Sea Surface Temperature Variability Research Articles

Conceptual Models for Exploring Sea-Surface Temperature Variability Vis-à Long-Range Weather Forecasting

This paper analyzes the ability of three conceptual stochastic models (one-box, two-box, and diffusion models) to reproduce essential features of sea surface temperature variability on intra-annual time scales. The variability of sea surface temperature, which is particularly influenced by feedback mechanisms in ocean surface–atmosphere coupling processes, is characterized by power spectral density, commonly used to analyze the response of dynamical systems to random forcing. The models are aimed at studying local effects of ocean–atmosphere interactions. Comparing observed and theoretical power spectra shows that in dynamically inactive ocean regions (e.g., north-eastern part of the Pacific Ocean), sea surface temperature variability can be described by linear stochastic models such as one-box and two-box models. In regions of the world ocean (e.g., north-western Pacific Ocean, subtropics of the North Atlantic, the Southern Ocean), in which the observed sea surface temperature spectra on the intra-annual time scales do not obey the ν−2 law (where ν is a regular frequency), the formation mechanisms of sea surface anomalies are mainly determined by ocean circulation rather than by local ocean–atmosphere interactions. The diffusion model can be used for simulating sea surface temperature anomalies in such areas of the global ocean. The models examined are not able to reproduce the variability of sea surface temperature over the entire frequency range for two primary reasons; first, because the object of study, the ocean surface mixed layer, changes during the year, and second, due to the difference in the physics of processes involved at different time scales.

Enhanced interaction between ENSO and the South Atlantic subtropical dipole over the past four decades

AbstractThis study investigates the relationship between sea surface temperature (SST) anomalies in the subtropical Atlantic Ocean, as represented by the Southern Atlantic subtropical dipole (SASD), and SST anomalies in the tropical Pacific Ocean, identified by the El Niño‐Southern Oscillation (ENSO). Contrary to the previously held notion of a weak relationship between SASD and ENSO as suggested by earlier literature, our analysis reveals a substantial inverse correlation between the two. This correlation exhibits significant multi‐decadal variability, which has notably intensified over the most recent two decades compared with the preceding two decades. This intensification in the SASD–ENSO inverse correlation may be attributed to the shift in ENSO regime from predominance of eastern Pacific El Niño to central Pacific El Niño events around the turn of the century. This transition triggers wavetrains that propagate along different paths, consequently influencing the South Atlantic subtropical high and inducing alterations in anomalous SST patterns in the subtropical Atlantic Ocean. These findings advance our comprehension of the interactions between South Atlantic and Pacific SST variations, which strongly influence rainfall patterns, particularly in South America and southern Africa. Understanding such teleconnection holds promise for improving sub‐seasonal to seasonal precipitation predictions in these regions.

Evaluation of CMIP6 models for sea surface temperature and sea surface salinity variability over the Arabian Sea

The capability of Coupled Model Inter-comparison Project Phase-6 (CMIP6) is analysed in simulating the seasonal variability of Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) over the Arabian Sea in this study. The historical simulations of 11 CMIP6 models, which come from various source are validated with the observational data (HadISST for SST and SODA for SSS). This study covers the Arabian Sea, spanning 5°S to 32°N and 33°E to 80°E from 1985 to 2014 (30 years). Seasonal mean climatology and annual cycle of SST and SSS are estimated over the Arabian Sea. The deviation of coupled model-simulated SST and SSS from the observation is quantified by determining the biases during all the seasons. Four distinct model evaluation measures are used to access the performance of CMIP6 models: root mean square error (RMSE), standard deviation (STD), correlation coefficient (CC) and interannual variability skill score (IVS). CMCC-CM2-SR5, NESM3 and IITM-ESM models for SST yield CC/RMSE values of 0.988/0.174, 0.983/0.204 and 0.963/0.299, respectively. CMCC-CM2-SR5, IPSL-CM6A-LR and CanESM5 models for SSS yield CC/RMSE values of 0.956/0.035, 0.937/0.042 and 0.820/0.069, respectively. Thus, these models having higher CC and lower RMSE among all models, give better performance. The present study is important as the future scenarios can be predicted using the best models.

Variability of SST through Koopman Modes

Abstract The majority of dynamical systems arising from applications show a chaotic character. This is especially true for climate and weather applications. We present here an application of Koopman operator theory to tropical and global sea surface temperature (SST) that yields an approximation to the continuous spectrum typical of these situations. We also show that the Koopman modes yield a decomposition of the datasets that can be used to categorize the variability. Most relevant modes emerge naturally, and they can be identified easily. A difference with other analysis methods such as empirical orthogonal function (EOF) or Fourier expansion is that the Koopman modes have a dynamical interpretation, thanks to their connection to the Koopman operator, and they are not constrained in their shape by special requirements such as orthogonality (as it is the case for EOF) or pure periodicity (as in the case of Fourier expansions). The pure periodic modes emerge naturally, and they form a subspace that can be interpreted as the limiting subspace for the variability. The stationary states therefore are the scaffolding around which the dynamics takes place. The modes can also be traced to the Niño variability and in the case of the global SST to the Pacific decadal oscillation (PDO). Significance Statement We compute the Koopman modes for the tropical and global SST, demonstrating that significant dynamical modes can be identified also in complex high-dimensional datasets with continuous spectra. The Koopman modes are then used to identify the stationary subspace, namely, the limit subspace for the invariant evolution of the system.

Influence of Kuroshio Extension’s sea surface temperature variability on the North Pacific atmosphere and Pacific Decadal Precession

Recent research has revealed links between a quasi-decadal mode of climate variability over the North Pacific – the Pacific Decadal Precession (PDP) – and the North Pacific’s western boundary current’s extension – the Kuroshio Extension (KE). It is suggested that on decadal time scales the PDP both responds to and influences the KE variability. A question yet to be answered is whether it is the large-scale or the mesoscale variations of the KE region that link with the PDP evolution. Using high-resolution sea surface temperature data (1981–2018) from the global ocean Operational Sea Surface Temperature (SST) and Sea Ice Analysis, low-resolution Extended Reconstructed SST (ERSST) version 3b data (1949–2018), geopotential height reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) we find that it is the large-scale variations in the KE region that correlate best with the PDP-like response in the overlying and downstream atmosphere as compared to the mesoscale variations. In particular, the second mode of the large-scale KE region, which is characterized by the warming (cooling) of the ocean south (north) of the KE, sets up a PDP-like north-south atmospheric pressure dipole over the North Pacific Ocean by altering the large-scale baroclinicity of the atmosphere and zonal intensification of the subtropical jet stream. In turn, there is a reduction in the zonal propagation of stationary wave energy and an enhancement of the climatological zonal wave heights over North America, which results in a downstream response over the North American continent and the formation of a subsequent east-west pressure dipole over the North Pacific and North American continent. As a result, there is a strong correlation between large-scale SST variations in the KE region and the evolution of the PDP over the next three years.

Sea surface temperature and current-related parameters affecting local adaptation of scalloped spiny lobster population in Indonesia’s archipelagic system

The Scalloped Spiny lobster (Panulirus homarus, Linnaeus 1758) known as one of the commercially harvested Panilurid lobster. This species was distributed widely across continents. Indonesia, as one of the largest archipelagic systems in the world, was also distribution area of the Scalloped Spiny lobster. These facts have led to questions regarding spiny lobster harvest and culture management by considering population differentiation and habitat fragmentation on complex and distinct archipelagic islands. Our investigation was conducted using high-density SNPs datasets from several spiny lobsters harvested from five locations in Indonesia. We found strong differentiation among spiny lobster populations clustered into 3 sub-populations. Environment association analysis and Fst analysis revealed outlier loci significantly associated with Sea Surface Temperature variation and potentially correlated with Sea Current-related parameters. The evidence of a structured population of scalloped spiny lobsters in Indonesia can serve as a consideration in the management of spiny lobsters.

Ocean feedback on 50–80-day boreal winter intraseasonal oscillation in the Indian Ocean: comparison of atmospheric and coupled CESM2 simulations

Abstract The role of sea surface temperature (SST) variability in the boreal winter intraseasonal oscillation (ISO) over the tropical Indian Ocean (TIO) is investigated in this study using the Community Earth System Model Version 2 (CESM2). An atmospheric general coupled model (AGCM) run forced by daily SSTs derived from a parallel coupled circulation model (CGCM) run is compared with reanalysis and the original coupled simulation. The comparison of atmospheric-coupled GCMs reveals the frequency and spatial tendency of the SST effect on the atmospheric ISO. The lower-frequency component of the ISO over the TIO is significantly intensified in the AGCM run. The SST-to-atmosphere effect is most prominent over the southeastern TIO. In the AGCM run, low-level wind anomalies related to SST appear west of the ISO convection center, contrasting with the previous findings that the SST effect appears east of the convection center.

Feedbacks, Pattern Effects, and Efficacies in a Large Ensemble of HadGEM3‐GC3.1‐LL Historical Simulations

AbstractClimate feedbacks over the historical period (here defined as 1850–2014) have been investigated in large ensembles of historical and single forcing experiments (hist‐ghg, hist‐aer, and hist‐nat), with 47 members for each experiment. Across the historical ensemble with all forcings, a range in estimated Effective Climate Sensitivity (EffCS) between approximately 3–6 K is found, a considerable spread stemming solely from initial condition uncertainty. The spread in EffCS is associated with varying Sea Surface Temperature (SST) patterns seen across the ensemble due to their influence on different feedback processes. For example, the level of polar amplification is strongly correlated with the amount of sea ice melt per degree of global warming. This mechanism is related to the large spread in shortwave clear‐sky feedbacks and is the main contributor to the different forcing efficacies seen across the different forcing agents, although in HadGEM3‐GC3.1‐LL these differences in forcing efficacy are shown to be small. The spread in other feedbacks is also investigated, with the level of tropical SST warming strongly correlated with the longwave clear‐sky feedbacks, and the local surface‐air‐temperatures well correlated with the spread in cloud radiative effect feedbacks. The metrics used to understand the spread in feedbacks can also help to explain the disparity between feedbacks seen in the historical experiment simulations and modeled estimate of the feedbacks seen in the real world derived from an atmosphere‐only experiment prescribed with observed SSTs (termed amip‐piForcing).

Role of atmospheric and oceanic processes on interannual summertime (2016–2017) decrease of sea ice in the Antarctic regions of the Southern Ocean

In this article, the interannual variability of sea ice in the Antarctic sea ice regions between 2013–2018 is studied using a global ocean sea ice coupled model and satellite observation. The numerical model reasonably well simulates satellite observed interannual variability of sea ice concentration (SIC) and sea surface temperature (SST) in the Antarctic regions of Southern Ocean during all four austral seasons; summer (December–February), autumn (March–May), winter (June–August), and spring (September–November).A comparison of satellite and model shows that, during last two decades between 2001–2020, summertime of 2016–2017 had the lowest (highest) SIC (SST) across the Antarctic sea ice regions. Mixed layer heat budget analysis has been performed to comprehend how thermodynamic processes affect changes in SIC and SST in the Antarctic sea ice regions. The strong positive net atmospheric heat flux and the negative ocean vertical entrainment during summertime of 2016–2017 resulted in increased SST compared to other years, which lead to decreased SIC during above years. Also, loss of sea ice during summertime of 2016–2017 in the Antarctic sea ice regions are linked with significant decrease of wind stress magnitude and increase of wind stress curl.

Global warming and coastal protected areas: A study on phytoplankton abundance and sea surface temperature in different regions of the Brazilian South Atlantic Coastal Ocean.

In this study, we examined the relationship between sea surface temperature (SST) and phytoplankton abundance in coastal regions of the Brazilian South Atlantic: São Paulo, Paraná, and Santa Catarina, and the Protection Area of Southern right whales (Eubalaena australis) in Santa Catarina (APA), a conservation zone established along 130 km of coastline. Using SST and chlorophyll-a (Chl-a) data from 2002 to 2023, we found significant differences in SST between the regions, with São Paulo having the highest SST, followed by Paraná and Santa Catarina. All locations showed a consistent increase in SST over the years, with North Santa Catarina, APA and São Paulo experiencing the lowest rate of increase. Correlation analyses between SST and Chl-a revealed a stronger inverse relationship in North Santa Catarina and APA, indicating an increased response of Chl-a to SST variations in this region. The presence of protected area appears to play an essential role in reducing the negative impacts of increasing SST. Specifically, while there is a wealth of research on the consequences of global warming on diverse coastal and oceanic areas, heterogeneity among different settings persists and the causes for this necessitating attention. Our findings have implications for both localized scientific approaches and broader climate policies, emphasizing the importance of considering coastal ecosystem resilience to climate change in future conservation and adaptation strategies.

Drivers of future extratropical sea surface temperature variability changes in the North Pacific

Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability (and correspondingly marine heatwave intensity) on monthly timescales and longer is spatially heterogeneous. We examined these projected changes (between 1960–2000 and 2060–2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change.

Decreased ENSO post-2100 in response to formation of a permanent El Niño-like state under greenhouse warming

Under transient greenhouse warming, El Niño-Southern Oscillation (ENSO) is projected to increase pre-2100, accompanied by an easier establishment of atmospheric convection in the equatorial eastern Pacific, where sea surface temperature (SST) warms faster than surrounding regions. After 2100, how ENSO variability may change remains unknown. Here we find that under a high emission scenario, ENSO variability post-2100 reverses from the initial increase to an amplitude far smaller than that of the 20th century. The fast eastern warming persists and shrinks the equatorial Pacific non-convective area, such that establishing convection in the non-convective area, as during an El Niño, requires smaller convective anomaly, inducing weaker wind anomalies leading to reduced ENSO SST variability. The nonlinear ENSO response is thus a symptom of the persistent El Niño-like warming pattern. Therefore, the oscillatory ENSO impact could be replaced by that from the permanent El Niño-like mean condition with cumulative influences on affected regions.

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.

Investigating the seasonal SST Predictability in the Northern Tropical Atlantic Ocean in an ensemble prediction system

The present study comprehensively investigates the practical and intrinsic predictability of the sea surface temperature (SST) in the Northern Tropical Atlantic (NTA) based on the 138-year-long coupled hindcasts with a recently developed seasonal ensemble prediction system. This system can yield skillful deterministic predictions for the prominent warm and cold events at least 6 months ahead. Notably, it excels in providing probabilistic predictions for below- and above-normal events rather than for neutral events. The predictability of SST in the NTA undergoes remarkable seasonal variation with two peaks of predictability targeted at April and October regardless of the lead time. Various sources of predictability for these target months are revealed. For the target month of April, the preceding remote forcing from the El Niño–Southern Oscillation (ENSO) in the tropical Pacific Ocean combined with local signal results in the phase locking of the SST variation and seasonality of signal component over the NTA. This ultimately contributes to the high predictability targeted at April. However, From the perspective of potential predictability of the predictability targeted at October, which has been rarely mentioned in previous studies. It is also encouraging that, similar to the Indian Ocean Dipole, ENSO and the signal-to-noise ratio of the system mainly contribute to predictability beyond persistence at long lead times for the spring SST in the NTA. This indicates that potential future ENSO improvements may leave much room for improvement in the current SST prediction in the NTA.

Variations in the Central Mode Water in the North Pacific as a manifestation of the Pacific Decadal Oscillation

The North Pacific Central Mode Water (CMW) is examined from the viewpoint of its volume variations. The volume of the CMW layers thicker than 150m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$150 m$$\\end{document} is used as an index of CMW variations, which successfully represents the year-to-year and decadal variations in the CMW volume. The CMW index shows the variation close to that of the Pacific Decadal Oscillation (PDO). The CMW variation is strongly tied with large-scale, dominant variations in sea surface temperature (SST), surface dynamic height (SDH), and sea surface height (SSH) anomalies in the North Pacific, with a significant correlation with the PDO. Year-to-year and decadal variations of CMW volume in May to July are significantly correlated with the wintertime Aleutian Low and 500hPa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$hPa$$\\end{document} geopotential height variations, which indicates that the Aleutian Low induces the eastward extension/retreat of the strong winter westerlies and resultant net surface heat flux anomaly over the CMW distribution region. Thus, the CMW volume variation can be regarded as a significant manifestation of the PDO. The SST, SDH, and SSH anomalies are associated with the surface cooling in the northern sector of the CMW distribution region. On the other hand, the SDH and SSH anomalies throughout a year and the SST anomaly in the cold season in the southeastern sector of the CMW region are formed due to the heaving of isopycnal surfaces in the subsurface layer above the CMW in response to its volume variations.

A new, high‐resolution atmospheric dataset for southern New Zealand, 2005–2020

AbstractThe regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south‐west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large‐scale SST and local‐scale high‐mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16‐year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high‐mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near‐surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high‐mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16‐year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ‐PROXY_16yrWRF).

Reconstruction of the marine carbonate system at the Western Tropical Atlantic: trends and variabilities from 20 years of the PIRATA program

The Western Tropical Atlantic Ocean (WTAO) is crucial for understanding CO2 dynamics due to inputs from major rivers (Amazon and Orinoco), substantial rainfall from the Intertropical Convergence Zone (ITCZ), and CO2-rich waters from equatorial upwelling. This study, spanning 1998 to 2018, utilized sea surface temperature (SST) and sea surface salinity (SSS) data from the PIRATA buoy at 8°N 38°W to reconstruct the surface marine carbonate system. Empirical models derived total alkalinity (TA) and dissolved inorganic carbon (DIC) from SSS, with subsequent estimation of pH and fCO2 from TA, DIC, SSS, and SST data. Linear trend analysis showed statistically significant temporal trends: DIC and fCO2 increased at a rate of 0.7 µmol kg-1 year-1 and 1.539 µatm year-1, respectively, and pH decreased at a rate of -0.001 pH units year-1, although DIC did not show any trend after data was de-seasoned. Rainfall analysis revealed distinct dry (July to December) and wet (January to June) seasons, aligning with lower and higher freshwater influence on the ocean surface, respectively. TA, DIC, and pH correlated positively with SSS, exhibiting higher values during the dry season and lower values during the wet season. Conversely, fCO2 correlated positively with SST, showcasing higher values during the wet season and lower values during the dry season. This emphasizes the influential roles of SSS and SST variability in CO2 solubility within the region. Finally, we have analysed the difference between TA and DIC (TA-DIC) as an indicator for ocean acidification and found a decreasing trend of -0.93 ± 0.02 μmol kg-1 year-1, reinforcing the reduction in the surface ocean buffering capacity in this area. All trends found for the region agree with data from other stations in the tropical and subtropical Atlantic Ocean. In conclusion, the use of empirical models proposed in this study has proven to help filling the gaps in marine carbonate system data in the Western Tropical Atlantic.

Southern Hemisphere Circumpolar Wavenumber‐4 Pattern Simulated in SINTEX‐F2 Coupled Model

AbstractInterannual sea surface temperature (SST) variations in the subtropical‐midlatitude Southern Hemisphere are often associated with a circumpolar wavenumber‐4 (W4) pattern. This study is the first attempt to successfully simulate the SST‐W4 pattern using a state‐of‐the‐art coupled model called SINTEX‐F2 and clarify the underlying physical processes. It is found that the SST variability in the southwestern subtropical Pacific (SWSP) plays a key role in triggering atmospheric variability and generating the SST‐W4 pattern during austral summer (December‐February). In contrast, the tropical SST variability has a very limited effect. The anomalous convection and associated divergence over the SWSP induce atmospheric Rossby waves confined in the westerly jet. Then, the synoptic disturbances circumnavigate the subtropical Southern Hemisphere, establishing an atmospheric W4 pattern. The atmospheric W4 pattern has an equivalent barotropic structure in the troposphere, and it interacts with the upper ocean, causing variations in mixed layer depth due to latent heat flux (LHF) anomalies. As incoming climatological solar radiation goes into a thinner (thicker) mixed layer, the shallower (deeper) mixed layer promotes surface warming (cooling). This leads to positive (negative) SST anomalies, developing the SST‐W4 pattern during austral summer. Subsequently, anomalous entrainment due to the temperature difference between the mixed layer and the water below the mixed layer, anomalous LHF, and disappearance of the overlying atmospheric W4 pattern cause the decay of the SST‐W4 pattern during austral autumn. These results indicate that accurate simulation of the atmospheric forcing and the associated atmosphere‐ocean interaction is essential to capture the SST‐W4 pattern in coupled models.

Impacts of the Madden‐Julian Oscillation on Marginal Sea Heatwaves of the Southeastern Tropical Indian Ocean

AbstractMarginal seas of the southeastern tropical Indian Ocean (SETIO), including the South Java coasts and the Timor and Arafura Seas, are hotspots of large‐scale marine heatwaves (MHWs) in austral summer with notable impacts on local ecosystems. Yet, the prediction of these MHWs remains challenging. By analyzing 39 MHWs and 45 marine cold‐spells (MCSs) observed in the summers of 1982–2021, this study reveals a robust modulation effect of the Madden‐Julian Oscillation (MJO) on these events. The results show that 56% of MHWs and 51% of MCSs overlapped with MJO events, and their duration is systematically longer than others. Moreover, MHWs in phases 3–4 and MCSs in phases 6–7 of the real‐time multivariate MJO (RMM) index were nearly 2–3 times stronger in intensity than the others. The passage of MJOs drives prominent intraseasonal sea surface temperature (SST) variability in the SETIO through perturbing surface heat fluxes, which in turn modulates the occurrence of MHWs and MCSs. During RMM phases 1–2 (convective center over of the east of Africa or the central‐western Indian Ocean), the SETIO is controlled by a dry condition, with increased surface insolation and suppressed latent heat release. These anomalous fluxes drive a persistent SST increase, facilitating the emergence of strong and prolonged MHWs in the subsequent RMM phases 3–4. Further analysis suggests an 18% probability for MHWs in the SETIO 12 ± 10 days after Phase 1. Therefore, the MJO contributes to the predictability of MHWs/MCSs of the SETIO, with implications for predicting climate extremes and hazards.

Exploring the Factors Controlling the Annual Range of Amazon Precipitation

Abstract The annual range (AR) of precipitation in the Amazon River basin has increased steadily since 1979. This increase may have resulted from natural variability and/or anthropogenic forcing, such as local land-use changes and global warming, which has yet to be explored. In this study, climate model experiments using the Community Earth System Model, version 2 (CESM2), were conducted to examine the relative contributions of sea surface temperatures (SSTs) variability and anthropogenic forcings to the AR changes in the Amazon rainfall. With CESM2, we design several factorial simulations, instead of actual model projection. We found that the North Atlantic SSTs fluctuation dominantly decreases the precipitation AR trend over the Amazon by −85%. In contrast, other factors, including deforestation and carbon dioxide, contributed to the trend changes, ranging from 25% to 35%. The dynamic component, specifically the tendency of vertical motion, made negative contributions, along with the vertical profiles of moist static energy (MSE) tendency. Seasonal-dependent changes in atmospheric stability could be associated with variations in precipitation. It is concluded that surface ocean warming associated with the North Atlantic natural variability and global warming is the key factor in the increased precipitation AR over the Amazon from 1979 to 2014. The continuous local land-use changes may potentially influence the precipitation AR in the future. Significance Statement The annual range (AR) in precipitation, the difference between wet- and dry-season precipitation, has increased from 1979 to 2014 in the Amazon. This increase may have resulted from global warming, deforestation, and sea surface temperature variability in North Atlantic and Pacific. To explore the role of each of these factors in altering the Amazon precipitation AR, five experiments were designed in the climate model (CESM). Among these experiment results, the effect of North Atlantic SSTs was the strongest. In the future, deforestation, global warming, and different ocean temperature states in the North Atlantic and Pacific may become increasingly influential on the changes in precipitation. Further investigation is needed to ascertain how the AR of precipitation in the Amazon will change.