Greenhouse Gas Forcing a Necessary Causation for Marine Heatwaves Over the Northeast Pacific Warming Pool

Over the last decade, the northeast Pacific (NP) experienced strong marine heatwaves (MHWs) that produced devastating marine ecological impacts and received major societal concerns. Here, we assess the link between the well-mixed greenhouse gas (GHG) forcing and the occurrence probabilities of the duration and intensity of the NP MHWs. To begin with, we apply attribution technique on the SST time series, and detect a region of systematically and externally-forced SST increase -- the long-term warming pool -- co-located with the past notably Blob-like SST anomalies. The anthropogenic signal has recently emerged from the natural variability of SST over the warming pool, which we attribute primarily to increased GHG concentrations, with anthropogenic aerosols playing a secondary role. With extreme event attribution technique, we further show that GHG forcing is a necessary, but not a sufficient, causation for the multi-year persistent MHW events in the current climate, such as that happened in 2019/2020 over the warming pool. However, the occurrence of the 2019/2020 MHW was extremely unlikely in the absence of GHG forcing. Thus, as GHG emissions continue to firmly rise, it is very likely that GHG forcings will become a sufficient cause for events of the magnitude of the 2019/2020 record event.

Bringing physical reasoning into statistical practice in climate-change science

The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the ‘frequentist’ tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p-values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequentist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of probability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribution. These examples show how the principles can be applied, in order to develop better scientific practice.“La théorie des probabilités n’est que le bon sens reduit au calcul.” (Pierre-Simon Laplace, Essai Philosophiques sur les Probabilités, 1819).“It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude.” (Harold Jeffreys, Theory of Probability, 1st edition, 1939).

Projected Changes in the Atmospheric Dynamics of Climate Extremes in France

Extremes of temperature, precipitation and wind have caused damages in France, in the agriculture, transportation and health sectors. Those types of events are largely driven by the atmospheric circulation. The dependence on the global climate change is not always clear, and it is the subject of extreme event attribution (EEA). This study reports an analysis of the atmospheric circulation over France for seven events that struck France in the 21st century, in various seasons. We focus on the atmospheric dynamics that leads to those extremes and examine how the probability of atmospheric patterns and their predictability responds to climate change. We analyse how the features of those events evolve in simulations following an SSP585 scenario for future climate. We identify how thermodynamical and dynamical changes of the atmosphere affect the predictability of the atmospheric circulation. Those using a range of CMIP6 simulations helps determining uncertainties linked to climate models.

Attributing compound events to anthropogenic climate change

Extreme event attribution answers the question whether and by how much anthropogenic climate change has contributed to the occurrence or magnitude of an extreme weather event. It is also used to link extreme event impacts to climate change. Impacts, however, are often related to multiple compounding climate drivers. Because extreme event attribution typically focuses on univariate assessments, these assessments might only provide a partial answer to the question of anthropogenic influence to a high-impact event. We present a theoretical extension to classical extreme event attribution for certain types of compound events. Based on synthetic data we illustrate how the bivariate fraction of attributable risk (FAR) differs from the univariate FAR depending on the extremeness of the event as well as the trends in and dependence between the contributing variables. Overall, the bivariate FAR is similar in magnitude or smaller than the univariate FAR if the trend in the second variable is comparably weak and the dependence between both variables is moderate or high, a typical situation for temporally co-occurring heatwaves and droughts. If both variables have similarly large trends or the dependence between both variables is weak, bivariate FARs are larger and are likely to provide a more adequate quantification of the anthropogenic influence. Using multiple climate model large ensembles, we apply the framework to two case studies, a recent sequence of hot and dry years in the Western Cape region of South Africa and two spatially co-occurring droughts in crop-producing regions in South Africa and Lesotho.

A dynamical adjustment perspective on extreme event attribution

Here we demonstrate that dynamical adjustment allows a straightforward approach to extreme event attribution within a conditional framework. We illustrate the potential of the approach with two iconic extreme events that occurred in 2010: the early winter European cold spell and the Russian summer heat wave. We use a dynamical adjustment approach based on constructed atmospheric circulation analogues to isolate the various contributions to these two extreme events using only observational and reanalysis datasets. Dynamical adjustment results confirm previous findings regarding the role of atmospheric circulation in the two extreme events and provide a quantitative estimate of the various dynamic and thermodynamic contributions to the event amplitude. Furthermore, the approach is also used to identify the drivers of the recent 1979–2018 trends in summer extreme maximum and minimum temperature changes over western Europe and western Asia. The results suggest a significant role of the dynamic component in explaining temperature extreme changes in different regions, including regions around the Black and Caspian Seas as well as central Europe and the coasts of western Europe. Finally, dynamical adjustment offers a simple and complementary storyline approach to extreme event attribution with the advantage that no climate model simulations are needed, making it a promising candidate for the fast-track component of any real-time extreme event attribution system.

Anthropogenic warming forces extreme annual glacier mass loss

Glaciers are unique indicators of climate change. While recent global-scale glacier decline has been attributed to anthropogenic forcing, direct links between human-induced climate warming and extreme glacier mass-loss years have not been documented. Here we apply event attribution methods to document this at the regional scale, targeting the highest mass-loss years (2011 and 2018) across New Zealand’s Southern Alps. Glacier mass balance is simulated using temperature and precipitation from multiple climate model ensembles. We estimate extreme mass loss was at least six times (2011) and ten times (2018) (>90% confidence) more likely to occur with anthropogenic forcing than without. This increased likelihood is driven by present-day temperatures ~1.0 °C above the pre-industrial average, confirming a connection between anthropogenic emissions and high annual ice loss. These results suggest that as warming and extreme heat events continue and intensify, there will be an increasingly visible human fingerprint on extreme glacier mass-loss years in the coming decades.

Anthropogenic warming forces extreme annual glacier mass loss

Glaciers are unique indicators of climate change. While recent global-scale glacier decline has been attributed to anthropogenic forcing, direct links between human-induced climate warming and extreme glacier mass-loss years have not been documented. Here we apply event attribution methods to document this at the regional scale, targeting the highest mass-loss years (2011 and 2018) across New Zealand’s Southern Alps. Glacier mass balance is simulated using temperature and precipitation from multiple climate model ensembles. We estimate extreme mass loss was at least six times (2011) and ten times (2018) (>90% confidence) more likely to occur with anthropogenic forcing than without. This increased likelihood is driven by present-day temperatures ~1.0 °C above the pre-industrial average, confirming a connection between anthropogenic emissions and high annual ice loss. These results suggest that as warming and extreme heat events continue and intensify, there will be an increasingly visible human fingerprint on extreme glacier mass-loss years in the coming decades.

Pathways and pitfalls in extreme event attribution

The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.