Based on an elliptic orbit representation of the yearly varying annual cycles of the Northern Hemisphere stratospheric polar vortex (SPV) from 1979 to 2021, we develop a statistical model to predict the parameters of the SPV’s elliptic orbit on a yearly basis. The predictors include indices describing the phase of key climate modes, such as ENSO and the quasi-biennial oscillation (QBO), as well as the initial state of the polar stratosphere, all derived from prior seasons. Our results demonstrate that the predicted annual SPV evolution, initialized on October 1, provides skillful forecasts with anomaly correlation skill exceeding 0.7 throughout the November-to-March period. In particular, our forecasts can accurately predict the timing and magnitude of peak vortex strength, the timing of the final warming, as well as providing insights into the sub-seasonal evolution of the vortex.

How to cite: Secor, M.: Long-Lead Forecasts of the Yearly Varying Annual Evolution of the Northern Hemisphere Stratospheric Polar Vortex, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3789, https://doi.org/10.5194/egusphere-egu25-3789, 2025.

The Madden-Julian Oscillation (MJO) is a critical component of tropical intraseasonal variability, influencing global weather patterns. This study investigates the interaction between the MJO and oceanic waves, specifically Kelvin waves, using indices from Wheeler and Hendon (2004) for the MJO and Rydbeck (2019) for sea surface height (SSH) anomalies. Our methodology involves cross-referencing the phases of the MJO with the phases of the Kelvin Index. We contrast the MJO days with significant oceanic Kelvin wave activity with those when the Kelvin wave signal is weak. By analyzing these intersections, we aim to elucidate the coupling of oceanic waves and the MJO across the three major ocean basins.

Our findings indicate that during significant Kelvin wave activity, there is enhanced convection and more clearly defined oceanic wave structures within the MJO phases in the Pacific basin. This suggests a strong coupling between atmospheric and oceanic processes, where the presence of Kelvin waves can amplify convective activity associated with the MJO. Additionally, we observed that during periods of weak Kelvin wave signals, the MJO tends to be weaker, with more diffuse wave structures. Conversely, in the Atlantic basin, MJO’s impact on ocean Kelvin waves involves episodes of atmospheric convective anomalies over the Amazon, which tend to be related with stronger MJO previous activity in the western Pacific. For the Indian basin, the methodology is able to discriminate the oceanic Kelvin waves triggered by equatorial wind stress anomalies associated with MJO.

We also evaluate 30-yr long simulations from storm-resolving coupled models performed in the framework of EU-NextGEMS project to evaluate their performance in simulating MJO’s footprint on the ocean.

This study provides new insights into the complex dynamics of the MJO and its interaction with oceanic waves, highlighting the importance of considering both atmospheric and oceanic components in understanding tropical variability. The results have significant implications for improving the predictability of the MJO and its associated weather impacts, offering potential advancements in climate modeling and forecasting.

How to cite: Belinchón Martín, F.: The coupling of MJO with oceanic Kelvin waves in the three major oceanic basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4325, https://doi.org/10.5194/egusphere-egu25-4325, 2025.

In the tropics, deep convection triggers upper-level quasi-stationary Rossby waves that propagate to higher latitudes and influence local climate patterns. This study examines the teleconnection between the Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, and Antarctica, using daily gridded observational datasets (precipitation from CMAP, 250-hPa geopotential height, 2 m air temperature, and 10 m winds from ERA5, and sea ice concentration from NSIDC) and the Linear Response Theory Method (LRTM) across all southern seasons during 1979-2014. Our results reveal that MJO-driven variations in surface temperature and winds substantially affect Antarctic sea ice concentration throughout the year. In austral summer and autumn, significant sea ice responses are evident in both the eastern (Lazarev Sea to Somov Sea) and western Antarctic sectors (Ross Sea, Amundsen Sea, and Weddell Sea). During summer, the most notable sea ice changes occur in MJO phases 1 and 5, while in autumn, the most potent responses are associated with phases 1–3. Conversely, in winter and spring, the sea ice responses are primarily restricted to the western Antarctic sectors (Ross Sea, Amundsen Sea, Bellingshausen Sea, and Weddell Sea). All MJO phases exert a pronounced influence on sea ice in winter, whereas in spring, phases 1 and 5 dominate. The LRTM effectively elucidates the mechanisms underlying these changes, attributing the observed sea ice variability to wind-driven forcing, thermal advection, or their combined effects.

How to cite: Sen, A., Deb, P., Matthews, A., and Joshi, M.: Investigating the role of the Madden-Julian Oscillation in Antarctic sea ice variability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6102, https://doi.org/10.5194/egusphere-egu25-6102, 2025.

In this study, the Betts-Miller-Janjić (BMJ) convective adjustment scheme in the Weather Research and Forecasting (WRF) model version 4.0 was used to investigate the effect of its α-parameter, which influences the first-guess potential temperature reference profile on the Madden‒Julian oscillation (MJO) propagation and structure. This study diagnosed the MJO active phase composites of the MJO-filtered outgoing longwave radiation (OLR) during the December-to-January (DJF) period of 2006–2016 over the Indian Ocean (IO), Maritime Continent (MC), and western Pacific (WP). The results show that the MJO-filtered OLR intensity, propagation pattern, and MJO classification (standing, jumping, and propagating clusters) are sensitive to the α-value, but the phase speeds of propagating MJOs are not. Overall, with an increasing α-value, the simulated MJO-filtered OLR intensity increases, and the simulated propagation pattern is improved. Results also show that the intensity and propagation pattern of an eastward-propagating MJO are associated with MJO circulation structures and thermodynamic structures. As α increases, the front Walker cell and the low-level easterly anomaly are enhanced, which premoistens the lower troposphere and triggers more active shallow and congestus clouds. The enhanced shallow and congestus convection preconditions the lower to middle troposphere, accelerating the transition from congestus to deep convection, thereby facilitating eastward propagation of the MJO. Therefore, the simulated MJO tends to transfer from standing to eastward propagating as α increases. In summary, increasing the α-value is a possible way to improve the simulation of the structure and propagation of the MJO.

How to cite: Zhu, X., Zhong, Z., and Lu, Ｗ.: Effect of Parameter Variation in the BMJ Scheme on the Simulation of MJO Propagation and Structure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7776, https://doi.org/10.5194/egusphere-egu25-7776, 2025.

Tropical cyclones (TCs), which often form over the western North Pacific (WNP), have a large socioeconomic impact and result in destructive damage in East Asian countries. Therefore, it is crucial to estimate TCs characteristics and predict TCs using the model. This study analyzed the subseasonal predictability of TC activities over the WNP region from June to September, using 24 years (1993–2016) of 21-member ensemble hindcasts generated by the Global Seasonal Forecast System version 6 (GloSea6). We analyzed TC activities using dynamic genesis potential index (DGPI) developed by Wang and Murakami, and tracking algorithm (i.e., TempestExtremes (TE)). Compared to IBTrACS best track data, these two methods captured TC genesis points well and showed high correlation in TC genesis density. However, particularly in the South China Sea (SCS), a negative bias was observed in TE, while GPI exhibited a positive or zero bias. Despite using the same input data, different results were observed in this region, and we analyzed the reasons for this discrepancy in two parts. First, why does such a bias occur in DGPI? Second, what causes the differences between DGPI and TE? We used ERA5 data to analyze the relative error and bias of DGPI and examined how westerly wind biases in GloSea6 influenced wind shear and omega errors. In conclusion, one of the key reasons for the differences between the two methods was attributed to the wind shear error induced by the westerly wind bias.

How to cite: Kim, E., Kim, T., and Cha, D.-H.: Subseasonal activities of tropical cyclones over the western North Pacific in GloSea6, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7926, https://doi.org/10.5194/egusphere-egu25-7926, 2025.

Despite recent advances, forecasting European weather on a seasonal timescale remains challenging for both numerical and statistical methods. Weather regimes (WRs), which represent recurrent, quasi-stationary, and persistent states of the atmospheric circulation, are well known to exert considerable influence over the European weather, offering a promising window of opportunity for sub-seasonal to seasonal forecasting. However, while much research has focused on the study of the correlation and the impacts of the WRs on the European weather, the estimation of ground-level climate variables, such as temperature and precipitation, from Euro-Atlantic WRs remains largely unexplored and limited to linear methods.

In this study, we present an AI model designed to reconstruct monthly mean anomalies of the European temperature and precipitation based on the dominant WRs. The model can capture and introduce complex non-linearities in the relation between multiple WRs, describing the state of the Euro-Atlantic atmospheric circulation, and the corresponding surface temperature and precipitation anomalies in Europe. The ability to reconstruct anomalies from WRs constitutes only a portion of the overall challenge. Predicting WRs on a monthly timescale is inherently difficult, and such forecasts are inevitably affected by errors, which can propagate and influence the quality of the reconstructed anomalies. In view of future developments, we examine the effect of inaccuracies in the WRs estimation on the anomalies reconstruction, establishing a lower bound on the WRs prediction accuracy required to outperform the ECMWF seasonal forecast system, SEAS5.

The model utilizes the monthly averages of weather regimes (WRs) to reconstruct the monthly averages of two-meter temperature and total precipitation anomalies during winter (DJF) and summer (JJA). ERA5 and NOAA-CIRES-DOE Twentieth Century Reanalysis datasets are used to compute the WRs and train the AI framework. Using ERA5 as the ground truth, the reconstruction performance is assessed through commonly used metrics, including mean squared error (MSE), anomaly correlation coefficient (ACC), and coefficient of efficiency (CE).

The results presented underline the importance of developing reliable WRs forecasting methods alongside reconstruction models to fully realize the potential of WRs-based forecasting systems. Our findings demonstrate that WRs-based anomaly reconstruction powered by AI-tools offers a viable pathway to better understand and predict seasonal variations.

How to cite: Camilletti, A., Tomasi, E., Franch, G., and Cristoforetti, M.: AI-based reconstruction of European temperature and precipitation anomalies from the Euro-Atlantic weather regimes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9206, https://doi.org/10.5194/egusphere-egu25-9206, 2025.

Summer heatwaves are a major concern for electricity distribution companies due to the high electrical loads they can place on urban distribution networks. These load peaks, driven by increased cooling demand, pose a serious threat to network infrastructure by accelerating the deterioration of underground components. During the summer, these components are prone to failure, resulting in cascading blackouts across multiple urban areas. In addition to meteorological forecasting of heat waves, it is therefore crucial to accurately estimate the probability that the electrical load in urban areas will exceed pre-defined thresholds.

In this study, temperature outputs from sub-seasonal forecasts are used to derive probabilistic forecasts of the expected electrical load. A machine learning approach is used, focusing on a single grid point representing the urban area of Milan. The chosen algorithm is Random Forest, where the target variable is the daily electrical load in Milan. The period used to train and validate the algorithm ranges from 2013 to 2023, and the predictors include the Degree Days (DD) and the "week of the year", since the electrical load shows strong seasonal variations.

The time series of the daily load in Milan, used to train the model, shows a significant shift from 2020 onwards due to the pandemic and the associated lockdowns, resulting in lower load values on average with respect to the 2013-2019 period. To ensure comparability between the pre-pandemic and the post-pandemic period (2021-2023), the historical series were detrended using a seasonal trend decomposition (STL) based on LOESS (Locally Estimated Scatterplot Smoothing), making the series almost stationary over the period analysed.

With the detrended electricity load time series, two forecasting models, both based on Random Forest, were implemented and tested. The first, called the Ensemble Model, trains the Random Forest with the Degree Days (DD) derived from ERA5 temperatures for 2013-2019 and applies the learned relationship to each of the bias-corrected seasonal S2S ensemble members for 2021-2023 to predict the electric load in the test period. The final load prediction in this case is the ensemble mean load.

The second approach, called the Quantile-Based model, uses the quantiles of the DD distribution derived from the bias-corrected S2S temperatures as predictors, providing greater flexibility for different ensemble configurations (e.g. 50 or 100 members). It is also tailored to specific forecast lead times and includes a simplified version based on the DD median.

The models have been evaluated using both deterministic and probabilistic metrics. The results indicate that while both models provide more reliable load forecasts than climatology, the Quantile-Based model outperforms the Ensemble Model beyond the third forecast week. It provides probability distributions that are more centred on the observed load, thereby improving forecast reliability.

These forecasting methods can help distribution system operators to address critical peak demand issues with preventive or more timely interventions.

How to cite: Bonanno, R. and Collino, E.: Probabilistic Load Forecasting for the City of Milan based on Subseasonal Predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10659, https://doi.org/10.5194/egusphere-egu25-10659, 2025.

Focusing on the intraseasonal variability (ISV) of the Siberian high (SH), this study found that the SH presents two main periods of 10–25 and 25–70 days, which dominated the cold waves in the winter of 1995/96 and 2005/06 over eastern China (EC), respectively. The influence of these two ISV on East Asian climate is reflected in the evaluation of the East Asian winter monsoon and surface air temperature. The southeastward and downward Rossby wave activity indicates that the upper-level Ural anticyclone is the key to the SH ISV. By utilizing a transformed vorticity budget analysis, distinct dynamic processes in 10–25-day and 25–70-day variability of the SH were further revealed. Forcing from the mean flow acts as a guiding role in both 10–25-day and 25–70-day variability that induces the Ural anticyclone to propagate westward and eastward, respectively. Forcing from the ISV flow is similar to that from the mean flow with a smaller intensity. The dynamic synoptic eddy feedback positively contributes to both the 10–25-day and 25–70-day variability. It promotes (restrains) the westward (eastward) propagation of the Ural anticyclone in the 10–25-day (25–70-day) variability, which may be the main reason for these two distinct ISV of the SH.

How to cite: Ren, H. and Zhou, F.: Distinct dynamic processes in 10–25-day and 25–70-day variability of the Siberian High, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11278, https://doi.org/10.5194/egusphere-egu25-11278, 2025.

Estimating power load, a crucial variable, is essential for optimizing power grid management, especially when forecasts are made months in advance. Weather conditions significantly influence power load; for example, high temperatures lead to increased energy demand for cooling during the summer. Utilizing seasonal weather forecasts to predict future power load represents a promising research direction in this field.

This study utilizes the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model, which is currently one of the most advanced in seasonal forecasting, to predict power load in a large region of Italy. Given the coarse spatial resolution (~30 km) of SEAS5, developing an application that forecasts the monthly aggregated power load for a large region such as the North Italy market area was appropriate. The method involves calculating degree-days from the predicted temperature and other predictors and then employing a multiple linear regression model to estimate the power load.

The monthly aggregated power load for North Italy is estimated using seasonal forecast data from the ECMWF SEAS5 model at a 0.25° resolution, covering the period from July 2017 to June 2024. The ECMWF SEAS5 system has been providing operational forecasts since 2017, and forecasts are made for horizons ranging from 2 to 7 months ahead. The earlier period (1993-2016) is used for bias correction of the SEAS5 forecasts by comparing them with the ERA5 reanalysis dataset.

Measured load data are retrieved from the European Network of Transmission System Operators for Electricity (ENTSO-E) portal. The data from 2020 are excluded, as they are considered an anomaly, to avoid negatively impacting the training of the forecasting system.

Daily forecast data, predicted 2 to 7 months in advance, are used to calculate degree days and other predictors, which are then translated into predicted power load on a seasonal scale by the multi-linear regression. While daily forecasts at the seasonal scale typically exhibit very low or no skill, we managed to retain some skill by aggregating them over a one-month period. Specifically, this application used forecasts with daily time resolution to estimate monthly cumulative degree days derived from the SEAS5 model data.

The meteorological variables considered include daily maximum and minimum temperatures as well as daily cumulative solar irradiance, spatially aggregated for the area of interest (Northern Italy). To calculate Heating Degree Days (HDD) and Cooling Degree Days (CDD), thresholds of 18°C and 21°C, respectively, were used, reflecting the characteristics of the selected region.

The load forecasting system was evaluated using commonly used metrics, including the mean absolute percentage error (MAPE), mean error, and correlation. The system demonstrates highly promising results, proving to be more skillful up to 7 months ahead compared to climatology and persistency approaches. In these alternative methods, mean meteorological data are used as predictors instead of SEAS5 data (climatology), or the previous year's monthly load observations are directly used as load predictions (persistency).

How to cite: Sperati, S. and Alessandrini, S.: Predicting the Power Load of a Market Area in Italy at the Seasonal Scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15260, https://doi.org/10.5194/egusphere-egu25-15260, 2025.

Accurate weather and climate predictions are crucial for agriculture, water management, and disaster preparedness across Africa. However, several studies have highlighted the need to improve rainfall prediction at short-range, medium-range, sub-seasonal, and seasonal timescales. The inability of numerical weather prediction models to reliably capture probabilities of near-normal rainfall, coupled with their overconfidence, poses a significant challenge for many operational weather and climate prediction centers in Africa.

To address these challenges, we developed a machine learning (ML)-based framework for March–May (MAM) seasonal rainfall forecasting within East Africa Region, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGB) models. These models leverage key climatic indicators, including the Indian Ocean Dipole (IOD), Mozambique Channel Trough (MOZ), and Oceanic Niño Index (ONI), computed as lagged indices (December–January) to capture antecedent conditions driving seasonal rainfall. About fifteen climate drivers computed from winds, soil moisture and sea surface temperatures were used as inputs for the machine learning models outputting MAM seasonal total rainfall.

Feature selection using mutual information scoring identified predictors with the strongest relationships to rainfall variability. Separate ML models were developed for each IGAD country to account for the spatial heterogeneity of climatic drivers, ensuring localized precision. A fair forecast performance of the RF and XGB models was achieved so far and also offering advantages in handling complex non-linear relationships.

This study demonstrates the potential of integrating ML with traditional forecasting methods to address the limitations of current model products, providing improved predictions to inform disaster risk reduction and climate adaptation strategies. By advancing the understanding of rainfall drivers, this work supports actionable decision-making for climate resilience within the East Africa region.

How to cite: Chinyoka, S., Steeneveld, G.-J., Vila-Guerau de Arellano, J., Gudoshava, M., Seid Endris, H., and Atheru, Z.: Advancing March-May seasonal rainfall prediction for the East Africa region through Machine Learning to facilitate agriculture management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21268, https://doi.org/10.5194/egusphere-egu25-21268, 2025.

The Madden-Julian Oscillation (MJO) is the most dominate mode of subseasonal-to-seasonal (S2S) variability, which bridges global weather and climate (Zhang, 2013). The MJO has been recognized as a source of predictability of the global weather on the S2S time scales and can influence onset of the El Nino (e.g., Kerns and Chen, 2021, Jauregui and Chen 2024a, 2024b). We developed a new capability by tracking the multiscale systems like the MJO, atmospheric rivers (ARs), jet stream, the ITCZ, easterly waves, tropical cyclones (TCs), and mesoscale convective systems (MCSs) using Multiscale Objects-Tracking and AI Climate Modeling for Extremes (Mosaic4E). One of the unique capabilities of Mosaic4E is the MJO Large-scale Precipitation Tracking (LPT, Kerns and Chen 2016, 2022) that can identify the MJO large-scale convective heating over the Northern Hemisphere (NH) and Southern Hemisphere (SH) can be used to study teleconnection patterns that are fundamental to extreme rainfall, heat waves and drought, which is not possible with the traditional MJO RMM index. When the MJO convection/precipitation is in the NH, it has a direct impact on the blocking patterns influencing the heatwaves and flooding and drought events. The MJO influence on the global high-impact weather involving heavy rainfall and flooding such as tropical cyclones (TCs) and the atmospheric rivers (ARs) are investigated using Mosaci4E. It is found that the number of TC activities increases 50-100% when MJO LPTs ended up over NH than when is in SH. Similar results are found for the MJO LPTs impacts on the ARs and extreme rainfall over the CONUS. The MJO-LPT represents the S2S time scale bridging the weather and climate and is a key for better understanding and predicting extreme events. The multiscale tracking capability will be enhanced by AI/ML tools in Mosaic4E for identifying, understanding, and predicting the extreme events. Mosaic4E is developed and tested using satellite and in situ observations as well as the ERA5 reanalysis data from 1979-2024. Mosaic4E has shown high skills in coastal flooding over the US and is currently tested globally. The satellite observation and reanalysis data are used to evaluate global weather and climate models.  Results show that most models overproduce precipitation over land in non-LPTs and underestimate large-scale precipitation over the oceans compared with the observations. For example, the MJO contributes up to 40-50% of the observed annual precipitation over the Indio-Pacific warm pool region, which are usually much less in the models because of models’ inability to represent the MJO dynamics. Furthermore, the spatial variability of precipitation ENSO is more pronounced in the observations than models.

How to cite: Chen, S., Kerns, B., Mazza, E., and Jauregui, Y.: Global Impacts of MJO Large-Scale Precipitation on Tropical Cyclones, Atmospheric Rivers, and Extreme Rainfall, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21170, https://doi.org/10.5194/egusphere-egu25-21170, 2025.