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Abstract
All analyses of the impact of urban heat islands (UHIs) on in situ temperature observations suffer from inhomogeneities or biases in the data. These inhomogeneities make urban heat island analyses difficult and can lead to erroneous conclusions. To remove the biases caused by differences in elevation, latitude, time of observation, instrumentation, and nonstandard siting, a variety of adjustments were applied to the data. The resultant data were the most thoroughly homogenized and the homogeneity adjustments were the most rigorously evaluated and thoroughly documented of any large-scale UHI analysis to date. Using satellite night-lights–derived urban/rural metadata, urban and rural temperatures from 289 stations in 40 clusters were compared using data from 1989 to 1991. Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures. It is postulated that this is due to micro- and local-scale impacts dominating over the mesoscale urban heat island. Industrial sections of towns may well be significantly warmer than rural sites, but urban meteorological observations are more likely to be made within park cool islands than industrial regions.
Abstract
All analyses of the impact of urban heat islands (UHIs) on in situ temperature observations suffer from inhomogeneities or biases in the data. These inhomogeneities make urban heat island analyses difficult and can lead to erroneous conclusions. To remove the biases caused by differences in elevation, latitude, time of observation, instrumentation, and nonstandard siting, a variety of adjustments were applied to the data. The resultant data were the most thoroughly homogenized and the homogeneity adjustments were the most rigorously evaluated and thoroughly documented of any large-scale UHI analysis to date. Using satellite night-lights–derived urban/rural metadata, urban and rural temperatures from 289 stations in 40 clusters were compared using data from 1989 to 1991. Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures. It is postulated that this is due to micro- and local-scale impacts dominating over the mesoscale urban heat island. Industrial sections of towns may well be significantly warmer than rural sites, but urban meteorological observations are more likely to be made within park cool islands than industrial regions.
Questions have been raised about whether poor siting practices that have existed in recent years at some in situ weather-observing stations are causing a bias in U.S. temperature change analysis. This potential bias was examined using homogeneity-adjusted maximum, minimum, and mean temperature data from five stations in eastern Colorado—two with good current siting and three with poor current siting. No siting-induced bias was found in the homogeneity-adjusted data. Furthermore, the results indicate that homogeneity-adjusted time series from the stations with poor current siting represent the temperature variability and change in the region as a whole quite well because they are very similar to the time series from stations with excellent siting.
Questions have been raised about whether poor siting practices that have existed in recent years at some in situ weather-observing stations are causing a bias in U.S. temperature change analysis. This potential bias was examined using homogeneity-adjusted maximum, minimum, and mean temperature data from five stations in eastern Colorado—two with good current siting and three with poor current siting. No siting-induced bias was found in the homogeneity-adjusted data. Furthermore, the results indicate that homogeneity-adjusted time series from the stations with poor current siting represent the temperature variability and change in the region as a whole quite well because they are very similar to the time series from stations with excellent siting.
Abstract
Decreasing pan evaporation trends in many regions of the world have been viewed as evidence of a decrease in the terrestrial evaporation component of the hydrologic cycle. However, some researchers suggest that the relationship between pan evaporation and terrestrial evaporation depends on the environment in which the measurements are recorded and that pan evaporation trends run counter to trends in terrestrial evaporation in some climates. To determine whether evidence of this kind of relationship exists in the observational record, pan evaporation trends were compared with precipitation trends in eight regions within the United States. To the extent that warm-season precipitation can be used as an indicator of surface evaporation, these results support the view that pan evaporation and actual evaporation can be inversely related.
Abstract
Decreasing pan evaporation trends in many regions of the world have been viewed as evidence of a decrease in the terrestrial evaporation component of the hydrologic cycle. However, some researchers suggest that the relationship between pan evaporation and terrestrial evaporation depends on the environment in which the measurements are recorded and that pan evaporation trends run counter to trends in terrestrial evaporation in some climates. To determine whether evidence of this kind of relationship exists in the observational record, pan evaporation trends were compared with precipitation trends in eight regions within the United States. To the extent that warm-season precipitation can be used as an indicator of surface evaporation, these results support the view that pan evaporation and actual evaporation can be inversely related.
Abstract
Urban heat island (UHI) analyses for the conterminous United States were performed using three different forms of metadata: nightlights-derived metadata, map-based metadata, and gridded U.S. Census Bureau population metadata. The results indicated that metadata do matter. Whether a UHI signal was found depended on the metadata used. One of the reasons is that the UHI signal is very weak. For example, population was able to explain at most only a few percent of the variance in temperature between stations. The nightlights metadata tended to classify lower population stations as rural compared to map-based metadata while the map-based metadata urban stations had, on average, higher populations than urban nightlights. Analysis with gridded population metadata indicated that statistically significant urban heat islands could be found even when quite urban stations were classified as rural, indicating that the primary signal was coming from the relatively high population sites. If ∼30% of the highest population stations were removed from the analysis, no statistically significant urban heat island was detected. The implications of this work on U.S. climate change analyses is that, if the highest population stations are avoided (populations above 30 000 within 6 km), the analysis should not be expected to be contaminated by UHIs. However, comparison between U.S. Historical Climatology Network (HCN) time series from the full dataset and a subset excluding the high population sites indicated that the UHI contamination from the high population stations accounted for very little of the recent warming.
Abstract
Urban heat island (UHI) analyses for the conterminous United States were performed using three different forms of metadata: nightlights-derived metadata, map-based metadata, and gridded U.S. Census Bureau population metadata. The results indicated that metadata do matter. Whether a UHI signal was found depended on the metadata used. One of the reasons is that the UHI signal is very weak. For example, population was able to explain at most only a few percent of the variance in temperature between stations. The nightlights metadata tended to classify lower population stations as rural compared to map-based metadata while the map-based metadata urban stations had, on average, higher populations than urban nightlights. Analysis with gridded population metadata indicated that statistically significant urban heat islands could be found even when quite urban stations were classified as rural, indicating that the primary signal was coming from the relatively high population sites. If ∼30% of the highest population stations were removed from the analysis, no statistically significant urban heat island was detected. The implications of this work on U.S. climate change analyses is that, if the highest population stations are avoided (populations above 30 000 within 6 km), the analysis should not be expected to be contaminated by UHIs. However, comparison between U.S. Historical Climatology Network (HCN) time series from the full dataset and a subset excluding the high population sites indicated that the UHI contamination from the high population stations accounted for very little of the recent warming.
The Global Historical Climatology Network version 2 temperature database was released in May 1997. This century-scale dataset consists of monthly surface observations from ~7000 stations from around the world. This archive breaks considerable new ground in the field of global climate databases. The enhancements include 1) data for additional stations to improve regional-scale analyses, particularly in previously data-sparse areas; 2) the addition of maximum–minimum temperature data to provide climate information not available in mean temperature data alone; 3) detailed assessments of data quality to increase the confidence in research results; 4) rigorous and objective homogeneity adjustments to decrease the effect of nonclimatic factors on the time series; 5) detailed metadata (e.g., population, vegetation, topography) that allow more detailed analyses to be conducted; and 6) an infrastructure for updating the archive at regular intervals so that current climatic conditions can constantly be put into historical perspective. This paper describes these enhancements in detail.
The Global Historical Climatology Network version 2 temperature database was released in May 1997. This century-scale dataset consists of monthly surface observations from ~7000 stations from around the world. This archive breaks considerable new ground in the field of global climate databases. The enhancements include 1) data for additional stations to improve regional-scale analyses, particularly in previously data-sparse areas; 2) the addition of maximum–minimum temperature data to provide climate information not available in mean temperature data alone; 3) detailed assessments of data quality to increase the confidence in research results; 4) rigorous and objective homogeneity adjustments to decrease the effect of nonclimatic factors on the time series; 5) detailed metadata (e.g., population, vegetation, topography) that allow more detailed analyses to be conducted; and 6) an infrastructure for updating the archive at regular intervals so that current climatic conditions can constantly be put into historical perspective. This paper describes these enhancements in detail.
Abstract
At the National Climatic Data Center, two basic approaches to making homogeneity adjustments to climate data have been developed. The first is based on the use of metadata (station history files) and is used in the adjustments made to the U.S. Historical Climatology Network monthly dataset. The second approach is non-metadata based and was developed for use with the Global Historical Climatology Network dataset, since there are not extensive station history files for most stations in the dataset. In this paper the two methodologies are reviewed and the adjustments made using each are compared, then the results are discussed. Last, some brief guidelines on the limitations and uses of these data are provided.
Abstract
At the National Climatic Data Center, two basic approaches to making homogeneity adjustments to climate data have been developed. The first is based on the use of metadata (station history files) and is used in the adjustments made to the U.S. Historical Climatology Network monthly dataset. The second approach is non-metadata based and was developed for use with the Global Historical Climatology Network dataset, since there are not extensive station history files for most stations in the dataset. In this paper the two methodologies are reviewed and the adjustments made using each are compared, then the results are discussed. Last, some brief guidelines on the limitations and uses of these data are provided.
Abstract
The effect of the Luers–Eskridge adjustments on the homogeneity of archived radiosonde temperature observations is evaluated. Using unadjusted and adjusted radiosonde data from the Comprehensive Aerological Reference Dataset (CARDS) as well as microwave sounding unit (MSU) version-d monthly temperature anomalies, the discontinuities in differences between radiosonde and MSU temperature anomalies across times of documented changes in radiosonde are computed for the lower to midtroposphere, mid- to upper troposphere, and lower stratosphere. For this purpose, a discontinuity is defined as a statistically significant difference between means of radiosonde–MSU differences for the 30-month periods immediately prior to and following a documented change in radiosonde type. The magnitude and number of discontinuities based on unadjusted and adjusted radiosonde data are then compared. Since the Luers–Eskridge adjustments have been designed to remove radiation and lag errors from radiosonde temperature measurements, the homogeneity of the data should improve whenever these types of errors dominate.
It is found that even though stratospheric radiosonde temperatures appear to be somewhat more homogeneous after the Luers–Eskridge adjustments have been applied, transition-related discontinuities in the troposphere are frequently amplified by the adjustments. Significant discontinuities remain in the adjusted data in all three atmospheric layers. Based on the findings of this study, it appears that the Luers–Eskridge adjustments do not render upper-air temperature records sufficiently homogeneous for climate change analyses. Given that the method was designed to adjust only for radiation and lag errors in radiosonde temperature measurements, its relative ineffectiveness at producing homogeneous time series is likely to be caused by 1) an inaccurate calculation of the radiation or lag errors and/or 2) the presence of other errors in the data that contribute significantly to observed discontinuities in the time series.
Abstract
The effect of the Luers–Eskridge adjustments on the homogeneity of archived radiosonde temperature observations is evaluated. Using unadjusted and adjusted radiosonde data from the Comprehensive Aerological Reference Dataset (CARDS) as well as microwave sounding unit (MSU) version-d monthly temperature anomalies, the discontinuities in differences between radiosonde and MSU temperature anomalies across times of documented changes in radiosonde are computed for the lower to midtroposphere, mid- to upper troposphere, and lower stratosphere. For this purpose, a discontinuity is defined as a statistically significant difference between means of radiosonde–MSU differences for the 30-month periods immediately prior to and following a documented change in radiosonde type. The magnitude and number of discontinuities based on unadjusted and adjusted radiosonde data are then compared. Since the Luers–Eskridge adjustments have been designed to remove radiation and lag errors from radiosonde temperature measurements, the homogeneity of the data should improve whenever these types of errors dominate.
It is found that even though stratospheric radiosonde temperatures appear to be somewhat more homogeneous after the Luers–Eskridge adjustments have been applied, transition-related discontinuities in the troposphere are frequently amplified by the adjustments. Significant discontinuities remain in the adjusted data in all three atmospheric layers. Based on the findings of this study, it appears that the Luers–Eskridge adjustments do not render upper-air temperature records sufficiently homogeneous for climate change analyses. Given that the method was designed to adjust only for radiation and lag errors in radiosonde temperature measurements, its relative ineffectiveness at producing homogeneous time series is likely to be caused by 1) an inaccurate calculation of the radiation or lag errors and/or 2) the presence of other errors in the data that contribute significantly to observed discontinuities in the time series.
The current network of internationally exchanged in situ station data is not distributed evenly nor densely around the globe. Consequently, the in situ data contain insufficient information to identify fine spatial structure and variations over many areas of the world. Therefore, satellite observations need to be blended with in situ data to obtain higher resolution over the global land surface. Toward this end, the authors calibrated and independently verified an algorithm that derives land surface temperatures from the Special Sensor Microwave/Imager (SSM/I). This study explains the technique used to refine a set of equations that identify various surface types and to make corresponding dynamic emissivity adjustments. This allowed estimation of the shelter height temperatures from the seven channel measurements flown on the SSM/I instrument. Data from first-order in situ stations over the eastern half of the United States were used for calibration and intersatellite adjustment. The results show that the observational difference between the in situ point measurements and the SSM/I-derived areal values is about 2°C with statistical characteristics largely independent of surface type. High-resolution monthly mean anomalies generated from the U.S. cooperative network served as independent verification over the same study area. This verification work determined that the standard deviation of the monthly mean anomalies is 0.76°C at each 1° × 1° grid box. This level of accuracy is adequate to blend the SSM/I-derived temperature anomaly data with in situ data for monitoring global temperature anomalies in finer detail.
The current network of internationally exchanged in situ station data is not distributed evenly nor densely around the globe. Consequently, the in situ data contain insufficient information to identify fine spatial structure and variations over many areas of the world. Therefore, satellite observations need to be blended with in situ data to obtain higher resolution over the global land surface. Toward this end, the authors calibrated and independently verified an algorithm that derives land surface temperatures from the Special Sensor Microwave/Imager (SSM/I). This study explains the technique used to refine a set of equations that identify various surface types and to make corresponding dynamic emissivity adjustments. This allowed estimation of the shelter height temperatures from the seven channel measurements flown on the SSM/I instrument. Data from first-order in situ stations over the eastern half of the United States were used for calibration and intersatellite adjustment. The results show that the observational difference between the in situ point measurements and the SSM/I-derived areal values is about 2°C with statistical characteristics largely independent of surface type. High-resolution monthly mean anomalies generated from the U.S. cooperative network served as independent verification over the same study area. This verification work determined that the standard deviation of the monthly mean anomalies is 0.76°C at each 1° × 1° grid box. This level of accuracy is adequate to blend the SSM/I-derived temperature anomaly data with in situ data for monitoring global temperature anomalies in finer detail.
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.
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.
Attribution of extreme events shortly after their occurrence stretches the current state-of-theart of climate change assessment. To help foster the growth of this science, this article illustrates some approaches to answering questions about the role of human factors, and the relative role of different natural factors, for six specific extreme weather or climate events of 2011.
Not every event is linked to climate change. The rainfall associated with the devastating Thailand floods can be explained by climate variability. But long-term warming played a part in the others. While La Niña contributed to the failure of the rains in the Horn of Africa, an increased frequency of such droughts there was linked to warming in the Western Pacific– Indian Ocean warm pool. Europe's record warm temperatures would probably not have been as unusual if the high temperatures had been caused only by the atmospheric flow regime without any long-term warming.
Calculating how the odds of a particular extreme event have changed provides a means of quantifying the influence of climate change on the event. The heatwave that affected Texas has become distinctly more likely than 40 years ago. In the same vein, the likelihood of very warm November temperatures in the UK has increased substantially since the 1960s.
Comparing climate model simulations with and without human factors shows that the cold UK winter of 2010/2011 has become about half as likely as a result of human influence on climate, illustrating that some extreme events are becoming less likely due to climate change.
Attribution of extreme events shortly after their occurrence stretches the current state-of-theart of climate change assessment. To help foster the growth of this science, this article illustrates some approaches to answering questions about the role of human factors, and the relative role of different natural factors, for six specific extreme weather or climate events of 2011.
Not every event is linked to climate change. The rainfall associated with the devastating Thailand floods can be explained by climate variability. But long-term warming played a part in the others. While La Niña contributed to the failure of the rains in the Horn of Africa, an increased frequency of such droughts there was linked to warming in the Western Pacific– Indian Ocean warm pool. Europe's record warm temperatures would probably not have been as unusual if the high temperatures had been caused only by the atmospheric flow regime without any long-term warming.
Calculating how the odds of a particular extreme event have changed provides a means of quantifying the influence of climate change on the event. The heatwave that affected Texas has become distinctly more likely than 40 years ago. In the same vein, the likelihood of very warm November temperatures in the UK has increased substantially since the 1960s.
Comparing climate model simulations with and without human factors shows that the cold UK winter of 2010/2011 has become about half as likely as a result of human influence on climate, illustrating that some extreme events are becoming less likely due to climate change.
