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This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of "intermodel empirical orthogonal function" analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom. [ABSTRACT FROM AUTHOR]

In the 2022 summer, western–central Europe and several other regions in the northern extratropics experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change using observation-driven soil moisture estimates and climate models. We find that in western–central Europe, a June–August root zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate but would have occurred only about once per century during preindustrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observation-based products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that under 2 ∘ C global warming, 2022-like soil drought conditions would become twice as likely for western–central Europe compared to today and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

Central European winters have warmed markedly since the mid-20th century. Yet cold winters are still associated with severe societal impacts on energy systems, infrastructure and public health. It is therefore crucial to anticipate storylines of worst-case cold winter conditions, and to understand whether an extremely cold winter, such as the coldest winter in the historical record of Germany in 1963 (−6.3 °C or −3.4 σ seasonal DJF temperature anomaly relative to 1981–2010), is still possible in a warming climate. Here, we first show based on multiple attribution methods that a winter of similar circulation conditions to 1963 would still lead to an extreme seasonal cold anomaly of about −4.9 to −4.7 °C (best estimates across methods) under present-day climate. This would rank as second-coldest winter in the last 75 years. Second, we conceive storylines of worst-case cold winter conditions based on two independent rare event sampling methods (climate model boosting and empirical importance sampling): winter as cold as 1963 is still physically possible in Central Europe today, albeit very unlikely. While cold winter hazards become less frequent and less intense in a warming climate overall, it remains crucial to anticipate the possibility of an extreme cold winter to avoid potential maladaptation and increased vulnerability. [ABSTRACT FROM AUTHOR]

Heavy rainfall in East Africa between late 2019 and mid 2020 caused devastating floods and landslides throughout the region. These rains drove the levels of Lake Victoria to a record-breaking maximum in the second half of May 2020. The combination of high lake levels, consequent shoreline flooding, and flooding of tributary rivers caused hundreds of casualties and damage to housing, agriculture and infrastructure in the riparian countries of Uganda, Kenya and Tanzania. Media and government reports linked the heavy precipitation and floods to anthropogenic climate change, but a formal scientific attribution study has not been carried out so far. In this study, we characterise the spatial extent and impacts of the floods in the Lake Victoria basin, and then investigate to what extent human-induced climate change influenced the probability and magnitude of the record-breaking lake levels and associated flooding, by applying a multi-model extreme event attribution methodology. Using remote sensing-based flood mapping tools, we find that more than 29 thousand people living within a 50 km radius of the lake shorelines were affected by floods between April and July 2020. Precipitation in the basin was the highest recorded in at least three decades, causing lake levels to rise by 1.21 m between late 2019 and mid 2020. The flood, defined as a 6-month rise in lake levels as extreme as that observed in the lead-up to May 2020, is estimated to be a 63-year event in the current climate. Based on observations and climate model simulations, the best estimate is that the event has become more likely by a factor of 1.8 in the current climate compared to a pre-industrial climate, and that in the absence of anthropogenic climate change an event with the same return period would have led lake levels to rise by 7 cm less than observed. Nonetheless, uncertainties in the attribution statement are relatively large due to large natural variability, and include the possibility of no observed attributable change in the probability of the event (probability ratio, 95 % confidence interval 0.8–15.8) or in the magnitude of lake level rise during an event with the same return period (magnitude change, 95 % confidence interval 0–14 cm). In addition to anthropogenic climate change, other possible drivers of the floods and their impacts include human land and water management, the exposure and vulnerability of settlements and economic activities located in flood-prone areas, and modes of climate variability that modulate seasonal precipitation. The attribution statement could be strengthened by using a larger number of climate model simulations, as well as by quantitatively accounting for non-meteorological drivers of the flood and potential unforced modes of climate variability. By disentangling the role of anthropogenic climate change and natural variability in the high-impact 2020 floods in the Lake Victoria basin, this paper contributes to a better understanding of changing hydrometeorological extremes in East Africa and the African Great Lakes region. [ABSTRACT FROM AUTHOR]

In the 2022 summer, West-Central Europe and several other northern-hemisphere mid-latitude regions experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change, using observation-driven soil moisture estimates and climate models. We find that in West-Central Europe, a June-August root-zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate, but would have occurred only about once per century during pre-industrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold, but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observationbased products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that in a 2 °C world, 2022-like soil drought conditions would become twice as likely for West-Central Europe compared to today, and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

Risk modellers in the insurance industry use catastrophe models to estimate the distribution of possible damage from natural catastrophes. The output from catastrophe models is often adjusted to create alternative risk scenarios. These adjustments are made for many reasons, such as to reflect different scientific hypotheses, different interpretations of historical data or different scenarios related to climate variability and climate change. Models that present the output in a list of simulated synthetic events with their associated damage (so‐called event loss tables) can be adjusted rather easily, since information about desired adjustments is typically expressed in terms of changes in the properties of events. Models that present the output in a list of simulated synthetic years (so‐called year loss tables) are harder to adjust, however, because the occurrences of the events are hard‐wired into the simulated years. A method is described that allows the adjustment of the results in a year loss table by the application of weights to the years. The weights are calculated in such a way as to capture the specified changes in properties of the underlying events. The method is demonstrated by applying it to output from a catastrophe model and using it to quantify the changes in US hurricane wind damage due to shifts between long‐term average, active and inactive levels of hurricane activity. It is shown that the method works well by comparing the results with more accurate results derived directly from the underlying event loss table. [ABSTRACT FROM AUTHOR]