40 years of changes in sea surface temperature along the Western Iberian Coast

Biguino, Beatriz; Antunes, Carlos; Lamas, Luísa; Jenkins, Luke; Dias, João Miguel; Haigh, Ivan D.; Brito, Ana C.

doi:10.1016/j.scitotenv.2023.164193

Cited by 4 publications

(5 citation statements)

References 43 publications

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“…Despite the obvious differences, we argue that comparing our results with the CCI product is relevant. Firstly, the CCI SST product is one of the most used datasets for longterm SST studies [4], and secondly it shows the best agreement with in situ observations [16]. Given the differences in the SST product generation and the SST anomaly calculation described above, the overall high agreement between the CCI and TIMELINE time series makes us confident about our results.…”

Section: Comparison Between Timeline and CCI Productmentioning

confidence: 60%

“…Because of the length of the AVHRR time series, sensors are distributed across several subsequent platforms as new generations have been launched (in the case of this study, 14,16,17,18,and 19), where each sensor has a different spectral response curve. Furthermore, the NOAA platforms experience orbit drift, which leads to changing observation times throughout the time series [45].…”

Section: The Timeline Sst Product and The Generation Of The Anomaly T...mentioning

confidence: 99%

“…Many studies were conducted on regionally and globally increasing SST. Far less attention has been given to SST trends in coastal areas [16]. During the last three decades, more than 70% of the world's coastal areas experienced significant increases in SST at a mean rate of 0.25 • C/decade.…”

Section: Introductionmentioning

confidence: 99%

“…These regions underly many local and remote forcing factors like wind direction, cloudiness, ocean currents, thermocline depth, and upwelling intensity [18]. While upwelling can buffer the effects of global warming nearshore [19], many coastal areas experience higher warming rates than open waters [16]. Little attention has been given to SST trends in coastal areas, and regional variations and changes in the seasonality are yet not fully understood [16].…”

Section: Introductionmentioning

confidence: 99%

“…While upwelling can buffer the effects of global warming nearshore [19], many coastal areas experience higher warming rates than open waters [16]. Little attention has been given to SST trends in coastal areas, and regional variations and changes in the seasonality are yet not fully understood [16]. Also, the authors of [17] emphasize the need for highspatial-resolution studies to close this gap.…”

Section: Introductionmentioning

confidence: 99%

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First Analyses of the TIMELINE AVHRR SST Product: Long-Term Trends of Sea Surface Temperature at 1 km Resolution across European Coastal Zones

Reiners,

Obrecht,

Dietz

et al. 2024

Remote Sensing

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Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions.

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Section: Comparison Between Timeline and CCI Productmentioning

confidence: 60%