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This paper develops trend estimation techniques for monthly maximum and minimum temperature time series observed in the 48 conterminous United States over the last century. While most scientists concur that this region has warmed on aggregate, there is no a priori reason to believe that temporal trends in extremes and averages will exhibit the same patterns. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent (statistically). Previous authors have suggested that minimum temperatures are warming faster than maximum temperatures in the United States; such an aspect can be investigated via the methods discussed in this study. Here, statistical models with extreme value and changepoint features are used to estimate trends and their standard errors. A spatial smoothing is then done to extract general structure. The results show that monthly maximum temperatures are not often greatly changing-perhaps surprisingly, there are many stations that show some cooling. In contrast, the minimum temperatures show significant warming. Overall, the southeastern United States shows the least warming (even some cooling), and the western United States, northern Midwest, and New England have experienced the most warming.

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.

a b s t r a c tPrescribed fire can release herbaceous forages from woody plant competition thus promoting increased forage plant production, vigor, and accessibility. Prescribe fire also consumes standing litter thereby improving forage quality and palatability. Consequently, prescribed fire is commonly considered an effective tool for manipulating livestock distribution on rangelands. Efficacy of this tool on mesic sagebrush steppe, however, has received little research attention. Beginning in 2001, resource selection by beef cows under a mid-summer (July) grazing regime was evaluated using global positioning system (GPS) collars for 2 years prior to and for up to 5 years after a fall prescribed fire was conducted on mesic sagebrush steppe in the Owyhee Mountains of southwestern Idaho, USA. Cattle selected for burned areas during the first, second, and fifth postfire years. Cattle had exhibited neutral selectivity towards these areas, during one of the two prefire years. Burning in the uplands reduced cattle use of near-stream habitats but only during the second postfire year. Differences in phenological timing of grazing may account for differences in cattle response to burning noted between this study and one conducted nearby under a spring (May) grazing regime. This is a case study and caution should be taken in extrapolating these results.Published by Elsevier Ltd.

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Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United StatesHighlights d Time series models can feature non-stationarity and correlation in COVID-19 outcomes d Data-driven algorithms detect change points in COVID-19 outcomes due to policy changes d Five patterns with geographical similarities are found in the COVID-19 trajectory d The COVID-19 trajectory changes in about 10-14 days after policy implementation

SUMMARYMost of the long memory estimators for stationary fractionally integrated time series models are known to experience non-negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this paper, we propose bias reduction methods for a lag-one sample autocorrelation-based moment estimator. In order to reduce the bias of the moment estimator, we explicitly obtain the exact bias of lag-one sample autocorrelation up to the order n −1 . An example where the exact first-order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. We show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation.

This paper develops methods to estimate a long‐term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long‐term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009.