Climate Estimates and Plant-Climate Relationships
Details on Spatial Extents, Temporal Information and Data Elements
- All of North America (Preferred) (longitude -177 to -52 and latitude 13.9 to 80 degrees) was recently built to support new research. It includes all the data used in the original western North America (including Mexico) extents plus data from eastern U.S. and Canada. We do not have a lot of experience using this surface yet.
- Western United States (Depreciated) (westUS: longitude -125 to -102 and latitude 31 to 51 degrees) is that used by Rehfeldt et al. 2006 and Rehfeldt (2006). This extent is only used in our species modeling, the original data used by Rehfeldt et al. 2006 and Rehfeldt (2006) have been incorporated into the North American work.
- Western North America (Depreciated) (westNA: longitude -177 to -97 and latitude 25 to 79 degrees) is based on from the western U.S. (including Alaska), western Canada, and northern Mexico). This surface is based on more data than used to fit the original western U.S. surfaces, even within the original westUS extent. It is the extent we use for all our current work at the one used when custom data requests are made for western North America.
- Mexico (Depreciated) (Mexico: longitude -118 to -74 and latitude 13.9 to 33 degrees) falls under work done in cooperation with Cuauhtemoc Saenz-Romero, Universidad Michoacana de San Nicolas de Hidalgo, Mexico (firstname.lastname@example.org). Weather stations included are from Mexico, parts of Southeastern U.S., and few stations from Cuba, Guatemala, and Belize. A paper has be re-submitted describing this work. See Publications.
We work with thin-plate splines (ANUSPLIN Version 4.3) which provide the ability to make point predictions. For mapping, we generally use grid cell size of 0.00833333 decimal degrees (about 1 km), but grids of other resolutions could be used.
- Contemporary climate for the climate normal period from 1961 to 1990.
- Future climates for the nominal years 2030 (average of 2026 to 2035), 2060, and 2090 based on updating the contemporary data with projections from three General Circulation Models.
The ANUSPLIN model directly predicts monthly values for:
- average mean daily temperature
- average minimum temperature
- average maximum temperature
- total precipitation
From these variables, several derived climate variables (see New Algorithms Used For Some Derived Variables) are computed using methods presented by Rehfeldt (2006). The variables included are:
- d100 — Julian date the sum of degree-days >5 degrees C reaches 100
- dd0 — Degree-days <0 degrees C (based on mean monthly temperature)
- dd5 — Degree-days >5 degrees C (based on mean monthly temperature)
- fday — Julian date of the first freezing date of autumn
- ffp — Length of the frost-free period (days)
- gsdd5 — Degree-days >5 degrees C accumulating within the frost-free period
- gsp — Growing season precipitation, April to September
- map — Mean annual precipitation
- mat_tenths — Mean annual temperature
- mmax_tenths — Mean maximum temperature in the warmest month
- mmindd0 — Degree-days <0 degrees C (based on mean minimum monthly temperature)
- mmin_tenths — Mean minimum temperature in the coldest month
- mtcm_tenths — Mean temperature in the coldest month
- mtwm_tenths — Mean temperature in the warmest month
- sday — Julian date of the last freezing date of spring
- smrpb — Summer precipitation balance: (jul+aug+sep)/(apr+may+jun)
- smrsprpb — Summer/Spring precipitation balance: (jul+aug)/(apr+may)
- smrp — Summer precipitation: (apr+may)
- winp — Winter precipitation: (nov+dec+jan+feb)
We used these alone and in combination in our analyses. Typical combinations include:
- adi — Annual dryness index, dd5/map or sqrt(dd5)/map (once named ami annual moisture index)
- sdi — Summer dryness index, gsdd5/gsp or sqrt(gsdd5)/gsp (once named smi, summer moisture index)
- pratio — Ratio of summer precipitatioin to total precipitation, gsp/map
These variables are computed for the points where we have vegetation observations for the purpose of building functions that predict vegetation. The variables are computed for Asciigrid maps for the purpose of making predictions of vegetation and mapping those predictions.