Detection and Attribution of Climate Change

This talk develops a mathematical model and statistical methods to analyze snow presence/absence. The methods are applied to satellite based observations which are part of the Climactic Data Record during the 1967-2020 time period.  A two-state Markov chain modeling snow absence/presence is equipped with periodic dynamics to account for seasonality.  The model allows one to characterize the autocorrelation structure of the 0-1 data.  Uncertainty estimates permit statistical testing of trends.  Among trustworthy grids, snow presence is seen to be declining in almost twice as many grids as it is advancing.

Detection and attribution (D&A) of climate change trends is commonly performed using a variant of Hasselmann’s “optimal fingerprinting” method, which involves a linear regression of historical climate observations on corresponding output from numerical climate models. However, it has long been known in the field of time series analysis that regressions of “non-stationary” or “trending” variables are,in general, statistically inconsistent and often spurious. When non-stationarity is caused by “integrated” processes, as islikely the case for climate variables, consistency of least-squares estimators depends on “cointegration” of regressors. Thisstudy has shown, using an idealized linear-response-model framework, that if standard assumptions hold then the optimalfingerprinting estimator is consistent, and hence robust against spurious regression. In the case of global mean surfacetemperature (GMST), parameterizing abstract linear response models in terms of energy balance provides this result withphysical interpretability. Hypothesis tests conducted using observations of historical GMST and simulation output from 13CMIP6 general circulation models produced no evidence that standard assumptions required for consistency were violated.It is therefore concluded that, at least in the case of GMST, detection and attribution of climate change trends is very likelynot spurious regression. Furthermore, detection of significant cointegration between observations and model output indicatesthat the least-squares estimator is “superconsistent”, with better convergence properties than might previously have beenassumed. Finally, a new method has been developed for quantifying D&A uncertainty, exploiting the notion of cointegrationto eliminate the need for pre-industrial control simulations.

Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on mean and extreme precipitation. Here, we propose a novel approach to regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is minimized. In a single framework, we are able to simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Greenhouse gas emissions increase mean and extreme precipitation from rain gauge measurements across all seasons. Aerosol emissions offset these increases in the winter and spring but appear to enhance rainfall during the summer and fall. Our results show that conflicting literature on trends in precipitation over the historical record can be explained by equal and opposite aerosol and greenhouse gas signals. At the scale of the United States, individual climate models reproduce observed changes due to anthropogenic forcing but cannot confidently determine whether these emissions sources increase or decrease rainfall.

Extreme event attribution is about estimating probabilities of extreme weather events and characterizing how they have changed, or will change in the future, as a consequence of greenhouse-gas-induced climate change. The approach proposed here is conditional in the sense that the extreme event probabilities are conditioned on some regional weather variable, such as summer mean annual temperature averaged over a grid box, that  we may reasonably hope to be well represented by climate models. The estimation problem is in three steps, (a) modeling the conditional distribution of extremes given the regional variable, (b) modeling the conditional distribution of the regional variable given climate model output, (c) combining steps (a) and (b) to model the conditional distribution of extremes given climate model output at various time points in the past and, using forward simulations of climate models, the future. The analysis relies entirely on public data sources including daily station data from the Global Historical Climatological Network, gridded temperature monthly averages from the Climate Research Unit of the University of East Anglia, and climate model from the CMIP6 archive. As an example, I estimate the probability of a temperature over 40 degrees C at London’s Heathrow Airport (an event that occurred on July 19 this year) based on data available prior to 2022. The estimated probability is about 7 times larger for 2022 than for the mid-20th century, but is projected to grow rapidly in the future, especially under the pessimistic ssp585 emissions scenario.

We develop a flexible dynamic quantile linear model (exDQLM) whichenables versatile, structured, and informative estimation of theevolution of a given quantile of an environmental variable ofinterest. In addition, our approach considers a transfer functionextension to our exDQLM as a method to quantify nonlinearrelationships between a specific quantile of a climatologicalresponse and a lagged input. We apply our method to the estimationof a high quantile over time of the integrated water vapor transport(IVT) magnitude. IVT is the primary component of many detectionschemes for atmospheric rivers. Our transfer function exDQLM is usedto to describe both the immediate and lagged effects of ENSO on theestimation of the .85 quantiles of IVT of the California coastduring the last four decades. The results show a significant butcomplex association with past values of ELI, a recently developedoceanic index that provides a summary of the sea surface temperaturefor the equatorial Pacific alternative to ENSO.

Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.

The field of detection and attribution fundamentally poses a causal question: was a certain observed event or trend caused by human influence on the climate system? The two most common forms of D&A focus on either a “fingerprint” of climate change, or specific extreme events. While these two forms use fundamentally different methods, both typically rely on climate model simulations of both the signal (climate change) and the noise (internal variability). Thus, in theory it is critical to assess whether these properties are being properly simulated. In practice, however, this presents a challenge because it can be difficult to know either quantity from the observations. Here, we present a methodology to create “observational large ensembles” (Obs-LEs) that contain multiple realizations of internal variability comparable to climate model ensembles, but that are constrained by the observational record. The validity of the approach is assessed using the CESM1 Large Ensemble (CESM1-LE) as a testbed through determining whether a single member (analogous to the observations) can be used to reproduce the spread across the full ensemble. Focusing on winter precipitation in the continental United States, we demonstrate that the Obs-LE approach successfully reproduces important characteristics of variability in the CESM1-LE using only a single member, including the magnitude of very extreme events that could not be easily estimated using the observational record. Further, there are key differences in the spatial pattern and magnitude of internal variability between CESM1-LE and the “GPCC-synth-LE”, an observational ensemble based on GPCC precipitation data. We suggest the observationally-based large ensembles should be incorporated in D&A frameworks as alternative estimates of internal variability.

Numerical climate models are complex and combine a large number of physical processes. They are key tools in quantifying the relative contribution of potential anthropogenic causes (e.g., the current increase in greenhouse gases) on high-impact atmospheric variables like heavy rainfall or temperatures. These so- called climate extreme event attribution problems are particularly challenging in a multivariate context, that is, when the atmospheric variables are measured on a possibly high-dimensional grid. In addition, global climate models like any in sillico numerical experiments are affected by different types of bias. In this talk, I will discuss about how to combine to two statistical theories to assess causality in the context of extreme event attribution. In addition, the question of uncertainties quantification that remains a challenge in any climate attribution analysis will be explored from various directions. In particular, a simple model bias correction step for records will described in details. To illustrate our approach, we infer emergence times in precipitation from the CMIP5 and CMIP6 archives. Joint work with Anna Kiriliouk, Paula Gonzalez Soulivanh Thao and Julien Worms Biblio: – Naveau P. and S. Thao Multi-model errors and emergence times in climate attribution studies, journal of climate, (2022) – Worms J. and P. Naveau. Record events attribution in climate studies. Envirmetrics (2022, in press). ⟨hal-02938596⟩ -Kiriliouk, A., and P. Naveau, 2020: Climate extreme event attribution using multivariate peaks- over-thresholds modeling and counterfactual theory. Ann. Appl. Stat., 14 (3), 1342–1358

Scientists interested in climate change detection and attribution ask many questions in their analyses.  Some of these are basic: are observed trends compatible with internal variability? Is there evidence for an externally forced response in observations?  Some are more complex: does the forced response depend on the background climate state?  What is the spatiotemporal structure of climate noise, and is it changing?  Do multiple forcing agents interact with one another?  How do we select the best models to explain observed change?  I will present an adaptable, hierarchical Bayesian framework for detection and attribution and show how it can be adapted to answer a wide range of questions.  I will argue that Bayesian methods best allow analysts to track and understand how observational, structural, and parametric uncertainties contribute to our ability to detect and attribute climate change.

This talk will highlight the different pathways whereby climate mechanisms engender extreme events as well as harness that information to improve extreme event prediction. Since the beginning of the 21st century, a great deal of research effort has been undertaken toward uncovering the changing pattern of climate extremes. Aimed to be inclusive, this talk will review four parts of the climate extremes research: forcings, processes, regionality, and prediction. These four parts of climate science illuminate the progress toward developing actionable information to foresee the likelihood of a climate extreme, and so get ready for the event and its consequences.