Natural Ecosystems

Global-ocean carbon and biogeochemical response to input from rivers and coastal wetlands

Abstract: Large amounts of terrestrial carbon and nutrients are routed to the ocean through the Land-Ocean Aquatic Continuum (LOAC). Once in coastal waters, these terrestrial inputs impact ocean carbon chemistry. Lateral carbon export from rivers has been estimated to be responsible for global-ocean outgassing of roughly 0.45 Pg C yr-1. However, the biogeochemical pathway for this outgassing has not yet been quantified. In this study, we have carried out a set of model sensitivity experiments, in which we introduce terrestrial carbon and nutrients in the ECCO-Darwin global-ocean biogeochemistry state estimate. We compute daily riverine export by combining the GlobalNEWS2.0 watershed model with point-source freshwater discharge from the JRA55-do atmospheric reanalysis. We quantify the litter and soil carbon pool for mangrove forests worldwide and the tidally-driven flux from this intertidal carbon pool to the open ocean following the time-volume change of water estimated from the combination of the FES2014 barotropic tidal model and the Global LiDAR Lowland Digital Terrain Model. We evaluate the impact of terrestrial exports on the global ocean by comparing a suite of experiments against a baseline simulation that does not include terrestrial carbon and nutrient export for the 1995–2017 period. Our study explores the role of terrestrial carbon and nutrients in the ocean’s biological and carbon chemistry. By including processes that occur at the land-ocean interface, we aim for an improved understanding of how the LOAC impacts global carbon cycling.

Modeling Impacts of Wildfires on Soil Thermal and Hydrological and Carbon Dynamics in Northern High Latitudes

Abstract: Wildfires significantly affect vegetation, soil thermal and hydrological as well as carbon dynamics. This study uses a process-based biogeochemistry modeling framework that is incorporated with land surface energy balance, soil thermal and hydrological dynamics and their effects on carbon and nitrogen cycling to simulate these dynamics and carbon budget in northern high latitudes. Here we present our model results on North American boreal forests from 1986 to 2020 using satellite-derived burn severity data. We find that fires remove ecosystem carbon through combustion emissions and reduce net ecosystem production, making the ecosystem lose 3.5 Pg C during 1986-2020 and changing the boreal forests from a carbon sink to a source in the region. Our modeling also suggests that fire-impacted canopy influences surface energy balance, inducing significant summer soil temperature changes, affecting nitrogen mineralization rate and plant nitrogen uptake, thereby changing plant net primary productivity; the altered soil temperature also affects soil carbon decomposition. As a result, the canopy effects on surface energy balance significantly affect boreal forest ecosystem carbon sink and source activities in the region. Currently we are examining the wildfire impacts on permafrost dynamics and hydrological cycle as well as carbon and nitrogen dynamics in northern Eurasia.

A Global Comparison of Marine Chlorophyll Variability Observed in Eulerian and Lagrangian Perspectives

Authors' Summary: The differences in phytoplankton variability through time observed at fixed locations (Eulerian perspective) or following water parcels (Lagrangian perspective) are poorly understood. We created a large set of satellite chlorophyll matched time series pairs in the Eulerian and Lagrangian perspective, using global drifter trajectories as an approximation of how surface ocean currents move.

We found that for most ocean locations, chlorophyll variability measured in Eulerian and Lagrangian perspectives is not different. In high latitude zones, chlorophyll appears to vary similarly over large areas. However, in localized regions of the ocean, such as western boundary currents and upwelling regions, chlorophyll variability in these two perspectives may significantly differ. The causes are linked to the specific ocean dynamics of each area.

Challenges and opportunities in connecting gene count observations with ocean biogeochemical models: Reply to Zehr and Riemann (2023)

Abstract: As authors of Meiler et al. (2022), we welcome Zehr and Riemann's (2023) comment and discussion. We agree, of course, with the general statement that “quantification of gene copy numbers is valuable in marine microbial ecology” and wish to clarify that one of the purposes of Meiler et al. (2022) was to address the specific challenge of using a compilation of quantitative polymerase chain reaction (qPCR) nifH data to evaluate the skill of biogeochemical models. In that particular case, the data were most helpful in constraining the range of diazotrophs, but several sources of uncertainty limited more detailed quantitative evaluations. This was not intended to imply a lack of value or promise for such applications of qPCR data: we believe that testing and constraining biogeochemical and ecological models will be an important application of qPCR data, yet the quantitative interface between molecular data and biogeochemical models remains at its infancy.

In the following, we first provide a background perspective for the Meiler et al. (2022) study, pointing out why observations and simulations are rooted in different currencies. We then discuss in more detail some of the specific points raised by Zehr and Riemann (2023) and highlight why further efforts toward intercalibration of currencies used to measure and simulate marine microbial populations is particularly significant if we are to fully exploit the data in biogeochemical and climate modeling applications. We end by summarizing some potentially fruitful avenues for future effort stimulated by this dialog.

Assessing the performance of backscattering-based phytoplankton carbon algorithms. Global Biogeochemical Cycles

Authors' Summary: Phytoplankton contribute roughly half of the photosynthesis on earth and fuel fisheries around the globe. Yet, few direct measurements of phytoplankton concentration are available. Frequently, concentrations of phytoplankton are instead estimated using the optical properties of water. Backscattering is one of these optical properties, representing the light being scattered backwards. Previous studies have suggested that backscattering could be a good method to estimate phytoplankton concentration. However, other particles that are present in the ocean also contribute to backscattering.

In this paper we examine how well backscattering can be used to estimate phytoplankton. To address this question, we use data from drifting instruments that are spread across the ocean and a computer model that simulates phytoplankton and backscattering over the global oceans.

We find that by using backscattering, phytoplankton can be overestimated/underestimated on average by ∼20%. This error differs between regions, and can be larger than 100% at high latitudes. Computer simulations allowed us to quantify spatial and temporal variability in backscattering signal composition, and thereby understand potential errors in inferring phytoplankton with backscattering, which could not have been done before due to the lack of phytoplankton data.

Assessing the performance of backscattering-based phytoplankton carbon algorithms

Authors' Summary: Phytoplankton contribute roughly half of the photosynthesis on earth and fuel fisheries around the globe. Yet, few direct measurements of phytoplankton concentration are available. Frequently, concentrations of phytoplankton are instead estimated using the optical properties of water. Backscattering is one of these optical properties, representing the light being scattered backwards. Previous studies have suggested that backscattering could be a good method to estimate phytoplankton concentration. However, other particles that are present in the ocean also contribute to backscattering. In this paper we examine how well backscattering can be used to estimate phytoplankton.

To address this question, we use data from drifting instruments that are spread across the ocean and a computer model that simulates phytoplankton and backscattering over the global oceans. We find that by using backscattering, phytoplankton can be overestimated/underestimated on average by ∼20%. This error differs between regions, and can be larger than 100% at high latitudes. Computer simulations allowed us to quantify spatial and temporal variability in backscattering signal composition, and thereby understand potential errors in inferring phytoplankton with backscattering, which could not have been done before due to the lack of phytoplankton data.

Global climate-change trends detected in indicators of ocean ecology

Abstract: Strong natural variability has been thought to mask possible climate-change-driven trends in phytoplankton populations from Earth-observing satellites. More than 30 years of continuous data were thought to be needed to detect a trend driven by climate change.

Here we show that climate-change trends emerge more rapidly in ocean colour (remote-sensing reflectance, R) because R is multivariate and some wavebands have low interannual variability. We analyse a 20-year R time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and find significant trends in R for 56% of the global surface ocean, mainly equatorward of 40°.

The climate-change signal in R emerges after 20 years in similar regions covering a similar fraction of the ocean in a state-of-the-art ecosystem model, which suggests that our observed trends indicate shifts in ocean colour—and, by extension, in surface-ocean ecosystems—that are driven by climate change. On the whole, low-latitude oceans have become greener in the past 20 years.

Editor's Summary: An analysis of satellite data from July 2002–June 2022 shows that ocean colour, or remote-sensing reflectance, changed significantly during this period, and that this trend is likely to be driven by climate change.

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