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Appendix .
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global vegetation model (DGVM) that simulates vegetation distribution, biogeochemical
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cycling and wildfire in a highly interactive manner. The model simulates potential
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vegetation that would occur without direct intervention by humans but indirect effects
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such as increasing greenhouse gas concentrations, grazing and fire suppression can be
1. The Dynamic global vegetation model MC2
MC2, the C++ version of its predecessor MC1 (Bachelet et al. 2001), is a dynamic
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included. The model does not simulate individual species but for each grid-cell, it
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simulates competition between a woody and an herbaceous lifeforms. Each grid cell is
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simulated independently (time before space), with no cell-to-cell communication. The
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model is composed of three modules that are described below.
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1.1. Biogeography module
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The biogeography module is composed of two parts: a lifeform interpreter and a
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vegetation classifier. The model simulates vegetation types that are each composed of a
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mixture of two lifeforms, herbaceous (referred to as grass) and woody (referred to as
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tree), the relative dominance of which varies as a function of climatic conditions that are
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calculated by the lifeform interpreter, similar to Neilson (1995) biogeography rule set.
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Plant functional types include evergreen needleleaf, deciduous needleleaf, evergreen
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broadleaf and deciduous broadleaf trees (shrubs are considered small trees and are
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included in the term "tree" in the rest of the document), as well as C3 (cool or temperate)
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and C4 (warm or subtropical) grasses (the term grass encompasses forbs and sedges).
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The tree phenology and leaf morphology are determined at each annual time step
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as a function of the minimum temperature of the coldest month (minimum Tmin) and of
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the growing (i.e. warm) season precipitation that have been smoothed over 15 years. The
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smoothing function progressively diminishes the influence of each past year’s climate on
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the smoothed climate variables to take into account the inherent inertia of long-lived
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woody types to short-term climate variability (Daly et al. 2000).
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The C3/C4 grass functional types are determined by the ratio of C3/C4 grass productivity,
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which depends on the temperature of the three consecutive warmest months, subject to
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the same 15-year climate smoothing function. Warmer temperatures favor C4 grasses.
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The balance between trees and grasses is determined by simulating the
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competition between these two lifeforms for light, water and nutrients, as mediated by
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fire. The resulting biomasses along with smoothed climate thresholds are used in the
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vegetation classifier to define the vegetation types. There are 36 vegetation types
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available for the globe, each defined by the association of a tree/shrub and either a C3 or
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a C4 grass. High latitude vegetation types are defined by growing degree-days that define
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their climate zone.
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1.2 Biogeochemistry module
The biogeochemistry model is a modified version of the CENTURY model
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(Metherell et al. 1993, Parton et al. 1994) that simulates the cycling of carbon and
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nitrogen among plant parts, multiple classes of litter, and soil organic matter pools. Live
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and dead plant components include leaves, fine and coarse branches, fine and coarse
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roots. Dead herbaceous material composes the standing dead compartment. Dead plant
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material is transferred to aboveground or belowground litter compartments that
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decompose into three soil carbon pools of increasingly slower turnover rates, releasing
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CO2 fluxes defined as heterotrophic respiration as described in Century model (Metherell
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et al. 1993). Decomposition rates are influenced by soil texture, soil moisture and
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temperature, as well as the existing soil carbon content and the nutrient content of the
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dead material.
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Tree and grass production rates are based on maximum monthly rates that are
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interpolated between lifeform-dependent parameter values, depending on the mixture of
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tree and grass functional types set by the biogeography module. For example, the climate
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indices may indicate 25% deciduousness so the model calculates a production rate that
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takes the weighted average of deciduous tree productivity (x0.25) and evergreen tree
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productivity (x0.75). The maximum production rate thus obtained is then multiplied by
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limiting-factors scalars related to temperature, soil available water, and atmospheric CO2
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that differ for trees vs. grasses (Bachelet et al. 2001). In the case of trees, a scalar related
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to leaf area index (LAI defined as one-sided leaf area per unit ground area), is added to
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address self-shading in estimating the fraction of incoming light intercepted by trees. For
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grasses, scalars incorporating the effects of shading by trees and standing dead grass are
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also included. The temperature scalars are based on mean monthly surface soil
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temperature, as affected by canopy shading and reduction of outgoing long-wave
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radiation (Parton et al. 1994). Allocation algorithms distribute carbon and nitrogen to
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various plant parts following the same logic as in the CENTURY model (Metherell et al.
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1993, Parton et al. 1994).
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The model includes a fertilization effect of elevated atmospheric CO2
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concentration that enhances water use efficiency (~8% reduction of transpiration and
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~8% increase in NPP at 550ppm).
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The biogeochemistry module also simulates actual and potential
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evapotranspiration (AET and PET) and soil water content in multiple soil layers, the
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number of which depends on the total soil depth that is an input to the model. Tree leaf
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and grass moisture contents are calculated as functions of the ratio of tree or grass
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available water to PET. These are interpreted as live fuel moisture contents by the fire
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module and affect fire behavior.
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1.3 Fire module
The fire module simulates the occurrence, behavior and effects of fire and was
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originally designed to project the large, severe fires that account for the bulk of observed
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fire impacts in the conterminous U.S. (Lenihan et al. 1998 and 2008, Conklin et al.
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2015). The module includes a set of mechanistic fire behavior and effects functions
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(Rothermel 1972; Peterson and Ryan 1986; van Wagner 1993) embedded in a structure
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that enables two-way interactions with the biogeochemistry and biogeography modules.
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Live and dead fuel loads in 1-hr, 10-hr, 100-hr and 1000-hr fuel classes are estimated
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from the carbon pools simulated in the biogeochemistry module. Allometric functions
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relate tree carbon pool sizes to height, crown base height and bark thickness for an
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average-sized tree. Empirical parameters are used to determine when crown (as opposed
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to surface) fires occur and project fire effects on vegetation (mortality only versus
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biomass consumption and emissions).
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The moisture content of the different fuel classes and the potential fire behavior
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are calculated each day based on pseudo-daily data interpolated from the monthly climate
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inputs. For temperature and relative humidity, a linear interpolation between monthly
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values is used to generate daily values. For precipitation, the number of precipitation
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events is estimated with a regression function derived from weather station data archived
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by the National Climate Data Center (Lenihan et al. 1998). Monthly values are divided by
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the number of precipitation events per month and resulting values are randomly assigned
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to days within each month. Moisture contents of plant parts passed from the
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biogeochemistry module determine live fuel moisture contents. A combination of the
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Canadian Fine Fuel Moisture Code (Van Wagner and Pickett 1985) and the National Fire
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Danger Rating System (Bradshaw et al. 1983) is used to estimate dead fuel moisture
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contents.
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Potential fire behavior (including rate of spread) is calculated each day based on
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daily-interpolated fuel loads, moisture contents and weather. Potential fire behavior is
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modulated by vegetation type, which affects fuel properties and realized wind speeds
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(e.g. higher for grasslands than forest). Actual fire is simulated whenever the calculated
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rate of spread is greater than zero and user-specified thresholds are exceeded for the fine
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fuel moisture code (FFMC) and the build up index (BUI) of the Canadian fire weather
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index system. These two indices are inverse functions of fine fuel and coarser fuel
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moisture contents, respectively, as specified by Van Wagner and Pickett (1985). Only
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one fire is simulated per year per cell on the first day when all thresholds are exceeded.
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Note that the day and year of fire may vary from cell to cell, given the independent
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simulation of each cell.
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Rogers et al. (2011) added an algorithm to MC1 to simulate fire suppression using
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thresholds for three fire intensity metrics: rate of spread (ros), fireline intensity (fli), and
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energy release component (erc).
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2. Input data
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2.1. Soils
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The MC2 model requires inputs of soil depth, texture and bulk density. Soils data
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from Kern (1994; 1995; 2000) were obtained from Dr. Ray Drapek (USFS PNW) who
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reprojected the original 1km data to a 30 arc-second grid using the “majority” rule such
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that the soil-related value that occupies the majority of the area of a particular grid-cell
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gets assigned to the entirety of that grid-cell.
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Available gridded soil datasets for the USA include “no soil” values for peatland
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as well as locations currently under glacier/snow. For historical runs, we can simply mask
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out those gridcells but for runs into the future there is high probability that permafrost
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would melt and high elevation peatlands will dry up is real (Turetsky et al. 2011). At
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lower elevations and/or latitudes such as in Florida, decreases in precipitation and
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increased evapotranspiration has already caused extensive subsidence of native peatlands
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(e.g. Aich and Dreschel 2011) when they are being drained for agriculture. While the
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current model version cannot simulate peatland processes, it will simulate vegetation
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shifts where snow/ice disappears following warming but because we do not calculate the
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water budget of warmer latitude peatlands we do not simulate the invasion of drying
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peatlands by terrestrial vegetation. The Harmonized World Soil Database (HWSD) that
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includes a variable called "topsoil organic carbon" was used to both to distinguish high
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elevation/latitude peatland (that we simply masked) from ice/snow fields and to fill in
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soil attributes for the latter. In western states (Rocky mountains and westward), lakes
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(Great Salt Lake, Klamath Lake, Salton Sea, etc) were also masked.
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2.2. Climate
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Historical Climate
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Climate inputs to the model include monthly precipitation, mean vapor pressure
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or dew point temperature, and mean daily maximum and minimum temperatures
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averaged over each month. Historical climate data (1895-2010) were acquired from the
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PRISM group at Oregon State University (Daly et al. 2008) at 30 arc-second.
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Future Climate
Future climate projections (2010-2100 and 2006-2100 for the land-use runs) were
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originally acquired from the WCRP (World Climate Research Programme) CMIP3
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(Coupled Model Intercomparison Project phase 3) multi-model database website
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(https://esg.llnl.gov:8443/home/publicHomePage.do). The three IPCC SRES A2, A1B
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and B1 greenhouse gas emission scenarios (Nakićenović et al. 2000) and three GCMs
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bracket the projected range of temperature increases across the U.S.: CSIRO Mk3.5
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(Gordon, 2010), CGCM3 (Flato et al. 2000) and MIROC 3.5 medres (Hasumi & Emori,
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2004) (henceforth CSIRO, CGCM3 and MIROC).
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For each climate variable and each future month, anomalies between future and
mean monthly historical (baseline: 1971-2000) GCM climate were calculated for each
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GCM grid cell over the conterminous U.S.. Differences were used for temperature and
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ratios for precipitation and vapor pressure (capped at a maximum value of five).
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Note: The MC2 model, like MC1, also requires annual ambient CO2 associated with both
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historical and each of the future emission scenarios. These data were provided by R.
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Stouffer (pers. comm.).
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Strided Input Data
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We created "strided" or sub-sampled input datasets where regularly spaced
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samples were taken in both latitudinal and longitudinal dimensions. We sub-sampled the
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30 arc-second (~800m) dataset to a resolution of 5 arc-minutes using a stride of 10 (every
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10th row and every 10th column). This subset is able to represent the conterminous U.S.
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in a 691 x 297 grid and have a higher probability of including the observed variability in
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climate values than would interpolation or aggregation of values to the coarser grid. The
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resulting subset is similar in appearance to a high-resolution dataset, hence allowing
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visual verification of the model results. Samples spaced at 5 arc-minutes in both
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dimensions provide a reasonable compromise of size (hence speed) and inclusion of
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representative data. This sub-sampled dataset allowed multiple runs for calibration
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purposes and ensured reasonable time for processing results and generating model runs to
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2100 to further evaluate trends in C cycle dynamics. When results of the full-resolution
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(30 arc sec) simulations were compared with the strided results, regional summaries were
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virtually identical. Consequently they were used to generate summary statistics.
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2.3. On-the-fly Downscaling
Because of the size of the input data and the number of combinations of models
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and emissions scenarios to run, storing all of the downscaled input climatologies (~1.5TB
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of storage) has become a challenge. We developed a system of "on-the-fly" downscaling,
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in which we stored anomalies of all the climate variables at the GCM resolution, along
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with values of the historical climate for the same baseline period at the 30 arc-second
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resolution. The GCM resolution is sufficiently coarse that the anomalies require only a
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small amount of disk space. This allowed efficient bias-correction downscaling by simply
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interpolating the GCM anomalies to the 30 arc-second grid and adding (or multiplying
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by) the historical PRISM (Daly et al. 1994) monthly baseline climate (1971-2000) at that
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same scale. The downscaled future climate was produced as temporary files, which were
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discarded upon completion of the model run.
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On-the-fly downscaling required an efficient interpolation routine that included an
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algorithm used for interpolation. Since interpolation can be viewed as an example of
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sampling, it can be approached using the very substantial body of known practices used
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in digital signal processing. A key result in that field is the Shannon-Nyquist sampling
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theorem, which describes the conditions under which artefacts (aliasing) can be
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introduced into sampled data. Many commonly used interpolation algorithms, such as
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bilinear and splined interpolation, can introduce such artefacts. Consequently we
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developed an interpolation routine (C++ in-house program) with a Gaussian interpolation
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filter, avoiding aliasing, with the filter size set according to the resolution of each GCM
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dataset.
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2.4. Land use
To initialize vegetation cover, a USGS land-use file (Sohl et al. 2012 and 2014,
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Sleeter et al. 2012) was used as input, whereby MC2-generated potential vegetation
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categories were replaced by new categories (developed, agriculture, mining, managed
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forests - see Table 1) where the input data indicated a managed vegetation type. Because
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the land-use time series started in 1992, the model was run with potential natural
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vegetation with fire suppression until 1991 and only then was initialized with land-use.
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Because projected future land-use based on the SRES emission projections (Nakićenović
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et al. 2000) started in 2006, future climate projections were used starting in 2006 for the
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land-use runs.
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3. Run protocol
The DGVM runs in three distinct phases producing results that are used as input
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for the next phase. First, the static biogeography model MAPSS (Neilson 1995) uses
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monthly mean climate (historical averages for 30 years, 1895-1924) to generate a map of
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potential vegetation distribution. During the second part of the equilibrium phase, the
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DGVM biogeochemistry module uses the same average climate iteratively to calculate
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the size of the carbon and nitrogen pools associated with each vegetation type for each
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grid cell while allowing for prescribed vegetation-specific fire return intervals. The
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equilibrium phase ends when the resistant soil carbon pool size changes by less than 1%
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from one year to the next. Consequently the duration of this phase varies across the map
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depending on the type of vegetation cover (from a few decades in the Great Plains
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grasslands up to 3000 years in the rain forests of the Pacific Northwest).
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During the 2nd, spinup phase, the model is run, also iteratively, using a detrended
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monthly historical climate time series (1895 to 2009) to capture the interannual
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variability and allow for readjustments of vegetation type and carbon pool sizes in
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response to dynamic wildfires. The time series is adjusted such that the climate variable
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means match the first 30 years of the historical period (1895-1924) and allow for a
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smooth transition between spinup and transient historical climate. The spinup phase ends
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when the net biome production (net ecosystem production minus carbon consumed by
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wildfire) reaches an equilibrium state near zero (600 years for this project).
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During the third, transient phase, the model is run first with a time series of
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historical climate data starting in 1895 and then, starting in 2006 and ending 2100, with
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future climate projections available from regional or global climate models that include
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both interannual variability and long-term trends.
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