SNAP GeoNetwork
SNAP GeoNetwork (Scenarios Network for Alaska & Arctic Planning)
Scenarios Network for Alaska & Arctic Planning
Continents, countries, sea regions of the world.
ALFRESCO Model outputs - linear coupled - Annual
This set of files includes annual model outputs from ALFRESCO, a landscape scale fire and vegetation dynamics model. These specific outputs are from the Integrated Ecosystem Model (IEM) project, and are from the linear coupled version using AR4/CMIP3 climate inputs (IEM Generation 1-AR4) and AR5/CMIP5 climate inputs (IEM Generation 1-AR5). These outputs include data from model rep 171(IEM Generation 1-AR4) and rep 26 (IEM Generation 1-AR5), referred to as the “best rep” out of 200 replicates. The best rep was chosen through comparing ALFRESCO’s historical fire outputs to observed historical fire patterns. Single rep analysis is not recommended as a best practice, but can be used to visualize possible changes. Climate models and emission scenarios: IEM Generation 1-AR4/CMIP3 CCCMA-CGCMS-3.1 MPI-ECHAM5 under the SRES A1B scenario IEM Generation 1-AR5/CMIP5 MRI-CGCM3 NCAR-CCSM4 under RCP 8.5 scenario Variables include: Veg: The dominant vegetation for this cell. Current values are: 0 = Not Modeled 1 = Black Spruce 2 = White Spruce 3 = Deciduous Forest 4 = Shrub Tundra 5 = Graminoid Tundra 6 = Wetland Tundra 7 = Barren / Lichen / Moss 8 = Temperate Rainforest Age: This the age of the vegetation in each cell. An Age value of 0 means it transitioned in the previous year. Basal Area: The accumulation of basal area of white spruce in tundra cell, and is influenced by seed dispersal, growth of biomass, climate data, and other factors. units = m^2 / ha Burn Severity: This is a categorical burn severity level of the previous burn in the current cell, influenced by fire size and slope. For example, a burn severity value in a file with year 1971 in the file name means that the severity level given to that file occurred in the fire that occurred in year 1970. 0=No Burn 1=Low 2=Moderate 3=High w Low Surface Severity 4=High w/ High Surface Severity Fire Scar: These are the unique fire scars. Each cell has three values. Band 1 - Year of burn Band 2 - Unique ID for the simulated fire for that simulation year Band 3 - Whether or not the cell was an ignition location for a fire. There will only be 1 ignition cell per fire per year. 0 = not ignition 1 = ignition point For background on ALFRESCO, please refer to: Is Alaska's Boreal Forest Now Crossing a Major Ecological Threshold? Daniel H. Mann, T. Scott Rupp, Mark A. Olson, and Paul A. Duffy Arctic, Antarctic, and Alpine Research 2012 44 (3), 319-331 http://www.bioone.org/doi/abs/10.1657/1938-4246-44.3.319
This dataset consists of 6000 GeoTIFFs produced by the Geophysical Institute Permafrost Lab (GIPL) Permafrost Model. Six distinct CMIP5 model-scenario combinations were used to force the GIPL model output. Each model-scenario combination includes annual (2021-2120) summaries of the following ten variables: - Mean Annual Ground Temperature (MAGT) at 0.5 m below the surface (°C) - MAGT at 1 m below the surface (°C) - MAGT at 2 m below the surface (°C) - MAGT at 3 m below the surface (°C) - MAGT at 4 m below the surface (°C) - MAGT at 5 m below the surface (°C) - Mean Annual Surface (i.e., 0.01 m depth) Temperature (°C) - Permafrost top (upper boundary of the permafrost, depth below the surface in m) - Permafrost base (lower boundary of the permafrost, depth below the surface in m) - Talik thickness (perennially unfrozen ground occurring in permafrost terrain, m) There are 1000 GeoTIFF files per model-scenario combination. The model-scenario combinations are: - GFDL-CM3, RCP 4.5 - GFDL-CM3, RCP 8.5 - NCAR-CCSM4, RCP 4.5 - NCAR-CCSM4, RCP 8.5 - A 5-Model (GFDL-CM3, NCAR-CCSM4, GISS-E2-R, IPSL-CM5A-LR, MRI-CGCM3) Average, RCP 8.5 - A 5-Model (GFDL-CM3, NCAR-CCSM4, GISS-E2-R, IPSL-CM5A-LR, MRI-CGCM3) Average, RCP 4.5 The file naming convention is `gipl_model_scenario_variable_year.tif` for example: `gipl_GFDL-CM3_rcp45_talikthickness_m_2090.tif` Each GeoTIFF uses the Alaska Albers (EPSG:3338) projection and has a spatial resolution of 1 km x 1 km. All rasters in this dataset have indentical extents, spatial references, and metadata objects. Once extracted, the entire dataset (all 6000 GeoTIFFs) requires 39 GB of disk space. Data are compressed into ten .zip files, one per variable. Each archive will contain all model-scenario combinations and all years for that variable. Each .zip file contains 600 GeoTIFFs. This research was funded by the Broad Agency Announcement Program and the U.S. Army Engineer Research and Development Center and Cold Regions Research and Engineering Laboratory (ERDC-CRREL) under Contract No. W913E521C0010. The GIPL2-MPI/GCM simulations were supported in part by the high-performance computing and data storage resources operated by the Research Computing Systems Group at the University of Alaska Fairbanks Geophysical Institute.
Historical wind data at Alaska airports, 1980-2019
These wind speed and direction data are the underlying data displayed in the interactive webtool at http://snap.uaf.edu/tools/airport-winds. Original wind speed/direction observations were made by Automated Surface Observing System (ASOS) and the Automated Weather Observing System (AWOS) stations, and we accessed these data via the Iowa Environmental Mesonet (IEM). These observations were hourly in most cases, and we filtered data to routine measurements (nearest to clock hour) where measurements were more frequent than hourly to generate a true hourly dataset, save for periods of missing data. We used data from 166 weather stations located across Alaska, selected from a pool of 185 stations available in the IEM database for 1980-2019. For inclusion in the app and this dataset, a station must have a reasonably complete record, and must have begun measurements before June 6, 2010. We applied a spike-filtering algorithm to detect spurious spikes and dips, and a changepoint detection plus quantile mapping adjustment to statistically account for the possibility of sensors changing location, height, or surroundings such that the long term (month-scale) wind regimes were affected. **Methodology** --- All hourly ASOS/AWOS wind speed and direction data available via the Iowa Environmental Mesonet AK ASOS network were accessed and assessed for completeness (185 stations), and 166 of those stations were determined to be sufficiently complete for climatological analysis. Those data were cleaned to produce regular hourly data, and adjusted via a combination of changepoint analysis and quantile mapping to correct for potential changes in sensor location and height. **Attribute Description** --- ts: timestamp (YYYY-mm-dd HH:MM:SS) ws: wind speed (mph) wd: wind direction (degrees) Station identifiers used for locations is available at: https://www.faa.gov/air_traffic/weather/asos/?state=AK
ASOS/AWOS records made available by the Iowa State University Iowa Environmental Mesonet (IEM) at https://mesonet.agron.iastate.edu/request/download.phtml?network=AK_ASOS; Attribute Accuracy: Spike-filtering of hourly ASOS/AWOS data to remove spurious wind speeds. Some non-spurious spikes may have been removed, and it is also likely that not all spurious observations were captured. Wind speed values were adjusted for some stations based on a changepoint detection algorithm applied to the monthly mean time series. This was used to identify potential changes in sensor height or location, or other changes that could cause systematic shifts in observed wind regimes. Quantile mapping was used to adjust wind speeds prior to the changepoint to match the distribution of speeds after the changepoint. Completeness: There are multiple instances of missing data, due to multiple causes. Those causes are: Lack of hourly reporting, particularly in first half of spanning time period Missed/removed hourly observations, present in source data Observations removed by spike-filtering algorithm during processing.
Community Charts Temperature and Precipitation
Mean temperature and precipitation values extracted at community locations across Alaska and Canada from downscaled raster datasets containing historical and projected estimates for these variables.
Projected Decadal Averages of Monthly Snow-Day Fraction 771m CMIP5/AR5
These files include downscaled projections of decadal average monthly snow-day fraction ("fs", units = percent probability from 1 – 100) for each month of the decades from 2010-2019 to 2090-2099 at 771 x 771 m spatial resolution. Each file represents a decadal average monthly mean. Output is available for the CCSM4, GFDL-CM3, GISS-E2-R, IPSL-CM5A-LR, and MRI-CGCM3 models and three emissions scenarios (RCP 4.5, RCP 6.0 and RCP 8.5). These snow-day fraction estimates were produced by applying equations relating decadal average monthly temperature to snow-day fraction to downscaled decadal average monthly temperature. Separate equations were used to model the relationship between decadal monthly average temperature and the fraction of wet days with snow for seven geographic regions in the state: Arctic, Western Alaska, Interior, Cook Inlet, SW Islands, SW Interior, and the Gulf of Alaska coast, using regionally specific logistic models of the probability that precipitation falls as snow given temperature based on station data fits as in McAfee et al. 2014. These projections differ from McAfee et al. 2014 in that updated CMIP5 projected temperatures rather than CMIP3 temperatures were used for the future projections. Although the equations developed here provide a reasonable fit to the data, model evaluation demonstrated that some stations are consistently less well described by regional models than others. It is unclear why this occurs, but it is likely related to localized climate conditions. Very few weather stations with long records are located above 500m elevation in Alaska, so the equations used here were developed primarily from low-elevation weather stations. It is not clear whether the equations will be completely appropriate in the mountains. Finally, these equations summarize a long-term monthly relationship between temperature and precipitation type that is the result of short-term weather variability. In using these equations to make projections of future snow, as assume that these relationships remain stable over time, and we do not know how accurate that assumption is. These snow-day fraction estimates were produced by applying equations relating decadal average monthly temperature to snow-day fraction to downscaled projected decadal average monthly temperature. The equations were developed from daily observed climate data in the Global Historical Climatology Network. These data were acquired from the National Climatic Data Center in early 2012. Equations were developed for the seven climate regions described in Perica et al. (2012). Geospatial data describing those regions was provided by Sveta Stuefer. Perica, S., D. Kane, S. Dietz, K. Maitaria, D. Martin, S. Pavlovic, I. Roy, S. Stuefer, A. Tidwell, C. Trypaluk, D. Unruh, M. Yekta, E. Betts, G. Bonnin, S. Heim, L. Hiner, E. Lilly, J. Narayanan, F.Yan, T. Zhao. 2012. NOAA Atlas 14. Precipitation-Frequency Atlas of the United States.
Projected Derived DOF/DOT/LOGS - 2km CMIP5/AR5
This set of files includes downscaled projections of decadal means of annual day of freeze or thaw (ordinal day of the year), and length of growing season (numbers of days, 0-365) for each decade from 2010 - 2100 at 2km x 2km meter spatial resolution. Each file represents a decadal mean of an annual mean calculated from mean monthly data. ---- The spatial extent includes Alaska, the Yukon Territory, British Columbia, Alberta, Saskatchewan, and Manitoba. Each set of files originates from one of five top ranked global circulation models from the CMIP5/AR5 models and RPCs, or is calculated as a 5 Model Average. 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 thaw and freeze. 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. Please note that these maps represent climatic estimates only. While we have based our work on scientifically accepted data and methods, uncertainty is always present . Uncertainty in model outputs tends to increase for more distant climatic estimates from present day for both historical summaries and future projections.
Historical and Projected Decadal Average Monthly Snowfall Equivalent and the Ratio of Snowfall Equivalent to Precipitation 771m CMIP5/AR5/CRU TS3.1
These files include climatological summaries of downscaled historical and projected decadal average monthly snowfall (i.e. snow-water) equivalent (SWE) in millimeters, the ratio of snowfall equivalent to precipitation, and future change in snowfall for October-March at 771-meter spatial resolution across the state of Alaska. Data are for summary October to March Alaska climatologies for: 1) historical and future snowfall equivalent (SWE), produced by multiplying snow-day fraction by decadal average monthly precipitation and summing over 6 months from October to March to estimate the total SWE on April 1. 2) historical and future ratio of SWE to precipitation (SFEtoP), SFEtoP is the ratio of October to March total SWE to October to March total precipitation is calculated as total SWE / total precipitation (expressed as percent, 0-100). 3) future change in snowfall equivalent relative to historical ("dSWE"), calculated as (SWE future – SWE historical) / SWE historical (no units, multiply by 100 to obtain percent). The historical reference period is 1970-1999, (file name “H70.99”), calculated from downscaled CRU TS 3.1 data Future climatologies (both RCP 4.5 and 8.5) are for: - 2020s (2010-2039) - 2050s (2040-2069) - 2080s (2070-2099) across 5 GCMs: NCAR-CCSM4, GFDL-CM3, GISS-E2-R, IPSL-CM5, and MRI-CGCM3 as well as a 5-model mean (“5MM”). Following Elsner et al. (2010), <0.1 is rain dominated, 0.1 < SFE:P < 0.4 is transitional, and >0.4 is snow dominated. Only calculated for historical reference climatology 1970-1999 and three future climatologies: 2010-2039, 2040-2069, and 2070-2090, with each climatology representing the mean of three decadal averages from the available decadal grids. Snow fraction data used can be found here: http://ckan.snap.uaf.edu/dataset/projected-decadal-averages-of-monthly-snow-day-fraction-771m-cmip5-ar5 http://ckan.snap.uaf.edu/dataset/historical-decadal-averages-of-monthly-snow-day-fraction-771m-cru-ts3-0-3-1 Precipitation data used can be found here: http://ckan.snap.uaf.edu/dataset/projected-monthly-and-derived-precipitation-products-771m-cmip5-ar5 http://ckan.snap.uaf.edu/dataset/historical-monthly-and-derived-precipitation-products-771m-cru-ts * Note: In Littell et al. 2018, "SWE" is referred to as "SFE", and "SFEtoP" as "SFE:P"
Annual ALFRESCO Model Outputs with Historical Fire Scars
These GeoTIFFs include annual spatial representations of the following variables produced through summarization of ALFRESCO model outputs across 200 replicates: Flammability: likelihood of a pixel to burn across 200 replicates Modal vegetation type: statistical mode of vegetation type across 200 replicates Percent vegetation type: percent of each possible vegetation type across 200 replicates These outputs were derived from AR5/CMIP5 climate inputs, historical fire inputs from the Alaska Interagency Coordination Center (AICC), and several fire management options (FMO) inputs.
Historical and Projected Rain On Snow (ROS) events across the State of Alaska and Surrounding Regions from 1979 to 2100
Rain on snow (ROS) events were derived from 20km dynamically downscaled ERA-Interim reanalysis and global climate model (GCM) climate projections data. The GCM data were from RCP 8.5 of GFDL-CM3 and NCAR-CCSM4. The amount of liquid precipitation for each day is provided in the database for each grid cell and was determined to be a ROS event by the temperature being at or near freezing and/or the presence of snow on the ground.
These data represent the highest quality climate projections for Alaska and Western Canada. They are updated to improve quality as issues are discovered.
20 km annual wet day totals for Alaska, 1980-2100
This dataset consists of single band GeoTIFFs containing total annual counts of wet days for each year from 1980-2100 for one downscaled reanalysis (ERA-Interim, 1980-2015) and two downscaled CMIP5 global climate models driven under the RCP 8.5 baseline emissions scenario (NCAR-CCSM4 and GFDL-CM3, 2006-2100), all derived from the same dynamical downscaling effort using the Weather Research and Forecasting (WRF) model (Version 3.5). A day is counted as a "wet day" if the total precipitation for that day is 1 mm or greater.
Arctic and Subarctic Engineering Design Tool- Technology Transfer UFC 3-130 (Year 1 Contract Report W913E5-21-C0010)
Statistically downscaled projections of snow/rain partitioning for Alaska
DOI for USGS ScienceBase record about this dataset
Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios
Scenarios Network for Alaska and Arctic Planning
Historical Monthly Precipitation Products - 10 min CRU TS 3.22
This set of files includes downscaled historical estimates of monthly total precipitation (in millimeters, no unit conversion necessary, rainwater equivalent) from 1901 - 2013 (CRU TS 3.22) at 10 min x 10 min spatial resolution with global coverage. The downscaling process utilizes CRU CL v. 2.1 climatological datasets from 1961-1990.
Historical Monthly Precipitation - 1 km CRU TS
This set of files includes downscaled historical estimates of monthly total precipitation (in millimeters) at 1 kilometer spatial resolution. Each file represents a single month in a given year. The original SNAP downscaled precipitation product at 2 kilometer spatial resolution was resampled to 1 kilometer spatial resolution via bilinear interpolation to create these data for input to the Integrated Ecosystem Model (IEM). Please note that this data is used to fill in a gap in available data for the IEM and does not constitute a complete or precise measurement of this variable in all locations.