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  • Author or Editor: Kenneth E. Kunkel x
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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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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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Jay Lawrimore, Thomas R. Karl, Mike Squires, David A. Robinson, and Kenneth E. Kunkel

Abstract

The 100 most severe snowstorms within each of six climate regions east of the Rocky Mountains were analyzed to understand how the frequency of severe snowstorms is associated with seasonal averages of other variables that may be more readily predicted and projected. In particular, temperature, precipitation, and El Niño/La Niña anomalies from 1901 to 2013 were studied. In the southern United States, anomalously cold seasonal temperatures were found to be more closely linked to severe snowstorm development than in the northern United States. The conditional probability of occurrence of one or more severe snowstorms in seasons that are colder than average is 80% or greater in regions of the southern United States, which was found to be statistically significant, while it is as low as 35% when seasonal temperatures are warmer than average. This compares with unconditional probabilities of 55%–60%. For seasons that are wetter (drier) than average, severe snowstorm frequency is significantly greater (less) in the Northern Plains region. An analysis of the seasonal timing of severe snowstorm occurrence found they are not occurring as late in the season in recent decades in the warmest climate regions when compared to the previous 75 years. Since 1977, the median date of occurrence in the last half of the cold season is six or more days earlier in the Southeast, South, and Ohio Valley regions than earlier in the twentieth century. ENSO conditions also were found to have a strong influence on the occurrence of the top 100 snowstorms in the Northeast and Southeast regions.

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Kenneth E. Kunkel, David R. Easterling, David A. R. Kristovich, Byron Gleason, Leslie Stoecker, and Rebecca Smith

Abstract

Daily extreme precipitation events, exceeding a threshold for a 1-in-5-yr occurrence, were identified from a network of 935 Cooperative Observer stations for the period of 1908–2009. Each event was assigned a meteorological cause, categorized as extratropical cyclone near a front (FRT), extratropical cyclone near center of low (ETC), tropical cyclone (TC), mesoscale convective system (MCS), air mass (isolated) convection (AMC), North American monsoon (NAM), and upslope flow (USF). The percentage of events ascribed to each cause were 54% for FRT, 24% for ETC, 13% for TC, 5% for MCS, 3% for NAM, 1% for AMC, and 0.1% for USF. On a national scale, there are upward trends in events associated with fronts and tropical cyclones, but no trends for other meteorological causes. On a regional scale, statistically significant upward trends in the frontal category are found in five of the nine regions. For ETCs, there are statistically significant upward trends in the Northeast and east north central. For the NAM category, the trend in the West is upward. The central region has seen an upward trend in events caused by TCs.

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Rajarshi Das Bhowmik, A. Sankarasubramanian, Tushar Sinha, Jason Patskoski, G. Mahinthakumar, and Kenneth E. Kunkel

Abstract

Most of the currently employed procedures for bias correction and statistical downscaling primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature, ignoring the interdependency between the two variables. In this study, a multivariate approach, asynchronous canonical correlation analysis (ACCA), is proposed and applied to global climate model (GCM) historic simulations and hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to downscale monthly precipitation and temperature over the conterminous United States. ACCA is first applied to the CNRM-CM5 GCM historical simulations for the period 1950–99 and compared with the bias-corrected dataset based on quantile mapping from the Bureau of Reclamation. ACCA is also applied to CNRM-CM5 hindcasts and compared with univariate asynchronous regression (ASR), which applies regular regression to sorted GCM and observed variables. ACCA performs better than ASR and quantile mapping in preserving the cross correlation at grid points where the observed cross correlations are significant while reducing fractional biases in mean and standard deviation. Results also show that preservation of cross correlation increases the bias in standard deviation slightly, but estimates observed precipitation and temperature with increased likelihood, particularly for months exhibiting significant cross correlation. ACCA also better estimates the joint likelihood of observed precipitation and temperature under hindcasts since hindcasts estimate the observed variability in precipitation better. Implications of preserving cross correlations across climate variables for projecting runoff and other land surface fluxes are also discussed.

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Kenneth E. Kunkel, Karen Andsager, Xin-Zhong Liang, Raymond W. Arritt, Eugene S. Takle, William J. Gutowski Jr., and Zaitao Pan

Abstract

A regional climate model simulation of the period of 1979–88 over the contiguous United States, driven by lateral boundary conditions from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis, was analyzed to assess the ability of the model to simulate heavy precipitation events and seasonal precipitation anomalies. Heavy events were defined by precipitation totals that exceed the threshold value for a specified return period and duration. The model magnitudes of the thresholds for 1-day heavy precipitation events were in good agreement with observed thresholds for much of the central United States. Model thresholds were greater than observed for the eastern and intermountain western portions of the region and were smaller than observed for the lower Mississippi River basin. For 7-day events, model thresholds were in good agreement with observed thresholds for the eastern United States and Great Plains, were less than observed for the most of the Mississippi River valley, and were greater than observed for the intermountain western region. The interannual variability in frequency of heavy events in the model simulation exhibited similar behavior to that of the observed variability in the South, Southwest, West, and North-Central study regions. The agreement was poorer for the Midwest and Northeast, although the magnitude of variability was similar for both model and observations. There was good agreement between the model and observational data in the seasonal distribution of extreme events for the West and North-Central study regions; in the Southwest, Midwest, and Northeast, there were general similarities but some differences in the details of the distributions. The most notable differences occurred for the southern Gulf Coast region, for which the model produced a summer peak that is not present in the observational data. There was not a very high correlation in the timing of individual heavy events between the model and observations, reflecting differences between model and observations in the speed and path of many of the synoptic-scale events triggering the precipitation.

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