High resolution interpolation of climate scenario change factors for the conterminous USA derived from AR4 General Circulation Model simulations

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Price, David T.
Originator: McKenney, Daniel W.
Originator: Siltanen, R. Martin
Originator: Papadopol, Pia
Originator: Lawrence, Kevin
Originator: Joyce, Linda A.
Originator: Coulson, David P.
Publication_Date: 2011
Title:
High resolution interpolation of climate scenario change factors for the conterminous USA derived from AR4 General Circulation Model simulations
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: USDA Forest Service, Rocky Mountain Research Station
Online_Linkage: https://doi.org/10.2737/RDS-2011-0023
Description:
Abstract:
Projections of future global climate have been developed by numerous climate modeling groups around the world; however, this data is often at spatial scales much larger than the spatial scale of resource management. This study develops a set of change factors that can be used with a user-selected historical climate data set to create climate change projections at the spatial scale of approximately 9.25 kilometer grid. Climate projection output was obtained from four well-established general circulation models (GCM) forced by each of three greenhouse gas (GHG) emissions scenarios namely A2, A1B, and B1, from the Special Report on Emissions Scenarios, and used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. Monthly data for the period 1961-2100 were downloaded mainly from Third Coupled Model Intercomparison Project (CMIP3) through the Program for Climate Model Diagnosis and Intercomparison (PCMDI) web portal. Climate variables included monthly mean daily maximum and minimum temperatures, precipitation, solar radiation, wind speed, and vapor pressure (used to calculate specific humidity). All variables are expressed as changes relative to the simulated monthly means for 1961-1990, which corrected for GCM bias in reproducing past climate and allowed future projected trends to be compared directly. Each month value at each GCM grid node was normalized either by subtracting (for temperature variables) or dividing by (for other climate variables) the mean of that month's value for the 30-year baseline period 1961-1990. The normalized data (or "deltas") we then formatted for input to ANUSPLIN thin-plate software. The downscaling procedure used ANUSPLIN software package to fit a two-dimensional spline function to each month's change data for each of the six normalized climate variables at a spatial resolution of 5 arcminutes (0.0833 degrees) longitude and latitude. Data for the United States and Canada were extracted. Alaska data are available through the R&D Data Archive (Price et al. 2011: https://doi.org/10.2737/RDS-2011-0022). Data for the conterminous United States are contained in this data publication.

This research was a collaborative effort between scientists at the Canadian Forest Service and scientists at the USDA Forest Service.
Purpose:
The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (https://www.fs.usda.gov/research/inventory/rpaa). For 2010 RPA Assessment, an integrated modeling framework will be used in which the potential implications of climate change can be analyzed across some resource areas (Langner et al. 2012).
Supplemental_Information:
Original metadata date is December 2011. Minor metadata updates on 03/08/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 11/30/2016, 10/27/2022, and 06/11/2024.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2000
Ending_Date: 2100
Currentness_Reference:
Publication date
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent:
Five arcminute grids falling all or part within conterminous United States.
Bounding_Coordinates:
West_Bounding_Coordinate: -125
East_Bounding_Coordinate: -66.583333
North_Bounding_Coordinate: 49.41667
South_Bounding_Coordinate: 25
Bounding_Altitudes:
Altitude_Minimum: -282
Altitude_Maximum: 14505
Altitude_Distance_Units: feet
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: climate scenario
Theme_Keyword: GCM
Theme_Keyword: downscaling
Theme_Keyword: interpolation
Theme_Keyword: ANUSPLIN
Theme_Keyword: National Center for Atmospheric Research
Theme_Keyword: NCAR
Theme_Keyword: Community Climate System Model
Theme_Keyword: CCSM
Theme_Keyword: Canadian Centre for Climate Modelling and Analysis
Theme_Keyword: CCCma
Theme_Keyword: Coupled Global Climate Model
Theme_Keyword: CGCM
Theme_Keyword: Commonwealth Scientific and Industrial Research Organisation
Theme_Keyword: CSIRO
Theme_Keyword: Climate System Model
Theme_Keyword: Centre for Climate System Research
Theme_Keyword: CCSR
Theme_Keyword: National Institute for Environmental Studies
Theme_Keyword: NIES
Theme_Keyword: Model for Interdisciplinary Research on Climate
Theme_Keyword: MIROC
Theme_Keyword: grid level
Theme_Keyword: 5 arcminute
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: climatologyMeteorologyAtmosphere
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Climate change
Theme_Keyword: Climate change effects
Theme_Keyword: Climatology
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: conterminous United States
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the Canadian Forest Service and U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Price, David T.; McKenney, Daniel W.; Siltanen, R. Martin; Papadopol, Pia; Lawrence, Kevin; Joyce, Linda A.; Coulson, David P. 2011. High resolution interpolation of climate scenario change factors for the conterminous USA derived from AR4 General Circulation Model simulations. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. https://doi.org/10.2737/RDS-2011-0023
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Person: David P. Coulson
Contact_Position: Statistician
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
We wish to acknowledge the support provided by the global climate modeling community, and their willingness to contribute and share data through web-based data archives, notably the World Climate Research Program (WCRP) working group on coupled modelling for their roles and willingness to share the WCRP Coupled Model Intercomparison Project (CMIP3) data through the Program for Climate Model Diagnosis and Intercomparison (PCMDI) web portal. Further, we greatly appreciate the availability of data provided by:

1. The Canadian Centre for Climate Modelling and Analysis (CCCma)

2. The U.S. National Center for Atmospheric Research (NCAR), and G Strand of the U.S. University Corporation for Atmospheric Research (UCAR)

3. The Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO), particularly M. Collier, M. Dix and T. Hirst of the Marine and Atmospheric Research Division (CMAR)

4. In Japan, the Centre for Climate System Research (CCSR), together with the University of Tokyo, the National Institute for Environmental Studies (NIES) and the Frontier Research Center for Global Change.

This research used data provided by the Community Climate System Model (CCSM) project (https://www.cesm.ucar.edu/models/ccsm3.0/), supported by the Directorate for Geosciences of the US National Science Foundation and the Office of Biological and Environmental Research of the US Department of Energy. Any redistribution of CCSM data must include this data acknowledgment statement.

The authors request that any users of the scenario data presented in this report also provide appropriate acknowledgements to the respective GCM groups, as identified above.

We also acknowledge the support of the USDA Forest Service for this research.
Cross_Reference:
Citation_Information:
Originator: Price, David T.
Originator: McKenney, Daniel W.
Originator: Joyce, Linda A.
Originator: Siltanen, R. Martin
Originator: Papadopol, Pia
Originator: Lawrence, Kevin
Publication_Date: 2011
Title:
High Resolution Interpolation of Climate Scenarios for Canada Derived from General Circulation Model Simulations
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: Information Report
Issue_Identification: NOR-X-421
Publication_Information:
Publication_Place: Edmonton, Alberta, Canada
Publisher: Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre
Cross_Reference:
Citation_Information:
Originator: Langner, Linda L.
Originator: Daniels, Amy E.
Originator: Joyce, Linda A.
Publication_Date: 2012
Title:
Future Scenarios: a technical document supporting the Forest Service 2010 RPA assessment
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: General Technical Report
Issue_Identification: RMRS-GTR-272
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station
Other_Citation_Details:
34 p.
Online_Linkage: https://doi.org/10.2737/RMRS-GTR-272
Cross_Reference:
Citation_Information:
Originator: Joyce, Linda A.
Originator: Price, David T.
Originator: McKenney, Daniel W.
Originator: Siltanen, R. Martin
Originator: Papadopol, Pia
Originator: Lawrence, Kevin
Originator: Coulson, David P.
Publication_Date: 2011
Title:
High resolution interpolation of climate scenarios for the conterminous United States and Alaska derived from general circulation model simulations
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: General Technical Report
Issue_Identification: RMRS-GTR-263
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station
Other_Citation_Details:
87 p.
Online_Linkage: https://doi.org/10.2737/RMRS-GTR-263
Cross_Reference:
Citation_Information:
Originator: Langner, Linda L.
Publication_Date: 2012
Title:
Future of America’s Forest and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: General Technical Report
Issue_Identification: WO-87
Publication_Information:
Publication_Place: Washington, DC
Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station
Other_Citation_Details:
198 p.
Online_Linkage: https://doi.org/10.2737/WO-GTR-87
Cross_Reference:
Citation_Information:
Originator: Price, David T.
Originator: McKenney, Daniel W.
Originator: Siltanen, R. Martin
Originator: Papadopol, Pia
Originator: Lawrence, Kevin
Originator: Joyce, Linda A.
Originator: Coulson, David P.
Publication_Date: 2011
Title:
High resolution interpolation of climate scenario change factors for Alaska derived from AR4 General Circulation Model simulations
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: USDA Forest Service, Rocky Mountain Research Station
Online_Linkage: https://doi.org/10.2737/RDS-2011-0022
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The climate projection data have a history of several developers. Each developer documents the accuracy of the data at their step. The global data from the climate models were obtained through the Coupled Model Intercomparison Project 3 (CMIP3) data portal. Model documentation is available at: https://pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php. Documentation on the climate models is also given in the methods section below. Price and colleagues document the downscaling procedure and any problems with the climate data (Price et al. 2011: Information Report NOR-X-421, Joyce et al. 2011).
Logical_Consistency_Report:
See model documentation referenced in the Attribute Accuracy section above and also Price et al. 2011 (Information Report NOR-X-421) and Joyce et al. 2011.
Completeness_Report:
These data are provided as ASCII text files of change factors, where the change factors are differences in degrees Celsius for temperatures, unscaled ratios for all other variables. The first six lines describe the data structure:

ncols 702
nrows 294
xllcorner -125
yllcorner 25
cellsize 0.083333338
NODATA_value -9999

This translated to 702 columns by 294 rows starting at in the lower left had corner of a grid block with the starting coordinates being -125 degrees longitude, 25 degrees latitude and the cell size being 5 arcminutes. Missing values are -9999.

This file structure is suitable for importation into ESRI GIS software and conversion to other file structures, such as raster files.
Lineage:
Methodology:
Methodology_Type: Lab
Methodolgy_Identifier:
Methodolgy_Keyword_Thesaurus:
None
Methodology_Keyword: high resolution, statistical downscaling, ANUSPLIN
Methodology_Description:
The simulated climate variables to be downscaled in this study were monthly means of daily surface temperature (minimum and maximum), monthly precipitation, global downward solar radiation, and wind speed. Monthly mean atmospheric vapor pressure was estimated from the simulated monthly mean specific humidity and sea-level atmospheric pressure.

The following general circulation models (GCMs) were selected for this study:

1. CGCM31MR - Canadian Centre for Climate Modelling and Analysis (CCCma) Third Generation Coupled Global Climate Model Version 3.1, Medium Resolution (T47). (https://www.ec.gc.ca/ccmac-cccma/default.asp?lang=En&n=1299529F-1).

2. NCARCCSM3 - U.S. National Center for Atmospheric Research (NCAR), (USA), Community Climate System Model Version 3.0 (T85) (https://www.cesm.ucar.edu/models/ccsm3.0/).

3. CSIROMK35 - Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mk3.5 Climate System Model (T63) (https://www.cmar.csiro.au/e-print/open/gordon_2002a.pdf; https://www-pcmdi.llnl.gov/ipcc/model_documentation/CSIRO-Mk3.5.htm).

4. MIROC32MR - Japanese Centre for Climate System Research (CCSR), University of Tokyo; National Institute for Environmental Studies (NIES) and Frontier Research Center for Global Change (FRCGC) Model for Interdisciplinary Research on Climate (MIROC) Version 3.2 Medium Resolution (T42).

The majority of the GCM, scenario, and variable data were downloaded from the Coupled Model Intercomparison Project (Phase 3) (CMIP3) at the Program for Climate Model Diagnosis and Intercomparison (PCMDI) web portal at https://esg.llnl.gov:8443/index.jsp. The major advantage obtaining the climate model data from the CMIP3 project was standardization of format, variable names, units, and other aspects, which facilitated comparison among models. The Canadian Centre for Climate Modelling and Analysis (CCCma) website serves data for CGCM31MR and other Canadian climate models (https://www.ec.gc.ca/ccmac-cccma/default.asp?lang=En&n=1299529F-1). Daily minimum and maximum temperature data for CGCM31MR were obtained from this source because they were not available from CMIP3. The Earth System Grid (ESG) data portal of the University Corporation for Atmospheric Research (https://www.earthsystemgrid.org/home.html) serves data standardized to the specifications of the US National Center for Atmospheric Research model. The complete and consistent NCARCCSM3 data set for the A2 scenario was available only from this data portal. Complete error-free data sets of simulated specific humidity at surface elevation were obtained for only 10 of the 12 GCM projections from PCMDI. For the NCARCCSM3 model forced by the A2 emissions scenario, a complete time series of surface specific humidity data was located at the Earth System Grid data portal of the University Corporation for Atmospheric Research (https://www.ccsm.ucar.edu/models/ccsm3.0/). For the CSIROMK35 forced by the A1B emissions scenario, data for surface specific humidity were unavailable from PCMDI but were obtained directly from CSIRO (https://www.cmar.csiro.au).

The downscaling method used in this study was the delta method, where the GCM data are normalized to a historical reference period, so that bias in the GCM's estimates of observed values can be removed. This methodology follows Price et al. (2000, 2004) and McKenney et al. (2006). These procedures were built around spatial interpolation of the GCM output data by means of ANUSPLIN software (Hutchinson et al. 2009, Hutchinson 2015) where the monthly data values were treated as simulated records obtained from a 'virtual climate station' located at the GCM grid-node coordinates. The monthly values (including calculated vapor pressure data) were converted to monthly change factors, with the averages of the simulated monthly values for the 30-year period 1961-1990 used as a baseline. In the case of daily minimum and maximum temperature, the change factor was computed as the arithmetic difference between the monthly value and the corresponding 30-year average of the same temperature variable for that month. For all other variables, the change factor was the ratio of the monthly value to the mean for that month over the period 1961-1990.

The change factors were interpolated by means of the ANUSPLIN software to create time series for the period over which the AR4 simulations were carried out (generally from 1961 to 2100). The thin-plate spline method can be described as a multidimensional, nonparametric curve-fitting technique, although ANUSPLIN can be configured in other ways (Hutchinson 2015, Hutchinson et al., 2009). A fixed signal model, rather than a standard optimization model, was used because the input data were anomalies, rather than actual climate values (McKenney et al. 2006, Hutchinson 2015). We note there is no inherent statistical relationship between these anomalies and the independent variables (longitude and latitude). A fixed signal of 60% of the data points (GCM grid cell values) produced reasonable results (e.g., to avoid singularities ["bulls eyes"] in the resultant climate change scenario models). An ANUSPLIN model was generated for each monthly variable, which was then used to create gridded data covering North America at a spatial resolution of 5 arcminutes. The monthly grids of interpolated change factors were generated in ARC/INFO ASCII format, with a cell size of 5 arcminute (300 arcsecond) latitude X longitude (approximately 9.25 kilometer square at the equator), covering the domain from 168 to 52 degrees West (W) and from 25 to 85 degrees North (N) (1,392 columns X 720 rows). It should be noted that the grids are simply a convenient expression of the fitted spline functions mapped over the region of interest. Data for the conterminous United States were extracted. Alaska data are available in a separate data archive (Price et al. 2011: https://doi.org/10.2737/RDS-2011-0022).

The study provides a suite of change factors for 12 climate scenarios, resulting in a range of potential future climates for assessing possible impacts of a changing climate on natural resources and ecosystems. These change factors can be combined with a baseline historical climate data set of the users choice. Because GCMs typically have very low horizontal resolution, their representation of topographic effects on local climate is necessarily poor. For this reason, the normalized and interpolated GCM data (the change factors here) should be combined with climatological data for the 1961-1990 reference period interpolated to the same resolution used here in their development to generate the actual climate projections. In this way, the effects of local spatial variability on real climate are captured and can be combined with the trends in climate projected by the GCMs.

Additional information on the downscaling methods can be found in Price et al. (2000, 2004); Price et al. (2011: Information Report NOR-X-421); Joyce et al. (2011).
Methodology_Citation:
Citation_Information:
Originator: Hutchinson, Michael F.
Publication_Date: 2015
Title:
ANUSPLIN Software Version 4.4
Geospatial_Data_Presentation_Form: website including manual and software information
Other_Citation_Details:
Website last accessed July 15, 2015
Online_Linkage: https://fennerschool.anu.edu.au/research/products/anusplin-vrsn-44
Methodology_Citation:
Citation_Information:
Originator: Hutchinson, Michael F.
Originator: McKenney Daniel W.
Originator: Lawrence, Kevin
Originator: Pedlar, John H.
Originator: Hopkinson, Ron F.
Originator: Milewska, Ewa
Originator: Papadopol, Pia
Publication_Date: 2009
Title:
Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Journal of Applied Meteorology and Climatology
Issue_Identification: 48: 726-741
Online_Linkage: https://doi.org/10.1175/2008JAMC1979.1
Methodology_Citation:
Citation_Information:
Originator: Price, David T.
Originator: McKenney, Daniel W.
Originator: Nalder, Ian A.
Originator: Hutchinson, Michael F.
Originator: Kestevan, Jennifer L.
Publication_Date: 2000
Title:
A comparison of statistical and thin-plate spline methods for spatial interpolation of Canadian monthly mean climate data
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Agricultural and Forest Meteorology
Issue_Identification: 101: 81-94
Other_Citation_Details:
https://doi.org/10.1016/S0168-1923(99)00169-0
Methodology_Citation:
Citation_Information:
Originator: Price, David T.
Originator: McKenney, Daniel W.
Originator: Papadopol, Pia
Originator: Logan, T.
Originator: Hutchinson, Michael F.
Publication_Date: 2004
Title:
High resolution future scenario climate data for North America
Geospatial_Data_Presentation_Form: conference proceedings
Series_Information:
Series_Name: American Meteorological Society Annual Meetings
Issue_Identification: August 2004
Publication_Information:
Publication_Place: Vancouver
Online_Linkage: https://ams.confex.com/ams/AFAPURBBIO/techprogram/paper_78202.htm
Methodology_Citation:
Citation_Information:
Originator: McKenney, Daniel W.
Originator: Papadopol, Pia
Originator: Campbell, K.L.
Originator: Lawrence, Kevin
Originator: Hutchinson, Michael F.
Publication_Date: 2006
Title:
Spatial models of Canada- and North America-wide 1971/2000 minimum and maximum temperature, total precipitation and derived bioclimatic variables
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: Frontline Technical Note
Issue_Identification: 106
Publication_Information:
Publication_Place: Sault Ste. Marie, Ontario, Canada
Publisher: Canadian Forestry Service, Great Lakes Forestry Centre
Other_Citation_Details:
9 p.
Process_Step:
Process_Description:
No process steps have been described for this data set
Process_Date: Unknown
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 294
Column_Count: 702
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: conterminous United States
Entity_Type_Definition:
The United States of America (US or USA) is a country that contains 50 states. The conterminous United States refers to the portion of the US that contains the 48 contiguous states.
Entity_Type_Definition_Source:
United States federal government
Overview_Description:
Entity_and_Attribute_Overview:
FILENAME: <model>_<scenario>_<variable>_anom_<year>_<month>_r48.grd

DESCRIPTION: Each file is raster file (ASCII space-delimited) and contains climate data change factor for one model, one scenario, one variable, one year, and one month. The four models are CGCM31MR, CSIROMK35, NCARCCSM3, and MIROC32MR. The scenarios are A1B, A2, and B1. Variables are dimensionless ratios for precipitation, solar radiation, vapor pressure, and wind speed. The variables minimum and maximum mean daily temperature are differences in degrees Celsius. Data are available for the years 2001-2100 and each month of the year (1-12).

HEADER: Each file has a header defining the number of columns (ncols), number of rows (nrows), cell-size (size in decimal degrees), missing value code, longitude of the lower left corner of the grid (xllcorner) and latitude of the lower left corner of the grid (yllcorner):

ncols 702
nrows 294
xllcorner -125
yllcorner 25
cellsize 0.083333337679505
NODATA_value -9999.
Entity_and_Attribute_Detail_Citation:
none provided
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Contact_Voice_Telephone: see Contact Information
Contact Instructions: This contact information was current as of June 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2011-0023
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: ASCII
Format_Version_Number: see Format Specification
Format_Specification:
Comma-delimited ASCII text file (CSV)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2011-0023
Fees: none
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Metadata_Reference_Information:
Metadata_Date: 20240611
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Person: David Coulson
Contact_Position: Statistician
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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