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Kevin P. Gallo

Abstract

Adjustments to data observed at pairs of climate stations have been recommended to remove the biases introduced by differences between the stations in time of observation, temperature instrumentation, latitude, and elevation. A new network of climate stations, located in rural settings, permits comparisons of temperatures for several pairs of stations without two of the biases (time of observation and instrumentation). The daily, monthly, and annual minimum, maximum, and mean temperatures were compared for five pairs of stations included in the U.S. Climate Reference Network. Significant differences were found between the paired stations in the annual minimum, maximum, and mean temperatures for all five pairs of stations. Adjustments for latitude and elevation differences contributed to greater differences in mean annual temperature for four of the five stations. Lapse rates computed from the mean annual temperature differences between station pairs differed from a constant value, whether or not latitude adjustments were made to the data. The results suggest that microclimate influences on temperatures observed at nearby (horizontally and vertically) stations are potentially much greater than influences that might be due to latitude or elevation differences between the stations.

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Timothy W. Owen
and
Kevin P. Gallo

Abstract

The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the “heat-island” effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900–90 interval. Retrospective grid-based populations, within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.

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Kevin P. Gallo
and
Timothy W. Owen

Abstract

Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (T sfc). The relationships between surface- and satellite-derived variables were developed during 1989–91 and tested on data acquired during 1992–93. The urban–rural differences in air temperature were linearly related to urban–rural differences in the NDVI and T sfc. A statistically significant but relatively small (less than 40%) amount of the variation in these urban–rural differences in air temperature [the urban heat island (UHI) bias] was associated with variation in the urban–rural differences in NDVI and T sfc. A comparison of the satellite-based estimates of the UHI bias with population-based estimates of the UHI bias indicated similar levels of error. The use of satellite-derived data may contribute to a globally consistent method for analysis of the urban heat island bias.

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Arthur T. DeGaetano
,
Robert J. Allen
, and
Kevin P. Gallo

Abstract

A subset of stations from the daily U.S. Historical Climatology Network (HCN) is used as a basis for a historical database of temperature extreme occurrence in the United States. The dataset focuses on daily temperature occurrences that exceed (fall below) the 90th (10th) percentiles of daily maximum and minimum temperature. Using a variety of techniques, the temperature extreme occurrence data are homogenized to account for nonclimatic shifts resulting from station relocations, changes in instrument type, and variations in the time of observations. Given the daily resolution of the extreme data, these potential sources of inhomogeneity require testing and adjustment using methods other than those conventionally used with mean temperature data. A data estimation technique, specific to extremes, is also used to produce serially complete exceedence records. Stations are also identified based on their current degree of urbanization using satellite observations. The dataset is intended to provide a research-quality source of temperature extreme data, analogous and complementary to the daily HCN dataset.

Two analyses are presented that illustrate the influence of adjustment. The change in temperature extreme occurrence with time reverses at between 15% and 20% of the HCN stations depending upon whether adjusted or unadjusted series is used. Changes in the distribution of extreme occurrences during drought and nondrought years are also shown to occur.

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Kevin P. Gallo
,
David R. Easterling
, and
Thomas C. Peterson

Abstract

The diurnal temperature range (DTR) at weather observation stations that make up the U.S. Historical Climatology Network was evaluated with respect to the predominant land use/land cover associated with the stations within three radii intervals (100, 1000, and 10 000 m) of the stations. Those stations that were associated with predominantly rural land use/land cover (LULC) usually displayed the greatest observed DTR, whereas those associated with urban related land use or land cover displayed the least observed DTR. The results of this study suggest that significant differences in the climatological DTR were observed and could be attributed to the predominant LULC associated with the observation stations. The results also suggest that changes in the predominant LULC conditions, within as great as a 10 000 m radius of an observation station, could significantly influence the climatological DTR. Future changes in the predominant LULC associated with observation sites should be monitored similar to the current practice of monitoring changes in instruments or time of observation at the observations sites.

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John R. Christy
,
William B. Norris
, and
Kevin P. Gallo
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John R. Christy
,
William B. Norris
,
Kelly Redmond
, and
Kevin P. Gallo

Abstract

A procedure is described to construct time series of regional surface temperatures and is then applied to interior central California stations to test the hypothesis that century-scale trend differences between irrigated and nonirrigated regions may be identified. The procedure requires documentation of every point in time at which a discontinuity in a station record may have occurred through (a) the examination of metadata forms (e.g., station moves) and (b) simple statistical tests. From this “homogeneous segments” of temperature records for each station are defined. Biases are determined for each segment relative to all others through a method employing mathematical graph theory. The debiased segments are then merged, forming a complete regional time series. Time series of daily maximum and minimum temperatures for stations in the irrigated San Joaquin Valley (Valley) and nearby nonirrigated Sierra Nevada (Sierra) were generated for 1910–2003. Results show that twentieth-century Valley minimum temperatures are warming at a highly significant rate in all seasons, being greatest in summer and fall (> +0.25°C decade−1). The Valley trend of annual mean temperatures is +0.07° ± 0.07°C decade−1. Sierra summer and fall minimum temperatures appear to be cooling, but at a less significant rate, while the trend of annual mean Sierra temperatures is an unremarkable −0.02° ± 0.10°C decade−1. A working hypothesis is that the relative positive trends in Valley minus Sierra minima (>0.4°C decade−1 for summer and fall) are related to the altered surface environment brought about by the growth of irrigated agriculture, essentially changing a high-albedo desert into a darker, moister, vegetated plain.

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Kevin P. Gallo
,
Timothy W. Owen
,
David R. Easterling
, and
Paul F. Jamason

Abstract

The 1221 weather observation stations that compose the U.S. Historical Climatology Network were designated as either urban, suburban, or rural based on data from the Defense Meteorological Satellite Program Operational Linescan System (OLS). The designations were based on local and regional samples of the OLS data around the stations (OLS method). Trends in monthly maximum and minimum temperature and the diurnal temperature range (DTR) were determined for the 1950–96 interval for each of three land use/land cover (LULC) designations. The temperature trends for the OLS-derived designations of LULC were compared to similarly designated LULC based on (i) map- (Operational Navigation Charts) and population-based estimates of LULC (ONCP method), and (ii) LULC designations that resulted from of a survey of the network station operators. Although differences were not statistically significant, the DTR trends (degrees Celsius per 100 years) did differ between the LULC classes defined by the OLS method, from −0.41 for the rural stations to −0.86 for the urban stations. Trends also differed, although not significantly, between the methods used to define an LULC class, such that the trends in rural DTR varied from −0.41 for the OLS defined stations to −0.67 for the ONCP defined stations. Although the trends between classes were not significantly different, they do present some contrasts that might confound the interpretation of temperature trends when the local and regional environments associated with the analyzed stations are not considered. The general (urban, suburban, or rural) LULC associated with surface observation stations appears to be one of the factors that can influence the trends observed in temperatures and thus should be considered in the analysis and interpretation of temperature trends.

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David L. Epperson
,
Jerry M. Davis
,
Peter Bloomfield
,
Thomas R. Karl
,
Alan L. McNab
, and
Kevin P. Gallo

Abstract

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Initial correlation analyses revealed that data from 700 mb were sufficient to represent the upper-air or background climate. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%–95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. The R 2's for the GHCN models ranged between 0,86 and 0.95, whereas the R 2's for the COOP models ranged between 0.64 and 0.92. In addition, root-mean-square errors (rmse's) were over 3°C for GHCN models and over 2°C for COOP models for winter months, and near 2°C for GHCN models and near 1.5°C for COOP models for summer months. The results of this study—a large amount of explained variance and a relatively small rmse—indicate the usefulness of these models for predicting surface temperatures. Urban landscape data are incorporated into these models in Part II of this study to estimate the urban bias of surface ternperatures.

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David L. Epperson
,
Jerry M. Davis
,
Peter Bloomfield
,
Thomas R. Karl
,
Alan L. McNab
, and
Kevin P. Gallo

Abstract

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

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