How to cite: Chug, D., Chen, C., Dominguez, F., and Grimm, A.: Subseasonal Effects of Large-Scale Soil Moisture Anomalies over Southeastern South America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3333, https://doi.org/10.5194/egusphere-egu22-3333, 2022.

How to cite: Williams, N., Scaife, A., and Screen, J.: Weak ENSO teleconnections contribute to the signal-to-noise paradox , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3880, https://doi.org/10.5194/egusphere-egu22-3880, 2022.

Tropical Cyclones (TCs) from the Eastern Pacific (EP) and North Atlantic (NA) Ocean commonly make landfall in the continental landmass of Middle America. These meteorological phenomena not only can cause floods and socioeconomic impacts, but they can also transport such heavy amounts of water that one event refill lakes, rivers, aquifers, and dams up to 100% during periods of prolonged droughts in arid and semiarid regions. This water resource can be used for agricultural and livestock activities, which are essential for Mexico and Central America’s countries. That is why local decision-makers are interested in having seasonal forecasts of tropical cyclone activity for the region. Current seasonal forecasts of tropical cyclone activity only focus on providing a number of TCs for the whole basin. However, local decision makers need information about possible affected regions at least 2 months in advance of the TC season peak (July-August-September for EP and August-September-October for the NA). This work is aimed at exploring a statistical-dynamical method for creating a seasonal forecast of TCs for Middle America.  We track TC-like vortices in five Coupled Global Models:  ECMWF, Météo-France, UKMO, DWD and CMCC during the 1993-2015 period (climatology period) and using two initial conditions: 1st July and 1st August for a three-month forecast. Our preliminary results show that three of the five models have the skill to adequately forecast the standardized track density anomaly and the TC activity per tercile (above-normal, normal, and below-normal) over the EP and NA basins. However, most of the models overestimate the activity. as indicated by the Brier Score (BS) and the Ranked Probability Skill Score (RPSS). Additionally, we present a statistical analysis of the type of tracks that are more important for the region and discuss how these types of tracks can be predicted depending on ENSO phase. We conclude that some models are useful to predict the TC activity 3 months in advance (dynamical approach), which can be combined with a statistical approach to provide more information about the type of TC track and possible affected regions.

How to cite: Vargas Hernandez, F. and Dominguez Sarmiento, C.: Seasonal Tropical Cyclone Forecasts for the Middle America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1398, https://doi.org/10.5194/egusphere-egu22-1398, 2022.

The last few years have seen an ever growing interest in weather predictions on sub-seasonal time scales ranging from 2 weeks to about 2 months. By forecasting aggregated weather statistics, such as weekly precipitation, it has indeed become possible to overcome the theoretical predictability limit of 2 weeks, bringing life to time scales which historically have been known as the “predictability desert”. The growing success at these time scales is largely due to the identification of weather and climate processes providing sub-seasonal predictability, such as the Madden-Julian Oscillation (MJO) and anomaly patterns of global sea surface temperature (SST), sea surface salinity, soil moisture and snow cover. Although much has been gained by these studies, a comprehensive analysis of potential predictors and their relative relevance to forecast sub-seasonal rainfall is still missing.

At the same time, data-driven machine learning (ML) models have proved to be excellent candidates to tackle two common challenges in weather forecasting: (i) resolving the non-linear relationships inherent to the chaotic climate system and (ii) handling the steadily growing amounts of Earth observational data. Not surprisingly, a variety of studies have already displayed the potential of ML models to improve the state-of-the-art dynamical weather prediction models currently in use for sub-seasonal predictions, in particular for temperatures, precipitation and the MJO. It seems therefore inevitable that the future of sub-seasonal prediction lies in the combination of both the dynamical, process-based and the statistical, data-driven approach. 

In the advent of this new age of combined Neural Earth System Modeling, we want to provide insight and guidance for future studies (i) to what extent large-scale teleconnections on the sub-seasonal scale can be resolved by purely data-driven models and (ii) what the relative contributions of the individual large-scale predictors are to make a skillful forecast. To this end, we build neural networks to predict sub-seasonal precipitation based on a variety of large-scale predictors derived from oceanic, atmospheric and terrestrial sources. As a second step, we apply layer-wise relevance propagation to examine the relative importance of different climate modes and processes in skillful forecasts.

Preliminary results show that the skill of our data-driven ML approach is comparable to state-of-the-art dynamical models suggesting that current operational models are able to correctly model large-scale teleconnections within the climate system. The ML model achieves highest skills over the tropical Pacific, the Maritime Continent and the Caribbean Sea, in agreement with dynamical models. By investigating the relative importance of those large-scale predictors for skillful predictions, we find that the MJO and processes associated with SST anomalies like the El Niño-Southern Oscillation, the Pacific decadal oscillation and the Atlantic meridional mode all play an important role for individual regions along the tropics.

How to cite: Rieger, N., Corral, Á., Olmedo, E., Magnusson, L., Ferranti, L., Pappenberger, F., and Turiel, A.: Identifying relevant large-scale predictors for sub-seasonal precipitation forecast using explainable neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7530, https://doi.org/10.5194/egusphere-egu22-7530, 2022.

The impacts of the Madden-Julian Oscillation (MJO) on the South American monsoon season (December, January, and February – DJF) and their possible changes during positive (El Niño – EN) and negative (La Niña – LN) phases of the El Niño-Southern Oscillation (ENSO) are analyzed in the UK Met Office Unified Model Global Ocean Mixed Layer configuration (MetUM-GOML3). Experiments sixty years long, with and without ENSO cycle, considering lower (200 km) and higher (90 km) spatial resolution, are performed to assess if the ENSO influences MJO characteristics such as the phase distribution, propagation, convection, and teleconnections to South America (SA). The analyzes use daily continental precipitation data, daily global outgoing longwave radiation (OLR), and zonally asymmetric streamfunction computed with daily wind data. Composites of daily filtered anomalies in the 20-90 day band are assessed. Simulations without ENSO show (1) an established MJO extratropical teleconnection triggered by enhanced convection in the central-east subtropical South Pacific (SP) (source region), and its strongest impact on precipitation over SA in phase 8, earlier than in observations (phase 1); (2) an extratropical teleconnection via Rossby wave train, triggered by suppressed convection over the same region, with strongest impact on precipitation over SA in phase 4, with opposite sign; (3) increased horizontal resolution enhances the MJO convection and the anomalous circulation-precipitation dipole over SA, mainly over subtropical SA. However, the extratropical teleconnections via Rossby wave train at upper levels are slightly shifted east at higher resolution due to an enhanced SA westerly jet with respect to the lower resolution. The ENSO affects the basic state and the MJO convective anomalies, which modulate the MJO teleconnections and their impacts on SA in simulations with ENSO cycles. The EN (LN) basic state improves (worsens) MJO eastward propagation and its convection. However, both EN and LN states produce enhanced convection over the source region in phases 8+1, while suppressed convection over the same region in phase 4 is simulated only in EN. The extratropical teleconnections via Rossby wave train (phases 8+1, 4) and their impacts are stronger under ENSO with respect to those in simulations without ENSO. Hence, both ENSO states in the model generate forcing in the central-east subtropical SP that more efficiently triggers teleconnections than simulations without ENSO, indicating nonlinear ENSO effects on MJO anomalies over SA. As the MJO and its teleconnections improve during ENSO, other coupled global climate models (CGCMs) may reproduce these features, and subseasonal to seasonal (S2S) predictions to SA may be better forecast when ENSO and MJO peak in DJF, though the MJO impacts in phase 1 remain challenging.

Keywords: Coupled global models; ENSO-MJO Interaction; South American monsoon; Teleconnections.

How to cite: Fernandes, L., Grimm, A., and Klingaman, N.: MJO impacts on South America monsoon season and their modulation by ENSO in MetUM-GOML3 model , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10781, https://doi.org/10.5194/egusphere-egu22-10781, 2022.

The simulated Northern Hemisphere stationary wave (SW) field is investigated in 11 subseasonal-to-seasonal (S2S) models. It is shown that while most models considered can well-simulate the stationary wavenumbers 1 and 2 during the first two weeks of integration, they diverge from observations following week 3. Those models with a poor resolution in the stratosphere struggle to simulate the waves, both in the troposphere and the stratosphere, even during the first two weeks, and biases extend from the troposphere all the way up to the stratosphere. Focusing on the tropospheric regions where SWs peak in amplitude reveals that the models generally do a better job in simulating the Northwest Pacific stationary trough, while certain models struggle to simulate the stationary ridges both in Western North America and the North Atlantic. In addition, a strong relationship is found between regional biases in the stationary height field and model errors in simulated upward propagation of planetary waves into the stratosphere. In the stratosphere, biases mostly are in wave-2 in those models with high stratospheric resolution, whereas in those models with low resolution in the stratosphere, a wave-1 bias is evident, which leads to a strong bias in the stratospheric mean zonal circulation due to the predominance of wave-1 there. Finally, biases in both amplitude and location of mean tropical convection and the subsequent subtropical downwelling, are identified as possible contributors to biases in the regional SW field in the troposphere.

How to cite: Schwartz, C., Garfinkel, C., Domeisen, D., Yadav, P., and Chen, W.: Stationary Wave Biases and Their Effect on Upward Troposphere - Stratosphere Coupling in Sub-seasonal Prediction Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10898, https://doi.org/10.5194/egusphere-egu22-10898, 2022.

The Madden-Julian Oscillation (MJO) is one of the leading sources of tropical and extra-tropical predictability on subseasonal-to-seasonal timescales, but numerical models often suffer from systematic errors in capturing the MJO dynamics. Large-scale convection associated with the MJO is initiated over the Indian Ocean and propagates eastward across the Maritime Continent (MC) and into the western Pacific. As an MJO event enters the MC, it often weakens or completely dissipates due to complex interactions between the large-scale MJO and the MC landmass and its topography. This MC barrier effect is responsible for the dissipation of 40-50% of observed MJO events, though the exact nature of the barrier effect is unclear. Common mechanisms include the physical barrier of the islands of the MC, and the dynamical barrier of strong diurnally driven circulations that exist around those islands. The MC barrier effect is often exaggerated in when it comes to MJO prediction.

In this study, we examine convection-permitting, atmosphere-ocean coupled model simulations of an MJO event to determine how the MJO responds to physical and dynamical changes implemented over the MC region. In addition to the control simulation with real topography, we introduce two idealized simulations – (1) where we flatten the topography of the MC to sea level, but leave the land-sea distribution as is, and (2) where we entirely remove the MC islands and replace them with a 50-m deep ocean. How the MJO responds to the implemented changes can help us determine whether some physical processes that occur over the MC are more detrimental to MJO propagation than others. The differences between the control simulation and the first scenario can tell us about the physical barrier effect of the MC on MJO propagation. The complete removal of land in the second scenario also removes the diurnal changes associated with air-sea boundaries (e.g., land-sea breezes and convergence zones between islands), exploring whether the barrier effect of the MC on the MJO is more dynamically driven.

Results show that flattening the MC terrain only has a small impact on large-scale MJO characteristics. However, as expected, removing the land, and diurnal cycle associated with it, drastically smooths the MJO’s propagation and the produced MJO shows no sign of dissipation over the MC region. We examine the model simulations to gain insight on what physical processes are behind the changes among model simulations and to expose some modeling difficulties that could contribute to numerical models’ exaggerating the effects of the MC barrier effect.

How to cite: Savarin, A. and Chen, S.: Understanding the Barrier Effects of the Maritime Continent on MJO Prediction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10984, https://doi.org/10.5194/egusphere-egu22-10984, 2022.

The performance of sub-seasonal to seasonal prediction models, particularly outlining the role of the stratosphere in representing the surface climate viz. precipitation and temperature associated with the Indian Summer Monsoon (ISM), has been examined in this study. The hindcast data from two configurations of a fully coupled model part of the UK Met Office seasonal prediction system that differ only in vertical resolution namely Glosea4 L38 (GL38, Low Top) and Glosea4 L85 (GL85, High Top) have been used. In addition to this, the hindcast data from the updated version of the model i.e. Glosea5 (GL5) is also analyzed, which resembles the GL85 in case of vertical resolution thereby including an exclusively well-resolved stratosphere (unlike GL38) but with finer horizontal resolution than the later. It has been found that the GL85 is performing much better by eliminating the dry bias, particularly over the central Indian region as compared to the GL38 and GL5. The same implications are seen in the inter-annual variabilities produced by the models as GL85 is showing better results and closer to the observation in reproducing interannual variability of both precipitation and temperature. A large part of the inter-annual variations can be explained by the internal variability of the models but other important modes of inter-annual variability are also needed to explain the noted year-to-year fluctuations in these models.  The impact of resolving the stratosphere on the temperature is not significant, as both GL38 and GL85 are producing similar biases over the ISM domain, and overall GL5 is showing better results. Furthermore, the influence of resolving stratosphere in representing surface climate by two versions of a CMIP5 model, CMCC-CM (Low Top) and CMCC-CMS (High Top) is also examined, and the improvement has been observed in the case of the high top model. Moreover, the circulation associated with the ISM for the models has also been analyzed to relate the model performance in reproducing the precipitation. The Somali jet is stronger in the high-top models leading to more moisture transport and convergence over the Indian land. In contrast, the Somali jet is shifted southwards and weaker in low-top version leading to more rainfall over the equatorial Indian Ocean and relatively less over India. The increase in vertical resolution (from GL38 to GL85) yields good results in representing precipitation, however, the increase in horizontal resolution (from GL85 to GL5) keeping the vertical resolution same has not been useful as it leads to the drier bias over the region.

Keyword: sub-seasonal prediction to seasonal, hindcast, High Top/Low Top, Indian Summer Monsoon

How to cite: Upadhyaya, P., Mishra, S. K., Jain, S., and Salunke, P.: Seasonal Prediction of Indian Summer Monsoon: Influence of Well-resolved Stratosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11136, https://doi.org/10.5194/egusphere-egu22-11136, 2022.

Air-sea coupling is critical in influencing atmospheric temperature and precipitation. The effect of greenhouse gases has influenced atmospheric variability and extreme events. Understanding and quantifying the effect of air-sea feedback on atmospheric variability and extremes remains unknown.

In this work we show results obtained from two numerical experiments. We use the HadSM4 configuration that couples the HadAM4 model at N144 resolution with a Slab Ocean to generate a large ensemble (~1000 members) of realizations of the 2013-14 October-March winter season, forced with a calibrated ocean heat convergence flux.

A twin experiment is performed by forcing HadAM4 with the diagnosed SST and sea ice from the ensemble, yielding a new ensemble with identical realizations of SST and sea ice. The only difference between the two ensembles is the enabling or disabling of the feedback of air-sea heat fluxes on SST.

While the impact of the feedback on the mean climate is relatively small, we show that its influence has important consequences for the variability of many important quantities, including air-sea fluxes and return periods of extreme events.

How to cite: Aengenheyster, M., Sparrow, S., Watson, P., Wallom, D., Zanna, L., and Allen, M.: Impact of sub-seasonal atmosphere-ocean interactions in a large ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12033, https://doi.org/10.5194/egusphere-egu22-12033, 2022.

One of the great concerns of the scientific community in the last decade concerns climate change and its consequences for humanity. The Brazilian hydric planning has faced constant challenges to guarantee supply and energy. In the various regions of the country, serious water shortages have been observed in recent years, due to the scarcity of rainfall, which has become more frequent and intense over the years. Due to its vast territorial extension and topographic complexity, Brazil has different precipitation regimes. Therefore, the study of changes in hydro-meteorological data time series is of extreme importance for the management of water resources. In this context, an analysis of the variability of the historical series of inflowing water flows of the main reservoirs in the country is fundamental for the understanding of the processes involved in drought episodes, in view of the significant impact that these oscillations can produce on Brazilian hydric planning. It is known that the variability of the flow in these reservoirs is closely related to the rainfall regime of each region, which in turn is influenced by climatic variability. Among the climatic variability stands out the Madden-Julian Oscillation (MJO) or 30-60-day Oscillation, which is a mode of intraseasonal climate variability that plays a key role in precipitation over much of South America. Based on this theme, the present work aims to analyze, quantify and predict the influence of MJO on precipitation and consequent hydro-energy variability in Brazil, considering the significant impact that these oscillations can produce on the economy of the country. In this way, we sought to determine what is the contribution of this oscillation to the variability of tributary flows, in the period from 1990 to 2016, data obtained through the National Water Agency (ANA), in order to contribute substantially to the improvement of hydroclimatic forecasts. After the identification of the extemos events of the historical series (positive and negative anomalies of affluent flow) the following analyses were carried out through the method of Wavelets, with the objective of identifying the intensity and the temporal scale of the most expressive phenomena acting in each reservoir of this study. Then a filter was applied on the Wavelets in order to highlight the intraseasonal scale (MJO) and smooth the interannual scale to identify what is the contribution of this phenomenon for each of the regions studied. Finally, in possession of these results, a comparison was made between them and the precipitation simulated by the Brazilian Global Atmospheric Model (BAM) for the same period, with the objective of analyzing the precipitation simulations and their influence on the levels of the country's water reservoirs. In view of this information, the analysis of BAM simulations will be of extreme importance in the comparison between simulations of precipitation and the levels of the reservoirs studied, which can contribute to the forecast and decision-making regarding the management of water resources and thus efficiently improve administration and investments throughout the sector, reducing the degree of vulnerability currently faced by the country.

How to cite: Kunzler, S., Boiaski, N., Ferraz, S., Herdies, D., and Bresciani, C.: Variability of the Inflow at the Intraseasonal Scale and Relationship with Precipitation from the BAM Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8807, https://doi.org/10.5194/egusphere-egu22-8807, 2022.

    This study examines the impacts of the Madden-Julian Oscillation (MJO) on extratropical prediction skill in the Northern Hemisphere during the extended winter, using subseasonal-to-seasonal reforecasts. All four models examined in this study showed a sensitivity of the prediction skill in the North Pacific basin to the initial MJO amplitude at lead weeks 4 and 5. In the ECMWF model, for example, pattern anomaly correlation coefficient (PACC) skill of 300-hPa geopotential height at week 4 becomes higher when the model is initialized with strong MJO than weak MJO. An improved PACC skill with strong MJO is also found in surface air temperature prediction, primarily over the United States. Although not always statistically significant, the similar results are also found in other three reforecasts. The changes in extratropical prediction skill seem to be linked with the MJO prediction skill and the amplitude of the predicted MJO in the first 3 weeks of forecast lead, both of which are higher with strong MJO at initial state than those without active MJO. In addition, the impact of the MJO initial condition on the extratropical prediction is different for each MJO phase. The prediction skill mainly changes in the region where observed teleconnection pattern is consistent across events for each phase, verifying the causality between the MJO and the extratropical prediction skills.

How to cite: Kim, H., Kim, H., and Son, S.-W.: The influence of MJO initial condition on the extratropical prediction skills in subseasonal-to-seasonal prediction model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13017, https://doi.org/10.5194/egusphere-egu22-13017, 2022.

The aim of the Subseasonal to Seasonal (S2S) Prediction Project Real Time Pilot (RTP) Initiative is to identify best practices for the development of useful and usable, user-orientated S2S forecasts. Typically, S2S forecasts are only available to researchers with a 3-week lag, but this can represent a barrier to the development of user-orientated applications as it prevents users from being able to understand the utility of this information in real time within their decision-making frameworks. To accomplish the aims of the RTP, the initiative looked to engage with existing user-orientated projects and offer them the opportunity to access real time S2S forecast information to enable better end-to-end development and evaluation of applications. To ensure that sufficient time was available for projects and users to use and become familiar with the real time S2S forecasts, an agreement was reached where S2S forecasts would be available in real time, to a small set of projects for a 2-year period (November 2019 up to November 2021). This has since extended up to November 2022.

To address the aims of the S2S RTP a series of feedback activities have been undertaken with the 16 projects involved. This has included the dissemination and analysis of 2 sets of questionnaires, followed by more detailed semi-structured interviews and subsequent synthesis. All feedback activities were inclusive of researchers and users participating in the initiative. This presentation will describe the different approaches projects have taken in the development of S2S forecast applications, focussing on co-production and user engagement activities across the value chain. Benefits, opportunities and challenges to using co-production methods in the development of user-orientated forecasts are identified through the feedback activities and wider literature. These findings suggest that the application of co-production methods remains novel in the S2S time range, with time and resource availability for stakeholder engagement posing a challenge. However, the feedback indicates that where bi-directional interaction is sustained, positive feedback mechanisms can develop, which build trust, strengthen collaborative working arrangements and enhance forecast product development specific to user requirements.

How to cite: Robbins, J. and Simmonds, R.: Using the Subseasonal-to-Seasonal (S2S) Real Time Pilot (RTP) Initiative to understand the challenges and opportunities of co-production in S2S forecast application development, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8008, https://doi.org/10.5194/egusphere-egu22-8008, 2022.

Subseasonal-to-seasonal (S2S) forecasts are bridging the gap between weather forecasts and long-range predictions. Decisions in various sectors are made in this forecast timescale, therefore there is a strong demand for this new generation of predictions. While much of the focus in recent years has been on improving forecast skill, if S2S predictions are to be used effectively, it is important that along with scientific advances, we also learn how best to develop, communicate and apply these forecasts.

In this presentation, we present recent progress in the applications of S2S forecasts. We summarise case studies from a recently-published applications community review paper in the Bulletin of the American Meteorological Society (BAMS), covering sectoral applications of S2S predictions from around the world, including public health, disaster preparedness, water management, telecommunications, energy and agriculture. Involving over 60 authors and drawing from the recent advances and experience of researchers and users working with S2S forecasts globally, we explore the value of applications-relevant S2S predictions through a series of sectoral cases where uptake is starting to occur.

From across 12 case studies, we show that:

• The S2S forecasting timescale is a new concept for many users. While the additional value of S2S forecasts for decision-making is increasingly gaining interest among users, incorporating probabilistic ensemble S2S forecasts into existing operations is not trivial.
• Barriers to widespread adoption of S2S forecasts include lack of access to the forecasts and the co-production to tailor forecasts to user needs, as well as varying ‘in house’ expertise in how to interpret and effectively apply them. This can create a ‘knowledge-value’ gap in some instances.
• S2S forecasts do not produce a ‘go/no go’ answer of how a user should respond to a potential hazard; instead they provide additional, supplementary ‘situational awareness’ information that can be used to support decision-making on S2S timescales.

While S2S forecasting is still a maturing discipline globally, this publication marks a significant step forward in moving from potential to actual S2S forecasting applications – a collective body of evidence demonstrating both skill and utility across sectors that places user needs and applications at the forefront of S2S forecast development.

Our paper, ‘Advances in the application and utility of subseasonal-to-seasonal predictions’, is available from BAMS as an open access publication: https://doi.org/10.1175/BAMS-D-20-0224.1.

How to cite: White, C. J., Domeisen, D. I. V., Charlton-Perez, A. J., Olaniyan, E., González Romero, C., Muñoz, Á. G., Graham, R. J., Acharya, N., Coelho, C. A. S., DeFlorio, M. J., Manrique-Suñén, A., Graham, R. M., Tozer, C. R., Brayshaw, D. J., Di Giuseppe, F., and Wetterhall, F.: Recent advances in the application and utility of subseasonal-to-seasonal predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6186, https://doi.org/10.5194/egusphere-egu22-6186, 2022.

Climate change is likely to impact the balance of worldwide food exchange networks and food security. Hence, the use of seasonal forecasts of precipitation and temperature may be regarded as essential for stakeholders to perform timely choices concerning the strategies required to maximize the expected cereal yield outcomes in the harvest period. The availability and ease-of-use of the seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) system 5 (SEAS5) may be an important asset to help implement these strategies by decision makers. Nevertheless, uncertainties and reduced skill may hinder the use of such forecasts for numerous applications. Thus, this work intends to analyse the added value of using dynamical forecasts when compared to using persistent anomalies of climate conditions, with the aim of predicting the production of wheat and barley yields in Iberia. First, empirical models involving annual wheat and barley yields in Spain and monthly values of precipitation and temperature are developed with ECMWF ERA5 reanalysis. Then, dynamical and persistence forecasts are issued at different lead times, and the skill of the forecasted yield is verified through different metrics. Results presented here show that wheat and barley yield anomaly forecasts (dynamical and persistent) start to gain skill later in the season (e.g., April) and show that the added value of using the SEAS5 forecast as an alternative to persistence varies between 6 and 16 %, with better results in the southern Spain regions.

The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – Instituto Dom Luiz.

How to cite: Bento, V., Russo, A., Dutra, E., Ribeiro, A., Gouveia, C., and Trigo, R.: Seasonal forecasts of cereal crop yields in Iberia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12389, https://doi.org/10.5194/egusphere-egu22-12389, 2022.

The PISSARO project focuses on atmospheric and oceanic forecasting at the subseasonal scale for applications over the South West Indian Ocean basin (SWIO). It is a collaborative academic research project, developed and conducted in partnership with stakeholders from Reunion and Seychelles and a panel of scientific experts in subseasonal forecasting. The aim of this project is to evaluate, improve and valorize subseasonal forecasting data. For this purpose, we mainly use the data archived into the the S2S (Subseasonal-to-Seasonal prediction project) data base in order to 1) evaluate the quality of subseasonal forecasts for tropical cyclones and weather patterns, and 2) develop forecast products suitable for potential users. This project focuses on the SWIO, which has been little studied by the S2S community until now. The different territories of the SWIO are subject to extreme events and a significant cyclonic activity. It is important to take into account the specificities of this region in order to improve their warning systems.

The ambition to deploy early warning tools cannot be achieved without discussions between potential users and S2S experts. The users specify the characteristics of the products to be developed so that they offer an asset for decision-making, and the experts assess the feasibility of these products. In the presentation, we will first discuss the importance of collaboration between the users and the experts within the project using two concrete actions: the animation of a monthly experimental forecasting briefing with operational forecasters and the participation in conferences in the humanitarian field. Then, we will present the subseasonal forecasting products which are under development for the anticipation of cyclonic risk at monthly scale in the SWIO basin. To address the urgency of the need of the disaster risk reduction, we first made a basic adaptation of already existing tropical cyclone occurrence probability and rainfall forecasting products into products interpretable by non-meteorological users.

We consider that a crucial information from the S2S data base to provide to users is the level of uncertainty. However, estimating the quality of S2S forecasts is not straighforward. It is actually difficult to match a forecasted cyclone to an actual observed cyclone, let alone detect a false alarm. To this end, we are working on the classification of S2S tropical cyclone trajectories with clustering methods and we will show the first results. We aim to exploit the ensemble character of the subseasonal forecast for the development of future S2S-derived forecast products that would provide probabilities of scenarios of potential trajectories (based on these clusters).

How to cite: Vérèmes, H., Malardel, S., Bonnardot, F., Labbé, L., Langlade, S., Peyrillé, P., Charpigny, S., Lefort, T., and Mékiès, D.: The PISSARO project: subseasonal impact-based forecasts of the cyclonic activity in the South West Indian Ocean basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-41, https://doi.org/10.5194/egusphere-egu22-41, 2022.

Climate variations have the potential to strongly affect aquaculture production. By having access to reliable predictions at extended and long-range lead times, aquaculture can take preventative measures. For instance, variability in water temperature influences the growth and mortality rates of farmed fish. Fish farmers can, if they have reliable forecasts, take action against unfavorable changes in water temperature by moving the sea cages and alter feeding schemes and slaughter times accordingly. In this way, one can minimize production loss, and production can become more sustainable. We present how sub-seasonal forecasts  from ECMWF can be used to provide skilful forecasts at lead times of two to four weeks at various fish farm locations in Norway by including post-processing methods that use on-sight observations as a predictor. Sub-seasonal forecasts are expected to capture grid scale variations and larger-scale phenomena in sea temperature. However, fish farms often lie in complex coastal areas and are therefore prone to local effects like river runoff and smaller scale currents, which are not adequately represented in the sub-seasonal forecast models.  First, we assess the forecast skill for all seasons for the fish farms along the Norwegian coast. The Norwegian fish farms are located in various regions, from off-shore to practically closed-off fjord environments. It is clear that forecast skill is reduced the further in the fjords  the fish farms are located. Post-processing the forecasts by including information on the persistence of water temperatures improves the skill in the fjords, compared to using the ECMWF sub-seasonal forecasts alone. The post-processing model is simple to implement and may enhance water temperature forecast skill in regions that are influenced by local processes. Moreover, this overview of forecast skill may guide forecasters and fish farmers on when, and where, to trust the sub-seasonal forecasts, which is crucial for decision making and can be beneficial for the economy and the industry’s  environmental sustainability.

How to cite: Auestad, H., Sørland, S. L., Wulff, O., Kolstad, E., and Sobolowski, S.: Verification of sub-seasonal sea surface temperature forecasts for fish farms along the Norwegian coast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8324, https://doi.org/10.5194/egusphere-egu22-8324, 2022.

The forecast lead time from the medium-range (15 days) to seasonal (up to several months) has the potential to be very useful for decision makers who rely on in hydrometeorological forecasts. Recently many forecasting systems, such as the IFS at ECMWF, are developing into fully integrated earth modeling systems by including the representation of the most relevant coupled processes such as ocean coupling, sea-ice interaction and troposphere-stratosphere feedbacks already at day 1. The immediate consequence of this new approach is that forecasting skills beyond the first two weeks might have increased to provide useful and "actionable" information to the end user. This is not only true for the meteorological output, but also for the many sectoral applications that relies on those atmospheric forcings. This study explores the sub-seasonal to seasonal predictability for a hydrological application over Europe forced by seasonal and sub-seasonal meteorological model output. The model system used was the seamless version of the European Flood Awareness System (EFAS) which combines the 46-day ECMWF Ensemble prediction system (EPS) with the seasonal forecasts (System-4). This provides biweekly forecast updates with a maximum horizon of 7 months. The forecast was evaluated against a water balance run forced with  observed meteorological input for a period of 20 years. The results show that the predictability window for river discharge at a number of locations extends to 31 days on average; beyond this limit climatology is as good as or better than dynamical forecast model. However, there are both spatial and and seasonal variations to this limit. Large river basins tends to extend the predictability to up to 45 days and there is a very relevant increase in the predictability up to 60 days for low-flow events. This indicates that a hydrological drought early warning system could provide skillful information of anomalous conditions almost at the season onset.

How to cite: Di Giuseppe, F. and Wetterhall, F.: The skill of sub-seasonal hydrological prediction over Europe , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9437, https://doi.org/10.5194/egusphere-egu22-9437, 2022.

Reliable sub-seasonal forecasts for precipitation and temperature are crucial to many sectors including agriculture, public health and renewable energy production. Since the forecast skill of numerical weather forecasts for lead times beyond two weeks is limited, the World Meteorological Organization launched a Challenge to improve Sub-seasonal to Seasonal Predictions using Artificial Intelligence, which was held from June to October 2021. Within the framework of this challenge, we have developed a hybrid forecasting model based on a convolutional neural network (CNN) that combines post-processing ideas with meteorological process understanding to improve sub-seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF).

Here, we present a refined version of our model that predicts tercile probabilities for biweekly averaged temperature and accumulated precipitation for weeks 3 – 4 and 5 – 6. Our model is trained on limited-area patches that are sampled from global predictor fields. It uses anomalies of large-scale predictors and features derived from the target variable forecasts as inputs. Spatial probabilistic forecasts are obtained by estimating coefficient values for local, spatially smooth basis functions as outputs of the CNN. Our CNN model provides calibrated and skillful probabilistic predictions, and clearly improves over climatology and the respective ECMWF baseline forecast in terms of the ranked probability score for weeks 3 – 4 and 5 - 6.

How to cite: Horat, N. and Lerch, S.: Convolutional neural networks for skillful global probabilistic predictions of temperature and precipitation on sub-seasonal time-scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-920, https://doi.org/10.5194/egusphere-egu22-920, 2022.

With the continuously increasing share of renewables in the electricity mix, the sub-seasonal predictions of 100 m wind speed and surface temperature, if skillful, can provide significant socio-economic value to the energy sector. In this study, we develop a novel hybrid statistical-dynamical probabilistic prediction model to improve the skill of sub-seasonal predictions of 100 m wind speed (U100) and 2 m temperature (T2m). For the statistical part, multivariate statistical analysis is carried out between the observed gridded large-scale fields such as the geopotential height at 500 hPa (Z500) and the gridded predictand (U100 or T2m) over Europe to obtain weather regimes conditioned on the targeted predictand. The relationship between the predictor and the predictand is then used to 'reconstruct' sub-seasonal predictions of U100 and T2m based on the predictions of Z500, which are more skillful than the surface variables. This is applied on sub-seasonal predictions from the European Centre for Medium-Range Weather Forecasts. The new 50 ensemble members of 'reconstructed' surface fields are combined with the original 50 members of dynamical/direct predictions. The resulting hybrid prediction ensemble is found to be generally more skillful than the dynamical predictions on sub-seasonal timescales.

How to cite: Goutham, N., Plougonven, R., Omrani, H., Parey, S., Tantet, A., Tankov, P., Hitchcock, P., and Drobinski, P.: Improving the skill of sub-seasonal forecasts of wind speed and surface temperature using information from large-scale fields: a proof of concept, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1891, https://doi.org/10.5194/egusphere-egu22-1891, 2022.

Extended range forecasts can directly contribute to positive health and economic outcomes, making sub-seasonal forecasting highly relevant for society. However, the summer temperature prediction skill over Europe (for both average and extreme temperatures) quickly decreases beyond timescales of two weeks. The origins of prediction errors of sub-seasonal forecast systems in the onset, intensity, and duration of hot temperature events are not yet fully understood. We investigate the predictability and drivers of the prediction skill of hot events in the sub-seasonal forecast system of the ECMWF (European Centre for Medium-Range Weather Forecasts). The analysis is conducted over six European regions and for different lead times (7-21 days) during the period 1998-2017. The onset and intensity of hot temperature events is better predicted by the ECMWF model at shorter lead times, but there are lower errors in duration at longer lead times. Compared to ERA-Interim reanalysis data, the ECMWF model overestimates the duration and underestimates the intensity of hot extremes for all European regions and lead times considered. Overall, the errors in hot event duration and intensity increase in the higher temperature percentiles, with large inter-event variability in the errors estimated for the 50-75 percentile range. 

How to cite: Pyrina, M. and Domeisen, D.: Predictability of onset, duration, and intensity of hot temperature events in the ECMWF subseasonal forecast system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2421, https://doi.org/10.5194/egusphere-egu22-2421, 2022.

Severe weather associated with organized convective systems is becoming more intense globally and is also observed in the Arabian Peninsula (AP). The extreme rainfall-associated flooding in low soil infiltration region like the AP often lead to significant social and economic losses within a very short period. Improving forecast capability at sub-seasonal to seasonal (S2S) timescale can potentially assist disaster risk mitigation, and water resource management.

A series of S2S regional climate model reforecasts were completed using the Weather Research and Forecasting Model (WRF) at convective-permitting resolution (4 km) for the AP. We dynamically downscale 20 years of winter season from the European Centre of Medium-range Weather Forecasts (ECMWF) S2S reforecast product. WRF simulations were initialized weekly with 1-month simulation duration between November and April.  A total of 191,400 hindcast days have been generated to evaluate the predictability of winter rainfall associated with convective activities.

Methods designed to evaluate the S2S forecast skills considers the probability of detection of precipitation at neighboring grids, determining the rate of forecast agreements between ensemble members, and running evaluation of the probability of forecast from 1-week to 4-week lead time. We evaluated all rain gauge measurement and gridded precipitation datasets available for the study period and determined the following datasets as our ground-base reference: satellite based Global Precipitation Mission (GPM) and 4-km reanalysis data produced by the King Abdullah University of Science and Technology (KAUST-RA). The WRF S2S downscaled reforecasts significantly improved from the driving ECMWF reforecast climatology, as evaluated against  the GPM and the KAUST-RA dataset. Our WRF results also produced reasonable winter precipitation climatology over the AP as compared to the satellite observations and high-resolution reanalysis products, at 1-week, 2-week, 3-week and 4-week forecast lead times.

How to cite: Chang, H.-I., Castro, C. L., Luong, T. M., Risanto, C. B., and Hoteit, I.: Analyses of convective event climatology in the Arabian Peninsula and forecast opportunity at S2S time scale  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8965, https://doi.org/10.5194/egusphere-egu22-8965, 2022.

Accurate weather forecasts can help to reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models are representing more physical processes, and can increasingly benefit from assimilating comprehensive Earth observation data. However, this increased complexity presents a challenge for pinpointing weaknesses in the forecast models’ process representations, which is needed to support continuous improvement in forecast accuracy.

In this study, we use a comprehensive set of observation-based ecological, hydrological and meteorological variables to study their potential for explaining temperature forecast errors at the weekly time scale. For this purpose, we computed Spearman correlations between each considered variable and the forecast error obtained from the ECMWF S2S re-forecasts dataset with lead times between 1 and 6 weeks. This is done across the globe for the time period 2001-2017. The results suggest that circulation-related variables such as wind speed and spatial pressure differences are overall most strongly related to forecast errors across the globe, suggesting that an improved representation of the large-scale circulation in the forecast model has the greatest potential to improve temperature forecasts. At the same time we found particular regions and seasons in which other variables are more strongly related to forecast errors, for  instance: i) during the growing season in Central Europe, Central Africa and Northern South America, the vegetation greenness and soil moisture are relevant, and ii) meteorological variables such as solar radiation, precipitation and sea surface temperature are relevant in Asia and Eastern Europe during boreal summer and autumn. Additionally, we found that the actual values of the variables are generally more strongly related to the forecast errors than their anomalies, pointing towards a systematic nature of the errors. Towards longer lead times, in contrast, the relevance of anomalies increases while correlations with the absolute values decrease. This highlights that biophysical information beyond the mean seasonal cycle can be informative for temperature forecasts. 

Our identification of variables related to forecast errors can inform the development of forecast models and data assimilation schemes, considering that most of the highlighted variables have corresponding satellite datasets available in near-real-time on a global scale.

How to cite: Ruiz-Vásquez, M., Oh, S., Brenning, A., Balsamo, G., Weber, U., Reichstein, M., Koster, R., and Orth, R.: Exploring the relationship between S2S temperature forecasting errors and Earth system variables, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9889, https://doi.org/10.5194/egusphere-egu22-9889, 2022.

The need to build reliable weather forecasting systems for subseasonal to seasonal (S2S) timescales has never been greater as the world continues to experience increased numbers of extreme weather events. This study addresses the skill gap between numerical weather prediction (NWP) and seasonal forecasting by proposing a daily probabilistic forecast model that predicts 2-meter temperature and total precipitation on a global scale. It combines multimodal data (e.g., physics-based ensembles, climate modes, recent climatology) into feature vectors given as inputs to three ML models: Extreme Gradient Boosting, U-Net, and Natural Gradient Boosting. We use Bayesian hyperparameter search and leave-one-year-out RPSS cross-validated scores to accelerate learning and ensure generalizability. Our method consistently outperforms both ECMWF 46-day forecasts and climatology. We find that augmenting physics-based issued forecasts with other sources of predictability greatly improves the performance of the underlying dynamical models. We hope that by improving the physics-based probabilistic forecasts, we will unlock skill in predicting climate extremes-oriented indices. Subsequent representation learning models may be trained to efficiently navigate the ensembles' uncertainty space and estimate the likelihood of extreme events.

How to cite: Zaytar, M. A., Zadrozny, B., Watson, C., Salles Civitarese, D., Eben Vos, E., Michael Mathonsi, T., and Lukhetho Mashinini, T.: ML-based Probabilistic Prediction of 2m Temperature and Total Precipitation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11063, https://doi.org/10.5194/egusphere-egu22-11063, 2022.

Persistence may be regarded as a baseline to forecasting, not only at short time-scales, but on subseasonal to seasonal time scales as well.  The present study explores the persistence of monthly mean temperatures in the dense network of long time series in metropolitan France.  The data reveals very high persistence in coastal areas, both at the Mediterranean Sea and at the Atlantic coast.  However, this persistence has a high seasonal variability; it is very high in the summer, but low in the winter, suggesting strong dependence of the persistence on static stability.  There are signs of negative correlation between mean temperatures of adjacent months in the autumn in inland areas and in the winter in S-France.  These features may possibly be attributed to soil moisture and regional impact of cold spells on the atmospheric circulation.     

How to cite: Avrillaud, S. and Olafsson, H.: Persistence in Mean Monthly Temperatures in France, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11718, https://doi.org/10.5194/egusphere-egu22-11718, 2022.

In a previous transfer project T1 of the SFB-Transregio “Waves to Weather” (W2W) a strategy was designed to classify precipitation extremes in Northern Italy and to provide additional information on the physical and dynamical drivers associated with it. Building on this, in collaboration with ARPAE-SIMC and ECMWF, we designed a new transfer project called TEX (Towards seamless prediction of EXtremes). The project has the final goal to expand and generalize this dynamical methodology to other regions and into the sub-seasonal forecast range (10-30 days). In this contribution, we present the first results concerning the validity of this method in the medium-range forecast. In particular, we show the accuracy of the random forest classification method, essentially based also on atmospheric upper-level predictors, in recognizing days with a high probability of extreme precipitation events compared to a forecast based only on precipitation outputs. These results, which are still referring to the test area of N-Italy, are preparatory for a further generalization to different areas and at a longer forecast horizon.

How to cite: Grazzini, F., Grams, C., and Craig, G.: Classification and prediction of days with extreme rainfall using random forest approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11954, https://doi.org/10.5194/egusphere-egu22-11954, 2022.