Heat extremes in Western Europe increasing faster than simulated due to atmospheric circulation trends.

Over the last 70 years, extreme heat has been increasing at a disproportionate rate in Western Europe, compared to climate model simulations. This mismatch is not well understood. Here, we show that a substantial fraction (0.8 °C [0.2°-1.4 °C] of 3.4 °C per global warming degree) of the heat extremes trend is induced by atmospheric circulation changes, through more frequent southerly flows over Western Europe. In the 170 available simulations from 32 different models that we analyzed, including 3 large model ensembles, none have a circulation-induced heat trend as large as observed. This can be due to underestimated circulation response to external forcing, or to a systematic underestimation of low-frequency variability, or both. The former implies that future projections are too conservative, the latter that we are left with deep uncertainty regarding the pace of future summer heat in Europe. This calls for caution when interpreting climate projections of heat extremes over Western Europe, in view of adaptation to heat waves.

Advancing research on compound weather and climate events via large ensemble model simulations.

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events - such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events - require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.

Atmospheric circulation compounds anthropogenic warming and impacts of climate extremes in Europe.

Diagnosing dynamical changes in the climate system, such as those in atmospheric circulation patterns, remains challenging. Here, we study 1950 to 2021 trends in the frequency of occurrence of atmospheric circulation patterns over the North Atlantic. Roughly 7% of atmospheric circulation patterns display significant occurrence trends, yet they have major impacts on surface climate. Increasingly frequent patterns drive heatwaves across Europe and enhanced wintertime storminess in the northern part of the continent. Over 91% of recent heatwave-related deaths and 33% of high-impact windstorms in Europe were concurrent with increasingly frequent atmospheric circulation patterns. While the trends identified are statistically significant, they are not necessarily anthropogenic. Atmospheric patterns which are becoming rarer correspond instead to wet, cool summer conditions over northern Europe and wet winter conditions over continental Europe. The combined effect of these circulation changes is that of a strong, dynamically driven year-round warming over most of the continent and large regional and seasonal changes in precipitation and surface wind.

Ocean and land forcing of the record-breaking Dust Bowl heatwaves across central United States.

The severe drought of the 1930s Dust Bowl decade coincided with record-breaking summer heatwaves that contributed to the socio-economic and ecological disaster over North America's Great Plains. It remains unresolved to what extent these exceptional heatwaves, hotter than in historically forced coupled climate model simulations, were forced by sea surface temperatures (SSTs) and exacerbated through human-induced deterioration of land cover. Here we show, using an atmospheric-only model, that anomalously warm North Atlantic SSTs enhance heatwave activity through an association with drier spring conditions resulting from weaker moisture transport. Model devegetation simulations, that represent the wide-spread exposure of bare soil in the 1930s, suggest human activity fueled stronger and more frequent heatwaves through greater evaporative drying in the warmer months. This study highlights the potential for the amplification of naturally occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in a warmer world.

Revisiting the dynamic and thermodynamic processes driving the record-breaking January 2014 precipitation in the southern UK.

Many attribution studies of precipitation extreme events have attempted to estimate the thermodynamic contribution (linked to temperature changes) and the dynamic contribution (linked to the atmospheric circulation). Those studies are based on statistical decompositions of atmospheric fields, and essentially focus on the horizontal motion of the atmosphere. This paper proposes a framework that decomposes those terms from first physical principles, which include the vertical atmospheric motion that has often been overlooked. The goal is to take into account the driving processes of the extreme event. We revisit a recent example of extreme precipitation that was extensively investigated through its relation with the atmospheric circulation. We find that although the horizontal motion plays a minor (but important) role, the vertical motion yields a dominating contribution to the event that is larger than the thermodynamic contribution. This analysis quantifies the processes leading to high winter precipitation rates, and can be extended for further attribution studies.

The hammam effect or how a warm ocean enhances large scale atmospheric predictability.

The atmosphere's chaotic nature limits its short-term predictability. Furthermore, there is little knowledge on how the difficulty of forecasting weather may be affected by anthropogenic climate change. Here, we address this question by employing metrics issued from dynamical systems theory to describe the atmospheric circulation and infer the dynamical properties of the climate system. Specifically, we evaluate the changes in the sub-seasonal predictability of the large-scale atmospheric circulation over the North Atlantic for the historical period and under anthropogenic forcing, using centennial reanalyses and CMIP5 simulations. For the future period, most datasets point to an increase in the atmosphere's predictability. AMIP simulations with 4K warmer oceans and 4 × atmospheric CO2 concentrations highlight the prominent role of a warmer ocean in driving this increase. We term this the hammam effect. Such effect is linked to enhanced zonal atmospheric patterns, which are more predictable than meridional configurations.

Dynamical proxies of North Atlantic predictability and extremes.

Atmospheric flows are characterized by chaotic dynamics and recurring large-scale patterns. These two characteristics point to the existence of an atmospheric attractor defined by Lorenz as: "the collection of all states that the system can assume or approach again and again, as opposed to those that it will ultimately avoid". The average dimension D of the attractor corresponds to the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of D has proved challenging. Moreover, D does not provide information on transient atmospheric motions, such as those leading to weather extremes. Using recent developments in dynamical systems theory, we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at lead times of over two weeks. Instantaneous properties of the attractor therefore provide an efficient way of evaluating and informing operational weather forecasts.

Attribution of extreme weather and climate-related events.

Extreme weather and climate-related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human-induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23-41. doi: 10.1002/wcc.380 For further resources related to this article, please visit the WIREs website.

A model-tested North Atlantic Oscillation reconstruction for the past millennium.

The North Atlantic Oscillation (NAO) is the major source of variability in winter atmospheric circulation in the Northern Hemisphere, with large impacts on temperature, precipitation and storm tracks, and therefore also on strategic sectors such as insurance, renewable energy production, crop yields and water management. Recent developments in dynamical methods offer promise to improve seasonal NAO predictions, but assessing potential predictability on multi-annual timescales requires documentation of past low-frequency variability in the NAO. A recent bi-proxy NAO reconstruction spanning the past millennium suggested that long-lasting positive NAO conditions were established during medieval times, explaining the particularly warm conditions in Europe during this period; however, these conclusions are debated. Here, we present a yearly NAO reconstruction for the past millennium, based on an initial selection of 48 annually resolved proxy records distributed around the Atlantic Ocean and built through an ensemble of multivariate regressions. We validate the approach in six past-millennium climate simulations, and show that our reconstruction outperforms the bi-proxy index. The final reconstruction shows no persistent positive NAO during the medieval period, but suggests that positive phases were dominant during the thirteenth and fourteenth centuries. The reconstruction also reveals that a positive NAO emerges two years after strong volcanic eruptions, consistent with results obtained from models and satellite observations for the Mt Pinatubo eruption in the Philippines.

Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms.

The rapid development of wind energy has raised concerns about environmental impacts. Temperature changes are found in the vicinity of wind farms and previous simulations have suggested that large-scale wind farms could alter regional climate. However, assessments of the effects of realistic wind power development scenarios at the scale of a continent are missing. Here we simulate the impacts of current and near-future wind energy production according to European Union energy and climate policies. We use a regional climate model describing the interactions between turbines and the atmosphere, and find limited impacts. A statistically significant signal is only found in winter, with changes within ±0.3 °C and within 0-5% for precipitation. It results from the combination of local wind farm effects and changes due to a weak, but robust, anticyclonic-induced circulation over Europe. However, the impacts remain much weaker than the natural climate interannual variability and changes expected from greenhouse gas emissions.

Historical phenology: grape ripening as a past climate indicator.

French records of grape-harvest dates in Burgundy were used to reconstruct spring-summer temperatures from 1370 to 2003 using a process-based phenology model developed for the Pinot Noir grape. Our results reveal that temperatures as high as those reached in the 1990s have occurred several times in Burgundy since 1370. However, the summer of 2003 appears to have been extraordinary, with temperatures that were probably higher than in any other year since 1370.