ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-1265-2018Biophysics and vegetation cover change: a process-based evaluation framework
for confronting land surface models with satellite observationsBiophysics and vegetation cover changeDuveillerGregorygregory.duveiller@ec.europa.euhttps://orcid.org/0000-0002-6471-8404ForzieriGiovannihttps://orcid.org/0000-0002-5240-1303RobertsonEddyLiWeihttps://orcid.org/0000-0003-2543-2558GeorgievskiGoranLawrencePeterWiltshireAndyCiaisPhilippePongratzJuliahttps://orcid.org/0000-0003-0372-3960SitchStephenArnethAlmutCescattiAlessandroEuropean Commission Joint Research Centre (JRC), Ispra, ItalyMet Office, Exeter, UKLaboratoire des Sciences du Climat et de l'Environnement (LSCE),
Gif-sur-Yvette, FranceMax-Planck Institut für Meteorologie, Hamburg, GermanyNational Center for Atmospheric Research (NCAR), Boulder, USACollege of Life and Environmental Sciences, University of Exeter, Exeter, UKKarlruher Institut für Technologie (KIT), Garmisch-Partenkirchen,
GermanyGregory Duveiller (gregory.duveiller@ec.europa.eu)13July20181031265127921February201821March201828June20182July2018This 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://essd.copernicus.org/articles/10/1265/2018/essd-10-1265-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/1265/2018/essd-10-1265-2018.pdf
Land use and land cover change (LULCC) alter the biophysical
properties of the Earth's surface. The associated changes in vegetation cover
can perturb the local surface energy balance, which in turn can affect the
local climate. The sign and magnitude of this change in climate depends on
the specific vegetation transition, its timing and its location, as well as on
the background climate. Land surface models (LSMs) can be used to simulate
such land–climate interactions and study their impact in past and future
climates, but their capacity to model biophysical effects accurately across
the globe remain unclear due to the complexity of the phenomena. Here we
present a framework to evaluate the performance of such models with respect
to a dedicated dataset derived from satellite remote sensing observations.
Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are
combined with satellite observations to analyse the changes in radiative and
turbulent fluxes caused by 15 specific vegetation cover transitions across
geographic, seasonal and climatic gradients. The seasonal variation in net
radiation associated with land cover change is the process that models
capture best, whereas LSMs perform poorly when simulating spatial and
climatic gradients of variation in latent, sensible and ground heat fluxes
induced by land cover transitions. We expect that this analysis will help
identify model limitations and prioritize efforts in model development as
well as inform where consensus between model and observations is already
met, ultimately helping to improve the robustness and consistency of model
simulations to better inform land-based mitigation and adaptation policies.
The dataset consisting of both harmonized model simulation and remote sensing
estimations is available at 10.5281/zenodo.1182145.
Introduction
Terrestrial vegetation regulates land–climate interactions through both
biogeochemical and biogeophysical mechanisms. The role of vegetation from the
biogeochemical side relies on its capacity to act either as a carbon sink or a
carbon source (Le Quéré et al., 2016). Changes in land cover, which
are dominated by forest area loss, have a pronounced effect on climate by
reducing the terrestrial carbon stocks (Canadell and Raupach, 2008). However,
land cover also controls both radiative and non-radiative biophysical surface
properties of vegetation that influence the water, momentum and energy
budgets (Bonan, 2008). Land use and land cover change (LULCC) alters these
biophysical properties and in turn affects the local climate through changes
in the surface energy balance (Anderson et al., 2011; Bonan, 2008; Davin and
de Noblet-Ducoudré, 2010; Lee et al., 2011; Mahmood et al., 2014; Pielke
et al., 2011). For instance, a vegetation cover transition from forest to
grassland typically causes an increase in albedo (because grasses are
generally brighter than trees and in cold climates grasses have a more
homogeneous snow cover during the cold season; see Betts and Ball, 1997;
Jackson et al., 2008; Loranty et al., 2014), but also a decrease in summer
evapotranspiration (because grasses have lower aerodynamic conductance, see
Bonan, 2008, and they typically have shallower roots and thus cannot access
water in deeper soil horizons, e.g. Canadell et al., 1996; Fan et al., 2017;
Oliveira et al., 2005). The result of competing biophysical processes on the
surface energy balance varies spatially and temporally and can lead to
warming or cooling depending on the specific vegetation change and on the
background climate (e.g. presence of snow or soil moisture) (Pitman et al.,
2011). In some cases, the associated changes in biophysical properties may
offset the intended biogeochemical effects of land-based mitigation (Betts,
2000). Yet, policies tackling climate mitigation through land management
focus only on biogeochemical mechanisms and neglect their biophysical
consequences.
Recent advances have demonstrated that satellite remote sensing observations
can provide valuable diagnostics of the effect of vegetation cover change on
their biophysical properties (Alkama and Cescatti, 2016; Duveiller et al.,
2018b; Forzieri et al., 2017; Li et al., 2015; Zhao and Jackson, 2014).
Biophysical surface properties, such as albedo (Schaaf et al., 2002) and land
surface temperature (Wan, 2008), are typically more accessible to remote
sensing instruments than biogeochemical properties like carbon stocks and
fluxes. The latent heat flux of the land can also be estimated from remote
sensing observations using data-driven models (McCabe et al., 2016; Miralles
et al., 2011; Mu et al., 2007), while the residual sensible and ground heat
fluxes can be obtained from a combination of such datasets by imposing the
closure of the surface energy balance (Duveiller et al., 2018b; Forzieri et
al., 2017). To exploit such datasets for analysing the biophysical effects of
LULCC, two different approaches are typically adopted. The first focuses on
places where vegetation cover has changed over a period of time and compares
the situation before and after this event, taking care in controlling for the
effects of inter-annual climatic variability over a local window (e.g.
Silvério et al., 2015; Alkama and Cescatti, 2016). The second approach
relies on a space-for-time substitution that isolates the potential impact of
a land cover transition by comparing neighbouring areas with similar
environmental conditions but contrasting vegetation (e.g. Zhao and Jackson
2014; Li et al., 2015; Peng et al., 2014; Duveiller et al., 2018b). Both
appear to yield similar results (Li et al., 2016), but the space-for-time
approach allows the exploration of more transitions and over a larger spatial extent
since it is not limited to places where actual change has occurred (Duveiller
et al., 2018b).
Flowchart resuming the processing steps undertaken for the present
study. The part in the grey box corresponds to work done in a previous study
(Duveiller et al., 2018b).
The biophysical consequences of LULCC are known to depend on the background
climate (Pitman et al., 2011; Winckler et al., 2017b), which in turn varies
with climate change (IPCC, 2014). To better anticipate these changes it is
necessary to predict these biophysical effects with robust model frameworks.
Land surface models (LSMs) are used to represent terrestrial processes within
Earth system models, in which they simulate both the carbon cycle and
land-atmosphere fluxes of energy, water and momentum. Initiatives like the
Land-Use and Climate, Identification of Robust Impacts (LUCID) project (de
Noblet-Ducoudré et al., 2012; Pitman et al., 2009) have attempted to
evaluate the capacity of LSMs to represent biophysical effects of LULCC by
inter-comparing several simulations of past LULCC and showing large
discrepancies amongst models, especially in separating between turbulent
fluxes. Such inter-comparison exercises should also continue within broader
initiatives such as the Land Use Model Inter-comparison Project (LUMIP)
contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6)
(Lawrence et al., 2016). However, there is a lack of model evaluation against
observation-driven datasets, in which the spatial, temporal and climatic
patterns can be evaluated at finer scale. Confrontation with observations
could considerably contribute towards improving the robustness and
consistency of models, but requires special attention to ensure simulations
and observations are comparable regarding how vegetation cover is
implemented and how biophysical processes are represented.
This study presents a framework for process-oriented model evaluation
specifically tailored towards analysing how local biophysical effects of
vegetation cover change are represented in LSMs. Simulations from four major
LSMs are confronted with satellite remote sensing observations across
geographic, seasonal and climatic dimensions for a range of vegetation
transitions and for different components of the surface energy balance. The
main objectives of this study are to create a harmonized multi-dimensional
dataset, to illustrate its content and to demonstrate its utility by
evaluating the agreement amongst models and against satellite observations.
Material and methods
Isolating the effect of vegetation cover change from both model simulations
and observations in order to make them comparable requires a series of
dedicated processing steps. To assist the reader in following the methodology
developed in this work, Fig. 1 summarizes the main steps in a synthetic
flowchart.
Remote sensing estimations
The observation part of the analysis is based on satellite remote sensing
observations to assess the effects of vegetation on the surface energy
balance for different vegetation cover types (Duveiller et al., 2018a). This
remote sensing dataset (RS dataset for short) consists of spatially and
seasonally explicit estimates of changes in surface properties following
specific vegetation transitions. These surface properties are albedo, land
surface temperature (LST) and evapotranspiration (ET), obtained from the
respective MODIS products MCD43C3 (Schaaf et al., 2002), MYD11C3 (Wan, 2008)
and MOD16A2 (Mu et al., 2011). The changes in these variables are calculated
at the original scale of the product 0.05∘, but the dataset is
provided at a spatial resolution of 1∘, with each cell representing
the mean changes occurring at the finer scale of 0.05∘. This coarser
spatial resolution is necessary for a specific step to ingest CERES EBAF
surface radiation data (Kato et al., 2013) in the processing chain, but is
also ideal to align the dataset with the simulations of LSMs. The data
represent a multi-annual average year with a monthly temporal resolution.
This synthetic year is constructed from the median values for a given month
over the period 2008–2012 for every 0.05∘ pixel. The original land
cover map used to build this dataset is the ESA CCI land cover map for 2010
(ESA, 2017), but with a simplified reclassification of land cover types into
major vegetation classes according to the International Geosphere-Biosphere
Programme (IGBP) classification scheme. A total of 45 distinct vegetation
transitions are provided in the RS dataset. Although these are referred to as
vegetation transitions, the information does not come from observations over
transient vegetation changes, but rather from paired observations of distinct
vegetation cover types at the same location. As a result, values for a given
pair are only available where there is sufficient spatial abundance, or
co-occurrence, of both vegetation types. For more details on the dataset and
how it was produced, readers may refer to Duveiller et al. (2018a).
The surface property variables from the RS dataset are net radiation (Rn),
latent heat flux (LE), and the sum of sensible and ground heat flux
(H + G). Sensible and ground heat fluxes have to be considered together
because they cannot be directly retrieved from satellites and are computed as
a residual flux from the closure of the surface energy balance. However, it
can be considered that H + G is dominated by H since the ground heat
values are generally much smaller and can be neglected at annual scale. In
this study net radiation is considered positive when the flux goes from the
atmosphere to the ground, while latent, sensible and ground heat fluxes are
positive when they exit from the surface to the atmosphere.
Land surface model simulations
To simulate the biophysical effects of local vegetation transitions that are
comparable to the RS dataset, we need to run LSMs forced by a realistic
climate and with idealized vegetation distributions. The four models
evaluated here are ORCHIDEE (Krinner et al., 2005), JULES (Best et al., 2011;
Clark et al., 2011), JSBACHv3.1 (Reick et al., 2013) and CLM4.5 (Oleson et
al., 2013). The forcing consists of historic climate data from CRU-NCEP v6,
and observationally derived global atmospheric CO2 concentration (Le
Quéré et al., 2015). Models were spun up for steady state in biomass
pools and leaf area index (LAI) and then forced with transient CRU-NCEP v6
reconstructed climate and CO2 from 1950 or earlier, and up until 2014.
To obtain values of the surface variables of interest (Rn, LE and H + G)
that are coherent with those of the RS dataset, the median monthly values of
these fluxes from 2008 until 2012 was calculated.
Models differ in how they represent the surface energy balance per plant
functional type (PFT) at the sub-grid level. Not all models can calculate
heat fluxes per PFT within a grid cell, and thus some need to resort to flux
aggregation at grid cell level to derive resulting variables such as
temperature. To overcome this problem and isolate the effect of vegetation
cover change on the surface energy budget, simulations are made in which the
entire grid cell is covered by a single PFT. Separate simulations are run
for each PFT of every model, in which the entire surface of the Earth is
covered by a single PFT. The effect of a change in PFT can then be retrieved
by subtracting values of biophysical fluxes between the two corresponding
simulations. Since there is no feedback between the vegetation and the
climate in this set-up, having such homogeneous distributions of vegetation
across vast geographic extents does not generate climate biases outside of
the grid cell.
Harmonizing vegetation classes
The models differ in how they represent vegetation using different PFTs, each
with their own parametrization. To facilitate the harmonization with the IGBP
vegetation classes in the remote sensing dataset, only 6 broad vegetation
classes are considered: evergreen broadleaf trees (EvgTr), deciduous
broadleaf trees (DecTr), needleleaf trees (NedTr), shrubs
(Shrub), grasses (Grass) and crops (Crops). A
total of 15 transitions (from paired comparisons of PFTs) are thus available
and can also be used to represent inverse transitions (e.g. ΔLE for
DecTr to Crops is equal to -ΔLE for Crops
to DecTr). To obtain these broad vegetation classes from the RS
dataset, the IGBP classes of evergreen and deciduous needleleaf forest (ENF
and DNF, respectively) were merged into NedTr, whereas classes not
represented by models or mixed classes such as woody savannas (SAV), mixed
forests (MF) and wetlands (WET) have been omitted. The other three classes
(Shrub, Grass and Crops) can be directly assigned
with their corresponding classes in the scheme used in the RS dataset (SHR,
GRA and CRO).
Summary of how the specific plant functional types (PFTs) of the
different land surface models (LSMs) are mapped into the six broad vegetation
classes used in this study. Model-specific nomenclature of PFT is used in
italics.
Vegetation classes used inthis paperJSBACHJULESCLMORCHIDEEBroadleaf evergreen trees (EvgTr)Tropical broadleaf evergreen are placed in grid cells with tropical climatea; Extra-tropical evergreen populate the remaining vegetated areas.Broadleaf treesBroadleaf evergreen treesTropical broadleaf evergreen are placed in grid cells with tropical climatea; Temperate broadleaf evergreen populate the remaining vegetated areas.Broadleaf deciduous trees (DecTr)Tropical broadleaf deciduous are placed in grid cells with tropical climatea; Extra-tropical broadleaf deciduous populate the remaining vegetated areas.Broadleaf treesBroadleaf deciduous treesTropical broadleaf deciduous are placed in grid cells with tropical climatea. Temperate broadleaf summergreen are placed in grid cells with arid and temperate areasa. Boreal broadleaf summergreen populate the remaining vegetated areas.Needleleaf trees (NedTr)Coniferous deciduous are placed in grid cells where they are dominant according to the default PFT distributionb. Coniferous evergreen populate the remaining vegetated areas.Needleleaf treesNeedleleaf treesBoreal needleleaf summergreen trees are placed in grid cells where they are dominant according to the default PFT distributionbBoreal needleleaf evergreen populate the remaining boreal and polar areasa. Temperate needleleaf evergreen populate the remaining tropical, temperate and arid areasa.Shrubs (Shrub)Deciduous shrubs are placed in grids where they are dominant in the default PFT distributionb; Raingreen shrubs populate the remaining vegetated areas.ShrubsShrubsNo PFTs are available.Grasses (Grass)Grid cell assigned either C3grass or C4grass, basedon the dominant photosynthetic pathway at grid cell levelb.C4grassGrid cell assigned either C3crop or C4crop, based on the dominant photosynthetic pathway at grid cell levelbGrid cell assigned either C3grass or C4grass, based on the dominant photosynthetic pathway at grid cell levelb.Crops (Crops)Grid cell assigned either C3crop or C4crop, based on the dominant photosynthetic pathway at grid cell levelb.C3 grassStandard C3crop(representing wheat)Grid cell assigned either C3crop or C4crop, based on the dominant photosynthetic pathway at grid cell levelb.
The spatial merging of different PFTs into
single simulation layers requires assignment from ancillary maps indicated by
the following superscripts: a Köppen–Geiger classification
(Kottek et al., 2006) to distinguish arid, tropical, temperate and boreal
climate zones; b default PFT distribution of JSBACH including
dominant photosynthetic pathways (Knorr and Heimann, 2001).
The harmonization of the different modelled PFTs to these 6 broad classes
required the use of some decision rules that are summarized in Table 1. PFTs
whose differences relate to their climatic regime (such as “Tropical
broadleaf deciduous” and “Temperate broadleaf deciduous” in ORCHIDEE) are
geographically separated and can be aggregated into a single global PFT. The
spatial representation of the climate zones is taken from the revisited
Köppen–Geiger classification product (Kottek et al., 2006). A similar
approach is adopted to keep all needleleaf tree PFTs in a single layer, as
the deciduous needleleaf trees are predominantly located in a well-defined
geographic area in Siberia without a strong overlap with evergreen needleleaf
trees. For grasses and crops, LSMs typically make a separation between
C3 and C4 systems for carbon fixation, which is not currently
feasible to detect from remote sensing observations (and thus is absent in
the RS dataset). For the two classes, Grass and Crops, the
decision rule adopted is to assign the dominant photosynthetic pathway
(C3 or C4) within a grid cell to the entire grid cell. Since
different models may have a different default PFT distribution map, the PFT
distribution of JSBACH (based on work by Knorr and Heimann, 2001) is selected
here as reference and used for the harmonization of the other models as well.
An exception to this rule is applied for JULES, which represents crops as
grasses. In this case, to maximize information content the Crops
class is assigned exclusively with the C3 grass PFT and the
Grass class contains only the C4 grass PFT. There are some
further model-specific details in the harmonization procedure. For CLM, the
Crops simulation is composed exclusively of the generic C3
crop. Even if some LSMs can simulate some crop managements options, such as
irrigation, this has been switched off to maximize inter-comparability
amongst model runs. Regarding trees, JULES does not distinguish PFTs based on
phenology, considering only the difference between broadleaf and needleleaf
trees. The EvgTr and DecTr simulations will therefore be
identical in JULES. Shrubs are simulated as a PFT by all models except for
ORCHIDEE, for which the Shrub class remains empty.
As mentioned previously, the RS dataset can only provide reliable
estimations where the vegetation types of interest locally co-exist.
Although the models could theoretically simulate the vegetation even where
is does not naturally occur, the harmonized dataset presented here only
includes simulated values over the areas where the RS data are available.
Inter-comparison of how the land surface models and remote sensing
(RS) estimate a change in latent heat flux (LE) for the vegetation transition
from evergreen broadleaf trees to crops (Crops – EvgTr). We summarize the
data within three different comparison spaces to illustrate (a) the
spatial variability, representing the annual mean for each pixel;
(b) the seasonal variability; and (b) the variability in
climate space, represented by mean annual temperature and annually cumulated
precipitation.
Protocol to evaluate agreement
The harmonized dataset is presented and analysed along three different
bi-dimensional spaces. The first is geographic space, in which mean annual
values per pixel are obtained by averaging all monthly observations. The
second is labelled seasonal space, in which averages are made along
latitudinal bands for each month, illustrating the seasonal course of the
variables, such as in Hovmöller diagrams (Hovmöller, 1949). The third
is climatic space, in which variables are analysed along temperature and
precipitation gradients. The climatic axes of this space are mean annual
temperature and annually cumulated precipitation for the period 2008–2012
based on the CRU TS4.00 climate data (Harris et al., 2014). The rationale
behind using these spaces is to encourage more process-based model evaluation
by ensuring that the agreement or disagreement between models and observations
is coherent spatially, seasonally and climatically.
In this context of quantifying the biophysical effects of vegetation cover
change, neither the satellite-derived estimations nor any of the model
simulations can pretend to be an absolute reference, as all of them have some
level of assumptions and uncertainties. While observation-driven datasets are
usually taken as a reference over model simulations, in some cases the
latter can also serve to evaluate the quality of the former (Massonnet et
al., 2016). In order to evaluate the agreement without setting a single
product as a reference, we measure agreement based on a metric that has the
property of being symmetric. This means the value of the metric remains
numerically unchanged whether it is applied to products X and Y, or if
these are inverted. This is not the case when using a coefficient of
determination R2 from a standard regression of Y on X, which differs
from that of a regression of X on Y. Beyond being symmetric, the index of
agreement λ that we use is also dimensionless, bounded (between 0 for
no agreement to 1 for perfect agreement) and easy to compute (Duveiller et
al., 2016). Furthermore, its interpretation is relatively intuitive for
practitioners since its value is the same as that of the familiar correlation
coefficient r when there are no additive or multiplicative bias
contributing to the disagreement between X and Y. When there are biases,
its value reduces proportionately to this bias. For two sets, X and Y, each
containing n values, the index is defined as follows:
λ=1-n-1∑i=1n(Xi-Yi)2σX2+σY2+(μX-μY)2+κ,
where μ and σ represent the mean and standard deviation,
respectively. κ is a term set to zero if the correlation between X
and Y is positive, and otherwise set as follows:
κ=2∑i=1n(Xi-μX)(Yi-μY).
Besides this index of agreement, the analysis also uses the correlation
coefficient r and the mean absolute bias B as defined by the following
formulas:
r=n-1∑i=1n(Xi-X‾)(Yi-Y‾)σXσY,B=∑i=1n|Xi-Yi|n.
Same as Fig. 2 for a change in the residual flux of the energy
balance composed of both sensible heat and ground heat fluxes (H + G) for
the vegetation transition from deciduous broadleaf trees to needleleaf trees
(DecTr – NedTr).
Same as Fig. 2 for a change in the net radiative flux (Rn) for the
vegetation transition from grasses to needleleaf trees (Grass – NedTr).
Results
Due to its high dimensionality, it is challenging to illustrate exhaustively
all the facets of information represented in the dataset. Therefore, this
section starts by describing a selection of cases of how different products
portray changes in geographic, seasonal and climatic space of a given
variable following a specific vegetation cover transition.
The first case, shown in Fig. 2, consists of changes in LE resulting from the
conversion of evergreen broadleaf trees to crops (EvgTr to Crops)
corresponding to a common land cover change associated with tropical
deforestation. It is clear from this figure that not all models reproduce the
expected effects of tropical deforestation (i.e. a reduction in LE due to
crops having shallower roots and thus less access to water for transpiration)
that are seen in the RS dataset. JSBACH and CLM see an increase in mean
annual LE following deforestation in large parts of the world. ORCHIDEE and
JULES generally predict the sign correctly, a reduction of LE, but for JULES
the behaviour in the seasonal and climatic space shows contrasting patterns
to those of RS, while those of JSBACH might be more in line despite the
constant bias of this model. Please note that in this and similar figures
presented in this work, data for a given transition are only available for
areas where there is a local co-occurrence of the two vegetation types
according to the land cover distribution used in the RS dataset.
The second case, displayed in Fig. 3, concerns changes in H + G following
the conversion of broadleaf deciduous to needleleaf trees (DecTr to
NedTr). This transition can represent for instance changes in
planted forest species, such as what occurred for most of the past 250 years
in Europe (Naudts et al., 2016). According to the RS dataset, this change is
associated with a general increase in H + G, particularly in the summer
period and across all latitudes (at least those in which there is a joint
presence of DecTr and NedTr, and thus a higher likelihood
that this conversion occurs). ORCHIDEE and JSBACH do not capture the sign of
change in H + G dynamic, sometimes showing a reduction in H + G that
is quite large for ORCHIDEE in the summertime and in warmer climates. CLM and
JULES show more consistent patterns with RS. However, JULES overestimates the
magnitude of change, especially in warmer climates, while CLM simulates a
higher rise in H + G in spring in northern mid-latitudes to high latitudes that is not
present in the observations.
The effect on net radiation caused by vegetation change from grasses to
needleleaf trees (Grass to NedTr) is the third case,
illustrated in Fig. 4. This transition reflects the effect of a northward
expansion of the boreal forests into tundra, which is expected to happen as
the temperatures in the higher latitudes increase. It also represents land
abandonment and reforestation in the mid-latitudes. The general direction of
change is captured by all models, showing how transforming grasslands to
forests leads to an increase in net radiation, mostly due to the increase in
shortwave radiation absorbed by the darker canopy. However, the geographic,
seasonal and climatic patterns differ from observations and vary across the
models. Seasonally, the RS dataset illustrates the strong snow effect on
albedo in northern latitudes in spring, when the radiation load increases
substantially as the days become longer while snow cover remains, thus
amplifying the albedo differences between snow-covered grasses and darker
evergreen trees. The higher increases in Rn that are present in the RS
observations along the mountain ranges in North America are only captured by
JULES. The magnitude of the spring albedo effect for this transition is
slightly underestimated in ORCHIDEE and overestimated in JSBACH and CLM,
although for CLM it appears to extend more in time and latitude than what is
reported in the RS dataset. While these discrepancies might be due more to
misrepresentation of snow-related processes within the models than to
misrepresentation of vegetation, it remains a good example of a process-based
model evaluation, since it focuses on the comprehensive biophysical effect of
the process of vegetation cover change.
Summary of the agreement between land surface models amongst
themselves and with the remote sensing estimations for the vegetation
transition from evergreen broadleaf trees to crops (Crops – EvgTr). The
agreement is measured using the index of agreement λ (size of the
squares), the Pearson correlation coefficient r (size of the circles, red
border indicates negative correlation) and the absolute bias (colour of the
symbols). The data used to calculate these metrics are the values previously
averaged in bins according to the spatial, seasonal and climatic analysis
spaces shown in Fig. 2. Hence, these metrics relate only to areas where both
vegetation types locally co-exist in reality. The fluxes represented are net
radiation (Rn), latent heat flux (LE), and the combination of the sensible and
ground heat fluxes (H + G).
For any vegetation transition, the similarities and discrepancies, both
amongst models and with the RS dataset, can be summarized synthetically in a
single diagram for all fluxes and for the three spaces under investigation
(geographic, seasonal and climatic). Figure 5 shows such a diagram for the
case of the tropical deforestation transition EvgTr to
Crops, the same transition which is represented in Fig. 2. Every
panel in Fig. 5 provides an inter-comparison of the pair-wise agreement
between products either with the index λ, using squares in the lower
right triangle of the panel, or with the correlation coefficient, r, using
circles in the upper left triangle. Whilst the sizes of the symbols represent
the relative value of the metric, the colour provides the value of the mean
absolute bias. For two products X and Y, all metrics (λ, r and
B) are calculated using the respective equations with the aggregated values
over the three reasoning spaces, i.e. the binned values in geographic,
seasonal and climatic space, such as those represented in Fig. 2. Generally
Rn is better represented across the board, especially the seasonal patterns,
and LE tends to suffer larger biases. The agreement amongst the models can be
also gauged by comparing the size of the symbols within the triangles of
Fig. 5. Analogous plots to those in Fig. 5 are available for all 15
transitions in the Supplement.
To provide a general overview for all transitions, Fig. 6 shows the agreement
for each model with the RS dataset for all three fluxes averaged over
geographic, seasonal and climatic space. The transitions are ordered
according to the magnitude of gross transitions that have occurred in the
recent past between 2000 and 2015 according to the annual ESA CCI annual land
cover maps (ESA, 2017), with EvgTr to Crops being the
largest. Generally, the agreement in geographic space is very poor, followed
by occasional agreement in climatic space, and then more frequent agreement
regarding the seasonal cycle. Net radiation is the variable that models
simulate best, particularly the seasonal patterns, while H + G come
second and LE comes third. Overall, the transition in which there is highest
agreement between models and RS across all variables and spaces is
DecTr to Crops, while the one with least agreement is
Grass to Crops.
No model stands out as having consistently better performance than the
others. Models differ in which transitions they simulate best. JULES performs
best for NedTr to Grass, CLM for EvgTr to
Crops, and both JSBACH and ORCHIDEE for DecTr to
Crops. When looking at each variable across transitions and facets,
the models showing highest mean agreement for LE, H + G and Rn are JULES,
CLM and JULES respectively. Disregarding the situations when agreement is
very poor, rare are the cases when all models similarly agree with RS, i.e.
when all colours in a single box of Fig. 6 have similar colours. Those that
stand out always involve Rn and are seasonal agreement for Grass to
DecTr and both seasonal and climatic agreement for DecTr to
Crops. There are also cases in which a single model stands out as
having much higher agreement than all the others, such as CLM for the
spatial agreement of Rn for EvgTr to Crops, JULES for the
seasonal agreement of Rn for NedTr to DecTr, JSBACH for
climatic agreement of H + G for NedTr to Shrub and
ORCHIDEE for climatic agreement of H + G for NedTr to
Crops.
Summary of the agreement between models and remote sensing for all
fluxes and all transitions in each of three facets of analysis: spatial,
seasonal and climatic. The position of each triangle represents one of the
four land surface models as shown in the top corner: ORCHIDEE (ORC), the
Community Land Model (CLM), JSBACH (JSB) and JULES (JUL). The fluxes
represented are net radiation (Rn), latent heat flux (LE), and the combination
of the sensible and ground heat fluxes (H + G). The colour of the
triangle represents the value of the index of agreement λ. Below
each transition label, the number n provides the total number of original
individual spatio-temporal records used to calculate each metric. The order
of the transitions (from top to bottom) corresponds to the order of gross
changes that have occurred between 2000 and 2015 according the ESA CCI land
cover maps, which is provided below each transition label in megahectares
(Mha).
A final synoptic summary is provided in Fig. 7 that encompasses all
transition and all fluxes and separates the total agreement in the recurrent
three spaces: geographic, seasonal and climatic. A further division is made by
distinguishing between the mean agreement amongst models (analogously to the
triangle of values mentioned for Fig. 5) and the mean model agreement with
remote sensing. The more salient feature is that models seem to agree most
over Europe. Inter-model agreement is also high over northern America, but
with the notable exception of the southeast of the United States. Inter-model
agreement is also higher in drier and colder areas. However, for many of
these areas models do not agree with RS. Western Canada and southern
Australia appear as the places where there is the strongest agreement with
RS, while the tropics show decisively lower agreement. The higher agreement
amongst models in northern latitudes is maintained across the seasons, but
the agreement with RS is only high in spring, probably due to the capacity of
some models to catch the snow-induced albedo changes when trees are replaced
by shrubs or grasses. The overall agreement in climate space indicates how
for warm climates, models agree amongst themselves less in the more humid
conditions, while there is generally a large disagreement with RS for all
conditions. In colder climates, inter-model agreement is high but agreement
with RS is higher for wetter conditions.
Agreement amongst models and between models and remote sensing for
all transitions and all fluxes together. The agreement amongst
models (a) is calculated as the mean of all λ values
calculated for each model pairs, while the agreement with remote
sensing (b) is the mean values of λ for each model with
respect to the remote sensing dataset.
Discussion
This model-evaluation framework specifically targeting the biophysical
effects of LULCC is unique in that it brings model simulations and
observation-driven estimates together. By focusing on a model set-up with
prescribed homogeneous vegetation types within grid cells, the biophysical
impacts of specific LULCC transitions within the models can be recombined to
match RS observations without requiring a complex disaggregation of the
energy balance fluxes per sub-grid PFT. The resulting harmonized dataset
should be of interest for a range of stakeholders. Model developers will
find it useful to assess how their model performs with respect to other
models and to an observational benchmark, which in turn can serve to
identify areas across geographic, seasonal and climate space where model
development efforts should be prioritized. Developers of LSMs that are not
included in this study can follow the protocol and use the dataset to
evaluate the resulting model performance. Model users can use the dataset
and the analysis to choose the LSM that performs best over their areas of
interest. For people making decisions based on conclusions derived from
model outputs, the dataset and the evaluation can provide a welcome overview
of model performance across space, time and climate zones, along with an
overall an idea of the current level of uncertainty associated with using
these tools for estimating the biophysical impacts of LULCC.
The overall picture of the general benchmarking exercise of model
performances is not encouraging. For various vegetation transitions, models
do not even agree amongst themselves on the magnitude nor the sign of the
change. The study confirms with observational data what previous analyses had
reported based on model inter-comparisons regarding how models have more
difficulties to simulate turbulent fluxes than radiative ones (de
Noblet-Ducoudré et al., 2012), despite that the former have been shown to
drive the local temperature response to land cover and management (Bright et
al., 2017). As can be expected, the seasonal patterns observed in the RS
dataset are better simulated than climatic or spatial patterns. Models are
especially poor in capturing the spatial patterns, arguably because LSMs
typically use the same parameterization for a given PFT across the globe,
thereby disregarding the spatial variability of traits that can naturally
occur, which in turn is sampled by the observation-driven estimates. In this
sense, some model improvement could come by adopting the concept of optical
functional types, based on traits detectable by remote sensing (Ustin and
Gamon, 2010).
Models also tend to agree more amongst themselves than with observations.
This may stem from similarities in the construction of models and their
underlying assumptions. Europe may come out as a place of higher inter-model
agreement because vegetation models were based heavily on information on
temperate ecosystems, resulting in a better representation of temperature
deciduous systems than drought-deciduous systems (Morales et al., 2005). Data
for other ecosystems have only become available in more recent decades.
Inter-model agreement, and to a lesser extent RS-model agreement, is also
higher in the areas where flux measurements from eddy-covariance towers,
frequently used for carbon cycle calibration, are denser (Schimel et al.,
2015). This converges towards an evident conclusion that strengthening the
observational base is still essential to ensure the quality of model results.
As discussed in Duveiller et al. (2018b), the RS dataset also has shortcomings, and these may partly
explain discrepancies with respect to model simulations. A valid criticism is
that the RS dataset relies on an evapotranspiration product (Mu et al., 2011)
that has been shown to underperform compared to other satellite-driven
products both at local (Michel et al., 2016) and at global scales (Miralles
et al., 2016). However, this was the only available product at the
sufficiently fine spatial resolution of 0.05∘, and despite the
underperformance, ancillary analyses in Duveiller et al. (2018a, b) suggest
that the product quality is sufficient for the purpose of studying local
differences.
Some caveats regarding the specific model set-up need to be highlighted. The
first relates to the spatial scale at which processes are represented. The
use of homogeneous PFTs across grid cells successfully isolates the effect
of a total vegetation cover change within an LSM grid, but this has a
natural trade-off: intrinsically heterogeneous ecosystems, such as
savanna systems and taiga, could not be evaluated as these are represented by models
using a mixture of tree and grass PFTs. A dedicated evaluation could be done
in a more sophisticated version of this work using the savanna class
transitions in the RS dataset (which was not used here), but would require
the use of the same prescribed mixture of trees and grasses for all models.
Such an exercise would probably reveal strong changes in biophysical effects
linked to canopy roughness. Another option could be to do the entire
exercise on LAI, which can integrate this heterogeneity, comparing changes
in modelled LAI with LAI estimated from satellite remote sensing. A related
caveat in the current set-up linked to mixed systems is that a within-grid
cell bias may result from the mismatch between climate and vegetation. In an
all-forest simulation over a grid cell subjected to the real climate
observed over a savanna, the trees may be more stressed than if they were
mixed with low-evaporative grasses. Therefore, transitions of forests to
grasses reported in this dataset may be overestimating the reduction of
processes such as evapotranspiration over savanna regions. Overall, these
issues illustrate how increasing the spatial resolution of the model
simulations to better match that of observations should improve their
inter-comparisons. This would result both from a better characterization of
vegetation heterogeneity, but also from enabling LSMs to better resolve
local climate variability and the resulting biophysical effects of LULCC.
Another particularity of the model set-up employed here is that only the
local first-order biophysical effects of LULCC are explored. Non-local
effects related to LULCC occurring elsewhere, as explored by modelling
exercises such as Winckler et al. (2017a), are not considered here because
they cannot be directly estimated by remote sensing diagnostics. Given that
the analysis is also based on uncoupled LSMs runs, there are no possible
feedbacks of the vegetation cover change on global climate, nor are there any
local atmospheric feedbacks. Considering second-order effects stemming from
the bi-directional land–climate interactions would require using LSMs coupled
with a general circulation model within an Earth system model in which some
cells are affected by LULCC, but this is beyond the scope of the present
work.
The inter-comparison exercise presented here can be extended and improved.
Doing so could further address other limitations of the current set-up, such
as the fact that only a single source of meteorological forcing data is used
to run the model simulations. Such datasets, based on climate reanalysis, can
be particularly prone to errors and uncertainties in data-poor regions.
Initiatives such as LUMIP (Lawrence et al., 2016) could use the present
framework to run LSMs with different forcing datasets and evaluate how the
simulated biophysical impacts of LULCC are sensitive to the quality of the
input data. Another criticism of the inter-comparison is the mismatch between
model runs, which do not explicitly include the effects of land management,
and the RS dataset, which intrinsically does, simply because these are
present in the observations. The biophysical impacts of land management
changes have been shown to be as important as the effects of land cover
change (Luyssaert et al., 2014), and could thus further account for the
discrepancies between models and observations. The exercise could thus be
extended with runs that include management for the models which can
effectively simulate it, and it could also evaluate the improvement based on the RS
benchmark. Finally, the exercise could be improved by extending the
observational part to an ensemble of RS datasets. The input biophysical
variables used to construct the RS dataset, namely albedo, land surface
temperature and evapotranspiration, could be derived from different satellite
instruments and based on other, ideally better algorithms. Ultimately, the RS
dataset could be based on products from geostationary satellites to be able
to study the diurnal patterns of biophysical effects of LULCC and how these
are represented in the models.
The dataset consisting of both harmonized model simulation
and remote sensing estimations is freely available in Zenodo:
10.5281/zenodo.1182145 (Duveiller et al., 2018c).
Conclusions
This paper presents a process-oriented model evaluation framework for
biophysical effects of vegetation cover change. A harmonized
multi-dimensional dataset has been generated including dedicated simulations
from four major LSMs along with observation-driven estimations based on
satellite imagery. The analysis of these data along geographic, seasonal and
climate dimensions results in an overview of model performance that can
serve to highlight hotspots of agreement and disagreement both amongst
models and with respect to an observational benchmark. The overall capacity
of current LSMs to represent biophysical effects of LULCC is low. The
seasonal cycle of radiative fluxes is the process that models capture best,
whilst performance drops considerably when considering spatial and climatic
gradients for all fluxes. We anticipate that the dataset will serve to
identify specific model shortcomings with respect to observations and to
other models, but also to highlight where models can be trusted more and
where model development should be prioritized. This should in turn
contribute to the larger goal to develop and inform land-based mitigation
and adaptation policies that account for both biogeochemical and biophysical
vegetation impacts on climate. Improving the robustness and consistency of
land-surface models is essential to develop and inform land-based mitigation
and adaptation policies that account for both biogeochemical and biophysical
vegetation impacts on climate.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-10-1265-2018-supplement.
GD, AC and SS conceived the study. ER, WL, GG and PL did the model runs,
which GF harmonized together. GD did all the analyses and wrote the
paper with contribution of all the authors.
The authors declare that they have no conflict of
interest.
Acknowledgements
The study was funded by the FP7 LUC4C project (grant no.
603542) Edited by: David
Carlson Reviewed by: Wim Thiery and one anonymous referee
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