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1. Mechanisms of Decadal North Atlantic Climate Variability and Implications for the Recent Cold Anomaly

   https://doi.org/10.1175/JCLI-D-20-0464.1  

   Årthun, Marius; Wills, Robert C.; Johnson, Helen L.; Chafik, Léon; Langehaug, Helene R. (May 2021, Journal of Climate)

   null (Ed.)

   Abstract Decadal sea surface temperature (SST) fluctuations in the North Atlantic Ocean influence climate over adjacent land areas and are a major source of skill in climate predictions. However, the mechanisms underlying decadal SST variability remain to be fully understood. This study isolates the mechanisms driving North Atlantic SST variability on decadal time scales using low-frequency component analysis, which identifies the spatial and temporal structure of low-frequency variability. Based on observations, large ensemble historical simulations, and preindustrial control simulations, we identify a decadal mode of atmosphere–ocean variability in the North Atlantic with a dominant time scale of 13–18 years. Large-scale atmospheric circulation anomalies drive SST anomalies both through contemporaneous air–sea heat fluxes and through delayed ocean circulation changes, the latter involving both the meridional overturning circulation and the horizontal gyre circulation. The decadal SST anomalies alter the atmospheric meridional temperature gradient, leading to a reversal of the initial atmospheric circulation anomaly. The time scale of variability is consistent with westward propagation of baroclinic Rossby waves across the subtropical North Atlantic. The temporal development and spatial pattern of observed decadal SST variability are consistent with the recent observed cooling in the subpolar North Atlantic. This suggests that the recent cold anomaly in the subpolar North Atlantic is, in part, a result of decadal SST variability. 

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2. Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

   https://doi.org/10.1029/2023GL108099  

   Davenport, Frances V; Barnes, Elizabeth A; Gordon, Emily M (June 2024, Geophysical Research Letters)

   Abstract We use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world. 

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3. Enhanced Atlantic Meridional Mode predictability in a high-resolution prediction system

   https://doi.org/10.1126/sciadv.ado6298  

   Zhang, Qiuying; Chang, Ping; Fu, Dan; Yeager, Stephen G; Danabasoglu, Gokhan; Castruccio, Frederic; Rosenbloom, Nan (August 2024, Science Advances)

   Accurate prediction of sea surface temperatures (SSTs) in the tropical North Atlantic on multiyear timescales is of paramount importance due to its notable impact on tropical cyclone activity. Recent advances in high-resolution climate predictions have demonstrated substantial improvements in the skill of multiyear SST prediction. This study reveals a notable enhancement in high-resolution tropical North Atlantic SST prediction that stems from a more realistic representation of the Atlantic Meridional Mode and the associated wind-evaporation-SST feedback. The key to this improvement lies in the enhanced surface wind response to changes in cross-equatorial SST gradients, resulting from Intertropical Convergence Zone bias reduction when atmospheric model resolution is increased, which, in turn, amplifies the positive feedback between latent and sensible surface heat fluxes and SST anomalies. These advances in high-resolution climate prediction hold promise for extending tropical cyclone forecasts at multiyear timescales. 

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4. Separating internal and forced contributions to near term SST predictability in the CESM2-LE

   https://doi.org/10.1088/1748-9326/acfdbc  

   Gordon, E M; Barnes, E A; Davenport, F V (October 2023, Environmental Research Letters)

   Abstract An open question in the study of climate prediction is whether internal variability will continue to contribute to prediction skill in the coming decades, or whether predictable signals will be overwhelmed by rising temperatures driven by anthropogenic forcing. We design a neural network that is interpretable such that its predictions can be decomposed to examine the relative contributions of external forcing and internal variability to future regional sea surface temperature (SST) trend predictions in the near-term climate (2020–2050). We show that there is additional prediction skill to be garnered from internal variability in the Community Earth System Model version 2 Large Ensemble, even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. We further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of decadal variability for the grid points within each region. 

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5. Toward Predicting Changes in the Land Monsoon Rainfall a Decade in Advance

   https://doi.org/10.1175/JCLI-D-17-0521.1  

   Wang, B; Li, J; Cane, MA; Liu, J; Webster, PJ; Xiang, B; Kim, H-M; Cao, J; Ha, K-J (January 2018, Journal of climate)

   Predictions of changes of the land monsoon rainfall (LMR) in the coming decades are of vital importance forsuccessful sustainable economic development. Current dynamic models, though, have shown little skill in thedecadal prediction of the Northern Hemisphere (NH) LMR (NHLMR). The physical basis and predictability forsuch predictions remain largely unexplored. Decadal change of the NHLMR reflects changes in the total NHcontinental precipitation, tropical general circulation, and regional land monsoon rainfall over northern Africa,India, East Asia, and North America. Using observations from 1901 to 2014 and numerical experiments, it isshown that the decadal variability of the NHLMR is rooted primarily in (i) the north–south hemispheric thermalcontrast in the Atlantic–Indian Ocean sector measured by the North Atlantic–south Indian Ocean dipole(NAID) sea surface temperature (SST) index and (ii) an east–west thermal contrast in the Pacific measured by anextended El Niño–Southern Oscillation (XEN) index. Results from a 500-yr preindustrial control experimentdemonstrate that the leading mode of decadal NHLMR and the associated NAID and XEN SST anomalies maybe largely an internal mode of Earth’s climate system, although possibly modified by natural and anthropogenicexternal forcing. A 51-yr, independent forward-rolling decadal hindcast was made with a hybrid dynamic con-ceptual model and using the NAID index predicted by a multiclimate model ensemble. The results demonstratethat the decadal changes in the NHLMR can be predicted approximately a decade in advance with significantskills, opening a promising way forward for decadal predictions of regional land monsoon rainfall worldwide. 

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