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Temperature and precipitation equations in ClimCalc were derived from regression
analysis of long-term monthly mean weather station data (1950-1980; n = 164 for
temperature, n = 310 for precipitation) against latitude, longitude and
elevation. Monthly coefficients for geographic coordinates reflect seasonally
shifting gradients with latitude and/or distance from the seacoast. Elevation
coefficients reflect orographic effects (precipitation) and environmental lapse
rates (temperature). Coefficients for annual precipitation indicate an average
increase of 74 cm per 1000 m increase in elevation. Seasonal patterns in
precipitation vary as a function of distance from the seacoast, with coastal
sites tending to receive a greater fraction of precipitation during the winter
than during the summer (e.g. Boston, MA). Sites further inland typically show
either no seasonal pattern (e.g. Albany, NY) or drier winters than summers
(e.g. Burlington, VT). Elevation coefficients derived from temperature data
indicate steeper lapse rates for minimum daily temperatures than maximum daily
temperatures. This pattern likely results from the occurrence of free
convection during the day, which tends to dampen vertical temperature gradients.
Observed lapse rates averaged -5.4 deg C per 1000 m for maximum daily temperatures
and -7.6 deg C per 1000 m for minimum temperatures with an overall average of -6.5
deg C per 1000 m.
Solar radiation algorithms were derived by combining standard trigonometric equations for potential radiation with measured surface radiation from available radiation monitoring stations. At each station, the monthly ratio of measured to potential radiation was calculated as a means of quantifying the degree of atmospheric interference. This ratio is close to 0.5 for most months (indicating that surface radiation is approximately 50% of that received at the top of the atmosphere) with the exception of November and December, which are consistently lower due to increased cloud cover. Wet atmospheric deposition of nitrogen and sulfur was analyzed using data from the National Atmospheric Deposition Program. For nitrate, sulfate and ammonium, a more than two-fold linear decrease was observed from western New York and Pennsylvania to Eastern Maine. In ClimCalc, wet deposition estimates are derived by combining these regional concentration gradients with estimated precipitation amount. For dry deposition, regional patterns were determined using data for particle and gas concentrations collected by the National Dry |
Deposition Network (NDDN) and several other sources, in combination
with estimates of deposition velocities. Contrary to wet deposition, the
dominant air concentration trends were steep decreases from south to north,
creating a regional gradient in total deposition (wet + dry) from the southwest
to northeast (figure 2). This contrast between
wet and dry deposition trends suggests that within the northeast, the two
deposition forms are received in different proportions from different source
areas: wet-deposited materials primarily from industrial areas to the west and dry
deposited materials from urban areas along the southern portions of the region.
To learn more about climate in the northeastern U.S. please visit the New England Climate Initiative Further reading about these analyses and ClimCalc applications: Aber, J.D., S.V. Ollinger, C.A. Federer, P.B. Reich, M.L. Goulden, D.W. Kicklighter, J.M. Melillo, and R.G. Lathrop. 1995. Predicting the effects of climate change on water yield and forest production in the northeastern U.S. Climate Research. 5:207-222. Ollinger, S.V., J.D. Aber, G.M. Lovett, S.E. Millham and R.G. Lathrop. 1993. A spatial model of atmospheric deposition for the northeastern U.S. Ecological Applications. 3: 459-472 Ollinger, S.V., J.D. Aber, C.A. Federer, G.M. Lovett, and J. Ellis. 1995. Modeling physical and chemical climatic variables across the northeastern U.S. for a Geographic Information System. USDA Forest Service General Technical Report NE-191. Ollinger, S.V., J.D. Aber, and P.B. Reich. 1997. Simulating ozone effects on forest productivity: interactions among leaf-, canopy- and stand-level processes . Ecological Applications. Ollinger, S.V., J.D. Aber, and C.A. Federer. 1998. Estimating regional forest productivity and water yield using an ecosystem model linked to a GIS. Landscape Ecology. 13:323-334. Jenkins, J.C., D.W. Kicklighter, S.V. Ollinger, J.D. Aber and J.M. 1999. Melillo. Sources of variability in NPP predictions at a regional scale: A comparison using PnET-II and TEM 4.0 in northeastern forests. Ecosystems 2:555- 570
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