A USER'S GUIDE TO THE VEMAP PHASE I DATABASE November 1996 (Updated 09/21/99) Nan Rosenbloom Timothy G.F. Kittel To accompany CDROM and World Wide Web versions of: THE VEMAP PHASE I DATABASE: AN INTEGRATED INPUT DATASET FOR ECOSYSTEM AND VEGETATION MODELING FOR THE CONTERMINOUS UNITED STATES T.G.F. Kittel, N.A. Rosenbloom, T.H. Painter, D.S. Schimel, H.H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E.R. Hunt, Jr. Ecosystem Dynamics and the Atmosphere Section Climate and Global Dynamics Division National Center for Atmospheric Research and Climate System Modeling Program University Corporation for Atmospheric Research TABLE OF CONTENTS 1 INTRODUCTION 1.1 VEMAP and the VEMAP Database 1.2 Citations and User Access Acknowledgments 1.3 VEMAP Mailing List 2 DATA FILE ACCESS 2.1 CDROM 2.2 UCAR World Wide Web Site 2.3 UCAR Anonymous FTP Server 2.4 GNU Compressed UNIX Tarfiles 3 THE VEMAP GRID 4 FILE STRUCTURE 4.1 SVF File Format 4.2 Binary File Format 4.3 ASCII Column Format for Site Files 5 SCALING FACTORS, BACKGROUND VALUES, AND LANDCOVER MASK 5.1 Scaling Factors 5.2 Background Values 5.3 Landcover Mask 6 ROAD MAP TO FILE AND VARIABLE DESCRIPTIONS 7 GEOREFERENCING VARIABLES AND CELL AREAS 7.1 Summary of Geographic Variables 7.2 Filename Protocol 7.3 Variable Descriptions 8 DAILY, MONTHLY, AND ANNUAL CLIMATE DATASETS 8.1 Summary of Climate Variables 8.2 Climate Filename Protocol 8.3 Creation of Climate Variables 8.4 Maximum, Minimum, and Mean Temperature 8.5 Precipitation 8.6 Solar Radiation 8.7 Humidity 8.8 Surface Wind Speed 9 SOILS 9.1 Summary of Soil Variables 9.2 Soil Filename Protocol 9.3 Creation of the VEMAP Soils Dataset 9.4 Hierarchical Division of Soils into Mineral and Organic Components and Texture Classes 9.5 Soil Files 10 VEGETATION 10.1 Summary of Vegetation Variables 10.2 Creation of the Vegetation Dataset 10.3 Vegetation Files 11 CLIMATE CHANGE SCENARIOS 11.1 Summary of Climate Scenario Files 11.2 Scenario Filename Protocol 11.3 Development of Climate Change Scenarios 11.4 Climate Change Scenario Variables 11.5 Creation of New Climates: Application of Change Fields to Base Climate and Tests for Physical Constraints 12 SITE FILES 12.1 Site File Content and Structure 12.2 Site File Naming Protocol 13 ACKNOWLEDGMENTS 14 CONTACTS 15 REFERENCES A1 APPENDIX 1: CDROM AND FTP SITE DIRECTORY STRUCTURE A2 APPENDIX 2: DETERMINATION OF ABSOLUTE SOIL AREA FOR EACH CELL A2.1 Absolute Area for Soil Modes and Components A2.2 Application to Model Outputs A3 APPENDIX 3: AGGREGATION OF KUCHLER VEGETATION CODES TO VEMAP VEGETATION TYPES A4 APPENDIX 4: STATE IDENTIFICATION NUMBERS A5 APPENDIX 5: VEMAP MAILING LIST A5.1 Description of the VEMAP Mailing List A5.2 How to Subscribe to the VEMAP Mailing List A5.3 Listserver Commands LIST OF TABLES AND FIGURES Table 1. Datasets available on the VEMAP CDROM, Web site, and FTP site Table 2. VEMAP grid corners defining the minimum bounding rectangle Table 3. Format of data records in site files Table 4a. Number of background cells (excluding wetlands) Table 4b. Number of background cells (including wetlands) Table 5. Geographic variables Table 6. Climate variables Table 7. Soil variables Table 8. Vegetation variable name code and description Table 9. VEMAP vegetation types Table 10. Availability of climate variables for each climate scenario Table A3.1 Aggregation of Kuchler vegetation types to VEMAP vegetation types Table A3.2 Kuchler Vegetation Type Names and Identifying Codes Figure 1. Layout of the VEMAP gridded array Figure 2. Schematic illustration of relationships among radiation variables in the climate dataset ------ 1 INTRODUCTION 1.1 VEMAP and the VEMAP Database The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multi-institutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation-type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The VEMAP exercise allows us to identify important commonalties and differences among model controls and responses. Where the models differ, the comparision highlights areas of uncertainty or error and identifies problems for future research. Inter-model differences also help to quantify the uncertainty in modeled responses to changing climate and other drivers. The completed Phase I of the project was structured as a sensitivity analysis, with factorial combinations of climate (current and projected under doubled CO2), atmospheric CO2, and mapped and model-generated vegetation distributions. The highly structured nature of the intercomparison allowed rigorous analysis of results, while constraining the range of questions explored. Maps of climate, climate change scenarios, soil properties, and potential natural vegetation were prepared as common boundary conditions and driving variables for the models (Kittel et al. 1995). As a consequence, differences in model results arose only from differences among model algorithms and their implementation rather than from differences in inputs. Results from VEMAP I are reported in VEMAP Members (1995) and selected files are available through UCAR's anonymous FTP server (see Section 2.3). The VEMAP input database for the Phase I model intercomparison is documented in this Technical Note. It includes compiled and model- generated datasets of long-term mean climate, soils, vegetation, and climate change scenarios for the conterminous United States. The data are on a 0.5 degree latitude/longitude grid. There are both daily and monthly representations of the mean climate. The climate data and climate change scenarios are presented in both gridded and time-sequential format. We developed the time-sequential, "site" file format to facilitate extractions of information for individual grid cells (Sections 4.3 and 12). 1.2 Citations and User Access Acknowledgments The citations for the VEMAP database are: Kittel, T.G.F., N.A. Rosenbloom, T.H. Painter, D.S. Schimel, H.H. Fisher, A. Grimsdell, VEMAP Participants1, C. Daly, and E.R. Hunt, Jr. (1996) The VEMAP Phase I Database: An Integrated Input Dataset for Ecosystem and Vegetation Modeling for the Conterminous United States. CDROM and World Wide Web (URL=http://www.cgd.ucar.edu/vemap/). Kittel, T.G.F., N.A. Rosenbloom, T.H. Painter, D.S. Schimel, and VEMAP Modeling Participants2 (1995) The VEMAP integrated database for modeling United States ecosystem/vegetation sensitivity to climate change. Journal of Biogeography 22(4- 5):857-862. An additional reference for VEMAP is: VEMAP Members3 (1995) Vegetation/Ecosystem Modeling and Analysis Project (VEMAP): Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochemical Cycles 9(4):407-437. Users are requested to acknowledge that access to the dataset was provided by the Climate System Modeling Program, University Corporation for Atmospheric Research, and the Ecosystem Dynamics and the Atmosphere Section, Climate and Global Dynamics Division, National Center for Atmospheric Research. Development of the VEMAP database was supported by NASA Mission to Planet Earth, Electric Power Research Institute (EPRI), USDA Forest Service Southern Region Global Change Research Program, and NSF-ATM Climate Dynamics Program through the University Corporation for Atmospheric Research's Climate System Modeling Program. 1.3 VEMAP Mailing List We have instituted an email list to keep VEMAP users informed of updates, future releases, and other information related to the VEMAP database. Our intent is to use this service as an electronic message board to quickly and easily disseminate pertinent database information. Archived list messages are available using the get command described in Appendix 5.3. For information on how to subscribe to the VEMAP mailing list, see Appendix 5. 2 DATA FILE ACCESS The VEMAP database is available on CDROM, through the Internet on the VEMAP Web site, or via anonymous FTP from the UCAR anonymous FTP server. 2.1 CDROM The VEMAP database CDROM contains the complete set of input files used for the VEMAP model intercomparison. We present the monthly data files in both gridded format (SVF) and time-sequential columnar format (site files), and the daily variables in binary format. Table 1 contains a list of the files available on the CDROM and Appendix A1 provides an overview of the CDROM directory structure. A README file resides under each subdirectory describing the files within that directory. We have included example postscript images under the subdirectory /images. Updated documentation and files for the CDROM can be found on the UCAR World Wide Web site: http://www.cgd.ucar.edu/vemap/CDROM_updates.html or obtained through the anonymous FTP site (see Section 2.3), under the /vUPDATES subdirectory. Table 1. Datasets available on the VEMAP CDROM, Web site (www), and FTP site. Category File Structure File Format Access Georeferencing Gridded data SVF* CDROM,www,FTP Monthly Climate Gridded data SVF CDROM,www,FTP Daily Climate Sequential data IEEE Binary CDROM,www,FTP Soil Gridded data SVF CDROM,www,FTP Vegetation Gridded data SVF CDROM,www,FTP Climate Scenarios Gridded data SVF CDROM,www,FTP Site Files Sequential data ASCII Columnar CDROM,www,FTP Bulk Transfer Multiple files UNIX Tarfiles www,FTP Phase I Model Results Gridded data SVF www,FTP *SVF = ASCII gridded "Single Variable Format" (see Section 4.1) 2.2 UCAR World Wide Web Site The VEMAP files are accessible from the Internet. Using an Internet browser (e.g., Mosiac, Netscape, etc.), enter the URL: http://www.cgd.ucar.edu/vemap/ Note for Macintosh Users: If you are using an early version of Netscape on a Macintosh, you may have difficulty downloading files. In this case, it is advisable to obtain the VEMAP files via anonymous FTP from ftp.ucar.edu (see Section 2.3). The VEMAP home page contains a short description of the VEMAP project and a directory to additional pages. To gain access to the VEMAP dataset from the VEMAP home page, click on "Access to the VEMAP Dataset" under the heading, "The VEMAP Dataset". A listing of available data files by dataset category is given in Table 1. 2.3 UCAR Anonymous FTP Server - ftp.ucar.edu To gain access to the VEMAP files via anonymous FTP from the UCAR FTP site, type: > ftp ftp.ucar.edu Name: anonymous Password: ftp> cd cgd/vemap ftp> cd ftp> get Available datasets are listed in Table 1 and the FTP directory structure is presented in Appendix 1. 2.4 GNU Compressed UNIX Tarfiles (Web Site and FTP Site Only) 2.4.1 Description of GNU Compressed Tarfiles We have archived multiple files into compressed UNIX tarfiles for more efficient data transfer. For example, all 12 gridded monthly maximum temperature files are stored in TX_MON.tar.gz (a .gz file suffix indicates that the tarfile has been compressed using the GNU software utility gzip, see below). Tarfiles can be found on the FTP site under the subdirectory /tarFiles. To use these files, first FTP the desired tarfile to your home machine (remember to set the transfer mode to binary before FTP'ing the tarfiles). Then, decompress the .gz file and extract the archived files using the GNU gzip and tar commands appropriate to your machine. For example, on a UNIX system, type: > gunzip > tar xvf This process will create a complete set of files in your current local directory. The tarfiles frequently contain a considerable number of files and require sufficient space in your current directory. For a complete listing of the space required for the contents of each tarfile, download the file: /tarFiles/README.tarFiles from ftp.ucar.edu. Characteristic decompressed and/or extracted individual file sizes are: Daily binary: ~4700 kBytes/file Gridded SVF: ~33 kBytes/file Columnar Site: ~370 kBytes/file 2.4.2 How to Obtain Free GNU Software The gzip utility is provided by the Free Software Foundation GNU project. It is available for multiple system platforms, and may be freely downloaded from the Internet. The Macintosh gzip version is available at: http://persephone.cps.unizar.es/general/gente/spd/gzip/gzip.html UNIX and MS-DOS versions, along with other GNU software, can be found at: ftp://prep.ai.mit.edu/pub/gnu/ 2.4.3 Hints for Using gzip on Selected Systems UNIX: To compress files, use the command gzip; gunzip will decompress a .gz file. The command man gzip will provide more information on how to use gzip. MS-DOS: To compress files, use the command gzip; gzip -d will decompress a .gz file. The command gzip -h will provide more information on how to use gzip. Macintosh: The Macintosh web site provides full instructions on how to use MacGzip. 3 THE VEMAP GRID The grid used for the VEMAP coverage is a 0.5 degree latitude 0.5 degree longitude grid covering the conterminous U.S. Grid edges are aligned with 1.0 degree and 0.5 degree latitude-longitude lines; grid centers are located at 0.25 degree and 0.75 degree latitude-longitude intersections. Latitude and longitude for each cell are included in the VEMAP dataset (Section 7.3). The grid's minimum bounding rectangle (MBR) is defined by grid domain corners given in Table 2. The full 0.5 degree VEMAP grid contains 5520 cells, with 115 columns and 48 rows. Table 2. VEMAP grid corners defining the minimum bounding rectangle (MBR). Grid Position Longitude* Latitude Lower Left Corner -124.5 25.0 Upper Right Corner -67.0 49.0 *Negative longitudes are degrees West. 4 FILE STRUCTURE We use three file formats throughout the VEMAP dataset: (1) ASCII SVF format for gridded monthly current climate, climate change scenarios, soils, vegetation, and georeferencing files. (2) Binary time-sequential format for daily climate data. Each record contains the "characteristic year" of daily data for a grid cell (Section 8.3). Records are indexed by grid cell. (3) ASCII column format for sequential monthly climate data and climate change scenarios. Each line presents 12 monthly values for a single grid cell. Records are indexed by grid cell (site files, Section 12). 4.1 SVF File Format All gridded VEMAP data files are in an ASCII format based on, but not identical to, the SVF format specified by the GENAMAP Geographic Information System (GIS). Typical SVF files have 2 header lines followed by a 6-digit integer array. In contrast, VEMAP files have 5 header lines. The first 2 lines are a VEMAP data access policy statement, followed by a blank line. These first 3 lines must be removed in order to convert the file to standard SVF format. The 4th header line is a title line identifying the gridded variable and its units. For continuous data (i.e., non-categorical datasets), we also include the scale factor used to convert values to stored integers (Section 5.1). Division by this factor will restore the original value. Version number and revision date are also in the title line. The 5th header line gives the gridded array's column and row indices (as four 6-digit integers): 1, 115 and 1, 48. The header lines are followed by the gridded VEMAP integer array, which is dimensioned 115 columns by 48 rows (Section 3). The 6- digit integers in the VEMAP array include at least one blank space so that values in the file are space delimited. The array starts in the northwest corner of the grid, with the column index running west to east and row index running north to south (Fig. 1). Column 1 115 Row 1 1 2 3 4 5 6... ...115 116 117 118... ...230 231 ... 48 ...5520 Figure 1. Layout of the VEMAP gridded array, with grid cell ID numbers. The full grid contains 5520 grid cells, 3261 of which are within the boundaries of the conterminous U.S. and predominantly covered by land (see Section 5.2). Background cells (ocean and inland water cells) are assigned the value of -9999. 4.2 Binary File Format All daily variables are stored in IEEE binary format. We have provided a FORTRAN program to read the binary files: /programs/biread.f Each binary file contains 365 days of data for the 3261 grid cells with landcover in the U.S. Background grid cells are not included. The files begin with two header lines containing information about the data. The first lists the variable name, units, scaling factor, and version number. The second describes the content of each data record with the following string: gridpt|lon|lat|day (1->365) The two header lines are followed by 3261 data records. Each record includes the grid point identifying number (ID), longitude, latitude, and a year's worth of scaled daily values. Only one year of data is given per grid cell, representing a "characteristic" year (see Section 8.3). Grid cell ID numbers begin at the top left corner of the grid and proceed left to right, top to bottom (Fig. 1). Daily files on the NCAR FTP site or the WWW are available in GNU compressed format (Section 2.4) to speed FTP transmission. 4.3 ASCII Column Format for Site Files Site files contain the monthly climate data and scenarios in column format, with each record containing 12 monthly values for a single variable. This time-sequential format was developed to facilitate data extraction for individual grid cells. Site files contain 8 header lines, beginning with a 2 line VEMAP data policy statement, followed by a blank line. The 4th header line is a title line identifying the gridded variable and its units. For continuous data (i.e., non-categorical datasets), we also include the scale factor used to convert values to stored integers (see Section 5.1). Division by this factor will restore the original value. Version number and revision date are also in the title line. The next 4 lines provide column headings for the data records. In addition to the 12 monthly data values, each record contains auxillary geographic information for each cell: grid cell ID, latitude, longitude, elevation, VEMAP vegetation type (Section 10 and Table 9), Kuchler vegetation type (Appendix Table A3.2), and state identification number. The format of each data record is given in Table 3. VEMAP vegetation types listed in the site files are from version 2 of vveg (vveg.v2, Table 9). State identifying codes are listed in Appendix 4. Order of monthly values is January to December. Table 3. Format of data records in site files. Variable Variable Variable Column Column Scaling Type Width Start End Factor (columns) Grid Cell ID numeric 4 1 4 1 Latitude numeric 4 7 10 100 Longitude numeric 5 12 16 -100 Elevation numeric 4 19 22 1 vveg2 Vegetation numeric 2 27 28 1 Code Kuchler numeric 3 32 34 1 Vegetation Code State ID numeric 2 39 40 1 12 Monthly numeric 6 41 112 (*) Values *Scaling factor for monthly values is stored in the 4th header line. Site files include only non-background grid cells, so that there are 3261 data records per file. 5 SCALING FACTORS, BACKGROUND VALUES, AND LANDCOVER MASK 5.1 Scaling Factors In SVF, site, and daily binary files, data values are represented by scaled integers. We produced these integers by multiplying the original data values by a scaling factor (e.g., 100.0, 0.001) which is included in the fourth header line of the SVF and site files and first header of the daily binary files. Division by this scaling factor will restore the original value. For example, if the listed value equals 297 and the scaling factor equals 10.0, the actual value equals: (297/10.0 = 29.7). 5.2 Background Values Data files contain roughly 2200 cells that are outside the physical or political boundaries of the conterminous U.S. (i.e., outside the VEMAP domain). In the SVF gridded files, these cells are set to the stored background value of -9999. In addition, cells dominated by large inland water bodies (e.g., Lake Michigan, Great Salt Lake) are also set to -9999. In some files, generally those containing VEMAP Phase I model results, cells classified as wetlands (in vveg.vx, Table 9) are also set to background. Counts of background cells and data cells, either including or excluding wetlands, are given in Tables 4a and 4b, respectively. Table 4a. Number of background cells and cells within the VEMAP domain with land cover (including wetlands). Cell Type Count Non-background grid cells 3261 (including wetlands) Background cells (-9999) 2259 Total 5520 Table 4b. Number of background cells (with wetland cells set to -9999) and cells within the VEMAP domain with land cover. Cell Type Count Non-background grid cells 3168 (excluding wetlands) Background cells (-9999) 2352 (with wetland cells set to -9999) Total 5520 Most VEMAP data files contain data for wetland cells, such that the non-background cell count for data files is 3261 cells (Table 4a). Exceptions are latitude and longitude files (Section 7) in which all cells are filled with data (non-background cell count = 5520), and elevation and vegetation files (non-background cell count = 3385). Typical non-background grid cell counts for VEMAP Phase I results files are 3168 cells because most of the models were not run for the wetland cells (Table 4b). 5.3 Landcover Mask Any soil or gridded climate file can be used as a VEMAP domain land mask. In these files, background values (-9999) indicate cells outside the domain or over inland water bodies, and all other values identify non-water cells within the domain. 6 ROAD MAP TO FILE AND VARIABLE DESCRIPTIONS In the following sections (Sections 7 - 11), we describe VEMAP database variables and associated files. Each of these sections follows this general outline: * Summary of available variables * File naming protocol * Derivation of variables (for most sections) * Description of individual variables For subsections that describe individual variables (e.g., Section 7.3.1), subsection headings include the variable name code used in filenames (in parentheses) and units (in square brackets). Descriptions include data sources and derivations where appropriate. At the end of each subsection, names of gridded SVF files, daily binary files (when present), and scaling factors are listed. We list a background cell value of "N/A" if all cells are filled with data. 7 GEOREFERENCING VARIABLES AND CELL AREAS 7.1 Summary of Geographic Variables The VEMAP dataset includes three georeferencing and three cell area variables (Table 5). These are described in more detail in Section 7.3. On the CDROM and FTP site, these data files are located under the subdirectory /geog. Note that the area variables are related: varea = (areap/100) * area Table 5. Geographic variables. Variable name codes are those used in filenames (Section 7.2). Variable Description Name Code elev Average grid cell elevation lat Latitude of grid cell center lon Longitude of grid cell center area Absolute area of a grid cell areap Percent of a grid cell covered by land and within U.S. borders varea Absolute area of a grid cell covered by land and within U.S. borders 7.2 Filename Protocol The filename protocol for area and georeferencing files is: VAR where: VAR = Variable name elev elevation [m] lat latitude [degrees and decimal degrees] lon longitude [degrees and decimal degrees] area absolute area [km2] areap percent VEMAP domain area [%] varea absolute VEMAP domain area [km2] 7.3 Variable Descriptions 7.3.1 Elevation (VAR = elev) [m] Elevation was aggregated from 10-minute Navy Fleet Numeric Oceanographic Center (NFNOC 1985) data (C. Vorosmarty, personal communication). Aggregated elevation for each 0.5 degree cell was computed as a simple mean of nine 10-minute grid cell modal values. Elevations for inland water bodies are included; non- background cell count = 3385 (see Section 5.2). Gridded SVF file: elev Scaling factor: 1.0 7.3.2 Latitude (VAR = lat) [degrees and decimal degrees] Latitude of grid cell center. Positive for North latitudes. All cells are filled with latitude values; there are no background cells. Gridded SVF file: lat Scaling factor: 100.0 7.3.3 Longitude (VAR = lon) [degrees and decimal degrees] Longitude of cell center. Scaling factor gives negative degrees for West longitudes. All cells are filled with longitude values; there are no background cells. Gridded SVF file: lon Scaling factor: -100.0 7.3.4 Area (VAR = area) [km2] Absolute area of a grid cell. Determined by coordinate geometry. Gridded SVF files: area Scaling factor: 1.0 7.3.5 Percent Land Area (VAR = areap) [%] Percent of the area of a 0.5 degree latitude/longitude grid cell that is covered by land and within the VEMAP domain (the conterminous U.S.). Derived from the Kern U.S. EPA 10-km gridded soil coverage (Section 9), this is the number of non- zero 10-km pixels relative to the total number of pixels in a 0.5 degree cell. Gridded SVF files: areap Scaling factor: 1.0 7.3.6 Absolute Land Area (VAR = varea) [km2] Absolute area of a grid cell that is covered by land and within the VEMAP domain (the conterminous U.S.). Absolute land area is determined as: varea = (area) * (areap/100) Gridded SVF files: varea Scaling factor: 1.0 8 DAILY, MONTHLY, AND ANNUAL CLIMATE DATASETS 8.1 Summary of Climate Variables The database includes 21 climate variables (Table 6), which are described in Sections 8.4 - 8.8, and are presented in daily, monthly, and annual files. Section 8.3 discusses development of the daily and monthly versions. On the CDROM and FTP site, these data are in the subdirectories /daily and /monthly. Selected climate variables are also available in site file format (Sections 4.3 and 12). Table 6. Climate variables. Variable name codes are those used in filenames (Section 8.2). Variable Name Description Code tx, tn, tm Maximum, minimum, mean temperature r_atmax, r_atmin Record absolute maximum, minimum temperature r_mtmax, r_mtmin Month of occurrence of record absolute maximum, minimum temperature c_atmax, c_atmin Characteristic year absolute maximum, minimum temperature c_mtmax, c_mtmin Month of occurrence of characteristic year absolute maximum, minimum temperature p Accumulated precipitation sr Total incident solar radiation at surface fsr 'sr' as fraction potential total solar radiation at top of atmosphere (total atmospheric transmissivity: clear sky + cloud effects) fsr_sfc 'sr' as fraction potential total solar radiation at surface (cloud transmissivity) psr Potential total solar radiation at top of atmosphere psr_sfc Potential total solar radiation at surface irr Mean daily irradiance vp Vapor pressure rh Relative humidity (mean for daylight hours) w Wind speed 8.2 Climate Filename Protocol 8.2.1 Gridded Monthly and Annual SVF Files The filename protocol for gridded monthly and annual climate SVF files, with the exception of gridded absolute temperature files (Section 8.2.2), is: VAR.MMM where: VAR or = Variable name VAR_sfc tx maximum temperature [degrees C] tn minimum temperature [degrees C] tm mean temperature [degrees C] p precipitation [mm] sr total incident solar radiation [kJ/m2] fsr 'sr' as fraction potential total solar radiation at top of atmosphere [0-1] fsr_sfc 'sr' as fraction potential total solar radiation at surface [0-1] psr potential total solar radiation at top of atmosphere [kJ/m2] psr_sfc potential total solar radiation at surface [kJ/m2] irr mean irradiance [W/m2] vp vapor pressure [mb] rh mean daylight relative humidity [%] w wind speed [m/s] .MMM = Period month (jan, feb, etc.) or annual (ann) 8.2.2 Gridded Absolute Temperature SVF Files Protocol for naming gridded absolute temperature and month of occurrence of absolute temperature files is: P_VAR where: P_ = Period r 20-year WGEN record c characteristic year VAR = Variable atmax absolute maximum temperature atmin absolute minimum temperature mtmax month of occurrence of absolute maximum temperature (e.g., 7 = July) mtmin month of occurrence of absolute minimum temperature (e.g., 1 = January) 8.2.3 Binary Daily Files Filenames for binary daily files follow the form: VAR. BI, or VAR_sfc. BI where: VAR = Variable name BI = Binary daily file and where variable name codes are the same as for monthly and annual SVF files (Section 8.2.1). 8.3 Creation of Climate Variables The VEMAP dataset includes daily, monthly, and annual climate data for the conterminous U.S. including maximum, minimum, and mean temperature, precipitation, solar radiation, and humidity. Seasonal mean surface wind speed is also provided. The monthly, seasonal, and annual data are long-term climatological means and are on the CDROM and FTP site under the subdirectory /monthly. Annual averages are simple means of the 12 monthly fluxes. The daily set presents a "characteristic year" in which monthly averages or accumulations of the daily values match the long-term monthly climatology but where the daily series has variances and covariances characteristic of a station's weather record. The daily data are on the CDROM and FTP site in the subdirectory /daily. We used two processes to create the daily climate data (Kittel et al. 1995): (1) statistical simulation of daily temperature and precipitation records, and (2) empirical estimation of corresponding daily radiation and humidity records. 8.3.1 Temperature and Precipitation Records In the first process, we generated one year of daily precipitation and maximum and minimum temperature for each VEMAP grid cell. These records were produced using a stochastic daily weather generator, WGEN (Richardson 1981, Richardson and Wright 1984), which we modified to better utilize temporal statistics created by its accompanying parameterization program, WGENPAR. Parameterization of WGEN was based on daily records from 870 stations. WGEN was run for each grid cell with parameters assigned from the closest station. Climate records created by WGEN have realistic daily variances and temporal autocorrelations (e.g., persistence of wet and dry days) and maintain physical relationships between daily precipitation and temperature. For example, in the WGEN records, days with precipitation tend to have lower maximum temperatures than days with no precipitation. To obtain the one year daily series, we first produced a 20-year weather record using WGEN. From this 20-year record, we derived the VEMAP characteristic year by choosing 12 individual months whose monthly means most closely matched the corresponding long- term historical monthly means (e.g., January from year 5, February from year 2, etc.). Daily values of the selected months were adjusted so that their monthly sum (for precipitation) or mean (for temperature) exactly matched the historical long-term monthly means. As the final step in this process, we determined the absolute maximum and minimum temperatures and their month of occurence for the characteristic year (c_atmax, c_mtmax, c_atmin, c_mtmin). We also saved "record" absolute maximum and minimum temperatures and their month of occurence (r_atmax, r_mtmax, r_atmin, r_mtmin) from the full 20-year WGEN simulation which includes interannual variation about the long-term mean. 8.3.2 Solar Radiation and Humidity Records We used CLIMSIM (Running et al. 1987) to generate daily records of solar radiation and surface air humidity from daily maximum and minimum temperatures and precipitation. We produced 6 solar radiation variables: total incident solar radiation at the surface (sr), sr as a fraction of potential total solar radiation at the top of the atmosphere (fsr) and at the surface (fsr_sfc), potential total solar radiation at the top of the atmosphere (psr) and at the surface (psr_sfc), and mean daily irradiance at the surface (irr). Humidity variables generated were vapor pressure (vp) and mean daylight relative humidity (rh). Because of biases in the method used in CLIMSIM to generate humidities from daily minimum temperature (Kimball et al. 1996), daily vapor pressure values were adjusted so that monthly means match the long-term means of Marks (1990). More details on this adjustment are given in Section 8.7.1. Monthly means of solar radiation and humidity variables were created from the daily CLIMSIM output. Because the solar radiation and humidity data are based on temperatures and precipitation that are constrained to match their long-term means and because the humidity data are additionally constrained by the Marks (1990) means, monthly means of the solar radiation and humidity dailies are taken to represent the climatological means of these variables. For radiation variables, monthly and annual files contain either averages or totals of daily values. To distinguish between these, refer to units and file descriptions (e.g., "Average monthly file" vs. "Total monthly file") in Section 8.6. 8.4 Maximum, Minimum, and Mean Temperature [degrees C] 8.4.1 Maximum, Minimum, and Mean Temperature (VAR = tx, tn tm) [degrees C] Long-term monthly mean daily maximum and minimum temperatures were interpolated to the VEMAP grid from 4613 station 1961-1990 normals (NCDC 1992, dataset TD-9641). Station values were adiabatically lowered to sea level (Marks and Dozier 1992), interpolated to the 0.5 degree VEMAP grid, and then re-adjusted to the new grid elevation. Mean temperatures were computed as a simple average of the gridded maximum and minimum monthly temperatures. We then generated daily maximum and minimum temperatures for each grid point, as described in Section 8.3.1. Daily temperatures were constrained in the generation process so that their monthly means matched the interpolated long-term monthly normals. Daily mean temperatures are not provided. Daily binary files: tx.BI tn.BI Average monthly SVF files: tx.MMM tn.MMM tm.MMM Average annual SVF files: tx.ann tn.ann tm.ann Scaling factor: 10.0 8.4.2 Record Absolute Maximum and Minimum Temperature (VAR = r_atmax, r_atmin) [degrees C] Absolute daily maximum and minimum temperature in the 20-yr WGEN record. SVF file: r_atmax r_atmin Scaling factor: 10.0 8.4.3 Month of Occurrence of Record Absolute Maximum and Minimum Temperature (VAR = r_mtmax, r_mtmin) [month id: 1-12] The month of occurrence of absolute maximum and minimum temperature in the 20-yr WGEN record. Month identifier runs from 1 to 12, corresponding to months January through December. SVF file: r_mtmax r_mtmin Scaling factor: 1.0 8.4.4 Characteristic Year Absolute Maximum and Minimum Temperature (VAR = c_atmax, c_atmin) [degrees C] Absolute maximum and minimum temperature found in the VEMAP characteristic year. SVF file: c_atmax c_atmin Scaling factor: 10.0 8.4.5 Month of Occurrence of Characteristic Year Absolute Maximum and Minimum Temperature (VAR = c_mtmax, c_mtmin) [month id: 1- 12] The month of occurrence of absolute maximum and minimum temperature found in the VEMAP characteristic year. Month identifier runs from 1 to 12, corresponding to months January through December. SVF format: c_mtmax c_mtmin Scaling factor: 1.0 8.5 Precipitation (VAR=p) [mm/day, month, or year] Long-term mean monthly precipitation was spatially aggregated from a 10-km gridded U.S. dataset developed using PRISM by Daly et al. (1994). PRISM models precipitation distribution by (1) dividing the terrain into topographic facets of similar aspect, (2) developing precipitation-elevation regressions for each facet type for a given region based on station data, and (3) using these regressions to spatially extrapolate station precipitation to 10-km cells that are on similar facets. We generated daily precipitation for each grid point using WGEN, as described in Section 8.3. Daily values were constrained such that monthly rainfall accumulations for each grid point matched the long- term monthly means. Note: Units and scaling factors differ for daily, monthly, and annual files. Daily binary files: p.BI [mm/day] Scaling factor: 10.0 Total monthly SVF files: p.MMM [mm/month] Total annual SVF file: p.ann [mm/year] Scaling factor: 1.0 8.6 Solar Radiation 8.6.1 Relationship Among Solar Radiation Variables Six solar radiation variables are included in the climate dataset (Table 6). These variables are either measures of solar radiation inputs at the top of the atmosphere (psr) and the surface (psr_sfc, sr, and irr) or of cloud and total transmissivity (fsr_sfc and fsr, respectively). Relationships among these variables on a daily basis are illustrated in Fig. 2 and are as follows. (1) Potential total incident solar radiation at the surface (psr_sfc) is the potential at the top of the atmosphere (psr) reduced by clear sky effects on transmissivity, such that: psr_sfc = psr * (clear sky transmissivity) (2) Total incident solar radiation at the surface (sr) is derived from potential solar radiation at the top of the atmosphere (psr) diminished by total atmospheric (clear sky and cloud) effects on transmissivity (fsr, ranging from 0 to 1), so that: sr = psr * fsr (3) Total incident solar radiation at the surface (sr) is also related to potential at the surface (psr_sfc) (which accounts for only clear sky effects on transmissivity), by further reducing psr_sfc by cloud effects: sr = psr_sfc * fsr_sfc where fsr_sfc is cloud transmissivity (0 - 1). (4) Atmospheric transmissivity variables are related to each other, such that total atmospheric transmissivity (fsr) is the product of cloud (fsr_sfc) and clear sky transmissivities: fsr = fsr_sfc * (clear sky transmissivity) (5) Daily mean surface irradiance for daylight hours (irr) is derived from sr and day length, such that, with unit conversion: irr = sr * (1 day/day length) * (1000J/1kJ) where day length is in seconds. Note that because radiation variables were determined on a daily basis, these relationships do not precisely hold for monthly averages or accumulations (see notes in Sections 8.6.5 and 8.6.6). Top of Atmosphere psr psr clear sky transmissivity total transmissivity (fsr) = clear sky + cloud transmissivity cloud transmissivity (fsr_sfc) Surface sr psr_sfc Figure 2. Schematic illustration of relationships among radiation variables in the climate dataset. 8.6.2 Total Incident Solar Radiation (VAR = sr) [kJ m-2 day-1 or kJ m-2 yr-1] Total incident solar radiation at the surface. Generated by CLIMSIM, sr is based on daily potential solar radiation at the top of the atmosphere (psr) and an estimate of daily atmospheric transmissivity (reported in this dataset as "fraction potential total solar radiation", fsr), such that: sr(daily) = psr_daily * fsr(daily) We report sr as daily (sr.BI) and monthly (sr.MMM) average values, and as an annual summation of daily values (sr.ann). Daily binary files: sr.BI [kJ m-2 day-1] Average monthly SVF files: sr.MMM [kJ m-2 day-1] Total annual SVF file: sr.ann [kJ m-2 yr-1] Scaling factor: 1.0 8.6.3 Surface Total Solar Radiation as Fraction of Top of Atmosphere Potential Total Solar Radiation (VAR = fsr) [fraction, 0-1] Ratio of total incident solar radiation at the surface (sr) to potential total solar radiation at the top of the atmosphere (psr), or total atmospheric transmissivity. CLIMSIM generates fsr as an estimate of atmospheric transmissivity (reported as "trans" in CLIMSIM). In CLIMSIM, atmospheric transmissivity is estimated first from clear sky transmissivity, which is a function of elevation. Clear sky transmissivity is then diminished by a surrogate for cloudiness, based on the occurrence of precipitation and the diurnal temperature range using the method of Bristow and Campbell (1984). Daily temperatures and precipitation used in these calculations are from the WGEN-generated record (tx.BI, tn.BI, p.BI). We report fsr as daily (fsr.BI) and monthly (fsr.MMM) average values, and as an average of the 12 monthly mean values (fsr.ann). Daily binary files: fsrday.BI Average monthly SVF files: fsr.MMM Average annual SVF file: fsr.ann Scaling factor: 1000.0 8.6.4 Surface Total Solar Radiation as Fraction of Surface Potential Total Solar Radiation (VAR_sfc = fsr_sfc) [fraction, 0-1] Ratio of total incident solar radiation at the surface (sr) to potential solar radiation at the surface (psr_sfc), or cloud transmissivity. Because psr_sfc already accounts for clear sky transmissivity, fsr_sfc represents a further reduction in transmissivity due to cloud cover. (See discussion of transmissivity calculations in the subsection on fsr, Section 8.6.3.) Therefore, fsr_sfc can be used as a surrogate for percent possible hours of sunshine or for (1 - % cloudiness). However, these 3 variables are not strictly the same. Percent hours of sunshine is determined at meteorological stations by a sunshine switch, and percent cloudiness by hourly observations of fractional cloud cover. We report fsr_sfc as daily (fsr_sfc.BI) and monthly (fsr_sfc.MMM) average values, and as an annual average of the monthly means (fsr_sfc.ann). Daily binary files: fsr_sfc.BI Average monthly SVF files: fsr_sfc.MMM Average annual SVF file: fsr_sfc.ann Scaling factor: 1000.0 8.6.5 Potential Total Solar Radiation at the Top of the Atmosphere (VAR = psr) [kJ m-2 day -1, mo-1, or yr-1] Monthly and annual accumulated potential total incident solar radiation generated by CLIMSIM. Potential total solar radiation is based on latitude and solar geometry using the method outlined by Gates (1981). We report psr as daily (psr.BI), monthly (psr.MMM), and annual (psr.ann) accumulations of daily values. Note: Because psr, fsr, and sr were determined on a daily basis, it is not possible to reproduce the monthly sr value based on monthly accumulated psr and mean monthly fsr values (i.e., [sr.MMM] * [days/month] != [psr.MMM] * [fsr.MMM]). Daily binary files: psr.BI [kJ m-2 day-1] Total monthly SVF files: psr.MMM [kJ m-2 mo-1] Total annual SVF file: psr.ann [kJ m-2 yr-1] Scaling factor: 0.01 8.6.6 Potential Total Solar Radiation at the Surface (VAR_sfc = psr_sfc) [kJ m-2 day -1] Monthly and annual averaged daily potential total incident solar radiation at the surface generated by CLIMSIM. Potential total solar radiation at the top of the atmosphere (based on latitude and solar geometry, Gates 1981) is modified by clear sky transmissivity to estimate potential solar radiation at the surface. We report psr_sfc as daily values (psr_sfc. BI), and monthly (psr_sfc.MMM) and annual (psr_sfc.ann) averages. Note: Because psr_sfc, fsr_sfc, and sr were determined on a daily basis, it is not possible to reproduce the monthly sr value based on the mean monthly psr_sfc and the mean monthly fsr_sfc values (i.e., [sr.MMM] != [psr_sfc.MMM] * [fsr_sfc.MMM]). Daily binary files: psr_sfc.BI [kJ m-2 day-1] Average monthly SVF files: psr_sfc.MMM [kJ m-2 day-1] Average annual SVF file: psr_sfc.ann [kJ m-2 day-1] Scaling factor: 0.01 8.6.7 Daily Mean Irradiance (VAR = irr) [W m-2] Daily mean irradiance for daylight hours, derived from CLIMSIM calculations of total incident solar radiation (sr.BI) and day length. Daily binary files: irr.BI [W/m2] Average monthly SVF files: irr.MMM [W/m2] Average annual SVF file: irr.ann [W/m2] Scaling factor: 100.0 8.7 Humidity 8.7.1 Vapor Pressure (VAR = vp) [mb] Vapor pressures were generated by CLIMSIM using WGEN-produced daily minimum temperature. CLIMSIM estimates surface air humidity by assuming that dew point temperature is equal to daily minimum temperature. To account for arid regions where the minimum temperature may not be an adequate estimate of dew point temperature, we modified vapor pressure and relative humidity values to more closely match long-term monthly means calculated by Marks (1990) (Kittel et al. 1995). If the Marks vapor pressure was less than CLIMSIM monthly mean vapor pressure, daily vapor pressures were adjusted by the corresponding monthly ratio: ratio(month) = [vp_MARKS(month)/vp_CLIMSIM(month)] If the Marks vapor pressure was equal to or higher than CLIMSIM (ratio < 1.0), no adjustment was made to daily vp and rh. New monthly mean vapor pressures were calculated from the adjusted values. Daily binary files: vp.BI Average monthly SVF files: vp.MMM Average annual SVF file: vp.ann Scaling factor: 100.0 8.7.2 Mean Daylight Relative Humidity (VAR = rh) [%] Generated by CLIMSIM with WGEN-generated temperature input (see Section 8.7.1). The mean is for daylight hours, as CLIMSIM calculates relative humidity relative to the saturated vapor pressure for a computed daylight-period temperature mean. If daily vapor pressures were adjusted (see Section 8.7.1), relative humidities were modified accordingly. Daily binary files: rh.BI Average monthly SVF files: rh.MMM Average annual SVF file: rh.ann Scaling factor: 10.0 8.8 Surface Wind Speed (VAR=w) [m/s] Grid-averaged seasonal wind speed at 10-meter height. These data are based on a 10-km EPA dataset (Marks 1990), which is in turn based on DOE seasonal (3-month) mean wind speeds with some topographic adjustment (Elliott et al. 1986). Wind speeds reported here in monthly files are the same within each season (e.g., winter = January, February, March). Average monthly SVF files: w.MMM Average annual SVF file: w.ann Scaling factor: 10.0 9 SOILS 9.1 Summary of Soil Variables The soils dataset includes 18 variables (Table 7). These are described in more detail in Sections 9.3 - 9.5. For most variables, soil data are provided for 2 layers: (1) 0 -> 50 cm (2) 50 -> 150 cm Relationships among area variables (ma, oa, map, tap) are presented in Appendix 2.1. On the CDROM and FTP site, soil data can be found in the /soil subdirectory. Table 7. Soil variables. Variable name codes and layer codes (L) are those used in filenames (Section 9.2). Variable Name Layers (L*) Description Code modes - Number of modal soil profiles per cell map - Percent areal coverage of mineral soil component within a modal or average soil (Corresponding variable for organic soil component is not included; 1 - map) tap - Percent areal coverage of a given modal soil (or average soil) within the VEMAP land area of a grid cell ma, oa - Absolute areal coverage of mineral (or organic) soil component within a modal or average soil mbd 1, 2 Bulk density mz, oz - Soil depth msa,msi,mcl,moc 1, 2** Texture: % sand, silt, clay, organic content mrf, orf 1, 2 Rock fragments mwh, owh 1, 2 Water holding capacity tsoc, tsoc20 (0-100 cm, Soil organic carbon 0- 20 cm) *layer ID code, L: 1 = 0 - 50 cm, 2 = 50 - 150 cm **moc is not available for layer 2 9.2 Soil Filename Protocol File names for modal soils follow the form: CVARL_mM and for average soils: CVARL_ave where: C = Soil component type m mineral soil o organic soil t total (both components combined) VAR = Variable name Mineral soils only (C = m) ap areal coverage of mineral soil [% of modal soil area] sa sand content of mineral soil [% by weight] si silt content of mineral soil [% by weight] cl clay content of mineral soil [% by weight] bd bulk density of mineral soil [g/cm3] oc organic content of mineral soil [% by weight] Mineral and organic soils (C = m, o) wh water holding capacity [cm H2O] z depth [cm] a area [km2] rf rock fragments [% by volume] Total (C = t; omitted for modes) modes number of modal soil profiles per cell tap mode area [% of cell land area] tsoc soil organic carbon (0-100 cm) [mg C/ha] tsoc20 soil organic carbon (0-20 cm) [mg C/ha] L = Layer 1 0 - 50 cm 2 50 - 150 cm _mM or .mM = Modal soil profile id# (M = 1 to 4, with m1 representing the most dominant profile _ave or .ave = Average soil profile 9.3 Creation of the VEMAP Soils Dataset 9.3.1 Source Data Soil properties were based on a 10-km gridded EPA soil database developed by Kern (1994, 1995). Two soil coverages are provided in the Kern dataset: one from the USDA Soil Conservation Service (SCS) national soil database (NATSGO) and the other from the United Nations Food and Agriculture Organization soil database (FAO 1974- 78). Only the SCS NATSGO soils are included in the VEMAP set. 9.3.2 Modal Representation of Cell Soil Data Physical consistency in soils data was incorporated by representing a grid cell's soil by a set of dominant (modal) soil profiles, rather than by a simple average of soil properties. Because soil processes, such as soil organic matter turnover and water balance, are non-linearly related to soil texture and other soil parameters, simulations based on dominant soil profiles and their frequency distribution can account for soil dynamics that would be lost if averaged soil properties were used. To spatially aggregate Kern data to the 0.5 degree grid, we used cluster analysis to group the subgrid 10-km elements into up to 4 modal soil catagories (Kittel et al. 1995). In this statistical approach, cell soil properties are represented by the set of modal soils, rather than by an "average soil." We also provide cell- averaged soil data. See Appendix 2 for determination of the absolute area represented by the entire cell (or by each modal soil within a cell) and the application of these quantities to model results. These areal values are included in the database as variables ma and oa (Section 9.5.4) and varea (Section 7.3.6). 9.3.3 Missing Code When a soil mode is not present, the cell value is set to a stored value of -98 (i.e., -98 is not scaled by the scaling factor). If a mode is absent, no additional soil profiles are present for the cell from that mode level on. For example, if mode 3 is not present, then all the soil information for that cell is contained in the previous modes (modes 1 and 2), and mode 4 will also be absent. 9.4 Hierarchical Division of Soils into Mineral and Organic Components and Texture Classes Structure of the VEMAP soil dataset follows the hierarchical division of a cell's soil in the Kern sets. In the Kern SCS NATSGO database, each soil type is represented by 2 component soils: a mineral soil (C=m) and an organic soil (C=o), each with its own profile of soil properties. Both mineral and organic soils are further differentiated into rock fragments and finer elements. Rock fractions are presented as a percentage of the entire soil volume for each of these component soils. VEMAP Phase I simulations used the mineral soil component of mode 1 soils. The finer elements of the mineral soil are defined texturally in terms of mineral (msa, msi, mcl) and organic (moc) content. Percent organic matter (by weight) is relative to the combined mineral and organic fractions of the mineral soil. Percent by weight of sand, silt, and clay are relative to the mineral portion only. Values are averages for each modal (or cell average) soil. Percent sand, silt, and clay add up to 100% (+/- 1% due to rounding error). 9.5 Soil Files 9.5.1 Modes per Cell (VAR = modes) [units = number of modes] Number of modal soils per cell. Number of modes range from 1 to 4. Gridded SVF files: modes Scaling factor: 1.0 9.5.2 Mineral Soil Percent Areal Coverage within a Given Modal (or Average) Soil (CVAR = map) [% of modal soil area, or % of area of all modal soils] Relative area covered by mineral soils as percent of the total area covered by a given modal soil (map_mM) or for all soils in a cell (map_ave). Note that the area covered by organic soils equals (1 - map) for the corresponding modal soil or cell average. Gridded SVF files: Mineral soils map_mM map_ave Scaling factor: 1.0 9.5.3 Modal Soil Percent Areal Coverage (CVAR = tap) [% of cell land area] Relative area covered by modal soil M as percent of area covered by land (and within U.S. borders) for each 0.5 degree grid cell. Includes both mineral and organic components. Gridded SVF files: tap_mM Scaling factor: 1.0 9.5.4 Absolute Areal Coverage of a Modal (or Average) Mineral or Organic Soil (CVAR = ma, oa) [km2] Areal coverage of the mineral (ma) or organic (oa) component soil in a cell for either a modal (_mM) or average (_ave) profile. (See Appendix 2.1 for calculation of this variable.) Gridded SVF files: Mineral soils ma_mM ma_ave Organic soils oa_mM oa_ave Scaling factor: 1.0 9.5.5 Bulk Density (CVAR = mbd) [g/cm3] Bulk density of the mineral soil component for layer L and soil mode M (or cell average). Gridded SVF files: Mineral soils mbdL_mM mbdL_ave Scaling factor: 100.0 9.5.6 Soil Depth (CVAR = mz, oz) [cm] Soil depth for mineral and organic soils for soil mode M (or cell average). Gridded SVF files: Mineral soils mz_mM mz_ave Organic soils oz_mM oz_ave Scaling factor: 1.0 9.5.7 Texture (CVAR = msa, msi, mcl, moc) [% by weight] Percent sand, silt, and clay of mineral portion of mineral soil and percent organic content of entire mineral soil (see Section 9.4) for layer L and soil mode M (or cell average). Note: This coverage is for mineral soils only. sand/silt/clay - Gridded SVF files: sand msaL_mM msaL_ave silt msiL_mM msiL_ave clay mclL_mM mclL_ave Scaling factor: 1.0 organic matter - (for layer 1 only) Gridded SVF files: moc1_mM moc1_ave Scaling factor: 100.0 9.5.8 Rock Fragments (CVAR = mrf, orf) [% by volume] Rock fragments for mineral and organic soils for layer L and soil mode M (or cell average). Gridded SVF files: Mineral soils mrfL_mM mrfL_ave Organic soils orfL_mM orfL_ave Scaling factor: 1.0 9.5.9 Water Holding Capacity (CVAR = mwh, owh) [cm H2O] For mineral soils, Kern (1995) provides water holding capacity (WHC) based on Rawls et al. (1982) and Saxton et al. (1986). We used the Rawls et al. WHC for layer 1, because this method utilizes organic matter content, and the Saxton et al. WHC for layer 2, where organic content information is not present (Saxton et al. calculations are based only on % sand/silt/clay). For WHC of organic soils, Kern used Paivanen (1973) and Boelter (1969). There was an error in the original Kern WHC values for layer 1. This is corrected as per Kern (1996). Gridded SVF files: Mineral soils mwhL_mM mwhL_ave Organic soils owhL_mM owhL_ave Scaling factor: Mineral soils 10.0 Organic soils 100.0 9.5.10 Soil Organic Carbon (CVAR = tsoc20, tsoc) [mg C/ha] Soil organic carbon (SOC) for 0-20 cm and 0-100 cm layers for modal and average soils. Calculated from mean SOC values in the Kern EPA soil database, based on SCS NATSGO data. SOC is for both mineral and organic soil combined. SCS data are for current SOC levels, including for agricultural soils where present. Kern's SOC values are adjusted for rock fragment content and actual soil depth. SOC 0-20 cm was derived using mean SOC values for 4 soil layers in the Kern EPA database by (1) integrating between 15 and 20 cm along a spline function that was fit to values for 0-8, 8-15, 15-30, and 30-75 cm layers and (2) adding the integrated value to the sum of 0-8 and 8-15 cm SOC. Gridded SVF files: tsoc20_mM tsoc20_ave tsoc_mM tsoc_ave Scaling factor: 1.0 10 VEGETATION 10.1 Summary of Vegetation Variables The vegetation dataset includes one variable: vegetation type (Table 8). This coverage is of potential natural vegetation under current conditions (see Section 10.2). We include the original coverage used in VEMAP Phase I simulations (vveg.v1), as well as a slightly modified version (vveg.v2). Vegetation files can be found in the subdirectory /geog on the CDROM and FTP site. Table 8. Vegetation variable name code and description. Variable Name Description Code vveg Current distribution of VEMAP vegetation class Table 9. VEMAP vegetation types: vveg identifying code and corresponding VEMAP vegetation type. Where type description differs between vveg versions, the version is identified in parentheses. vveg Code Vegetation Type TUNDRA 1 Tundra FOREST 2 Boreal Coniferous Forest (includes Boreal/Temperate Transitional and Temperate Subalpine Forests) 3 Maritime Temperate Coniferous Forest 4 Continental Temperate Coniferous Forest 5 Cool Temperate Mixed Forest 6 Warm Temperate/Subtropical Mixed Forest 7 Temperate Deciduous Forest 8 Tropical Deciduous Forest (not present)** 9 Tropical Evergreen Forest (not present) XEROMORPHIC WOODLANDS and FORESTS 10 Temperate Mixed Xeromorphic Woodland 11 Temperate Conifer Xeromorphic Woodland 12 Tropical Thorn Woodland (not present) SAVANNAS 13 Temperate/Subtropical Deciduous Savanna (.v1) Temperate Deciduous Savanna (.v2) 14 Warm Temperate / Subtropical Mixed Savanna 15 Temperate Conifer Savanna 16 Tropical Deciduous Savanna (not present) GRASSLANDS 17 C3 Grasslands (includes Short, Mid-, and Tall C3 Grasslands) 18 C4 Grasslands (includes Short, Mid-, and Tall C4 Grasslands) SHRUBLANDS 19 Mediterranean Shrubland 20 Temperate Arid Shrubland 21 Subtropical Arid Shrubland EXCLUDED SURFACE TYPES 90 Ice (not present) 91 Inland Water Bodies (includes ocean inlets) 92 Wetlands (includes floodplains and strands) ** not present = vegetation type is not present in the current distribution of types for the U.S. on the 0.5 degree grid (vveg.v1, vveg.v2). These types are included because they are outputs of VEMAP biogeographical models where vegetation distribution could change under altered climate and CO2 forcing, and they were used as inputs to selected biogeochemical model runs. 10.2 Creation of the Vegetation Dataset Vegetation types are defined physiognomically in terms of dominant lifeform and leaf characteristics (including leaf seasonal duration, shape, and size) and, in the case of grasslands, physiologically with respect to dominance of species with the C3 versus C4 photosynthetic pathway (Table 9). The physiognomic classification criteria are based on our understanding of vegetation characteristics that influence biogeochemical dynamics (Running et al. 1994). The U.S. distribution of these types is based on a 0.5 degree latitude/longitude gridded map of Kuchler's (1964, 1975) potential natural vegetation provided by the TEM group (D. Kicklighter and A.D. McGuire, personal communication). Kuchler's map is based on current vegetation and historical information and, for purposes of VEMAP Phase I model experiments, is presumed to represent potential vegetation under current climate and atmospheric CO2 concentrations (355 ppm). The aggregation of Kuchler to VEMAP vegetation types for versions 1 and 2 is given in Appendix 3. 10.3 Vegetation Files 10.3.1 vveg.v1 Current distribution of potential natural vegetation, aggregated from Kuchler's (1964, 1975) potential natural vegetation map (Appendix 3). VEMAP Phase I used vveg.v1 for simulations that input current potential natural vegetation. Gridded SVF file: vveg.v1 Scaling factor: 1.0 10.3.2 vveg.v2 Similar to vveg.v1 but with slight variations in vegetation distribution based on a modification of the VEMAP aggregation of Kuchler types (Appendix 3). The updated distribution (.v2) is used in the site files (see Section 12). Gridded SVF file: vveg.v2 Scaling factor: 1.0 11 CLIMATE CHANGE SCENARIOS 11.1 Summary of Climate Scenario Files There are 8 climate change scenarios in the VEMAP database (Table 10, Section 11.3.2). These are based on doubled-CO2 climate model experiments and are described in Section 11.3. Not all variables are available for each scenario (Table 10). We report changes as either differences or change ratios, depending on the variable (Section 11.3, Table 10). The scenarios can be found in the directory /scenario on the CDROM and FTP site. Table 10. Availability of climate variables for each climate scenario and description of the change field (diff = difference, ratio = change ratio). Climate scenarios are based on climate model experiments discussed in Section 11.3. Climate Scenario Variab Change CCC GFDL GFDL GFDL GISS RegCM OSU UKMO le Field R15 R15 R30 Name Type Q- flux tx diff X tn diff X t, tm diff X X X X X X X X rh diff X X X X X X p ratio X X X X X X X X sr ratio X X X X X X X X vp ratio X X X X X X X w ratio X X X X X X X X 11.2 Scenario Filename Protocol The naming protocol for scenario files is: VAR_GGG.MMM where: VAR = Variable name t, tm surface air mean temperature difference (2xCO2-1xCO2) tx, tn surface air maximum or minimum temperature difference (2xCO2-1xCO2) rh relative humidity difference (2xCO2-1xCO2) p precipitation change ratio (2xCO2/1xCO2) sr total incident solar radiation change ratio (2xCO2/1xCO2) vp surface vapor pressure change ratio (2xCO2/1xCO2) w surface wind speed change ratio (2xCO2/1xCO2) _GGG = Climate model experiment ccc CCC gf1 GFDL R15 gfq GFDL R15 Q-flux gf3 GFDL R30 gis GISS mm4 RegCM (MM4) osu OSU ukm UKMO .MMM = Period month (e.g., jan, feb) or annual (ann) 11.3 Development of Climate Change Scenarios 11.3.1 Overview Climate scenarios from eight climate change experiments are included in the database. Seven of these experiments are from atmospheric general circulation model (GCM) 1xCO2 and 2xCO2 equilibrium runs (Section 11.3.2). These GCMs were implemented with a simple "mixed-layer" ocean representation that includes ocean heat storage and vertical exchange of heat and moisture with the atmosphere, but omits or specifies (rather than calculates) horizontal ocean heat transport. The eighth scenario is from a limited-area nested regional climate model (RegCM) experiment for the U.S. (see Section 11.3.2) which was supported by the Model Evaluation Consortium for Climate Assessment (MECCA). The CCC and GFDL R30 runs are among the high resolution GCM experiments reported in IPCC (1990). Changes in monthly mean temperature and relative humidity were represented as differences (2xCO2 climate value - 1xCO2 climate value) and those for monthly precipitation, solar radiation, vapor pressure, and horizontal wind speed as change ratios (2xCO2 climate value/1xCO2 climate value). GCM grid point change values were derived from archives at the National Center for Atmospheric Research (NCAR; Jenne 1992) and spatially interpolated to the 0.5 degree VEMAP grid. Wind speed changes are for the lowest model level. For GISS runs, we calculated winds from vector components and then determined the change ratio. Values from the 60-km RegCM grid were reprojected to the 0.5 degree grid. For calculation of relative humidity changes, see Section 11.3.3. Vapor pressure (and relative humidity) were not available for the CCC run; relative humidity changes were not determined for the RegCM experiment. A key issue in the generation of altered climates based on climate model output is the strong possibility of physical inconsistencies in the new climates. Change ratios from the NCAR archive have an imposed upper limit of 5.0, providing some constraint on these changes. An exception is that the GISS wind speed change ratios do not have this limit imposed (most GISS wind speed change ratios were less than 5). In the creation of the climates, we suggest additional checks for physical consistency in Section 11.5. For a discussion of the utility and limitations of using climate model experiment outputs for exploring ecological sensitivity to climate change, see Sulzman et al. (1995). 11.3.2 Model Experiments The 8 climate model experiments are: CCC - Canadian Climate Centre (Boer, McFarlane, and Lazare 1992) GISS - Goddard Institute for Space Studies (Hansen et al. 1984) GFDL - Geophysical Fluid Dynamics Laboratory. Three experiments: (1) GFDL R15: R15 (4.5 degree by 7.5 degree grid) runs without Q- flux corrections (Manabe and Wetherald, 1987). (2) GFDL R15 Q-flux: R15 resolution (4.5 degree by 7.5 degree grid) runs with Q-flux corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990). (3) GFDL R30: R30 (2.22 degree by 3.75 degree grid) run with Q-flux corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990). OSU - Oregon State University (Schlesinger and Zhao 1989) UKMO - United Kingdom Meteorological Office ("UKLO" low resolution run; Wilson and Mitchell 1987) RegCM (MM4) - National Center for Atmospheric Research (NCAR) nested regional climate model (climate version of the Pennsylvania State University/NCAR mesoscale model MM4; Giorgi, Brodeur and Bates 1994). Conterminous U.S. simulations were on a 60-km interval grid and were driven by 1x and 2xCO2 equilibrium GCM runs (Thompson and Pollard 1995a, 1995b). 1x and 2xCO2 RegCM runs were each 3 years in length. Climate changes were based on averages for these runs. 11.3.3 Determination of Surface Humidity Change Surface humidity is reported in the NCAR archives as mixing ratio (r) for OSU and GFDL runs and as specific humidity (q) for UKMO and GISS runs; no humidity variable was archived for CCC runs. We converted q and r to vapor pressure and calculated a change ratio. Determination of new monthly mean daytime relative humidities (RH) from monthly change ratios of vapor pressure (VP) on a monthly basis and independent of a base or control climate is problematic. This is because of non-linear relationships among VP, RH, and temperature and between daily mean daylight temperature and monthly temperature means. While recognizing these limitations, we estimated monthly mean RH for each scenario from corresponding monthly VP and temperature means, mimicking the daily method in CLIMSIM. New climate monthly values were constrained to be between 0 and 100%. We assumed that changes in monthly mean RH are a good estimate of changes in monthly mean daylight RH. 11.4 Climate Change Scenario Variables All variables in the scenario dataset are change fields. The reader is referred to the section on methods and cautions for creating new climate inputs based on these fields (Section 11.5). 11.4.1 Difference Fields Temperature - [degrees C] Difference in monthly or annual mean monthly temperature. Gridded SVF files: t_GGG.MMM Scaling factor: 10.0 Relative humidity - [%] Difference in monthly or annual mean daylight relative humidity (see Section 11.3.3). Gridded SVF files: rh_GGG.MMM Scaling factor: 10.0 11.4.2 Change Ratios [ratio, 0-1] Precipitation - Change ratios for monthly or annual accumulated precipitation. Gridded SVF files: p_GGG.MMM Scaling factor: 1000.0 Solar radiation - Change ratios for monthly or annual mean total incident solar radiation. Gridded SVF files: sr_GGG.MMM Scaling factor: 1000.0 Vapor pressure - Change ratios for monthly or annual mean vapor pressure. Gridded SVF files: vp_GGG.MMM Scaling factor: 1000.0 Wind speed - Change ratios for monthly or annual mean near-surface wind speed. Gridded SVF files: w_GGG.MMM Scaling factor: 1000.0 11.5 Creation of New Climates: Application of Change Fields to Base Climate and Tests for Physical Constraints 11.5.1 Creation of Altered Climate Fields To create new climates for a given scenario, modify monthly or daily VEMAP base climate (Section 8) by monthly scenario change fields according to the following processes. Then check for physical inconsistencies (Section 11.5.2) (1) For maximum, minimum, and mean temperature and for relative humidity: Add the corresponding month's temperature or relative humidity differences to the base climate's monthly or daily values. (2) For precipitation, solar radiation, vapor pressure, and wind speed: Multiply base climate monthly or daily values by the corresponding monthly change ratios. Note that these procedures may not result in daily RH values that are strictly consistent with the new daily temperature and vapor pressure record because RH, vapor pressure, and temperature changes are applied evenly across a month. 11.5.2 Checks for Physical Consistency We recommend that users of the climate scenarios apply the following rules to limit physical inconsistencies arising from the generation of altered climates: (1) Apply an upper limit of 5.0 on RegCM (MM4) change ratio values and on GISS wind speed change ratios. This avoids extreme values and maintains consistency with the upper limit already built into the change fields for the other models. (2) For solar radiation: Limit new values of total incident solar radiation (sr) so as not to exceed potential solar input at the surface (psr_sfc). (3) For vapor pressure: Check that new vapor pressure values do not exceed saturated vapor pressure (vpsat). To calculate saturated VP based on daylight average temperature (tdaylt), we present here code adapted from CLIMSIM that is consistent with that used in the calculation of daily relative humidity (Section 8.3.2): tdaylt = [{tmax - [(tmax+tmin)/2]} * 0.35] + [(tmax+tmin)/2] vpsat = 6.1078 * exp[(17.269 * tdaylt)/(237.3 + tdaylt)] Where tmin and tmax are minimum and maximum temperatures, respectively. This constraint is appropriately applied on a daily basis. When applied monthly, it may overly constrain monthly mean vapor pressures. (4) For relative humidity: Set any relative humidity values greater than one hundred percent to 100% and values less than zero percent to 0%. (5) For wind speed: Use caution in deciding whether or not to apply surface wind speed changes. Changes in wind speed from the GCM runs are locally extreme (e.g., by a factor of 3 or more). Wind change fields were not used in the VEMAP I simulations. These tests do not cover all possible physical inconsistencies, but provide a minimum set of checks. Note that for any month in which rules (2) - (5) are applied, monthly means of new daily values may not exactly match new monthly values that are obtained by applying monthly changes to VEMAP base climate monthly means. This is because the above constraints have differential effects when applied at daily versus monthly timesteps. 12 SITE FILES 12.1 Site File Content and Structure Site files contain monthly climate and scenario data in column format. We developed this time-sequential format to facilitate the extraction of data for individual stations. README files included under the /siteFiles directory give instructions on how to find a particular grid cell. Site files omit background grid cells, with a new line for each grid cell (3261 data records). Each file lists 12 monthly values (January-December) as a single record. A record also contains geographic information about the associated grid point such as latitude, longitude, elevation, state identification number, and Kuchler and VEMAP vveg.v2 vegetation types (See Section 4.3). 12.2 Site File Naming Protocol The naming protocol for the files is VAR or VAR_GGG, where VAR describes the variable (as in Section 8.2.1) and, in the case of climate scenario files, GGG gives the climate model experiment from which the scenarios were extracted (as in Section 11.2). If the filename does not include a GGG suffix, the data were extracted from the monthly climate files. 13 ACKNOWLEDGMENTS Development of the VEMAP database was supported by VEMAP sponsors (NASA Mission to Planet Earth, Electric Power Research Institute, and USDA Forest Service Southern Region Global Change Research Program) and by the National Science Foundation Climate Dynamics Program through UCAR's Climate System Modeling Program (CSMP). We thank Lou Pitelka, Susan Fox, Tony Janetos, and Hermann Gucinski for their support of VEMAP. Thanks to Donna Beller, Hank Fisher, Alison Grimsdell, and Tom Painter for programming and data management support, Susan Chavez for administrative support, Gaylynn Potemkin for manuscript preparation, Roy Barnes, Chris Daly, Filippo Giorgi, E. Raymond Hunt, Jr., Roy Jenne, Dennis Joseph, Jeff Kern, Danny Marks, Christine Shields, Dennis Shea, and Will Spangler for access to datasets and model output, and Jeff Kuehn and NCAR's Climate and Global Dynamics Division for computer systems support. We thank Rick Katz, Dennis Shea, David Schimel, VEMAP participants, and other users for document review and dataset evaluation. Linda Mearns, Rick Katz, and Dennis Shea also provided comments on daily climate dataset design. We wish to thank Genasys II, StatSci, and NCAR's Scientific Computing Division for technical support. NCAR is supported by the National Science Foundation. 14 CONTACTS Direct enquiries and comments regarding the VEMAP dataset to: Nan Rosenbloom telephone: 303-497-1617 email: nanr@ucar.edu Tim Kittel telephone: 303-497-1606 email: kittel@ucar.edu Mailing address and fax number are: Ecosystem Dynamics and the Atmosphere Section Climate and Global Dynamics Division NCAR P.O. Box 3000 Boulder, CO 80307-3000 USA Fax: 303-497-1695 15 REFERENCES Boelter, D.H. (1969) Physical properties of peats as related to degree of decomposition. Soil Sci. Soc. Am. J. 33:606-609. Boer, G.J., N.A. McFarlane and M. Lazare (1992) Greenhouse gas- induced climate change simulated with the CCC second- generation general circulation model. J. Climate 5:1045-1077. Bristow, K.L., and G.S. Campbell (1984) On the relationship between incoming solar-radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology 31:159-166. Daly, C. R.P. Neilson, and D.L. Phillips (1994) A statiscal- topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteorol. 33:140-158. Elliott, D.L., C.G. Holladay, W.R. Barchet, H.P. Foote, and W.F. Sandusky (1986) Wind Energy Resource Atlas. Solar Technical Information Program. U.S. Department of Energy. Washington, D.C. 210 pp. FAO/UNESCO (United Nations Food and Agriculture Organization/United Nations Educational, Scientific, and Cultural Organization) (1974-78) Soil Map of the World. Volumes I-X. FAO, Paris. Gates, D.M. (1981) Biophysical Ecology. Springer-Verlag, New York, p. 611. Giorgi, F., C.S. Brodeur, and G.T. Bates (1994) Regional climate change scenarios over the United States produced with a nested regional climate model. J. Climate 7:375-399. Hansen, J., A. Lacis, D. Rind, G. Russell, P. Stone, I. Fung, R. Ruedy, and J. Lerner (1984) Climate sensitivity: Analysis of feedback mechanisms. Pp 130 - 163, in: Climate Processes and Climate Sensitivity. J.E. Hansen and T. Takahashi (eds). Geophysical Monograph 29. American Geophysical Union, Washington, D.C. IPCC (1990) Climate Change: The IPCC Scientific Assessment. J.T. Houghton, G.J. Jenkins, and J.J. Ephraums (eds). Intergovernmental Panel on Climate Change. Cambridge University Press, New York. 365 pp. Jenne, R.L. (1992) Climate model description and impact on terrestrial climate. Pp. 145 - 164, in: Global Climate Change: Implications, Challenges and Mitigation Measures. S.K. Majumdar, L.S. Kalkstein, B. Yarnal, E.W. Miller, and L.M. Rosenfeld (eds). Pennsylvania Academy of Science. Kimball, J. S.W. Running, and R. Nemani (1996) An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology, in press. Kern, J.S. (1994) Spatial patterns of soil organic carbon in the contiguous United States. Soil Sci. Soc. Am. J. 58:439-455. Kern, J.S. (1995) Geographic patterns of soil water-holding capacity in the contiguous United States. Soil Sci. Soc. Am. J. 59:1126-1133. Kern, J.S. (1996) Errata to "Geographic patterns of soil water- holding capacity in the contiguous United States". Soil Sci. Soc. Am. J., in preparation. Kittel, T.G.F., D.S. Ojima, D.S. Schimel, R. McKeown, J.G. Bromberg, T.H. Painter, N.A. Rosenbloom, W.J. Parton, and F. Giorgi (1996) Model-GIS integration and dataset development to assess terrestrial ecosystem vulnerability to climate change. Pp. 293-297, in: GIS and Environmental Modeling: Progress and Research Issues. M.F. Goodchild, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning (eds). GIS World, Inc., Ft. Collins, CO. Kittel, T.G.F., N.A. Rosenbloom, T.H. Painter, D.S. Schimel, and VEMAP Modeling Participants (1995) The VEMAP integrated database for modeling United States ecosystem/vegetation sensitivity to climate change. J. Biogeog. 22:857-862. Kuchler, A.W. (1964) Manual to Accompany the Map, Potential Natural Vegetation of the Conterminous United States. Spec. Pub. No. 36. American Geographical Society, New York. 143 pp. Kuchler, A.W. (1975) Potential Natural Vegetation of the Conterminous United States. (2nd ed.) (Map 1:3,168,000) American Geographical Society, New York. Manabe, S. and R.T. Wetherald (1987) Large-scale changes in soil wetness induced by increase in carbon dioxide. J. Atmos. Sci. 44:1211-1235. Manabe, S. and Wetherald, R.T. (1990) [Reported in: Mitchell, J.F.B., S. Manabe, V. Meleshko, T. Tokioka. Equilibrium Climate Change and its Implications for the Future. Pp. 131- 172, in: Climate Change: The IPCC Scientific Assessment. Houghton, J.T., G.J. Jenkins, and J.J. Ephraums (eds). Cambridge University Press, Cambridge, UK.] Marks, D. (1990) The sensitivity of potential evapotranspiration to climate change over the continental United States. Pp. IV- 1 - IV-31, in: Biospheric Feedbacks to Climate Change: The Sensitivity of Regional Trace Gas Emissions, Evapotranspiration, and Energy Balance to Vegetation Redistribution. H. Gucinski, D. Marks, and D.P. Turner (eds). EPA/600/3-90/078. U.S. Environmental Protection Agency, Corvallis, OR. Marks, D. and J. Dozier (1992) Climate and energy exchange at the snow surface in the alpine region of the Sierra Nevada: 2. Snow cover energy balance. Water Resources Research 28:3043-3054. NFNOC (Navy Fleet Numeric Oceanographic Center) (1985) 10-minute Global Elevation Terrain, and Surface Characteristics. (Re- processed by NCAR and NGDC). NOAA National Geophysical Data Center. Digital dataset. NCDC (National Climatic Data Center) (1992) 1961-1990 Monthly Station Normals Tape. U.S. Department of Commerce. Digital dataset: TD 9641. Paivanen, J. (1973) Hydraulic conductivity and water retention in peat soils. Acta Forestalia Fennica 129:1-70. Rawls, W.L., D.L. Brakensiek, and K.E. Saxton (1982) Estimation of soil water properties. Trans. American Society of Agricultural Engineers 25:1316-1320. Richardson, C.W. (1981) Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resources Research 17:182-190. Richardson, C.W. and D.A. Wright (1984) WGEN: A Model for Generating Daily Weather Variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8. 83 pp. Running, S.W., R.R. Nemani, and R.D. Hungerford (1987) Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis. Can. J. For. Res. 17:472-483. Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick (1986) Estimating generalized soil-water characteristics from texture. Soil Sci. Am. J. 50:1031-1036. Schlesinger, M.E. and Z.C. Zhao (1989) Seasonal climate changes induced by doubled CO2 as simulated by the OSU atmospheric GCM- mixed layer ocean model. J. Climate 2:459-495. Sulzman, E.W., K.A. Poiani, and T.G.F. Kittel (1995) Modeling human-induced climatic change: A summary for environmental managers. Environmental Management 19:197-224. Thompson, S.L. and D. Pollard (1995a) A global climate model (GENESIS) with a land-surface-transfer scheme (LSX). Part 1: Present-day climate. J. Climate 8:732-761. Thompson, S.L. and D. Pollard (1995b) A global climate model (GENESIS) with a land-surface-transfer scheme (LSX). Part 2: CO2 sensitivity. J. Climate 8:1104-1121. VEMAP Members (1995) Vegetation/Ecosystem Modeling and Analysis Project: Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochem. Cycles 9:407-437. Wetherald, R.T. and S. Manabe (1990) [Reported in: Cubasch, U., and R.D. Cess. Processes and Modeling. Pp. 69-91, in: Climate Change: The IPCC Scientific Assessment. Houghton, J.T., G.J. Jenkins, and J.J. Ephraums (eds). Cambridge University Press, Cambridge, UK.] Wilson, C.A. and J.F.B. Mitchell (1987) A doubled CO2 climate sensitivity experiment with a global climate model including a simple ocean. J. Geophys. Res. 92 (D11):13,315-13,343. A1 APPENDIX 1: CDROM AND FTP SITE DIRECTORY STRUCTURE Directories on the CDROM (Section 2.1) and FTP site (Section 2.3) have the following structure: /daily /docs /geog /images /monthly /programs /soil /mineral /organic /total /scenario /ccc /gfdl_qfx /gfdl_r15 /gfdl_r30 /giss /mm4 /osu /ukmo /siteFile /tarFiles (FTP and Web site only) /vresults (FTP and Web site only) /bgc /biome2 /century /doly /mapss /tem /vUPDATES (FTP and Web site only) Readme files residing in each subdirectory describe files in that directory. A2 APPENDIX 2: DETERMINATION OF ABSOLUTE SOIL AREA FOR EACH CELL A2.1 Absolute Area for Soil Modes and Components A useful quantity for spatially explicit modeling is the absolute area represented by each cell (varea, Section 7.3.6) or portion of a cell being simulated (such as the area for mode 1's mineral soil). The soils database includes the area covered by mineral and organic components of the modal and average soils (ma and oa, Section 9.5.4). These were determined as follows: For mineral soils: Absolute areal cover of mode M mineral soils for a cell = (relative extent of mineral soils within mode M soils) ¥ (relative extent of mode M soils within VEMAP land area) ¥ (absolute area of VEMAP land area in a cell) or, in terms of the datasets by filename: ma_mM = (map_mM/100) * (tap_mM/100) * varea and for average soils, by filename: ma_ave = (map_ave/100) * (tap_ave/100) * varea For organic soils: Absolute areal cover of mode M organic soils for a cell = (relative extent of organic soils within mode M soils) ¥ (relative extent of mode M soils within VEMAP land area) ¥ (absolute area of VEMAP land area in a cell) or, in terms of the datasets by filename: oa_mM = [1 - (map_mM/100)] * (tap_mM/100) * varea and for average soils, by filename: oa_ave = [1 - (map_ave/100)] * (tap_ave/100) * varea A2.2 Application to Model Outputs Model experiments can be run either with the dominant soil, average soil, or a suite of modal types. In the first and second cases, where a single modal or average mineral soil is assumed to represent the land area for a cell, then model output can be multiplied by the cell's land area within the VEMAP domain: Model variable cell total = (model variable) * varea * km2/(model unit area) where varea is the area of VEMAP land area in a cell (Section 7). In the third case, model outputs must be weighted by the relative areal coverage of each soil category in each cell to give results for the entire cell. For simulations run with all soil modes (1-4) and/or both soil components (organic and mineral), weighted model outputs (e.g., for net primary production) are generated by the following method (Kittel at al. 1996). Outputs for each soil component and mode are multiplied by the cell area represented by the corresponding modal soil, summed across modes to provide a weighted total for each component. If both components are present, component totals are summed, again weighted by corresponding areas. For a more explicit description of how to determine the weighted total for each component and both combined, see the the VEMAP User's Guide on the World Wide Web (http://www.cgd.ucar.edu/vemap/users_guide.html), or order the "User's Guide to the VEMAP Phase I Database" (NCAR Technical Note TN-431+IA) available from: EDAS NCAR Box 3000 Boulder, CO 80307-3000 A3 APPENDIX 3: AGGREGATION OF KUCHLER VEGETATION CODES TO VEMAP VEGETATION TYPES Table A3.1 Aggregation of Kuchler vegetation types to VEMAP vegetation types (vveg versions 1 and 2, Section 10). Names of Kuchler types are given in Table A3.2. VVEG VEMAP Vegetation Kuchler Vegetation Types Type vveg.v1 vveg.v2 ________________________________________________________________ 1 Tundra 52 52 2 Boreal coniferous forest 15, 21, 93, 96 15, 21, 93, 96 forest 3 Temperate maritime 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6 coniferous forest 4 Temperate 8, 10, 11, 12, 13, 8, 10, 11, 12, 13, continental coniferous 14, 16, 17, 18, 19, 14, 16, 17, 18, forest 20, 95 19, 20, 95 5 Cool temperate 28, 106, 107, 108, 106, 107, 108, mixed forest 109, 110 109, 110 6 Warm temperate/ 29, 89, 90, 111, 26, 28, 29, 89, subtropical mixed forest 112 90, 111, 112 7 Temperate deciduous forest 26, 98, 99, 100, 98, 99, 100, 101, 101, 102, 103, 104 102, 103, 104 8 Tropical deciduous forest not present not present 9 Tropical evergreen forest not present not present 10 Temperate mixed 30, 31, 32, 36, 37 30, 31, 32, 36, 37 xeromorphic woodland 11 Temperate conifer 23 23 xeromorphic woodland 12 Tropical thorn woodland not present not present 13 (v1) Temperate/ 61, 71, 81, 82, 84, 71, 81, 82, 84, 88 deciduous savanna 87, 88 (v2) Temperate/subtropical deciduous savanna 14 Warm temperate/subtropical 60, 62, 83, 85, 86 60, 61, 62, 83, mixed savanna 85, 86, 87 15 Temperate conifer savanna 24 24 16 Tropical deciduous savanna not present not present 17 C3 grasslands 47, 48, 50, 51, 63, 47, 48, 50, 51, 64, 66, 67, 68 63, 64, 66, 67, 68 18 C4 grasslands 53, 54, 65, 69, 70, 53, 54, 65, 69, 74, 75, 76, 77 70, 74, 75, 76, 77 19 Mediterranean shrubland 33, 34, 35 33, 34, 35 20 Temperate arid shrubland 38, 39, 40, 46, 55, 38, 39, 40, 46, 56, 57 55, 56, 57 21 Subtropical arid shrubland 41, 42, 43, 44, 45, 41, 42, 43, 44, 58, 59 45, 58, 59 90 Ice not present not present 91 Inland water bodies no symbol no symbol 92 Wetlands 49, 78, 79, 80, 92, 49, 78, 79, 80, 94, 113, 114 92, 94, 113, 114 Table A3.2 Kuchler Vegetation Type Names and Identifying Codes (Kuchler 1964, 1975). Code Kuchler Vegetation Type _____________________________________ WESTERN FORESTS Needleleaf Forests 1 Spruce-cedar hemlock forest 2 Cedar-hemlock-Douglas fir forest 3 Silver fir-Douglas fir forest 4 Fir-hemlock forest 5 Mixed conifer forest 6 Redwood forest 7 Red fir forest 8 Lodgepole pine-subalpine forest 9 Pine-cypress forest 10 Ponderosa shrub forest 11 Western ponderosa forest 12 Douglas fir forest 13 Cedar-hemlock-pine forest 14 Grand fir-Douglas fir forest 15 Western spruce-fir forest 16 Eastern ponderosa forest 17 Black Hills pine forest 18 Pine-Douglas fir forest 19 Arizona pine forest 20 Spruce-fir-Douglas fir forest 21 Southwestern spruce-fir forest 22 Great Basin pine forest 23 Juniper-pinyon woodland 24 Juniper steppe woodland Broadleaf forests 25 Alder-ash forest 26 Oregon oakwoods 27 Mesquite bosques Broadleaf and needleleaf forests 28 Mosaic of numbers 2 and 26 29 California mixed evergreen forest 30 California oakwoods 31 Oak-juniper woodland 32 Transition between 31 and 37 WESTERN SHRUB AND GRASSLAND Shrub 33 Chaparral 34 Montane chaparral 35 Coastal sagebrush 36 Mosaic of numbers 30 and 35 37 Mountain mahogany-oak scrub 38 Great Basin sagebrush 39 Blackbrush 40 Saltbush-greasewood 41 Creosote bush 42 Creosote bush-bur sage 43 Palo verde-cactus shrub 44 Creosote bush-tarbush 45 Ceniza shrub 46 Desert: vegetation largely absent Grasslands 47 Fescue-oatgrass 48 California steppe 49 Tule marshes 50 Fescue-wheatgrass 51 Wheatgrass-bluegrass 52 Alpine meadows and barren 53 Grama-galleta steppe 54 Grama-tobosa prairie Shrub and grasslands combinations 55 Sagebrush steppe 56 Wheatgrass-needlegrass shrubsteppe 57 Galleta-three awn shrubsteppe 58 Grama-tobosa shrubsteppe 59 Trans-Pecos shrub savanna 60 Mesquite savanna 61 Mesquite-acacia savanna 62 Mesquite-live oak savanna CENTRAL AND EASTERN GRASSLANDS Grasslands 63 Foothills prairie 64 Grama-needlegrass-wheatgrass 65 Grama-buffalo grass 66 Wheatgrass-needlegrass 67 Wheatgrass-bluestem-needlegrass 68 Wheatgrass-grama-buffalo grass 69 Bluestem-grama prairie 70 Sandsage-bluestem prairie 71 Shinnery 72 Sea oats prairie 73 Northern cordgrass prairie 74 Bluestem prairie 75 Nebraska Sandhills prairie 76 Blackland prairie 77 Bluestem-sacahuista prairie 78 Southern cordgrass prairie 79 Palmetto prairie Grassland and forest combinations 80 Marl-Everglades 81 Oak savanna 82 Mosaic of numbers 74 and 100 83 Cedar glades 84 Cross timbers 85 Mesquite-buffalo grass 86 Juniper-oak savanna 87 Mesquite-oak savanna 88 Fayette prairie 89 Blackbelt 90 Live oak-sea oats 91 Cypress savanna 92 Everglades EASTERN FORESTS Needleleaf forests 93 Great Lakes spruce-fir forest 94 Conifer bog 95 Great Lakes pine forest 96 Northeastern spruce-fir forest Broadleaf forests 98 Northern floodplain forest 99 Maple-basswood forest 100 Oak-hickory forest 101 Elm-ash forest 102 Beech-maple forest 103 Mixed mesophytic forest 104 Appalachian oak forest 105 Mangrove Broadleaf and needleleaf forests 106 Northern hardwoods 107 Northern hardwoods-fir forest 108 Northern hardwoods-spruce forest 109 Transition between numbers 104 and 106 110 Northeastern oak-pine forest 111 Oak-hickory-pine forest 112 Southern mixed forest 113 Southern floodplain forest 114 Pocosin 115 Sand pine scrub 116 Subtropical pine forest A4 APPENDIX 4: STATE IDENTIFICATION NUMBERS State identification codes used in the site files (Section 12) are: 1 Alabama 2 Arizona 3 Arkansas 4 California 5 Colorado 6 Connecticut 7 Delaware 8 Florida 9 Georgia 10 Idaho 11 Illinois 12 Indiana 13 Iowa 14 Kansas 15 Kentucky 16 Louisiana 17 Maine 18 Maryland 19 Massachusetts 20 Michigan 21 Minnesota 22 Mississippi 23 Missouri 24 Montana 25 Nebraska 26 Nevada 27 New Hampshire 28 New Jersey 29 New Mexico 30 New York 31 North Carolina 32 North Dakota 33 Ohio 34 Oklahoma 35 Oregon 36 Pennsylvania 37 Rhode Island 38 South Carolina 39 South Dakota 40 Tennessee 41 Texas 42 Utah 43 Vermont 44 Virginia 45 Washington 46 West Virginia 47 Wisconsin 48 Wyoming 49 unassigned 50 Alaska A5 APPENDIX 5: VEMAP MAILING LIST A5.1 Description of the VEMAP Mailing List The VEMAP mailing list is called "vemap_users" and is a moderated list as opposed to a discussion list. A moderated list allows only the list owner to send messages to list subscribers. A discussion list allows any individual subscriber to send messages to the entire list. A5.2 How to Subscribe to the VEMAP Mailing List In order to subscribe to the VEMAP users list, send a message to: majordomo@ucar.edu Leave the subject blank, then type in the body of the letter: subscribe vemap_users end The end command is optional and is only needed if your outgoing email messages include an appended footer, or signature (e.g., your name and address appended to the bottom of each message). In response to this message, you will receive an email reply from majordomo@ucar.edu telling you of your successful subscription. If you wish to unsubscribe at any time, use the instructions below. A5.3 Listserver Commands In the description below, items contained in [ ]'s are optional. When providing the item, do not include the [ ]'s or <>'s shown with the command. subscribe [
] Subscribe yourself (or
if specified) to the named . unsubscribe [
] Unsubscribe yourself (or
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