Spatial Climatology

Crown of the Continent Ecosystem (CCE)

Spatial Climatology

Project Description

Project Summary:

As a premiere ecosystem in North America (Hauer et al. 2007) and one of the world’s most intact temperate ecosystems (Pederson et al. 2010), the Crown of the Continent Ecosystem (CCE) straddles the Rocky Mountains from southern Alberta and British Columbia to northern Montana. With public property comprising more than 80% of its 43,700 km2 land area, the CCE includes many well-known protected areas such as the Waterton-Glacier International Peace Park comprising both Waterton Lakes National Park in Alberta and Glacier National Park in Montana, and wilderness areas such as the Bob Marshall, Great Bear, Scapegoat, Rattlesnake, and Mission Mountains (Long 2007; Prato and Fagre 2007). The highly heterogeneous topography of the CCE and its location between maritime and continental climates makes it home to a high diversity of plants and animals (Nature Conservancy). In turn, the CCE is a critical area for maintaining both biodiversity and ecological integrity in North America. The CCE is also a source for 19 major rivers (Long 2007) and contains important headwaters for a large area of the continent with water flowing to the Columbia River basin, the Saskatchewan River basin, and the Missouri River basin (Hauer et al. 2007)

We are currently performing a climate impacts assessment of the CCE to seehow projected changes in temperature and precipitation could affect the region’s ecosystem energy and water constraints and drive changes invegetation productivity in the 2020s (2010-2039), 2040s (2030 – 2069) and 2080s (2070 – 2099). We have generated a fine resolution (~800m) spatialclimatology of the region and used it downscale GCM-derived Pacific Northwest climate projections (B1 and A1B scenarios) provided by the ClimateImpacts Group at the University of Washington (Mote and Salathé 2009). In this preliminary downscaling effort, we used the simple delta downscaling method along with a stochastic weather generator. We forced the Biome-BGC ecosystem model with the downscaled climatology to model ecosystem changesfrom 1970 – 2001 for evergreen needleleaf forest, grass, deciduous broadleaf forest, and shrub functional types.

Initial results show a dramatic release of energy constraints and thecrossing of critical temperature thresholds for much of the landscape along with overall decreased snow pack and increased water stress. Althoughvegetation productivity appears to increase for much of the CCE, the release of temperature constraints appears limited in dramatically increasingproductivity due to the enhanced water stress. Since current precipitation projections are highly variable and the projections used in this initialstudy show little change in precipitation, future work will focus on better quantifying the thresholds that could tip the balance between the lessoning of energy constraints and the increasing of water constraints within the CCE.

Scientists

Science Team:

Steve Running

Jared Oyler

Daymet

Spatial Climatology

Project Description

Spatial Bioclimatology

Project Summary:

Meteorology at the land surface affects many processes in the terrestrial biogeochemical system. Measurements of near-surface meteorological conditions are made at many locations, but we are often faced with having to perform ecosystem process simulations in areas where no meteorological measurements have been taken. In some cases it is possible to install new instrumentation for a particular study, but there are many situations where this is not a feasible solution. These problems are particularly important for simulations over large regions, where the number of simulation points is likely to be far greater than the number of observation stations. Starting from the MT-CLIM logic, we developed a set of algorithms to interpolate daily weather observations between observation locations, and to extrapolate from low elevations (where measurements are commonly made) to higher elevations.

1980-2008 DAYMET Dataset is available online

Details

Daymet is a group of computer programs that produce surfaces of daily temperature, precipitation, radiation, and humidity over large regions, taking into account the effects of complex terrain. Observations can be included from an arbitrarily large number of stations. The relationships of temperature and precipitation to elevation are determined directly from the observations. In addition to the daily observations from a network of stations, Daymet requires digital elevation data for the region of interest.

The interpolation method is based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations. Sensitivity to the typical heterogeneous distribution of stations is accomplished with an iterative station density estimation algorithm.

The extrapolation with elevation depends on the station observations and the station elevations. These relationships vary in space and time, and Daymet makes a new diagnosis of these relationships for each spatial modeling unit and for each day of observed conditions.

Precipitation estimates are performed in two steps: first a binary estimate of precipitation occurrence, and contingent on occurrence, an estimation of precipitation amount, corrected for elevation effects.

Radiation and humidity are estimated using the same relationships as in the MT-CLIM model, following the completion of temperature and precipitation estimates.

Sample Images

Scientists

Science Team:

Peter Thornton

Publications

Publications:

Bristow, K.L., and G.S. Campbell, 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31:159-166.

Running, S.W., R.R. Nemani, and R.D. Hungerford, 1987. Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evaporation and photosynthesis. Canadian Journal of Forest Research, 17:472-483.

Glassy, J.M., and S.W. Running, 1994. Validating diurnal climatology of the MT-CLIM model across a climatic gradient in Oregon. Ecological Applications, 4(2):248-257.

Kimball, J.S., S.W. Running, and R. Nemani, 1997. An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology, 85:87-98.

Thornton, P.E., S.W. Running, and M.A. White, 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology, 190:214-251.

Thornton, P.E., and S.W. Running, 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93:211-228.

MT-CLIM

Spatial Climatology

Project Description

Project Summary:

Download MT-CLIM 4.3

Meteorology at the land surface affects many processes in the terrestrial biogeochemical system. Measurements of near-surface meteorological conditions are made at many locations, but we are often faced with having to perform ecosystem process simulations in areas where no meteorological measurements have been taken. In some cases it is possible to install new instrumentation for a particular study, but there are many situations where this is not a feasible solution. We developed MT-CLIM to address the problems of estimating daily near-surface meteorological parameters from nearby observations. It is designed to handle the special problems encountered in mountainous terrain, but it is applicable in flat terrain as well.

Details

MT-CLIM is a computer program that uses observations of daily maximum temperature, minimum temperature, and precipitation from one location (the "base") to estimate the temperature, precipitation, radiation, and humidity at another location (the "site"). The base and the site can be at different elevations, and can have different slopes and aspects. Better results are obtained when the base and site are relatively close to one another (at the bottom and the top of a valley, for example).

Temperature estimates at the site are based on the base temperatures and a user-supplied temperature lapse rate. Separate lapse rates can be supplied for daily maximum and minimum temperature. Precipitation estimates at the site are based on the daily record of preciupitation from the base, and a user-specified ratio of annual total precipitation between the site and the base.

The estimation of radiation and humidity are more complex, since these parameters are not assumed to be measured at the base station. Humidity estimates are based on the observation that daily minimum temperature is usually very close to dewpoint temperature. The MT-CLIM algorithm includes corrections to this assumption for arid climates. Radiation estimates are based on the observation that the diurnal temperature range (from minimum temperature to maximum temperature) is closely related to the daily average atmospheric transmittance. In conjunction with information about the latitude, elevation, slope, and aspect of the site, this relationship can be used to estimate daily total radiation with a typical error range of +/- 15%. Some of the limitations of MT-CLIM are the use of a single base station for observations and the need for the user to specify the temperature and precipitation relationships with elevation. We have developed an expanded version of the MT-CLIM logic, called Daymet, that uses observations from a large number of base stations.

Publications

Publications:

Bristow, K.L., and G.S. Campbell, 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31:159-166.

Glassy, J.M., and S.W. Running, 1994. Validating diurnal climatology of the MT-CLIM model across a climatic gradient in Oregon. Ecological Applications, 4(2):248-257.

Kimball, J.S., S.W. Running, and R. Nemani, 1997. An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology, 85:87-98.

wxTopo

Spatial Climatology

Project Description

Project Summary:

Due to the predicted changes in climate, the effects of climate change have increasingly become a central focus of many regional and landscape-scale ecological/hydrological impact assessments. From evapotranspiration and photosynthesis to decomposition and respiration, climate is a key driver of many ecosystem-related processes. Thus, climate-related variables are the most important set of input variables for ecosystem computer models and analyses. Modeling studies within smaller regions present a challenge, however, due to the mismatch in scale between many of the general climate models (GCMs) and more local climate-forcing factors and related ecological/hydrological processes. The limitations of GCM projections are especially apparent in topographically complex landscapes where terrain features frequently drive rapid changes in temperature and precipitation over relatively small spatial scales. Consequently, smaller scale historical spatial climate datasets that account for local terrain influences are necessary to downscale GCM climate projections and drive ecosystem/hydrology models at regional and landscape scales.

While several smaller scale conterminous United States climate datasets are currently available (e.g.—PRISM, Daymet) they do not contain all the necessary climate variables for process-based models, do not have the required temporal extent and resolution, or do not properly account for local terrain influences. Given these limitations, development of a new climate dataset, TopoMet, is currently underway. TopoMet will build upon previous MT-CLIM and Daymet logic, but also incorporate atmospheric reanalysis data and empirical-statistical downscaling (ESD) techniques to model not only elevation lapse rates, but also other regional and landscape-scale terrain forcings such as cold air drainage and slope/aspect variations. TopoMet will cover the conterminous United States from 1950 to present at a 1 km spatial scale and a daily time step. The following variables will be included: daily minimum/maximum temperature, precipitation, humidity, and solar radiation.

Crown of the Continent Climatology

To prepare for TopoMet development, a prototype spatial climate dataset (1970-present, 1 km resolution) was created for the Crown of the Continent Ecosystem (CCE) in western Montana. While this prototype did not integrate atmospheric reanalysis data, terrain climate forcings were still modeled by combining the daily interpolation logic of Daymet with the monthly PRISM dataset. Projected changes in Pacific Northwest temperature and precipitation (Climate Impacts Group, University of Washington) were then applied to the dataset to drive the Biome-BGC ecosystem model into the future and assess possible future climate impacts in the CCE.

Example Images