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The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula. [ABSTRACT FROM AUTHOR]

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature–emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and −1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2 at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR. [ABSTRACT FROM PUBLISHER]

The Caribbean Sea and adjacent land areas are highly sensitive to the projected impacts of global climate change. The countries bordering the Caribbean Sea depend heavily on coastal and marine assets as a major source of livelihood support. Rising sea surface temperatures (SSTs) are known to be associated with coral bleaching, ocean acidification, and other phenomena that threaten livelihoods in the region. The paucity of SST systematic observations in both the Caribbean Sea and adjoining Western Atlantic waters is a limiting factor in the projection of future climate change impacts on the region's marine resources. Remote sensing of SST by satellites began only within the last three decades and although the data collected so far might be insufficient to provide conclusive definitions of long-term SST variations in the Caribbean waters, these data along with the output from climate model simulations provide a useful basis for gaining further insights into plausible SST futures under IPCC SRES scenarios. In this paper, we examine the recent SST records from the NESDIS AVHRR satellite data and NOAA Optimum Interpolation (OI) sea surface temperature V2 and provide a comparative analysis of projected SST changes for the Caribbean Sea up to the end of the twenty-first century, under the SRES A2 and B2 scenarios' simulations of the sea surface skin temperatures (SSsT) using the Hadley Centre's regional model, PRECIS. The implications of these projected SST changes for bleaching of coral reefs, one of the region's most valuable marine resource, and for rainfall are also discussed. [ABSTRACT FROM AUTHOR]

Extreme precipitation events induced by tropical cyclones have increased frequency and intensity, significantly impacting human socioeconomic activities and ecological environments. This study systematically examines the spatiotemporal characteristics of these events across Hainan Island and their influencing factors using GsMAP satellite precipitation data and tropical cyclone track data. The results indicate that while the frequency of typhoon events in Hainan decreased by 0.3 events decade−1 from 1949 to 2020, extreme precipitation events have increased significantly since 2000, especially in the eastern and central regions. Different typhoon tracks have distinct impacts on the island, with Track 1 (Northeastern track) and Track 2 (Central track) primarily affecting the western and central regions and Track 3 (Southern track) impacting the western region. The impact of typhoon precipitation on extreme events increased over time, being the greatest in the eastern region, followed by the central and western regions. Incorporating typhoon precipitation data shortened the recurrence interval of extreme precipitation in the central and eastern regions. Diurnal peaks occur in the early morning and late evening, primarily affecting coastal areas. Typhoon duration (CC_max = 0.850) and wind speed (CC_max = 0.369) positively correlated with extreme precipitation, while the pressure was negatively correlated. High sea surface temperature areas were closely associated with extreme precipitation events. The atmospheric circulation indices showed a significant negative correlation with extreme precipitation, particularly in the western and central regions. ENSO events, especially sea surface temperature changes in the Niño 1 + 2 region (−0.340 to −0.406), have significantly influenced typhoon precipitation characteristics. These findings can inform region-specific disaster prevention and mitigation strategies for Hainan Island. [ABSTRACT FROM AUTHOR]

The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatio-temporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed. [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]

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05° from satellite observations, to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05–0.1°, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~ 0.04 K in global area coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods. [ABSTRACT FROM AUTHOR]

The Pacific Decadal Oscillation (PDO) regime is a major factor not only for the physical properties of the ocean but also for fishery and water resources. However, only a few studies have examined the impact of the PDO on the marine ecosystem in the East/Japan Sea. Therefore, in this study, the relationship between PDO and primary production (PP), and subsequent effects on the marine ecosystem were investigated in the East/Japan Sea using satellite data sets. PDO index showed a negative relationship with sea surface temperature (SST) and the contribution of the small phytoplankton to the total PP during the study period, whereas the mixed layer depth (MLD) and the PP showed a positive relationship with PDO index. The shallower MLD during the negative PDO phase indicates that vertical mixing may be weakened due to the stronger stratification caused by the higher SST than observed during the positive PDO phase. Consequently, we hypothesized that weakened vertical mixing may reduce nutrient supply to the euphotic layer, providing small‐sized phytoplankton favored environmental conditions during the negative PDO. It is noteworthy that PDO‐induced shoaling of the MLD was mainly observed in winter, which may influence the annual PP of the following year. This study shows that the annual PP in the East/Japan Sea can be largely affected through interactions between SST, MLD and subsequent changes in nutrient regime according to the PDO regime, which subsequently affects potential fishery resources in the East/Japan Sea. Plain Language Summary: Pacific Decadal Oscillation (PDO) is very important climatological factor that can affect ocean environmental conditions and its various components. However, only a few studies were conducted to examine the impact of the PDO on the marine ecosystem in the East/Japan Sea. In order to understand how the marine ecosystem will change according to the PDO in the East/Japan Sea, we investigated changes in marine environmental conditions according to the PDO using satellite data sets. Sea surface temperature was warmer, and mixed layer depth was shallower during the negative phase of the PDO. In addition, total primary production of phytoplankton was lower, while the contribution of small‐sized phytoplankton to the total primary production was higher during the negative phase. The higher ocean surface temperature and shallower mixed layer depth suggest that the ocean is more stratified, which can lead to weakening of vertical mixing. Weakened vertical mixing may reduce nutrient supply to the euphotic layer, providing small‐sized phytoplankton favored environmental conditions. Primary production of phytoplankton in the East/Japan Sea can be affected through environmental changes in related to the PDO, which can subsequently affect potential fisheries in the East/Japan Sea. Key Points: Various marine environmental conditions in the East/Japan Sea were closely related to the Pacific Decadal Oscillation (PDO)Primary production and phytoplankton community structure in the East/Japan Sea could be changed according to the PDOA conceptual model for the responses of phytoplankton and potential changes according to the PDO phase in the East/Japan Sea was suggested [ABSTRACT FROM AUTHOR]

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño). [ABSTRACT FROM AUTHOR]

Understanding variations in marine phytoplankton primary production (PP) is crucial for assessing the response of the marine environment to climate change and for quantifying the ocean carbon cycle. However, the spatiotemporal variability of size-fractionated PP in the South China Sea (SCS) remains ambiguous. Our study investigated the monthly, seasonal, and inter-annual variability of size-fractionated PP in the SCS using satellite observations from 2002 to 2022. There were noticeable seasonal and monthly variations in size-fractionated PP, with notably high PP values appearing during the cold season. The disparities in distribution and the distinct fluctuation patterns between size-fractionated PP suggest that total PP alone is not a comprehensive indicator of marine ecosystem health. Over the past two decades in the SCS, there were more pronounced decreases in total, pico-, and nano-PP, whereas micro-PP displayed no significant trend. The most pronounced decline occurred in the northern SCS, contrasted by increases in coastal areas. These size-fractionated PP anomalies showed strong correlations with climate change indices, highlighting the impact of environmental factors on these anomalies, such as sea surface temperature, mixed layer depth, and wind speed. Our findings emphasize the importance considering size-fractionated PP to gain a more nuanced understanding of the ocean carbon cycle and the marine ecosystem's response to climate changes. • Reveals significant seasonal and interannual variations in size-fractionated phytoplankton PP in the SCS. • Highlights the influence of SST, MLD and wind speed on anomalies in size-fractionated phytoplankton PP. • Emphasizes that climate variability has a significant impact on variations in size-fractionated phytoplankton PP. [ABSTRACT FROM AUTHOR]

A double intertropical convergence zone (ITCZ) with poor equatorial rainfall over the eastern Pacific Ocean distinctively occurs in March–April. Here we find that double ITCZs and equatorial precipitation are strongly linked to meridionally symmetric (or asymmetric) sea surface temperature (SST) anomaly patterns during eastern Pacific (EP)‐type (or central Pacific [CP]‐type) El Niño episodes. EP‐type El Niño weakens the north–south asymmetry of the eastern Pacific double ITCZs by moving them equatorward. In contrast, CP‐type El Niño strengthens the north–south asymmetry of the double ITCZs by strengthening the northern ITCZ and weakening the southern ITCZ. While EP‐type El Niño brings unique flourishing Galápagos Islands owing to warm equatorial SST anomalies with major local and remote climate effects, CP‐type El Niño causes drier Galápagos Islands by decreasing equatorial eastern Pacific SST. This study highlights the distinctly different climate effects of EP‐ and CP‐type El Niños in the region. [ABSTRACT FROM AUTHOR]

Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth's ecosystem; therefore, assessing themhas becomemore important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical) approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated. [ABSTRACT FROM AUTHOR]

Increased intensity and duration of droughts and high-temperature events have been associated with forest decline in many parts of the world, and these decline events are expected to become more common under climate change. There is, therefore, a need for monitoring and modeling of forest decline. We used a regional forest condition monitoring program (DEBOSCAT) to study the spatial distribution of decline events in 2012 in Catalonia (Northeastern Spain) and their relationship with climatic factors. In 2012, this dataset was collected after an extraordinarily dry summer, and allowed the study of decline events in eight dominant tree species. We fitted a logistic model to predict forest decline probability as a function of species, precipitation and temperature anomalies, solar radiation, and remotely sensed soil moisture data from the Soil Moisture and Ocean Salinity Mission (SMOS). Broadleaved species were more affected by decline events than conifers. The statistical model explained almost 40% of forest decline occurrence, wherein almost 50% of this variability was explained by species effect, with broadleaved trees being generally more sensitive to the studied factors than conifers. Climatically wetter areas and those more exposed to radiation were more likely to be affected, suggesting better adaptation of forests in dry areas. In general, more damaged forests were characterized by high-positive temperature anomalies, lower than average rainfall, and low soil moisture in summer 2012. The most vulnerable species was Fagus sylvatica, a Euro-Siberian species, contrasting with Pinus halepensis, a typically Mediterranean species, which showed low sensitivity to drought. [ABSTRACT FROM PUBLISHER]

Remotely derived environmental variables, including sea surface height anomaly (SSHA), sea surface temperature (SST), chlorophyll-a (chl-a), eddy kinetic energy (EKE), mixed layer data set of argo float (MLD), Niño 3.4 index, and bigeye tuna catch data for the period 1997-2008, were used to analyse ocean climate variability and how they relate to the hotspots of bigeye tuna catch in the eastern Indian Ocean off Java. The empirical orthogonal function (EOF) was performed to obtain a more detailed structure of the spatio-temporal ocean variability in the region. The results showed that the first EOF modes of chl-a, SSHA, and SST accounted for 42.8%, 36.5%, and 27.4% of total variance, respectively, and these corresponded to the interannual signal. The maps of spatial patterns of the first EOF modes of SSHA, SST, and chl-a gave very typical values for cold-water SSHA, low SST, and high chl-a concentration along the southern coast of the Indonesian archipelago; and warm-water SSHA, high SST, and low chl-a concentration in the offshore region to make frontal areas along the latitudinal line around 10-12° S. The EOF analysis further revealed a strong relationship between the El Niño event and favourable oceanographic conditions, resulting in a significant increase in bigeye tuna catch. The average hook rate of 0.71 (0.43) was recorded during El Niño (La Niña). Major hotspots were located away from the confluence region and frontal areas around 11-16° S and 110-118° E and were thus demonstrated as the most favourable oceanographic conditions for bigeye tuna fishing in the eastern Indian Ocean off Java. [ABSTRACT FROM AUTHOR]

A 30-yr climate data record (CDR) of sea surface temperature (SST) has been produced with daily gap-free analysis fields for the North Sea and the Baltic Sea region from 1982 to 2012 by combining the Pathfinder AVHRR satellite data record with the Along-Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) dataset and with in situ observations. A dynamical bias correction scheme adjusts the Pathfinder observations toward the ARC and in situ observations. Largest Pathfinder-ARC differences are found in the summer months, when the Pathfinder observations are up to 0.4°C colder than the ARC observations on average. Validation against independent in situ observations shows a very stable performance of the data record, with a mean difference of −0.06°C compared to moored buoys and a 0.46°C standard deviation of the differences. The mean annual biases of the SST CDR are small for all years, with a negligible temporal trend when compared against drifting and moored buoys. Analysis of the SST CDR reveals that the monthly anomalies for the North Sea, the Danish straits, and the central Baltic Sea regions show a high degree of correlation for interannual and decadal time scales, whereas the monthly variability differs from one region to another. The linear trends of the 1982-2012 SST anomaly time series range from 0.037°C yr−1 for the North Sea to 0.041°C yr−1 for the Baltic Sea. [ABSTRACT FROM AUTHOR]

Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia's blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement. [ABSTRACT FROM AUTHOR]

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol. [ABSTRACT FROM AUTHOR]

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite's overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel's data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell's data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements. [ABSTRACT FROM AUTHOR]

Over the last century, climate change has impacted the physiology, distribution, and phenology of marine and terrestrial primary producers worldwide. The study of these fluctuations has been hindered due to the complex response of plants to environmental forcing over large spatial and temporal scales. To bridge this gap, we investigated the synchrony in seasonal phenological activity between marine and terrestrial primary producers to environmental and climatic variability across northern Patagonia. We disentangled the effects on the biological activity of local processes using advanced time-frequency analysis and partial wavelet coherence on 15 years (2003–2017) of data from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Terra and Aqua satellites and global climatic variability using large-scale climate indices. Our results show that periodic variations in both coastal ocean and land productivity are associated with sea surface temperature forcing over seasonal scales and with climatic forcing over multi-annual (2–4 years) modes. These complex relationships indicate that large-scale climatic processes primarily modulate the synchronous phenological seasonal activity across northern Patagonia, which makes these unique ecosystems highly exposed to future climatic change. [ABSTRACT FROM AUTHOR]