CL4.10 | Challenges in climate prediction: multiple time-scales and the Earth system dimensions
Challenges in climate prediction: multiple time-scales and the Earth system dimensions
Co-organized by BG9/NP5/OS1
Convener: Andrea Alessandri | Co-conveners: Yoshimitsu Chikamoto, Tatiana Ilyina, June-Yi Lee, Xiaosong Yang
Orals
| Wed, 17 Apr, 08:30–10:15 (CEST)
 
Room 0.49/50
Posters on site
| Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30
 
Hall X5
Posters virtual
| Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00
 
vHall X5
Orals |
Wed, 08:30
Wed, 10:45
Wed, 14:00
One of the big challenges in Earth system science consists in providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. The resulting data have the potential to be translated into climate information leading to a better assessment of global and regional climate-related risks.
The main goals of the session is (i) to identify gaps in current climate prediction methods and (ii) to report and evaluate the latest progress in climate forecasting on subseasonal-to-decadal and longer timescales. This will include presentations and discussions of the developments in predictions for the different time horizons from dynamical ensemble and statistical/empirical forecast systems, as well as the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, exploration of artificial-intelligence methods, etc.
Following the new WCRP strategic plan for 2019-2029, prediction enhancements are solicited from contributions embracing climate forecasting from an Earth system science perspective. This includes the study of coupled processes between atmosphere, land, ocean, and sea-ice components, as well as the impacts of coupling and feedbacks in physical, hydrological, chemical, biological, and human dimensions. Contributions are also sought on initialization methods that optimally use observations from different Earth system components, on assessing and mitigating the impacts of model errors on skill, and on ensemble methods.
We also encourage contributions on the use of climate predictions for climate impact assessment, demonstrations of end-user value for climate risk applications and climate-change adaptation and the development of early warning systems.
A special focus will be put on the use of operational climate predictions (C3S, NMME, S2S), results from the CMIP6 decadal prediction experiments, and climate-prediction research and application projects.
An increasingly important aspect for climate forecast's applications is the use of most appropriate downscaling methods, based on dynamical, statistical, artificial-intelligence approaches or their combination, that are needed to generate time series and fields with an appropriate spatial or temporal resolution. This is extensively considered in the session, which therefore brings together scientists from all geoscientific disciplines working on the prediction and application problems.

Orals: Wed, 17 Apr | Room 0.49/50

Chairpersons: June-Yi Lee, Tatiana Ilyina, Andrea Alessandri
08:30–08:31
08:31–08:41
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EGU24-15488
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CL4.10
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ECS
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solicited
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On-site presentation
Filippa Fransner, Marie-Lou Bachèlery, Shunya Koseki, David Rivas, Noel Keenlyside, Nicolas Barrier, Matthieu Lengaigne, and Olivier Maury

The variability and predictability of the Tropical Atlantic primary productivity remains little explored on interannual-to-decadal time scales. Here, we  present the results of two studies, in which find a decadal scale variability in phytoplankton abundance that can be predicted three years ahead. The predictions are made with NorCPM, which is a fully coupled climate prediction model with ocean biogeochemistry that assimilates temperature and salinity to reconstruct past variability. From these reconstructions, predictions are initialized that are run freely ten years ahead. We find that the predictability is a result of nutrient pulses that are advected with the southern branch of the South Equatorial Current from the most southern part of the Atlantic, and that then get caught in the Equatorial undercurrent before they reach the surface in the Tropical Atlantic Ocean. A more detailed analysis is being done in order to pinpoint the underlying mechanisms in a forced ocean model, where we find a link to the Pan-Atlantic decadal oscillation.

How to cite: Fransner, F., Bachèlery, M.-L., Koseki, S., Rivas, D., Keenlyside, N., Barrier, N., Lengaigne, M., and Maury, O.: Phytoplankton predictability in the Tropical Atlantic - triggered by nutrient pulses from the South, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15488, https://doi.org/10.5194/egusphere-egu24-15488, 2024.

08:41–08:45
08:45–08:55
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EGU24-16456
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CL4.10
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Virtual presentation
Hongmei Li, Tatiana Ilyina, István Dunkl, Aaron Spring, Sebastian Brune, Wolfgang A. Müller, Raffaele Bernardello, Laurent Bopp, Pierre Friedlingstein, William J. Merryfield, Juliette Mignot, Michael O'Sullivan, Reinel Sospedra-Alfonso, Etienne Tourigny, and Michio Watanabe

The imperative to comprehend and forecast global carbon cycle variations in response to climate variability and change over recent decades and in the near future underscores its critical role in informing the global stocktaking process. Our study investigates CO2 fluxes and atmospheric CO2 growth through ensemble decadal prediction simulations using Earth System Models (ESMs) driven by CO2 emissions with an interactive carbon cycle. These prediction systems provide valuable insights into the global carbon cycle and, therefore, the variations in atmospheric CO2. Assimilative ESMs with interactive carbon cycles effectively reconstruct and predict atmospheric CO2 and carbon sink evolution. The emission-driven prediction systems maintain comparable skills to conventional concentration-driven methods, predicting 2-year accuracy for air-land CO2 fluxes and atmospheric CO2 growth, with air-sea CO2 fluxes exhibiting higher skill for up to 5 years. Our multi-model predictions for the next year, along with assimilation reconstructions, for the first time contribute to the Global Carbon Budget 2023 assessment. We plan regular updates and the involvement of more ESMs in future assessments. Ongoing efforts include implementing seasonal-scale predictions for skill improvement. Furthermore, we assess uncertainty contributions to CO2 flux and growth predictions, revealing the comparable impacts of internal climate variability and diverse model responses, particularly at a lead time of 1-2 years. Notably, the effect of CO2 emission forcing rivals internal variability at a 1-year lead time. Large uncertainties in CO2 responses to initial states of ENSO are observed, stemming from both model responses and internal variability. The challenge lies in addressing the scarcity and uncertainty of data for initialization and obtaining precise external forcings to enhance the reliability of predictions. The further advancements involve not only addressing comprehensive bias correction but also implementing statistical methods to enhance dynamical predictions.

How to cite: Li, H., Ilyina, T., Dunkl, I., Spring, A., Brune, S., Müller, W. A., Bernardello, R., Bopp, L., Friedlingstein, P., Merryfield, W. J., Mignot, J., O'Sullivan, M., Sospedra-Alfonso, R., Tourigny, E., and Watanabe, M.: Advancements and Challenges in Assessing and Predicting the Global Carbon Cycle Variations Using Earth System Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16456, https://doi.org/10.5194/egusphere-egu24-16456, 2024.

08:55–09:05
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EGU24-3134
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CL4.10
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On-site presentation
André Düsterhus and Sebastian Brune

Climate predictions focus regularly on the predictability of single values, like means or extremes. While these information offer important insight into the quality of a prediction system, some stakeholders might be interested in the predictability of the full underlying distribution. These allow beside evaluating the amplitude of an extreme also to estimate their frequency. Especially on decadal time scales, where we verify multiple lead years at a time, the prediction quality of full distributions may offer in some applications important additional value.

In this study we investigate the predictability of the seasonal daily 2m-temperature on time scales of up to ten lead years within the MPI-ESM decadal prediction system. We compare yearly initialised hindcast simulations from 1960 onwards against estimates for climatology and uninitialised historical simulations. To verify the predictions we demonstrate a novel approach based on the non-parametric comparison of distributions with the integrated quadratic distance (IQD).

We show that the initialised prediction system has advantages in particular in the North Atlantic area and allow so to make reliable predictions for the whole temperature distribution for two to ten years ahead. It also demonstrates that the capability of initialised climate predictions to predict the temperature distribution depends on the season. Finally, we will also discuss potential opportunities and pitfalls of such approaches.

How to cite: Düsterhus, A. and Brune, S.: Decadal predictability of seasonal temperature distriubutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3134, https://doi.org/10.5194/egusphere-egu24-3134, 2024.

09:05–09:15
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EGU24-3274
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CL4.10
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ECS
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On-site presentation
Yong-Yub Kim, June-Yi Lee, Axel Timmermann, Yoshimitsu Chikamoto, Sun-Seon Lee, Eun Young Kwon, Wonsun Park, Nahid A. Hasan, Ingo Bethke, Filippa Fransner, Alexia Karwat, and Abhinav R.Subrahmanian

Here we present a new seasonal-to-multiyear earth system prediction system which is based on the Community Earth System Model version 2 (CESM2) in 1° horizontal resolution. A 20- member ensemble of temperature and salinity anomaly assimilation runs serves as the initial condition for 5-year forecasts. Initialized on January 1st of every year, the CESM2 predictions exhibit only weak climate drift and coupling shocks, allowing us to identify sources of multiyear predictability. To differentiate the effects of external forcing and natural climate variability on longer-term predictability, we analyze anomalies calculated relative to the 50-member ensemble mean of the CESM2 large ensemble. In this presentation we will quantify the extent to which marine biogeochemical variables are constrained by physical conditions. This analysis provides crucial insights into error growth of phytoplankton and the resulting limitations for multiyear predictability.

How to cite: Kim, Y.-Y., Lee, J.-Y., Timmermann, A., Chikamoto, Y., Lee, S.-S., Kwon, E. Y., Park, W., A. Hasan, N., Bethke, I., Fransner, F., Karwat, A., and R.Subrahmanian, A.:  A Multi-year Climate Prediction System Based on CESM2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3274, https://doi.org/10.5194/egusphere-egu24-3274, 2024.

09:15–09:25
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EGU24-1120
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CL4.10
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ECS
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On-site presentation
Emanuele Di Carlo, Andrea Alessandri, Fransje van Oorschot, Annalisa Cherchi, Susanna Corti, Giampaolo Balsamo, Souhail Boussetta, and Timothy Stockdale

Vegetation is a highly dynamic component of the Earth System. Vegetation plays a significant role in influencing the general circulation of the atmosphere through various processes. It controls land surface roughness, albedo, evapotranspiration and sensible heat exchanges among other effects. Understanding the interactions between vegetation and the atmosphere is crucial for predicting climate and weather patterns. This study explores how better representation of vegetation dynamics affects climate predictions at decadal timescale and how surface characteristics linked to vegetation affect the general circulation at local, regional and global scales. We used the latest satellite datasets of vegetation characteristics and developed a new and improved parameterization for effective vegetation cover. We implemented the new parameterization in the land surface scheme Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL), which is embedded in the EC-Earth model. 

The enhancement of the model's vegetation variability significantly improves the prediction skill of the model for several parameters, encompassing both surface and upper-level elements such as 2-metre temperature, zonal wind at 850 hPa and mean sea level pressure. The improvement is particularly evident over Euro-Asian Boreal forests. In particular, a large-scale effect on circulation emerges from the region with the most 2-metre temperature improvement, over Eastern Europe. 

The incorporation of an effective vegetation cover also introduces heightened realism in surface roughness and albedo variability. This, in turn, leads to a more accurate representation of the land-atmosphere interactions. The regression analysis of surface roughness and albedo with 2-metre temperature, mean sea level pressure and wind (both at surface and 850 hPa) reveals a robust relationship across the entire northern hemisphere. This relation between the surface and the atmosphere is notably absent in the standard configuration model, where the vegetation is prescribed by a dynamical vegetation module.

These findings underscore the substantial impact of vegetation cover on the general circulation, particularly in the northern hemisphere, and emphasise its crucial role in improving prediction skills. Furthermore, they highlight the challenges faced by modern earth system models in accurately representing several processes connecting the land surface and the atmosphere.

How to cite: Di Carlo, E., Alessandri, A., van Oorschot, F., Cherchi, A., Corti, S., Balsamo, G., Boussetta, S., and Stockdale, T.: Effects of the realistic vegetation cover representation on the large-scale circulation and predictions at decadal time scale., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1120, https://doi.org/10.5194/egusphere-egu24-1120, 2024.

09:25–09:35
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EGU24-16402
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CL4.10
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On-site presentation
Matteo Zampieri, Karumuri Ashok, Andrea Toreti, Davide Bavera, and Ibrahim Hoteit

Compound climate anomalies pose escalating risks in the context of climate change, with anomalous heat and drought presenting significant stressors to both ecosystems and society. The simultaneous occurrence of these events can be influenced by land surface processes such as the soil moisture – air temperature coupling. However, the long-term variability of this coupling remains unexplored. Here, using a combination of observations and multi-model ensemble forecasts dating back to the 1980s, we examine the global land exposure to higher than normal probabilities of concurrent hot temperature anomalies and drought on a monthly scale. Our findings confirm that drought substantially shapes the spatial distribution of heat-related risks on a global scale, offering a crucial predictive factor for these combined events. Traditionally, defining heat anomalies for non-adaptive systems involves fixed reference temperature thresholds. Using this method, our analysis reveals that the portion of global land experiencing drought-conditioned hot temperature anomalies has tripled in less than three decades. Surprisingly, the global level of spatial coupling appears to be declining. However, this outcome heavily depends on the specific definition of heat risk employed. By employing a time-dependent temperature threshold that considers changes in the climate's mean state due to both global warming and natural variability, a different picture emerges. Using the latter method, the level of spatial coupling demonstrates persistence and stability. Importantly, this method is better suited to assessing risks for adaptive systems and is more consistent with our current understanding of the underlying processes. Our study strongly advocates for tailoring hazard definitions to the specific processes and systems under investigation. Additionally, it underscores the pivotal role of operational sub-seasonal and seasonal forecasts in early warning systems, crucial for societal adaptation in the face of global warming.

How to cite: Zampieri, M., Ashok, K., Toreti, A., Bavera, D., and Hoteit, I.: On the stationarity of the global spatial dependency of heat risk on drought., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16402, https://doi.org/10.5194/egusphere-egu24-16402, 2024.

09:35–09:45
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EGU24-5484
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CL4.10
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ECS
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On-site presentation
Gabriel Narváez and Constantin Ardilouze

Accurate seasonal streamflow forecasts (SSF) are crucial for disaster prevention, water management, agriculture, and hydropower generation. A global approach becomes imperative in regions lacking forecast systems. The Météo-France seasonal prediction system (MF System 8 - SYS8), contributing to Copernicus Climate Change Services (C3S), employs a fully coupled Atmosphere-Ocean General Circulation Model (AOGCM) with an advanced river routing component (CTRIP) interacting with the ISBA land-surface scheme. This study evaluates the skill of the SYS8 global SSF through hindcast river discharges. This work is part of the European project CERISE, which aims to enhance the C3S seasonal forecast portfolio by improving land initialisation methodologies.

SYS8 derives land initial conditions from a historical initialisation run where land (such as soil moisture and river discharges) is weakly constrained, contrasting with the atmosphere and ocean counterparts, which are nudged to the ERA5 and GLORYS re-analysis. This study improves the initialisation run by relaxing soil moisture to fields reconstructed from an offline land simulation.  Daily streamflow ensemble hindcasts of 25 members are generated in a  0.5° grid, with a lead time of up to 4 months initialised on the first day of May and August between 1993-2017. May and August initialisations allow forecasting of summer (JJA) and fall (SON) seasons. Actual forecast skill is assessed against streamflow observations in 1608 monitored basins worldwide (with areas > 3000 km2) using deterministic and probabilistic metrics. The classical Ensemble Streamflow Prediction approach (ESP) serves as a benchmark to evaluate the control SYS8 SSF skill and the additional skill of soil moisture nudging.

Globally, hindcast skill improves with enhanced land-surface initial conditions, especially during summer. Lower latitudes (<50°N) exhibit increased skill, while higher and cooler latitudes may lead to overestimated streamflow magnitude and oscillation amplitude due to soil moisture constraints. Local skill degradation will be discussed. Still, positive results support ongoing efforts to enhance land initialisation through a global land data assimilation system.

How to cite: Narváez, G. and Ardilouze, C.: Global Streamflow Seasonal Forecasts: Impact of soil moisture initialization in a novel two-way AOGCM-River Routing coupling approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5484, https://doi.org/10.5194/egusphere-egu24-5484, 2024.

09:45–09:55
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EGU24-7918
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CL4.10
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ECS
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On-site presentation
Fousiya Thottuvilampil Shahulhameed, Jonathan Beuvier, and Damien Specq

Research and development activities around the current Météo-France operational seasonal forecasting system (System 8) are underway to upgrade it to the next version (System 9), along with efforts to improve the initialization of its components. Among these components, sea ice is particularly challenging to initialize. At present, a coupled-nudged initialisation strategy, based on a high-resolution configuration of the CNRM-CM6 climate model, is employed to initialise the System 8, except for the sea-ice. In order to get initial states of sea ice that are consistent with the forecasting model, our procedure consists in making a preliminary continuous run where the ocean and sea ice models are integrated in stand-alone mode, with forcing at the surface from an atmosphere reanalysis.

However, in the current operational System 8 – based on the NEMO 3.6 ocean model and the GELATO sea ice model – the initial states of sea ice generated with this procedure are not fully realistic. Results show that the sea ice thickness over the Arctic region in the System 8 initial states is underestimated compared to the reference data. Numerous sensitivity experiments were carried out with the current NEMOv3.6-GELATO system, leading to some minor improvements. Thus, an upgraded version of the ocean model (NEMO version 4.2) coupled to a new sea-ice component (SI3) has been tested (in stand-alone mode, not coupled to the atmosphere) to see if the use of more recent versions of ocean and sea-ice models leads to some improvements in the Arctic sea ice representation. The results are encouraging as the representation of sea ice variables in the Arctic is improved compared to the old version.

This incites our team to foresee that System 9 will indeed incorporate the NEMO4.2 and SI3 models, and that the same initialization procedure as before (using these new models) will provide sea-ice initial states closer to those observed.

 

 

How to cite: Thottuvilampil Shahulhameed, F., Beuvier, J., and Specq, D.: Generation of sea ice initial conditions for the next Météo-France seasonal forecasting system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7918, https://doi.org/10.5194/egusphere-egu24-7918, 2024.

09:55–10:05
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EGU24-18766
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CL4.10
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On-site presentation
Stefano Materia, Constantin Ardilouze, and Ángel G. Muñoz

While subseasonal forecasts often exhibit limited skill across mid-latitudes, occasional improvements are observed in specific locations during certain periods, known as "windows of opportunity." Understanding the causal factors behind these windows is complex due to the diverse and interdependent nature of predictors, their spatial and temporal variability, and the challenges in establishing causality relationships. 

Traditional lagged-correlations methods provide only a partial view, lacking insights into causality. Based on previous work on the role of land surface processes, multi-model subseasonal model skill assessment and the use of causality metrics in predictions across timescales (e.g. Ardilouze et al., 2020, 2021; Materia et al 2020, 2022; Muñoz et al., 2023), here we propose an approach based on the Liang-Kleeman information flow, allowing the assessment of statistically significant causal links across various lead times.

Applied to reforecast and reanalysis data, our framework successfully identifies significant predictability drivers -involving sea-surface temperatures, atmospheric circulation and remote and local land-surface processes-, revealing their interference (interplay), evolving patterns and prevalence from seasonal to subseasonal scales. 

Furthermore, the comparison between reanalysis and reforecast results aids in assessing the capability of models to capture these causality features, suggesting additional ways to conduct model diagnostics. We illustrate here the theoretical background by showcasing the causal factors influencing a window of opportunity identified from a multimodel subseasonal reforecast.

 

References

Ardilouze, C., Materia, S., Batté, L., Benassi, M., & Prodhomme, C. (2020). Precipitation response to extreme soil moisture conditions over the Mediterranean. Climate Dynamics, 1, 1–16. https://doi.org/10.1007/S00382-020-05519-5/TABLES/2

Ardilouze, C., Specq, D., Batté, L., & Cassou, C. (2021). Flow dependence of wintertime subseasonal prediction skill over Europe. Weather and Climate Dynamics, 2(4), 1033-1049. https://doi.org/10.5194/wcd-2-1033-2021 

Materia, S., Muñoz, Á. G., Álvarez-Castro, M. C., Mason, S. J., Vitart, F., & Gualdi, S. (2020). Multi-model subseasonal forecasts of spring cold spells: potential value for the hazelnut agribusiness. Weather and Forecasting. https://doi.org/10.1175/waf-d-19-0086.1 

Materia, S., Ardilouze, C., Prodhomme, C., & et al. (2022). Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime. Climate Dynamics, 58, 1943–1963. https://doi.org/10.1007/s00382-021-05815-8

Muñoz, Á. G., Doblas-Reyes, F., DiSera, L., Donat, M., González-Reviriego, N., Soret, A., Terrado, M., & Torralba, V. (2023). Hunting for “Windows of Opportunity” in Forecasts Across Timescales? Cross it. EGUGA, EGU-15594. https://doi.org/10.5194/EGUSPHERE-EGU23-15594 

How to cite: Materia, S., Ardilouze, C., and Muñoz, Á. G.: Deciphering Prediction Windows of Opportunity: A Cross Time-Scale Causality Framework  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18766, https://doi.org/10.5194/egusphere-egu24-18766, 2024.

10:05–10:15
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EGU24-6970
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CL4.10
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Virtual presentation
Yosuke Fujii, Ichiro Ishikawa, and Shoji Hirahara

“Synergistic Observing Network for Ocean Prediction (SynObs)” is a project of the United Nations Decade of Ocean Science for Sustainable Development. SynObs aims to find the way to extract maximum benefits from the combination among various ocean observation platforms, including satellite and in situ observations. A major ongoing effort led by SynObs is the international multi-system OSEs/OSSEs. In this activity, various operational centers and research institutes participating will conduct Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs) using a variety of ocean or coupled ocean-atmosphere prediction systems with the common setting to evaluate ocean observation impacts which are robust for most ocean prediction systems. More than 10 ocean prediction systems with various model resolutions and diverse data assimilation methods are used in this activity, and impacts of various observation data, including satellite sea surface temperature and height, Argo floats, and tropical mooring buoys, will be evaluated.

The activity is divided into two parts. The first part is the ocean prediction OSEs. In this part, we run several ocean reanalysis runs assimilating different observation datasets at least for 2020 (preferably extended to 2022), and conduct 10-day ocean predictions from the reanalysis fields of every 5 days. Three-dimensional oceanic temperature, salinity, and velocity fields with the 1/10-degree resolution, and several two-dimensional diagnostics with the 1/4-degree resolution will be analyzed. The second part is the subseasonal-to-seasonal (S2S) OSEs. Here, we run several ocean reanalysis runs for 2003-2022, and conduct 1-month (4-month) coupled predictions from the reanalysis fields of every month (twice a year). We will evaluate the impacts of ocean observation data on the long-term reanalysis and S2S predictions using the coupled prediction systems. We also plan to conduct OSSEs using multiple ocean prediction systems in order to assess newly emerging or future observing systems, such as SWOT, ocean gliders, etc. 

We are currently conducting the S2S OSEs using a Japanese operational global ocean data assimilation and coupled prediction system for S2S forecasts. We are now conducting OSEs assimilating no in situ observations and withholding temperature and salinity profiles observed by Argo floats. In the presentation, we will introduce the results and the perspective of the collaborative activities.

How to cite: Fujii, Y., Ishikawa, I., and Hirahara, S.: Early results of OSEs conducted for the SynObs international multi-system OSE effort using an Japanese operational system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6970, https://doi.org/10.5194/egusphere-egu24-6970, 2024.

Posters on site: Wed, 17 Apr, 10:45–12:30 | Hall X5

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 12:30
Chairpersons: Tatiana Ilyina, June-Yi Lee, Andrea Alessandri
X5.196
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EGU24-995
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CL4.10
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ECS
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Highlight
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Thomas Dal Monte, Andrea Alessandri, Annalisa Cherchi, and Marco Gaetani

Droughts are characterized by prolonged and severe deficits in precipitation that can extend in time, over a season, a year or more. They are confined to specific climatic zones but can manifest in both high and low rainfall regions. Contributing factors include temperatures, strong winds, low relative humidity, and the characteristics of rainfall. Drought events are characterized through indices that can be categorized based on the specific impacts they are associated with, such as meteorological, agricultural, or hydrological effects. Using such indices for drought characterization serves multiple purposes, including detection, assessment, and representation of drought conditions within a particular region. Seasonal precipitatio is essential for social and economic development and activities, hence. Reliable seasonal forecasts, especially regarding extreme precipitation events, become crucial for sectors like agriculture and insurance. Europe, and in particular the Mediterranean region, is expected to be considerably affected under climate change. The northern regions are anticipated to exhibit higher variability, increasing the risk of floods, while the southern areas may face decreased rainfall, prolonged dry spells, and intensified evaporation, potentially leading to more frequent drought occurrences.

This research aims to evaluate the prediction skill for extreme drought events at the seasonal time-scale using the SPI and SPEI indices over the EURO-Mediterranean area. The use of SPEI also takes into account the effect of temperature on the water balance, given by the calculation of potential evapotranspiration within it, which can be crucial in a context of global warming. We consider the seasonal forecasts provided by the Copernicus multi-system and we use the Brier Skill Score metric for the assessment of the performance. The objective is to understand potential predictability factors of these indices within the study area. The results show a positive performance for most of the areas examined, between 60 and 80 percent of the entire area for both indices. This led us to investigate possible optimization strategies to increase the skill in the area.

Using the multi-model approach we optimize the prediction skill obtaining considerable performance in forecasting drought conditions. Different multi-model strategies are compared, including the selection or aggregation of available forecasts to achieve the best overall performance in the area. We show that multi-model optimization can indeed provide valuable probabilistic predictions of seasonal drought events in many areas of the Euro-Mediterranean that could be useful for the decision-making process of the affected end users.

How to cite: Dal Monte, T., Alessandri, A., Cherchi, A., and Gaetani, M.: Assessing the predictability of Euro-Mediterranean droughts through seasonal forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-995, https://doi.org/10.5194/egusphere-egu24-995, 2024.

X5.197
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EGU24-1356
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CL4.10
Hu Lei, Duan Wansuo, and Feng Rong

    Considering the limitations of current ensemble forecasting initial perturbation methods in describing the interactions among various spheres of the Earth system, this study proposes a new method called the Coupled Conditional Nonlinear Optimal Perturbation (C-CNOP), which incorporates the effect of muti-sphere coupling uncertainties. By applying the C-CNOP method to ensemble forecasting of El Niño-Southern Oscillation (ENSO), which is a typical tropical Pacific ocean-atmosphere coupling phenomenon, the study demonstrates that the C-CNOP method can generate coupled initial perturbations (CPs) that much appropriately consider the effect of initial uncertainties of coupled ocean-atmosphere system. It is revealed that the CPs significantly improve ENSO ensemble-mean forecast skill of the time variability of Niño3.4 sea surface temperature anomalies (SSTAs) and the spatial variability of ENSO mature-phase SSTA. Particularly, despite the weakest ocean-atmosphere coupling in the tropical Pacific during the boreal spring and summer, CPs can still capture the uncertainties of the weak coupling when predicting from these seasons, which suppresses the rapidly growing of ENSO prediction errors caused by the strongest ocean-atmosphere coupling instability during these seasons, and thus effectively extends the lead time of ENSO forecasting. Hence, the C-CNOP method is an approach to produce ensemble forecasting initial perturbation that can consider the effect of initial coupling uncertainties much appropriately, which is expected to play an important role in future Earth climate system predictions with further research.

How to cite: Lei, H., Wansuo, D., and Rong, F.: Coupled Conditional Nonlinear Optimal Perturbations and its applications to ENSO ensemble forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1356, https://doi.org/10.5194/egusphere-egu24-1356, 2024.

X5.198
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EGU24-1407
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CL4.10
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ECS
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Highlight
Dong Tong and Dahai Liu

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable regions such as coastal areas of China. The response of vegetation growth to extreme climates and the time lag-accumulation relationship still require further exploration. We characterize the vegetation growth status by solar-induced chlorophyll fluorescence (SIF), analyzed the vegetation dynamic in coastal areas of China from 2000 to 2019, also explored the spatiotemporal pattern of vegetation, and assessed the response of vegetation to extreme climate in term of time lag-accumulation by combines gradual analysis and abrupt analysis. The results showed that (1) Coastal areas of China were sensitive to global climate change, with extreme high temperatures and extreme precipitation increasing from 2000 to 2019, and the warming in high latitudes was greater than in low latitudes, while the increase in precipitation was concentrated in the southern regions, which are already water-rich. (2) The vegetation in coastal areas of China improved significantly, with gradual analysis showed that the vegetation improvement area accounts for 94.12% of the study area, and the abrupt analysis showed that the majority (69.78%) of the vegetation change types were "monotonic increase", with 11.77% showing "increase with negative break" and 9.48% "increases to decreases." (3) Significant lag-accumulation relationships were observed between vegetation and extreme climate in coastal areas of China, and the time-accumulation effects was stronger than time-lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2-3 months. These findings contribute to filling gaps in understanding the time lag-accumulation effects of extreme climates on vegetation in sensitive coastal regions. It provides a foundational basis for predicting the growth trend of coastal vegetation, environmental changes and ecosystem evolution, which is essential for a comprehensive assessment of coastal ecological security.

How to cite: Tong, D. and Liu, D.: Time Lag and Cumulative Effects of Extreme Climate on Coastal Vegetation in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1407, https://doi.org/10.5194/egusphere-egu24-1407, 2024.

X5.199
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EGU24-11948
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CL4.10
David Stainforth

Much effort goes into studying the causes of systematic errors in Earth System Models (ESMs). Reducing them is often seen as a high priority. Indeed, the development of Digital Twin approaches in climate research is founded on the idea that a sufficiently good model would be able to provide reliable and robust, conditional predictions of climate change (predictions conditioned on scenarios of future greenhouse gas emissions). Here, “reliable” encapsulates the idea that the predictions are suitable for use by society in anticipating and planning for future climate change, and “robust” encapsulates the idea that they are unlikely to change as the models are improved and developed.

Such an approach, however, begs the question, when is a model sufficiently realistic to be able to provide reliable, detailed predictions? A physical processes view of current ESMs suggests that they are not close to this level of realism while a nonlinear dynamical systems perspective raises questions over whether it will ever be possible to achieve such reliability for the types of regionally-specific, extrapolatory, climate change predictions that we may think society seeks.

Given this context, multi-model and perturbed-physics ensembles are often seen as a means to quantify uncertainty in conditional, climate change predictions (commonly referred to as “projections” in the scientific community). In the IPCC atlas (https://interactive-atlas.ipcc.ch/) the most easily accessible output is the multi-model median with the 10th, 25th, 75th and 90th percentiles of the multi-model distribution also prominent. This presentation in terms of probabilities implies that the probabilities themselves have meaning to the users of the data - most users are likely to take them as probabilities of different outcomes in reality. Unfortunately multi-model ensembles cannot be interpreted that way because we have no metric for the shape of model space nor any idea of how to explore it, so the ensemble members cannot be taken as independent samples of possible models. Perturbed-parameter ensembles work in a more defined space of possible model-versions but the shape of that space is also undefined and as a result the ensemble-based probabilities are again arbitrary.

When seeking the best possible information for society, multi-model and perturbed physics ensembles would benefit from targeting diversity: the greatest possible range of responses given a particular model structure. Model emulators could be used to systematise this process. Such an approach would provide more reliable information. It changes the question, however, from “when is a model sufficiently realistic” to “how unrealistic does a model have to be to be uninformative about extrapolatory future climatic behaviour?”

In this presentation I will discuss and elaborate on these issues.

 

References:

Stainforth, D., “What we do with what we’ve got”, Chapter 21 in “Predicting Our Climate Future: What we know, what we don’t know and what we can’t know”, Oxford University Press, 2023.

Stainforth, D.A. et al., Confidence, uncertainty and decision-support relevance in climate predictions, Phil.Trans.Roy.Soc., 2007.

Stainforth, D.A. et al., Issues in the interpretation of climate model ensembles to inform decisions, Phil.Trans.Roy.Soc., 2007.

How to cite: Stainforth, D.: What is the Target for Multi-Model and Perturbed-Physics Ensembles?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11948, https://doi.org/10.5194/egusphere-egu24-11948, 2024.

X5.200
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EGU24-12988
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CL4.10
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ECS
Qing Lin, Yanet Díaz Esteban, Fatemeh Heidari, Edgar Fabián Espitia Sarmiento, and Elena Xoplaki

Copernicus Climate Change Service provides seasonal forecasts for meteorological outlooks several months in advance and can provide indications of future climate risks on a global scale. Using downscaling techniques, global variables can be transferred to the high-resolution regional scale, allowing the information to be elaborated for extreme events detection and further implementing and coupling with hydrological models for regional hazard prediction, thus serving agriculture and energy, improving planning for tourism and other sectors.

In this study, we applied a new CNN-based architecture for temperature and precipitation downscaling. Both variables are downscaled from 1 degree to 1 arcminute to fulfill the requirements as an input to the hydrological models. The architecture implements an auto-encoder/decoder structure to extract the data relations. The system is trained with seasonal forecast inputs and observation data to establish the relation between both scales. The model is then evaluated with the validation period from the observation data to achieve the best performance, changing network structures and tuning different network hyper-parameters. The results show a good fit for the observation data on the monthly scale, providing enough details in the downscaling product. Finally, the best-performing networks for downscaling temperature and precipitation are selected and could be extended for further utilization.

How to cite: Lin, Q., Díaz Esteban, Y., Heidari, F., Espitia Sarmiento, E. F., and Xoplaki, E.: A CNN-based Downscaling Method of C3S Seasonal Forecast: Temperature and Precipitation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12988, https://doi.org/10.5194/egusphere-egu24-12988, 2024.

X5.201
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EGU24-13811
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CL4.10
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ECS
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solicited
Alexia Karwat, June-Yi Lee, Christian Franzke, and Yong-Yub Kim

Climate extremes, such as heat waves, heavy precipitation, intense storms, droughts, and wildfires, have become more frequent and severe in recent years as a consequence of human-induced climate change. Estimating the predictability and improving prediction of the frequency, duration, and intensity of these extremes on seasonal to multi-year timescales are crucial for proactive planning and adaptation. However, climate prediction at regional scales remains challenging due to the complexity of the climate system and limitations in model accuracy. Here we use a large ensemble of simulations, assimilations, and reforecasts using Community Earth System Model version 2 (CESM2) to assess the predictability of statistics of climate extremes with lead times of up to 5 years. We show that the frequency and duration of heat waves during local summer in specific regions are predictable up to several months to years. Sources of long-term predictability include not only external forcings but also modes of climate variability across time scales such as El Niño and Southern Oscillation, Pacific Decadal Variability, and Atlantic Multidecadal Variability. This study implies opportunities to deepen our scientific understanding of sources for long-term prediction of statistics of climate extremes and the potential for the associated disaster management.

How to cite: Karwat, A., Lee, J.-Y., Franzke, C., and Kim, Y.-Y.: Estimating Seasonal to Multi-year Predictability of Statistics of Climate Extremes using the CESM2-based Climate Prediction System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13811, https://doi.org/10.5194/egusphere-egu24-13811, 2024.

X5.202
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EGU24-16842
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CL4.10
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Highlight
June-Yi Lee, Yong-Yub Kim, and Jeongeun Yun

The demand for decision-relevant and evidence-based near-term climate information is increasing. This includes understanding and explaining the variability and changes in ecosystems to support disaster management and adaptation choices. As climate prediction from seasonal to decadal (S2D) expands to encompass Earth system dimensions, including terrestrial and marine ecosystems, it is crucial to deepen our scientific understanding of the long-term predictability sources for ecosystem variability and change. Here we explore to what extent terrestrial ecosystem variables are driven by large-scale - potentially predictable -climate modes of variability and external forcings or whether regional random environmental factors are dominant. To address these issues, we utilize a multi-year prediction system based on Community Earth System Model version 2 (CESM2).  The system consists of 50-member uninitialized historical simulations, 20-member ocean assimilations, and 20-member hindcast initiated from every January 1st integrating for 5 years from 1961 to 2021. The key variables assessed are surface temperature, precipitation, soil moisture, wildfire occurrence, and Gross Primary Productivity. Our results suggest that land surface processes and ecosystem variables over many parts of the globe can be potentially predictable 1 to 3 years ahead originating from anthropogenic forced signals and modes of climate variability, particularly El Nino and Southern Oscillation and Atlantic Multi-decadal variability. These global modes of climate variability shift regional temperature and precipitation patterns, leading to changes in soil moisture, wildfire occurrence, and terrestrial productivity.  

How to cite: Lee, J.-Y., Kim, Y.-Y., and Yun, J.: Exploring Sources of Multi-year Predictability of Terrestrial Ecosystem, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16842, https://doi.org/10.5194/egusphere-egu24-16842, 2024.

X5.203
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EGU24-15829
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CL4.10
Andrea Alessandri, Emanuele Di Carlo, Franco Catalano, Bart van den Hurk, Magdalena Alonso Balmaseda, Gianpaolo Balsamo, Souhail Boussetta, and Tim Stockdale

Vegetation is a relevant and highly dynamic component of the Earth system and its variability – at seasonal, interannual, decadal and longer timescales – modulates the coupling with the atmosphere by affecting surface variables such as roughness, albedo and evapotranspiration. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictability and prediction at the seasonal timescale. To this aim, the observational constraints from the latest generation satellite dataset of vegetation Leaf Area Index (LAI) have been integrated in the modeling, including a parameterization of the effective vegetation cover as a function of LAI. The improved vegetation representation is implemented in HTESSEL, which is the land surface model included in the seasonal forecasting (ECMWF SEAS5) systems used in this work.

Our results show that the realistic representation of vegetation variability has significant effects on both potential predictability and actual prediction skill at the seasonal time scale. It is shown a significant improvement of the skill in predicting boreal winter (December-January-February; DJF) 2m Temperature (T2M) at 1-month lead time especially over Euro-Asian boreal forests; the improvement is at least in part due to the more realistic representation of the interannual albedo variability that is related to the changes in vegetation shading over snow. Remarkably, from the region with the most considerable T2M improvement originates a large-scale ameliorating effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. The results indicate that the coupling with the improved vegetation might operate by amplifying locally the signal originating from the North Atlantic sector, therefore improving both potential predictability and actual skill over the region. Concurrently, the improved predictability and skill over the Euro-Asian forests appears to feedback to the large-scale circulation enhancing the representation of the circulation pattern and associated interannual anomalies.

How to cite: Alessandri, A., Di Carlo, E., Catalano, F., van den Hurk, B., Balmaseda, M. A., Balsamo, G., Boussetta, S., and Stockdale, T.: The role of realistic vegetation variability in climate predictability and prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15829, https://doi.org/10.5194/egusphere-egu24-15829, 2024.

Posters virtual: Wed, 17 Apr, 14:00–15:45 | vHall X5

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 18:00
Chairpersons: Yoshimitsu Chikamoto, Xiaosong Yang
vX5.20
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EGU24-6494
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CL4.10
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solicited
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Highlight
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Liwei Jia, Thomas Delworth, and Xiaosong Yang

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of humid heat extremes months in advance are essential for developing strategies to help communities build more resilience to the risks associated with extreme events. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0-1 months in advance in the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. The sea surface temperature (SST) at the tropical North Atlantic (TNA) basin is found as the primary driver of the prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in TNA favor the heat and moisture flux transported from the gulf of Mexico to the SEUS. This research demonstrates the role of slowly-varying sea surface conditions in modifying large-scale environments that contribute to the predictions of HHE in SEUS. The results are potentially applicable for developing early warning systems of HHE. 

How to cite: Jia, L., Delworth, T., and Yang, X.: Seasonal predictions of summer humid heat extremes in the southeastern United States driven by sea surface temperatures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6494, https://doi.org/10.5194/egusphere-egu24-6494, 2024.

vX5.21
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EGU24-4083
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CL4.10
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Highlight
Yoshimitsu Chikamoto, Hsin-I Chang, Simon Wang, Christopher Castro, Matthew LaPlante, Bayu Risanto, Xingying Huang, and Patrick Bunn

Predicting extreme precipitation events at subseasonal timescales is a critical challenge in Earth system science. This study advances climate predictability by employing dynamical downscaling, specifically focusing on convection-permitting modeling in the Southern Plains of the United States. Two contrasting extreme precipitation periods in Texas, the extremely dry May of 2011 and the abnormally wet May of 2015, were selected for analysis. To enhance subseasonal climate forecasting, we integrated the Weather Research and Forecasting (WRF) model with the decadal climate prediction system based on the Community Earth System Model (CESM). Evaluating the impact of dynamical downscaling on the prediction of extreme precipitation events, our study demonstrates how high-resolution downscaling enhances model skill in capturing these events. The findings hold the potential to significantly contribute to improving climate predictions and assessing regional climate-related risks, aligning with the session's goals.

How to cite: Chikamoto, Y., Chang, H.-I., Wang, S., Castro, C., LaPlante, M., Risanto, B., Huang, X., and Bunn, P.: Enhancing Subseasonal Climate Predictions through Dynamical Downscaling: A Case Study in the Southern Plains of the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4083, https://doi.org/10.5194/egusphere-egu24-4083, 2024.

vX5.22
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EGU24-11927
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CL4.10
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Highlight
Xiaosong Yang, Thomas Delworth, Liwei Jia, Nathaniel Johnson, Feiyu Lu, and Colleen McHugh

Solar energy plays a crucial role in the transition towards a sustainable and resilient energy future. One challenge that remains is the considerable year-to-year variation in solar energy resources. As a result, precise seasonal solar energy predictions become pivotal for effective energy system planning and operation.  This study employs GFDL’s GFDL’s Seamless System for Prediction and Earth System (SPEAR) to evaluate seasonal solar irradiance prediction across the United States.  Notably, SPEAR demonstrates high skill in predicting solar irradiance particularly in the western United States. Furthermore, we conduct an advanced predictability analysis to pinpoint the underlying physical drivers contributing to this skillful solar energy prediction.  The outcomes of this research offer substantial potential benefits to stakeholders within the energy sector by providing predictable information regarding year-to-year fluctuations in solar energy resources.

How to cite: Yang, X., Delworth, T., Jia, L., Johnson, N., Lu, F., and McHugh, C.: Seasonal prediction of solar energy resources in the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11927, https://doi.org/10.5194/egusphere-egu24-11927, 2024.