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Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States.

The impacts of inland flooding caused by tropical cyclones (TCs), including loss of life, infrastructure disruption, and alteration of natural landscapes, have increased over recent decades. While these impacts are well documented, changes in TC precipitation extremes—the proximate cause of such inland flooding—have been more difficult to detect. Here, we present a latewood tree-ring–based record of seasonal (June 1 through October 15) TC precipitation sums (ΣTCP) from the region in North America that receives the most ΣTCP: coastal North and South Carolina. Our 319-y-long ΣTCP reconstruction reveals that ΣTCP extremes (≥0.95 quantile) have increased by 2 to 4 mm/decade since 1700 CE, with most of the increase occurring in the last 60 y. Consistent with the hypothesis that TCs are moving slower under anthropogenic climate change, we show that seasonal ΣTCP along the US East Coast are positively related to seasonal average TC duration and TC translation speed.

In recent decades, South Asia has experienced declining air quality, with much of the attention being focused on extremely high levels of particulate matter. Here, we analyze tropospheric ozone (O3), formaldehyde (HCHO), and nitrogen dioxide (NO2) to assess other measures of air quality across South Asia from 2008 to 2018. The IASI-Forli retrieved tropospheric ozone data was validated with ozonesonde, reanalysis (ERA5), satellite (TES), and model simulation products (GEOS-Chem and TOMCAT/SLIMCAT). Space-based observations of these three trace gases were used to conduct a spatio temporal analysis over South Asia using trend analysis (Theil-Sen and linear regression), change-point detection (Pettitt's test), and hotspot identification (Getis-Ord Gi*). We used the formaldehyde-nitrogen dioxide ratio (FNR) to identify NOx limited, VOC limited, and transitional regimes in South Asia. Counter to previous studies, a statistically significant decrease of HCHO (-0.0041 DU yr-1) and O3 (-0.064 DU yr-1) was detected for South Asia; however, NO2 is increasing the 0.001 DU yr-1 over South Asia during 2008-18. The Indo-Gangetic Plains emerged as being critically affected by the three trace gases. Certain parts of southern and south-eastern India are gradually emerging as NO2 and HCHO hotpots. No significant O3 hotspots were discernible, though coldspots existed along the Himalaya belt of India, Nepal, and Bhutan and mountainous tracts of Pakistan. FNR indicates the reduction of NOx in NOx-limited regime of the Indo-Gangetic Plains reduced the formation of tropospheric O3 over South Asia.

Tree rings are widely considered to be a reliable proxy record of variations in climate and soil moisture. Here, using data from the Midwest United States (US), we provide documentation of a deteriorating relationship between radial tree growth and drought that is consistent across multiple species and locations. We find that traditional methods for drought reconstructions produce models that have rapidly declining validation statistics in recent decades. Split-sample calibration-verification that uses the first and second halves of the record can be problematic, as those two samples may not represent a sufficiently wide range of soil moisture conditions. To investigate this problem, we develop a randomized validation procedure that generates an empirical distribution of calibration and validation statistics. We place validation statistics derived from traditional methods in the generated distribution and compare them to a stratified approach that ensures each calibration model is composed of a sample that includes both dry and wet years. We find that the deteriorating relationship between tree growth and soil moisture is an artifact of the absence of drought over an extended period of time. A model that forces each calibration period to contain extreme drought years is statistically validated.. Nonetheless, if the current pluvial continues in the Midwest US, the linear relationship between tree rings and soil moisture will likely continue to deteriorate to the point where tree rings in the region will have a reduced ability to estimate past drought conditions.

Regional and local climate extremes, and their impacts, result from the multifaceted interplay between large-scale climate forcing, local environmental factors (physiography), and societal vulnerability. In this paper, we review historical and projected changes in temperature and precipitation extremes in the United States, with a focus on strengths and weaknesses of (1) commonly used definitions for extremes such as thresholds and percentiles, (2) statistical approaches to quantifying changes in extremes, such as extreme value theory, and (3) methods for post-processing (downscaling) global climate models (GCMs) to investigate regional and local climate. We additionally derive regional and local estimates of changes in temperature extremes by applying a quantile mapping approach to high-resolution gridded daily temperature data for 6 U.S. sub-regions. Consistent with the background warming in the parent GCMs, we project decreases in regional and local cold extremes and increases in regional and local warm extremes throughout the domain, but the downscaling approach removes bias and produces substantial spatial variability within the relatively small sub-regions. We finish with recommendations for future research on regional climate extremes, suggesting that focus be placed on improving understanding of extremes in the context of large-scale circulation and evaluating the corresponding cascade of scale interactions within GCMs.

Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

Spatially weighted averages of Palmer Drought Severity Index (PDSI) over central and southern California show that the 1 year 2014 drought was not as severe as previously reported, but it still is the most severe in the 1895–2014 instrumental record. Using the typical adjustment procedure that matches the mean and standard deviation of tree ring PDSI values to those of instrumental data shows over 10 droughts from 800 to 2006 that were more severe than the 1 year 2014 drought, with the 2014 drought having a return period of 140–180 years. Quantile mapping allows for a closer correspondence between instrumental and tree ring PDSI probability distributions and produces return periods of 700–900 years for the 1 year 2014 drought. Associated cumulative 3 and 4 year droughts, however, are estimated to be much more severe. The 2012–2014 drought is nearly a 10,000 year event, while the 2012–2015 drought has an almost incalculable return period and is completely without precedent.

Using a spatial percentile approach, we explore the magnitude of temperature anomalies across the Northern and Southern Hemispheres. Linear trends in spatial percentile series are estimated for 1881–2013, the most recent 30 year period (1984–2013), and 1998–2013. All spatial percentiles in both hemispheres show increases from 1881 to 2013, but warming occurred unevenly via modification of cold anomalies, producing a reduction in spatial dispersion. In the most recent 30 year period, trends also were consistently positive, with warm anomalies having much larger warming rates than those of cold anomalies in both hemispheres. This recent trend has largely reversed the decrease in spatial dispersion that occurred during the twentieth century. While the period associated with the recent slowdown of global warming, 1998–2013, is too brief to estimate trends reliably, cooling was evident in NH warm and cold anomalies during January and February while other months in the NH continued to warm.

This study estimated the trends in the number of days that fall within the near-zero (0 °C) range of the temperature continuum. This narrow range has importance for potential transportation hazards and freeze-thaw cycles. While the tails of the air-temperature distribution and their trends often are closely examined under the climate change context, the frequency and trend of other portions of the air-temperature distribution can be equally important, as many societal impacts are caused by events in the non-tail region, such as near-zero °C temperatures (NZT). Examining the trend of the number of NZT days over the conterminous USA for the period of 1948–1949 through 2010–2011, we found three distinct spatial clusters. The most distinctive spatial clusters are found along the West Coast (positive temperature trends leading to a decrease in NZT days), the High Plains and Northern Rockies (positive temperature trends leading to an increase in NZT days), and the southeastern USA (negative temperature trends l...

Time trends in annual land-surface precipitation during the twentieth and early twenty-first centuries and their spatial patterns are estimated from gridded (at a 0.5° × 0.5° spatial resolution) ra...

A new hybrid empirical downscaling technique is presented and applied to assess 21st century projections of maximum and minimum daily surface air temperatures (Tmax, Tmin) over the Midwestern USA. Our approach uses multiple linear regression to downscale the seasonal variations of the mean and standard deviation of daily Tmax and Tmin and the lag-0 and lag-1 correlations between daily Tmax and Tmin based on GCM simulation of the large-scale climate. These downscaled parameters are then used as inputs to a stochastic weather generator to produce time series of the daily Tmax and Tmin at 26 surface stations, in three time periods (1990–2001, 2020–2029, and 2050–2059) based on output from two coupled GCMs (HadCM3 and CGCM2). The new technique is demonstrated to exhibit better agreement with surface observations than a transfer-function approach, particularly with respect to temperature variability. Relative to 1990–2001 values, downscaled temperature projections for 2020–2029 indicate increases that range (across stations) from 0.0 K to 1.7 K (Tmax) and 0.0 K to 1.5 K (Tmin), while increases for 2050–2059 relative to 1990–2001 range from 1.4 K to 2.4 K (Tmax) and 0.8 to 2.2K (Tmin). Although the differences between GCMs demonstrate the continuing uncertainty of GCM-based regional climate downscaling, the inclusion of weather-generator parameters represents an advancement in downscaling methodology. Copyright © 2006 Royal Meteorological Society

A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

In climatology, spatial representativeness can be measured as the degree to which an instrumental temperature record resolves the climatic variability across an area. Some station records may disproportionately resolve characteristics of their immediate surroundings and, therefore, have less utility in representing climate (or weather) over larger spatial scales. To evaluate spatial representativeness of historical air-temperature records, a modified geostatistical procedure was developed. The procedure measures the spatial representativeness of a station based on variogram parameter estimates—in this case the nugget. This application is novel for two reasons: (1) variogram models are fit to station-specific or "point-centered" semivariance and (2) variogram models are fit to semivariance computed at intervals in a time series. Fitting variograms to point-centered semivariance may be used to create time series of nuggets for each station in a network. Nugget time series may show subsets of a time series t...

Using daily minimum air-temperature (Tmin) data from the state of Illinois, the dates of spring and fall freezes – and the resulting growing-season length – are examined for trends during theperiod 1906–1997. Of the stations in the Daily Historical Climate Network, mostshow trends toward earlier spring freezes; however, trends in fall freezes are not consistent over the station network. Although the time series are highly variable (noisy), results suggest that the growing-season length in Illinois became roughly one week longer during the 20thcentury. To examine how changing freeze-date statistics relate to changing air-temperature probability distributions, percentiles of Tmin formoving 10-year periods were analyzed for trends during the typical times for spring and fall freezes in Illinois (i.e., the months of April and October). The lower portion of the April probability distribution shows substantially larger warming (0.5–0.7 ° C/100 yrs) than the upper portion of the distribution (0.2–0.3 ° C/100 yrs), suggesting that although cold events are warming during April, warm events are not warming as fast. Conversely, the lower portion of the October probability distribution shows modest cooling in Tmin (–0.2 ° C/100yrs for the 10th percentile), while middle and upper portions of the distribution show very large rates of cooling (up to –1.5 ° C/100 yrs for the 40th–70th percentiles). Analysis ofthe entire probability distribution provides a more-comprehensive perspective on climatic change than does the traditional focus on central tendency.

Using data from the contiguous USA, the relationship between mean and standard devi- ation of daily air temperature was estimated on a monthly timescale from 1948 to 1997. In general, there is either an inverse relationship or a weak relationship between mean and standard deviation. For both daily maximum and daily minimum air temperature, the inverse relationship is spatially coherent for one-third to two-thirds of the contiguous USA for most months. The inverse relationship also is fairly strong, with typical reductions in standard deviation ranging from 0.2 to 0.5°C for every 1°C increase in mean temperature. A weaker, direct relationship between mean and standard devi- ation occurs in some northern states, primarily during spring and fall months. Using the predominant inverse and weak relationships as historical analogs for future climatic change suggests that interdi- urnal variability of air temperature should either decrease or remain unchanged under warming con- ditions. Although the variability of air temperature may decrease or remain unchanged at most loca- tions in the contiguous USA, the probability of extremely high air temperatures should still increase, depending on the magnitude of changes in mean air temperature and the nature of the variance response.

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.

Hourly wind data from a network of climate stations in the north-central United States (drawn from the states of Illinois, lowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota, and Wisconsin) are analyzed to evaluate the efficacy of spatial analyses of near-surface wind speed and power. Spatial autocorrelation functions (acfs) were calculated at a number of timescales: annual, monthly, daily, and hourly. Annual wind speeds have virtually no coherent distance-decay relationship; monthly data produce a more consistent relationship, but still exhibit a large amount of scatter. Both daily and hourly data have classical decay with increasing distance between stations and there appears to be an optimal level of temporal aggregation, near the daily timescale, for spatial analysis of wind. In general, however, spatial acfs overestimate the spatial coherence of both wind speed and power. Temporal nonstationarities in wind data (i.e., diurnal and annual cycles) bias spatial autocorrelation functi...

Spatial and temporal sampling errors inherent in large-scale, weather-station (raingauge) climatologies of precipitation are evaluated. A primary goal is to assess whether more representative large-scale precipitation climatologies emerge when (i) more station means are included, even when they are based on unequal periods of record, or (ii) fewer station means are included but all are derived from the same period of record. Observations drawn from the Historical Climatology Network (HCN) are used to estimate temporally averaged precipitation over lo-, 20-, and 30-year intervals at 457 stations within the USA. Two strategies for estimating these ‘observed’ means are examined, one based on temporal ‘substitution’ within each station record, and the other based on spatial interpolation from surrounding stations. Temporally estimated m-year means were obtained by substituting other m-year means, from within the same station record, for each ‘observed’ m-year mean, where m is the length of the averaging period of interest. Spatially interpolated m-year means were estimated from m-year means associated with nearby stations. Climatologies containing a greater number of station averages, even if they are computed over unequal averaging periods, appear to better represent the space-time variability in mean precipitation than climatologies containing fewer, but tem orall indicate that the within-station-record substitution of means is about 1.3 to 2.5 times more accurate than is interpolation from surrounding station means. Within-station substitution errors-associated with estimating any 10-year mean precipitation from any other 10-year mean-for example, were about 8 per cent of the long-term spatial precipitation mean for the USA, or 67.6 mm. Spatially interpolated 10-year means, from nearby stations, were in error by more than 10 per cent, or 88.8 mm on average. Much of the space-time variability in mean precipitation was not resolved adequately by the 457 HCN stations, especially high-frequency spatial variability caused by orographic and convective mechanisms. For many regions of the world, temporally homogeneous precipitation station networks are considerably more sparse than in the USA, further degrading the reliability of interpolated and spatially integrated mean precipitation fields derived solely from those networks.

Networks of near-surface climate stations often produce samples of air temperature that are spatially uneven and sparse. Spatial samples of air temperature, as a result, affect both the regional depiction of air temperature anomalies and large-scale spatial averages. A resampling procedure is used here to illustrate the general problem of how spatially variable station networks can influence estimates of climate change. As an example, the warm air temperature anomalies that occurred during 1988 are resampled using station networks from other years. During 1988, areas in North America and northern Asia experienced very warm conditions, producing a terrestrially averaged air temperature anomaly of over 0.4{degrees}C. If the 1988 air temperature anomalies were resampled using the 1981 station network, the warm event in northern Asia would not have been detected due to missing air temperatures over northern Russian during 1981. As a result, the 1981 station network would have estimated the 1988 terrestrial air temperature anomaly to be 0.25{degrees}C. Alternatively, the station network of 1988 was biased toward areas that had warm 1988 air temperatures and would estimate the 1988 air temperature anomaly to be 0.52{degrees}C. In addition to problems in estimating the terrestrially averaged air temperature anomaly, regional climatic variability ismore » altered by resampling the 1988 anomalies with other networks. The sampling problems illustrated here also show the importance of having free and open exchanges of data worldwide. National networks of climatic stations are crucially important not only in the analysis of regional climatic variability, but also in the detection of global-scale climatic change.« less

Uneven and changing spatial distributions of air temperature stations can produce unrepresentative samples of the space-time variability of near-surface air temperature. Over the last century, station networks have varied from less than 300 stations that are largely limited to the Northern Hemisphere to nearly 1700 stations that are fairly well distributed over the terrestrial surface. As a result, estimates of air temperature change derived from historical observation networks often contain network-induced variability. Spatial resampling methods are used to estimate network-induced variability in terrestrially averaged air temperature anomalies and trends. Random resampling from the station networks allows pseudo confidence intervals and other statistics of variability to be estimated. Network-induced variability appears to be substantial during the late 1800s, especially in the Southern Hemisphere. Most networks from the 1900s and the Northern Hemisphere, however, appear to produce reliable estimates of spatially averaged air temperature anomalies and trends. A non-random, but historically appropriate, resampling method - sampling a given year's air temperature anomaly field using another year's station distribution - also is used. Using 1987 - one of the warmest years on record - as an example, station distributions from 1881-1988 are used to sample the 1987 air temperature anomaly field. This sampling procedure produces spatially averaged air temperature anomalies for 1987 that vary by over 0.3°C. A combinatorial process of resampling a given year's anomaly field is repeated for every year and every station network to produce terrestrial average air temperature anomaly estimates that vary by more than 0.3°C solely due to network changes.

© 1994 by the Royal Meteorological Society. Available from the publisher's website at http://dx.doi.org/10.1002/joc.3370140405

[1] With the recent development of high-quality, digital daily air-temperature archives over North America, flexible approaches that evaluate trends in time-varying percentiles are capable of estimating climate-change parameters from all portions of air-temperature frequency distributions. Using these methods, intense warming is found in the lowest minimum temperatures over western and central North America. During the months of January through March, the lower tail of the daily minimum air-temperature distribution over western North America has warmed at rates exceeding 3°C/50yr. Daily maximum air temperatures also are increasing over western Canada and Alaska at this time, but all parts of the frequency distribution are warming equally. Other times of year in western North America, as well as much of eastern North America, show little change in either minimum or maximum air temperature during the last half-century.