Displays

The key to better prediction of S2S variability and weather regimes in a changing climate lies with improved understanding of the fundamental nature of S2S phase space structure and associated predictability and dynamical processes. The latter can be decomposed into a finite number of relatively large-scale discrete-like Rossby waves with coherent space-time characteristics using Empirical Normal Mode (ENM) analysis. ENM analysis is based on principal component analysis, conservation laws and normal mode theories. These modes evolve in a complex manner through nonlinear interactions with themselves and transient eddies and weak dissipative processes. Within this atmospheric dynamic framework, we will discuss the teleconnections and the 35-day wave resonance of the North Atlantic Oscillation using recent diagnostics and numerical experiments.

References

Brunet, G. and J. Methven, 2018: Identifying wave processes associated with predictability across time scales: An empirical normal mode approach. Book chapter in Sub-seasonal to seasonal prediction: The gap between weather and climate forecasting. Editors A.W. Robertson and F. Vitart, Elsevier. p.1-42

Brunet, G. 1994:  Empirical normal mode analysis of atmospheric data. J. Atmos. Sci., 51, 932-952.

Lin, H., G. Brunet, and J. Derome, 2009: An observed connection between the North Atlantic Oscillation and the Madden-Julian Oscillation.  J. Climate, 22, 364-380.

Lin, H., G. Brunet and J. Derome 2007: Intraseasonal Variability in a Dry Atmospheric Model, J. Atmos. Sci., 64, 2442-2441.

How to cite: Brunet, G., Martinez, Y., Lin, H., and Bernier, N.: On the Resonances and Teleconnections of the North Atlantic and Madden-Julian Oscillations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3951, https://doi.org/10.5194/egusphere-egu2020-3951, 2020.

State-of-the-art Subseasonal-to-Seasonal (S2S) forecast systems correctly simulate the main properties of weather regimes, like their spatial structures and their average frequencies. However, they are still unable to skillfully predict the observed frequencies of occurrence of weather regimes after the first ten days or so. Such a limitation severely restrict their application to develop climate service products, for example to forecast events with a strong impact on society, such as droughts, heat waves or cold spells.

This work describes two novel corrections that can be easily applied to any weather regime classification, to significantly enhance the S2S predictability of the frequencies of the weather regimes. The first one is based on the idea of weighting the daily observed anomaly fields of the variable used to cluster the atmospheric flow by the Anomaly Correlation Coefficient (ACC) of the same variable, just before clustering it. In this way, the clustering algorithm gives more importance to the areas where the forecast system is better in predicting the circulation variable. Thus, it is forced to generate the most predictable regimes. The second correction consists in the ACC weighting of the daily forecasted anomalies before the assignation of the daily fields to the observed regimes, to give more importance to the grid points where the forecast system has more skill. Hence, the forecasted time series of the regimes is more similar to the observed one.

Two sets of four regimes each were validated, one defined by k-means clustering of SLP from NCEP reanalysis over the Euro-Atlantic region during lasts 40-years (1979-2018) for October to March, and another for April to September. Forecasts proceed from the 2018 version of the Monthly Forecast System developed by the European Centre for Medium-Range Weather Forecasts (ECMWF-MFS). Predictability was measured in cross-validation by the Pearson correlations between the forecasted and observed weekly frequencies of occurrence of the regimes, for each of the 52 weekly start dates of the year separately and for a 20-years hindcast period (1998-2017).

Results show that with both corrections described above, Pearson correlations increase up to r = +0.5, depending on the start date and forecast time. Average increase over all start dates is of r = +0.2 at forecast days 12-18 and r = +0.3 at forecast days 19-25 and 26-32. The gain is spread quite evenly along the start dates of the year.

Beyond the Euro-Atlantic region, these two corrections can be easily transferred to any area of the world. They may be employed to correct seasonal predictions of weather regimes too (results in progress). Besides, their application is straightforward and provides a significant skill gain at a negligible computational cost for potentially all S2S forecast systems and regime classifications. We foresee that they might also benefit forecasts of atmospheric teleconnections. For all these reasons, we warmly recommend the S2S community to take advantage of this 'low-hanging fruit'.
 

How to cite: Cortesi, N., Torralba, V., Lledó, L., Manrique-Suñén, A., Gonzalez-Reviriego, N., Soret, A., and Doblas-Reyes, F. J.: Predictable weather regimes at the S2S time scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1580, https://doi.org/10.5194/egusphere-egu2020-1580, 2020.

Extreme states of the winter stratosphere, such as sudden stratospheric warmings (SSWs) or an extremely strong stratospheric polar vortex (SPV), can affect surface weather over the North-Atlantic European region on subseasonal time scales. Here we investigate the occurrence of Atlantic-European weather regimes during different stratospheric conditions in winter and their link to large-scale weather events in European sub-regions. We further elucidate if the large-scale flow regime in the North Atlantic at SSW onset determines the subsequent downward impact.

Anomalous stratospheric conditions modulate the occurrence of weather regimes which project strongly onto the NAO and the likelihood of their associated weather events. In contrast weather regimes which do not project strongly onto the NAO are not affected by anomalous stratospheric conditions. These regimes provide pathways to unexpected weather events in extreme stratospheric polar vortex states. For example, Greenland blocking (GL) and the Atlantic Trough (AT) regime are the most frequent large-scale flow patterns following SSWs. While in Central Europe GL provides a pathway to cold and calm weather, AT provides a pathway to warm and windy weather. The latter weather conditions are usually not expected after an SSW. Furthermore, we find that a blocking situation over western Europe and the North Sea (European Blocking) at the time of the SSW onset favours the GL response and associated cold conditions over Europe. In contrast, an AT response and mild conditions are more likely if GL occurs already at SSW onset. An assessment of forecast performance in ECMWF extended-range reforecasts suggests that the model tends to forecast too cold conditions following weak SPV states.

In summary, weather regimes and their response to anomalous SPV states importantly modulate the stratospheric impact on European surface weather. In particular the tropospheric impact of SSW events critically depends on the tropospheric state during the onset of the SSW. We conclude that a correct representation of weather regime life cycles in numerical models could provide crucial guidance for subseasonal prediction.

References:

Beerli, R., and C. M. Grams, 2019: Stratospheric modulation of the large-scale circulation in the Atlantic–European region and its implications for surface weather events. Q.J.R. Meteorol. Soc., 145, 3732–3750, doi:10.1002/qj.3653.

Domeisen, D. I. V., C. M. Grams, and L. Papritz, 2020: The role of North Atlantic-European weather regimes in the surface impact of sudden stratospheric warming events. Weather and Climate Dynamics Discussions, 1–24, doi:https://doi.org/10.5194/wcd-2019-16.

How to cite: Grams, C. M., Beerli, R., Büeler, D., Domeisen, D. I. V., Papritz, L., and Wernli, H.: The mutual impact of weather regimes and the stratospheric circulation on European surface weather, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11513, https://doi.org/10.5194/egusphere-egu2020-11513, 2020.

The representation of upward and downward stratosphere-troposphere coupling and its influence on the teleconnections of the Madden Julian oscillation (MJO) to the European sector is examined in five subseasonal-to-seasonal (S2S) models. We show that while the models simulate a realistic stratospheric response to transient anomalies in troposphere, they overestimate the downward coupling. The models with a better stratospheric resolution capture a more realistic stratospheric response to the MJO, particularly after the first week of the integration. However, in all models examined here the connection between the MJO and vortex variability is weaker than that observed. Finally, we focus on the MJO-SSW teleconnection in the NCEP model, and specifically initializations during the MJO phase with enhanced convection in the west/central pacific (i.e. 6 and 7) that preceded observed SSW. The integrations that simulated a SSW (as observed) can be distinguished from those that failed to simulate a SSW by the realism of the Pacific response to MJO 6/7, with only the simulations that successfully simulate a SSW capturing the North Pacific low. Furthermore, only the simulations that capture the SSW, subsequently simulate a realistic surface response over the North Atlantic and Europe.

How to cite: Schwartz, C. and Garfinkel, C.: Troposphere-Stratosphere Coupling In S2S Models and Its Importance for a Realistic Extratropical Response to the Madden-Julian Oscillation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7171, https://doi.org/10.5194/egusphere-egu2020-7171, 2020.

This study investigates the prediction skill of the sub-seasonal prediction model that can depend on the choice of dynamical cores: the finite volume (FV) dynamical core on a latitude-longitude grid system versus spectral element (SE) dynamical core on a cubed-sphere grid system. Recent researches showed that the SE dynamical core on a uniform grid system increases parallel scalability and removes the need for polar filters mitigating uncertainty in climate prediction, particularly for the Arctic region. However, it remains unclear whether the choice of dynamical cores can actually yield significant skill changes or not. To tackle this issue, we implemented a sub-seasonal prediction model based on the Community Atmospheric Model version 5 (CAM5) by incorporating the above two dynamical cores with virtually the same physics schemes. Sub-seasonal prediction skills of the SE dynamical core and FV dynamical core are verified with ERA-interim reanalysis during the early winter (November – December) and the late winter (January – February) from 2001/2002 to 2017/2018. The prediction skills of the two different dynamical cores were significantly different regardless of the virtually same physics schemes. In the ocean, the predictability of the SE dynamical core is similar to the FV dynamical core, mostly because of our simulation configuration imposing the same boundary and initial conditions at the surface. Notable differences in the one-month predictability between the two cores are found for the wintertime Arctic and mid-latitudes, particularly over North America and Eurasia continents. With the one-month lead, SE dynamical core exhibited higher predictability over North America in late winter, whereas the FV dynamical core showed relatively higher predictability in East Asia and Eurasia in early winter. One of the reasons for these differences may be the different manifestations of Arctic-midlatitudes linkage in the two dynamical cores; the SE dynamical core captures warmer Arctic and colder mid-latitudes relatively well than the FV dynamical core. Therefore, we conclude that the careful choice of dynamical cores of sub-seasonal prediction models is needed.

How to cite: Kim, H.-R., Kim, B.-M., Jun, S.-Y., and Choi, Y.-S.: How sensitive is the sub-seasonal prediction to the choice of dynamical cores in the atmospheric model?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8310, https://doi.org/10.5194/egusphere-egu2020-8310, 2020.

This study uses initialized forecasts to evaluate the wintertime North Atlantic response to an increase of ocean model resolution from ~100 km (LRO) to ~25 km (HRO) in the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS). Importantly, the simulated impacts are timescale-dependent such that impacts in subseasonal and seasonal forecasts cannot be extrapolated from multidecadal climate experiments. In general, mean biases are reduced in HRO relative to LRO configurations and the impact is increased at longer lead times. At subseasonal to seasonal lead times, surface heating anomalies over the Gulf Stream are associated with local increases to the poleward heat flux associated with transient atmospheric eddies. In contrast, surface heating anomalies in climate experiments are balanced by changes to the time-mean surface winds that resemble the steady response under linear dynamics. Some aspects of air-sea interaction exhibit a clear improvement with increased resolution at all lead times. However, it is difficult to identify the impact of increased ocean eddy activity in the variability of the overlying atmosphere. In particular, atmospheric blocking and the intensity of the storm track respond more strongly to mean biases and thus have a larger response at longer lead times. Finally, increased ocean resolution drives improvements to subseasonal predictability over Europe. This increase in skill seems to be a result of improvements to the Madden Julian Oscillation and its associated teleconnections rather than changes to air-sea interaction in the North Atlantic region.

How to cite: Roberts, C., Vitart, F., Balmaseda, M., and Molteni, F.: Impacts of ocean model resolution in S2S forecasts with the ECMWF coupled model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3565, https://doi.org/10.5194/egusphere-egu2020-3565, 2020.

Convective-permitting modeling (CPM) yields step improvements in the representation of precipitation, as has been demonstrated in applications of numerical weather prediction and climate modeling. While CPM has been used in the context of historical climate simulations and climate change projections, its application to the sub-seasonal to seasonal (S2S) forecast timescale (weeks to months) is comparatively underexplored. New, long-term S2S reforecast products have recently been generated from operational global forecast models, for example as part of the S2S Project and North American Multimodel Ensemble (NMME). These are analogous to CMIP models used for climate change projection. It is now technically possible to dynamically downscale these reforecast data to CPM scale, to asess potential improvement in S2S forecast skill and create new S2S forecast metrics for extreme events. The Coordinated Regional Ensemble Downscaling Experiment (CORDEX) provides an existing robust community framework that can be leveraged to dynamically downscale S2S reforecast data, in a globally unified way. This overview presentation will highlight outcomes from a community discussion on this topic that took place at the 2019 Latsis Symposium "High-Resolution Climate Modeling: Perspectives and Challenges" at ETH Zurich, including a summary of the current state of the science, collective identification of research priorities, and proposed action items proceeding forward.

How to cite: Castro, C. L., Chang, H.-I., Prein, A. F., and Bukovsky, M.: Convective-Permitting Modeling for Retrospective Subseasonal-to-Seasonal (S2S) Forecasting Using the Framework of the Coordinated Regional Ensemble Downscaling Experiment (CORDEX) , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12667, https://doi.org/10.5194/egusphere-egu2020-12667, 2020.

We will first examine the skill of probabilistic tropical cyclone (TC) occurrence and intensity (ACE - accumulated cyclone energy) predictions in the Subseasonal to Seasonal (S2S) dataset. We show that some of the models in the S2S dataset have skill in predicting TC occurrence 4 weeks in advance. In contrast, only one of the models (ECMWF) has skill in predicting the anomaly of TC occurrence from the seasonal climatology beyond week 1. For models with significant mean biases, calibrating the forecast can improve the models’ prediction skill. In contrast, for models with small mean biases, calibration does not guarantee an improvement in model skill as measured by the Brier Skill Score. 

We then focus only on the ECMWF model and using cluster analysis examine the sensitivity of the North Atlantic TC tracks biases to various factors, such as model resolution, lead time, and tracking. We also explore how well the ECMWF North Atlantic TC model tracks in each cluster simulate the known response to climate modes, such as ENSO and MJO. By applying simple bias corrections to each cluster of Atlantic TC tracks, we examine if we can improve the model skill in landfall prediction in the US and Caribbean.

How to cite: Camargo, S., Lee, C.-Y., Vitart, F., Sobel, A., Tippett, M., Wang, S., and Camp, J.: Tropical cyclone activity prediction on subseasonal time-scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3158, https://doi.org/10.5194/egusphere-egu2020-3158, 2020.

The main atmospheric teleconnection patterns in the Southern Hemisphere are the Southern Annular Mode (SAM) and the Pacific South American (PSA). The SAM has opposite atmospheric anomalies between high and middle latitudes and it is linked with the polar vortex intensity and jet streams. PSA shows a wavetrain pattern from tropical to the extratropical atmosphere over the South Pacific Ocean triggered by convection in the tropical Indian, Maritime Continent and tropical Pacific. These modes modulate the atmospheric circulation variability and have an influence on the precipitation over Southern Hemisphere continents, mainly in South America (SA). Global models are able to represent these modes in climate simulations of seasonal timescale. The objective of this study is to analyse these teleconnections in hindcasts of subseasonal timescale and the relations to precipitation anomalies over South America. Predictions in the subseasonal time scale of austral summer are very important for several sectors of Southeastern and Southern regions of SA, as these are very populated regions and have agriculture and the largest hydropower,  which are very much affected by precipitation extremes, both excess and lack of rain. Two models of the S2S project (ECMWF and NCEP) are used for the summer seasons of 1999 to 2011 and the patterns are compared to ERA5 reanalyses and GPCP data. EOF analyses of geopotential at 200 hPa and regression analyses against precipitation show the patterns and the influences over South America. The SAM pattern is represented in predictions of 1 to 4 weeks in advance, and PSA pattern, from 1 to 3 weeks in advance. Then, the atmospheric circulation and meteorological variables composites of extreme positive and negative amplitudes of SAM and PSA are analysed to interpret precipitation anomalies during these specific periods for predictions of weeks 2 and 3.

How to cite: Cavalcanti, I. and Barreto, N.: Teleconnection patterns in the Southern Hemisphere in subseasonal to seasonal models hindcasts and influences on South America, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2755, https://doi.org/10.5194/egusphere-egu2020-2755, 2020.

In 2018 the long rains season in Kenya (March-May) was the wettest ever recorded. The country experienced several multi-day heavy rainfall episodes, leading to dam collapse, land and mudslides. 186 people died due to flooding and 300,000 were left displaced. 

The Kenya Meteorological Department issued several advisories during the season that warned of heavy rainfall events a few days before their occurrence. Ahead of this no warnings were given.

However subseasonal forecasts gave strong indications of the heaviest rainfall episodes, several weeks in advance. With this extra lead time, preparedness actions may have been taken in order to reduce flood risk and save lives. 

To this end, the ForPAc project (Toward Forecast-Based Preparedness Action) has been working in partnerships across Kenya and the UK to evaluate and build trust in subseasonal forecasts, and explore preparedness actions which could be taken in response. Most recently ForPAc has been granted access to real-time subseasonal data as part of phase two of the S2S pilot.

In this presentation we will first show analysis of the S2S hindcasts over East Africa, demonstrating the relatively high levels of subseasonal forecast skill and linking this to a strong MJO teleconnection that models capture relatively well.

In the second part we will describe work with stakeholders to co-design forecast products derived from the S2S data, concluding with a report on the forecasts for the ongoing 2020 long rains season and an evaluation of the way in which these have influenced disaster preparedness.

How to cite: MacLeod, D., Kilavi, M., Mwangi, E., Otieno, G., Graham, R., and Todd, M.: Subseasonal forecasts for humanitarian decision-making in Kenya: understanding forecast skill and the latest results from the S2S ForPAc real-time pilot study., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3533, https://doi.org/10.5194/egusphere-egu2020-3533, 2020.

How to cite: Brayshaw, D., Gonzalez, P., and Ziel, F.: A new approach to subseasonal multi-model forecasting: Online prediction with expert advice , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17663, https://doi.org/10.5194/egusphere-egu2020-17663, 2020.

Extreme events such as droughts, heat waves and floods can have significant and long lasting financial, infrastructural and environmental impacts. While probabilistic seasonal outlooks are commonplace, there are relatively few probabilistic outlooks available on multiweek timescales. Additionally, many services focus on the middle of the distribution of possible outcomes – e.g., forecasts of probability of above or below median, or probability of mean conditions exceeding some threshold. These do not encompass the types of extreme events that can be the most damaging, such as several consecutive days of extreme heat, unusually large numbers of cold days in a season, or an extended period where rainfall is in the lowest decile of historical years.

Advance warning of extreme events that impact particular industries enable managers to put in place response measures which can help to reduce their losses. This can involve:

• Active responses which aim to reduce the severity of the impact. For example, losses in dairy production due to extreme heat can be mitigated by adjusting grazing rotations such that cows are in shadier paddocks during these events
• Defensive responses which aim to account for any losses incurred due to an event. For example, the purchase of new farm equipment can be deferred if a forecast extreme event indicates a likely unavoidable financial loss in the near future

To meet this need, the Australian Bureau of Meteorology is developing a suite of forecast products communicating risk of extreme events using data from the Bureau’s new seasonal forecasting system ACCESS-S. Each prototype forecast product is trialed with external users through a webpage to assess usefulness and popularity.

How to cite: de Burgh-Day, C., Hudson, D., Alves, O., Griffiths, M., Marshall, A., and Young, G.: Forecasting climate extremes to aid decisions on multi-week timescales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6523, https://doi.org/10.5194/egusphere-egu2020-6523, 2020.

Renewable electricity is a key enabling step to globally decarbonise the energy sector. Europe is at the forefront of renewable deployment and this has dramatically increased the weather sensitivity of the continent's power systems. Despite the importance of weather to energy systems, the meteorological drivers remain difficult to identify, and are poorly understood. This study presents a new and generally applicable approach, targeted circulation types (TCTs). In contrast to standard meteorological circulation typing methods, such as weather regimes, TCTs convolve the weather sensitivity of an impacted system of interest (in this case, the electricity system) with the intrinsic structures of the atmospheric circulation to identify its meteorological drivers.

A new, freely available, 38 year reanalysis-based reconstruction of daily electricity demand, wind power and solar power generation across Europe is created and used to identify the winter large‐scale circulation patterns of most interest to the European electricity grid. TCTs are shown to provide greater explanatory power for power system variability and extremes compared with standard weather regime analysis. Two new pairs of atmospheric patterns are highlighted, both of which have marked and extensive impacts on the European power system. The first pair resembles the meridional surface pressure dipole of the North Atlantic Oscillation, but shifted eastward into Europe and noticeably strengthened, while the second pair is weaker and corresponds to surface pressure anomalies over Central Southern and Eastern Europe. These patterns are shown to be robust features of the “present-day” European power system.

 The use of TCTs to increase the utility and skill of subseasonal forecasts during the winter season is discussed.  It is shown that TCTs provide additional useful information compared to standard “grid-point” or weather-regime techniques for applications in energy system forecasting and operations.

How to cite: Bloomfield, H., Brayshaw, D., Charlton-Perez, A., Gonzalez, P., and Livings, D.: Understanding and forecasting the subseasonal meteorological drivers of the European electricity system in winter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19129, https://doi.org/10.5194/egusphere-egu2020-19129, 2020.

Skilful onset forecasts are highly sought after in West Africa, due to the importance of monsoon onset for agriculture, disease prevalence and energy provision. With research on the sub-seasonal timescale bridging the gap between weather and seasonal forecasts, sub-seasonal forecasts may provide useful information in the period preceding monsoon onset. This study explores sub-seasonal monsoon onset forecasts over West Africa using three operational ensemble prediction systems (ECMWF, UKMO, and NCEP) from the Sub-seasonal to Seasonal (S2S) prediction project database in order to determine the spatial scale and lead time at which sub-seasonal forecasts can provide useful monsoon onset information. Current research and operational methods of determining onset are identified and compared. The effect of spatial averaging on onset forecasting and skill is explored by comparing regional [Coast, Forest, Transition and North] and local forecasts at 4 major cities over Ghana.

‘This work was supported by UK Research and Innovation as part of the Global Challenges Research Fund, African SWIFT programme, grant number NE/P021077/1’

How to cite: Thompson, E., Wainwright, C., Hirons, L., Marques de Andrade, F., and Woolnough, S.: Sub-seasonal Monsoon Onset forecasting over West Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-542, https://doi.org/10.5194/egusphere-egu2020-542, 2020.

This study evaluated the basic performance of the subseasonal prediction using an ensemble hindcast runs for 20 years (1991-2010) produced by KMA GloSea5. The KMA GloSea5 is global prediction system for subseasonal-to-seasonal time scale, based on the fully-coupled atmosphere, land, ocean, and sea-ice model. To examine the fidelity of the system to reproduce and to forecast phenomena, this study focused on three important aspects: systematic biases of hindcast climatology, error diagnostics related to precipitation, and prediction skill of major climate variability. The major results show the overestimated precipitation over the western Pacific. Precipitation errors related to the enhanced convection processes, it leads to decreased incoming surface heat fluxes by clouds. As a result, SST can be decreased by cloud-radiation processes as well as ocean mixing processes. This study includes the evaluation and the identification of the systematic biases in the global prediction model. Also it focuses on the prediction skill of East Asian summer and winter monsoon with its interaction between tropics or arctic climate, which are major drivers of weather and climate variability in East Asia.

How to cite: Ham, S. and Jeong, Y.: Assessment of subseasonal prediction skill of the KMA GloSea5 hindcast experiment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4302, https://doi.org/10.5194/egusphere-egu2020-4302, 2020.

Using daily minimum temperature of 77 stations in Gansu in spring 1981-2018,temporal and spatial distribution characteristics of strong cold air are analyzed in spring in Gansu Province in the past 38 years. The frequency of strong cold air in spring in Gansu was the lowest in 1980s，it increased since the new century. Strong cold air in the whole province and Hedong area mainly appeared in March and April, The strong cold air in Hexi area is more than April and May. The frequency of strong cold air in Hexi area is two times of that in Hedong area. Using NCEP daily 500hPa height field data for 1981-2018 and quasi 150 day rhythm method, the prediction of extended period of the strong cold air in spring in Gansu province was studied. The threshold value of circulation similarity is determined , evaluation criteria and multiple screening are established. Developing  evaluation criteria and multilayer screening, and selecting 4 typical weather forecasts of strong cold air in spring in Gansu province by calculating similarity coefficients and determining thresholds. In the case of 4 typical fields being applied at the same time, the prediction accuracy is obviously improved, the null rate is reduced to zero, and the omission rate is greatly reduced, which provides a new idea for the extended forecast of the strong cold air in Gansu.

How to cite: Lin, S., Li, D., Lu, G., and Liu, W.: Application of quasi 150 day rhythm method in the prediction of strong cold air extension period in spring in Gansu Province, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4332, https://doi.org/10.5194/egusphere-egu2020-4332, 2020.

        The sub-seasonal characteristics and prediction of rainfall over the South China Sea and surrounding areas during spring-summer transitional season (April-May-June) are investigated using a full set of hindcasts generated by the Dynamic Extended Range Forecast operational system version 2.0 (DERF2.0) of Beijing Climate Center, China Meteorological Administration. The onset and development of Asian summer monsoon and the seasonal migration of rain belt over East Asia can be well depicted by the model hindcasts at various leads. However, there exist considerable differences between model results and observations, and model biases depend not only on lead time, but also on the stage of monsoon evolution. In general, forecast skill drops with increasing lead time, but rises again after lead time becomes longer than 30 days, possibly associated with the effect of slowly-varying forcing or atmospheric variability. An abrupt turning point of bias development appears around mid-May, when bias growths of wind and precipitation exhibit significant changes over the northwestern Pacific and South Asia, especially over the Bay of Bengal and the South China Sea. This abrupt bias change is reasonably captured by the first two modes of multivariate empirical orthogonal function analysis, which reveals several important features associated with the bias change. This analysis may provide useful information for further improving model performance in sub-seasonal rainfall prediction.

How to cite: Li, Q., Wang, J., Yang, S., Wang, F., Wu, J., and Hu, Y.: Sub-seasonal prediction of rainfall over the South China Sea and its surrounding areas during spring-summer transitional season, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4337, https://doi.org/10.5194/egusphere-egu2020-4337, 2020.

Rapidly ascending air streams in midlatitude cyclones – so-called warm conveyor belts (WCBs) – affect the lifecycle of blocking anticyclones. WCBs are usually identified by selecting coherent bundles of rapidly ascending trajectories. Their calculation, however, requires data at a high spatio-temporal resolution and is computationally expensive. To identify WCBs in expansive data sets such as ensemble reforecasts or climate model projections, alternative approaches are necessary. 

In this study we introduce a logistic regression model which is capable of identifying the inflow, ascent, and outflow phase of WCBs based on Eulerian input parameters. Validation against a Lagrangian-based dataset confirms that the logistic model is reliable in replicating the climatological frequency of WCBs as well as the footprints of WCBs at instantaneous time steps. 

Second, we employ the statistical model to verify the representation of WCBs in ECMWF’s sub-seasonal reforecasts. Overall the reforecasts depict frequencies of WCBs across seasons relatively well at all lead times. A correction of biases in the meteorological parameters for the logistic model partly removes existing biases in the reforecast WCB climatology. However, the bias-corrected  forecast skill still rapidly decays leaving useful skill only up to around day 8. These results corroborate that synoptic-scale activity might hinder accurate forecasts into sub-seasonal time scales for the extratropical large-scale circulation. Future work will elucidate if and in which situation poor skill for WCBs dilutes skill for Atlantic-European weather regimes on sub-seasonal time scales.

How to cite: Quinting, J. F., Wandel, J., Büeler, D., and Grams, C. M.: Using a statistical model to verify warm conveyor belts in ECMWF’s sub-seasonal forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5413, https://doi.org/10.5194/egusphere-egu2020-5413, 2020.

A severe cold spell with surface temperatures reaching 10 K below climatology hit Eurasia during late February/early March 2018. This cold spell was associated with a Scandinavian blocking pattern followed by an extreme negative North Atlantic Oscillation (NAO) phase. Here we explore the predictability of this cold spell/NAO event using ensemble forecasts from the Subseasonal-to-Seasonal (S2S) archive. We find that this event was predicted with the observed strength roughly 10 days in advance. However, the probability of occurrence of the cold spell was doubled up to 25 days in advance, when a sudden stratospheric warming (SSW) occurred. Our results indicate that the amplitude of the cold spell was increased by the regime shift to the negative NAO phase at the end of February, which was likely favored by the SSW. We quantify the contribution of the SSW to the enhanced extended-range forecast skill for this particular event by running forecast ensembles in which the evolution of the stratosphere is nudged to a) the observed evolution, and b) a time invariant state. In the experiment with the observed stratospheric evolution nudged, the probability of occurrence of a strong cold spell is enhanced to 45%, while it is at its climatological value of 5% when the stratosphere is nudged to a time invariant state. These results showing enhanced predictability of surface extremes following SSWs extend previous observational evidence, which is mostly based on composite analyses, to a single event. Our results support that it is the subsequent evolution throughout the lower stratosphere following the SSW, rather than the occurrence of the SSW itself, that is crucial in coupling to large-scale tropospheric flow patterns. However, we caution that probabilistic gain in predictability alone is insufficient to conclude about a causal link between the SSW and the cold spell event.

How to cite: Kautz, L.-A., Polichtchouk, I., Birner, T., Garny, H., and Pinto, J.: Enhanced extended-range predictability of the 2018 late-winter Eurasian cold spell due to the stratosphere, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6838, https://doi.org/10.5194/egusphere-egu2020-6838, 2020.

Subseasonal forecasts (ranging between 2 weeks and 2 months) have been the subject of attention in many operational weather forecasts centers and by the research community in recent years. This growing attention stems from the value of these forecasts for society and from the scientific challenges involved. The scientific challenges of capturing and representing key processes and teleconnections which are relevant at these scales are significant. One example is temperature extremes associated with weather extremes like heatwaves and droughts that can have severe consequences in nature and human health, among others. Some of the limitations in forecast skill arise from the limits of predictability of the chaotic earth system. Model error is also likely to play a relevant role. In this study, we investigate systematic model biases, their evolution with lead time and potential links with forecast skill.

This study assessed the skill and biases of the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal forecast in predicting the daily temperature extremes in the Northern Hemisphere. These forecasts are from an experimental setup of ECMWF extended-range forecast system. The forecasts compromise 11 ensemble members with weekly starting dates between 9 April to 30 July extending up to 6 weeks lead with a 20-years hindcast period (1998-2017). The forecasts were performed by the coupled ECMWF systems with TcO199 horizontal resolution (about 50km) in the atmosphere and 1x1 degree ocean. A particular focus is given to Europe and to two other regions that were identified with large systematic errors. The data used in this work consisted of the daily maximum and minimum two-meter temperature, precipitation and other surface fluxes that are aggregated into weekly means and verified against ERA5. 

The evaluation of systematic biases in daily temperature extremes shows a clear increase with lead time, which is widespread on a hemispheric scale. The spatial patterns of model error growth with lead time are reasonably similar between daily maximum and minimum temperatures. However, the amplitude of the errors is remarkably different with general cold bias of daily maximum and warm bias of daily minimum that consistently grow with forecast lead time. Despite the consistent error growth with lead time, there are clear differences between the forecasts initialized in late Spring (April-May) and those in Summer (June-July). These biases are not fully collocated in two regions in the Northern Hemisphere showing the largest warm temperature biases: Central US and East of Caspian Sea. The warm biases are consistent with an underestimation of precipitation and dry soil moisture, compared to ERA5, but only over the East Caspian region.  Forecasts skill assessed via the anomaly correlation shows that the temperature forecasts are skillful up to week 2, with a drop in skill from week 3 onwards. This drop in skill is consistent over all the European domain. Similar results are found for precipitation, but with ACC at week 2 comparable with those of temperature at week 3. 

How to cite: Johannsen, F., Dutra, E., and Magnusson, L.: Evaluation of systematic biases and skill of summer subseasonal forecasts in the ECMWF system , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21676, https://doi.org/10.5194/egusphere-egu2020-21676, 2020.

In the framework of the MEDSCOPE project, a forecasting chain is developed at Météo-France for hydrological long term predictions over the Euro-Mediterranean region, from one month up to seven months. This new prototype is based on the Météo-France System 6 global seasonal forecast system. Atmospheric forecasts are interpolated to 5.5 km and corrected by the statistical method ADAMONT using the UERRA regional atmospheric reanalysis as reference. These high resolution forecasts drive the physically-based model SURFEX coupled to CTRIP providing seasonal forecasts of surface variables : river discharges, soil wetness indices, snow water equivalent.

A forecast using the climatology (ESP approach) has been produced on the period 1993-2016. It is use to explore the sources of predictability in the different watersheds (Ebro, Po, Rhône). Predictability is mostly coming from the snow pack built during the winter and the soil moisture evolution in spring and summer. A hindcast on the period 1993-2016 is produced to assess the added value of the seasonal forecast compared to the climatology for the end-users in agriculture and energy.

How to cite: Dayon, G., Besson, F., Soubeyroux, J.-M., Viel, C., and Marson, P.: Seasonal forecast of water resources over the Euro-Mediterranean region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20246, https://doi.org/10.5194/egusphere-egu2020-20246, 2020.

A purely statistical machine learning (ML) approach was applied to forecast near-surface temperature and precipitation anomalies over land areas in the Northern Hemisphere and Tropics. A high number of principal components (PCs) from the key variables, most importantly sea surface temperatures and the near-tropopause geopotential from reanalyses, was used as predictors to forecast the 2-weekly mean predictand anomalies in each location. Separate models were fitted for different seasons and lead times in the range of 1–6 weeks.

To select and weight the predictors and to reduce the risk of overfitting, such ML methods as least absolute shrinkage and selection operator (LASSO) regularization and ensembling based on random sampling of the predictor data were used in addition to the dimensionality reduction with PCs.

Skill analysis of the independent test sample results show that both the climatological and persistence reference forecasts were inferior compared to the ML approach on average, with all lead times, and in the majority of the target grid cells. Also, the ML approach achieved a skill that was generally comparable to the European Centre for Medium-Range Weather Forecasts (ECMWF) dynamical model.

Previously, these particular ML methods have been shown to work in a regional approach in Europe for seasonal time scales. According to the new results, they also work in the near-global domain and in the challenging subseasonal time scales.

How to cite: Kämäräinen, M.: Promising subseasonal forecasting results based on machine learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19133, https://doi.org/10.5194/egusphere-egu2020-19133, 2020.

How to cite: Kim, B.-M., Kim, H.-R., Choi, Y.-S., Lee, Y., and Yang, G.-H.: Sub-seasonal prediction of Arctic sea ice concentration using time series forecasting technique, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18021, https://doi.org/10.5194/egusphere-egu2020-18021, 2020.

In recent years, the summer rainfall shows an increasing trend in Northwest China. Based on the NCEP/NCAR reanalysis data, the RESST data from NOAA and the precipitation data from 351 meteorological observation stations in Northwest China from 1981-2018, the dominant modes of summer precipitation anomalies, the corresponded circulation characteristic and the main influence systems were analyzed by diagnostic methods. There were three dominant EOF modes about summer rainfall, the first one showed the same anomaly in whole region, the second showed a inverse pattern between the east and west, and the third showed the opposite anomaly between the south and north. The variance contribution of the first mode accounted for 20% and the first mode was represented as the primary mode in the subsequent analysis. The high impact region of circulation which affected the precipitation in Northwest China was the middle and high latitudes area of Eurasia and the subtropical area: for the first mode’s positive phase, the 500hPa height field showed a "+ - +" distribution in the middle latitude of Eurasia, while on the 200hPa wind field, there was an anticyclone near the Ural and a cyclone near Lake Baikal, it also has an anticyclone on the Chinese mainland, this configuration will facilitates the strengthening of westerly jets. The tropical Pacific and the North Atlantic are the main external forcing signals of the circulation pattern: SST characteristics showed that the negative phase of the North Atlantic SST Tripole in spring, from winter of the previous year to summer of the current year, SST of the equatorial Middle East Pacific developed from warm to cold. The distribution of 500 hPa height field corresponding to the main mode of summer precipitation in Northwest China is similar to that of EU remote correlation type. An index(I_Hgt) was defined to reflect circulation patterns in mid-latitude and subtropical regions, when the index is positive/negative, most of the precipitation in northwest China is more/less. After 2000, the correlation between the two increased significantly. Given the performance of the I_Hgt index in describing the summer precipitation, it could be used as a good indicator in the monitoring and prediction of the summer precipitation in Northwest China.

How to cite: liu, L., lu, G., wei, D., li, D., wang, X., and wang, F.: Analysis of the Dominant Spatial Patterns of Summer Precipitation and Circulation Characteristics in Northwest China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8775, https://doi.org/10.5194/egusphere-egu2020-8775, 2020.

                Heat waves have important impacts on society and our environment. In Europe for instance, the summer of 2003 caused upwards of 40000 fatalities. They also impact the crop production, ecosystems, and infrastructures. In a warming climate, heat wave intensity and frequency are likely to increase with potentially more dramatic consequences.

                Considering this, it is crucial to forecast such extreme events and therefore gain a better understanding of their triggering processes. The determination of these processes requires to identify heat wave patterns (timing and location) together with the correlated large-scale circulation patterns. This will enable to devise early warning systems, that could help mitigate the impact.

                This work is part of an ongoing PhD project focusing on improving the forecast of heat waves at sub-seasonal time scale. The main objectives are to evaluate the link between large scale weather patterns and severe warm events over Europe and measure current level of predictive skill. The first part will focus on defining an objective criteria to identify heat wave events in the ERA5 reanalaysis dataset from ECMWF. The identification of heat waves depends on three main criteria: temperature threshold, spatial and temporal extension. Meaning that the temperature should exceed a defined threshold over a large enough region and for a long enough period. We will consider daily means as well as maximum and minimum values of 2m temperature. We will identify the circulation patterns (persistent high pressure systems) associated with heat wave events and analyse the key differences with persistent high pressure systems that are not associated with heat waves.

                This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grand agreement No 813844.

How to cite: Rouges, E., Ferranti, L., Kantz, H., and Pappenberger, F.: Heatwaves over Europe: Identification and connection to large-scale circulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5619, https://doi.org/10.5194/egusphere-egu2020-5619, 2020.

The downward influence of the stratosphere on the troposphere can be significant during boreal winter when the polar vortex is most variable, when major circulation changes in the stratosphere can impact the tropospheric flow. These strong and weak vortex events, the latter also referred to as Sudden Stratospheric Warmings (SSWs), are capable of influencing the tropospheric circulation down to the sea level on timescales from weeks to months. Thus, the occurrence of stratospheric polar vortex events influences the seasonal predictability of sea level pressure (SLP), which is, over the Atlantic sector, strongly linked to the North Atlantic oscillation (NAO).
We analyze the influence of the polar vortex on the seasonal predictability of SLP in a seasonal prediction system based on the mixed resolution configuration of the coupled Max-Planck-Institute Earth System Model (MPI-ESM), where we investigate a 30 member ensemble hindcast simulation covering 1982 -2016. Since the state of the polar vortex is predictable only a few weeks or even days ahead, the seasonal prediction system cannot exactly predict the day of occurrence of stratospheric events. However, making use of the large number of stratospheric polar vortex events in the ensemble hindcast simulation, we present a statistical analysis of the influence of a correct or incorrect prediction of the stratospheric vortex state on the seasonal predictability of SLP over the North Atlantic and Europe.

How to cite: Baehr, J., Wett, S., Dobrynin, M., and Domeisen, D.: Importance of Stratospheric Polar Vortex Events for Seasonal Predictability of Sea Level Pressure over the North Atlantic and Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11067, https://doi.org/10.5194/egusphere-egu2020-11067, 2020.

How to cite: Dutta, U., Hazra, A., Chaudhari, H., Saha, S. K., and Pokhrel, S.: Is Tuning of Auto-conversion Important for the Realistic Simulation of Indian Summer Monsoon Intraseasonal Oscillations and MJO in Coupled Climate Model?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-735, https://doi.org/10.5194/egusphere-egu2020-735, 2020.

The WWRP/WCRP Sub-seasonal to Seasonal Prediction (S2S) database contains real-time and re-forecasts from 11 operational centres. Several S2S models are initialized frequently with a small ensemble size (e.g. 4 ensemble members every day). In order to inflate the ensemble size, real-time forecasts are produced by combining all the forecasts produced over a window of several days to produce a “lagged ensemble” in which ensemble members have different lead times. The other S2S models are initialized less frequently (e.g. once or twice a week) but with a large ensemble size (e.g. 51 members). This initialization strategy is referred to as “burst sampling”. Both strategies have advantages and inconvenience and it is not clear which strategy is optimal for sub-seasonal prediction. 
The ECMWF sub-seasonal forecasts are produced using the burst-sampling strategy: a 51-member ensemble is run twice a week (every Monday and Thursday). A large set of re-forecasts, run on a daily basis, have been produced to assess the potential benefit of replacing this current ensemble configuration by a lagged-ensemble approach. We are interested in answering the following two questions, if the current 51-member ensemble run twice a week is replaced by a sub-seasonal ensemble run every day with an ensemble size Ne:

• What is the minimum value of Ne so that there is a lagged ensemble forecast (Nd forecast days combined) which is at least as skilful as the current system on Mondays and Thursdays?

• For a given value of Ne, what is the optimal number Nd of forecast days to combine? Greater values of Nd produce larger lagged ensemble size, but also reduce the accuracy of the forecasts by adding ensemble members with older start dates. 

Results indicate that:

1. A lagged ensemble is more beneficial in the Tropics than in the Northern Extratropics particularly for shorter lead times (weeks 1 and 2).  

2. The minimum daily ensemble size to produce sub-seasonal forecasts (beyond week 1) at least as skilful as the current ECMWF forecasts on Mondays and Thursdays is Ne=20 with an optimal number of lag days Nd=3. The values of Ne (Nd) decrease (increase) with increased lead time. 

These results suggest that a lagged-ensemble could be a viable alternative to the current ensemble extended-range forecasting system at ECMWF. 

How to cite: Vitart, F.: Lagged ensemble vs burst sampling strategy for initializing sub-seasonal forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2680, https://doi.org/10.5194/egusphere-egu2020-2680, 2020.

In recent years, persistent heavy air pollution (PHP) events occurred frequently over the Beijing-Tianjin-Hebei (BTH) region in China, which posed a great threat to human health. The pollution was characterized by fine particulate matter smaller than 2.5 μm in diameter (PM_2.5). This study investigates the evolution of PHP over the BTH region and its relation to the atmospheric quasi-biweekly oscillation in winters of 2013–2017. A PHP event is defined as three or more consecutive days with daily mean PM_2.5 concentration exceeding 150 μg m^-3. We observed a significant periodicity of 10–16 days of the PM_2.5 concentration, which notably contributes to the occurrence of PHP. According to the quasi-biweekly variation of PM_2.5, the life cycle of PHP events are divided into eight phases. The phase composites of circulation anomalies show that the atmospheric quasi-biweekly oscillation provides favorable conditions for the persistence of wintertime PM_2.5 pollution. During the PHP events, the quasi-biweekly southerly anomalies prevail persistently over eastern China. The East Asian winter monsoon is weakened and more moisture is transported to the BTH region continuously. The anomalous warming in the lower troposphere indicates a stable stratification on the quasi-biweekly time scale. In the mid-troposphere, the oscillation of East Asian trough’s intensity is significantly correlated with the PHP events. Further lead-lag correlation analysis suggested that the quasi-biweekly oscillation of East Asian trough can be traced back to a precursor signal over northwestern Eurasia about 11 days earlier, through a southeastward wave train propagation. Therefore, the meteorological conditions conducive to PHP over the BTH region can be predicted on the quasi-biweekly time scale.

How to cite: Gao, L., Wang, T., Ren, X., Zhuang, B., Li, S., Yao, R., and Yang, X.: Impact of atmospheric quasi-biweekly oscillation on the persistent heavy PM2.5 pollution over Beijing-Tianjin-Hebei region, China during winter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1398, https://doi.org/10.5194/egusphere-egu2020-1398, 2020.

Sub-seasonal precipitation forecasts during the boreal summer monsoon season are very valuable for flood and drought mitigation over China. Here, we evaluate the sub-seasonal precipitation forecast skills of 11 dynamic models from the Sub-seasonal to Seasonal (S2S) Prediction Project at various spatial and temporal scales. For ensemble mean forecasts, most models show significant correlations with observations at both grid and basin scales with lead time up to 2 weeks. When the lead time is beyond week-2, significant correlations are only observed over southeast and western China at the grid scale. Spatial aggregation helps improve week-3-4 average forecast skills at basin scales; significant correlations can be found for all hydroclimatic regions over China. For ensemble forecasts, most S2S models produce skilful forecasts at basin scale as measured by discrimination scores. Both the El Niño-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO) have an impact on precipitation forecast skills at week-3-4. In particular, forecast skill improvement is most pronounced when the forecasts are initialized during active MJO center located in Maritime Continent (Phase 4~5). The results here will help inform the usefulness of sub-seasonal forecasts for hydrological modelling for drought and flood mitigation.

How to cite: Li, Y., Wu, Z., He, H., Wang, Q., Wasko, C., Li, T., and Lu, G.: Sub-seasonal precipitation forecast skills over China during the boreal summer monsoon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1859, https://doi.org/10.5194/egusphere-egu2020-1859, 2020.

Despite being one of the driest places in the world, the Arabian Peninsula (AP) occasionally experiences extreme precipitation events associated with organized convections. On 25 November 2009, for instance, a cutoff low driven rainfall exceeding 140 mm over a 6-hour period triggered a flash flood event in Jeddah, Saudi Arabia, claiming hundreds of lives and substantially damaging infrastructure. Similar extreme precipitation events have occurred in subsequent years. To assess the potential predictability of extreme precipitation in the Arabian Peninsula, we perform retrospective forecast simulations for several extreme events occurring over the period 2000 to 2018, out to a sub-seasonal timescale (3-4 weeks). Using the Advanced Research version of Weather Research and Forecasting Model (WRF-ARW), we dynamically downscale 11 ensemble members of the European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal reforecasts at convective-permitting resolution (4 km). WRF simulated precipitation is evaluated against various precipitation products, including the Global Precipitation Measurement (GPM) system, Climate Prediction Center morphing technique (CMORPH), and the Saudi Ministry of Water and Electricity(MOWE) and the Presidency of Meteorology and Environment(PME) regional rain gauge measurements. The convective-permitting WRF simulations substantially improve the representation of precipitation relative to the ECMWF reforecast, in terms of spatial distribution and timing. A specific focus in the presentation of the results will be on the potential value added by the use of convective-permitting modeling (CPM) to forecasting extreme events at sub-seasonal timescales. The predictability of the synoptic pattern could be the key for CPM sub-seasonal-type forecast for the AP.

How to cite: Luong, T. M., Risanto, C. B., Chang, H.-I., Dasari, H. P., Attada, R., Castro, C. L., and Hoteit, I.: Simulating extreme precipitation over the Arabian Peninsula using a convective-permitting sub-seasonal reforecast product, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4059, https://doi.org/10.5194/egusphere-egu2020-4059, 2020.

In late January to early February 2019, wide-spread flooding, strong winds and relatively cold temperatures over the north-eastern Australian state of Queensland led to the loss of an estimated 625,000 cattle and 48,000 sheep. The system that caused these impacts was a quasi-stationary monsoon depression that lasted close to 10 days, bringing weekly rainfall totals above 1000 mm in some locations, maximum temperatures 8–12°C below average, and sustained wind speeds of 30-40 km/h. The same weather event caused inundation and damage to more than 3000 homes over the eastern Queensland coastal city of Townsville with an insurance cost of over $1.2 billion AUD (https://www.afr.com/companies/financial-services/insurers-reveal-townsville-flood-cost-warn-region-is-unprofitable-20190804-p52do5). Observations and reanalysis confirm that an active Madden-Julian Oscillation pulse stalled over the western Pacific during the period of the flooding. To the south, a blocking anticyclone over the northern Tasman Sea promoted onshore easterly flow, and with it, the relatively low apparent temperatures (Cowan et al. 2019).

In the days before the event, the Australian Bureau of Meteorology issued the monthly rainfall outlook for February which provided little indication of the upcoming extreme event. At the time of the event, there was a 50% chance of an El Niño developing during the boreal spring, meaning a tendency towards warmer and drier conditions across the northeast. Here we show that forecasts from the Bureau's newly developed dynamical subseasonal-to-seasonal (S2S) prediction system – Australian Community Climate Earth-System Simulator Seasonal version 1 (ACCESS-S1) – of the weekly-averaged conditions were more skilful. The ACCESS-S1 99-member ensemble forecast a more than doubling of the probability of extreme weekly rainfall totals a week prior to the floods, along with increased probabilities of extremely low maximum temperatures and high winds. Ensemble-mean weekly rainfall amounts, however, were considerably underestimated by ACCESS-S1, even in forecasts initialised at the start of the peak flooding week. This is consistent with other state-of-the-art dynamical S2S prediction systems. Yet one individual ensemble member of ACCESS-S1 managed to forecast close to 85% of the rainfall magnitude across the most heavily impacted region of northwest Queensland in a week 2 forecast. This suggests current S2S prediction systems like ACCESS-S1 are capable at getting close to predicting record-breaking events with at least one week's lead-time. It also appears that accurate prediction beyond two weeks (i.e., a week 3 forecast) of an event like the northern Queensland floods is more difficult to achieve.

Reference:

Cowan et al. (2019): Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019, Weather and Climate Extremes, 26, 100232, https://doi.org/10.1016/j.wace.2019.100232.

How to cite: Cowan, T., Wheeler, M., Griffiths, M., de Burgh-Day, C., and Hawcroft, M.: Sub-seasonal prediction of the extreme weather conditions associated with the northeastern Australia floods in February 2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2785, https://doi.org/10.5194/egusphere-egu2020-2785, 2020.

The Quasi-Biweekly Oscillation (QBWO) mode with 10-20-day time scale over the tropical western Pacific (TWP) in boreal winter (December-February), characterized by westward-northwestward propagation from the dateline to the east coast of Philippines (EPH) identified by the first two EEOF modes, is investigated based on the daily mean OLR and ERA-Interim reanalysis datasets from 1979 to 2015. The suppressive (active) QBWO-related convection heating located near EPH at peak day (day 0), results in anomalous divergence (convergence) wind to the south of Japan at upper troposphere due to the heat release. The divergent circulations can advect climatological absolute vorticity, then leads to positive (negative) Rossby wave source, which could propagate eastward. Therefore, a Rossby wave train (RWT) with equivalent barotropical structure over Pacific originated from the south of Japan is observed one/two days later. This wave train propagates northeastward into Alaska and then southeastward into southern North America. The meridional wind associated with the cyclonic/anticyclonic anomalies of RWT advects climatological thermal condition dominating the local temperature tendency over North America. Thus, a significant warming (cooling) over central North America is found at day +4 consistent to the anomalous southerlies (northerlies). In addition, both the barotropical energy conversion (CK) and baroclinic energy conversion (CP) contribute to the RWT on a time scale of 10-20 days maintained against dissipation.

How to cite: Dong, Z. and Wang, L.: Quasi-Biweekly Oscillation over the tropical western Pacific in boreal winter： Its climate influences on North America, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3835, https://doi.org/10.5194/egusphere-egu2020-3835, 2020.

Subseasonal prediction skill of precipitation is typically low. Sometimes, however, forecasts are accurate and it would be useful to end-users to assess a priori if this might be the case. We use a 20-year hindcast data set of the ECMWF S2S prediction system and identify periods of high forecast confidence, evaluating model skill of precipitation forecasts for these periods compared to lower confidence predictions.

From reanalysis data, we derive a set of circulation patterns, called archetypes, that represent the broad-scale atmospheric circulation over Australia. These archetypes are combinations of ridges and troughs, and yield different precipitation patterns depending on the location of these features. In the literature, a typical application of circulation patterns is assigning daily reanalysis fields to the closest-matching pattern, thus obtaining conditional distributions of precipitation corresponding to key modes of atmospheric variability. A problem common to such analyses is that the precipitation distributions associated with the circulation patterns can be too similar; distinct distributions are required in order for the patterns to be useful in estimating precipitation. We show that by subsampling the archetype occurrences only when they are particularly well-matched to the underlying field, the conditional precipitation distributions become more distinct.

We subsample hindcast fields in the same way, obtaining a sample of periods when the model is confident about its prediction of the upcoming archetype. We then calculate model skill in predicting precipitation for three regions in southern Australia during such periods compared to when the model is not confident about the predicted archetype. Our results suggest that during periods of forecast confidence, precipitation skill is greater than normal for shorter leads (up to ten days) in two of the three regions (the Murray Basin and Western Tasmania). Skill for the third region (Southwest Western Australia) is greater during confident periods for lead times greater than one week, although this is marginal.

How to cite: Richardson, D., Risbey, J., and Monselesan, D.: Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation in southern Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1981, https://doi.org/10.5194/egusphere-egu2020-1981, 2020.

The succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large scale flow. Some aggregated statistic however, like the average temperature exceeding a threshold, can have extended predictability when adequate spatial scales, temporal scales and thresholds are chosen. This study benchmarks how the forecast skill horizon of probabilistic 2-meter temperature forecasts from the ECMWF sub-seasonal forecast system evolves with varying scales and thresholds. We apply temporal aggregation by rolling window averaging and spatial aggregation by hierarchical clustering. We verify 20 years of re-forecasts against the E-OBS data set and find that European predictability extends at maximum up to week 4. Simple aggregation and standard statistical post-processing extend the forecast skill horizon with two and three skillful days on average, respectively.
The intuitive notion that higher levels of aggregation capture the larger scale and lower frequency variability and therefore tap into an extended predictability, holds in many cases. However, we show that the effect can saturate and that regional optimums exist, beyond which extra aggregation reduces the forecast skill horizon. We expect that such windows of predictability result from specific physical mechanisms that only modulate and extend predictability locally. To optimize sub-seasonal forecasts for Europe, aggregation should in certain cases thus be limited.

How to cite: van Straaten, C., Whan, K., Coumou, D., van den Hurk, B., and Schmeits, M.: The influence of aggregation and statistical post-processing on the sub-seasonal predictability of European temperatures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2454, https://doi.org/10.5194/egusphere-egu2020-2454, 2020.

Damages in cities resulting from climate change are made irregularly and untypically, thus difficult to predict due to heavily concentrated buildings and population, etc. This study aims to introduce the results of our Urban Climatic Environment Assessment Model System(Model System hereinafter) as well as its construction, which is designed to provide impact assessment of heat waves in cities, to reduce damages, and to build capacities against it.

Our Model System is based on the Unified Model(UM : an integrated model of Korea Meteorological Administration), and satellite data is necessary to verify the Model System. However, we have developed high resolution (10m ~ 100m) urban assessment model to analyze the impacts of heat waves in city of Gwangju to help local government by developing and implementing environmental policies. The outputs of our Model System will contribute to the decision making.

Following two approaches were considered for impact assesment. Firstly, high spatial resolution model (in 10m to 100m level) using ensemble and down-scaling techniques can help identification of vulnerable areas in the city. Also, analyzed data can be linked to local GIS and land use map for analysis and assessment of the heat waves, which enables to make 48h heat wave forecast.

Secondly, CFD micro-scale analysis using super-computer enables to analyze the vulnerable areas with components of : temperature, wind, humidity, solar radiation quantity, cloud cover, etc. Data achieved via our Model System will be used as objective and scientific basis for developing heat wave policies. It will also give guidance for heat wave early warning.

It is expected that local governments can utilize our Model System to identify and analyze patterns and characteristics of heat waves in the city, and make decisions and develop environment-related policies on the objective and scientific basis preemptive response for vulnerable areas in the region.

Keywords : heat waves, Urban Climatic Environment Assessment Model System, spatial resolution, ensemble average, down-scaling, CFD, micro-scale, Early warning system

* This research was supported by a grant from Research Program funded by International Climate & Environment Center(ICEC).

How to cite: Oh, B., Hwang, C., Yun, W., and Kim, J.: A Study on the Operation of Early Warning System for Heat Waves in Gwangju Based on the Urban Climatic Environment Assessment Model System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3238, https://doi.org/10.5194/egusphere-egu2020-3238, 2020.

The continuous increase of computational power and improvement of numerical weather prediction systems in recent decades has allowed extending the operational weather forecast horizon into sub-seasonal time scales (10 – 60 days). On these scales, quasi-stationary, persistent, and recurrent large-scale flow patterns, so-called weather regimes, explain most of the regional surface weather variability and are thus of primary interest in sub-seasonal forecasting for the respective region. Here, we assess the skill of sub-seasonal reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) for predicting 7 year-round weather regimes in the Atlantic-European region. We primarily show that forecast skill considerably differs for different flow situations and seasons. We further elucidate the effect of model calibration on forecast skill: simply removing the model bias is shown to hardly affect and for some flow situations even reduce forecast skill, which indicates that flow-dependent model calibration techniques might be more useful for sub-seasonal weather regime forecasts. Finally, we give an outlook on how lower-frequency climate modes such as the stratospheric polar vortex as well as midlatitude synoptic-scale activity such as warm conveyor belts may enhance or dilute flow-dependent forecast skill.

How to cite: Büeler, D., Quinting, J. F., Wandel, J., and Grams, C. M.: Flow-dependent sub-seasonal forecast skill for Atlantic-European weather regimes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5662, https://doi.org/10.5194/egusphere-egu2020-5662, 2020.

How to cite: Strommen, K.: Jet Latitude Regimes and the Predictability of the North Atlantic Oscillation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5838, https://doi.org/10.5194/egusphere-egu2020-5838, 2020.

This study explores the spatial variations and physical mechanisms of 10–30-day rainfall anomalies over southeastern China based on daily station-observed rainfall data for the period 1979‒2015. Empirical orthogonal function analysis shows that the dominant spatial distribution of 10–30-day rainfall anomalies is a monopole pattern over the south of the middle and lower reaches of the Yangtze River Valley (SMLY). Lead-lag composites reveal that the evolution of such a monopole pattern depends on the coordinated influences of 10–30-day atmospheric intraseasonal oscillations (ISOs) from the tropics and mid-high latitudes. In the upper troposphere, the southeastward-propagating Rossby wave train from the mid-high latitudes, which presents as anomalous anticyclones and cyclones alternating over eastern Europe to the southeastern coastal area of China, induces strong ascents (descents) over the SMLY via vorticity advection. Circulation anomalies associated with tropical ISO over East Asia/Western North Pacific trigger a vertical cell with strong updraft (downdraft) over the SMLY and downdraft (updraft) to the south, further enhancing the ascents (descents) over the SMLY, forming the wet (dry) phases of 10–30-day rainfall anomalies. Moreover, due to the meridional non-uniformity of ISO-related diabatic heating along the Indian Ocean longitudes, an anticyclone (cyclone) is generated over the central Indian–northern Bay of Bengal, which tends to anchor the anomalous ascents (descents) over the SMLY through its interaction with the intraseasonal Rossby wave from mid-high latitudes, thus favoring the persistence of wet (dry) phases of the 10–30-day SMLY rainfall anomalies.  

How to cite: Li, J. and Mao, J.: Coordinated influences of the tropical and extratropical intraseasonal oscillations on the 10–30-day variability of the summer rainfall over southeastern China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6438, https://doi.org/10.5194/egusphere-egu2020-6438, 2020.

A substantial amount of subseasonal-to-seasonal sea-ice variability is potentially predictable, but improved model biases and initialization techniques are needed to realize this potential. Forecasts for other Earth System components can be expected to benefit from improved sea-ice forecasts as well, because the presence of sea ice drastically alters exchanges of heat and momentum between the atmosphere and the ocean. Here, we present the impact of initializing subseasonal forecasts with observed sea-ice thickness. The newly developed sea-ice thickness data set CS2SMOS that we use is derived from radar altimetry and L-band radiance satellite observations. It allows for the first time a spatially complete view of pan-Arctic ice thickness on a near-daily basis during the freezing season. The ingestion of this data into the ECMWF ocean reanalysis system improves subseasonal forecasts of the Arctic ice edge during the melting season by up to 10%. Sea-surface temperature forecasts at high latitudes are also significantly improved during the melting season, because an improved prediction of ice-free date allows an improved forecast of the amount of seasonal warming. These results illustrate the potential for improving subseasonal-to-seasonal predictions by initializing the sea-ice thickness.

How to cite: Tietsche, S., Balan Sarojini, B., Mayer, M., Zuo, H., Vitart, F., and Balmaseda, M.: Improving sea-ice cover and SST forecasts by sea-ice thickness initialization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9729, https://doi.org/10.5194/egusphere-egu2020-9729, 2020.

The alternation of seasons over tropical northern Africa is associated with the occurrence of devastating diseases such as meningitis, Lassa fever and malaria. These tropical diseases are associated with specific atmospheric conditions. Thus, meningitis is one of the most endemic diseases observed over this region with a prevalence period up to 7 months (December-June). Previous studies based on the link between atmospheric conditions and the occurrence of meningitis outbreaks have shown that this disease develops under dry and dusty atmospheric conditions which are difficult to represent in numerical weather and climate models. However, the onset, breakup, and sub-seasonal variability of meningitis outbreaks are not well documented. The objective of this study is to identify the local and synoptic drivers favoring the large occurrence of this disease over the meningitis belt in order to improve its predictability by numerical weather and climate models on intra-seasonal and seasonal timescales. This study focuses on two cases studies of meningitis epidemics over Niger in 2009 and 2015. The case study of 2009 started early with a duration of more than eight weeks. The second case study was shorter than the first one. It took three weeks and was observed at the end of the dry season. Based on ERA5 data, surface dust concentration observations and satellite data, a further analysis of the role of climate metrics on the triggering of meningitis epidemics on intra-seasonal timescales at local and large scale atmospheric conditions will be presented.

How to cite: Dione, C., Diouf, M. D., Ogwang, B. A., Adefisan, E. A., Woolnough, S., Senghor, H., and Hirons, L.: Relationship between meningitis occurrence and atmospheric conditions over the African meningitis belt, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7924, https://doi.org/10.5194/egusphere-egu2020-7924, 2020.

The CAFE Project is a Marie S. Curie Innovative-Training-Network (ITN) project funded by the EU. The ultimate goal of the CAFE project is to contribute to the improvement of sub-seasonal predictability of extreme weather events. This will be addressed through a structured and cross-disciplinary program, training 12 early stage researchers who undertake their PhD theses. CAFE brings together a team of co-supervisors with complementary expertise in climate science, meteorology, statistics and nonlinear physics.

The CAFE team comprises ten beneficiaries (seven academic centres, one governmental agency, one intergovernmental agency and one company: ARIA, CRM, CSIC, ECMWF, MeteoFrance, MPIPKS, PIK, TUBAF, UPC, UR) and ten partner organizations (CEA and Munich Re, among them).

CAFE research is organized into three main lines: Atmospheric and oceanic processes, Analysis of extremes, and Tools for predictability, all focused on the sub-seasonal time scale. This includes the study of Rossby wave packets, Madden-Julian oscillation, Lagrangian coherent structures, ENSO-related extreme weather anomalies, cascades of extreme events, extreme precipitation, large-scale atmospheric flow patterns, and stochastic weather generators, among other topics.

Information about the CAFE project will be updated at:

http://www.cafes2se-itn.eu/

https://twitter.com/CAFE_S2SExtrem

This project receives funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.

How to cite: Corral, A. and the CAFE-H2020-MSCA-ITN Team: Climate Advanced Forecasting of sub-seasonal Extremes (CAFE), ITN Project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8907, https://doi.org/10.5194/egusphere-egu2020-8907, 2020.

The Madden-Julian Oscillation (MJO) is a well-known source of predictability on sub-seasonal-to-seasonal (S2S) time scales and a major driver of intraseasonal weather variability around the globe. For example, the MJO’s interaction with and influence on daily regional weather in the Maritime Continent-Southeast Asia (MC-SEA) region is thought to be most pronounced during boreal winter (November through February), given that the amplitude of MJO activity is often much stronger during that period compared to other times of the year.

In this study, we examine the relationship of the MJO to eight weather regimes (WR) that have been previously defined for Singapore and the MC-SEA region using k-means clustering of daily sounding data from reanalysis. These weather regimes cover the whole annual cycle of rainfall with well-defined peak frequency times and mean spatial structures that correspond to the seasonal movement of the Inter-tropical Convergence Zone (ITCZ) across the Equator. Following previous work, we use a statistical method to compute the lagged relationship between each MJO phase and daily WR occurrence between December 1980 - November 2014 to quantify the change in the likelihood that a certain regime will occur relative to climatology, given an MJO phase in advance. Bimonthly analysis indicates that strong lag relationships exist between MJO phases and certain regimes in different two-month periods, thus giving potential predictability of the type of mean weekly weather in the MC-SEA up to 3-4 weeks ahead. In addition, we consider the modulation of the MJO-WR relationships stratified by the ENSO phase to determine whether the expected WR frequency response to MJO activity varies substantially in different background states.

How to cite: Hassim, M. E. and Lee, J.: The potential predictability of Singapore and Maritime Continent weather regimes in relation to the MJO and ENSO, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12422, https://doi.org/10.5194/egusphere-egu2020-12422, 2020.

The increase in societal exposure and vulnerability to drought, call to move from post-crisis to pre-impact drought risk management. Accurate and timely information of evolving drought conditions is crucial to take early actions to avoid devastating long-term impacts. A previous study indicated that a statistical empirical method, the ensemble streamflow prediction system (ESP; an ensemble based on reordering historical data), represents a computationally fast alternative to dynamical prediction applications for drought prediction (Turco et al. 2017). Extending this work, here we present an assessment of the ability of the ESP method in predicting the drought of 2017 in Spain considering also the uncertainties coming from the observations. For this, four different datasets are used: that cover a period of 36 years (1981-2017) and with a spatial resolution of 0.25 x 0.25º based on observations of interpolated stations (E-OBS, AEMET), on reanalysis data (ERA5), and on combining stations and satellite data (CHIRPS). Meteorological droughts are defined using the Standardized Precipitation Index aggregated over the months April–September. All the datasets show a similar spatial pattern, with most of the domain suffering extreme drought conditions. In addition, the ESP system achieves reasonable skill in predicting this drought event 2 months in advance with, again, similar pattern among the different datasets. These results suggest the feasibility of the development of an operational early warning system, also considering that the data of CHIRPS and ERA5 are updated every month, i.e., that are available for near-real time applications.

References

Turco, M., et al. (2017). Summer drought predictability over Europe: empirical versus dynamical forecasts. Environmental Research Letters, 12(8), 084006.

Acknowledgments

The authors acknowledge the ACEX project (CGL2017-87921-R) of the Ministerio de Economía y Competitividad of Spain. AHM thanks his predoctoral contract FPU18/00824 to the Ministerio de Ciencia, Innovación y Universidades of Spain. M.T. has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00).

How to cite: Torres Vázquez, M. Á., Halifa Marín, A., Montávez, J. P., and Turco, M.: Monitoring and predicting the outstanding 2017 drought in Spain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15225, https://doi.org/10.5194/egusphere-egu2020-15225, 2020.

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth and more frequent travels. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection once they are back in the country if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, environmental suitability for presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. Here, we develop a subseasonal to seasonal monitoring and forecasting system for environmental suitability of transmission of Aedes-borne diseases for the US, Central America, the Caribbean and northern South America, using multiple calibrated ento-epidemiological models, climate models, and quality-controlled temperature observations. We show that the predictive skill of this new system is higher than that of any of the individual models, and illustrate how a combination of deterministic and probabilistic forecasts can inform key prevention and control strategies.

How to cite: DiSera, L., Munoz, A., and Chourio, X.: Subseasonal Forecasting of Aedes-borne Disease Transmission, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22137, https://doi.org/10.5194/egusphere-egu2020-22137, 2020.

Increasingly, operational forecasting centres are producing sub-seasonal forecasts, targeted at lead times of 3-6 weeks. These aim to fill the gap between conventional 2-week weather forecasts and longer term seasonal outlooks. However it is often difficult for end-users to know how these sub-seasonal forecasts can be best utilised, and how skilful they are for predicting variables of real world interest.

Much prior work on sub-seasonal forecasts has focused on assessing skill scores for large-scale smooth fields of mid- or upper-tropospheric variables, or else has looked at heavily time-averaged quantities. How to extend the lessons of these studies to user applications is not always obvious.

We take a more applied approach, focused on the chaotic and variable weather of Western Europe. We use sub-seasonal temperature forecasts alongside real-world French energy price and demand data in order to directly calculate the financial value of subseasonal forecasts to users in the energy sector. Using this new, real-world framework we make an estimate of cost-loss ratios and so can compare to the results of a simpler potential economic value model.

How to cite: Dorrington, J., Finney, I., Weisheimer, A., and Palmer, T.: Quantifying the usefulness of European subseasonal forecasts using a real-world energy-sector framework, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5358, https://doi.org/10.5194/egusphere-egu2020-5358, 2020.