The Land Use and Climate Across Scales Flagship Pilot
Study (LUCAS FPS) is a coordinated community effort to improve the
integration of land use change (LUC) in regional climate models (RCMs) and
to quantify the biogeophysical effects of LUC on local to regional climate
in Europe. In the first phase of LUCAS, nine RCMs are used to explore the
biogeophysical impacts of re-/afforestation over Europe: two
idealized experiments representing respectively a non-forested and a
maximally forested Europe are compared in order to quantify spatial and
temporal variations in the regional climate sensitivity to forestation. We
find some robust features in the simulated response to forestation. In
particular, all models indicate a year-round decrease in surface albedo,
which is most pronounced in winter and spring at high latitudes. This
results in a winter warming effect, with values ranging from +0.2 to +1
K on average over Scandinavia depending on models. However, there are also a
number of strongly diverging responses. For instance, there is no agreement
on the sign of temperature changes in summer with some RCMs predicting a
widespread cooling from forestation (well below -2 K in most regions), a
widespread warming (around +2 K or above in most regions) or a mixed
response. A large part of the inter-model spread is attributed to the
representation of land processes. In particular, differences in the
partitioning of sensible and latent heat are identified as a key source of
uncertainty in summer. Atmospheric processes, such as changes in incoming
radiation due to cloud cover feedbacks, also influence the simulated
response in most seasons. In conclusion, the multi-model approach we use
here has the potential to deliver more robust and reliable information to
stakeholders involved in land use planning, as compared to results based on
single models. However, given the contradictory responses identified, our
results also show that there are still fundamental uncertainties that need
to be tackled to better anticipate the possible intended or unintended
consequences of LUC on regional climates.
Introduction
Land use change (LUC) affects climate through biogeophysical processes
influencing surface albedo, evapotranspiration and surface roughness
(Bonan, 2008; Davin and de
Noblet-Ducoudré, 2010). The quantification of these effects is still subject
to particularly large uncertainties, but there is growing evidence that LUC
is an important driver of climate change at local to regional scales. For
instance, the Land-Use and Climate, IDentification of robust impacts (LUCID)
model intercomparison indicated that while LUC likely had a modest
biogeophysical impact on global temperature since the pre-industrial era, it
may have affected temperature in a similar proportion to greenhouse gas
forcing in some regions (de Noblet-Ducoudré et al., 2012).
Results from the Coupled Model Intercomparison Project Phase 5 (CMIP5)
confirmed the importance of LUC for regional climate trends and for
temperature extremes (Kumar et al., 2013;
Lejeune et al., 2017, 2018).
In this light, it is particularly important to represent LUC forcings not
only in global climate models but also in regional climate simulations. Yet,
LUC forcings were not included in previous regional climate model (RCM) intercomparisons
(Christensen
and Christensen, 2007; Jacob et al., 2014; Mearns et al., 2012; Solman et
al., 2013), which are the basis for numerous regional climate change
assessments providing information for impact studies and the design of
adaptation plans (Gutowski Jr. et al.,
2016). RCMs have been applied individually to explore different aspects of
land use impacts on regional climates (Davin
et al., 2014; Gálos et al., 2013; Lejeune et al., 2015; Tölle et
al., 2018; Wulfmeyer et al., 2014), but the robustness of such results is
difficult to assess due to their reliance on single RCMs and due to the lack of
a common protocol. There is therefore a need for a coordinated effort to
better integrate LUC effects in RCM projections. The Land Use and Climate
Across Scales (LUCAS) initiative (https://www.hzg.de/ms/cordex_fps_lucas/, last access: 10 February 2020)
has been designed with this goal in mind. LUCAS is endorsed as a Flagship
Pilot Study (FPS) by the World Climate Research Program-Coordinated Regional
Climate Downscaling Experiment (WCRP-CORDEX) and was initiated by the
European branch of CORDEX (EURO-CORDEX) (Rechid et al.,
2017). The objectives of the LUCAS FPS are to promote the inclusion of the
missing LUC forcing in RCM multi-model experiments and to identify the
associated impacts with a focus on regional to local scales and considering
timescales from extreme events to seasonal and multi-decadal trends and
variability. LUCAS is designed in successive phases that will go from
idealized to realistic high-resolution scenarios and intends to cover both
land cover changes and land management impacts.
In the first phase of LUCAS, which is the focus of this study, idealized
experiments over Europe are performed in order to benchmark the RCM
sensitivity to extreme LUC. Two experiments (FOREST and GRASS) are performed
using a set of nine RCMs. The FOREST experiment represents a maximally
forested Europe, while in the GRASS experiment trees are replaced by
grassland. Comparing FOREST to GRASS therefore indicates the theoretical
potential of a maximum-forestation (encompassing both reforestation and
afforestation) scenario over Europe. Given that forestation is one of the
most prominent land-based mitigation strategies put forward in scenarios
compatible with the Paris Agreement goals (Grassi
et al., 2017; Griscom et al., 2017; Harper et al., 2018), it is essential to understand its full consequences beyond CO2 mitigation.
These experiments are not meant to represent realistic scenarios, but they
enable a systematic assessment and mapping of the biogeophysical impact of
forestation across regions and seasons. Experiments of this type have
already been performed using single regional or global climate models
(Cherubini
et al., 2018; Claussen et al., 2001; Davin and de Noblet-Ducoudré, 2010;
Strandberg et al., 2018), but here they are performed for the first time
using a multi-model ensemble approach, thus providing an unprecedented
opportunity to assess uncertainties in the climate response to vegetation
perturbations. In the following, we focus on the analysis of the surface
energy balance and temperature response at the seasonal timescale, while
future studies within LUCAS will explore further aspects (sub-daily
timescale and extreme events, land–atmosphere coupling, etc.). We aim to
quantify the potential effect of forestation over Europe, identify robust
model responses, and investigate the possible sources of uncertainty in the
simulated impacts.
MethodsRCM ensemble
Two experiments (GRASS and FOREST) were performed with an ensemble of nine
RCMs, whose names and characteristics are presented in Table 1. All
experiments were performed at 0.44∘ (∼50 km) horizontal
resolution on the EURO-CORDEX domain (Jacob et al., 2014) with
lateral boundary conditions and sea surface temperatures prescribed based on
6-hourly ERA-Interim reanalysis (Dee et al., 2011).
The simulations are analysed over the period 1986–2015, and the earlier years
(1979–1985 or a subset of these years depending on models; see Table 1)
were used as spin-up period. The model outputs were aggregated to monthly
values for use in this study. When showing results averaged across all nine
RCMs, we refer to it as the multi-model mean (MMM).
A notable characteristic of the multi-model ensemble is that some RCMs share
the same atmospheric scheme (i.e. same version and configuration) but are
coupled to different land surface models (LSMs) or share the same LSM in
combination with different atmospheric schemes (see Table 1). This allows us to
evaluate the respective influence of atmospheric versus land process representation. For instance, the same version of COSMO-CLM (CCLM) is used
in combination with three different LSMs (TERRA_ML, VEG3D and
CLM4.5). Comparing results from these three CCLM-based configurations
enables us to isolate the role of land process representation in this
particular model. Conversely, CLM4.5 is used in combination with two
different RCMs (CCLM and RegCM), which allows us to diagnose the influence of
atmospheric processes on the results. Different configurations of WRF (Weather Research and Forecasting) are
also used: WRFa-NoahMP and WRFb-NoahMP differ only in their atmospheric
set-up, while WRFb-NoahMP and WRFb-CLM4.0 share the same atmospheric set-up
but with different LSMs.
While the simulations we present are not suitable for model evaluation
because of the idealized land cover characteristics, it is worthwhile to
note that the RCMs included here have been part of previous evaluation
studies over Europe (e.g. Kotlarski et al., 2014; Davin et al., 2016). Although for a given RCM the
model version and configuration may differ from previously evaluated
configurations, the systematic biases highlighted in these previous studies
are likely still relevant here. In particular, a majority of RCMs suffer
from predominantly cold and wet biases in most European regions, while the
opposite is true in summer in Mediterranean regions
(Kotlarski et al., 2014). The conditions that are too dry over
southern Europe have been related in particular to land surface process representation including evapotranspiration (Davin et al., 2016).
FOREST and GRASS vegetation maps
Two vegetation maps have been created for use in the Phase 1 LUCAS
experiments (Fig. S1 in the Supplement). The vegetation map used in the experiment FOREST is meant
to represent a theoretical maximum of tree coverage, while in the vegetation
map used in the experiment GRASS, trees are entirely replaced by grassland.
The starting point for both maps is a MODIS-based present-day land cover map
at 0.5∘ resolution (Lawrence and Chase, 2007)
providing the global distribution of 17 plant functional types (PFTs). Crops
and shrubs which are present in the original map are not considered in the
FOREST and GRASS experiments and are set to zero. To create the FOREST map,
the fractional coverage of trees is expanded until trees occupy 100 % of
the non-bare soil area. The proportion of various tree types (i.e.
broadleaf to needleleaf and deciduous to evergreen) is conserved as in the
original map as well as the fractional coverage of bare soil, which prevents
expanding vegetation on land areas where it could not realistically grow
(e.g. in deserts). If no trees are present in a given grid cell with less
than 100 % bare soil, the zonal mean forest composition is taken as a
representative value. This results in a map with only tree PFTs (PFT names)
and bare soil, all other vegetation types being shrunk to zero. It is
important to note that this FOREST map does not represent a potential
vegetation map, which would imply a more conservative assumption in terms of
forest expansion potential. Indeed, trees can grow even in regions where
they would not naturally occur because of various human interventions
(assisted afforestation, forest management, fire suppression, etc.). This
FOREST map is therefore in line with the idea of considering both
reforestation and afforestation potential, while still excluding forest
expansion over dryland regions where irrigation measures would likely be
necessary.
The GRASS map is then derived from the FOREST map by converting all tree
PFTs into grassland PFTs, the C3-to-C4 ratio being conserved as in the
original MODIS-based map as well as the bare soil fraction.
Since the various RCMs use different land use classification schemes (see
Table 1), the PFT-based FOREST and GRASS maps were converted into
model-specific land use classes for implementation into the respective RCMs.
The specific conversion rules used in each RCM are summarized in Table 1
(note that for three out of the nine RCMs, no conversion was required). Urban
areas, inland water and glacier, if included in a given RCM, were conserved
as in the standard dataset of the respective RCM.
Names and characteristics of the RCMs used. NET-Temperate: needleleaf evergreen tree – temperate; NET-Boreal: needleleaf evergreen
tree – boreal; NDT-Boreal: needleleaf deciduous tree – boreal; BET-Tropical: broadleaf evergreen tree – tropical; BET-Temperate: broadleaf evergreen tree
– temperate; BDT-Tropical: broadleaf deciduous tree – tropical;
BDT-Temperate: broadleaf deciduous tree – temperate; BDT-Boreal: broadleaf
deciduous tree – boreal; BES-Temperate: broadleaf evergreen shrub –
temperate; BDS-Temperate: broadleaf deciduous shrub – temperate; BDS-Boreal: broadleaf deciduous shrub – boreal. Institution IDs are as follows: JLU – Justus-Liebig-Universität Gießen; BTU:
Brandenburgische Technische Universität; KIT – Karlsruhe Institute of Technology; ETH – Eidgenössische Technische Hochschule Zürich; SMHI – Swedish Meteorological and Hydrological Institute; ICTP – International Centre for Theoretical Physics; GERICS – Climate Service Center Germany; IDL – Instituto Amaro Da Costa; UHOH – University of Hohenheim; AUTH – Aristotle University of Thessaloniki.
Model nameCCLM-TERRACCLM-VEG3DCCLM-CLM4.5RCARegCM-CLM4.5REMO-iMOVEWRFa-NoahMPWRFb-NoahMPWRFb-CLM4.0Institute IDJLU/BTU/CMCCKITETHSMHIICTPGERICSIDLUHOHAUTHRCMCOSMO_5.0_clm9COSMO_5.0_clm9COSMO_5.0_clm9RCA4RegCM4.6.1 (Giorgi et al., 2012)REMO2009WRF381WRF381WRF381Land settings Representation of sub-grid-scale vegetation heterogeneitysingle classsingle classtile approachtile approachtile approachtile approachsingle classsingle classtile approachLeaf area indexprescribed seasonal cycle (sinus function depending on altitude and latitude with vegetation-dependent minimum and maximum values)prescribed seasonal cycle (sinus function depending on altitude and latitude with vegetation-dependent minimum and maximum values)prescribed seasonal cycle based on MODIS (Lawrence and Chase, 2007)Calculated monthly based on vegetation type, soil temperature and soil moistureprescribed seasonal cycle based on MODIS (Lawrence and Chase, 2007)Calculated daily based on atmospheric forcing and soil moisture stateprescribed seasonal cycle based on lookup tablesprescribed seasonal cycle based on lookup tablesprescribed seasonal cycle based on MODIS (Lawrence and Chase, 2007)Total soil depth and number of hydrologically/thermally active soil layersnine thermally active layers down to 7.5 m; first eight hydrologically active down to 3.9 mnine layers down to 7.5 m15 layers for thermal calculations down to 42 m; first 10 hydrologically active down to 3.43 mfive layers down to 2.89 m15 layers for thermal calculations down to 42 m; first 10 hydrologically active down to 3.43 mfive thermally active layers down to 10 m; one water bucketfour layers down to 1 mfour layers down to 1 m10 layers down to 3.43 mAtmospheric settings Initialization and spin-upInitialization with ERA-Interim, 1979–1985 as spin-upInitialization with ERA-Interim, 1979–1985 as spin-upInitialization with ERA-Interim, 1979–1985 as spin-upInitialization with ERA-Interim, 1979–1985 as spin-upInitialization with ERA-Interim except soil moisture, which is based on a climatological average (Giorgi et al., 1989); 1985 as spin-upInitialization with ERA-Interim, 1979–1985 as spin-upInitialization with ERA-Interim, 1979–1985 as spin-upInitialization with ERA-Interim, 1983–1985 as spin-upInitialization with ERA-Interim, 1984–1985 as spin-upLateral boundary formulationDavies (1976)Davies (1976)Davies (1976)Davies (1976) with a cosine-based relaxation functionGiorgi et al. (1993)Davies (1976)exponential relaxationexponential relaxationexponential relaxationBuffer (no. of grid cells)1313138128151010No. of vertical levels404040242327504040Turbulence and planetary boundary layer schemeLevel 2.5 closure for turbulent kinetic energy as prognostic variable (Mellor and Yamada, 1982)Level 2.5 closure for turbulent kinetic energy as prognostic variable (Mellor and Yamada, 1982)Level 2.5 closure for turbulent kinetic energy as prognostic variable (Mellor and Yamada, 1982)(Vogelezang and Holtslag, 1996)The University of Washington turbulence closure model (Bretherton et al., 2004; Grenier et al., 2001)Vertical diffusion after Louis (1979) for the Prandtl layer, extended level-2 scheme after Mellor and Yamada (1974) in the Ekman layer and the free atmosphere including modifications in the presence of cloudsMYNN (Mellor–Yamada–Nakanishi–Niino model) Level 2.5 PBL (planetary boundary layer) (Nakanishi and Niino, 2006; Nakanishi and Niino, 2009)MYNN Level 2.5 PBL (Nakanishi and Niino, 2006; Nakanishi and Niino, 2009)MYNN Level 2.5 PBL (Nakanishi and Niino, 2006; Nakanishi and Niino, 2009)Radiation schemeRitter et al. (1992)Ritter et al. (1992)Ritter et al. (1992)Savijärvi and Savijärvi (1990); Wyser et al. (1999)Radiative transfer model from the NCAR Community Climate Model 3 (CCM 3) (Kiehl et al., 1996)Morcrette et al. (1986) with modifications for additional greenhouse gases, ozone and various aerosols.Rapid Radiative Transfer Model (RRTMG) scheme (Iacono et al., 2008)RRTMG scheme (Iacono et al., 2008)RRTMG scheme (Iacono et al., 2008)
Continued.
Model nameCCLM-TERRACCLM-VEG3DCCLM-CLM4.5RCARegCM-CLM4.5REMO-iMOVEWRFa-NoahMPWRFb-NoahMPWRFb-CLM4.0Institute IDJLU/BTU/CMCCKITETHSMHIICTPGERICSIDLUHOHAUTHRCMCOSMO_5.0_clm9COSMO_5.0_clm9COSMO_5.0_clm9RCA4RegCM4.6.1 (Giorgi et al., 2012)REMO2009WRF381WRF381WRF381Atmospheric settings Convection schemeTiedtke (1989)Tiedtke, (1989)Tiedtke (1989)Bechtold et al. (2001)Tiedtke (1996) for cumulus convection(Tiedtke, 1989) with modifications after Nordeng (1994)Grell and Freitas (2014) for cumulus convection and Global/Regional Integrated Model system (GRIMs) Scheme (Hong et al., 2013) for shallow convection(Kain, 2004); no shallow convection(Kain, 2004); no shallow convectionMicrophysics schemeone-moment cloud microphysics scheme (Seifert and Beheng, 2001)one-moment cloud microphysics scheme (Seifert and Beheng, 2001)one-moment cloud microphysics scheme (Seifert and Beheng, 2001)values from tablesSubgrid Explicit Moisture scheme (SUBEX) (Pal et al., 2000)Sundqvist (1978); Roeckner et al. (1996)two-moment, six-class scheme (Lim and Hong, 2010)Thompson et al. (2004)Thompson et al. (2004)Greenhouse gaseshistorical (Meinshausen et al., 2011)historical (Meinshausen et al., 2011)historical (Meinshausen et al., 2011)historical (Meinshausen et al., 2011)historical (Meinshausen et al., 2011)historical (Meinshausen et al., 2011)historical (Meinshausen et al., 2011)constant (CO2=379 ppm)constant (CO2=379 ppm)Aerosolsconstant (Tanré, 1984)Tegen et al. (1997) climatologyconstant (Tanré, 1984)constantnot accounted forconstant (Teichmann et al., 2013)Tegen et al. (1997) climatologyTegen et al. (1997) climatologyTegen et al. (1997) climatologyResultsTemperature response
The effect of forestation (FOREST minus GRASS) on seasonal mean winter
2 m temperature is shown in Fig. 1. All RCMs simulate a warming
pattern which is strongest in the northeast of Europe. This warming effect
weakens toward the southwest of the domain even changing sign for instance
in the Iberian Peninsula (except for REMO-iMOVE). In summer (Fig. 2), there
is a very large spread of model responses with some RCMs predicting a
widespread cooling from forestation (CCLM-TERRA and RCA), a widespread
warming (RegCM-CLM4.5, REMO-iMOVE and the WRF models) or a mixed response
(CCLM-VEG3D and CCLM-CLM4.5). Overall this highlights the strong seasonal
contrasts in the temperature effect of forestation and the larger
uncertainties associated with the summer response.
Seasonally averaged 2 m temperature (FOREST minus
GRASS) for winter (DJF).
Seasonally averaged 2 m temperature (FOREST minus
GRASS) for summer (JJA).
Looking separately at the response for daytime and nighttime 2 m
temperatures also indicates important diurnal contrasts. The winter warming
effect is stronger and more widespread for daily maximum temperature (Fig. 3), while daily minimum temperature shows a more contrasted cooling–warming
dipole across the domain (Fig. 5). In summer, diurnal contrasts are even
more pronounced with a majority of models showing an opposite sign of change
for daily maximum and minimum temperatures over most of Europe (Figs. 4 and
6), namely a daytime warming effect and a nighttime cooling effect.
Exceptions are RCA and CCLM-TERRA, which indicate a cooling for both daily
maximum and minimum temperatures and REMO-iMOVE exhibiting a warming for
both daytime and nighttime.
Seasonally averaged daily maximum 2 m temperature
(FOREST minus GRASS) for winter (DJF).
Seasonally averaged daily maximum 2 m temperature
(FOREST minus GRASS) for summer (JJA).
In terms of magnitude, the temperature signal is substantial. In all RCMs,
there is at least one season with absolute temperature changes above 2∘ in some regions, for instance in winter and spring over northern
Europe (Fig. S2). The magnitude of changes is even more pronounced for daily
maximum temperature.
Seasonally averaged daily minimum 2 m temperature
(FOREST minus GRASS) for winter (DJF).
Seasonally averaged daily minimum 2 m temperature
(FOREST minus GRASS) for summer (JJA).
Surface energy balance
Changes in surface energy fluxes over land are summarized for eight European
regions (the Alps, the British Isles, eastern Europe, France, the Iberian
Peninsula, the Mediterranean, mid-Europe and Scandinavia) as defined in the
PRUDENCE project (Christensen et al., 2007). Here we discuss
results for two selected regions representative of northern Europe
(Scandinavia; Fig. 9) and southern Europe (the Mediterranean; Fig. 10),
while results for the full set of regions are provided in the Supplement (Figs. S11 to S18). One of the most robust features across models
and seasons is an increase in surface net shortwave radiation. This increase
is a direct consequence of the impact of forestation on surface albedo.
Indeed all RCMs consistently simulate a year-round decrease in surface
albedo due to the lower albedo of forest compared to grassland (Fig. S7).
This decrease is strongest in winter and at high latitudes owing to the snow-masking effect of forest. However, the strongest increase in net shortwave
radiation occurs in spring and summer in both regions because incoming
radiation is higher in these seasons, thus implying a larger surface
radiation gain despite the smaller absolute change in albedo. Notable
outliers are REMO-iMOVE, exhibiting a smaller albedo decrease across all
seasons and thus a less pronounced increase in net shortwave radiation, and
CCLM-TERRA and RCA, which despite the albedo increase simulate a net
shortwave radiation decrease in summer (only over Scandinavia in the case of
RCA). In the latter two models, an increase in evapotranspiration triggers
an increase in cloud cover and a subsequent decrease in incoming shortwave
radiation (not shown) offsetting the change in surface albedo. The spatial
pattern of surface net shortwave radiation change is relatively consistent
across RCMs in winter with maximum net shortwave radiation increases well
above 10 W m-2 in high-elevation regions and the northeast of Europe
(Fig. 7). In summer, the magnitude of net shortwave radiation changes is
overall larger as is the inter-model spread (Fig. 8). CCLM-TERRA is the
only RCM to simulate a widespread decrease in net shortwave radiation, while
RCA and CCLM-VEG3 also simulate net shortwave radiation decreases in some
areas in particular in northern Europe. All other RCMs simulate a widespread
increase in net shortwave radiation over land, with WRFa-NoahMP and
WRFb-NoahMP exhibiting the strongest increase with values well above 20 W m-2 in most regions.
Seasonally averaged net surface shortwave radiation
(FOREST minus GRASS) for winter (DJF).
Seasonally averaged net surface shortwave radiation
(FOREST minus GRASS) for summer (JJA).
To a large extent, sensible heat flux follows shortwave radiation changes
(i.e. a majority of models suggest an increase in sensible heat). This is
also largely the case for ground heat flux (calculated here indirectly as
the residual of the surface energy balance), which increases in autumn,
winter and spring in most models due to the overall increase in absorbed
radiation. Changes in the latent heat flux exhibit a higher degree of
disagreement across models and seasons. For instance in spring, latent heat
flux increases together with sensible heat over Scandinavia (Fig. 9), while
it decreases in most models over the Mediterranean (Fig. 10). In summer, the
agreement is low over Scandinavia, and there is a tendency for decreasing
latent heat in the Mediterranean. At the European scale, there is a clear
tendency of increasing latent heat flux in spring particularly over northern
Europe, whereas in summer most RCMs (with the exception of CCLM-TERRA)
indicate both increasing and decreasing latent heat depending on regions
(Fig. S10).
Changes in temperature and in surface energy balance
components (FOREST minus GRASS) averaged over Scandinavia for DJF, MAM, JJA
and SON. Results for other regions are shown in the Supplement.
Changes in temperature and in surface energy balance
components (FOREST minus GRASS) averaged over the Mediterranean for DJF,
MAM, JJA and SON. Results for other regions are shown in the Supplement.
Origin of the inter-model spread
Changes in albedo and in the partitioning of turbulent heat fluxes are
essential in determining the temperature effect of forestation. The dominant
influence of albedo decrease is evident in winter and spring over northern
Europe as illustrated for instance by the quasilinear inter-model
relationship between the magnitude of changes in albedo and in 2 m
temperature over Scandinavia in spring (Fig. 11a). The role of turbulent
heat fluxes partitioning can be illustrated by examining changes in
evaporative fraction (EF), calculated as the ratio between latent heat and
the sum of latent and sensible heat. The advantage of using EF instead of
latent heat flux is that the former provides a metric relatively independent
of albedo change (since albedo change does influence the magnitude of
turbulent heat fluxes through changes in available energy). Taking the
example of Scandinavia in summer (Fig. 11b), it appears that there is a
relatively linear relationship between changes in temperature and in EF. In
other words, models showing a decrease in EF following forestation tend to
simulate a warming and models showing an increase in EF simulate a cooling.
Illustrative relationships between changes (FOREST minus
GRASS) in 2 m temperature and albedo in spring (a) and between changes
in 2 m temperature and EF (evaporative fraction) in summer (b) for
Scandinavia.
In order to assess more systematically the role of individual drivers across
regions and seasons, we perform a regression analysis using changes in
albedo, EF and incoming surface shortwave radiation as explanatory variables
and 2 m temperature as the variable to be explained. The rationale for
using albedo, EF and incoming surface shortwave radiation as explaining
factors is that the first two capture the intrinsic LUC-induced changes in
land surface characteristics representing respectively the radiative and
non-radiative impacts of LUC, whereas incoming surface shortwave radiation
captures some of the potential subsequent atmospheric feedbacks (e.g. through cloud cover changes). Here we discuss the results of the regression
analysis for Scandinavia and the Mediterranean (Fig. 12), while results for
the full set of regions are provided in the Supplement (Figs. S19 and S20). Combining albedo, EF and incoming surface shortwave radiation
into a multiple linear regression effectively explains a large fraction of
the inter-model variance of the simulated temperature response (around
80 % of variance explained for both regions and all seasons except winter
where the explained variance is much lower). Albedo change alone explains
the largest part of the inter-model variance in spring over Scandinavia and
in winter over the Mediterranean, indicating a dominance of radiative
processes during these seasons. EF change alone explains the largest part of
the inter-model variance in summer over Scandinavia and in spring, summer
and autumn over the Mediterranean. Finally, incoming surface shortwave
radiation explains a substantial part of the inter-model variance across
most seasons although it is not a dominating factor. It is important to note
the two main caveats of this simplified approach: (1) the explanatory
variables are likely not fully independent due to the tightly coupled
processes in the models; (2) other factors not included as explanatory
variables may contribute to the temperature response (e.g. changes in
surface roughness, other atmospheric feedbacks). Nevertheless, the fact that
a large part of the variance can be explained by this simple linear model is
an indication of the essential role of these selected processes. An
exception is the winter season during which a very limited part of the
inter-model spread can be explained, suggesting that other processes may
play a dominant role. One potential process that could explain differences
across RCMs is the occurrence of precipitation feedbacks. We note however
that precipitation changes are small in all RCMs with no clear consensus
among models (Fig. S5). One possible exception is the summer precipitation
decrease in WRFa-NoahMP, which could be related to the use of the
Grell–Freitas convection scheme (Table 1), while precipitation is less
affected in WRFb-NoahMP and WRFb-CLM4.0, which use the Kain–Fritsch scheme.
The stronger summer temperature increase in WRFa-NoahMP compared to
WRFb-NoahMP and WRFb-CLM4.0 may therefore be linked to this precipitation
feedback.
Fraction of inter-model variance in 2 m temperature
change (FOREST minus GRASS) explained by changes in albedo, evaporative
fraction, incoming surface shortwave radiation or the three combined. Alb:
inter-model correlation (Rsquared) between changes in albedo and 2 m
temperature. EF: inter-model correlation (Rsquared) between changes in
evaporative fraction and 2 m temperature. SWin: inter-model correlation
(Rsquared) between changes in incoming surface shortwave radiation and 2 m temperature. Alb + EF + SWin: Rsquared of a
multi-linear regression combining the three predictors. Results for other
regions are shown in the Supplement.
Comparing results from different RCMs sharing either the same LSM or the
same atmospheric model can help provide additional insights into the
respective role of land versus atmospheric processes. By comparing for
instance the temperature response across RCMs (Figs. 1 to 6), it appears, in
summer particularly, that the three RCMs based on CCLM (i.e. same
atmospheric model with three different LSMs) span almost the full range of
RCM responses while CCLM-CLM4.5 and RegCM-CLM4.5 (i.e. same LSM and
different atmospheric models) have generally similar patterns of change.
This suggests that the summer temperature response to forestation is
conditioned primarily by land process representation more than by
atmospheric processes. To quantify objectively the level of similarity or
dissimilarity between different RCMs, we compute the Euclidean distance
across latitude and longitude between each RCM pairs for each season for
differences in 2 m temperature and precipitation. This distance matrix
is then used as a basis for a hierarchical clustering applying the Ward's
clustering criterion (Ward, 1963). For the 2 m temperature
response, the cluster analysis indicates a relatively high degree of
similarity in winter between RCMs sharing the same atmospheric scheme, as
illustrated in particular by the clustering of CCLM-TERRA and CCLM-CLM4.5
and of WRFb-NoahMP and WRFb-CLM4.0 (Fig. 13). In contrast, CCLM-TERRA and
CCLM-CLM4.5 are relatively far apart in summer suggesting a stronger
influence of land processes during this season. This tendency, however, does
not arise in the WRF-based RCMs, with WRFb-NoahMP and WRFb-CLM4.0 showing a
high degree of similarity even in summer. A possible explanation could be
that NoahMP and CLM4.0 are structurally less different than TERRA and
CLM4.5.
Dendrogram of the clustering analysis based on the
2 m temperature response (FOREST minus GRASS) for DJF and JJA. The
underlying distance matrix between RCM pairs is based on the Euclidean
distance across latitude and longitude for the given season.
Discussion and conclusions
Results from nine RCMs show that, compared to grassland, forests imply
warmer temperatures in winter and spring over northern Europe. This result
is robust across RCMs and is a direct consequence of the lower albedo of
forests, which is the dominating factor during these seasons. In summer and
autumn, however, the RCMs disagree on the direction of changes, with
responses ranging from a widespread cooling to a widespread warming above 2∘ in both cases. Although albedo change plays an important role in all
seasons by increasing absorbed surface radiation, in summer inter-model
differences in the temperature response are to a large extent induced by
differences in EF. These conclusions are overall consistent with previous
studies based on global climate models. Results from the LUCID and the CMIP5
model intercomparisons have indeed highlighted a robust, albedo-induced,
winter cooling effect due to past deforestation at mid-latitudes
(Lejeune et al., 2017), in other words
implying a winter warming effect of forestation. On the other hand, no
robust summer response has been identified in these intercomparisons, mainly
attributed to a lack of agreement across models concerning
evapotranspiration changes (Lejeune
et al., 2017, 2018; de Noblet-Ducoudré et al., 2012).
Resolving this lack of consensus will require intensified efforts to
confront models and observations and identify possible model deficiencies
(Boisier
et al., 2013, 2014; Duveiller et al., 2018a; Meier et al., 2018). For
instance, a key feature emerging from observation-based studies is the fact
that mid-latitude forests are colder during the day and warmer during the
night compared to grassland (Duveiller et al., 2018b; Lee et
al., 2011; Li et al., 2015). It is striking that none of the LUCID and CMIP5
models reflect this diurnal behaviour (Lejeune et al., 2017), nor do the RCMs
analysed in this study (i.e. a majority of RCMs have a diurnal signal
opposite to observations, two other RCMs indicate a cooling effect of
forests for both day and night, and one exhibits a warming effect for both day
and night). It is however important to note that this apparent contradiction
may not be only attributable to model deficiencies and could be in part
related to discrepancies on the scale of processes considered in models and
observations. Indeed, observation-based estimates capture mainly local
changes in surface energy balance and temperature due to land cover and are
unlikely to reflect the type of large-scale atmospheric feedbacks triggered
in coupled climate models (especially given the large-scale nature of the
forest expansion considered in our experiments). Similarly, the fact that a
majority of RCMs simulate a summer decrease in evapotranspiration over many
regions following forestation is at odds with current observational evidence
(Chen
et al., 2018; Duveiller et al., 2018b; Meier et al., 2018) and might play a
role in the simulated summer daytime warming in most RCMs. Although the
reasons behind this behaviour may be model-specific, some recent work based
on the CLM4.5 model, which is used in two of the RCMs here, sheds some light
on the possible processes involved (Meier et al., 2018). It was found
that while evapotranspiration is higher in spring under forested conditions
in CLM4.5, trees become more water stressed than grassland in summer (even
under equivalent soil moisture conditions) in particular due to unrealistic
choices of root distribution, photosynthetic parameters and water uptake
formulation. After improvement of these aspects in CLM4.5,
evapotranspiration was found to be more realistically simulated, also resulting in an improved daytime temperature difference between grassland and
forest (Meier et al., 2018). An
important insight from this first phase of RCM experiments is therefore that particular attention should be given to model evaluation and benchmarking
in future phases of the LUCAS initiative.
An additional insight from this study concerns the role of land versus
atmospheric processes. Some of the participating RCMs share the same
atmospheric scheme (i.e. the same version and configuration) but are coupled to
different land surface models or share the same land surface model in
combination with different atmospheric schemes. This represents a unique
opportunity to objectively determine the origin of uncertainties in the
simulated response. For instance, we find that land process representation
is heavily involved in the large model spread in summer temperature
response. The range of responses generated by using three different LSMs
within the same atmospheric scheme (CCLM) is almost as large as the full
model range in summer. Supporting this conclusion, a simple regression-based
analysis shows that, except in winter, changes in albedo and EF can explain
most of the inter-model spread in temperature sensitivity, in other words
indicating that land processes primarily determine the simulated temperature
response. Atmospheric processes can nevertheless also play a substantial or
even dominant role for example in winter or for other variables such as
precipitation.
In this first phase of LUCAS, we relied on idealized experiments at
relatively low resolution (50 km) to gain insights into the biogeophysical
role of forests across a range of European climates. Future phases of LUCAS
will evolve toward increasing realism for instance by (1) investigating
transient historical LUC forcing as well as RCP (representative concentration pathways)-based LUC scenarios, (2) considering a range of land use transitions beyond grassland to forest
conversion and (3) assessing the added-value of higher (kilometre-scale)
resolution when assessing local LUC impacts. Finally, the most
societally relevant adverse effects or benefits from land management
strategies may become apparent only when addressing changes in extreme
events such as heatwaves or droughts (Davin et al., 2014; Lejeune et
al., 2018), an aspect which will receive more attention in future analyses
based on LUCAS simulations.
Data availability
The data and scripts used are available upon request from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/esd-11-183-2020-supplement.
Author contributions
ELD, DR, MB, RMC, EC, PH, LLJ, EK, KR, MR, PMMS, GS, SS, GS, MHT and KWS
performed the RCM simulations, using vegetation maps produced by ELD. ELD
designed the research, analysed the data and wrote the paper. All
authors contributed to interpreting the results and revising the text.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Edouard L. Davin acknowledges support from the Swiss National Science Foundation
(SNSF) through the CLIMPULSE project and thanks the Swiss National
Supercomputing Centre (CSCS) for providing computing resources. Rita M. Cardoso
and Pedro M. M. Soares acknowledge the projects LEADING (PTDC/CTA-MET/28914/2017)
and FCT- UID/GEO/50019/2019 – Instituto Dom Luiz. Peter Hoffmann is funded by
the Climate Service Center Germany (GERICS) of the Helmholtz-Zentrum
Geesthacht in the frame of the HICSS (Helmholtz-Institut Climate Service
Science) project LANDMATE. Lisa L. Jach, Kirsten Warrach-Sagi and Volker Wulfmeyer
acknowledge support by the state of Baden-Württemberg through bwHPC and
thank the Anton and Petra Ehrmann-Stiftung Research Training Group
“Water-People-Agriculture” for financial support. The work of Eleni Katragkou
and Giannis Sofiadis was supported by computational time granted from the Greek
Research & Technology Network (GRNET) in the National HPC facility – ARIS
– under project ID pr005025_thin. Nathalie de Noblet-Ducoudré
thanks the “Investments d'Avenir” Programme overseen by the French
National Research Agency (ANR) (LabEx BASC; ANR-11-LABX-0034). RCA
simulations were performed on the Swedish climate computing resource Bi
provided by the Swedish National Infrastructure for Computing (SNIC) at the
Swedish National Supercomputing Centre (NSC) at Linköping University. G.
Strandberg was partly funded by a research project financed by the Swedish
Research Council VR (Vetenskapsrådet) on “Quantification of the
biogeophysical and biogeochemical forcings from anthropogenic deforestation
on regional Holocene climate in Europe, LandClim II”. Susanna Strada has been
supported by the TALENTS3 Fellowship Programme (FP code 1718349004) funded
by the autonomous region Friuli Venezia Giulia via the European Social Fund
(Operative Regional Programme 2014–2020) and administered by the AREA
Science Park (Padriciano, Italy). CCLM-TERRA simulations were performed at
the German Climate Computing Center (DKRZ) through support from the Federal
Ministry of Education and Research in Germany (BMBF). Merja H. Tölle
acknowledges the funding of the German Research Foundation (DFG) through
grant 401857120. We thank Richard Wartenburger for providing the R scripts
that have been used to perform the cluster analysis. We acknowledge the
support of LUCAS by WCRP-CORDEX as a Flagship Pilot Study.
Financial support
This research has been supported by the Swiss National Science Foundation (grant no. 200021_172715).
Review statement
This paper was edited by Somnath Baidya Roy and reviewed by two anonymous referees.
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