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Normal temperatures, which are calculated by the National Climatic Data Center for locations across the country, are quality-controlled, smoothed 30-yr-average temperatures. They are used in many facets of media, industry, and meteorology, and a given day's normal maximum and minimum temperatures are often used synonymously with what the observed temperature extremes “should be.” However, allowing some leeway to account for natural daily and seasonal variations can more accurately reflect the ranges of temperature that we can expect on a particular day—a “normal range.” Providing such a range, especially to the public, presents a more accurate perspective on what the temperature “usually” is on any particular day of the year. One way of doing this is presented in this study for several locations across North Carolina. The results yield expected higher variances in the cooler months and seem to well represent the varied weather that locations in North Carolina tend to experience. Day-to-day variations in the normal range are larger than expected, but are retained rather than smoothed. The method is simple and applicable to any location with a complete 30-yr record and with a temperature variance time series that follows a bell curve. The normal-range product has many potential applications.
Normal temperatures, which are calculated by the National Climatic Data Center for locations across the country, are quality-controlled, smoothed 30-yr-average temperatures. They are used in many facets of media, industry, and meteorology, and a given day's normal maximum and minimum temperatures are often used synonymously with what the observed temperature extremes “should be.” However, allowing some leeway to account for natural daily and seasonal variations can more accurately reflect the ranges of temperature that we can expect on a particular day—a “normal range.” Providing such a range, especially to the public, presents a more accurate perspective on what the temperature “usually” is on any particular day of the year. One way of doing this is presented in this study for several locations across North Carolina. The results yield expected higher variances in the cooler months and seem to well represent the varied weather that locations in North Carolina tend to experience. Day-to-day variations in the normal range are larger than expected, but are retained rather than smoothed. The method is simple and applicable to any location with a complete 30-yr record and with a temperature variance time series that follows a bell curve. The normal-range product has many potential applications.
The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement.
A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover, rather than providing additional independent information, the use of the data from poorly sited stations provides a false sense of confidence in the robustness of the surface temperature trend assessments.
The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement.
A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover, rather than providing additional independent information, the use of the data from poorly sited stations provides a false sense of confidence in the robustness of the surface temperature trend assessments.
