Decadal prediction represents a source of near-term climate information that has the potential to support climate-related decisions in key socio-economic sectors that are influenced by climate variability and change. While the research to illustrate the ability of decadal predictions in forecasting the varying climate conditions on a multi-annual timescale is rapidly evolving, the development of climate services based on such forecasts is still in its early stages. This study showcases the potential value of decadal predictions in the development of climate services. We summarize the lessons learnt from coproducing a forecast product that provides tailored and user-friendly information about multi-year drought conditions for the coming five years over global wheat harvesting regions. The interaction between the user and climate service provider that was established at an early stage and lasted throughout the forecast product development process proved fundamental to provide useful and ultimately actionable information to the stakeholders concerned with food production and security. This study also provides insights on the potential reasons behind the delayed entry of decadal predictions in the climate services discourse and practice, which were obtained from surveying climate scientists and discussing with decadal prediction experts.

How to cite: Solaraju-Murali, B., Bojovic, D., Gonzalez-Reviriego, N., Nicodemou, A., Terrado, M., Caron, L.-P., and Doblas-Reyes, F. J.: Better late than never: arrival of decadal predictions to the climate services arena, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8296, https://doi.org/10.5194/egusphere-egu23-8296, 2023.

Current global climate models typically fail to fully resolve mesoscale ocean features (with length scales on the order of 10 km), such as the western boundary currents, potentially limiting climate predictability over decadal timescales. This study incorporates high-resolution eddy-resolving ocean (HR: 0.1°) in a suite of CESM model experiments that capture these important mesoscale ocean features with increased fidelity. Compared with eddy-parametrized ocean (LR: 1°) experiments, HR experiments show more realistic climatology and variability of sea surface temperature (SST) over the western boundary currents and eddy-rich regions. In the North Atlantic, the inclusion of mesoscale ocean processes produces a more realistic Gulf Stream and improves both localized rainfall patterns and large-scale teleconnections. We identify enhanced decadal SST predictability in HR over the western North Atlantic, which can be explained by the strong vertical connectivity between SST and sub-surface ocean temperature. SST is better connected with slower processes deep down in HR, making SST more persistent (and predictable). Moreover, we detect a better representation of the air-sea interactions between SST and low-level atmosphere over the Gulf Stream, thus improving low-frequency rainfall variations and extremes over the Southeast US. The results further imply that high-resolution GCMs with increased ocean model resolution may be needed in future climate prediction systems.

How to cite: Zhang, W., Kirtman, B., Siqueira, L., and Clement, A.: Decadal Climate Variability and Predictability with a High-resolution Eddy-resolving Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7676, https://doi.org/10.5194/egusphere-egu23-7676, 2023.

We introduce a simple data assimilation approach applied to the coupled global climate model EC-Earth3.3.1, aiming at producing initial conditions for decadal climate hindcasts and forecasts. We rely on a small selection of assimilated variables, which are available in a consistent manner for a long period, providing good spatial coverage for large parts of the globe, that is sea-surface temperatures (SST) and near-surface winds.

Given that these variables play a role directly at or very close to the ocean-atmosphere interface, we assume a comparably strong cross-component impact of the data assimilation. Starting from five different free-running CMIP6-historical simulations in 1900, we first apply surface restoring in the model’s ocean component towards monthly means of HadISST1. After integrating this five-member ensemble with only assimilating SST for the period 1900-1949, we start additionally assimilating (nudging) 6-hourly near-surface winds (vorticity and divergence) taken from the ERA5 reanalysis from 1950 onwards. To mitigate the risk of model drifts after initializing the decadal predictions and to account for known instationary biases of the model, we assimilate anomalies of all variables that are calculated based on a 30-year running mean.

By assimilating near-surface data over several decades before entering the actual period targeted by the decadal hindcasts/forecasts for CMIP6-DCPP, we expect the coupled model to be able to ingest a significant share of observed climate evolution also in deeper ocean layers. This would then potentially serve as a source of predictive skill on interannual-to-decadal timescales.

We show that the presented assimilation approach is able to force the coupled model’s evolution well in phase with observed climate variability, positively affecting not only near-surface levels of the atmosphere and ocean but also deeper layers of the ocean and higher levels of the atmosphere as well as Arctic sea-ice variability. However, we also present certain problematic features of our approach. Two examples are significantly strengthened low-frequency variability of the AMOC and a wind bias resulting into generally reduced evaporation over ocean areas.

How to cite: Kruschke, T., Karami, M. P., Docquier, D., Schenk, F., Fuentes Franco, R., Willén, U., Wang, S., Wyser, K., Fladrich, U., and Koenigk, T.: A simple coupled assimilation approach for improved initialization of decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8750, https://doi.org/10.5194/egusphere-egu23-8750, 2023.

How to cite: Drews, A., Schmith, T., Yang, S., Olsen, S., Tian, T., Devilliers, M., Wang, Y., and Keenlyside, N.: Role of the subpolar North Atlantic region in skillful climate predictions for high northern latitudes: A pacemaker experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13375, https://doi.org/10.5194/egusphere-egu23-13375, 2023.

Climate extremes can impact societies in various ways: from nuances in daily lives to full humanitarian crises. Droughts  are usually slow onset extremes but can be highly disruptive and affect millions of people every year. Warm temperature extremes (e.g. heat waves) can exacerbate droughts and their impacts and trigger a faster drought evolution. Combined drought and heat waves can lead to devastating consequences. For example, 2022 was a very active year in terms of drought or combined drought and heat waves, affecting particularly hard several regions of the world (e.g. Europe, China, southern South America and East Africa). In a context of risk management and civil protection, the use of operationally available seasonal climate forecasts can provide actionable information to reduce the risks and the impacts of these events on societies with different levels of development and adaptive capacities. 

Within the Copernicus Emergency Management Service (CEMS), the European and Global Drought Observatories (EDO and GDO, respectively) provide real time drought and temperature extreme monitoring products freely available and displayed through two dedicated web services. Recent efforts have been targeting the optimal integration and use of multi-system forecasting products to enhance the early warning component of the service. This contribution provides an overview of first results in terms of  initial multi-model skill assessment of forecasts available through the Copernicus Climate Change Service (C3S). It also discusses future avenues to improve skill in regions with limited predictability, for example by applying physically-based sampling techniques.    

How to cite: Acosta Navarro, J. C. and Toreti, A.: Seasonal forecasting of drought and temperature extremes as part of the Copernicus Emergency Management Service (CEMS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5602, https://doi.org/10.5194/egusphere-egu23-5602, 2023.

The European North West shelf seas (NWS) support economic and environmental interests of several adjacent populous countries. Forecasts of physical marine variables on the NWS for upcoming months – an important decision-making timescale – would be useful for many industries. However, currently there is no operational seasonal forecasting product deemed sufficient for capturing the high variability associated with shallow, dynamic shelf waters. Here, we identify the dominant sources of seasonal predictability on the shelf and quantify the extent to which empirical persistence relationships can produce skilful seasonal forecasts of the NWS at the lowest level complexity. We find that relatively skilful forecasts of the typically well-mixed Winter and Spring seasons are achievable via persistence methods at a one-month lead time. In addition, incorporating observed climate modes of variability, such as the North Atlantic Oscillation (NAO), can significantly boost persistence for some locations and seasons, but this is dependent on the strength of the climate mode index. However, even where high persistence skill is demonstrated, there are sizeable regions exhibiting poor predictability and skilful persistence forecasts are typically limited to ≈ one-month lead times. Summer and Autumn forecasts are generally less skilful owing largely to the effects of seasonal stratification which emphasises the influence of atmospheric variability on sea surface conditions. As such, we also begin incorporating knowledge of future atmospheric conditions to forecasting strategies. We assess the ability of an existing global coupled ocean-atmosphere seasonal forecasting system to exceed persistence skill and highlight areas where additional downscaling efforts may be needed.

How to cite: Atkins, J., Tinker, J., Graham, J., Scaife, A., and Halloran, P.: Seasonal forecasting of the European North West Shelf: Quantifying the persistence of the physical marine environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6000, https://doi.org/10.5194/egusphere-egu23-6000, 2023.

For several years the Met Office has produced a seasonal outlook for the UK every month, which is issued to the UK Government and contingency planners.  The outlook gives predictions of the probability of having average, low, or high seasonal mean UK temperature and precipitation for the coming three-months.  In recent years, there has been increasing demand from sectors such as energy and insurance to include similar probabilistic predictions of UK wind speed: both for the seasonal mean and for measures of extreme winds such as storm numbers.  In this presentation we show the skill of the Met Office’s GloSea system in predicting seasonal (three-month average) UK mean wind and a measure of UK storminess throughout the year, and discuss the drivers of predictability.  Skill in predicting the UK mean wind speed and storminess peaks in winter (December–February), owing to predictability of the North Atlantic oscillation.  In summer (June–August), there is evidence that a significant proportion of variability in UK winds is driven by a Rossby wave train which the model has little skill in predicting. Nevertheless there are signs that the wave is potentially predictable and skill may be improved by reducing model errors.

How to cite: Lockwood, J., Stringer, N., Hodge, K., Bett, P., Knight, J., Smith, D., Scaife, A., Patterson, M., Dunstone, N., and Thornton, H.: Seasonal prediction of UK mean and extreme winds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13639, https://doi.org/10.5194/egusphere-egu23-13639, 2023.

Earth system predictability on decadal timescales can arise from both low frequency internal variability as well as from anthropogenically forced long-term changes. However, on these timescales, the chaotic nature of the climate system makes skillful predictions difficult to achieve even if we include information from climate change projections. Furthermore, it is difficult to separate the contributions from internal variability and external forcing to predictability. One way to improve skill is through identifying and harnessing initial conditions with more predictable evolution, so-called state-dependent predictability. We explore a neural network approach to identify these opportunistic initial states in the CESM2 large ensemble and subsequently explore how predictability may manifest in a future climate, influenced by both forced warming and internal variability. We use an interpretable neural network to demonstrate that internal variability will continue to play an important role in future climate predictions, especially for states of increased predictability.

How to cite: Gordon, E. and Barnes, E.: An interpretable neural network approach to identifying sources of predictability in the future climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8000, https://doi.org/10.5194/egusphere-egu23-8000, 2023.

The state-of-the-art climate models suffer from significant sea surface temperature (SST) biases in the tropical Indian Ocean (TIO), greatly damaging the climate prediction and projection. In this study, we investigate the multidecadal atmospheric bias teleconnections caused by the TIO SST biases and their impacts on the simulated atmospheric variability. A set of century long simulations forced with idealized SST perturbations, resembling various persistent TIO SST biases in coupled climate models, are conducted with an intermediate complexity climate model. Bias analysis is performed using the normal-mode function decomposition which can differentiate between balanced and unbalanced flow regimes across spatial scales. The results show that the long-term atmospheric circulation biases caused by the TIO SST biases have the Matsuno-Gill-type pattern in the tropics and Rossby wavetrain distribution in the extratropics, similar to the steady state response to tropical heating. The teleconnection between the tropical and extratropical biases is set up by the Rossby wavetrain emanating from the subtropics. Over 90% of the total bias energy is stored in the zonal modes k≤6, and the Kelvin modes take 50-65% of the total unbalanced bias energy. The spatial and temporal variabilities have different responses to positive SST biases. In the unbalanced regime, variability changes are confined in the tropics, but the spatial variability increases whereas the temporal variability decreases. In the balanced regime, the spatial variability generally increases in the tropics and decreases in the extratropics, whereas the temporal variability decreases globally. Variability responses in the tropics are confined in the Indo-west Pacific region, and those in the extratropics are strong in the Pacific-North America region and the Europe. In the experiment with only negative SST biases, spatial and temporal variabilities increase in both regimes. In addition, the comparison between experiments indicates that the responses of the circulation and its variability are not sensitive to the structure and location of the TIO SST biases.

How to cite: Zhao, Y.-B., Žagar, N., Lunkeit, F., and Blender, R.: Long-term atmospheric bias teleconnection and the associated spatio-temporal variability originating from the tropical Indian Ocean sea surface temperature errors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16899, https://doi.org/10.5194/egusphere-egu23-16899, 2023.