Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).

Seamless operational climate prediction --ranging from hours to multiple decades-- is a sound theoretical possibility that in practice has not been fully realised  yet. While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales.

Because different models and strategies are used at different timescales by both national and international decadal, seasonal and subseasonal forecasting centers (e.g. Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017,Mahmood et al., 2021), and skill is different at those timescales, it is key to guarantee that a physically consistent "bridging" between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work. As recent research suggests, forecasts at different timescales can be merged to mimic the idea of seamless forecasts (e.g. Befort et al., 2022). 

Here, a method for merging forecasts across timescales (Muñoz et al., 2023) is discussed and illustrated. The approach is based on the identification of "bridges of opportunity" via a cross-wavelet spectral analysis of causal information flows between prediction systems. The new method enables to formally calculate the timing and length of the windows of opportunity, and the optimal temporal aggregation to conduct the bridging of the forecast systems. The approach is illustrated for operational (a) subseasonal-to-seasonal, and (b) seasonal-to-interannual forecast systems.

How to cite: Muñoz, Á. G.: Forecasting across timescales by crossing “bridges ofopportunity”, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-925, https://doi.org/10.5194/ems2024-925, 2024.

The El Niño–Southern Oscillation (ENSO) is a prominent interannual signal in the global climate system with widespread climatic influence. Our current understanding of ENSO predictability is based mainly on long-term retrospective forecasts obtained from intermediate complexity and hybrid coupled models. Compared with those models, complicated coupled general circulation models (CGCMs) include more realistic physical processes and have the potential to reproduce the ENSO complexity. However, hindcast studies based on CGCMs have only focused on the last 20–60 yrs. In this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models. 

How to cite: Liu, T.: ENSO predictability in the CESM model from 1880 to 2017, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-155, https://doi.org/10.5194/ems2024-155, 2024.

Long-lead climate predictions are increasingly in demand due to climate change. Through its well-known atmospheric teleconnections El Niño Southern Oscillation (ENSO) is a leading source of seasonal and interannual climate predictability, but currently operational ENSO forecasts are limited to about two seasons in advance. At the same time the scientific literature pointing to the feasibility of ENSO forecasts one year and even more in advance is increasing. The early anticipation of ENSO could prepare vulnerable communities around the world and help mitigate its most devastating impacts such as droughts, floods, poor harvests, and the spread of infectious diseases. Here we showcase real-time forecasts of the recent 2023/24 El Niño at lead times up to 1.5 years in advance of an expected peak in December 2023. We use a previously validated statistical ENSO model that relies on surface and subsurface ocean temperatures and zonal wind stress as predictors, and includes various dynamic components in the form of time-varying cyclical components. Real-time early forecasts with the same model were also issued for the 2015/16 and 2018/19 El Niños, when the long-lead forecasts were also coupled to an impact dengue fever model for the city of Machala in Ecuador to predict the probability of a dengue outbreak in the region up to 11 months in advance. In both cases the dengue predictions were successful indicating an outbreak in 2016 and a low dengue season in 2019. Therefore, such longer-lead ENSO forecasts could directly facilitate decision-making in the health sector, but also other key sectors of society.

How to cite: Petrova, D., Rodó, X., Koopman, S. J., Tzanov, V., and Cvijanovic, I.: Long-Lead ENSO Predictability: the 2023/24 El Niño, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-868, https://doi.org/10.5194/ems2024-868, 2024.

The implementation of adaptation policies requires seamless and relevant information on the evolution of the climate over the next decades. Decadal climate predictions are subject to drift because of intrinsic model errors and their skill may be limited after a few years or even months depending on the region. Non-initialized ensembles of climate projections have large uncertainties over the next decades, encompassing the full range of uncertainty attributed to internal climate variability. Providing the best climate information at regional scale over the next decades is therefore challenging. Recent studies have started to address this challenge by constraining uninitialized projections of sea surface temperature using decadal predictions or using a storyline approach to constrain uninitialized projections of the Atlantic Meridional Overturning Circulation using observations. Here, we develop a new method combining both observational and forecast information to provide the best climate information over the next decades. First, we select a sub-ensemble of non-initialized climate simulations based on their similarity to observed predictors with multi-decadal signal potential over Europe, such as Atlantic multi-decadal variability (AMV). Then, we try to further refine this sub-ensemble of trajectories by selecting a subset based on its consistency with decadal predictions. This study presents a comparison of these different methods for constraining surface temperatures in the Europe regions as defined in the IPCC over the next decades, focusing on CMIP6 non-initialized simulations. A retrospective evaluation is carried out over the historical period to assess the ability of this method to provide a good climate information in the near-term climate.

How to cite: Bonnet, R., Sanchez-Gomez, E., Boé, J., and Cassou, C.: Constraining near-term climate projections by combining observations with decadal predictions, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-125, https://doi.org/10.5194/ems2024-125, 2024.

We present the development of the initialisation strategy for seasonal to decadal climate predictions based on the ICON-XPP model, within DWD’s Innovation in the Applied Research and Development (IAFE) program. The ICON-XPP model is based on several well-established model components:  the ICON-NWP, operational weather forecast model at the DWD, as atmospheric model, ICON-O as ocean model, JSBACH as land model and uses a hydrological discharge model. To develop a weakly coupled data assimilation system for ICON-XPP, we use the experience build on a former ICON-ESM version (Pohlmann et al 2023). In our weakly coupled data assimilation framework, we use two different assimilation methods for atmosphere and ocean. We initialize the ocean component of the climate system through a monthly assimilation of salinity and temperature profiles from the EN4 dataset. For this we use a localised singular evolutive interpolated Kalman filter implemented via the Parallel Data Assimilation Framework (PDAF, Nerger 2020). The atmosphere component is initialised by nudging temperature, pressure and horizontal wind fields of the ERA5 reanalysis. We conduct our experiments with a 25-member ensemble, which we put together from three different historical runs started from different states of our piControl run. In the atmosphere ICON-XPP is run as R2B5 (~80km resolution) with 130 vertical levels and in the ocean we use a resolution of ~40km (R2B6) with 72 vertical levels. Our assimilation experiments start from 1990 after a ten-year assimilation spin-up of the ocean. We show the results of our experiments with the weakly coupled data assimilation system and discuss its challenges.

How to cite: Sgoff, C., Pohlmann, H., Brune, S., Pham, T. V., Schneidereit, A., Steinert, T., and Fröhlich, K.: Weakly coupled data assimilation for climate predictions with ICON-XPP, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-950, https://doi.org/10.5194/ems2024-950, 2024.

Past observational studies and numerical experimentation suggest that spring soil moisture anomalies in the northern hemisphere can induce significant anomalies in local summer temperature and precipitation as well as in large-scale planetary wave patterns. This is particularly the case in regions where soil-moisture memory is long and there are strong soil-moisture-atmosphere interactions. However, recent studies, where soil-moisture initial conditions of dynamical forecasting systems are scrambled, have found more modest impacts suggesting that current seasonal forecasting systems may not accurately capture this source of potential predictability.

Here we present an evaluation of two important aspects in the dynamical models that contribute to the multi-model seasonal forecasting system of the Copernicus Climate Change Service (C3S). Firstly, assessing the skill of soil-moisture in seasonal hindcasts and secondly analysing the realism of soil-moisture-atmosphere coupling strength. Both of which need to be well represented to achieve accurate predictions. These links between soil-moisture, evapotranspiration and temperature are investigated by way of correlation metrics to identify and compare locations where evaporation is soil-moisture limited or energy limited in seasonal hindcasts and observations.

A key finding of this analysis is that in the summer hindcasts, initialised in May, tend to misrepresent the precise location of the so-called North America coupling “hotspot”, an important region for land-atmosphere coupling. The soil-moisture atmosphere coupling tends to be too weak in the models in the western USA and too strong in the east. Both the reasons for this misplacement and the impact of this on forecasts of temperature, precipitation and large-scale planetary waves will be investigated.  

How to cite: Day, J., Stockdale, T., and Vitart, F.: How well do dynamical seasonal forecasts capture soil-moisture atmosphere coupling?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-738, https://doi.org/10.5194/ems2024-738, 2024.

Certain atmospheric conditions provide windows of opportunity for sub-seasonal prediction, such that forecasts initialized at these times maintain good predictive skill further out into the future than usual. Such windows of opportunity are for example given by certain phases of the Madden-Julian oscillation and the state of the stratospheric polar vortex at forecast initialization.

These findings come from retrospective analyses of forecast skill that often investigate only one or two drivers of sub-seasonal predictability at a time. What is lacking is investigating multiple drivers and comparing their relative importance, which we propose to achieve by means of explainable machine learning (ML) techniques. Furthermore, whereas previous ML-based studies of atmospheric predictability quantified uncertainty in terms of deterministic error or ensemble spread as a proxy for forecast skill, we here aim to predict the probabilistic forecast skill itself. The result is an operational decision support tool that can inform users, at forecast initialization time, of the skill expected at sub-seasonal lead times.

Therefore we are presenting, for the first time to our knowledge, an explainable ML model (based on random forests) that learns to predict directly the skill of the ECMWF extended-range forecasts at several weeks’ lead time, given the atmospheric state at initialization. We use reanalysis data as ground truth and focus on the skill of weekly aggregated statistics of geopotential height at 500 hPa, a variable of particular interest to the renewable energy industry since it tracks the synoptic-scale weather patterns controlling European energy production and demand.

We expect that our forecast skill prediction model will enable enhanced risk management for sub-seasonal forecast users, such as stakeholders in the renewable energy industry, and thereby accelerate the transition to a decarbonized energy system. From a scientific perspective, interrogating our ML model with explainability techniques should yield new insights into the sources of sub-seasonal predictability.

How to cite: Mârza, A.-C., Domeisen, D. I. V., and Meyer, A.: Machine learning-driven assessment and prediction of sub-seasonal forecast skill, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-267, https://doi.org/10.5194/ems2024-267, 2024.

The North Atlantic Oscillation (NAO) represents the dominant mode of atmospheric circulation variability over the North Atlantic, driving winter weather conditions over a large part of the Euro-Mediterranean sector. Seasonal forecast systems have demonstrated some predictive skill for wintertime NAO, related to the enhanced ability of dynamical models to correctly represent possible sources of NAO predictability. However, increasing the predictive skill at the seasonal timescale over the European domain is still considered a major challenge.

In this work the aim is to extract the potential hidden skill in a dynamical seasonal forecast ensemble by properly selecting relevant realizations. The idea is to define a reduced ensemble better performing in terms of NAO predictions and to assess the performance of this subsample compared to the full ensemble.

Different subsampling criteria have been tested and verified. On the one hand, under the assumption that the ensemble average represents the most predictable path, the members simulating at the beginning of the forecast a NAO state closest to the ensemble mean NAO are selected. On the other hand, the realizations that resemble a reliable and independent estimate of the winter NAO, derived from the autumn conditions of a set of established dynamical predictors, are taken into consideration.

The comparison between the results obtained with the full ensemble and these subsampled ensembles reveals the potential for a significant improvement in the prediction of 2m temperature and precipitation anomalies, therefore representing a valuable strategy for possible real-time operational applications both in a multi-model and in a single model framework.

How to cite: Benassi, M., Athanasiadis, P., Borrelli, A., Cavicchia, L., Gualdi, S., Hashemi Devin, M., Sanna, A., and Tibaldi, S.: A NAO-based subsampling approach for winter seasonal prediction , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-529, https://doi.org/10.5194/ems2024-529, 2024.

Accurate seasonal forecasts can be very useful for taking adaptation and prevention measures to unfavourable weather conditions, such as droughts, heatwaves, extreme precipitation events or high fire risk conditions. In a region with a very marked seasonal cycle and a high inter-annual variability of atmospheric conditions such as the Iberian Peninsula, the demand for improved seasonal forecasting becomes even more compelling. 
Currently operational seasonal forecasting systems have considerable scope for improvement in their ability to predict mid-latitude temperature or precipitation. However, some of these systems have shown some skill in predicting, months in advance, the phase of some modes of variability such as the North Atlantic Oscillation.
Here we show that seasonal forecast of surface variables (such as 2m air temperature or total precipitation) can be improved by taking advantage of the models' skill in predicting the main modes of variability in Europe. First, a quantification of the theoretical potential for improving the forecasts is carried out using an ensemble-member weighting technique that increase the statistical weight of those members whose variability mode configuration is closer to the observed configuration. Then, we assess the actual potential for improving the forecasts by using a two steps prediction: (1) a multi-system forecast of the corresponding variability mode configuration and (2) a seasonal forecast of the surface variables in which we use the first step forecast of the variability mode configuration to weight the ensemble members. Different verification metrics were used to quantify the skill of the forecasts, both deterministic and probabilistic, all defined as in the WMO forecast guidance.
The results show that ensemble-member weighting method with information on variability modes is a window of forecast opportunity that is able to improve seasonal forecasts if further research is conducted.

How to cite: Senande-Rivera, M., Domínguez-Alonso, M., and Rodríguez-Guisado, E.: Improving seasonal forecasts for the Iberian Peninsula through climate variability modes, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1011, https://doi.org/10.5194/ems2024-1011, 2024.

In the context of climate change adaptation, the use of climate predictions is steadily gaining importance. One critical challenge to consider when dealing with climate simulation outputs is the systematic bias affecting the modelled data. While bias correction methods are commonly employed in impact models to assess the effect of climate events on human activities, their effectiveness is often reduced in the case of extreme events, due to the scarcity of data for these low-probability and high-impact phenomena. 

This study, conducted as part of the European project FOCUS-Africa, is dedicated to advancing innovative climate services in the southern regions of Africa. Our primary objective is to respond to the needs of risk assessment studies, focusing on the impact of extreme events and their implications for climate change adaptation. To this end, we designed a novel bias correction method to consistently correct extreme events of temperature and precipitation, but is adaptable to other climate variables, such as wind speed. Our approach conceptually extends one of the classic Quantile Mapping (QM) methods by improving the description of the tail ends of the distribution through a generalised extreme value distribution (GEV) fitting. Our methodology also incorporates a downscaling component. QM is indeed frequently both as a bias correction method and for downscaling simulations to finer observed scales. Therefore, our method not only corrects the climate data but also enhances the raw resolution of the model outputs (typically around 100 km) to match the 9 km grid of the observational reference. 

In this study, we applied our technique to daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7, and GCFS2.1, developed respectively by ECMWF, Météo-France, and DWD. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5-Land. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction, highlighting the challenge of obtaining robust statistics due to the lack of information about extreme events.

How to cite: Trentini, L., Venturini, M., Guerrini, F., Dal Gesso, S., Calmanti, S., and Petitta, M.: An advanced bias correction technique for improved Seasonal Forecasts: a focus on extreme events in Southern Africa, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-604, https://doi.org/10.5194/ems2024-604, 2024.

Climate change has a noticeable impact on the Mediterranean region, causing changes in the seasonal patterns and yearly variations of important meteorological variables along with the occurrence, strength, duration, and timing of unusual and extreme events. While the Mediterranean is recognized as one of the  prominent global hot spot for climate impacts, it's imperative to dissect its sub-regions more meticulously. These areas exhibit significant meteorological shifts due to climate change and merit classification as unique hotspots. The Mediterranean Hotspot Index (MED-HOT index) is utilized to identify hotspot sub-regions in the Mediterranean region, followed by a detailed assessment of the seasonal data on these regions.

MED-HOT index is designed to assess regional climate extremes by simultaneously analyzing  changes in frequency and intensity of key climatic variables, specifically precipitation and temperature. The MED-HOT index offers a comprehensive assessment of the major climate challenges in the Mediterranean, including heatwaves, extreme rainfall, and drought events, pinpointing regions requiring necessitating immediate attention and intervention . It calculated utilizing ERA5 daily maximum and minimum temperatures as well as precipitation data spanning into two time periods between 1981 to 2020 across 30 Mediterranean subregions. The analysis of the MED-HOT index reveals that Mediterranean hotspot region identification primarily relied on changes in the frequency of extremes, as the contribution of intensity changes was less important. Notably the change in extreme maximum temperature appears to play a crucial role in the most subregions. On an annual basis, the primary hot spots in the Mediterranean are identified as northern Greece, southern Italy, and the southeastern Mediterranean.

Moreover, this study examined the performance of ECMWF-SEAS5 in predicting extreme temperature and precipitation in the Mediterranean hot spot regions. It assessed the quality of seasonal temperature and precipitation forecasts over the subregions by analyzing predictions from all members of the hindcasts and forecasts ensemble across the two seasons (winter and summer). The analyses included seasonal mean fields, anomaly correlations, statistical indicators, and seasonality index. ECMWF-SEAS5 successfully replicated the extreme precipitation anomalies observed in recent decades, with effectiveness varying based on lead time and subregion. Nevertheless, the system exhibited only moderate predictive ability in regions with medium and low predictability.

Acknowledgments

The work was supported by PREVENT project. This project has received funding from Horizon Europe programme under Grant Agreement No: 101081276.

How to cite: Anagnostopoulou, C., Lazoglou, G., Papadopoulos-Zachos, A., Georgiades, P., Velikou, K., Manios, E. M., and Zittis, G.: Seasonal Data Evaluation in MED-HOT Index Hotspots: A Climatological Perspective on Mediterranean Subregions, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-580, https://doi.org/10.5194/ems2024-580, 2024.