Theoretical and Applied Climatology
https://doi.org/10.1007/s00704-020-03400-3
ORIGINAL PAPER
Comparison of GCM and RCM simulated precipitation
and temperature over Central America and the Caribbean
Alejandro Vichot-Llano 1
Abel Centella-Artola 1
&
Daniel Martinez-Castro 1,2 & Filippo Giorgi 3 & Arnoldo Bezanilla-Morlot 1 &
Received: 24 May 2019 / Accepted: 17 September 2020
# Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract
The climate features over Central America and the Caribbean are simulated with the regional climate model RegCM4 to analyze
the performance of the model in reproducing precipitation and temperature patterns over the region. Results from RegCM4 and
the driving global climate model (GCM) HadGEM2-ES (HadG) are compared against gridded observations for a period from
January 1980 to December 2004 using a perturbed physics ensemble formed by four configurations of the RegCM4 using
different combinations of four convection schemes run over land and ocean areas: Kain Fritsch (Kf), Emanuel (Em), Grell
(Ge), and Tiedtke (Tk). The added value of RegCM4 relative to the GCM simulation is also estimated, with focus over four
subregions, using several metrics calculated on the RegCM4 as well as the GCM grids. The RegCM4 downscaling, expressed by
the ensemble mean ENS, adds significant details to the HadG simulation of the temperature field patterns, showing smaller biases
of up to ± 1.6 °C at the RCM resolution. Regarding the spatial patterns of precipitation, the HadG has overall higher correlation
values with observations than ENS. However, the regional model provides more detailed spatial distributions, decreasing the bias
by more than 1 mm/day over some of the islands. It also captures extreme precipitation events that are underestimated in the
HadG simulation, even after upscaling to the GCM resolution grid. Concerning the performance of the different RegCM4
convection scheme combinations, KfEm and EmEm show the highest skill values for precipitation, while for temperature
TkEm and GeEm are the best performing. This highlights that no individual scheme outperforms the others in all respects, while
the application of the averaged ensemble technique provides the best results.
1 Introduction
The use of regional climate models (RCMs) driven by global
climate models (GCMs) to produce climate simulations or
projections at a higher scale than that of the GCM has become
a common practice in the last decades (Giorgi 2019). The
advantages and disadvantages of RCMs have been discussed
in many papers (Anthes et al. 1989; Centella-Artola et al.
2015; Feser 2006; Giorgi and Gutowski 2015; Giorgi and
Mearns 1991; Castro et al. 2005; Martínez-Castro et al.
2006, 2016, 2018; Prömmel et al. 2010; Rockel et al. 2008;
* Alejandro Vichot-Llano
alejandrovichot.llano@gmail.com; alejandro.vichot@insmet.cu
1
Instituto de Meteorología, Loma de Casablanca S/N, Havana, Cuba
2
Instituto Geofísico del Perú, Lima, Perú
3
Earth Physics Section, International Centre for Theoretical Physics,
Trieste, Italy
Vichot-Llano et al. 2014; Winterfeldt and Weisse 2009). One
of the most debated issues concerning regional climate modeling is the quantitative representation of the added value (AV)
of going from the lower GCM resolution to the higher RCM
one (Giorgi and Gutowski 2015; Torma et al. 2015;
Rummukainen 2016; Di Luca et al. 2012, 2013a, b, 2015,
2016). This problem arises because the identification of AV
depends on the analyzed variable and the applied metrics and
is also related to the morphological structure of the domain
and study areas, particularly if complex topography and coastlines are involved (Mearns et al. 2003).
Several studies on AV have adopted diverse metrics to quantify it. For example, Sun et al. (2005) used a scale separation
technique for precipitation in Northeast Brazil, finding that the
regional model was able to provide accurate small-scale details
that were missed by the global model. As another example, the
RCM performance has been assessed by comparing with observations to evaluate the spatial and temporal patterns of the model
results by Castro et al. (2005). On the other hand, Prömmel et al.
(2010) used temporal correlation and bias metrics, to find the
A. Vichot-Llano et al.
RCM AV in temperature surrounding the Alps during the winter
and in topographically complex regions in the summer. Another
research aimed at finding improvement in the precipitation skills
by RCMs was made by De Sales and Xue (2013), who found
AV over the southern part of South America using a verification
of the intensity scale. Di Luca et al. (2012) developed a potential
AV metrics and determined that the highest potential for precipitation AV is on shorter time scales and near complex topography. Similar conclusions were found by Lucas-Picher et al.
(2017) for North America, who also showed AV in the simulation of lake-effect snow, sea breeze–driven precipitation and
wind intensity and direction over coastal areas.
As a final example, Torma et al. (2015) used different metrics
to show substantial AV by high-resolution RCMs in simulating
precipitation patterns, daily precipitation rate distributions, and
precipitation extremes over the Alpine region. This work was
extended by Giorgi et al. (2016) to the case of summer precipitation change signals over the region. They found that the RCMs
produced a change signal of the opposite sign compared with that
of the driving GCMs because of increased high elevation convection not simulated by the GCMs. The RCM signal was also
more consistent with observed trends over the region.
All these examples clearly show that the issue of the identification of AV is complex and depends on the variables and regional climate types being considered, which may require
targeted analyses, specific to different regional settings (Giorgi
and Gutowski 2015; Rummukainen 2016; Di Luca et al. 2012,
2013a, b, 2015, 2016; Torma et al. 2015). According to these
considerations, the main objective of the present paper is to analyze the AV for variables such as precipitation and temperature
as simulated by a driving GCM, the HadGEM2-Es (HadG), and
the regional climate model RegCM4 (Giorgi et al. 2012) over
Central America and the Caribbean. This region is especially
suitable for such an analysis because of the presence of islands,
complex topography, and coastlines. Our study is based on data
from four simulations using different cumulus schemes, which
allows us to determine how the findings are specific to a particular configuration of the model. The analysis is focused in the
comparison with the GCM output patterns, assessing the dependence on the quality of the boundary forcing fields. Both the
individual simulations and the ensemble mean (ENS) are
assessed using several metrics at both the GCM and RCM resolution grids, so that we can assess the AV also when the RCM
data are upscaled onto the GCM grid.
2 Methods and data
2.1 Model description, data, and numerical
experiments
The latest version of the regional climate model RegCM4
developed at the International Centre for Theoretical Physics
(ICTP) (Giorgi et al. 2012) is used in this work (Giorgi et al.
1993, 1999; Pal et al. 2007). The model uses a finitedifference discretization and a hydrostatic dynamical core
(Grell et al. 1994) on an Arakawa B horizontal grid
(Arakawa and Lamb 1977). The RegCM4 has capability of
multiple physics schemes, and here we use the NCAR radiation scheme (Kiehl et al. 1996), the biosphere–atmosphere
transfer scheme (BATS) (Dickinson et al. 1993) to represent
soil–vegetation–atmosphere interaction processes, the nonlocal planetary boundary layer scheme of Holtslag et al.
(1990), the ocean surface flux scheme of Zeng and
Dickinson (1998), and the explicit moisture representation of
resolvable scale cloud and precipitation of Pal et al. (2007).
2.1.1 Convective schemes
The RegCM4 system has multiple options for representing
cumulus convection, with the capability of using different
schemes over land and ocean areas (Giorgi et al. 2012). In this
paper, four different cumulus parameterization scheme configurations are used over land: the Grell (1993) scheme (GE),
the Emanuel (Emanuel 1991) scheme (EM), the Tiedtke (TK)
scheme (Tiedtke 1989), and the parameterization of Kain and
Fritsch (1989) (KF). The EM scheme is applied over ocean in
all cases. The influence of the GE, EM, and TI schemes in the
performance of RegCM4 was tested by the authors in previous
studies (Martínez-Castro et al. 2016, 2018; Vichot-Llano et al.
2014), while the KF was implemented in the last version of the
model and has been recently tested with good results in the
tropical regions of India and West Africa by Huang and Gao
(2017), Adeniyi (2019), and Maity (2020).
2.2 Initial conditions and reference observation
datasets
HadGEM2-ES (hereafter as HadG) (Martin et al. 2011) is
used to produce initial and boundary conditions for
RegCM4. Its atmospheric component has 38 levels, extending
to nearly 40-km height. The global grid used by the model has
192 × 145 grid cells, with a horizontal resolution of 1.25
degrees of latitude by 1.875 degrees of longitude. The variables used to drive the RegCM4 are as follows: air temperature, relative humidity, horizontal wind components, and
mean sea level pressure. Sea surface temperature (SST) is
obtained by interpolation of the monthly average values of
the Reynolds and Smith (1994) dataset. With a horizontal
resolution of 10 min, the topography and land-use data are
obtained from the United States Geological Survey (USGS)
and Global Land Cover Characterization (GLCC, Loveland
et al. 2000), respectively. To evaluate the model, different
datasets are used: the reanalysis ERA-Interim (ERAI) from
the European Center of Medium-Range Weather Forecasts
(ECMWF) (Dee et al. 2011; Simmons et al. 2007) at a
Comparison of GCM and RCM simulated precipitation and temperature over Central America and the Caribbean
Table 1 Definitions of the 5
experiments
Experiment acronym
Definition
GeEm
EmEm
TkEm
KfEm
ENS
RegCM4: GE over land, EM over ocean driven by HadGM2-ES
RegCM4: EM over land and ocean
RegCM4:TK over land, EM over ocean
RegCM4: KF over land, EM over ocean
Average of GeEm, EMEm, TkEM and KfEm
HadG
HadGM2-ES output
horizontal resolution of 1.5° × 1.5° (ERAI); the Climate
Research Unit (CRU) of the University of East Anglia dataset
for temperature and precipitation with a horizontal resolution
of 0.5° × 0.5°, version 3.10 (Mitchell and Jones 2005; Harris
et al. 2014); and the Climate Hazards Group InfraRed
Precipitation with Station data (CHIRPS) (Funk et al. 2015),
5 × 5 km horizontal resolution also for precipitation. Other
datasets with coarser temporal and horizontal resolution are
mainly used to evaluate the larger-scale precipitation patterns
and the annual cycle. These include the climate prediction
center merged analysis of precipitation (CMAP) dataset (Xie
and Arkin 1997) and the GPCP (Global Precipitation Climate
Project, Adler et al. 2003).
The model data and the observations are interpolated onto
two representative regular grids, at grid spacing of 0.25° and
1.875° corresponding with the RCM and GCM model grids,
respectively. This allows a direct assessment and intercomparison of the results across resolutions. Following Torma et al.
(2015), for the interpolation we use the distance-weighted
method in CDO (Schulzweida 2019). The domain (Fig. 1)
includes the Greatest and Lesser Antilles, and the
Interamerican Seas and its surrounding territories, including
part of the Atlantic and pacific Oceans. However, most of the
analysis is focused in a study area including the Caribbean
Region and part of central America (central rectangle in Fig.
1), and particularly in four subregions, defined following previous work (Martinez-Castro et al. 2018).
2.3 Numerical experiments
2.4 Metrics
The study areas, resolution, and domain were chosen from
previous studies (Vichot-Llano et al. 2014; Martinez-Castro
et al. 2018;). In total, four RCM simulations were completed
at 25-km grid spacing for the 25-year-long present-day period
January 1979–December 2004 using the GCM output for initial and lateral boundary conditions. The first year (1979) is
removed from the analysis as model spin-up. Table 1 shows
the acronyms for the four experiments, along with the convective parameterizations used over land and sea.
Fig. 1 Domain with five
subregions defined as: study area
(SA), Greater Antilles (GA), and
Central America: north-east
(CAMNE), east (CAME), and
west (CAMW).
The analysis of the AV is focused on the spatial distribution of
mean precipitation and temperature and on the daily precipitation
probability distribution functions (PDFs), whose right tail is evaluated using as metrics the fraction of precipitation accounted for
by events above the 95th percentile (R95) (Giorgi et al. 2014;
Sillmann et al. 2013). In addition, we also use Taylor diagrams,
which provide information on the spatial correlation and standard
deviation, the bias, the root mean square error (RMSE), and the t
A. Vichot-Llano et al.
Fig. 2 Spatial variability of the mean precipitation (a, c, e, and g) and bias
(b, d, f, and h; statistically significant biases are shown by hatched areas)
during the dry season against CHIRPS for the period 1980–2004,
interpolated on a 1.875 grid (GCM-res) and 0.25 (RCM-res) degrees.
From top to bottom: HadG (a, b; e, f) and the ENS simulations (c, d; g,
h) (mm/day)
test value to calculate statistical significance. The dry (Nov–Apr)
and rainy (May–Oct) seasons are analyzed separately, as defined
by Lecha and Paz (1994). Following Di Luca et al. (2012), a
specific AV metrics, the AV index, or AVI, is used to estimate
whether the RCM adds value over the lower resolution GCM.
This is defined by:
3 Results
AVI ¼ ðGCM−ObsÞ2 −ðRCM−ObsÞ2
ð1Þ
In this way, the RCM generates some AV if its RCM
square error is smaller than the GCM one, i.e., if AVI is
positive.
Fig. 3 Same as Fig. 2, but during the rainy season
3.1 Precipitation
3.1.1 AV in the representation of the mean dry and wet
season climate
The increase in detail for the precipitation field with higher
resolution is visually evident during both the dry and rainy
seasons in ENS, as is the deterioration of this signal at the
coarsest resolution for the GCM simulations relative to the
CHIRPS observations (Figs. 2 and 3). ENS reproduces quite
Comparison of GCM and RCM simulated precipitation and temperature over Central America and the Caribbean
well the observed spatial pattern of precipitation in the dry and
rainy seasons, capturing the relative maxima over both Central
American coasts. However, the Pacific coast maximum is
overestimated and the Atlantic one is underestimated, with
biases statistically significant at the 90% confidence level
(Figs. 2 and 3, d and h). The detailed spatial information
represented by the observations is only captured by ENS, even
at the coarse resolution. For the Caribbean Islands, the improvement is more marked, as can be seen from the simulation
of precipitation maxima, coincident with the observed ones in
the mountainous regions of Cuba and La Española. ENS outperforms the HadG’s results with lower biases over the
smallest islands, and even regions such as the Bahamas without significant biases during the dry season.
In general, the ENS performance is better throughout
the domain, with smaller values of root mean squared
error (RMSE) (between 1 and 3 mm/day) than the
HadG during the rainy season, except over Guatemala
and El Salvador, where the values are between 3 and
5.8 mm/day, with permanent biases over this region.
The range of the bias values during the dry season
(around 1.8 and 3 mm/day) are similar to the ones in
HadG.
Figure 4 presents Taylor diagrams for the four individual
RegCM4 configurations, HadG and ENS for precipitation in
the two seasons over the study area at the two resolution grids.
At the finer scale, the standard deviation and spatial correlation values are similar across simulations and resolutions. For
the wet season, the highest correlation value is shown by the
global model, even if the spatial details of the observed spatial
patterns are better reproduced by the regional model. This can
be explained by the relative displacement of the precipitation
regions, which is greater for the high-resolution case, and
occurs mainly in the rainy period, when precipitation is mostly
Fig. 4 Taylor diagram of mean precipitation over land points (1980–2004). From top to bottom: dry and rainy seasons. From left to right: GCM
resolution and RCM resolution. Reference data: CHIRPS
A. Vichot-Llano et al.
convective, and related to local processes. In the lower resolution distributions, the precipitation areas are more expanded
and the agreement with observations is better. Only TkEm
appears out of range because of its poor ability to produce
precipitation during the wet season, a result consistent with
Vichot-Llano et al. (2014) and Martínez-Castro et al. (2016,
2018). At the GCM resolution, all the simulations show similar values for the standard deviation and correlation.
3.1.2 AV in the representation of the annual cycle
The dominant annual cycle of the Central American region,
except for the central part of its Atlantic coast, is monsoonal,
with highest temperatures in April, just before the summer
rains, and minimum temperatures in January, related with
strong trade winds. Precipitation over most of Central
America, including the Yucatan peninsula, is characterized
by two maxima in June and September, an extended dry season from November to May, and a shorter dry season in July–
August, known as the midsummer drought (MSD). The main
dry season of winter and early spring is more intense on the
Pacific slopes of the isthmus, due to the seasonal reversal of
the winds on the Pacific side which blow offshore during
winter and to the migration of the intertropical convergence
zone (ITCZ) which shifts to its southernmost position from
February to March (Taylor and Alfaro 2005). The Caribbean
islands have two main seasons. The rainy season, with higher
values of temperature and accumulated precipitation, occurs
during the boreal summer, including part of spring and autumn, although its limits depend on the subregion of the
Caribbean. The dry season occurs during the boreal winter,
with minimum temperatures above 18 °C. In the small islands
and coastal areas of the Greater Antilles, the sea breeze attenuates the diurnal temperature cycle, limiting the temperature
extremes. The direct influence of the ITCZ on precipitation in
the Caribbean islands is not significant. The MSD is also
present in most of the Caribbean, particularly in the Greater
Antilles (Taylor and Alfaro 2005).
An additional way to detect the AV from the RCMs is
through their representation of the annual cycle of precipitation, which is shown in Fig. 5. In general, HadG, ENS, and all
its members are capable to reproduce the two maximum peaks
that are characteristic of the region, with a bimodal precipitation regime during the rainy season.
However, some discrepancies arise about the timing and
month of occurrence of the maxima and the MSD. There is a
general agreement between the model simulations and observations in the time of the first maximum in May-July, with the
exception of CAME, for which the models reproduce the first
maximum in June, while the observations show it in July.
Regarding the intensity of the maxima, there is a general
agreement among observations, except for CRU, with a precipitation value well over those of the other datasets for three
of the subregions. There is also a good agreement among
model simulations except TkEm, which in CAMW overestimates substantially the value of the maximum. CAME is a
particular region, where there is not a marked climatological
bimodal precipitation regime (Magaña et al. 1999; Reuscher
et al. 2008); however, the observational datasets show a second weak peak in October (Curtis and Gamble 2007). This is
captured by ENS and its members, while HadG simulates it
incorrectly in time (two months in advance) and intensity.
The models have more discrepancies in simulating the second maximum, especially its time of occurrence. In the case of
Fig. 5 Precipitation annual cycle at both horizontal resolution over the four subregions defined in Fig. 1 (mm/day)
Comparison of GCM and RCM simulated precipitation and temperature over Central America and the Caribbean
GA, the observations show it in September but HadG and all
the RegCM4 configurations produce it in October. In the case
of CAME, where the bimodal pattern is not so marked, ENS
correctly situates the second maximum in October, while HadG
produces a very weak maximum in September–October. For
CAMW, there are discrepancies among observational datasets,
so that ENS follows CHIRPS and CRU, while HadG is closer
to GPCP and CMAP, and for CAMNE, RegCM4 and particularly ENS locate the peak correctly while HadG lags 1 month.
In general, the models underestimate the second peak, but ENS
gives a much better estimation than HadG, which is a clear
indication of AV. Regarding the relative minimum of precipitation, defining the MSD, it is underestimated by all the model
simulations, which leads to an overestimate of the MSD.
The effect of the increase in resolution does not influence
much the annual cycle, as can be seen by comparing each of
the four panels at the right half of the figure with the corresponding ones of the left half. The difference in the simulations by ENS and HadG is significant in CAME and CAMNE,
but it does not depend much on the output grid resolution.
3.1.3 AV in representation of extreme rainfall
The R95 metrics provides information about the extremes tail
of the PDF distribution, accounting for the fraction of
precipitation above the 95th percentile (Giorgi et al. 2014;
Sillmann et al. 2013; Torma et al. 2015). Figure 6 shows the
spatial distribution of R95 during the dry and rainy seasons
comparing the HadG and the ENS performance against
CHIRPS. The observed and simulated R95 during the dry
season have common areas with high values over the western
coast of Central America and the Lesser Antilles. A second
maximum over the Bahamas is not captured, while a third
maximum over the Colombia coast can be seen in both simulations, although it is underestimated by HadG. The performance of the models is quite good at both resolutions, but in
general the GCM tends to underestimate this metrics, missing
the high values and high-resolution details, specifically over
the Caribbean sea and over the Greater and Lesser Antilles,
where ENS captures the patterns even after upscaling to the
GCM resolution grid.
The performances of ENS and HadG are quite similar during the rainy season, reproducing well the maximum over the
Bahamas and around the Lesser Antilles. Even if HadG reproduces these two maxima, the signal is weaker than ENS and
CHIRPS, and the maximum observed over the north of SouthAmerica is only captured by ENS. Comparing against
CHIRPS with information only over land and at the highest
original grid resolution, the ENS performance is particularly
good and better than that of HadG, showing AV in this
Fig. 6 Spatial distribution of simulated and observed R95 during the dry and rainy season. From top to bottom: CHIRPS (a–b; g–h), HadG (c–d; i–j), and
ENS (e–f; k–l)
A. Vichot-Llano et al.
metrics. The moderate precipitation maximum shown in
CHIRS over the western part of Cuba is greatly overestimated
by HadG, with an improvement by the regional model which
shows a much reduced overestimation limited to the southwest coastal and sea region including the small island Isla de
la Juventud.
Figure 7 shows the Taylor diagram for R95, clearly revealing the AV of the RegCM4 at both resolutions against the
GCM. During the dry season, ENS and all its members show
higher correlation values than HadG, with the AV being even
clearer at the coarse resolution, where the HadG shows the
poorest correlation and standard deviation values. For the
rainy season, the ENS performance is even better in relation
to HadG, who has metrics values out of range. However, the
correlation decreases with increasing resolution of the grid,
from a value of 0.6 during the dry season at the GCM grid
and to less than 0.4 at the RCM grid. During the rainy season,
the values are around 0.3 and for the high-resolution grid. This
indicates that, even if in general the increase of resolution
leads to more realistic fine scale detail, the exact placement
of the orographic maxima deviates from observations. This
may be due to the relatively smooth model topography even
at 25 km resolution, resulting from a topography smoother
employed in the model to increase numerical stability
(Giorgi et al. 2012).
The PDFs of all individual simulations along with the
corresponding observations at both resolution grids during
the rainy season are shown in Fig. 8. Here and also during
the dry season (not shown), the AV of the RCM simulations
relative to the GCM ones is noticeable. The simulations by
the individual configurations of RegCM4 overestimate the
high-intensity tail of the observed distribution, producing
Fig. 7 Taylor diagram of R95 over land points (1980–2004). From top to bottom: dry and rainy seasons. From left to right: GCM grid and RCM grid
Comparison of GCM and RCM simulated precipitation and temperature over Central America and the Caribbean
Fig. 8 Probability distribution functions during the rainy season at the coarsest and finest resolution grids
events of the highest magnitudes exceeding 600 mm/day,
where the observations only reach between 200 and 300
mm/day. This may be due to the occurrence of occasional
numerical grid point storms which are characteristic of climate models especially in tropical regimes (Giorgi 1991;
Giorgi and Marinucci 1991). ENS improves this problem
by filtering out the contributions of individual point storm
events, and indeed, over the entire study region, the
RegCM4 PDFs match well the CHIRPS ones. Conversely,
HadG underestimate the PDFs substantially when looking
at the entire analysis region. Clearly, ENS matches best the
CHIRPS observations compared with HadG, while the
HadG matches best ERAI, as both have a relatively coarse
resolution.
For the coarse-resolution grid, ENS substantially improves
the PDFs compared with the HadG revealing significant AV.
Here, even when the GCM matches the ERAI performance, it
still underestimates medium to high intensity events compared
with CHIRPS. Generally, the tails in the GCM run are shorter
than the RCM ones and underestimate the intensity of the
events because of the poor representation of topography or
low spatial resolution.
Figure 9 shows the spatial distribution of the added value
index, AVI, defined by Di Luca et al. (2015), and previously
applied by Torma et al. (2015), according to Eq. (1). Positive
AVI regions represent ENS AV. In this case, the highresolution model tends to improve the performance of HadG
to a greater extent over the Caribbean Islands, specifically
where HadG fails in the precipitation performance, because
of the resolution and the topography information at the
original resolution. Also over Central America and Florida,
the performance of ENS adds value. The regions with positive
AVI are more extended during the rainy season for both the
coarse and high-resolution grids.
3.2 Temperature
3.2.1 AV in the representation of the temperature patterns
Figure 10 shows the temperature biases for the wet and dry
seasons in HadG and ENS. HadG heavily overestimates the
temperature field at both grids, the bias being statistically significant at the 95% confidence level over La Española, the
Bahamas, and Central America, and being also high over
Cuba and Florida. This is clearly a result of the coarse resolution of the GCM which does not capture properly the coastlines and topography of islands and peninsulas. ENS reproduces quite well the spatial pattern of temperature in both
seasons, with biases around ± 1.6 at the RCM resolution.
This is one of the clearest examples of the AV introduced by
the regional model, due to the better representation of the fine
scale physiography of the region.
The Taylor diagrams in Fig. 11 produced for the entire
study area at the two resolution grids show that for ENS both
the standard deviation (close to the reference) and correlation
values (around 0.9) are close to observations and better than
HadG at both resolutions and seasons. In particular, also at the
coarse resolution, ENS and its members show higher performance comparing with HadG.
A. Vichot-Llano et al.
Fig. 9 Added value index AVI of the RegCM4 precipitation relative to the driving GCM (only over land), using CHIRPS as reference, at the coarse
resolution (left) and at the high resolution (right). From top to bottom: dry (a–b) and rainy (c–d) seasons
The AVI index for temperature is shown in Fig. 12. The
application of the RCM improves the performance of HadG to
a great extent over the whole analysis area in both seasons, but
more clearly during the dry season. It is clear how the regional
Fig. 10 Statistical significance biases over the hatched areas (b, d, f, and h) during the dry (top) and rainy (bottom) season against CRU for the period
1980–2004 interpolated on 1.875 (GCM-res) and 0.25 (RCM-res) degrees. To the left: HadG and to the right: ENS simulations (°C)
Comparison of GCM and RCM simulated precipitation and temperature over Central America and the Caribbean
Fig. 11 Taylor diagram of mean temperature (1980–2004). From top to bottom: dry and rainy seasons. From left to right: GCM grid and RCM grid,
against CHIRPS
model with a better representation of the topography and distribution of small islands in the region makes the temperature
field closer to observations relative to the GCM, particularly
in mountainous regions of Central America, Cuba and La
Española, the narrow western extreme of Cuba, The
Bahamas and the Lesser Antilles.
4 Summary and conclusions
In this study we compared an ensemble of RegCM4 simulations of present-day climate with the driving GCM (HadG)
over Central America and the Caribbean region for precipitation and temperature. The results clearly show the need to use
high-resolution regional models for climate studies in the region
because of the complex orography and land-sea contrasts associated with the presence of islands and peninsulas. High
resolution is also needed to simulate climate extremes, and
our results in fact show that HadG fails to represent the tail of
the PDFs and the R95 patterns, whereas the RegCM4 ensemble
considerably improved this aspect of the simulation. The added
value of using the regional model nested in the global model
was found for most of the tested variables and climate features.
Substantial AV of RCM downscaling and upscaling was
found when using the AVI metric for both variables, with the
ENS showing improved performance also compared with the
individual ensemble members. Spatial patterns, standard deviation, and correlations also mostly improved relative to
HadG, even when upscaled at the GCM grid resolution, except for the case of fine scale precipitation correlations due to
some displacement of small-scale orographic maxima. The
improvement compared with the GCM was particularly evident for temperature (all metrics) and for precipitation PDFs
and extremes.
A. Vichot-Llano et al.
Fig. 12 Added value of the RegCM4 temperature relative to the driving GCM, using CRU as reference, at the coarse resolution (left) and at the highest
resolution (right) for temperature. From top to bottom: dry (a–b) and rainy (c–d) seasons
Concerning the performance of the different RegCM4 convection schemes, for precipitation, KfEm and EmEm showed
the highest skill values, while for temperature TkEm and
GeEm are the best performing, consistently with previous results (Martínez-Castro et al. 2016, 2018). Therefore, there is
no individual scheme that outperforms the others in all respects, and in fact, in general terms, the application of the
averaged ensemble technique provided the best results.
The global model and the configurations of the regional
model reproduced the bimodal annual precipitation cycle for
the Greater Antilles and three subregions in the Central
America isthmus, with the presence of the midsummer
drought, with different time intervals and intensity, but the
use of the regional model improved the estimations of the
positions and magnitudes of the maxima and minimum of
the cycle.
Our results confirm the potential of RCMs to substantially
improve the representation of fine scale climate processes
compared with coarse-resolution GCMs in areas characterized
by complex morphological features (such as Central America
and the Caribbean). They also point to the usefulness of using
ensemble approaches compared with individual models. We
are using the same ensemble approach to the production of
future climate change scenarios over the region, which will be
reported in future papers.
Acknowledgments Special thanks to the ICTP “Earth System Physics”
(ESP) group for providing the regional climate model RegCM4, access to
the “Argo” supercomputer cluster and technical assistance and to the
Associateship Program, funded by the Simons Foundation. We also thank
the “Caribbean Community Climate Change Centre” (CCCCC) for its
support.
Funding This study was supported by funding from Technology and
Environment (CITMA) of Cuba through the project SUPERCLIMA of
the Institute of Meteorology of Cuba (INSMET) and the “Sandwich
Training Educational Program” (STEP) of the International Centre for
Atmospheric Physics (ICTP).
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