What might the Northwestern US look like over the course of the 21st century as a result of anthropogenic climate change? To address this question a coordinated effort using state of the science models including the latest climate, hydrologic and vegetation models was used to illustrate potential scenarios of the future Northwest environment. The end product was a consistent set of projections of climate change for the Northwest's natural resources that provides critical information for local and regional adaptation planning.
Global climate model (GCM) output from the 5th Coupled Model Inter-Comparison Project (CMIP5) as used in the Fifth Assessment Report of the Intergovernmental Climate Change (IPCC) were used as a basis for projecting future climate. A total of 20 different GCMs under 2 emission pathways (RCP4.5, RCP8.5) were used to provide a probabilistic view of future changes in climate. GCM outputs were statistically downscaled (i.e. remapped onto finer grids) using MACA (Multivariate Adaptive Constructed Analogs) to translate coarse scale projections to spatial scales that are useful for modeling efforts. Downscaled climate data was then used as inputs to both two hydrological models, VIC and ULM, and two vegetation models, MC2 and 3-PG. The Integrated Scenarios dataset is then composed of the outputs from the downscaled climate data, the hydrologic models and the vegetation models.
The Integrated Scenarios climate data came from the application of MACA to the GCM data from CMIP5. The MACA (Multivariate Adaptive Constructed Analogs, Abatzoglou and Brown, 2012) method is a statistical downscaling technique that removes biases in GCM outputs by utilizing the statistics of observations from a training dataset and downscales meteorological variables to a finer grid by using a pattern matching constructed analogs algorithm. The MACA data is available as monthly data on a 4-km grid (MACAv1-METDATA) and as daily/monthly data on a 1/16-deg grid (~6 km, MACAv2-LIVNEH) depending on which training observation dataset (i.e. METDATA or LIVNEH datasets) was used for the statistical mapping. Meteorological variables provided are min/max temperature, precipitation, relative/specific humidity, dew point temperature, wind speed/components and downward solar radiation.
The Variable Infiltration Capacity model (VIC, Liang et al. 1994) is a large-scale, semi-distributed hydrologic model that simulates the hydrology in land-surface processes by incorporating variable vegetation, soil types and topography. The VIC data is available as daily/monthly data on a 1/16-deg (~6km) grid for well over 20 variables from the simulations, including snow water equivalent, soil moisture, runoff, streamflow and evapotranspiration. The VIC data is available for the 10 ‘best performing’ GCM (Rupp et al., 2013) and for both RCP scenarios.
MC2 is a dynamic global vegetation model that simulates vegetation types, ecosystem fluxes of carbon, nitrogen and water, as well as wildfire occurrence and impacts. MC2 took MACA downscaled climate data at a monthly time step over a 4-km grid and simulated biogeography, biogeochemistry and fire disturbance. MC2 was run in two different modes: with and without fire suppression as a management practice. The MC2 results are available as annual aggregations on a 4-km grid of well over 20 variables from the simulations, including soil moisture, stream flow, leaf area index, carbon stocks and fluxes, burned area, biome/ecosystem/primary production and tree and vegetation cover. The MC2 data is available for all 20 GCMs and for both RCP scenarios.
3-PG (Physiological Principles Predicting Growth, Coops et al. 1998) is a model that simulates the change in net primary production (i.e. photosynthesis) as a forest ages. It is a generalized forest carbon allocation model and is here used on a 50-year old conifer forest. 3-PG took downscaled climate data at a monthly time step over a 4-km grid to constrain photosynthesis and simulated five consecutive 50 year simulations of forest growth for the decades from the 1950s to the 2090s. The 3-PG data is available as decadal aggregations on a 4-km grid of 3 variables from the simulations: potential leaf area index, net primary productivity and carbon storage in wood. The 3-PG data is available for only 3 GCMs (2 used RCP 8.5 and 1 used RCP 4.5).
Katherine is a postdoctoral fellow working under Dr John Abatzoglou, a climatologist in the Department of Geography at the University of Idaho. Katherine's background is in computation and statistics. Katherine has been working on the statistical downscaling of global climate ...