Geology topics

Christopher Daly

Publications and source records attributed to Christopher Daly.

3 recordsLinked to original sources

USGS2010

Comparing maps of mean monthly surface temperature and precipitation for Alaska and adjacent areas of Canada produced by two different methods

Maps of mean monthly surface temperature and precipitation for Alaska and adjacent areas of Canada, produced by Oregon State University's Spatial Climate Analysis Service (SCAS) and the Alaska Geospatial Data Clearinghouse (AGDC), were analyzed. Because both sets of maps are generally available and in use by the community, there is a need to document differences between the processes and input data sets used by the two groups to produce their respective set of maps and to identify similarities and differences between the two sets of maps and possible reasons for the differences. These differences do not affect the observed large-scale patterns of seasonal and annual variability. Alaska is divided into interior and coastal zones, with consistent but different variability, separated by a transition region. The transition region has high interannual variability but low long-term mean variability. Both data sets support the four major ecosystems and ecosystem transition zone identified in our earlier work. Differences between the two sets of maps do occur, however, on the regional scale; they reflect differences in physiographic domains and in the treatment of these domains by the two groups (AGDC, SCAS). These differences also provide guidance for an improved observational network for Alaska. On the basis of validation with independent in situ data, we conclude that the data set produced by SCAS provides the best spatial coverage of Alaskan long-term mean monthly surface temperature and precipitation currently available.

Alaska, British Columbia, Northwest Territories, Y↗

USGS2008

Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States

Spatial climate data sets of 1971–2000 mean monthly precipitation and minimum and maximum temperature were developed for the conterminous United States. These 30‐arcsec (∼800‐m) grids are the official spatial climate data sets of the U.S. Department of Agriculture. The PRISM (Parameter‐elevation Relationships on Independent Slopes Model) interpolation method was used to develop data sets that reflected, as closely as possible, the current state of knowledge of spatial climate patterns in the United States. PRISM calculates a climate–elevation regression for each digital elevation model (DEM) grid cell, and stations entering the regression are assigned weights based primarily on the physiographic similarity of the station to the grid cell. Factors considered are location, elevation, coastal proximity, topographic facet orientation, vertical atmospheric layer, topographic position, and orographic effectiveness of the terrain. Surface stations used in the analysis numbered nearly 13 000 for precipitation and 10 000 for temperature. Station data were spatially quality controlled, and short‐period‐of‐record averages adjusted to better reflect the 1971–2000 period. PRISM interpolation uncertainties were estimated with cross‐validation (C‐V) mean absolute error (MAE) and the 70% prediction interval of the climate–elevation regression function. The two measures were not well correlated at the point level, but were similar when averaged over large regions. The PRISM data set was compared with the WorldClim and Daymet spatial climate data sets. The comparison demonstrated that using a relatively dense station data set and the physiographically sensitive PRISM interpolation process resulted in substantially improved climate grids over those of WorldClim and Daymet. The improvement varied, however, depending on the complexity of the region. Mountainous and coastal areas of the western United States, characterized by sparse data coverage, large elevation gradients, rain shadows, inversions, cold air drainage, and coastal effects, showed the greatest improvement. The PRISM data set benefited from a peer review procedure that incorporated local knowledge and data into the development process.

International Journal of Climatology↗

USGS2004

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895—1993 on a 0.5° latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers.

Climate Research↗