Elevation Gradients and Climatic Consequences

Steep topography usually results in gradients in surface meteorological elements. Sometimes these gradients are extremely sharp. Frequent or persistent gradients are expressed in climatic statistics as well. Most commonly, higher elevations are wetter and cooler than lower elevations. The magnitude of these climate gradients vary both spatially and temporally, generally on smaller scales for the former and on a greater variety of scales for the latter. Orographic contributions to precipitation vary on hourly to annual scales, and temperature inversions of different durations can alter or reverse the vertical temperature lapse rate normally found in the atmosphere. The presence of these factors affects the probability distributions of climate elements as a function of elevation. This leads in turn to consequences for ecology, resource management, and data. Orographic enhancement of Sierra precipitation varies by a factor of about three on seasonal time scales, and more on shorter scales. Particularly strong gradients in temperature climate are observed along the California coast, resulting in large changes in long-term climatological probability distributions over quite short distances in elevation. These have significant implications for plant life. For specific noteworthy events, such as the California heat wave of July 2006, striking differences were seen over a horizontal distance of merely 2-3 km along the Big Sur Coast, related entirely to elevation. There is evidence of differential warming with elevation between California's Central Valley and the Sierra Nevada. As a practical matter, the three-dimensional correlation fields of weather and climate elements in topographically diverse regions, on differing time scales, have complex structure, but also have certain regularities. This makes quality control of weather and climate data sets in highly diverse topography much more challenging. Quality control decisions that do not properly take this correlation structure (which varies in time) into account can result in degraded data sets, a variety of Type I and Type II errors, and paradoxically, hinder or prevent the discovery and description of the effects of climate gradients by incorrectly altering the data sets needed to uncover and quantify the relationships.