AS1.3
Orals: Fri, 28 Apr | Room 1.85/86
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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-9342, https://doi.org/10.5194/egusphere-egu23-9342, 2023.
The COVID-19 pandemic, recent extreme weather events around the globe and the invasion of Russian forces in Ukraine have led to a disrupted global food market. As the 12th largest global wheat exporter, Kazakhstan is fundamental for regional and global food security. Timely and reliable predictions of Kazakh wheat production could therefore improve food security planning and management in Central Asia and beyond.
In this session, we want to present a statistical weather-driven yield forecast model that is run with publicly available weather and yield data and requires low computational power, making it easily replicable. Decision makers in Kazakhstan have expressed high interest in using the forecast model as a replenishment to currently applied work-intensive forecasting methods. We stringently evaluated our model in a double out-of-sample validation and used it to forecast total national wheat production in a fully blind run for 2022.
Our results show that the model can successfully hindcast wheat yields at the oblast (regional) level up to two months before the harvest. The hindcast of wheat yields for 1993 to 2021 produces a median R2 of 0.69 for the full model run and R2 values of 0.60 and 0.37 for two levels of out-of-sample validations, respectively. Based on these yield estimates we provide a robust hindcast of the total wheat production for Kazakhstan with an R2 value of 0.86 (0.81 and 0.73 for two levels of out-of-sample validations). We forecast total wheat production in Kazakhstan for 2022 to be 12.4 million tonnes and thus 5 % above the production of the last year.How to cite: Romanovska, P., Schauberger, B., and Gornott, C.: Wheat yields in Kazakhstan can successfully be forecasted using a statistical crop model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1286, https://doi.org/10.5194/egusphere-egu23-1286, 2023.
Dry and heat spells have the potential to alter the maize crop’s physiology and ultimately result in low yield. This study proposes a methodology of moving away from the generic dry spell definitions by using the water requirement satisfaction index (WRSI) output from the Tropical Applications of Meteorology using SATellite data – AgriculturaL Early waRning sysTem (TAMSAT-ALERT) as a metric of the impact such events have on the maize crop. We define these objectively identified events as impactful dry spells (IDS). The total exceedance of the maize crop’s upper-temperature optimal threshold is used to capture extreme temperatures during the growing season and is defined as impactful heat spells (IHS). Results of our analysis show that some seasons with relatively low yields were associated with the co-occurrence of severe IDS and IHS events specifically in the establishment and vegetative development phases. This implies that operational drought monitoring models with methodologies similar to that of TAMSAT-ALERT have the potential of inadvertently underestimating yield since they are incapable of incorporating the impact of heat on the crop’s physiology. We also investigate the rainfall characteristics based on the concept of Shannon entropy and show that seasons with relatively high (low) simulated yields are associated with (un)evenly distributed rainfall. Seasons of low simulated yield are shown to be associated with the southward displacement of Tropical Temperate Troughs; an anomalously weak (strong) Angola Low, Mascarene High-Pressure, and St. Helena High-Pressure systems (Mozambique Channel Trough and Botswana High), coupled with regional inhibitive atmospheric conditions for convective cloud development. This is the opposite of atmospheric patterns in seasons with a high simulated yield.
How to cite: Masukwedza, G. I., Todd, M., Lazenby, M., and Boult, V.: Extreme agrometeorological events over Zimbabwe: Characteristics and drivers, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10345, https://doi.org/10.5194/egusphere-egu23-10345, 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, 24–28 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, 24–28 Apr 2023, EGU23-360, https://doi.org/10.5194/egusphere-egu23-360, 2023.
Posters on site: Fri, 28 Apr, 14:00–15:45 | Hall X5
Numerical predictions for a lead time of 2 to 4 weeks, a timescale known as subseasonal, has only in recent years begun transitioning from research to operational settings. One experiment dedicated to that effort is the Subseasonal Experiment (SubX). In here, SubX multi-model ensemble (MME) mean precipitation forecast (2017-2021) for days 8 to 14 (week-2 forecast) is used as a covariate in logistic regression models to predict fire risk in the Amazon. The hybrid (dynamical and statistical) modelling approach describes the NextGen methodology aimed at improving forecast outcomes at the seasonal and subseasonal time scales. In a complementary experiment, a vegetation health index (VHI) is added to SubX precipitation forecasts as a predictor to fires. The findings show that fire risk can be skillfully assessed in most of the Amazon where fires occur regularly. In some sectors, SubX week-2 precipitation alone is a reliable predictor of fire risk, but the addition of VHI results both in (i) a larger portion of the Amazon domain with skillful forecasts and; (ii) higher skill in some sectors. The added information provided by VHI as a predictor is most relevant where the mosaic of land covers includes savannas and grassland, whereas SubX precipitation can be used as the sole predictor for week-2 fire risk forecast where the mosaic of land cover is dominated by forests. The operationalization of the methods presented in this study could allow for better preparedness and fire risk reduction in the Amazon with a lead time greater than a week.
How to cite: Fernandes, K., Bell, M., and Muñoz, Á.: Subseasonal fire forecast in the Amazon using week-2 precipitation forecast combined with a vegetation health indicator, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2085, https://doi.org/10.5194/egusphere-egu23-2085, 2023.
In this ongoing study we aim at using machine learning algorithms to better understand and improve southern Europe summer heatwave prediction on sub-seasonal to seasonal timescales (S2S). Summer heatwaves are extreme events that have large socio-economic impacts on mortality rate, crop yields, energy demand or water resources and southern Europe is particularly prone and vulnerable to such events.
To do this, we train a convolutional network coupled with a multilayer perceptron to forecast with a 15-day and 1-month lead times the occurrence and intensity of heatwave in summer. This forecast model is trained with ERA5 data. The predictors fed to this model are monthly means of the SST, local soil moisture, outgoing longwave radiation, snow cover and sea-ice cover. The target is a monthly-mean heatwave index integrated over a sub-area of southern Europe.
Here, we will present the initial results of this ongoing work and the next steps, focusing first on the Iberian Peninsula only.
How to cite: Drouard, M., Pérez-Aracil, J., Barriopedro, D., Zaninelli, P. G., Garrido-Perez, J. M., Fister, D., Salcedo-Sanz, S., and García-Herrera, R.: S2S prediction of summer heatwaves in the Iberian Peninsula using convolutional networks , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5742, https://doi.org/10.5194/egusphere-egu23-5742, 2023.
Tropical Cyclone (hereafter, TC), a most destructive weather phenomenon that causes enormous socio-economic damage, occurs around 25 times every year in the western North Pacific, of which Korea is directly or indirectly affected by about 3 to 4 TCs every year. Even if it is affected by a small number of TCs, the damage could be unimaginably large. To preemptively prepare and respond to TCs, predictability on the sub-seasonal to seasonal (S2S) time-scale, over two weeks to two months is being emphasized. In this study, the characteristics of TCs in sub-seasonal forecasting with the Global Seasonal Forecast System 5 (GloSea5) of the Korea Meteorological Administration (KMA) were assessed over the western North Pacific (WNP). The predictability of GloSea5 was examined for its ability to reproduce observed TC climatology as well as changes in TC genesis with the El Niño-Southern Oscillation (ENSO) and a 1998/1999 climate regime shift. GloSea5 showed skilful performance in simulating the frequency and genesis spatial distribution of TCs in climatology and both extreme ENSO phases. Synoptic fields related to TC genesis were also reasonably captured, despite some systematic biases in those. GloSea5 performed well in terms of characteristics of changes in TC genesis due to the climate regime shift. However, there were biases in TC frequency before the regime shift and in changes in TC-related environmental fields. This study implies that GloSea5, which has a good predictive skill for TC genesis over the WNP, can be used as an operational model for sub-seasonal TC forecasting, although it requires continuous improvements to reduce systematic errors
How to cite: Kim, T., Kim, E., Lee, M., Cha, D.-H., Lee, S.-M., Lee, J., and Boo, K.-O.: Characteristics of Tropical cyclones in sub-seasonal forecasting with GloSea5: Predictability in extreme ENSO phases and a climate regime shift, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14682, https://doi.org/10.5194/egusphere-egu23-14682, 2023.
Current sub-seasonal prediction systems are traditionally based on models developed for numerical weather prediction. We present a different approach wherein we develop a sub-seasonal prediction system using a coupled Earth system model, the Max-Planck-Institute Earth system model (MPI-ESM), developed primarily for the use in climate prediction. We present results from initialized sub-seasonal reforecasts for the time period 1993-2017 from a 1st generation (CMIP5) seasonal-turned-sub-seasonal prediction system based on MPI-ESM including different components of the Earth system: atmosphere, land surface, ocean, and marine ecosystems. With our system we find (1) that atmospheric variables can be predicted with a quality and prediction horizon similar to what is found within the range of current sub-seasonal to seasonal prediction systems, (2) that extreme events as diverse as heatwaves over land, storm severity over Europe, and sudden stratospheric warmings can be skilfully predicted one to a few weeks ahead, (3) that sea surface temperatures can be skilfully predicted in the majority of large marine ecosystems for several weeks ahead, and (4) that sea ice area in the majority of Arctic seas can be skillfully predicted several weeks ahead. Our findings indicate that a coupled Earth system model like MPI-ESM can already be seamlessly used for sub-seasonal to seasonal (to decadal) climate predictions of different domains of the Earth system. Ultimately these results ask for the seamless approach to be embedded into the development of future coupled Earth system models.
How to cite: Koul, V., Brune, S., Febre, C., Domeisen, D. I. V., and Baehr, J.: A sub-seasonal to seasonal prediction system with MPI-ESM, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12537, https://doi.org/10.5194/egusphere-egu23-12537, 2023.
Reliable (sub) seasonal (S2S) forecasts remain a huge scientific challenge. The lead-time is too long to benefit from the atmosphere’s inertial memory, but too short for the atmosphere’s boundary conditions to be felt strongly. Only for specific "windows of predictability" (i.e. specific regions, timescales and climatic background states), skillful forecasts are possible, in an otherwise largely unpredictable future. Due to a number of successes in S2S forecasting, the interest in machine learning (ML) is growing fast. However, we argue there is a need for more standardization, consensus on best practices, higher efficiency, and higher reproducibility. Typical S2S ML use-cases, such as (1) pure statistical forecasting based on observations, (2) transfer learning, and (3) post-processing of dynamical model ensembles, require a large coding and preprocessing effort. Such experiments are not trivial to set up, and without sufficient experience and expertise there is a large risk of improper cross-validation and/or improper and non-standard verification.
Within a 3-year project, we are developing a high-level Python package called s2spy. Our aim is to make ML workflows more transparent and easier to build, and to facilitate standardization and collaboration across the S2S community. s2spy also contributes to a higher reproducibility and works towards a wider acceptance of standards and best practices. We will present our vision and the capabilities of our package, show-casing that we can build a model from raw climate data up to verification and explanation in only a few lines of code.
How to cite: Liu, Y., Schilperoort, B., van Ingen, J., Vijverberg, S., Kalverla, P., and Coumou, D.: s2spy, a package to boost (sub) seasonal forecasting with artificial intelligence, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-953, https://doi.org/10.5194/egusphere-egu23-953, 2023.
Beginning July 2020, the Niño 3.4 index crossed below the threshold to La Niña conditions and remained below a -0.4 sea surface temperature anomaly through the spring of 2023, impacting agriculture, livelihoods, and communities around the world. What caused this prolonged La Niña event and why was it sustained? How did the interaction between the different modes of climate variability influence the event? The internal dynamics of ENSO, the Indian Ocean Dipole, and the Madden-Julian Oscillation are studied here through a non-linear approach utilizing compositing techniques and both linear and non-linear wave superposition to identify what caused and prolonged the 2020-2023 La Niña event.
How to cite: DiSera, L. and Muñoz, Á. G.: The 2020-2023 La Niña: Did Cross-timescale Interference Fuel this Multi-year Event?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16412, https://doi.org/10.5194/egusphere-egu23-16412, 2023.
Extended medium-range prediction targets a period of up to 30 days, which is a longer period than medium-range (up to 15 days) and shorter than seasonal (up to 3 months) forecast. The atmospheric response to the initial condition significantly impacts predictability in medium-range prediction while ocean response which is a slower change compared to the atmosphere is also an important factor in extended medium-range prediction. Thus, it is important to consider not only initial forcing but also air-sea interaction containing ocean response in extended medium-range prediction. The interactions in the earth system model can be considered among the atmosphere, ocean, sea-ice, and ocean wave by coupling of each components. The Korean Integrated Model (KIM) system, which is a global atmospheric forecast model, was developed by the Korea Institute of Atmospheric Prediction Systems. Recently, the ocean and sea-ice model components have been coupled with the KIM atmosphere system, and continuous efforts are being made to improve its performance. The air-sea interaction in an atmosphere-ocean coupled system can be considered by exchanging the variables that require interaction between components with a coupler. The bulk type exchange method basically transfers state variables such as temperature, pressure, and wind, which are used to get flux variables that contain interacting information among the atmosphere, ocean, and sea-ice. The bulk method is simple but the energy budget at the interface among the model components may become inconsistent due to the use of different formulas during calculation of the flux variables. In this study, exchange variables are changed by replacing atmospheric state variables with flux and momentum variables, which are the final form used in the ocean model. It is found that the corrected flux and momentum of the ocean surface resulting from the flux type exchange method change the ocean structure, particularly over the low latitude region. The atmosphere reacts to the changed ocean state, affecting not only the lower atmosphere but also the upper atmosphere. The results show that the flux type variable exchange method has advantages for considering air-sea interaction, which would improve the performance of extended medium-range weather forecast compared to the bulk type exchange method.
Keywords: extended medium-range forecast, coupled model, air-sea interaction, bulk type method
Acknowledgement
This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01210.
How to cite: Yeo, N., Chang, E.-C., Song, H., Park, J., Lee, E., and Koo, M.-S.: Impact of flux type variable exchange method in the atmosphere-ocean coupled version of the Korean Integrated Model (KIM) system for extended medium-range weather forecast, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11267, https://doi.org/10.5194/egusphere-egu23-11267, 2023.
The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in one day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the S2S Prediction Project Database, from May 1 to Oct. 31, 1999, to 2010. The ensemble reforecasts are collected from eleven operational centers, the BoM, CMA, ECCC, ECMWF, HMCR, ISAC, JMA, KMA, METFR, NCEP, and UKMO. In both observation and these eleven forecast models, the DTCP is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, BSISO1 and BSISO2. During BSISO1 phases 1, 5, 6, 7, and 8, the DTCP in the northwestern Pacific region is ~3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of BSISO2, the DTCP is ~2.5 times higher. Among the eleven models, the ECMWF model best reproduces the climatological DTCP and its modulation by the BSISO in the western North Pacific region, followed by NCEP, KMA, JMA models. Using the DTCP metric, the highest debiased Brier Skill Score of the eleven models is from ECMWF, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11 to 30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases. The updated results based on the real-time tropical cyclone forecasts of the S2S Prediction Project Database from these eleven systems will also be discussed.
How to cite: Wang, X., Waliser, D., Vitart, F., Jiang, X., and Asharaf, S.: Evaluating Western North Pacific Tropical Cyclone Forecast in the Subseasonal to Seasonal Prediction Project Database, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6511, https://doi.org/10.5194/egusphere-egu23-6511, 2023.
Changes of tropical wave-modes due to climate change will impact the predictability of the tropical atmosphere, and may impact extratropical weather as well. The simulations of convectively coupled equatorial waves and the Madden-Julian Oscillation (MJO) are considered in 13 state-of-the-art models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We look at the wave-modes using frequency-wavenumber power spectra of the models and observations for Outgoing Longwave Radiation and zonal winds at 250 hPa. We analyze the spectra of the historical simulations and end of 21st century projections for the SSP245 and SSP585 scenarios. The models simulate a spectrum quantitatively resembling that observed, though systematic biases exist. Most models project a future increase in power spectra for the MJO, while nearly all project a robust increase for Kelvin waves (KW) and weaker power values for most other wavenumber-frequency combinations. Models with a more realistic MJO in their control climate tend to simulate a stronger future intensification. In addition to strengthening, KW also shift toward higher phase speeds. The net effect is a more organized tropical circulation on intraseasonal timescales, which may contribute to higher intrinsic predictability in the tropics and to stronger teleconnections in the extratropics. In addition, those projected changes might be due to extratropical forcings, and more specifically due to changes in the North Pacific subtropical jet.
How to cite: Bartana, H., Garfinkel, C., Shamir, O., and Rao, J.: Projected Future Changes in Equatorial Wave Spectrum in CMIP6, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16731, https://doi.org/10.5194/egusphere-egu23-16731, 2023.
Extreme stratospheric polar vortex events, such as sudden stratospheric warmings (SSW) or extremely strong polar vortex (SPV) states, can have a prolonged downward impact, influencing surface weather for several weeks to months. These events often lead to changes in the midlatitude storm track position and associated cyclone frequency over the North Atlantic and Europe. Such changes can result in infrastructure damage and health impacts due to cyclone-associated extreme winds and the risk of flooding or heavy snowfall. However, there exists a strong inter-event variability in these downward impacts on the tropospheric storm track, leading to opposite predictions of the storm track response. Therefore, identifying the biases in the forecast of the downward impact of stratospheric polar vortex extremes can improve the predictability of extratropical winter storms on subseasonal-to-seasonal timescales, and has the potential to benefit society and stakeholders.
Using ECMWF reanalysis data and ECMWF reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project database, we investigate the stratospheric influence on extratropical cyclones, identified with a cyclone detection algorithm. Following SSWs, there is an equatorward shift in cyclone frequency over the North Atlantic in reforecasts, and a poleward shift is observed after SPV events, consistent with the response in reanalysis. However, less than 70% of the reforecasts capture the sign of the cyclone frequency response over the North Atlantic during weeks 1-2 after SSWs, and less than 50% of the reforecasts capture the response during weeks 3-4. The cyclone forecasts following SPV events are generally more successful. We further discuss the differences in predictability of extratropical cyclones between the two types of stratospheric extremes.
The results provide new insights on the role of the stratosphere in subseasonal variability and predictability of extratropical cyclones during winter that can be used for forecasting their frequency and surface impacts.
How to cite: Gerstman, H., Büeler, D., Wulff, C. O., Sprenger, M., and Domeisen, D.: The influence of the stratosphere on the North Atlantic storm track predictability in subseasonal-to-seasonal reforecasts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-553, https://doi.org/10.5194/egusphere-egu23-553, 2023.
France is committed to achieving climate neutrality by 2050. In this respect, the heating sector, one of the largest energy-consuming sectors in France, is undergoing rapid electrification. In 2022, electricity contributed to the heating of more than 40% of French dwellings. As a result, the French electricity demand is increasingly becoming thermosensitive. Accordingly, for every 1°C drop in temperature below the threshold (i.e., 15°C) during winter, the electricity demand increases by ~2.4 GW in France. With a notable share of French nuclear reactors reaching their end of service life, several recent episodes of widespread cold spells over France have raised concerns about energy security. Hence, anticipating cold spells well in advance is increasingly becoming important for the smooth operation of the energy sector. In this regard, we assess the predictability of several recent episodes of cold spells on seasonal timescales over France using the seasonal predictions from the European Centre for Medium-Range Weather Forecasts. Additionally, we test a recently developed statistical downscaling methodology in forecasting cold spells over France, using the forecasts of upper-level fields, which are better predicted than the surface fields. On comparing the dynamical and statistical predictions, we show that the statistical predictions, relying upon the information contained in the better-predicted upper-level fields, perform significantly better than the dynamical counterparts in predicting cold spells beyond a month.
How to cite: Goutham, N., Omrani, H., Himych, O., and Plougonven, R.: How well in advance can we predict cold spells over France?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5201, https://doi.org/10.5194/egusphere-egu23-5201, 2023.
During a Madden-Julian Oscillation (MJO) event, anomalous convection triggers a dynamical response with anomalous large-scale ascent and upper-tropospheric divergence outside the tropics creates interaction between the MJO and the extratropical weather, modes of global circulation and climate variability. The MJO is known to have an impact on China rainfall and regional circulation with enhanced/ suppressed rainfall in South China during the propagation of the MJO from the Indian Ocean into the western Pacific. As the MJO is considered a major source of predictability at subseasonal time scales, it is important to understand how climate models are representing the MJO and its remote effects. This study is aimed to investigate the modelled MJO and associated local effects in China precipitation using the Met Office Unified Model (MetUM). It was found that the response of the rainfall over South China is asymmetric, with the enhancement of rainfall during the Indian Ocean convective phases (phase 2) of the MJO being much stronger than the suppression during the west Pacific phases (phase 6). This response signal was mostly due to the increase in probability of extreme precipitation events rather than the increase in number of rainy days. Analysis of the modelled MJO and associated response shows, although the MJO is more realistically represented in the atmosphere-ocean coupled simulation, the atmosphere-only simulation showed more evidence of MJO-related remote effects in the rainfall patterns over China. The ocean-coupled simulation shows no significant response to the propagation of MJO-associated convection whereas the atmosphere-only simulation shows the correct pattern of enhancement and suppression of rainfall and associated regional circulation pattern changes. The differences found in the representation of remote effects between atmosphere-only and ocean-coupled simulations may be attributed to the air-sea interaction processes and to fundamentally different mean-state biases which affect not only the representation of the MJO but also the propagation of MJO-induced Rossby waves.
How to cite: Carvalho, M. J., Xavier, P., and Furtado, K.: MJO-related China rainfall teleconnections in the MetUM , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2752, https://doi.org/10.5194/egusphere-egu23-2752, 2023.
A new method of classification based upon an empirical orthogonal functions (EOFs) analysis of zonal wind anomalies of the 70 winters from 1950 to 2020, extracted from ERA5, revealed that the winter stratosphere tends to follow four independent scenarios. The first three scenarios: the January, the February, and the Double modes, are all characterized by a perturbed evolution of the polar vortex due to significant sudden stratospheric warmings (SSWs) occurring in mid-winter, generally causing the reversing of zonal winds. Unsurprisingly, these modes contain the information of preferential important SSWs’ timings, events including minor and major SSWs, and final stratospheric warming’s timings. Thus, their patterns show that the mid-winter is often anti-correlated with the winter end. This result is consistent with the conclusion done in a recent study showing that the polar vortex on a given month is anti-correlated with its state 2-3 months earlier. While the last scenario illustrates an unperturbed polar vortex evolution during winter for which only the final stratospheric warming’s timing differs, either early and dynamical or late and radiative.
The study of the mean evolutions of wave-1 and wave-2 amplitude anomalies associated with these four scenarios reveals that they possess singular dynamic behavior, especially for the wave-1 activities, which are consistent with their mean evolutions of zonal mean zonal winds. Indeed, we found that the wave-1 activity drops systematically for each scenario when zonal winds weaken due to an important. In contrast, it is not the case for the wave-2 activity.
How to cite: Mariaccia, A., Keckhut, P., and Hauchecorne, A.: New classification showing the stratospheric memory concept: towards a better seasonal prediction , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1376, https://doi.org/10.5194/egusphere-egu23-1376, 2023.
Unprecedented heavy rainfall reaches the warming Earth more frequently, creating the need for effective risk-warning alerts that utilize subseasonal-to-seasonal (S2S) forecasting to integrate information from nowcasting, weather, and seasonal predictions. A record-breaking flooding event occurred in Zhengzhou, Henan Province of China during 17–23 July 2021, causing 398 total of deaths and vast economic losses.
A number of studies have shown this super severe heavy flooding occurred under the background of multiscale circulation interactions and the impacts of remote tropical cyclones. Here, we evaluated the predictability of this extreme rainfall event and the impacts of tropical cyclones (TCs) using subseasonal-to-seasonal (S2S) operational models. Most S2S models can reasonably predict the wet-in-north and dry-in-south monthly rainfall pattern over China in July. Only four models captured the location, probability, and sudden intensification of the Zhengzhou rainfall extremes in advance of one week, largely due to their reasonable prediction of the variability of the western North Pacific subtropical high in mid-latitudes. Although the chance of exceeding the new record daily rainfall is only approx. 0.7% in mid-late July, they provide a high probability of this heavy weekly rainfall one week in advance. However, the S2S models still underestimated the super extremeness of this event. The prediction discrepancies came from the poor predictability of Typhoon IN-FA and its impact on the daily evolution of the extreme rainfall event, even within a few days forecast lead. Compared with the observation, the prediction bias of tropical disturbance changed the environmental monsoon airflow to induce the earlier warning of rainfall extremes prior to the formation of IN-FA. After the formation of IN-FA, the prediction bias of the typhoon’s moving speed distorted the typhoon location, which incorrectly predicted the moisture convergence center and underestimated their remote impacts on this heavy rainfall event. Future research should improve our awareness of the challenges that remain in the S2S forecasts.
How to cite: Yan, Y., Zhu, C., and Liu, B.: Subseasonal prediction of the July 2021 extreme rainfall event over Henan China in S2S forecasting systems, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15077, https://doi.org/10.5194/egusphere-egu23-15077, 2023.
Posters virtual: Fri, 28 Apr, 14:00–15:45 | vHall AS
Subseasonal forecasting is forecasting of the weather parameters, mainly temperature and precipitation, two weeks to two months in advance. Sub-seasonal variability accounts for a substantial portion of the summer rainfall over India. Prediction of sub-seasonal climate is of immense societal importance in agriculture planning, water management, emergency planning, etc. Using various weather parameters and ECMWF dynamical model forecasts as predictors, this study tries to investigate the weekly forecast of temperature and precipitation at 2-week, 3-week, and 4-week forecast horizon over India using a computationally inexpensive machine learning model-MultiLLR, which prunes out irrelevant predictors and integrates remaining predictors linearly for each target date. The model’s predictions calculated over the years 2019-2022 are as skilful as IMD’s Extended Range Forecasting System (ERFS). The skill of the model is better in the coastal region than in the inland part of India.
How to cite: Jadhav, P., Op, S., and Thelliyil, S.: Subseasonal forecasting of temperature and precipitation over India using a machine learning approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16178, https://doi.org/10.5194/egusphere-egu23-16178, 2023.
Teleconnection patterns associated with the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO) impact weather and climate phenomena in the Pacific–North American region and beyond, and therefore accurately simulating these teleconnections is of importance for seasonal and subseasonal forecasts. Systematic biases in boreal midwinter ENSO and MJO teleconnections are found in eight subseasonal to seasonal (S2S) forecast models over the Pacific–North America region. All models simulate an anomalous 500-hPa geopotential height response that is too weak. This overly weak response is associated with overly weak subtropical upper-level convergence and a too-weak Rossby wave source in most models, and in several models there is also a biased subtropical Pacific jet, which affects the propagation of Rossby waves. In addition to this overly weak response, all models also simulate ENSO teleconnections that reach too far poleward toward Alaska and northeastern Russia. The net effect is that these models likely underestimate the impacts associated with the MJO and ENSO over western North America, and suffer from a reduction in skill from what could be achieved.
How to cite: Schwartz, C., Garfinkel, C., Chen, W., Li, Y., Yadav, P., and Domeisen, D. I. V.: The Winter North Pacific Teleconnection in Response to ENSO and the MJO in Operational Subseasonal Forecasting Models Is Too Weak, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8258, https://doi.org/10.5194/egusphere-egu23-8258, 2023.
Rain-fed agriculture constitutes more than 95 % of cropland in Germany. It depends heavily on rainfall patterns and the water storage capacities of top soil layers. Intense summer droughts with long-lasting lack of precipitation leads to yield loss in wheat, corn and sugar beet production in the last years 2018, 2019, 2020 and 2022. Hence, these drought events increase the requirement of long-range forecasts for precipitation and soil moisture, which could provide useful predictions for agricultural applications.
Here a coupled modelling attempt is presented, that combines the extended-range ENS-forecasts from the European Centre for Medium-Range Weather Forecasts (ECWMF) with the soil-vegetation-atmosphere-transfer (SVAT) impact model AMBAV to simulate the top soil moisture for subseasonal forecasts on a downscaled 5x5 km grid in Germany. A quality assessment of forecast ensemble means from July 2022 to November 2022 has been done with the corresponding hindcasts for the preceding 20 years. The mean squared error skill score (MSESS) of weekly averages reveals a significant forecast skill up to 4-6 weeks for soil moisture in the upper 60 cm in comparison to an AMBAV analysis run based on gridded weather station data. In contrast, the precipitation forecast skill is much lower and achieve only adequate forecast skill with lead times up to two weeks. Due to the low variability and persistence of soil moisture values, it is proposed, that this storage variable is well suited for climate services like agricultural drought predicting systems on subseasonal time scales. It could offer guidance with sufficient reliability for medium-term management adjustments like irrigation planning or reduced fertilizer usage in case of expected severe drought periods. Overall, the results of this study show the potential of subseasonal soil moisture forecasts for agricultural applications. Further research is needed to verify these findings and to extend the forecast analysis period to the entire year. Then the impact modelling system might contribute to the adaptation of agriculture to climate change in Germany.
How to cite: Leppelt, T.: Quality assessment of subseasonal soil moisture forecasts for agricultural applications in Germany, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11833, https://doi.org/10.5194/egusphere-egu23-11833, 2023.
Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).
While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales. Since different models and strategies are used at different timescales by both national and international seasonal and subseasonal forecasting centers (Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017), and skill is different at those timescales, it is key to guarantee that a physically consistent “bridging” between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work.
Here, we propose and explore a new methodology –that we call the Xit (“cross-it”) operator– based on the Liang-Kleeman information flow (e.g., Tawia Hagan et al. 2019) and wavelet spectra and entropy (e.g., Zunino et al. 2007), to “bridge” forecasts at different timescales in a smooth and physically-consistent manner.
In summary, the Xit operator (1) conducts a wavelet spectral analysis (e.g., Ng and Chan 2013, Zunino et al. 2007) and (2) a non-stationary time-frequency causality analysis (e.g., Tawia Hagan et al. 2019, Liang 2015) on forecasts at different timescales to assess cross-timescale coherence and physical consistency in terms of various sources of predictability. In principle, the approach permits to identify which “intrinsic” periods/scales (i) in the timescale continuum (t) are more suitable for the bridging to occur, and/or which ones can produce more skillful forecasts, by pointing to particular target times—i.e., potential windows of opportunity (Mariotti et al. 2020)—in the forecast period where wavelet entropy (uncertainty) is lower.
While the first component of the Xit operator, i.e., the wavelet spectral and entropy analysis (Zunino et al. 2007), is designed to identify the optimal time-frequency bands for cross-timescale bridging, the fact that two forecast systems (e.g., a subseasonal and a seasonal) exhibit significant wavelet coherence does not imply that bridging those systems will provide physically-consistent predictions. The second component of the Xit operator, i.e., the non-stationary causality analysis (Tawia Hagan et al. 2019), is thus designed to assess physical consistency of the bridging by analyzing the causal link between different climate drivers (acting at different timescales) and the forecast variable of interest.
How to cite: Muñoz, Á. G., Doblas-Reyes, F., DiSera, L., Donat, M., González-Reviriego, N., Soret, A., Terrado, M., and Torralba, V.: Hunting for “Windows of Opportunity” in Forecasts Across Timescales? Cross it, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15594, https://doi.org/10.5194/egusphere-egu23-15594, 2023.
