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Abstract:

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Abstract:

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.

Abstract:

Observations are increasingly used to constrain multi-model projections for future climate assessments. This study assesses the performance of five constraining methods, which have previously been applied to attempt to improve regional climate projections from CMIP5-era models. We employ an out-of-sample testing approach to assess the efficacy of these constraining methods when applied to “pseudo-observational” datasets to constrain future changes in the European climate. These pseudo-observations are taken from CMIP6 simulations, for which future changes were withheld and used for verification. The constrained projections are more accurate and broadly more reliable for regional temperature projections compared to the unconstrained projections, especially in the summer season, which was not clear prior to this study. However, the constraining methods do not improve regional precipitation projections. We also analysed the performance of multi-method projections by combining the constrained projections, which are found to be competitive with the best-performing individual methods and demonstrate improvements in reliability for some temperature projections. The performance of the multi-method projection highlights the potential of combining constraints for the development of constraining methods.

Abstract:

Let (Formula presented.) be a field with (Formula presented.), where (Formula presented.) denotes the maximal pro-2 quotient of the absolute Galois group of a field (Formula presented.). We prove that then (Formula presented.) admits a (non-trivial) valuation (Formula presented.) which is 2-henselian and has residue field (Formula presented.). Furthermore, (Formula presented.) is a minimal positive element in the value group (Formula presented.) and (Formula presented.). This forms the first positive result on a more general conjecture about recovering (Formula presented.) -adic valuations from pro- (Formula presented.) Galois groups which we formulate precisely. As an application, we show how this result can be used to easily obtain number-theoretic information, by giving an independent proof of a strong version of the birational section conjecture for smooth, complete curves (Formula presented.) over (Formula presented.), as well as an analogue for varieties.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>The widespread destruction and insurance losses incurred by midlatitude storms every year makes it an imperative to study how storms change with climate change. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small climate change signal in variables such as windspeed compared to the noise of weather, as well as the high resolutions required to represent the dynamic processes in the storms. The midlatitude cyclone Eunice hit the South of the UK on February 18, 2022. Here, we assess how Eunice was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, thus significantly increasing our confidence in its ability to model the key physical underlying processes and how they repsond to climate change. Using modified boundary conditions for the greenhouse gas concentrations and changed initial conditions for the 3D ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times of eight, four and two days, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution of a single event enables combining event specificity and a focus on dynamic changes with the assessment of changes in risks from strong winds. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.&amp;#xD;</jats:p>