United States Historical Climatology Network Research Articles

Observed Daily Temperature Variability and Extremes over Southeastern USA (1978–2017)

This study presents an analysis of extreme temperature events over southeastern USA from 1978 to 2017. This region is part of the so-called ‘warming hole’ where long-term surface temperature trends are negative or non-significant, in contrast with the remainder of the country. This study examines whether this distinctive characteristic reflects on the region’s trends in temperature extremes. Daily maximum and minimum temperatures from the US Historical Climatology Network were used to compute extreme indices recommended by the Expert Team on Climate Change Detection and Indices. Temperature extreme indices computed for all stations using the RClimDex package were gridded onto a regular latitude–longitude grid, and a spatiotemporal analysis of associated trends was performed. The results point to a tendency toward warming due to increasing trends in the annual occurrence of the hottest day, the warmest night, warm days, warm nights, summer days, tropical nights, and warm spells, as well as decreases in cool nights, cool days, and frost days. Statistically significant trend changes over large portions of the Southeast were dominated by increases in the frequency of the coldest night, summer days, and warm nights, and decreases in cool nights and frost days. Comparison of our results with other global and regional studies indicate that most of the extreme temperature changes over the Southeast are consistent with findings from other parts of the United States (US) and the world. Overall, this study shows that being part of the ‘warming hole’ does not preclude southeastern US from an intensification of temperature extremes, whether it is an increase in warm extremes or a decrease in cold ones. Further, the results suggest that, should the current trends continue in the long term, the Southeast will not be considered as being part of a warming hole anymore.

Spatiotemporal Analysis of Extreme Precipitation in the Missouri River Basin from 1950 – 2019

AbstractExtreme precipitation events are challenging to local and regional stakeholders across the United States. The Missouri River Basin (MoRB), covering an area over 1.29 million km2, is prone to extreme precipitation events. These events are exacerbated by the complex terrain in the west and the numerous weather and climate features which impact the region on a seasonal/annual basis (low-level jets, mesoscale convective systems, extreme cold air intrusions, etc.). Without an in-depth analysis of extreme precipitation in the MoRB, the evolving nature of extreme precipitation is not known. This warrants an analysis of extreme precipitation, especially relating to sub-annual variations when extreme precipitation is more impactful. To this end, data from 131 United States Historical Climatology Network (USHCN) stations were used to determine the nature of extreme precipitation from 1950 – 2019. Annual 99th percentile and annual station maximum precipitation events occur more frequently in the eastern MoRB than in the western MoRB, in line with the annual precipitation climatology. Results show that 99th percentile events and annual station maximum precipitation events are becoming more frequent across the MoRB. Through analysis of 3-month extreme precipitation trends, areas in the eastern and southern MoRB are shown to have an increasing event frequency and intensity. Frequency shifts in the 99th percentile events, however, have occurred across the entire region. The increasing frequency of extreme events in the western MoRB represent a significant change for the hydroclimate of the region. Overall, the results from this work show that MORB extreme precipitation has increased in frequency and intensity during the 1950 – 2019 period.

Weather, climate, politics, or God? Determinants of American public opinions toward global warming

ABSTRACT2012 American National Election Study data supplemented with monthly temperature data from the United States Historical Climatology Network are used to examine how religion, politics, and weather and climate affect views of global warming. Evangelical fundamentalism is the most consistent factor among all religion-related variables in determining perceptions of global warming, those more oriented toward evangelical fundamentalism being less likely than others to believe in the existence, anthropogenic cause, and adverse impact of global warming. Controlling for the effects of traditional political predisposition variables, including party identification and political ideology, support for the Tea Party plays a dominant role in determining views of global warming; support for the Tea Party leading to higher likelihood of disbelief in the existence, anthropogenic cause, and negative impact of global warming. Warming winters coupled with cooling springs of the past decade are positively related with belief in the existence of global warming.

Spatial and temporal variations in the accuracy of meteorological drought indices

Meteorological drought indices are commonly calculated using data from weather stations and then interpolated to create a map of moisture conditions. These maps are used to communicate drought information to decision makers and the general public. This study analyzes five of the factors (drought index, interpolation method, seasonality, climate region, and station density) that influence the accuracy of these maps. This study compared the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) using data from the Cooperative Observer Network (COOP) and United States Historical Climatology Network (USHCN). The accuracy of the drought maps varied significantly over time and space. The most significant factor affecting the accuracy of the meteorological drought maps was seasonality. Errors were higher in regions (e.g., southeastern U.S.), and months (e.g., summer), dominated by convective precipitation. The choice of interpolation method also had an influence. We found that Ordinary Kriging (OK) performed better than Inverse Distance Weighting (IDW) in all cases and therefore it was recommended for interpolating drought indices. Not surprisingly, maps that were created using more stations (COOP) were more accurate. The normalized errors of SPI and SPEI were very similar and so the choice of drought index had little impact on the accuracy of the drought maps.

Historical trends in precipitation, temperature and drought in the Alabama–Coosa–Tallapoosa and Apalachicola–Chattahoochee–Flint river basins

ABSTRACTUsing station data from the United States Historical Climatology Network, we preformed a running trend analysis of temperature, precipitation and drought in the Alabama–Coosa–Tallapoosa and Apalachicola–Chattahoochee–Flint river basins for the time period 1895–2012 on annual and monthly timescales.Our study found that long‐term precipitation and temperature trends were statistically detectible but relatively slight in the order of an increase at 3 mm per decade for precipitation and a decline of 0.02 °C per decade for temperature. Running trend analyses for the time period 1895–2012 found field‐significant and relatively large annual precipitation increases and temperature decreases between the 1950s and early 1980s associated with the 'warming hole'. The magnitude of precipitation trends for the aforementioned period was in the range of 7 mm per year for precipitation and 0.6 °C per decade for temperature. For temperature, we observed generally decreasing maximum, minimum and mean temperatures in the 1960s and 1970s and increasing temperatures from the 1970s to present. Minimum temperatures particularly showed a strong increase in recent summer months in the range of 0.3 °C per decade.Trends in the diurnal temperature range showed a recent narrowing during summer and fall months. Differences in the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) did not show a strong effect of temperature on drought for this region.

Climate change vulnerability assessment in Georgia

Climate change is occurring in the Southeastern United States, and one manifestation is changes in frequency and intensity of extreme events. A vulnerability assessment is performed in the state of Georgia (United States) at the county level from 1975 to 2012 in decadal increments. Climate change vulnerability is typically measured as a function of exposure to physical phenomena (e.g., droughts, floods), sensitivity to factors affecting the social milieu, and the capacity of a given unit to adapt to changing physical conditions. The paper builds on previous assessments and offers a unique approach to vulnerability analyses by combining climatic, social, land cover, and hydrological components together into a unified vulnerability assessment, which captures both long-term and hydroclimatic events. Climate change vulnerability indices are derived for the 1980s, 1990s, 2000s, and 2010s. Climate change exposure is measured as: 1) departure of decadal mean temperature and precipitation from baseline temperature and precipitation (1971–2000) using the United States Historical Climatology Network version 2.5 and 2) extreme hydroclimatic hazards indicated by flood, heat wave and drought events. Sensitivity and adaptive capacity are measured by well-established conceptualizations and methods built derived from socioeconomic variables. Impervious surface and flood susceptibility area are also incorporated to account for place-based vulnerability.Anomalies in temperature and precipitation with an overall trend towards drying and warming have been observed. The anomalous cooling period in Georgia during the 1970–1980 period as well as the post-1980 warm-up have been captured with a clearly established increase in extreme hydroclimatic events in recent decades. Climate vulnerability is highest in some metropolitan Atlanta and coastal counties. However, the southwestern region of Georgia, and part of the rural Black belt region are found to be especially vulnerable to climate change.

Six temperature and precipitation regimes of the contiguous United States between 1895 and 2010: a statistical inference study

This paper describes six different temporal climate regimes of the contiguous United States (CONUS) according to interdecadal variations of surface air temperature (SAT) and precipitation using the United States Historical Climatology Network (USHCN) monthly data (Tmax, Tmin, Tmean, and precipitation) from 1895 to 2010. Our analysis is based on the probability distribution, mean, standard deviation, skewness, kurtosis, Kolmogorov-Smirnov (KS) test, and Welch’s t test. The relevant statistical parameters are computed from gridded monthly SAT and precipitation data. SAT variations lead to classification of four regimes: 1895–1930 (cool), 1931–1960 (warm), 1961–1985 (cool), and 1986–2010 (warm), while precipitation variations lead to a classification of two regimes: 1895–1975 (dry) and 1976–2010 (wet). The KS test shows that any two regimes of the above six are statistically significantly different from each other due to clear shifts of the probability density functions. Extremes of SAT and precipitation identify the ten hottest, coldest, driest, and wettest years. Welch’s t test is used to discern significant differences among these extremes. The spatial patterns of the six climate regimes and some years of extreme climate are analyzed. Although the recent two decades are the warmest among the other decades since 1895 and many hottest years measured by CONUS Tmin and Tmean are in these two decades, the hottest year according to the CONUS Tmax anomalies is 1934 (1.37 °C), which is very close to the second Tmax hottest year 2006 (1.35 °C).

Temperature variability and early clustering of record-breaking events

As the number of climatological studies using record-breaking statistics is growing rapidly, understanding the sensitivity of the chosen time period becomes essential. To that end, here we examine the evolving variability of monthly mean temperatures and its dependence on beginning and final year. Specifically, we use an index, α, based on record-breaking statistics and employing reversibility such that 〈α〉=0 indicates no trend in variability. Generally, 〈α〉 has decreased between 1900 and 2013, indicating decreasing variability relative to early decades for stations from the contiguous USA (United States Historical Climatology Network, version 2.5). We find, somewhat surprisingly, that the observed decrease is due to an early excess of records beginning in 1917 (record low value) and 1921 (record high value). While detailed results depend on whether the data is gridded, detrended, etc., the general finding appears remarkably robust and holds globally as well.

Are actual weather and perceived weather the same? Understanding perceptions of local weather and their effects on risk perceptions of global warming

I use one nationally representative sample from CBS News/New York Times Poll on Environment (2007), supplemented with monthly temperature and precipitation data from the United States Historical Climatology Network and extreme weather events data from National Climatic Data Center, to examine how different weather measures and individuals’ socio-demographic background, political predisposition, and beliefs about global warming affect the perception of local weather. Beliefs about global warming are found to play a dominant role in determining the perception of local weather. Specifically, people who view that global warming is causing an immediate impact, is serious, and needs to be a priority are much more likely to perceive a strange pattern of weather in the recent past. This finding conforms to motivated reasoning which refers to the tendency to interpret evidence to confirm the preexisting beliefs and predisposition. Among all the weather measures, the total number of extreme weather events in the past three years appears to be the only significant indicator of perceived weather. Specifically, individuals who have experienced more extreme weather events in their home counties are more likely than those elsewhere to perceive the weather is stranger than usual. I further examine how actual weather and perceived weather together affect perceptions of global warming. The results illustrate that the perception of local weather has stronger explanatory power than actual weather. People who perceive the strange weather patterns are much more likely than others to see the immediate impacts and priority of global warming. Future studies are needed to investigate the dynamic relationship between perceptions of global warming and perceptions of weather and climate. In addition, more studies are needed to further examine how the public forms their opinions toward various specific types of the local weather and climate.

How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana

The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings.

A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change

By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA’s Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements model-based predictions. Second, our results show that subtle differences in the character of thermal forcing can substantially alter the timing of leaf emergence and canopy development. By explicitly comparing and contrasting the timing of thermal forcing and leaf phenology in 2010 and 2012, we show that even though temperatures were warmer in 2012 than in 2010, the nature and timing of thermal forcing in 2010 lead to leaf emergence that was almost a week earlier than 2012.

Regional trends and local variability in monsoon precipitation in the northern Chihuahuan Desert, USA

In the southwestern United States, monsoon precipitation can affect changes to the land surface, vegetation communities and ecosystem services. To better understand monsoon precipitation, we quantified change in precipitation properties from 1910 to 2010 at 22 sites across the northern Chihuahuan Desert using United States Historical Climatology Network daily data. We also assessed precipitation variability at the Sevilleta National Wildlife Refuge (SNWR) – located at the desert's ecological boundary – using daily data from a recent 10-year period. Evaluating precipitation at these locations allows for comparison of precipitation variability between ecologically-stable ecoregions and the less-stable boundaries where ecological change may be more likely to occur. Regional data from 1910 to 2010 show an increase in the number of precipitation events, a decrease in their magnitude, and also an increase in the length of extreme periods with and without precipitation. At the SNWR, total precipitation is influenced by a small number of large events, while the majority of events (65%) have an insignificant effect. These analyses suggest that local variability in precipitation may be greater than is often attributed to the summer monsoon, and the difference between wet and dry monsoons depends on the occurrence of a small number of large events.

A spatiotemporal analysis of U.S. station temperature trends over the last century

This study presents a nonlinear spatiotemporal analysis of 1167 station temperature records from the United States Historical Climatology Network covering the period from 1898 through 2008. We use the empirical mode decomposition method to extract the generally nonlinear trends of each station. The statistical significance of each trend is assessed against three null models of the background climate variability, represented by stochastic processes of increasing temporal correlation length. We find strong evidence that more than 50% of all stations experienced a significant trend over the last century with respect to all three null models. A spatiotemporal analysis reveals a significant cooling trend in the South‐East and significant warming trends in the rest of the contiguous U.S. It also shows that the warming trend appears to have migrated equatorward. This shows the complex spatiotemporal evolution of climate change at local scales.

Trends in precipitation and temperature in Florida, USA

Summary Annual, seasonal, and monthly trends in precipitation, mean temperature, maximum temperature, minimum temperature, and temperature range were evaluated using stations from the United States Historical Climatology Network (USHCN) for the time periods 1895–2009 and 1970–2009 for the state of Florida. The significance and magnitude of station trends were determined using the non-parametric Mann–Kendall test and Sen’s slope, respectively. The collective, field significance of trends were evaluated using a Monte Carlo permutation procedure. Field significant trends in seasonal precipitation were found in only the June–August and March–May seasons for the 1895–2009 and 1970–2009 time periods, respectively. Significant decreasing trends in monthly precipitation were found in the months of October and May for the 1895–2009 and 1970–2009 time periods, respectively. Field significant trends were found for all temperature variables for both time periods, with the largest number of stations with significant trends occurring in the summer and autumn months. Trends in mean, maximum, and minimum temperature were generally positive with a higher proportion of positive trends in the 1970–2009 period. The spatial coherence of trends in temperature range was generally less compared to other temperature variables, with a larger proportion of stations showing negative trends in the summer and positive trends at other times of the year and more negative trends found in the 1970–2009 period. Significant differences in temperature trends based on the surrounding land use were found for minimum temperature and temperature range in the 1970–2009 period indicating that data homogenization of the USHCN temperature data did not fully remove this influence. The evaluation of trends based on station exposure ratings shows significant differences in temperature variables in both the 1895–2009 and 1970–2009 time periods. Systematic changes in trends can be seen in the 1980s, the period of widespread conversion from liquid-in-glass to electronic measurement, indicating that some of the differences found may be due to uncorrected inhomogeneities. Since notable differences were found between differently rated stations pre-1940, a time which the present-day rating should have little to no influence, attribution of differences based on station rating should be done with caution.

Evaluation of NARR and CLM3.5 outputs for surface water and energy budgets in the Mississippi River Basin

[1] The North American Regional Reanalysis (NARR) and Community Land Model (CLM, version 3.5) outputs are analyzed to characterize the surface water and energy budgets in the Mississippi River Basin (MRB). NARR and CLM performance are evaluated with reference to energy flux observations from 16 AmeriFlux sites in MRB. The issue of point-scale observations versus climate model grid cell outputs is addressed by analyzing the spatial variability in long-term monthly precipitation and temperature observations from 71 United States Historical Climatology Network stations in Indiana and Illinois. The model outputs are also evaluated for their ability to capture spatial and temporal variability in total runoff. Compared to average values at 11 AmeriFlux sites in MRB, NARR show higher biases (compared to CLM) in incoming solar radiation (24%), sensible heat flux (27%), and latent heat flux (59%), whereas CLM show smaller biases (compared to NARR) in incoming solar radiation (0.5%), sensible heat flux (−2%), and latent heat flux (11%). Seasonal cycle of observed sensible heat flux in the crop region shows two peaks (bimodal pattern), which is captured by NARR, but CLM do not show any bimodal pattern. Based on 25 years (1980–2004) monthly climatology in MRB, NARR has 11% energy balance closing error (latent + sensible + ground heat flux = 1.11 net radiation) and 12% water balance closing error (evapotranspiration + runoff = 1.12 precipitation), whereas CLM does not have water and energy balance closing errors, primarily due to model design. In comparison to the observed mean annual runoff of 237 mm/yr based on 1988–1999 data in MRB, NARR and CLM mean annual runoff values are 89 mm/yr and 281 mm/yr, respectively. Overall, CLM provides relatively better characterization of surface water and energy fluxes in the MRB compared to NARR.

A spatial entropy analysis of temperature trends in the United States

[1] The United States Historical Climatology Network (USHCN) temperature database shows a significant upward trend over the past half century. In this investigation, we calculate the spatial entropy (dissimilarity or disorder) associated with the temperature trends of 1,221 stations in the fully adjusted USHCN. We find that over the network, the spatial entropy levels are significantly and positively related to the observed temperature trends suggesting that stations most unlike their neighbors in terms of temperature change tend to have a higher temperature trend than their neighbors. These results suggest that the USHCN contains some questionable warming signals at some stations, despite the many attempts to quantitatively control for these contaminants.

Analysis of adjustments to the United States Historical Climatology Network (USHCN) temperature database

[1] The United States Historical Climatology Network (USHCN) temperature database is commonly used in regional climate analyses. However, the raw temperature records in the USHCN are adjusted substantially to account for a variety of potential contaminants to the dataset. We compare the USHCN data with several surface and upper-air datasets and show that the effects of the various USHCN adjustments produce a significantly more positive, and likely spurious, trend in the USHCN data.

Spatial grouping of United States climate stations using a hybrid clustering approach

AbstractThe development of a hybrid clustering technique based on the geographic proximity of observing stations and some application‐driven measure of statistical similarity (in this case rank correlation) is described. The procedure is then applied to temperature and precipitation data from the United States (US) Historical Climatology Network. The resulting station groups provide some insight into the number of observation stations that are necessary to monitor adequately the climate of the US.Based on temperature data alone, a 287‐station subset of the original 1145 sites would be adequate to account for 80% of the spatial variability in seasonal temperature across the US. Geographically the distribution of these stations would be relatively sparse in the centre of the country with higher station density along the East Coast and from the Rocky Mountains to the West Coast. Generally, the temperature clusters match the existing US climate divisions to some extent. To monitor adequately the spatial variability of precipitation, a network of similar size could be used. However, such a network would only account for 65% of the spatial variability in precipitation. In this case, fairly uniform station density is indicated across the country with the highest station density in Florida and the Dakotas. A similar number of stations, but with slightly different geographic groupings would be adequate to monitor precipitation and temperature simultaneously. Copyright © 2001 Royal Meteorological Society

Updated Population Metadata for United States Historical Climatology Network Stations

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 m...

The Effects of Data Gaps on the Calculated Monthly Mean Maximum and Minimum Temperatures in the Continental United States: A Spatial and Temporal Study

Abstract Gaps in otherwise regularly scheduled observations are often referred to as missing data. This paper explores the spatial and temporal impacts that data gaps in the recorded daily maximum and minimum temperatures have on the calculated monthly mean maximum and minimum temperatures. For this analysis 138 climate stations from the United States Historical Climatology Network Daily Temperature and Precipitation Data set were selected. The selected stations had no missing maximum or minimum temperature values during the period 1951–80. The monthly mean maximum and minimum temperatures were calculated for each station for each month. For each month 1–10 consecutive days of data from each station were randomly removed. This was performed 30 times for each simulated gap period. The spatial and temporal impact of the 1–10-day data gaps were compared. The influence of data gaps is most pronounced in the continental regions during the winter and least pronounced in the southeast during the summer. In the n...