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Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

Synechococcus is the second most abundant phytoplanktonic organism on Earth, and its wide genetic diversity allowed it to colonize all the oceans except for polar waters, with different clades colonizing distinct oceanic niches. In recent years, the use of global metagenomics data sets has greatly improved our knowledge of “who is where” by describing the distribution of Synechococcus clades or ecotypes in the open ocean. Marine Synechococcus comprise a numerically and ecologically prominent phytoplankton group, playing a major role in both carbon cycling and trophic networks in all oceanic regions except in the polar oceans. Despite their high abundance in coastal areas, our knowledge of Synechococcus communities in these environments is based on only a few local studies. Here, we use the global metagenome data set of the Ocean Sampling Day (June 21 st , 2014) to get a snapshot of the taxonomic composition of coastal Synechococcus communities worldwide, by recruitment on a reference database of 141 picocyanobacterial genomes, representative of the whole Prochlorococcus , Synechococcus , and Cyanobium diversity. This allowed us to unravel drastic community shifts over small to medium scale gradients of environmental factors, in particular along European coasts. The combined analysis of the phylogeography of natural populations and the thermophysiological characterization of eight strains, representative of the four major Synechococcus lineages (clades I to IV), also brought novel insights about the differential niche partitioning of clades I and IV, which most often co-dominate the Synechococcus community in cold and temperate coastal areas. Altogether, this study reveals several important characteristics and specificities of the coastal communities of Synechococcus worldwide. IMPORTANCE Synechococcus is the second most abundant phytoplanktonic organism on Earth, and its wide genetic diversity allowed it to colonize all the oceans except for polar waters, with different clades colonizing distinct oceanic niches. In recent years, the use of global metagenomics data sets has greatly improved our knowledge of “who is where” by describing the distribution of Synechococcus clades or ecotypes in the open ocean. However, little is known about the global distribution of Synechococcus ecotypes in coastal areas, where Synechococcus is often the dominant phytoplanktonic organism. Here, we leverage the global Ocean Sampling Day metagenomics data set to describe Synechococcus community composition in coastal areas worldwide, revealing striking community shifts, in particular along the coasts of Europe. As temperature appears as an important driver of the community composition, we also characterize the thermal preferenda of 8 Synechococcus strains, bringing new insights into the adaptation to temperature of the dominant Synechococcus clades.

In 2016, northern France experienced an unprecedented wheat crop loss. The cause of this event is not yet fully understood, and none of the most used crop forecast models were able to predict the event (Ben-Ari et al., 2018). However, this extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn–winter and abnormally high precipitation during the spring season. Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss. This work is motivated by the question of whether the 2016 meteorological conditions were the most extreme possible conditions under current climate, and what the worst-case meteorological scenario would be with respect to warm winters followed by wet springs. To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue-based importance sampling algorithm that was recently introduced into this field of research (Yiou and Jézéquel, 2020). This algorithm is a modification of a stochastic weather generator (SWG) that gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This approach is inspired by importance sampling of complex systems (Ragone et al., 2017). This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way. This paper explains how an SWG for extreme winter temperature and spring precipitation can be constructed in order to generate large samples of such extremes. We show that with some adjustments both types of weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. We find that the number of cold days in late autumn 2015 was close to the plausible minimum. However, our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the relation of crop loss in 2016 to climate variability is not yet fully understood, these results indicate that similar events with higher impacts could be possible in present-day climate conditions.

Concentration variability in the fast-response tracer dataset for continuous, near-surface, point source releases in the urban core from the Joint Urban 2003 field study is analyzed. Concentration variability for conditionally and unconditionally sampled time series is characterized by probability densities, concentration fluctuation intensity, skewness, and kurtosis. Significant day-night differences in plume dispersion are observed. Relative to daytime, nighttime plumes were more likely to have reduced concentration fluctuation intensities, higher normalized surface concentrations, suppressed vertical mixing, and a greater prevalence of Gaussian-like distributions rather than log-normal or mixed mode distributions. This was in spite of the similar stability and turbulence conditions in the urban core for day and night. The potential roles of flow meander and thermal stability in explaining these differences are examined. Probability densities of concentration are found to be a strong function of fluctuation intensity. There are few differences in probability densities between day and night when classified by fluctuation intensity. There are no appreciable differences between conditional and unconditional probability densities and only small differences between conditional and unconditional sampling statistics relative to the larger differences usually observed in more homogeneous settings. Fluctuation intensity, skewness, and kurtosis are higher for the daytime experiments, and closer to the source, but show little difference between conditional and unconditional results over most of their range of values. The log-normal distribution provides a better overall fit to a broader range of the dataset than the exponential or clipped-normal distributions.