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Synoptic‐scale patterns associated with daily temperature and precipitation extremes in Alaska are identified and evaluated for daily variability in order to understand consistency in forcing mechanisms associated with extreme events as well as the tendency for each pattern to produce an extreme event. Daily station data at five locations for the 29‐year period from 1982 to 2010 are used. The widely recognized ClimDex indices are used to identify extreme high temperature, low temperature, and single‐day precipitation events. Pressure patterns during extreme events are evaluated seasonally for summer (JJA) and winter (DJF) at mean sea level pressure, 700 and 500‐hPa geopotential heights. Temperature extremes are largely characterized by synoptic‐scale patterns associated with substantial warm and cold air advection, and precipitation extremes are associated with moisture advection from the North Pacific. Extreme warm and cold temperature events are generally associated with more consistent daily synoptic‐scale patterns than precipitation. Considerable dissimilarities among daily synoptic‐scale patterns associated with extreme events based on location, type of extreme event and season highlight the need for careful consideration in the practice of applying synoptic‐scale forcing patterns to forecast or downscale extreme weather events in Alaska. [ABSTRACT FROM AUTHOR]

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet‐day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA‐derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet‐day threshold increases and models using PCA‐derived predictors tend to outperform those based on grid cell predictors. Each model for wet‐day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN‐based models and those with larger predictor suites tend to have the smallest bias. [ABSTRACT FROM AUTHOR]

The occurrence of extreme climate events (ECEs) in the Amazon basin (AMZ) and northeast Brazil (NEB), such as torrential rains and severe droughts, varies in both spatial and temporal scales. Spatial analysis of trends allows observing changes in behaviour and determining in which regions a particular variable has been experiencing changes over time. Thus, the objective of this study is to analyse trends of 21 climate extremes indices, relative to maximum and minimum precipitation and temperature, as defined by the World Meteorological Organization, for the AMZ and NEB. The 21 indices were selected according to meteorological and climate characteristics of the regions. Through annual analysis it was possible to observe an increase in most of the climate extremes indices for air temperature in all AMZ and NEB subregions. As for extreme precipitation, only a few of the selected indices presented significant increase and/or decrease in their values. Overall, the eastern Amazon subregion in the AMZ presented the highest significant indices for temperature and precipitation. In NEB, both Northern Coast and Southern Coast subregions presented substantial increase or decrease in precipitation and temperature indices. Location of the reference points selected for each of the 11 homogeneous precipitation subregions in the AMZ and the NEB. [ABSTRACT FROM AUTHOR]

The Expert Team on Climate Change Detection and Indices (ETCCDI) proposed a set of 27 core indices, describing extreme weather and climate events based on daily temperature and precipitation data. There are six percentile‐based temperature extreme indices: four exceedance rates (ERs) (TN10p, TN90p, TX10p and TX90p) and two durations (cold spell duration [CSDI] and warm spell duration [WSDI]), derived by using percentiles for calendar days during the base period 1961–1990 as thresholds. The widely used empirical quantile (percentile) estimator or its bootstrap resampling adjustment may result in inhomogeneity in the derived annual ER series or in their linear trends, as well as seasonally varying biases in the monthly ER series. We present a new data set of the six indices for the historical and three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5) scenarios simulated by 19 global climate models participating the Coupled Model Intercomparison Project Phase 5, generated via the semi‐parametric quantile regression approach. Percentiles estimated upon the independence condition, inhomogeneity and seasonal biases are removed from the new data set, so that the changes in temperature extremes can be authentically revealed from the climatological distribution point of view. Compared with its counterpart produced by the CLIMDEX project, the new data set shows similar spatial and temporal change patterns under the RCP scenarios, but with much smaller magnitudes. By the end of 21st century, TN90p increases to 17.3, 24.5 and 40.0%, and TX90p increases to 16.9, 22.1 and 35.3%, under RCP2.6, RCP4.5 and RCP8.5, respectively. WSDI increases to 35 days under RCP8.5. For the three increasing indices, the most significant changes occur in tropical and extratropical regions. The greatest increases in TN90p and TX90p occur in southeast Asia and Amazon Basin, up to 85 and 68% in the summer under RCP8.5; that in WSDI occurs in Amazon Basin up to 120 days under RCP8.5. [ABSTRACT FROM AUTHOR]

Extreme temperatures can injure or kill organisms and can drive evolutionary patterns. Many indices of extremes have been proposed, but few attempts have been made to establish geographic patterns of extremes and to evaluate whether they align with geographic patterns in biological vulnerability and diversity. To examine these issues, we adopt the CLIMDEX indices of thermal extremes. We compute scores for each index on a geographic grid during a baseline period (1961-1990) and separately for the recent period (1991-2010). Heat extremes (temperatures above the 90th percentile during the baseline period) have become substantially more common during the recent period, particularly in the tropics. Importantly, the various indices show weak geographic concordance, implying that organisms in different regions will face different forms of thermal stress. The magnitude of recent shifts in indices is largely uncorrelated with baseline scores in those indices, suggesting that organisms are likely to face novel thermal stresses. Organismal tolerances correlate roughly with absolute metrics (mainly for cold), but poorly with metrics defined relative to local conditions. Regions with high extreme scores do not correlate closely with regions with high species diversity, human population density, or agricultural production. Even though frequency and intensity of extreme temperature events have - and are likely to have - major impacts on organisms, the impacts are likely to be geographically and taxonomically idiosyncratic and difficult to predict. [ABSTRACT FROM AUTHOR]

ABSTRACT Extreme temperature and precipitation events in Alaska are examined in an ensemble of global climate models (GCMs) and an atmospheric reanalysis. Extreme monthly maximum and minimum temperature and the monthly maximum 5-day precipitation amount are evaluated for a 30-year historical period and two 30-year future time slices (2050s and 2080s). Although biases exist, models capture the spatial pattern and seasonality of the extremes depicted by the ERA-40 reanalysis. Discrepancies between station data (Anchorage, Fairbanks and Barrow) and GCMs/reanalysis are larger than the model-reanalysis differences, and are consistent with (1) surface elevation differences arising from the models' resolution and (2) gauge undercatch of precipitation in the station data. GCMs project future changes that are 2-4 times larger than the across-model standard deviations. The largest changes projected by the GCMs are significantly different from the historical mean at the 95% confidence level. Changes in extreme minimum temperature and extreme 5-day precipitation projections are larger than changes in means. The extreme minimum temperatures are projected to increase 2-3 times as much as the extreme maximum temperatures in all seasons except summer, with the largest increases of extreme minima in coastal regions. By the 2080s, the increases in all three extremes indices are twice as large in the Representative Concentration Pathway (RCP) 8.5 as in the RCP 4.5 scenario. The magnitude of the projected increase of maximum 5-day precipitation is largest in southern and inland areas, although the percentage increase is largest in the north. In the RCP 8.5 simulations, the inter-annual variability of extreme temperatures narrows by the end of the century, most notably in autumn. Record-breaking 5-day precipitation events become more common in the RCP 8.5 than in the RCP 4.5 scenario. [ABSTRACT FROM AUTHOR]