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A REGIONAL CLIMATE STUDY OF CENTRAL AMERICA USING THE MM5 MODELING SYSTEM: RESULTS AND COMPARISON TO OBSERVATIONS

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The Mesoscale Modeling system, version 3.6 (MM5) regional modeling system has been applied to Central America and has been evaluated against National Oceanic and Atmospheric Administration/National Climatic Data Center (NOAA/NCDC) daily observations and the Global Precipitation Climatology Project (GPCP) precipitation data. We compare model results and observations for 1997 and evaluate various climate parameters (temperature, wind speed, precipitation and water vapor mixing ratio), emphasizing the differences within the context of the station dependent geographical features and the land use (LU) categories. At 9 of the 16 analyzed stations the modeled temperature, wind speed and vapor mixing ratio are in agreement with observations with average model-observation differences consistently lower than 25%. MM5 has better performance at stations strongly impacted by monsoon systems, regions typified by low topography in coastal areas and areas characterized by evergreen, broad-leaf and shrub land vegetation types. At four stations the model precipitation is about a factor of 3–5 higher than the observations, while the simulated wind is roughly twice what is observed. These stations include two inland stations characterized by croplands close to water bodies; one coastal station in El Salvador adjacent to a mountain-based cropland area and one station at sea-level. This suggests that the model does not adequately represent the influence of topographic features and water bodies close to these stations. In general, the model agrees reasonably well with measurements and therefore provides an acceptable description of regional climate. The simulations in this study use only two seasonal maps of land cover. The main model discrepancies are likely attributable to the actual annual cycle of land–atmosphere vapor and energy exchange that has a temporal scale of days to weeks. These fluxes are impacted by surface moisture availability, albedo and thermal inertia parameters. Copyright  2006 Royal Meteorological Society.
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  INTERNATIONAL JOURNAL OF CLIMATOLOGY  Int. J. Climatol. 26 : 2161–2179 (2006)Published online 6 July 2006 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1361 A REGIONAL CLIMATE STUDY OF CENTRAL AMERICA USING THE MM5MODELING SYSTEM: RESULTS AND COMPARISON TO OBSERVATIONS JOSE L. HERNANDEZ, a JAYANTHI SRIKISHEN, b DAVID J. ERICKSON III, a , * ROBERT OGLESBY b and DANIEL IRWIN ba Climate and Carbon Research Institute, National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge,Tennessee 37830, USA b  Marshall Space Flight Center, National Aeronautics and Space Administration, Huntsville, Alabama, USA Received 26 September 2005 Revised 16 February 2006  Accepted 10 April 2006  ABSTRACTThe Mesoscale Modeling system, version 3.6 (MM5) regional modeling system has been applied to Central Americaand has been evaluated against National Oceanic and Atmospheric Administration/National Climatic Data Center(NOAA/NCDC) daily observations and the Global Precipitation Climatology Project (GPCP) precipitation data. Wecompare model results and observations for 1997 and evaluate various climate parameters (temperature, wind speed,precipitation and water vapor mixing ratio), emphasizing the differences within the context of the station dependentgeographical features and the land use (LU) categories. At 9 of the 16 analyzed stations the modeled temperature, windspeed and vapor mixing ratio are in agreement with observations with average model-observation differences consistentlylower than 25%. MM5 has better performance at stations strongly impacted by monsoon systems, regions typified bylow topography in coastal areas and areas characterized by evergreen, broad-leaf and shrub land vegetation types. Atfour stations the model precipitation is about a factor of 3–5 higher than the observations, while the simulated windis roughly twice what is observed. These stations include two inland stations characterized by croplands close to waterbodies; one coastal station in El Salvador adjacent to a mountain-based cropland area and one station at sea-level. Thissuggests that the model does not adequately represent the influence of topographic features and water bodies close tothese stations. In general, the model agrees reasonably well with measurements and therefore provides an acceptabledescription of regional climate. The simulations in this study use only two seasonal maps of land cover. The main modeldiscrepancies are likely attributable to the actual annual cycle of land–atmosphere vapor and energy exchange that has atemporal scale of days to weeks. These fluxes are impacted by surface moisture availability, albedo and thermal inertiaparameters. Copyright  2006 Royal Meteorological Society. KEY WORDS: climate; regional modeling; Central America; diagnostic analysis; land use 1. INTRODUCTIONThe evaluation of models against observations is fundamental in climate studies since it reveals variousmisrepresentations in parameterization schemes leading to bias in estimating environmental properties andfluxes at a variety of time and spatial scales. Regional models used in climate and forecasting allow thestudy of the evolution of such geophysical properties and air–land interaction processes because of couplingan atmospheric model to a land surface model (LSM). Research on lower atmosphere and land surfaceproperty exchange have revealed the importance of considering several parameters, such as surface albedo,evapotranspiration, roughness length, soil properties and vegetation type. These variables are required tohave an adequate description of energy and water vapor exchange that ultimately influences the atmosphericboundary layer dynamics (Charney et al ., 1977; Dickinson, 1983; Avissar and Verstraete, 1990; Henderson-Sellers et al ., 1993; Yongjiu et al ., 2003). Current high-resolution regional models coupled with improved *Correspondence to: David J. Erickson III, Climate and Carbon Research Institute, National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6016, 865-574-3136, USA; e-mail: ericksondj@ornl.com Copyright  2006 Royal Meteorological Society  2162 J. L. HERNANDEZ ET AL . LSMs have shown a reasonable representation of land–atmosphere interactions by incorporating a range of different land surface properties. These improvements have allowed a realistic depiction of diurnal, short-term and seasonal cycles of heat, momentum and water (Betts et al ., 1997; Ek  et al ., 2003; Chen and Dudhia,2001b). Investigations evaluating the interactions among microphysics, cumulus, radiation, planetary boundarylayer (PBL) and surface processes have contributed to the creation of state-of-the-art models like MM5.The fifth-generation Mesoscale Model (Anthes and Warner, 1978; Grell et al ., 1994) is used worldwidein forecasting and climate studies. The MM5 offers several options related to the coupling to LSMs andschemes of atmospheric physics, giving reasonable estimates for land surface heat fluxes and atmosphericsolar radiative transfer (Chen and Dudhia, 2001a). For instance, the MM5 land surface schemes range fromsimple five-layer vertical diffusion descriptions to sophisticated multilayered models allowing the predictionof soil moisture, temperature profiles and interactions with the PBL. Even with these advances in modeling,simulations of small-scale ( ≤ 20 km) processes and properties under different model options and environmentalsettings present a challenge. We carried out simulations with MM5 to explore the performance of this modelin an annual simulation for Central America using observations from stations deployed under distinct land-cover categories and elevations. The dry and wet seasons are also investigated with the Global PrecipitationClimatology Project (GPCP) data (Huffman et al ., 2001) and model-observation comparisons were completedfor the rate of precipitation.Seasonal controls on Central American regional climate are known, e.g. the Pacific and Atlantic monsoonsystems, trade winds, topographic effects, seasonal displacement of the Intertropical Convergence Zone(ITCZ) and land surface type. However, many uncertainties remain on subseasonal to interannual timescales.For example, on intraseasonal timescales, the midsummer drought (MSD) on the Pacific side of CentralAmerica is well documented, but little is known about why it occurs (Maga˜na et al ., 1999). In CentralAmerica a bimodal annual cycle in precipitation with intensification around the May–June–July (MJJ)and the August–September–October (ASO) time periods are observed. The Atlantic and Pacific Oceanhydrologic cycles obviously are important in regional rainfall activity in Central America. Hastenrath (1976)studied the driest and wettest summers in the Caribbean using rainfall records from 1911 to 1972 to finda correlation between elevated sea-surface temperatures (SSTs) in the equatorial Pacific with a drier seasonin the Caribbean. Rogers (1988) documented a drying influence of El Ni˜no during the late rainfall seasonover Central America. Positive anomalies in the sea-surface temperature of the tropical North Atlantic areassociated with an enhancement of precipitation in the MJJ time period. Taylor et al . (2002) suggest that adecrease in rainfall during the ASO time period is strongly influenced by El Ni˜no/La Ni˜na events and that there is a robust relationship between an east–west gradient of SST with rainfall rates during the ASO timeperiod.A reliable evaluation of regional climate over the last several decades in Central America requires maps of land-cover categories that reflect changes in land surface features corresponding to the study period. Globalforest assessments from the Food and Agriculture Organization (FAO) indicate that the Central Americanregion has undergone dramatic changes in land cover due to deforestation over the last several decades. Thesehuman induced alterations to vegetation coverage cause changes in land surface geophysics and the surfaceenergy balance, resulting in significant climate change in tropical regions (Snyder et al ., 2004). The ForestResources Assessment (FRA, 2000a,b) show that annual rates of deforestation in Central America duringthe 1980s and 1990s were among the highest in the world, particularly in small countries like El Salvador(4.6%), Belize (2.3%) and Guatemala (1.7%). Archard et al . (2002) estimated changes in humid tropical forestdistributions using satellite imagery to refine the calculation of carbon fluxes in the global carbon budget.Archard et al . (2002) found several hot spots of deforestation in Central America, particularly in Honduras,Nicaragua, Belize and Guatemala with almost the same loss rates as estimated for East Asia, a region thathas experienced the highest rates of deforestation on Earth. A dramatic example of a land use (LU)–climaterelationship is illustrated by the tropical montane cloud forests (TMCF), which are typically located highin mountains where orographic clouds form due to the forced rising of trade winds. Lawton et al . (2001)investigated TMCF and showed that deforested lowlands in Costa Rica remain relatively cloud free, whilethe forested counterparts develop a dry season of cumulus clouds. They evaluated simulations from regionalmodels with realistic moisture advection across the model boundaries to compare results using forested and Copyright  2006 Royal Meteorological Society Int. J. Climatol. 26 : 2161–2179 (2006)DOI: 10.1002/joc  A REGIONAL CLIMATE STUDY USING THE MM5 MODELING SYSTEM 2163pasture surface cover. In the present study, MM5 is coupled to a five-layer soil vertical diffusive model anda simple cumulus parameterization (Grell and Devenyi, 2002). The model uses information on land surfacecharacteristics from two seasonal maps of 25 vegetation and land-cover categories from the United StatesGeological Survey (USGS). MM5 is currently using the most recent version of land-cover maps for regionalclimate models from USGS, which has been generated using the 1993 normalized difference vegetation index(NDVI) measured by the advanced very high-resolution radiometer (AVHRR) on board of National Oceanicand Atmospheric Administration (NOAA) satellites (Loveland et al ., 2000). In our work, we want to study theperformance of MM5 to evaluate the daily to annual variability of the regional climate in Central America. Webase this evaluation on model-observation comparisons of four parameters: temperature, wind speed, watervapor mixing ratio and precipitation.Climate studies from high-resolution regional models are very scarce in the Central American region. Here,we present a diagnostic study of the MM5 modeling system adapted to describe the regional climate in CentralAmerica (92.5 ° –77 ° W; 7 ° –18.5 ° N) for a one-year period (1997) at a resolution of 20 km. The National Centerfor Environmental Protection (NCEP) reanalysis data (Kalnay et al ., 1996) were used as boundary conditionsin the annual integration. MM5 allows nested simulations, which in our study were set to 60 and 20 km.Since we focus on local to small region climate variability and diagnostic analysis, we prefer to use the20 km resolution results in our investigation. We compare MM5 results with NOAA/NCDC observationsand GPCP monthly data in the seven countries in Central America and southern Mexico. Our analysisincludes an examination of climate statistics at 16 observational stations. We explore model-measurementdiscrepancies within the context of land-cover features (including vegetation type) of the regions surroundingthe meteorological stations. We examine temperature, wind, water vapor mixing ratio and precipitation ratesand relate these to atmospheric transport and land surface climate parameters.2. MODEL DESCRIPTION AND OBSERVATIONAL DATAThe MM5 modeling system is designed to simulate mesoscale and regional-scale atmospheric circulations. Itbasically consists of a series of components that horizontally interpolate terrestrial data; interpolates globalor meteorological records on pressure levels to a predefined horizontal grid; establishes lateral and initialconditions; and finally carries out regional simulations under various land and atmospheric physics options.Lateral and lower boundary conditions are necessary in weather forecasting and regional climate simulationsand the NCEP reanalysis provides those conditions for the annual integration carried out in this work.Soil and vegetation features are incorporated in the description of the atmosphere–land interactions throughparameterizations of plant mediated effects in the land surface scheme used. We use the MM5 version 3.6in this study to conduct an annual simulation using settings described and referenced in the MM5 user’sguide (Dudhia et al ., 2005). We configured our model experiments using the cloud radiation scheme thataccounts for longwave and shortwave interactions with explicit cloud and clear air (Dudhia, 1989), a simplesingle-cloud scheme useful for small grid sizes (Grell et al ., 1994), a simple ice scheme where the clouds, rainwater and nonconvective precipitation are resolved through microphysics adding ice phase processes (Dudhia,1989). We used the boundary layer parameterization of Hong and Pan (1996). A five-layer soil model withlevels at 1, 2, 4, 8 and 16 cm that employs a one dimensional heat diffusion equation is applied to predictthe vertical soil temperature profile (Dudhia, 1996). This model estimates the temperature profile consideringthe thermal inertia and moisture availability provided as initial conditions for each category in the USGS LUmaps. According to Chen and Dudhia (2001b), although there is no explicit representation of vegetation inthis model, MM5 behaves well with regard to many atmospheric variables. The land surface scheme in MM5results in simulations similar to advanced LSMs in forecasting simulations. We recognize the importance of having improved representations of land surface processes through an advanced LSM; however, they requireadditional fields and they are computationally expensive particularly for long climate simulation as proposedhere.We used meteorological station measurements and GPCP precipitation data to evaluate the MM5 modelresults. The meteorological measurements are taken from the Global Surface Summary of Day , which is Copyright  2006 Royal Meteorological Society Int. J. Climatol. 26 : 2161–2179 (2006)DOI: 10.1002/joc  2164 J. L. HERNANDEZ ET AL . supported by the National Climatic Data Center (NCDC) and is available athttp://www.ncdc.noaa.gov/.Thedata are from 1994 to the present, with daily average records updated weekly from a network of about 8000stations around the world. To carry out the model evaluation we consider 16 stations in Central America thatare characterized by different geographical conditions: plains, mountains, coastal regions on the Pacific andAtlantic oceans and the various land-cover categories. There are more than 16 stations for Central Americain the NCDC data set; however, several of these stations suffer from incomplete records with gaps of severalmonths. For this reason we select a subset with the longest records during the study period, year 1997.Because of the limited number of stations in the region, this study of MM5 model evaluation is meant topresent preliminary conclusions and guidelines for future research. The GPCP monthly precipitation databegin in 1979 and go through 2005. These precipitation estimates have been constructed by merging infraredand microwave satellite estimates of precipitation with rain gauge analysis data from more than 6000 stations.Adler et al ., (2003) presents an exhaustive analysis of the GPCP data and estimates a random and samplingerror of 10–30% over regions of significant rainfall ( > 100 mm/month), particularly in the tropics. We compareGPCP precipitation rates with those derived from MM5 convective and nonconvective precipitation.3. STUDY AREAThe Mesoamerican region (Sader et al ., 2001), which is part of the corridor between North and South America,consists of seven countries (Panama, Costa Rica, Nicaragua, Honduras, El Salvador, Guatemala and Belize)and southern Mexico. Mesoamerica is bordered by the Atlantic Ocean and Pacific Ocean, which influencethe climate variability in both coastal regions as some observational and reanalysis studies have shown. Forinstance, Enfield and Alfaro (1999) used coarse (2 . 5 ° × 2 . 5 ° latitude–longitude) satellite-raingauge modelanalysis data to find that the climatological behavior of the rainy season in Central America is more stronglyrelated to the tropical Atlantic sea-surface temperature anomaly than its counterpart in the tropical EasternPacific. In a detailed description of the interannual variability, Taylor et al . (2002), found that the influence of the tropical Atlantic wanes in the late season, when the equatorial Pacific and Atlantic become relatively moreimportant modulators. Maga˜na et al . (1999) considered 1 ° data derived from satellite and observational stationsto study the annual cycle of precipitation over the Caribbean and Central America. According to Maga˜na et al . (1999) a bimodal distribution of precipitation exists with maxima in the June and September–Octobertime periods and a minimum in July–August not associated with the ITCZ, but controlled by trade windintensification and the topography of Central America. Although the regional climate is in general well-defined, there are some particular climate features that depend on the combined effects of the proximity tothe coasts, the meridional migration of the ITCZ and topography ( < 4200 m in Guatemala, < 3800 m in CostaRica, < 2850 m in Honduras and < 400 m mostly in the study region). For instance, in Guatemala (in the northportion of the study region) there are two regimes of precipitation. Over the plains on the Atlantic coast of Guatemala the rainy season lasts 7 months while on the Pacific coast of Guatemala, dominated by the SierraMadre, there are only 2 months of rainfall activity. In Nicaragua (in the midlatitudes of Mesoamerica), thePacific region experiences two well-defined dry and rainy seasons of 6 months each with annual precipitationof 1000–2000 mm/year. In the same country, the large plains in the region bordering the Atlantic Oceanexperience longer rainy seasons (9–11 months) with annual precipitation that exceeds by a factor of 2 thosecharacteristics of the near Pacific coastal region. Panama, at the lowest latitudes, has precipitation patternsmore influenced by the ITCZ, with a minimum of rainfall around July and two rainy seasons in the April–Juneand August–November time periods. As expected in coastal regions, the monsoon strongly influences thestrength, duration and spatial distribution of temperature, winds and precipitation through differential heatingbetween land and ocean. In addition, Central America has a variety of land-cover types (mainly crop lands,mixed land types and forests) and water bodies (e.g. the Nicaragua and Managua lakes) influencing theland-atmosphere exchange of heat and moisture.Figure 1 presents the distribution of LU derived from the USGS, which is divided into 25 categories,and the borders of the seven countries in Central America. Figure 2 shows the percentage of land-covertypes in the study region indicating large areas covered by tropical forest (evergreen broad-leaf, needle leaf  Copyright  2006 Royal Meteorological Society Int. J. Climatol. 26 : 2161–2179 (2006)DOI: 10.1002/joc
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