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The article focuses on debate over the extent warmer temperatures in Arctic regions has on climate and weather extremes at lower latitudes. It examines the role of the polar vortex in weather systems which can result in cold spells in mid-latitudes and warmer weather in the Arctic and mentions cooling over central Asia and eastern United States compared to that seen previously. It considers a null hypothesis on the thermodynamic aspects of climate change and predictions of numerical models.

Greenhouse gas (GHG) emissions affect precipitation worldwide. The response is commonly described by two timescales linked to different processes: a rapid adjustment to radiative forcing, followed by a slower response to surface warming. However, additional timescales exist in the surface-warming response, tied to the time evolution of the sea-surface-temperature (SST) response. Here, we show that in climate model projections, the rapid adjustment and surface mean warming are insufficient to explain the time evolution of the hydro-climate response in three key Mediterranean-like areas--namely, California, Chile, and the Mediterranean. The time evolution of those responses critically depends on distinct shifts in the regional atmospheric circulation associated with the existence of distinct fast and slow SST warming patterns. As a result, Mediterranean and Chilean drying are in quasiequilibrium with GHG concentrations, meaning that the drying will not continue after GHG concentrations are stabilized, whereas California wetting will largely emerge only after GHG concentrations are stabilized. The rapid adjustment contributes to a reduction in precipitation, but has a limited impact on the balance between precipitation and evaporation. In these Mediterranean-like regions, future hydro-climate-related impacts will be substantially modulated by the time evolution of the pattern of SST warming that is realized in the real world. [ABSTRACT FROM AUTHOR]

The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes's theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes's theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years. Significance Statement: Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes. [ABSTRACT FROM AUTHOR]

The springtime persistent rainfall (SPR) is the major rainy period before the onset of summer monsoon in East Asia, which profoundly affects the regional and even global hydrological cycle. Despite the great importance of the mechanical and thermal effects of the Tibetan Plateau (TP) large-scale orography on the formation of SPR, the impact of small-scale orography over the TP remains poorly understood. Here we show that upward-propagating orographic gravity waves (OGWs), which occur as the subtropical westerlies interact with the TP's small-scale orography, contribute importantly to the SPR. The breaking of OGWs induces a large zonal wave drag in the middle troposphere, which drives a meridional circulation across the TP. The rising branch of the meridional circulation acts to lower the pressure and increase the meridional pressure gradient to the south of the TP by dynamically pumping the lower-tropospheric air upwards. The southwesterly monsoonal flow on the southeastern flank of the TP thus intensifies and transports more water vapor to East Asia, resulting in an enhancement of the SPR. This finding helps more completely understand the impacts of TP's multiscale orography on the SPR and provides a new perspective on the westerly-monsoon synergy in East Asia. [ABSTRACT FROM AUTHOR]

To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k‐means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro‐Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub‐seasonal and interannual time‐scales. We find that sub‐seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual time‐scales relations between the occurrence rates of the regimes and the El Niño Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual time‐scales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO‐index where the signal strength in the observations is underestimated by a factor of 2 in the model. Our regime analysis suggests that this signal‐to‐noise problem for the NAO‐index is primarily related to those atmospheric flow patterns associated with the negative NAO‐index as we find poor predictability for the corresponding NAO− regime. [ABSTRACT FROM AUTHOR]

Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections. [ABSTRACT FROM AUTHOR]

This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August and mid-September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first-ever-observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by ∼50% in the midstratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-1 activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the southern annular mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July. [ABSTRACT FROM AUTHOR]

The atmospheric temperature distribution is typically described by its mean and variance, while higher-order moments, such as skewness, have received less attention. Skewness is a measure of the asymmetry between the positive and negative tails of the distribution, which has implications for extremes. It was recently shown that near-surface temperature in the Southern Hemisphere is positively skewed on the poleward side of the storm tracks and negatively skewed on the equatorward side. Here we take a dynamical approach to further study what controls the spatial structure of the near-surface temperature distribution in this region. We employ a tracking algorithm to study the formation, intensity, and movement of warm and cold temperature anomalies. We show that warm anomalies are generated on the equatorward side of the storm tracks and propagate poleward, while cold anomalies are generated on the poleward side and propagate equatorward. We further show that while the perturbation growth is mainly achieved through linear meridional advection, it is the nonlinear meridional advection that is responsible for the meridional movement of the temperature anomalies and therefore to the differential skewness. The projected poleward shift and increase of the temperature variance maximum in the Southern Hemisphere under global warming is shown to be composed of a poleward shift and increase in the maximum intensity of both warm and cold anomalies, and a decrease in their meridional displacements. An analytic expression is derived for the nonlinear meridional temperature tendency, which captures the spatial structure of the skewness and its projected changes. [ABSTRACT FROM AUTHOR]

• EC-Earth large ensembles are used to drive hydrological models of catchments in Great Britain. • Estimates show the changing chance of unprecedented high temperatures and low rainfall. • Climate storylines describe changes in future dry spring-summers, autumn-winters and multiple dry winters. • Storylines can be used to stress test hydrological systems and inform decision-making. The UK has experienced recurring hydrological droughts in the past and their frequency and severity are predicted to increase with climate change. However, quantifying the risks of extreme droughts is challenging given the short observational record, the multivariate nature of droughts and large internal variability of the climate system. We use EC-Earth time-slice large ensembles, which consist of 2000 years of data each for present-day, 2°C and 3°C conditions relative to pre-industrial, to drive hydrological models of river catchments in Great Britain (GB) to obtain a large set of plausible droughts. Since future warming is certain, the uncertainty in drought is mainly associated with uncertainty in precipitation. Estimates of unprecedented extremes show that the chance of a summer month in a given year drier than the observed driest summer (1995) is projected to increase with future warming (from 9% in the present-day (PD) to 18% in a 3°C warmer world (3C) for southeast England). For winter, the chance of a dry winter month drier than the observed driest winter (1991–92) slightly decreases (from 10% - PD to 8% − 3C for southeast England) but the chance of the driest winter does not change significantly with future warming. We add value to these probabilistic estimates by sampling for physical climate storylines of drought sequences characterised by dry spring-summers, autumn-winters and consecutive dry winters. Dry spring-summers are estimated to become drier with future warming primarily driven by reduced precipitation in summer. Dry autumn-winters may become wetter mainly driven by the general trend of more precipitation in winter although drought conditions triggered by moderate autumn–winter precipitation deficits may worsen given the higher likelihood of being followed by a dry summer. Similarly, drought impacts of consecutive dry winters, a particular risk for slow-responding catchments in the English Lowlands, may worsen with future warming as the intervening summer is projected to become hotter and drier. These storylines can be used to stress-test hydrological systems and inform decision-making. [ABSTRACT FROM AUTHOR]

With climate extremes hitting nations across the globe, disproportionately burdening vulnerable developing countries, the prompt operation of the Loss and Damage fund is of paramount importance. As decisions on resource disbursement at the international level, and investment strategies at the national level, loom, the climate science community's role in providing fair and effective evidence is crucial. Attribution science can provide useful information for decision makers, but both ethical implications and deep uncertainty cannot be ignored. Considering these aspects, we articulate a vision that integrates established attribution methods and multiple lines of evidence within a coherent logical framework., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

There is a widely accepted gap between the production and use of climate information. It is also widely accepted that at least part of the reason for this situation lies in the challenge of bridging between what may be characterized as ''top-down'' approaches to climate information on the global scale, and local decision contexts, which necessarily take a ''bottom-up'' perspective, in which climate change is just one factor among many to consider. We here reflect on the insights provided in a different context-that of economics-by E.F. Schumacher in his celebrated book Small is Beautiful (1973), to see what light they might shed on this challenge, with a focus on climate-change science for adaptation. Schumacher asked how economics might look if it was structured "as if people mattered". We ask the same question of climate-change science, and find many parallels. One is the need to grapple with the complexity of local situations, which can be addressed by expressing climate knowledge in a conditional form. A second is the importance of simplicity when dealing with deep uncertainty, which can be addressed through the use of physical climate storylines. A third is the need to empower local communities to make sense of their own situation, which can be addressed by developing ''intermediate technologies'' that build trust and transparency. Much of climate-change science is necessarily big science. We argue that in order to make climate information useable for adaptation, it is also necessary to discover the beauty of smallness., (© The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences.)

In a recent very influential court case, Juliana v. United States , climate scientist Kevin Trenberth used the "storyline" approach to extreme event attribution to argue that greenhouse warming had affected and will affect extreme events in their regions to such an extent that the plaintiffs already had been or will be harmed. The storyline approach to attribution is deterministic rather than probabilistic, taking certain factors as contingent and assessing the role of climate change conditional on those factors. The US Government's opposing expert witness argued that Trenberth had failed to make his case because "all his conclusions of the injuries to Plaintiffs suffer from the same failure to connect his conditional approach to Plaintiffs' local circumstances." The issue is whether it is possible to make statements about individual events based on general knowledge. A similar question is sometimes debated within the climate science community. We argue here that proceeding from the general to the specific is a process of deduction and is an entirely legitimate form of scientific reasoning. We further argue that it is well aligned with the concept of legal evidence, much more so than the more usual inductive form of scientific reasoning, which proceeds from the specific to the general. This has implications for how attribution science can be used to support climate change litigation. "The question is", said Alice, "whether you can make words mean different things." "The question is", said Humpty Dumpty, "which is to be master - that's all." (Lewis Carroll, Alice's Adventures in Wonderland)., (© The Author(s) 2021.)

Compound flooding arises from storms causing concurrent extreme meteorological tides (that is the superposition of storm surge and waves) and precipitation. This flooding can severely affect densely populated low-lying coastal areas. Here, combining output from climate and ocean models, we analyse the concurrence probability of the meteorological conditions driving compound flooding. We show that, under a high emissions scenario, the concurrence probability would increase globally by more than 25% by 2100 compared to present. In latitudes above 40 o north, compound flooding could become more than 2.5 times as frequent, in contrast to parts of the subtropics where it would weaken. Changes in extreme precipitation and meteorological tides account for most (77% and 20%, respectively) of the projected change in concurrence probability. The evolution of the dependence between precipitation and meteorological tide dominates the uncertainty in the projections. Our results indicate that not accounting for these effects in adaptation planning could leave coastal communities insufficiently protected against flooding., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)

The Southern Hemisphere zonal circulation manifests a downward influence of the stratosphere on the troposphere from late spring to early summer. However, the strength and timescale of the connection, given the stratospheric state, have not been explicitly quantified. Here, SH zonal wind reanalysis time series are analyzed with a methodology designed to detect the minimal set of statistical predictors of multiple interacting variables via conditional independence tests. Our results confirm from data that the variability of the stratospheric polar vortex is a predictor of the tropospheric eddy-driven jet between September and January. The vortex variability explains about 40% of monthly mean jet variability at a lead time of 1 month and can entirely account for the observed jet persistence. Our statistical model can quantitatively connect the multidecadal trends observed in the vortex and jet during the satellite era. This shows how short-term variability can help understand statistical links in long-term changes., (©2019. The Authors.)

Climate science seeks to make statements of confidence about what has happened, and what will happen (conditional on scenario). The approach is effective for the global, thermodynamic aspects of climate change, but is ineffective when it comes to aspects of climate change related to atmospheric circulation, which are highly uncertain. Yet, atmospheric circulation strongly mediates climate impacts at the regional scale. In this way, the confidence framework, which focuses on avoiding type 1 errors (false alarms), raises the prospect of committing type 2 errors (missed warnings). This has ethical implications. At the regional scale, however, where information on climate change has to be combined with many other factors affecting vulnerability and exposure-most of which are highly uncertain-the societally relevant question is not 'What will happen?' but rather 'What is the impact of particular actions under an uncertain regional climate change?' This reframing of the question can cut the Gordian knot of regional climate change information, provided one distinguishes between epistemic and aleatoric uncertainties-something that is generally not done in climate projections. It is argued that the storyline approach to climate change-the identification of physically self-consistent, plausible pathways-has the potential to accomplish precisely this., Competing Interests: I declare I have no competing interests.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a 'storyline' approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change., Competing Interests: Compliance with ethical standardsThe authors declare that they have no conflict of interest.

Lagged correlation analysis is often used to infer intraseasonal dynamical effects but is known to be affected by nonstationarity. We highlight a pronounced quasi 2 year peak in the anomalous zonal wind and eddy momentum flux convergence power spectra in the Southern Hemisphere, which is prima facie evidence for nonstationarity. We then investigate the consequences of this nonstationarity for the Southern Annular Mode and for eddy momentum flux convergence. We argue that positive lagged correlations previously attributed to the existence of an eddy feedback are more plausibly attributed to nonstationary interannual variability external to any potential feedback process in the midlatitude troposphere. The findings have implications for the diagnosis of feedbacks in both models and reanalysis data as well as for understanding the mechanisms underlying variations in the zonal wind.

A recent intercomparison exercise proposed by the Working Group for Numerical Experimentation (WGNE) revealed that the parameterized, or unresolved, surface stress in weather forecast models is highly model-dependent, especially over orography. Models of comparable resolution differ over land by as much as 20% in zonal mean total subgrid surface stress ( τ tot ). The way τ tot is partitioned between the different parameterizations is also model-dependent. In this study, we simulated in a particular model an increase in τ tot comparable with the spread found in the WGNE intercomparison. This increase was simulated in two ways, namely by increasing independently the contributions to τ tot of the turbulent orographic form drag scheme (TOFD) and of the orographic low-level blocking scheme (BLOCK). Increasing the parameterized orographic drag leads to significant changes in surface pressure, zonal wind and temperature in the Northern Hemisphere during winter both in 10 day weather forecasts and in seasonal integrations. However, the magnitude of these changes in circulation strongly depends on which scheme is modified. In 10 day forecasts, stronger changes are found when the TOFD stress is increased, while on seasonal time scales the effects are of comparable magnitude, although different in detail. At these time scales, the BLOCK scheme affects the lower stratosphere winds through changes in the resolved planetary waves which are associated with surface impacts, while the TOFD effects are mostly limited to the lower troposphere. The partitioning of τ tot between the two schemes appears to play an important role at all time scales.

In recent decades, the Arctic has been warming and sea ice disappearing. By contrast, the Southern Ocean around Antarctica has been (mainly) cooling and sea-ice extent growing. We argue here that interhemispheric asymmetries in the mean ocean circulation, with sinking in the northern North Atlantic and upwelling around Antarctica, strongly influence the sea-surface temperature (SST) response to anthropogenic greenhouse gas (GHG) forcing, accelerating warming in the Arctic while delaying it in the Antarctic. Furthermore, while the amplitude of GHG forcing has been similar at the poles, significant ozone depletion only occurs over Antarctica. We suggest that the initial response of SST around Antarctica to ozone depletion is one of cooling and only later adds to the GHG-induced warming trend as upwelling of sub-surface warm water associated with stronger surface westerlies impacts surface properties. We organize our discussion around 'climate response functions' (CRFs), i.e. the response of the climate to 'step' changes in anthropogenic forcing in which GHG and/or ozone-hole forcing is abruptly turned on and the transient response of the climate revealed and studied. Convolutions of known or postulated GHG and ozone-hole forcing functions with their respective CRFs then yield the transient forced SST response (implied by linear response theory), providing a context for discussion of the differing warming/cooling trends in the Arctic and Antarctic. We speculate that the period through which we are now passing may be one in which the delayed warming of SST associated with GHG forcing around Antarctica is largely cancelled by the cooling effects associated with the ozone hole. By mid-century, however, ozone-hole effects may instead be adding to GHG warming around Antarctica but with diminished amplitude as the ozone hole heals. The Arctic, meanwhile, responding to GHG forcing but in a manner amplified by ocean heat transport, may continue to warm at an accelerating rate.

Background: Exposure to solar ultraviolet-B (UV-B) radiation is a major source of vitamin D3. Chemistry climate models project decreases in ground-level solar erythemal UV over the current century. It is unclear what impact this will have on vitamin D status at the population level. The purpose of this study was to measure the association between ground-level solar UV-B and serum concentrations of 25-hydroxyvitamin D (25(OH)D) using a secondary analysis of the 2007 to 2009 Canadian Health Measures Survey (CHMS)., Methods: Blood samples collected from individuals aged 12 to 79 years sampled across Canada were analyzed for 25(OH)D (n = 4,398). Solar UV-B irradiance was calculated for the 15 CHMS collection sites using the Tropospheric Ultraviolet and Visible Radiation Model. Multivariable linear regression was used to evaluate the association between 25(OH)D and solar UV-B adjusted for other predictors and to explore effect modification., Results: Cumulative solar UV-B irradiance averaged over 91 days (91-day UV-B) prior to blood draw correlated significantly with 25(OH)D. Independent of other predictors, a 1 kJ/m² increase in 91-day UV-B was associated with a significant 0.5 nmol/L (95% CI 0.3-0.8) increase in mean 25(OH)D (P = 0.0001). The relationship was stronger among younger individuals and those spending more time outdoors. Based on current projections of decreases in ground-level solar UV-B, we predict less than a 1 nmol/L decrease in mean 25(OH)D for the population., Conclusions: In Canada, cumulative exposure to ambient solar UV-B has a small but significant association with 25(OH)D concentrations. Public health messages to improve vitamin D status should target safe sun exposure with sunscreen use, and also enhanced dietary and supplemental intake and maintenance of a healthy body weight.