Satellite Lidar Measurements as a Critical New Global Ocean Climate Record

The year 2023 marked the tenth anniversary of the first published description of global ocean plankton stocks based on measurements from a satellite lidar. Diverse studies have since been conducted to further refine and validate the lidar retrievals and use them to discover new characteristics of plankton seasonal dynamics and marine animal migrations, as well as evaluate geophysical products from traditional passive ocean color sensors. Surprisingly, all of these developments have been achieved with lidar instruments not designed for ocean applications. Over this same decade, we have witnessed unprecedented changes in ocean ecosystems at unexpected rates and driven by a multitude of environmental stressors, with a dominant factor being climate warming. Understanding, predicting, and responding to these ecosystem changes requires a global ocean observing network linking satellite, in situ, and modeling approaches. Inspired by recent successes, we promote here the creation of a lidar global ocean climate record as a key element in this envisioned advanced observing system. Contributing to this record, we announce the development of a new satellite lidar mission with ocean-observing capabilities and then discuss additional technological advances that can be envisioned for subsequent missions. Finally, we discuss how a potential near-term gap in global ocean lidar data might, at least partially, be filled using on-orbit or soon-to-be-launched lidars designed for other disciplinary purposes, and we identify upcoming needs for in situ support systems and science community development

An Operational Overview of the EXport Processes In the Ocean From RemoTe Sensing (EXPORTS) Northeast Pacific Field Deployment

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal. The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set

Open ocean particle flux variability from surface to seafloor

The sinking of carbon fixed via net primary production (NPP) into the ocean interior is an important part of marine biogeochemical cycles. NPP measurements follow a log‐normal probability distribution, meaning NPP variations can be simply described by two parameters despite NPP’s complexity. By analyzing a global database of open ocean particle fluxes, we show that this log‐normal probability distribution propagates into the variations of near‐seafloor fluxes of particulate organic carbon (POC), calcium carbonate, and opal. Deep‐sea particle fluxes at subtropical and temperate time‐series sites follow the same log‐normal probability distribution, strongly suggesting the log‐normal description is robust and applies on multiple scales. This log‐normality implies that 29% of the highest measurements are responsible for 71% of the total near‐seafloor POC flux. We discuss possible causes for the dampening of variability from NPP to deep‐sea POC flux, and present an updated relationship predicting POC flux from mineral flux and depth

Patterns and Processes of Salt Efflorescences in the McMurdo region, Antarctica

Evaporite salts are abundant around the McMurdo region, Antarctica (~78°S) due to very low precipitation, low relative humidity, and limited overland flow. Hygroscopic salts in the McMurdo Dry Valleys (MDVs) are preferentially formed in locations where liquid water is present in the austral summer, including along ephemeral streams, ice-covered lake boundaries, or shallow groundwater tracks. In this study, we collected salts from the Miers, Garwood, and Taylor Valleys on the Antarctic continent, as well as around McMurdo Station on Ross Island in close proximity to water sources with the goal of understanding salt geochemistry in relationship to the hydrology of the area. Halite is ubiquitous; sodium is the major cation (ranging from 70%–90% of cations by meq kg-1 sediment) and chloride is the major anion (\u3e50%) in nearly all samples. However, a wide variety of salt phases and morphologies are tentatively identified through scanning electron microscopy (SEM) and X-ray diffraction (XRD) work. We present new data that identifies trona (Na3(CO3)(HCO3)·2H2O), tentative gaylussite (Na2Ca(CO3)2·5H2O), and tentative glauberite (Na2Ca(SO4)2) in the MDV, of which the later one has not been documented previously. Our work allows for the evaluation of processes that influence brine evolution on a local scale, consequently informing assumptions underlying large-scale processes (such as paleoclimate) in the MDV. Hydrological modeling conducted in FREZCHEM and PHREEQC suggests that a model based on aerosol deposition alone in low elevations on the valley floor inadequately characterizes salt distributions found on the surfaces of the soil because it does not account for other hydrologic inputs/outputs. Implications for the salt distributions include their use as tracers for paleolake levels, geochemical tracers of ephemeral water tracks or “wet patches” in the soil, indicators of chemical weathering products, and potential delineators of ecological communities

Sampling uncertainties of particle size distributions and derived fluxes

In this study, we provide a method to quantify the uncertainty associated with sampling particle size distributions (PSD), using a global compilation of Underwater Vision Profiler observations (UVP, version 5). The UVP provides abundant in situ data of the marine PSD on global scales and has been used for a diversity of applications, but the uncertainty associated with its measurements has not been quantified, including how this uncertainty propagates into derived products of interest. We model UVP sampling uncertainty using Bayesian Poisson statistics and provide formulae for the uncertainty associated with a given sampling volume and observed particle count. We also model PSD observations using a truncated power law to better match the low concentration associated with rare large particles as seen by the UVP. We use the two shape parameters from this statistical model to describe changes in the PSD shape across latitude band, season, and depth. The UVP sampling uncertainty propagates into an uncertainty for modeled carbon flux exceeding 50%. The statistical model is used to extend the size interval used in a PSD-derived carbon flux model, revealing a high sensitivity of the PSD-derived flux model to the inclusion of small particles (80–128 μm). We provide avenues to address additional uncertainties associated with UVP-derived carbon flux calculations

Comparison of model-predicted phytoplankton division rates with measure steady-state rates in PO₄-limited chemostat cultures.

(A) Circles = cell division rates observed by Laws et al. [32] for Tetraselmis suecica (Kylin) Butcher. Solid line = predicted division rates assuming an average cell size of 12 μm and a maximum division rate (μm) of 1.19 d-1 [32]. (B) Circles = cell division rates observed by Laws et al. [55] for Pavlova lutheri (Droop) Green. Solid line = predicted division rates assuming an average cell size of 6 μm and a μm of 0.98 d-1 [55].</p

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Short-term and acclimated production-resource relationships for light-limited and nutrient-limited phytoplankton populations.

(A) Short-term (20 minute) carbon-specific 14C uptake as measured by Fisher & Halsey [76] for Thalassiosira pseudonana (Hustedt) Hasle et Heimdal (CCMP 1355) cultures acclimated to a light-limited growth rate of 0.85 d-1. Dashed line = fit of Eq 5. (B) Cell division rates observed by Laws & Bannister [54] for Thalassiosira weissflogii (previously, Thalassiosira fluviatilis) acclimated to a range in growth irradiance (Ig, x-axis). Solid line = fit of Eq 6. Dashed line = application of Eq 5. (C) Short-term (8 minute) PO4 uptake (atto-mol = 10−15 mmol) measured by Laws et al. [55] for Pavlova lutheri (Droop) J.C. Green maintained in chemostats at a PO4-limited growth rate of 0.48 d-1 and then rapidly exposed to a range of concentrations (x-axis). Dashed line = fit of Eq 7. (D) Cell division rates observed by Laws et al. [32] for Tetraselmis suecica (Kylin) Butcher in steady-state PO4-limited chemostat cultures. Solid line = fit of Eq 8. Dashed line = application of Eq 7. (c,d) x-axis = measured far-field PO4 concentration ().</p

Diffusion-supported phytoplankton division rates as a function of cell size predicted for a range in far-field nutrient concentrations (<i>S</i>_<i>∞</i>) reflective of highly oligotrophic to highly eutrophic natural waters.

(A-C) Non-diatoms. (D-F) Diatoms. (A,D) Lower heavy black line = initial prediction for diffusion-limited growth at all cell sizes. Upper heavy black line = division rate prediction if following cellular surface:volume ratios. Grey lines = size-dependent division rates for S∞ ranging from 1 nM to 3 μM (blue labeling). Red lines = division rates for biologically-available nitrogen concentrations of 3 nM to 17 nM typical of S∞ values in oligotrophic ocean gyres. (B,E) Same data as in left column but with normal y-axis and log-transformed x-axis to better reveal size-dependent division rates of small cells. (C,F) Same data as in left column but with normal axes. Blue line = envelope in size-dependent maximum division rates (μm) from Behrenfeld et al. [53].</p

Conceiving the discreteness of phytoplankton communities.

(A) Cell abundances for populations of a single cell size required for the spatial (DVλ) and temporal (DVτ) distribution variables defined by Siegel [12] to have a value of one, indicating direct competition for resources is prevalent. Note, these threshold values are notably larger than most natural population abundances. (B) Average number of body lengths between individual phytoplankton cells (left axis, solid line) and average population cell size (right axis, dashed line) for modeled phytoplankton communities with size distributions reflective of natural populations (see text). Cell size is calculated as the cell diameter of the average cell volume. Bottom and top axis give total phytoplankton carbon biomass (Cphyto) and approximate corresponding chlorophyll concentrations. (C) Depiction of phytoplankton in natural waters where cells are distantly spaced and resource acquisition is limited to discrete boundary layers around each cell (outer circles with inward pointing arrows) and has no immediate impact on the far-field resource pool (S∞) experienced by all cells.</p