Andrew Rhines

Frequent summer temperature extremes reflect changes in the mean, not the variance

Hansen et al. (1) demonstrated that the probability of extremely hot summers has markedly increased because the mean of the distribution of seasonally averaged temperatures has increased. However, the authors also implied that the variance of the distribution has increased, a result that differs from regional studies that show changes in the extremes are consistent with a simple shift in the mean (2, 3). Here we extend the spatially aggregated distribution analysis of Hansen et al. to show that once issues related to normalizations, trends, and reductions in surface-station density are accounted for, there is no indication that variability about the mean has increased, at least using these methods and data.

U.S. Daily Temperatures: The Meaning of Extremes in the Context of Nonnormality

AbstractVariations in extreme daily temperatures are explored in relation to changes in seasonal mean temperature using 1218 high-quality U.S. temperature stations spanning 1900–2012. Extreme temperatures are amplified (or damped) by as much as ±50% relative to changes in average temperature, depending on region, season, and whether daily minimum or maximum temperature is analyzed. The majority of this regional structure in amplification is shown to follow from regional variations in temperature distributions. More specifically, there exists a close relationship between departures from normality and the degree to which extreme changes are amplified relative to the mean. To distinguish between intraseasonal and interannual contributions to nonnormality and amplification, an additional procedure, referred to as z bootstrapping, is introduced that controls for changes in the mean and variance between years. Application of z bootstrapping indicates that amplification of winter extreme variations is generally ...

The changing shape of Northern Hemisphere summer temperature distributions

The occurrence of recent summer temperature extremes in the midlatitudes has raised questions about whether and how the distributions of summer temperature are changing. While it is clear that in most regions the average temperature is increasing, there is less consensus regarding the presence or nature of changes in the shape of the distributions, which can influence the probability of extreme events. Using data from over 4000 weather stations in the Global Historical Climatology Network-Daily database, we quantify the changes in daily maximum and minimum temperature distributions for peak summer in the Northern Hemisphere midlatitudes during 1980–2015 using quantile regression. A large majority (87–88%) of the trends across percentiles and stations can be explained by a shift of the distributions with no change in shape. The remaining variability is summarized through projections onto orthogonal basis functions that are closely related to changes in variance, skewness, and kurtosis. North America and Eurasia show significant shifts in the estimated distributions of daily maximum and minimum temperatures. Although no general change in summer variance is found, variance has regionally increased in Eurasia and decreased in most of North America. Changes in shape that project onto the skewness and kurtosis basis functions have a much smaller spatial scale and are generally insignificant.

Identification and interpretation of nonnormality in atmospheric time series

Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed.

Sea Ice and Dynamical Controls on Preindustrial and Last Glacial Maximum Accumulation in Central Greenland

AbstractGreenland has experienced large changes since the last glacial with its summit warming by approximately 21°C, average accumulation rates tripling, and annual amplitudes of temperature and accumulation seemingly declining. The altered seasonal cycle of accumulation has been attributed to a combination of the large-scale dynamical response of the North Atlantic storm track to surface boundary conditions and the modulation of moisture availability due to changes in winter sea ice cover. Using atmospheric simulations of preindustrial and glacial climate, the contributions of these two mechanisms are evaluated. Estimates of moisture source footprints make it possible to distinguish between long-range transport related to the storm track and regional transport from the ocean surface near Greenland. It is found that the contribution of both mechanisms varies significantly with the background climate. With greater ice cover and the North Atlantic storm track locked to the topographically enhanced stationa...

Seasonally Resolved Distributional Trends of North American Temperatures Show Contraction of Winter Variability

AbstractThere is considerable interest in determining whether recent changes in the temperature distribution extend beyond simple shifts in the mean. The authors present a framework based on quantile regression, wherein trends are estimated across percentiles. Pointwise trends from surface station observations are mapped into continuous spatial fields using thin-plate spline regression. This procedure allows for resolving spatial dependence of distributional trends, providing uncertainty estimates that account for spatial covariance and varying station density. The method is applied to seasonal near-surface temperatures between 1979 and 2014 to unambiguously assess distributional changes in the densely sampled North American region. Strong seasonal differences are found, with summer trends exhibiting significant warming throughout the domain with little distributional dependence, while the spatial distribution of spring and fall trends show a dipole structure. In contrast, the spread between the 95th and ...

Global Relationships between Cropland Intensification and Summer Temperature Extremes over the Last 50 Years

AbstractConversion of native ecosystems to cropland and the use of irrigation are considered dominant pathways through which agricultural land-use change alters regional climate. Recent research proposes that increases in cropland productivity, or intensification, also influences climate through increasing evapotranspiration. Increases in evapotranspiration are expected to have the greatest temperature influence on extremely hot summer days with high vapor pressure deficits. Here, the generalizability and importance of such relationships are assessed by examining historical land-use and climate trends in seven regions across the globe, each containing a major temperate or subtropical cropping area. Trends in summer high-temperature extremes are sequentially compared against trends in cropland area, area equipped for irrigation, precipitation, and summer cropping intensity. Trends in temperature extremes are estimated using quantile regression of weather station observations, and land-use data are from agr...