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.

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.

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.

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.

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.

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.

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.

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.

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.

“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.