Statistical significance of seasonal warming/cooling trends

Abstract

The question whether a seasonal climate trend (e.g., the increase of summer temperatures in Antarctica in the last decades) is of anthropogenic or natural origin is of great importance for mitigation and adaption measures alike. The conventional significance analysis assumes that (i) the seasonal climate trends can be quantified by linear regression, (ii) the different seasonal records can be treated as independent records, and (iii) the persistence in each of these seasonal records can be characterized by short-term memory described by an autoregressive process of first order. Here we show that assumption ii is not valid, due to strong intraannual correlations by which different seasons are correlated. We also show that, even in the absence of correlations, for Gaussian white noise, the conventional analysis leads to a strong overestimation of the significance of the seasonal trends, because multiple testing has not been taken into account. In addition, when the data exhibit long-term memory (which is the case in most climate records), assumption iii leads to a further overestimation of the trend significance. Combining Monte Carlo simulations with the Holm-Bonferroni method, we demonstrate how to obtain reliable estimates of the significance of the seasonal climate trends in long-term correlated records. For an illustration, we apply our method to representative temperature records from West Antarctica, which is one of the fastest-warming places on Earth and belongs to the crucial tipping elements in the Earth system.climate | long-term persistence | seasonal trends | statistical significance | multiple testing I n the last decades, estimations of the magnitude of deterministic trends in natural records have become an important issue, due to anthropogenic global warming (1). Although the estimation of a trend by linear regression is an easy task, the estimation of its statistical significance and its error bar is complicated, because the natural persistence of the records also becomes an issue.In the absence of persistence (white noise) as well as in shortterm persistent records, the distribution of the trend follows a Student's t distribution from which the significance S , its p value p = 1 − S , and the error bars of the trend can be determined (see, e.g., refs. 2-4 and Methods). In many natural records like temperature data, river flows, sea level heights, wind fields, midlatitude cyclones, or Antarctic sea ice extent, the assumption of white noise or short-term memory is not valid, due to strong longterm memory in the data (5-27) (Methods).Here we consider seasonal temperature records. A "season" can be a calendar day (without leap day), a week, a month, or combinations of months like meteorological winter, spring, summer, and autumn. Let us consider a daily mean temperature record with a length of L years. When a season is defined as a certain calendar day, the corresponding seasonal record consists of the L temperature data at that calendar day (e.g., January 10), in chronological order. By definition, ...

Role of the South Pacific Convergence Zone in West Antarctic Decadal Climate Variability

“…Next, we examine the circulation and climate impacts grouped into the extended warm and cold seasons, DFJMAM and JJASON, respectively (Figure ). During DJFMAM (Figures a and c), the SPCZ induces a strong anticyclone in the ASL region that produces, via thermal advection, cooling across the Antarctic Peninsula and Bellingshausen Sea and warming across the Ross Ice Shelf (consistent with the recent warming at McMurdo station located on the western Ross Ice Shelf; Ludescher et al, ). In JJASON (Figures b and d), the ASL is deepened west of its mean position in the northern Ross Sea and an anticyclone is located over the Bellingshausen Sea.…”

The Springtime Influence of Natural Tropical Pacific Variability on the Surface Climate of the Ross Ice Shelf, West Antarctica: Implications for Ice Shelf Thinning

“…While no major surface melting has been documented in this region during spring, surface melting is commonly observed over the Ross Ice Shelf during summer 24 – 27 , and it is likely to become more frequent by the end of the century 31 , 32 . This westward shift in spring warming is confirmed by the sparse station observations in the region, which show over recent decades a reduction in warming at Byrd station in central West Antarctica 3 , while McMurdo station located on the western Ross Ice Shelf exhibits a large springtime warming trend that is stronger than any Antarctic station during any season 37 . The cause of the shift in spring warming is suggested to be tied to anomalous meridional winds 3 due to an increase in cyclonic circulation offshore over the Ross Sea 35 , 38 , 39 , resulting in local changes to thermal advection 35 , 36 and sea ice concentration 10 .…”

Bayesian model selection for complex dynamic systems

“…Finally, it is important to note that we neglect any long-range correlations in the measured temperature data, because the likelihood function (low-level model) does not factorize for long-range correlated parameters, as required by our iterative inference method. While our method provides a solution to the weighting problem in multi-model climate projections, it may therefore still underestimate the uncertainty of the temperature predictions 63 – 65 .…”