Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

Abstract

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems and the population’s traditional livelihoods. In the Lena River catchment, Eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We focus on the Lena River catchment, where significant environmental changes are already apparent. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scale. The method yields dissimilarities or emergence levels ranging from 0 to 100 % and the direction of change as continuous time series itself. For the Lena River catchment, on average, emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools.

The timing of unprecedented hydrological drought under climate change

“…The regional average FDDs were analyzed to identify pronounced departures from the historical variability ranges. Distinct from several ToE studies that applied the signal-to-noise ratio 6,17 , Kolmogorov-Smirnov test 11,16,18 , probability ratio 25 , or Hellinger distance metric 21 , in this study, we applied the method proposed by Mora et al 9 to estimate the point in time at which regional drought characteristics deviated significantly from past regional drought characteristics. We identified the first year when the regional average FDD time series exceeded the corresponding maximum value during the historical baseline period (1865-2005) and subsequently remained beyond this historical range for a certain number of consecutive years afterward (Supplementary Fig.…”

“…In general, ToE is defined as the time at which a climate change signal emerges from natural variability, indicating the beginning of a new regime. Previous studies have applied the ToE framework to study hydroclimatic variables [9][10][11] , fire 12 , and biodiversity 13 , as well as average [14][15][16][17][18][19][20][21] or extreme [22][23][24][25] conditions of temperature or precipitation; several different approaches have been proposed to detect the relevant significant shifts.…”

Time of emergence of compound events: contribution of univariate and dependence properties

“…Methodologies for ToE based on statistical tests have also been developed, which estimate the first period for which the distribution of the climate metric is significantly and permanently different from a baseline period distribution (e.g. using Kolmogorov-Smirnov tests; Mahlstein et al, 2012;Gaetani et al, 2020;Pohl et al, 2020). To define the emergence of CE probabilities, we propose to assess probabilities in a 30-year window sliding over the period 1871-2100 and compare their values with respect to a baseline period's probability.…”