Global Ocean Temperature Research Articles

Forecasting crude oil prices with global ocean temperatures

This paper explores the role of ocean temperature (OT) in forecasting crude oil prices. Global OT shows statistically and economically significant predictive power, with corresponding in-sample and out-of-sample R2s of 1.81 % and 1.74 %, respectively. Both southern and northern hemisphere OTs can predict future oil prices, and the former is more predictive. Compared to the Atlantic, OTs in the Indian and Pacific Oceans display considerable predictive gains. In addition, we find that OTs have stronger forecasting power for crude oil prices in the recent period, with the in-sample R2 of global OT for the recent decade's sample reaching as high as 2.74 %. Correspondingly, we find that OTs' predictive power in the full sample can be explained through two channels: market risk and investor attention. In the recent period, in addition to these two channels, OTs have also influenced crude oil prices through the oil fundamental channel.

Enhanced ocean heat storage efficiency during the last deglaciation

Proxy reconstructions suggest that increasing global mean sea surface temperature (GMSST) during the last deglaciation was accompanied by a comparable or greater increase in global mean ocean temperature (GMOT), corresponding to a large heat storage efficiency (HSE; ∆GMOT/∆GMSST). An increased GMOT is commonly attributed to surface warming at sites of deepwater formation, but winter sea ice covered much of these source areas during the last deglaciation, which would imply an HSE much less than 1. Here, we use climate model simulations and proxy-based reconstructions of ocean temperature changes to show that an increased deglacial HSE is achieved by warming of intermediate-depth waters forced by mid-latitude surface warming in response to greenhouse gas and ice sheet forcing as well as by reduced Atlantic meridional overturning circulation associated with meltwater forcing. These results, which highlight the role of surface warming and oceanic circulation changes, have implications for our understanding of long-term ocean heat storage change.

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP5–8.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

CODC-v1: a quality-controlled and bias-corrected ocean temperature profile database from 1940–2023

High-quality ocean in situ profile observations are fundamental for ocean and climate research and operational oceanographic applications. Here we describe a new global ocean subsurface temperature profile database named the Chinese Academy of Science (CAS) Oceanography Data Center version 1 (CODC-v1). This database contains over 17 million temperature profiles between 1940–2023 from all available instruments. The major data source is the World Ocean Database (WOD), but CODC-v1 also includes some data from some Chinese institutes which are not available in WOD. The data are quality-controlled (QC-ed) by a new QC system that considers the skewness of local temperature distributions, topographic barriers, and the shift of temperature distributions due to climate change. Biases in Mechanical Bathythermographs (MBTs), eXpendable Bathythermographs (XBTs), and Bottle data (OSD) are all corrected using recently proposed correction schemes, which makes CODC-v1 a bias-corrected dataset. These aspects ensure the data quality of the CODC-v1 database, making it suitable for a wide spectrum of ocean and climate research and applications.

Prediction of ocean temperature based on ARIMA model

As an important parameter of the Earth's ecosystem, ocean temperature has always been a hot research field for scholars, and the rise in ocean temperature caused by global warming is a topic related to the fate of humanity. The prediction of future ocean temperature is of great significance. The ARIMA model, as a typical prediction model, is highly favored by scholars from various countries due to its accuracy and adaptability. In this paper, the ARIMA multiplication model with seasonal effect is used to forecast the trend of ocean temperature change in the next year, and to prediction graph as well as specific reference values are given. The conclusion is as follows: The global average monthly ocean temperature will continue to rise in the next year. Therefore, this article believes that the government should promptly introduce relevant policies to address the rise in ocean temperature caused by global warming, and people should also fulfill their obligations to protect the environment.

Projected impacts of global warming scenarios on marine ecosystems: Insights from the CMIP6 model

Rising oceanic temperatures and increased heat content have triggered a decline in marine biodiversity and the degradation of ecosystem services. This study employs the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze variables within the Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) framework. Focusing on two contrasting Shared Socioeconomic Pathways (SSPs) - SSP1-2.6 (low-warming scenario) and SSP5-8.5 (high-warming scenario) - we assess future impacts of climate-related factors on marine ecosystems across major oceanic regions. Under the high-warming SSP5-8.5 scenario, global ocean temperature rise, coupled with declining surface pH levels, nitrate concentrations, and plankton biomass/productivity, is evident. Regional reductions in plankton biomass/productivity, especially pronounced at higher latitudes, are observed in both SSP scenarios. Nutrient cycling emerges as a pivotal factor influencing plankton communities, particularly ammonium regulation in the Southern Ocean. This research emphasizes the urgent need to curb greenhouse gas emissions to mitigate global warmings profound effects on marine ecosystems.

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

Circulation patterns associated with trends in summer temperature variability patterns in North America

This study improves the understanding of circulation patterns associated with regional temperature trends by characterizing boreal summer temperature variability patterns in North America using rotated S-mode principal component analysis. We analyzed gridded observational 2-m temperature datasets and the ERA5 reanalysis temperature dataset to examine the climate patterns associated with long-term trends and inter-annual variability of temperature variability patterns in North America. Our analysis revealed significant trends among some classified temperature variability patterns from 1979 to 2022 summers, with inter-annual amplitudes (i.e., a departure from the mean state) signaling toward the warm regime. The anticyclonic circulation anomaly over the temperature coherent regions associated with Greenland/northeastern Canada, and Alaska, respectively, is linked to an increase in warm air advection and above-average temperatures, while cyclonic circulation over the northeast Pacific coast enhanced warm air advection and temperature increases in the coherent region comprising the northwestern portion of North America. The increase in global mean land and ocean temperatures is strongly associated with the long-term increase in the amplitude of atmospheric circulations associated with warm regimes in parts of North America. At the interannual time scale, temperature increase over Greenland/northeastern Canada is strongly associated with the negative phase of the Arctic Oscillation. These findings highlight the modulating effects of global temperature increase and warming of the western tropical Pacific Ocean on the increasing amplitude of circulations associated with warm regimes in North America. Our results further indicate that the enhancement of anticyclonic circulations over the Arctic contributes to nearly 68% of the observed reduction in sea ice extent.

Research On the Evaluation of Green Gdp System Based on Comprehensive Evaluation Method

In order to adjust traditional GDP to achieve a comprehensive measure of the sustainability of national economies and to optimize national policy-making, this paper constructs an assessment system of 11 indicators to measure GGDP in 3 dimensions: economic development, resource use and environmental pollution, and selects 24 countries as samples. Based on Coupled EWM-PCA, we construct Green Adjustment Coefficient (GAC) to adjust GDP to GGDP. Impact measurement based on Spearman Correlation. Results show that GGDP has the greatest correlation(-0.9) to suppress global CO2 emissions, followed by global temperature (-0.7) and global ocean temperature (-0.6).

Persistence in Climate Risk Measures

In this short note, we investigate persistence in two climate risk measures – climate policy uncertainty index (cpu_index) and the Global Land and Ocean Temperature Anomalies (GLOT). Using the fractional integration method, we find that cpu_index and GLOT exhibit high, but mean reverting persistence, both in full and sub-samples.

Concrete Autoencoder for the Reconstruction of Sea Temperature Field from Sparse Measurements

This paper presents a new method for finding the optimal positions for sensors used to reconstruct geophysical fields from sparse measurements. The method is composed of two stages. In the first stage, we estimate the spatial variability of the physical field by approximating its information entropy using the Conditional Pixel CNN network. In the second stage, the entropy is used to initialize the distribution of optimal sensor locations, which is then optimized using the Concrete Autoencoder architecture with the straight-through gradient estimator for the binary mask and with adversarial loss. This allows us to simultaneously minimize the number of sensors and maximize reconstruction accuracy. We apply our method to the global ocean under-surface temperature field and demonstrate its effectiveness on fields with up to a million grid cells. Additionally, we find that the information entropy field has a clear physical interpretation related to the mixing between cold and warm currents.

South Pacific Ocean Dynamics Redistribute Ocean Heat Content and Modulate Heat Exchange With the Atmosphere

AbstractThe correlation of global ocean surface temperatures with ocean heat content (OHC) at interannual to decadal time scales shows wind‐driven ocean circulation plays a fundamental role in the Earth's energy balance. Wind‐driven baroclinic Rossby waves contribute to the adjustment of the ocean circulation to the winds modulating OHC at mid‐latitudes. Here we use observational records, a reanalysis and a Rossby wave model to quantify the contribution of the waves to the variations in air‐sea heat flux. We find that Rossby waves crossing the South Pacific at 35°S can explain up to 70% of the interannual variance of the heat flux. The heat exchange attributed to the waves, ranging from −9 to 21 W m−2, has contributed to the multi‐year increase in heat in the central Pacific. Heat fluxes due to Rossby waves could be forecast to provide predictability of this component of the heat exchange with the atmosphere.

Glacial‐Interglacial Controls on Ocean Circulation and Temperature During the Permo‐Carboniferous

AbstractThe apex of Earth's penultimate icehouse during the Permo‐Carboniferous coincided with dramatic glacial‐interglacial fluctuations in atmospheric CO2, sea level, and high‐latitude ice. Global transformations in marine fauna also occurred during this interval, including a rise to peak foraminiferal diversity, suggesting that glacial‐interglacial climate change impacted marine ecosystems. Nevertheless, changes in ocean circulation and temperature over the Permo‐Carboniferous and their influence on marine ecosystem change are largely unknown. Here, we present simulations of glacial and interglacial phases of the latest Carboniferous‐early Permian (∼305‐295 Ma) using the Community Earth System Model version 1.2 to provide estimates of global ocean circulation and temperature during this interval. We characterize general patterns of glacial and interglacial surface ocean currents, temperature, and salinity, and compare them to the documented abundance and distribution of Permo‐Carboniferous marine fauna as well as a preindustrial climate simulation. We then explore how glacial‐interglacial changes in atmospheric CO2, sea level, and high‐latitude ice extent impact thermohaline circulation. We find that glacial‐interglacial changes in equatorial surface temperatures are consistently ∼3–6°C. Ocean circulation is stronger overall in the glacial simulation, particularly as lower atmospheric CO2 enables deep convection in the Northern Hemisphere. Wind‐driven circulation, heat transport, and upwelling intensity are stronger overall in the Permo‐Carboniferous superocean relative to the preindustrial oceans at the same level of atmospheric CO2. We also find that CO2‐induced glacial conditions of the early Permian may have promoted foraminiferal diversity through increased thermal gradients and suppressed riverine input in marine shelf environments.

The Influence of Solar Activity on Snow Cover over the Qinghai–Tibet Plateau and Its Mechanism Analysis

Using global ocean vertical temperature anomaly data, we identified that a significant response of the sea temperature anomaly (STA) to the solar radio flux (SRF) exists. We found that the STA exhibited a significant correlation with Asian summer and winter precipitation, among which the response from the Qinghai–Tibet Plateau (the QTP) was particularly noticeable. Based on NCEP/NCAR reanalysis data, the latent heat flux (LHF) anomaly, which plays a key role in winter precipitation in China, especially over the QTP, showed a significant response to the SRF in the Pacific. The results demonstrated the bottom-up mechanism of impact of solar activity (SA) on the plateau snow through sea–air interaction. Meanwhile, a top-down mechanism was also present. When the SRF was high, the stratospheric temperature in the low and mid-latitudes increased and the temperature gradient pointed to the pole to strengthen the westerly wind in the mid-latitudes. The EP flux showed that atmospheric long waves in the high altitudes propagated downward from the stratosphere to the troposphere. A westerly (easterly) wind anomaly occurred in the south (north) of the QTP at 500 hPa and the snowfall rate over the QTP tended to increase. When the SRF was low, the situation was the opposite, and the snowfall rate tended to decrease. The model results confirmed that when total solar irradiance (TSI) became stronger (weaker), both of the solar radiation fluxes at the top of the atmosphere and the surface temperature over the QTP increased (decreased), the vertical updraft intensified (weakened), and the snowfall rate tended to increase (decrease) accordingly. These conclusions are helpful to deepen the understanding of SA’s influence on the snow cover over the QTP.

Editor’s Corner

While you’re reading the fall issue, I’m actually writing this editorial in June, and it’s been H-O-T here in Nashville, elsewhere throughout the country, and the world. According to the National Oceanic and Atmospheric Administration, the average global land and ocean surface temperature from January to May was 1.53 degrees F (0.85 of a degree C) above the twentieth century average of 55.5 degrees F (13.1 degrees C), ranking as the sixth-warmest January–May period in the global climate record. Stay hydrated, folks!

Change of Global Ocean Temperature and Decadal Variability under 1.5 °C Warming in FOAM

The rise in atmospheric CO2 concentration is regarded as the dominant reason for observed warming since the mid-20th century. Based on the Paris Agreement target, this research designs three conceptual pathways to achieve the warming target of 1.5 °C above the pre-industrial level by using the Fast Ocean Atmosphere Model. The three different scenarios contain one equilibrium experiment (equilibrium, EQ) and two transient experiments (never-exceed pathway, NE; overshoot pathway, OS). Then, we choose a ten year average that achieves 1.5 °C warming to calculate the climatology of the warming situation. Since OS achieves 1.5 °C twice, we obtain four warming situations to explore the response of ocean temperature. In 2100, the global ocean temperature increases over the global region, except the surface of the Southern Ocean. The difference in heat content mainly depends on the cumulative force of CO2 concentration. It is worth pointing out that during the increase in warming, the ocean surface temperature and heat content start to respond in different hemispheres. The weakening of decadal variability in the North Pacific and North Atlantic is robust in all three scenarios. However, there is a tremendous growth in the low-pass ocean surface temperature standard deviation in the Southern Ocean in EQ, which is different to NE and OS, and causes the increase in global mean total standard deviation. The shortening of decadal variability can only be seen from the EQ power spectrum, while NE and OS have similar power spectra with pre-industrial runs. It suggests that all previous studies that use equilibrium experiments data may have overestimated the shortening of decadal variability under global warming.

Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning

The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remote sensing techniques allow us to retrieve and reconstruct the 3D ocean temperature structure by combining surface remote sensing observations with in situ float observations. This study proposed a new deep learning method, Convolutional Long Short-Term Memory (ConvLSTM) neural networks, which combines multisource remote sensing observations and Argo gridded data to reconstruct and produce a new long-time-series global ocean subsurface temperature (ST) dataset for the upper 2000 m from 1993 to 2020, which is named the Deep Ocean Remote Sensing (DORS) product. The data-driven ConvLSTM model can learn the spatiotemporal features of ocean observation data, significantly improves the model’s robustness and generalization ability, and outperforms the LighGBM model for the data reconstruction. The validation results show our DORS dataset has high accuracy with an average R2 and RMSE of 0.99/0.34 °C compared to the Argo gridded dataset, and the average R2 and NRMSE validated by the EN4-Profile dataset over the time series are 0.94/0.05 °C. Furthermore, the ST structure between DORS and Argo has good consistency in the 3D spatial morphology and distribution pattern, indicating that the DORS dataset has high quality and strong reliability, and well fills the pre-Argo data gaps. We effectively track the global ocean warming in the upper 2000 m from 1993 to 2020 based on the DORS dataset, and we further examine and understand the spatial patterns, evolution trends, and vertical characteristics of global ST changes. From 1993 to 2020, the average global ocean temperature warming trend is 0.063 °C/decade for the upper 2000 m. The 3D temperature trends revealed significant spatial heterogeneity across different ocean basins. Since 2005, the warming signal has become more significant in the subsurface and deeper ocean. From a remote sensing standpoint, the DORS product can provide new and robust data support for ocean interior process and climate change studies.

Effects of elevated temperature on reproduction and larval settlement in Leptastrea purpurea.

As global ocean temperatures continue to rise, severe declines in coral reef health and diversity are reported on a global scale. Recovery of coral reefs relies on reproduction and increased rates of successful recruitment, which can vary tremendously across coral species. We investigated the effects of increased temperatures in the environment of parental colonies on larval production, size, settlement and survival, in the heat-resistant coral Leptastrea purpurea in Guam. Thanks to two tank experiments (eleven and four weeks, respectively) conducted over two consecutive years we found that larvae released by heat-treated parents (30 °C) were significantly smaller in size but greater in number, had normal settlement behavior and increased post-settlement survival rates compared to those released by control parent colonies (28 °C). We conclude that changes in the environment of parental L. purpurea colonies trigger an anticipatory maternal effect which leads to the release of preconditioned larvae with an increased chance of survival.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00338-022-02241-y.

Effects of Temperature and Salinity on Growth, Metabolism and Digestive Enzymes Synthesis of Goniopora columna.

Simple SummaryCoral reefs are important habitats for marine life and have high commercial uses, which can be used in biomedicine, aquariums and tourism promotion. In recent years, climate change has posed a serious threat to coral survival. Therefore, it is very important to explore the effects of temperature and salinity changes on coral physiology. In addition to understanding the threat of climate change to corals, it can also be used as an important environmental indicator for coral large-scale aquaculture. This study aims to investigate the effects of different temperatures and salinity on the body composition, digestive enzymes and metabolism of G. columna, to explore the physiological and metabolic threats caused by climate change to G. columna, and to understand the most suitable temperature and salinity for G. columna growth.Climate change is causing dramatic changes in global ocean temperature and salinity, threatening coral survival. Coral growth and metabolism are greatly affected by the temperature, salinity and feeding time of the environment. In order to explore the threats to coral survival caused by climate change, this study will investigate the changes in body composition, digestive enzymes and metabolism of G. columna at different temperatures and salinities. A maximum G. columna growth rate was observed at 25 °C and 30–35 psu salinity. The G. columna could survive in a wide salinity range of 25–40 psu. However, the maximum number and weight of G. columna polyps was determined at 30–35 psu. Furthermore, 30–35 psu salinity at 25 °C led to the best G. columna growth and survival, mainly because of their enhanced nutrient absorption rate, polyp expansion rate, metabolic rate and adaptability. Comparing various salinity-temperature treatment groups, all obtained values for growth, behavior and metabolism were significantly higher (p < 0.05) for 30 psu at 25 °C than other treatment groups resulting in maximum G. columna yield. In addition, the optimal timing of G. columna feeding was assessed by studying changes in body composition and digestive enzymes within 24 h of feeding. The results showed that G. columna has higher protein and protease activity between 6:00 a.m. to 12:00 noon. Therefore, at 25 °C, 30–35 psu and feeding will enhance G. columna growth and survival.

Finite difference schemes for time-dependent convection &lt;i&gt;q&lt;/i&gt;-diffusion problem

&lt;abstract&gt;&lt;p&gt;The energy balance ordinary differential equations (ODEs) model of climate change is extended to the partial differential equations (PDEs) model with convections and &lt;italic&gt;q&lt;/italic&gt;-diffusions. Instead of integer order second-order partial derivatives, partial &lt;italic&gt;q&lt;/italic&gt;-derivatives are considered. The local stability analysis of the ODEs model is established using the Routh-Hurwitz criterion. A numerical scheme is constructed, which is explicit and second-order in time. For spatial derivatives, second-order central difference formulas are employed. The stability condition of the numerical scheme for the system of convection &lt;italic&gt;q&lt;/italic&gt;-diffusion equations is found. Both types of ODEs and PDEs models are solved with the constructed scheme. A comparison of the constructed scheme with the existing first-order scheme is also made. The graphical results show that global mean surface and ocean temperatures escalate by varying the heat source parameter. Additionally, these newly established techniques demonstrate predictability.&lt;/p&gt;&lt;/abstract&gt;