Impacts of land cover changes and global warming on climate in Colombia during ENSO events

Abstract

Colombia is highly vulnerable to climate change which may be intensified due to the climatic effects of regional deforestation. Here, we quantify the impact of historical (1900–2011) land cover changes (LCC) and of global warming during ENSO events (CC) on precipitation, temperature and surface energy balance components by running the Weather Research and Forecasting model WRF v3.9 at 10 km resolution. We find that historical anthropogenic CC causes a mean temperature increase of 0.77 ± 0.02 °C in Colombia, which is more pronounced in high altitudes. Precipitation is enhanced by 0.98 ± 0.30 mm/day (+ 9%), particularly over forested areas and reduced at the Pacific coast. LCC imply a reduction of precipitation particularly above the Andes (− 0.48 ± 0.10 mm/day) and Caribbean Coast (− 0.67 ± 0.12 mm/day), where LCC effects dampen CC effects by 24% and 72%, respectively. La Niña tends to intensify LCC and CC effects in the Andes but dampens them at the Coast, roughly by a factor of two compared to El Niño impacts in both regions. At the subregional level, LCC and CC can have impacts of similar magnitude on precipitation highlighting the need to precisely account for both drivers in hydroclimatic assessments. Contrary to almost all observations and similar simulations with climate models, WRF simulates a cooling bias after historical deforestation in Colombia, even with alternative WRF land surface models. We identify two main sources of biases in the default WRF parametrization to explain this inaccuracy: (1) surface shortwave radiation reflected after deforestation is overestimated; (2) associated evapotranspiration loss is underestimated. Improved model representation and validation of tropical vegetation properties are necessary to provide more robust and confident projections

Full text

Vol.:(0123456789)
1 3
Climate Dynamics (2023) 61:111–129
https://doi.org/10.1007/s00382-022-06545-1
Impacts ofland cover changes andglobal warming onclimate
inColombia duringENSO events
AstridManciu1,2 · AnjaRammig1· AndreasKrause1· BenjaminRaphaelQuesada2
Received: 17 January 2022 / Accepted: 15 October 2022 / Published online: 10 November 2022
© The Author(s) 2022
Abstract
Colombia is highly vulnerable to climate change which may be intensified due to the climatic effects of regional deforestation.
Here, we quantify the impact of historical (1900–2011) land cover changes (LCC) and of global warming during ENSO events
(CC) on precipitation, temperature and surface energy balance components by running the Weather Research and Forecast-
ing model WRF v3.9 at 10km resolution. We find that historical anthropogenic CC causes a mean temperature increase of
0.77 ± 0.02°C in Colombia, which is more pronounced in high altitudes. Precipitation is enhanced by 0.98 ± 0.30mm/day
(+ 9%), particularly over forested areas and reduced at the Pacific coast. LCC imply a reduction of precipitation particularly
above the Andes (−0.48 ± 0.10mm/day) and Caribbean Coast (−0.67 ± 0.12mm/day), where LCC effects dampen CC
effects by 24% and 72%, respectively. La Niña tends to intensify LCC and CC effects in the Andes but dampens them at the
Coast, roughly by a factor of two compared to El Niño impacts in both regions. At the subregional level, LCC and CC can
have impacts of similar magnitude on precipitation highlighting the need to precisely account for both drivers in hydrocli-
matic assessments. Contrary to almost all observations and similar simulations with climate models, WRF simulates a cool-
ing bias after historical deforestation in Colombia, even with alternative WRF land surface models. We identify two main
sources of biases in the default WRF parametrization to explain this inaccuracy: (1) surface shortwave radiation reflected
after deforestation is overestimated; (2) associated evapotranspiration loss is underestimated. Improved model representation
and validation of tropical vegetation properties are necessary to provide more robust and confident projections.
Keywords Land cover change· Climate change· Surface energy balance· Deforestation· Regional climate modelling
1 Introduction
Since preindustrial times, mean surface air temperature has
risen globally by 1°C due to anthropogenic emissions of
greenhouse gases from fossil fuel burning and land use and
land cover changes (LCC, IPCC 2018). Especially the trop-
ics are recently experiencing accelerating deforestation rates
(Hansen etal. 2013, Song etal. 2018). Deforestation influences
climate by reducing carbon stocks and releasing carbon into
the atmosphere (Pan etal. 2011), leading to global warming
(biogeochemical feedback to deforestation) and by alterations
of regional energy, momentum and water fluxes (biophysical
feedbacks), which can outbalance biogeochemical effects and
lead regionally to net cooling or additional warming (Claussen
etal. 2001). Jia etal. (2019) suggest that on a global scale, bio-
geochemical effects dominate the interplay of both feedbacks.
However, the net impacts of LCC highly vary in space and
time and may have contrasting consequences depending on
latitude (Davin and de Noblet-Ducoudré 2010). Modelling and
observational studies suggest that boreal regions experience a
cooling of surface air temperature, while lower latitudes show
warming in response to local deforestation (Alkama and Ces-
catti 2016; Findell etal. 2017). Colombia, a megadiversity hot-
spot and one of the top 12 deforesting countries (FAO 2015),
is particularly affected by such LCC impacts, which in tropi-
cal countries are expected to amplify the warming from CC.
Every year, roughly 180,000ha (IDEAM 2017, 2018, 2019) of
* Benjamin Raphael Quesada
benjamin.quesada@urosario.edu.co
Astrid Manciu
astrid.manciu@gmail.com
1 TUM School ofLife Sciences Weihenstephan, Technical
University ofMunich, Hans–Carl–von–Carlowitz–Platz 2,
85354Freising, Germany
2 Faculty ofNatural Sciences, “Interactions Climate‐
Environment (ICE)” Research Group, Earth System Sciences
Program, Universidad del Rosario, Bogotá, Colombia
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112 A.Manciu et al.
1 3
forests in Colombia are lost due to agricultural expansion, cat-
tle ranching, urbanization and timber extraction. At the same
time, the intensity of extreme weather events associated with
the El Niño-Southern Oscillation (ENSO), e.g. droughts or
floods, is increasing (Ávila etal. 2019; Hoyos etal. 2013).
Thus, the question arises of how such climate events and LCC
are interacting.
Understanding deforestation and global warming impacts
on climatic variables in Colombia is crucial because this coun-
try concentrates very high levels of ecosystem services, terrain
complexity, species richness and water provision. Modelling
this region is challenging because global climate models cannot
resolve small-scale terrain complexity inherent to the presence
of the three Andean cordilleras (Posada-Marín etal. 2018; Espi-
noza etal. 2020). Regional Climate Models (RCMs) can repre-
sent local, small-scale phenomena more accurately than global
General Circulation Models (GCMs) or Earth System Models
(ESM). One of the most widely used RCMs is the Weather
Research and Forecasting model (WRF) which has proven its
capability to dynamically downscale global reanalysis data from
coarse to high resolution in good agreement with observations
(e.g. Posada-Marín etal. 2018; Caldwell etal. 2009; Lee and
Berbery 2012). In complex terrain domains, WRF is considered
to produce climatic features even better than the original reanal-
ysis data sets due to more accurate representation of orography
(e. g.Heikkilä etal. 2010; Sun etal. 2016; Soares etal. 2012;
Gao etal. 2012; Jiménez-Esteve etal. 2018).
To date, no impact study for Colombia exists that evaluates
the impacts of LCC on climate at high spatial resolution. Thus,
we present here the first high-resolution, dynamical downscal-
ing study investigating LCC and CC impacts in Colombia.
We perform sensitivity simulations with WRF v3.9, forcing
the model with historical land cover and climate input and
compare the results against present-day conditions. Our aims
are to answer the following questions:
(1) How do historical LCC and CC influence surface air
temperature and precipitation in different regions of
Colombia, during ENSO events (2009–2011)?
(2) What is the dominant forcing for regional climate
changes in Colombia?
(3) How are land cover changes and global warming
impacts modulated by ENSO?
(4) How is the surface energy balance affected by different
scenarios of land cover change in Colombia?
2 Materials andmethods
2.1 Study site
Colombia is located in the northwest of South America and
contains five major biogeographic regions with contrasting
biophysical and land cover properties: Colombian Amazon,
Caribbean, Pacific Coast, Orinoco plains (Llanos orien-
tales) and Andes. The three branches (cordilleras) of the
Andes, which cross the country in the center and west,
confers Colombia a unique and complex orography with
altitudes ranging between 5800m and sea level, resulting
in a wide variety of climatic and vegetation zones. Local-
scale phenomena such as inter-Andean valley circulations
but also large-scale oscillations in the Atlantic and Pacific,
interactions with the Amazon and Orinoco basin influence
Colombia’s climatic variability (Poveda etal. 2011; Espi-
noza etal. 2020). On the inter-annual scale however, the
hydrological variability is dominated by ENSO, with El
Niño causing less evapotranspiration followed by less recy-
cled precipitation, consequently fewer river discharges, and
less cloud cover which facilitates more radiation and eventu-
ally higher temperatures (Poveda etal. 2011). La Niña, the
opposing ENSO-phase, has a reversed effect. ENSO impacts
the western Andean branch first while the eastern branch is
affected with a little time lag and less strong (Poveda etal.
2011). Generally speaking, Colombia experiences a drier
and prolonged dry season during El Niño, and a wetter and
prolonged wet season during La Niña.
2.2 Model settings
In order to simulate climate response to LCC and CC
in Colombia, we use WRF version 3.9.1.1 (Skamarock
etal. 2008). This non-hydrostatic, fully compressible
and terrain-following sigma coordinate model (Powers
etal. 2017) has been proven to perform well in high-
resolution regional modelling studies and is one of the
world’s most widely used numerical weather prediction
models. The simulations are executed by the Advanced
Research WRF (ARW) dynamical core of wrf.exe. It con-
tains basic dynamical equations for advection, Coriolis,
pressure gradient terms, buoyancy and diffusion. Options
on microphysics, radiation schemes and planetary bound-
ary layers are set in namelist.input. Lookup tables for
soil characteristics and land cover and vegetation prop-
erties set important physical features like minimum and
maximum albedo per land use category, surface rough-
ness, rooting depth, which the model accesses during the
model run.
The simulations are carried out on two regional
domains encompassing a large external domain (D01)
covering large parts of Central America, Northern South
America, Amazon basin, the Caribbean Sea and Eastern
Pacific Ocean, along with an internal (nested) domain cov-
ering continental Colombian territory (D02, see Fig.1),
excluding the Colombian islands of San Andres, Providen-
cia and Santa Catalina off the coast of Nicaragua.
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113Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
1 3
All simulations are performed from January 1st, 2009 to
December 31st, 2011, excluding the first month from the
analysis due to spin-up time, covering two major ENSO
events with extreme socio-economic consequences in
Colombia (El Niño 2009–2010 and La Niña 2010–2011,
Hoyos etal. (2013), Comisión Económica para América
Latina y el Caribe (2012)) as well as some ENSO-neu-
tral periods (i.e. March-July 2009, March-June 2010 and
May–July 2011 For this period, we find an average Oceanic
Niño Index (ONI) value (based on the 3-month running
mean of SST anomalies in the Niño 3.4 region [5°N-5°S,
120°-170°W]) of −0.34 (as computed by the Climate Pre-
diction Center: https:// origin. cpc. ncep. noaa. gov/ produ cts/
analy sis_ monit oring/ ensos tuff/ ONI_ v5. php), meaning that
our simulation period (2009–2011) is slightly biased towards
more representative of weak La Niña/Neutral periods. It
appears that Summer 2010-Spring 2011 was characterized
by an Eastern-Pacific (EP) La Niña event of Medium grade
(although a strong minimum trimestrial ONI of −1.6) while
Summer 2009-Spring 2010 was marked by a Central-Pacific
(CP) El Niño event of Medium grade (with an amplitude of
1.7°C) (Ren etal. 2018).
We used two-way nesting to allow interactions between
inner and outer domain which is the recommended method
for simulations lasting longer than a few days (Wang etal.
2017). Both domains have 35 vertical levels. D01 has a
horizontal resolution of 30km. A finer 10km resolution for
D02 was chosen as a trade-off between computational time
and high resolution of fine-scale climate events. Initial and
boundary conditions for the simulations were retrieved from
the 6-hourly, 0.75° × 0.75° gridded ERA-Interim reanalysis
dataset provided by the European Center for Medium-Range
Weather Forecasts (ECMWF, Dee etal. 2011).
To avoid large deviations between simulation and driving
fields (Bowden etal. 2012) we use spectral nudging on both
domains during the whole simulation run, which has been
shown to improve simulated results especially in high-res-
olution downscaling studies (e.g. Chotamonsak etal. 2012;
Bowden etal. 2012; Heikkilä etal. 2010). Additionally, Paul
etal. (2016) found a reduction in precipitation bias, specifi-
cally in rainy regions. Nudging, which comprises relaxation
techniques, maintains on the one hand large-scale features
from the input data driving field and on the other hand, sim-
ulates small-scale features. To let the model develop its own
mesoscale and synoptic structures in the surface-near tropo-
sphere, we decide to nudge zonal and meridional wind (U
and V), temperature (T) and geopotential height (PH) only
above the planetary boundary layer additionally to switch-
ing off nudging in the first ten levels from the bottom of
the model. Similar to Posada-Marín etal. (2018) and Paul
etal. (2016), we use a relaxation time of approximately one
hour, which corresponds to a value of 0.0003 for U, V, T and
PH nudging coefficient (guf, gt, gph). The top wave number,
which is the number of waves contained in the domain, is
set to three in x and y direction, with three being the maxi-
mum one that is nudged, to capture ERA-Interim features
with wavelengths of approximately 1200km and upwards
(the WRF domain size is about 3000km × 2940km in zonal
and meridional directions, respectively). Nudging is exerted
every 6h, which coincides with the frequency of our ERA-
Interim reanalysis data.
We also rely on previous WRF studies in Colombia or in
nearby countries that tested several microphysics, cumulus,
longwave radiation, and planetary boundary layer schemes
(Arregocés etal. 2021; Núñez 2014; García 2014; Posada-
Marín etal. 2018; González-Rojí etal. 2022) to optimize
our CTRL configuration. However, the sensitivity of our
results to an exhaustive panel of WRF schemes is beyond
the scope of this study. In consequence, further physical
parametrization schemes considered include the microphys-
ics scheme of the WRF single moment 6-class (Hong and
Lim 2006), the new version of the Rapid Radiative Trans-
fer Model (RRTMG) for shortwave and longwave radiation
(Iacono etal. 2008), the Noah land surface model (Tewari
etal. 2004), the Monin–Obukhov similarity scheme for the
surface-layer (Jiménez etal. 2012) and the Yonsei Univer-
sity (YSU) planetary boundary layer (Hong etal. 2006) as
D01
D02
Topographic Height in m
Fig. 1 WPS Domain Configuration and study areas – D01 spans
3000km × 2940km in zonal and meridional direction and has a res-
olution of 30km. D02 spans 1390km × 1900 km and has a resolu-
tion of 10km. Focus regions for analysis are located in the southern
Andes and at the Caribbean coast in the north of Colombia
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114 A.Manciu et al.
1 3
well as the Kain-Fritsch (KF) scheme cumulus convection
parametrization (Kain 2004). We use adaptive time-stepping
to avoid the model run crashing when calculating too high
windspeed on steep slopes.
We stress here that those model settings (simulation
months, schemes, boundary conditions, spin-up, nudging)
are similar to the ones used in other recent studies on the
detection or attribution of regional and local climatic events,
and this standard nudging setting strongly limits the overall
model internal variability across simulations (Teklay etal.
2019; Wang etal. 2020; Glotfelty etal. 2021; Lu etal. 2021).
Meanwhile, across the simulation period, we find no signifi-
cant drift in the differences between simulations (< 5%, not
shown) and we perform sensitivity experiments in Sect.4.4
to albedo parametrization and physical schemes to explain
responses and potential biases.
2.3 Experimental setup
In total, we conduct five main simulations, each one repre-
senting a different land cover and/or climate input, for both
domains:
(1) CTRL simulation:
forced by ERA-Interim data for 2009-2011, with
standard WRF-built-in land cover map
(2) WITHOUT_LCC simulation:
forced by ERA-Interim data for 2009-2011, prein-
dustrial land use/land cover (potential natural vegeta-
tion)
(3) WITHOUT_CC simulation:
forced by “pseudo anthropogenically-unforced”
ERA-Interim data for 2009-2011, with standard WRF-
built-in land cover map
(4) WITHOUT_CC_LCC simulation:
forced by “pseudo anthropogenically-unforced”
ERA-Interim data for 2009-2011, preindustrial land
use/land cover
(5) 100% DEFORESTATION Simulation:
forced by ERA-Interim data for 2009-2011 with
100% replacement of forest by cropland and pasture
Those simulations are performed with the same physical
parameterization schemes and parameters. Land use cat-
egories in the baseline simulation (simulation 1) are based
on the USGS land-cover classification (24 categories, see
Fig.2a). For Colombia, this means that it is largely cov-
ered by natural vegetation: evergreen broadleaf forest (EBF,
Amazonas, Chocó) and savanna (eastern Llanos). Urban and
agricultural land is located mostly between the central and
the eastern Andean branch, and in the north at the Caribbean
coast. According to the built-in USGS land cover classifi-
cation, only a few pixels are classified as urban and built-
up land (Bogotá: 3 grid points; Cartagena, Cali and Bar-
ranquilla: 1 grid point, respectively). The most prominent
agricultural land use type is dryland cropland and pasture.
The Caribbean coast is dominated by a cropland/woodland
mosaic.
To retrieve LCC effects on climate, in simulation 2, we
changed all categories representing urban and agricultural
land to potential natural vegetation (EBF and grassland)
classes from inspecting coarse preindustrial land cover
maps suggested by Levavasseur etal. (2012) and Hengl etal.
(a) (b)
Fig. 2 a Land cover in Colombia as used in WRF based on USGS-24-category classification and b Extent of land use change in Simulations 2
and 4 (given is the landcover fraction changed in percent) and selection for regional analysis
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115Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
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(2018, simulation 2). Each grid cell consists of fractions
of more than one land cover type. Therefore, we decide to
replace the vegetation properties (minimum and maximum
values for albedo, leaf area index, emissivity, roughness
length, as well as green vegetation fraction, rooting depth
and stomatal resistance) in the classes “Urban and Built-
Up land”, “Dry cropland and pasture” as well as “Crop-
land/Woodland mosaic” with the properties of the class
“Evergreen Broadleaf Forest”. The properties of the classes
“Irrigated cropland and pasture”, “Mixed dryland/irrigated
cropland and pasture” and “Cropland/Grassland Mosaic” are
replaced by the vegetation properties of the class “Grass-
land” (see Fig.2b).
Simulation 3 is forced by near-preindustrial (~ 1900)
ERA-Interim data. ‘Pseudo anthropogenically-unforced
(PAU)’ ERA-Interim data consisted of reconstructing his-
torical climate as if there would be no anthropogenic global
warming influence. To achieve this, a three-step approach
was employed:
(a) Calculate 30-year monthly mean values for the periods
1900–1929 (~ near-preindustrial) and 1980–2009 (~ present-
day) of variables (VAR) needed to force WRF e.g. air tem-
perature, surface pressure, the two wind components and sea
surface temperature. 30-year monthly mean values were cal-
culated from the global reanalysis data set ERA-20C, equally
provided by the ECMWF.
(b) Calculate the difference between preindustrial and
present-day values of VAR.
(c) Create for each input variable new ERA-Interim PAU
data at each time step:
The underlying assumption is that the regional histori-
cal trend for those variables is mostly due to anthropogenic
global warming forcing. Simulation 4 combines the adjust-
ments made in simulation 2 and 3. The last simulation (sim-
ulation 5) represents a complete deforestation (100DEF)
scenario which is forced by ERA-Interim data (as in simu-
lation 1 and 2) with dryland cropland and pasture replacing
present-day EBF in the whole domain.
ΔVAR =VARpresent
−
day −VARpre
−
industrial
PAU ERA - Interim =ERA - Interim −ΔVA R
2.4 Statistical analysis andvisualization
To identify the relative contribution of LCC and CC, the area-
weighted mean values of variables over the 3year-simulation
period are calculated at the national level as well as for two
focus regions, namely the southern Colombian Andes (76.0°W
– 74.8°W, 5.2°N – 1.8°N) between the central and eastern cordill-
eras, and the Caribbean coast (76.5°W – 74.8°W, 11°N – 7.6°N)
in northern Colombia. These two regions were chosen because
historically they were particularly affected by LCC (see Fig.2a,
b). Consequently, they presumably exhibit the highest tempera-
ture and/or precipitation changes from LCC in the domain.
Simulation 2 to simulation 5 were compared against the
CTRL simulation. Hence, the effects were calculated as
shown in Table1.
We calculate the statistical significance of each simula-
tion pair and associated confidence intervals at 95% using a
Mann–Whitney-Wilcoxon non-parametric test, commonly
used in regional climate studies, without presuming the
shape of a variable distribution.
Further analysis focuses on the ENSO phases. Based on
the Multivariate ENSO Index (MEI, PSL 2020) and the Oce-
anic Niño Index (ONI, CPC 2020), the El Niño study period
is defined from June 2009 to March 2010, and La Niña from
July 2010 to May 2011.
3 Results
3.1 Evaluation ofmodel performance
Since WRF has already been shown to simulate tem-
peratures and precipitation satisfactorily in our model
domain (Posada-Marín etal. (2018), we used a similar
model configuration and visually evaluated model per-
formance by comparing annual temperature and pre-
cipitation to the available observational 30-years aver-
age (1981–2010) provided by the Colombian Institute
of Hydrology, Meteorology and Environmental Studies
(IDEAM 2015). Underlying per-pixel data of the Fig.3c,
d were not publicly available to calculate biases, nor the
2009–2011 average (IDEAM, pers.comm.). However,
visually, WRF is representing spatial patterns of annual
temperatures satisfactorily (Fig.3c). Only in the north-
ern lowlands and between the central and eastern Andes
Table 1 Method for calculating
the individual effects of land-
cover and climate change based
on the performed simulation
runs
Name of effect Abbreviation Calculation performed
Historical LCC effect only LCC effect CTRL simulation – without_LCC simulation
Historical CC effect only CC effect CTRL simulation – without_CC simulation
Combined effect – CTRL simulation – without_CC_LCC simulation
100% Deforestation 100DEF 100DEF simulation – CTRL simulation
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116 A.Manciu et al.
1 3
valley, it underestimates mean temperature by about 2°C.
WRF simulates peaks higher in altitude (i.e. colder than
reanalysis) and valleys lower in altitude on average (i.e.
warmer than reanalysis), as expected (see e.g. Figure11
lower panel of Posada-Marin etal. 2018). Precipitation
is generally overestimated across the whole domain but
follows the observed spatial patterns.
3.2 Regional impacts ofCC andLCC ontemperature
In our simulations, global warming (CC) since the beginning
of the last century increases regional temperature in Colom-
bia (+ 0.77 ± 0.02 °C, Fig.4b) while historical LCC and
100DEF lead to a significant weak cooling (−0.01 ± 0.01°C
and −0.16 ± 0.01°C, respectively; Fig.4a, c). Generally,
Fig. 3 Model evaluation of a simulated mean (2009–2011) annual 2m surface air temperature and b precipitation against c observed mean
(1981–2010) annual 2m surface air temperature and d precipitation provided by IDEAM. The color scale is the same for both sources
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117Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
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these effects are significantly lower at the Caribbean coast
(+ 0.58 ± 0.02°C for CC and −0.04 ± 0.01°C for histori-
cal LCC) than in the Andean region (+ 0.65 ± 0.02°C for
CC and −0.07 ± 0.01°C for historical LCC). The maps
show that the cooling effect from historical LCC is mainly
located in the Andean region over potentially historically
deforested areas, where forest was converted to cropland
and pasture (Fig.4a). CC effects are significantly stronger at
higher altitudes (+ 0.17°C/km above 500m at the national
level and + 0.12°C/km across an Andean transect, r > 0.6 for
both, in elevation-dependent warming vs. height diagram,
see Fig.5a, b).
Fig. 4 Changes in 2m surface air temperature (in °C) due to a LCC effect, b CC effect, c 100% deforestation effect and d combined effect
(LCC + CC). Minimum and maximum values are displayed above and below the color scale
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118 A.Manciu et al.
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3.3 Regional impacts ofCC andLCC onprecipitation
Historical LCC and 100DEF simulations both reduce
mean precipitation in Colombia by -0.08 ± 0.22mm/day
(−0.7%, not significant p > 0.05). In contrast, precipita-
tion significantly increases due to CC by 0.98 ± 0.30mm/
day (+ 9%). Combined effects are marginally smaller
(+ 0.91 ± 0.31mm/day) with negligible synergistic effects.
In terms of precipitation response, the Caribbean Coast
is more affected by historical LCC and 100DEF than the
Andean region. In the coastal region, LCC decreases pre-
cipitation by −0.67 ± 0.12mm/day (−7%), while 100DEF
leads to an increase of 0.44 ± 0.13mm/day (+ 5%). In con-
trast, the CC impact is by a factor of two higher in the
Andes than at the coast and at the national level. LCC
and 100DEF both decrease precipitation in the Andes by
− 0.48 ± 0.10mm/day (−4%) and − 0.18 ± 0.08mm/day
(−1.5%), respectively (see Fig.6a–d). Deforested areas
clearly show precipitation reduction (Fig.6a, c), while CC
and combined effects show opposite trends, except for the
Caribbean coast. Precipitation increase is also significantly
higher at higher elevations, albeit less significantly than
temperature (r < 0.3): by + 0.17mm/day/km above 500m
at the national level and by + 0.32mm/day/km across the
Andean transect (Fig.5c, d).
3.4 LCC andCC impacts ontemperature
andprecipitation modulated byENSO
When comparing simulated temperature changes in Colom-
bia during ENSO events over the study period (mean over
2009–2011), no remarkable differences are found during El
Niño (07-2009/03-2010) and La Niña (06-2010/05-2011;
see Fig.7a). The same holds truefor the Coast (Fig.7b).
In the Andean region, LCC and CC effects are slightly
enhanced in magnitude during La Niña (by −0.01 ± 0.01°C
–i.e. + 14%– and by + 0.04 ± 0.01°C –i.e. + 6%– respectively,
compared to the full simulation period, Fig.7c) and damp-
ened during El Niño (−0.02 ± 0.01°C – i.e. − 28%– and
−0.08 ± 0.01°C – i.e. −12%–, respectively).
−3
0
3
6
1000 2000 3000 4000
Hight in m
.. Difdaily precipiation in mm
Elevation dependent in crease in daily precipitation above 500m
y=1.28 +0.00017*height
r= 0.12
−1
0
1
2
3
4
1000 2000 3000
Hight in m
.. Difdaily precipia tion in mm
Elevation dependent increase in daily precipitation for latitudinal band 6.55809N
0.6
0.8
1.0
1000 2000 3000
Hight in m
.. Dif2mSurface Te mperatureinC
Elevatio n dependent warmin g fo r latitu dinal band 6.55809N
y=1.15 +0.00032*height
r=0.22
y=0.56 +0.00012*height
r=0.67
(c)
(b) (d)
Heigth in m
Heigth in m
0.5
1.0
1000 2000 3000 4000
Hight in m
.. Dif2mSurfa ce Te mperatureinC
Elevation dependent warmin g due to Climate Change
y=0.45 +0.00017*height
r=0.73
Heigth in m
1,0002,0003,0004,0001,0002,0003,0004.000
1,0002,0003,000
Heigth in m
1,0002,0003,000
0.8
0.6
0.6
1.0
1.0
Δ2msurface te mperaturein°C
(a)
6
3
0
-3
ΔDaily precipitationinmm
4
0
1
-1
2
3
ΔDaily precipitationinmm
Δ2msurfa ce temperaturein°C
Fig. 5 Relationship between CC-induced temperature/precipitation and altitude. Temperature (a, b) and precipitation (c, d) changes across dif-
ferent altitudes above 500m on national level (upper panels) and across a longitudinal transect (lower panels)
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119Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
1 3
The CC effect on precipitation depends on the ENSO
phases: it is weakened by El Niño (+ 0.62 ± 0.31mm/
day) but enhanced by La Niña (+ 1.08 ± 0.42mm/
day, Fig.7d). Combined effects respond in the same
pattern, although slightly weaker than the CC effect
(+ 0.55 ± 0.29mm/day and + 1.0 ± 0.4mm/day, for El
Niño and La Niña respectively). The Andean region
reacts in a similar pattern, just about twice as strong in
magnitude (Fig.7f). At the coast, an opposite reaction
can be observed: CC and combined effects are stronger
during El Niño and weaker during La Niña (Fig.7e).
Here, historical LCC reduces precipitation, especially
during La Niña (−0.84 ± 0.13mm/day). If the com-
plete domain would be deforested and replaced by dry-
land cropland and pasture, it would result in a general
decrease in precipitation which would hit the Andes the
Fig. 6 Same as Fig.4, but for changes in daily precipitation (in mm)
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120 A.Manciu et al.
1 3
strongest during La Niña event (−0.23 ± 0.09mm/day,
Fig.7f). On the contrary, the 100% deforestation case
increases precipitation at the coast across all studied
time periods, with its highest impact during La Niña
(+ 0.69 ± 0.19mm/day, Fig.7e).
3.5 Regional impacts ofhistorical LCC and100DEF
onsurface energy balance
Changes in the surface energy balance following histori-
cal LCC show a significant increase in
Δ
SW
↑
of 2.03W/
m2 (p < 0.05, Fig.8a), resulting in a relative cooling effect.
(d) (a)
(b) (e)
(f) (c)
Fig. 7 Temperature (a–c) and precipitation (d–f) changes due to LCC and CC in different Colombian regions: a and d Colombian mean, b and e
coastal mean, c and f Andean mean
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121Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
1 3
This represents a contribution of roughly 35% to the total
magnitude of change (i.e.
ΔSW ↑
divided by Δ sum of all
absolute flux changes).
A similar change in magnitude but with an opposite
sign for
ΔL
(−1.92W/m2, p < 0.05) has a warming effect
on national level, thereby balancing the cooling from
ΔSW ↑
.
Δ(H+G)
decrease and contribute also to warm-
ing, albeit the effect is smaller. Together, the heat fluxes
account for 50% of the total magnitude of change (same
as above: Δ non-radiative fluxes divided by Δ sum of all
absolute flux changes).
Downward radiative fluxes are slightly decreased and
contribute around 15% to the overall change. However,
on a local scale, changes in fluxes due to LCC effects are
imbalanced, especially at the coast, where the dominant
land cover was changed from EBF to a cropland/wood-
land mosaic (
ΔSW ↑
: + 12.43W/m2 vs.
ΔL
: −9.96W/m2;
p < 0.05). In the Andean region, where EBF was converted
to dryland cropland and pasture, these two fluxes are simi-
larly imbalanced (
ΔSW ↑
: + 6.80W/m2 vs
ΔL
: −4.26W/
m2, p < 0.05), pointing to a dominance of albedo-driven
cooling.
The 100DEF scenario with the replacement of all EBF by
cropland and pasture in the model domain (Fig.8b) reveals a
more drastic picture compared to historical LCC: a stronger
increase in reflected shortwave radiation from the surface
and a smaller reduction in latent heat, which is strongest
on country level. The effect exists also in the focal regions,
though weaker because these areas are already largely defor-
ested in the control simulation.
Albedo on average significantly rises by 0.008 and 0.04
following historical LCC and 100DEF in Colombia, respec-
tively. At the coast, the albedo increases by 0.045 due to his-
torical LCC and by 0.01 under 100DEF. The Andean region
shows an albedo increase of 0.02, respectively, after LCC
and complete deforestation.
4 Discussion
This study explores the effect of both land cover change and
global warming particularly during ENSO events, as well as
their combined effects on surface air temperature, precipi-
tation and surface energy fluxes, over Colombia, a climate
modeling coldspot.
The performance of the WRF model in simulating general
temperature and precipitation patterns in Colombia is sat-
isfactory for the purpose of this research (Fig.3; Sect.3.1).
An apparent overestimation of precipitation is found com-
paring our WRF CTRL simulation and IDEAsM patterns
(the national observational dataset, see Fig.3). This can be
explained because ERA-Interim already slightly overesti-
mates precipitation in Colombia (Posada-Marín etal. 2018),
our large-scale climatic input data to WRF. In addition,
Jin etal. (2010) and Teklay etal. (2019) explored differ-
ent model configurations with varying land surface models
(LSM) in a similar context and found that precipitation is
overestimated regardless of the LSM used. Another reason
for the discrepancy might be the simulation period of only
three years of the recent decade.
4.1 CC effect ontemperature andprecipitation
Interestingly, our results on temperature changes substan-
tially contrast with the official climate near-term projections
from the Colombian IDEAM in its Third National Commu-
nication on Climate change in Colombia (TCNCC, IDEAM
etal. 2017). Historical surface air temperature increases in
our study compare well with national averages published by
IDEAM (+ 0.77°C vs. + 0.8°C), however, spatial warming
patterns are very different (Fig.4b, d). Our simulations show
the highest temperature increases occurring above Andean
(b)
(a)
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
∆SW↑ ∆SW↓ +∆LW↓∆
L∆
H+∆G
in W/m
2
LCCeffectondifferent fluxes
Colombia CostaAn dina
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
∆SW↑ ∆SW↓ +∆LW↓∆
L∆
H+∆G
in W/m
2
Colombia CostaAn dina
100DEF effect on different fluxes
Fig. 8 a LCC and b 100DEF effect on different surface energy fluxes:
Shortwave reflected (ΔSW ↑), downward fluxes (ΔSW ↓ + ΔLW ↓),
latent heat flux (ΔL), sensible and ground heat flux (Δ H + ΔG)
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122 A.Manciu et al.
1 3
cordilleras and higher elevation areas (> 1°C, dark red in
Figs.4b, d and 5a, b), lower temperature increases in the
northern and western areas and intermediate increases in
the south and eastern parts (e.g. Llanos and Amazon Forest).
This is in contradiction with IDEAM results where higher
elevation areas warm less (1–1.5°C) and the Llanos or low
elevation areas more but more homogeneously (> 1.5°C,
Fig.4b, d). In other means, we show a positive elevation-
dependent warming (EDW) while the IDEAM showed the
opposite result (negative EDW). Confusingly, the IDEAM
states in this very same TCNCC, that the “most significant
increase in mean, minimum and maximum temperatures
will be in the Andean region, especially in high elevation
areas” (IDEAM etal. 2017). In the literature, there is grow-
ing recent evidence that the rate of warming is amplified
with elevation (Pepin etal. 2015; Wang etal. 2016; Hock
etal. 2019). However, in the tropics, there is a particular
problem with observational data from mountain regions,
which is both sparse and inhomogeneous (Pepin etal. 2015),
and EDW in the Tropical Andes is controversially discussed
in the literature (Vuille and Bradley 2000; Beniston etal.
1997; Hock etal. 2019; Rangwala and Miller; 2012). At least
three elements tend to reinforce here our results: (1) most
recent literature in the tropics based on observations show
more warming above high-elevation vs. low-elevation areas
(Rangwala and Miller 2012; Aguilar-Lome etal. 2019), (2)
GCMs and RCMs mostly show positive EDW in tropical
Andes (Rangwala and Miller 2012; Hock etal. 2019), (3)
WRF reproduces correctly the EDW in low latitude environ-
ments along elevational gradients (Gao etal. 2018; Expósito
etal. 2015; Lin etal. 2015).
Concerning precipitation changes, global warming inten-
sifies the global hydrological cycle (Jia etal. 2019), i.e. the
warming of surface air temperature features more evapora-
tion of water into the atmosphere, thus more water content
in the air, since warmer air can hold more water vapor. How-
ever, according to an average of four emission scenarios,
IDEAM etal. (2017) expect precipitation to decrease over
the Caribbean coast and the Colombian Amazonas (by
10–40% by 2071–2100 compared to the reference period
1976–2005) and to increase in the Andean region. Our
results suggest an overall precipitation increase in Colom-
bia in response to historical global warming, except for the
Pacific coast. Our findings are comparable to Skansi etal.
(2013), who analyzed station-based time series data from
1950–2010 in South America. Our findings show a drying
along the Pacific coast, which is confirmed at least for the
southern part by Skansi etal. (2013) but in contradiction
to Carmona and Poveda (2014). In WRF, the decrease in
Western Pacific Colombian coast precipitation in response to
global warming can be explained by less tropospheric winds
coming from the Pacific which usually advect moisture,
driven by less ocean-continent thermal contrast (not shown).
Furthermore, elevation-dependent rainfall changes under
global warming are poorly studied (Li etal. 2017) and we
are only aware of one study in the tropics (Urrutia and Vuille
2009, tropical Andes). This latter study does not show con-
sensual patterns of elevation-dependent rainfall changes
under global warming, which depends on the latitude or the
slope orientation. The authors found that “In general the
projected changes in precipitation are not as pronounced as
for temperature” and that precipitation is slightly higher in
the global warming scenario below 4000m.a.s.l. and slightly
less beyond that elevation (Urrutia and Vuille 2009). Our
results show a slightly higher rainfall increase under his-
torical global warming in Colombian Andean cordilleras vs.
other low-elevation areas (Figs.5b and 6c, d) which is a con-
sistent pattern with IDEAM near-term projections (IDEAM
etal. 2017) and some mid-latitude patterns (Tibetan Plateau,
Li etal. 2017) but differs from Urrutia and Vuille (2009).
Finally, our results suggest that CC effects are more domi-
nant on national climate in Colombia than LCC effects while
LCC affect rainfall substantially more than temperature at
regional and sub-regional scales, which is consistent with
most recent studies (Hock etal. 2019; Quesada etal. 2017a,
2017b). Moreover, at the subregional level (Coast or Andean
sub-region of Colombia), LCC effects are of the same mag-
nitude on precipitation as CC effects, which urges to sys-
tematically account for land-use and land-cover dynamics
in any departmental and regional mitigation and adaption
to climate change plans.
4.2 LCC effect onprecipitation andtemperature
A drying over tropical deforested areas is reported through-
out the literature (Perugini etal. 2017; Coe etal. 2017;
Spracklen and Garcia-Carreras 2015; Lejeune etal. 2015;
Saavedra etal. 2020; Sierra etal. 2021; Eiras-Barca etal.
2020) which is in good agreement with our results, although
the magnitude of precipitation change differs. Trees usually
have a deeper rooting depth and are therefore able to access
deep-lying groundwater even in dry periods. Removing
tropical forests and replacing them by crops and pastures
results in reduced evaporative leaves and canopies, foliar
density, surface roughness, stronger winds and an increased
albedo. Consequently, evapotranspiration and precipitation
(due to fewer clouds) are expected to diminish in tropical
agricultural areas. Furthermore, modeled (Sampaio etal.
2007; Nobre etal. 1991; Zemp etal. 2017) changes in pre-
cipitation show that drying is even more pronounced during
the dry seasons and in low latitude areas.
Reviews on GCM and RCM modeling studies with
a focus on Amazon deforestation report a precipitation
decrease (Spracklen and Garcia-Carreras 2015; Lejeune
etal. 2015; Sampaio etal. 2007). Lejeune etal. (2015) report
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123Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
1 3
a reduction of ~ − 0.7mm/day (−5 to −10%) across 28
GCM and RCM experiments, in agreement with Sprack-
len and Garcia-Carreras (2015), who report a decrease of
16.5 ± 13% across 44 simulations of complete Amazon
deforestation. This is similar to our findings of a 4% decrease
in precipitation (−184mm/year or −0.5mm/day in case
of total deforestation over the modeling domain) but more
pronounced in magnitude.
Though the comparison with other regional WRF studies
(Laux etal. 2017; Paul etal. 2016; Takahashi etal. 2017;
Quesada etal. 2017a) shows discrepancies, due to the spa-
tially specific influence of climatic phenomena, they agree
that deforestation leads to decreasing precipitation over
large-scale deforested areas.
Our simulations show that tropical deforestation causes
a slight cooling at country level and more enhanced cool-
ing over deforested grid-cells. Such cooling after deforesta-
tion simulated by default WRF parametrizations is at odds
against all observations and climate simulations about the
tropical deforestation impacts on temperature (Perugini
etal. 2017; Lawrence and Vandecar 2015; Jia etal. 2019).
The IPCC Special Report on Climate Change and Land
(Jia etal. 2019) concludes that there is high confidence that
large-scale deforestation leads to mean biophysical warm-
ing (+ 0.61 ± 0.48°C) over the entire tropics and especially
over deforested areas. After entire Amazon deforestation,
GCMs and RCMs reviewed by Lejeune etal. (2015) simu-
lated a temperature increase by 0.8°C on average, whereas
WRF in our study simulates a cooling of − 0.21 ± 0.02°C
over Colombian deforested grid-cells in the 100DEF sce-
nario. Perugini etal. (2017) reviewed eleven modeling stud-
ies in the tropics and found a mean temperature increase of
0.68°C (forest cropland/grassland) which is in agreement
with three tropical observational studies they investigated
(+ 0.41°C on average). Temperature changes derived from
satellite observations (+ 1.06°C in Alkama and Cescatti
(2016), + 1.34°C in Duveiller etal. (2018)) or from forest
change data (+ 0.38°C in Prevedello etal. (2019) confirm
the warming effect of deforestation in the tropics. However,
caution is needed as observational studies do not capture
nonlocal biophysical impacts from deforestation (Winckler
etal. 2017).
Only a few modeling studies found a cooling as a result
of tropical deforestation. For instance, Lejeune etal. (2015)
report a mean increase in surface air temperatures of + 1.2°C
across 28 GCM and RCM studies conducted for the Amazon
basin with only 3 studies showing cooling out of 28 in total.
Robertson (2019), using the HadGEM2-ES ESM, obtained
a cooling similar to our study (−0.08°C vs. −0.01°C) and
showed with a decomposition of the surface energy balance
that albedo and evapotranspiration responses were strongly
biased (overestimated and underestimated, respectively).
We suspected similar reasons being responsible for WRF
incorrectly simulating cooling after tropical deforestation
and thus conducted in the next section a decomposition of
our results as well as sensitivity experiments modifying
albedo parametrization and land surface models.
4.3 LCC andCC interactions withENSO
Studies investigating the interactions between LCC and
ENSO in tropical South America are rather rare. The con-
sensus, however, is that LCC enhances the ENSO signal
(Nobre etal. 2009; Beltrán-Przekurat etal. 2012; Bush etal.
2017; Zhang etal. 2009). Tölle etal. (2017), for example,
used the RCM COSMO-CLM and investigated conse-
quences of abrupt tropical deforestation during the ENSO
phases in South East Asia and concluded that they “can
amplify the impact of the natural mode ENSO”. Another
study by Chapman etal. (2020) on the island of Borneo
(Indonesia) confirms that deforestation under El Niño condi-
tions resulted in warmer and drier climate than under normal
conditions. Syktus etal. (2007), whose study domain was
Australia, came to the same conclusion. Bush etal. (2017)
suggest that the ENSO signal has been even amplifying since
humans started cultivating agricultural goods, which was
buffered before by natural vegetation. The buffering effect
has been confirmed by Meijide etal. (2018). However, our
results suggest that it depends on the region, land cover type,
ENSO phase and variable whether the LCC effect is ampli-
fied or not, i.e. there is no or little influence of LCC and
CC on temperature during ENSO, whereas precipitation
changes are more affected, especially during La Niña and at
the Coast. Beltrán-Przekurat etal. (2012) concluded from
their results that dry periods are enhanced by the occurrence
of ENSO, but that the type of land cover conversion also
plays an important role for the sign of change.
4.4 Decomposition ofenergy balance andmodel
sensitivity toalbedo parametrization andland
surface models (LSM)
At a regional scale, the energy balance is deeply modified
by forest losses. In the tropics, a deforestation-driven warm-
ing caused by decreased latent heat flux is expected to out-
balance the albedo-driven cooling. When decomposing the
energy balance into its single components and comparing it
to observed values provided by Duveiller etal. (2018), WRF
with the coupled Unified Noah LSM (Tewari etal. 2004),
which is used here, overestimates the change in reflected
shortwave radiation from the surface by a factor of 3.5 over
converted grid-cells (EBF → crop/grass, see Table2). At
the same time, the magnitude in latent heat change reduction
is underestimated by 41% in WRF. WRF simulates a latent
heat flux reduction of 9.6W/m2, while a reduction of 20 to
30W/m2 would be more realistic according to Duveiller
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124 A.Manciu et al.
1 3
etal. (2018), Nobre etal. (1991) and Snyder etal. (2004)
when forests are converted to cropland and/or pasture. Fur-
thermore, sensible heat is decreasing by -8.4W/m2 in WRF
following deforestation, instead of increasing by + 5W/
m2 to + 20W/m2 as suggested in the literature (Duveiller
etal. 2018; Nobre etal. 1991; Sampaio etal. 2007; Llopart
etal. 2018; Takahashi etal. 2017; Coe etal. 2017; Oliveira
etal. 2019). In summary, the most important reason for the
deforestation-temperature bias in WRF seems to be the high
overestimation of shortwave radiation reflected from the sur-
face after deforestation, while biases in the latent/sensible
heat partitioning tend to compensate each other. This is a
coherent finding compared to Sierra etal. (2021) who men-
tioned the WRF overestimation of the reflected shortwave
radiation and the strong underestimation of the sensible heat
flux and evaporation/latent heat after cooling in response to
forest loss in the tropical Amazon-Andes transition region.
Indeed, Robertson (2019) concluded, that “to reduce
this error the difference between the PFT’s albedo param-
eters should be reduced”. Following this recommendation,
we adjusted the land use category’s albedo parameters
and ran new WRF simulations. The default albedo ranges
from 0.17–0.23 for dryland cropland and pasture, from
0.19–0.23 for grassland and has a constant value of 0.12
for EBF. Observed values for albedo indicate that 0.13 to
0.19 for crops, 0.13 to 0.23 for grassland and 0.11 to 0.15
for tropical EBF are more appropriate (Cescatti etal. 2012,
Hänchen 2017; Prentice etal. 1992; Giambelluca etal.
1997). After running simulation 1 and 2 again with these
adjusted values (e.g. using minimum albedo of 0.11 and
a maximum albedo of 0.15 for EBF),
ΔSW ↑
is now only
slightly overestimated (33%). However, the underestima-
tion of
ΔL
is amplified (by 83%, see Table2).
Additional runs were conducted to explore the model’s
sensitivity to the LSM used. Table2 shows that the cool-
ing over deforested pixels is even more enhanced when
applying the Rapid Update Cycle (RUC) LSM (−0.13°C,
Benjamin etal. 2004) and the 5-Layer thermal diffusion
scheme (−0.13°C, Dudhia 1996). Only the Pleim-Xiu
LSM (Pleim and Xiu 1995) simulates a warming of
0.06°C over deforested grid-cells. Furthermore, a decom-
position of the alternative LSM’s energy balance shows
also changes in radiative and non-radiative fluxes that
are in stark contrast to observed changes (Duveiller etal.
2018, see Table2). Thus, we investigated several sources
of deforestation-induced temperature biases in WRF,
which were found in recent tropical studies performed with
WRF in the default configuration (Glotfelty etal. 2021;
Sierra etal. 2021).
Finally, we stress here that we are confident with WRF
responses to CC (Sect.4.2) as well as precipitation responses
to LCC: (1) vegetation model properties are the same
between CC simulations and climatic patterns are well rep-
resented by WRF in this region – see Sect.3.1 and Posada-
Marín etal. (2018); (2) precipitation responses after LCC
and tropical deforestation (100DEF) are consistent with the
observations in this tropical region (see Sect.4.3), (3) the
biases tend to cancel out: the underestimation of evapotran-
spiration loss after deforestation has an opposite effect with
respect to the overestimated cooling for precipitation: the
former tends to underestimate the magnitude of the reduced
precipitation response (more evapotranspiration leads to
more clouds and in turn more precipitation) while the latter
tends to overestimate it (cool air tends to hold less water
thus less precipitation, Mahmood etal. (2014); (4) across
the different WRF land cover schemes tested, precipitation
responses are not substantially affected and are systemati-
cally negative after deforestation (not shown).
5 Conclusion
Colombian climate regional modelling is challenging in
several respects: it has a highly uneven and mountainous
terrain highlighting the need for high-resolution model-
ling, it is a regional climate modelling coldspot (very few
studies), it is highly vulnerable territory to climate change
and biodiversity loss and thus, it calls for an urgent need
of hydroclimatic assessments (Poveda etal. 2011). In
this study, we quantified and disentangled the impact of
Table 2 Comparison of
simulated deforestation
responses in selected variables
using different land surface
models to observed variables
from Duveiller etal. (2018)
2m surface air temperature difference is given in °C, energy fluxes in W/m2. Values from Duveiller etal.
(2018) refer to a mean value for forest—> crop/grass conversions in the tropics, values from model simula-
tions refer to grid-cells with Evergreen Broadleaf Forest—> cropland and pasture conversion
Simulation ↓/variable →
ΔT2
ΔSW ↑
ΔLW ↑
ΔL
Δ(H+G)
Duveiller etal. (2018) + 1.34 5.47 7.53 −16.31 4.21
WRF (Noah, default LSM and albedo) −0.07 19.02 1.11 −9.64 −8.32
WRF (Noah, default LSM and adjusted albedo) −0.03 7.29 2.15 −2.77 −5.85
WRF RUC LSM −0.13 22.84 −1.44 −11.77 −6
WRF 5 layer thermal diffusion LSM −0.13 22.76 −0.35 −1.55 −14.15
WRF Pleim-Xiu LSM 0.06 −0.02 −1.4 0.83 0.53
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125Impacts ofland cover changes andglobal warming onclimate inColombia duringENSO events
1 3
historical LCC and CC on Colombian climate perform-
ing WRF simulations during ENSO events (2009–2011),
identifying the most dominant forcing and analyzing the
impacts of historical LCC on the surface energy balance
given several model configurations.
LCC reduces precipitation everywhere over deforested
areas, but especially in the Caribbean coast in the north.
Conversely, CC increases precipitation, specifically in the
Andean region. LCC effects are found to be strong driv-
ers of sub-regional precipitation changes: dampening CC
effects magnitude by 72% and 24% on average in Coastal
Caribbean and Andean regions, respectively. Combining
historical LCC and CC, temperature increases by 0.76°C,
especially in high altitudes. Conflicting with most other
modeling studies and observations, WRF simulates a tem-
perature decrease following historical LCC and 100DEF,
which is intensified over deforested areas.
Our results suggest that CC effects are more dominant
on regional climate in Colombia than LCC effects but both
effects are comparable in magnitude over deforested areas
or at the subregional level. La Niña tends to enhance the
studied effects across the country, whereas El Niño weak-
ens them, except at the coast where the pattern is mostly
inverted. In the Andes, La Niña tends to intensify LCC and
CC effects roughly to a factor of two compared to El Niño
impacts in both cases.
Our simulations indicate a serious bias in WRF con-
cerning an incorrect simulation of cooling after tropi-
cal deforestation, recently found as well in other tropi-
cal regions (Glotfelty etal. 2021; Sierra etal. 2021). We
further investigate this apparent bias because of its great
importance for climate studies using WRF with default
parametrization in tropical contexts and probably beyond.
We show here that WRF coupled with the Unified Noah
LSM, RUC LSM and the 5-layer thermal diffusion LSM
overestimates reflected surface shortwave radiation
response and underestimates evapotranspiration-driven
warming in the tropics which leads us to the assumption
that vegetation properties are not appropriately para-
metrized for tropical regions in all investigated LSMs in
WRF. We therefore recommend using default vegetation
properties of WRF with caution for temperature assess-
ments. Improved model representation of vegetation prop-
erties and more validation data in tropical areas are needed
to provide more robust and confident climate projections at
regional-to-local scale. We stress here that, although our
study has limits concerning the limited sensitivity experi-
ments across physical schemes or imprecise representa-
tion of land-cover characteristics, the model settings are
tuned to limit internal model variability maximizing the
signals response, following similar WRF climatic studies
(see Sects.2.2 and 2.4). Meanwhile, we provide statis-
tical significance to ensure robust messages throughout
this first-of-its-kind attribution study at high-resolution
(10km) analyzing global warming and land-cover changes
impacts on climate in this region. Although our simulation
period is limited (2009–2011), our results on the whole
period are likely to represent the simulated impact of his-
torical land cover changes and global warming on Colom-
bian climate during a climatological period, albeit slightly
skewed towards La Niña.
To avoid obvious temperature biases in WRF simula-
tions, in-depth analysis and further development of land
cover classification and the representation of vegetation
properties used by WRF is necessary. This includes a more
realistic and up-to-date representation of tropical land
cover data. USGS land use classification in WRF stand-
ard simulations dates back to the early 90s. Since then,
tropical deforestation and land use rose tremendously. A
thorough review of vegetation properties parametrization
is necessary to assure a good representation of tropical
land–atmosphere feedbacks. We implemented alternative
albedo values for Colombia’s most important land use cat-
egories and tested its sensitivity, which showed a substan-
tial improvement of simulated reflected shortwave radia-
tion. However, parametrizations of e.g. roughness length
and stomatal resistance, important parameters which drive
evapotranspiration, were not investigated, potentially
explaining that simulated latent heat fluxes were even
more biased. To understand the role of the ENSO phases
better, longer simulation studies are needed, in order to
have a more appropriate climatological period and an aver-
age of more ENSO events. In addition, a follow-up study
could investigate climate extreme indices like maximum
daily temperature and maximum daily precipitation to
provide evidence for LCC and CC impacts on extreme
weather events. Although land use data has been used for
regional climate simulations in the Neotropics (e.g. Sierra
etal. 2021; Saavedra etal. 2020) and although a more
recent update land use data already exists (years 2000s,
Eva etal. 2004), a newer high-resolution land-cover data-
set for Colombia spanning a climatological period would
represent a great input for more robust climate and hydro-
logical assessment in Colombia. Finally, it should be
noted that the official climatic projections by the TCNCC
(IDEAM etal. 2017)contains methodological shortcom-
ings, inconsistencies and need urgent reassessment by
the national communities of meteorology and climatology
(Arias etal. 2022).
Funding Open Access funding provided by Colombia Consortium. We
deeply acknowledge the services of the High Performance Computing
center hosted at Universidad del Rosario (Colombia) and Advanced
Computer Laboratory for Research as well as the useful technical help
provided (https:// www. urosa rio. edu. co/ Labor atorio- Compu tacion-
Avanz ada- Inves tigac ion/ Infra estru ctura/"). A.Manciu acknowledges
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

126 A.Manciu et al.
1 3
TUM university and the Hanns-Seidel-Stiftung for the Mobility
Fellowship as well as the Universidad del Rosario for supervision.
B.Quesada acknowledges Climat AmSud program (project 21-CLI-
MAT-13), Colombian-french researcher association COLIFRI in the
framework of the French FSPI fund on Renewable Energy ecosystem
in Orinoco Region project and the Starting Grant of Universidad del
Rosario for funding this work.
Data availability For this research, we did not use any new data for
simulating the studied effects. The ERA-Interim reanalysis dataset from
Dee etal. (2011), with which we forced the model, and the ERA-20C
dataset from Dee etal. (2016), that was used to create the forcing data
without Global Warming influence, is available for download from
the European Center for Medium-Range Weather Forecasts (ECMWF,
available at https:// www. ecmwf. int/ en/ forec asts/ datas ets/ browse- reana
lysis- datas ets) upon creation of a cost-free user account. The newly
simulated and processed data in this research is available in the Center
for Open Science repository under the following link: https:// osf. io/
pcrk5/? view_ only= 5f80e 18935 1b484 5bd9d f72b2 8ed87 fe.
Declarations
Conflict of interest The authors declare no conficts of interest or com-
peting interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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