Until now, scientists have not been able to predict warming of the ocean surface in the South Atlantic Ocean. But now, by using artificial intelligence they show that El Niño phenomenon in the region can be predicted up to 3 to 4 months ahead.
Read the article: Breakthrough in forecasting El Niño phenomenon in the Atlantic
Bachèlery ML, Brajard J, Patacchiola M, Illig S, Keenlyside N 2025: Predicting Atlantic and Benguela Niño events with deep learning. Sci. Adv.. https://doi.org/10.1126/sciadv.ads5185
Summary: Atlantic and Benguela Niño events substantially affect the tropical Atlantic region, with far-reaching consequences on local marine ecosystems, African climates, and El Niño Southern Oscillation. While accurate forecasts of these events are invaluable, state-of-the-art dynamic forecasting systems have shown limited predictive capabilities. Thus, the extent to which the tropical Atlantic variability is predictable remains an open question. This study explores the potential of deep learning in this context. Using a simple convolutional neural network architecture, we show that Atlantic/Benguela Niños can be predicted up to 3 to 4 months ahead. Our model excels in forecasting peak-season events with remarkable accuracy extending lead time to 5 months. Detailed analysis reveals our model’s ability to exploit known physical precursors, such as long-wave ocean dynamics, for accurate predictions of these events. This study challenges the perception that the tropical Atlantic is unpredictable and highlights deep learning’s potential to advance our understanding and forecasting of critical climate events.
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Looking back at the achievements of BCPU so far, Professor Noel Keenlyside is very pleased with what he sees. A new result on soil moisture for predicting heat waves adds promise for the future.
Read the article: Soil moisture: A future key to predict heat waves
Nair, A.S., Counillon, F., Keenlyside, N. 2024: Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation. Clim Dyn. https://doi.org/10.1007/s00382-024-07444-3
Summary: This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.
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Zheng, Y. X., S. L. Li, N. Keenlyside, S. P. He, Suo, L.L. 2024: Projecting spring consecutive rainfall events in the Three Gorges Reservoir based on triple-nested dynamical downscaling. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-023-3118-2
Summary: Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.
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Garcia-Oliva, L., Counillon, F., Bethke, I., Keenlyside, N. 2024: Intercomparison of initialization methods for Seasonal-to-Decadal Climate Predictions with the NorCPM. Clim Dyn. https://doi.org/10.1007/s00382-024-07170-w
Summary: Initialization is essential for accurate seasonal-to-decadal (S2D) climate predictions. The initialization schemes used differ on the component initialized, the Data Assimilation method, or the technique. We compare five popular schemes within NorCPM following the same experimental protocol: reanalysis from 1980 to 2010 and seasonal and decadal predictions initialized from the reanalysis. We compare atmospheric initialization—Newtonian relaxation (nudging)—against ocean initialization—Ensemble Kalman Filter—(ODA). On the atmosphere, we explore the benefit of full-field (NudF-UVT) or anomaly (NudA-UVT) nudging of horizontal winds and temperature (U, V, and T) observations. The scheme NudA-UV nudges horizontal winds to disentangle the role of wind-driven variability. The ODA+NudA-UV scheme is a first attempt at joint initialization of ocean and atmospheric components in NorCPM. During the reanalysis, atmospheric nudging improves the synchronization of the atmosphere and land components with the observed data. Conversely, ODA is more effective at synchronizing the ocean component with observations. The atmospheric nudging schemes are better at reproducing specific events, such as the rapid North Atlantic subpolar gyre shift. An abrupt climatological change using the NudA-UV scheme demonstrates that energy conservation is crucial when only assimilating winds. ODA outperforms atmospheric-initialized versions for S2D global predictions, while atmospheric nudging is preferable for accurately initializing phenomena in specific regions, with the technique’s benefit depending on the prediction’s temporal scale. For instance, atmospheric full-field initialization benefits the tropical Atlantic Niño at 1-month lead time, and atmospheric anomaly initialization benefits longer lead times, reducing hindcast drift. Combining atmosphere and ocean initialization yields sub-optimal results, as sustaining the ensemble’s reliability—required for ODA’s performance—is challenging with atmospheric nudging.
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Famooss Paolini, L., Omrani, N.-E., Bellucci, A., Athanasiadis, P.J., Ruggieri, P., Patrizio, C.R., Keenlyside, N. 2024: Nonstationarity in the NAO–Gulf Stream SST front interaction. J Clim. https://doi.org/10.1175/JCLI-D-23-0476.1
Summary: The interaction between the North Atlantic Oscillation (NAO) and the latitudinal shifts of the Gulf Stream sea surface temperature front (GSF) has been the subject of extensive investigations. There are indications of nonstationarity in this interaction, but differences in the methodologies used in previous studies make it difficult to draw consistent conclusions. Furthermore, there is a lack of consensus on the key mechanisms underlying the response of the GSF to the NAO. This study assesses the possible nonstationarity in the NAO–GSF interaction and the mechanisms underlying this interaction during 1950–2020, using reanalysis data. Results show that the NAO and GSF indices covary on the decadal time scale but only during 1972–2018. A secondary peak in the NAO–GSF covariability emerges on multiannual time scales but only during 2005–15. The nonstationarity in the decadal NAO–GSF covariability is also manifested in variations in their lead–lag relationship. Indeed, the NAO tends to lead the GSF shifts by 3 years during 1972–90 and by 2 years during 1990–2018. The response of the GSF to the NAO at the decadal time scale can be interpreted as the joint effect of the fast response of wind-driven oceanic circulation, the response of deep oceanic circulation, and the propagation of Rossby waves. However, there is evidence of Rossby wave propagation only during 1972–90. Here it is suggested that the nonstationarity of Rossby wave propagation caused the time lag between the NAO and the GSF shifts on the decadal time scale to differ between the two time periods.
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Ogilvie, A.E., King, L.A., Keenlyside, N., Counillon, F., Daviđsdóttir, B., Einarsson, N., Gulev, S., Fan, K., Koenigk, T., McGoodwin, J.R. and Rasmusson, M.H. 2024: Recent Ventures in Interdisciplinary Arctic Research: The ARCPATH Project. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-023-3333-x
Summary: This paper celebrates Professor Yongqi GAO’s significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies – ARCPATH (https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.
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Wu, J., H. Fan, S. Lin, W. Zhong, S. He, N. Keenlyside, Yang, S. 2024: Boosting effect of strong western pole of the Indian Ocean Dipole on the decay of El Niño events. npj Clim Atmos Sci. https://doi.org/10.1038/s41612-023-00554-5
Summary: The Indian Ocean Basin (IOB) mode is believed to favor the decay of El Niño via modulating the zonal wind anomalies in the western equatorial Pacific, while the contribution of the Indian Ocean Dipole (IOD) mode to the following year’s El Niño remains highly controversial. In this study, we use the evolution of fast and slow decaying El Niño events during 1950–2020 to demonstrate that the positive IOD with a strong western pole prompts the termination of El Niño, whereas a weak western pole has no significant effect. The strong western pole of a positive IOD leads to a strong IOB pattern peaking in the late winter (earlier than normal), enhancing local convection and causing anomalous rising motions over the tropical Indian Ocean and sinking motions over the western tropical Pacific. The surface equatorial easterly wind anomalies on the western flank of the sinking motions stimulate oceanic equatorial upwelling Kelvin waves, which shoal the thermocline in the eastern equatorial Pacific and rapidly terminate the equatorial warming during El Niño. However, a weak western pole of the IOD induces a weak IOB mode that peaks in the late spring, and the above-mentioned cross-basin physical processes do not occur.
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Boljka, L., Omrani, N.-E., Keenlyside, N. S. 2023: Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific. Weather Clim Dynam. https://doi.org/10.5194/wcd-4-1087-2023
Summary: A variety of statistical tools have been used in climate science to gain a better understanding of the climate system’s variability on various temporal and spatial scales. However, these tools are mostly linear, stationary, or both. In this study, we use a recently developed nonlinear and nonstationary multivariate time series analysis tool – multivariate empirical mode decomposition (MEMD). MEMD is a powerful tool for objectively identifying (intrinsic) timescales of variability within a given spatio-temporal system without any timescale pre-selection. Additionally, a red noise significance test is developed to robustly extract quasi-periodic modes of variability. We apply these tools to reanalysis and observational data of the tropical Pacific. This reveals a quasi-periodic variability in the tropical Pacific on timescales ∼ 1.5–4.5 years, which is consistent with El Niño–Southern Oscillation (ENSO) – one of the most prominent quasi-periodic modes of variability in the Earth’s climate system. The approach successfully confirms the well-known out-of-phase relationship of the tropical Pacific mean thermocline depth with sea surface temperature in the eastern tropical Pacific (recharge–discharge process). Furthermore, we find a co-variability between zonal wind stress in the western tropical Pacific and the tropical Pacific mean thermocline depth, which only occurs on the quasi-periodic timescale. MEMD coupled with a red noise test can therefore successfully extract (nonstationary) quasi-periodic variability from the spatio-temporal data and could be used in the future for identifying potential (new) relationships between different variables in the climate system.
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