CL5.3.4

Winter (DJF) 2020/21 in the North Atlantic/European sector was characterised by the negative phase of the North Atlantic Oscillation (NAO). However, this was not well forecast by the leading seasonal prediction systems. We focus on forecasts from GloSea5, which was the Met Office operational seasonal prediction system at the time. Forecasts initialised in November 2020, at the 1-month lead time, indicated that a positive NAO was likely, although a few ensemble members did agree with the eventual outcome. Analysis suggests that the sudden stratospheric warming (SSW) that occurred in early January 2021 and an active MJO in late January/early February 2021 probably contributed to the observed negative NAO. In particular, GloSea5 indicated a rather low probability for SSW activity, which may well have been exacerbated by the forecast of a stronger than observed La Niña by this system.

How to cite: Ineson, S., Lockwood, J., Stringer, N., Thornton, H., and Scaife, A.: Predictability of European Winter 2020/21, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9713, https://doi.org/10.5194/egusphere-egu22-9713, 2022.

During the 2019/2020 winter season the extremely high air temperature and precipitation were recorded over northern Eurasia, eastern Asia and eastern North America. Over the UK this winter was stormy and one of the wettest for the entire observational period. Moreover, it was the only winter without stable snow cover in the central East European Plain. The reason of such exceptional weather is the domination of a North Atlantic Oscillation (NAO) positive phase during the whole season.
Since the NAO effect on winter weather is strong, prediction of its phase is a challenge for all national meteorological services. Several of them predicted the positive sign of North Atlantic Oscillation phase for 2019/2020 winter season, but underestimated the magnitude and duration. Forecasts obtained from INM RAS climate model (INMCM5) demonstrated consistent results for the considered season.
In this work we use the INMCM5 to study the sources of the predictability of both the extremely positive NAO phase and the weather fields anomalies in the 2019/2020 winter season. In particular we consider whether the INM RAS climate model simulates the positive Indian Ocean dipole — positive North Atlantic Oscillation phase teleconnection.
The research was carried out during the student educational program "Computational Technologies, Higher Order Data Analysis and Modelling" at the Sirius University and partially supported by the Russian Science Foundation (project 20‑17‑00190).

How to cite: Tarasevich, M., Vorobyeva, V., Chernenkov, A., Gasanov, M., Bardashov, D., and Volodin, E.: Influence of the ocean initial state on the weather anomalies simulation for 2019/2020 winter in the INMCM5 seasonal hindcasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-599, https://doi.org/10.5194/egusphere-egu22-599, 2022.

We investigate the potential for enhancing the seasonal prediction skill of mid-latitude cyclonic activity, represented by eddy kinetic energy (EKE) at 250 hPa over the North Atlantic and Europe, in hindcast simulations with the Max Planck Institute Earth System Model (MPI-ESM) against the ECMWF ERA5 reanalysis. Our analysis focuses on wintertime months (December-March) from 1982 to 2019, with a 30-member seasonal hindcast ensemble initialized every November 1st. Based on the initial confirmation that in both ERA5 reanalysis and MPI-ESM hindcasts, the eddy-driven jet stream and the wintertime North Atlantic Oscillation (NAO) play a significant role in wintertime's spatial and temporal distribution of mid-latitude cyclonic activity, we perform ensemble subsampling.

Specifically, we sample each winter so that a northern position of the jet stream is consistent with a positive phase of the NAO and represents poleward enhanced EKE activity. In contrast, a southern position of the jet stream is consistent with a negative phase of the NAO and represents equatorward enhanced EKE activity. Preliminary analysis of the predictive skill of MPI-ESM hindcasts with respect to ERA5 shows that such subsampling with respect to a consistent representation of the jet stream position and the NAO phase leads to improvements over the skill from the 30-member ensemble mean, with significant correlations concentrated over areas of major frequency of storm tracks. Our results put into practical use that an enhanced representation of the large-scale climate variability plays a crucial role in the long-term prediction of high-frequency events such as mid-latitude cyclones.

How to cite: Aranyossy, A., Brune, S., Hellmich, L., Dobrynin, M., Krieger, D., and Baehr, J.: Seasonal Predictability of wintertime North Atlantic cyclonic activity through the NAO and the eddy-driven jet stream, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3948, https://doi.org/10.5194/egusphere-egu22-3948, 2022.

Since the 1980s, external forcing from increasing greenhouse gases and declining aerosols has had a large effect on European summer temperatures. The forcing therefore provides an important source of predictive skill, even for timescales as short as seasonal forecasts. However, the relative importance of forcing for seasonal forecasts has thus far not been quantified, particularly for skill on regional scales. In this study, we investigate forcing-induced skill by comparing the skill of the operational multi-model ensemble of seasonal predictions from the Copernicus climate change service (C3S) archive to that of an uninitialized ensemble of CMIP6 projections for European summers for the period 1993-2016.

We show that for some regions, such as northern Europe, the forced trend provides the primary source of 2m temperature skill in current seasonal forecast models at 2-4 month lead-times. Over some parts of northern Europe, summer correlation skill is actually higher in uninitialized predictions and in runs with long lead-times than at short lead-times suggesting that there may be problems with the initialization. Conversely, 2m temperature in the Mediterranean region is generally well predicted by seasonal forecast models out to 4-6 months due to a combination of dynamical skill and a strong forced trend.

We argue that the strong warming trends mean that even uninitialized predictions contain useful information for seasonal forecasts of European summer temperatures. However, the ability of current models to capture summer circulation patterns requires further investigation as it is still unclear whether the models are deficient in this regard or whether the summer is inherently unpredictable.

How to cite: Patterson, M., Weisheimer, A., Befort, D., and O'Reilly, C.: The strong role of external forcing in seasonal forecasts of European summer temperatures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1793, https://doi.org/10.5194/egusphere-egu22-1793, 2022.

Slow-varying atmospheric boundaries are the main sources of seasonal climate predictions, and their footprints on climate variables may be captured as predictable patterns. Based on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The main findings of this study are as following:

• The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 90° phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice.
• The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5.

How to cite: Fan, H., Wang, L., Zhang, Y., Tang, Y., Duan, W., and Wang, L.: Predictable Patterns of Wintertime Surface Air Temperature in Northern Hemisphere and Their Predictability Sources in the SEAS5, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1507, https://doi.org/10.5194/egusphere-egu22-1507, 2022.

Fishery sector is of vast importance to the Irish economy. In 2019 it has generated €577 million and employed 16 thousand. The ability to predict changes in the future stock will support adaptation and fish stock management. In decadal climate prediction, initialized predictions have demonstrated improved prediction skill for the North Atlantic. The different stages of fish development are dependent on oceanic variables like temperature and variability and so decadal prediction skill for those variables would allow to make statements on potential changes in fish stock. 

Our aim is to improve decadal prediction skill in the Northeast Atlantic. For this we apply ensemble subsampling, a process that selects those ensemble members for creating a subsampled ensemble mean, which perform best under evaluation by physically-based statistical predictors. Climate modes, like Subpolar Gyre (SPG) and the Atlantic Multidecadal Variability (AMV), interact with our region of interest and therefore we will use those to inform us about our subsampling decisions. Applying this methodology on seasonal scales has demonstrated improved prediction skill for other climate modes.

For this contribution we will investigate the application of subsampling on decadal scales for the Northeastern Atlantic on variables like temperature and salinity for different depth levels. The analysis will show how decadal prediction skill will change when wider oceanographic basin information, like SPG and AMV, are considered in the decadal predictions. We will discuss potential implications for a selection of species for the Irish fisheries sector, and with it the possibility for improving the current fish stock management systems in Ireland.

How to cite: O’Beirne, C., Vaughan, L., Koul, V., and Düsterhus, A.: Decadal prediction for Ireland and Irish Fisheries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8491, https://doi.org/10.5194/egusphere-egu22-8491, 2022.

Recently it has been shown that initialised climate predictions capture the decadal variability of the winter NAO with high skill. However, the signal from models is often hidden among their large internal variability, which results in a low signal-to-noise ratio. In this study, we quantify the skill of the North Atlantic eddy-driven jet’s location and intensity, both in summer and winter. We focus on multi-model decadal predictions made for CMIP6. Overall, we find that models feature a higher skill (as featured by the Anomaly Correlation Coefficient) in predicting the intensity of the jet than its location. For years 2-9, the high winter NAO skill is largely associated with skilful prediction of the jet speed. However, skill in summer is considerably worse than in winter, with models consistently failing to capture the observed southward shift of the Jet between the 1970s and 2010s. Finally, we also show that the skill for the winter NAO is sensitive to the period over which it is computed, and skill drops considerably when evaluating up to the present day, as models fail to capture the observed northern shift and strengthening of the winter eddy-driven jet over the period 2005-2020, as well as the positive trend in the winter NAO.

How to cite: Marcheggiani, A., Robson, J., Monerie, P.-A., Bracegirdle, T., and Smith, D.: Decadal predictability of the North Atlantic eddy-driven jet in winter and summer within CMIP6, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9892, https://doi.org/10.5194/egusphere-egu22-9892, 2022.

We have developed, implemented and preliminary evaluated the performance of the first “eddy-resolving” decadal prediction prototype system based on the MPI-ESM-ER model configuration with the aim to investigate potential improvements due to resolving oceanic eddies in interannual to decadal climate variability and in the prediction skill of the North Atlantic circulation and climate of the regions impacted by it (Europe, Nordic Seas, and Arctic). The MPI-ESM-ER setup is employing an eddy-resolving ocean component with a global resolution of 10 km and an atmospheric component with a resolution of 100 km (T127). The eddy-resolving simulations were compared with similar MPI-ESM-HR experiments conducted within the CMIP6 DCPP-A framework employing an eddy-permitting ocean configuration of 0.4° (~40km). Since both the radiative forcing (CMIP6), the assimilation procedure and ensemble generation are exactly identical, has allowed us to isolate the effect of resolving oceanic eddies (and topographic features) in MPI-ESM-ER prediction system. The variability of the sea surface temperature (SST) in the subpolar North Atlantic over the last decades is well reproduced by the initialized predictions, in contrast to the uninitialized historical simulations. Both prediction systems are able to reproduce the mid-1990s abrupt strong warming event, with a more realistic amplitude of the warming in the MPI-ESM-ER hindcasts. Moreover, there is a clear reduction in the systematic model bias by using an eddy-resolving ocean component in MPI-ESM-ER. All MPI-ESM-HR hindcasts are approximately 1°C too warm, but the MPI-ESM-ER hindcast ensemble is very close to the observations. Reducing the SST bias in the North Atlantic will have implications for other quantities than SST, such as storm tracks or blocking events over Europe. We have also investigated the impact of an “eddy-permitting” and an “eddy resolving” ocean configuration on the predictability of the 2015 record Subpolar North Atlantic “Cold Blob”. Predicting such extreme coupled climate phenomena over the North Atlantic-European region has proved to be very challenging for state-of-art prediction systems. However, we could demonstrate that our prediction system is able to reproduce the observed anomalies, but in years where it is absolutely necessary to forecast the atmosphere conditions too, it will require a large ensemble of hindcasts (of the order of 10 or more): two (out of five) ensemble members in MPI-ESM-HR and six (out of ten) ensemble members in MPI-ESM-ER configuration simulate an eastern subpolar North Atlantic “Cold Blob"" in 2015. One of the MPI-ESM-ER ensemble members even reproduces the full observed strength of the ""Cold Blob"", underlining the potential of high-resolution climate predictions. We could also demonstrate that using an eddy-resolving ocean (0.1°) considerably improves the model systematic bias over the North Atlantic subpolar gyre. Based on these promising results, we plan to investigate the predictability of other recent oceanic extreme climate phenomena.

How to cite: Matei, D., Lohmann, K., Gutjahr, O., and Jungclaus, J.: Towards an "eddy-resolving" climate prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12731, https://doi.org/10.5194/egusphere-egu22-12731, 2022.

We investigate the forced response of the North Atlantic Oscillation (NAO)  in large ensembles of climate models including simulations with historical  forcings and initialized decadal hindcasts.  The forced NAO in the  CMIP6 historical ensemble correlates significantly with observations after 1970. However, the forced NAO shows an apparent non-stationarity with significant correlations to observations only in the period after 1970 and in the period before 1890. We demonstrate that such apparent non-stationarity can be due to chance even when models and observations are independent. We find only weak evidence that initialization improves the skill of the NAO on decadal time-scales. Neither of the historical ensembles including only natural forcings, well-mixed greenhouse-gases, or anthropogenic aerosols show any skillful NAO. Our results question the possibility of useful decadal predictions of the NAO.

How to cite: Christiansen, B., Yang, S., and Matte, D.: On the forced response and decadal predictability of the North Atlantic Oscillation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7508, https://doi.org/10.5194/egusphere-egu22-7508, 2022.

Decadal climate predictions are a new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and lead times) with skill that can be used to develop a climate service and inform users in several sectors. Also, it can help to monitor improvements in current forecast systems. The Decadal Climate Prediction Project Component A (DCPP-A) of the Coupled Model Intercom-parison Project Phase 6 (CMIP6) now provides the most comprehensive set of retrospective decadal predictions from multiple forecast systems. The increasing availability of these simulations leads to the question of how to best post-process the raw output from the forecast systems so that the most useful and reliable information is provided to users.

This work evaluates the quality of deterministic and probabilistic forecasts for spatial fields of near-surface air temperature and precipitation, and time series of the Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available decadal predictions contributing to CMIP6/DCPP-A (169 members from 13 forecast systems). The predictions generally show high skill in predicting temperature and the AMV and GSAT time series, while the skill is more limited for precipitation. Also, different approaches for building a multi-model forecast are compared (pooling all ensemble members versus combining the averages from individual forecast systems), finding small differences. Besides, the multi-model ensemble is compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the uninitialized historical climate simulations (195 members from the same forecast systems as the decadal prediction members) to estimate the impact of initialization. An added value is found for temperature over several ocean and land regions, and for the AMV and GSAT time series, while it is more reduced for precipitation. Moreover, the full DCPP-A ensemble is compared to a sub-ensemble of predictions that could be provided in near real-time for a potential operational product generation. The comparison shows a benefit of using a large ensemble over several regions, especially for temperature. Finally, the implications of these results in a climate services context are discussed.

How to cite: Delgado-Torres, C., Donat, M. G., Gonzalez-Reviriego, N., Caron, L.-P., Athanasiadis, P. J., Bretonnière, P.-A., Dunstone, N. J., Ho, A.-C., Pankatz, K., Paxian, A., Pérez-Zanón, N., Samsó Cabré, M., Solaraju-Murali, B., Soret, A., and Doblas-Reyes, F. J.: Multi-model forecast quality assessment of CMIP6 decadal predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13156, https://doi.org/10.5194/egusphere-egu22-13156, 2022.