BLM REA YKL 2011 Decadal Average Length of Growing Season 2010s A2 Scenario

Metadata Updated: October 10, 2019

Some of the YKL rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This set of files includes downscaled projections of decadal means of annual length of growing season (number of days, 0-365) for the decades 2010-2019, 2020-2029, 2050-2059, and 2060-2069 at 771x771 meter spatial resolution. Each file represents a decadal mean of an annual mean calculated from mean monthly data. The spatial extent is clipped to a YKL REA boundary bounding box. Overview: This set of files is an average of the five top ranked global circulation models. These models are referred to by the acronyms: cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, and miroc3_2_medres. For a description of the model seletion process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174 cccma_cgcm31 - Canadian Centre for Climate Modelling and Analysis, Coupled General Circulation Model version 3.1 - t47, Canada mpi_echam5 - Max Planck Institute for Meteorology, European Centre Hamburg Model 5, Germany gfdl_cm21 - Geophysical Fluid Dynamics Laboratory, Coupled Model 2.1, United States ukmo_hadcm3 - UK Met Office - Hadley Centre, Coupled Model version 3.0, United Kingdom miroc3_2_medres - Center for Climate System Research, Model for Interdisciplinary Research on Climate 3.2(medres), Japan Each set of files represents the A2 projected emission scenario. ============================= Emission scenarios in brief: The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change. ============================= Downscaling: These files are bias corrected and downscaled via the delta method using PRISM (http://prism.oregonstate.edu/) 1971-2000 as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. For more detailed information on base input data (GCMs, historical data), emission scenarios, the downscaling process, or uncertainty, please go to http://www.snap.uaf.edu/ ========================================== Day of Freeze, Day of Thaw, Length of Growing Season calculations: Estimated ordinal days of freeze and thaw are calculated by assuming a linear change in temperature between consecutive months. Mean monthly temperatures are used to represent daily temperature on the 15th day of each month. When consecutive monthly midpoints have opposite sign temperatures, the day of transition (freeze or thaw) is the day between them on which temperature crosses zero degrees C. The length of growing season refers to the number of days between the days of freeze and thaw. This amounts to connecting temperature values (y-axis) for each month (x-axis) by line segments and solving for the x-intercepts. Calculating a day of freeze or thaw is simple. However, transitions may occur several times in a year, or not at all. The choice of transition points to use as the thaw and freeze dates which best represent realistic bounds on a growing season is more complex. Rather than iteratively looping over months one at a time, searching from January forward to determine thaw day and from December backward to determine freeze day, stopping as soon as a sign change between two months is identified, the algorithm looks at a snapshot of the signs of all twelve mean monthly temperatures at once, which enables identification of multiple discrete periods of positive and negative temperatures. As a result more realistic days of freeze and thaw and length of growing season can be calculated when there are idiosyncrasies in the data.

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Dates

Metadata Date December 20, 2017
Metadata Created Date October 10, 2019
Metadata Updated Date October 10, 2019
Reference Date(s) March 5, 2014 (publication)
Frequency Of Update asNeeded

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Harvested from DOI CKAN Harvest Source

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Resource Type Dataset
Metadata Date December 20, 2017
Metadata Created Date October 10, 2019
Metadata Updated Date October 10, 2019
Reference Date(s) March 5, 2014 (publication)
Responsible Party Scenarios Network for Alaska and Arctic Planning (SNAP) (Point of Contact)
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Bbox East Long -134.04
Bbox North Lat 71.430670
Bbox South Lat 49.106935
Bbox West Long -180.000000
Coupled Resource
Frequency Of Update asNeeded
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Harvest Object Id 5a83db88-3158-44cd-a436-c5b0efe3e7b6
Harvest Source Id 34ce571b-cb98-4e0b-979f-30f9ecc452c5
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Metadata Type geospatial
Progress completed
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