Future Sea Surface Temperature Research Articles

Future Amplification of Sea Surface Temperature Seasonality Due To Enhanced Ocean Stratification

AbstractIn many regions the projected future sea surface temperature (SST) response to greenhouse warming is larger in summer than in winter. What causes this amplification of the SST seasonal cycle has remained unclear. To determine robustness of the projected seasonal cycle intensification and ascertain underlying physical mechanisms we analyze a suite of historical and greenhouse warming simulations conducted with 13 coupled general circulation models in the Coupled Model Intercomparison Project Phase 5. In the Representative Concentration Pathway 8.5 scenario, the amplitude of SST seasonal cycle, defined as the difference between climatological maximum and minimum temperature, increases by 30% ± 20% on average by the end of 21st century. Analysis of a simplified mixed layer heat budget demonstrates that the amplification can be attributed to the increasing upper ocean stratification and hence shoaling of the annual‐mean mixed layer. The projected intensification of SST seasonality may have important implications for future changes in marine ecosystems.

Predictions of sardine and the Portuguese continental shelf ecosystem dynamics under future fishing, forced-biomass and SST scenarios

We used the Ecopath with Ecosim ecosystem model to assess the future effects of multiple stressors on sardine and the Portuguese continental shelf ecosystem. We assessed individual and combined impacts of changes in sardine fishing pressure, biomass of sardine competitors and predators and sea surface temperature (SST). This study demonstrated that the greatest impact on sardine stock is caused by projected SST rise whose effect is dominant and detrimental to sardine stock regardless of other conditions, including sardine fishing at maximum sustainable yield (FMSY). The largest impact on ecosystem stability, maturity and diversity of flows was observed under the forced-biomass scenarios that simulate changes in biomass of sardine predators and competitors. Moreover, these stressors alongside FMSY are projected to play an important role in the future evolution of the sardine stock. Results presented in this study can assist long-term and strategic management of the Iberian sardine stock.

Influence of Canary upwelling system on coastal SST warming along the 21st century using CMIP6 GCMs

Climate change is causing the global sea surface temperature (SST) to rise. However, warming rates are lower in coastal areas affected by the upwelling that buffers the SST warming. The influence of the Canary upwelling system on coastal SST throughout the 21st century was analyzed taking advantage of the high spatial resolution of the Global Climate Models (GCMs) from the CMIP6 project to capture upwelling features. Future SST changes were evaluated both on the coast and in their oceanic counterparts under the 5–8.5 socioeconomic pathway scenario. Results showed a latitudinal displacement of the upwelling system to the north by the end of the century. Middle and north latitudes showed a strengthening in the buffering effect of upwelling with a greater influence on the differences between coast and ocean. Oppositely, the southernmost latitudes showed a weakening causing a homogenization of the SST between coast and ocean.

Multiple Granularity Spatiotemporal Network for Sea Surface Temperature Prediction

Sea surface temperature (SST) prediction has an important practical value in marine disaster prevention and mitigation. Most current methods only use the temporal correlation of SST during prediction, but the spatial correlation is not considered, resulting in low prediction accuracies. In addition, the changing trend of SST as reflected by the single granularity feature is unreliable, and the degrees of dependence between historical SST and future SST tend to vary. In order to overcome these issues, the multiple granularity spatiotemporal network (MGSN) is proposed for SST prediction. The proposed method consists of three parts. First, a multibranch network structure is constructed to extract different temporal features of different granularities. Second, a temporal dependence representation module is developed to represent the different degrees of dependence between historical SST and predicted SST in the temporal dimension. Third, the spatiotemporal fusion prediction module is used to achieve a spatiotemporal prediction of the SST and fuse the prediction results of different granular features. Comparative experiments have been conducted. The experimental results show that the root-mean-square error (RMSE) of the proposed method is reduced by 0.1360, 0.1608, and 0.1448 compared with the RMSE of convolutional LSTM (ConvLSTM), when predicting SST for the next one day, three days, and seven days, respectively. Our method has strong spatiotemporal feature modeling capabilities and is suitable for regional SST prediction.

Understanding past and future sea surface temperature trends in the Baltic Sea

The Baltic Sea is one of the fastest-warming semi-enclosed seas in the world over the last decades, yielding critical consequences on physical and biogeochemical conditions and on marine ecosystems. Although long-term trends in sea surface temperature (SST) have long been attributed to trends in air temperature, there are however, strong seasonal and sub-basin scale heterogeneities of similar magnitude than the average trend which are not fully explained. Here, using reconstructed atmospheric forcing fields for the period 1850–2008, oceanic climate simulations were performed and analyzed to identify areas of homogenous SST trends using spatial clustering. Our results show that the Baltic Sea can be divided into five different areas of homogeneous SST trends: the Bothnian Bay, the Bothnian Sea, the eastern and western Baltic proper, and the southwestern Baltic Sea. A classification tree and sensitivity experiments were carried out to analyze the main drivers behind the trends. While ice cover explains the seasonal north/south warming contrast, the changes in surface winds and air-sea temperature anomalies (along with changes in upwelling frequencies and heat fluxes) explain the SST trends differences between the sub-basins of the southern part of the Baltic Sea. To investigate future warming trends climate simulations were performed for the period 1976–2099 using two RCP scenarios. It was found that the seasonal north/south gradient of SST trends should be reduced in the future due to the vanishing of sea ice, while changes in the frequency of upwelling and heat fluxes explained the lower future east/west gradient of SST trend in fall. Finally, an ensemble of 48 climate change simulations has revealed that for a given RCP scenario the atmospheric forcing is the main source of uncertainty. Our results are useful to better understand the historical and future changes of SST in the Baltic Sea, but also in terms of marine ecosystem and public management, and could thus be used for planning sustainable coastal development.

Pacific Warming Pattern Diversity Modulated by Indo‐Pacific Sea Surface Temperature Gradient

AbstractThe multi‐model ensemble mean sea surface temperature (SST) of CMIP5 models shows an El Niño‐like Pacific warming (PW) trend, contradictory with the observational result which manifests a La Niña‐like PW pattern. Here, we demonstrate that these two SST PWs coexist in the CMIP5 models and they are largely determined by the model's tropical Indo‐Pacific SST gradient. When the Pacific warms faster than the Indian Ocean (IO), the model tends to project an El Niño‐like PW pattern. In contrast, a La Niña‐like warming trend prevails if a more rapid IO warming is simulated. We suggest that the PW pattern in an individual model is nonstationary and may transform from a La Niña‐like to an El Niño‐like when its interbasin SST gradient changes with more robust warming in the Pacific, and vice versa. Our conclusions shed great light on the future SST PW pattern change projected by the coupled models.

Changing El Niño–Southern Oscillation in a warming climate

Originating in the equatorial Pacific, the El Niño–Southern Oscillation (ENSO) has highly consequential global impacts, motivating the need to understand its responses to anthropogenic warming. In this Review, we synthesize advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, there is an inter-model consensus of an increase in future ENSO rainfall variability. Now, however, it is apparent that models that best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, thereby, ENSO magnitude under greenhouse warming, as well as an eastward shift and intensification of ENSO-related atmospheric teleconnections — the Pacific–North American and Pacific–South American patterns. Such projected changes are consistent with palaeoclimate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability, though underpinned by increased equatorial Pacific upper-ocean stratification, is strongly influenced by internal variability, raising issues about its quantifiability and detectability. Yet, ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modelling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes and reducing uncertainties in projections. The El Niño–Southern Oscillation (ENSO) has global climatic implications, necessitating understanding of its observed and projected changes. This Review brings together knowledge of ENSO in a warming climate, revealing projected increases in ENSO magnitude, as well as ENSO-related rainfall and sea surface temperature variability.

Simulated Thermocline Tilt Over the Tropical Indian Ocean and Its Influence on Future Sea Surface Temperature Variability

AbstractMost coupled climate models simulate an overly shallow thermocline over the southeastern equatorial Indian Ocean (SEIO). Through the Bjerknes feedback loop, the associated unrealistic thermocline tilt leads to warmer sea surface temperature (SST) over the western equatorial Indian Ocean than the SEIO, which is conducive to equatorial easterly winds. Similar to phase 5 of the Coupled Model Intercomparison Project (CMIP), phase 6 of the CMIP continues to simulate such overly tilted thermocline. On the one hand, this leads to overly strong SST variability, and on the other hand, it provides an inherent limit for the thermocline to incline further under greenhouse warming. Consequently, models with a more realistic tilt in the present‐day climate project a greater shoaling of the thermocline under global warming, which facilities upwelling of cool subsurface water, leading to stronger increase in SST variability over the SEIO, compared to models with an overly large thermocline tilt.

Future Changes in the Global and Regional Sea Level Rise and Sea Surface Temperature Based on CMIP6 Models

Estimating future sea level rise (SLR) and sea surface temperature (SST) is essential to implement mitigation and adaptation options within a sustainable development framework. This study estimates regional SLR and SST changes around the Korean peninsula. Two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) scenarios and nine Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations are used to estimate the changes in SLR and SST. At the end of the 21st century, global SLR is expected to be 0.28 m (0.17–0.38 m) and 0.65 m (0.52–0.78 m) for SSP 1–2.6 and SSP5-8.5, respectively. Regional change around the Korean peninsula (0.25 m (0.15–0.35 m; SSP1-2.6) and 0.63 m (0.50–0.76 m; SSP5-8.5)) is similar with global SLR. The discrepancy between global and regional changes is distinct in SST warming rather than SLR. For SSP5-8.5, SST around the Korean peninsula projects is to rise from 0.49 °C to 0.59 °C per decade, which is larger than the global SST trend (0.39 °C per decade). Considering this, the difference of regional SST change is related to the local ocean current change, such as the Kuroshio Current. Additionally, ocean thermal expansion and glacier melting are major contributors to SLR, and the contribution rates of glacier melting increase in higher emission scenarios.

UNCERTAINTY ASSESSMENT OF FUTURE CHANGE PROJECTION TO TYPHOON PROPERTIES AND MAXIMUM STORM SURGE HEIGHT DEPENDING OF FUTURE SEA SURFACE TEMPERATURE AND GREENHOUSE GAS EMISSION SCENARIOS

In order to evaluate the future storm surge risk at the national scale, it is necessary to evaluate typhoon characteristics for a country-specific in prior to conducting storm surge simulation using them. When projecting future changes of tropical cyclones (TC) by using the atmospheric general circulation model (AGCM), there are several uncertainties due to model resolution, model physics parameterization, given sea surface temperature (SST) under future climate condition, and global warming scenarios. The uncertainties stemming from physics and numerical modeling configuration can be reduced by improving the accuracy of AGCMs, while those from the global warming scenario and future SST condition are unable to be. This study assessed uncertainties in projecting future change to typhoon properties such as tracks, frequency and intensity and extreme storm surge height (SSH) depending of future SST and greenhouse gas emission scenarios.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Wzp35k4tyhM

Redistribution of salmon populations in the northeast Pacific ocean in response to climate

AbstractSpecies that migrate long distances or between distinct habitats— for example, anadromous or catadromous fish—experience the consequences of climate change in each habitat and are therefore particularly at risk in a changing world. Studies of anadromous species often focus on freshwater despite the ocean's disproportionate influence on survival and growth. To understand a prominent anadromous species’ response to ocean climate, we use a new spatio‐temporal model jointly estimating the ocean distribution of all major fall‐run Chinook salmon (Oncorhynchus tshawytscha, Salmonidae) stocks from California to British Columbia over 40 years. We model hundreds of millions of tagged individuals, finding that different stocks have fundamentally different ocean distributions, distinct associations with sea surface temperature (SST), and contrasting distributional responses to historical ocean SST variation. We show species‐level estimates of ocean distribution that ignore among‐stock variation will lead to errant predictions of spatial distribution. Using future (2030–2090) SST projections to model focal stocks of fisheries importance we predict substantial ocean redistribution in response to SST change. Predicted aggregate distributional changes do not follow a simple, poleward shift. Instead, we predict net movement into some ocean regions (British Columbia, central California) but net movement out of others (northern California, Washington). Distribution shifts have implications for both major fisheries and marine mammal predators of Chinook salmon. We focus on the consequences of spatial changes in ocean distribution, but our approach provides a general structure to link marine and freshwater components of anadromous species under climate change.

Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

Assessment of uncertainties in projecting future changes to extreme storm surge height depending on future SST and greenhouse gas concentration scenarios

We assess uncertainties in projecting future changes in extreme storm surge height (SSH) based on typhoon data extracted from ensemble experiment results with four sea surface temperature (SST) conditions and three global warming scenarios using a single atmospheric global climate model. In particular, this study focus on typhoons passing around the Korean Peninsula (KP) defined as the region of 32 to 40° N and 122 to 132° E. It is predicted the number of the typhoons affecting the KP will decrease by 4~73% while their strength will increase by 0.8~1.4% under the given future conditions. The locations of genesis and lysis of the typhoons are expected to be shifted towards the northwest and northeast for all ensemble experiment conditions, respectively. However, the extent of their change varies depending on the future SST and global warming conditions. Storm surge simulations were carried out by using predicted typhoon data as an external force. It is found that future SST patterns and climate warming scenarios affect future typhoon characteristics, which influences values of extreme SSH and locations of the vulnerable area to storm surge under the future climate conditions. In particular, the values of extreme SSH and the locations of the vulnerable area to storm surge appear to be strongly influenced by both pathway and frequency of intense typhoons.

A Migration Model to Predict the Distribution of Scottish Herring and Mackerel

To identify climate-related habitat changes and variations in abundance and distribution of herring and mackerel around the waters of Scotland, a new habitat suitability index (HSI) model and Markov Model were developed in this study. The study only used the distribution of annual catch and fishing efforts. The suitability index (SI) was constructed by newly defined estimate-catch-per-unit-effort (ECPUE). Through the voting method and data of sea surface temperature (SST), the habitat suitability index model (HSI) was established. Based on the HSI, Markov Model was established to predict the movement of shoals. Using the predicted data of future SST, this model can simulate the changes of fish distribution.

Global and regional evolution of sea surface temperature under climate change

Sea surface temperature (SST) has important local and remote influence on global climate through the distribution and transport of heat and moisture, which in turn affect precipitation and air temperature patterns across the globe. Thus, this work aims to assess the worldwide regionalization of the SST evolution for the 21 st century under the influence of climate change by means of: 1) division of the worldwide SST data in regions applying a K-means cluster procedure and validation of the most suitable CMIP5 models by a comparative analysis with SST data from Era-Interim reanalysis; 2) calculation of SST trends along the 21 st century and assessment of future SST differences between RCP 4.5 and RCP 8.5 greenhouse gas emission scenarios and historical SST data. Global climate model (GCM) projections show a SST warming worldwide although with different intensity depending on the region. Northern Hemisphere regions present a higher seasonal thermal amplitude comparing with equivalent regions in the Southern Hemisphere. Northern Hemisphere has also a higher annual SST increment than clusters in the Southern Hemisphere, independently of the future time period and climate scenario. The Northern Sub-Tropical cluster (STRN) shows the highest significant increment (4.34 °C on RCP 8.5). Under the RCP 4.5 climate scenario, SST trends from 1975 to 2100 range from 0.075 °C dec −1 at South Polar cluster (PRS) to 0.21 °C dec −1 at STRN cluster. Relatively to RCP 8.5 climatic scenario, SST trends change from 0.13 °C dec −1 at PRS cluster to 0.36 °C dec −1 at the North Polar cluster (PRN). • Identification of CMIP5 models with best SST performance. • The optimal worldwide SST division is eight clusters. • Northern Hemisphere clusters will present a higher SST increment.

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

A Look at the Relationship between the Large-Scale Tropospheric Static Stability and the Tropical Cyclone Maximum Intensity

AbstractVarious modeling and observational studies have suggested that tropical cyclone (TC) intensity tends to increase in the future due to projected warmer sea surface temperature (SST). This study examines the effects of the tropospheric stratification that could potentially offset the direct increase of TC intensity associated with the warmer SST. Using reanalysis datasets and TC records in the northwestern Pacific and the North Atlantic basins, it is shown that there exists a consistently negative correlation between the annually averaged TC intensity and the basinwide average of the tropospheric static stability. This negative correlation is more robust in the northwestern Pacific basin when using the TC lifetime maximum intensity but is somewhat less significant in the North Atlantic basin. Further separation of the troposphere into a lower (1000–500 hPa) and an upper layer (500–200 hPa) reveals that it is the upper-tropospheric static stability that plays a more dominant role in governing the TC intensity variability. The negating effects of a stable troposphere on TC intensity as found in this study suggest a partial offset of the projected increase in the TC potential intensity due to the future warmer SST. Thus, the tropospheric static stability is one of the key large-scale factors that need to be properly taken into account in studies of long-term TC intensity change.

A Bayesian Structural Time Series Approach for Predicting Red Sea Temperatures

Sea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long-term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment management of the Red Sea ecosystem. In this work, we build a data-driven Bayesian structural time series model and show its effectiveness in predicting future SST seasons with a high accuracy, and identifying the main predictive factors of future SST variability among a large number of factors, including regional SST and large-scale climate indices. The modeling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for five months ahead.

Spatio-temporal predictions of SST time series in China’s offshore waters using a regional convolution long short-term memory (RC-LSTM) network

ABSTRACT Sea surface temperature (SST) research is the basis of our understanding of local and global climate characteristics. Significant effort has been focused on the acquisition of accurate prediction results. Although a large amount of SST data is available due to the development of storage media, there are limited studies on the prediction of future SST values in a spatiotemporal manner. Most previous studies ignore the regional distribution information in natural geographical location, which leads to the loss of regional information and hinders further regional analysis. In this work, we have addressed this issue by presenting Regional Convolution long short-term memory (RC-LSTM) model with spatio-temporal information processing capability. To the best of our knowledge, this is the first time that SST time series has been used for region prediction based on deep learning. Daily SST from January 2000 to December 2012 from China’s offshore waters is adopted to conduct experiments. The local correlation and global coherence of each time series are extracted by the multi-layer neural network in the model. The structure of the RC-LSTM model includes a convolutional layer with region information extraction capabilities, and a long short-term memory (LSTM) layer. The RC-LSTM model performs well over both the South China Sea and the East China Sea, as determined by the root mean squared error (RMSE) and the accuracy (Acc). The experimental results show that our model is more accurate than traditional prediction models and has higher generalization ability, and is therefore more practical.

Future projections of Indian Summer Monsoon under multiple RCPs using a high resolution global climate model multiforcing ensemble simulations

Present-day simulations (1983–2003) of a global climate model of 60-km resolution with three deep convection schemes are analysed to find the best scheme for simulation of mean Indian summer monsoon rainfall (ISMR) and its variability. Multiforcing ensemble projections with the best scheme are carried out under multiple Representative Concentration Pathways (RCPs) (based on various socio-economic and technological development at the end of the century), viz. RCP2.6, RCP4.5, RCP6.0 and RCP8.5, forced with four patterns of future sea surface temperature (SST) change for each scenario; one with mean SST changes projected by 28 Coupled Model Intercomparison Project Phase-5 (CMIP5) models and the rest obtained from subgroups of CMIP5 models grouped through cluster analysis of tropical SST changes. These are analysed for future (2079–2099) changes in surface air temperature ($$\hbox {T}_{s}$$) and rainfall which show overall increase over India except for rainfall reduction over Western Ghats. We find that combination of enhanced atmospheric water vapour content and increased vertically integrated low level moisture transport into the subcontinent as the major contributing factors for future intensification of ISMR. Extreme events show increase in warm days with significant increase in warm nights. Percentage of grid points showing increased extreme rainfall increases from low to high emission scenario. The high-resolution model enables to study projected changes over India at homogeneous zones level. The maximum increase in $$\hbox {T}_{s}$$ and rainfall occurs over Western Himalaya and Northeast hilly region respectively. Consistent with future increase in $$\hbox {T}_{s}$$ and rainfall, their extreme events also increase over all the homogeneous zones.