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1SUPPLEMENTARY TEXT
2 Model set up for simulating land cover change impacts
3 The Regional Atmospheric Modeling System (RAMS) [Pielke et. al., 1992] is a
4nonhydrostatic numerical modeling system that utilizes finite difference approximations
5to solve conservation equations of mass, momentum, heat, and solid and liquid phases of
6water. The finite difference equations were solved within a grid structure using a polar
7stereographic projection in the horizontal, and a terrain following sigma coordinate
8system in the vertical [Mahrer and Pielke, 1975]. Cloud and precipitation processes were
9represented in the model as implicit Kain-Fritsch [Kain and Fritsch, 1993] convective
10parameterization scheme. A multi layer soil model [Tremback and Kessler, 1985] and a
11vegetation model [Walko et al., 2000] represented the various land surface processes.
12 The initial atmospheric conditions and temporally varying lateral boundary
13forcing for RAMS was provided by NCEP reanalysis [Kalnay et al., 1996], upper air and
14surface data [Ray et al., 2009]. A nudging option was used along the lateral boundaries,
15where the time series of atmospheric dynamic and thermodynamic field analysis was
16relaxed to the atmospheric conditions along the lateral boundaries towards observations.
17This was achieved by nudging the current value of a variable at a grid point along the
18lateral boundaries by an amount proportional to difference between the current and future
19values where the future value was prescribed by the objective analysis of the
20meteorological fields. Five points along the lateral boundaries were nudged with nudging
21strength exponentially decreasing towards the domain interior. A nudging time scale of
22900s was used. 1Page 2 of 5
1 The Klemp and Wilhelmson [1978] lateral boundary conditions were applied to
2the coarse grid, in which the normal velocity component specified at the lateral boundary
3was effectively advected from the interior assuming a propagation speed. This boundary
4condition allowed disturbances to propagate out of the model domain without strongly
5reflecting back into the interior. The atmospheric radiative transfer scheme of Mahrer
6and Pielke [1975] that accounts for the effects of water vapor in the atmosphere was
7utilized in this study. In the horizontal a deformation based scheme was used to represent
8diffusion, while in the vertical, diffusion was parameterized using the Mellor and
9Yamada [1982] scheme.
10 The United State Geological Survey (USGS) 1 km resolution topography data
11was used to specify the terrain in the simulations. Leaf Area Index (LAI), a crucial input
12characteristic for the vegetation parameterization within RAMS, was specified using
13Moderate Resolution Imaging Spectroradiometer (MODIS) derived LAI at 1 km spatial
14resolution [Myneni et al., 1997; Knyazikhin et al., 1998] available at eight-day intervals.
15The LAI values used in this study is based on MODIS imagery acquired over the study
16area during the time period 1-14 January 2001.
17 The LEAF-2 vegetation model in RAMS assigns fixed characteristics such as
18albedo, roughness length, and LAI, to each land cover type. This then varies as a function
19of season in the model. For the current land use scenario, the spatial distribution of the
20initial LAI is specified using the more representative MODIS derived LAI dataset.
21Average values of the LAI found over remnant evergreen broadleaf forests (5.1) and over
22remnant deciduous broadleaf forests (3.9) were prescribed for the corresponding forest 1Page 3 of 5
1types for the Mesoamerican Biological Corridor scenario. For woodlands and wooded
2grasslands the values used were 3.34 and 3.32 respectively.
3 Locations that are currently forested and would be forested also in the
4Mesoamerican Biological Corridor scenario were assumed to have LAI that are exactly
5the same as they are currently. Similarly there are several locations that are currently
6deforested and would have be so in changed land cover scenario. At these locations the
7LAI prescribed similar to the current values. Locations prescribed with land cover
8different from those of the current land cover are prescribed the average LAI values
9found from satellite observations of LAI.
10 The soil depth for the study area, reported in the FAO soil database [Webb et al.,
111992; FAO 1971-1981; Gerakis and Baer, 1999], varies from 2.0 m to 2.5 m over this
12region. An average value of 2.0 m was chosen as the depth of the soil layer. The soil
13moisture profiles were derived from long time integration of the MM5 using the (Oregon
14State University) OSU LSM with the outer domain of 60km and inner domain of 20 km.
15The MM5 model initialized with NCEP reanalysis data [Kalnay et al., 1995] was run for
162 years (1997 to 1998) and the soil moisture from December 31, 1998 was used to
17initialize all the models. The values were 0.32, 0.32, 0.33, and 0.34 at 10cm, 40cm,
18100cm and 200cm. Another parameter that was prescribed in the RAMS and also derived
19from the long-term integration of MM5 was the difference between lowest atmospheric
20temperature and soil temperature. All the values from MM5 were domain averaged where
21the domain was nearly identical to the one being used for the RAMS simulations (96W
22to 86W and 13N to 19N). Adequate representation of deep soil water access by forests
23within RAMS requires characterization of root profiles within the forest, and also 1Page 4 of 5
1observation of soil moisture at depths greater than 1m. Forest vegetation in RAMS was
2provided with a rooting depth of 2m whereas the wooded grassland type vegetation was
3provided with rooting depths of 1.0 consistent with field observations. However, note that
4the difference in soil moisture values between the surface and depths where the forest
5vegetation can access soil moisture is only around 16%.
6 The RAMS, initialized using 1st January 1997, 1998, 1999, 2000 and 2001 (i.e.
7five dry seasons), was integrated for a time period of 3 months for the four land use
8scenarios. The simulations used a time step of 300 seconds for the coarse grid. The
9tendencies from the radiative transfer calculations are updated once every 1200 seconds.
10The analysis fields derived from NCEP reanalysis, available every 6 hours, were used to
11nudge the lateral boundaries. Note that additional atmospheric information was not
12provided in this Type II dynamical downscaling simulations [Castro et al., 2005; Lo et
13al., 2007] which according to Ray et al., [2010] could provide incorrect simulation results
14as large as the signal being measured.
15
16
17References
18Castro, C.L., R.A. Pielke Sr., and G. Leoncini (2005), Dynamical downscaling: 19 Assessment of value retained and added using the Regional Atmospheric Modeling 20 System (RAMS), J. Geophys. Res., doi:10.1029/2004JD004721 21Food and Agriculture Organization - United Nations Educational, Scientific, and Cultural 22 Organization (FAO-UNESCO), 1971-1981, Soil Map of the World, 1:5,000,000, 23 Volumes II-X. UNESCO, Paris, France 24Gerakis, A., and B. Baer, 1999: A computer program for soil textural classification, Soil 25 Science Society of America Journal, 63, 807-808 26Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Handin, M. Iredell, S. 27 Saha, G. White, J. Woollen,Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. 28 Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Renolds, R. Jenne, and 1Page 5 of 5
1 D. Joseph, 1996: The NCEP / NCAR 40-year Reanalysis Project. Bull. Amer. Meteor. 2 Soc., 77, 437-471 3Klemp, J. B. and R. B. Wilhelmson, 1978: The simulation of three-dimensional 4 convective storm dynamics. J. Atmos. Sci., 35, 1070-1096 5Knyazikhin, Y., J. V. Martonchik, R. B. Myneni, D. J. Diner, and S. W. Running, 1998: 6 Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of 7 absorbed photosynthetically active radiation from MODIS and MISR data, J. 8 Geophys. Res., 103, 32257-32275 9Lo, J.C.-F., Z.-L. Yang, and R.A. Pielke Sr. (2008), Assessment of three dynamical 10 climate downscaling methods using the Weather Research and Forecasting (WRF) 11 Model, J. Geophys. Res., doi:10.1029/2007JD009216 12Mahrer, Y., and R. A. Pielke, 1975: A numerical study of air flow over mountains using 13 the two-dimensional version of the University of Virginia mesoscale model, J. Atmos. 14 Sci., 32, 2144-2155 15Myneni, R. B., R. R. Nemani, and S. W. Running, 1997: Estimation of global leaf area 16 index and absorbed PAR using radiative transfer model, IEEE Trans. Geosci. Remote 17 Sens, 35, 1380-1393 18Mellor G. L., Yamada T., 1982: Development of a turbulence closure model for 19 geophysical fluid problems, Rev. Geophys., 20, 851-875 20Pielke, R. A, W. R. Cotton, R. L. Walko, C. J. Tremback, W. A. Lyons, L. D. Grasso, M. 21 E. Nicholls, M. D. Moran, D. A. Wesley, T. J. Lee, and J. H. Copeland, 1992: A 22 comprehensive meteorological modeling system – RAMS, Meteor. Atmos. Phys., 49, 23 69-91 24Ray, D. K., R. A. Pielke Sr., U. S. Nair, R. M. Welch, and R. O. Lawton (2009), 25 Importance of land use versus atmospheric information verified from cloud 26 simulations from a frontier region in Costa Rica, J. Geophys. Res., 27 doi:10.1029/2007JD009565 28Ray, D. K., R. A. Pielke, Sr., U. S. Nair, and D. Niyogi (2010), Roles of atmospheric and 29 land surface data in dynamic regional downscaling, J. Geophys. Res., 115, D05102, 30 doi:10.1029/2009JD012218 31Tremback, C. J., Kessler, R., A surface temperature and moisture parameterization for 32 use in mesoscale numerical models, 1985: Proceedings of 7th AMS Conference on 33 Numerical Weather Prediction. June 17-20. Montreal, Quebec, Canada, Amer. 34 Meteor. Soc., Boston, 355-358 35Walko, R.L., L. E. Band, J. Baron, T.G.F. Kittel, R. Lammers, T.J. Lee, D.S. Ojima, R.A. 36 Pielke, C. Taylor, C. Tague, C.J. Tremback, and P.L. Vidale, 2000: Coupled 37 atmosphere-biophysics-hydrology models for environmental modeling, J. Appl. 38 Meteor., 39, 931-944 39Webb, R. W., C. E. Rosenzweig, and E. R. Levine, 1992: A global data set of soil particle 40 size properties, NASA Techical Memorandum 4286, 1992