Sea Surface Temperature Patterns Research Articles

Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward.

Quantifying Downstream Climate Impacts of Sea Surface Temperature Patterns in the Eastern Tropical Pacific Using Clustering

El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures, limiting the ability to link individual ENSO flavors with downstream phenomena. Hierarchical and k-means clustering methods are used to construct Eastern Pacific patterns from the ERSST dataset spanning 1950 to 2021. Cluster analysis allows for the direct linkage of individual SST years/seasons to ENSO phase, providing insight into ENSO flavors and associated downstream impacts. In this study, four clusters are revealed, each depicting unique SST patterns influenced by ENSO and Pacific Meridional Mode (PMM) phases. A case study demonstrating the utility of the clusters was also carried out using accumulated cyclone energy (ACE) in the Atlantic and Eastern Pacific basins. Results showed that Eastern Pacific (EP) El Niño suppresses Atlantic tropical cyclone (TC) activity, while Central Pacific (CP) La Niña enhances it. Further, EP El Niño, coupled with positive PMM, amplifies ACE. Ultimately, the methods used herein offer a cleaner analysis tool for identifying dominant SSTA patterns and employing those patterns to diagnose downstream climatic effects.

The Impact of Interactive Ocean Dynamics on Atlantic Sea Surface Temperature Variability

Abstract The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere–ocean–ice model to the same atmosphere–ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic multidecadal variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both the reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

What Determines the East Asian Winter Temperature during El Niño? — Role of the Early-Onset El Niño and Tropical Indian Ocean Warming

Abstract Atmospheric teleconnections from the Pacific El Niño are key to determining the East Asian winter climate. Using the database for Policy Decision-making for Future climate change (d4PDF) large ensemble simulations, the present study investigates a mechanism for the warm and cold East Asian winters during El Niño with a focus on atmospheric teleconnections triggered by anomalous sea surface temperature (SST) patterns in the tropical Indo-Pacific. Our results show that the Western Pacific (WP) teleconnection pattern plays a primary role in the warm winters in East Asia. The WP pattern tends to appear in years when both an early El Niño and the positive phase of the Indian Ocean dipole mode (IOD) develop in boreal autumn. In those years, the tropical Indian Ocean (TIO) strongly warms in the following winter, forming a distinct zonal contrast in precipitation anomalies over the tropical Indo-Pacific through a reduced Walker circulation. The Rossby wave source anomalies indicate that the WP pattern is associated with the weakened Indo-Pacific Walker circulation. By contrast, the WP pattern does not dominate in the cold winters due to the absence of strong TIO warming. The present study proposes a mechanism that promotes excitation of the WP pattern through the upper-troposphere divergence in East Asia associated with the Walker circulation modulated by the tropical Indo-Pacific interbasin interaction.

Weakened western Indian Ocean dominance on Antarctic sea ice variability in a changing climate.

Patterns of sea surface temperature (SST) anomalies of the Indian Ocean Dipole (IOD) exhibit strong diversity, ranging from being dominated by the western tropical Indian Ocean (WTIO) to the eastern tropical Indian Ocean (ETIO). Whether and how the different types of IOD variability patterns affect the variability of Antarctic sea ice is not known, nor is how the impact may change in a warming climate. Here, we find that the leading mode of austral spring Antarctic sea ice variability is dominated by WTIO SST variability rather than ETIO SST or El Niño-Southern Oscillation. WTIO warm SST anomalies excite a poleward-propagating Rossby wave, inducing a tri-polar anomaly pattern characterized by a decrease in sea ice near the Amundsen Sea but an increase in regions on both sides. Such impact has been weakening in the two decades post-2000, accompanied by weakened WTIO SST variability. Under greenhouse warming, climate models project a decrease in WTIO SST variability, suggesting that the reduced impact on Antarctic sea ice from the IOD will likely to continue, facilitating a fast decline of Antarctic sea ice.

Long-term variations in terrestrial carbon cycles and atmospheric CO2 levels: Exploring impacts on global ecosystem and climate in the aftermath of end-Cretaceous mass extinction

The short-term climate and environmental consequences (<∼1.0–10 Kyr) immediately following the end-Cretaceous extinction have been attributed to transient effects originating from Deccan volcanism and the Chicxulub impact. These two events have received significant attention for their role in precipitating extreme climate conditions. In contrast, the long-term (> ∼100 Kyr) climate and environmental changes of the post-extinction have been extensively studied in the marine systems, yet their ramifications within terrestrial ecosystems are less well-characterized and require further elucidation. This study presents a carbon isotope analysis of pedogenic carbonates from matured calcisols in the Nanxiong Basin, South China, to reconstruct carbon cycles and atmospheric CO2 concentrations (pCO2) spanning from 76.0 Ma to 62.0 Ma, aiming to decipher the long-term terrestrial environmental and climatic conditions. Results show that δ13C values range from −12.4‰ to −5.0‰ (mean − 9.0‰) and pCO2 varies between ∼250 ppmV and ∼ 2200 ppmV (mean 920 ppmV). When combined with δ13C data from Songliao Basin, NE China, the carbon cycle variation exhibits a 13C collapse and smooth towards subsequent rebound, indicating a process of ∼600 Kyr deterioration, ∼600 Kyr stabilization and ∼ 400 Kyr recovery in the terrestrial ecosystem. The fluctuation of estimated pCO2 aligns with the change pattern of sea surface temperatures, attesting to the strong perturbation of climate and environment at the same pace as the carbon cycle variations. It is proposed that ecosystems and environments in both terrestrial and surface ocean experienced a more unstable, difficult and erratic recovery process and were much more sensitive to climatic changes than in deep ocean for ∼1.5 million years in the aftermath of the end-Cretaceous mass extinction. In addition, the divergence of proxy variations from expected effects implies the Deccan eruption and Chicxulub impact could not have played a long-term role in governing the climatic and environmental perturbations following the Cretaceous-Paleogene Boundary.

Exploring temporal and spatial SST patterns and their impact in the Arabian Sea: Insights from the regional ocean modeling system

The Regional Ocean Modeling System (ROMS) is integrated across the Arabian Sea (AS) from 1992 to 2021 and covers 33°E to 80°E and 5°N to 32°N at high horizontal resolutions of 1/4°(∼25 km). The study demonstrates that the variability is controlled by a seasonal high-resolution setup, with superior performance for various seasons during the months of December-January-February (DJF), March-April-May (MAM), June-July-August-September (JJAS) and October–November (ON). The Sea Surface Temperature (SST) anomalies in the JJAS season werestudied. SST dominates the seasonal variability of the Indian summer monsoon (ISM). Our results show that the ROMS can simulate seasonal variability and its effects on the upper ocean properties over the AS. In addition, the impact of monthly variation is controlled. In recreating the spatio-temporal distribution of surface as well as subsurface hydrographic parameters such as surface and subsurface temperature, the simulation results are reasonable like observation and reanalysis.

Spatial pattern of sea surface temperature and chlorophyll-a trends in relation to hydrodynamic processes in the Alborán Sea

Environmental conditions such as temperature, planktonic biomass, and ocean currents play an important role in the development and distribution of marine species. This work aims to estimate, in a high spatial resolution, the actual trends of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) and to assess the relationship with the local hydrodynamic conditions in the Alborán Sea. To investigate these objectives, time series of SST and Chl-a of satellite sensor data were analyzed during 20 years from January 2001 to December 2020, using the Seasonal-Trend-Loess (STL) decomposition method and the Mann-Kendall seasonality test. The results, obtained with a 95% of confidence, showed that the Alborán Sea basin is subject to sea surface warming evaluated at 0.027 ± 0.008 ° C per year, related to the warming of the Atlantic water mass, which contributes to a decrease of productivity evaluated at -0.0024 ± 0.0003 μg /l per year of Chl-a concentration. These trends are not homogeneous over the entire basin area but show a large regional variation between different parts of the Alborán Sea due to the hydrodynamic process of the Atlantic Jet - Western Alboran Gyre system (AJ-WAG), which is more active especially in summer/autumn seasons and contribute largely to these changes by mixing the waters of the Atlantic Ocean and the Mediterranean Sea.

Deep learning with autoencoders and LSTM for ENSO forecasting

AbstractEl Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep learning to predict the Niño 3.4 index by encoding non-linear sea surface temperature patterns in the tropical Pacific using an autoencoder neural network. The resulting encoded patterns identify crucial centers of action in the Pacific that serve as predictors of the ENSO mode. These patterns are utilized as predictors for forecasting the Niño 3.4 index with a lead time of at least 6 months using the Long Short-Term Memory (LSTM) deep learning model. The analysis uncovers multiple non-linear dipole patterns in the tropical Pacific, with anomalies that are both regionalized and latitudinally oriented that should support a single inter-tropical convergence zone for modeling efforts. Leveraging these encoded patterns as predictors, the LSTM - trained on monthly data from 1950 to 2007 and tested from 2008 to 2022 - shows fidelity in predicting the Niño 3.4 index. The encoded patterns captured the annual cycle of ENSO with a 0.94 correlation between the actual and predicted Niño 3.4 index for lag 12 and 0.91 for lags 6 and 18. Additionally, the 6-month lag predictions excel in detecting extreme ENSO events, achieving an 85% hit rate, outperforming the 70% hit rate at lag 12 and 55% hit rate at lag 18. The prediction accuracy peaks from November to March, with correlations ranging from 0.94 to 0.96. The average correlations in the boreal spring were as large as 0.84, indicating the method has the capability to decrease the spring predictability barrier.

Drivers of Laptev Sea interannual variability in salinity and temperature

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r&gt;0.8) and provides notable improvements compared to the reanalysis product used in this study (r&gt;0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

The relationship between December haze pollution in the Sichuan Basin and preceding climate factors

AbstractHaze pollution (HP) is one of the most serious disasters in the Sichuan Basin (SCB), adversely affecting human health, transportation, tourism and agriculture. HP in December is severe and differs from the haze days in January and February in terms of their temporal variations. To improve the understanding of December haze days in the SCB (DHDSCB), this study not only shows the atmospheric circulations and local meteorological circumstances related with the haze variation, but also analyses its relationship with the prior atmospheric apparent heat source over the Tibetan Plateau (TP) and the sea surface temperature (SST). The key circulation system associated with the interannual variation of the DHDSCB is that the SCB is controlled by the positive geopotential height anomalies and anomalous anticyclonic circulations with weak southwestward water vapour transport. Moreover, unfavourable local meteorological circumstances limit the vertical (horizontal) dispersion of pollutants. In addition, the DHDSCB during 1981–2022 is also found to have significant relationships with two preceding climate factors, which are the atmospheric apparent heat source (Q1) over the western TP in preceding November and the SST difference between the western Maritime Continent and western Australia in preceding autumn. The enhancement of Q1 over the western TP in preceding November is closely related to the midlatitude North Atlantic–southern Mediterranean Middle East–Arabian Sea–TP and its downstream region teleconnection pattern. Consequently, the anticyclonic circulation prevails over the SCB, and SCB is with less precipitation and more haze days in December. The western Maritime Continent and western Australia SST pattern in preceding Autumn is correlated with the Bay of Bengal–eastern TP and its downstream–Japan–Bering Sea teleconnection pattern, providing unfavourable moisture condition for the precipitation over the SCB and leading to the onset, development and maintenance of static weather in December. Therefore, the Q1 over the western TP and the SST pattern over the western Maritime Continent and western Australia may be potential indicators for the subseasonal prediction of HP in the SCB in December.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

Emergent Constraint on Projection of the North Pacific Monsoon Trough and Its Implications for Typhoon Activity Using CMIP6 Models

AbstractThe North Pacific monsoon trough (NPMT) is an imperative large‐scale circulation pattern influencing tropical cyclone (TC) activity in the western North Pacific (WNP), thus its future change has great implications for the WNP TC activity. Here future change in the NPMT and its uncertainty are examined by using 35 climate models from Phase six of Coupled Model Intercomparison Project (CMIP6). Due to the El Niño‐like sea surface temperature (SST) pattern, the multi‐model ensemble (MME) mean projects an eastward extension of the NPMT in the latter half of the 21st century. However, considerable inter‐model uncertainty exists in the projection, which is associated with the diversity in the zonal SST gradient across the tropical Pacific. This diversity in the projected SST is largely rooted in the simulated precipitation biases in the tropical Pacific in the historical experiments of CMIP6 models, which can modulate future Pacific SST distribution through the shortwave‐SST feedback. This linkage between biases in historical climate and future climate allows us to constrain the projection by using observational data sets. Emergent constraint using observational precipitation data set reduces the projection uncertainty by 48% and projects an eastward extension of the north Pacific NPMT by 6.2°. This eastward extension of the NPMT indicates an eastward migration of TC genesis location, and elongated TC track and thus strengthened TC intensity, suggesting an increased TC‐related disaster for residents near the WNP.

Pliocene Weakening of Gradients in Temperature but Not in Productivity in the Eastern Equatorial Pacific

AbstractThe modern eastern equatorial Pacific Ocean (EEP) exhibits strong upwelling, producing pronounced gradients in sea surface temperature (SST), nutrient concentration, and biological productivity between 80° and 140°W. During the globally warmer late Pliocene (3.0–3.6 Ma), the EEP may have experienced permanent El Niño‐like conditions, supported by a reduced SST gradient across the equatorial Pacific. However, the weakened east‐west SST gradient has been controversial, with disparate results depending on the proxy used to monitor Western Warm Pool SSTs. We present new Pliocene alkenone‐based SST and paleoproductivity records from four Ocean Drilling Program (ODP) cores spanning an east‐west transect across the EEP, which present an internally consistent picture of SST and productivity gradients in the modern cold tongue, resolved at orbital‐scale variability. Strong agreement between core top reconstructions and satellite estimates indicates that alkenone paleotemperature and paleoproductivity proxies are appropriate for reconstructing Pliocene EEP conditions. The average SST gradient between 90° and 120°W was reduced from the modern 1.8°C gradient to 0.9°C in the late Pliocene. Despite the weakened SST gradient, the surface productivity gradient was stronger during the late Pliocene compared to modern, based on calibrated X‐ray fluorescence biogenic opal and alkenone average accumulation rates. Contrary to modern El Niño SST and productivity patterns, reduced Pliocene surface productivity did not accompany the weakened SST gradient. Instead, strong Pliocene biogenic opal and alkenone concentration accumulation gradients in the eastern EEP suggest that subsurface tilting of the nutricline and thermocline persisted to supply vigorous upwelling of warm but nutrient‐rich subsurface waters in a warmer climate.

Atmospheric Rivers are Responsible for Cyclicity in Sierra Nevada Precipitation

Abstract Cool-season (November–March) precipitation contributes critically to California’s water resources and flood risk. In the Sierra Nevada, approximately half of cool-season precipitation is derived from a small proportion of storms classified as atmospheric rivers (ARs). The frequency and intensity of ARs are highly variable from year to year and unreliable climate teleconnections limit forecasting. However, previous research provides intriguing evidence of cycles with biennial (2.2 years) and decadal (10–20 years) periodicities in Sierra Nevada cool-season precipitation, suggesting it is not purely stochastic. To identify the source of this cyclicity, we decompose daily precipitation records (1949–2022) into contributions from ARs versus non-ARs, as well as into variations in frequency and intensity. We find that the biennial and decadal spectral peaks in Sierra Nevada precipitation totals are entirely due to precipitation delivered by ARs, and primarily due to variations in the frequency of days with AR precipitation. While total non-AR precipitation correlates with sea surface temperature (SST) and atmospheric pressure patterns associated with the El Niño–Southern Oscillation, AR precipitation shows no consistent remote teleconnections at any periodicity. Supporting this finding, atmospheric simulations forced by observed SSTs do not reproduce the biennial or decadal precipitation variations identified in observations. These results, combined with the lack of long-term stable cycles in previously published tree-ring reconstructions, suggest that the observed biennial and decadal quasi-cyclicity in Sierra Nevada precipitation is unreliable as a forecasting tool. Significance Statement In California’s Sierra Nevada, where most of the state’s above-ground water resources originate, cool-season precipitation totals exhibited year-to-year and decadal cyclicity over the past century. Long-range forecasts are notoriously unskillful in this region, so nonrandom cycles would be intriguing to water managers challenged to simultaneously minimize flood and drought risk. Over 1949–2022, precipitation cycles were driven by variations in the number of atmospheric river (AR) storms per year even though ARs account for just half of total precipitation. These findings bring us a step closer to understanding the causes of precipitation cyclicity, but we find no evidence that the cycles were underpinned by larger-scale ocean–atmosphere circulations so we caution against relying on continued cycles into the future.

Sea surface temperature driven modulation of decadal co-variability in mean and extreme precipitation

This study investigates the role that sea surface temperature (SST) variability plays in modulating the relationship between decadal-scale mean precipitation and monthly-scale extreme precipitation using the Australian Community Climate and Earth System Simulator Earth System model (ACCESS ESM1.5) climate model. The model large ensemble successfully reproduces the observed strong co-variability between monthly mean rainfall and wet extreme rainfall, defined as monthly rainfall totals above the 95th percentile. Removing SST variability in the ACCESS ESM1.5 model significantly weakens the co-variability between mean and wet extremes over most of the globe, showing that SSTs play a key role in modulating this co-variability. The study identifies Pacific and Atlantic SST patterns as the main drivers of the decadal scale co-variability in mean and extreme wet precipitation. On the other hand, observations and model results show that co-variability between mean and dry extremes is generally weaker than for wet extremes, with highly regional signals. Model experiments also show that SST variability plays a weaker role in modulating the co-variability between the mean precipitation and dry extremes as compared to wet extremes. These results suggest that stochastic atmospheric variability plays a stronger role in generating dry precipitation extremes compared SST forcing.

Enhanced North Pacific Victoria mode in a warming climate

The Pacific Decadal Oscillation (PDO), renowned as the dominant sea surface temperature (SST) fluctuation in the North Pacific and extensively scrutinized for its extensive influence on global climate patterns, stands in stark contrast to the Victoria mode (VM). Traditionally, the VM, representing the second most prominent SST pattern in the North Pacific, has not garnered comparable attention. However, our investigation unveils a remarkable surge in the low-frequency VM variability, spanning periods greater than 8 years, over the course of a century. Astonishingly, this enhanced VM variability now surpasses the PDO’s variability in recent decades, signifying a notable shift. Consequently, the heightened VM variability assumes newfound significance in shaping climate systems across the entire North Pacific region and in distant locales. This intensified VM behavior could be attributed to amplified atmospheric variability in the Hawaiian region, primarily stemming from the reinforced variability in the tropical central Pacific (CP) SST in recent decades. As greenhouse warming escalates CP SST variability, the VM’s enhanced variability may further intensify, yielding broader and more profound repercussions in the future.

Marine heatwaves and cold spells in the Brazil Overshoot show distinct sea surface temperature patterns depending on the forcing

The Brazil Current Overshoot regions feature the strongest sea surface temperature variations over the global ocean but forecast skill for these extreme variations is limited. Here we investigate the drivers of marine heatwaves and cold spells in this region and find that ocean flows and sea surface heat fluxes are the primary drivers. We also find that sea surface temperature extremes driven by air-sea heat fluxes produce consistent changes in sea surface temperature and related atmospheric variables over a wide horizontal area, whereas ocean flow-driven sea surface temperature extremes exhibit low consistency in air variables and limited consistency in the spatial extent of sea variables across most sea surface temperature extreme events. These differing air-sea characteristics associated with extremes driven by different drivers offer potential avenues for improving prediction skills and advancing the understanding of three-dimensional air-sea impacts associated with extreme events.

Inconsistent Atlantic Links to Precipitation Extremes over the Humid Tropics

This study investigates extreme wet and dry conditions over the humid tropics and their connections to the variability of the tropical ocean basins using observations and a multi-model ensemble of 24 state-of-the-art coupled climate models, for the 1930–2014 period. The extreme wet (dry) conditions are consistently linked to Central Pacific La Niña (Eastern Pacific El Niño), the weakest being the Congo basin, and homogeneous patterns of sea surface temperature (SST) variability in the tropical Indian Ocean. The Atlantic exhibits markedly varying configurations of SST anomalies, including the Atlantic Niño and pan-Atlantic decadal oscillation, with non-symmetrical patterns between the wet and dry conditions. The oceanic influences are associated with anomalous convection and diabatic heating partly related to variations in the strength of the Walker Circulation. The observed connection between the Amazon basin, as well as the Maritime continent, and the Indo-Pacific variability are better simulated than that of the Congo basin. The observed signs of the Pacific and Indian SST anomalies are reversed for the modelled Congo basin extreme conditions which are, instead, tied to the Atlantic Niño/Niña variability. This Atlantic–Congo basin connection is related to a too southerly location of the simulated inter-tropical convergence zone that is associated with warm SST biases over the Atlantic cold tongue. This study highlights important teleconnections and model improvements necessary for the skillful prediction of extreme precipitation over the humid tropics.

Evaluating the Performance of the North Pacific Victoria Mode as an ENSO Predictor Based on Multimodel Ensemble Hindcasts

Abstract The Victoria mode (VM), similar to the Pacific meridional mode (PMM), is forced by North Pacific Oscillation atmospheric variability. Both the boreal spring VM and PMM can trigger the onset of El Niño–Southern Oscillation (ENSO) events in the following winter. Previous studies have examined the precursor relationship between the PMM and ENSO based on a subset of models drawn from the North American Multimodel Ensemble (NMME) system. They suggested that the PMM can act as a precursor to El Niño events, whereas it fails to predict La Niña events. Utilizing the hindcasts of these models from NMME, this study further investigates the role of the VM as an ENSO predictor to examine the real usefulness of the VM for ENSO prediction. When compared with the PMM, the VM can predict both El Niño and La Niña events with some skill, showing that the VM seems to be a more reliable predictor of ENSO. We found that the unique role of the VM in ENSO prediction originates from the symmetric impact of the VM on ENSO events. The VM, as a basin-scale sea surface temperature (SST) pattern, combines the role of the SST over the subtropical northeastern Pacific that is similar to the PMM in initializing El Niño events with that of the SST over the western North Pacific that is different from PMM in initializing La Niña events, resulting in the symmetric effect of the VM on ENSO prediction. Thus, it is useful to consider VM variability as a reference for ENSO prediction. Significance Statement We aim to investigate whether the robust relationship between the boreal spring Victoria mode (VM) and the following winter ENSO in observations and climate models has any real predictive use, thus bridging the gap between theory and practical application. Our analyses based mainly on model hindcast datasets examine the forecasting skill of the VM for El Niño and La Niña events. We also reveal the symmetric impact of VM on ENSO through the analysis of observations, which explains the VM’s skill in predicting both El Niño and La Niña events. This study deepens our understanding of the effect of North Pacific SST variability on ENSO.