More Like this

1. Pantropical Indo-Atlantic temperature gradient modulates multi-decadal AMOC variability in models and observations

   https://doi.org/10.1038/s41612-023-00489-x  

   Ferster, Brady S; Borchert, Leonard F; Mignot, Juliette; Menary, Matthew B; Cassou, Christophe; Fedorov, Alexey V (December 2023, npj Climate and Atmospheric Science)

   Abstract Interconnections between ocean basins are recognized as an important driver of climate variability. Recent modeling evidence suggests that the North Atlantic climate can respond to persistent warming of the tropical Indian Ocean sea surface temperature (SST) relative to the rest of the tropics (rTIO). Here, we use observational data to demonstrate that multi-decadal changes in pantropical ocean temperature gradients lead to variations of an SST-based proxy of the Atlantic Meridional Overturning Circulation (AMOC). The largest contribution to this temperature gradient-AMOCconnection comes from gradients between the Indian and Atlantic Oceans. TherTIOindex yields the strongest connection of this tropical temperature gradient to theAMOC. Focusing on the internally generated signal in three observational products reveals that an SST-basedAMOCproxy index has closely followed low-frequency changes ofrTIOtemperature with about 26-year lag since 1870. Analyzing the pre-industrial control simulations of 44 CMIP6 climate models shows that theAMOCproxy index lags simulated mid-latitudeAMOCvariations by 4 ± 4 years. These model simulations reveal the mechanism connectingAMOCvariations to pantropical ocean temperature gradients at a 27 ± 2 years lag, matching the observed time lag in 28 out of the 44 analyzed models. rTIO temperature changes affect the North Atlantic climate through atmospheric planetary waves, impacting temperature and salinity in the subpolar North Atlantic, which modifies deep convection and ultimately the AMOC. Through this mechanism, observed internalrTIOvariations can serve as a multi-decadal precursor ofAMOCchanges with important implications forAMOCdynamics and predictability. 

   more » « less
2. 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. 

   more » « less
3. Investigation of the Drivers of Sea Surface Salinity and North Atlantic Climate Variability Using a Stochastic Model

   https://doi.org/10.1175/JCLI-D-24-0694.1  

   Liu, Glenn; Kwon, Young-Oh; Frankignoul, Claude (October 2025, Journal of Climate)

   Abstract The role of ocean dynamics in Atlantic climate variability and predictability is often studied through the lens of sea surface temperature (SST). Unlike SST, sea surface salinity (SSS) is not directly damped by surface fluxes, and its low-frequency variability responds primarily to oceanic processes. This study investigates the drivers of SSS variability using a stochastic model hierarchy to disentangle oceanic and atmospheric contributions to Atlantic climate variability, in particular, the role of local vertical processes. Representation of SST and SSS persistence and variance is especially improved by the introduction of damping of anomalies below the mixed layer, though SSS anomalies remain too persistent. The effect of SST–evaporation feedback on SSS is comparatively smaller except in regions with strong SST–SSS correlation. Despite the lack of representation of geostrophic advection, the stochastic model successfully reproduces spatial patterns of recurring SST/SSS anomalies in the Community Earth System Model 1 (CESM1) Large Ensemble at monthly to interannual time scales. At multidecadal time scales, the stochastic model is unable to simulate the amplitude of SST/SSS variability, but their spatial patterns are broadly reproduced, suggesting that direct atmospheric forcing and local vertical processes are important for capturing these features. Further analysis of the processes missing from the stochastic model suggests that the lack of geostrophic advection is largely responsible for too persistent SSS in the stochastic model, while the lack of interannual mixed-layer depth variability explains the underestimated persistence and variance in some regions for both SST and SSS. 

   more » « less
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. 

   more » « less
5. 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. 

   more » « less