Climate pathways behind phytoplankton-induced atmospheric warming

We investigate the ways in which marine biologically mediated heating increases the surface atmospheric temperature. While the effects of phytoplankton light absorption on the ocean have gained attention over the past years, the impact of this biogeophysical mechanism on the atmosphere is still unclear. Phytoplankton light absorption warms the surface of the ocean, which in turn affects the air–sea heat and CO2 exchanges. However, the contribution of air–sea heat versus CO2 fluxes in the phytoplankton-induced atmospheric warming has not been yet determined. Different so-called climate pathways are involved. We distinguish heat exchange, CO2 exchange, dissolved CO2, solubility of CO2 and sea-ice-covered area. To shed more light on this subject, we employ the EcoGEnIE Earth system model that includes a new light penetration scheme and isolate the effects of individual fluxes. Our results indicate that phytoplankton-induced changes in air–sea CO2 exchange warm the atmosphere by 0.71 ∘C due to higher greenhouse gas concentrations. The phytoplankton-induced changes in air–sea heat exchange cool the atmosphere by 0.02 ∘C due to a larger amount of outgoing longwave radiation. Overall, the enhanced air–sea CO2 exchange due to phytoplankton light absorption is the main driver in the biologically induced atmospheric heating.

Air-Sea CO2 Exchange in the Strait of Gibraltar

The seasonal and spatial variability of the CO2 system and air-sea fluxes were studied in surface waters of the Strait of Gibraltar between February 2019 and March 2021. High-resolution data was collected by a surface ocean observation platform aboard a volunteer observing ship. The CO2 system was strongly influenced by temperature and salinity fluctuations forced by the seasonal and spatial variability in the depth of the Atlantic–Mediterranean Interface layer and by the tidal and wind-induced upwelling. The changes in seawater CO2 fugacity (fCO2,sw) and fluxes were mainly driven by temperature despite the significant influence of non-thermal processes in the southernmost part. The thermal to non-thermal effect ratio (T/B) reached maximum values in the northern section (>1.8) and minimum values in the southern section (<1.30). The fCO2,sw increased with temperature by 9.02 ± 1.99 μatm °C–1 (r2 = 0.86 and ρ = 0.93) and 4.51 ± 1.66 μatm °C–1 (r2 = 0.48 and ρ = 0.69) in the northern and southern sections, respectively. The annual cycle of total inorganic carbon normalized to a constant salinity of 36.7 (NCT) was assessed. Net community production processes described 93.5–95.6% of the total NCT change, while air-sea exchange and horizontal and vertical advection accounted for <4.6%. The fCO2,sw in the Strait of Gibraltar since 1999 has been fitted to an equation with an interannual trend of 2.35 ± 0.06 μatm year–1 and a standard error of estimate of ±12.8 μatm. The seasonality of the air-sea CO2 fluxes reported the behavior as a strong CO2 sink during the cold months and as a weak CO2 source during the warm months. Both the northern and the southern sections acted as a net CO2 sink of −0.82 and −1.01 mol C m–2 year–1, respectively. The calculated average CO2 flux for the entire area was −7.12 Gg CO2 year–1 (−1.94 Gg C year–1).

Quantifying the Robustness of Complex Networks with Heterogeneous Nodes

The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the `fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erdős–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the CO2 exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.

A monthly surface pCO₂ product for the California Current Large Marine Ecosystem

To calculate the direction and rate of carbon dioxide gas (CO2) exchange between the ocean and atmosphere, it is critical to know the partial pressure of CO2 in surface seawater (pCO2(sw)). Over the last decade, a variety of data products of global monthly pCO2(sw) have been produced, primarily for the open ocean on 1° latitude by 1° longitude grids. More recently, monthly products of pCO2(sw) that are more finely spatially resolved in the coastal ocean have been made available. A remaining challenge in the development of pCO2(sw) products is the robust characterization of seasonal variability, especially in nearshore coastal environments. Here we present a monthly data product of pCO2(sw) at 0.25° latitude by 0.25° longitude resolution in the Northeast Pacific Ocean, centered around the California Current System (CCS). The data product (RFR-CCS; Sharp et al., 2021; https://doi.org/10.5281/zenodo.5523389) was created using the most recent (2021) version of the Surface Ocean CO2 Atlas (Bakker et al., 2016) from which pCO2(sw) observations were extracted and fit against a variety of satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from moored autonomous surface platforms, and find that the data product effectively captures seasonal pCO2(sw) cycles at nearshore mooring sites. This result is notable because alternative global products for the coastal ocean do not capture local variability effectively in this region. We briefly review the physical and biological processes — acting across a variety of spatial and temporal scales — that are responsible for the latitudinal and nearshore-to-offshore pCO2(sw) gradients seen in RFR-CCS reconstructions of pCO2(sw).