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Anthropogenically-induced global warming is expected to decrease primary productivity in the subtropical oceans by strengthening stratification of the water column and reducing the flux of nutrients from deep-waters to the sunlit surface layers. Identification of such changes is hindered by a paucity of long-term, spatially-resolved, biological time-series data at the basin scale. This paper exploits Atlantic Meridional Transect (AMT) data on physical and biogeochemical properties (1995–2014) in synergy with a wide range of remote-sensing (RS) observations from ocean colour, Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and altimetry (surface currents), combined with different modelling approaches (both empirical and a coupled 1-D Ecosystem model), to produce a synthesis of the seasonal functioning of the North and South Atlantic Sub-Tropical Gyres (STGs), and assess their response to longer-term changes in climate. We explore definitive characteristics of the STGs using data of physical (SST, SSS and peripheral current systems) and biogeochemical variables (chlorophyll and nitrate), with inherent criteria (permanent thermal stratification and oligotrophy), and define the gyre boundary from a sharp gradient in these physical and biogeochemical properties. From RS data, the seasonal cycles for the period 1998–2012 show significant relationships between physical properties (SST and PAR) and gyre area. In contrast to expectations, the surface layer chlorophyll concentration from RS data (CHL) shows an upward trend for the mean values in both subtropical gyres. Furthermore, trends in physical properties (SST, PAR, gyre area) differ between the North and South STGs, suggesting the processes responsible for an upward trend in CHL may vary between gyres. There are significant anomalies in CHL and SST that are associated with El Nino events. These conclusions are drawn cautiously considering the short length of the time-series (1998–2012), emphasising the need to sustain spatially-extensive surveys such as AMT and integrate such observations with models, autonomous observations and RS data, to help address fundamental questions about how our planet is responding to climate change. A small number of dedicated AMT cruises in the keystone months of January and July would complement our understanding of seasonal cycles in the STGs.

Chlorophyll-a (Chl a) is a key parameter for the assessment of water quality in coastal and shelf environments. The availability of satellite ocean colour offers the potential of monitoring these regions at unprecedented spatial and temporal scales, as long as a high level of accuracy can be achieved. To use satellite derived Chl a to monitor these environments, it is imperative that rigorous accuracy assessments are undertaken to select the most accurate ocean colour algorithm(s). To this end, the accuracy of a range of ocean colour Chl a algorithms for use with Medium Imaging Resolution Spectrometer (MERIS) Level 2 (L2) Remote Sensing Reflectance (R-rs), using two different atmospheric correction (AC) processors (COASTCOLOUR and MERIS Ground Segment processor version 8.0 - MEGS8.0), were assessed in North West European waters. A total of 594 measurements of R-rs(lambda) and/or Chl a were made in the North Sea, Mediterranean Sea, along the Portuguese Coast English Channel and Celtic Sea between June 2001 and March 2012, where Chl a varied from 0.2 to 35 mg m(-3). The following algorithms were compared: MERIS Case 1 water Chl a algorithm OC4Me, the MERIS Case 2 algorithm Algal Pigment 2 (AP2), the MODIS-Aqua Case 1 Chl a algorithm OC3 adapted for MERIS (OC3Me), the MODIS-Aqua Garver-Siegel-Maritorena algorithm (GSM) adapted for MERIS and the Gohin et al. (2002) algorithm for MERIS (005Me). For both COASTCOLOUR and MEGS8.0 processors, OC5Me was the most accurate Chl a algorithm, which was within similar to 25% of in situ values in these coastal and shelf waters. The uncertainty in MEGS8.0 R(rs)4442) (similar to 17%) was slightly higher compared to COASTCOLOUR (similar to 12%) from 0.3 to 7 mg m(-3) Chl a, but for R-rs(560)the uncertainty was lower for MEGS8.0 (similar to 10%) compared to COASTCOLOUR (similar to 13%), which meant that MEGS8.0 Chl a was more accurate than COASTCOLOUR for all of the Chl a algorithms tested. Compared to 005Me, OC4Me tended to over-estimate Chl a, which was caused by non-algal SPM especially at values >14 g M-3. GSM also over-estimated Chl a, which was caused by variations in absorption coefficient of coloured dissolved organic matter at 442 nm (a(CDOM)(442)). AP2 consistently under-estimated Chl a, especially when non-algal SPM was >4 g m(-3). (C) 2016 Published by Elsevier Inc. All rights reserved. Natural Environment Research Council's National Capability - The Western English Channel Observatory (WCO), NEODAAS European Union - INTERREG IVA 2 Mers Seas Zeeen Cross-border Cooperation Programme [07-027-FR-ISECA] European Space Agency [4000113691/15/I-LG] AQUA-USERS project (EU 7th Framework Programme) [607325] Natural Environment Research Council [pml010008, pml010007]