AS1.3 | Subseasonal-to-Seasonal Prediction, Processes and Applications
EDI
Subseasonal-to-Seasonal Prediction, Processes and Applications
Convener: Christopher White | Co-conveners: Daniela Domeisen, Marisol Osman, Joanne Robbins, Frederic Vitart
Orals
| Tue, 16 Apr, 08:30–12:30 (CEST)
 
Room 0.11/12
Posters on site
| Attendance Tue, 16 Apr, 16:15–18:00 (CEST) | Display Tue, 16 Apr, 14:00–18:00
 
Hall X5
Posters virtual
| Attendance Tue, 16 Apr, 14:00–15:45 (CEST) | Display Tue, 16 Apr, 08:30–18:00
 
vHall X5
Orals |
Tue, 08:30
Tue, 16:15
Tue, 14:00
This session invites contributions spanning all aspects of prediction, predictability and applications on the Subseasonal-to-Seasonal (S2S) (i.e., 2 weeks to 2 months) lead time range. The session welcomes contributions on the following:

(a) The Madden Julian Oscillation (MJO) and other modes of variability impacting the S2S timescale;
(b) Tropical/extratropical wave dynamics;
(c) Teleconnections and cross-timescale interference of climate modes of variability;
(d) Stratosphere-troposphere coupling, land-atmosphere coupling, ocean-atmosphere coupling;
(e) Studies of predictability and predictive skill of atmospheric or surface variables such as sea ice, snow cover, and land surface;
(f) Use of AI/ML methods for S2S prediction, post-processing and attribution;
(g) Case studies of extreme or high-impact event prediction on the S2S timescale; and
(h) User applications, impact studies and climate services on the S2S timescale including, including impact-focused modelling studies and examples of how S2S-derived information can be integrated into decision support systems at the local, regional or international level.

Session assets

Orals: Tue, 16 Apr | Room 0.11/12

Chairpersons: Christopher White, Steffen Tietsche
Large-scale processes and sources of forecast skill
08:30–08:40
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EGU24-7705
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ECS
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solicited
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Highlight
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On-site presentation
Constantin Ardilouze and Aaron Boone

Accurate soil moisture initial conditions in dynamical subseasonal forecast systems are known to improve the temperature forecast skill regionally, through more realistic water and energy fluxes at the land-atmosphere interface. Recently, results from the GEWEX-GASS LS4P (Impact of initialized land temperature and snowpack on sub-seasonal to seasonal prediction) multi-model coordinated experiment have provided evidence of the primal contribution of the initial surface and subsurface soil temperature over the Tibetan Plateau for capturing a hemispheric scale atmopsheric teleconnection leading to improved subseasonal forecasts. Yet, both the soil temperature and water content are key components of the soil enthalpy and we hypothesize that properly initializing one of them without modifying the other in a consistent manner can alter the soil thermal equilibrium, thereby potentially reducing the benefit of land initial conditions on subsequent atmospheric forecasts. This study builds on the protocol of the above-mentioned multi-model experiment, by testing different land initialization strategies in an Earth system model. Results of this pilot study suggest that a better mass and energy balance in land initial conditions of the Tibetan Plateau triggers a wave train which propagates through the northern hemisphere mid-latitudes, resulting in an improved large scale circulation and temperature anomalies over multiple regions of the globe. While this study is based on a single case, it strongly advocates for enhanced attention towards preserving the soil energy equilibrium at initialization to make the most of land as a driver of atmospheric extended-range predictability.

How to cite: Ardilouze, C. and Boone, A.: Soil enthalpy: an unheeded source of subseasonal predictability?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7705, https://doi.org/10.5194/egusphere-egu24-7705, 2024.

08:40–08:50
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EGU24-20993
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ECS
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Virtual presentation
Lin Yao, Da Yang, James Duncan, Ashesh Kumar Chattopadhyay, Pedram Hassanzadeh, Wahid Bhimji, and Bin Yu

The Madden-Julian Oscillation (MJO) is a large-scale tropical phenomenon where fluctuations of clouds, rainfall, winds, and pressure propagate eastward around the globe every 30 to 90 days on average. The MJO has significant impacts on weather and climate both locally and globally. Despite its importance, forecasting the MJO remains challenging due to the limitations of traditional numerical and statistical methods. To address this, machine learning has emerged as a promising avenue for MJO forecasting (Martin et al. 2022, Silini et al. 2021, Delaunay and Christensen 2022). Apart from accurate forecasts emphasized in previous research, our study aims to get more physical insights: we build a predictive and interpretable convolution neural network (CNN) and unravel what tropical waves at which spatial scales are essential for MJO forecasting.

Our CNN model takes tropical reanalysis maps as input and predicts the MJO index, achieving forecast skills comparable to NCEP Climate Forecast System (CFSv2). This level of skill is state-of-art in interpretable neural networks. To understand what information is crucial to our MJO forecast, we decompose the output of each convolution layer into tropical waves at different zonal scales. We find that the CNN focuses on large-scale patterns whose zonal scale is above 2500 km. In fact, even when fed exclusively with large-scale features as input, the CNN achieves MJO forecasts akin to the skill of the original model. Furthermore, the CNN chooses to reconstruct large-scale features from input containing solely small-scale features instead of relying directly on small scales for forecasting. This reconstruction further emphasizes the critical role of large-scale patterns in MJO predictions.

In future research, we plan to perform a systematic analysis to evaluate the contribution of different tropical waves to MJO forecasting. We will also simplify the model architecture to facilitate better understanding. Additionally, we plan to incorporate more previous time steps as input memories to enhance forecast accuracy. This work represents a promising advance towards economic yet precise MJO forecasting.

How to cite: Yao, L., Yang, D., Duncan, J., Kumar Chattopadhyay, A., Hassanzadeh, P., Bhimji, W., and Yu, B.: Machine Learning Models Use Large Scale Signals to Forecast the MJO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20993, https://doi.org/10.5194/egusphere-egu24-20993, 2024.

08:50–09:00
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EGU24-1591
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ECS
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On-site presentation
Eliza Karlowska, Adrian Matthews, Benjamin Webber, Tim Graham, and Prince Xavier

The diurnal cycle of SST (dSST) is influenced by the development of diurnal warm layers in the upper ocean. Observations show that the dSST rectifies intraseasonal SSTs, potentially leading to changes in intraseasonal weather patterns such as the Madden-Julian Oscillation (MJO). Here we analyze 15-day forecast composites of the coupled ocean-atmosphere and the atmosphere-only configurations of the Numerical Weather Prediction (NWP) models of the UK Met Office to show that a strong dSST in the coupled model leads to a faster MJO propagation compared with the atmosphere-only version of the model. A set of experiments using the coupled model was designed to reduce the strength of the dSST by imposing instant vertical mixing in the top 5 and 10 m of the ocean model. On a 15 lead-day time scale, weakening the dSST slows the MJO phase speed in the coupled model. On a 7 lead-day time scale, all coupled model runs display an underlying 5% increase in the MJO phase speed compared to the atmosphere-only model due to the presence of thermodynamic coupling unrelated to the dSST. The MJO phase speed increase due to the dSST is linearly related to the mean tropical dSST at lead day 1 in the coupled model. An additional 4% of the MJO phase speed increase between the control coupled model and the atmosphere-only model on a 7 lead-day timescale can be attributed to the presence of the dSST in the coupled model. Over 15 lead days, the coupled model produces a two-way feedback between the MJO and the dSST. The MJO conditions set the strength of the dSST in the coupled model. Consistent with observations, the dSST in the coupled model rectifies intraseasonal anomalies of SSTs such that stronger dSST leads to positive intraseasonal SST anomalies. The MJO convection response to these SST anomalies peaks 7 days later, and subsequently feeds back onto SST anomalies. The phase relationship between MJO convection, dSST and intraseasonal SST anomalies is consistent with the relationship between dSST and MJO propagation speed. Overall, our experiments demonstrate the importance of high vertical resolution of the upper ocean in predicting the eastward propagation of the MJO in an NWP setting, potentially creating repercussions for seasonal predictions and climate projections should this feedback be unrepresented in the models.

How to cite: Karlowska, E., Matthews, A., Webber, B., Graham, T., and Xavier, P.: Process-based analysis of the MJO phase speed error in the coupled NWP model of the UK Met Office: a two-way feedback between the MJO and the diurnal warm layers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1591, https://doi.org/10.5194/egusphere-egu24-1591, 2024.

09:00–09:10
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EGU24-2747
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On-site presentation
Susmitha Joseph, Avijit Dey, Raju Mandal, Mahesh Kalshetti, Ravuri Phani, Shubham Waje, and Atul Sahai

Subseasonal predictions with a time scale of 2-4 weeks, which fills the gap between the weather and seasonal forecasts, are limited by the uncertainties arising from the initial conditions as well as the model physics. Therefore, to develop an efficient subseasonal prediction system, both these uncertainties need to be addressed. With this background, a multi-physics multi-ensemble approach has been adopted to develop a competent second-generation subseasonal prediction system at the Indian Institute of Tropical Meteorology (IITM), Pune, India. The first-generation prediction system developed at IITM is run operationally at the India Meteorological Department and has useful skills for up to two weeks.

A combination of physics perturbations and initial condition perturbations with a total of 18 ensemble members is present in the system. This system has been experimentally run since May 2022. The hindcast runs during 2003-2018 are also made on-the-fly. The initial results indicate a considerable improvement in the forecast skill compared to its predecessor and have reasonable deterministic prediction skill for up to three weeks. The system could provide skilful prediction of the subseasonal variations during the two contrasting monsoon seasons 2022 (above normal) and 2023 (below normal) 2-3 weeks in advance.

How to cite: Joseph, S., Dey, A., Mandal, R., Kalshetti, M., Phani, R., Waje, S., and Sahai, A.: Development of a Multi-physics Multi-ensemble Subseasonal Prediction System and its Real-time Performance during Contrasting Indian monsoons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2747, https://doi.org/10.5194/egusphere-egu24-2747, 2024.

09:10–09:20
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EGU24-2890
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ECS
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On-site presentation
Guokun Dai, Mu Mu, Xueying Ma, and Yangjiayi Gao

Utilizing the Community Atmospheric Model version 4, the influence of Arctic sea ice concentration (SIC) on the predictability of the Ural Blocking (UB) in subseasonal timescale is investigated. Taking the zonal flows as the reference states, the optimal Arctic SIC perturbations that trigger zonal flows into UB events on subseasonal timescale are obtained with the conditional nonlinear optimal perturbation (CNOP) approach. The numerical results show that the Arctic SIC decline in the Greenland, Barents and Okhotsk Seas can trigger zonal flows into UB events on a timescale of four pentads (20 days). Further diagnosis shows that the SIC decline in these regions locally warms the low troposphere via diabatic processes in the first pentad. Then, dynamic processes, such as temperature advection, modulate the temperature in the middle troposphere and weaken the meridional temperature gradient between the Arctic and mid-latitudes upstream of the Ural sector. The weakened meridional temperature gradient further decelerates the background zonal flow near the Ural sector and triggers UB formation in four pentads. After that, the optimal Arctic SIC perturbations that have great influences on subseasonal UB predictions are also obtained with CNOP approach. It is found that SIC increase in the Greenland Sea, Barents Sea, and Okhotsk Sea would weaken the UB intensity while SIC decline in these regions would strengthen it. Further diagnoses show that the physical mechanisms are similar to those triggering UB formation. Moreover, utilizing the observing system simulation experiments, it is shown that targeted observations in the Barents Sea, Greenland Sea, and Okhotsk Sea can remarkably improve the prediction skills of UB in the fourth pentad. Numerical results show that targeted observations have a positive effect on 75% of 160 experiment members, reduce 35% forecast errors of the fourth pentad mean blocking index, and perform even better when the original forecast errors are greater. Further diagnosis shows that the improvement is related to the well-described westerly winds in the Ural region and its adjacent regions, corresponding to the more skillful predictions of blocking circulations. The above results supply a theoretical base for the design of Arctic SIC observations and more skillful subseasonal predictions for mid-latitude extreme weather.

How to cite: Dai, G., Mu, M., Ma, X., and Gao, Y.: Influence of Arctic sea ice concentration on extreme Ural blocking predictability in subseasonal timescales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2890, https://doi.org/10.5194/egusphere-egu24-2890, 2024.

09:20–09:30
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EGU24-6229
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On-site presentation
Adrian Matthews, Daniel Skinner, and David Stevens

The extratropical response to the Madden-Julian Oscillation (MJO) is modulated by two prominent modes of low-frequency sea surface temperature (SST) variability: the Atlantic Multidecadal Variability (AMV) and the Pacific Decadal Oscillation (PDO). Utilizing the UK Earth System Model (UKESM) 1100 year pre-industrial control simulation from CMIP6, this study offers a unique opportunity to explore decadal variability with an extensive dataset, surpassing the limitations of previous studies which focussed on reanalysis products.

The results underscore a statistically significant influence of both AMV and PDO on the extratropical response across all MJO phases. Non-linear interactions between the MJO teleconnection and SST forcing are observed prominently in the modification of the response to MJO phase 6 (enhanced convection over the western Pacific), with AMV+ and PDO+ background states amplifying distinct teleconnection patterns, notably the negative North Atlantic Oscillation (NAO-) and the deepened Aleutian Low responses, respectively. These changes are greater in magnitude than would be expected from the linear superposition of the individual atmospheric responses to the SST mode and the MJO. The amplification of the MJO phase 6 teleconnection to the North Atlantic aligns with prior research based on ERA5 reanalysis data.

While modulation of the response to MJO phase 3 (enhanced convection over the eastern Indian Ocean) is evident, it is less pronounced compared to phase 6, and the mechanisms via which it acts are less clear. Intriguingly, alterations in the teleconnection, such as a weaker Aleutian Low during PDO+, contradict the anticipated modulation. Since MJO phase 3 and PDO+ tend to weaken and strengthen the Aleutian Low, respectively, it would be reasonable to expect that these effects would cancel. Instead, the weakening of the Low after MJO phase 3 is increased during PDO+.

A possible mechanism for the modulation of the teleconnections is a linear superposition of Rossby wave modes excited by the MJO, contingent upon the SST state. In the case of MJO phase 6, this corresponds to an amplification of the existing modes, and hence of the expected response. For MJO phase 3, however, there is an indication that other Rossby wave modes may also be excited in certain SST states, leading to interference which is out of phase with the primary response.

Acknowledging the limitations of observational and reanalysis datasets, this study underscores the pivotal role of climate models in the effective study of decadal and multi-decadal variability. Importantly, the study has significant implications for extratropical forecasting over the coming decades. The modulation of the MJO teleconnection by AMV and PDO suggests modifications in predictability, crucial for refining forecasting techniques. Furthermore, these results provide a contextual foundation for studies examining MJO teleconnections in future climates, enabling a more accurate dissection of responses influenced by internal and anthropogenically forced variability.

How to cite: Matthews, A., Skinner, D., and Stevens, D.: Decadal variability of the extratopical response to the MJO: AMV and PDO modulation in the UKESM climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6229, https://doi.org/10.5194/egusphere-egu24-6229, 2024.

09:30–09:40
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EGU24-6688
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On-site presentation
Chidong Zhang

One main justification for subseasonal-to-seasonal (S2S) prediction is its identified sources of predictability. These sources include slowly varying phenomena, such as the MJO, stratospheric conditions, upper-ocean heat content, soil moisture, and sea ice. In practice, however, these presumed sources of S2S predictability have become the main targets of S2S prediction. For example, predicting the MJO, especially its propagation over the Indo-Pacific Maritime Continent, has been challenging. This raises a fundamental question: What are the predictability sources of the MJO? For global coupled prediction models, the primary sources of predictability are initial conditions and the governing laws. It is unclear, however, what elements in the initial conditions are more important to MJO prediction than others. It can be argued that the current practice of initializing forecasts using a single state of the system may not be optimal. Embedded initial conditions may provide an additional source of predictability that has yet to be fully explored.

How to cite: Zhang, C.: Sources of S2S and MJO predictability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6688, https://doi.org/10.5194/egusphere-egu24-6688, 2024.

09:40–09:50
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EGU24-8357
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On-site presentation
Jadwiga Richter, Anne Glanville, Teagan King, Sanjiv Kumar, Stephen Yeager, Yanan Duan, Megan Fowler, Abby Jaye, Jim Edwards, Julie Caron, Paul Dirmeyer, Gokhan Danabasoglu, and Keith Oleson

Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a unique set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3-4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3-6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.

How to cite: Richter, J., Glanville, A., King, T., Kumar, S., Yeager, S., Duan, Y., Fowler, M., Jaye, A., Edwards, J., Caron, J., Dirmeyer, P., Danabasoglu, G., and Oleson, K.: Quantifying sources of subseasonal prediction skill in CESM2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8357, https://doi.org/10.5194/egusphere-egu24-8357, 2024.

09:50–10:00
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EGU24-9510
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ECS
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On-site presentation
Jonas Spaeth, Philip Rupp, Hella Garny, and Thomas Birner

Extreme events of the stratospheric polar vortex can modulate subsequent surface weather at subseaonal to seasonal (S2S) timescales. Moreover, they are considered to form windows of opportunity for tropospheric forecasting. This study aims to improve understanding of how the canonical surface response of polar vortex events translates into modulated surface predictability. 

First, we confirm that in the ECMWF extended-range prediction model, the mean signal of weak (strong) polar vortex events projects onto a negative (positive) phase of the North Atlantic Oscillation. The associated equatorward (poleward) shift of the eddy-driven jet then enhances or suppresses synoptic variability in specific regions. By constructing a leadtime, seasonal and model version-dependent climatology of forecast ensemble spread, we link these regions to anomalous forecast uncertainty. For example, sudden stratospheric warmings (SSWs) are followed by a southerly jet shift, which translates into suppressed Rossby wave breaking over Northern Europe, resulting in anomalously high forecast confidence in that region.

In general, both signatures in the mean and spread can contribute to predictability. However, when forecasts are compared to reanalyses, they manifest differently in different skill scores, such as the Root-Mean-Squared Error or the Continuously Ranked Probability Skill Score. We therefore discuss how separate consideration of anomalies in the ensemble mean and ensemble spread may aid to interpret predictability following polar vortex events.

Finally, we apply the diagnostics also to tropical teleconnections. We find indications that windows of forecast opportunity might be dominated by stratospheric polar vortex variability over the Atlantic and by ENSO variability over the Pacific.

How to cite: Spaeth, J., Rupp, P., Garny, H., and Birner, T.: Stratospheric impact on subseasonal forecast uncertainty in the Northern extratropics , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9510, https://doi.org/10.5194/egusphere-egu24-9510, 2024.

10:00–10:10
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EGU24-14626
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ECS
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On-site presentation
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Philip Rupp, Hilla Afargan-Gerstman, Jonas Spaeth, and Thomas Birner

Weather forecasts at subseasonal-to-seasonal (S2S) timescales have little or no deterministic forecast skill in the troposphere. Individual ensemble members are uncorrelated and span a range of scenarios that are possible for the given set of boundary conditions. The uncertainty of such probabilistic forecasts is then determined by this range of scenarios – often quantified in terms of ensemble spread. For certain boundary conditions, the ensemble spread can be highly anomalous, with conditions associated with reduced spread sometimes referred to as „windows of opportunity“. Various dynamical processes can affect the ensemble spread within a given region, including extreme weather events present in individual members. For geopotential height forecasts over Europe, such extremes are mainly comprised of synoptic storms travelling on the North Atlantic storm track.

We use ECMWF re-forecasts from the S2S database to investigate the connection between storm characteristics and increases in ensemble spread in more detail. We find that the presence of storms in individual ensemble members at s2s time scales forms a major contribution to the geopotential height forecast uncertainty over Europe. In our study, we quantify the magnitude of this contribution and analyse the underlying dynamics, using both Eulerian and Lagrangian frameworks. We further show that certain atmospheric conditions, like various blocked weather regimes, are associated with reduced geopotential height ensemble spread over Europe due to changes in the North Atlantic storm track and associated anomalies in storm density. This connection sheds light on the occurrence of some “windows of opportunity” in the troposphere on S2S time scales.

How to cite: Rupp, P., Afargan-Gerstman, H., Spaeth, J., and Birner, T.: The impact of storm event likelihood on the forecast uncertainty over Europe at S2S time scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14626, https://doi.org/10.5194/egusphere-egu24-14626, 2024.

10:10–10:15
Coffee break
Chairpersons: Steffen Tietsche, Christopher White
Extremes, impacts & applications
10:45–10:55
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EGU24-3242
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ECS
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solicited
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Highlight
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On-site presentation
Pauline Rivoire, Sonia Dupuis, Antoine Guisan, and Pascal Vittoz

Extreme meteorological events such as frost, heat, and drought can induce significant damage to vegetation and ecosystems. In particular, heat and drought events are projected to become more frequent in a changing climate. On the subseasonal-to-seasonal (S2S) forecasting timescale, skillful forecasts of hydro-meteorological hazards combined with targeted actions can prevent various vegetation damage and large-scale impacts (e.g. agriculture and food security, wildfire risk management, forest management,  biodiversity and flora protection,etc.).

We here focus on forest damage in Europe, defined as negative anomalies of the normalized difference vegetation index (NDVI). Compound drought and heat wave events are known to trigger low NDVI events in summer. A dry summer combined with warm and moist conditions during the previous winter can also have a negative impact. However, to our knowledge, there exists no comprehensive study of hydro-meteorological drivers triggering forest damage in Europe. Hence, the goal of our study is a) finding the optimal variables to predict summer forest damage in Europe, and b) assessing the S2S forecast skill of these variables. We develop an automated procedure to systematically identify hydro-meteorological conditions leading to forest damage, up to 18 months prior to occurrence. We train a model using AVHRR remote sensing observation of NDVI for the impact data, and ERA5 and ERA5-Land reanalysis datasets for the explicative variables. These variables include temperature, precipitation, dew point temperature, surface latent heat flux, soil moisture, and soil temperature. To bridge the research gap between the S2S forecasts of hydrometeorological variables and vegetation damage, we assess the forecast skill of variables from the S2S hindcast database of ECMWF identified as responsible for low NDVI events. The idea is to determine to what extent S2S models can predict conditions triggering forest damage, by identifying the sources of predictability or potential need for improvement.

How to cite: Rivoire, P., Dupuis, S., Guisan, A., and Vittoz, P.: Predicting Forest Damage in Europe: A Subseasonal-to-Seasonal Forecasting Approach for Hydro-meteorological Drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3242, https://doi.org/10.5194/egusphere-egu24-3242, 2024.

10:55–11:05
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EGU24-4208
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Highlight
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On-site presentation
Han-Yu Hsu and Hsiao-Chung Tsai

The main objective of this study is to assess typhoon precipitation forecast skill on the subseasonal timescale. The 20-year reforecasts from the ECMWF 46-day ensemble (ENS) are utilized to compare with gridded surface observations in Taiwan. The analysis focuses on the dates when typhoons affect Taiwan (117-129°E and 19-28°N). 15 ENS grids around Taiwan area are used with the grid size of 0.8 x 0.8 degree. Historical rainfall observations are provided by the Central Weather Administration (CWA), which the observations from the surface stations are interpolated into a resolution of 1km x 1km grid box. A comparison between the ENS forecast data and gridded CWA rainfall observations is performed by searching the optimal percentile rank (PR) of gridded CWA rainfall that has the smallest mean difference against the ENS data. The result reveals that the ENS can somewhat capture the rainfall contrast between the mountainous area and plain area, despite its relatively lower horizontal resolution. However, the difference between ENS rainfall forecasts and surface observations significantly increases for the forecasts beyond 72 hours, due to the model's coarser resolution and typhoon track forecast errors.

The ENS typhoon track forecast errors in weeks 1-4 are analyzed by comparing the ensemble vortex tracks with the JTWC best tracks. The track forecast error is decomposed into the along-track (AT) and cross-track (CT) components. The analysis result shows negative mean AT errors, indicating slower translation speed biases in the model. The mean AT errors could reach up to 400 km for the 168 h forecasts after TC formations.

Given the significant typhoon track forecast errors, using the raw ENS rainfall forecasts for the operational TC forecasting/outlook become challenging. In response, we have developed a statistical Quantitative Precipitation Forecast (QPF) model to predict typhoon rainfall, considering the track biases in the ENS forecasts. The forecast tools developed in this study will be integrated into CWA’s subseasonal typhoon forecast system to support water resources management and disaster risk reduction.

How to cite: Hsu, H.-Y. and Tsai, H.-C.: Subseasonal Typhoon Precipitation Forecast in Taiwan Area Using the ECMWF Reforecasts: Forecast Verification and Application, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4208, https://doi.org/10.5194/egusphere-egu24-4208, 2024.

11:05–11:15
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EGU24-4665
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Highlight
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On-site presentation
Xiaochun Wang and Frederic Vitart

The real-time WWRP/WCRP Subseasonal to Seasonal (S2S) Prediction Project Phase 2 database was used to evaluate the prediction skill of tropical cyclone from eleven forecasting systems for the North Western Pacific. The variable introduced to evaluate S2S tropical cyclone prediction is daily tropical cyclone probability, which is the occurrence probability of tropical cyclone within 500 km in one day. Using such a definition, the occurrence of tropical cyclone is a dichotomous event. The skill of S2S tropical cyclone prediction can be evaluated using debiased Brier Skill Score, which is the traditional Brier Skill Score with impact of forecast ensemble size removed. Sensitivity tests were conducted to analyze the influence of difference in temporal window and radius in the definition of daily tropical cyclone probability. It is demonstrated that though the daily tropical cyclone probability would vary with a changed radius and temporal window, the debiased Briere Skill Score does not change much since it is related with the ratio of mean error of model forecast and the mean error of a reference climatological forecast. The robustness of the prediction skill indicates the suitability of using the daily tropical cyclone probability and debiased Brier Skill Score to measure tropical cyclone prediction skill at S2S timescale. Compared with the prediction skill of the S2S Prediction Project Phase 1, the real-time S2S tropical cyclone prediction is improved for some forecast systems. Some early results by combining multi-model tropical cyclone forecasts to improve tropical cyclone prediction will also be presented.

How to cite: Wang, X. and Vitart, F.: Evaluating Real-time Subseasonal to Seasonal Tropical Cyclone Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4665, https://doi.org/10.5194/egusphere-egu24-4665, 2024.

11:15–11:25
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EGU24-1706
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ECS
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On-site presentation
Raju Mandal, Susmitha Joseph, Atul Kumar Sahai, Avijit Dey, Phani Murali Krishna, Dushmanta Pattanaik, Manpreet Kaur, and Nirupam Karmakar

Cold wave (CW) events over India are usually observed during the boreal winter months, November to February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large-scale features associated with these CW events are also discussed.

The CW-prone region has been identified by utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades. By calculating the standardized area-averaged (over the CW-prone region) Tmin anomalies time series, the CW events are identified from 1951 to 2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events, even under the general warming scenarios. It is found that the long CW events (>7 days) are favoured by the La-Nina condition, and short CW events (≤7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the northwest of Indian longitude with the very slow movement of the westerly trough to the east is found to be associated with the long CW events. In contrast, in the case of short events, the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW-prone region up to 2-3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications, it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time despite its uncertainties in space and time. 

How to cite: Mandal, R., Joseph, S., Sahai, A. K., Dey, A., Krishna, P. M., Pattanaik, D., Kaur, M., and Karmakar, N.: Real-time subseasonal prediction of cold waves over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1706, https://doi.org/10.5194/egusphere-egu24-1706, 2024.

11:25–11:35
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EGU24-4245
|
On-site presentation
Qiyu Zhang, Mu Mu, Guodong Sun, and Guokun Dai

Land surface processes are strongly associated with heat waves (HWs). However, how the uncertainties in land surface processes owing to inaccurate physical parameters influence subseasonal HW predictions has rarely been explored. To examine the impact of parameter errors of land surface processes on the uncertainty of subseasonal HW predictions, five strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR) are investigated. Based on the Weather Research and Forecasting (WRF) model, the conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach is employed to address the aforementioned issues.

Numerical results demonstrate that the CNOP-P type errors of physical parameters cause large prediction errors for five HW event onsets. Two types of CNOP-Ps are obtained for HW events, called the type-1 CNOP-P and the type-2 CNOP-P. The type-1 (type-2) CNOP-P causes an approximately 3 °C (2 °C) warm (cold) bias during the HW period. Surface sensible and latent heat flux errors, especially flux exchange between vegetation canopy and canopy air, provide considerable uncertainty in subseasonal HW predictions. The type-1 (type-2) CNOP-P exhibits an underestimation (overestimation) of transpiration. Furthermore, it should be noted that the type-1 CNOP-P results in a substantial difference in soil moisture, a phenomenon that is demonstrated to be challenging to observe in the type-2 CNOP-P. The results indicate that understanding vegetation-atmosphere dynamics is crucial for improving subseasonal HW predictions. Jointly lowering soil-atmosphere and vegetation-atmosphere uncertainty can notably improve subseasonal HW prediction skills.

How to cite: Zhang, Q., Mu, M., Sun, G., and Dai, G.: Impact of Uncertainties in Land Surface Processes on Subseasonal Predictability of Heat Waves Onset Over the Yangtze River Valley, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4245, https://doi.org/10.5194/egusphere-egu24-4245, 2024.

11:35–11:45
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EGU24-9738
|
ECS
|
On-site presentation
Duncan Pappert, Alexandre Tuel, Dim Coumou, Mathieu Vrac, and Olivia Martius

Persistent summer weather can result in extreme events with enormous socio-economic impacts; recent summers in Europe have notably demonstrated this. The dynamics that cause persistent surface weather, as well as potential changes under anthropogenic climate change, are the subject of active scientific debate. Summertime atmospheric dynamics have nevertheless received less attention and we are far from obtaining a comprehensive understanding of the mechanisms involved in the formation of persistent weather conditions in summer. This study investigates the drivers responsible for making some surface extreme events more prone to being long-lasting than others.

Gaining a comprehensive understanding of such processes poses challenges due to the complex interactions of variables and fluxes operating at various timescales – from individual weather events (daily to weekly), to the general circulation of the atmosphere and its modulation by specific changes in sea surface temperature or soil moisture interactions (monthly, seasonal to interannual). Furthermore, studies are recently observing that persistent (quasi-stationary or recurrent) circulation patterns do not necessarily always translate to extreme events and persistence at the surface. This discussion extends to open questions about, such as the potential role of soil moisture preconditioning in extending the lifetime of these events.

Starting from an impact-based definition of persistent hot conditions for different European regions, we characterise their persistence by looking at the associated circulation patterns and surface conditions. Through a comparison of long-lived (persistent) and short-duration events, we discern dynamical differences and regional variations that shed light on the common ingredients and potential mechanisms influencing the persistence of extreme heat events in summer. We use the ERA5 reanalysis dataset to take advantage of its high spatiotemporal resolution and relatively long temporal coverage from the 1950s up to today.

A deeper investigation into the dynamical processes controlling persistent surface conditions over Europe in summer is essential for improved predictability at the sub-seasonal to seasonal (S2S) timescale, and it holds significant relevance for risk preparedness. Results from the study aim to advance the discussion on summer dynamics, weather persistence and climate impacts.

How to cite: Pappert, D., Tuel, A., Coumou, D., Vrac, M., and Martius, O.: The dynamics of persistent hotspells in European summers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9738, https://doi.org/10.5194/egusphere-egu24-9738, 2024.

11:45–11:55
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EGU24-975
|
Virtual presentation
Sarmistha Singh and Sidhan Valiya Veetil

Drought, an extreme meteorological phenomenon, has significant impacts on a country's social, economic, and environmental stability. Early prediction of drought is crucial to provide warning and preparedness measures. Sub-seasonal prediction, which encompasses a few weeks to a few months ahead, is a critical timescale with limited memory of initial conditions, and not significantly controlled by boundary conditions. Presently, dynamical models have drawn much attention in the sub-seasonal precipitation forecast, however, the accuracy in drought prediction remains low. Currently, various dynamical models such as North American Multi-Model Ensemble (NMME) provide sub-seasonal prediction of hydro-meteorological variables for the entire globe. The efficacy of NMME model output for sub-seasonal drought prediction has not been explored in India. Also, a comprehensive study regarding the inclusion of climate indices as potential predictors for S2S drought prediction is lacking in the literature. We have investigated the potentiality of NMME precipitation output for sub-seasonal drought prediction over India and found out that the NMME model output doesn’t show a reasonable S2S forecast for 3-months standardized precipitation index (SPI3). Further, the study utilized data-driven models such as auto-regression, support vector regression (SVR), XGboost, and recurrent neural network (RNN) with climatic indices and previous month lagged value as predictors to improve the prediction skill. The results show that statistical models are superior to dynamic models. Although the previous monthly data is adequate for lead 1 drought prediction for most of the grids over India, the inclusion of climatic oscillation information was found to be the potential predictor and necessary for higher lead predictions. For example, the western disturbance index helped predict droughts at 2-months lead for the Northwest region of India. Moreover, the wavelet-based post-processing technique has shown the potential to enhance drought predictions significantly. The outcomes of this study will provide an outlook for the sub-seasonal to seasonal drought prediction over India and aid in the improvement of decision-making.

How to cite: Singh, S. and Valiya Veetil, S.: Sub-seasonal to seasonal (S2S) prediction of droughts over India using different data-driven models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-975, https://doi.org/10.5194/egusphere-egu24-975, 2024.

11:55–12:05
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EGU24-11637
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ECS
|
On-site presentation
Swatah Snigdha Borkotoky, Kathleen Schiro, and Kevin Grise

Large-scale (synoptic to planetary), quasi-stationary circulation patterns in the atmosphere modulate the local weather dynamics from seasonal to sub-seasonal scale. These circulation patterns are known as Weather Regimes (WRs) and are a prominent feature in the midlatitudes. Most studies so far have focused on specific regions (such as the west coast of the United States or the European sector), and during a specific time of the year (namely the boreal winter season). Little work has been done on understanding the spatiotemporal characteristics (frequency, duration, and orientation) of seasonal North American WRs and how they affect local weather, especially in terms of extremes. This study aims to fill this knowledge gap with an investigation of North American WRs independently for all four seasons. Using a k-means clustering algorithm on daily geopotential height anomalies (de-seasonalized at monthly scale) at the 500-hPa pressure level, we identify five WRs in each of the four seasons across three independent reanalysis datasets: 1) MERRA2; 2) ERA5; and 3) NCEP-NCAR Reanalysis 1, for the period 1980-2022. Initial analysis shows that the spatial patterns of these WRs are robust but have non-trivial differences in the frequency and duration of occurrences across different reanalysis datasets. Additionally, we explore the occurrence of local extreme weather (precipitation and temperature) across the contiguous United States (CONUS) during the presence of these seasonal WRs. This study aims to improve the understanding of the seasonal to sub-seasonal variations of North American WRs and their influence on local extreme weather.

How to cite: Borkotoky, S. S., Schiro, K., and Grise, K.: Seasonal classification of North American weather regimes and their effect on extreme weather, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11637, https://doi.org/10.5194/egusphere-egu24-11637, 2024.

12:05–12:15
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EGU24-11702
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Virtual presentation
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William Seviour, Amy Butler, Chaim Garfinkel, and Peter Hitchcock and the SNAPSI Working Group 2

Sudden stratospheric warming events (SSWs)–in which the westerly polar vortex rapidly breaks down during winter–are  some of the most dramatic examples of dynamical variability in Earth’s atmosphere. It is now well established that SSWs are, on average, followed by large scale anomalies in near-surface circulation patterns, including an equatorward shift of the eddy driven jet that can persist for several months. These anomalies have, in turn, been related to an increase in the likelihood of a variety of high impact weather extremes. However, not all SSWs are followed by impactful weather events; equally, most winter weather extremes are not preceded by SSWs.

Here we will discuss the extent to which the occurrence of individual extreme weather events and their impacts can be attributed to polar stratospheric variability, drawing upon new results from the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project (Working Group 2). This project involves a set of controlled subseasonal hindcast experiments, targeted at three SSW case study events, in which the stratospheric state can be either freely-evolving or nudged towards a climatological or observational state. These simulations reveal that the stratospheric evolution can more than double the regional risk of extreme temperature, rainfall, and snow events. We will go on to explore the attribution of the subsequent impacts of these weather extremes, including on the energy sector, health, and wildfires.  

How to cite: Seviour, W., Butler, A., Garfinkel, C., and Hitchcock, P. and the SNAPSI Working Group 2: Attributing the role of sudden stratospheric warming events in surface weather extremes and their impacts: insights from SNAPSI Working Group 2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11702, https://doi.org/10.5194/egusphere-egu24-11702, 2024.

12:15–12:25
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EGU24-17920
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Highlight
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On-site presentation
Yuhei Takaya, Toshichika Iizumi, Yuji Masutomi, and Toshiyuki Nakaegawa

Seasonal forecasting has the potential to support agricultural activities by offering crop-yield forecasts and facilitating measures to mitigate weather-related damages. This study aims to enhance the application of subseasonal to seasonal (S2S) forecasts in agriculture by evaluating them through tailored verification methods that consider crop calendars and areas.

The verification employs the so-called 1-norm continuous ranked probability score (CRPS), which utilizes the absolute norm instead of a square to quantify forecast errors. While the 1-norm CRPS is not a proper score and does not suit for ensemble forecast verification, it offers an advantage in terms of user-friendliness. Specifically, the score is proportional to the expectation of the absolute error, and thus, it is easier to relate the outcomes of crop models under the assumption of linearity compared to other scores like the ordinal CRPS.

Crop regions and seasons for major commodity crops such as wheat, rice, and maize were identified using global datasets of crop yields and crop calendars. Using the crop calendar information, we can assess the within-season forecast performance in relation to crop growth stages globally. Reforecast data from seasonal forecasts archived by the EU-funded Copernicus Climate Change Service (C3S) were evaluated, allowing for a multi-model comparison of forecast skill. The presentation illustrates a set of example verification products targeted to the common commodity crops. A comprehensive overview of forecast skill for the target crops is anticipated to facilitate a dialogue between meteorological and agricultural experts, thereby enhancing the usability of the seasonal forecast.

How to cite: Takaya, Y., Iizumi, T., Masutomi, Y., and Nakaegawa, T.: Verification of seasonal forecast for facilitating agricultural applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17920, https://doi.org/10.5194/egusphere-egu24-17920, 2024.

12:25–12:30

Posters on site: Tue, 16 Apr, 16:15–18:00 | Hall X5

Display time: Tue, 16 Apr, 14:00–Tue, 16 Apr, 18:00
Chairpersons: Christopher White, Daniela Domeisen
X5.1
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EGU24-375
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ECS
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Luis Lazcano and Christian Dominguez

The Intraseasonal Oscillation (ISO) is commonly divided into two oscillations: the Madden-Julian Oscillation (MJO), which commonly occurs from November to April in winter, and the Boreal Summer Intraseasonal Oscillation (BSISO), which occurs from May to October. Recent studies have classified these two modes into different types using cluster analysis. Here, we analyze the oceanic and atmospheric variables from the reanalysis ERA5 to determine the influence of MJO and BSISO over the Tropical Americas during the period 1980-2018. We also evaluate how the models of the S2S represent the diverse types of MJO and BSISO by using the Pearson correlation, the root mean square error, and the Brier skill score.

The analysis shows that the four MJO types (slow, fast, stationary, and jumping) exhibit no convective signal over the Tropical Americas and the three BSISO types (canonical, north dipole, and east-expansion) have a strong signal on OLR, winds at 850 and 200 mb over the Tropical Americas. Considering the MJO types, the jumping and slow MJO reveal a small warm pool area, areas where the sea surface temperatures (SSTs) are higher than 28.5°C, over the Mexican Pacific, while the stationary and fast MJOs do not reach such high temperatures. Slow (fast) MJO has strong negative (positive) anomalies in SSTs over the central and Eastern Pacific Ocean. Considering the BSISO types, the canonical BSISO has the strongest westerly burst signal before the initiation of the BSISO events over the Maritime Continent, followed by easterly winds later. In contrast, the east-expansion BSISO shows weaker winds and negative OLR anomalies over Mexico. The northward dipole produces a small warm pool area over the Eastern Pacific Ocean when compared to the canonic and east expansion BSISO.

We conclude that the MJO and BSISO types have different physical mechanisms for modulating the intraseasonal changes in the atmospheric and oceanic variables over the Tropical Americas. We also find that the ECMWF model has the best correlation skill when compared to other models from the S2S project.

How to cite: Lazcano, L. and Dominguez, C.: Subseasonal forecast of the MJO over Tropical America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-375, https://doi.org/10.5194/egusphere-egu24-375, 2024.

X5.2
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EGU24-572
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ECS
Pratibha Gautam, Rajib Chattopadhyay, Gill Martin, Susmitha Joseph, and Atul kumar Sahai

This study focuses on the soil moisture characteristics and its role in supporting the continental tropical convergence zone (CTCZ) during the active phase of the monsoon. Like rainfall, land surface parameters (soil moisture and evaporation) also show intraseasonal oscillation. Furthermore, the sub-seasonal and seasonal features of soil moisture are different from each other. During the summer monsoon season, the maximum soil moisture is found over western coastal regions, central parts of India, and the northeastern Indian subcontinent. However, during active phases of the monsoon (i.e., on sub-seasonal timescales), the maximum positive soil moisture anomaly was found in northern India. Land surface characteristics (soil moisture) also play a pre-conditioning role during active phases of the monsoon over the monsoon core zone of India. When it is further divided into two boxes, the north monsoon core zone and the south monsoon core zone, it is found that the preconditioning depends on that region's soil type and climate classification. Also, we calculate the moist static energy (MSE) budget during the monsoon phases to show how soil moisture feedback affects the boundary layer MSE and rainfall. A similar analysis is applied to the model run, but it cannot show the realistic preconditioning role of soil moisture and its feedback on the rainfall as in observations. We conclude that to get proper feedback between soil moisture and precipitation during the active phase of the monsoon in the model, the pre-conditioning of soil moisture should be realistic.

How to cite: Gautam, P., Chattopadhyay, R., Martin, G., Joseph, S., and Sahai, A. K.: Intraseasonal Oscillation of Land Surface Moisture and  its role in the maintenance of land CTCZ during the active  phases of the Indian Summer Monsoon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-572, https://doi.org/10.5194/egusphere-egu24-572, 2024.

X5.3
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EGU24-1244
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ECS
Can Cao and Zhiwei Wu

Recent studies suggest that La Niña events can be classified into two categories: mega La Niña and equatorial La Niña. The understanding of the variations in boreal summer intraseasonal oscillation (BSISO) behaviors between such two conditions remains uncertain. Results in this work show during equatorial La Niña summers, in conjunction with the more adequate intraseasonal column-integrated moisture anomalies, the weaker intraseasonal outgoing longwave radiation anomalies are observed over the western North Pacific (WNP) at 3 pentads lag of the peak phase for the Maritime Continent (MC) BSISO events than during mega conditions. Such changes are closely linked with the different propagation features, specifically northwestward and northeastward propagations under mega and equatorial conditions respectively. The distinct propagations under these two conditions could be partly explained by the background column-integrated moisture anomalies. Under equatorial conditions, the less sufficient background moisture anomalies over the tropical western Pacific (WP), in comparison to mega conditions, suppress the activities of the BSISO and its northwestward propagation here. Meanwhile, the enhanced moisture anomalies over the northwestern MC and its surrounding area (NWMC) facilitate the northeastward propagation. Under mega conditions, the background moisture anomalies over the tropical WP are not significant. The southward moisture anomaly gradient over the NWMC hinders the meridional northward propagation and makes some BSISO activities move to the tropical WP region, performing the zonal westward propagation as a whole. The moisture budget and multi-scale interaction diagnoses also emphasize the significant role of the propagation change in the moisture tendency difference averaged over the WNP. Moreover, the extratropical circulation anomalies associated with the MC BSISO events are also discussed. These findings provide new insights into BSISO activity and offer potential improvements for subseasonal forecast.

How to cite: Cao, C. and Wu, Z.: Distinct changes in boreal summer intraseasonal oscillation over the western North Pacific under mega and equatorial La Niña conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1244, https://doi.org/10.5194/egusphere-egu24-1244, 2024.

X5.4
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EGU24-1376
Baoqiang Xiang

Subseasonal prediction of extremes has emerged as a top forecast priority but remains a great challenge. In this work, we explored two physical modes controlling the subseasonal variation and prediction of land cold extremes over Eurasia: the so-called North Atlantic Oscillation (NAO) and the Eurasian Meridional Dipole mode (EMD). The ECMWF model has shown its skill in predicting the Eurasian land cold extremes 2-4 weeks in advance mainly because of the skillful prediction of NAO and EMD. Further, we separated these observed events into the good prediction and poor prediction groups for those two modes to reveal the potential factors influencing the subseasonal prediction of land cold extremes. It is found that the good prediction group has a stronger initial amplitude and longer persistence, while the poor prediction group has a relatively weaker initial amplitude but rapid intensification. For EMD, the predictability is mainly due to the skillful prediction of the Ural blocking which is further traced back to the stratospheric variations.  

How to cite: Xiang, B.: The window of opportunity for subseasonal land cold extreme prediction over Eurasia  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1376, https://doi.org/10.5194/egusphere-egu24-1376, 2024.

X5.5
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EGU24-1548
Masaru Inatsu, Mio Matsueda, Naoto Nakano, and Sho Kawazoe

The hypothesis that predictability depends on the atmospheric state in the planetary-scale low-frequency variability in boreal winter was examined.We first computed six typical weather patterns from 500-hPa geopotential height anomalies in the Northern Hemisphere using self-organizing map (SOM) and k-clustering analysis. Next, using 11 models from the subseasonal-to-seasonal (S2S) operational and reforecast archive, we computed each model’s climatology as a function of lead time to evaluate model bias. Although the forecast bias depends on the model, it is consistently the largest when the forecast begins from the atmospheric state with a blocking-like pattern in the eastern North Pacific. Moreover, the ensemble-forecast spread based on S2S multimodel forecast data was compared with empirically estimated Fokker– Planck equation (FPE) parameters based on reanalysis data. The multimodel mean ensemble-forecast spread was correlated with the diffusion tensor norm; they are large for the cases when the atmospheric state started from a cluster with a blocking-like pattern. As the multimodel mean is expected to substantially reduce model biases and may approximate the predictability inherent in nature, we can summarize that the atmospheric state corresponding to the cluster was less predictable than others.

How to cite: Inatsu, M., Matsueda, M., Nakano, N., and Kawazoe, S.: Prediction Skill and Practical Predictability Depending on the Initial Atmospheric States in S2S Forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1548, https://doi.org/10.5194/egusphere-egu24-1548, 2024.

X5.6
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EGU24-2784
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ECS
Linyun Yang, Haoming Chen, and Shuyu Wang

This study investigates the influence of the boreal summer intraseasonal oscillation (BSISO) on 10-30-day summer rainfall anomalies in Southwestern China (SWC) under the effects of Qinghai-Tibetan Plateau monsoon (QTPM) based on ERA5 reanalysis data and CN05.1 precipitation in 1981-2018. The results show that the 10-30-day rainfall anomalies in SWC have significant and joint feedback to variation of the second component of BSISO (BSISO2) and QTPM at lagging strong (weak) BSISO events by 0-12 days. Their lagged causal linkage and corresponding physical processes have been revealed by causal effect networks and composite analyses, which are most significant at 4-day and 12-day lag. Simultaneously, BSISO2 can induce wetter 10-30-day rainfall over southern SWC by motivating water vapor transport from the Bay of Bengal towards Yunnan province. More importantly, BSISO2 can modulate a northwest-propagating wave train from the western north Pacific towards SWC at the upper troposphere by vertical wave energy transport, which blocks the wave train propagating from the Lake Balkhash to east China–Japan most significantly at a 4-day lag and leads to drier eastern SWC. The process can be influenced by QTPM significantly which leads to the response of 10-30-day rainfall over SWC with lags of 0-12 days. Specifically, same-phase QTPM can trigger more active wave train propagation from high-latitude while opposite-phase QTPM enhances the low-latitude wave energy transport. The interference then facilitates baroclinic structure over eastern SWC at lagging 12 days with positive precipitation anomalies for same-phase events and negative precipitation for opposite-phase events.

How to cite: Yang, L., Chen, H., and Wang, S.: The joint effects of the boreal summer intraseasonal oscillation and Qinghai-Tibetan Plateau monsoon on the precipitation over Southwestern China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2784, https://doi.org/10.5194/egusphere-egu24-2784, 2024.

X5.7
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EGU24-2862
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ECS
Daehyun Kang, Daehyun Kim, and Seon-Yu Kang

The Madden-Julian Oscillation (MJO) is the dominant intraseasonal variability of eastward propagating atmospheric disturbances in the tropics. From its vast impacts on the sub-seasonal extreme events and predictability, the mean states controlling the MJO activity have been investigated. For example, the robust relationship between the Quasi-Biennial Oscillation (QBO) and the MJO has been suggested in the past several years. In the easterly QBO winters, the MJO exhibits stronger activity than the westerly QBO winters. 
Our study suggests another crucial factor that affects the MJO: a meridional humidity gradient of the atmospheric column in the vicinity of the Maritime Continent. With the change in the shape of the column humidity distribution, MJO variance shows a robust interannual modulation regardless of the QBO. The northward (southward) extension of the moisture increases (decreases) the mean state meridional humidity gradient, which leads to MJO development (decay) over the MC with increasing (decreasing) horizontal moisture advection. This robust relationship between mean state humidity and MJO activity is investigated in the CMIP6 models as two aspects: i) interannual variation of MJO and ii) future change in MJO. Both simulated MJO activities are largely affected by the mean state MHG, supporting the robust role of mean state moisture on the MJO shown in the observations. The results of this study provide a further understanding of seasonal MJO activity and sub-seasonal predictability.MJO activity and sub-seasonal predictability.

How to cite: Kang, D., Kim, D., and Kang, S.-Y.: Robust Relationship between Mean State Moisture and Interannual MJO Activity in Observations and CMIP6 Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2862, https://doi.org/10.5194/egusphere-egu24-2862, 2024.

X5.8
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EGU24-3148
Xin Qi, Jing Yang, Yongkang Xue, Qing Bao, Guoxiong Wu, and Duoying Ji

The precipitation over the eastern Tibetan Plateau (ETP, here defined as 29°–38°N, 91°–103°E) usually exhibits significant subseasonal variation during boreal summer. As the hot spot of land-air interaction, the influences of ETP surface soil temperature (Tsoil) on the local precipitation through subseasonal land-air interaction are still unclear but urgently needed for improving subseasonal prediction. Based on station and reanalysis datasets of 1979–2018, this study identifies the evident quasi-biweekly (QBW) (9–30 days) periodic signal of ETP surface Tsoilvariation during the early summer (May–June), which results from the anomalies of southeastward propagating mid-latitude QBW waves in the mid-to-upper troposphere. The observational results further show that the maximum positive anomaly of precipitation over the ETP lags the warmest surface Tsoil by one phase at the QBW timescale, indicating that the warming surface Tsoil could enhance the subseasonal precipitation. The numerical experiments using the WRF model further demonstrate the effect of warming surface Tsoil  on enhancing the local cyclonic and precipitation anomaly through increasing upward sensible heat flux, the ascending motion, and water vapor convergence at the QBW timescale. In contrast, the effect of soil moisture over the ETP is much weaker than Tsoil  at the subseasonal timescale. This study confirms the importance of surface Tsoil over the ETP in regulating the precipitation intensity, which suggests better simulating the land thermal feedback is crucial for improving the subseasonal prediction.

How to cite: Qi, X., Yang, J., Xue, Y., Bao, Q., Wu, G., and Ji, D.: Subseasonal Warming of Surface Soil Enhances Precipitation Over the Eastern Tibetan Plateau in Early Summer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3148, https://doi.org/10.5194/egusphere-egu24-3148, 2024.

X5.9
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EGU24-3194
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ECS
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Highlight
Kyle Lesinger and Di Tian

Global warming is accelerating drought onset, causing more frequent flash drought events. These events occur at the subseasonal timescale in which rapid decreases in root-zone soil moisture (RZSM) increase risks of crop failure, wildfire, and heat stress globally. However, forecasting soil moisture and flash droughts at lead times beyond 2 weeks remains a significant challenge. Recently, machine learning methods with historical reanalysis data have shown improved forecast accuracy compared to state-of-the-art numerical weather prediction methods, but they can only produce skillful forecast within 10 days. Here we show that a convergence forecast model combining a deep learning approach with subseasonal retrospective forecasts (reforecast) from numerical models produces skillful subseasonal soil moisture and flash drought forecasts at lead times beyond 2 weeks. We train a deep learning architecture on combinations of reanalysis and reforecast from 2000 to 2015 and validate results during the testing period from 2018 to 2019. The subseasonal forecast skill of soil moisture of the convergence forecast model is much higher than those of current state-of-the-art numerical forecast models, deep learning bias corrected numerical forecast models, or the reanalysis-based deep learning models, which showed no skill after 2 weeks lead time. The convergence model also showed significantly improved performance for predicting flash droughts compared to the original or deep learning bias corrected numerical forecast models or reanalysis-based deep learning models.  A permutation analysis indicates that reanalysis precursors and soil moisture reforecast at lead times within 2 weeks both contribute significantly to the forecast skill at longer lead times. The convergence forecast model provides accurate and efficient subseasonal soil moisture and flash drought forecasting and is promising for accurately forecasting key variables and extreme events at the subseasonal timescale.

How to cite: Lesinger, K. and Tian, D.: Converging Deep Learning and Numerical Prediction for Skillful Subseasonal Soil Moisture and Flash Drought Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3194, https://doi.org/10.5194/egusphere-egu24-3194, 2024.

X5.10
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EGU24-3737
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ECS
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Highlight
Yeon-Soo Jang, Hyung-Gyu Lim, Sang-Yoon Jun, and Jong-Seong Kug

Despite current global warming due to increasing greenhouse gases, severe cold winters have devastated the East Asia in recent decades. Efforts are being made to predict cold events using dynamic models and physically-based statistical models. In this study, we explore the potential predictability of the East Asian winter surface temperature by establishing a multiple linear regression model based on three precursors of time-evolved preconditions: 1) autumn Arctic sea-ice loss, 2) northern Eurasian sea level pressure pattern, and 3) the El Niño-Southern Oscillation (ENSO). Reduced autumn Arctic sea-ice was favorable for extreme cold events in the East Asia. Furthermore, the autumn Arctic sea-ice loss was accompanied by cyclonic circulations over northern Eurasia in November, which could have led to cold anomalies over the East Asia in the late winter. The preconditioning deep convection in La Niña events is a well-known indicator of exerted atmospheric wave propagation, resulting in cold winters over the East Asia. We suggested here that by combining Arctic sea-ice, atmospheric circulations, and ENSO, the predictability of East Asian winter surface temperature variability could be improved.

How to cite: Jang, Y.-S., Lim, H.-G., Jun, S.-Y., and Kug, J.-S.: Arctic sea ice loss and La Niña as precursors of extreme East Asian cold winters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3737, https://doi.org/10.5194/egusphere-egu24-3737, 2024.

X5.11
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EGU24-3796
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ECS
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Highlight
Yangjiayi Gao, Mu Mu, and Guokun Dai

The predictability of certain extreme weather events can exceed the traditional two weeks by considering the boundary conditions. Targeted observations in sensitive areas on Arctic sea ice concentration (SIC) can improve the extended-range (4 pentads) forecast skills of long-lasting and strong Ural blocking (UB). The sensitive areas are determined based on the SIC optimally growing boundary errors, obtained by the conditional nonlinear optimal perturbation method. The sensitive areas are mainly located in the Barents Sea, Greenland Sea, and Okhotsk Sea. The results of observing system simulation experiments for 8 UB cases indicate that the targeted observations can remarkably improve the prediction skills of UB in the 4th pentad. Targeted observations have a positive effect on 75% of 160 experiment members, reduce 35% forecast errors of the 4th pentad mean blocking index, and perform even better when the original forecast errors are greater. Further diagnosis shows that targeted observations contribute to more accurate SIC boundary conditions in the Barents Sea, Greenland Sea, and Okhotsk Sea and reduce temperature errors in the lower and middle troposphere. It further results in well-described westerly winds in the Ural region and its adjacent regions, corresponding to the more skillful predictions of blocking circulations. The above results supply a theoretical base for the design of Arctic SIC observations and more skillful extended-range predictions for mid-latitude extreme weather.

How to cite: Gao, Y., Mu, M., and Dai, G.: Targeted Observations on Arctic Sea Ice Concentration for Improving Extended-range Prediction of Ural Blocking, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3796, https://doi.org/10.5194/egusphere-egu24-3796, 2024.

X5.12
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EGU24-3798
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ECS
Xiran Xu, Lei Wang, Tao Wang, and Gang Chen

The summer northern annular mode (NAM) variability plays a crucial role in the summer climate variability and extremes of the Northern Hemisphere. In this study, we report a significant negative correlation between the March NAM and summer NAM during 1979–2022 and reveal the role of the spring stratosphere in this seasonal linkage. Particularly, it is found that the negative phase of March NAM features a strong meridional shear in the extended-North-Atlantic jet, which tends to generate planetary scale Rossby waves that propagate upward and poleward into the stratosphere. This increased stratospheric planetary wave activity in March transitions to weakened wave activity in May, leading to positive zonal wind anomalies in the polar stratosphere in May, extending downward to the troposphere in June and promoting the formation and persistence of positive summer NAM. The results provide both statistical and dynamical evidence for the role of the spring stratosphere in connecting the spring and summer circulation. 

How to cite: Xu, X., Wang, L., Wang, T., and Chen, G.: The role of stratospheric processes in the trans-seasonal connection between spring and summer northern annular modes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3798, https://doi.org/10.5194/egusphere-egu24-3798, 2024.

X5.13
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EGU24-4272
Hsiao-Chung Tsai, Han-Yu Hsu, Tzu-Ting Lo, and Meng-Shih Chen

This study uses the ECMWF 46-day ensemble to evaluate the subseasonal forecasts of tropical cyclones (TCs) in the western North Pacific, including TC formations, tracks, intensity, and precipitation forecasts. TC formations and the subsequent tracks are objectively detected in both real-time forecasts and also the 20-year ECMWF reforecasts. Additionally, a spatial-temporal track clustering technique is utilized to group similar vortex tracks in the 101-member real-time forecasts for operational application. The forecast verification focuses on evaluating the influence of large-scale environmental factors on TC forecast skills during weeks 1-4, such as the Western North Pacific Summer Monsoon (WNPSM), Madden Julian Oscillation (MJO), and Boreal Summer Intraseasonal Oscillation (BSISO). The Precision-Recall (PR) curve is used to represent the imbalanced TC data instead of the Receiver Operating Characteristic (ROC) curve. Better TC forecast skills are observed if model initialized on MJO Phases 6 and 7 for the week-1 forecasts, and on MJO Phases 4 and 5 for the weeks 2 and 3 forecasts. Also, TC forecast skills are better if the cumulative percentage of the WNPSM index (Wang et al. 2001) is larger than 60%. This study also investigats the TC precipitation forecast skill around Taiwan area.

The evaluation results obtained from this study has been integrated into the TC Tracker 2.0 system developed by Central Weather Administration (CWA). The system can generate a "Subseasonal TC Threat Potential Forecast" product to assist in disaster mitigation and water resources management for the Water Resources Agency. More details about the subseasonal TC forecast verifications and applications will be presented in the meeting

How to cite: Tsai, H.-C., Hsu, H.-Y., Lo, T.-T., and Chen, M.-S.: Verifications of Week-1 to Week-4 Tropical Cyclone Forecasts in the Western North Pacific from the ECMWF 46-Day Ensemble, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4272, https://doi.org/10.5194/egusphere-egu24-4272, 2024.

X5.14
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EGU24-4955
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ECS
Xiaojing Li

Considering the significant differences in the rainfall characteristics over East Asia between the early [May–June (MJ)] and late [July–August (JA)] summer, this study investigates the subseasonal predictability of the rainfall over East Asia in early and late summer, respectively. Distinctions are obvious for both the spatial distribution of the prediction skill and the most predictable patterns, that is, the leading pattern of the average predictable time (APT1) between the MJ and JA rainfall. Further analysis found that the distinct APT1s of MJ and JA rainfall are attributable to their different predictability sources. The predictability of the MJ rainfall APT1 is mainly from the boreal intraseasonal oscillation signal, whereas that of the JA rainfall APT1 is provided by the Pacific–Japan teleconnection pattern. This study sheds light on the temporal variation of predictability sources of summer precipitation over East Asia, offering a possibility to improve the summer precipitation prediction skill over East Asia through separate predictions for early and late summer, respectively.

How to cite: Li, X.: Subseasonal Predictability of Early and Late Summer Rainfall Over East Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4955, https://doi.org/10.5194/egusphere-egu24-4955, 2024.

X5.15
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EGU24-5422
Jingyuan Xi

Summer monsoon precipitation over the Bay of Bengal (BoB) has pronounced intraseasonal variability (ISV), which has a close relationship to the local intraseasonal sea surface temperature (SST). Before heavy precipitation, intraseasonal SST in the BoB often has a warm anomaly and propagates northward, which drives the atmosphere and tends to trigger the convection. Besides the local air-sea interaction, the ISV of SST in the Arabian Sea (AS) also has an effect on the precipitation over the BoB. Results show that a prominent heavy precipitation usually occurs when the warm intraseasonal SST anomaly appears early in the AS and moves northward prior to that emerges in the BoB. The warm SST anomaly in the AS affects the sea level pressure and then trigger a southwestly wind anomaly in the center of AS. This wind anomaly promotes the wind convergence moving northward from the southern tip of Indian peninsula to the north India and northern BoB, which directly influence the vertical moisture advection and finally the precipitation. Understanding this process will be helpful to improve the predictive skill of the ISVs during the Indian Summer Monsoon.

How to cite: Xi, J.: Influence of Intraseasonal Variability of Sea Surface Temperature in the Arabian Sea on the Summer Monsoon Precipitation Over the Bay of Bengal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5422, https://doi.org/10.5194/egusphere-egu24-5422, 2024.

X5.16
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EGU24-6452
Stephanie Rushley, Matthew Janiga, and Carolyn Reynolds

The Navy Earth System Prediction Capability (ESPC) is the Navy’s coupled ocean-atmosphere-sea ice model.  The current version of the Navy ESPC has 16 ensemble members and been operational since August 2020. The Navy ESPC has known biases in Madden-Julian Oscillation (MJO), which has a too strong amplitude and too fast propagation speed. During boreal winter, the MJO in the Navy ESPC is too strong due to biases in the vertical motion, which supports larger vertical moisture advection.  The MJO is too strong in this season due to excessive evaporation in the western Pacific supporting moistening to the east of the MJO convective center.  In this study, we examine the boreal winter MJO in the operational Navy ESPC ensemble.  We use process oriented diagnostics to explore the local and remote sources of biases that drive good and poor MJO forecasts. 

MJO forecasts are split into those that are well predicted and those that are poorly predicted.  Individual MJO events are tracked following Chikira (2014), using Hovmöllers of MJO filtered OLR averaged between 10N and 10S.  The MJO forecast performance is determined by comparing the forecasted MJO to the observed MJO based on the magnitude of the maximum amplitude of the MJO, the phase speed, duration of the event, and the location of the MJO convection.  Using the moisture mode framework, we examine the maintenance and propagation of moisture anomalies to identify how the local and remote sources of error affect MJO skill.  We use a moisture budget analysis to diagnose and understand the difference between the forecasts that performed well and those that performed poorly.  Additionally, we examine the effects that these forecast errors in the MJO have on extratropical cyclones, surface winds, and clouds in the Navy ESPC and how biases in the extratropics affect the skill of MJO-teleconnections.

How to cite: Rushley, S., Janiga, M., and Reynolds, C.: Local and remote sources of error inMJO forecasts in the Navy ESPC , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6452, https://doi.org/10.5194/egusphere-egu24-6452, 2024.

X5.17
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EGU24-7386
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Highlight
Jeong-Min Park, Dasom Lee, Kwanchul Kim, Seong-min Kim, Gahye Lee, and Kwon Ho Lee

Recently, it has been noticed that weather and climate changes over the Arctic and mid-latitude regions may have influenced the particulate matter concentrations and haze over East Asia. Among the various weather and climate conditions and climate indices could be an important factor in affecting variation of particulate matter (PM) concentrations. In this study, we examined the long-term changes in the sea ice cover, soil moisture, near-surface temperature and its link with the lower atmospheric circulation over Arctic and mid-latitude from 1950 to 2022, using modern reanalysis datasets. Long-term analyses show negative trends in sea ice cover over the Arctic and positive trends in near-surface temperature and SST, implying atmospheric stagnant and variation of PM concentration. Additionally, climate indices, related to teleconnection between the Arctic region and mid-latitude, co-related with understanding air quality. Based on climate indices, we have developed the air quality prediction model for reflecting variations in weather and climate conditions. Therefore, the findings in this study can likely be used for actual prediction systems based on long-term weather measurement datasets over the Arctic region.

Acknowledgment: This research was supported by a National Research Foundation of Korea Grant from the Korean Government (MSIT ; the Ministry of Science and ICT) (NRF- 2023M1A5A1090715).

How to cite: Park, J.-M., Lee, D., Kim, K., Kim, S., Lee, G., and Lee, K. H.: Weather and Climate conditions over the Arctic and mid-latitude regions affecting air quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7386, https://doi.org/10.5194/egusphere-egu24-7386, 2024.

X5.18
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EGU24-8918
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ECS
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Highlight
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Selina M. Kiefer, Sebastian Lerch, Patrick Ludwig, and Joaquim G. Pinto

For many practical applications, e.g. agricultural planning, skillful weather predictions on the subseasonal timescale (2-4 weeks in advance) are key for making sensible decisions. Since traditional numerical weather prediction (NWP) models are often not capable of delivering such forecasts, we use an alternative forecasting approach combining both, physical knowledge and statistical models. Selected meteorological variables from ERA-5 reanalysis data are used as predictors for wintertime Central European mean 2-meter temperature and the occurrence of cold wave days at lead times of 14, 21 and 28 days. The forecasts are created by Quantile Regression Forests in case of continuous temperature values and Random Forest Classifiers in case of binary occurrence of cold wave days. Both model types are evaluated for the winters 2000/2001 to 2019/2020 using the Continuous Ranked Probability Skill Score for the continuous forecasts and the Brier Skill Score for the binary forecasts. As a benchmark model, a climatological ensemble obtained from E-OBS observational data is considered. We find that the used machine learning models are able to produce skillful weather forecasts on all tested lead times. As expected, the skill depends on the exact winter to be forecasted and generally decreases for longer lead times but is still achieved for individual winters and in the 20-winter mean at 28 days lead time. Since machine learning models are often subject to a lack of interpretability and thus considered to be less trustworthy, we apply Shapley Additive Explanations to gain insight into the most relevant predictors of the models’ predictions. The results suggest that both Random-Forest based models are capable of learning physically known relationships in the data. This is, besides the capability of producing skillful forecasts on the subseasonal timescale, a selling point of the combination of physical knowledge and statistical models. Finally, we compare the skill of our statistical models to subseasonal state-of-the-art NWP forecasts.

How to cite: Kiefer, S. M., Lerch, S., Ludwig, P., and Pinto, J. G.: Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal Timescales?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8918, https://doi.org/10.5194/egusphere-egu24-8918, 2024.

X5.19
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EGU24-9494
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ECS
Improving Subseasonal-to-Seasonal Prediction of Summer Extreme Precipitation over Southern China Based on a Deep Learning Method
(withdrawn)
Yang Lyu, Xiefei Zhi, Shoupeng Zhu, and Yan Ji
X5.20
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EGU24-11457
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ECS
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Highlight
Rebecca Wiegels, Luca Glawion, Julius Polz, Christian Chwala, Jan Niklas Weber, Tanja C. Schober, Christof Lorenz, and Harald Kunstmann

Seasonal predictions are essential in mitigating damage to people and nature as a result of climate change and extreme events by improving timely decision-making particularly for water and irrigation management. The newly constructed Grand Ethiopian Renaissance Dam, located in the Blue Nile (BN) Basin in Ethiopia at the border to Sudan, increases the urgency of optimized transboundary water management and improved seasonal predictions. However, the global seasonal forecasting systems have known limitations such as biases and drifts. Specifically at regional level, such as in the highlands of Ethiopia, the seasonal predictions need accurate post-processing. Recent developments have shown the large potential of Deep Learning (DL) applications to improve weather and climate predictions. The goal of this study is to improve the global seasonal forecasting system SEAS5 of ECMWF specifically for the BN Basin using DL approaches such as conventional Convolutional Neural Networks (CNN) or more advanced Adaptive Fourier Neural Operators (AFNO). We present first results for improving and downscaling SEAS5 global seasonal precipitation forecasts in the BN Basin with a particular emphasis on ensemble generation and calibration. The neural networks are trained with ERA5-Land-reanalysis data as a ground-truth, which has a higher resolution than SEAS5 (~9km compared to ~36km). This additional downscaling step allows us to consider the high variations in precipitation intensities in the Ethiopian highlands. The results show that the applied DL models have high potential in improving forecasting scores such as the continuous ranked probability skill score. They therefore allow for improved timely decision-making for water management in the transboundary BN Basin.

How to cite: Wiegels, R., Glawion, L., Polz, J., Chwala, C., Weber, J. N., Schober, T. C., Lorenz, C., and Kunstmann, H.: Deep Learning improved seasonal forecasts for the Blue Nile Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11457, https://doi.org/10.5194/egusphere-egu24-11457, 2024.

X5.21
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EGU24-13143
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ECS
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Yuna Lim, Andrea Molod, Randal Koster, and Joseph Santanello

Land-atmosphere (L-A) coupling can significantly contribute to subseasonal-to-seasonal (S2S) prediction. During periods of strong L-A coupling, land-atmosphere feedbacks are expected to enhance the memory of the system and therefore also the predictability and prediction skill. This study aims to evaluate S2S prediction of ambient surface air temperature under conditions of strong versus weak L-A coupling in forecasts produced with NASA’s state-of-the-art GEOS S2S forecast system. Utilizing three L-A coupling metrics that together capture the connection between the soil and the free atmosphere, enhanced prediction skill for surface air temperature is observed for 3-4 week boreal summer forecasts across the eastern Great Plains when strong L-A coupling is detected at this lead by all three indices. The forecasts with strong L-A coupling in these “hot spot” regions exhibit warm and dry anomalies, signals that are well simulated in the model. Overall, this study provides insight into how better capturing relevant L-A coupling processes might improve prediction on subseasonal-to-seasonal timescales.

How to cite: Lim, Y., Molod, A., Koster, R., and Santanello, J.: Land-Atmosphere Coupling Simulation and Its Role in Subseasonal-to-Seasonal Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13143, https://doi.org/10.5194/egusphere-egu24-13143, 2024.

X5.22
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EGU24-14585
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Highlight
OKYeon Kim, Seul-Hee Im, and Gaeun Kim

We explored the objective methods to improve long-range forecasting through enhanced forecast skills and integrated forecast information. The objective process we used in this study includes the selection of monitoring factors for more reliable monthly seasonal forecasts. Therefore, we chose the three most significant monitoring factors, i.e., ENSO, snow cover over Eurasia Continent and Arctic sea ice. We first examined the effect and response of the monitoring factors on the boreal winter temperature in South Korea. To improve the information related to the ENSO in seasonal forecasting, the impact of the tropical precipitation which act as an oceanic ENSO forcing was investigated. As one of the important monitoring factors for boreal winter temperature prediction, we analyzed the availability of the index describing austral Eurasian snow cover. We also analyzed the usage of Arctic conditions for predicting monthly temperature for boreal winter. We then investigated how well the effect and response of the factors are simulated in the operational seasonal models. Finally, the link between observation-based monitoring factors and model-based prediction is proposed for objective forecasting.

How to cite: Kim, O., Im, S.-H., and Kim, G.: Improved long-range forecasts in South Korea through integrated forecast information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14585, https://doi.org/10.5194/egusphere-egu24-14585, 2024.

X5.23
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EGU24-15134
Adel Imamovic, Dominik Büeler, Maria Pyrina, Vincent Humphrey, Christoph Spirig, Lionel Moret, and Daniela Domeisen

Given the limited skill of precipitation forecasts, the question arises to what extent ensemble forecasting systems can be used for early warning systems that require longer lead times, such as drought early warning. In this study, we use ECMWF’s IFS extended range forecasts, statistically downscaled to a 2 km grid encompassing Switzerland, to quantify the spatially and seasonally stratified predictability of several precipitation statistics. Consistent with existing analyses we find the predictability of extratropical instantaneous precipitation to be limited to week 1. However, when considering accumulated precipitation and the standardized precipitation index (SPI) forecasts, which is commonly used for drought management, the forecasts are skillful well into week 3. This extension in predictability horizon is attributed to the characteristic of accumulated precipitation, which is less sensitive to differences in timing of precipitating systems. The enhanced predictability of SPI enhances the utility of extended range forecasts for monthly drought forecasts. We discuss the practical applicability of these findings in the context of the new Swiss drought early warning and monitoring platform, planned for operations in 2025. Leveraging the enhanced predictability of SPI, this platform stands to benefit from our research outcomes, providing stakeholders with tools for proactive drought management and response strategies. 

How to cite: Imamovic, A., Büeler, D., Pyrina, M., Humphrey, V., Spirig, C., Moret, L., and Domeisen, D.: How far in advance can we skillfully predict meteorological drought indices?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15134, https://doi.org/10.5194/egusphere-egu24-15134, 2024.

X5.24
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EGU24-15210
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ECS
Miriam Rodriguez

In recent years, deep learning (DL) models have been shown to be able to make competitive forecasts of El Niño Southern Oscillation (ENSO). In most cases, due to the short observational record, the outputs of global circulation models (GCM) are used to train DL models. However, GCMs themselves show biases when modeling ENSO dynamics, such as the lack of phase-locking behavior, shifted precipitation trends, or missing El Niño - La Niña asymmetry. The biases of the GCMs are likely inherited by the DL models during pre-training, raising the question of how we can obtain unbiased DL ENSO models while pre-training on GCM output. In this study, we contend that a possible solution to correct these biases is to use well-established domain adaptation methods, which allow DL models to account for shifts in data distribution between training and validation data sets. In particular, we use a ConvLSTM network trained on CESM2 simulations where we first use a supervised objective to fine-tune our model to reanalysis data. Secondly, we employ test-time training to adapt our model for the domain shift between CESM2 and reanalysis data. This study serves as a first step toward comparing domain adaptation techniques for data-driven seasonal-to-annual DL models in a limited data regime.

How to cite: Rodriguez, M.: Domain adaptation for deep learning ENSO forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15210, https://doi.org/10.5194/egusphere-egu24-15210, 2024.

X5.25
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EGU24-16022
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ECS
Jan Niklas Weber, Christof Lorenz, Tanja Schober, Rebecca Wiegels, and Harald Kunstmann

Droughts, prolonged heat-waves, heavy precipitation events and large-scale flooding - the last years have demonstrated that global climate change is already hitting hard in many places of the Earth. This, inevitably, leads to increased water stress that requires a more sustainable and timely water management across scales. In particular, for optimized use of water resources for irrigation or hydropower generation, it is essential to know their expected availability in the coming months all over the world. This sub-seasonal to seasonal temporal domain, from weeks to months ahead, is addressed by seasonal forecasting systems such as SEAS5, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). These systems have the potential to provide essential data for enhancing water management practices. Without a bias correction though, the data exhibit a notable deficiency in skill. We have shown for several regions of the world that the “Bias Correction and Spatial Disaggregation” method (BCSD) can improve the forecasting skill substantially. Our next step is now to expand our efforts from the regional to the global scale, i.e., to provide the BCSD-forecasts for the entire globe. Here, the challenge lies in significantly reducing the computational demand for the bias correction: Presently, the BCSD requires several days to execute on a global scale. However, if such forecasts should be used as decision support, a timely provision is crucial.

We therefore present a method to achieve this task: The utilization of fixed Cumulative Distribution Functions (CDFs) rather than their recalculation for each pixel has the potential to enhance the computational efficiency of the bias correction. This approach not only significantly reduces the required data volume but also improves accessibility. To further achieve transferability of the system, we also demonstrate the performance of this system in a containerized environment. Our goal is to achieve a globally corrected SEAS5 forecasts within a time frame of ideally less than one day. With the provision of these bias-corrected data in near-real time, better estimations become available for direct utilization by water managers or as input for subsequent modeling processes.

How to cite: Weber, J. N., Lorenz, C., Schober, T., Wiegels, R., and Kunstmann, H.: Global bias-corrected seasonal forecasts: Towards efficient and near real-time solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16022, https://doi.org/10.5194/egusphere-egu24-16022, 2024.

X5.26
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EGU24-16753
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ECS
Minjie Yu

The seasonal cycle (SC) anomalies of winter surface air temperature (SAT) over China mainly include three modes: consistent changes throughout the winter, inverse changes in the early and late winter, and opposite changes in the southern and northern China, respectively.  The positive EOF1 phase (i.e., uniformly warming throughout winter) can be attributed to global warming, especially in the North Atlantic and tropical Pacific. The EOF2 is mainly related to the dipole sea surface temperature (SST) pattern in the North Atlantic. In the early winter, the Rossby wave originating from North Atlantic strengthens Ural blocking high (UBH) and Siberian high (SH) in the early winter, resulting in cold SAT anomalies in most of China. While the large-scale zonal circulation with weakened SH has transformed SAT over China into a warm state in the later winter. The EOF3 can be attributed to the tripole SST in the North Atlantic and El Niño-like SST pattern in the tropical Pacific. In December, the Rossby wave train originating from the mid-latitudes of the North Atlantic Ocean enhances cold air activity in the Northern Hemisphere, causing cold SAT anomalies in Northeast China, while the dominating southerly winds in southern China cause warm SAT anomalies. In the late winter, the large-scale circulation resembles negative AO phase, resulting in the northerly winds and cold SAT anomalies in the northern China. Meanwhile, the anomalous anticyclonic circulation in the Northwest Pacific causes warm SAT anomalies in southern China. Therefore, the combined effects of tropical and extratropical SST should be considered when predicting interannual variability of winter SAT anomalies over China.

How to cite: Yu, M.: Diversity of seasonal cycle anomalies of surface air temperature in winter over China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16753, https://doi.org/10.5194/egusphere-egu24-16753, 2024.

X5.27
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EGU24-17318
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ECS
Camille Le Coz, Alexis Tantet, Rémi Flamary, and Riwal Plougonven

Combining ensemble forecasts from different models into a multi-model ensemble (MME) have been shown to improve forecast skill at different time-scales, including the sub-seasonal to seasonal (S2S) one. Here, we investigate a new method to build such MME based on barycenter.

Recognizing ensemble forecasts as discrete probability distributions, we work directly in the probability distribution space. This allows us to use existing tools in this space, and in particular the concept of barycenter. The barycenter of a collection of distributions (or the ensemble forecasts here) is the distribution that best represents them, based on a given metric. The barycenter can thus be seen as the combination of these distributions, and so used to build a MME. We compare two barycenters based on different metrics: the L2 and the Wasserstein distances. The Wasserstein distance corresponds to the cost of the optimal transport between two distributions and has interesting properties in the distribution space. We compare it to the L2-barycenter which is in fact shown to be equivalent to the well-known “pooling” MME method (i.e. the concatenation of the different ensembles members). Another interesting point of the barycenters is that they allow you to give different weights to the models and so to easily build a weighted-MME. The weights have an important impact on the skill of the MMEs. We are thus optimizing the weights by learning them from the data using cross-validation on the forecasts.

The two barycenter-based MMEs are applied to the combination of the models from the S2S project’s database. Their performances are evaluated for the prediction of weekly 2m-temperature during European winter with respect to different metrics. As a proof of concept, we first start with the combination of two models, namely the European Centre Medium-Range Weather Forecasts (ECMWF) and the National Center for Environmental Prediction (NCEP) models. We show that the two MMEs are generally able to perform as well or better than both the single-models, but that the best combination method depends on the chosen metric. We then extend the barycenter approach to the combination of more models, of which we will discuss preliminary results.

How to cite: Le Coz, C., Tantet, A., Flamary, R., and Plougonven, R.: A barycenter-based approach for the multi-model ensembling of subseasonal forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17318, https://doi.org/10.5194/egusphere-egu24-17318, 2024.

X5.28
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EGU24-18260
Svetlana Loza, Marylou Athanase, Longjiang Mu, Jan Streffing, Antonio Sánchez-Benítez, Miguel Andrés-Martínez, Lars Nerger, Tido Semmler, Dmitry Sidorenko, and Helge Goessling

Predictive skills of coupled sea-ice/ocean and atmosphere models are limited by the chaotic nature of the atmosphere. Assimilation of observational information on ocean hydrography and sea ice allows to obtain a coupled-system state that provides a basis for subseasonal-to-seasonal ocean and sea-ice forecast (Mu et al., 2022). However, if the atmosphere is not additionally constrained, the quasi-random atmospheric states within an ensemble forecast lead to a fast divergence of the ocean and sea-ice states, degrading the system’s performance with respect to the sea ice forecasts. As reported previously, imposing an additional constraint by nudging large-scale winds to the ERA5 reanalysis data (Sánchez-Benítez et al., 2021; Athanase et al., 2022) improves predictive skills of the AWI Coupled Prediction System (AWI-CPS, Mu et al. 2022) with regard to sea ice drift (Losa et al., 2023). Here we provide results based on a much more extensive set of ensemble-based data assimilation experiments spanning the time period from 2002 to 2023 and a series of long forecast experiments over 2010 – 2023, initialized in four different seasons. We compare the performance of forecasts initialized from two sets of data assimilation experiments, with and without atmospheric wind nudging. The additional relaxation of the large-scale atmospheric circulation to the ERA5 reanalysis data for the initialization leads to reasonable atmospheric forecast skill on weather timescales: Despite the simple technique, the coarse resolution compared to NWP systems, and the limited optimization efforts, 10-day forecasts of the 500 hPa geopotential height are about as skillful as the best performing NWP forecasts were about 10 –15 years ago. Among other aspects, this leads to significantly improved subseasonal-to-seasonal sea-ice concentration and thickness forecasts.

 

Athanase, M., Schwager, M., Streffing, J., Andrés-Martínez, M., Loza, S., and Goessling, H.: Impact of the atmospheric circulation on the Arctic snow cover and ice thickness variability , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5836, https://doi.org/10.5194/egusphere-egu22-5836, 2022.

Losa, S. N., Mu, L., Athanase, M., Streffing, J., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H. F.: Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14227, https://doi.org/10.5194/egusphere-egu23-14227, 2023.

Mu, L. , Nerger, L. , Streffing, J. , Tang, Q. , Niraula, B. , Zampieri, L., Loza, S. N. and Goessling, H. F. (2022): Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System , Journal of Advances in Modeling Earth Systems, 14 (12) . doi: 10.1029/2022ms003176

Sánchez-Benítez, A. , Goessling, H. , Pithan, F. , Semmler, T. and Jung, T. (2022): The July 2019 European Heat Wave in a Warmer Climate: Storyline Scenarios with a Coupled Model Using Spectral Nudging , Journal of Climate, 35 (8), pp. 2373-2390 . doi: 10.1175/JCLI-D-21-0573.1

How to cite: Loza, S., Athanase, M., Mu, L., Streffing, J., Sánchez-Benítez, A., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H.: Improving daily-to-seasonal sea ice forecasts of the AWI coupled prediction system with sea-ice and ocean data assimilation and atmospheric large-scale wind nudging., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18260, https://doi.org/10.5194/egusphere-egu24-18260, 2024.

X5.29
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EGU24-19883
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ECS
Boreal summer extratropical intraseasonal waves over the Eurasian continent and real-time monitoring metrics
(withdrawn after no-show)
Tao Zhu
X5.30
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EGU24-19908
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ECS
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Highlight
Zhou Jiahui and Fei Liu

Accurate subseasonal forecast of East Asian summer monsoon precipitation (EASM) is pivotal, impacting the livelihoods of billions. However, the proficiency of state-of-the-art subseasonal-to-seasonal (S2S) models in forecasting precipitation remains constrained. We developed a convolutional neural network regression model, harnessing the more reliably predicted atmospheric variables from dynamic models to enhance their forecast skills for precipitation. The outcomes of the CNN model are promising: a 12% increase in accuracy and a 10% reduction in RMSE for precipitation forecast at the lead time of one week. The predictive skill of dynamic models for atmospheric variables shows a significant correlation with the performance of the CNN model. Ablation experiments on various predictors reveal that xx is the most influential factor affecting the CNN model's performance.

How to cite: Jiahui, Z. and Liu, F.: Improving sub-seasonal forecasting of East Asian monsoon precipitation with deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19908, https://doi.org/10.5194/egusphere-egu24-19908, 2024.

X5.31
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EGU24-20725
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ECS
Michael Secor, Yueyue Yu, Jie Sun, Ming Cai, and Xinyue Luo

The year-to-year varying annual evolutions of the stratospheric polar vortex (SPV) have an important downward impact on the weather and climate from winter to summer and thus potential implications for seasonal forecasts. This study constructs a parametric elliptic orbit model for capturing the annual evolutions of mass-weighted zonal momentum at 60° N (MU) and total air mass above the isentropic surface of 400 K (M) over the latitude band of 60–90° N from 1 July 1979 to 30 June 2022. The elliptic orbit model naturally connects two time series of a nonlinear oscillator. As a result, the observed coupling relationship between MU and M associated with SPV as well as its interannual variations can be well reconstructed by a limited number of parameters of the elliptic orbit model. The findings of this study may pave a new way for short-time climate forecasts of the annual evolutions of SPV, including its temporal evolutions over winter seasons as well as the spring and fall seasons, and timings of the sudden stratospheric warming events by constructing its elliptic orbit in advance.

How to cite: Secor, M., Yu, Y., Sun, J., Cai, M., and Luo, X.: A Parametric Model of Elliptic Orbits for Annual Evolutions of Northern Hemisphere Stratospheric Polar Vortex and Their Interannual Variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20725, https://doi.org/10.5194/egusphere-egu24-20725, 2024.

Posters virtual: Tue, 16 Apr, 14:00–15:45 | vHall X5

Display time: Tue, 16 Apr, 08:30–Tue, 16 Apr, 18:00
Chairperson: Frederic Vitart
vX5.1
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EGU24-14701
Michael Sprenger, Dominik Büeler, and Heini Wernli

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to sub-seasonal numerical weather prediction might therefore hinder exploiting the potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 21 years (2000 – 2020) of sub-seasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. Overall, the reforecasts reproduce the climatology of cyclone frequency and life-cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis (locally and upstream), size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest deficit of cyclones of up to 15% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones are too deep in both ocean basins during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime and differences between the native spatial resolutions of the reforecasts and the verification dataset might explain this intensity bias in some cases, but there are likely further so far unidentified processes involved. While the patterns of cyclone frequency and life cycle biases often appear in lead time weeks 1 and 2, their magnitudes typically grow further at sub-seasonal lead times and, in some cases, saturate in weeks 5 and 6 only. Most of the dynamical sources of these biases thus likely appear in the early medium range, but biases on longer timescales probably contribute to their further increase with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in sub-seasonal weather forecasts. Given the considerable biases during summer, when sub-seasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.

How to cite: Sprenger, M., Büeler, D., and Wernli, H.: Northern Hemisphere extratropical cyclone biases in ECMWF sub-seasonal forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14701, https://doi.org/10.5194/egusphere-egu24-14701, 2024.