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Ken Ferschweiler (CBI) used climate data from the PRISM group (Chris Daly, Oregon State University) at 4kmx4km spatial grain across the conterminous USA to generate a climatology or baseline. He then created future climate change scenarios using statistical downscaling and created anomalies from the Hadley CM3 General Circulation Model (GCM) run through the A2 emission scenario (SRES - special report on emission scenarios published in 2000). To run the MAPSS model (Neilson 1995), average monthly precipitation values were calculated for the period 2045-2060. This dataset shows the standard deviation of the annual precipitation for that period.
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Ken Ferschweiler (CBI) used climate data from the PRISM group (Chris Daly, Oregon State University) at 4kmx4km spatial grain across the conterminous USA to generate a climatology or baseline. He then created future climate change scenarios using statistical downscaling and created anomalies from the Hadley CM3 General Circulation Model (GCM) run through the A2 emission scenario (SRES - special report on emission scenarios published in 2000). To run the MAPSS model (Neilson 1995), average monthly temperatures were calculated for the period 2045-2060. This dataset shows the standard deviation of the annual mean temperature for that period.
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Ken Ferschweiler (CBI) used climate data from the PRISM group (Chris Daly, Oregon State University) at 4kmx4km spatial grain across the conterminous USA to generate a climatology or baseline. He then created future climate change scenarios using statistical downscaling and created anomalies from the Hadley CM3 General Circulation Model (GCM) run through the A2 emission scenario (SRES - special report on emission scenarios published in 2000). To run the MAPSS model (Neilson 1995), average monthly precipitation values were calculated for the period 2045-2060. This dataset shows the mean anomaly (difference) in the annual precipitation for that period with respect to the 1971-2000 baseline period.
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Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM...
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Ken Ferschweiler (CBI) used climate data from the PRISM group (Chris Daly, Oregon State University) at 4kmx4km spatial grain across the conterminous USA to generate a climatology or baseline. He then created future climate change scenarios using statistical downscaling and created anomalies from the Hadley CM3 General Circulation Model (GCM) run through the A2 emission scenario (SRES - special report on emission scenarios published in 2000). To run the MAPSS model (Neilson 1995), average monthly temperatures were calculated for the period 2045-2060. This dataset shows the mean anomaly (difference) in the annual mean temperature for that period with respect to the 1971-2000 baseline period.
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To meet the climate change planning and adaptation needs of Alaska managers and decision makers, I developed a set of statewide summaries of available climate change projections that can be further subset using GIS techniques for requests by management unit, watershed, or other location. This facilitates the development of tailored climate futures for decision makers’ regional or subregional management context. This file describes the source data and summaries for purposes of technical /scientific documentation. The methods and presentation for these datasets were adapted from products in previous USGS-approved IP products for the AKCASC Building Resilience Today project (e.g, Community of Kotlik et al. 2019)....
Abstract (from http://link.springer.com/article/10.1007/s10584-016-1598-0): Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies...
The MC2 model projects an overall increase in carbon capture in conterminous United States during the 21st century while also simulating a rise in fire causing much carbon loss. Carbon sequestration in soils is critical to prevent carbon losses from future disturbances, and we show that natural ecosystems store more carbon belowground than managed systems do. Natural and human-caused disturbances affect soil processes that shape ecosystem recovery and competitive interactions between native, exotics, and climate refugees. Tomorrow's carbon budgets will depend on how land use, natural disturbances, and climate variability will interact and affect the balance between carbon capture and release.
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Ken Ferschweiler (CBI) used climate data from the PRISM group (Chris Daly, Oregon State University) at 4kmx4km spatial grain across the conterminous USA to generate a climatology or baseline. He then created future climate change scenarios using statistical downscaling and created anomalies from the Hadley CM3 General Circulation Model (GCM) run through the A2 emission scenario (SRES - special report on emission scenarios published in 2000). To run the MAPSS model (Neilson 1995), average monthly temperatures were calculated for the period 2045-2060. This dataset shows the change in leaf area index simulated by MAPSS compared to the historical period (1968-1999) based on PRISM climate data.
This fact sheet provides highlights from a comprehensive U.S. Geological Survey report that evaluates six widely used downscaled climate projections covering the southeastern United States and recommends best practices for use of downscaled datasets for ecological modeling and decision-making.
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Ken Ferschweiler (CBI) used climate data from the PRISM group (Chris Daly, Oregon State University) at 4kmx4km spatial grain across the conterminous USA to generate a climatology or baseline. He then created future climate change scenarios using statistical downscaling and created anomalies from the Hadley CM3 General Circulation Model (GCM) run through the A2 emission scenario (SRES - special report on emission scenarios published in 2000). To run the MAPSS model (Neilson 1995), average monthly temperatures were calculated for the period 2045-2060. This dataset shows the change in runoff simulated by MAPSS compared to the historical period (1968-1999) based on PRISM climate data.
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Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM...
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This dataset represents projected survey and managed species probability of occupancy for the climate of 2061-2090, averaged from the occurrence probabilities of 130 survey and managed species. The survey and manage species are rare localized species of concern under the Northwest Forest plan, consisting of 75 species of fungi, 21 species of lichen, 10 species of bryophytes, 8 species of vascular plants, 12 species of mollusks, 2 species of amphibians, one mammal, and one bird. Climate data were drawn from three representative climate projections: lowest warming (GCM GISS_ER with IPCC storyline B1), moderate warming (GCM ECHAM5 SRES with storyline A2), and highest warming (GCM IPSL_CM4 with storyline A2). In Carroll...
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This dataset represents projected survey and manage species probability of occupancy for the climate of 2011-2040, averaged from the occurrence probabilities of 130 survey and manage species. The survey and manage species are rare localized species of concern under the Northwest Forest plan, consisting of 75 species of fungi, 21 species of lichen, 10 species of bryophytes, 8 species of vascular plants, 12 species of mollusks, 2 species of amphibians, one mammal, and one bird. Climate data were drawn from three representative climate projections: lowest warming (GCM GISS_ER with IPCC storyline B1), moderate warming (GCM ECHAM5 SRES with storyline A2), and highest warming (GCM IPSL_CM4 with storyline A2). In Carroll...
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The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL,...
map background search result map search result map Change in Leaf Area Index (2045-2060) Simulated by MAPSS using HadCM3 GCM under A2 scenario (Western USA) Change in Runoff (2045-2060) Simulated by MAPSS using HadCM3 GCM under A2 scenario (Western USA) Standard Deviation of Annual Temperature (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Standard Deviation of Annual Precipitation (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Average Annual Precipitation Anomaly (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Average Annual Temperature Anomaly (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Pacific Northwest Predicted Survey and Manage Species Occupancy for 2061-2090 Pacific Northwest Predicted Survey and Manage Species Occupancy for 2011-2040 California Basin Characterization Model Downscaled Climate and Hydrology Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Geospatial and climatic data layers for coastal and temperate rainforest biome Final NPLCC Project Report - NPLCC Cross-Border Final Report & Appendices 2014 Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Final Report South Central Climate Projections Evaluation Project (C-PrEP) South Central Climate Projections Evaluation Project (C-PrEP) Geospatial and climatic data layers for coastal and temperate rainforest biome Final NPLCC Project Report - NPLCC Cross-Border Final Report & Appendices 2014 Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Final Report Data Integration Workshop in Support of the Coastal Temperate Rainforest of Southeast Alaska and British Columbia Pacific Northwest Predicted Survey and Manage Species Occupancy for 2061-2090 Pacific Northwest Predicted Survey and Manage Species Occupancy for 2011-2040 California Basin Characterization Model Downscaled Climate and Hydrology South Central Climate Projections Evaluation Project (C-PrEP) South Central Climate Projections Evaluation Project (C-PrEP) Change in Leaf Area Index (2045-2060) Simulated by MAPSS using HadCM3 GCM under A2 scenario (Western USA) Change in Runoff (2045-2060) Simulated by MAPSS using HadCM3 GCM under A2 scenario (Western USA) Standard Deviation of Annual Temperature (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Standard Deviation of Annual Precipitation (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Average Annual Precipitation Anomaly (2045-2060) from HadCM3 GCM under A2 scenario (Western USA) Average Annual Temperature Anomaly (2045-2060) from HadCM3 GCM under A2 scenario (Western USA)