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Hydrological climate change projections forCentral America

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Hydrological climate change projections for Central America

Hugo G. Hidalgo a,b, , Jorge A. Amador a,b, Eric J. Alfaro a,b,c, Beatriz Quesada d,b

a Escuela de Física, University of Costa Rica, 2060-Ciudad Universitaria Rodrigo Facio San Pedro, San José, Costa Ricab Center for Geophysical Research, University of Costa Rica, 2060-Ciudad Universitaria Rodrigo Facio San Pedro, San José, Costa Ricac Center for Research in Marine Sciences and Limnology, University of Costa Rica, 2060-Ciudad Universitaria Rodrigo Facio San Pedro, San José, Costa Ricad Department of Earth Sciences, Uppsala University, Villav. 16, 752 36 Uppsala, Sweden

a r t i c l e i n f o

Article history:Received 9 November 2012Received in revised form 27 February 2013Accepted 4 May 2013Available online 13 May 2013This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief

Keywords:HydrometeorologySurface water hydrologyVariable Inltration Capacity ModelIntertropical Convergence Zone (ITCZ)Mid-Summer Drought (MSD)Caribbean Low-Level Jet (CLLJ)

s u m m a r y

Runoff climate change projections for the 21st century were calculated from a suite of 30 General Circu-lation Model (GCM) simulations for the A1B emission scenario in a 0.5 0.5 grid over Central America.The GCM data were downscaled using a version of the Bias Correction and Spatial Downscaling (BCSD)method and then used in the Variable Inltration Capacity (VIC) macroscale hydrological model. TheVIC model showed calibration skill in Honduras, Nicaragua, Costa Rica and Panama, but the results forsome of the northern countries (Guatemala, El Salvador and Belize) and for the Caribbean coast of CentralAmerica was not satisfactory. Bias correction showed to remove effectively the biases in the GCMs.Results of the projected climate in the 2050–2099 period showed median signicant reductions in pre-cipitation (as much as 5–10%) and runoff (as much as 10–30%) in northern Central America. Thereforein this sub-region the prevalence of severe drought may increase signicantly in the future under thisemissions scenario. Northern Central America could warm as much as 3 C during 2050–2099 and south-ern Central America could reach increases as much as 4 C during the same period. The projected dry pat-tern over Central America is consistent with a southward displacement of the Intertropical ConvergenceZone (ITCZ). In addition, downscaling of the NCEP/NCARReanalysis data from 1948 to 2012 and posteriorrun in VIC, for two locations in the northern and southern sub-regions of Central America, suggested that

the annual runoff has been decreasing since ca. 1980, which is consistent with the sign of the runoff changes of the GCM projections. However, the Reanalysis 1980–2012 drying trends are generally muchstronger than the corresponding GCM trends. Amongthe possible reasons for that discrepancy are modeldeciencies, amplicationof the trendsdue to constructive interference with natural modesof variabilityin the Reanalysis data, errors in the Reanalysis (modeled) precipitation data, and that the drying signal ismore pronounced than predicted by the emissions scenario used. A few studies show that extrapolationsof future climate from paleoclimatic indicators project a wetter climate in northern Central America,which is inconsistent with the modeling results presented here. However, these types of extrapolationsshould be done with caution, as the future climate responds to an extra forcing mechanism (anthropo-genic) that was not present prehistorically and therefore the response could also be different than inthe past.

2013 Elsevier B.V. All rights reserved.

1. Introduction

In Central America ( Fig. 1), droughts and oods are aconsequence of the high spatial and temporal variability of climate(Westerberg, 2011 ). Such climate variability is the result of variousnaturally occurring phenomena at different spatial and temporalscales (Amador et al., 2006; Waylen et al., 1996 ), and the

interaction between thepredominant ow andthecomplex terrainof the region ( Amador et al., 2010 ). The start and end date of therainy season and the Mid-Summer Drought (MSD; Magaña et al.,1999 ) for example, are subject to interannual variability associatedwith the temperature anomalies of the east-equatorial Pacic andthe Tropical North Atlantic (Eneld and Alfaro, 1999 ). The climatevariability at seasonal and interannual scales, in particular thoseassociated with El Niño-Southern Oscillation (ENSO), has signi-cant socioeconomic impacts on the countries of Central America(IPCC, 1997; Waylen et al., 1996 ). In addition, climate changecouldsignicantly affect the hydrological cycle, altering the intensity anddistribution of precipitation, runoff and recharge, producing di-verse impacts in different natural ecosystems and human activities(IPCC, 1997). Just in the last 5 years or so, the economies of the

0022-1694/$ - see front matter 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.jhydrol.2013.05.004

Corresponding author at: Escuela de Física, University of Costa Rica, 2060-Ciudad Universitaria Rodrigo Facio San Pedro, San José, Costa Rica. Tel.: +506 25 1150 96.

E-mail addresses: hugo.hidalgo@ucr.ac.cr (H.G. Hidalgo), jorge.amador@ucr.ac.cr(J.A. Amador), erick.alfaro@ucr.ac.cr (E.J. Alfaro), beatriz.quesada@geo.uu.se(B. Quesada).

Journal of Hydrology 495 (2013) 94–112

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Journal of Hydrology

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Central America countries have suffered losses by several hundredmillion United States (US) dollars due to hydrometeorologicalevents. In fact, the Intergovernmental Panel on Climate Change(IPCC) determined that Mesoamerica (central Mexico, Guatemala,

Belize, Honduras and El Salvador along with the Pacic coast of Nicaragua and northern Costa Rica) is a region of the world thatcould increase its vulnerability to extreme climatic events in thefuture ( IPCC, 2007a, 2012). Moreover, Central America has beenidentied as a climate change ‘‘hot-spot’’ ( Giorgi, 2006). Hydro-power generation, cereal production and cattle ranching are justsome examples of activities that are especially vulnerable tochanges in the future availability of water, in particular in CostaRica and Panama ( IPCC, 2007a). Also vulnerable to such changesare the wetlands, which cover a signicant part of the CentralAmerican territory and are known to help in mitigating some of the effects of climate change. Their destruction could have severerepercussions on both the environment and society ( Rojas et al.,2003 ).

It is necessary to know which aspects of global climate changewould affect more directly the hydrology and the water resources

of the Central American region. In order to produce reliable projec-tions of future climate it is necessary to depend on climatic andhydrological models, and downscaling techniques to obtain the re-quired temporal and spatial coverage demanded by regional stud-ies of (hydro) climatic change. These models are also very valuablein studies of climate change impacts in diverse sectors (e.g. ecosys-tems, agriculture, power generation, economy, public health).

The main objective of this article was to determine the impactof climate change in the regional hydrology of Central America. Asecondary goal was to show the uncertainty in several models inthe nal downscaled runoff estimates associated with the differ-ences in the precipitation and temperature of 21st century projec-tions from 30 different runs of General Circulation Models (GCMs).For this reason in many parts of the study ensembles were not pro-duced, but instead the results of individual models were kept apart,using boxplots that show the spread of the uncertainty associatedwith the different climate projections from the GCMs. In otherwords we did not work with aggregates or ensembles, but the 30GCM runs were downscaled, resampled, and ran them throughVIC. Sometimes we refer to the median climatic changes of allthe models, but we considered these individual changes (2000–2099) from the 30 runs according to the individual correspondingbaseline (1950–1999) runs. Models that were far from the generaltendency of the group were identied as outliers and thereforewere tagged as model runs that depict a future that is out of therange of the whiskers of the model-to-model variability. Anothersecondary objective was the determination of the impacts atmonthly to annual time-scales. For example we were interestedin verifying the changes in the MSD mentioned by Rauscher et al.(2008) in regard to future MSD behavior as part of the annual cycleof precipitation in the Pacic coast of Central America.

Thedata from the GCMs were downscaledusing a modied ver-sion of the Bias Corrected and Spatial Downscaling Method (BCSD;Wood et al., 2004 ). The skill of the downscaling procedure is dis-cussed in Maurer andHidalgo (2008) and Maurer et al. (2010) . Biascorrection of the GCM data is a necessary step in downscaling, asGCMs are known to present considerable biases in the mean andstandard deviation for both precipitation and temperature esti-mates (Hidalgo and Alfaro, 2012a; Maurer et al., 2010 ). This typeof spatial downscaling consists of a process of interpolating GCMdata or their anomalies into the ner resolution grid. A nal pro-cess of resampling (described in a later section) transformed thedownscaled monthly GCM data into daily estimates of precipita-tion and temperature. The downscaled estimates were then usedas input in the Variable Inltration Capacity (VIC) macroscalehydrological model (Liang et al., 1994 ) with the objective to obtainregionalestimatesof runoffat a resolution of 0.5 0.5 . Details onthe VIC model are discussed in a latter section.

The procedure of downscaling GCM estimates and then usingthe input in hydrological models is common in studies of variabil-

ityandclimate change, (for examples see Barnett et al., 2008; Clokeet al., 2010; Das et al., 2011; Hidalgo et al., 2009; Maurer et al.,2009; Pierce et al., 2008 ). For example, in the particular case of Central America, Maurer et al. (2009) studied the climate changeimpacts in the Rio Lempa basin. The authors found precipitationreductions of 5–10% for the B1 and A2 scenarios respectively forthe period 2070–2099 compared to the baseline scenario from1961 to 1990. Median reservoir reductions for 2070–2099 were10% and 13% for the B1 and A2 scenarios respectively.

Using a regional model, Karmalkar et al. (2011) found signi-cant decreases in future precipitation in the dry season of CentralAmerica under the A2 emission scenario. Neelin et al. (2006) foundan agreement between models in depicting a drying pattern overthe Caribbean/Central American region at the end of the century

(2077–2099). Data from stations (1950–2002) and satellite(1979–2003) showed decreasing precipitation trends over the

(a)

(b)

Fig. 1. Study area and locations of the stream-gages (top) and meteorologicalgridded data (bottom).

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region, in particular in the satellite data and in northern CentralAmerica. However, as it was mentioned by Imbach et al. (2012)and Rauscher et al. (2008) , the evidence of observed precipitationtrends in Central America is mixed and depends on the databaseused. For example in Aguilar et al. (2005) there is a variety of po-sitive and negative signs of the observed precipitation trends(1961–2003 and 1971–2003) for stations over the region, resultingin a lack of a coherent signal andsignicant trends. In Aguilar et al.study, indexes used to represent extreme rainfall show similarinconsistent trend signs as the ones found for precipitation totals(PRCPTOT), but more stations showed positive trends than forPRCPTOT, indicating some positive tendency for larger precipita-tion events ( Aguilar et al., 2005 ). Using 17 GCMs, Rauscher et al.(2008) cite a decrease in summer precipitation, an intensicationof the MSD and a southern shift of the Eastern Pacic IntertropicalConvergence Zone (ITCZ) as responses of climate change in the re-gion. Using a collection of 19 coupled and 12 uncoupled modelruns from the IPCC Assessment Report 4, Martin and Schumacher(2011) showed that all models captured the 3-D location of theCaribbean Low-Level Jet (CLLJ; Amador, 1998 ), a fundamental cli-mate feature, for its relationship to regional precipitation ( Amador,2008 ), in future projections of regional climate. However, the mod-els failed to simulate the observedmagnitude of its semiannual cy-cle, especially in regard to the intense CLLJ July peak ( Amador et al.,2010 ). The ability of the models to simulate the correlation be-tween the CLLJ and regional precipitation varied based on seasonand region. During summer over the Caribbean, the correlationbetween the precipitation anomalies and the CLLJ index used byMartin and Schumacher (2011) resulted in negative values, inaccordance to the observed. As far as the MSD is concerned, theseauthors claimed that a link between the ability of models to pro-duce a summer CLLJ peak and the MSD was established, however,it is necessary to clarify that their MSD denition differs from thatdiscussed by Magaña et al. (1999) . For temperature, the averageannual warming for the projections for Central America at theend of the 21st century ranges from 2.5 C to 3.5 C dependingon the location ( Hidalgo and Alfaro, 2012b ). Conversely, observedtemperature increases (1900–2010) for Central America are onthe order of approximately 1 C (Corrales, 2010 ). There are generaltrends pointing to a reduction in cold nights and days, and an in-crease in warm nights and days over the region (Alexander et al.,2006 ). Corrales (2010) mentions that climate scenarios for CentralAmerica project a drier future for the region, along with a change inseasonality of precipitation, which could have implications forwater resources in the future. Using a vegetation model (not ahydrological model), Imbach et al. (2012) studied changes in vege-tation and runoff in Central America using 136 GCM runs. Theyconcluded that runoff will decrease across the region even in areaswhere precipitation increases, as warmer temperatures will in-crease evapotranspiration.

It should be mentioned that the results of this study would beuseful for agriculture and to have an estimate of future conditionsthat may affect other human and environmental systems (e.g.wildres potentials, water availability, drought frequency, andindication of some potential social impacts such as possible popu-lation migrations). Another aspect that helps planning is to allowthe determination of the model-to-model uncertainty in the futureclimate and the estimation of the median direction of the precipi-tation, temperature and runoff changes. The study is not suitablefor characterizing extremes such as oods, ecosystems healthand infrastructural design. For that, a ner spatial and temporalresolution is needed.

This article is organized using the following structure: the nextsection is Data, the following section is a description of the Vari-

able Inltration Capacity hydrological model and a brief presenta-tion of some of the analysis procedures, then the Results are

analyzed, the Discussion section is next and nally the Conclusionsare put forward.

2. Data and methods

Global climate simulations corresponding to monthly precipita-tion, temperature andsurface wind velocities for the climate of the20th Century (known as 20c3m runs) and climate projections forthe 21st Century for the A1B greenhouse gas emission scenariowere obtained from the US Lawrence Livermore National Labora-tory Program for Climate Model Diagnosis and Intercomparison(PCMDI, 2010) and from the Intergovernmental Panel on ClimateChange (IPCC, 2010). These data were collected as a response of an activity of the World Climate Research Programme (WCRP) of the World Meteorological Organization (WMO) and constitutesphase 3 of theCoupled Model Intercomparison Project (CMIP Phase3) in support of research relied on by the 4th Assessment Report(AR4) of the IPCC (Meehl et al., 2007 ). Redundant runs from the

PCMDI and IPCC datasets were compared and discarded. Onlythose models that had complete precipitation and temperatureruns for all of the following periods were considered in the analy-sis: (a) climate of the 20th Century or 20c3m type of simulations(covering the time period 1950–1999), (b) the climate change pro- jection for the horizon 1 or CC1 (2000–2049), and (c) the climatechange projection for the horizon 2 or CC2 (2050–2099). Some of the models that had more than one climate realization were alsoconsidered in the analysis. There were a total of N = 30 simulationsthat met these requirements. Some of the models selected did nothave wind velocity data available and therefore the analysis of thewind data was circumscribed to N = 21 simulations. The list of models and runs can be found in Table 1 . Since the original GCMdata have monthly resolution we will not interpret results at the

sub-monthly level from the hydrological model. Daily variabilityassociated with extremes for example, cannot be captured with

Table 1

General Circulation Model runs used in this study.

# Model name Run #

1 GISS-AOM 12 GISS-AOM 23 CGCM2 14 CGCM2 25 CGCM3.1(47) 1

6 CGCM3.1(47) 27 CGCM3.1(63) 18 GFDL-CM2.0 19 GFDL-CM2.1 1

10 CM3 111 INM-CM3.0 112 IPSL-CM4 113 GISS-EH 114 GISS-EH 215 ECHAM4a 116 ECHAM5a 117 ECHAM5a 218 ECHO-G 119 ECHO-G 220 FGOALS 121 FGOALS 222 CSIRO-MK3.0a 1

23 BCCR-BCM2.0 124 CCSM3a 125 CCSM3a 226 UKMO-HadCM3 127 UKMO-HadGEM1 128 MIROC3.2(hires)a 129 MIROC3.2(medres) a 130 MIROC3.2(medres) a 2

a These runs do not have surface wind data available.

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the modeling scheme presented here. To do this properly we willneed daily data from the GCMs and a downscaling method thatpreserves the daily variability such as Constructed Analogues(e.g. Hidalgo et al., 2008 ) or through the use of a regional model(e.g. Karmalkar et al., 2011 ).

Global climate data from their original resolution were interpo-lated to the resolution of the coarser model (2 latitude 5 longi-tude) by the nearest grid-point method, but considering separateinterpolations for the ocean and land grid-points selected accord-ingly to the individual land-sea masksof themodels. The data werevisually inspected at selected grid-points in Central America. Thedata were also changed to the same units and same le formatfor the rest of the study. An analysis on how these models repro-duce the 20th century climate represented in the US National Cen-ter of Environmental Prediction (NCEP) and US National Center forAtmospheric Research (NCAR) Reanalysis ( Kalnay et al., 1996; Kis-tler et al., 2001 ) database can be found in Hidalgo and Alfaro(2012a) . This analysis showed that in general the models presentlarge biases in mean and standard deviation of precipitation pat-terns, while temperature patterns are better modeled (for this rea-son a bias-correction procedure was implemented in this study). Italso showed that, once the models were ranked using several met-rics, they could be grouped to produce ensembles of 1,2,3, . . .30models in order of rank. It was found that an ensemble of around7 of the best models produced the best skill in reproducing ‘‘ob-served’’ climatic patterns from the Reanalysis. Entering the modelsat random into the ensemble showed that the maximum skill wasfound at around 7 models also. In other words, when using ensembles (which is not the case in the present study) using asingle model or using all the models may not provide the bestskill, but instead there is an optimum number of models to beused.

The VIC model uses parameters that characterize soil, vegeta-tion and snow distribution properties (snow bands informationwas not used in this analysis as temperatures areabove the thresh-old for snow accumulation). The uncalibrated parameters for Cen-tral America were obtained from global simulations from Nijssenet al. (2001a) at a resolution of0.5 . In Nijssen et al. (2001a) thesoilparameters were manually calibrated for selected river basinsaround the world. However none of the selected basins in Nijssen’sstudy was located in Central America. For this reason, an automaticcalibration procedure was necessary to be applied to VIC beforeusing the model to describe hydrological variability and changein the region.

In order to calibrate the model it is necessary to match the mod-el results with naturalized streamow (or runoff) observations.Streamow data were obtained from the Global River DischargeDatabase ( http://www.rivdis.sr.unh.edu/ , Vörösmarty et al., 1996,1998 ) and from the Global Runoff Data Centre (GRDC)Streamow Database ( http://www.bafg.de/GRDC/EN/Home/home-

page__node.html ). The data are available at monthly time scales,and although information about the quality of the data and alsoof the degree of impairment by dams is not available from theoriginal sources or in the databases, it is expected that the

interpolation of the streamowdata into a runoffgrid couldreducesome of the errors of individual gages, particularly in regions withthe highest density of observations. The gages cover different peri-ods, but it was found that most of them covered the 1969–1979period, and therefore the calibration was performed in that period.Validation was performed in the 1980–1984 period. The location of the streamgages can be found in Fig. 1a.

The 1958–1990 observed daily meteorological forcing dataused in the study (hereinafter ne-scale observations) comesfrom two different sources. Precipitation was obtained from theCRN073 database ( Magaña et al., 1999 ), while maximum temper-ature, minimum temperature and wind velocity were obtainedfrom Maurer et al. (2009) . The location of the gridded data isshown in Fig. 1b. The data from Maurer et al. (2009) also containdaily precipitation values, but a preliminary analysis showed thatthe CRN073’s precipitation resulted in better calibration statistics.A possible reason for this is that the Maurer et al. (2009) data areglobal while CRN073 data are regional for Central America, andthus supposedly a greater effort was spent by Magaña et al. onnding stations from the region. Unfortunately we could not val-idate this further as the cited references to these two databasesdo not describe the stations used. Although the CRN073 databasehas data available up to the year 1999, visual inspection of thedata showed a disproportionate increase in precipitation after1991, and for this reason it was decided to leave the 1992–1999 data out of the analysis.

As was mentioned before, each basin’s streamow data were di-vided by their corresponding tributary area. Then the resultingrunoff data were linearly interpolated into the grid. At each grid-point the runoff was calibrated by nding the VIC parameters thatresulted in the maximum Nash–Sutcliffe coefcient ( Nash andSutcliffe, 1970 ) between modeled and observed runoff using theShufex Complex Evolution algorithm ( Duan et al., 1992, 1993 ).The Nash–Sutcliffe coefcient varies from minus innity to 1, with1 representing a perfect agreement between modeled and ob-served runoff values and minus innity is associated with no skill.The parameters were constrained to the ranges obtained from theminimum and maximum global values for each parameter fromNijssen et al. (2001a) as can be seen in Table 2 . Six soil parameters,dened in Table 2 , were calibrated: b, Ds, Dsmax , Ws, and thedepths of soil layers 2 and 3. All other soil and vegetation param-eters were obtained from Nijssen et al. (2001a) .

3. The Variable Inltration Capacity Model

VIC was developed at the Universities of Washington andPrinceton and simulates a complete suite of hydrological variablesobtained from the surface and energy balance near the surfacesuch as soil moisture, surface runoff, base ow, evapotranspirationand others, using daily meteorological data and parameterized soiland vegetation properties. The land surface is modeledusing a mo-saic conguration of vegetation layers, while the subsurface ow issimulatedusing three soil layers of different thickness ( Liang et al.,1994; Shefeld et al., 2004 ). Other VIC characteristics are the

Table 2

Parameters of the VIC model calibrated in this experiment.

Parameter Range Units Description

b 0.01–0.35 Variable inltration curve parameterDs 0.01–0.50 Fraction of Dsmax where non-linear base ow beginsDsmax 10–99 mm/d Maximum base ow velocityWs 0–0.80 Fraction of maximum soil moisture content above which nonlinear base ow occursLayer 2 depth 0.18–0.27 m Depth of layer 2Layer 3 depth 0.50–4.00 m Depth of layer 3

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hydrological models as presented here can be found in Troy et al.(2008) , and Duan et al. (1992, 1993) . In particular, a similar ap-proach as the one in Troy et al. (2008) was used in order to ndthe parameters over a grid of runoff estimates, instead of calibrat-ing individual basins. Following this approach, thestreamow datafor each basin was divided by its respective area to obtain runoff

timeseries. All the timeseries were interpolated into a 0.5 0.5and themodel was calibrated at each individual grid-point. VICcal-ibration in other regions (such as the western US where the modelhas been used extensively) have been done traditionally by hydro-logical basin and not by distributed runoff (as in Troy et al. (2008) ).In the western US the meteorological and land-surface databases

Fig. 3. Results of calibration, validation and simulationof monthly total runoff for two gridpointsnear Tegucigalpa (Honduras) and San Jose(Costa Rica) for the period 1958–1991.

Fig. 4. Nash–Sutcliffe coefcient between observed and modeled runoff during calibration for a grid-point that showed the best overall t. The x-axis of each sub-gure

corresponds to each of the VIC model parameters of Table 2. The grid-point is located near Tegucigalpa, Honduras. Around 3000 runs of the model with different sets of parameters are shown.

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grid-point close to San Jose, Costa Rica (southern part of CentralAmerica) are also shown. Observations at the two above men-tioned sites showed in most years a reduction of the streamowin July, a consequence of the appearance of the MSD on the Paci-c slope of the region. This annual feature in the streamow iswell captured during the periods used for calibration and valida-tion, however, the MSD signal is not very clear in the simulationperiod. As can be seen, for that last grid-point there is a tendencyfor underestimating some of the peaks and overestimating thelow-ows, but in general the Nash–Sutcliffe coefcients are largeenough to guarantee some skill in reproducing hydrologic vari-ability in that sub-region. Note that the source of the underesti-

mation of variability in Fig. 3 is not the aggregation of GCMssince VIC was forced with CRN073 and Maurer et al. (2009) , butinstead may be caused by: (1) a limitation of the hydrologicalmodel to capture some of the peaks (more in San Jose and lessin Tegucigalpa), or (2) problems in the CRN073 precipitationand Maurer et al. (2009) meteorological data, or (3) problemswith the observed streamow data that were used for calibration.Analysis of the results for the best validated grid-point showedindication of moderate equinality for some of the parameters(Fig. 4). That is, some of the parameters did not exhibit a deniteoptimum value and therefore there is not a single optimumparameter set for this particular grid-point. Equinality is a

Fig. 7. Projected changes (with respect to 1950–1999) in temperature, precipitation andrunoff for two gridpointsnear Tegucigalpa (Honduras) and San Jose (Costa Rica). The

spreadof theboxes represent thedifferentvaluesfor allruns in Table 1 . Outliers are shown with thesymbol ‘‘ ⁄ ’’. Boxes in boldface represent those changes in which themeanof the values for all runs (without considering outliers) is signicantly different from zero at the p = 0.05 level.

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problem common to hydrological models; however the modelvalidates well for a number of grid-points as was shown in Figs. 2and 3 .

Results of the downscaling procedure were evaluated at thecoarse scale and at the ne scale. At the coarse scale the bias cor-rection applied was simpler than the method described by Woodet al. (2002, 2004) . This is because the climate change tempera-tures were extreme compared to the baseline period and the t-ting of a distribution to nd the estimates at the tails assuggested in Wood et al. (2002, 2004) resulted in probabilitiesof exceedance equal to 1 for a range of high temperatures. Forthis reason, the bias correction that was applied to the GCM dataconsisted in removing the mean of the GCM and adding the meanof the aggregated ne-scale observations (hereinafter coarsenedobservations) for the grid points that were contained in any cor-responding GCM grid-point. In addition the GCM anomalies weredivided by the standard deviation of the model and multiplied bythe standard deviation of the coarsened observations. This pro-cess was performed separately for each month of the year(Fig. 5). An alternative analysis was performed by selecting sev-eral GCM grid-points, close to the coordinate to be downscaledweighting them by their correlation. This alternative analysis re-sulted in similar results (not shown).

The skill of the bias correction in producing modeled climato-logical patterns that match the observed patterns from the coars-ened observations was evaluated using the pattern Skill Score(SS ) of Pierce et al. (2009) :

SS ¼ r 2m ;o ½r 2

m ;o ð S m =S o Þ 2 ½ðm o Þ=S o2 ð1 Þ

where r m,o is the spatial correlation between modeled (i.e. GCM)and ‘‘observed’’ (i.e. coarsened observation) patterns, S m and S oare the sample spatial standard deviations for the modeled and ob-

served patterns respectively. The ratio S m/S o is denotedas c in Pierceet al. (2009) . The m and o over-bars correspond to the spatial

average of the modeled and observed climate patterns respectively.SS varies from minus innity (no skill) to 1 (perfect match betweenthe patterns). Zero SS values correspond to cases in which themeanof theobservations is reproduced correctly by themodel in a certainregion, but only as a featureless uniform pattern ( Pierce et al.,2009). Inspection of the right hand side of Eq. (1) shows that SS iscomposed of three squared terms, and therefore SS can also be ex-pressed as:

SS ¼ RHO CBIAS UBIAS ð2 Þ

where RHO is the square of the spatial correlation between the ob-served and modeled patterns; and CBIAS and UBIAS are the Condi-tional and Unconditional Biases respectively (see Pierce et al.,2009). Note that SS not only reects correlation coherence betweenthe patterns but also biases play an important role in affecting SS’ svalues.

Fig. 6 show the values of the SS in reproducing mean precipita-tion patterns before and after bias correction for the model runs

shown in Table 1. As can be seen the nal values are close to 1.It should be noted that the mean and standard deviation of thecoarsened observations were calculated using the available datafrom 1959 to 1991, while Fig. 6 shows the biases in the modeledpatterns during the 1950–1999 baseline period (for this reasonthe resulting SSs after bias-correction are close but not exactlyequal to 1). It is also important to note that the bias correctiontechnique, in general, improves the CGM model Skill Scores moreduring summer.

The bias-correctedGCMmonthly anomalies were linearly inter-polated to the ner grid. The climatological ne-scale observationswere added to the local anomalies. VIC requires as a minimum in-put daily data parameters of precipitation, maximumtemperature,minimum temperature and wind velocity. Climatological values of

wind velocity from Maurer et al. (2009) were used as input in allVIC runs as there are no winds available for all GCM models and

Fig. 8. Projection of annual total runoff for two grid-points near Tegucigalpa (Honduras) and San Jose (Costa Rica) from 1950 to 2099. The spread of the boxes represent thedifferent valuesfor allruns in Table 1 . Theboxesshow the25th, the50th andthe 75th percentiles. Thewhiskersextend1.5 theinterquartile rangeor to theextendof thedata.Data outside the whiskers (outliers) are shown with the symbol ‘‘ ⁄ ’’.

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the downscaling procedure has not been tested for wind velocity.The monthly values were resampled to daily values by ndingthe month in the historical daily ne scale observations that hada closer monthly average than the monthly average of the

downscaled pattern to be resampled. The daily data selected wasrescaled by a factor (multiplicative for precipitation and additivefor temperature) in order to make the monthly average (or accu-mulation for precipitation) exactly the same as the month to be

Fig. 9. Mean differences (in percentage) in the annual minimum and maximum runoff and in the percentiles 25, 50 and 75 between the projections from 2000 to 2049compared to the values from 1950 to 1999. Themedian of all the runs were used in this gure. Only those grid-points with Nash–Sutcliffe validationcoefcients greater thanzero were shown in the gure.

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the role of the SST warming variations and effect of warm ENSOevents in the region ( Rauscher et al., 2011 ). Several questions arisewith the intensity of the reduction and the nature of the proposedmechanisms. If some of the models did not properly capture theMSD, how is the ensemble able to represent this reduction signif-icantly during summer? Since most of the approaches used coarseresolution data, is it possible to identify ENSO effects in a regionthat has such a different precipitation patterns in the Pacic andCaribbean slopes ( Magaña et al., 1999 ) and that respond so differ-ently to ENSO events ( Amador et al., 2003 )? Besides that, as it iswell known, the CLLJ response to ENSO is different from summerto winter depending on the sign of the ENSO anomaly. The CLLJis stronger (weaker) for summer (winter) during warm ENSO epi-sodes, being the converse true for cold events ( Amador, 2008;

Amador et al., 2003, 2006 ). The use of statistical downscaling asit is used here allows the characterization of ner scale features

such as the MSD. This is important because as it was mentionedbefore, the raw GCM data may not capture important features of Central America climate. The precipitation reductions are ampli-ed in Tegucigalpa’s mean runoff projected decits (on the orderof 15% for CC1 to 30% for CC2) during a large part of the wet season(May–September), with smaller but signicant reductions in therunoff means of some of the other months. It should be noted thatfor the CC2 scenario the median runoff reduction during June andespecially July are greater than the median reductions for theneighboring wet season (May–November) months, suggesting anintensication of the MSD at the end of the century that has beenreported in Rauscher et al. (2008) .

In terms of drought it was evident a signicant reduction inrunoff in the northern part of Central America, as shown in the

reductions of the annual runoff means and of the runoff percen-tiles, in particular at the end of the century ( Figs. 8–10). Southern

Fig. 12. Projection of percentage of area under drought from 1950 to 2099. Drought was dened as the condition when the annual runoff is below the 25th percentile of thedata from 1950 to 1999. The spread of the boxes represent the different values for all runs in Table 1. Boxplot limits are similar to Fig. 8. Only those grid-points with Nash–Sutcliffe validation coefcients greater than zero were used in the gure.

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Central America (Costa Rica and Panama) showed less impact inprecipitation and runoff, albeit with warmer climates than the restof the region.

Consistently with Hidalgo and Alfaro (2012b) the domain-aver-age annual temperaturecould increase by around3 C at the endof the century when considering the median of all the runs analyzed(Fig. 11). It is interesting to note that in Fig. 11 there are two mod-els that are clearly away from the distribution of the rest of themodels: (a) the item 28 from Table 1 (MIROC3.2(hires), run 1) thatshowed a disproportionate warming in the future, especially after2040 and (b) the item 16 from Table 1 (ECHAM5, run 1) thatshowed little temperature sensitivity. Both models, (but in partic-ular item 16), tend to be low-skill outliers when compared to thedistribution of the temperature skill of all the models in reproduc-

ingmonthly mean andstandarddeviation patternsof the20th cen-tury climate contained in the NCEP/NCAR Reanalysis (not shown).

The percentage areas of Central America under drought and un-der severe drought have great inter-run uncertainty especiallyafter 2020, but the projections of the median drought and severedrought coverage suggested increases in the frequency of wide-spread droughts in the region, in particular in the northern part(Figs. 12 and 13 ). This is generally consistent with the consensusof the global precipitation and runoff change patterns for the A1Bscenario depicted in Figs. 3.3 and 3.5 of synthesis report of IPCC(2007b) .

5. Analysis of the NCEP/NCAR Reanalysis results

In order to determine if there are runoff changes during the his-

torical period that are consistent with the runoff reductions pro- jected by the GCMs, the Reanalysis precipitation and temperature

Fig. 13. Same as Fig. 12 but for the severe drought (runoff below or equal to the 10th percentile). Boxplot limits are similar to Fig. 8. Only those grid-points with Nash–Sutcliffe validation coefcients greater than zero were used in the gure.

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the climate and that this limitation is also present in the down-scaled precipitation data. Another possibility is that the wide-spread drying pattern is consistent with a southern displacementof the ITCZ, and perhaps this reduction is more important thanany smaller scale features caused by strengthening of the tradewinds. Moreover, another possibility is that even though there isan increase in the moisture transport, there is not a ux conver-gence over the region, usually related with most of the precipita-tion systems in the isthmus; however to conrm or deny this

requires an analysis that is beyond the scope of this work.

During the northern hemisphere winter the drying occurred ina region climatologically dry in these models, while during thesummer the drying occurred when the ITCZ is over Central Amer-ica and brings a lot of moisture to the region. This suggests a pro- jected southward shift in the July position of the ITCZ in thefuture. Although the future drying of Central America ( Imbachet al., 2012; Neelin et al., 2006; Rauscher et al., 2008 ) and thesouthward displacement of the ITCZ ( Rauscher et al., 2008 ) areconsistent with previous modeling studies; paleoclimatic proxy

data suggest that during prehistoric periods when the earth has

Fig. 16. Median zonal (U), meridional (V) and magnitude of surface wind velocity climatologies for climate change projections from a subset of 21 GCMs from Table 1 thathave wind data. Positive zonal (meridional) wind velocities are directed eastward (northward). Numbers in square brackets are the latitude and longitude of the grid-point.The boxplots represent the spread of the models during the baseline (1950–1999) scenario.

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been relatively warmer, the ITCZ tends to be shifted northward(Koutavas and Lynch-Stieglitz, 2005; Sachs and Myhrvold, 2011;Sachs et al., 2009 ). As a consequence, it has been extrapolatedthat warming (natural or anthropogenic) would result in a shiftto a more northward position of the ITCZ – and therefore an in-crease of precipitation in northern Central America instead of areduction – ( Koutavas and Lynch-Stieglitz, 2005; Sachs andMyhrvold, 2011; Sachs et al., 2009 ). This discrepancy betweenmodels and paleoclimatic evidence deserves more thought. Onone hand the comparisons with the paleo record may or maynot be appropriate in regards to GHG forcing -as compared tothe orbital forcing of Holocene climate patterns for this region.Some cautions regarding extrapolations to those records is in or-der. The inconsistencies between the resolved Holocene may bedue to the difference between uniform global forcing of GHGsversus the hemispherically asymmetric orbital forcing. This couldresult that the anthropogenic climate change in precipitation overCentral America could be completely different than the naturalsignal present in paleoclimatic data, and therefore the anthropo-genic signal would indeed include a southern shift of the ITCZassociated with warming. On the other hand Sachs and Myhrvold(2011) mention that ‘‘computer-based models have not accu-rately reproduced past and present rainfall patterns in the tro-pics’’. We should be aware of the limitations of the GCMs inthis region. For this, we refer to previous studies that analyzedthe skill of the models in reproducing large-scale climatic featuressuch as ENSO and the PDO (Pierce et al., 2008, 2009 ), the ITCZ(Delworth et al., 2012; Hirota et al., 2011; Liu et al., 2012;Rauscher et al., 2008 ), the MSD (Rauscher et al., 2008 ), the CLLJ(Martin and Schumacher, 2011 ), the intraseasonal variability( Jiang et al., 2012) and also our own study ( Hidalgo and Alfaro,2012a,b ) in which we found that the models in the Eastern PacicSeascape (close to the Central America region) present largebiases in reproducing the historical mean and standard deviationof precipitation patterns, while temperature patterns are bettermodeled. (For this reason, a bias-correction procedure wasneeded and was successfully implemented as part of the down-scaling procedure.) Although these studies have shown limita-tions on the GCMs, (some of them mentioned before), there issome skill in reproducing some of the most important climaticfeatures. The fact that the Reanalysis-BCSD-VIC runoff results dur-ing the historical period are showing a drying signal in the laterpart of the record that is consistent with the GCM results givesus some condence on the sign of future runoff changes projectedby GCMs. However, as was seen in the previous section, theReanalysis-BCSD-VIC 1980–2012 runoff trends for two locationsare located in the lower tail of the distribution of GCM trendsfor the same period suggesting limitations in the GCMs, in themethodology or in the Reanalysis.

It should be noted that at least one previous study ( Ruosteenoja

et al., 2003 ) and a few of the models analyzed here (see Fig. 7) sug-gested the possibility of precipitation increases in Central Americadueto climatechange. However, themedianofthe30 runs analyzedhere, the Reanalysis runoff trends since 1980 and the results fromother previous climate changestudies is that theregionwill experi-ence drier conditions. Rauscher et al. (2011) explain that regionalvariations inseasurface temperature(SST) warmingmayplaya roleto explain theMesoamericandryingunder the global warming sce-nario.These authors suggest that SSTs over thetropical NorthAtlan-tic (TNA) do not warm as much as the surrounding oceans and thetroposphere senses a TNA that is cooler than the tropical Pacic,increasing thestrength of theNorthAtlantic subtropicalhigh. Addi-tionally, the warm ENSO-like state simulated by the models in theeastern tropical Pacic could decrease the precipitation overMeso-

america,as warmENSOevents areassociatedwithdryingoverMes-oamerica ( Eneld, 1996; Eneld and Alfaro, 1999 ).

7. Conclusions

The VIC model has been calibrated for Central America. Theexperiment showed mixed results. There are areas of CentralAmerica that showed acceptable calibration and validation, espe-cially in parts of Honduras, Nicaragua, Costa Rica and Panama,but the results for some of the northern countries (Guatemala, El

Salvador and Belize) were not satisfactory. It is important to noticethe lack of skill of the model in reproducing the Caribbean coast’srunoff. Lack of streamow data for calibration and possibly higheruncertainty in the gridded historical meteorological data used incalibration could be the causes behind this problem. There is de-nitely a need for more data for use in studies of climate variabilityand change, as it is a challenge to produce such studies with cur-rent limitations in the availability of data.The median model re-sults ( Fig. 7) suggest that A1B projections of Central America’smedian temperature changes could reach values as high as 2–4 C during the 2050–2099 period on some of the months anddepending on the location, with the southern part showing thewarmer temperatures. Precipitation changes showed a distinctdrying pattern with maximum values of around 5–10% dependingon location with the driest part of the pattern located in the north-ern part of the region. When the percentage changes were consid-ered, the reductions in precipitation resulted in an amplication of these changes in terms of runoff, resulting in possible reductionsfrom 10% to 30% at the end of the century (2050–2099) for thisemissions scenario. Consistent with other studies, the largest dry-ness occurred during June and July, suggesting an intensication of the MSD. There was a signicant signal that the percentage area of Central America under drought and severe drought could increasein the future under this emissions scenario, in particular associatedwith the dryness that is expected in the northern part of the region,but there was great model-to-model uncertainty about theultimate extension of the dryness. The drying in the northernsub-region is widespread during the winter and more localizedduring the summer.

Analysis of the Reanalysis runoff trends from 1980 to 2012show that, for Tegucigalpa and San Jose, the drying trends are sig-nicantly larger than the trends found for the GCMs during thesame period. Among the possible reasons for that discrepancyare model deciencies, amplication of the trends due to construc-tive interference with natural modes of variability in the Reanaly-sis data, errors in the Reanalysis (modeled) precipitation data, andthat the drying signal is more pronounced than predicted by theemissions scenario used.

The projections results were consistent with previous modelingstudies, but were inconsistent with extrapolations of the climateusing paleoclimatic data that suggest that prehistoric epochs of rel-atively warm conditions have been associatedwith a northernshiftof the ITCZ, and therefore with more precipitation over northern

Central America. More research is needed in order to better under-stand if there is a serious deciency of the models or if theseextrapolations are incorrect since anthropogenic climate responseis different than previous periods of natural global warming.

Acknowledgements

This work was partially nanced by projects ( 805-A9-224, 808- A9-180 , 805-A9-532 and 805-A8-606 ) from the Center for Geophys-ical Research (CIGEFI) and the Marine Science and Limnology Re-search Center (CIMAR) of the University of Costa Rica (UCR) andthrough a ‘‘Fondo de Estímulo’’ grant from UCR. Thanks for thelogistics support of the School of Physics of UCR. J.A., E.A. and

H.H. were funded through an Award from Florida Ice and FarmCompany. H.H. was also funded through a grant from the

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Panamerican Institute of Geography and History ( GEOF.02.2011).The authors thank Ed Maurer from Santa Clara University who pro-vided data from Maurer et al. (2009) and land-surface data. Theauthors would like to thank two anonymous reviewers for theirconstructive comments.

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