Sea Surface Temperature Evolution Research Articles

The South Atlantic Dipole via multichannel singular spectrum analysis

This study analyzes coupled atmosphere–ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959–2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest–northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño–Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic’s Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability.

Potential Near‐Term Wetting of the Southwestern United States if the Eastern and Central Pacific Cooling Trend Reverses

AbstractNear‐term projections of drought in the southwestern United States (SWUS) are uncertain. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce these observed SST trends, and they may continue doing so in the future. Here, we assess the sensitivity of SWUS precipitation projections to future SST trends using a Green's function approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. It is critical to consider the impact of possible SST pattern trends on SWUS precipitation trends until we fully trust SST evolution in climate models.

Future Changes in the Intensity and Duration of Marine Heat and Cold Waves: Insights from Coupled Model Initial-Condition Large Ensembles

Abstract The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

On the Non‐Linear Nature of Long‐Term Sea Surface Temperature Global Trends

AbstractThe year‐to‐year rate and acceleration of the changes in global sea surface temperatures (SSTs) for 1870–2022 are determined using non‐linear techniques. Our methodology identifies the non‐linear, long‐term tendencies of the warming, revealing subtle but essential features of the SST changes that are impossible to identify by linear techniques. Our analysis identifies inhomogeneous patterns of SST evolution. For instance, in most equatorial regions, including the western Pacific, the acceleration of the warming is positive, and sustained warming replaces the cooling at the beginning of the records. On the contrary, the warming acceleration in the central and eastern equatorial Pacific is negative, indicating a sustained warming at the beginning of the record that weakens over the years, resulting in a stagnated warming or even a cooling in recent years. We identify a strong warming with a negative acceleration for the North Atlantic. We hypothesize that this warming is part of the multidecadal variability of SST rather than a long‐term trend.

Baseline Climatology of the Canary Current Upwelling System and Evolution of Sea Surface Temperature

Global climate change has induced a rise in sea surface temperature (SST), although this increase is not uniform across the world. Significant variations exist between coastal and offshore waters, particularly in regions affected by upwelling processes. This study focuses on the Canary Current Upwelling System (CCUS), stretching from Northwest Iberia to Northwest Africa. High-resolution remotely sensed SST data (0.05°) from the ODYSSEA Level 4 Sea Surface Temperature Reprocessed dataset were validated with in situ measurements and employed to establish a regional climatological baseline for 1982–2012. Subsequent years were compared to this baseline to construct SST anomaly maps, revealing SST changes since 2012. The study area was further divided into sub-regions for comparative analysis. Results indicate that SST consistently increased at a higher rate offshore compared to the adjacent nearshore regions. A reference dataset spanning 1951–1981 was used to gauge SST variability between the two baselines. SST exhibited a 0.59 °C increase from 1951–1981 to 1982–2012, with a slowing of SST trends beyond the 1982–2012 baseline. This research offers valuable insights into the climatological dynamics of the CCUS. These findings enhance our understanding of this critical coastal system’s climatology, laying the groundwork for future investigations into evolving climate patterns in coastal regions.

Prediction of Summer Precipitation in North China: Role of the Evolution of Sea Surface Temperature Anomalies from Boreal Winter to Spring

Abstract Prediction of summer precipitation in north China (NCP) has long been a challenge partly because its low correlation with previous sea surface temperature (SST) anomalies (SSTA) limits the application of SST in NCP prediction. This study aims to extract optimal predictors of NCP from the SST field using an objective method—empirically optimal screening (EOS). It finds that the optimal precursory signal of NCP lies in the change of SSTA from winter to spring rather than the SSTA itself. This study identifies two optimal precursory signs predicting a positive (negative) NCP anomaly: the anomalous SST cooling (warming) from winter to spring in the coastal area of Somalia and Peru. Interestingly, these two presummer conditions have considerable independence, but they lead to a similar summer development of La Niña (El Niño). In summer, the tropical precipitation anomaly pattern associated with La Niña (El Niño) development excites a meridional wave train over the western Pacific and the circumglobal teleconnection in the Northern Hemisphere. Both of the anomalous wave trains show abnormal high (low) pressure over northeast Asia, which induces the south (north) wind anomalies over north China and produces abundant (deficient) precipitation there. These results highlight the importance of the SST evolution from winter to spring, break through the limitation of SST application in NCP prediction, and thus bring a prospect of improving NCP forecast skills. Significance Statement Sea surface temperature (SST) anomalies are most used as predictors in climate prediction. However, the forecast of summer precipitation in north China is limited by its low correlation with prior SST anomalies. In this paper, we find that the optimal precursory signal of north China precipitation (NCP) is not the SST anomaly itself, but the changes of SST anomalies from winter to spring in the coastal area of Somalia and Peru. These two precursory signals are almost independent yet indicate similar summer situations leading to NCP anomaly. These results highlight the importance of the dynamic evolution of sea surface temperature in improving the forecast skill of NCP.

Can Tibetan Plateau Snow Depth Influence the Interannual Association between Tropical Indian Ocean Sea Surface Temperatures and Rapidly Intensifying Typhoons?

Abstract This study finds that the observed decrease in winter–spring Tibetan Plateau snow depth since 2000 has played an important role in weakening the correlation between rapidly intensifying tropical cyclones over the western North Pacific and tropical Indian Ocean sea surface temperatures (SSTs). Tibetan Plateau snow depth modulates convective activity through changes in the South Asian high. Increased Tibetan Plateau snow depth promotes basinwide tropical Indian Ocean cooling in spring through a Gill-type response. In the following seasons, downward latent heat flux anomalies associated with a weaker monsoon circulation contributes to warm SST anomalies over the western tropical Indian Ocean, thus favoring a positive phase of the Indian Ocean dipole. This evolution of tropical Indian Ocean SSTs associated with anomalously high Tibetan Plateau snow depth potentially weakens the relationship between rapidly intensifying tropical cyclone frequency and spring tropical Indian Ocean SSTs. When the effect of Tibetan Plateau snow depth is removed via partial correlation analysis, we find a significant relationship between spring tropical Indian Ocean SSTs and rapidly intensifying tropical cyclones as well as corresponding large-scale environmental factors. The results of this study enhance understanding of changes in tropical cyclone intensity and have implications for seasonal forecasting of tropical cyclone intensity over the western North Pacific basin. This study also emphasizes the importance of Tibetan Plateau thermal forcing in atmosphere–ocean coupling.

Storylines of Maritime Continent dry period precipitation changes under global warming

The dry half of the year from May to October over the Maritime Continent (MC) has experienced unprecedented damages from forest fires in recent decades. The observed interannual rainfall variability during this period is closely tied to sea surface temperature (SST) variability over the equatorial Pacific (EP). Therefore, the future evolution of EP SST can be expected to influence the climatological precipitation over the MC. Whilst multi-model means (MMMs) suggest a future drying trend over the south-western part of the MC, there is considerable model uncertainty. Here, using a storyline approach with the 38 climate models from Coupled Model Intercomparison Project Phase 6, we distinguish the model uncertainty associated with changes in the zonal EP SST gradient from that associated with the basin-wide EP (BEP) warming. We find that an increase in east-to-west EP SST gradient would bring more rainfall over the north-eastern regions including northern Borneo, Sulawesi and New Guinea. In contrast, the intensity of the basin-wide warming of EP SST is directly linked with the drying response seen over the south-western MC in the MMM. This drying affects the highly vulnerable regions of Sumatra and Kalimantan for forest fires. Our results suggest that a storyline under higher BEP warming accompanied by an El-Niño like change in zonal SST gradient would lead to even drier climatic conditions over these key regions. However, the observed record of more than one hundred years favours a storyline of lower BEP warming accompanied by a La-Niña like change in zonal SST gradient, which would lead to minimal drying over the south-western MC and wetter conditions over the north-eastern parts of the MC.

A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions

Many coupled models are unable to accurately depict the multi-year La Niña conditions in the tropical Pacific during 2020–22, which poses a new challenge for real-time El Niño–Southern Oscillation (ENSO) predictions. Yet, the corresponding processes responsible for the multi-year coolings are still not understood well. In this paper, reanalysis products are analyzed to examine the ocean–atmosphere interactions in the tropical Pacific that have led to the evolution of sea surface temperature (SST) in the central-eastern equatorial Pacific, including the strong anomalous southeasterly winds over the southeastern tropical Pacific and the related subsurface thermal anomalies. Meanwhile, a divided temporal and spatial (TS) 3D convolution neural network (CNN) model, named TS-3DCNN, was developed to make predictions of the 2020/21 La Niña conditions; results from this novel data-driven model are compared with those from a physics-based intermediate coupled model (ICM). The prediction results made using the TS-3DCNN model for the 2020–22 La Niña indicate that this deep learning–based model can capture the two-year La Niña event to some extent, and is comparable to the IOCAS ICM; the latter dynamical model yields a successful real-time prediction of the Niño3.4 SST anomaly in late 2021 when it is initiated from early 2021. For physical interpretability, sensitivity experiments were designed and carried out to confirm the dominant roles played by the anomalous southeasterly wind and subsurface temperature fields in sustaining the second-year cooling in late 2021. As a potential approach to improving predictions for diversities of ENSO events, additional studies on effectively combining neural networks with dynamical processes and mechanisms are expected to significantly enhance the ENSO prediction capability.摘要2020–22年间热带太平洋经历了持续性多年的拉尼娜事件, 多数耦合模式都难以准确预测其演变过程, 这为厄尔尼诺-南方涛动(ENSO)的实时预测带来了很大的挑战. 同时, 目前学术界对此次持续性双拉尼娜事件的发展仍缺乏合理的物理解释, 其所涉及的物理过程和机制有待于进一步分析. 本研究利用再分析数据产品分析了热带东南太平洋东南风异常及其引起的次表层海温异常在此次热带太平洋海表温度(SST)异常演变中的作用, 并构建了一个时空分离(Time-Space)的三维(3D)卷积神经网络模型(TS-3DCNN)对此次双拉尼娜事件进行实时预测和过程分析. 通过将TS-3DCNN与中国科学院海洋研究所(IOCAS)中等复杂程度海气耦合模式(IOCAS ICM)的预测结果对比, 表明TS-3DCNN模型对2020–22年双重拉尼娜现象的预测能力与IOCAS ICM相当, 二者均能够从2021年初的初始场开始较好地预测2021年末 El Niño3.4区SST的演变. 此外, 基于TS-3DCNN和IOCAS ICM的敏感性试验也验证了赤道外风场异常和次表层海温异常在2021年末赤道中东太平洋海表二次变冷过程中的关键作用. 未来将神经网络与动力 模式模式间的有效结合, 进一步发展神经网络与物理过程相结合的混合建模是进一步提高ENSO事件预测能力的有效途径.

The Cretaceous (early Albian to early Campanian) biostratigraphy and palaeotemperature reconstruction of the eastern Tethys: Calcareous nannofossil evidence from southern Tibet, China

Studies of geochemical proxies, such as carbonate δ18O and TEX86, show that the Earth's climate has changed significantly during the Cretaceous Period. However, knowledge about the sea surface temperature (SST) history of the eastern Tethys is limited. Based on the calcareous nannofossil record of the Nirang section, Tethys Himalayas, this paper attempts to establish the biostratigraphic framework and SST evolution of the Cretaceous Period in the southern Tibet. A total of 131 species were identified from the studied succession. Twelve bioevents were recognized, allowing for early Albian to the early Campanian (CC8 to CC18 biozones) in the section. Meanwhile, a disconformity within 81–71 Ma was also identified. The SST evolution was analysed by the calcareous nannofossils modified temperature index (mTI), and the results reveal that the long-term SST changes in the Tethys Himalayas of southern Tibet experienced progressive warming from the early Albian to the early Turonian and the warm interval persisted until the early Campanian. Short-term warming and cooling events, e.g., rapid warming in the early and late Albian, subsequent cooling in the latest Albian and peak warmth at the Cenomanian–Turonian transition, are also recorded in the study area. The SST history of the Albian-Cenomanian in the eastern Tethys can be well correlated to the western Tethys (central Italy). However, the compilation of TEX86 data indicates substantial global cooling during the Coniacian to Campanian. This study suggests that the northward drift of the Indian continent towards the equator is likely to be responsible for the sustained warmth in the Tethys Himalayas from the middle Turonian to the early Campanian.

Sea surface temperature evolution of the North Atlantic Ocean across the Eocene–Oligocene transition

Abstract. A major step in the long-term Cenozoic evolution toward a glacially driven climate occurred at the Eocene–Oligocene transition (EOT), ∼34.44 to 33.65 million years ago (Ma). Evidence for high-latitude cooling and increased latitudinal temperature gradients across the EOT has been found in a range of marine and terrestrial environments. However, the timing and magnitude of temperature change in the North Atlantic remains highly unconstrained. Here, we use two independent organic geochemical palaeothermometers to reconstruct sea surface temperatures (SSTs) from the southern Labrador Sea (Ocean Drilling Program – ODP Site 647) across the EOT. The new SST records, now the most detailed for the North Atlantic through the 1 Myr leading up to the EOT onset, reveal a distinctive cooling step of ∼3 ∘C (from 27 to 24 ∘C), between 34.9 and 34.3 Ma, which is ∼500 kyr prior to Antarctic glaciation. This cooling step, when compared visually to other SST records, is asynchronous across Atlantic sites, signifying considerable spatiotemporal variability in regional SST evolution. However, overall, it fits within a phase of general SST cooling recorded across sites in the North Atlantic in the 5 Myr bracketing the EOT. Such cooling might be unexpected in light of proxy and modelling studies suggesting the start-up of the Atlantic Meridional Overturning Circulation (AMOC) before the EOT, which should warm the North Atlantic. Results of an EOT modelling study (GFDL CM2.1) help reconcile this, finding that a reduction in atmospheric CO2 from 800 to 400 ppm may be enough to counter the warming from an AMOC start-up, here simulated through Arctic–Atlantic gateway closure. While the model simulations applied here are not yet in full equilibrium, and the experiments are idealised, the results, together with the proxy data, highlight the heterogeneity of basin-scale surface ocean responses to the EOT thermohaline changes, with sharp temperature contrasts expected across the northern North Atlantic as positions of the subtropical and subpolar gyre systems shift. Suggested future work includes increasing spatial coverage and resolution of regional SST proxy records across the North Atlantic to identify likely thermohaline fingerprints of the EOT AMOC start-up, as well as critical analysis of the causes of inter-model responses to help better understand the driving mechanisms.

Multi‐Scale Temporal Variability of Turbulent Mixing During a Monsoon Intra‐Seasonal Oscillation in the Bay of Bengal: An LES Study

AbstractA process study using large‐eddy simulations is carried out to explore the dominant 1‐D processes that affect mixed layer (ML) properties during an event of summer Monsoon Intra‐seasonal Oscillations (MISO) in the Bay of Bengal (BOB). These simulations use realistic air‐sea fluxes and initial conditions that were collected during the summer 2018 MISO‐BOB field experiment to explore the roles of thermal inversion layer (TIL) and Langmuir turbulence (LT) in modulating ML properties. The simulations span an active period with heavy rain and strong winds and a break period with strong solar heat flux and little rain. The mixed layer depth (MLD), sea surface temperature (SST) and sea surface salinity (SSS) are most affected by the presence of near‐inertial oscillations, solar heating and precipitation, all of which occur at different timescales. The subsurface warming induced by the TIL reduces the SST variability at the MISO timescale when compared with the simulation without TIL. Comparison of simulations with and without LT indicates that LT enhances subsurface warming during the active phase and reduces diurnal SST modulation during the break phase. Simulations with 1‐D mixing models show a wide disparity in the evolution of MLD, SST, and SSS.

Estimating predictability of a dynamical system from multiple samples of its evolution.

Natural and social systems exhibit complex behavior reflecting their rich dynamics, whose governing laws are not fully known. This study develops a unified data-driven approach to estimate predictability of such systems when several independent realizations of the system's evolution are available. If the underlying dynamics are quasi-linear, the signal associated with the variable external factors, or forcings, can be estimated as the ensemble mean; this estimation can be optimized by filtering out the part of the variability with a low ensemble-mean-signal-to-residual-noise ratio. The dynamics of the residual internal variability is then encapsulated in an optimal, in a Bayesian sense, linear stochastic model able to predict the observed behavior. This model's self-forecast covariance matrices define a basis of patterns (directions) associated with the maximum forecast skill. Projecting the observed evolution onto these patterns produces the corresponding component time series. These ideas are illustrated by applying the proposed analysis technique to (1) ensemble data of regional sea-surface temperature evolution in the tropical Pacific generated by a state-of-the-art climate model and (2) consumer-spending records across multiple regions of the Russian Federation. These examples map out a range of possible solutions-from a solution characterized by a low-dimensional forced signal and a rich spectrum of predictable internal modes (1)-to the one in which the forced signal is extremely complex, but the number of predictable internal modes is limited (2). In each case, the proposed decompositions offer clues into the underlying dynamical processes, underscoring the usefulness of the proposed framework.

Drivers of Late Miocene Tropical Sea Surface Cooling: A New Perspective From the Equatorial Indian Ocean

AbstractDuring the late Miocene, global cooling occurred alongside the establishment of near‐modern terrestrial and marine ecosystems. Significant (3°C–5°C) sea surface cooling from 7.5 to 5.5 Ma is recorded by proxies at midlatitudes to high latitudes, yet the magnitude of tropical cooling and the role of atmospheric carbon dioxide (pCO2) in driving this trend are debated. Here, we present a new orbital‐resolution sea surface temperature (SST) record spanning the late Miocene to earliest Pliocene (9–5 Ma) from the eastern equatorial Indian Ocean (International Ocean Discovery Program Site U1443) based on Mg/Ca ratios measured in tests of the planktic foraminifer Trilobatus trilobus. Our SST record reveals a 3.2°C decrease from 7.4 to 5.8 Ma, significantly increasing previous estimates of late Miocene tropical cooling. Analysis of orbital‐scale variability shows that before the onset of cooling, SST variations were dominated by precession‐band (19–23 kyr) variability, whereas tropical temperature became highly sensitive to obliquity (41 kyr) after 7.5 Ma, suggesting an increase in high‐latitude forcing. We compare a revised global SST database with new paleoclimate model simulations and show that a pCO2 decrease from 560 to 300 ppm, in the range suggested by pCO2 proxy records, could explain most of the late Miocene sea surface cooling observed at Site U1443. Using our new Site U1443 record as representative of tropical SST evolution, estimated meridional SST gradients suggest a much more modest increase over the late Miocene than previously suggested, in agreement with modeled meridional SST gradients.

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Abstract. The El Niño–Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño), and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: central Pacific (CP) and eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and re-analysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similarly to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs for trends after 1950, but not for the full observed period (1896 onwards). When considering future changes, we do not project a change in CP frequency or amplitude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and amplitude projections. Finally, we find that models show differences in projected precipitation and sea surface temperature (SST) pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in individual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models because of the large spread of results found within a single model due to internal variability alone.

Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea

Sea ice loss and accelerated warming in the Barents Sea have recently been one of the main concerns of climate research. In this study, we investigated the trends and possible relationships between sea surface temperature (SST), sea ice concentration (SIC), and local and large-scale atmospheric parameters over the last 39 years (1982 to 2020). We examined the interannual and long-term spatiotemporal variability of SST and SIC by performing an empirical orthogonal function (EOF) analysis. The SST warming rate from 1982 through 2020 was 0.35 ± 0.04 °C/decade and 0.40 ± 0.04 °C/decade in the ice-covered and ice-free regions, respectively. This climate warming had a significant impact on sea-ice conditions in the Barents Sea, such as a strong decline in the SIC (−6.52 ± 0.78%/decade) and a shortening of the sea-ice season by about −26.1 ± 7.5 days/decade, resulting in a 3.4-month longer summer ice-free period over the last 39 years. On the interannual and longer-term scales, the Barents Sea has shown strong coherent spatiotemporal variability in both SST and SIC. The temporal evolution of SST and SIC are strongly correlated, whereas the Atlantic Multidecadal Oscillation (AMO) influences the spatiotemporal variability of SST and SIC. The highest spatial variability (i.e., the center of action of the first EOF mode) of SST was observed over the region bounded by the northern and southern polar fronts, which are influenced by both warm Atlantic and cold Arctic waters. The largest SIC variability was found over the northeastern Barents Sea and over the Storbanken and Olga Basin. The second EOF mode revealed a dipole structure with out-of-phase variability between the ice-covered and ice-free regions for the SST and between the Svalbard and Novaya Zemlya regions for SIC. In order to investigate the processes that generate these patterns, a correlation analysis was applied to a set of oceanic (SST) and atmospheric parameters (air temperature, zonal, and meridional wind components) and climate indices. This analysis showed that SST and SIC are highly correlated with air temperature and meridional winds and with two climate indices (AMO and East Atlantic Pattern (EAP)) on an interannual time scale. The North Atlantic Oscillation (NAO) only correlated with the second EOF mode of SST on a decadal time scale.

Warmer western tropical South Atlantic during the Last Interglacial relative to the current interglacial period

The Last Interglacial (LIG, 129–116 thousand years ago) is an excellent case study for global warming scenarios and a target for proxy-model comparisons. The LIG global average sea surface temperature (SST) was ~0.5 °C higher than pre-industrial (PI). Contrary to the global average, tropical SST proxy compilations and model simulations show a negative anomaly in LIG SST relative to PI. Here, we present a LIG SST record from marine sediment core GL-1180 retrieved from the western tropical South Atlantic (WTSA). The SST record is based on Mg/Ca ratios of planktonic foraminifera Globigerinoides ruber (white). Our results indicate a warmer LIG in the WTSA relative to PI and Holocene conditions. We show that a positive LIG SST anomaly in tropical regions can be explained by polar sea ice loss during the LIG, which warms the ocean surface all the way into the tropics. The disagreement between proxy results and model simulations from the fourth phase of the Paleoclimate Modelling Intercomparison Project could result from uncertainties in our proxy for SST and/or limitations of numeric models in capturing transient forcings and feedbacks in Earth's climate system and imprecise boundary conditions. Additional studies are warranted to better constrain the LIG SST evolution in tropical regions.

Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0

Abstract. The global impact of an El Niño–Southern Oscillation (ENSO) event can differ greatly depending on whether it is an eastern Pacific (EP)-type event or a central Pacific (CP)-type event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean–atmosphere dynamics that underpin ENSO, achieving skilful forecasts for the spatial patterns of SST anomalies out to 1 year ahead. Furthermore, it is of great significance to analyse the precursors of EP-type or CP-type events and identify targeted observation sensitive areas for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have the greatest impacts on the evolution of ENSO. We use the saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the northern Pacific. It indicates that the subtropical precursors seem to favour the generation of the CP-type El Niño and that the EP-type El Niño is more related to the tropical thermocline dynamics. Furthermore, the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of the two ENSO types.

The multifaceted history of the Walker Circulation during the Plio-Pleistocene

The Walker Circulation (WC) is an east-west trending band of atmospheric circulation cells along the equator and the predominant controller of heat and moisture transport in the tropics. Its variability is closely linked to the sea-surface temperature (SST) changes across the Pacific, the Indian and the Atlantic Oceans and can have pronounced effects on the humidity regimes of the adjacent continents. In recent years, the evolution of the WC during the Plio- and Pleistocene epochs has been intensely studied in the context of the effectiveness of the tropics in modulating global climate change (e.g., the intensification of Northern Hemisphere glaciation).However, the onset of the modern WC pattern as well as its global impact during the Plio- and Pleistocene is controversially assessed in the literature. For its onset, previous studies have suggested dates ranging between 2.4 and 0.8 million years ago (Myr), while its argued impact ranges from – crucially influencing the increase of Northern Hemisphere ice sheet growth by channelling heat and moisture from the tropics into the high latitudes to – having no effect on global ice volume changes. In order to achieve a comprehensive understanding of the spatiotemporal evolution of the WC during this time frame, we statistically analysed 30 globally distributed SST records covering the low and high latitudes between 3.5 and 1.5 Myr, encompassing the Late Pliocene to Early Pleistocene. We utilized a statistical change-point regression model to determine significant change points in the SST evolution of the (sub)-tropics and high latitudes that potentially relate to changes in the WC. We find that the WC experienced a multifaceted evolution between the Late Pliocene and the Early Pleistocene with significant transitional steps at ∼2.7 and ∼2.1 - Ma. Our results suggest after the Late Pliocene, a pre-modern WC set in, which was characterized by a progressively strengthened Pacific Walker Cell alongside a weakened Indian Ocean Walker Cell. This change was potentially triggered by the constriction of the Indonesian seaway, an important transmitter between the Pacific and Indian Ocean. The ensuing mode of the WC intensified until ∼2.1 Myr, when SST values around the global scale signalled a progressive strengthening of the Indian Walker Cell in phase with the progressive strengthening of the Pacific and Atlantic Cells. Our findings indicate that a shift from a pre-modern to a modern-like WC potentially only occurred during the mid-Pleistocene.

A data-model perspective on the Brazilian margin surface warming from the Last Glacial Maximum to the Holocene

The western South Atlantic along the Brazilian margin is an important region for the Atlantic Meridional Overturning Circulation (AMOC) because surface currents in this area transfer warm and salty waters from the Southern Hemisphere to the North Atlantic. Although the number of sea surface temperature (SST) reconstructions has grown in this region, it has been challenging to explain changes in different and sometimes proximal cores. To understand the SST evolution of the Brazilian margin from the Last Glacial Maximum (LGM) to the late Holocene, we present the first SST stack (BRSST stack) for this region. We compare the BRSST stack with the outputs of the transient climate model simulation TraCE-21ka. The BRSST stack shows an LGM cooling of 1.43 °C (from −1.31 to −1.55 °C, 2σ) relative to the late Holocene, followed by deglacial warming starting at ∼ 18.8 ka. TraCE-21ka simulates this early onset of the last deglaciation. Sensitivity experiments suggest that the input of meltwater from retreating ice sheets in the Northern Hemisphere triggered the post-LGM warming, which was subsequently sustained by increasing atmospheric CO2. Deglacial millennial-scale events of AMOC slowdown produced large-scale warming of the Brazilian margin not clearly distinguished by some previous studies. Analyzing our stack in its segregated components, i.e., the North Brazil Current (NBCSST stack) and Brazil Current (BCSST stack) – we noted in-phase warming at the onset of the last deglaciation, which is not found in TraCE-21ka simulations. We attributed this to underestimating the meltwater influence over the tropical Brazilian margin by the model. BRSST stack also presents episodes of abrupt cooling near periods of fast sea-level rise during the last deglaciation, which may be due to rapid AMOC reinvigoration or freshening of the Southern Ocean. Holocene climate resembles that recorded by other compilations, with no clear Holocene thermal maximum but presenting a cooling trend during the late Holocene possibly related to intensified volcanic activity. Our study indicates that the future human-induced SST change expected for the end of the 21st-century will overcome the background of natural climate variability of the last 22,000 years for the Brazilian margin.