Recent studies have used ensemble-spread-based diagnostics to understand atmospheric sources of uncertainty, and its growth and propagation. We aim to provide confidence in the diagnostics-based approach, and understand its limitations, by comparing Eulerian and Lagrangian geopotential height uncertainty growth diagnostics calculated from 12 different ensemble prediction systems available in The International Grand Global Ensemble. In addition to a Lagrangian growth-rate diagnostic that uses the ensemble mean velocity (LGR), an alternative form (LGR2) is derived that includes all the uncertainty transport terms and that, for variables with tracer-like characteristics, also represents the non-conservative sources of uncertainty. Good agreement between ensemble prediction systems is found in the magnitude and distribution of all growth-rate diagnostics applied in the midlatitude and polar regions for a lead-time range starting at 48 hr and ending at between 96 hr and 192 hr, with the best agreement for the largest ensemble size of 50 members. In these situations, ensemble-spread-based diagnostics provide a consistent measure of uncertainty growth. However, at shorter and longer lead times, and in tropical regions, ensemble prediction system dependence is greater. In addition, LGR and LGR2 are compared for the fields of geopotential height and potential vorticity (a tracer-like variable) using data from the European Centre for Medium-Range Weather Forecasts operational archive. For geopotential height, LGR2 is found to include a significant non-advective component; therefore, restricting the use of this diagnostic to variables that have tracer-like characteristics is recommended. This study provides confidence in, and constraints on, the use of spread-based diagnostics for understanding the sources and transport of uncertainty growth that limit predictability.

Abstract. A new dataset, Reanalysis-Augmented Best Tracks for Tropical Cyclones (RABTracks), is created for the study of Tropical Cyclones (TCs) globally. The goal of RABTracks is to bring together multiple sources of information on historical tropical cyclones in one place. To do so, we augment the International Best Track Archive for Climate Stewardship (IBTrACS) with track data from five analyses (four reanalyses and one operational analysis), using two different tracking methods, in all basins, from 1940 to present. This provides us, in particular, with extended tracks that reconstruct the early and late stages of each recorded cyclone. It is also an opportunity to gather information on cyclones’ intensity, size, and to retrieve information about the cyclones’ nature at different stages, derived from the Cyclone Phase Space (Hart, 2003), and SyCLoPS classification (Han and Ullrich, 2025). This dataset has a high potential to support robust TC-related climatological studies. It was also designed to be easy to extend with additional data, and all the code for creating the dataset, making the analyses and adding new data is provided in an open-source repository.

A method for identifying tropical cyclones (TCs) from cyclone tracks is introduced which is based solely on the physical structure of the cyclone and not tuned to maximize the match between reanalysis, which poorly capture TC structure and intensity, and recorded TCs (IBTrACS), which excludes some tropical storms. The method addresses two questions: Does the cyclone have TC structure (deep warm core and symmetric, WCS)? Is it intensifying (Warm core, symmetric, and intensifying: WCSI)? Intensification is important to remove decaying extratropical cyclones (ETCs) which can exhibit WCS structure. The WCSI criteria matches 73-75% of IBTrACS TCs in reanalysis data (ERA5), but 18% of WCSI tracks have no match in IBTrACS. These scores are mostly limited by the data, not the WCSI criteria. Firstly, ERA5 strongly underestimates the intensity of TCs, and most missed TCs are weak storms, have underestimated intensity in ERA5, or both. Applying WCSI to JRA3Q (a reanalysis that assimilates IBTrACS) gives fewer misses, although JRA3Q still underrepresentents TC intensity. Secondly, more than half of the WCSI tracks not matching IBTrACS correspond to recorded tropical disturbances that did not develop into TCs (invests). After accounting for tracks matching to invests, only 7% of tracks are false alarms and correspond to (in order of importance) unrecorded tropical disturbances, Indian monsoon lows, and polar lows. This method is a consistent framework that can be used for investigating TCs in larger datasets where there are no observations, such as climate simulations, or unseen events in ensemble forecasts and hindcasts.

Equatorial waves are a driver of heavy rainfall events in Southeast (SE) Asia. These heavy rainfall events can be very damaging, resulting in landslides and flooding, but remain challenging to predict. Previous studies have demonstrated the role single equatorial waves play in heavy rainfall and the usefulness of this information in aiding forecasting. Less is known about the impacts when multiple equatorial waves co-occur. Here, dynamically identified equatorial waves are used to construct a "compound" equatorial wave phase space to explore statistically how different combinations of the phases of Kelvin, equatorial Rossby, and westward-moving mixed Rossby-gravity (WMRG) waves are related to the intensity of precipitation in the SE Asia region. A demonstration of the methodology focuses on heavy rainfall in boreal winter in Peninsular Malaysia in relation to Kelvin and WMRG co-occurrence. We show that heavy rainfall is significantly increased in Peninsular Malaysia during coincident Kelvin and WMRG waves, with heavy rainfall probability increased by up to seven times relative to climatology, compared with twice when activity from only one wave type is considered. Other regions profoundly impacted by wave co-occurrence include the South Philippines, Sumatra, and Borneo; rainfall in 14 of the 15 regions examined showed significant relationships between heavy rainfall and compound wave occurrence. Moreover, it is found that in some cases the occurrence of both waves together, rather than a single wave alone, is a requirement for increased heavy rainfall probability. A table summarising the findings for all individual regions of SE Asia is produced and can aid forecasters in forecasting extreme weather events.

The North Atlantic jet (NAJ), a fast-flowing westerly wind in the upper troposphere, influences the movement of extreme weather systems and affects the safety of commercial flights. In recent decades, the summer NAJ exhibited a substantial equatorward shift over the eastern Atlantic, contrasting with the poleward shift of zonal-mean jet. However, whether this equatorward shift is driven by external forcing or internal variability remains unclear. Here, we show that the recent equatorward shift of the summer NAJ was dominated by internal climate variability. Using simulations from state-of-the-art numerical models and reanalyses, we identify the recent decadal component of the North Atlantic warming hole in summer as the key factor. Through turbulent heat release, this distinctive pattern altered the local atmospheric thermal structure, causing the shift of the summer NAJ via thermal wind response and eddy feedback. However, this internal variability-dominated situation is not expected to persist. Our results indicate that as early as the 2050s, the latitudinal shift of the summer NAJ is projected to emerge beyond the range of internal climate variability.

In recent years there has been an advance towards coupled Earth-system models for weather forecasting. For example, the European Centre for Medium-Range Weather Forecasts now implements ocean-ice coupling with dynamic sea ice in the Integrated Forecasting System (IFS) at all time ranges. This has the potential to improve weather forecasts in the polar regions, where sea ice influences the overlying atmosphere directly by turbulent exchange, especially in the rapidly warming summer-time Arctic where thinner and more mobile ice is susceptible to rapid change. In this study, we investigate the sensitivity of IFS (cycle 47r1) weather forecasts to sea-ice coupling representation in the summer-time Arctic by comparing three sets of forecasts that are coupled with (i) dynamic sea ice in operational configuration, (ii) static sea ice, and (iii) dynamic sea ice with additional thermodynamic (surface temperature and albedo) coupling. It is found that dynamic sea ice improves predictions of sea ice and the ice edge compared with persistence, especially in the vicinity of Arctic cyclones. The dynamic sea-ice forecasts exhibit lower near-surface temperatures (up to 0.5 degrees C) compared with static sea-ice forecasts where ice loss has occurred, and differences in near-surface winds of up to 0.5 ms-1$$ \mathrm{m}{\mathrm{s}}<^>{-1} $$, consistent with changing surface roughness over the marginal ice zone. The forecasts with additional thermodynamic coupling have near-surface temperatures that are up to 1 degrees C cooler over ice than the operational configuration (correcting a known warm bias), consistent with a more stable boundary layer (BL) and weaker near-surface winds. The influence of sea-ice coupling above the BL is small, with differences in cyclone forecasts being smaller than the spread of the operational ensemble. This study highlights the influence of ocean-ice coupling in weather forecasts for the summer-time Arctic, and the potential gains from improving its representation.

Coupled Numerical Weather Prediction (NWP) models have only recently been implemented for short-term environmental prediction and both challenges and benefits are evident in polar regions. Their simulation of surface exchange over sea ice depends on the model's sea-ice characteristics, however these are hard to constrain due to a lack of in situ and accurate remotely sensed observations. We focus on the Fram Strait region during peak melt conditions and during the passage of an Arctic cyclone: very challenging conditions for coupled NWP. We use in situ aircraft observations from the Arctic Summertime Cyclones field campaign in July-August 2022, plus satellite products, to evaluate a set of 5-day forecasts from the Met Office Unified Model. Our model set ups are based on operational GC4 (Global Coupled 4) and developmental GC5 (Global Coupled 5) configurations, which use the CICE5.1 and SI3 sea-ice models respectively. We find a combination of deficiencies in the simulated sea-ice field, due to initialization and modeling problems. An initially low concentration of sea ice results in excessive absorption of shortwave radiation by the ocean, leading to excessive basal melting of the sea ice, and further sea-ice loss; leading to relatively poorly simulated sea-ice fields in general. In contrast, the passage of an Arctic cyclone and its impact on sea-ice velocities are captured well. Although we demonstrate several deficiencies in the short-term forecasts of two state-of-the-art coupled NWP models, we also find promising aspects of model performance and some clear benefits from a fully coupled atmosphere-ice-ocean system.

The dynamics of atmospheric disturbances are often described in terms of displacements of air parcels relative to their locations in a notional background state. Modified Lagrangian Mean (MLM) states have been proposed by M. E. McIntyre using the Lagrangian conserved variables potential vorticity and potential temperature to label air parcels, thus avoiding the need to calculate trajectories explicitly. Methven and Berrisford further defined a zonally symmetric MLM state for global atmospheric flow in terms of mass in zonal angular momentum (z) and potential temperature (θ) coordinates. We prove that for any snapshot of an atmospheric flow in a single hemisphere, there exists a unique energy-minimising MLM state in geophysical coordinates (latitude and pressure). Since the state is an energy minimum, it is suitable for quantification of finite amplitude disturbances and examining atmospheric instability. This state is obtained by solving a free surface problem, which we frame as the minimisation of an optimal transport cost over a class of source measures. The solution consists of a source measure, encoding surface pressure, and an optimal transport map, connecting the distribution of mass in geophysical coordinates to the known distribution of mass in (z, θ). We show that this problem reduces to an optimal transport problem with a known source measure, which has a numerically feasible discretisation. Additionally, our results hold for a large class of cost functions, and generalise analogous results on free surface variants of the semi-geostrophic equations.

We evaluate the skill and jumpiness of the ECMWF medium-range ensemble (ENS) in predicting tropical cyclone genesis in the Atlantic basin. Focusing on the probabilistic performance of the ENS, we assess how far in advance the ENS can predict genesis, quantify the consistency (jumpiness) from run to run, and investigate what factors influence the skill and consistency. We find that first indications of genesis are picked up at least 7 days ahead in 50% of the observed cases, although strong signals often only appear less than 3 days before genesis. There are significant regional differences, with observed genesis events predicted 2-3 days earlier in the eastern Atlantic than in other areas. The genesis probabilities can be jumpy from run to run, and the jumpiest cases are in the more skillful regions (central and eastern Atlantic) and for situations where the initial signal for genesis appears at longer lead time. In the eastern Atlantic, there is a tendency for the ENS tracks to reach tropical storm strength earlier and further east than observed; this model bias can affect both skill and jumpiness of the genesis forecasts. Our results provide guidance to forecasters on how to use and interpret the ENS predictions. Areas for future work include the link between early intensification in the eastern Atlantic and African easterly wave activity, the relationship between skill and the TC development pathways, and the impact of systematic analysis differences between 0000 UTC and 1200 UTC on forecast intensity. SIGNIFICANCE STATEMENT: Forecasting where and when tropical cyclones will appear increases the lead time at which decision-makers can begin to take preparatory mitigating action. Numerical weather prediction models can provide important guidance but sometimes are not consistent from one run to the next. We evaluate the skill and consistency of a state-of-the-art global model in predicting the formation of tropical cyclones up to 10 days ahead and provide guidance to forecasters on how to use and interpret the model predictions. We show that the formation of tropical cyclones can be predicted 2-3 days earlier in the eastern Atlantic than in the western Atlantic and identify some of the factors influencing both skill and consistency.

Melt ponds play a key role in the Arctic sea-ice surface energy budget. Their reduced albedo compared to the surrounding ice and snow surfaces increases the absorption of short-wave radiation and enhances ice melt. Further, melt ponds affect atmosphere-ice-ocean surface turbulent exchanges of heat, moisture and momentum, which influence the structure of the overlying boundary layer. Simulation of melt ponds and surface exchange over sea ice in coupled numerical weather prediction models depends on parameterization schemes that need further development. However, the relationship between sea ice surface conditions and the overlying boundary layer is difficult to constrain due to the lack of in-situ observations in Arctic regions. We carried out the Arctic Summertime Cyclones project field campaign in July-August 2022 to make observations of sea-ice surface exchange and cyclone dynamics. Using the British Antarctic Survey MASIN Twin Otter aircraft we observed a range of sea ice surface types, some with a very high melt pond fraction during warm melt conditions, and the overlying atmospheric boundary layer. Using these observations to evaluate forecasts from the UK Met Office Unified Model, we show that a combination of deficiencies in the model sea ice field, melt pond representation and surface exchange parameterizations are linked to errors in the simulated boundary layer structure. In particular, the model consistently exhibits surface temperature and albedo biases over sea ice with melt ponds that act as sources of error in the surface energy budget.

Using reanalysis and model data, this study investigates potential vorticity (PV) generated by diabatic cooling over the Tibetan Plateau (TP) and its associated climatic effects on adjacent regions. Results suggest that anomalous diabatic cooling over the TP exhibits a bottom-heavy vertical structure, which leads to the generated positive PV anomaly being confined within a shallow near-surface layer. The total PV generation anomaly within this layer is determined by the anomalous surface PV generation flux that is thermally driven by anomalous surface cooling. Further analysis revealed a significant interannual relationship linking stronger near-surface PV generation over the TP with anomalously warm winters in eastern China. The proposed mechanism involves PV generation by surface cooling, PV transport by katabatic flows, and an attendant weakened Siberian High. The surface cooling anomaly over the TP generates a positive PV anomaly within the near-surface layer, while simultaneously induces a form of anomalous katabatic flow down the slopes of the TP. This anomalous katabatic flow transports positive PV within the near-surface layer into the region of the Siberian High, increasing the local positive PV anomaly and weakening the Siberian High. A weaker Siberian High is associated with weaker winter monsoon flow and anomalous southerlies, leading to a warm surface air temperature anomaly over eastern China. The validity of the proposed mechanism was corroborated by numerical modeling. Relative to the well-studied winter blocking effect of the TP, this study offers and emphasizes new aspects of the TP's climatic effects driven by its wintertime thermal conditions. SIGNIFICANCE STATEMENT: The purpose of this study was to better understand the thermal effects of the Tibetan Plateau on surrounding regions during winter. We found that wintertime surface cooling over the Tibetan Plateau generates positive potential vorticity and induces downslope katabatic flows within the near-surface layer. The generated potential vorticity transported by these katabatic flows exerts substantial climatic impacts on surrounding areas. This result importantly reveals that the wintertime thermal effects of the Tibetan Plateau could be critical in driving the climate of eastern China. In addition to consideration of the blocking effect of the Tibetan Plateau, our results suggest that greater attention should be given to its thermal effects during wintertime.

Near stationarity of large-scale Rossby waves can be associated with extreme seasons e.g. high seasonal rainfall or persistent hot or cold weather. Here we investigate the dependence of quasi-stationary waves on the structure the background zonal flow. Idealised experiments are performed using a global primitive equation model with a weak relaxation to a baroclinically unstable background zonal mean state in which the latitude and strength of the jet can controlled. This unstable background state generates sustained wave activity through repeated baroclinic lifecycles.To link the structure of the jet the evolution of Rossby waves, modes of variability are extracted using the Empirical Normal Mode (ENM) technique. This technique extracts the dominant modes of variability which like dynamical modes are orthogonal with respect to a psuedo-momentum (wave activity) norm. Due to the choice of norm these modes possess an intrinsic linear phase-speed determined by their structure in the same way it would be for dynamical modes. It is found that despite the non-linearity of the simulations the flow is dominated by a few ENMs whose depend systematically on the background state jet latitude and strength in a manner which can be well understood through the dynamics of modes. It is suggested that seasons with anomalously persistent mid-latitude Rossby wave activity may be related to the interannual variability in the background state zonal flow.

Critical infrastructure, such as telecommunications networks and hospitals, are in many cases required to have reserve power systems in place, mitigating transmission network failures and protecting against national power grid outage. A previous case study of Great Britain (GB) telecommunications assets implemented a temperature-driven model of infrastructure electricity demand (Fallon et al., 2023), used to plan reserve capacity installation sufficient to meet the highest anticipated 5-day periods of energy consumption (or other regulatory targets). Extending this work with climate models (UKCP18), we demonstrate that the capacity planning framework reliant upon reanalysis observations underestimates capacity installation appropriate to meet historic weather risk, while assessments are improved using historic period climate model outputs. Additionally, climate projections simulating future periods support further upgrading the installed reserve capacity beyond historic requirements. Quantile-correcting bias adjustments of climate model outputs can address significant discrepancy between the model world and observations temperature distributions across the historic period (model timespan where global climate matches recent observations). Uncorrected, this climate model error leads to an exaggerated frequency of extreme temperature events, hence overestimating the reserve capacity requirement. But under a quantile-correcting approach, assuming a consistent underlying representation of the weather dynamics, the temperature distribution is adjusted to match the reanalysis distribution. Temperature delta-shifts are calculated to represent the GB historic period climate variability observed across model ensemble members. The resulting infrastructure electricity demand timeseries are compared against timeseries produced from historic period temperature data adjusted by quantile delta mapping, demonstrating that reanalysis data alone is insufficient to capture the greater reserve capacity requirements predicted by quantile delta-mapping of climate model outputs in the historic time period. Using future period climate model outputs, we compare three alternative treatments of model temperature timeseries simulating future climate: a delta-shift adjustment of reanalysis data, a regional trend-preserving mean bias adjustment, and quantile delta mapping. In each case, reserve capacity requirements increase (5% to 10% increase in a world 2.0°C above pre-industrial temperatures). There is significant variability across different model ensemble members, and sensitivity to individual weather years. Reserve system operators can use the approaches outlined to make an informed assessment of the need for upgrading or installing new reserve systems, ensuring the stability and resilience of critical infrastructure assets. The consistent trajectories across different approaches and model ensemble members may improve confidence in results, whilst individual model ensemble members can be investigated to identify potential ‘worst case’ outcomes.

Recent work within the WCSSP FORSEA project and its successor FORWARDS has demonstrated that a hybrid statistical-dynamical forecasting technique combining model ensemble forecasts of equatorial waves with climatological rainfall statistics conditioned on wave phase and amplitude can provide additional skill in predicting high impact weather. The underlying rationale for the technique is twofold. Firstly that high impact rainfall events in the tropics are commonly associated with presence of equatorial waves; and secondly that while global models can adequately predict the evolution of dynamical structure of equatorial waves on time-scales of several days they do not predict the relationship between waves and rainfall well. In tests using the Met Office Global and Regional Forecasting System (MOGREPS) the hybrid forecast is found to outperform model rainfall forecasts from both the global and regional convection permitting versions of MOGREPS, however a weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill and further improvements in the method may be obtained by taking into consideration the effects of wave-superposition and interaction. To ascertain whether forecasts can be further improved by better predictions of wave amplitude and phase we compare to hypothetical best-case hybrid forecast computed using wave amplitudes and phases taken from reanalysis. This best-case scenario indicates that errors in forecasting all wave types diminish the hybrid forecast's skill, with the most significant reduction observed for Kelvin waves, suggesting that a significant improvement in the prediction of the propagation of equatorial waves would have a significant impact on rainfall prediction in the tropics.

The THINICE field campaign, based in Svalbard in August 2022, provided unique observations of summertime Arctic cyclones, their coupling with cloud cover, and their interactions with tropopause polar vortices and sea ice conditions. THINICE was motivated by the need to advance our understanding of these processes and to improve coupled models used to forecast weather and sea ice, as well as long-term projections of climate change in the Arctic. Two research aircraft were deployed with complementary instrumentation. The Service des Avions Fran & ccedil;ais Instrument & eacute;s pour la Recherche en Environnement (Safire) Aerei da Trasporto Regionale 42 (ATR42) aircraft, equipped with the radar-lidar (RALI) remote sensing instrumentation and in situ cloud microphysics probes, flew in the midtroposphere to observe the wind and multiphase cloud structure of Arctic cyclones. The British Antarctic Survey Meteorological Airborne Science Instrumentation (MASIN) aircraft flew at low levels measuring sea ice properties, including surface brightness temperature, albedo and roughness, and the turbulent fluxes that mediate exchange of heat and momentum between the atmosphere and the surface. Long-duration instrumented balloons, operated by WindBorne Systems, sampled meteorological conditions within both cyclones and tropospheric polar vortices across the Arctic. Several novel findings are highlighted. Intense, shallow low-level jets along warm fronts were observed within three Arctic cyclones using the Doppler radar and turbulence probes. A detailed depiction of the interweaving layers of ice crystals and supercooled liquid water in mixed-phase clouds is revealed through the synergistic

Recent work has demonstrated that skilful hybrid statistical-dynamical forecasts of heavy rainfall events in Southeast Asia can be made by combining model forecasts of the phases and amplitudes of Kelvin, Rossby, and westward-moving Rossby gravity waves with climatological rainfall statistics conditioned on these waves. This study explores the sensitivity of this hybrid forecast to its parameter choices and compares its skill in forecasting extreme rainfall events in the Philippines, Malaysia, Indonesia, and Vietnam to that of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The hybrid forecast is found to outperform both the global and convection-permitting ensemble in some regions when forecasting the most extreme events; however, for less extreme events, the ensemble is found more skilful. A weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill of all for almost all definitions of extreme event and in most regions. To quantify the influence of errors in the predicted wave state on the skill of the hybrid forecast, the skill of a hypothetical best-case forecast was also calculated using reanalysis data to specify the wave amplitudes and phases. This best-case forecast indicates that errors in the forecasts of all wave types reduce the skill of hybrid forecast; however, the reduction in skill is largest for Kelvin waves. The skill in convection-permitting models is greater than for global models in the regions where Kelvin waves dominate, but the added value of limited-area high-resolution forecasts is hampered by the poor representation of Kelvin waves in the parent global model. We compare probabilistic forecasts of extreme precipitation within southeast Asia for a global and convection-permitting ensemble and compare them with the forecasts of a hybrid statistical-dynamical method (hybrid model) based on equatorial wave information. We highlight which types of equatorial waves are most relevant for the various regions within southeast Asia and show that this hybrid model outperforms both the global and convection-permitting model in some regions within southeast Asia when forecasting the most extreme events.image

Ensemble clustering is an efficient ensemble post-processing approach that distils an ensemble forecast into its prevalent forecast scenarios by grouping similar ensemble members together - something which is increasingly important in a world where ensemble data volumes are rapidly increasing. The application of a suitable clustering method combined with appropriate forecast visualisation allows a forecaster to effectively focus on the key possible outcomes, simplify the message and characterise and communicate forecast uncertainty more easily. For example, output may be presented as (1) probabilities of each cluster occurring, (2) representative or central members from each cluster showing alternative forecast directions, and (3) probabilities of threshold exceedance under each cluster.Operationally, the Met Office runs a probabilistic weather pattern forecasting tool called Decider (Neal et al., 2024), where ensemble members are clustered according to their allocation to one of 30 predefined weather patterns (circulation types). This allows for changes in the large-scale circulation to be identified at a range of lead times and is useful for many downstream applications where the same weather patterns have been related to specific impacts. To be used alongside this, a prototype feature-based clustering approach is being trialled, which is the focus of this talk. Here, k-medoids clustering is applied to Fractions Skill Score (FSS) distances between members, using identified features in each member. These features may represent areas of hazardous weather or mesoscale or synoptic features, such as areas of heavy rainfall, damaging wind speeds, or weather fronts. The methods being trialled follow those initially developed by Boykin (2022) in PhD work at the University of Reading in collaboration with the Met Office, and include several post-processing steps. These steps are designed to (1) determine spatial distances between objects and cluster on those distances, (2) identify an optimal number of clusters, (3) identify windows of interest where clusters become more distinct, and (4) identify representative members within each cluster, within the time window, to provide plausible forecast scenarios or evolutions with associated probabilities. Multiple configurations of the prototype feature-based clustering approach have been trialled and early results will be discussed.ReferencesBoykin, K.A. (2022) Extracting likely scenarios from Ensemble Forecasts in real time. PhD in Atmosphere, Oceans and Climate, Department of Meteorology, University of Reading, DOI: 10.48683/1926.00111270.Neal, R., Robbins, J., Crocker, R., Cox, D., Fenwick, K., Millard, J., Kelly, J. (2024) A seamless blended multi-model ensemble approach to probabilistic medium-range weather pattern forecasts over the UK. Meteorological Applications, 31(1), e2179.

Jet streams play an important role in determining weather variability and extremes. A better understanding of the mechanisms driving long-term changes in the jet is essential to successfully anticipate extreme meteorological events. This study analyzes the intensification trend of the North Atlantic jet using the ERA5 reanalysis and investigates the dynamical mechanisms involved. The results highlight the importance of an increase in diabatic heating in the free troposphere below the jet entrance over the Gulf Stream sector. This change in diabatic heating modifies the jet directly and produces a local intensification and a slight poleward shift. A two-dimensional frontal-geostrophic model illustrates this mechanism by considering the enhanced diabatic heating associated with the baroclinic growth of extratropical cyclones. The change in diabatic heating also affects the jet indirectly by increasing the mean baroclinicity and subsequent eddy momentum flux convergence. This indirect mechanism has also an effect downstream, where there is an acceleration of the jet core and reduced westerlies along the flanks, reducing the width of the jet. An idealized warming experiment confirms this mechanism by determining the jet response downstream of an idealized land-sea contrast. Finally, using a single-model ensemble of fully coupled climate simulations, we show that the differences in the evolution of the North Atlantic jet are related to the latitude of the increase in baroclinicity, which has a large spread. What emerges from the model hierarchy is a consistent dynamical chain of mechanisms associated with the intensification trend of the North Atlantic jet stream.

The Borneo vortex (BV) is a synoptic-scale vorticity feature found in the South China Sea near Borneo during extended Boreal winter, which can bring heavy rain to the region. Predicting this rainfall is difficult. Therefore, a better understanding of the structure of these vortices and their interaction with equatorial waves could aid forecasters. Here we divide the BVs found from 41-years of October-March ERA5 data into five clusters based on their tracks identified using relative vorticity maxima. These clusters capture distinct phenomena: vortices moving westwards across the South China Sea, vortices tracking along the north and northwest sides of Borneo, vortices sitting on the west side of Borneo, and vortices that initiate on the northwest side of Borneo, cross the equator and track eastwards along the south coast of Borneo. These clusters have a strong seasonal dependence related to the strength and southward propagation of the northeasterly flow and therefore cold-surge type. The Madden-Julian oscillation (MJO) is considerably less important than the cold surge for modulating vortex frequency but has a similar order of magnitude impact on vortex rainfall. Kelvin waves strongly modulate rainfall from all BVs. Westward-moving mixed Rossby-gravity (WMRG) and Rossby n = 1 (R1) waves modify frequency, rainfall, and vorticity through modification of environmental vorticity and northeasterly flow. These properties are highest when the BV is within or on the leading edge of the positive vorticity phase of R1 waves (in the northern hemisphere) or WMRG waves. Westward-moving vortices north of 4 degrees N are often embedded in and move with R1 or WMRG waves. Examining case studies in detail, we find BVs typically extend upward to 500-400 hPa but can reach to 300 hPa, and those near the equator may not always have closed streamlines. Under vertical wind shear they may tilt, usually to the west.

Accurate representation of near-tropopause fields is important for the forecast skill of numerical weather prediction models, yet there remain significant systematic forecast errors in the region of the tropopause. Although extratropical near-tropopause humidity, temperature, and wind model biases have been documented from several models, more knowledge of their causes is required as a step towards reducing these biases. Typically, a moist bias is present in the lowermost stratosphere in the analyses used to initialise forecasts, which leads to a growing cold bias in the lowermost stratosphere over the course of the forecasts due to long-wave radiative cooling. Experiments are conducted with the European Centre of Medium-Range Weather Forecasts global forecast system where the humidity in a layer 0-4 km above the tropopause in the extratropics is reduced to correct the moist bias in the initial conditions. In these experiments, the lowermost stratosphere cold bias growth is halved compared with the control and gradually remoistens, returning to typical analysis values with a half-life of around 8-9 days. The reduction in cooling in the lowermost stratosphere is due to a reduction in long-wave radiative emission from the water vapour above the tropopause. The main contributors to the remoistening are resolved advective transport and parametrised turbulent mixing in the model, the cloud microphysical process rates being similar in the modified and control experiments. The biases in near-tropopause moisture transport are almost independent of horizontal resolution. These results show that the temperature bias in the extratropical lower stratosphere can be reduced by correcting the collocated moist bias, but the moist bias cannot be fixed by solely correcting the initial conditions and further model improvements are also required to reduce cross-tropopause moisture transport. A moist-biased extratropical lowermost stratosphere in operational forecasts leads to the development of a collocated cold bias. Here, we apply an initial correction to the specific humidity of the model that reduces the cold bias; however, water vapour is reintroduced to the lower stratosphere of the free-running model during the 15-day forecast period at a rate insensitive to horizontal resolution. It is shown that vertical diffusion and advection are key contributors to this model moisture bias. image