Sea Surface Temperature Variability Research Articles

Regional Variation in Extratropical North Atlantic Air‐Sea Interaction 1960–2020

AbstractAir‐sea interaction in late boreal winter is studied over the extratropical North Atlantic (NA) during 1960–2020 by examining the relationship between sea‐surface temperature (SST) and total turbulent heat flux (THF). The two quantities are positively correlated on interannual timescales over the central‐midlatitude and subpolar NA, suggesting the atmosphere on average drives SST and THF variability is independent of SST. On decadal timescales and over the central‐midlatitude NA the correlation is negative, suggesting ocean processes on average drive SST and THF variability is sensitive to SST. The correlation is positive over the subpolar NA. There, interannual and decadal THF variability is governed by the North Atlantic Oscillation (NAO). During the major late 20th and early 21st century SST increase in the subpolar NA diminishing oceanic heat loss associated with a weakening NAO was observed. This study suggests that the atmosphere is more sensitive to SST over the central‐midlatitude than subpolar NA.

Spatial and Temporal Variability of Ocean Thermal Energy Resource of the Pacific Islands

A lack of natural resources drives the oil dependency in Pacific Island Countries and Territories (PICTs), hampering energy security and imposing high electricity tariffs in the region. Nevertheless, the Western Equatorial Pacific is known for its large Sea Surface Temperature (SST) and deep-sea water (DSW) temperature difference favorable for harvesting thermal energy. In this study, we selected 18 PICTs in the western Equatorial Pacific to estimate Annual Energy Production (AEP) for a 1 MW class Ocean Thermal Energy Conversion (OTEC) plant. We combined the DSW temperature from the mean in situ Argo profiles and 1 km resolution satellite SST data to estimate the thermal energy resource resolving the fine features of the island coastline. Furthermore, the twenty-year-long SST dataset was used to analyze the SST variability. The analysis showed that Equatorial islands and Southern islands have the highest inter-annual variability due to El Nino Southern Oscillation (ENSO). The power density varied from 0.26 to 0.32 W/m2 among the islands, with the lowest values found for the southernmost islands near the South Equatorial Countercurrent. Islands within the South Equatorial Current, Equatorial Undercurrent, and North Equatorial Countercurrent showed the highest values for both power density and gross power. Considering a 1 MW class OTEC plant, Annual Energy Production (AEP) in 2022 varied from 7 GWh to 8 GWh, with relatively low variability among islands near the Equator and in low latitudes. Considering the three variables, AEP, SST variability, and distance from the shore, Nauru is a potential candidate for OTEC, with a net power of 1.14 MW within 1 km from the shore.

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

AbstractWe 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.

The Atlantic Niño Mode: A Thermodynamic or a Dynamic Phenomenon?

AbstractThe Atlantic Niño is an important source of the year‐to‐year variability of the tropical Atlantic, consisting in an irregular oscillation of the Sea Surface Temperature (SST) in the eastern tropical Atlantic. The physical mechanism underlying this oscillation is topic of debate. Some theories, known as dynamical, suggest that the Atlantic Niño is driven by internal wave dynamics. Other theories, termed thermodynamic, propose that the eastern tropical Atlantic SST variability is caused by thermodynamic processes associated with heat fluxes at the ocean surface and/or heat transport by oceanic currents. Here, we examine the SST variability from 1940 to 2022 and find that it can be described in terms of two spatial modes: one in the central and the other in eastern tropical Atlantic. However, focusing on the period from 1993 to 2022, these patterns behave differently. In 1993–2009 they concur in determining the eastern tropical Atlantic SST variability, while in 2010–2022 they act in opposite direction. Therefore, we use Sea Surface Height data and heat fluxes advected by oceanic currents to explore the relative contribution of wave dynamics and heat advection to the eastern tropical Atlantic SST fluctuation. We show that, between 1993 and 2010, the eastern tropical Atlantic SST variability is mainly determined by heat advected from the south by horizontal currents, while between 2010 and 2022 it can be explained in terms of wave dynamics. This finding suggests that the two spatial patterns determining the eastern tropical Atlantic SST variability, are two distinct physical phenomena forced by different physical mechanisms.

Multi‐Year Potential Predictability of the Wintertime Heavy Precipitation Potentials in East Asia

AbstractWe validate the multiyear potential predictability of wintertime heavy precipitation potential in East Asia by combining initialized decadal hindcasts of the global climate model and large ensemble simulations from a high‐resolution global atmospheric model. By analyzing a set of initialized hindcasts, the major predictive components of sea surface temperature (SST) variability beyond interannual timescales are identified as high‐latitudes multidecadal variability and the so‐called trans‐basin variability (TBV). A set of 100 ensemble simulations using a high‐resolution atmospheric model showed a significantly large signal‐to‐noise ratio for the wintertime heavy precipitation potential in East Asia, which is closely related to the TBV. When the SST around the maritime continent is higher, the anomalously low pressure in the northwestern Pacific enhances low‐level cold air transport due to the winter monsoon. Consequently, the resultant weaker baroclinicity in the lower atmosphere reduces storm activity and wintertime heavy precipitation potential in East Asia.

Influence of the sea surface temperature of equatorial Pacific Ocean on the tropical cyclones activity

Introduction. Today, the study of climate is one of the most important tasks of modern science. Particular attention is paid to issues of climate variability on different time scales. The El Niño – Southern Oscillation (ENSO) is the leading signal of interannual climate variability that influences the weather and climate of the globe. It has been revealed that during the onset of its different phases, weather and climate anomalies (up to extreme ones) are observed not only over the tropical zone of the Pacific Ocean, but also in extratropical latitudes, which are transmitted through teleconnections in the ocean-atmosphere system. Numerous studies have shown that ENSO modulates tropical cyclone activity in the northwest Pacific Ocean. The purpose of the study is to determine the relationship between sea surface temperature (SST) changes in the equatorial Pacific Ocean and the frequency of tropical cyclones of varying intensity in the northwestern Pacific Ocean under current climatic conditions. Materials and methods of research. The work uses data of the number of cases and intensity of tropical cyclones (TCs) for the period 1991-2020, and series of indices ONI and SOI. Using these data, statistics on the frequency of TCs in the study area were calculated and Pearson correlation coefficients were calculated. The scientific novelty lies in obtaining quantitative characteristics of the relationship between SST and the number of TCs of varying intensity in modern climatic conditions. The practical significance of the work lies in the fact that TCs are associated with the occurrence of severe weather conditions, which, when a TC reaches the densely populated coast of Southeast Asia, can pretend to be significant losses. Therefore, information that can be used in forecasting TCs and their trajectories is very high importance. Research results. Analysis of the frequency of TCs of different intensity in the northwestern Pacific Ocean during the 1991-2020 showed that there was a decrease in the number of tropical depressions and typhoons. However, there has been a slight increase in the occurrence of tropical storms. The results of the analysis of the influence of SST variability in the equatorial Pacific Ocean on the frequency of TCs, showed that there is a statistically significant relationship only with the frequency of typhoons. The establishment of positive SST in the center of the equatorial Pacific Ocean, associated with the El Niño episode, is associated with an increase in typhoons in the northwestern Pacific Ocean.

Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model

Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies.

Moderate Indian Ocean Dipole dominates spring fire weather conditions in southern Australia

Patterns of the Indian Ocean Dipole (IOD) exhibit strong diversity, ranging from being dominated by the western tropical Indian ocean (WTIO) to by the eastern tropical Indian ocean. How the different types of the IOD variability patterns affect Australian fires differently is unknown, nor is it certain how the impacts may change under greenhouse warming. Here, we find that the moderate IOD, dominated by WTIO sea surface temperature (SST) variability, plays a primary role in affecting southern Australian fire weather conditions during austral spring. During a positive moderate IOD, broad-scaled warm SST anomalies in WTIO force an atmospheric stationary Rossby wave with a high-pressure anomaly over southern Australia. This elevated pressure and associated anomalous atmospheric conditions provide suitable fire weather with hot, dry, and windy conditions, raising fire risks in southern Australia. Such impact is distinctively different from that strong IOD-induced. As predicted by climate models, decreased moderate IOD variability in the future will result in weakened Australian fire weather responses.

First Analyses of the TIMELINE AVHRR SST Product: Long-Term Trends of Sea Surface Temperature at 1 km Resolution across European Coastal Zones

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions.

Numerical Modeling of Atmospheric Temperature and Stratospheric Ozone Sensitivity to Sea Surface Temperature Variability

The results of numerical experiments with a chemistry–climate model of the lower and middle atmosphere are presented to study the sensitivity of the polar stratosphere of the Northern and Southern Hemispheres to sea surface temperature (SST) variability, both as a result of interannual variability associated with the Southern Oscillation, and because of long-term increases in SST under global warming. An analysis of the results of model experiments showed that for both scenarios of SST changes, the response of the polar stratosphere for the Northern and Southern Hemispheres is very different. In the Arctic, during the El Niño phase, conditions are created for the polar vortex to become less stable, and in the Antarctic, on the contrary, for it to become more stable, which is expressed in a weakening of the zonal wind in the winter in the Arctic and its increase in the Antarctic, followed by a spring decrease in temperature and concentration of ozone in the Antarctic and their increase in the Arctic. Global warming creates a tendency for the polar vortex to weaken in winter in the Arctic and strengthen it in the Antarctic. As a result, in the Antarctic, the concentration of ozone in the polar stratosphere decreases both in winter (June–August) and, especially, in spring (September–November). Global warming may hinder ozone recovery which is expected as a result of the reduced emissions of ozone-depleting substances. The model results demonstrate the dominant influence of Brewer–Dobson circulation variability on temperature and ozone in the polar stratosphere compared with changes in wave activity, both with changes in SST in the Southern Oscillation and with increases in SST due to global warming.

Logistic curve modelling of sea surface temperature and latent heat flux variability in the Tropical Indian Ocean

The Tropical Indian Ocean (TIO) is one of the most vulnerable regions to climate change due to its unique ocean-atmosphere interactions. The region is characterized by warm temperatures, high evaporation rates, and strong convection, making it particularly vulnerable to greenhouse gases. This susceptibility leads to an overall trend of warming across the Earth's surface and atmosphere, intensifying sea surface temperatures (SST) and increasing evaporation rates, consequently influencing Surface Latent Heat Flux (SLHF). The strong influence of both SST and SLHF on atmospheric circulation and precipitation patterns makes them critical factors in determining the region's climate, particularly in monsoon-dominated regions. Analysis reveals a consistent upward trend in both SST and SLHF in the central region of TIO. To model this, standard Logistic Curve Model (LCM) has been applied to these parameters averaged over the central region of the TIO. The model is run for a period of 20 years from 2001 to 2020, grouped into four lustrums. The LCM-derived SST and SLHF are in good consistent with observed datasets during the above periods with, correlation coefficients ranging from 0.92 to 0.96 for SST and 0.86–0.90 for SLHF. Extending the model spatially across the entire TIO region explains the ability to project fluctuations in SST and SLHF values across different seasons. These findings highlight the model's relevance for capturing short-term, long-term, and seasonal variability of the parameters, providing important insights into regional climate dynamics.

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

Competing impacts of tropical Pacific and Atlantic on Southern Ocean inter-decadal variability

The observed Southern Ocean sea surface temperature (SST) has experienced prominent inter-decadal variability nearly in phase with the Inter-decadal Pacific Oscillation (IPO), but less associated with the Atlantic Multidecadal Variability (AMV), challenging the prevailing view of Pacific-Atlantic synergistic effects. Yet, the mechanisms of distinct trans-hemispheric connections to the Southern Ocean remain indecisive. Here, by individually constraining the observed cold-polarity and warm-polarity IPO and AMV SSTs in a climate model, we show that the IPO is influential in initiating a basin-wide Southern Ocean response, with the AMV secondary. A tropical Pacific-wide cooling triggers a basin-scale Southern Ocean cold episode through a strong Rossby wave response to the north-to-south cross-equatorial weakened Hadley circulation. By contrast, due to the competing role of tropical Pacific cooling, an Atlantic warming partly cools the Southern Ocean via a weak Rossby wave response to the south-to-north cross-equatorial enhanced Hadley circulation. Conversely, tropical Pacific warming leads to a warm Southern Ocean episode. Our findings highlight that properly accounting for the tropical Pacific SST variability may provide a potential for skillful prediction of Southern Ocean climate change and more reliable estimates of climate sensitivity, currently overestimated by the misrepresented Southern Ocean warming.

Global Multiproxy ENSO Reconstruction Over the Past Millennium

AbstractEl Niño‐Southern Oscillation (ENSO) is the leading mode of interannual climate variability that affects climate and society across the world. However, our understanding of ENSO variability is currently lacking due to short instrumental observations, which limit our confidence in predicting its future occurrence and impact. In this study, we use the nested principal component regression method to reconstruct the ENSO index based on a global‐scale proxy network of stable oxygen isotopes (δ18O). We account for the teleconnection changes that influence proxy records corresponding to different ENSO phases by using the isotope‐enabled Community Earth System Model. According to the precipitation δ18O–ENSO relationship, we reconstruct the ENSO index over the last eight hundred years based on the first two leading modes of the selected proxy records. The skill of ENSO reconstruction improves with an increased number of δ18O series distributed over the ENSO‐affected regions. Our new reconstruction shows high consistency with existing ENSO reconstructions during periods of strong ENSO variability, indicating that the global proxy records capture enhanced teleconnection signals during the phase of strong ENSO amplitude. There is a weak El Niño response 1 year after the large volcanic eruptions in our reconstruction, and the amplitude of ENSO is significantly correlated with volcanic intensity. The strengthened energetic interactions between the ocean and atmosphere following large volcanic eruptions result in enhanced sea surface temperature variability.

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.

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

Winter “warm Arctic-cold Eurasia” pattern and its statistical linkages to oceanic precursors during the era of satellite observations

A striking recurrent feature of winter climate variability is the “warm Arctic-cold Eurasia” (WACE) pattern of opposite sign anomalies of surface air temperature (SAT) in the Barents Sea region and midlatitude Eurasia. Its origins and mechanisms are hotly debated, and its predictability remains unknown. This study investigates statistical relationships of the winter WACE dipole with concurrent anomalies of atmospheric circulation and oceanic precursors during the era of satellite observations. The results reveal a high potential for seasonal predictability of not only the WACE dipole but also several related indicators of winter climate variability, including the Arctic and Eurasian SAT anomalies. During subperiods of extreme covariability between the Arctic and Eurasian SATs around the early 1980s and late 2000s, most of the WACE variability is explained by ocean temperature and surface turbulent heat flux anomalies in the Barents Sea region during the preceding months. Anomalies in summer Atlantic water temperature (AWT) and autumnal sea surface temperature (SST) in this region explain about 70–80% of the variance of the following winter WACE variability during all events of strong Arctic-Eurasian SAT covariability. Analysis of SST variability in the Arctic-North Atlantic region suggests that the winter WACE link to the summer AWT anomalies reflects an atmospheric response to a large-scale surface reemergence of ocean temperature anomalies. However, this linkage had been robust only until the early 2000s. Since then, the winter WACE variability has been strongly related to autumnal SST anomalies in the Barents Sea region and the North Pacific.

Determining sea-level rise in the Caribbean: A shift from temperature to mass control

Tropical Small Island Developing States (SIDS), such as those in the Caribbean, are among the most vulnerable to the impacts of climate change, most notably sea-level rise. The current sea-level rise in the Caribbean is 3.40 ± 0.3 mm/year (1993–2019), which is similar to the 3.25 ± 0.4 mm/year global mean sea-level (GMSL) rise (1993–2018). Throughout the year, Caribbean seasonal sea-level variability is found to respond to sea surface temperature variability. Over the past few decades, the trend in Caribbean Sea-level rise is also found to be variable. Satellite altimetry and steric sea-level records of the Caribbean region reveal a shift in the late 2003-early 2004, which separates two distinct periods of sea-level rise. Thermal expansion dominates the sea-level trend from 1993–2003. Following this period, there is an increased trend in sea-level rise, with a dominance of mass changes from 2004–2019, as confirmed by GRACE data. During this period, the sea-level trend is 6.15 ± 0.5 mm/year, which is 67% faster than the most recent estimates of global mean sea-level rise provided by the Intergovernmental Panel on Climate Change (3.69 ± 0.5 mm/year for the period 2006–2018). Despite its reduced importance, increasing temperatures contribute greatly to sea-level rise in the Caribbean region through thermal expansion of ocean water, hence there is a need to limit the current trend of global warming.

Impacts of Atmospheric Internal Variations on the Variability of Sea Surface Temperature Based on the Hydra‐SINTEX Model

AbstractOcean–atmosphere interactions largely control the variabilities of the climate system on Earth. However, how much atmospheric internal signals contribute to climate variabilities remains uncertain over many parts of the globe. Here, we develop an interactive ensemble coupled model (called Hydra‐SINTEX) to investigate the influences of atmospheric internal variations (AIVs) on the mean‐states and variability of the climate system. The results show that, while climatological mean‐states are little affected, the AIVs can largely influence climate variabilities over the globe. We pay particular attention to two regions, that is, the tropical eastern Indian Ocean, which is the key area of the Indian Ocean Dipole (IOD), and the subtropical North Pacific. We found that sea surface temperature (SST) variabilities in these two regions are much reduced without the AIVs but with distinct mechanisms. Without the AIVs, the intensity of the IOD is largely reduced in association with weakened air–sea coupling in the tropics. This indicates the importance of atmospheric noise forcing on the development of the IOD. In contrast, the reduction of SST variability in the subtropical North Pacific is caused by the absence of the AIVs that are generated by both mid‐latitude atmospheric processes and weakened remote influence of the tropical SST in accordance with the reduced SST signals there.