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Atmospheric and Climate Sciences, 2016, 6, 445-474
Published Online July 2016 in SciRes. http://www.scirp.org/journal/acs
http://dx.doi.org/10.4236/acs.2016.63036
Assessment of Climate Change in Nicaragua:
Analysis of Precipitation and Temperature
by Dynamical Downscaling over a 30-Year
Horizon
Josep Maria Solé1, Raúl Arasa1, Miquel Picanyol1, Mª Ángeles González1,
Anna Domingo-Dalmau1, Marta Masdeu1, Ignasi Porras1, Bernat Codina1,2
1Technical Department, Meteosim S.L., Barcelona, Spain
2Department of Astronomy and Meteorology, University of Barcelona, Barcelona, Spain
Received 3 June 2016; accepted 11 July 2016; published 14 July 2016
Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Abstract
The present study has generated and analyzed Climate Change projections in Nicaragua for the
period 2010-2040. The obtained results are to be used for evaluating and planning more resilient
transport infrastructures in the next decades. This study has focused its efforts to pay attention
into the effect of Climate Change on precipitation and temperature from a mean and extreme
event perspective. Dynamical Downscaling approach on a 4 km resolution grid has been chosen as
the most appropriate methodology for the estimation of the projected climate, being able to ac-
count for local-scale factors like complex topography or local land uses properly. We selected MPI-
ESM-MR as the global climate model with the best skill scores in terms of precipitation and tem-
perature in Nicaragua. MPI-ESM-MR was coupled to a mesoscale model. We chose WRF mesoescale
model as the most appropriate regional model and we optimized their physical and dynamical op-
tions in order to minimize the model uncertainty in Nicaragua. For this, model output against the
available in-situ measurements from the national meteorological station network and satellite
data were compared. Climate change signal was estimated by comparing the different climate sta-
tistics calculated from a model run over an historical period, 1980-2009, with a model run over a
projected period, 2010-2040. The obtained results from the projected climate show an increase of
the mean temperature between 0.6˚C and 0.8˚C and an increase of the number of days per year
with maximum daily temperatures higher than 35˚C. Regarding precipitation, annual projected
amounts do not change remarkably with respect to the historical period. However, significant
changes in the distribution of the precipitation within the wet period (May-October) were ob-
How to cite this paper: Solé, J.M., Arasa, R., Picanyol, M., González, M.A., Domingo-Dalmau, A., Masdeu, M., Porras, I. and
Codina, B. (2016) Assessment of Climate Change in Nicaragua: Analysis of Precipitation and Temperature by Dynamical
Downscaling over a 30-Year Horizon. Atmospheric and Climate Sciences, 6, 445-474.
http://dx.doi.org/10.4236/acs.2016.63036
J. M. Solé et al.
served. Moreover, an increment between 5% and 10% of the number of days without precipita-
tion is expected. Finally, Intensity-Duration-Frequency (IDF) projected curves show an increment
of the rainfall intensity and an increment of extreme precipitation event frequency, especially in
the Caribbean basin.
Keywords
WRF, Climate Change, Global Warming, Dynamical Downscaling, Precipitation Projections, IDF
1. Introduction
Climate change is one of the main challenges of modern society. The impact of climate change is global and de-
localized of anthropogenic emissions that induce it and, for this reason, it is essential to achieve global agree-
ments to combat it. Global warming and climate change are mainly caused by emissions of greenhouse gases
and aerosols emitted by industry, mainly traffic and residential heating [1]. For years, nations around the world
have tried to achieve compromises to reduce these emissions and they have organized meetings and conventions
to reach binding agreements. And in the last annual conference of parties (COP), 195 countries adopted the first
universal climate agreement (United Nations Conference on Climate Change in Paris, COP’21,
http://www.cop21.gouv.fr/en).
Climate change affects meteorology and climate, modifying the Earth radiation balance, and consequently,
affecting meteorological variables as temperature and precipitation, weather patterns like El Niño, and altering
the natural frequency of extreme events as hurricanes and tropical cyclones. But their impacts cover all aspects
of the society, affecting transports, infrastructures, agriculture, economy, ecosystems, vegetation, land uses and
regional planning, migrations, etc.
Nicaragua is particularly vulnerable to the effects of Climate Change due to its location in the inter-tropical
convergence zone, being one of the economies most exposed to climate hazards. The highest risk area is the
northern Caribbean coast, gradually decreasing towards to south, and floods and landslides are recurrent in the
country. In the recent history, these impacts have produced direct and indirect economical losses. It was esti-
mated that annual economic losses due to extreme weather events (e.g. hurricanes, tropical storms, floods and
landslides, excluding droughts) between 1990 and 2012 were 1.89% of GDP [2]. For example, hurricane Félix
(2007) caused damage equivalent to 14.4% of GDP, while extreme and heavy precipitations that affected the
northwest of the country in 2011 and 2012 caused damages equal to 3% and 6.8% of GDP respectively accord-
ing to data from World Bank. Hurricane Mitch (1998) is considered to date as the most serious climate disaster
in the recent history of Central America, affecting 90% of the territory and causing 3800 fatalities in Nicaragua,
and numerous people are displaced. In contrast to this, a part of the territory of Nicaragua is also prone to severe
drought, especially along the “dry corridor” of Central America that affects 20% of the territory of Nicaragua.
For all these reasons, Nicaragua is a highly vulnerable region to its current climate, and climate change effects
can even increase the existent hazards or cause new ones. Trying to reduce the impact of these events, the Gov-
ernment of Nicaragua and other multilateral institutions as the Nordic Development Fund or the World Bank
have promoted different studies with the aim to increase the adaptability of the country to the effects of Climate
Change [3]-[6].
Climate research centers have addressed the climate change analysis by global modeling, projecting the future
climate under various scenarios of anthropogenic emissions. Due to computational limitations, the usual resolu-
tion of global climate models is very low and, therefore, only allows reproducing synoptic scale phenomena.
Consequently, and due to the geographical features of Nicaragua and its topographic complexity, the results of
the global climate models cannot be used for the evaluation of the local effects of climate change over Nicara-
gua. To address this problem it is necessary to apply techniques of downscaling that allow increasing the resolu-
tion of the results obtained through global climate models [7] [8]. There are two kinds of downscaling: statistical
and dynamical regionalization. The first one corresponds to a mathematical or statistical approach, based on the
definition of statistical patterns using historical weather information from measurement data [9]-[11], and dy-
namical downscaling which corresponds to a physical approach, based on the equations governing the behavior
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of the atmosphere [11]-[14]. Dynamical downscaling is a technique more expensive than statistical downscaling
from the point of view of computational cost.
The study we present here is innovative for two reasons: 1) it is the first time that the technique of dynamical
downscaling using a mesoscale meteorological model is applied over Nicaragua, and few studies of dynamical
downscaling have been realized in Central America; and 2) the study uses and configures, for the first time in
Nicaragua, a mesoscale meteorological model, increasing extensively the horizontal resolution used in the pre-
vious studies in the country, obtaining the local effect of climate change for all of Nicaragua in a 4 km horizon-
tal grid.
Description of the climate features of Nicaragua and their climatic regions is presented in section 2. In section
3 a complete description of the dynamical downscaling methodology defined and developed is shown. An anal-
ysis of the results obtained is presented in section 4 and, finally, conclusions are reported in section 5.
2. Geographic Information, Climate Features and Climate Regions of Nicaragua
Nicaragua is located in Central America in tropical latitudes between 10˚N and 15˚N. It borders both by Carib-
bean Sea and North Pacific in east and west boundaries respectively and by Costa Rica and Honduras in south
and north boundaries respectively. Nicaragua is divided into 17 departments and Managua is the capital city
(Figure 1).
Nicaragua extends an area about 130,000 km2 containing a wide range of climates and land uses. Nicaraguan
topography is complex consisting of a Central Highlands which divides the country into the Pacific and Carib-
bean lowlands. The Pacific lowland consists of a flat area with a volcanic chain and two freshwater lakes: Coci-
bolca and Xolotlán with an extension about 8200 and 1000 km2. Central Highlands comprise a triangular area
with ridges from 900 to 1800 m.a.s.l composed by “Cordillera Isabelia” y “Cordillera Amerrisque” (Figure 1).
The Caribbean lowland is sparsely settled and includes coastal plains and the lower area of Río San Juan.
Nicaragua. Main geographical features of the country are: Cocibolca and Xolotlán lakes (also called Nicara-
gua and Managua lakes respectively), connected by the Tipitapa River; Mogoton is the highest mountain in Ni-
caragua, reaching 2107 m.a.s.l, located in the Cordillera Isabelia in the northern portion of the central mountain
range, which runs from northwest to southeast through the center of the country; and main rivers are the San
Juan, Coco, Grande and Escondido.
Climate features in Nicaragua are driven by multiple factors which can act simultaneously and are related each
other. The proximity to the Pacific Ocean and the Caribbean Sea, its tropical location and its complex topography
(a) (b)
Figure 1. Geographical features and location of meteorological stations from INETER network with precipitation measurements
and temperature measurements (a). NP represents North Pacific; CP—Central Pacific; SP—South Pacific; NZ—North Zone;
CZ—Central Zone; NC—North Caribbean; SC—South Caribbean. And territorial division of Nicaragua (b).
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result in a complex meteorological system highly variable in time and space.
Due to its tropical latitudes, Nicaragua is within the Intertropical Convergence Zone (ITCZ) depending on the
season. Therefore, this region (in the Northern Hemisphere) is typically influenced by Northeast trade winds and
an important convection activity.
Due to the influence of the Caribbean Sea, Nicaragua is periodically affected by hurricanes and tropical cyc-
lones normally occurring from May to October. At the same time, Nicaragua is located under the influence of
the Pacific Ocean which configures a rainfall regime based on two seasons: a wet season from May to October
and a dry season from November to April. Due to its proximity to the Pacific Ocean, Nicaragua is under the ef-
fect of ENSO (El Niño-Southern Oscillation) which can alter the rainfall regime oceanic year-to-year. A posi-
tive-El Niño-(or a negative-La Niña) anomaly of Sea Surface Temperature in the Easter Equatorial Pacific
Ocean (defined as Niño 3.4 region) is normally correlated with a strong (or a weak) dry period in the Pacific Ba-
sin and, at the same time, is correlated with an increase(or decrease) of Caribbean hurricanes frequency in the
Caribbean Basin respectively.
Indeed, Nicaragua has a very complex topography with volcanic structures and abrupt mountain ranges which
divide the country in 7 climate regions which are defined by INETER (Nicaraguan Institute of Territorial Stu-
dies) and shown in Figure 1. The main singularities of each in terms of temperature and precipitation are de-
scribed below based on temperature and precipitation monthly-mean data of INETER meteorological network.
As can be seen in Figure 1, temperature and precipitation meteorological stations are not distributed spatially
uniformly, but also they are denser in the Pacific regions than in the Caribbean regions.
• North Pacific (NP): It is located on the northwestern sector with presence of several volcanic cones and low
topography complexity. Temperature annual profile is based on monthly mean temperatures above 26˚C
throughout the year with maximum temperatures in April reaching monthly means above 28˚C. Precipitation
is based on a two-season profile with a dry season from December to April and a wet season from May to
November. Within the wet season a double peak is observed with a first precipitation maximum in May-
June and a second one in October. Annual precipitation is normally between 1000 and 2000 mm.
• Central Pacific (CP): It is located on the central western sector with presence of several volcanic cones and
low topography complexity. Temperature annual profile and precipitation profile is very similar to North
Pacific profile, although the presence of Cocibolca and Xolotlán lakes implies a slight softening of precipi-
tation and temperature profiles in comparison with North Pacific. Annual precipitation is normally between
1000 and 2000 mm.
• South Pacific (SP): It is located on the southwestern sector with presence of several volcanic cones and low
topography complexity. Temperature annual profile and precipitation profile is very similar to North Pacific
and Central Pacific profile. The presence of Cocibolca and Xolotlán clearly affect to the precipitation profile
with a double-peak less noticeable in the wet period. Annual precipitation is normally between 1000 and
2000 mm.
• North Zone (NZ): It is located on the northern sector with presence of the highest and the most abrupt
mountain ranges with a high topography complexity. Temperature annual profile shows mean temperatures
above 22 degrees throughout the year with maximum temperatures in April-May. Precipitation profile
shows a two-season profile as shown in Pacific regions with a strong dry period from December to April and
a weak wet period from May to November. Annual precipitation is normally below 1000 mm.
• Central Zone (CZ): It is located on the southern sector with presence of the high mountain range like Amer-
risque. Temperature annual profile shows temperatures above 25 degrees throughout the year with maxi-
mum temperatures in April-May. Precipitation profile shows a two-season profile as shown in Pacific re-
gions with a strong dry period from December to April and a wet period from May to November. The
double-peak in the wet period is less noticeable due to the influence of lakes. Annual precipitation is nor-
mally between 1000 and 2000 mm.
• North Caribbean (NC) and South Caribbean (SC): It is located on the northeastern and southeastern sector
respectively. Its temperature annual profile shows soft temperatures above 24 degrees with maximum tem-
peratures in April-May. Precipitation is present throughout the year, although a substantial decrease occurs
between December and April. Annual precipitation is normally between 2000 and 4000 mm.
3. Methodology: Dynamical Downscaling
Dynamical downscaling for climate applications is defined as a technique based on coupling a global climate
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model (GCM) and a regional climate model (RCM) [15]. The regional model is running over a limited area and
is forced by initial and boundary conditions from the global climate model.
In order to implement this methodology we have followed different steps described below in a brief summary
and extended it in the next subsections.
1) Selection of the global climate model (GCM) reproducing the best possible the atmospheric conditions of
Nicaragua.
2) Analysis and selection of the most convenient Representative Concentration Pathway (RCP) defined as
different radiative forcing sin relation to different levels of global warming.
3) Selection and configuration of the regional climate model in order to reduce the uncertainty and increase
the accuracy as much as possible in Nicaragua.
4) Validation of the obtained regional climate model configuration through a historical climatic run of 30
years long forced by reanalysis.
5) Design of the regional climate simulations in terms of time segmentation and decision about the historical
and the projected time periods.
6) Execution of modeling regional climate runs with the regional model forced by the selected GCM in step
(1).
7) Climate change signal estimation by comparing historical and projected runs.
3.1. Global Climate Model
The selection of the most appropriated GCM has involved three different data platforms: Earth System Grid
Generation (ESGF) Peer-to-peer enterprise system, CERA WW3-Gateway and CISL Research Data Archive. In
these databases numerous and multiple GCM are available. We selected the most convenient according to the
following criteria: 1) GCM must be included in the 5th Coupled Model Intercomparison Project (CMIP5); 2) si-
multaneous availability of historical and projected GCM runs; 3) simultaneous availability of 6-hourly data of
temperature, geopotential height, specific humidity, wind velocity and wind direction. As a result, 13 GCM are
considered (Table 1).
The selected GCM runs have been validated with temperature and precipitation data from satellite and reana-
lysis. The validation has been performed in terms of spatial means on Nicaragua delimited by 10 - 15˚N/91 -
96˚W. We used the following databases:
• Delaware (satellite). To evaluate the temperature over land. Period evaluated: 1980-2009
(http://www.esrl.noaa.gov/psd/data/gridded/data.UDEL_AirT_Precip.html).
Table 1. GCM considered evaluating their accuracy over Nicaragua.
GCM Grid resolution Research center
Bcc-csm 2.81 Beijing Climate Center, China Meteorological Administration
CanCM4 2.81 Canadian Centre for Climate Modeling and Analysis
CCSM4 1.25 NCAR Community Climate System Model
CMCC-CM 0.75 Centro Euro-Mediterraneo per Cambiamenti Climatici
CNRM-CM5 1.41 Centre National de Recherches Meteorologiques/Centre Europeen de
Recherche et Formation Avancees en Calcul Scientifique
FGOALS-g2 2.81 Institute of Atmospheric Physics, Chinese Academy of Sciences
GFDL-CM2p1 1.25 Geophysical Fluid Dynamics Laboratory
IPSL-CM5A-LR 3.75 Institute Pierre-Simon Laplace
HadGEM2-ES 1.88 Met Office
MIROC5 1.41 University of Tokyo, National Institute for Environmental Studies, and
Japan Agency for Marine-Earth Science and Technology
MPI-ESM-LR 1.88 (45 vertical layers) Max Planck Institute for Meteorology
MPI-ESM-MR 1.88 (95 vertical layers) Max Planck Institute for Meteorology
MRI_CGCM3 1.13 Meteorological Research Institute-Japan
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J. M. Solé et al.
• GPCC (satellite). To evaluate precipitation over land. Period evaluated: 1980-2009
(ftp://ftp.dwd.de/pub/data/gpcc/html/fulldata_v6_doi_download.html).
We have evaluated the differences between modeled and observed values using root mean square error
(RMSE) and Pearson correlation coefficient (r) for: monthly average precipitation and monthly average temper-
ature at 2 meters (Table 2). Results show that MPI-ESM-MR is the GCM that reproduces in the most appro-
priated way the atmospheric conditions of Nicaragua in terms of precipitation and temperature. The obtained
results are consistent with those obtained by Hidalgo and Alfaro [16]. In this study, MPI-ESM-MR is identified
as one of the best playing conditions for Central America. Results are complemented with the monthly evolution
of temperature and monthly precipitation for every GCM and satellite data (Figure 2).
Radiative scenario
Global Climate models use different climate scenarios based on future projections of radiative forcing asso-
ciated to different levels of global warming. These scenarios are known as Representative Concentration Path-
ways (RCPs). The Intergovernmental Panel on Climate Change (IPCC) defines up to 4 RCPs scenarios, identi-
fied as RCP2.6, RCP4.5, RCP6.0 and RCP8.5, corresponding to the radiative forcings for the year 2100 of 2.6
W/m2 [17], 4.5 W/m2 [18], 6.0 W/m2 [19] [20] and 8.5 W/m2 [21], respectively. High radiative forcing implies
less mitigation measures and higher temperatures, whereas, a less radiative forcing implies more mitigation
measures and lower temperatures. Moreover the highest radiative forcing scenario (RCP8.0) corresponds to the
worst scenario from the point of view of emissions and, therefore, from the point of view of temperature, but not
from the point of view of precipitation, because the relationship is not direct for this variable.
It is impossible to define a scenario as more or less likely than the other, as this will depend on the current and
future evolution of greenhouse gases (GHG) emissions, as well as the evolution of many socioeconomic and
geopolitical variables. However, for the next thirty years, the application of one or another should not be a factor
involving drastic differences in the corresponding climate regional simulations, being 2.999 W/m2, 3.411 W/m2,
3.146 W/m2 and 3.993 W/m2 the radiative forcing projected in the year 2040 for the scenarios RCP2.6, RCP4.5,
RCP6.0 and RCP8.5, respectively. For this reason, and because mitigation measures of GHG emissions and
strategies and technologies for emissions reduction are already being implemented, the RCP4.5 has been the ra-
diative scenario selected to conduct the dynamical downscaling.
3.2. Regional Model
The regional and mesoscale meteorological model used for the study has been the Weather Research and
Table 2. Correlation coefficient (r) and RMSE calculated using monthly average temperature and accumulated
precipitation diagnosed by GCMs and information from satellite and/or reanalysis. Bold mark the optimal
values for global model and shaded in blue values corresponding to the MPI-ESM-MR model.
GCM Correlation coefficient (r) Root Square Mean Error (RMSE)
Temperature Precipitation Temperature (˚C) Precipitation (mm)
BCC-CSM 0.89 0.81 0.49 124
CANCM4 0.76 0.35 0.83 170
CCSM4 0.88 0.88 0.75 136
CMCC-CM 0.93 0.96 0.60 54
CNRM-CM5 0.95 0.98 2.55 52
FGOALS-g2 −0.34 −0.09 1.57 164
GFDL-CM2p1 0.58 0.79 1.14 133
HADCM3 0.89 0.91 0.60 119
IPSL-CM5A-LR 0.78 0.87 1.64 170
MIROC5 0.45 0.45 0.87 134
MPI-ESM-LR 0.97 0.98 0.37 45
MPI-ESM-MR 0.99 0.99 0.32 31
MRI-CGCM3 0.91 0.94 0.95 108
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J. M. Solé et al.
Figure 2. Monthly average temperature (top) and monthly average accumulated precipitation (bottom) diagnosed by the
different GCMs evaluated and satellite data.
Forecasting-Advanced Research (WRF-ARW) version 3.7 [22], developed by the National Center of Atmos-
pheric Research (NCAR). It is a universally used community mesoscale model and a state-of-the-art atmospher-
ic modeling system that is applicable for meteorological research, climate scenarios and numerical weather pre-
diction. WRF is a fully compressible and non-hydrostatic model with terrain-following hydrostatic pressure
coordinate. In the next sections, we show the WRF domain, the experiments realized to obtain the best configu-
ration of WRF for Nicaragua and the design of the climate simulations.
Simulation domains and resolution
In Figure 3, we show modeling domains used for simulations. The WRF model is built over a mother domain
(d01) with 108 km spatial resolution, centered at 12.15˚N, 86.27˚W, and with a domain size of 9396 × 6588 km2.
It comprises Central America, northern South America and southern North America. The first nested domain
(d02), with a spatial resolution of 36 km and with a domain size of 5112 × 4140 km2, covers the Atlantic and
Pacific region of Central America. The third domain (d03), with a spatial resolution of 12 km and with a domain
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J. M. Solé et al.
d01, d02, d03 and d04
d04
Figure 3. Modeling domains for simulations.
size of 1776 × 1416 km2, covers Nicaragua, Costa Rica, Panamá, Honduras, El Salvador, Guatemala and Jamai-
ca. And finally, the fourth domain (d04) covers fully Nicaragua with a spatial resolution of 4 km and with a do-
main size of 688 × 640 km2.
The number of vertical levels used is 30. These vertical layers cover the whole troposphere with a resolution
decreasing slowly with height in order to capture low-level flow details.
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J. M. Solé et al.
Sensitive analysis and calibration
Different options that WRF offers can be combined in many different ways. WRF has different parameteriza-
tions for microphysics, radiation (long and short wave), cumulus, surface layer, planetary boundary layer (PBL)
and land surface as physical options. To obtain WRF highest accuracy, it is essential to carry out a sensitive
analysis of these different options by numerical experiments. In the same way, the definition of the simulation
domains, spin up, vertical resolution or nesting architecture determine the accuracy, and therefore uncertainty, of
WRF results [23] [24]. In this sense, and with the aim to obtain the best WRF configuration for climate applications
in Nicaragua, numerical experiments corresponding to different physical and dynamical options have been tested.
There are multiple and numerous combinations of WRF options, being not feasible to analyze all of them. For
this reason, and as the goal is to analyze the variables precipitation and temperature, we focus our attention on
the study of cumulus, planetary boundary layer parameterization and surface layer scheme options. And we have
fixed the rest of options following the NCAR recommendations for regional climate runs. In this way we have
used WSM6 [25], Noah LSM [26] and CAM [27] as microphysical, land surface scheme and radiation options
respectively.
A total of 17 experiments have been evaluated progressively, as Table 3 shows. Seven of them by varying
Table 3. Numerical experiments developed and corresponding physics and dynamical options.
Experiment PBL Surface Layer Cumulus Knievel Diffusion Rayleigh
Relaxation
INI YSU [30] MM5 similarity Kain-Fritsh [31] No No
CUM1 YSU MM5 similarity Multi-scale KF [32] No No
CUM2 YSU MM5 similarity Grell 3D [33] No No
CUM3 YSU MM5 similarity New SAS [34] No No
CUM4 YSU MM5 similarity Tiedtke [35] No No
CUM5 MYJ [36]a Eta similaritya Zhang-McFarlane [37] No No
CUM6 YSU MM5 similarity
Tiedtke with CUM
option disabled in d03 No No
CUM7 MYJa Eta similaritya
Zhang-McFarlane with
CUM option disabled
in d03
No No
PBL1 MYJa Eta similaritya Best cumulus
configuration selected No No
PBL2 QNSE [38] QNSE
Best cumulus
configuration selected No No
PBL3 ACM2 [39] MM5 similarity Best cumulus
configuration selected No No
PBL4 MYNN3 [40] MYNN Best cumulus
configuration selected No No
PBL5 UW [41] MM5 similarity
Best cumulus
configuration selected No No
PBL6 GBM [42] MM5 similarity
Best cumulus
configuration selected No No
PBL7 Shing-Hong [43] MM5 similarity Best cumulus
configuration selected No No
DIN1
Best PBL
configuration
selected
Surface Layer selected
associated to the best PBL
configuration
Best cumulus
configuration selected Yes No
DIN2
Best PBL
configuration
selected
Surface Layer selected
associated to the best PBL
configuration
Best cumulus
configuration selected No Yes
Best
Configuration
Best PBL
configuration
selected
Surface Layer selected
associated to the best PBL
configuration
Best cumulus
configuration selected
Default diffusion
option or Knievel
option
Rayleigh
relaxation
term or not
Rayleigh
aPBL and surface layer modified due to model restrictions.
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cumulus parameterizations, seven experiments by varying PBL and surface layer schemes (at the same time due
to model restrictions) and two of them using different dynamical options. The first numerical experiment cor-
responds to the default WRF options corrected by microphysical and radiation options recommended for region-
al climate (defined as INI experiment). Secondly, cumulus experiments (CUM) are analyzed. Cumulus parame-
terization is used to predict the collective effects of convective clouds at smaller scales as a function of larg-
er-scale processes and conditions. Once cumulus option is selected, experiments of PBL and surface layer have
been carried out (PBL experiments). PBL and surface layer schemes define boundary layer fluxes (heat, mois-
ture, momentum) and the vertical diffusion process. Finally, Knievel diffusion [28] and Rayleigh relaxation as
dynamical options are evaluated (DIN experiments). The rest of dynamical options in WRF are fixed to default
options. For this sensitivity analysis the initial and boundary conditions for domain d01 are updated every six
hours using the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis
[29].
To calibrate the WRF model we have run the model during the period comprised between May 2005 and
April 2006. This period has been considered representative of the climate average for all the country compar-
ing observed values provided by different databases (GPCP: Global Precipitation Climatology Project; TRMM:
Tropical Rainfall Measuring Mission; NOAA/ESRL/PSD: Earth System Research Laboratory—Physical
Sciences Division ) for a 30-years period and for the period selected. In Figure 4 and Figure 5 we show a com-
parison of selected variables during the 30-year period and the evaluation period for calibrating the mesoscale
model.
Figure 4. Monthly average temperature for Nicaragua from GPCP and TRMM considering the period 1980-2009 (GPCP)
and the period 1998-2014 (TRMM), and for the chosen representative period (May 2005-April 2006).
Figure 5. Monthly average temperature for Nicaragua from the NOAA/ESRL/PSD considering the entire period (1980-2009)
and for the chosen representative period (May 2005-April 2006).
0
100
200
300
400
May
June
July
August
September
Octo ber
Nove mber
December
January
February
March
April
Precipit ation (mm)
Monthly Average Precipitation
May 2005 -April 2006 GPCP
May 2005 -April 2006 TRMM
Average TRMM
Average GPCP
23
24
25
26
27
28
May
June
July
August
September
Octo ber
Nove mber
December
January
February
March
April
Temperature (°C)
Monthly Average Temperature
Average NOAA/ESRL/PSD
May 2005 -April 2006 NOAA/ESRL/PSD
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We have evaluated the differences between modeled and measured values using mean bias (MB), root mean
square error (RMSE) and Pearson correlation coefficient (r) for the next variables: monthly average precipitation,
monthly average temperature, and monthly maximum and minimum temperature. Measured values of precipita-
tion and temperature have been provided by the Nicaraguan Institute of Territorial Studies (INETER) and it has
been considered 148 meteorological stations with precipitation data and 45 with temperature data. Results ob-
tained from the validation of WRF numerical experiment are showed in Table 4. In this case, correlation coeffi-
cient is much more restrictive than the same used for the global model. Regional model validation uses the in-
formation provided by all monitoring stations, whilst global model uses satellite info treated as only one source
of information for the comparison, being Nicaragua treated as only one point for the analysis.
Results show that more influential experiments are PBL experiments and cumulus experiments for tempera-
ture and precipitation respectively. In the case of temperature, WRF underestimate temperature for all the expe-
riments realized. The use of one or other experiment yields a RMSE between 2.0˚C and 2.7˚C with a high corre-
lation coefficient (0.89 - 0.92). Experiments DIN1 and DIN2 minimize the error of this variable. Parallel results
have been obtained when the evaluated variable is the monthly maximum or minimum temperature. In the case
of precipitation, we can see that the use of one or other combination of parameterization, modifies drastically the
accuracy of the results. WRF model working with default options of cumulus (Kain-Fritsch scheme) and PBL
(YSU scheme) present a RMSE of 223 mm, whereas the optimum combination working with Tiedtke and
ACM2 as cumulus and PBL schemes, reduces up to a 48% the uncertainty of the model, also considering
changes in the dynamical options in comparison with initial experiment. If we analyze the reduction of the un-
certainty for intra-annual periods, we observe that reduction is more important for the wet period (54%) than for
the dry period (32%). Patterns observed for precipitation and temperature are reproduced in most of the weather
stations used.
Modeling approach and design of the simulations
There are two approaches to run climate simulations of a long time period using mesoscale or regional models
Table 4. Statistical evaluation of the different numerical experiments carried out for the
monthly average precipitation and monthly average temperature. In bold the optimum value
for each statistical.a
Experiment Monthly average precipitation Monthly average temperature
MB (mm) RMSE (mm) r MB (˚C) RMSE (˚C) r
INI 112 223 0.74 −2.0 2.2 0.90
CUM1 35 133 0.73 −2.1 2.3 0.90
CUM2 63 168 0.77 −2.3 2.5 0.91
CUM3 6 133 0.65 −2.0 2.3 0.89
CUM4 7 116 0.73 −2.0 2.2 0.91
CUM5 143 231 0.74 −2.3 2.5 0.90
CUM6 6 96 0.79 −2.6 2.7 0.91
CUM7 56 143 0.74 −2.1 2.3 0.90
PBL1 7 130 0.69 −1.7 2.0 0.91
PBL2 12 118 0.73 −2.1 2.3 0.91
PBL3 −5 110 0.74 −1.7 2.0 0.91
PBL4 6 121 0.71 −2.3 2.5 0.90
PBL5 3 117 0.72 −1.9 2.2 0.91
PBL6 −4 114 0.73 −1.8 2.1 0.92
PBL7 3 115 0.73 −2.0 2.2 0.91
DIN1 −9 111 0.75 −1.7 2.0 0.91
DIN2 8 107 0.75 −1.7 2.0 0.91
aThe uncertainty showed corresponds to the sum of the uncertainty due to the model and due to the observations.
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[44]-[46]. The first of one is based on running only one simulation along the time of interest. Otherwise, the
same long period could be splitted into subperiods. In this last case, boundary and initial conditions must be rei-
nitialized every time. Different authors have evaluated the differences between one or other approaches, con-
cluding that, in general, that segmented or reinitialized simulations offer better results [47]-[50]. For these rea-
son we have considered reinitialized simulations to conduct the climate simulations of these study.
Our study is focused in obtaining the signal of climate change for the next 30 years after 2010. For this reason,
the long time period of interest has been the period comprised between 2010 and 2040 (defined as projected pe-
riod), and the historical period of reference has been the period comprised between 1980 and 2010 (defined as
historical period). Usually, thirty years is the minimum time considered as representative for climate applica-
tions. Every simulated year has been splitted into 365 reinitialized simulations with a time length of 36 hours
each, discarding the first 12 hours considered as spin-up time. In Table 5 we show a brief summary of the main
characteristics of all WRF simulations realized.
Previously to conduct climate simulations over the projected period, we have evaluated the accuracy of the
methodology using measurements during the historical period. The methodology of evaluation is the same as
that shown in the section of sensitive analysis and calibration. Results of validation are showed in Table 6. In
the case of temperature we can observe that the model tends to underestimate the values and offer a RMSE
around 2˚C. For climatic regions, the best results are reproduced in North Pacific and North Caribbean. Correla-
tion coefficient is very high, 0.91 for the full period, being higher during the dry period (0.93). On the other
hand, WRF modeling tends to lightly underestimate precipitation during the dry period and lightly overestimate
during the wet period. RMSE is 113 mm for the full period, being higher during the wet period (151 mm) and
lower during the dry period (53 mm). For climatic regions, the best results are reproduced in North Caribbean
and South Caribbean. Correlation coefficient is high, 0.72, so the model acceptably reproduces the evolution of
this variable.
Once WRF has been evaluated using CFSR as initial and boundary conditions, the same procedure has been
followed using MPI-ESM-MR. CFSR represents the best state of the atmosphere because it incorporates satellite
information, metars, radio soundings, weather information from stations managed by weather national services,
etc. And therefore, results obtained using MPI-ESM-MR as climate global model, are worst than those obtained
using CFSR. Nevertheless, errors obtained with MPI-ESM-MR are not very different than those obtained work-
ing with CFSR, and common patterns can be found. In Figure 6 we show the differences between RMSE calcu-
lated using CFSR-WRF simulations versus MPI-ESM-MR-WRF simulations. Results show that the geographi-
cal distribution of the uncertainty is similar and results obtained using MPI-ESM-MR-WRF are reasonable in
comparison with CFSR-WRF.
4. Results and Discussion
Dynamical downscaling methodology generates a high amount of results. To do a comprehensive analysis is
necessary to prepare a complete statistical treatment of the obtained outputs. The different climatic statistics
calculated could be classified depending on the treatment of the time variable. We can distinguish between in-
tra-annual, inter-annual and multiannual statistics. The analyzed variables are the following: temperature (daily
Table 5. Main characteristics of WRF simulations used for the climate study.
Initial and
boundary
conditions Period WRF characteristics Goal
CFSR
(reanalysis)
May 2005-April
2006 3 Nested domains (CFSR): 36-12-4 km
4 Nested domains (MPI): 108-36-12-4 km
Maximum horizontal resolution: 4km
Vertical layers: 30
Nesting: one-way
Reinitialized simulations
Length time: 36 hours
Spin-up: 12 hours
Time-Step: 200 minutes (CFSR) and 600
minutes (MPI)
To calibrate the WRF model
1980-2009 To validate the WRF model during a climate
period representative and to check well-accuracy
of WRF simulations forced by the GCM selected
MPI-ESM-MR
(GCM)
RCP 4.5
1980-2009 To obtain climate values representatives of a
historical period
2010-2040 To
obtain climate future projections of temperature
and precipitation
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Table 6. Statistical evaluation of the historical period simulated for the monthly average precipitation and monthly average
temperature.Results are expressed for the full period (complet period between 1980 and 2009), wet period (only those
months comprised between May and October and corresponding to the years 1980-2009), and dry period (only those months
comprised between November and April and corresponding to the years 1980-2009). Results are shown for all Nicaragua and
for each climatic region.
Period Monthly average precipitation Monthly average temperature
MB (mm) RMSE (mm) r MB (˚C) RMSE (˚C) r
All Nicaragua
Full period 1 113 0.72 −1.8 2.0 0.91
Wet period 13 151 0.53 −1.6 1.8 0.90
Dry period −11 53 0.66 −2.0 2.3 0.93
North Pacific
Full period −38 119 0.78 −1.3 1.6 0.69
Wet period −66 164 0.61 −1.1 1.4 0.65
Dry period −10 35 0.70 −1.4 1.7 0.72
Central Pacific
Full period −14 99 0.74 −1.6 1.9 0.87
Wet period −19 135 0.55 −1.4 1.7 0.85
Dry period −8 36 0.63 −1.8 2.1 0.91
South Pacific
Full period −6 120 0.67 −1.7 1.9 0.68
Wet period 1 161 0.35 −1.5 2.7 0.63
Dry period −13 56 0.59 −1.8 2.0 0.72
North Zone
Full period 10 99 0.73 −2.2 2.4 0.88
Wet period 32 133 0.55 −1.9 2.1 0.87
Dry period −12 42 0.57 −2.5 2.7 0.90
Central Zone
Full period 21 126 0.72 −1.7 1.9 0.90
Wet period 50 167 0.46 −1.5 1.7 0.87
Dry period −8 61 0.65 −1.9 2.0 0.92
North Caribbean
Full period −9 156 0.65 −1.3 1.6 0.79
Wet period 11 198 0.32 −1.0 1.3 0.79
Dry period −29 95 0.62 −1.6 1.8 0.71
South Caribbean
Full period −7 149 0.75 −1.9 2.1 0.71
Wet period 8 177 0.59 −1.7 1.9 0.59
Dry period −21 114 0.64 −2.0 2.2 0.71
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Figure 6. Graphical representation of the RMSE associated to the variables montlhly average temperature and monthly
average precipitation for the WRF simulations forced by CFSR and MPI-ESM-MR.
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average temperature, monthly average temperature, monthly maximum temperature, daily maximum tempera-
ture, number of days with temperature exceeding 35˚C), precipitation (monthly, annual, rainfall intensity and
number of days with and without precipitation). Numeric temperature threshold value (35˚C) has been selected
calculating the 99th percentile for the warmer region of Nicaragua (North Pacific).
In order to analyze the extreme climatology associated to the rainfall intensity, we have used the IDF curves
[51]. For each variable we have generated historical and projected maps, anomalies analysis (defined as the dif-
ference between a result over the projected period and the historical period), probability density representation,
year-per-year evolution, and monthly evolutions. We have considered the climate change signal over the dry and
wet known period and we have obtained conclusions for every climatic region and every department in Nicara-
gua.
IDF curves have been generated for return periods of 1.5, 2, 5, 10, 15, 25, 50, 100 and 500 years and durations
of 10 and 30 minutes, 1, 2, and 3 hours, 1 and 5 days. To adjust IDF curves we have followed the next proce-
dure:
1) For each duration, we have calculated maximum values analyzing the database composed by 30 maximum
annual values for the historical period and for the projected period.
2) For each duration, we have adjusted a generalized extreme values (GEV) function [52]. This function cor-
responds to a Frechet, Gumbel or Weibull function depending on the value of a parameter included in the GEV
function. We have applied an L-Moments method [53] in order to obtain the optimized parameters of the GEV
function.
3) Finally, return period values are inferred from GEV distributions.
Only the most relevant results will be presented in this paper. In the next sections we show results analyzed
for the projection of temperature, accumulated precipitation and IDF curves.
4.1. Projection of Temperature
The projection of the temperature shows that there is a clear trend of increasing temperatures, both maximum
and daily average for every climatic region. From the analysis of probability density representation, a genera-
lized increase in temperature for all months of the year is observed, resulting in a shift of the mean values of
temperatures of the distribution and, at the same time, a decrease in the amplitude of the temperature range, es-
pecially for the Pacific regions during the months of July and August. During the warmest months we obtain the
highest increases.
In Figure 7 we show a probability density representation for the maximum daily temperature corresponding
to every month over the North region (area where the most remarkable increments take place). The same trend
can be obtained by analyzing annual profiles of monthly temperatures. In Figure 8 we show this monthly profile
for the North zone and the North Pacific region. The increase is generalized for all climate regions of Nicaragua,
increasing the 30-year average from 24.7˚C to 25.9˚C in the North Pacific; from 24.6˚C to 25.4˚C in the Central
Pacific; from 25.3˚C to 25.9˚C in the South Pacific; from 21.0˚C to 21.8˚C in the North region; from 23.2˚C to
24.0˚C in the Central region; from 23.6˚C to 24.4˚C in the North Caribbean; and from 23.4˚C to 24.1˚C in the
South Caribbean. In Figure 8 we can observe that the shift between the historical and the projected profile is not
within the inter-annual variability (represented by the standard deviation), and therefore, this is a clear evidence
of climate change.
The analysis of the projection of the temperature is completed by comparing the geographical distribution of
average temperature and daily maximum temperature between historical and projected periods. This comparison
concludes that a temperature increase between 0.6˚C and 0.8˚C for the whole country is projected, being more
significant for the daily average temperature than the daily maximum temperature. In Figure 9 we show the map
of the anomaly (or the difference between projected and historical period) for the average temperature and daily
maximum temperature. We observe higher anomalies for the average temperature in the northern area, whereas
for the daily maximum temperature the highest anomalies are located in the north-eastern North Pacific, in the
northern North Pacific and isolated locations in the Northern Zone.
We observe an increment of the number of days per year with temperatures exceeding 35˚C (Figure 10). We
can infer that there is an increasing trend in the number of days, as well as the extension of the territory covered
by these exceedances. If currently higher values of 35˚C are measured in the Departments of Chinandega and
León, future projections show that these exceedances will be recorded in these Departments and in the Depart-
ment of Managua or at points in the North Atlantic Autonomous region.
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Maxim um dail y temper ature
Figure 7. Probability density representation corresponding to the maximum daily temperature in the historical and projected
period for the climatic region of North Zone.
Figure 8. Annual profile corresponding to the monthly average temperature in the historical and projected period for the
climatic region of North Zone and North Pacific. SD corresponds to the standard deviation.
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Figure 9. Map of the difference of the average temperature and daily maximum temperature between projected and historical
period.
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Figure 10. Maps of the number of days which temperatures exceed 35˚C: historical period and projected period.
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4.2. Projection of Accumulated Precipitation
The comparison between probability density representations show that the pattern of intra-annual variability is
maintained in the future projection, with a slight increase in the number of days with lower rainfall during the
months of July, August and September over the Pacific climate regions. In Figure 11 we show the representa-
tion corresponding to the North Pacific. Similar behavior is observed during the months of July and August in
the South Caribbean. Whilst in the North Caribbean a significant decrease in the number of days with lower pre-
cipitation during the months of July and August and an increase in the number of days with higher precipitation
is observed. An increased number of days with higher rainfall in the Pacific region are also observed during Oc-
tober.
From the differences between the monthly evolutions of accumulated precipitation we infer a significant in-
crease during the months of June and October and a slight decrease in the months of July, August and Septem-
ber over the Pacific regions. The increases are significant; the largest increases projected will be reproduced in
June (177 to 255 mm in the North Pacific; 170 to 234 mm in the Central Pacific, 201 to 236 mm in the South
Pacific). The behavior observed for the Pacific corresponds to an intensification of the monthly accumulated
precipitation during the beginning and at the end of the wet period, and a trend towards weakening during the
inner months of the wet period. However, increases and decreases observed are within the inter-annual variabil-
ity, characterized by the standard deviation. In Figure 12 we show the annual profile calculated for the North
Pacific. For the other regions, the differences between the intra-annual patterns of accumulated precipitation are
much lower, showing a slight increase in June and a slight reduction in September in the North region; a slight
increase in September and slight reductions in February and October for the North Caribbean region; and a
slight decrease in February in the South Caribbean. However, annual values obtained for the historical and pro-
jected period are very similar and not significant differences are obtained.
Analyzing the anomaly associated to the number of days per year without precipitation, we conclude that an
increasing trend in this parameter is observed for all climatic regions. This tendency becomes more significant in
the Caribbean regions and in the North region, with increases up to 9% - 10%. In other climatic regions this in-
crease is lower (5% - 6%). In the same way, the number of wet days (days with precipitation higher than 0 mm)
is reduced, both during the dry period as well as the wet period. This negative anomaly is higher during the dry
period, and greater intensity in the Departments of Chinandega, Granada, León, Managua and Masaya (Figure
13).
Finally, we show the map of the anomaly for the accumulated precipitation during the wet and dry periods
(Figure 14). Results indicate an overall negative anomaly of this variable for whole Nicaragua during the dry
period. This anomaly is more significant in the Department of Estelí and in the border between the Departments
of Carazo and Rivas. In the case of the wet period, results indicate a dominant slight negative anomaly in the
Figure 11. Probability density representation corresponding to the daily precipitation in the historical and projected period
for the climatic region of North Pacific.
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Caribbean regions, and the Departments of Chontales and Río San Juan. However there is a slight tendency to-
ward positive anomaly in the Departments of Chinandega, Estelí, León and Managua.
4.3. Projection of IDF Curves
Finally, IDF curves for the historical and projected periods are compared. In the North Pacific region (Figure 15)
Figure 12. Annual profile corresponding to the monthly accumulated precipitation in the historical and projected period for
the climatic region of North Pacific.
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Figure 13. Map of the difference of the number of wet days (defined as those with precipitation > 0 mm) between projected
and historical period corresponding to the dry period and wet period.
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Figure 14. Map of the difference of the accumulated precipitation between projected and historical period corresponding to
the dry period and wet period.
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Figure 15. IDF curves corresponding to North Pacific and North Caribbean.
we observe a very slight increase of the intensity of precipitation for 10-minutes duration, from 128 mm/h in the
historical period to 132 mm/h in the projected period for a return period of 10 years; from 148 to 154 mm/h for a
return period of 25 years; and from 162 to 169 mm/h for the return period of 50 years. Increases are similar for
durations of 30 minutes and 1 hour. Similar pattern is observed in the South Pacific and no significant changes
have been observed in the Central Pacific. On the other hand, in the Caribbean regions the changes become
more remarkable, with increases for all durations (Figure 15). The singularity in this case is that in the projected
period the curve associated to the return period of 25 years practically coincides with the curve associated to the
return period of 50 years, which means that the same intensities will become more frequent in the projected pe-
riod for this climatic region. For the same rainfall intensity, the return period, defined as the average time be-
tween two consecutive phenomena of the same intensity, is reduced from 50 years to 25 years. In the case of the
inner regions of Nicaragua, North and Central region, we observe very slight increases and reductions, respec-
tively, of the rainfall intensity for each return period and duration.
Similar results can be obtained from the comparison of rainfall intensity maps (Figure 16). There is a clear
upward trend in the rainfall intensity in the projection period, being more significant in the Departments of Ca-
razo, Chinandega, León and Managua for low durations (10 minutes and 1 hour). Finally, in the case of the du-
ration of 5 days we also observe a significant increase in the rainfall intensity, being more significant in all De-
partments of the Pacific coast.
5. Conclusions
In this paper climate change projections over Nicaragua have been obtained and analyzed. This work has fo-
cused on the achievement of future climate projections of temperature and precipitation over Nicaragua with a
thirty years horizon. The aim to do these projections is to prepare and adapt the infrastructures of the country to
the effects of climate change, thereby increasing resilience to this phenomenon.
To do this, we have designed a dynamical downscaling methodology based on the coupling of global and re-
gional models. We have followed steps that have enabled us to minimize the uncertainty of the method and to
customize the methodology to Nicaraguan features, being the first time that a mesoscale model is used in the
country. By comparison with satellite data information we have concluded that MPI-ESM-MR is the global cli-
mate model that best reproduces the atmospheric conditions of Nicaragua, obtaining high correlation coeffi-
cients for monthly average temperature and monthly precipitation using satellite data. In the same way, the re-
sulting root mean square error is 0.32˚C and 31 mm for monthly average temperature and accumulated precipita-
tion respectively. RCP4.5 has been chosen as the most convenient radiative scenario, being the scenario that
agrees with the current world climate agreements. Anyway, the selection of one or another scenario is not relevant
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(a)
(b)
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(c)
(d)
Figure 16. Maps of rainfall intensity corresponding to a return period of 10 years and temporal lenght of 10 minutes ( (a) and
(b) refers to historical and projected period respectively) and corresponding to a return period of 50 years and temporal
length of 5 days ( (c) and (d) refers to historical and projected period respectively).
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because their 8-radiative forcing is very similar for the next 30 years.
On the other hand, WRF-ARW mesoscale model has been configured, tested and evaluated over Nicaragua.
Using a representative period, we have selected the optimum WRF configuration, and we have been able to re-
duce monthly average precipitation model uncertainty up to 52%, in comparison with the results obtained using
a configuration based on default options. We conclude that cumulus and planetary boundary layer schemes are
the most influencing parameterizations in terms of temperature and precipitation accuracy. Tiedtke, as cumulus
scheme, and ACM2, as PBL scheme, have been found to be the ones that minimize the model uncertainty of
temperature and precipitation in Nicaragua. Working with this optimum configuration and comparing model
output and observed values during a 30-year period, 1980-2009, we have obtained correlation coefficients of
0.72 and 0.91, and root mean square errors of 2.0˚C and 113 mm, for monthly average precipitation and monthly
average temperature respectively, using information from 148 meteorological stations.
To analyze the climate change signal we have compared mesoscale simulations with a 4 km horizontal resolu-
tion covering all Nicaragua for a historical period, 1980-2009, and for a projected period, 2010-2040. Results
indicate that an increase in temperature between 0.6˚C and 0.8˚C is expected. This increase will affect all the
country, with special relevance in the north. Higher temperatures will affect the number of days with tempera-
tures higher than 35˚C. Results show an increment of this parameter, more important in the north and during the
wet period (May to October).
Regarding the precipitation, we observe changes in the monthly precipitation patterns in the regions of the
Pacific, increasing the value of the accumulated precipitation at the beginning and at the end of the wet period,
and reducing the same value at the inner months. However, the annual precipitation is similar, without signifi-
cant changes, and within year-to-year variability. Furthermore, an increment of the number of dry days (defined
as those without precipitation-0 mm) between 5% and 10% is expected, being more significant during the dry
period (November to April). And from the analysis of the IDF curves, we conclude that an increment of the
rainfall intensity is expected. We have observed higher 10-minute, 30-minute and hourly rainfall (between 3 and
10%) for the return periods of 10, 25 and 50 years. In the Caribbean regions, the IDF curves for 25 years in the
projected period are very similar to the same curve for 50 years in the historical period. This means that the
same rainfall intensity will be more frequent in the future.
Acknowledgements
This work has been developed within the framework of the contract ES-007-2015 funded by the Nordic Devel-
opment Fund and partly funded by the Spanish Government through PTQ-12-05244. Authors are especially
grateful to Dr. José Antonio Milán and Eng. Marcio Baca from the Nicaraguan Institute of Territorial Studies for
their valuable cooperation and support and for providing meteorological measurements. The authors gratefully
acknowledge the technicians at the Ministry of Transport and Infrastructures of the Government of Nicaragua,
IDOM consultants, the Nordic Development Fund and Mr. Eduardo Acuña for their collaboration and support.
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