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The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime-mean and extreme-wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the 'Scandinavian Blocking' and 'North Atlantic Oscillation negative' regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 1.5 and 2.0, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-)seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential.

Weather regimes have been defined over multiple regions and used in a range of practical applications, including subseasonal‐to‐seasonal forecasting and climate model evaluation. Despite their widespread use, the extent to which regimes reflect physical modes of the atmosphere is seldom investigated. Here, we adopt a year‐round classification of four North American weather regimes, with a fifth “no regime” class, and leverage dynamical systems theory to investigate their dynamical properties. We find that when the atmospheric flow is assigned to a regime, it displays persistent characteristics and a lifecycle‐like temporal evolution. We further find that, regardless of season, these characteristics are enhanced when the atmospheric flow displays a comparatively strong projection onto the cluster‐mean of the regime to which it is assigned (while the reverse is true for a weaker projection). We interpret these results as evidence that the four North American weather regimes are physically‐meaningful, with a clear dynamical footprint. Plain Language Summary Surface weather and extremes in North America can be related to a small number of atmospheric patterns stretching over scales of thousands of kilometers. These patterns, known as weather regimes, vary over timescales of several days to weeks and occur repeatedly: depending on the exact definition, between a third and a fifth of all days typically fall within a given regime. Weather regimes have a number of practical applications, hinging around making statistical predictions of the weather over a large region several weeks in advance. Even though they have proven useful in practice, there is no simple way of determining to what extent weather regimes are actually linked to the dynamics and physics of the atmosphere. While different definitions of weather regimes exist for different seasons, there have been recent efforts to identify weather regimes that may be applicable year‐round. In this study, we adopt a four‐regime, year‐round classification for the North American continent, and investigate whether the regimes have a clear physical footprint. We find that when the atmosphere falls within a given regime, it displays persistent flow characteristics with a systematic evolution in time. These results support interpreting the regimes as physically‐meaningful, rather than simply a statistical characterization of atmospheric patterns. Key Points Four year‐round North American regimes exhibit a clear dynamical footprint, supporting their interpretation as physically‐meaningful The regimes are persistent atmospheric states with a coherent lifecycle‐like temporal evolution The stronger the atmospheric flow projects onto the assigned regime, the more evident the above properties become

The simulation of quasi-persistent regime structures in an atmospheric model with horizontal resolution typical of the Intergovernmental Panel on Climate Change fifth assessment report simulations, is shown to be unrealistic. A higher resolution configuration of the same model, with horizontal resolution typical of that used in operational numerical weather prediction, is able to simulate these regime structures realistically. The spatial patterns of the simulated regimes are remarkably accurate at high resolution. A model configuration at intermediate resolution shows a marked improvement over the low-resolution configuration, particularly in terms of the temporal characteristics of the regimes, but does not produce a simulation as accurate as the very-high-resolution configuration. It is demonstrated that the simulation of regimes can be significantly improved, even at low resolution, by the introduction of a stochastic physics scheme. At low resolution the stochastic physics scheme drastically improves both the spatial and temporal aspects of the regimes simulation. These results highlight the importance of small-scale processes on large-scale climate variability, and indicate that although simulating variability at small scales is a necessity, it may not be necessary to represent the small-scales accurately, or even explicitly, in order to improve the simulation of large-scale climate. It is argued that these results could have important implications for improving both global climate simulations, and the ability of high-resolution limited-area models, forced by low-resolution global models, to reliably simulate regional climate change signals.

Weather regimes are recurrent and quasi‐stationary large‐scale atmospheric circulation patterns, typically linking to surface weather. Two commonly used sets of weather regimes are wintertime North American and Euro‐Atlantic regimes. Notwithstanding recent evidence pointing to a connection between winter weather in North America and Europe, there is little knowledge on the possible relation between North American and Euro‐Atlantic regimes. Here, we find that specific pairs of North American and Euro‐Atlantic regimes show a close visual and statistical correspondence. Moreover, the joint analysis of the two sets of regimes can provide medium‐range statistical predictability for anomalies in their occurrence frequencies. Conditioning on North American weather regimes also results in anomalies in both the large‐scale circulation during specific Euro‐Atlantic regimes, and the associated European surface weather. We conclude that there is a benefit in conducting joint analyses of North American and European weather regimes, as opposed to considering the two in isolation. Plain Language Summary Wintertime weather in Europe is closely related to large‐scale atmospheric patterns occurring over scales of thousands of kilometers. These patterns, termed weather regimes, are relatively persistent in time, and occur repeatedly. Such weather regimes have been used for many applications, including predicting the weather several weeks in advance. Weather regimes conceptually similar to those identified for Europe have also been determined for North America. In this study, we look at whether and how North American and European weather regimes are related. We show that the two sets of weather regimes have a statistical link, and that accounting for North American regimes can help to gain a more detailed understanding of how European regimes relate to weather in Europe. Key Points Specific pairs of North American and Euro‐Atlantic weather regimes show a close relation A joint analysis of the two sets of regimes can provide statistical predictability for anomalies in their occurrence frequencies North American regimes have a clear footprint on the European surface weather associated with Euro‐Atlantic regimes

Atmospheric prediction at 2–6 weeks lead time (so‐called subseasonal‐to‐seasonal timescales) entails large forecast uncertainty. Here we investigate the flow‐dependence of this uncertainty during Boreal winter. We categorize the large‐scale flow using North Atlantic‐European weather regimes. First, we show that forecast uncertainty of near‐surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy‐driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime‐uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe. Plain Language Summary Weather is chaotic, and forecasts several weeks ahead are quite uncertain. Nevertheless, the degree of uncertainty varies, which can be relevant for long‐term planning in various sectors, including agriculture, energy supply, and public health. Here we show that the degree of uncertainty depends on the weather at the time of forecast start, which we categorize using eight characteristic weather patterns for the North Atlantic and Europe. We analyze a large set of forecasts during winter, with lead times up to 6 weeks. For example, persistent high‐pressure systems over Greenland are known to favor low temperatures over northern Europe. Our results indicate that, in addition, temperatures are highly variable in these cases, leading to unusually high forecast uncertainty. In contrast, uncertainty of near‐surface air pressure tends to decrease due to less frequent storms over the North Atlantic. Furthermore, we show that forecast uncertainty of the weather patterns themselves varies, which is useful when large‐scale flow conditions are more critical than local weather. We analyze forecasts under different circulation conditions in the Arctic stratosphere, as these can have long‐lasting impacts on surface weather. We find forecasts of weather patterns become less uncertain when the circumpolar winds in the Arctic stratosphere are weak. Key Points Ensemble spread of near‐surface weather in subseasonal forecasts is sensitive to the prevailing North Atlantic‐European weather regime Greenland blocking is linked to the smallest ensemble spread of geopotential but to the largest spread of temperature over northern Europe Weak polar vortex states are followed by reduced forecast uncertainty of weather regimes but increased uncertainty of European temperature

Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts. Plain Language Summary In 2017 and 2023, California experienced unexpectedly wet conditions despite predictions of dry winters due to La Niña. In 2016, seasonal predictions in California favored wet conditions due to the very strong El Niño, but the season was normal‐to‐dry statewide. Understanding relationships between El Niño/La Niña and recurring atmospheric weather patterns driving individual storms is needed to improve seasonal forecasts. We studied historical relationships between weather patterns that bring rain and snow to the region and the El Niño Southern Oscillation (ENSO). We find ENSO influences important characteristics of weather patterns once they make landfall in California, making El Niño storms generally wetter in coastal southern California and Desert Southwest. However, ENSO does not strongly affect how often these patterns occur in a season, which makes seasonal precipitation forecasts challenging. The frequency of certain weather patterns not tied to ENSO played important roles in the unusual rainfall of 2017, the heavy snowfall of 2023, and the drier than expected winter of 2016. Understanding these weather patterns provides operationally and scientifically relevant context for future seasonal forecasts by highlighting that while ENSO only minimally influences the frequency of certain impactful storm types, it does change the precipitation characteristics of these storms. Key Points Weather regime type and frequency are key drivers of winter seasons with anomalous precipitation and/or snow accumulation in California ENSO does not modulate the seasonal frequency of weather regimes impacting the coast, presenting a challenge for seasonal forecasting ENSO modulates synoptic circulation characteristics of key weather regimes which produces the canonical ENSO‐precipitation relationship

In this work we performed an analysis on the impacts of blocking episodes on seasonal and annual European precipitation and the associated physical mechanisms. Distinct domains were considered in detail taking into account different blocking center positions spanning between the Atlantic and western Russia. Significant positive precipitation anomalies are found for southernmost areas while generalized negative anomalies (up to 75 % in some areas) occur in large areas of central and northern Europe. This dipole of anomalies is reversed when compared to that observed during episodes of strong zonal flow conditions. We illustrate that the location of the maximum precipitation anomalies follows quite well the longitudinal positioning of the blocking centers and discuss regional and seasonal differences in the precipitation responses. To better understand the precipitation anomalies, we explore the blocking influence on cyclonic activity. The results indicate a split of the storm-tracks north and south of blocking systems, leading to an almost complete reduction of cyclonic centers in northern and central Europe and increases in southern areas, where cyclone frequency doubles during blocking episodes. However, the underlying processes conductive to the precipitation anomalies are distinct between northern and southern European regions, with a significant role of atmospheric instability in southern Europe, and moisture availability as the major driver at higher latitudes. This distinctive underlying process is coherent with the characteristic patterns of latent heat release from the ocean associated with blocked and strong zonal flow patterns. We also analyzed changes in the full range of the precipitation distribution of several regional sectors during blocked and zonal days. Results show that precipitation reductions in the areas under direct blocking influence are driven by a substantial drop in the frequency of moderate rainfall classes. Contrarily, southwards of blocking systems, frequency increases in moderate to extreme rainfall classes largely determine the precipitation anomaly in the accumulated totals. In this context, we show the close relationship between the more intrinsic torrential nature of Mediterranean precipitation regimes and the role of blocking systems in increasing the probability of extreme events.

The North Atlantic sea surface temperature exhibits fluctuations on the multidecadal time scale, a phenomenon known as the Atlantic Multidecadal Oscillation (AMO). This letter demonstrates that the multidecadal fluctuations of the wintertime North Atlantic Oscillation (NAO) are tied to the AMO, with an opposite-signed relationship between the polarities of the AMO and the NAO. Our statistical analyses suggest that the AMO signal precedes the NAO by 10-15 years with an interesting predictability window for decadal forecasting. The AMO footprint is also detected in the multidecadal variability of the intraseasonal weather regimes of the North Atlantic sector. This observational evidence is robust over the entire 20th century and it is supported by numerical experiments with an atmospheric global climate model. The simulations suggest that the AMO-related SST anomalies induce the atmospheric anomalies by shifting the atmospheric baroclinic zone over the North Atlantic basin. As in observations, the positive phase of the AMO results in more frequent negative NAO-and blocking episodes in winter that promote the occurrence of cold extreme temperatures over the eastern United States and Europe. Thus, it is plausible that the AMO plays a role in the recent resurgence of severe winter weather in these regions and that wintertime cold extremes will be promoted as long as the AMO remains positive.

Recently, much attention has been devoted to better understand the internal modes of variability of the climate system. This is particularly important in mid-latitude regions like the North-Atlantic, which is characterized by a large natural variability and is intrinsically difficult to predict. A suitable framework for studying the modes of variability of the atmospheric circulation is to look for recurrent patterns, commonly referred to as Weather Regimes. Each regime is characterized by a specific large-scale atmospheric circulation pattern, thus influencing regional weather and extremes over Europe. The focus of the present paper is the study of the Euro-Atlantic wintertime Weather Regimes in the climate models participating to the PRIMAVERA project. We analyse here the set of coupled historical simulations (hist-1950), which have been performed both at standard and increased resolution, following the HighresMIP protocol. The models’ performance in reproducing the observed Weather Regimes is assessed in terms of different metrics, focussing on systematic biases and on the impact of resolution. We also analyse the connection of the Weather Regimes with the Jet Stream latitude and blocking frequency over the North-Atlantic sector. We find that—for most models—the regime patterns are better represented in the higher resolution version, for all regimes but the NAO-. On the other side, no clear impact of resolution is seen on the regime frequency of occurrence and persistence. Also, for most models, the regimes tend to be more tightly clustered in the increased resolution simulations, more closely resembling the observed ones. However, the horizontal resolution is not the only factor determining the model performance, and we find some evidence that biases in the SSTs and mean geopotential field might also play a role.

Using high temporal resolution satellite observations and reanalysis data, we classify daily weather into distinct regimes and quantify their associated cloud radiative effect (CRE) to better understand the roles of various weather systems in affecting Earth's top‐of‐atmosphere radiation budget. These regimes include non‐precipitation, drizzle, wet non‐storm, and storm days, which encompass atmospheric rivers (AR), tropical storms (TS), and mesoscale convection systems (MCS). We find that precipitation (wet) days account for roughly 80% (60%) of global longwave (LW) and shortwave (SW) CREs due to their large frequency and high intensity in CRE. Despite being rare globally (13%), AR, TS, and MCS days together account for 32% of global LW CRE and 27% of SW CRE due to their higher intensity in LW and SW CRE. These results enhance our understanding of how various weather systems, particularly severe storms, influence Earth's radiative balance, and will help to better constrain climate models. Plain Language Summary Using detailed satellite observations and reanalysis data, we categorize daily weather patterns into different types and measure the cloud radiative effects (CRE) associated with each type. The weather patterns we study include non‐precipitation days, drizzle, wet non‐storm days, and storm days, which include events like atmospheric rivers, tropical storms, and mesoscale convective systems. We found that precipitation days, which include both drizzle and wet days, contribute to about 80% of global longwave (LW) and shortwave (SW) CRE due to their high frequency and intensity. Even though storm days are rare globally (only 13%), they collectively contribute to around 32% of global LW CRE and 27% of SW CRE because of their stronger impact on both LW and SW CRE. These findings are important for understanding how different weather systems influence the Earth's radiation balance and will help improve the accuracy of climate models. Key Points Cloud radiative effect (CRE) associated with various daily weather regimes including atmospheric rivers (ARs), tropical storms (TSs), and mesoscale convection systems (MCSs) are derived using satellite observations and reanalysis data Precipitation (wet) days account for roughly 80% (60%) of global longwave (LW) and shortwave (SW) CRE due to their large frequency and high intensity in CRE Despite their rare occurrence, AR, TS, and MCS days together account for 32% of LW CRE and 27% of SW CRE due to their higher intensity CRE