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Allan Frei, Kenneth E. Kunkel, and Adao Matonse

Abstract

Recent analyses of extreme hydrological events across the United States, including those summarized in the recent U.S. Third National Climate Assessment (May 2014), show that extremely large (extreme) precipitation and streamflow events are increasing over much of the country, with particularly steep trends over the northeastern United States. The authors demonstrate that the increase in extreme hydrological events over the northeastern United States is primarily a warm season phenomenon and is caused more by an increase in frequency than magnitude. The frequency of extreme warm season events peaked during the 2000s; a secondary peak occurred during the 1970s; and the calmest decade was the 1960s. Cold season trends during the last 30–50 yr are weaker. Since extreme precipitation events in this region tend to be larger during the warm season than during the cold season, trend analyses based on annual precipitation values are influenced more by warm season than by cold season trends. In contrast, the magnitude of extreme streamflow events at stations used for climatological analyses tends to be larger during the cold season: therefore, extreme event analyses based on annual streamflow values are overwhelmingly influenced by cold season, and therefore weaker, trends. These results help to explain an apparent discrepancy in the literature, whereby increasing trends in extreme precipitation events appear to be significant and ubiquitous across the region, while trends in streamflow appear less dramatic and less spatially coherent.

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Nancy E. Westcott, Steven E. Hollinger, and Kenneth E. Kunkel

Abstract

This study evaluated the suitability of rain estimates based on the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network to estimate yield response to rainfall on a county scale and to provide real-time information related to crop stress resulting from deficient or excessive precipitation throughout the summer. The relationship between normalized corn yield and rainfall was examined for nine states in the central United States for 1997–99 and 2001–02. Monthly rainfall estimates were computed employing multisensor precipitation estimate (MPE) data from the National Centers for Environmental Prediction and quality-controlled (QC_Coop) and real-time (RT_Coop) NWS cooperative gauge data. In-season MPE rain estimates were found to be of comparable quality to the postseason QC_Coop estimates for predicting county corn yields. Both MPE and QC_Coop estimates were better related to corn yield than were RT_Coop estimates, presumably because of the lower density of RT_Coop gauges. Large corn yields typically resulted when May rain was less than 125 mm and July rain was greater than 50 mm. Low yields often occurred when July rainfall was less than 100 mm. For moderate July rains (50–100 mm), positive and negative normalized yields resulted. Parameterization of heat stress (number of July days > 32.2°C) improved the correlation between rainfall and normalized corn yield, particularly for years with the poorest yield-vs-rain relationship (1998 and 1999). For the combined analysis years, the multiple regression correlation coefficient was 0.56, incorporating May and July rainfall and July heat stress and explaining 31% of the variance of normalized corn yield. Results show that MPE rainfall estimates provide timely yield projections within the growing season.

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Jinsheng You, Kenneth G. Hubbard, Saralees Nadarajah, and Kenneth E. Kunkel

Abstract

The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.

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Mary Schoen Petersen, Peter J. Lamb, and Kenneth E. Kunkel

Abstract

A semiphysical solar radiation (SR) model is implemented to generate a new historical daily SR database for 53 locations in nine Midwestern and six adjacent states (available from the Midwestern Climate Center). This model estimates daily SR using standard hourly meteorological observations (surface atmospheric pressure and dewpoint temperature; cloud height and fractional sky cover by layer) as well as time of day, day of year, latitude/longitude, and the daily presence/absence of snow cover as input. Because of an extensive effort to interpolate for missing input (especially cloud) data, the daily SR dataset generated is 92% complete for all 53 stations for 1948–91, and 99% complete for the 43 stations with continuous hourly meteorological observations that commenced during 1945–50 and extended through 1991. Consistent with previous work, the model validates favorably against sets of daily SR measurements from (three) contrasting parts of the study region, and so its output is used here without adjustment.

Analyses of the dataset document the basic Midwestern spatial and temporal SR variability since the mid-to late 1940s. The spatial variation of calendar monthly mean SR is dominated by a near-meridional (north-eastward) decrease in fall and winter. This fundamental pattern is substantially perturbed from midspring through summer by subregional-to-mesoscale variability around and across the Great Lakes. Time series of individual monthly station mean SR values exhibit a pronounced, regionwide 1945–91 downtrend for August–November. This decline is strongest (∼12%) and most statistically significant (>99% level) for October in a belt extending east-southeastward from west-central Wisconsin across southern lake Michigan and western Lake Erie to western Pennsylvania. The SR trends for December–July are largely positive but of lesser spatial coherence, temporal consistency, and statistical significance.

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Kenneth E. Kunkel, Stanley A. Changnon, and Robin T. Shealy

Abstract

Long-term (1921–90) daily precipitation data from 242 stations were used to identify heavy multiday precipitation events (exceeding the threshold for a 1-yr recurrence interval) that were found to be closely related to flood events. The number of events were aggregated over 5-yr (pentad) periods and compared with total pentad precipitation. Although a strong positive correlation was found, this was due entirely to the event contributions to the total precipitation. When event precipitation was subtracted from total precipitation, no statistically significant correlation was found. The frequency distribution of precipitation totals in nonevent weeks was also found to be similar in years with few or no events compared to years with several events. These findings suggest that the occurrence of these events is not strongly linked to longer-term persistent climate anomalies. These relatively few events make an important contribution to long-term precipitation variability, accounting for about half of the interpentadal variability.

To provide information for determining hydrologic impacts from the results of GCMs, a study of spatial precipitation variability during heavy events was undertaken. For each event at each station, grid-average precipitation was calculated for 2° latitude × 2.5° longitude, and 4° latitude × 5° longitude grid cells. The ratio of grid average to heavy-event precipitation totals was determined. These relationships could be used to assess the probability of flood-producing, localized precipitation extremes from GCM grid-average precipitation.

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Kenneth E. Kunkel, Karen Andsager, and David R. Easterling

Abstract

This paper describes the results of an analysis of trends in short duration (1–7 days) extreme precipitation events that have a recurrence interval of 1 yr or longer for stations in the United States and Canada. This definition of extreme precipitation was chosen because such events are highly correlated with hydrologic flooding in some U.S. regions. The dominant temporal characteristic of a national event composite index is significant low-frequency variability. There were lengthy periods of a below-average number of events in the 1930s and 1950s and an above-average number of events in the early 1940s, early 1980s, and 1990s. Regional variations often differ substantially from the national composite. A simple linear analysis indicates that the overall trend covering the period 1931–96 has been upward at a highly statistically significant rate over the southwest United States and in a broad region from the central Great Plains across the middle Mississippi River and southern Great Lakes basins. The national trend for the United States is upward at a rate of 3% decade−1 for the period 1931–96. While the annual trend for Canada is upward for the period 1951–93, it is not statistically significant. Although the high statistical significance of the results is partially a consequence of the low frequency during the 1930s and 1950s located in the first half of the record, the latter half of the record exhibits an upward trend nearly identical to the entire record. However, an analysis of a 101-yr record of midwestern stations shows that heavy precipitation event frequencies around the turn of the twentieth century (1896–1906) were higher than for other periods of comparable length, except for 1986–96. Although data were not available in digital form to extend the analysis back to 1896 for the entire United States, the midwestern analysis shows that interpretation of the recent upward trends must account for the possibility of significant natural forcing of variability on century timescales.

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Kenneth E. Kunkel, Thomas R. Karl, and David R. Easterling

Abstract

A Monte Carlo analysis was used to assess the effects of missing data and limited station density on the uncertainties in the temporal variations of U.S. heavy precipitation event frequencies observed for 1895–2004 using data from the U.S. Cooperative Observer Network (COOP). Based on the actual availability of long-term station data, the effects of limited spatial density were found to be of greater importance than those of missing data. The Monte Carlo simulations indicate that there is a high degree of statistical confidence that the recent elevated frequencies in the United States are the highest in the COOP record since 1895, at least for event definitions using return periods of 5 yr or shorter. There is also high confidence that elevated frequencies seen early in the record are higher than those measured in the 1920s and 1930s, and are not simply an artifact of the limited spatial sampling. The statistically significant shift from high to low values in the early portion of the record, a reflection of natural variability, should not be ignored when interpreting the elevated levels of the most recent decades. Nevertheless, it does appear that the recent elevated levels exceed the variations seen in the earlier part of the record since 1895. The confidence in these statements decreases as the return period increases because of the diminishing number of events in the sample. When a linear trend is fit to the entire 1895–2004 period, the trends are positive and different from zero with a high level of statistical confidence for all return periods from 1 to 20 yr.

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Kenneth E. Kunkel, Roger A. Pielke Jr., and Stanley A. Changnon

This paper reviews recent work on trends during this century in societal impacts (direct economic losses and fatalities) in the United States from extreme weather conditions and compares those with trends of associated atmospheric phenomena. Most measures of the economic impacts of weather and climate extremes over the past several decades reveal increasing losses. But trends in most related weather and climate extremes do not show comparable increases with time. This suggests that increasing losses are primarily due to increasing vulnerability arising from a variety of societal changes, including a growing population in higher risk coastal areas and large cities, more property subject to damage, and lifestyle and demographic changes subjecting lives and property to greater exposure.

Flood damages and fatalities have generally increased in the last 25 years. While some have speculated that this may be due in part to a corresponding increase in the frequency of heavy rain events, the climate contribution to the observed impacts trends remains to be quantified. There has been a steady increase in hurricane losses. However, when changes in population, inflation, and wealth are considered, there is instead a downward trend. This is consistent with observations of trends in hurricane frequency and intensity. Increasing property losses due to thunderstorm-related phenomena (winds, hail, tornadoes) are explained entirely by changes in societal factors, consistent with the observed trends in the thunderstorm phenomena. Winter storm damages have increased in the last 10–15 years and this appears to be partially due to increases in the frequency of intense nor'easters. There is no evidence of changes in drought-related losses (although data are poor) and no apparent trend in climatic drought frequency. There is also no evidence of changes in the frequency of intense heat or cold waves.

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Stanley A. Changnon, Kenneth E. Kunkel, and Beth C. Reinke

The short but intense heat wave in mid-July 1995 caused 830 deaths nationally, with 525 of these deaths in Chicago. Many of the dead were elderly, and the event raised great concern over why it happened. Assessment of causes for the heat wave–related deaths in Chicago revealed many factors were at fault, including an inadequate local heat wave warning system, power failures, questionable death assessments, inadequate ambulance service and hospital facilities, the heat island, an aging population, and the inability of many persons to properly ventilate their residences due to fear of crime or a lack of resources for fans or air conditioning. Heat-related deaths appear to be on the increase in the United States. Heat-related deaths greatly exceed those caused by other life-threatening weather conditions. Analysis of the impacts and responses to this heat wave reveals a need to 1) define the heat island conditions during heat waves for all major cities as a means to improve forecasts of threatening conditions, 2) develop a nationally uniform means for classifying heat-related deaths, 3) improve warning systems that are designed around local conditions of large cities, and 4) increase research on the meteorological and climatological aspects of heat stress and heat waves.

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Kenneth E. Kunkel, Xin-Zhong Liang, Jinhong Zhu, and Yiruo Lin

Abstract

The observed lack of twentieth-century warming in the central United States (CUS), denoted here as the “warming hole,” was examined in 55 simulations driven by external historical forcings and in 19 preindustrial control (unforced) simulations from 18 coupled general circulation models (CGCMs). Twentieth-century CUS trends were positive for the great majority of simulations, but were negative, as observed, for seven simulations. Only a few simulations exhibited the observed rapid rate of warming (cooling) during 1901–40 (1940–79). Those models with multiple runs (identical forcing but different initial conditions) showed considerable intramodel variability with trends varying by up to 1.8°C century−1, suggesting that internal dynamic variability played a major role at the regional scale. The wide range of trend outcomes, particularly for those models with multiple runs, and the small number of simulations similar to observations in both the forced and unforced experiments suggest that the warming hole is not a robust response of contemporary CGCMs to the estimated external forcings. A more likely explanation based on these models is that the observed warming hole involves external forcings combined with internal dynamic variability that is much larger than typically simulated.

The observed CUS temperature variations are positively correlated with North Atlantic (NA) sea surface temperatures (SSTs), and both NA SSTs and CUS temperature are negatively correlated with central equatorial Pacific (CEP) SSTs. Most models simulate rather well the connection between CUS temperature and NA SSTs. However, the teleconnections between NA and CEP SSTS and between CEP SSTs and CUS temperature are poorly simulated and the models produce substantially less NA SST variability than observed, perhaps hampering their ability to reproduce the warming hole.

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