Sensitivity of the simulated Oxygen Minimum Zone to biogeochemical processes at an oligotrophic site in the Arabian Sea

Abstract

14 15Oxygen minimum zones (OMZs) are large, low-oxygen areas in the global oceans. Although 16OMZs represent a serious threat to ecosystem functioning and services, our capability of modelling 17 the main biogeochemical processes driving OMZ dynamic are still limited. Here we performed a 18 full sensitivity analysis of a complex ecosystem model to rank the most important biogeochemical 19 parameters influencing the simulation of the OMZ at an oligotrophic site in the open Arabian Sea. 20We applied a one-dimensional configuration of the European Regional Seas Ecosystem Model 21 (ERSEM) -here advanced by including denitrification -coupled with the General Ocean 22Turbulence Model (GOTM). The coupled model was skilled in simulating the vertical gradients of 23 climatological data of oxygen and nutrients. The sensitivity analysis of the model was carried out in 24 two steps: i) a preliminary Morris screening analysis of 207 ERSEM parameters, which selected the 25 three most influential groups of parameters; and ii) a subsequent Monte Carlo sampling-based 26 2 analysis for ranking the importance of the 38 parameters within the three selected groups. Overall, 27 the four most important parameters for the simulation of the minimum oxygen concentration were 28 found to be: 1) the cubic half saturation constant for oxygenic control of denitrification; 2) the 29 parameter regulating the fraction of ingested matter excreted by heterotrophic nanoflagellates; 3) 30 the bacterial efficiency at low oxygen levels; and 4) the specific rate of bacterial release of capsular 31 material. Based on these findings, and assuming that the ranking of the model parameters reflects 32 the relevance of the process they characterize, we present a conceptual model describing the most 33 important biogeochemical processes affecting the OMZ at the study site. Our results suggest that 34 including bacteria explicitly in ecosystem models is useful to simulate and predict OMZs, provided 35 that efforts are invested in estimating parameters characterizing the microbial loop in marine 36 ecosystems. 37 38

Reviews and syntheses: Assessment of Biogeochemical Models in the Marine Environment

“…Nutrients such as nitrate, phosphorous, and silica have been well represented by several biogeochemical models (Aumont et al, 2015;Das et al, 2019;Dutkiewicz et al, 2015;Jung et al, 2019b;Lachkar et al, 2019;Pant et al, 2018;Le Quéré et al, 2016;Sankar et al, 2018). The PFTs based models such as PISCES-NEMO, DARWIN-MITgcm, and TOPAZ-NEMO have shown well representation of surface nutrients distribution in the global oceans with r (> 0.9 for P and N; ~0.85 for S), > 0.9 for all nutrients, and > 0.95 for all nutrients respectively.…”

“…Moreover, TOPAZ and PISCES coupled to NEMO (Jung et al, 2019) respectively were also implemented to study the global oxygen minimum zone. ERSEM-Princeton/Mellor-Yamada lacks horizontal advective processes which could be enhanced through considering the diurnal physical processes while ERSEM-GOTM ignores the episodic intrusion of oxygen within the oxygen minimum zone (Sankar et al, 2018) (Sankar et al, 2018) (Blackford and Burkill, 2002). Thus, the models have shown contradictions between the estimates and climatological seasonal cycles of oxygen at depths which might be attributed to the lack of lateral circulation in the model.…”

Modelling mixotrophic functional diversity and implications for ecosystem function

“…We used an approach based on the Monte-Carlo ensemble technique to rank the importance of the input parameters (Saltelli et al, 2008;Sankar et al, 2018). This technique allows the detection of the parameters (and thus of the respective processes and functional types) that each targeted output is most sensitive to.…”

“…Random values were generated assuming a range of ± 30% of the reference value of the input parameters (e.g. Sankar et al, 2018). The analyses were performed using a Python code developed for the purpose.…”

A global sensitivity analysis approach for marine biogeochemical modeling

“…It reduces the input space dimension. Then, in a second step, a Monte Carlo samplingbased sensitivity and ranking analysis is run (see, e.g., Sankar et al, 2018) or Sobol' indices are computed (see, e.g., Morris et al, 2014;Wang et al, 2018).…”