Detecting spatial patterns with the cumulant function. Part II: An application to El Nino

The spatial coherence of a measured variable (e.g. temperature or pressure) is often studied to determine the regions where this variable varies the most or to find teleconnections, i.e. correlations between specific regions. While usual methods to find spatial patterns, such as Principal Components Analysis (PCA), are constrained by linear symmetries, the dependence of variables such as temperature or pressure at different locations is generally nonlinear. In particular, large deviations from the sample mean are expected to be strongly affected by such nonlinearities. Here we apply a newly developed nonlinear technique (Maxima of Cumulant Function, MCF) for the detection of typical spatial patterns that largely deviate from the mean. In order to test the technique and to introduce the methodology, we focus on the El Nino/Southern Oscillation and its spatial patterns. We find nonsymmetric temperature patterns corresponding to El Nino and La Nina, and we compare the results of MCF with other techniques, such as the symmetric solutions of PCA, and the nonsymmetric solutions of Nonlinear PCA (NLPCA). We found that MCF solutions are more reliable than the NLPCA fits, and can capture mixtures of principal components. Finally, we apply Extreme Value Theory on the temporal variations extracted from our methodology. We find that the tails of the distribution of extreme temperatures during La Nina episodes is bounded, while the tail during El Ninos is less likely to be bounded. This implies that the mean spatial patterns of the two phases are asymmetric, as well as the behaviour of their extremes.Comment: 15 pages, 7 figure

Return times of hot and cold days via recurrences and extreme value theory

International audienceIn this paper we introduce a model evaluation and comparison metric based on the methodology introduced in Faranda et al (2013) to assess biases and their potential origins in a historical model simulation against long-term reanalysis. The metric is constructed by exploiting recent results of dynamical systems theory linking rare recurrences to the classical statistical theories of extreme events for time series. We compute rare recurrences for 100 years daily mean temperatures data obtained in a model with historical greenhouse forcing (the Institut Pierre-Simon Laplace, IPSL-CM5 model) and compare them with the same quantities obtained from two datasets of reanalysis (20 Century Reanalysis and ERA 20C). The period chosen for the comparison is 1900-2000 and the focus is on the European region. We show that with respect to the traditional approaches, the recurrence technique is sensitive to the change in the size of the selection window of extremes due to the conditions imposed by the dynamics. Eventually, we study the regions which show robust biases with respect to all the techniques investigatin

Data-Adaptive Wavelets and Multi-Scale Singular Spectrum Analysis

Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series of length $N$ whose intermittency can give rise to the divergence of their variance. SSA relies on the construction of the lag-covariance matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W = M Dt, with Dt the time step. The proposed multi-scale SSA is a local SSA analysis within a moving window of width M <= W <= N. Multi-scale SSA varies W, while keeping a fixed W/M ratio, and uses the eigenvectors of the corresponding lag-covariance matrix C_M as a data-adaptive wavelets; successive eigenvectors of C_M correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency domain, by a suitable localization of the signal's covariance matrix. We present several examples of application to synthetic signals with fractal or power-law behavior which mimic selected features of certain climatic and geophysical time series. A real application is to the Southern Oscillation index (SOI) monthly values for 1933-1996. Our methodology highlights an abrupt periodicity shift in the SOI near 1960. This abrupt shift between 4 and 3 years supports the Devil's staircase scenario for the El Nino/Southern Oscillation phenomenon.Comment: 24 pages, 19 figure

Was the cold European winter 2009-2010 modified by anthropogenic climate change? An attribution study

An attribution study has been performed to investigate the degree to which the unusually cold European winter 2009-2010 was modified by anthropogenic climate change. Two different methods have been included for the attribution: one based on a large HadGEM3-A ensemble and one based on a statistical surrogate method. Both methods are evaluated by comparing simulated winter temperature means, trends, standard deviations, skewness, return periods, and 5 % quantiles with observations. While the surrogate method performs well, HadGEM3-A in general underestimates the trend in winter by a factor of 2/3. It has a mean cold bias dominated by the mountainous regions and also underestimates the cold 5 % quantile in many regions of Europe. Both methods show that the probability of experiencing a winter as cold as 2009-2010 has been reduced by approximately a factor of two due to anthropogenic changes. The method based on HadGEM3-A ensembles gives somewhat larger changes than the surrogate method because of differences in the definition of the unperturbed climate. The results are based on two diagnostics: the coldest day in winter and the largest continuous area with temperatures colder than twice the local standard deviation. The results are not sensitive to the choice of bias correction except in the mountainous regions. Previous results regarding the behavior of the measures of the changed probability have been extended. The counter-intuitive behavior for heavy-tailed distributions is found to hold for a range of measures and for events that become more rare in a changed climate

Human influence on climate in the 2014 southern England winter floods and their impacts

A succession of storms reaching Southern England in the winter of 2013/2014 caused severe floods and £451 million insured losses. In a large ensemble of climate model simulations, we find that, as well as increasing the amount of moisture the atmosphere can hold, anthropogenic warming caused a small but significant increase in the number of January days with westerly flow, both of which increased extreme precipitation. Hydrological modelling indicates this increased extreme 30-day-average Thames river flows, and slightly increased daily peak flows, consistent with the understanding of the catchment’s sensitivity to longer-duration precipitation and changes in the role of snowmelt. Consequently, flood risk mapping shows a small increase in properties in the Thames catchment potentially at risk of riverine flooding, with a substantial range of uncertainty, demonstrating the importance of explicit modelling of impacts and relatively subtle changes in weather-related risks when quantifying present-day effects of human influence on climate

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed Sea Surface Temperature of the 54 year period 1960-2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land-atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution

Compound Climate Events and Extremes in the Midlatitudes: Dynamics, Simulation, and Statistical Characterization

The workshop, conducted virtually due to travel restrictions related to COVID-19, gathered scientists from six countries and focused on the mechanistic understanding, statistical characterization, and modeling of societally relevant compound climate events and extremes in the midlatitudes. These ranged from co-occurring hot–humid or wet–windy extremes, to spatially compounding wet and dry extremes, to temporally compounding hot–wet events and more. The aim was to bring together selected experts studying a diverse range of compound climate events and extremes to present their ongoing work and outline challenges and future developments in this societally relevant field of research