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Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snow pack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. All models overestimate downward turbulent heat fluxes under stable stratification, a long-standing issue in weather and climate models. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

Marine heatwaves are on the rise: their frequency, intensity, and duration are expected to increase in a warming world. Yet it remains unclear whether local feedback processes could amplify extreme ocean temperatures. A prominent marine heatwave recently occurred in the Northeast Pacific Ocean in summer 2019, exhibiting the highest sea surface temperatures ever recorded in this area since the availability of satellite observations in 1979. Here, we use fully-coupled model experiments, termed “nudged storylines”, in which the evolution of large-scale winds in the free troposphere is nudged to the observed (reanalysed) one before and during the summer 2019 event, to generate close analogues of this record-breaking marine heatwave for past, present, and plausible future climates. We show in particular that future climate analogues of the marine heatwave may warm 50% more than what is expected from the projected global-mean ocean warming. Together with the rapid Northeast Pacific mean warming, air-sea feedback processes lead to a projected warming amplification of 1°C above the 1.9°C global-mean ocean temperature rise. Primary drivers of this amplification are a reduction in clouds and ocean mixed-layer depth, as well as anomalous air advection from fast-warming subpolar regions. Our results show that marine heatwave temperatures may warm substantially faster than the global and regional background temperature, increasing the stress on local ecosystems and fishery resources.

Siberia experienced an exceptionally warm first half of 2020, with temperatures peaking on 20th June, when the weather station at Verkhoyansk registered 38ºC – the highest-ever temperature recorded north of the Arctic Circle. This event led to a state of emergency declaration due to a wide range of natural and human disasters, including permafrost collapse or wildfires. Several previous attribution studies have found anomalous synoptic atmospheric circulation and human-caused climate change as major drivers of this event. However, it remains unclear how such an event would unfold in warmer climates.In this work, we have constructed analogues of this extreme event in past and future climates, so-called “storylines”, employing spectral nudging experiments using the coupled climate model AWI-CM1. In these simulations, large-scale free-troposphere winds are constrained toward ERA5 data, and the model is run for different boundary and initial conditions (pre-industrial, present, +2K, +3K, +4K climates). By doing so, this approach focuses on the less uncertain thermodynamic influence of climate on extreme events, constraining the much more uncertain dynamical changes.Our present-climate simulations realistically reproduce the observed Siberian heatwave and the exceptionally warm conditions before the event. When the different background climates are considered, on the one hand, a robust global warming amplification is obtained until spring. On the other hand, a future global warming dampening or even local cooling is observed in phase with the heatwave peak. We examine the mechanisms that damp warming during the heatwave analogue, especially changes in soil moisture and radiative fluxes.

Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

Extreme weather events are triggered by atmospheric circulation patterns and shaped by slower components, including soil moisture and sea surface temperature, and by the background climate. This separation of factors is exploited by the storyline approach in which an atmospheric model is nudged toward the observed dynamics using different climate boundary conditions to explore their influence. The storyline approach disregards uncertain climatic changes in the frequency and intensity of dynamical conditions, focusing instead on the thermodynamic influence of climate on extreme events. Here we demonstrate an advanced storyline approach that employs a coupled climate model (AWI-CM-1-1-MR) in which the large-scale free-troposphere dynamics are nudged toward ERA5 data. Five-member ensembles are run for present-day (2017–19), preindustrial, +2-K, and +4-K climates branching off from CMIP6 historical and scenario simulations of the same model. In contrast to previous studies, which employed atmosphere-only models, feedbacks between extreme events and the ocean and sea ice state, and the dependence of such feedbacks on the climate, are consistently simulated. Our setup is capable of reproducing observed anomalies of relevant unconstrained parameters, including near-surface temperature, cloud cover, soil moisture, sea surface temperature, and sea ice concentration. Focusing on the July 2019 European heat wave, we find that the strongest warming amplification expands from southern to central Europe over the course of the twenty-first century. The warming reaches up to 10 K in the 4-K-warmer climate, suggesting that an analogous event would entail peak temperatures around 50°C in central Europe. Significance Statement This work explores a new storyline method to determine the impact of climate change on specific recent extreme events. The observed evolution of the large-scale atmospheric circulation is imposed in a coupled climate model. Variations in climate parameters, including ocean temperatures and sea ice, are well reproduced. By varying the background climate, including CO2 concentrations, it is demonstrated how the July 2019 European heat wave could have evolved in preindustrial times and in warmer climates. For example, up to 10°C warmer peak temperatures could occur in central Europe in a 4°C warmer climate. The method should be explored for other types of extreme events and has the potential to make climate change more tangible and to inform adaptation measures.

Under the ongoing climate change, extreme weather events are becoming more prolonged, intense, and frequent; and this trend is expected to continue in a future warmer climate. Several studies have found that the synoptic atmospheric circulation at the time of the event is the main contributing factor in most cases. Moreover, they are shaped by slower processes, including sea-surface temperature and soil moisture, in turn influenced by the history of preceding weather patterns, and by the background climate. The separation of influencing components is exploited by the storyline approach, where an atmosphere model is nudged toward the observed dynamics using different climate boundary conditions. Thus, the storyline approach focuses on the less uncertain thermodynamic influence of climate on extreme events, disregarding the somewhat controversial dynamical changes. This approach provides a very efficient way of making the impacts of climate change more tangible to experts and non-experts alike as events fresh in the people's memory are reproduced in different plausible climates with just moderate computational resources.Spectral nudging experiments have been run with two coupled climate models, AWI-CM-1 and AWI-CM-3. In these simulations, the large-scale free-troposphere dynamics are constrained toward ERA5 data and the model is run for different boundary conditions. Here, the ocean and sea-ice state are consistently simulated, unlike previous studies which employed atmosphere-only models. Our setups reasonably reproduce daily to seasonal observed anomalies of relevant unconstrained parameters, including near-surface temperature, soil moisture or cloud cover. In particular, our configurations showed satisfactory skills in reproducing two different extreme events: the July 2019 European heat wave, and the July 2021 European extreme rainfall. Therefore, this methodology has been applied to study several extreme events in different climates. To do so, nudged simulations are branched off CMIP6 historical and scenario simulations of the same model. For the particular July 2021 extreme rainfall event, we have run five ensemble members for AWI-CM-1-1-MR for dynamical conditions from 1st January 2017 to 31st July 2021 in pre-industrial, present-day, +2K, and +4K climates. These simulations are complemented with similar experiments for AWI-CM-3. The most outstanding finding of these studies is a global warming amplification associated with some events, which exacerbates their exceptionality, especially in a high emission scenario.

Extreme weather events are triggered by atmospheric circulation patterns and shaped by slower components, including soil moisture and sea-surface temperature, and by the background climate. This se...

Under the current global warming trend, heatwaves are becoming more intense, frequent, and longer-lasting; and this trend will continue in the future. In this context, the recent 2019 summer was exceptionally hot in large areas of the Northern Hemisphere, with embedded heatwaves, as for example the June and July 2019 European events, redrawing the temperature record map in western Europe. Large-scale dynamics (associated with blockings or subtropical ridges) play a key role in explaining these-large scale events.Conceptually, global warming can be split into two different contributions: Dynamic and thermodynamic changes. Whereas dynamic changes remain highly uncertain, some thermodynamic changes can be quantified with higher confidence. We exploit this concept by studying how these recent European heatwaves would have developed in a pre-industrial climate and how it would develop in the future for 1.5, 2 and 4 ºC warmer climates (storyline scenarios). To do so, we employ the spectral nudging technique with AWI-CM (CMIP6 model, a combination of ECHAM6 AGCM + FESOM Sea Ice-Ocean Model). Large-scale dynamics are prescribed by reanalysis data (ERA5). Meanwhile, the model is run for different boundary conditions corresponding to preindustrial and future climates along the SSP370 forcing scenario. This approach can be useful to help understand and communicate what climate change will mean to people’s life and hence facilitate effective decision-making regarding adaptation to climate change, as we are quantifying how recent outstanding events would be modified by our climate action. Temperatures during the heatwaves often increase twice as much as global mean temperatures, especially in a future 4 ºC warmer climate. In this future climate, maximum temperatures can locally reach 50ºC in many western Europe countries. Nighttime temperatures would be similar to the daytime temperatures in a preindustrial world. The global warming amplification can be partly explained by a robust soil drying in the future 4 ºC warmer climate (exacerbated due to the June 2019 heatwave) which is transmitted to a robust increase in Bowen ratio. Importantly, by design of our study, this response occurs without any changes in atmospheric circulation.

This paper examines the characteristics of the heatwave that affected western and central Europe in June 2017. Using a novel algorithm, we show that its extension, intensity, and persistence were comparable to those of other European mega‐heatwaves, but it occurred earlier in the summer. The most affected area was Iberia, which experienced devastating forest fires with human casualties and the warmest temperatures of the reanalysis period from daily to seasonal scales. The peak of the mega‐heatwave displayed an unprecedented warm air intrusion due to a record‐breaking subtropical ridge with signatures closer to those of July and August. The atmospheric circulation was the main triggering factor of the event. However, thermodynamical changes of the last decades made a substantial contribution to the event, by increasing the likelihood of surpassing high‐temperature thresholds. This episode could be a good example of a coming future, with high‐summer mega‐heatwaves occurring earlier., This work was supported by the Ministerio de Economía y Competitividad through the PALEOSTRAT (CGL2015‐69699‐R) and ISIPEDIA (ERA4CS) (PCIN‐2017‐046) projects. Ricardo M. Trigo and Pedro M. Sousa are supported by project IMDROFLOOD: Improving Drought and Flood Early Warning, Forecast and Mitigation using real‐time hydroclimatic indicators, JPND‐WaterJPI/0004/2014, financed by FCT/MCTES (PIDDAC). A. Sánchez‐Benítez was funded by grant FPU15/03958 from the Ministerio de Educación, Cultura y Deporte (MECD).

The Canary Islands are characterized by their low availability of water resources and different studies have suggested a decrease of precipitation in the second half of the 20th century. Here, we have used the most complete precipitation data set available in the islands to create a dense high quality and homogeneous database, which was used to determine the spatio-temporal precipitation patterns in the archipelago and the influence of oceanic and atmospheric teleconnections. We have produced a robust regionalization of the Canary Islands precipitation, with three patterns that characterizing the south of the most montainous islands and El Hierro and La Gomera, the north of the most montainous islands and the easternmost arid islands (Lanzarote and Fuerteventura), respectively. All of them show high interannual variability, with no significant trends, except in a few cases. We highlight the strong influence of weather types and teleconections, modulated by the orography, with the highest influence recorded in the first pattern. It must be stressed that the oceanic teleconnections (in special with the tropical North Atlantic surface temperature) are more important than the North Atlantic Oscillation to explain interannual variability of precipitation.