Category: Publications

Projecting spring consecutive rainfall events in the Three Gorges Reservoir based on triple-nested dynamical downscaling

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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Intercomparison of initialization methods for Seasonal-to-Decadal Climate Predictions with the NorCPM

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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Nonstationarity in the NAO–Gulf Stream SST Front Interaction

Famooss Paolini, L., Omrani, N.-E., Bellucci, A., Athanasiadis, P.J., Ruggieri, P., Patrizio, C.R., Keenlyside, N. 2024: Non-stationarity 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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Recent Ventures in Interdisciplinary Arctic Research: The ARCPATH Project

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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Expanding influence of Atlantic and Pacific Ocean heat transport on winter sea-ice variability in a warming Arctic

Dörr, J., Årthun, M., Eldevik, T., Sandø, A. B. 2024: Expanding influence of Atlantic and Pacific Ocean heat transport on winter sea-ice variability in a warming Arctic. Geophys Res Lett Oceans. https://doi.org/10.1029/2023JC019900

Summary: The gradual anthropogenic-driven retreat of Arctic sea ice is overlaid by large natural (internal) year-to-year variability. In winter, sea-ice loss and variability are currently most pronounced in the Barents Sea. As the loss of winter sea ice continues in a warming world, other regions will experience increased sea-ice variability. In this study, we investigate to what extent this increased winter sea-ice variability in the future is connected to ocean heat transport (OHT). We analyze and contrast the present and future link between Pacific and Atlantic OHT and the winter Arctic sea-ice cover using simulations from seven single-model large ensembles. We find strong model agreement for a poleward expanding impact of OHT through the Bering Strait and the Barents Sea under continued sea-ice retreat. Model differences on the Atlantic side can be explained by the differences in the simulated variance of the Atlantic inflows. Model differences on the Pacific side can be explained by differences in the simulated strength of Pacific Water inflows, and upper-ocean stratification and vertical mixing on the Chukchi shelf. Our work highlights the increasing importance of the Pacific and Atlantic water inflows to the Arctic Ocean and highlights which factors are important to correctly simulate in order to capture the changing impact of OHT in the warming Arctic.

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Boosting effect of strong western pole of the Indian Ocean Dipole on the decay of El Niño events

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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Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific

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://wcd.copernicus.org/articles/4/1087/2023/wcd-4-1087-2023-discussion.html

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 wellknown 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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Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre

Polkova, I, Swingedouw, D., Hermanson, L., Köhl, A., Stammer, D., Smith, D., Kröger, J., Bethke, I., Yang, X., Zhang, L., Nicolì, D., Athanasiadis, P., Karami, P., Pankatz, K., Pohlmann, H., Wu, B., Bilbao, R., Ortega, P., Yang, S., Sospedra-Alfonso, R., Merryfield, W., Kataoka, T., Tatebe, H., Imada, Y., Ishii, M., Matear, R. 2023: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre. Front Clim. doi: https://doi.org/10.3389/fclim.2023.1273770

Summary: Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model’s climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.

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Forced and internal components of observed Arctic sea-ice changes

Dörr, J.S., Bonan, D.B., Årthun, M., Svendsen, L., Wills, R.C.J. 2023: Forced and internal components of observed Arctic sea-ice changes. The Cryosphere. https://doi.org/10.5194/tc-17-4133-2023

Summary: The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

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Supermodeling: improving predictions with an ensemble of interacting models

Schevenhoven , F., Keenlyside, N., Counillon, F., Carrassi, A., Chapman, W.E., Devilliers, M., Gupta, A., Koseki, S., Selten, F., Shen, M.L., Wang, S. 2023: Supermodeling: improving predictions with an ensemble of interacting models. BAMS. https://doi.org/10.1175/BAMS-D-22-0070.1

Summary: The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists.

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