Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean

Abstract

Abstract. Oceanic particulate organic carbon (POC) is a small but dynamic component of the global carbon cycle. Biogeochemical models historically focused on reproducing the sinking flux of POC driven by large fast-sinking particles (LPOC). However, suspended and slow-sinking particles (SPOC, here < 100 µm) dominate the total POC (TPOC) stock, support a large fraction of microbial respiration, and can make sizable contributions to vertical fluxes. Recent developments in the parameterization of POC reactivity in PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies model; PISCESv2_RC) have improved its ability to capture POC dynamics. Here we evaluated this model by matching a global 3D simulation and 1D simulations at 50 different locations with observations made from biogeochemical (BGC-) Argo floats and satellites. Our evaluation covers globally representative biomes between 0 and 1000 m depth and relies on (1) a refined scheme for converting particulate backscattering at 700 nm (bbp700) to POC, based on biome-dependent POC / bbp700 ratios in the surface layer that decrease to an asymptotic value at depth; (2) a novel approach for matching annual time series of BGC-Argo vertical profiles to PISCES 1D simulations forced by pre-computed vertical mixing fields; and (3) a critical evaluation of the correspondence between in situ measurements of POC fractions, PISCES model tracers, and SPOC and LPOC estimated from high vertical resolution bbp700 profiles through a separation of the baseline and spike signals. We show that PISCES captures the major features of SPOC and LPOC across a range of spatiotemporal scales, from highly resolved profile time series to biome-aggregated climatological profiles. Model–observation agreement is usually better in the epipelagic (0–200 m) than in the mesopelagic (200–1000 m), with SPOC showing overall higher spatiotemporal correlation and smaller deviation (typically within a factor of 1.5). Still, annual mean LPOC stocks estimated from PISCES and BGC-Argo are highly correlated across biomes, especially in the epipelagic (r=0.78; n=50). Estimates of the SPOC / TPOC fraction converge around a median of 85 % (range 66 %–92 %) globally. Distinct patterns of model–observations misfits are found in subpolar and subtropical gyres, pointing to the need to better resolve the interplay between sinking, remineralization, and SPOC–LPOC interconversion in PISCES. Our analysis also indicates that a widely used satellite algorithm overestimates POC severalfold at high latitudes during the winter. The approaches proposed here can help constrain the stocks, and ultimately budgets, of oceanic POC.

Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon

“…Here we define SPOC as the sum of the PISCES tracers for nanophytoplankton (PHY), microphytoplankton (PHY2), microzooplankton (ZOO) and small detritus (POC), and LPOC as the sum of large detritus (GOC) and mesozooplankton (ZOO2). These idealised fractions show good correspondence with those determined from BGC-Argo data (Galí et al, 2022). It is important to keep in mind that detrital POC is a variable proportion of total POC, which generally increases with depth.…”

“…To enable comparison between biogeochemical (BGC)-Argo data and model simulations, we developed a framework that is described in detail in the companion paper by Galí et al (2022). Briefly, particulate backscattering measurements acquired by Argo floats were converted to POC using depth-dependent empirical conversion factors and separated into two size fractions, small POC (SPOC) and large POC (LPOC), following Briggs et al (2020).…”

Biogeographical Classification of the Global Ocean From BGC‐Argo Floats

“…The high vertical and temporal resolution of b bp measurements in our data allows us to investigate the variability of the organic particle stocks between the productive and the mesopelagic zone for each biome. It is important to note, though, that as a result of removing spikes in b bp profiles, our results primarily reflect contributions of the small and/or suspended fraction of overall particle stocks (Briggs et al, 2011;Galí et al, 2021). With this, our estimates of transfer efficiency and particle stocks across different depth horizons should be interpreted as representing only a portion of the particle stocks, especially for high-latitude biomes.…”

“…In doing so, they provide a global template for contextualizing profile measurements of phytoplankton community structure and/or HPLC pigments from ship‐based research programs (e.g., JGOFS, AMT, and HOT), or for constraining estimates of subsurface phytoplankton distributions for modeling primary production (Westberry et al., 2008 ) or phytoplankton community structure (Morel & Berthon, 1989 ; Uitz et al., 2006 ). Various seasonal patterns regarding the dynamics of small particles at depth also highlight the further potential for using subsurface classifications to constrain estimates of parameters related to the biological carbon pump (Boyd et al., 2019 ; Galí et al., 2021 ).…”

“…Average b bp values within the mesopelagic zone (b bp_mz ) were calculated by integrating from Z p to Z m and normalizing to the difference in depth between Z m and Z p . The ratio of b bp_pz :b bp_mz was interpreted as a proxy for transfer efficiency (T effp ), following Galí et al (2021), albeit in our case based on comparative depth-integrated b bp values rather than POC estimates. Average b bp values were also calculated for each of the four equally sized depth ranges within the mesopelagic zone (b bp_m1 -b bp_m4 ) by integrating b bp profiles within each depth range and then normalizing to the width of the depth range (Figure S3 in Supporting Information S1).…”