Confounding climatic change: The problem of spatially unrepresentative air temperature records.

摘要

Assessment of long-term climatic change relies heavily on historical air temperature records. In the United States, these records are drawn from “high quality” cooperative weather stations. High quality generally implies long records with relatively few station moves, instrument changes, or other discontinuities that may degrade the record. With few exceptions, micro- and meso-scale environmental characteristics around weather stations have not been systematically analyzed for their influence on long-term air temperature records. Accurate estimation of long-term temperature change, however, may be hindered by non-optimal micro- and meso-scale environmental characteristics around weather stations.; One of the primary concerns of this work is to determine if local environmental characteristics influenced historical station records to the extent that spatial representativeness is limited and that network homogeneity is compromised. This research addresses potential scale-dependent inhomogeneities in historical air temperature records by examining the regional coherence of air temperature and diurnal temperature range. Computationally intensive resampling procedures and geostatistical semivariance functions are used to iteratively test the spatial representativeness of each station in the network. In addition to local environmental characteristics, this work also identifies operational inhomogeneities that degraded spatial representativeness.; Thirty-three stations (of 237) in the central United States subset of the Daily Historical Climatology Network were found to be spatially unrepresentative of regional-scale climatic variability. These stations deleteriously influenced the homogeneity (spatial patterns, averages, and trends) of long-term air temperature change. Since anomalous stations have even greater consequence in regional and local change assessments, they should be culled from or de-emphasized in historical climatology networks. Findings suggest a number of stations have records that, in sum, substantially increase the variability of the long-term regional climate signal. Although forcing mechanisms are not easily identified, maximum and minimum temperatures are both susceptible to site-specific forcing.