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Development of ocean measurement technologies can improve monitoring of the global Ocean Heat Content (OHC) and Heat Storage Rate (HSR) that serve as early-warning indices for climate-critical circulation processes such as the Atlantic Meridional Overturning Circulation and provide real-time OHC assessments for tropical cyclone forecast models. This paper examines the potential of remotely measuring ocean temperature profiles using a simulated Brillouin lidar for calculating ocean HSR. A series of data analysis ('Nature') and Observational Systems Simulation Experiments (OSSEs) were carried out using 26 years (1992–2017) of daily mean temperature and salinity outputs from the ECCOv4r4 ocean circulation model. The focus of this study is to compare various OSSEs carried out to measure the HSR using a simulated Brillouin lidar against the HSR calculated from the ECCOv4r4 model results. Brillouin lidar simulations are used to predict the probability of detecting a return lidar signal under varying sampling strategies. Correlations were calculated for the difference between sampling strategies. These comparisons ignore the measurement errors inherent in a Brillouin lidar. Brillouin lidar technology and instruments are known to contain numerous, instrument-dependent errors and remain an engineering challenge. A significant decrease in the ability to measuring global ocean HSRs is a consequence of measuring ocean temperature from nadir-pointing instruments that can only take measurements along-track. Other sources of errors include the inability to fully profile ocean regions with deep mixed layers, such as the Southern Ocean and North Atlantic, and ocean regions with high light attenuation levels. [ABSTRACT FROM AUTHOR]

Global warming is causing poleward expansion of species ranges. Temperate seas, in particular, are undergoing a process known as 'tropicalisation', i.e., the combination of sea-water warming and establishment of southern species. The Ligurian Sea is one of the coldest sectors of the Mediterranean and has thus been characterized by a dearth of warm-temperate species and a comparative abundance of cold-temperate species. This paper uses a time series of sea surface temperature (SST) and new records of thermophilic fish species to reconsider the biogeography of the Ligurian Sea. SST has risen by about 0.7 °C on average between 1948 and 2023, but two phases may be distinguished: a cool one (ended in the mid-1980s) and a warm one (still ongoing); the latter phase shows alternating periods of rapid warming and comparatively stationary temperature. The arrival of thermophilic species coincided with the periods of rapid warming; some of these species were established in the subsequent stationary periods. Heatwaves and climate-related diseases associated with the periods of rapid warming have caused mass mortalities of autochthonous species. Our knowledge on the biogeography of the Ligurian Sea was established during the cool phase; the present situation, however, calls for re-defining the chorological spectrum of the Ligurian Sea biota. [ABSTRACT FROM AUTHOR]

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper analyzed the different relationships between earlywood and latewood as well as total tree-ring growth and the climate factors and reconstructed 106 years of May–June mean temperature (TMJ) in the Tongbai Mountains based on the earlywood width chronology of Pinus taiwanensis Hayata. It also analyzed the linkages to the Indian and Pacific Oceans. This paper found that earlywood width chronology has better response to the climate factors than latewood width and total tree-ring width. This study also found that the main limiting factors that restrained radial growth of Pinus taiwanensis Hayata in the Tongbai Mountains were May–June mean temperature and mean maximum temperature. The reconstructed TMJ series have a better reliability and are significantly negatively correlated with sea surface temperature (SST) over the tropical Western Pacific Ocean and Indian Ocean and are significantly positively correlated with SST over the subtropical Pacific Ocean. Finally, the periodic fluctuations of TMJ in the Tongbai Mountains might be related to the quasi-biennial interannual oscillation of SST over the Indo-Pacific equatorial region (QBO). The results of this study are significant for further understanding and exploring forest growth and climate change in the climatic transition zone. The Tongbai Mountains are an ecologically sensitive region to climate change, where there lies a climatic transitional zone from a subtropical to a warm–temperate monsoon climate. The northern boundary of Pinus taiwanensis Hayata is here; thus, climate information is well recorded in its tree rings. Based on developed earlywood width (EWW), latewood width (LWW) and total ring width (RW) chronologies (time period: 1887–2014 year) of Pinus taiwanensis Hayata in the Tongbai Mountains in central China, this paper analyzed characteristics of these chronologies and correlations between these chronologies and climate factors. The correlation results showed that earlywood width chronology contains more climate information than latewood width chronology and total ring width chronology, and mean temperature and mean maximum temperature in May–June were the main limiting factors for radial growth of Pinus taiwanensis Hayata. The highest significant value in all correlation analyses is −0.669 (p < 0.05) between earlywood width chronology and May–June mean temperature (TMJ) in the pre-mutation period (1958–2005) based on mutating in 2006. Thus, this paper reconstructed May–June mean temperature using earlywood width chronology from 1901 to 2005 (reliable period of earlywood width chronology is 1901–2014). The reconstructed May–June mean temperature experienced eight warmer periods and eight colder periods and also showed 2–3a cycle change over the past 105 years. The spatial correlation showed that the reconstructed series was representative of the May–June mean temperature variation in central and eastern China and significant positive/negative correlation with the sea surface temperature (SST) of the subtropical Pacific Ocean and the tropical Western Pacific Ocean and Indian Ocean from the previous October to the current June. This also indicated that May–June mean temperature periodic fluctuations might be related to the quasi-biennial oscillation (QBO) in the tropical Western Pacific Ocean and Indian Ocean. The results of this study have extended and supplemented the meteorological records of the Tongbai Mountains and have a guiding significance for forest tending and management in this area. [ABSTRACT FROM AUTHOR]

In this paper, we provide a Lagrangian analysis of the anomalies in the moisture transport during two important drought events (1989/1990 and 2003) configured over the Danube River Basin (DRB) region. Firstly, we identified the drought episodes that occurred over the DRB in the period of 1980-2014 through the Standardized Precipitation Evapotranspiration Index (SPEI). SPEI was calculated using monthly Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 precipitation and potential evapotranspiration (PET) datasets with a spatial resolution of 0.5 degrees. The monthly SPEI-1 index was applied to identify the drought episodes and their respective indicators, including duration, severity, and intensity. Two significant drought events were selected: 1989/1990 (presenting dry conditions during October 1989-March 1990) and 2003 (presenting dry conditions during April 2003-September 2003). These events were associated with the two most severe SPEI-1 episodes identified over the DRB during 1980-2014. Then, an analysis of anomalies in the moisture transport was conducted in order to verify possible changes in the moisture supply from the climatological sources for the DRB during these episodes. The moisture transport analysis was performed through a Lagrangian approach, which uses the outputs of the FLEXiblePARTicle dispersion model FLEXPART integrated with one of the reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF): the ECMWF Re-Analysis (ERA)-Interim dataset. The DRB receives moisture from seven different moisture source regions: the North Atlantic Ocean (NATL), North Africa (NAF), the Mediterranean Sea (MED), the Black Sea (BS), the Caspian Sea (CS), the DRB, and Central and Eastern Europe (Rest of Land (RestL)). The analysis of drought events shows that the precipitation and moisture supply from the selected sources weakened mainly during both drought events. Anomalous subsidence and an increased PET also prevailed over the DRB during these SPEI-1 episodes. RestL and MED registered the most intensive reduction in the moisture supply over the DRB during both periods. [ABSTRACT FROM AUTHOR]

Simple Summary: Atlantic salmon (Salmo salar) has become a commodity worldwide. The culture of Atlantic salmon is by far the most well-developed branch of marine finfish aquaculture, with this species ranking among the top ten most highly produced in global aquaculture. While Atlantic salmon has been commonly farmed in sea cages located in colder waters (e.g., in Norway, Chile and Tasmania), these regions can experience the negative impacts of heat waves that push seawater temperature above values tolerated by this species. These climate-change-driven shifts in water temperature can be associated with mass mortality events and urgent actions are needed to cope with a changing ocean. This paper reviews the thermal limits of adult Atlantic salmon and lists the negative effects driven by heat stress. We highlight how biotechnology and the genetic diversity of wild populations may help producers to tackle this challenge. Selective breeding programs and other more advanced biotechnological solutions (e.g., gene editing) may play a key role in this quest to produce new strains of Atlantic salmon that more readily tolerate higher water temperatures, without compromising productivity and profitability. Atlantic salmon (Salmo salar) cage farming has traditionally been located at higher latitudes where cold seawater temperatures favor this practice. However, these regions can be impacted by ocean warming and heat waves that push seawater temperature beyond the thermo-tolerance limits of this species. As more mass mortality events are reported every year due to abnormal sea temperatures, the Atlantic salmon cage aquaculture industry acknowledges the need to adapt to a changing ocean. This paper reviews adult Atlantic salmon thermal tolerance limits, as well as the deleterious eco-physiological consequences of heat stress, with emphasis on how it negatively affects sea cage aquaculture production cycles. Biotechnological solutions targeting the phenotypic plasticity of Atlantic salmon and its genetic diversity, particularly that of its southernmost populations at the limit of its natural zoogeographic distribution, are discussed. Some of these solutions include selective breeding programs, which may play a key role in this quest for a more thermo-tolerant strain of Atlantic salmon that may help the cage aquaculture industry to adapt to climate uncertainties more rapidly, without compromising profitability. Omics technologies and precision breeding, along with cryopreservation breakthroughs, are also part of the available toolbox that includes other solutions that can allow cage farmers to continue to produce Atlantic salmon in the warmer waters of the oceans of tomorrow. [ABSTRACT FROM AUTHOR]

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change. [ABSTRACT FROM AUTHOR]

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge's parent node and a number of parent nodes from the edge's child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO's relevant knowledge, which may provide new references for global change research. [ABSTRACT FROM AUTHOR]

This is an update of Sobrino et al.'s paper, published in January 2020, which extends the calculation of the Earth's surface temperature to the period 2003–2019 and uses the new version 2019.0 for the sea surface temperature product MODIS, which is available from 15 January 2020 and replaces version 2014.0. The land surface temperature was estimated from the MCD11C1 product for the same period. The results corroborate the temperature anomalies retrieved from climate models and improve the comparison with global annual air temperatures estimated by the NOAA's National Climatic Data Center (NOAA-NCDC), with a correlation coefficient of 0.96. In addition, a trend of 0.021 ± 0.001 °C/year increase was found for the Earth's surface temperature in this 17-year period. [ABSTRACT FROM AUTHOR]

Climate related changes can have significant effects on Posidonia oceanica, an endemic seagrass species of the Mediterranean Sea (MEDIT). This seagrass is very important for many aspects of functioning of the sea but there is an increasing number of reports about the ongoing loss of its biomass and area coverage. We analysed multiyear data of the sea surface temperature (SST), sea level anomalies, ocean colour MODIS-A and ERA-Interim reanalysis. The results provide a description of current environmental conditions in the MEDIT and their spatial and temporal variability, including long-term trends. We defined regions where the extent of the P. oceanica meadows may be limited by specific environmental conditions. Light limitation is more severe near the northern and western coasts of the MEDIT, where the vertical diffuse attenuation coefficient is large. In the zone extending from the Gulf of Lion towards the south, significant wave heights reach large values. Wave action may destroy the plants and as a result the shallow water depth limit of P. oceanica meadows is most likely deeper here than in other regions. The highest SST values are documented in the south-eastern part of the Mediterranean Sea. In this area P. oceanica meadows are more endangered by the climate warming than in other regions where SSTs are lower. The absence of P. oceanica meadows in the south-eastern edge of the Mediterranean Sea can be attributed to high temperatures. Our conclusions are partly confirmed by the information about P. oceanica from the literature but more monitoring efforts are needed to fully describe current extent of the meadows and their shifts. Results presented in this paper can help with designing special programs to confirm the role of environmental conditions on the spatial distribution of P. oceanica and their future trends in the Mediterranean Sea. [ABSTRACT FROM AUTHOR]

This paper examines the hydroclimate history of the Eastern Mediterranean (EM) region during the 10th to 14th centuries C.E., a period known as the Medieval Climate Anomaly (MCA), a time of significant historical turmoil and change in the region. The study assembles several regional hydroclimatic archives, primarily the Dead Sea reconstructed lake level curve together with the recently extracted deep-lake sediment record, the Soreq Cave speleothem record and its counterpart, the EM marine sediment record and the Cairo Nilometer record of annual maximum summer flood levels in lower Egypt. The Dead Sea record is a primary indicator of the intensity of the EM cold-season storm activity while the Nilometer reflects the intensity of the late summer monsoon rains over Ethiopia. These two climate systems control the annual rainfall amounts and water availability in the two regional breadbaskets of old, in Mesopotamia and Egypt. The paleoclimate archives portray a variable MCA in both the Levant and the Ethiopian Highlands with an overall dry, early-medieval climate that turned wetter in the 12th century C.E. However, the paleoclimatic records are markedly punctuated by episodes of extreme aridity. In particular, the Dead Sea displays extreme low lake levels and significant salt deposits starting as early as the 9th century C.E. and ending in the late 11th century. The Nile summer flood levels were particularly low during the 10th and 11th centuries, as is also recorded in a large number of historical chronicles that described a large cluster of droughts that led to dire human strife associated with famine, pestilence and conflict. During that time droughts and cold spells also affected the northeastern Middle East, in Persia and Mesopotamia. Seeking an explanation for the pronounced aridity and human consequences across the entire EM, we note that the 10th–11th century events coincide with the medieval Oort Grand Solar Minimum, which came at the height of an interval of relatively high solar irradiance. Bringing together other tropical and Northern Hemisphere paleoclimatic evidence, we argue for the role of long-term variations in solar irradiance in shaping the early MCA in the EM and highlight their relevance to the present and near-term future. [ABSTRACT FROM AUTHOR]

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions. [ABSTRACT FROM AUTHOR]

In Eastern Boundary Upwelling Systems, cold coastal waters are separated from offshore by a strong cross-shore Sea Surface Temperature (SST) gradient zone. This upwelling front plays a major role for the coastal ecosystem. This paper proposes a method to automatically identify the front and define its main characteristics (position, width, and intensity) from high resolution data. The spatio-temporal variability of the front characteristics is then analyzed in a region off Central Chile (37°S), from 2003 to 2016. The front is defined on daily 1 km-resolution SST maps by isotherm T0 with T0 computed from mean SST with respect to the distance from the coast. The probability of detecting a front, as well as the front width and intensity are driven by coastal wind conditions and increased over the 2007–2016 period compared to the 2003–2006 period. The front position, highly variable, is related to the coastal jet configuration and does not depend on the atmospheric forcing. This study shows an increase by 14% in the probability of detecting a front and also an intensification by 17% of the cross-front SST difference over the last 14 years. No trend was found in the front position. [ABSTRACT FROM AUTHOR]