United States Historical Climate Network Research Articles

Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature

Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.

Did irrigation impact 20th century air temperature in the High Plains aquifer region?

This study investigates potential impacts of widespread adoption of irrigation on long-term temperatures over the High Plains aquifer (HPA) region of the Great Plains. It is well known that availability of soil moisture can modify near surface energy partitioning (latent vs. sensible) and temperature. This study provides an assessment of the changes in the historical near surface temperature records in the HPA region due to adoption of irrigation. Long-term growing season mean monthly maximum and minimum air temperature data from 24 irrigated and 26 non-irrigated sites were analyzed. These stations are part of the US Historical Climate Network (USHCN). This study reports that growing season mean maximum temperature (GTmax) at irrigated areas is predominantly cooler than non-irrigated areas with up to 1.01 °C cooling at some locations. A geographical variation in magnitude of this cooling is also observed. The majority of irrigated locations report warming in GTmin with up to 1.00 °C increases. The results are largely statistically significant. This paper suggests more focus on regional- and local-scale studies is needed to better understand impacts of land use changes on climate change and variability.

The identification of distinct patterns in California temperature trends

Regional changes in California surface temperatures over the last 80 years are analyzed using station data from the US Historical Climate Network and the National Weather Service Cooperative Network. Statistical analyses using annual and seasonal temperature data over the last 80 years show distinctly different spatial and temporal patterns in trends of maximum temperature (Tmax) compared to trends of minimum temperature (Tmin). For trends computed between 1918 and 2006, the rate of warming in Tmin is greater than that of Tmax. Trends computed since 1970 show an amplified warming rate compared to trends computed from 1918, and the rate of warming is comparable between Tmin and Tmax. This is especially true in the southern deserts, where warming trends during spring (March–May) are exceptionally large. While observations show coherent statewide positive trends in Tmin, trends in Tmax vary on finer spatial and temporal scales. Accompanying the observed statewide warming from 1970 to 2006, regional cooling trends in Tmax are observed during winter and summer. These signatures of regional temperature change suggest that a collection of different forcing mechanisms or feedback processes must be present to produce these responses.

Impacts of land use land cover on temperature trends over the continental United States: assessment using the North American Regional Reanalysis

AbstractWe investigate the sensitivity of surface temperature trends to land use land cover change (LULC) over the conterminous United States (CONUS) using the observation minus reanalysis (OMR) approach. We estimated the OMR trends for the 1979–2003 period from the US Historical Climate Network (USHCN), and the NCEP‐NCAR North American Regional Reanalysis (NARR). We used a new mean square differences (MSDs)‐based assessment for the comparisons between temperature anomalies from observations and interpolated reanalysis data. Trends of monthly mean temperature anomalies show a strong agreement, especially between adjusted USHCN and NARR (r = 0.9 on average) and demonstrate that NARR captures the climate variability at different time scales. OMR trend results suggest that, unlike findings from studies based on the global reanalysis (NCEP/NCAR reanalysis), NARR often has a larger warming trend than adjusted observations (on average, 0.28 and 0.27 °C/decade respectively).OMR trends were found to be sensitive to land cover types. We analysed decadal OMR trends as a function of land types using the Advanced Very High Resolution Radiometer (AVHRR) and new National Land Cover Database (NLCD) 1992–2001 Retrofit Land Cover Change. The magnitude of OMR trends obtained from the NLDC is larger than the one derived from the ‘static’ AVHRR. Moreover, land use conversion often results in more warming than cooling.Overall, our results confirm the robustness of the OMR method for detecting non‐climatic changes at the station level, evaluating the impacts of adjustments performed on raw observations, and most importantly, providing a quantitative estimate of additional warming trends associated with LULC changes at local and regional scales. As most of the warming trends that we identify can be explained on the basis of LULC changes, we suggest that in addition to considering the greenhouse gases–driven radiative forcings, multi‐decadal and longer climate models simulations must further include LULC changes. Copyright © 2009 Royal Meteorological Society

Trends in wintertime climate in the northeastern United States: 1965–2005

Humans experience climate variability and climate change primarily through changes in weather at local and regional scales. One of the most effective means to track these changes is through detailed analysis of meteorological data. In this work, monthly and seasonal trends in recent winter climate of the northeastern United States (NE‐US) are documented. Snow cover and snowfall are important components of the region's hydrological systems, ecosystems, infrastructure, travel safety, and winter tourism and recreation. Temperature, snowfall, and snow depth data were collected from the merged United States Historical Climate Network (USHCN) and National Climatic Data Center Cooperative Network (COOP) data set for the months of December through March, 1965–2005. Monthly and seasonal time series of snow‐covered days (snow depth >2.54 cm) are constructed from daily snow depth data. Spatial coherence analysis is used to address data quality issues with daily snowfall and snow depth data, and to remove stations with nonclimatic influences from the regional analysis. Monthly and seasonal trends in mean, minimum, and maximum temperature, total snowfall, and snow‐covered days are evaluated over the period 1965–2005, a period during which global temperature records and regional indicators exhibit a shift to warmer climate conditions. NE‐US regional winter mean, minimum, and maximum temperatures are all increasing at a rate ranging from 0.42° to 0.46°C/decade with the greatest warming in all three variables occurring in the coldest months of winter (January and February). The regional average reduction in number of snow‐covered days in winter (−8.9 d/decade) is also greatest during the months of January and February. Further analysis with additional regional climate modeling is required to better investigate the causal link between the increases in temperature and reduction in snow cover during the coldest winter months of January and February. In addition, regionally averaged winter snowfall has decreased by about 4.6 cm/decade, with the greatest decreases in snowfall occurring in December and February. These results have important implications for the impacts of regional climate change on the northeastern United States hydrology, natural ecosystems, and economy.

Spuriously induced precipitation trends in the southeast United States

This study uses correlation and multiple regression techniques to document differences in annual and seasonal precipitation trends between the NCDC Climate Division database and the United States Historical Climate Network (USHCN) in the southeast United States. Findings indicate that the majority of climate divisions have different temporal patterns than those depicted by the USHCN. They did not, however, consistently possess statistically significant relationships between the ratio (CDD/USHCN) and changes in mean station location as noted in other studies. It appears that other influences cause the majority of the variance between the two datasets. The fact that the two datasets do not consistently agree, however, suggests that spuriously induced trends may be present in the NCDC Climate Division database.

Observer Bias in Daily Precipitation Measurements at United States Cooperative Network Stations

The Cooperative Observer Program (COOP), established over 100 years ago, has become the backbone of temperature and precipitation data that characterize means, trends, and extremes in U.S. climate. However, significant and widespread biases in the way COOP observers measure daily precipitation have been discovered. These include 1) underreporting of light precipitation events (daily totals of less than 0.05 in., or 1.27 mm), and 2) overreporting of daily precipitation amounts evenly divisible by five- and/or ten-hundredths of an inch, that is, 0.10, 0.25, 0.30 in., etc. (2.54, 6.35, 7.62 mm, etc.). Observer biases were found to be highly variable in space and time, which has serious implications for the spatial and temporal trends and variations of commonly used precipitation statistics. In addition, it was found that few COOP stations had sufficiently complete data to allow the calculation of stable precipitation statistics for a stochastic weather simulation model. Out of more than 12,000 COOP stations nationally, only 784 (6%) passed data completeness and observer bias screening tests for the climatological period 1971–2000. Of the 1221 COOP stations selected for the U.S. Historical Climate Network (USHCN), which provides much of the country's official data on climate trends and variability over the past century, only 221 stations (18%) passed these tests. More effective training materials and regular communication with COOP observers could reduce observer bias in the future. However, it is unlikely that observer bias can be eliminated. One solution is to automate the COOP precipitation measurement system, but this is an expensive option, and may increase other biases associated with automated precipitation measurement. Further analyses are needed to better quantify and characterize observer bias, and to develop methods for dealing with its effects.

Spuriously induced temperature trends in the Southeast United States

This study uses correlation and multiple regression techniques to document differences in annual temperature trends between the National Climatic Data Center (NCDC) Climate Division Database (CDD) and the United States Historical Climate Network (USHCN) for the Southeast United States. Results indicate that an increase (decrease) in elevation and a northward (southward) shift in mean station location in the CDD correspond with decreases (increases) in temperature. Although the movement of station locations in the CDD showed only modest impacts on trends, the effects of the movements are statistically significant, and explain some of the variances in the temperature trends. Results therefore suggest that climate divisions with more rugged terrain and greater shifts in elevation are more susceptible to spuriously generated trends.

The GeoProfile metadata, exposure of instruments, and measurement bias in climatic record revisited

Station metadata plays a critical role in the accurate assessment of climate data and eventually of climatic change, climate variability, and climate prediction. However, current procedures of metadata collection are insufficient for these purposes. This paper introduces the GeoProfile as a model for documenting and visualizing enhanced spatial metadata. In addition to traditional metadata archiving, GeoProfiles integrate meso-scale topography, slope, aspect, and land-use data from the vicinity of climate observing stations (http://kyclim.wku.edu/tmp/geoprofiles/geoprofiles main.html). We describe how GeoProfiles are created using Geographical Information Systems (GIS) and demonstrate how they may be used to help identify measurement bias in climate observations due to undesired instrument exposures and the subsequent forcings of micro- and meso-environments. A study involving 12 COOP and US Historical Climate Network (USHCN) stations finds that undesirable instrument exposures associated with both anthropogenic and natural influences resulted in biased measurement of temperature. Differences in average monthly maximum and minimum temperatures between proximate stations are as large as 1.6 and 3.8 °C, respectively. In addition, it is found that the difference in average extreme monthly minimum temperatures can be as high as 3.6 °C between nearby stations, largely owing to the differences in instrument exposures. Likewise, the difference in monthly extreme maximum temperatures between neighboring stations are as large as 2.4 °C. This investigation finds similar differences in the diurnal temperature range (DTR). GeoProfiles helped us to identify meso-scale forcing, e.g. instruments on a south-facing slope and topography, in addition to forcing of micro-scale setting. Copyright  2006 Royal Meteorological Society.

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 27:151-170 (2004) - doi:10.3354/cr027151 VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA T. G. F. Kittel1,2,5,*, N. A. Rosenbloom1, J. A. Royle1,6, C. Daly3, W. P. Gibson3, H. H. Fisher1, P. Thornton1, D. N. Yates1, S. Aulenbach1, C. Kaufman1, R. McKeown2, D. Bachelet4, D. S. Schimel1, VEMAP2 Participants§ 1National Center for Atmospheric Research, PO Box 3000, Boulder, Colorado 80307, USA2Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA3Spatial Climate Analysis Service and 4Department of Bioengineering, Oregon State University, Corvallis, Oregon 97331, USAPresent addresses: 5INSTAAR, Campus Box 450, University of Colorado, Boulder, Colorado 80309-0450, USA6USDI Fis and Wildlife Service, Laurel, Maryland 20708, USA§R. Neilson, J. Lenihan, R. Drapek (USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA); D. S. Ojima, W. J. Parton (Natural Resource Ecology Laboratory, Colorado University, Fort Collins, CO, USA); J. M. Melillo, D. W. Kicklighter, H. Tian (Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA, USA); A. D. McGuire (US Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, Fairbanks, AK, USA); M. T. Sykes, B. Smith, S. Cowling, T. Hickler (Physical Geography & Ecosystems Analysis, Lund University, Sweden); I. C. Prentice (Max Planck Institute for Biogeochemistry, Jena, Germany); S. Running (University of Montana, Missoula, MT, USA); K. A. Hibbard (College of Forestry, Oregon State University, Corvallis, OR, USA); W. M. Post, A. W. King (Oak Ridge National Laboratory, Oak Ridge, TN, USA); T. Smith, B. Rizzo (Dept. of Environmental Sciences, University of Virginia, Charlottesville, VA, USA); F. I. Woodward (Dept. of Animal and Plant Sciences, University of Sheffield, UK) *Email: kittel@colorado.edu ABSTRACT: Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5° latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. KEY WORDS: Climate dataset · Climate variability · Climate change · Ecosystem dynamics · Vegetation · Ecological modeling · VEMAP · Geostatistics · Palmer Drought Severity Index · PDSI Full article in pdf format PreviousExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 27, No. 2. Online publication date: October 07, 2004 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2004 Inter-Research.

Are there spurious temperature trends in the United States Climate Division database?

The United States (U.S.) Climate Division data set is commonly used in applied climatic studies in the United States. The divisional averages are calculated by including all available stations within a division at any given time. The averages are therefore vulnerable to shifts in average station location or elevation over time, which may introduce spurious trends within these data. This paper examines temperature trends within the 15 climate divisions of New England, comparing the NCDC's U.S. Divisional Data to the U.S. Historical Climate Network (USHCN) data. Correlation and multiple regression revealed that shifts in latitude, longitude, and elevation have affected the quality of the NCDC divisional data with respect to the USHCN. As a result, there may be issues with regard to their use in decadal‐ to century‐scale climate change studies.

Lunar influence on diurnal temperature range

While variations in the diurnal temperature range (DTR) have been linked to atmospheric enhancement of greenhouse gases, it is useful to identify all contributing factors potentially acting upon the DTR. In this study, an area‐weighted record of daily temperatures from the United States Historical Climate Network is used to examine the influence of lunar phase variations on the DTR. For the period from 1950 to 1995, a statistically significantly higher DTR occurs near the full moon (∼10.23°C) while a lower DTR occurs near the new moon (∼ 10.13°C). This synodic monthly difference appears to be primarily influenced by warmer maximum daily temperatures occurring near the full moon. This finding allows evaluation of two potential extraterrestrial factors influencing terrestrial temperatures as a function of lunar phase: a) the barycenter effect which is defined as variations in terrestrial and lunar position around their mutual gravitational centroid over the course of synodic month, and b) reflected solar/infrared emission off the lunar surface over the course of a synodic month. While both the barycenter effect and the reflected solar and infrared emission off the lunar surface would influence the minimum temperatures, we suggest that the barycenter effect is likely the dominating extraterrestrial mechanism in explaining lunar phase variations in the diurnal temperature range because its additional influence on daily maximum temperatures.

Comparison of temporal and unresolved spatial variability in multiyear time-averages of air temperature

When compiling climatological means of air temperature, station data usually are selected on the basis of whether they exist within a fixed base period (e.g. 1961 to 1990). Within such analyses, station records that do not contain sufficient data during the base period or only contain data from other base periods are excluded. If between-station variability is of interest (e.g. a map or gridded field is needed), then removing such stations assumes that spatial interpolation to the location of culled stations is more reliable than using a temporal mean from a shorter or different averaging period — the latter is a process that we call 'temporal substitution.' Data from the United States Historical Climate Network (HCN) are used to examine whether spatial interpolation or temporal substitution is more reliable for multiyear averages of monthly and annual mean air temperature. After exhaustively sam- pling all possible 5-, 10-, and 30-yr averaging periods from 1921 to 1994, spatially averaged inter- polation and substitution errors are estimated for all months and for annual averages. For all months, temporal substitution produces lower overall error than traditional spatial interpolation for both 10- and 30-yr averages. Maps of mean absolute error (for all averaging periods) show that spatial interpolation errors are largest in mountainous regions while temporal substitution errors are largest in the north- central and eastern USA, especially in winter. A spatial interpolation algorithm (topographically aided interpolation, TAI) that incorporates elevation data reduces interpolation error, but also produces larger errors than temporal substitution for all months when using 30-yr averages and for all months except January, February, and March when using 10-yr averages. For 5-yr averages, however, TAI produces lower errors than temporal substitution, especially in winter. For the USA, therefore, it is suggested that for averaging periods less than 10 yr in length, elevation-aided spatial interpolation is preferable to temporal substitution. Conversely, for averaging periods longer than 10 yr in length, temporal substitu- tion is preferable to spatial interpolation. Analysis of the 1961 to 1990 period using a wide range of network densities demonstrates that temporal substitution generally is more reliable than spatial inter- polation of 30-yr averages, regardless of network density.

Urban Bias in Area-averaged Surface Air Temperature Trends

A data set derived from the United States Historical Climate Network has been compared to two global land-based temperature data sets that have been commonly cited in connection with the detection of the greenhouse effect and in other studies of climate change. Results indicate that in the United States the two global land-based temperature data sets have an urban bias between + 0.1°C and +0.4°C over the twentieth century (1901–84). This bias is as large or larger than the overall temperature trend in the United States during this time period, +0.16°C/84 yr. Temperature trends indicate an increasing temperature from the turn of the century to the 1930s but a decrease thereafter. By comparison, the global temperature trends during the same period are between +0.4°C/84 yr and +0.6°C/84 yr. At this time, we can only speculate on the magnitude of the urban bias in the global land-based data sets for other parts of the globe, but the magnitude of the bias in the United States compared to the overall temperature trend underscores the need for a thorough global study.