This data set provides environmental data that have been standardized and aggregated for use as input to carbon cycle models at global (0.5-degree resolution) and regional (North America at 0.25-degree resolution) scales.
The data were compiled from selected sources (Table 2) and integrated into gridded global and regional collections of climatology variables (precipitation, air temperature, air specific humidity, air relative humidity (NA only), pressure, downward longwave radiation, downward shortwave radiation, and wind speed), time-varying atmospheric CO2 concentrations, time-varying nitrogen deposition, biome fraction and type, land-use and land-cover change, C3/C4 grasses fractions, major crop distribution, phenology, multiple soil characteristics, and a land-water mask. The driver data are sufficient for carbon cycle model simulations from 1801 to 2010. The temporal resolution ranges from 3-hourly for climate to monthly for CO2-atm and phenology to annual for N-deposition and landcover.
These data were compiled specifically for the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as the prescribed model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The standardized data provided consistent model inputs to minimize the inter-model variability caused by differences in environmental drivers and initial conditions. Together with the sensitivity simulations, the standardized input data enable better interpretation and quantification of structural and parameter uncertainties of model estimates.
Data are provided in Climate and Forecast (CF) metadata convention compliant (version 1.4) netCDF-4 file formats. There are 3,152 *.nc4 data files with this data set.
The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities. The overall objective of MAST-DC was to provide data management support to NACP investigators and agencies performing modeling and synthesis activities.
Data and Documentation Access:
Get Data:
http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1220
Companion Documentation for this Data Set:
A copy of this user’s guide is included as a companion file.
Related Data Products:
MsTMIP Model Structure data
MsTMIP Model Output data (to be
archived)
Other Model Driver Data Archived at the ORNL DAAC:
VEMAP 2: U.S.
Monthly Climate, 1895-1993, Version 2
The
International Satellite Land Surface Climatology Project, Initiative II
(ISLSCP II)
Cite this data set as follows:
Wei, Y., S. Liu, D.N. Huntzinger, A.M. Michalak, N. Viovy, W.M. Post, C.R. Schwalm, K. Schaefer, A.R. Jacobson, C. Lu, H. Tian, D.M. Ricciuto, R.B. Cook, J. Mao, and X. Shi. 2014. NACP MsTMIP: Global and North American Driver Data for Multi-Model Intercomparison. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1220
Project: North American Carbon Program (NACP)
The NACP (Denning et al., 2005; Wofsy and Harriss, 2002) is a multidisciplinary research program to obtain scientific understanding of North America's carbon sources and sinks and of changes in carbon stocks needed to meet societal concerns and to provide tools for decision makers. Successful execution of the NACP has required an unprecedented level of coordination among observational, experimental, and modeling efforts regarding terrestrial, oceanic, atmospheric, and human components. The project has relied upon a rich and diverse array of existing observational networks, monitoring sites, and experimental field studies in North America and its adjacent oceans. It is supported by a number of different federal agencies through a variety of intramural and extramural funding mechanisms and award instruments. The Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) is the archive for the NACP data products.
Data Products (Wei et al. (in review))
The variables in this data set include climatology [precipitation, air temperature, air specific humidity, air relative humidity (NA only), pressure, downward longwave radiation, downward shortwave radiation, and wind speed], time-varying atmospheric CO2 concentrations, time-varying nitrogen deposition, biome fraction and type, land-use and land-cover change (LULCC), C3/C4 grasses fractions, major crop distribution, phenology, soil characteristics data, and a land-water mask (Table 2). The data are provided in a standard format for global (0.5 degree by 0.5 degree resolution) and regional (North American, 0.25 degree by 0.25 degree resolution) analyses.
For most data categories, the North American data sets are based on the same data sources as the global products. However, different climatology and soil data products were compiled for the two domains based primarily on the availability of these data at the spatial and temporal resolution needed for the model simulations. In order to meet the needs of MsTMIP, improvements were made to the quality and/or the spatial and temporal coverage and resolution of several of the original environmental data sets (Wei et al. (in review)).
The resulting standardized data are provided in Climate & Forecast (CF) 1.4 convention compliant netCDF version 4 format, which is supported by a wide range of programming APIs (e.g., C, C++, Fortran, Java, Perl) and multiple operating systems (e.g., Linux, Unix, Mac OS X, Windows). All drivers are saved in Coordinated Universal Time (UTC) with all sub-monthly drivers (e.g., climate) including leap years.
MsTMIP -- Model Intercomparison
This data set provided standardized environmental driver data for 22 terrestrial biosphere models (TBM) participating in the NACP Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). MsTMIP is a formal multi-scale and multi-model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales (Huntzinger et al., 2013; Wei et al., in review). MsTMIP builds upon current and past NACP synthesis activities, and was established to provide a consistent and unified modeling framework to isolate, interpret, and inform understanding of how TBM structural differences and associated internal parameters impact estimates of terrestrial ecosystem carbon dynamics. [Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration parameterization) in the models.] By prescribing a common experimental protocol (Huntzinger et al., 2013) with standardized driver data (this data set) and spin-up procedures for all model simulations (Wei et al., in review), the biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves and model-specific parameter values can be isolated and explained.
Data Synthesis Process (Wei et al. (in review))
Working closely with the core MsTMIP team, MAST-DC prepared the driver data to the requirements needed by the MsMTIP protocol and the individual modeling teams (20+). Based on project requirements, MAST-DC compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land-use and land-cover change (LULCC), C3/C4 grasses fractions, major crops, phenology, and soil data from identified sources into a standard format for global (0.5 degree by 0.5 degree resolution) and regional (North America, 0.25 degree by 0.25 degree resolution) model simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by changing the quality, the spatial and temporal coverage, resolution, or a combination of these as described in Section 5.
Table 1. Author’s afilliation and role in development of MsTMIP driver data
| Name | Institution | Role | |
|---|---|---|---|
| Yaxing Wei | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | weiy@ornl.gov | MAST-DC |
| Shishi Liu | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | carol.shishi@gmail.com | MAST-DC |
| Deborah H. Huntzinger | School of Earth Sciences and Environmental Sustainability & the Department of Civil Engineering, Construction Mgmt., and Environmental Engineering, Northern Arizona University, Flagstaff, AZ, USA | deborah.huntzinger@nau.edu | Core MsTMIP |
| Anna Michalak | Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA | michalak@stanford.edu | Core MsTMIP |
| Viovy Nicolas | Laboratoire des Sciences du Climat et l'Environnement, Paris, France | viovy@lsce.ipsl.fr | MsTMIP |
| Wilfred M. Post (retired) | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | mpost3116@gmail.com | MAST-DC |
| Christopher Schwalm | School of Earth Sciences and Environmental Sustainability & the Department of Civil Engineering, Construction Mgmt., and Environmental Engineering, Northern Arizona University, Flagstaff, AZ, USA | Christopher.Schwalm@nau.edu | Core MsTMIP |
| Kevin Schaefer | University of Colorado, National Snow and Ice Data Center, Boulder, CO, USA | kevin.schaefer@nsidc.org | Core MsTMIP |
| Andrew R. Jacobson | University of Colorado and NOAA Earth System Research Lab, Boulder, CO, USA | andy.jacobson@noaa.gov | Core MsTMIP |
| Chaoqun Lu | International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA | czl0003@auburn.edu | Modeler |
| Hanqin Tian | International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA | tianhan@auburn.edu | Modeler |
| Dan M. Ricciuto | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | ricciutodm@ornl.gov | Modeler |
| Robert B. Cook | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | cookrb@ornl.gov | MAST-DC |
| Jiafu Mao | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | maoj@ornl.gov | Modeler |
| Xiaoying Shi | Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA | shix@ornl.gov | Modeler |
Table 2. The MsTMIP Driver Data Summary
| Category | Data Source | Spatial Extent & Resolution (degrees) |
Native Temporal Period, Resolution | Extended Temporal Period, Resolution* | Variables (Units) |
|---|---|---|---|---|---|
| Climate |
CRUNCEP | Global (0.5) | 1901-2010, 6-hourly | 1801-2010, 6-hourly | Total Precipitation (mm) Air temperature Air specific humidity (g/g) Air relative humidity (NA only) Pressure (Pa) Downward longwave radiation (W/m2) Downward shortwave radiation Wind speed |
| NARR | NA (0.25) | 1979-2010, 3-hourly | 1801-2010, 3-hourly | ||
| Land Water Mask | CRUNCEP | Global (0.5) | Constant | Constant | Binary land vs. water map; 1 represents land; 0 represents water |
| NARR | NA (0.25) | ||||
| CO2 | Extended GLOBALVIEW-CO2 | Global (0.5) , NA (0.25) | 1700-2010, monthly | 1801-2010, monthly | Atmospheric CO2 concentration |
| Nitrogen Deposition | Enhanced Dentener | Global (0.5) , NA (0.25) | 1860-2050, annual | 1801-2010, annual | NHx-N deposition NOy-N deposition |
| Biome | SYNMAP | Global (0.5) , NA (0.25) | Constant Present = around year 2000 Potential = pre-industry, pre-agriculture |
Constant | Biome fraction Biome type |
| Land-Use and Land-Cover Change | SYNMAP + Hurtt | Global (0.5) , NA (0.25) | 1700-2010, annual | 1801-2010, annual | Biome fraction Biome type Biome type pure |
| C3 /C4 Grass | C3/C4 grass fraction | Global (0.5) , NA (0.25) | Constant Present = around year 2000 Potential = pre-industry, pre-agriculture (around year 1900 or before) |
Constant | Present relative fractions of
C3/C4 grasses Potential relative fractions of C3/C4 grasses |
| Major Crops | Monfreda et al. (2008) | Global (0.5) , NA (0.25) | Constant (year 2000) | Constant | Fraction of harvest area in each grid cell for maize, rice, soybean, and wheat |
| Phenology | GIMMSg | Global (0.5) , NA (0.25) | 1700-2010, monthly | 1801-2010, monthly | NDVI, LAI, fPAR |
| Soil | HWSD v1.1 | Global (0.5) | Constant | Constant | Soil layers Dominant soil type Reference soil depth Clay/sand/silt fractions pH Organic carbon Cation exchange capacity Reference bulk density Gravel content |
| Unified North American Soil
Database (UNASD) [STATSGO2 (US) + SLC v3.2 and v2.2 (CA) + HWSD 1.1] |
NA (0.25) |
Notes:
* Native temporal periods of environmental driver data sets were extended for the TBM simulation time period (1801-2010) defined by MsTMIP. Please refer to Data Acquisition Materials and Methods section to see how a data with shorter native temporal period are extended back to 1801 to address the needs of MsTMIP simulations.
CRUNCEP = Climate Research Unit, National Centers for Environmental Prediction
NARR = North American Regional Reanalysis
SYNMAP = SYNergetic land cover MAP
GIMMSg = Global Inventory Monitoring and Modeling System version g
NDVI = Normalized Difference Vegetation Index
LAI = Leaf Area Index
fPAR = canopy absorbed fraction of Photosynthetically Active Radiation
HWSD = Harmonized World Soil Database
STATSGO2 = State Soil Geographic data version 2
SLC = Soil Landscapes of Canada
2.1. Spatial Coverage
Sites: Global and North America
Site Boundaries:(All latitude and longitude given in decimal degrees)
| Site (Region) | Westernmost Longitude | Easternmost Longitude | Northernmost Latitude | Southernmost Latitude |
|---|---|---|---|---|
| Global (all land surface areas excluding Antarctica) | -178.750 | 179.950 | 89.750 | -78.250 |
| North America | -169.875 | -50.125 | 83.875 | 10.125 |
2.2. Spatial Resolution
Environmental model driver data are provided globally at 0.5 degree by 0.5 degree resolution and regionally over North America at 0.25 degree by 0.25 degree resolution.
2.3. Temporal Coverage
Environmental model driver data are provided in their respective "native" temporal period for both global and North America resolutions as shown in Table 2. The temporal ranges of the data were modified to enable carbon cycle model simulations from 1801 to 2010.
2.4. Temporal Resolution
For model simulations, global climate data are 6-hourly and North American climate data are 3-hourly. For both global and North American model simulations, CO2 concentrations and phenology are monthly, nitrogen deposition and land use and cover change are annual, and relative fractions of C3/C4 grasses, major crops, soils, and land/sea mask are constant.
All driver data are saved in Coordinated Universal Time (UTC) with all sub-monthly drivers (e.g., climate) including leap years. For models using an internal time step less than the driver data time step, the model linearly interpolated between weather data points, except for the down-welling shortwave radiation, where scaling using the cosine of the zenith angle was appropriate. If a model used a time step larger than the drivers, appropriate time averages or totals of the weather data were use. For example, a model with a 1-day time step would use 24-hour averages or totals.
The sub-daily climate data include leap years. If a model did not account for leap year, the MAST-DC removed February 29 from the driver data in leap years. They did not delete December 31, January 1, or any other day because this would have created a time lag between model output and the observations.
2.5. Data File Information
All MsTMIP model driver data files, including global data and North American data, are stored in CF metadata convention compliant (version 1.4) netCDF-4 file format. Climate variables, which have a fine temporal resolution, are provided in multiple netCDF files with each file containing one year of data. Other variables with a coarser temporal resolution, including CO2 concentration, phenology, nitrogen deposition, and land use change, are supplied in individual netCDF files covering the whole temporal period.
Missing Value: -999.0.
File naming convention:
All data files are named
mstmip_driver_XX_ZZ_variable__
where
XX=global or na (North America), ZZ=hd (half degree) or qd (quarter degree)
User Note: Be advised that all 3,152 files are listed in a flat structure. Also, in the tables below, "mstmip_driver" has been omitted from the file names in order to save space in the tables.
MsTMIP DATA FILES
Land-water Masks (two data files)
Global CRUNCEP Land Mask:
Spatial Resolution/Extent: 0.5
degree / W: -180, S: -90, E: 180, N: 90
Temporal Resolution/Extent: One
time
North America NARR Land Mask:
Spatial Resolution/Extent:
0.25 degree / W: -170, S: 10, E: -50, N: 84
Temporal Resolution/Extent:
One time
Table 3. Land-water Mask data files
| Data Files | Description | Units |
|---|---|---|
| mstmip_driver_global_hd_ |
Global land-water mask at 0.5 degree | 1 = land 0 = water |
| mstmip_driver_na_qd_ |
North American land-water mask at 0.25 degrees |
Climate Data (1,874 data files)
There are 1,650 global climate data files and 224 data files for North America (na). The files are described in tables 4 and 5 below.
Global Climate (CRU+NCEP)
Spatial Resolution/Extent: 0.5 degree / W: -180, S: -90, E: 180,
N: 90
Temporal Resolution/Extent: 6-hourly / 1901-2010
Number of
Files for Each Variable: 110 annual files
Table 4. Global Climate (CRU+NCEP) data files
| Variable | Example file name | Description | Units |
|---|---|---|---|
| lwdown | _global_hd_climate_lwdown_ |
6 hourly incoming longwave radiation | W/m2 |
| press | _global_hd_climate_press_1901_v1.nc4 | 6 hourly pressure | Pa |
| press_monthly_mean | _global_hd_climate_press_ |
Monthly mean pressure | |
| qair | _global_hd_climate_qair_2008_v1.nc4 | 6 hourly air specific humidity | g/g |
| qair_monthly | _global_hd_climate_qair_ |
Monthly mean air specific humidity | |
| rain | _global_hd_climate_rain_1901_ |
6 hourly total rainfall | mm |
| rain_total_monthly | _global_hd_climate_rain_ |
Monthly total rainfall | |
| swdown | _global_hd_climate_swdown_ |
6 hourly average incoming shortwave radiation | W/m2 |
| swdown_monthly_mean | _global_hd_climate_swdown_ |
Monthly mean incoming shortwave radiation | W/m2 |
| swdown_total_6hourly | _global_hd_climate_swdown_ |
6 hourly total incoming shortwave radiation energy | J/m2 |
| swdown_monthly_total | _global_hd_climate_swdown_ |
Monthly total incoming shortwave radiation energy | J/m2 |
| tair | _global_hd_climate_tair_1914_ |
6 hourly instantaneous air temperature at 2m | K |
| tair_monthly_mean | _global_hd_climate_tair_ |
6 hourly monthly mean air temperature | |
| uwind | _global_hd_climate_uwind_1978_ |
6 hourly wind speed of u wind component | m/s |
| vwind | _global_hd_climate_vwind_1978_ |
6 hourly wind speed of v wind component |
North American Climate (NARR)
Spatial Resolution/Extent: 0.25 degree / W: -170, S: 10, E: -50,
N: 84
Temporal Resolution/Extent: 3-hourly / 1979-2010
Number of
Files for Each Variable: 32 annual files
Table 5. North American Climate (NARR)
| Variable | Example File Name | Description | Cell Methods | Units |
|---|---|---|---|---|
| air_2m | _na_qd_climate_air_2m_1987_v1. |
Air temperature at 2m1 | Spatial: average Temporal: instantaneous value at start point of each 3-hour period | K |
| apcp | _na_qd_climate_apcp_1987_v1. |
Accumulated total precipitation at surface1 | Spatial: average Temporal: sum in a 3-hour period | kg/m2 |
| dlwrf | _na_qd_climate_dlwrf_2000_v1. |
Downward longwave radiation flux at surface1 | Spatial: average Temporal: average in a 3-hour period | W/m2 |
| dswrf | _na_qd_climate_dswrf_2000_v1. |
Downward shortwave radiation flux at surface2 | Spatial: average Temporal: average in a 3-hour period | W/m2 |
| rhum_2m | _na_qd_climate_rhum_2m_2000_ |
Relative humidity at 2m | Spatial: average Temporal: instantaneous value at start point of each 3-hour period | % |
| shum_2m | _na_qd_climate_shum_2m_2000_ |
Specific humidity at 2m1 | Spatial: average Temporal: instantaneous value at start point of each 3-hour period | kg/kg |
| wnd_10m | _na_qd_climate_wnd_10m_1996_ |
Wind Speed at 10m3 | Spatial: average Temporal: instantaneous value at start point of each 3-hour period | m/s |
Notes:
1Regridded to quarter degree resolution using distance-weighted average method.
2Regridded to quarter degree resolution using area-weighted average method, rescaled with analysis output from MTCLIM algorithm. Rows with missing values replaced with nearby values. Negative values replaced with 0.
3Wind speed was calculated from U and V wind components, then regridded to quarter degree resolution using distance-weighted average method.
Atmospheric CO2 Concentration (two data files)
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: Monthly / 1700 - 2010
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: Monthly / 1700 -
2010
Table 6. Atmospheric CO2 Concentration data files
| FILES | DESCRIPTION | UNITS |
|---|---|---|
| _global_hd_co2_v1.nc4 | Global CO2 concentration | ppm |
| _na_qd_co2_v1.nc4 | North American CO2 concentration |
Nitrogen Deposition (four data files)
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: Annual / 1860 - 2050
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: Annual / 1860 -
2050
Table 7. Nitrogen Deposition data files
| FILES | DESCRIPTION | UNITS |
|---|---|---|
| _global_hd_nitrogen_nhx_v1.nc4 | Global NHx-N deposition at half degrees | mgN/m2/yr |
| _global_hd_nitrogen_noy_v1.nc4 | Global NOy-N deposition at half degrees | |
| _na_qd_nitrogen_nhx_v1.nc4 | North American NHx-N deposition at 0.25 degrees | |
| _na_qd_nitrogen_noy_v1.nc4 | North American NOy-N deposition at 0.25 degrees |
Biome Data (two data files)
There are two global biome data files and two files for North America. The files are described in tables 8 and 9 below.
Global SYNMAP BIOME data:
Present SYNMAP:
Spatial Resolution/Extent: 0.5 degree / W:
-180, S: -90, E: 180, N: 90
Temporal Resolution/Extent: one time (around
2000)
Potential SYNMAP:
Spatial Resolution/Extent: 0.5 degree /
W: -180, S: -90, E: 180, N: 90
Temporal Resolution/Extent: one time
(pre-industry, pre-agriculture)
Table 8. SYNMAP Biome Classification data files
| FILES | DESCRIPTION | VARIABLE NAME (definition) | UNITS |
|---|---|---|---|
| _global_hd_biome_v1.nc4 | Present global SYNMAP | biome_frac (fraction of each biome type in each cell) | fraction |
| biome_type (dominant biome type in each cell) | none | ||
| _global_hd_biome_potveg_v1.nc4 | Potential global SYNMAP | biome_frac (fraction of each biome type in each cell) | fraction |
| biome_type (dominant biome type in each cell) | none |
North American SYNMAP Biome Classification (two data files)
Present SYNMAP:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: one time (around
2000)
Potential SYNMAP:
Spatial Resolution/Extent: 0.25 degree /
W: -170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: one time
(pre-industry, pre-agriculture)
Table 9. North American SYNMAP Biome Classification data files
| Data Files | Description | Variable name(definition) | Units |
|---|---|---|---|
| _na_hd_biome_v1.nc4 | Present North American SYNMAP | biome_frac (fraction of each biome type in each cell) | fraction |
| biome_type (dominant biome type in each cell) | none | ||
| _na_hd_biome_potveg_v1.nc4 | Potential North American SYNMAP | biome_frac (fraction of each biome type in each cell) | fraction |
| biome_type (dominant biome type in each cell) | none |
Land-Use and Land-Cover Change (Hurtt_SYNMAP data)
There are 622 data files of land use and land cover change; 311 global data files and 311 files for North America. The variables and files are described in the table below.
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: Annual / 1700 - 2010
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: Annual / 1700 -
2010
Table 10. Land-Use and Land-Cover Change data files
| Variable | Variable definition | Example file names | Description | Units |
|---|---|---|---|---|
| land_use_change (lulcc) | biome_frac | _global_hd_lulcc_1776_v1.nc4 _na_qd_lulcc_1776_v1.nc4 |
A 3 D array with dimensions latitude, longitude and merged time - varying fractional coverages for all 48 SYNMAP land cover classes. There are 311 files, one for each year. | fraction |
| biome_type_pure1 | A 3D array with dimensions latitude, longitude and time with dominant class only. | none | ||
| biome_type2 | A 3D array with dimensions latitude, longitude and time with dominant class only. | none |
Note: Hurtt_SYNMAP are yearly land-use and land-cover change data created by MsTMIP by merging a static satellite-based land cover product, SYNMAP, with the time-varying land use harmonization (LUH) data for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). See Data Acquisition Materials and Methods section for details.
1Dominant class was determined by first apportioning each SYNMAP mixed class (e.g., Shrubs & Grasses) to a pure class. That is, mixed classes were assumed to exhibit a 50 - 50 split and these fractional coverages were moved to their pure class analogues. After this step the dominant class was given by the pure SYNMAP class with the highest fractional coverage.
2Dominant class was given by the SYNMAP class with the
highest fractional coverage.
The SYNMAP land cover types are denoted as SYNMAP-Legend:
| Value | Life forms | Tree leaf type | Tree leaf longevity | % land |
|---|---|---|---|---|
| 1 | Trees | Needle | Evergreen | 9.8 |
| 2 | Trees | Needle | Deciduous | 1.7 |
| 3 | Trees | Needle | Mixed | 0.6 |
| 4 | Trees | Broad | Evergreen | 8.2 |
| 5 | Trees | Broad | Deciduous | 3 |
| 6 | Trees | Broad | Mixed | 0.5 |
| 7 | Trees | Mixed | Evergreen | 0.25 |
| 8 | Trees | Mixed | Deciduous | 0.03 |
| 9 | Trees | Mixed | Mixed | 3.5 |
| 10 | Trees & Shrubs | Needle | Evergreen | 1.5 |
| 11 | Trees & Shrubs | Needle | Deciduous | 0.2 |
| 12 | Trees & Shrubs | Needle | Mixed | 0.05 |
| 13 | Trees & Shrubs | Broad | Evergreen | 0.3 |
| 14 | Trees & Shrubs | Broad | Deciduous | 2.5 |
| 15 | Trees & Shrubs | Broad | Mixed | 0.2 |
| 16 | Trees & Shrubs | Mixed | Evergreen | 0.03 |
| 17 | Trees & Shrubs | Mixed | Deciduous | 0.04 |
| 18 | Trees & Shrubs | Mixed | Mixed | 0.3 |
| 19 | Trees & Grasses | Needle | Evergreen | 0.2 |
| 20 | Trees & Grasses | Needle | Deciduous | 0.03 |
| 21 | Trees & Grasses | Needle | Mixed | 0.01 |
| 22 | Trees & Grasses | Broad | Evergreen | 0.3 |
| 23 | Trees & Grasses | Broad | Deciduous | 2.15 |
| 24 | Trees & Grasses | Broad | Mixed | 0.15 |
| 25 | Trees & Grasses | Mixed | Evergreen | 0.005 |
| 26 | Trees & Grasses | Mixed | Deciduous | 0.02 |
| 27 | Trees & Grasses | Mixed | Mixed | 0.2 |
| 28 | Trees & Crops | Needle | Evergreen | 0.3 |
| 29 | Trees & Crops | Needle | Deciduous | 0.006 |
| 30 | Trees & Crops | Needle | Mixed | 0.003 |
| 31 | Trees & Crops | Broad | Evergreen | 0.7 |
| 32 | Trees & Crops | Broad | Deciduous | 1.1 |
| 33 | Trees & Crops | Broad | Mixed | 0.2 |
| 34 | Trees & Crops | Mixed | Evergreen | 0.01 |
| 35 | Trees & Crops | Mixed | Deciduous | 0.01 |
| 36 | Trees & Crops | Mixed | Mixed | 0.4 |
| 37 | Shrubs | - | - | 4.5 |
| 38 | Shrubs & Grasses | - | - | 8.3 |
| 39 | Shrubs & Crops | - | - | 0.4 |
| 40 | Shrubs & Barren | - | - | 10.5 |
| 41 | Grasses | - | - | 8.3 |
| 42 | Grasses & Crops | - | - | 1.5 |
| 43 | Grasses & Barren | - | - | 0.3 |
| 44 | Crops | - | - | 10.7 |
| 45 | Barren | - | - | 11.7 |
| 46 | Urban | - | - | 0.2 |
| 47 | Snow & Ice | - | - | 5.2 |
| 0 = water | ||||
C3 and C4 Grass Fraction (12 data files)
There are six global and six NA data files described in the table below.
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: One time
Present:
around year 2000
Potential: around year 1900 or before
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: One
time
Present: around year 2000
Potential: around year 1900 or before
Table 11. C3 and C4 Grass Fraction data files
| Data Files | Description | Units |
|---|---|---|
| _global_hd_C3_rfrac_potveg_v1.nc4 | Potential relative fraction of C3 grasses1 | fraction |
| _global_hd_C4_rfrac_potveg_v1.nc4 | Potential relative fraction of C4 grasses1 | |
| _global_hd_grass_frac_potveg_v1.nc4 | Potential relative fraction of grasses1 | |
| _global_hd_C3_rfrac_presentveg_v1.nc4 | Present relative fraction of C3 grasses2 | |
| _global_hd_C4_rfrac_presentveg_v1.nc4 | Present relative fraction of C4 grasses2 | |
| _global_hd_grass_frac_presentveg_v1.nc4 | Present relative fraction of grasses2 | |
| _na_qd_C3_rfrac_potveg_v1.nc4 | Potential relative fraction of C3 grasses1 | fraction |
| _na_qd_C4_rfrac_potveg_v1.nc4 | Potential relative fraction of C4 grasses1 | |
| _na_qd_grass_frac_potveg_v1.nc4 | Potential relative fraction of grasses1 | |
| _na_qd_C3_rfrac_presentveg_v1.nc4 | Present relative fraction of C3 grasses2 | |
| _na_qd_C4_rfrac_presentveg_v1.nc4 | Present relative fraction of C4 grasses2 | |
| _na_qd_C4_rfrac_presentveg_v1.nc4 | Present relative fraction of grasses2 |
Notes:
1based on the potential SYNMAP.
2based on the present SYNMAP.
Major Crop Types (eight data files)
There are four crop global data files and four crop data files for North America described below. The crop data includes maize, rice, soybean, and wheat.
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: One time / 2000
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: One time /
2000
Table 12. Major Crop Types data files
| Files | Description | Units |
|---|---|---|
| _global_hd_crop_frac_maize_v1.nc4 | Global maize fraction | fraction of harvest area in each cell1 |
| _global_hd_crop_frac_rice_v1.nc4 | Global rice fraction | |
| _global_hd_crop_frac_soybean_v1.nc4 | Global soybean fraction | |
| _global_hd_crop_frac_wheat_v1.nc4 | Global wheat fraction | |
| _na_qd_crop_frac_maize_v1.nc4 | North American maize fraction | |
| _na_qd_crop_frac_rice_v1.nc4 | North American rice fraction | |
| _na_qd_crop_frac_soybean_v1.nc4 | North American soybean fraction | |
| _na_qd_crop_frac_wheat_v1.nc4 | North American wheat fraction |
Notes: 1Some of the cells have fraction values greater than 1, because the crops in these cells were harvested more than once each year. Therefore, the harvest area can be greater than the physical cell area.
Phenology (622 data files)
There are 311 global phenology data files and 311 phenology data files for North America. The data include leaf area index (LAI), normalized difference vegetation index (NDVI), and the canopy absorbed fraction of photosynthetically active radiation (fPAR).
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: monthly / 1700 - 2010
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: monthly / 1700 -
2010
Table 13. Phenology data files
| Variable | Example file names | Units |
|---|---|---|
| LAI (Leaf area index) | _global_hd_phenology_1700_v1.nc4 _na_qd_phenology_1700_v1.nc4 |
m2/m2 |
| NDVI (Normalized Difference Vegetation Index) | unitless | |
| fPAR (canopy absorbed fraction of Photosynthetically Active Radiation) | unitless |
Note: For both global and NA, GIMMSg phenology data were harmonized with SYNMAP to provide LAI and NDVI values for each of the SYNMAP PFTs appearing in a 0.5 or 0.25 degree cell.
Soil Properties (two data files)
There are two soil data files and 22 soil attributes described in the tables below.
Global:
Spatial Resolution/Extent: 0.5 degree / W: -180, S:
-90, E: 180, N: 90
Temporal Resolution/Extent: One time
Source:
HWSD version 1.1
North America:
Spatial Resolution/Extent: 0.25 degree / W:
-170, S: 10, E: -50, N: 84
Temporal Resolution/Extent: One time
Source:
Unified North American Soil Database (UNASD) [U.S. General Soil Map (STATSGO2)
+ Soil Landscapes of Canada v3.2 and v2.2 + HWSD v1.1]
Table 14. Soil Properties data files
| Files | Description |
|---|---|
| mstmip_driver_global_hd_soil_v1. |
Gap-filled global soil data |
| mstmip_driver_na_hd_soil_ |
Gap-filled North American soil data |
Table 15. Soil Attributes
| Soil Property | Description | Units in HWSD v 1.1 | Units in UNASD |
|---|---|---|---|
| soil_code | soil mapping unit code | code | code |
| ref_depth | reference soil depth | code | code |
| roots | obstacles to roots (Europe only) | code | NA |
| il | impermeable layer (Europe only) | code | NA |
| t_cec_clay | topsoil CEC (clay) | cmol/kg | meq/100g |
| t_clay | topsoil clay fraction | % weight | % weight |
| t_gravel | topsoil gravel content | % volume | % volume |
| t_oc | topsoil organic carbon | % weight | % weight |
| t_ph_h20 | topsoil pH (H2O) | -log(H+) | -log(H+) |
| t_ref_bulk | topsoil bulk density | kg/dm3 | g/cm3 |
| t_sand | topsoil sand fraction | % weight | % weight |
| t_silt | topsoil silt fraction | % weight | % weight |
| t_usda_tex | topsoil USDA texture classification | name | name |
| s_cec_clay | subsoil CEC (clay) | cmol/kg | meq/100g |
| s_clay | subsoil clay fraction | % weight | % weight |
| s_gravel | subsoil gravel content | % volume | % volume |
| s_oc | subsoil organic carbon | % weight | % weight |
| s_ph_h20 | subsoil pH (H2O) | -log(H+) | -log(H+) |
| s_ref_bulk | subsoil bulk density | kg/dm3 | g/cm3 |
| s_sand | subsoil sand fraction | % weight | % weight |
| s_silt | subsoil silt fraction | % weight | % weight |
| s_usda_tex | subsoil USDA texture classification | name | name |
2.6. Companion File Information
A copy of this user’s guide is included as a companion file.
This data product contributes to a multidisciplinary research program to obtain scientific understanding of North America's carbon sources, carbon sinks, and changes in carbon stocks. This information is needed to meet societal concerns and to provide tools for decision makers.
Multi-model intercomparison projects (MIPs) help to characterize or synthesize current understanding of land-atmosphere carbon exchange, and inform the uncertainty or confidence surrounding projections of future exchange and feedbacks with the climate system. The previous NACP MIP conducted on a regional scale provided a comprehensive assessment of the range of estimates of land-atmosphere carbon exchange and uncertainties associated with such estimates, including uncertainties resulting not only from model formulation and assumptions, but also from the choice of environmental driver data and spin up procedures (Huntzinger et. al. 2012) . However, the lack of consistent forcing data and detailed simulation protocols have precluded the attribution of observed across-model variability to differences in modeling approaches. The goal of MsTMIP is to quantify, within a unified intercomparison framework, the contribution of model structural differences to across-model variability in estimates of land-atmosphere carbon exchange, thus providing the critical synthesis, benchmarking, evaluation, and feedback needed to improve the current state of the art in carbon cycle modeling. The MsTMIP experimental protocol (Huntzinger et al., 2013) specifies standard model inputs (this data set), simulations and simulation setup procedures, as well as required model output and format to ensure a valid and fair comparison of model results against one another and against available observations. Wei et al. (in review) outline key components of the MsTMIP environmental model driver data set.
Quality analyses of MsTMIP model driver data were conducted by MAST-DC in close collaboration with the MsMTIP team as described by Wei et al. (in review). These quality analyses are summarized below:
Data Requirements
In order to meet the objectives of MsTMIP’s experimental design, the goal was to provide modeling teams, to the extent possible, with a complete and consistent set of environmental driver data. This required MAST-DC to make improvements in the quality and/or the spatial and temporal coverage and resolution of several of the original environmental data sets. In addition to being of high quality, the environmental driver input data chosen for MsTMIP also needed to meet the following requirements:
For most data categories, the North American data sets are based on the same data sources as the global products. However, different climatology and soil data products were compiled for the two domains. This decision was driven primarily by the availability of these drivers at the spatial and temporal resolution needed for the regional simulations. In addition, by holding the source of other drivers constant between the global and North American simulations, MsTMIP created an opportunity to test the impact of the choice of climate and soil characteristics on model estimates.
Data Synthesis and Processing
Wei et al. (in review) describes how the MsTMIP model intercomparison driver data were compiled. This information is summarized below.
Global Climate: CRUNCEP. MAST-DC combined the Climate Research Unit CRU Time Series (TM) 3.2 (Mitchell and Jones, 2005) and the National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) Reanalysis 1 (Kalnay et al., 1996) gridded global climatology data sets to produce the “CRUNCEP” global climate data set. The original data sets have the following characteristics:
The new CRUNCEP data set provides a globally gridded (0.5 degree by 0.5 degree) and sub-daily (6-hourly) time-varying climatology product that spans the period between 1901 and 2010. It contains seven climatology variables; including incoming longwave and shortwave radiations, pressure, air specific humidity, precipitation, temperature, and wind. In the process of creating this new climatology product, MAST-DC also corrected known biases in temperature and shortwave radiation in the NCEP/NCAR Reanalysis product described by Zhao et al. (2006). [Zhao et al. (2006) showed that NCEP/NCAR Reanalysis climatology overestimates downward shortwave radiation, especially in non-tropical regions, and underestimates surface temperature for almost all latitudes. Biases in climatological variables can introduce substantial errors into Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) estimates.] By fusing NCEP/NCAR with the CRU climatology, MAST-DC forced the amplitude of CRUNCEP product to be consistent with the observation-based CRU climatology, while preserving the diurnal and daily variability in the NCEP/NCAR Reanalysis product.
Below is a summary of the fusion method used by MAST-DC:
North American Climate: NARR. The NCEP North America Regional Reanalysis (NARR) is a long-term, dynamically consistent, high-resolution, high-frequency, atmospheric and land surface hydrology data set (1979-present) for the North American domain (Mesinger et al., 2006). It has 3-hourly temporal resolution and 32 km spatial resolution. The NARR model uses the very high resolution NCEP Eta Model (32km / 45 layers) together with the Regional Data Assimilation System (RDAS) which, significantly, assimilates precipitation along with other variables. Seven mono-level NARR climate variables were selected for MsTMIP, including air temperature at 2 m (air.2m), accumulated total precipitation (apcp), downward longwave radiation flux (dlwrf), downward shortwave radiation flux (dswrf), relative humidity at 2 m (rhum.2m), specific humidity at 2m (shum.2m), and wind speed at 10 m (wnd.10m). The original NARR data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www.esrl.noaa.gov/psd/.
Additional processing was performed to convert original NARR data into the format required by MsTMIP and to further reduce the bias of certain NARR climate variables. This procedure included the regridding to 0.25 degree x 0.25 degree spatial resolution and improvements made to NARR precipitation and downward shortwave radiation flux.
The original NARR data are in Lambert Conformal Conic Projection and have a spatial resolution of 32 km, which is different from the Sphere-based Geographic Lat/Lon coordinate reference system and the 0.25 degree spatial resolution required by MsTMIP. The Spherical Coordinate Remapping and Interpolation Package (SCRIP) was utilized to regrid NARR variables to MsTMIP North America grids using two different methods: area-weighted average and distance-weighted average. Area-weighted average method was used for precipitation and radiation flux variables in order to preserve their total amounts in the North America region. Distance-weighted average method was used for other variables. The original NARR provides variables of U-direction wind speed (U-wnd.10m) and V-direction wind speed (V-wnd.10), which were combined together to calculate the surface wind speed variable prior to the regridding process.
Sun and Barros (2010) compared NARR with National Climatic Data Center (NCDC) rain gauge measurements and found that although NARR reproduces the spatial patterns of NCDC parameters, the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. Xie et al. (2003) found that the 2.5° pentad Global Precipitation Climatology Project (GPCP) analyses, which matched the monthly GPCP in magnitude, reproduced spatial distribution patterns of total precipitation with relatively high quality especially over land. Thus MAST-DC rescaled NARR 3-hourly precipitation with the GPCP monthly gridded data set version 2.1. The GPCP v2.1 consists of monthly total precipitation derived from satellite and gauge measurements (Adler et al., 2003). Although the GPCP product has a relatively coarse resolution of 2.5 degree, it presented the advantage of including a correction to compensate for systematic biases in gauge measurements due to wind, gauge wetting, and gauge evaporation. Since the NARR data set does not include such corrections, the complimentary use of the GPCP product helped to identify the effect of systematic errors in precipitation measurements. For each month, precipitation of all 3-hourly 0.25 degree NARR grids within each 2.5 degree GPCP grid were summed up and adjusted linearly to match the total precipitation amount in GPCP. MAST-DC analysis showed that rescaled NARR precipitation preserved both the magnitude and spatial pattern in most area of North America. Precipitation was reduced after rescaling in a few areas, including lower Alaska and Montana. Extreme rainfall events at the coastline of Gulf of Alaska and Central America were enhanced by the rescaling process.
Kennedy, et al. (2010) found NARR has significant positive bias for incoming shortwave radiation flux (dswrf) under clear- and all-sky conditions compared with ARM Southern Great Plains (SGP) site continuous forcing during the period 1999-2001. Comparing NARR incoming shortwave radiation flux with FLUXNET tower observations showed that NARR downward shortwave radiation is overestimated by about 30% overall with higher positive bias under cloudy conditions. The MTCLIM model version 4.3, a weather simulation model, was used to reduce this shortwave radiation bias. Given input data from one location, MTCLIM generates weather information for another location with potentially different elevation, slope, and aspect from the input location (Running et al., 1987, Thornton and Running, 1999). The input location is referred to as the “base station” while the new location for output is referred to as the “site.” In the case of NARR, the “site” is same as the “base station” which is the center point of every 0.25 degree grid cell in North America. The MTCLIM model was fed daily max/min temperature and daily total precipitation which were derived from the 3-hourly NARR original temperature and rescaled precipitation for each grid. The model then calculated the daily total shortwave radiation flux for that grid. The 3-hourly NARR dswrf variable values were then adjusted to match the total daily downward shortwave radiation generated from MTCLIMl. The rescaled NARR downward shortwave radiation have a 25%-30% decrease compared with original NARR data. Between 70°N and 80°N, the original NARR downward shortwave radiation almost has a flat curve. This issue was addressed in the reanalyzed NARR data.
Land-Water Mask. The MsTMIP land-water mask maps contain values 0 and 1, with 0 indicating water and 1 indicating land. The mask maps specify all land grid cells on which MsTMIP global and regional simulations were run. The selection criterion for land-water mask maps was to be consistent with climate driver data. For global land-water mask map, MAST-DC took the CRU-NCEP land water mask directly. For North America land-water mask map, MAST-DC took the original NARR mask, regridded it to 0.25 degree spatial resolution to derive a land fraction map using an area-weighted average method to preserve the total amount of land area, then used a threshold of 50% to classify regridded land fraction map into land-water mask map.
Atmospheric CO2 Concentration. The atmospheric CO2 concentration data prepared for MsTMIP is consistent with the GLOBALVIEW-CO2 2011 (henceforth GV) data product, the time series of historic atmospheric CO2 from Antarctic ice cores (MacFarling Meure et al., 2006), fossil fuel emissions (Marland et al., 2008), and Scripps CO2 program (SIO) atmospheric CO2 observations at Mauna Loa (MLO) and the South Pole (SPO). During the period of 1979-2010, when direct observations are available, CO2 concentrations were set directly to the GV marine boundary reference surface, interpolated from GV’s native latitude-time grid to that needed for MsTMIP simulations. For the period prior to 1979, MAST-DC preserved the mean annual cycle from GV and imposed this on a modeled CO2 surface that represented annual mean concentrations and a time-evolving meridional gradient. Following the methods of Conway and Tans (1999), the annual mean difference between Mauna Loa and South Pole in the GV product was modeled as a linear function of fossil fuel (FF) emissions (Marland et al., 2008). Extrapolated to zero FF emissions, the preindustrial MLO-SPO difference estimated in this manner is 0.3 ppm. Performing this same exercise using SIO observations instead of GV yielded a stronger dependence of the meridional gradient on FF emissions and a preindustrial MLO-SPO difference of -1.2 ppm. While it is possible that preindustrial southern hemisphere CO2 values exceeded those in the northern hemisphere (Conway and Tans, 1999), MAST-DC judged that it was more parsimonious to assume a small preindustrial inter-hemispheric CO2 gradient that the GV-based scheme achieves natively. The MsTMIP product agrees well with Scripps CO2 data before 1979 at SPO and MLO, and with Law Dome ice core data in Antarctica data (MacFarling Meure et al., 2006). It does not represent inter-annual variability other than that derived from variability in FF emissions, and it does not include speculative changes in the magnitude or phase of annual cycles of CO2 in the atmosphere.
Nitrogen Deposition. MAST-DC used the approach described in Tian et al. (2010) and Lu et al. (2012) to create a time-varying annual nitrogen deposition data set for both global (0.5 degree by 0.5 degree resolution) and North American (0.25 degree by 0.25 degree resolution) simulations based on Dentener (2006) maps of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O) deposition and to introduce spatial and temporal variations from nitrogen emissions. The original maps cover the years 1860, 1993, and 2050 and have a spatial resolution of 5 degree longitude by 3.75 degree latitude.
For the Dentener (2006) maps, the annual variation of nitrogen deposition rate from year 1890 to year 1990 was controlled by EDGAR-HYDE 1.3 nitrogen emission data (Van Aardenne et al., 2001) which provides information on annual totals of NH3 and NOx emissions from 10 anthropogenic sources within 1.0 ×1.0-degree grid cells for each decade. MAST-DC assumed that the temporal trends of NHx-N and NOy-N depositions were consistent with those of NH3 and NOx emissions between 1890 and 1990. MAST-DC also assumed that nitrogen deposition increased linearly over the time periods 1860-1890 and 1990-2050. Following these assumptions and the methods of Tian et al. (2010), the annual global 0.5 degree and North America 0.25 degree nitrogen deposition estimates were calculated by temporally and spatially interpolating the original Dentener’s nitrogen deposition data.
Specifically, the development of time-series nitrogen deposition data during 1890-1990 followed several assumptions:
NDi= NDi-1+ (EDj- EDj-1) /10 × (ND1990- ND1890) / (E1990- E1890)
Where NDi is N deposition rate for NHx or NOy in a specific year i (unit: mg N/m2/yr); correspondingly, EDj and EDj-1 are emission rates of NH3 or NOx in decade j and j-1.
Biome. The static satellite-based land cover product SYNMAP (Jung et al., 2006) was chosen for MsTMIP biome classification due to its: (1) reconciliation of multiple global land cover products (i.e., Global Land Cover Characterization Database (GLCC) (Hansen et al., 2000; Loveland et al., 2000), GLC2000 (2003), and the 2001 MODIS land cover product (Friedl et al., 2002); (2) global coverage at 1-km resolution; (3) general definition of classes based on life form, leaf type, and leaf longevity which allowed for simple mapping rules to plant functional types (PFTs) used in different TBMs, and (4) assumed representation of year 2000/2001 land cover/biome status. Generality was a key concern as PFT schemes used in TBMs vary widely. The original 1-km resolution SYNMAP data was upscaled by MAST-DC to 0.5-degree resolution for global and 0.25-degree for North America to derive two variables: land cover class fraction and dominant land cover class. The land cover class fraction variable provides fraction of each of the 48 land cover classes contained in each grid cell and the class with highest fraction value was selected as the dominant land cover class for that grid cell.
Land-use and Land-cover Change. For this data set, land-use and land-cover change was prescribed by merging SYNMAP (Jung et al., 2006) with the time-varying land use harmonization data from the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) (Hurtt et al., 2011). The SYNMAP-Hurtt product was chosen for MsTMIP based on its global coverage, inclusion of land use change fractions (required for a subset of participating models), overlap with the time horizon of MsTMIP simulations, and its use in the IPCC process.
The land use harmonization product (Hurtt et al., 2011) provides mapped fractional coverage and underlying annual land use transitions for six land use classes (primary land, secondary land, cropland, pasture, urban, and barren) at 0.5-degree by 0.5-degree spatial resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500–2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the Integrated Assessment Model (IAM) implementations of the Representative Concentration Pathways (RCPs) for the period 2005–2100. The historical land use harmonization data (1801-2005) were combined with the Representative Concentration Pathway (RCP) 4.5 scenario (2006-2010) to match the time horizon of MsTMIP model simulations (1801-2010).
SYNMAP and Hurtt were merged using one-to-one (direct mapping) and one-to-many mapping rules based on map intersection during their period of overlap, i.e., both products exist for 2000/2001. Direct mappings were used for those Hurtt types with direct analogues in the SYNMAP class structure (i.e., the class denoting urban and built-up land). For the remaining five Hurtt types, MAST-DC used a one-to-many mapping approach based on the temporal overlap of the two products in 2000/2001.
Table 16. One-to-many mapping rules used to merge Hurtt with SYNMAP
| Hurtt Class | SYNMAP Target Class |
|---|---|
| Croplands | Trees & Crops1, Shrubs & Crops, Grasses & Crops, Crops2 |
| Pasture | Trees & Grasses1, Shrubs, Shrubs & Grasses, Shrubs & Crops, Grasses, Grasses & Crops |
| Primary | Trees1, Shrubs, Shrubs & Grasses, Shrubs & Crops, Shrubs & Barren |
| Secondary | Trees1, Trees & Shrubs1, Trees & Grasses1, Trees & Crops1, Shrubs, Shrubs & Grasses, Shrubs & Crops |
| Barren | Shrubs & Barren, Grasses & Barren, Barren, Snow & Ice, Water3 |
Notes:
1All tree leaf types and longevities.
2The pure SYNMAP crop class was favored in the one-to-many mappings (relative weight increased by a factor of 5). This was based on Hurtt croplands being pure with in-pixel admixtures of other classes accounted for by Hurtt non-croplands as opposed to SYNMAPs mixed classes.
3The land mask used was all grid cells where the SYNMAP water class was less than unity.
One-to-many mappings involved three steps. Using an example of mapping Hurtt pasture to SYNMAP shrubs and grasses, the following processing took place:
Applying these rules resulted in the merged dynamic land cover product from 1700-2100 with all 48 SYNMAP classes on an annual time step.
C3 and C4 Grass Fractions. MAST-DC created global 0.5 degree C3 and C4 grasses relative fraction maps under the “present” climate state based on the CRU-NCEP mean monthly precipitation and temperature data between 2000-2010 using an approach described in Still et al. (2003) based on growing season temperature. For grid cells characterized as grasslands (or containing grasslands) the relative fraction map defines the fraction of those grasses that are C3 or C4, so that in each grid cell the C3 and C4 grass fractions sum to one regardless of the total percentage of grassland contained in the grid cell. The value is zero if no grass is present in a particular grid cell.
The grass fractions were calculated as follows:
Among the 12 months, if there was at least one month with monthly mean air temperature (T) above 22 degrees C and at the same time the monthly total rainfall (P) was above 25 mm in a grid cell, it was assumed that the C4 grass relative fraction to be equal to the number of months where C4 photosynthesis is favored relative to the number of growing season months with air T greater than 5 degrees C. Therefore, C4 grass relative fraction was calculated as:
[C4 grass relative fraction] = [number of months with T>22 degrees C and P>25 mm] / [number of months with T>5 degrees C]
[C3 grass relative fraction] = 1 − [C4 grass relative fraction]
The C3 and C4 grass fraction maps were then created through merging the C3 and C4 relative fraction maps with SYNMAP:
[C3/C4 grass fraction] = [C3/C4 grass relative fraction] − [SYNMAP grass fraction]
SYNMAP contains 13 land cover classes that include grasses land cover class, with 12 of them mixtures of grasses with trees/shrubs/crops/barren. For the mixed classes, it was assumed that grasses accounted for 50% area of a cell. The SYNMAP grass fraction in each cell was calculated as the sum of the grass fraction of all different classes included in the cell.
The North American C3 and C4 relative grassland fraction maps were created using the same approach with NARR climate data.
MsTMIP only provides a constant C3/C4 data product under "present" climate conditions. For models that needed time-varying C3/C4 grass fractions, the same approach was applied to historical land cover data and historical precipitation / temperature climate data to generate C3/C4 grassland maps for previous years.
Major Crops.MAST-DC identified and extracted four globally significant crop types (maize, rice, soybean, and wheat) from the Monfreda et al. (2008) global crop database for 2000 at a 5 min by 5 min (approximately 10 km by 10 km) spatial resolution. The original data was resampled to 0.5 degrees by 0.5 degrees (global) and 0.25 degrees by 0.25 degrees (North American) spatial resolutions. These major crop designations do not provide detailed model simulation prescription, but rather guidance for models that need to specify crop types or cropping systems.
Phenology. For models that use remote sensing products to prescribe plant phenology to calculate GPP or NPP, MAST-DC constructed monthly maps of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and absorbed fraction of Photosynthetically Active radiation (fPAR) consistent with the harmonized Hurtt-SYNMAP land cover change data on both global and North American grids for 1801-2010.
LAI and fPAR values were derived from the Global Inventory Monitoring and Modeling System version g (GIMMSg) NDVI data set (Tucker et al., 2005) which is a 15-day maximum value composite calculated from AVHRR at about 8km spatial resolution for 1982-2010 and adjusted for missing data, satellite orbit drift, sensor degradation, and volcanic aerosols. The 15-day GIMMSg NDVI for 1982-2010 was first converted to monthly mean composites for 12 months from January to December. This averaging process reduced noise in the data such as sudden and large changes due to cloud contamination. The monthly mean composites were then regridded to the 0.5 degrees grid for global and the 0.25 degrees grid for North America. MAST-DC then calculated the average seasonal cycle in monthly mean NDVI values and used them to calculate LAI and fPAR using methods described in Schaefer et al. (2002).
To harmonize phenology data with the the harmonized Hurtt-SYNMAP land cover change data, MAST-DC assumed that a pixel would consist of tiles, each corresponding to a different land use/cover class with fractional areas set by the MsTMIP coverage maps as a function of year from 1801 to 2010. Maps of LAI and fPAR were calculated assuming the entire land surface was one of the 12 SiB biome classes (Sellers et al., 1986) resulting in 12 sets of LAI and fPAR maps corresponding to the 12 SiB biome classes, all calculated from the same NDVI values but using different parameter values unique to each biome (Sellers et al., 1996). The 12 SiB biomes were then mapped to the 47 SYNMAP land use/cover types using one-to-one or one-to-many mapping, resulting in 47 sets of LAI and fPAR maps corresponding to the 47 SYNMAP classes. This two-step process was required because the parameters used to calculate LAI and fPAR were not available for each of the 47 SYNMAP types. Combining these 47 sets of LAI and fPAR maps and the yearly MsTMIP land-use and land-cover change data, time-evolving and land use/cover type explicit LAI and fPAR data products were created. If a grid cell did not contain a particular SYNMAP type for a specific year, a standard missing value was inserted into the corresponding LAI and fPAR maps. Participating model would then extract the LAI and fPAR values for a particular SYNMAP class and use them for the corresponding tile.
Global Soil: Gridded HWSD. Harmonized World Soil Database (HWSD) version 1.1 (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2011) was used as the source for MsTMIP global soil data. Each soil mapping unit in the HWSD is composed of several different soil units (or soil types) defined by major soil group code following a combined FAO-74/FAO-85/FAO-90 soil classification system. For the global simulations, the original HWSD was regridded to a spatial resolution of 0.5 degrees by 0.5 degrees by selecting the dominant soil type within each grid cell. Eight (8) physical and chemical soil properties associated with the dominant soil type in each soil layer were then selected (Table 14 above). One additional property, reference soil depth, was extracted from HWSD and provided as a proxy for mineral soil depth, even though this reference soil depth is not precise. Bulk density values that are overestimated in HWSD v1.1 for Andosols and Histosols soil types were corrected using the corresponding depth-weighted average values from ISRIC-WISE, version 1.0 (Batjes, 2008). The correction mainly impacts the North American boreal region and a few places of southeastern Asia where Andosols and Histosols dominate.
North American Soil: Unified North American Soil Database (UNASD). A new gridded database of harmonized soil physical and chemical properties for North America was created for MsTMIP by fusing the most recent regional soil information from U.S. STATSGO2, Canada soil databases versions 3.2 and 2.2, and the HWSD v1.1. The fused database was then harmonized into two standardized soil layers as for the HWSD. The top soil layer ranges from 0 to 30 cm and the sub soil layer ranges from 30 to 100 cm. A comparison with the subset of HWSD demonstrated pronounced difference in the spatial distributions of soil properties and soil organic carbon mass between the UNASD and HWSD, but overall the UNASD provides more accurate and detailed information particularly in Alaska and central Canada. The methods used to develop the UNASD and the comparisons with HWSD are described in detail in Liu et al. (2013).
This data set is available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Contact for Data Center Access Information:
E-mail: uso@daac.ornl.gov
Telephone: +1 (865) 241-3952
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