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SUPPLEMENTARY TEXT
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Model set up for simulating land cover change impacts
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The Regional Atmospheric Modeling System (RAMS) [Pielke et. al., 1992] is a
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nonhydrostatic numerical modeling system that utilizes finite difference approximations
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to solve conservation equations of mass, momentum, heat, and solid and liquid phases of
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water. The finite difference equations were solved within a grid structure using a polar
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stereographic projection in the horizontal, and a terrain following sigma coordinate
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system in the vertical [Mahrer and Pielke, 1975]. Cloud and precipitation processes were
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represented in the model as implicit Kain-Fritsch [Kain and Fritsch, 1993] convective
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parameterization scheme. A multi layer soil model [Tremback and Kessler, 1985] and a
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vegetation model [Walko et al., 2000] represented the various land surface processes.
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The initial atmospheric conditions and temporally varying lateral boundary
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forcing for RAMS was provided by NCEP reanalysis [Kalnay et al., 1996], upper air and
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surface data [Ray et al., 2009]. A nudging option was used along the lateral boundaries,
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where the time series of atmospheric dynamic and thermodynamic field analysis was
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relaxed to the atmospheric conditions along the lateral boundaries towards observations.
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This was achieved by nudging the current value of a variable at a grid point along the
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lateral boundaries by an amount proportional to difference between the current and future
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values where the future value was prescribed by the objective analysis of the
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meteorological fields. Five points along the lateral boundaries were nudged with nudging
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strength exponentially decreasing towards the domain interior. A nudging time scale of
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900s was used.
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The Klemp and Wilhelmson [1978] lateral boundary conditions were applied to
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the coarse grid, in which the normal velocity component specified at the lateral boundary
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was effectively advected from the interior assuming a propagation speed. This boundary
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condition allowed disturbances to propagate out of the model domain without strongly
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reflecting back into the interior. The atmospheric radiative transfer scheme of Mahrer
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and Pielke [1975] that accounts for the effects of water vapor in the atmosphere was
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utilized in this study. In the horizontal a deformation based scheme was used to represent
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diffusion, while in the vertical, diffusion was parameterized using the Mellor and
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Yamada [1982] scheme.
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The United State Geological Survey (USGS) 1 km resolution topography data
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was used to specify the terrain in the simulations. Leaf Area Index (LAI), a crucial input
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characteristic for the vegetation parameterization within RAMS, was specified using
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Moderate Resolution Imaging Spectroradiometer (MODIS) derived LAI at 1 km spatial
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resolution [Myneni et al., 1997; Knyazikhin et al., 1998] available at eight-day intervals.
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The LAI values used in this study is based on MODIS imagery acquired over the study
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area during the time period 1-14 January 2001.
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The LEAF-2 vegetation model in RAMS assigns fixed characteristics such as
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albedo, roughness length, and LAI, to each land cover type. This then varies as a function
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of season in the model. For the current land use scenario, the spatial distribution of the
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initial LAI is specified using the more representative MODIS derived LAI dataset.
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Average values of the LAI found over remnant evergreen broadleaf forests (5.1) and over
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remnant deciduous broadleaf forests (3.9) were prescribed for the corresponding forest
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types for the Mesoamerican Biological Corridor scenario. For woodlands and wooded
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grasslands the values used were 3.34 and 3.32 respectively.
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Locations that are currently forested and would be forested also in the
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Mesoamerican Biological Corridor scenario were assumed to have LAI that are exactly
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the same as they are currently. Similarly there are several locations that are currently
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deforested and would have be so in changed land cover scenario. At these locations the
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LAI prescribed similar to the current values. Locations prescribed with land cover
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different from those of the current land cover are prescribed the average LAI values
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found from satellite observations of LAI.
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The soil depth for the study area, reported in the FAO soil database [Webb et al.,
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1992; FAO 1971-1981; Gerakis and Baer, 1999], varies from 2.0 m to 2.5 m over this
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region. An average value of 2.0 m was chosen as the depth of the soil layer. The soil
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moisture profiles were derived from long time integration of the MM5 using the (Oregon
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State University) OSU LSM with the outer domain of 60km and inner domain of 20 km.
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The MM5 model initialized with NCEP reanalysis data [Kalnay et al., 1995] was run for
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2 years (1997 to 1998) and the soil moisture from December 31, 1998 was used to
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initialize all the models. The values were 0.32, 0.32, 0.33, and 0.34 at 10cm, 40cm,
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100cm and 200cm. Another parameter that was prescribed in the RAMS and also derived
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from the long-term integration of MM5 was the difference between lowest atmospheric
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temperature and soil temperature. All the values from MM5 were domain averaged where
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the domain was nearly identical to the one being used for the RAMS simulations (96W
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to 86W and 13N to 19N). Adequate representation of deep soil water access by forests
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within RAMS requires characterization of root profiles within the forest, and also
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observation of soil moisture at depths greater than 1m. Forest vegetation in RAMS was
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provided with a rooting depth of 2m whereas the wooded grassland type vegetation was
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provided with rooting depths of 1.0 consistent with field observations. However, note that
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the difference in soil moisture values between the surface and depths where the forest
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vegetation can access soil moisture is only around 16%.
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The RAMS, initialized using 1st January 1997, 1998, 1999, 2000 and 2001 (i.e.
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five dry seasons), was integrated for a time period of 3 months for the four land use
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scenarios. The simulations used a time step of 300 seconds for the coarse grid. The
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tendencies from the radiative transfer calculations are updated once every 1200 seconds.
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The analysis fields derived from NCEP reanalysis, available every 6 hours, were used to
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nudge the lateral boundaries. Note that additional atmospheric information was not
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provided in this Type II dynamical downscaling simulations [Castro et al., 2005; Lo et
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al., 2007] which according to Ray et al., [2010] could provide incorrect simulation results
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as large as the signal being measured.
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