at 0.25∘ resolution. To maximize spatial and temporal overlap with the
MODIS observations, we choose GLEAM version 3.1 a (hereafter referred to as
GLEAM), which incorporates reanalysis input besides satellite observations.
We compare the ET estimates for tall canopy and low vegetation to model
output for forests and open land, respectively. Since interception loss is
only estimated for tall canopy, it was fully attributed to ET from forests.
GETA 2.0 is a suite of global-scale fields of actual ET for
16 separate land cover types (LCTs), derived from a collection of in situ
measurements between 1850 and 2010. Using a linear mixed effect model with
air temperature, precipitation, and incoming shortwave radiation as
predictors, yearly ET estimates for each of these 16 different LCTs have been
obtained with a global coverage and 1∘ spatial resolution. We then use
the same land cover map employed for the CLM4.5 simulations to weigh the
different LCTs in this data set and retrieve an ET value for forest and open
land (see Sect. for more details). Since our CLM4.5
simulations were conducted without irrigation, we did not include the GETA
2.0 irrigation layer. We refer to this data set as GETA in this study.
Model evaluation
The forest signal in CLM4.5 is extracted by comparing the area-weighted mean
of the variables of interest over all forest tiles to its corresponding
values over open land tiles (i.e., grassland and cropland), similar to
. As such, it becomes possible to infer a forest signal for
every model grid cell containing any forest and any open land PFT, no matter
how small the fraction of the grid cell covered by these PFTs. The different
PFT tiles within a 0.5∘×0.5∘ grid cell in our CLM4.5
simulations are subject to the exact same atmospheric forcing and are hence
comparable to the almost local effect of forests retrieved at a resolution of
0.45∘×0.25∘ in MODIS. It needs to be noted that the
MODIS observations can only be retrieved under clear-sky conditions, thereby
potentially impairing the comparability to our CLM4.5 data which are not
filtered for clear-sky days. Nevertheless, it was decided to include cloudy
days for the analysis of the CLM4.5 simulations, to preserve the
comparability to studies which do not distinguish between cloudy and
clear-sky days e.g., GLEAM; GETA;.
The Köppen–Geiger climate zones used for the analysis.
A total of 12 of the 16 PFTs of CLM4.5 are attributed to either the forest or the
open land class as described in Table . Consistent with
, open land was considered the combination of grassland and
cropland. Hence, bare soil as well as shrubland are excluded from our
analysis. Forest and open land ET of GETA was aggregated similarly using the
same land cover (LC) map as in the CLM4.5 simulations, with the LCTs of GETA attributed to
the different CLM4.5 PFTs as listed in Table . To ensure a
consistent comparison with the LST data from MODIS, we derive a radiative
temperature (Trad) from the emitted longwave radiation output
(LWup) in CLM4.5 according to Stefan–Boltzmann's law assuming
that emissivity is 1 as in Eq. 4.10 of:
Trad=LWupσ4,
with σ being the Stefan–Boltzmann constant
(5.67×10-8Wm-2K-4). Hereafter, Trad will
be referred to as LST. For the local difference of forest minus open land in
albedo, ET, daily mean LST, daily maximum LST, and daily minimum LST, we will
use the symbols Δα(f-o), ΔET(f-o),
ΔLSTavg(f-o), ΔLSTmax(f-o), and
ΔLSTmin(f-o), respectively.
To evaluate the different
CLM4.5 simulations objectively, three different metrics are calculated over
the following eight Köppen–Geiger climate zones :
equatorial humid (E-h), equatorial seasonally dry (E-sd), arid (Arid), warm
temperate winter dry (T-wd), warm temperate summer dry (T-sd), warm temperate
fully humid (T-fh), snow warm summer (S-ws), and snow cold summer (S-cs)
(Fig. ). As a first metric, the area-weighted mean for a given
variable over these climate zones (Δx‾) is calculated as
follows:
Δx‾=∑iAiΔxi∑iAi,
where Δxi is the difference of forest minus open land in variable x of all the grid cells i belonging to the
respective climate zone and Ai their areas. Secondly, the CLM4.5 simulations are compared in terms of the area-weighted
root mean squared deviation (RMSD) to the observation-based data sources:
RMSD(Δx)=∑iAiΔxisim-Δxiobs2∑iAi,
where Δxisim and Δxiobs are the simulated and
observed differences of forest minus open land in variable x. RMSD for a
Köppen–Geiger climate zone is calculated from a data pool collecting all
monthly values with data in CLM4.5 and the given observational data which lie
within the respective climate zone (except when comparing to GETA for which
only long-term annual means are available).
Lastly, the index of agreement
IA; was calculated for the same data pools as RMSD.
This dimensionless metric describes the agreement between two data sets, with
0 indicating no agreement and 1 indicating perfect agreement. By definition,
this metric is set to 0 if the two compared data sets exhibit a negative
Pearson correlation. Since results of this metric generally support those of
RMSD, they are shown in the Appendix (Fig. ).
Seasonal and latitudinal variations of Δα(f-o) in (a) the MODIS
observations and (b) CLM-BASE. Points with a mean which is insignificantly different
from zero in a two-sided t test at 95 % confidence level are marked with a black dot.
Only grid cells containing valid data in the MODIS observations were considered for the analysis of CLM-BASE. All
data from the 2002–2010 analysis period corresponding to a given latitude and a given month
are pooled to derive the sample set for the test. Panel (c) shows the zonal annual mean of both
MODIS (in green along with the range between the 10th and 90th percentiles in grey) and CLM-BASE (in red, the range between the 10th and 90th
percentiles in orange).
Note that on this subfigure results have been smoothed with a 4∘ latitudinally running mean.
ResultsEvaluation of the local effect of forests in CLM4.5Albedo
The MODIS satellite observations and CLM-BASE agree on a generally
negative Δα(f-o) (Fig. ). Effectively, MODIS
observations show slightly positive Δα(f-o) for some
latitude–month combinations concentrated in the tropics and subtropics
(Fig. ); however, these differences are mostly insignificant and
must be considered in the light of uncertainties in the MODIS observations,
which are more sparse over these regions due to frequent cloud coverage
. The negative albedo difference is amplified towards the poles
and in wintertime due to the snow masking effect . Among the
non-snow climate zones, the albedo contrast between forest and open land is
strongest in the Arid and the T-sd climate zones (Fig. a). This
could be related to the occurrence of dry periods in these climate zones
during which open land dries out more easily than forests due to their
shallower root profiles . As green leaves have lower
albedo than dry leaves and the soil, the albedo contrast between the
still-green forest and the dried-out open land would be intensified in such a
scenario . Δα(f-o) tends to be more negative in
CLM-BASE than in the satellite observations in all Köppen–Geiger
climate zones, especially in the snow climate zones. RMSD values over the
climate zones exhibit similar tendencies as the magnitudes of mean Δα(f-o) and have roughly the same magnitude of mean Δα(f-o)
(Fig. a). The exception to this are the tropical climate zones
where the magnitude of RMSD is considerably higher than the mean values of
Δα(f-o). This is likely related to the fact that MODIS observes
only a weak albedo signal of forests in these climate zones.
Area-weighted annual mean over Köppen–Geiger climate zones Fig. of
(a)Δα(f-o), (b)ΔLSTavg(f-o), (c)ΔLSTmax(f-o), and
(d)ΔLSTmin(f-o)
in MODIS (green), CLM-BASE (red), and CLM-PLUS (orange). Only grid cells containing valid data in the MODIS
observations were considered for analysis of CLM4.5. Panel (e) shows the area-weighted mean over the Köppen–Geiger
climate zones of ΔET(f-o) in MODIS (green), GLEAM (light blue), GETA (dark blue), CLM-BASE (red), and
CLM-PLUS (orange), and (f) the area-weighted mean ET for each PFT analyzed in this study according to
the GETA (dark blue), CLM-BASE (red), and CLM-PLUS (orange). The acronyms of the PFTs are defined in Table .
RMSD of CLM-BASE (red) and CLM-PLUS (orange) against MODIS observations over
Köppen–Geiger climate zones Fig. of monthly (a)Δα(f-o),
(b)ΔLSTavg(f-o), (c)ΔLSTmax(f-o), and (d)ΔLSTmin(f-o).
Panel (e) shows
the RMSD over the Köppen–Geiger climate zones of ΔET(f-o) of CLM-BASE (red), and CLM-PLUS (orange)
against MODIS (green edge), GLEAM (light blue edge), and GETA (dark blue edge). The numbers indicate the size of the
data samples used for the calculation of RMSD.
Evapotranspiration
Annual mean ΔET(f-o) in (a) MODIS, (b) GLEAM, (c) GETA, (d) CLM-BASE, and
(e) CLM-PLUS. Panel (f) shows the zonal mean (thick line) and the range between the 10th and 90th percentiles (shading)
of MODIS (green), GLEAM (light blue, grey shading), GETA (dark blue), CLM-BASE (red), and
CLM-PLUS (orange). Note that on this subfigure results have been smoothed with a 4∘ latitudinally running mean.
Seasonal and latitudinal variations of ΔET(f-o) in (a) the MODIS and
(b) GLEAM observations, (c) CLM-DFLT, (d) CLM-BASE, and (e) CLM-PLUS. Points
with a mean which is insignificantly different from zero in a two-sided t test at 95 %
confidence level are marked with a black dot. All data from the 2002–2010 analysis period
corresponding to a given latitude and a given month are pooled to derive the sample set for the test.
All of the considered observation-based ET products indicate that annual mean ΔET(f-o) is positive in every climate zone,
despite considerable variations in the magnitude of this difference (Fig. e). GLEAM suggests a near-zero ΔET(f-o)
in the Arid climate zone most likely because it uses surface SM data as an input to estimate ET. Also, GLEAM exhibits positive
ΔET(f-o) throughout the year in the midlatitudes, unlike MODIS which has a negative ΔET(f-o) during winter
(Fig. ). Paired-site FLUXNET studies offer an additional opportunity to compare ET over forest and over open
land on a point scale. Overall, they report higher ET for tropical forests .
In the midlatitudes and high latitudes, a number of FLUXNET studies observe a positive ΔET(f-o) during summer, and a
near-zero negative ΔET(f-o) during winter, similar to MODIS Fig. ;.
On the other hand, negative ΔET(f-o) values have been observed at some paired FLUXNET sites in the tropics and in the
midlatitudes during summer .
The considered global ET data sets, however, consistently exhibit higher ET
over forests in most regions (Fig. ). This agreement across the
different independent global data sources gives some confidence in the fact
that ET is generally higher over forests. Nevertheless, it needs to be noted
that ΔET(f-o) GETA shows fundamentally different results when
considering the data over irrigated crops instead of data over rainfed crops
(resulting in negative ΔET(f-o) at many locations). Therefore,
distinguishing irrigated from rainfed crops in future evaluations would be
essential but remains beyond the scope of this study.
ET and latent heat flux in situ observations from various studies and the values in
CLM-BASE and CLM-PLUS at the respective locations. EBT indicates broadleaf evergreen tree, DBT
indicates broadleaf deciduous tree, and ENT indicates evergreen needleleaf tree.
Seasonal and latitudinal variations of ΔET(f-o) in (a) MODIS, (b) GLEAM,
and difference of forest minus open land in (c) total ET, (d) soil evaporation, (e) canopy
interception evaporation, and (f) vegetation transpiration in CLM-BASE. Points with a mean
which is insignificantly different from zero in a two-sided t test at 95 % confidence level
are marked with a black dot.
CLM-BASE exhibits considerable discrepancies in ΔET(f-o) to the
observation-based data sets both for the annual mean values
(Fig. ) and the seasonal cycle (Fig. ).
ΔET(f-o) in CLM-BASE is near zero in all climate zones
(Fig. e), and even negative in the E-sd climate zone, unlike the
global ET data sets which clearly suggest positive values. The large bias of
ΔET(f-o) in CLM-BASE is also apparent in the RMSD values, which
tend to be slightly larger than the observed mean signal (compare
Figs. e and e). A comparison of the absolute ET
values of each PFT in CLM-BASE versus the GETA data reveals that
CLM-BASE generally exhibits similar ET averages for needleleaf PFTs,
lower ET averages for broadleaf deciduous trees as well as crops, and higher
ET averages for non-arctic grasses and broadleaf evergreen trees
(Fig. f). Notably, evergreen and deciduous tropical broadleaf
trees as well as C4 grass have a bias larger than 0.2 mmday-1
relative to GETA. The biases of these PFTs can have a large effect on the
overall ΔET(f-o) as they cover a large proportion of the land surface
(9.5, 8.0, and 8.0 %, respectively). Similarly, CLM-BASE
overestimates ET compared to in situ measurements conducted over a pasture
site in the Amazon by and underestimates ET compared to
the two forest sites in Alaska reported in the study of
(Table ).
Interestingly, deciduous trees are mostly responsible for this discrepancy in
ΔET(f-o) at latitudes below 30∘ (Fig. ). In the
midlatitudes, on the other hand, both deciduous and evergreen trees show
lower ET than open land during summer and higher ET during winter, which is
inconsistent with GLEAM and, even more so, inconsistent with the
seasonally varying ΔET(f-o) in MODIS. Another noteworthy result is
that the SeSC configuration (i.e., CLM-BASE) appears to impair the
agreement on ΔET(f-o) between CLM4.5 and the observations
(Fig. ). CLM-DFLT exhibits a positive ΔET(f-o)
throughout the year except for the tropical dry season which is caused by
deciduous broadleaf trees exhibiting lower ET than open land
(Fig. a, b, and c). There are two potential reasons for the
negative bias in ΔET(f-o) introduced by SeSCs. First, the implicit
lateral GHF from open land towards forest which occurs in CLM-DFLT
(Fig. ) provides additional energy over forests for turbulent
heat fluxes. This energy source (sink) for forests (open land) is disabled by
SeSCs. Second, the lower soil temperature of forests in CLM-BASE
(Fig. ) reduces the specific humidity gradient between the soil
surface and the atmosphere and hence also the absolute soil evaporation. It
needs to be noted that the weaker agreement with observational data of
ΔET(f-o) in CLM-BASE than in CLM-DFLT does not necessarily
imply a worse representation of the evaporative processes in CLM-BASE
but could also originate from the fact that CLM4.5 was tuned to retrieve
realistic ET values on ShSCs.
To shed light on the origin of the ΔET(f-o) bias in CLM4.5, we
separately analyze the three components of ET in CLM4.5: soil evaporation
(including sublimation/evaporation from the snow- and water-covered fraction
of the soil), canopy interception evaporation, and vegetation transpiration
(VTR). As seen in Fig. d, there is a distinct band around the
Equator where soil evaporation is considerably lower in forests than in open
land. Interestingly, both the study of and ours show that the
lower soil evaporation signal only arises for the configuration with SeSCs
(data of CLM-DFLT are not presented here). Thus, lower soil evaporation
around the Equator in CLM-BASE is likely related to the diminution of the
soil temperature and of the available energy mentioned earlier in this
section. It appears reasonable that, in comparison with open land, forests
have lower soil evaporation since (1) the forest soil surface receives less
incoming solar radiation, (2) more of the incoming precipitation is
intercepted by the canopy, and (3) the water vapor concentrations within the
canopy are higher. Yet soil evaporation and canopy interception evaporation
contribute a larger proportion to total ET in CLM4.5 (31 and 19 %)
compared to GLEAM 14 and 10 %;. It is thus possible
that the strength of this effect is too large in CLM4.5. However, most ET
measurement techniques cannot distinguish among the different components of
ET, making it difficult to assess which partitioning is more realistic.
Overall, negative ΔET(f-o) values in CLM-BASE typically coincide
with negative differences for its VTR component, in particular during the wet season in the
tropics and subtropics and during summer at higher latitudes
(Fig. c and f), whereas negative values in the soil evaporation
difference are partly compensated by positive values in interception
evaporation (Fig. d and e). It is therefore likely that VTR is
the main driver behind the ΔET(f-o) bias even though the contribution
of the individual ET components to the total signal cannot be evaluated with observations. For
this reason, the modifications in the CLM-PLUS sensitivity experiment are
targeted at altering vegetation transpiration.
In summary, ΔET(f-o) in
CLM4.5 exhibits considerable discrepancies to the considered global ET
data sets and in situ observations. The SeSC configuration amplifies these
discrepancies, which are typically driven by the difference in VTR of forest
minus open land.
Land surface temperature
The overall local temperature impact of forests is the result of several
biogeophysical properties acting simultaneously. They include lower albedo of
forests (warming effect), higher surface roughness (cooling effect if land
surface is warmer than boundary layer), and alteration of the evaporative
fraction . For daily mean LST, forests
exhibit a cooling effect in MODIS except for the winter months at latitudes
exceeding 30∘ (Fig. a). This implies that the cooling
effects of higher surface roughness and generally higher evaporative fraction
over forests are stronger than the warming effect due to their lower albedo.
ΔLSTavg(f-o) and ΔLSTmax(f-o) are positive only under
the presence of snow, as Δα(f-o) is amplified due to the snow
masking effect moreover, sensible heat fluxes are often directed
towards the land surface during winter at high latitudes, resulting in warmer
forests due to their higher surface roughness inducing stronger turbulent
heat fluxes;. The observed magnitude of ΔLSTmax(f-o)
tends to be larger than that of ΔLSTavg(f-o) likely due to the
fact that the observed daytime effect is partly compensated by an opposing
nighttime effect (Fig. b, c, and d). MODIS exhibits an overall
cooling effect of forests on daily mean LST in all climate zones, including
the snow climate zone where the sign of the difference changes seasonally
(Fig. d). Further, this data set shows a slightly negative
ΔLSTmin(f-o) in tropical and subtropical regions and even a
positive ΔLSTmin(f-o) in the midlatitudes (Fig. g).
This nighttime signal in the midlatitudes is observed in several
observational studies but its source is not yet fully determined
.
CLM-BASE generally captures the sign and magnitude of
ΔLSTavg(f-o) and ΔLSTmax(f-o) compared to MODIS
(Fig. ). The SeSCs used in CLM-BASE allow for larger LST
differences between forest and open land than the default version of CLM4.5
(CLM-DFLT) on ShSCs, resulting in a better agreement with the observed
magnitudes. This is due to the fact that the GHF on ShSCs counteracts the
soil temperature difference and thereby also the LST difference between
forest and open land. Nevertheless, there are still some discrepancies
between the LST signal in CLM-BASE and the MODIS observations. It appears
that ΔLSTavg(f-o) in CLM-BASE has a positive bias in the
equatorial, the Arid, and the snow climate zones, and a negative bias in the
T-wd and T-fh climate zones (Fig. b). ΔLSTmax(f-o)
in CLM-BASE appears qualitatively similar to the MODIS observations
(Fig. d, e, and f) but is biased positively in all climate zones
(Fig. c). In contrast, daily minimum LST shows much larger
discrepancies between CLM-BASE and MODIS (Fig. g, h, and i).
In CLM-BASE, ΔLSTmin(f-o) is similar to
ΔLSTavg(f-o) and ΔLSTmax(f-o); i.e., forests have an
overall nighttime cooling effect in all climate zones except for the neutral
signal in the snow climate zones, whereas MODIS exhibits an only weak
nighttime cooling effect in the tropical climate zones and a clear nighttime
warming effect in all other climate zones (Fig. d). The weak
performance of CLM-BASE in terms of ΔLSTmin(f-o) is also
visible in the RMSD values which are considerably larger than the mean
ΔLSTmin(f-o) signal (compare Figs. d and d).
Seasonal and latitudinal variations of ΔLSTavg(f-o) in (a) the MODIS
observations, (b) CLM-DFLT, and (c) CLM-BASE. Points with a mean which is insignificantly
different from zero in a two-sided t test at 95 % confidence level are marked with a black dot.
Only grid cells containing valid data in the MODIS observations were considered for the analysis of CLM-DFLT and CLM-BASE.
All data from the 2002–2010 analysis period corresponding to a given latitude and a given month
are pooled to derive the sample set for the test. Panel (d) shows the zonal annual mean of MODIS
(green, range between the 10th and 90th percentiles in grey), CLM-DFLT (blue, range between the 10th and 90th percentiles in blue), and
CLM-BASE (red, range between the 10th and 90th percentiles in orange). Note that on this subfigure results have
been smoothed with a 4∘ latitudinally running mean.
The same was done for ΔLSTmax(f-o) in panels (e), (f), (g), and (h), and for ΔLSTmin(f-o)
in panels (i), (j), (k), and (l).
Seasonal and latitudinal variations of (a) daily maximum T2M difference of forest
minus open land and (b)ΔLSTmax(f-o) in CLM-BASE. Points with a mean which
is insignificantly different from zero in a two-sided t test at 95 % confidence level are
marked with a black dot. All data from the 2002–2010 analysis period corresponding to a
given latitude and a given month are pooled to derive the sample set for the test. Only
grid cells containing valid data in the MODIS observations were considered for the analysis.
Interestingly, and in contrast to LST, CLM4.5 simulates a small year-round
warming effect of forests on daily maximum 2 m air temperature (T2M,
Fig. ). This contradicts a number of observational studies which
show that the T2M difference of forest minus open land (ΔT2M(f-o)) has
the same sign but is attenuated compared to ΔLST(f-o). The fact that we use offline simulations in our
experiments might explain this behavior, because some land–atmosphere
feedbacks are not represented. However, report similar
discrepancies of ΔT2M(f-o) in CLM with observational data for coupled
simulations, suggesting that the behavior of ΔT2M(f-o) in our
simulations may not be related to the lack of atmospheric feedbacks.
Sensitivity experiment to alleviate ET biases in CLM4.5
In the previous section, striking discrepancies between the effect of forests
in CLM-BASE and observation-based data were found for ΔET(f-o). An
important driver responsible for these differences was identified to be VTR
(Fig. ). In addition, it became apparent that the SeSC
configuration impairs the ΔET(f-o) compared to the ShSC configuration
(Fig. ), despite improving ΔLSTavg(f-o) and
ΔLSTmax(f-o) (Fig. ). Hence, in this section, we aim
to improve the comparability of modeled ΔET(f-o) to observation-based
results by testing a modified parameterization of VTR in a sensitivity
experiment called CLM-PLUS. This model configuration comprises (1) a
shallower root distribution for open land PFTs, (2) a modified plant water
uptake scheme whereby plants only extract water from the 10 % of the roots
with best access to SM, (3) altered light limitation of photosynthesis
(decreased for C3 plants and increased for C4 plants), and (4) altered
Vcmax values to alleviate ET biases at PFT level compared to the GETA
data.
Δα(f-o) is only marginally affected by the modifications of
CLM-PLUS compared to CLM-BASE (Fig. a). This is expected
since the modifications are targeted at modifying VTR which is not linked
directly to albedo. ΔET(f-o) in CLM-PLUS becomes more positive
than in CLM-BASE in all climate zones, thereby better matching the
observation-based estimates (Fig. e). The improvement is also
apparent in the RMSD values which are reduced in CLM-PLUS for all data
sets and climate zones, except for GETA in the E-h climate zone
(Fig. e). The bias in average ET compared to GETA is smaller in
CLM-PLUS than in CLM-BASE for all PFTs except for boreal deciduous
needleleaf trees and crops (Fig. f). Some discrepancies with
observation-based ET products nevertheless remain. ΔET(f-o) in
CLM-PLUS is still mostly less positive compared to remote-sensing-based
observations and GETA, and remains of opposite sign during the warm season in
the temperate regions and in a narrow band around the Equator
(Figs. and e). This band originates from a negative
ΔET(f-o) around the western part of the Equator in Africa and over
Indonesia (Fig. ). GLEAM and GETA observations cover these areas
which explains the only moderate reduction of RMSD of CLM-PLUS against
GLEAM and the increase in RMSD against GETA in the E-h climate zone. On the
other hand, the RMSD against MODIS is reduced considerably in CLM-PLUS,
since MODIS observations are sparse over Africa and Indonesia
(Fig. e). Also, relative to the in situ observations of
, biases in CLM-PLUS are reduced, yet not completely
eliminated (Table ). As a consequence of the improved
ΔET(f-o), we find that CLM-PLUS partly alleviates the positive
bias in ΔLSTmax(f-o) compared to the MODIS data, especially in
the equatorial climate zone which also reduces the RMSD in all but the Arid
climate zone (Figs. c and c). This hints that a
realistic representation of ΔET(f-o) is crucial for resolving the
underestimated cooling effect of forests on daily maximum LST. Similarly,
RMSD of ΔLSTavg(f-o) decreases in the equatorial and Arid climate
zones, whereas it increases in the temperate and snow climate zones
(Fig. b). At the same time, the RMSD of ΔLSTmin(f-o)
is only marginally increased in all climate zones (Fig. d).
Discussion
The combination of SeSCs and the further modifications introduced in CLM-PLUS
led to substantial improvements in CLM4.5's capability to represent
forest/open land contrast. Nevertheless, some biases still persist. In
particular, CLM4.5 is still unable to represent the nighttime warming effect
of forests in the midlatitudes exhibited by observational data . Additionally, there is a remaining
positive bias of ΔLSTmax(f-o) compared with MODIS even though
this bias is alleviated to some extent due to the more positive
ΔET(f-o). Inadequate representation or omission of several processes
in CLM4.5 could be the source of these discrepancies with MODIS. The biases
in both ΔLSTmax(f-o) and ΔLSTmin(f-o) could be
alleviated by accounting for vegetation heat storage, a process which is
currently disregarded in CLM4.5. Observed diurnal vegetation heat storage
fluxes reach an amplitude of 10–20 Wm-2 in the midlatitudes and
high latitudes and
20–70 Wm-2 in the tropics . Fluxes of this magnitude are sufficient to considerably alter
the diurnal temperature cycle in forests and hence potentially resolve the
discrepancies in ΔLSTmax(f-o) and ΔLSTmin(f-o) of
CLM4.5 with MODIS. While ΔET(f-o) in CLM-PLUS is improved against
all the considered ET data sets in almost every climate zone, some biases
persist, especially concerning the seasonality in the midlatitudes and high latitudes
as well as annual mean values around the Equator. In CLM-PLUS, the focus
was on VTR, thereby neglecting the contribution from soil and interception
evaporation. However, soil evaporation is considerably lower over forests
around the Equator in CLM-PLUS which might explain the remaining negative
ΔET(f-o) in this region. We therefore encourage additional sensitivity
experiments which also focus on the other components of ET. When testing new
model configurations, care should be taken that the implemented modifications
do not impair other features of the model, related not only to the water but
also the energy and carbon budgets. Reassuringly, we find that global ET
averages are only weakly affected in the sensitivity experiment, with an
average of 1.43 mmday-1 in CLM-BASE compared to
1.41 mmday-1 in CLM-PLUS. These values lie within the range
of 1.2 to 1.5 mmday-1 estimated from
surface water budgets . Nevertheless, it would be desirable in
future studies to evaluate the biogeochemical effects of the different model
configurations investigated here alongside the biogeophysical effects.
For comparison with LST data, we used the radiative temperature in CLM4.5
rather than the more common T2M diagnostic which exhibits an
observation-contradicting sign in CLM4.5 during daytime (compare Figs. e and ). Such T2M-specific discrepancies with observations could be
related to a differing definition of T2M over forests in the model and
observations. For example, the differing sign of ΔT2Mmax(f-o) in
climate models using CLM and the observations of found in
might be related to the fact that T2M observations were
made 2 to 15 m above the forest canopy, whereas T2M of CLM4.5 lies within the
forest canopy . Therefore, T2M in CLM4.5 should be used with
care when comparing to observations.
There are several factors which may affect the comparability of the signal
extracted from our CLM4.5 simulations and the considered observational data
sets. (1) The different data sources use differing land cover information.
For example, GLEAM uses the MOD44B product which provides the fraction of
each grid cell covered by trees, non-tree vegetation, and non-vegetated land
surfaces, whereas MODIS uses the MCD12C1 product which provides the dominant
International Geosphere-Biosphere Programme (IGBP)
land cover type . Further, the definition of forest
and open land in the Li et al. (2015) data set can be a source of model–data
discrepancy. The methodology applied by relies on the definition
of a threshold (80 %) in the coverage of forest (open land) for a pixel to
be classified as forest (open land). There are therefore some mixing effects
between the forest and open land categories in this data set, whereas our
evaluation method isolates pure signals over forest and open land in CLM4.5.
In fact, MODIS albedo retrievals were found to underestimate albedo over
grass- and cropland, especially under the presence of snow, and overestimate
it over forests due to the heterogeneity of land cover within pixels
. Therefore, it is possible that the magnitude of
Δα(f-o) is underestimated in MODIS rather than overestimated in
CLM4.5. Consistently, in situ observations of paired forest and open land
sites support the higher Δα(f-o) found in CLM-BASE
. (2) MODIS LST data are retrieved under clear-sky
conditions only, whereas we do not mask out cloudy days in the evaluation of
the CLM4.5 simulations. (3) The overpass times of the MODIS satellite system
are at 01:30 LT and 13:30 LT, hence not necessarily coinciding with the daily
maximum and minimum LST in CLM4.5. (4) Finally, the meteorological
conditions within one search window of MODIS may vary among the different
pixels, whereas the different PFT tiles in our CLM4.5 simulations were
subject to the exact same atmospheric forcing. However, partly
accounted for this effect by applying an elevation adjustment. Moreover, they
found little sensitivity of the forest minus open land signal to the size of
the chosen window.
In this study, we focused on the contrast between forest and open land.
However, we acknowledge that future studies should consider other types of
land conversions or land management changes, as an increasing number of
studies have demonstrated that other LULCCs than de- or reforestation also
have remarkable biogeophysical effects e.g.,. The two new observation-based data sets of
and assess the biogeophysical
consequences of a series of different LULCCs globally, thereby enabling the
evaluations of the sensitivity to additional types of land cover in future
studies. An additional advantage of these two studies is that they both
provide a signal for a complete conversion from one land cover type to
another (i.e., they do not rely on a coverage threshold as MODIS). In our
evaluation approach, we focus on the local climatic impact of forests, thereby
neglecting feedback mechanisms between the atmosphere and the land surface.
While they appear to be relevant in many climate models , their evaluation is prevented by the lack of observations at the
moment.
Conclusions
In this study, we evaluate the representation of the local
biogeophysical effects of forests in the CLM4.5,
using recently published MODIS-based observations of the albedo,
ET, and LST difference
between forest and nearby open land. Given the uncertainties in
observation-based ET estimates, we further extend our evaluation for this
variable by including data from GLEAM v3.1a and GETA 2.0. In our model
evaluation, we extract a local signal of forests by analyzing PFT-level model
output, allowing for good comparability with the high-resolution satellite
observations. Further, we use a modified version of CLM4.5 which attributes a
separated soil column to each PFT, resulting in a
more realistic subgrid contrast between forest and open land.
Overall, the lower albedo over forests in CLM4.5 is in line with the MODIS
observations. However, the albedo contrast between forests and open land is
somewhat more pronounced in the model. Ground observations support the
stronger albedo contrast in CLM4.5, suggesting that MODIS albedo observations
should be used carefully when contrasting different land cover types, as
satellite observations tend to retrieve a mixed signal of various land cover
types due to their limited spatial resolution. By suppressing lateral ground
heat fluxes, the soil column separation considerably improved the
representation of the impact of deforestation on daily mean and maximum LST,
resulting in a good agreement with the MODIS observations. Both exhibit an
overall cooling effect of forests on these variables, except for winter at
latitudes exceeding 30∘. Nevertheless, it appeared that the LST
difference of forest minus open land in CLM4.5 tends to have a positive bias
compared to observational studies. Also, it emerged that caution is required
when comparing 2 m air temperature in CLM4.5 to observational data. This
variable is only diagnostic in CLM4.5 and might not conform with
measurements, despite realistic LST values. The nighttime warming effect of
forests in the midlatitudes, which emerged in a number of recent
observational studies, is not reproduced by CLM4.5. The biases in the daily
maximum and minimum LST signals of forests might be at least partly alleviated
by accounting for heat storage in the vegetation biomass. We therefore
encourage a modification of CLM which enables the representation of canopy
heat storage.
Observation-based ET estimates generally agree on higher ET over forests than
open land throughout the year at low latitudes and during summer at midlatitudes and
high latitudes. This was however not represented by the CLM4.5 configuration
using separated soil columns. In fact, the soil column separation impaired
the ET signal of forests in CLM4.5, despite improving the LST signal of
forests considerably. Hence, a complete evaluation and verification of this
modification of CLM4.5 should be undertaken before including it in future
versions of CLM. We succeeded in attenuating the biases in ET and also daily
maximum LST in a sensitivity experiment which incorporated modifications on
four aspects of the parameterization of vegetation transpiration: the root
distribution, a dynamic plant water uptake instead of the current static one,
the light limitation, and the maximum rate of carboxylation.
Historically, the most important LULCC process, deforestation,
is still ongoing in large parts South America, Africa, and southeast Asia. A
realistic representation of the biogeophysical effects of LULCC in climate
models is needed as a number of observational studies revealed that they can
have a considerable impact on the local climate. An appropriate
representation of the effects of LULCC is not only a feature land surface
models require to understand the climate of the past and
project future climate but is also a chance to achieve a more realistic
simulation of processes at the land surface. To this end, the analysis of
model output at PFT level can help reveal model deficiencies that
otherwise would have been hidden below the veil of grid-scale aggregation.
CLM4.5 is publicly accessible as described in
www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html (CESM Software Engineering Group, 2014).
The Model output and modifications to the model code are available from the corresponding authors
upon request. The GETA 2.01 database is available at http://sterlinglab.ca/databases (Sterling, 2018).
GLEAM version 3.1a can be downloaded from https://www.gleam.eu/#downloads (Martens et al., 2017) and the land cover specific variables used in this study are available upon
request from Brecht Martens. The MODIS-based data are available from Yan Li upon request.
Sensitivity of CLM4.5 to individual modifications
Here, we present a more detailed description and discussion of the individual
modifications described in Sect. . In order to isolate the
effect of the individual modifications, three additional sensitivity
experiments are presented: CLM-ROOT, CLM-10PER, and CLM-LIGHT.
Table shows which modifications of CLM4.5 are incorporated in
the different sensitivity experiments.
The PFT-specific values of Vcmax
(µmol m-2 s-1), ra, and rb in the default of CLM4.5 and in CLM-PLUS.
The land cover types from (GETA) used in this study and the numbers
of the respective PFTs in CLM4.5 applied to the different land cover types (Table ).
The fraction of the CLM4.5 grid cells covered by (a) bare soil,
(b) forest, (c) shrubland, and (d) open land.
Sensitivity to root distribution
In CLM4.5, ET is strongly and positively correlated to SM at most locations,
indicating that SM limitation exerts a strong control on the magnitude of ET
(not shown). In CLM-DFLT, where SM is the same for all PFTs within a grid
cell, forest mostly experiences higher SM stress except for the northern
high-latitude winter (Fig. a). Once the SeSCs are introduced in
CLM-BASE, the differences in the SM stress are also influenced by the
differences in SM, which in turn are affected by the various ET rates over
forest and open land. In other terms, it is possible that forests experience
less SM stress than open land but only because they evaporate less water, and
vice versa (Fig. b). We argue that the difference in the SM
stress of forest minus open land in CLM-DFLT is more representative,
because it is unaffected by the ET rates of the individual PFTs in this model
configuration. Under this assumption, forests are often more SM-limited than
open land in CLM4.5. In contrast, two observational studies comparing SM
profiles of forest and nearby pasture sites in the Amazon reveal that forests
have a considerably higher capacity to access water from the soil below a
depth of 2 m. Further, there are a number
of studies reporting increased forest ET during the dry season due to the
higher amount of incoming shortwave radiation, whilst the response is the
opposite over pasture . Altogether, these studies indicate that forest ET should be less
SM-limited than open land ET. It is thus possible that forests experience too
high and/or open land too little SM stress in CLM4.5.
GHF for forests (a, b) and open land (c, d) in CLM-DFLT (a, c)
and CLM-BASE (b, d). Positive values correspond to a heat flux out of the soil.
Difference in vertically averaged annual mean soil temperature of forest
minus open land in CLM-BASE.
Seasonal and latitudinal variations of βt-factor differences of forest
minus open land in (a) CLM-DFLT and (b) CLM-BASE. Points with a mean which is
insignificantly different from zero in a two-sided t test at 95 % confidence level
are marked with a black dot.
CLM4.5 accounts for SM
stress on VTR through a stress function βt, which ranges from
0 (when soil moisture limitation completely suppresses VTR) to 1
(corresponding to no SM limitation on VTR). This function is calculated
according to Eq. () as the sum of the root fraction in each soil
layer (ri) multiplied by a PFT-dependent wilting factor (wi). The
original root distributions in CLM4.5 were adapted from and
are rather similar for all PFTs, especially for needleleaf trees, broadleaf
deciduous trees, and grassland in the lower part of the soil
(Fig. ). Therefore, there is no considerable difference in the
default configuration of CLM4.5 regarding the ability to extract water from
the lower part of the soil between forests and open land PFTs (except for
broadleaf evergreen trees). Furthermore, all tree PFTs have a less negative
soil matrix potential at which the stomata are fully closed and opened than
the open land ones; i.e., tree PFTs have their permanent wilting point at a
higher SM content than open land and hence use water more conservatively. In
order to increase SM limitation for open land PFTs and thus reduce their
ability to extract water from the lower part of the soil, we conduct a
sensitivity experiment, called CLM-ROOT, with a much shallower root
distribution for open land PFTs. The new values for the root distribution
factors (ra and rb) are shown in Table and the resulting
root distribution in Fig. .
βt=∑iwiri
The modified root distributions strongly reduce the ET of non-arctic open
land PFTs, especially ET of C4 grass (Table ). Also, the ET
of grassland at the location of the pasture site in the Amazon in the study
of is considerably reduced during the dry period, even
overcompensating the positive bias in CLM-BASE (Table ). On
the other hand, it does not affect ET during the wet season, when ET is not
SM limited. Overall, this experiment reveals that modifying the root
distribution has high potential to alleviate biases of CLM4.5 in ET, except
for the arctic region where likely temperature and incoming shortwave
radiation are the main factors limiting VTR.
Sensitivity to dynamic plant water uptake
In the tropics, forests often exhibit increased ET during dry periods, due to
increased light availability , even
though the upper soil is dry, as they still have sufficient water supply from
the lower part of the soil . We aim to allow a
similar behavior in CLM4.5 by introducing a dynamic plant water uptake, where
plants only extract water from the 10 % of the roots with the highest
wilting factor (i.e., best access to SM) for the calculation of the
βt factor and the extraction of soil water (example in
Fig. ). The resulting model simulation, called CLM-10PER, was
conducted by adding this modification to the configuration from the CLM-ROOT
experiment.
This modification generally reduces SM stress for plants and hence increases
ET for all non-arctic PFTs (Table ). Its impact is limited for
arctic PFTs where temperature and shortwave radiation are more important
limiting factors of VTR than water availability. A notable improvement can be
observed for tropical deciduous broadleaf trees for which average ET is
increased by 0.11 mmday-1, thereby alleviating the negative bias
compared to GETA. Furthermore, it improves the seasonal dynamics of forest ET
in the tropics. With the 10 % modification, forests show increased ET during
the dry period at the forest site of . This is the case as
trees are now less SM-limited during the dry period than in CLM-BASE,
since they have a significant fraction of their roots in the still-moist
lower part of the soil, allowing them to exploit the increase in incoming
shortwave radiation. On the other hand, ET at the pasture site of
remains largely unaffected, as grassland has only limited
access to SM from the lower part of the soil due to the shallow root
distribution introduced in CLM-ROOT. It hence appears that a dynamic
plant water uptake could be crucial for the representation of the seasonal
dynamics of ET (and possibly photosynthetic activity in general) in the
tropics.
Area-weighted annual mean ET for each PFT analyzed in this study
according to the GETA data and in the different configurations of CLM4.5 and fraction
of the land surface covered by the different PFTs. The global
integral of annual ET is listed on the bottom.
Abbr.Full nameFrac.ET (mmday-1) (%)GETABASEROOT10PERLIGHTPLUSNET – temperateNeedleleaf evergreen tree – temperate3.21.741.781.781.811.841.75NET – borealNeedleleaf evergreen tree – boreal6.91.000.970.970.981.001.00NDT – borealNeedleleaf deciduous tree – boreal1.00.720.720.720.720.730.73EBT – tropicalBroadleaf evergreen tree – tropical9.53.473.703.703.783.873.52EBT – temperateBroadleaf evergreen tree – temperate1.52.582.612.612.662.702.60DBT – tropicalBroadleaf deciduous tree – tropical8.02.652.312.312.422.442.62DBT – temperateBroadleaf deciduous tree – temperate3.11.781.741.741.761.791.79DBT – borealBroadleaf deciduous tree – boreal1.31.231.081.081.081.101.13C3 arctic grass3.10.810.660.650.650.660.67C3 grass8.81.481.601.531.561.571.53C4 grass8.02.062.322.182.222.122.04CropC3 unmanaged rainfed crop101.901.761.701.731.741.73Total ET (km3yr-1) 70 22369 05970 32270 64969 023
ET and latent heat flux in situ observations from various studies and the values of
the different CLM4.5 sensitivity tests at the respective locations.
Vertical root fraction distribution of the different PFTs in the default
version of CLM4.5 and (in light blue) the modified root fraction distribution of open
land PFTs used in CLM-PLUS. The asterisks mark the reported maximum rooting depths of
for annual grass (yellow), evergreen needleleaf trees (dark blue), deciduous
broadleaf trees (light green), and evergreen broadleaf trees (dark green).
Example of the calculation of the βt factor with the 10 % modification. Shown are five soil
layers, with the fraction of the roots in these layers in brown and the wilting factor in blue. On the
bottom, the calculation of βt for this particular example with the 10 % modification (βt10PER) and
the default calculation in CLM4.5 (βtDFLT), assuming the roots not shown have a wilting factor of 0.
The root fractions eventually used to calculate βt10PER are shaded in red.
IA of CLM-BASE (red), and CLM-PLUS (orange) with MODIS observations
over Köppen–Geiger climate zones Fig. for monthly (a)Δα(f-o),
(b)ΔLSTavg(f-o), (c)ΔLSTmax(f-o), and (d)ΔLSTmin(f-o).
Panel (e) shows the
IA over the Köppen–Geiger climate zone for ΔET(f-o) of CLM-BASE (red), and CLM-PLUS (orange) with
MODIS (green edge), GLEAM (light blue edge), and GETA (dark blue edge). The numbers indicate the size of the data
samples used for the calculation of IA.
Seasonal and latitudinal variations of ΔET(f-o) in CLM-DFLT for (a) all tree PFTs
minus open land, (b) deciduous tree PFTs only minus open land, and (c) evergreen tree PFTs only minus open
land. Points with a mean which is insignificantly different from zero in a two-sided t test at 95 %
confidence level are marked with a black dot. All data from the 2002–2010 analysis period corresponding
to a given latitude and a given month are pooled to derive the sample set for the test. The same is done for
CLM-BASE in panels (d), (e), and (f).
Sensitivity to light limitation
As arctic PFTs are only weakly affected by the previously introduced
modifications of SM stress as well as the maximum rate of carboxylation
described in the next section, we performed a sensitivity experiment with
altered light limitation, which is called CLM-LIGHT. Since ET values are
strongly negatively biased for boreal deciduous broadleaf trees and C3
arctic grass (Table ), the light limitation of photosynthesis
for C3 plants was lessened by increasing the factor 0.5 in Eq. (8.7) of
to 0.6. Because ET of C4 grass exhibits a strong positive
bias, their quantum efficiency was reduced from 0.05 to
0.025 molCO2mol-1photon, thereby increasing their light
limitation.
Altering the light limitation of photosynthesis impacts ET in all climate
zones (Table ). Its impact is strongest in the tropics and
remains small in boreal regions. Of the C3 PFTs, tropical evergreen
broadleaf trees are impacted strongest. The implemented modification
alleviates the negative ET bias for evergreen broadleaf trees during the dry
season but slightly increases the positive bias during the wet season,
overall still leading to a further improvement of the difference between the
two seasons (Table ). Additionally, the increased light
limitation reduces ET of C4 grass during the wet season similar to the
observations over the grassland site in . This is likely
responsible for the increased ET during the dry season as well, since the
reduced SM consumption during the wet season is carried over to the following
dry season, therefore reducing the SM stress.
Sensitivity to the maximum rate of carboxylation
Vcmax appears to be a suitable parameter to tune VTR
values, since it is not well constrained from observations and VTR in models
is highly sensitive to this parameter . In CLM4.5, the values
reported by are used except for tropical evergreen broadleaf
trees, for which a higher value was chosen to alleviate model biases
. In order to test the sensitivity of the
PFT-specific ET values to Vcmax, we conduct a final sensitivity
experiment with new values of this parameter in addition to the other
modifications presented beforehand, with the aim to alleviate the biases to
GETA (Table ). Additionally, the minimum stomatal conductance of
C4 plants, which is by default 4 times larger than that of C3
plants, was reduced from 40 000 µmolm-2s-1 to
20 000 µmolm-2s-1 (see Eq. 8.1 in Oleson et al., 2013) in
this sensitivity experiment, which we call CLM-PLUS.
As already shown by , photosynthetic activity of C3 PFTs is
strongly influenced by the choice of Vcmax, except for the boreal ones
where light or temperature are more important limiting factors of
photosynthesis. The CLM-PLUS simulation alleviates biases in ET averaged
for the individual PFTs compared to GETA, in particular by reducing ET over
temperate evergreen needleleaf trees, both temperate and tropical evergreen
broadleaf trees, and C4 grass, as well as by increasing ET of tropical
deciduous broadleaf trees (Table ). The mismatch between
results of CLM4.5 and the in situ measurements of and
in the Amazon region are reduced in this new configuration
during the wet season but enhanced during the dry one
(Table ). As in the CLM-LIGHT experiment, the reduction of
C4 grass ET during the wet season at the pasture site of
is partly compensated by an ET increase during the dry
period. Overall, ET of C4 grass compares well with the mean value of GETA.
The in situ observations of , on the other hand, support a
stronger tuning for this particular PFT in order to further reduce its ET.
RM, ELD, and QL designed the study. RM performed the model simulations and
analyses.
YL, SS, and BM provided the MODIS, GETA, and GLEAM data, respectively. NS, WT, and MH contributed to the set up of the CLM4.5 simulations
and the processing of the model outputs. All authors contributed to writing and revising the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank Sonia Seneviratne for her comments on the analysis and are grateful
for the technical support of Urs Beyerle during the course of this project.
We would like to thank the two anonymous reviewers for their constructive
comments which helped to improve the quality of our manuscript considerably.
We acknowledge funding from the Swiss National Science Foundation (SNSF) and
the Swiss Federal Office for the Environment (FOEN) through the CLIMPULSE and
Hydro-CH2018 projects, and from the European Union's Horizon 2020 research and
innovation program under grant agreement no. 641816 (CRESCENDO).
Edited by: Akihiko Ito
Reviewed by: two anonymous referees
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