Trends in Extreme U.S. Temperatures

Lee, Jaechoul; Li, Shanghong; Lund, Robert

doi:10.1175/jcli-d-13-00283.1

Cited by 35 publications

(28 citation statements)

References 46 publications

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“…4, first and fourth rows) as climate warms. As previously found in other studies of climate model output (Kharin et al, 2007 and observations (Lee et al, 2014), the changes are unequal, with a larger amount of warming in cold extremes than in warm extremes. For warm extremes, location parameter changes are fairly uniform spatially and similar to changes in summer mean temperatures (see Fig.…”

Section: Gev Parameters In Pre-industrial and Future Climatessupporting

confidence: 78%

“…Over the past 2 decades, numerous studies have sought to identify changes in temperature extremes both in observations (Easterling et al, 2000;Shaby and Reich, 2012;Parey et al, 2013;Westra et al, 2013;Lee et al, 2014;Naveau et al, 2014) and in general circulation model (GCM) simulations of future climate (Kharin and Zwiers, 2000;Tebaldi et al, 2006;Kharin et al, 2007;Sterl et al, 2008;Frías et al, 2012;Kharin et al, 2013). A number of studies find that extreme changes follow closely with changes in means and standard deviations (e.g., de Vries et al, 2012;Parey et al, 2013).…”

Section: W K Huang Et Al: Temperature Extremes In Ccsm3mentioning

confidence: 99%

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Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Huang

Stein

McInerney

et al. 2016

Adv. Stat. Clim. Meteorol. Oceanogr.

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Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO 2 ) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.

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Section: Gev Parameters In Pre-industrial and Future Climatessupporting

confidence: 78%

Section: W K Huang Et Al: Temperature Extremes In Ccsm3mentioning

confidence: 99%

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Huang

Stein

McInerney

et al. 2016

Adv. Stat. Clim. Meteorol. Oceanogr.

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show abstract

“…This would change the Gaussian assumptions, say, to Poisson for tropical cyclone counts (Robbins et al 2011) or to generalized extreme values (Lee et al 2014) for temperature extremes. First, to handle monthly series, seasonal location parameters need to be added into (1).…”

Section: Summary and Commentsmentioning

confidence: 99%

Multiple Changepoint Detection Using Metadata

Lund

2015

Journal of Climate

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This paper examines multiple changepoint detection procedures that use station history (metadata) information. Metadata records are available for some climate time series; however, these records are notoriously incomplete and many station moves and gauge changes are unlisted (undocumented). The shift in a series must be comparatively larger to declare a changepoint at an undocumented time. Also, the statistical methods for the documented and undocumented scenarios radically differ: a simple t test adequately detects a single mean shift at a documented changepoint time, while a t max distribution is appropriate for a single undocumented changepoint analysis. Here, the multiple changepoint detection problem is considered via a Bayesian approach, with the metadata record being used to formulate a prior distribution of the changepoint numbers and their location times. This prior distribution is combined with the data to obtain a posterior distribution of changepoint numbers and location times. Estimates of the most likely number of changepoints and times are obtained from the posterior distribution. Simulation studies demonstrate the efficacy of this approach. The methods, which are applicable with or without a reference series, are applied in the analysis of an annual precipitation series from New Bedford, Massachusetts.

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“…Wehner (2013) used the EVT distributions to analyze 20-yr return values of seasonal extreme daily precipitation in the ensemble of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models and found that performance of regional models of approximately 50 km in horizontal resolution varied widely in their ability to reproduce observed precipitation extremes. In another study, Lee et al (2014) developed statistical models from the EVT to estimate trends in monthly extreme temperature events in the 48 contiguous United States (CONUS). The results showed that, while monthly maximum temperatures are not significantly changing, monthly minimum temperatures depict a significant warming trend.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

Ashouri

Sorooshian

Hsu

et al. 2016

Journal of Hydrometeorology

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This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S. Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

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Trends in Extreme U.S. Temperatures

Cited by 35 publications

References 46 publications

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Multiple Changepoint Detection Using Metadata

Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

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