Publication Details
- Title:
- Climate data for RPA 2020 Assessment: MACAv2 (METDATA) historical modeled (1950-2005) and future (2006-2099) projections for the conterminous United States at the 1/24 degree grid scale
- Author(s):
-
Joyce, Linda A.; Abatzoglou, John T.; Coulson, David P. - Publication Year:
- 2018
- How to Cite:
-
These data were collected using funding from the 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 both of these citations:
Joyce, Linda A.; Abatzoglou, John T.; Coulson, David P. 2018. Climate data for RPA 2020 Assessment: MACAv2 (METDATA) historical modeled (1950-2005) and future (2006-2099) projections for the conterminous United States at the 1/24 degree grid scale. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2018-0014
Abatzoglou, John T.; Brown, Timothy J. 2012. A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology, 32: 772–780. https://doi.org/10.1002/joc.2312
For further clarity, unless otherwise noted, the MACA datasets are made available with a Creative Commons CC0 1.0 Universal dedication. In short, John Abatzoglou waives all rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute, and perform the work, even for commercial purposes, all without asking permission. John Abatzoglou makes no warranties about the work, and disclaims liability for all uses of the work, to the fullest extent permitted by applicable law. (https://climate.northwestknowledge.net/MACA/MACAreferences.php) - Abstract:
- The 2020 Resources Planning Act (RPA) Assessment will include climate change as a driver affecting natural resources on all forest and rangelands in the United States. These climate projections, along with projections for population dynamics, economic growth, and land use change in the United States, comprise the RPA scenarios. The climate scenarios are the Representative Concentration Pathways (RCPs) RCP 4.5 and RCP 8.5 and the downscaled climate data is the MACAv2-METDATA developed by Abatzoglou and Brown (2012) and Abatzoglou (2013). This downscaled climate data set covers the conterminous United States at the grid size of approximately 4 kilometers (1/24 degree) on a side. The data set includes downscaled historical model output (1950-2005) and projections (2006-2099) for both the RCP 4.5 and the RCP 8.5 scenarios for five models: HadGEM2-ES365, MRI-CGCM3, CNRM-CM5, IPSL-CM5A-MR, and NorESM1-M. The variables available include, monthly average of daily mean near-surface specific humidity (huss), mean daily mean potential evapotranspiration (pet), mean daily maximum relative humidity (rhmax), mean daily minimum relative humidity (rhmin), monthly average of daily surface downwelling shortwave radiation (rsds), mean daily maximum air temperature (tasmax), mean daily minimum air temperature (tasmin), and monthly average of daily mean near-surface wind speed (was). With two climate scenarios and five models, ten different climate futures are available.
- Keywords:
- climatologyMeteorologyAtmosphere; Climate change; Climatology; monthly; precipitation; maximum temperature; minimum temperature; downwelling shortwave solar radiation; specific humidity; wind speed; potential evapotranspiration; minimum relative humidity; maximum relative humidity; CMIP5; gridded meteorological data; MACAv2; RPA Assessment; Resources Planning Act Assessment; conterminous United States
- Related publications:
- Abatzoglou, John T. 2013. Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology. 33: 121-131. https://doi.org/10.1002/joc.3413
- Abatzoglou, John T.; Brown, Timothy J. 2012. A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology. 32: 772-780. https://doi.org/10.1002/joc.2312
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- RDS-2018-0014_Metadata_Fileindex.zip (15 KB; sha256: defe87ce671994b49e9425bcd78d955ed954f37338b791fe4e2143c539b49325Checksum)
- RDS-2018-0014_Data_HUSS.zip (17.9 GB; SHA256: 4A09AB92F4385EB86128E500B96033E0199E604752D323F028C4BD5263FF54BCChecksum)
- RDS-2018-0014_Data_PET.zip (18.4 GB; SHA256: 719B5A67F454DED0DE1E61BB282F56A27AFF5973D3452DD4BF2B2D5CCEBBCF35Checksum)
- RDS-2018-0014_Data_PR.zip (20.2 GB; SHA256: 6882CE0CB1693D773EEF0A8A53E1E6E5C73A890EB887C2EF819A8334A970029BChecksum)
- RDS-2018-0014_Data_RHSMAX.zip (13.5 GB; SHA256: 23220337DDA0B66CD3DDA04C89ABF065909F2EEB40F578B2FD753B029D5F8B61Checksum)
- RDS-2018-0014_Data_RHSMIN.zip (14.4 GB; SHA256: 0284A8E899FC71A7885E9895B238DF49F27A1FA810D8F2E989C2E8DA861AF15BChecksum)
- RDS-2018-0014_Data_RSDS.zip (15.7 GB; SHA256: 356BC5B617331BED5BD6715D07C46902568A73785787608A2BE24F9A761D8017Checksum)
- RDS-2018-0014_Data_TASMAX.zip (14.4 GB; SHA256: 762019527EE79FB9B6C575A50790DE06A39362FECF6996700840D5C2136EF94EChecksum)
- RDS-2018-0014_Data_TASMIN.zip (14.3 GB; SHA256: CD5F911041D9BFACB7A04D79CA48DF534E1AED19E0A325FFA49E10CB9844C581Checksum)
- RDS-2018-0014_Data_WAS.zip (17.8 GB; SHA256: 86082909852D159B4DB9BED1CBFF18A87A2B25904A418E33A8FED3287F5F8868Checksum)
- RDS-2018-0014_Metadata_Fileindex.zip (15 KB;
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