Northern Hemisphere Climatology and Trends of Statistical Moments Documented from GHCN-Daily Surface Air Temperature Station Data from 1950 to 2010

Abstract

The first four statistical moments and their trends are calculated for the average daily surface air temperature (SAT) from 1950 to 2010 using the Global Historical Climatology Network-Daily station data for each season relative to the 1961-90 climatology over the Northern Hemisphere. Temporal variation of daily SAT probability distributions are represented as generalized linear regression coefficients on the mean, standard deviation, skewness, and kurtosis calculated for each 10-yr moving time window from 1950-59 to 2001-10. The climatology and trends of these statistical moments suggest that daily SAT probability distributions are non-Gaussian and are changing in time. The climatology of the first four statistical moments has distinct spatial patterns with large coherent structure for mean and standard deviation and relatively smaller and more regionalized patterns for skewness and kurtosis. The linear temporal trends from 1950 to 2010 of the first four moments also have coherent spatial patterns. The linear temporal trends in the characterizing statistical moments are statistically significant at most locations and have differing spatial patterns for different moments. The regionalized variations specific to higher moments may be related to the climate dynamics that contribute to extremes. The nonzero skewness and kurtosis makes this detailed documentation on the higher statistical moments useful for quantifying climate changes and assessing climate model uncertainties.

The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation

“…Since our all-season LIM does not perform equally well at all amplitudes and phases of the MJO, it is possible that an MJO behavior-, amplitude-or phase-dependent LIM, resulting in a piecewise-stationary linear model, could yield large increases in MJO forecast skill, provided any hindcast stitching procedures do not produce amplifying errors and that the observed model skill deficiencies are not merely manifestations of the intrinsic predictability of the tropical atmosphere itself. Lastly, it is well known that linear stochastic models with Gaussian white noise produce Gaussian forecasts, whereas high-frequency climate statistics are markedly non-Gaussian (Perron andSura 2013, Cavanaugh andShen 2014). An alternative mode reduction strategy, strategic choice of additional independent observable inputs containing MJO precursor and dynamics information, and/or augmentation of the system with correlated additive and multiplicative noise, suggested by Sura et al (2005), may improve forecasts by accounting for more aspects of variability, while maintaining the simplicity of a linear stochastic framework.…”

Short‐tailed temperature distributions over North America and implications for future changes in extremes

“…Donat and Alexander [] showed positive, but not significant, trends in skewness of gridded annual daily temperature over northern NA. Cavanaugh and Shen [] showed statistically significant trends in temperature skewness over NA at some observation stations. Rhines and Huybers [] and Weaver et al .…”

“…PDFs of daily 2 m temperature (T2m) exhibit marked departures from Gaussianity in the tails over much of North America (NA). Using station data, Cavanaugh and Shen [2014] documented the first four statistical moments of the temperature PDF, finding large and coherent regions of non-Gaussian distributions. Perron and Sura [2013] identified regions of non-Gaussian temperature PDFs in global reanalysis and Loikith et al [2013] documented wintertime skewness of T2m in reanalysis over NA.…”

Identification and interpretation of nonnormality in atmospheric time series

“…Surface temperature from the U.S. Global Historical Climatology Network (GHCN) has also been shown to exhibit significant nonnormality, as well as trends in higher-order moments and quantiles [Huybers et al, 2014;Cavanaugh and Shen, 2014].…”