Greenland blocking (GL) resembles the negative phase of the NAO and features a strong positive Z500 anomaly over Greenland and a zonally aligned negative anomaly stretching from the eastern North Atlantic into Northern Europe. The prevailing westerly flow is then deflected southward and extends into the Mediterranean. It causes melting events of the Greenland Ice Sheet which can impact global sea-level rise and has strong downstream impacts on Europe. It occurs year-round, although is more common in winter (11.7%) compared to summer (9.1%). GL is forecast with good ability by S2S models. This skill is driven by the performance in winter, when GL is persistent. In this study, we explore whether the skill of GL blocking can be linked to external meteorological drivers or the prevalence of specific meteorological features. Re-forecasts using the European Centre for Medium-Range Weather Forecasts for the 1999-2019 period are considered and compared against ERA Interim reanalysis over the same period. We focus on the factors affecting the skill, as depicted by the Brier Skill Score, from lead times 6 to 10 days, where the skill is 30% to 70% smaller than the skill at lead time 1 day.

Results show that most of the GL blocking events associated with low skill occur in spring. In this season, the model fails in forecasting the transition from Scandinavian Blocking to Greenland Blocking, in opposition to the rest of the seasons, when this transition is well predicted. The analysis of the role of large-scale processes that affect GL skill reveals that half of the forecasts of GL events initialized up to 30 days after a sudden stratospheric warming shows poor skill. In addition, the forecasts of GL events initialized with an active MJO in phase 6 and 7 present good skill whereas those forecast GL events initialized during an active MJO in phase 2 to 4 show poor skill. This link between large-scale factors and skill offers potential guidance in operational forecasting.

How to cite: Osman, M., Grams, C. M., and Beerli, R.: Factors influencing sub-seasonal forecast skill of Greenland Blockings, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5397, https://doi.org/10.5194/egusphere-egu23-5397, 2023.

We implement deep neural networks to forecast monthly precipitation over Europe. This architecture conformed by several convolutional layers and fully connected layers uses four different variables (surface temperature, west-east wind at 200 hPa, precipitation and sea level pressure) coming from seven different operational forecast systems (1. ECCC 2. MeteoFrance  3. DWD  4. JMA 5. NCEP  6. ECMWF  7. CMCC). The neural network is trained using observations from E-OBS, a gridded land-only observational dataset covering the whole European continent. This convolutional neural network is trained using the period 1993-2012 and the validation period is 2013-2016, which is the range that is available for all operational forecast systems. 

Comparing with precipitation from observations we show that forecasted precipitation from this Deep-Learning model shows small biases in the whole European continent when forecasting monthly precipitation, especially over Sweden (with a small overestimation of less than 0.2 mm/day). With some higher negative biases over Southern Europe (<-1 mm/day). In turn, the representation of the mean precipitation over specific months and seasons was also assessed, showing that during the validation period this method is able to reproduce properly the spatial features of mean precipitation over Europe and its intensity.

How to cite: Fuentes-Franco, R. and Zimmermann, K.: Deep-learning-based monthly precipitation forecast for Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1187, https://doi.org/10.5194/egusphere-egu23-1187, 2023.

The Indian Ocean Dipole (IOD) is a major source of seasonal climate variability in the
Indian Ocean. This dipole has strong impacts on the Indian Ocean region and through
teleconnections can influence the seasonal climate of remote regions like the North Pacific
and North Atlantic. A prominent example of this teleconnection from the IOD occurred
in the winter 2019/2020, where the IOD was in a positive state. This influenced the state
and predictability of the Northern Hemisphere extratropics. Thus, a good understand-
ing of the mechanism that transports information from the Indian Ocean to the North
Atlantic is desirable. In this contribution we investigate the special teleconnection of the
winter 2019/2020 and analyse the transport mechanism.
In model experiments with the OpenIFS from ECMWF we show that the NAO in the
winter 2019/2020 is influenced by the IOD and analyse the teleconnection mechanisms.
We use hindcast ensemble model experiments of the DJF season 2019/2020 to analyse
the behaviour of the IOD and its impact on the NAO. In the uncoupled OpenIFS the Sea
Surface Temperature (SST) boundary conditions are perturbed in regions of importance
to the NAO (like the ENSO region and the Indian Ocean). With these perturbations we
identify the relative importance of individual ocean regions to the state of the NAO in
the winter of 2019/2020.
We contrast the experiments with the perturbed SST conditions to the operational ECMWF
System5 forecast and ERA5. Experiments with the 2019/2020 SST’s in the In-
dian Ocean (with other boundary conditions set to climatology) reproduce many of the
observed atmospheric 2019/2020 features. In contrast, experiments with SST’s in the
Pacific show very different patterns to the observed 2019/2020 ones.
We identify eddy-mean-flow interactions as a mechanism that connects and transports
information from the Indian Ocean to the North Atlantic. With Hoskins E-Vectors we
show that anomalous eddy activity during IOD events impacts the position and strength
of the Northern Hemisphere extratropical jet. This interaction provides a teleconnection
mechanism in addition to the Rossby-wavetrain discussed in other studies.

How to cite: Hempel, T., Weisheimer, A., and Palmer, T.: The seasonal teleconnections of the Indian Ocean Dipole to the North Atlantic region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5665, https://doi.org/10.5194/egusphere-egu23-5665, 2023.

Madden-Julian Oscillation (MJO), an intraseasonal oscillation over the equatorial Indian ocean and Pacific, has profound impacts around the globe. Its extended-range life cycle (20-90 days) makes it the most important predictability source on subseasonal-to-seasonal timescales. While the mechanisms responsible for MJO's life cycle have been well  explored through the frameworks of moisture modes, and tropical wave dynamics, the mechanisms of initiation remain unsolved. By using linear inverse modeling (LIM) and incorporating different frameworks, this study investigates the processes resulting in MJO convection initiation. It is suggested that multi-scale interactions play a vital role in intraseasonal convection initiation over the Indian ocean. On intraseasonal timescales, the remnant of former MJO can create an environment favoring the convection development for the next event through modulating the prevailing circulations and moisture state (e.g., moisture advection). On shorter timescales (< 20 days), the optimal initial condition arises from the synoptic convergence/divergence of moisture flux, and the upper troposphere instability. 

How to cite: Chang, C.-H. and Tseng, K.-C.: The Optimal Initial Condition of MJO Development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3039, https://doi.org/10.5194/egusphere-egu23-3039, 2023.

As first explored by Thompson and Barnes (2014), hemispheric mean storm activity (or related quantities, such as the meridional eddy heat flux) exhibits periodicity on 20-30 day time scales.  They characterized this variability with the so-called Baroclinic Annular Mode, or BAM, a ring of enhanced eddy activity which is present in both hemispheres, but most pronounced in the south, which is less perturbed by zonal asymmetries relative to the north.   The mechanism behind this internally generated periodicity, however, has remained elusive.  We probe the dynamics and structure of the BAM on two fronts.  To understand the mechanism, we develop a minimal model that captures the essential dynamics: 2 layer quasi-geostrophic flow in a channel. By varying the geometry of the channel and the thermal and frictional forcing, we tease out the parameters that control the period and amplitude of the BAM.  The resulting changes in the BAM support the general framework of the charge-discharge mechanism proposed by Thompson and Barnes, but demand a more detailed explanation for the coupling between eddies and the mean baroclinicity that generates enhanced variability on subseasonal time scales.  On a second front, we apply dynamical mode decomposition (DMD) to atmospheric reanalyses of the Southern Hemisphere to quantify the structure of the southern BAM.  DMD captures BAM variability, providing additional information on relationships between the eddy kinetic energy and other mean and eddy quantities.  It suggests that moisture plays a fundamental role in the relationship between the eddy activity and baroclinicity, and that changes in stratification are more important than horizontal temperature gradients in the dynamics.    In this sense, the underlying BAM dynamics of vacillation between eddy and potential energy are remarkably robust, active in our moist atmosphere and in dry quasi-geostrophic systems where only the meridional temperature gradient can capture the available energy.

How to cite: Gerber, E., Youngs, M., and Pauluis, O.: The Dynamics and Structure of the Baroclinic Annular Mode, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6161, https://doi.org/10.5194/egusphere-egu23-6161, 2023.

Across East Africa, sub-seasonal rainfall variability predominantly depends on the phase of the Madden Julian Oscillation (MJO). Rainfall is enhanced during MJO phases 2 to 4, and suppressed during phases 6 to 8. Given that MJO-induced anomalous precipitation can persist beyond several days, a surface response is expected. Using earth observations and reanalysis data, in this presentation we will show how MJO-induced precipitation anomalies promote a surface response which feeds back onto local and regional atmospheric conditions.

              MJO-induced rainfall suppression across East Africa decreases surface soil moisture across the exit region of the Turkana jet. Reduced soil moisture increases surface sensible heat fluxes and elevates land surface temperatures. The drier and warmer surface reduces surface pressure and leads to an intensification of the Turkana jet. We conclude that on average approximately 11% of the anomalous jet speed is associated with surface-driven pressure fluctuations over the course of a single day. Since the Turkana jet controls moisture transport from low-lying regions of East Africa into Central Africa, we highlight that surface-induced jet variations impact rainfall totals across East Africa. Furthermore, due to the Turkana jet response to spatial variations in surface warming, we also identify that the magnitude of MJO-induced anomalous precipitation is influenced by surface conditions prior an MJO event. For example, when the surface over southern South Sudan is anomalously dry, MJO-induced precipitation suppression is greater. This presentation will highlight that to fully exploit predictability from the MJO, forecast models must correctly represent surface processes and land-atmosphere interactions. Future work evaluating sub-seasonal forecast models and improving the representation of land-atmosphere interactions will enhance the lead-time of early warning systems across East Africa.

How to cite: Talib, J., Taylor, C., Harris, B., and Wainwright, C.: MJO-induced land-atmosphere feedbacks across East Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1102, https://doi.org/10.5194/egusphere-egu23-1102, 2023.

Tropical cyclone precipitation (TCP) contributes a significant fraction of total annual rainfall and also is a frequent cause of extreme precipitation in many parts of the tropics. The climatology of TCP in the S2S models is characterized by dry biases in the North Atlantic and wet biases in most other basins,  specially in the Southern Indian Ocean and Australia. 
Biases in total precipitation (P), TCP and their ratio (TCP/P) are mostly positive in the multi-model ensemble mean and change very little with lead time. in these models the frequency biases are the dominant contribution to TCP biases. However, in some models, there are positive biases in average precipitation per each TC which contribute significantly to TCP biases at equatorial latitudes.

The prediction skill of these reforecasts is evaluated using skill scores such as the ranked probability skill score for TCP and the Brier Skill score for genesis and occurrence. The implication of these results is discussed for their relevance to mean and extreme precipitation prediction skill using S2S models.

How to cite: Garcia-Franco, J. L., Lee, C.-Y., Camargo, S., Tippett, M., Kim, D., Molod, A., and Lim, Y.-K.: Tropical cyclone precipitation skill in S2S models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7102, https://doi.org/10.5194/egusphere-egu23-7102, 2023.

Combining ensemble forecasts from several models has been shown to improve the skill of S2S predictions. One of the most used method for such combination is the “pooled ensemble” method, i.e. the concatenation of the ensemble members from the different models. The members of the new multi-model ensemble can simply have the same weights or be given different weights based on the skills of the models. If one sees the ensemble forecasts as discrete probability distributions, then the “pooled ensemble” is their (weighted-)barycenter with respect to the L2 distance.
Here, we investigate whether a different metric when computing the barycenter may help improve the skill of S2S predictions. We consider in this work a second barycenter with respect to the Wasserstein distance. This distance is defined as the cost of the optimal transport between two distributions and has interesting properties in the distribution space, such as the possibility to preserve the temporal consistency of the ensemble members.
We compare the L2 and Wasserstein barycenters for the combination of two models from the S2S database, namely ECMWF and NCEP. Their performances are evaluated for the weekly 2m-temperature over seven winters in Europe (land) in terms of different scores. The weights of the models in the barycenters are estimated from the data using grid search with cross-validation. We show that the estimation of these weights is critical as it greatly impacts the score of the barycenters. Although the NCEP ensemble generally has poorer skills than the ECMWF one, the barycenter ensembles are able to improve on both single-model ensembles (although not for all scores). At the end, the best ensemble depends on the score and on the location. These results constitute a promising first step before implementing this methodology with more than two ensembles, and ensembles having less contrasting skills.

How to cite: Le Coz, C., Tantet, A., Flamary, R., and Plougonven, R.: Optimal transport for the multi-model combination of sub-seasonal ensemble forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13445, https://doi.org/10.5194/egusphere-egu23-13445, 2023.

Boreal summer Intraseasonal Oscillation (BSISO), with its 20–90 day periodicity characterised by northward propagation over the northern Indian Ocean and eastward propagation over the equatorial region, acts as a major source of predictability in the intraseasonal time scale. Predicting the initiation of BSISO over the equatorial Indian Ocean is of vital importance in the prediction of BSISO's northward advancement over the ISM domain. This study tries to investigate where we stand in terms of predicting the BSISO initiation and propagation, making use of the reforecasts available from the different operational forecasting centres part of the Sub-Seasonal-to-Seasonal (S2S) prediction project. The BSISO convective initiations over the Equatorial Indian Ocean are objectively identified using OLR MJO Index(OMI), and the ability of the models to simulate the initiation and propagation of BSISO is assessed. The BSISO propagation skill, quantified in 9 S2S models, ranges from 11 to 29 days, while the BSISO initiation skill, quantified in 4 out of 9 models, ranges from 11 to 16 days, which is systematically lower compared to the skill of the BSISO non-initiation stages. Two major regions of BSISO initiation were identified, one over the Western Equatorial Indian Ocean and another over the Eastern equatorial Indian Ocean. Over these identified initiation regions, observation show a buildup (reduction) of lower tropospheric moisture before (after) the BSISO initiation. Out of the 9 models considered, few capture either the buildup or reduction, while the majority of the models show biases in capturing the moisture buildup and reduction. Previous studies have emphasised the role of background moisture in the propagation of BSISO. The relationship between the background moisture gradient over the ISM domain and the BSISO propagation prediction skill is examined in the S2S models and a positive relationship is found.

How to cite: Simon, D. and Joseph Mani, N.: Boreal Summer Intraseasonal oscillation convective initiations in S2S models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-861, https://doi.org/10.5194/egusphere-egu23-861, 2023.

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode which determines the active and break phases of the monsoon, and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the rainfall portion corresponding to MISO.

Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO using a novel method. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics, and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve significant improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10–30 day lead times, an interval that is generally considered as a predictability gap. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point towards a future of combining dynamical and data-driven forecasts for Earth system prediction.

How to cite: Bach, E., Krishnamurthy, V., Shukla, J., Mote, S., Sharma, A. S., Kalnay, E., and Ghil, M.: Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10243, https://doi.org/10.5194/egusphere-egu23-10243, 2023.

Seasonal forecasts of meteorological drought can help decision-making for weather-driven wildfires (Turco et al., 2018). However, one of the main drawbacks of drought prediction lies in the uncertainty of monitoring precipitation in near-real time. In this contribution we assess the predictability of the Standardized Precipitation Index (SPI) on a global scale, combining 11 datasets (DROP; Turco et al., 2020) as observed initial conditions with empirical and dynamic predictions of precipitation. The empirical predictions are based on the ensemble-based streamflow prediction system (ESP, an ensemble-based reordering of historical data) and the dynamics on the new generation seasonal prediction model developed by ECMWF (System 5; S5). Although both systems show comparable quality, S5 performs better at longer forecast timescales, especially over tropical regions.

Subsequently, we investigate whether the S5 seasonal forecasts can predict area burned anomalies on a global scale. To do so, we link the seasonal climate predictions of S5 to an empirical climate-fire model, using standard regression techniques in the framework of generalised linear models. The seasonal climate predictions of S5 have shown high and significant performance (with a mean relative operating characteristic “ROC” area value of 0.87) over a large fraction of the burnable area (~47%).

In summary, given that all data are publicy available in near real time, our results provide a basis for the development of a global probabilistic seasonal drought and burned area forecast product.

References

Turco, M., Jerez, S., Doblas-Reyes, F. J., AghaKouchak, A., Llasat, M. C., & Provenzale, A. (2018). Skilful forecasting of global fire activity using seasonal climate predictions. Nature Communications, 9(1), 1–9.

Turco, M., Jerez, S., Donat, M. G., Toreti, A., Vicente-Serrano, S. M., & Doblas-Reyes, F. J. (2020). A global probabilistic dataset for monitoring meteorological droughts. Bulletin of the American Meteorological Society, 101(10), E1628–E1644.

Acknowledgements

We acknowledge funding through the project ONFIRE, grant PID2021-123193OB-I00,funded by MCIN/AEI/ 10.13039/501100011033.

How to cite: Torres-Vázquez, M. Á., Gincheva-Norcheva, A., Halifa-Marín, A., Montavez, J. P., and Turco, M.: Probabilistic predictions of global fire activity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7750, https://doi.org/10.5194/egusphere-egu23-7750, 2023.

Climate change is causing disruptions in Earth's weather patterns, leading to an increase in the frequency and severity of extreme weather events such as droughts, floods, frost, and heatwaves. These events can impact food production and lead to challenges in meeting the food needs of a growing population. Previous research has documented the role of temperature and precipitation during the growing season in explaining crop yield variability. For example, droughts and extreme heat can reduce cereal production by 9-10%.

Current crop yield models use only a few meteorological variables to represent weather conditions. However, weather patterns or weather regimes, (i.e., persistent, and recurrent flow patterns of the large-scale atmospheric circulation) can provide a more comprehensive view of weather conditions, and can be used to predict and characterise extreme weather events and explain crop yield variation.

In this study, we first conducted a literature review to examine the links between extreme weather events, such as heat waves, and droughts with weather patterns and regimes. One of the main findings of that review was the need to define what extreme weather is in the context of agriculture. The new definition is based on studies that identified optimal and terminal weather conditions for winter wheat at specific phenological stages. Using this definition of extreme weather, we analyse historic yields in East Anglia, UK, forming statistically based relationships between low yield years with a set of classified weather patterns from the UK Met Office. We focused on the weather patterns frequency of occurrence and persistence with additional consideration given to potential microclimates as we compare the effects weather patterns have on a specific farm with a long-term data set to the effects of the larger region. Preliminary analyses shows that a small number of these weather patterns are associated with high impact weather events that cause yield limiting conditions or physical damage to the crop such as wind lodging.

It is hoped that further research will lead to the development of a next-generation crop yield variation model taking into account the weather patterns, which can provide longer-term predictions of regional crop yield variability to help agri-businesses, crop insurers and farmers to facilitate decision making, respond effectively to regional and global crop production shocks and food price spikes, and develop adaptation strategies to reduce the potential impact of extreme weather events.

How to cite: Knight, C., Khouakhi, A., and Waine, T.: Investigating the Role of Weather Patterns in Crop Yield Variability and Predictability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15043, https://doi.org/10.5194/egusphere-egu23-15043, 2023.

Information at the sub-seasonal to seasonal (S2S) time scale can be of high socio-economic value to a variety of users whose decision-making depends on climate variability. The usability of S2S forecasts generated by Numerical Weather Prediction (NWP) systems has increased over the years not only due to their skill improvement but also due to their potential to bridge the medium-range and seasonal horizons. The skill of the sub-seasonal (4-6 weeks ahead) and seasonal (6-12 months ahead) NWP-based forecasts in space and time depends on different factors, including the representation of the physical process, the initialization frequency and the spatial resolution. However, the NWP model setups differ between the two time horizons, and this consequently intrinsic differences between the two forecast products. To date, it has been subjectively accepted that during the first time horizons, e.g. up to 6 weeks ahead, the sub-seasonal forecasts are more informative than the seasonal forecasts, and hence all efforts on generating a seamless product are implemented through a direct merging of the two products. This unfortunately masks the potential for tailored seamless products that extract the best S2S information available.

Here, we evaluate the S2S hydro-meteorological forecasts from the ECMWF sub-seasonal (ENS-ER) and seasonal (SEAS5) products, aiming to identify their skill complementarity in space and time and further seamlessly communicate them for improved decision-making. Both the ENS-ER and the SEAS5 precipitation and temperature forecasts were bias-adjusted prior to forcing the E-HYPE hydrological model. The investigation focuses on the period 1999-2015. Overall, results highlight both spatial and temporal complementarities between the two systems, which is very encouraging for a seamless communication. In particular, ENS-ER-based hydro-meteorological forecast skill patterns appear to be more homogeneous spatially, while SEAS5-based forecasts ensure skill at longer forecast horizons. This diagnostic analysis is a step forward in hydro-climate services, indicating the tipping points in all European river systems for switching from ENS-ER to SEAS5 forecasts.

How to cite: Pechlivanidis, I. and Crochemore, L.: European S2S streamflow forecasting: Towards a seamless communication, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7168, https://doi.org/10.5194/egusphere-egu23-7168, 2023.

Forecasts on sub-seasonal to seasonal (S2S) timescales have huge potential to improve early warning and anticipatory action ahead of high impact events. However, fully realising this potential predictability requires reliable forecasts that are communicated effectively so that they can support appropriate preparedness action. This study reflects on the African SWIFT (Science for Weather Information and Forecasting Techniques) S2S forecasting testbed which brought together researchers, forecast producers and forecast users from a range of African and UK institutions. The testbed used a co-production approach to pilot the provision of real-time bespoke S2S forecast products for applications. The S2S testbed supported decision-makers in a range of sectors and contexts. For example, informing food security decisions and hydropower energy planning in East Africa, supporting agricultural decision-making across West Africa, and, in health applications, increasing the lead-time for potential disease outbreaks.

This study critically reflects on the benefits and challenges of the co-production process within the S2S applications context. Specifically, while having direct access to the real-time S2S data allowed user-guided iterations to products to make them more actionable for their specific context. Some key lessons for effective co-production emerged. First, it is critical to ensure there is sufficient resource to support co-production, especially in the early co-exploration of needs. Second, all the groups in the co-production process require capacity building to effectively work in new knowledge systems. Third, evaluation should be ongoing and combine meteorological verification with decision-makers feedback. Ensuring the sustainability of project-initiated services within the testbed hinges on integrating the knowledge-exchanges between individuals in the co-production process into shaping sustainable pathways for improved operational S2S forecasting within African institutions.

How to cite: Hirons, L.: Using a co-production approach to support effective application of S2S forecasts in Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9342, https://doi.org/10.5194/egusphere-egu23-9342, 2023.

Extreme precipitation events (EPE), especially those leading to floods and landslides, are devastating to society. Predicting these events in advance can help disaster managers to carry out plans of action to respond effectively to any oncoming adverse events. Sub-seasonal forecasts, which aim to predict the weather with 2 weeks to 2 months in advance, can help to provide valuable and actionable information to disaster managers. Given the potential usefulness to end users, it is vital to assess the skill of sub-seasonal forecasts in predicting EPEs. However, given that precipitation is known to be a difficult variable to predict, the lead time at which forecasts are skilful may be limited. This study, therefore, aims to assess at which lead time sub-seasonal forecasts of atmospheric drivers of EPEs are skilful.

The study investigates the skill of the European Centre for Medium-Range Weather Forecast (ECMWF) sub-seasonal reforecast in predicting EPE over Italy from 2001 to 2020. A total of 100 EPEs are used as case studies. The variables evaluated are total precipitation, mean sea level pressure, geopotential height at 500 hPa and specific humidity at 850 hPa. Variables are averaged over the 5 days surrounding the date of the EPE. ERA5 is used as the reference dataset. Both deterministic and probabilistic metrics are used to assess the skill of the reforecast.

Results show that the skill for precipitation is limited to the first two weeks. Nevertheless, the ECMWF sub-seasonal product is skilful in predicting the atmospheric fields associated with the selected EPEs, such as MSLP and geopotential height, showing both reliability and discrimination beyond two weeks.

How to cite: Scott, W., Gaetani, M., and Fosser, G.: Skill assessment of sub-seasonal forecasts of different atmospheric variables related to extreme precipitation events over Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10052, https://doi.org/10.5194/egusphere-egu23-10052, 2023.

The study focuses on identifying potential “windows of opportunity” for the enhanced predictability of extreme events, such as severe Northern Eurasian cold air outbreaks as these events have significant impacts on human health, energy use, agriculture and welfare.  The extended-range predictability of extreme events is closely related to the preceding large-scale circulation patterns and remote teleconnections. To assess the predictability of these events and attribute their causes we use ensemble hindcasts (i.e., reforecasts for dates in the past) from five prediction systems from the S2S database – namely, from the European Centre for Medium‐range Weather Forecasts (ECMWF), the United Kingdom Met Office (UKMO), Météo‐France (CNRM), Bureau of Meteorology (BoM), Japan Meteorological Agency (JMA). These models have long re-forecast periods and big ensemble sizes necessary to establish statistically robust results. Moreover, the comparison of the forecasts from these six models evaluates the ability of modern prediction systems to forecast extreme events well in advance and highlights the main sources of predictability. We subsample the hindcasts into two groups according to their skill to predict an extreme event beyond weather predictability horizon (lead time week 2 and 3) in order to study the systematic relationship between preceding conditions and the onset of extreme events. Next, we evaluate the flow configurations in the initial conditions: the state of the stratospheric polar vortex (SPV), the phase and amplitude of the Madden-Julian Oscillation (MJO) in the tropics, and the weather regimes over the North Atlantic and Europe. This analysis provides a systemic evaluation and understanding of the large-scale patterns that can potentially contribute to the onset of extreme events over Eurasia, therefore, extending their predictability. Our results show that in overall models tend to over-predict cold conditions after certain states of the remote drivers but there is case-to-case variability in the predictability of the individual events. Moreover, this study assesses and compares the results from several state-of-art predicting systems which provides useful information for model developers as well as for forecast users.

How to cite: Statnaia, I., Karpechko, A., and Järvinen, H.: Factors influencing subseasonal predictability of Northern Eurasian cold spells, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-360, https://doi.org/10.5194/egusphere-egu23-360, 2023.