Analysis of 23 Years of Daily Cloud-Free Chlorophyll and Suspended Particulate Matter in the Greater North Sea

Abstract

Satellite-derived estimates of ocean color variables are available for several decades now and allow performing studies of the long-term changes occurred in an ecosystem. A daily, gap-free analysis of chlorophyll (CHL) and suspended particulate matter (SPM, indicative of light availability in the subsurface) at 1 km resolution over the Greater North Sea during the period 1998–2020 is presented. Interannual changes are described, with maximum average CHL values increasing during the period 1998–2008, a slightly decreasing trend in 2009–2017 and an stagnation in recent years. The typical spring bloom is observed to happen earlier each year, with about 1 month difference between 1998 and 2020. The duration of the bloom (time between onset and offset) appears also to be increasing with time, but the average CHL value during the spring bloom does not show a clear trend. The causes for earlier spring blooms are still unclear, although a rising water temperature can partially explain them through enhanced phytoplankton cell division rates or through increased water column stratification. SPM values during winter months (prior to the development of the spring bloom) do not exhibit a clear trend over the same period, although slightly higher SPM values are observed in recent years. The influence of sea surface temperature in the spring bloom timing appears to be dominant over the influence of SPM concentration, according to our results. The number of satellites available over the years for producing CHL and SPM in this work has an influence in the total amount of available data before interpolation. The amount of missing data has an influence in the total variability that is retained in the final dataset, and our results suggest that at least three satellites would be needed for a good representation of ocean color variability.

Threshold indicators of primary production in the north-east Atlantic for assessing environmental disturbances using 21 years of satellite ocean colour

“…This has been related to shallower mixed layer depths limiting the resuspension of suspended material to the surface which results in more stratified, clearer waters, which in turn form higher biomass blooms. Similarly, in the greater North Sea, the spring bloom has been observed to be occurring earlier with a one month difference between 1998 and 2020 and longer duration, which is partially explained by rising seawater temperatures (Alvera-Azcarate et al, 2021). In addition, summer blooms have been observed to start and end later, and to be longer in duration with higher Chl a concentrations (Silva et al, 2021).…”

“…The method of merging the data uses SeaWiFS remote sensing reflectance as the reference where possible and normalises the data from the other sensors to this (Melin et al, 2017), so theoretically there should be no bias in the resulting Chl a concentrations. Any bias between these periods would arise from differences in cloud free observations from several different satellite sensors as opposed to one during the SeaWiFS-only period (Alvera-Azcarate et al, 2021). There are different merged ocean colour products available.…”

Improving operational ocean color coverage using a merged atmospheric correction approach

“…These blooms are generally composed of diatoms and Phaeocystis globose [16]. In recent years, blooms have been occurring earlier, likely in response to sea surface temperature increases and changes in nutrient outputs [17] [18]. The water type is turbid coastal to turbid coastal with high organic content.…”

Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea

“…Many satellite sensors rely on this part of the spectrum to measure, for example, sea surface temperature and ocean color. Besides clouds, other reasons for missing data include atmospheric dust, sun glint contamination, limited swath width, and high sensor-zenith angle (Feng and Hu, 2016;Mikelsons and Wang, 2019;Alvera-Azcàrate et al, 2021). The amount of missing data in satellite observations can therefore be substantial.…”