This research explores the predictability of seasonal storminess in the North Sea using machine learning methods and the weather model emulator ACE2, focusing on how the stratosphere and upper troposphere influence winter storms. Understanding the drivers of winter storminess is essential for improving sub-seasonal prediction skill in regions strongly affected by extratropical cyclones. Using ERA5 reanalysis data (1940–2024), we built a storminess index based on storm-event frequency, examined its relationship with large-scale atmospheric fields, and explored its predictability on seasonal timescales. We aim to predict North Sea storminess using two approaches: one based on the ACE2 climate emulator and another on the Random Forest machine learning algorithm. The ACE2 simulations show that by decreasing the stratospheric temperature and increasing the wind speed on 1 December, we can increase the emulated mean January surface wind speeds by about 0.5–3 m s−1 across much of the North Sea. Similar sensitivity emulations initialised at the start of other months, e.g. 1 November, failed to produce a meaningful response in the ensuing month. This suggests a dynamic link between early-winter stratospheric conditions and increased mid-winter surface storminess. For the Random Forest regression model, monthly means of air temperature, zonal wind at 70 hPa, and geopotential height at 200 hPa were used as predictors after dimensionality reduction using Principal Components Analysis (PCA). The highest skill was obtained when predicting January storminess from December fields, with a correlation of about 0.55–0.60, while predictive skill was substantially weaker and sometimes negative for other month-to-month predictions. This seasonal predictability pattern, derived from both the ACE2 emulations and the Random Forest model, follows the seasonal cycle of the polar vortex's average intensity. The circumpolar westerly jet strengthens from autumn and peaks in winter, when predictability is highest. This higher skill is likely linked to stronger stratosphere–troposphere coupling that peaks in late December and extends until mid-February, as polar vortex anomalies develop and begin to descend toward the surface. Both indicate that the seasonal predictability of storminess peaks in mid-winter, with a predictability lead time of about 4 to 6 weeks. It fades for the earlier and late winter periods. We found that North Sea storminess, measured as the monthly or seasonal number of storms, is not statistically correlated with storminess in any other region of the globe. This suggests that although individual seasons or months may show increased storminess over large areas, the statistical predictors of North Sea storminess must have a small-scale regional character specific to the North Sea. We also found that the North Sea storminess shows no statistical persistence from month to month. This suggests that although individual winters may display North Sea storminess across all winter months, predictors of North Sea monthly storminess should also have a short-term subseasonal temporal scale. Together, these two findings indicate that sea-surface temperatures are not an adequate statistical predictor of North Sea storminess, although they may play a role in individual winters. Overall, this research shows that stratospheric conditions play an essential role in shaping North Sea winter storminess and that machine learning methods can improve sub-seasonal predictions in this region.
Hailstorms are among the most damaging natural hazards in Switzerland, yet the large-scale processes governing their interannual variability remain poorly understood, limiting the potential for early prediction and risk preparedness. Using a 64-year reconstruction of past hail days (1959–2022) and ERA5 reanalysis data, we identify the dominant atmospheric, oceanic, and land-surface patterns associated with particularly active hail seasons north and south of the Swiss Alps. In both regions, active hail seasons are associated with recurrent large-scale circulation anomalies, characterized by a zonally or meridionally oriented Euro-Atlantic wave train, together with seasonally preconditioned background states in sea-surface temperatures, near-surface temperature, and the mid-tropospheric circulation. These conditions promote repeated occurrences of hail-favorable environments with warm and moist boundary layers, enhanced instability, and moderate convective inhibition. The identified patterns differ significantly from those in hail-sparse seasons and exhibit distinct regional differences: north of the Alps, moisture supply is linked primarily to Atlantic influences and continental evaporation, and the strongest seasonal-scale anomalies occur in temperature, indicating a predominantly temperature-limited regime. South of the Alps, hail activity is associated with frequent elevated dry-layer conditions and a stronger contribution from Mediterranean moisture, occurring in a generally more convection-favorable environment with weaker seasonal-scale anomalies. We further find wintertime precursors of active hail seasons, including continental cooling and Pacific SST anomalies resembling a positive Pacific Decadal Oscillation phase, pointing to low-frequency preconditioning through large-scale teleconnection pathways. Together, these results identify consistent circulation regimes and precursor signals that could underpin (sub-)seasonal hail prediction in Switzerland and Central Europe.
Mass coral bleaching events are becoming increasingly frequent over the Great Barrier Reef (GBR), posing a critical risk to Australia's marine ecosystems and the broader global ocean environment. These events are primarily driven by anomalously warm water temperatures, but their severity is strongly influenced by local cloud cover, which controls the amount of solar radiation reaching the ocean surface (including ultra-violet radiation which exacerbates bleaching). This study presents a characterization of the cloud and rainfall diurnal cycles over north-east Queensland during the coral bleaching season, providing a foundational step to untangling the complex relationships between clouds, rainfall, local-scale processes and the surface energy budget for this climate-sensitive region. Leveraging high-resolution Himawari-8 satellite brightness temperature data, C-band radar observations, and BARRA-R2 regional reanalysis, a multi-year analysis is conducted across three representative zones: coastal land, coastal ocean and open ocean. Results show that diurnal cycles vary distinctly by region and are strongly modulated by prevailing wind regimes. In general, westerly regimes are associated with clear skies and stronger daytime heating over the coastal ocean with enhanced convection over coastal land. In contrast, the frequently observed southeasterlies lead to relatively weaker development over the land and ocean. Cloud and rainfall maxima exhibit out-of-phase behavior between land and ocean, with rainfall often preceding cold cloud tops, indicative that cold brightness temperatures frequently correspond to decaying anvils rather than active convection. Latitudinal and topographic differences contribute to more intense convection near Cairns than Townsville. Variations in inland versus offshore propagation speeds further highlight regional complexity. Our findings emphasize the need for high-resolution simulations to better understand the convection initiation and propagation processes that shape cloud cover and rainfall variability over the GBR during the coral bleaching season, when cloud-radiation interactions may influence reef heat stress.
The teleconnections of the Quasi-Biennial Oscillation are revisited using similar to 65 000 years of model output contributed by four modeling centers to the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). The large ensemble size (at least 10, and in many cases 50) allows isolation of weak signals that are usually hidden by internal variability, as well as better quantification of the role of internal variability in possible model-observation discrepancies in the magnitude of the signals. All four models simulate a Holton-Tan effect, and two of the models also simulate a subtropical downward arching wind horseshoe teleconnection that is most prominent in the Pacific sector. The magnitudes of these teleconnections are statistically indistinguishable from those observed in two of the models but not in the other two; this is a notable improvement from previous work that analyzed small ensembles. These large-scale teleconnections lead to surface temperature and precipitation anomalies over the mid-latitude continents, including an impact on western North America surface temperature which appears to have not been noted before. Furthermore, all models show impacts of the QBO on tropical surface temperature and precipitation, however the nature of these responses differs across the models due, in part, to qualitatively different interactions with El Ni & ntilde;o. Remarkably, one of the models simulates a connection between the QBO and the Madden Julian Oscillation that mimics observations, although it remains too weak. Finally, the LESFMIP simulations allow an exploration of external forcings impacting the magnitude of teleconnections. Among these experiments, greenhouse gas forcing is seen to significantly strengthen the subtropical wind horseshoe of the QBO.
Atmospheric blocking is a key driver of midlatitude weather extremes, including heatwaves and cold spells. Yet general circulation models (GCMs) struggle to capture the frequency, persistence, and spatial characteristics of blocking. Here, we evaluate atmospheric blocking in next-generation storm-resolving Earth system models from the nextGEMS, EERIE, and DestinE projects, focusing on ICON and IFS-FESOM with similar to 10 km atmospheric and similar to 5 km ocean grid spacing. We also provide first insights into the IFS-FESOM under SSP3-7.0 forcing.Blocking frequency, duration, and size are assessed in historical simulations spanning 30 years for IFS and 27 years for ICON, relative to ERA5 reanalysis and a CMIP6 multi-model ensemble of eight models. We further examine links between blocking biases and the background flow, sea surface temperatures (SSTs), and storm-track activity. In the CMIP6 ensemble, persistent biases in blocking frequency, duration, and spatial extent are evident, particularly over the Euro-Atlantic sector, consistent with previous studies. Several of these biases persist in the storm-resolving coupled simulations or are even amplified, indicating that increased horizontal resolution alone does not systematically improve blocking representation. Among the storm-resolving models, performance varies regionally and seasonally. ICON exhibits larger winter biases, including overly zonal jets and an underestimation of Euro-Atlantic blocking compared to IFS. The coupled IFS configuration shows intermediate performance, reproducing some aspects of blocking variability but retaining substantial biases associated with SST errors and jet structure. In contrast, the atmosphere-only IFS simulation (IFS AMIP), which is forced with observed SSTs, reproduces blocking frequency and jet structure more realistically over both the North Atlantic and North Pacific. This highlights the strong sensitivity of blocking to sea surface temperatures and ocean-atmosphere coupling, and underscores the importance of realistic SST boundary conditions for improving blocking representation.Under SSP3-7.0 forcing, IFS projects reduced winter blocking at high latitudes (e.g., northern Europe) and reduced summer blocking frequency over the North Atlantic, northern Europe, and Russia. Changes in magnitude, spatial pattern, and persistence are often of the same order as the model biases, indicating that projected blocking responses are difficult to disentangle from systematic errors related to jet structure, SST biases, and storm-track activity.Overall, storm-resolving models show local improvements in blocking representation, particularly when forced with realistic SSTs. However, coupled simulations still exhibit large biases, underlining the need for further development of ocean-atmosphere coupling representation. These findings highlight both the potential and the current limitations of storm-resolving models for simulating and projecting persistent weather extremes in a warming climate.
Sea ice ensemble forecasts can be highly underdispersive, meaning that the ensemble spread is notably lower than the average forecast error. One common strategy to address underdispersion is to add stochastic perturbations to the forecasts. We detail the implementation of a stochastically perturbed parameterisation (SPP) scheme for SI3, the sea ice component used by the Integrated Forecast System (IFS), the forecast model used and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We then evaluate its impact on seasonal forecasts of Northern Hemisphere summer and winter. The inclusion of SPP is found to enhance ensemble spread for sea ice concentration (SIC) and sea ice thickness (SIT) forecasts by around 10 % relative to a forecast with no SPP, which results in a better calibrated probabilistic forecast. Some small but robust changes to the mean state are also found, including a general decrease in the mean SIC and a redistribution of the winter ice from the central Arctic to the ice edge. These changes reduce or increase the mean bias depending on the region. Changes to the mean and spread of the sea ice result in changes to the mean and spread of air temperature up to at least 850 hPa, altering the mean air temperature biases of the model. An apparent consequence of this is a significant increase in the anomaly correlation coefficients of 500 hPa geopotential height (Z500) over the Euro-Atlantic domain in winter, which partially projects onto the North Atlantic Oscillation. We conclude that sea ice stochastic perturbations can be a valuable contribution to increased reliability of seasonal forecasts of the sea ice itself and can impact seasonal forecasts of the atmosphere at high and mid latitudes.
The development of extratropical cyclones (ETCs) is often significantly altered by diabatic processes, yet the representation of these processes in numerical weather prediction models has been shown to lead to significant forecast biases. To provide a systematic quantification of 12-h ETC forecast errors, this study uses a cyclone-centred composite framework for North Atlantic wintertime (DJF) ETCs using the ERA5 reanalysis for the period 1979 to 2022. Cyclones are categorised into strong and weak diabatic heating at the time of their maximum intensification based on the domain-averaged 70th and 30th percentiles of vertically integrated diabatic heating. While both groups exhibit a systematic underestimation of cyclone intensity, the error structures are markedly distinct. The weak heating group is characterised by an intensity underestimation near the cyclone core, whereas the strong heating group features a pronounced southwestward displacement bias together with a domain-wide intensity underestimation. After removing the displacement bias, the strong heating group exhibits distinct structural errors. In the warm sector, a clear underestimation of moisture transport and temperature, combined with an underdeveloped upper-level ridge, indicates a mis-representation of the intense moisture transport pathways and associated warm-sector moist processes. Conversely, in the cold sector, low-level winds are overestimated within the cold conveyor belt (CCB), sting jet (SJ), and dry intrusion (DI) regions. The wind field biases are accompanied by a pronounced overestimation of 850 hPa kinematic frontogenesis near the centre. The strong frontogenesis is associated with an enhanced secondary circulation and vertical velocity, yielding the overestimation of total column liquid water observed along the bent-back warm front. In contrast, cyclones in the weak heating group exhibit an underestimation of wind speed and moisture near the centre, consistent with the near-centre intensity underestimation. Overall, our findings demonstrate the critical impact of diabatic heating on structural forecast biases, highlighting that the representation of moist processes and the interaction with atmospheric dynamics through diabatic processes is a key area for future model developments.
Tropical cyclones that move into the midlatitudes undergo changes in their structure and transition into extratropical cyclones. The process is known as extratropical transition (ET). ET can result in severe weather locally and also affect the weather downstream. Although the importance of ET has been recognised primarily in the Northern Hemisphere, there are only a handful of studies focusing on the Southern Hemisphere. The current study conducts a comprehensive synoptic-climatological analysis of ET over the Southern Hemisphere. We use a state-of-the-art low-pressure system detection and classification scheme to objectively track tropical cyclones and detect those that undergo ET based on ERA5 data. Our results show that ET preferentially occurs in the southwest Indian Ocean, off the northwest coast of Australia, and in the southwest Pacific. The ET fraction is higher in March–May and lower in January and February, and the latitude of ET also changes strongly with season. The observed seasonality is associated with meridional shifts in the large-scale circulation and sea surface temperature pattern. The changes in structural characteristics and background environment during ET are investigated via cyclone-centred composites. In general, the transitioning cyclone lies on the equatorward side of the jet entrance, with an upper-level trough approaching from the west and a ridge developing downstream. Highly asymmetric fields of vertical velocity and equivalent potential temperature advection are indicative of warm, moist, ascending (cold, dry, descending) air to the east (west), responsible for an increasingly asymmetric precipitation pattern. Case-to-case variability in synoptic configurations at ET is examined by applying K-means clustering on surface and upper-level fields, which identifies four distinct ET clusters. In particular, Clusters 2 and 3 feature the transitioning cyclone with a relatively strong intensity and high precipitation, accompanied by enhanced latent heat release in its southeastern sector. In the upper troposphere, the cyclone-associated divergent outflow impinges on the waveguide and enhances the potential vorticity gradient, leading to downstream jet streak formation and contributing to ridge development.
Weather regimes are quasi-stationary, persistent, and recurrent states of the large-scale extratropical circulation. Weather regimes explain most of the multi-day atmospheric variability on sub-seasonal time scales of 5 to 30 d. While regime definitions have been explored for the European region extensively, in recent years the existence of regimes in other world regions such as North and South America, and East Asia has been confirmed. Importantly, traditional regime definitions focus on a specific season and adapted approaches are needed for year-round applications. Using ERA-Interim reanalysis, Grams et al. (2017) introduced a year-round weather regime definition for the North Atlantic European region which accounts for inter-seasonal differences by construction. The study at hand now provides an update on ERA5 reanalysis data 1979–2019. It newly discusses commonalities, differences, and the rationale behind year-round North Atlantic European regimes compared to the canonical seasonal regimes, and presents a general overview of regime characteristics. The emphasis lays on inter-annual and intra-annual variability of regime occurrence. It is shown that the most extreme weather regime life cycles in terms of duration go along with extreme seasons in Europe, featuring heat waves, cold spells, or storm series. Finally, potential trends in regime occurrence are explored by extending the regime identification to the period 1950–2024. Overall inter-annual variability of regime occurrence dominates and there are hardly significant trends. Only Scandinavian Blocking shows a significant positive trend in summer and autumn in line with expected trends. The trend can be related to the thermal expansion of the troposphere under global warming but is highly sensitive to the methodology used. Next to present new insight in regime occurrence and trends, the paper aims to serve as a basis for subsequent work. It therefore also represents a thorough documentation of the seamless year-round definition of seven North Atlantic European weather regimes, and, accompanied with the open release of data and auxiliary scripts at Zenodo (Grams, 2025), it facilitates an easy start working with the year-round regimes.
This study explores mechanisms by which the Atlantic Multidecadal Variability (AMV) drives multidecadal changes in the West African Monsoon (WAM), with a focus on Sahel rainfall. We investigate the AMV-WAM connection through an energetic perspective using atmosphere–ocean coupled models forced by an idealized AMV sea surface temperature (SST) pattern. Results show that a positive AMV phase (anomalously warm North Atlantic) increases net energy input to the atmosphere via enhanced surface latent heat flux. The atmospheric circulation adjusts by exporting this excess energy from the North Atlantic. In the Tropical Atlantic and Africa, this is accomplished by anomalous southward cross-equatorial energy transport and a northward shift of the Intertropical Convergence Zone (ITCZ). Over West Africa, this ITCZ shift leads to increased and northward displaced Sahel rainfall. The monsoon intensification is dynamically consistent with enhanced low-level convergence and high-level divergence in the main ascent region and a decrease in mid-level dry-air intrusion, linked to a weakening of the shallow meridional circulation over the Sahara.
Abstract. Variability of the stratospheric polar vortex, particularly its dramatic breakdown during sudden stratospheric warming (SSW) events, has been linked to a number of surface weather extremes. However, attributing the role of stratospheric variability in a specific observed weather extreme, rather than an abstracted class of extremes, has proved highly challenging. Here we use an ensemble of subseasonal forecast simulations from 7 forecast systems participating in the Stratospheric Nudging and Predictable Surface Impacts (SNAPSI) project to carry out this task. By comparing the likelihood of extreme events in free-running forecasts to those with the zonal-mean stratospheric state nudged towards its observed or climatological evolution (while the troposphere is freely-evolving), we are able to calculate the changes in the risk and severity of extremes due to the occurrence, or non-occurrence, of an SSW. We focus on three case-study events: (i) the 2018 boreal SSW and subsequent Eurasian cold air outbreak and snowfall, (ii) the 2019 boreal SSW and subsequent North American cold air outbreak, and (iii) the 2019 austral near-SSW and subsequent Australian heat wave. Through an extreme value statistical analysis, we find in all three cases a significant stratospheric contribution to the risk of relevant weather extremes. In case (i), improving the SSW prediction by nudging as much as doubles the forecast risk of extreme Eurasian cold and UK snow. The differences in risk and severity between experiments nudged to the SSW and to climatology are relatively insensitive to the lead time before the cold air outbreak of case (i). By contrast, in case (ii) this difference only emerges at short lead times before the event, indicating a stratospheric influence on this event that is dependent on the tropospheric state. For case (iii) we find a stronger and more robust stratospheric impact on the severity of the Australian heat wave than on its risk, with the latter being highly sensitive to model bias. The methodology outlined here, including both the experimental design and the semi-parametric approaches for calculating risks, can be applied to attribute several other internal climate system drivers of extreme event risk.
The Madden–Julian Oscillation (MJO) is a key driver of global subseasonal-to-seasonal (S2S) climate variability, initiating teleconnections that affect weather patterns worldwide. Improving understanding of the factors that modulate MJO predictability is therefore critical for advancing S2S forecasting systems. Using a multi-model framework, we evaluate changes in MJO prediction skill between two periods (1981–1998 and 1999–2018) during austral summer (December–February) and examine the processes underpinning these differences. Our analysis reveals a pronounced decadal variation in MJO forecast skill, with high-skill years in 1981–1998 showing prediction lead times of around 10 d longer (based on the bivariate correlation of the Real-Time Multivariate MJO (RMM) index) than in 1999–2018, while low-skill years show little change. This asymmetric reduction coincides with stronger MJO amplitude in the earlier period, despite relatively stable model mean-state biases in tropical sea surface temperatures (SSTs) and lower-tropospheric moisture. Key findings include: (1) persistent moisture biases across both periods, yet higher skill in 1981–1998, suggesting that model systematic errors alone cannot explain the differences; (2) a stronger relationship between Quasi-Biennial Oscillation (QBO) and MJO forecast skill in the first period, independent of stratospheric resolution in the models; and (3) weakened coupling between the MJO and large-scale climate modes, including the QBO, El Niño–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), in 1999–2018, indicating reduced dynamical support for prediction. These results suggest that decadal variations in MJO forecast skill are strongly influenced by changes in the background dynamical environment.
Sting jets (SJs) are airstreams that can lead to exceptionally strong and damaging winds in intense extratropical cyclones. Whilst there is extensive evidence that SJ descent can be associated with the release of symmetric instability (SI), the individual diabatic processes driving the onset of this instability have not yet been identified and characterised. In our study we tackle this question by analysing a near-operational numerical weather prediction model simulation of Storm Ciarán (1 November 2023), that featured a SJ associated with damaging winds and characterised by the development of SI (indicated by negative potential vorticity, PV) during its evolution. Diabatic tendencies are included in the output of this simulation and are used in our study, including by being traced on Lagrangian trajectories, to illustrate the contributions of the individual diabatic processes to the onset of SI during the evolution of the SJ. The SJ in our simulation is consistent in terms of magnitude, timing and structure with operational forecasts, observations and literature. This SJ develops in an environment characterised by multiple bands of negative PV in the cloud head. It becomes part of one of such filaments as it ascends near the bent-back warm front before descending off the tip of the cloud head. The decrease in PV along the SJ captured by diabatic tendencies is mainly associated with four moist processes: condensation of water vapour, evaporation of cloud water, melting of ice and snow and sublimation of snow. The first three show large variability across trajectories and, particularly for condensation, positive and negative extremes near the trajectories. The limited decrease in PV caused by the sublimation of snow is instead robust and consistent across trajectories. The reduction in buoyancy caused by the cooling from snow sublimation and melting favours the start of SJ descent, rather than the continuation of ascent. Conditions allowing the formation of SJ-like airstreams from negative PV filaments persist as the storm develops and are associated with condensation and melting. Sublimation of snow is only present when conditions are most favourable and lead to the formation of the main SJ. The decrease in PV observed on the SJ trajectories at this time is only partially captured by diabatic tendencies. This substantial discrepancy exposes the limitations of the methodology and can be ascribed to the use of offline trajectories computed from hourly instantaneous model data in an environment characterised by fast-changing and non-linear processes and by fully three-dimensional and small-scale patterns. This is particularly true in the narrow region near the bent-back warm front and the tip of the cloud head, in which the SJ travels as SI develops along it. In summary, in this study we analyse diabatic tendencies in a model simulation of Storm Ciarán, acknowledging their limitations, to reveal the role of different moist processes in causing the onset of instability on a SJ and therefore driving its intensification. The complex interplay between these processes highlights the unique properties of the narrow region in the cloud head in which the SJ develops before descending towards the surface and bringing damaging winds.
Indian Summer Monsoon (ISM) rainfall is organized across multiple timescales, from diurnal convection to synoptic disturbances, intraseasonal oscillations, and the seasonal mean. Climate models often show different levels of skill at each of these timescales, raising an important question: how do scale-dependent biases shape overall monsoon variability? Here, we assess a medium-resolution (40 km), non-hydrostatic global model (ICON) together with five hydrostatic CMIP6-class models (CNRM, MPI, GFDL, MIROC6, and IITM-ESM; 50–190 km resolution). All simulations are evaluated in AMIP configuration against high-resolution IMERG observations during 1998–2014, allowing isolation of atmospheric sources of rainfall bias. Rainfall errors are strongly scale-dependent and exhibit clear land–ocean contrasts. At the diurnal scale, ICON reproduces amplitudes over the continent with a relatively small bias (∼ 5 %–10 %), whereas MPI overestimates land diurnal amplitude by more than 150 % with premature triggering. The CNRM and GFDL show early daytime convection and weak nocturnal rainfall, while MIROC6 and IITM-ESM exhibit reduced diurnal amplitude linked to convective and resolution limitations. Over the Bay of Bengal, ICON overestimates diurnal amplitude (∼ 60 %) and variance (∼ 180 %), whereas CMIP6 models underestimate nocturnal oceanic variability (amplitude < 40 %, variance < 60 %). At synoptic timescales (2–7 d), models differ in their ability to sustain organized monsoon disturbances. ICON and GFDL maintain realistic spatial structure with moderate suppression, while MPI underestimates synoptic variance by up to ∼ 70 %–80 %. Other models either has weakened synoptic activity or redistribute variability toward intermediate (10–20 d) bands. Across the ensemble, the 20–100 d intraseasonal band is systematically underestimated (by ∼ 30 %–60 %) in the AMIP framework, suggesting that coupled ocean–atmosphere feedbacks, among other factors, contribute to maintaining monsoon intraseasonal oscillations. Seasonal rainfall patterns reflect the combined effect of these multiscale biases. Models that maintain a balanced variance distribution across diurnal, synoptic, and intraseasonal bands show improved seasonal structure, whereas distortions at intermediate frequencies contribute to amplitude and migration errors. These results indicate that credible monsoon simulation depends not only on seasonal-mean accuracy but also on physically consistent variability across timescales. A scale-aware diagnostic framework is therefore essential for improving convective triggering, mesoscale organization, boundary-layer processes, and air–sea coupling in climate models.
Glacier recession gives rise to changes in land surface type and topography that are poorly represented in atmospheric models but may have important local impacts on climate. Implementing these changes in the Weather Research and Forecasting (WRF) model for the Jostedalsbreen ice cap in western Norway results in warmer and drier regional climate with less snow that can amplify glacier recession through a positive feedback effect. Most of the climatic response to glacier recession is related to the surface lowering associated with ice melt, resulting in reduced orographic lifting of moist air masses and higher surface pressure. The climatic response to glacier recession is largest where the ice melts but is also evident in adjacent valleys several kilometers away from the ice cap. While the warming by glacier recession amplifies effects of global warming, reduced precipitation counteracts the projected regional increase in precipitation. These findings should be included in estimates of glacier mass balance and have implications for agriculture, hydropower, tourism, and biodiversity around glacierised landscapes.
Atmospheric rivers (ARs) play a major role in transporting heat and moisture into the Arctic, yet their thermodynamic structure and regional impacts remain poorly understood. Here, we adopt a combined Eulerian-Lagrangian framework to investigate two intense ARs that penetrated into the central Arctic within one week in April 2020 during the MOSAiC field campaign. This study provides a comprehensive view of their large-scale dynamics, moisture sources, and thermodynamic evolution.The first AR entered the Arctic via the Siberian sector, driven by a highly anomalous quasi-stationary anticyclone over north-central Siberia. The second followed an Atlantic pathway and was associated with an unusually deep and persistent cyclone over Baffin Bay. Despite their distinct origins and pathways, both events produced extreme surface impacts, including widespread warming across Eurasia exceeding 9 degrees C over a 7 d period and intense precipitation along the Greenland coast and in the central Arctic. The events coincided with a notable decline in sea ice extent along eastern Greenland and in the Barents-Kara Sea, that is highly correlated with the AR-induced warming and rainfall.Backward trajectory analysis of parcels associated with extreme Arctic precipitation reveals distinct pathways and thermodynamic evolution. During both AR events, a subset of air parcels exhibiting classic AR characteristics is identified. These warm, moist, low-pressure airmasses ascend upon arrival and release intense precipitation. Moisture sources, however, differed by pathway: the Atlantic AR drew from the warm Gulf Stream region, while the Eurasian AR was fed by continental Eurasia. These findings highlight the diverse origins and mechanisms of ARs and their capacity to drive rapid Arctic climate and cryospheric changes.
Extreme temperature swings from one day to the next, whether warming or cooling, can significantly impact human health, ecosystems, and the economy. These effects may become more pronounced or attenuated in the future. Part 1 of this research identified the physical processes - advection, as well as adiabatic and diabatic temperature changes - that cause extreme day-to-day temperature (DTDT) fluctuations in the present climate. Extreme DTDT variations are projected to change under future warming; however, how and which processes drive these changes remain poorly understood. This study addresses this question globally by analysing physical processes in Community Earth System Model Large Ensemble (CESM-LE) simulations under a high-emission scenario, employing both Eulerian composite and Lagrangian backwards-trajectory analyses. The projected changes in (extreme) DTDT variations display a seasonal contrast: weakening in mid- to high latitudes and intensification in the tropics during December-February (DJF), while during June-August (JJA), tropical intensification is more widespread, and only some extratropical locations experience reductions in DTDT variations. The spatial pattern of projected changes in the DTDT variations is mostly associated with changes in the standard deviation of daily temperature, whereas changes in temporal autocorrelation give rise to regional variations in magnitude. In the extratropics during DJF, the weakening of DTDT extremes is mainly driven by reduced advection contributions due to Arctic amplification. However, during JJA, reductions in extremes result from changes in advection, diabatic, and adiabatic processes, with differences between events and regions in their relative contributions. Furthermore, changes in diabatic processes play a significant role in the projected intensification of extremes in JJA over land areas in the tropics and subtropics, while the tropical intensification during DJF results from local changes in diabatic and adiabatic processes. Our findings demonstrate that a regional and seasonal perspective that, in addition to the well-established role of advection, also accounts for diabatic and adiabatic heating processes is essential for understanding projected extreme DTDT changes and for developing suitable adaptation strategies.
The Eastern Mediterranean is a water-scarce, climate-sensitive region. Eastern-Mediterranean cyclones (EMCs) are a major contributor to precipitation totals and weather extremes, including heavy precipitation, strong winds, temperature extremes and dust storms, substantially impacting the population and natural environment. Understanding EMCs' variability and their associated impacts is essential for improving their predictability and forecasts. The large case-to-case variability of cyclone development and associated impacts calls for EMC classification that incorporates dynamical insight into EMC large-scale setting, their track characteristics and associated surface weather. Here we classify EMCs based on their associated upper-tropospheric potential vorticity (PV) structures, providing a novel process-based framework for quantifying cyclone-associated surface weather and extremes. Using the self-organising map (SOM) algorithm to categorise ERA5-based PV distributions during EMCs from 1979-2020, six distinct PV patterns highlight different governing large-scale and synoptic settings. Each EMC type involves distinct mean and extreme surface weather signatures. Two clusters with high PV anomalies over the eastern Mediterranean dominate the regional annual precipitation. A strong ridge upstream of the PV trough during Rossby wave breaking leads to enhanced precipitation, compared to a similar PV configuration with a weak ridge upstream. Temperature anomalies during the cyclone passage are strongly linked to upper-level PV patterns, with certain EMC types causing notable near-surface warm and cold temperature extremes. In transition and warm seasons, occasional extreme localised precipitation is found despite the prevalence of shallow thermal lows with weak upper-tropospheric PV anomalies and generally no/low precipitation amounts. While the annual frequency of EMCs exhibits no significant trend, some clusters show contrasting trends. A notable increase in the frequency of non-precipitating EMCs, indicates a potential shift toward drier, but occasionally more extreme conditions in the region. Through this classification approach the link between EMCs large-scale setting and their surface impacts is systematically mapped. These findings provide a framework for the evaluation of cyclones and their prediction, and may improve strategies for managing the societal and environmental impacts of EMCs at weather and climate timescales.
The global monsoon system is a lifeline for two-thirds of the world's population, as it is essential for tropical water security, food, and agriculture. However, its complex multiscale interactions challenge weather and climate models. This study investigates how horizontal grid spacing (80, 40, and 10 km) in the ICOsahedral Non-hydrostatic (ICON) model affects both the mean and the variability of Northern Hemisphere monsoons across diurnal, intraseasonal, and interannual timescales. All simulations show substantial skill in capturing the global monsoon system domain and its mean annual range of precipitation with a pattern correlation of > 0.7 and RMSE < 3 mm d-1. For the key Northern Hemisphere regional monsoons, South Asia (SAsiaM), West Africa (WAfriM) and North America (NAmerM), ICON achieves an accuracy > 80 % in capturing the observed monsoon domain. Crucially, the impact of grid spacing is strongly region-dependent and non-systematic. The finer grid spacing induces higher mean precipitation biases over continental SAsiaM, and WAfriM. Some of these biases are related to the intensity and location of moist monsoonal low-level jets, as well as their sensitivity to grid spacing. Furthermore, the fine grid spacing overestimates monsoon precipitation variability at interannual and intraseasonal scales, including intense precipitation frequency (> 10 mm d-1). This amplification stems primarily from enhanced grid-scale precipitation resulting from efficient microphysical processes, while convective precipitation exhibits limited sensitivity to grid spacing. Over NAmerM, biases are smaller and show minimal sensitivity to model grid spacing. Increased intraseasonal variance (2-30 d band) in the 10 km simulation is linked to more intense low-pressure synoptic systems over SAsiaM and intense African easterly wave activity over WAfriM. All simulations agree on the diurnal precipitation peak timing, with the 10 km simulation marginally performing better over continents. Our results demonstrate that fine grid spacing alone does not uniformly improve monsoon simulations. Some features, such as the precipitation diurnal cycle, are improved while existing biases in mean precipitation and variability are enhanced. This underscores the role of region-dependent sensitivity of grid spacing governing monsoon dynamics.
Atmospheric blocking often triggers extreme events and remains difficult for weather and climate models to represent due to the complex multi-scale processes in its lifecycle. While recent studies highlight the importance of latent heat release in building and maintaining the upper-level anticyclonic anomaly, different perspectives assign varying roles to dry and moist dynamics, and it is still unclear whether their relative roles differ across regions where blocking occurs. This study uses a quasi-Lagrangian potential vorticity (PV) framework applied to ERA5 (1979-2021) to investigate blocking in the North Atlantic-European sector from the perspective of four large-scale blocked weather regimes. We track negative upper-tropospheric PV anomalies (PVAs-) around blocked regime onset and quantify the processes governing their amplitude changes to assess the roles of dry and moist dynamics. Most PVAs- linked to blocked regime onset are not formed in situ but follow two main pathways, arriving either from upstream or from downstream. PVAs- intensify in the days before onset, with moist, divergence-related PV tendencies associated with warm conveyor belt activity and baroclinic PV tendencies contributing strongly to their amplification, independent of blocked regime type or pathway. The position of PVAs- relative to storm tracks determines the strength of the moist contribution, with moist processes exerting a greater influence within the midlatitude storm track over the North Atlantic. Consequently, the magnitude of PVA- amplification depends more on whether a PVA- arrives from upstream or downstream, since the pathway controls the timing, location, and strength of the moist-dynamical processes acting on it, than on the blocked regime type it eventually contributes to. This study highlights the synoptic-scale moist-dynamical evolution of PVAs- associated with different types of blocked regimes from a quasi-Lagrangian perspective. Complementing the quasi-Lagrangian analysis with previous insights from a Eulerian perspective provides a coherent view of blocked regime evolution, linking the remote moist amplification of PVAs- with the local formation of the regime pattern by anomaly re-arrangement, which is dominated by dry, quasi-barotropic dynamics. Given the key role of moist processes in PVA- amplification and the systematic biases of blocking in weather and climate models, our results emphasize the need for better representation of moist baroclinic eddies and scale interactions, from cloud microphysics to the synoptic scale.
