Evaluating Uncertainties in Marine Biogeochemical Models: Benchmarking Aerosol Precursors

Abstract

Abstract:The effort to accurately estimate global radiative forcing has long been hampered by a degree of uncertainty in the tropospheric aerosol contribution. Reducing uncertainty in natural aerosol processes, the baseline of the aerosol budget, thus becomes a fundamental task. The appropriate representation of aerosols in the marine boundary layer (MBL) is essential to reduce uncertainty and provide reliable information on offsets to global warming. We developed an International Ocean Model Benchmarking package to assess marine biogeochemical process representations in Earth System Models (ESMs), and the package was employed to evaluate surface ocean concentrations and the sea-air fluxes of dimethylsulfide (DMS). Model performances were scored based on how well they captured the distribution and variability contained in high-quality observational datasets. Results show that model-data biases increased as DMS enters the MBL, but unfortunately over three-quarters of the models participating in the fifth Coupled Model Intercomparison Project (CMIP5) did not have a dynamic representation of DMS. When it is present, models tend to over-predict sea surface concentrations in the productive region of the eastern tropical Pacific by almost a factor of two, and the sea-air fluxes by a factor of three. Systematic model-data benchmarking as described here will help to identify such deficiencies and subsequently lead to improved subgrid-scale parameterizations and ESM development.

Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System

“…Further, IOMB can be customized easily for different applications and can incorporate diagnostic updates and new observational datasets from end‐users. IOMB was used previously to benchmark aerosol precursors (Ogunro et al., 2018).…”

“…Here we conduct a model evaluation for several important biogeochemical and physical climate‐related variables for both CMIP5 and CMIP6 models using IOMB (the International Ocean Model Benchmarking [IOMB] software package) (Collier et al., 2018; Ogunro et al., 2018). The IOMB package can make quantitative comparisons between time‐dependent sequences of observed and simulated multidimensional fields of ocean biogeochemistry data, including sparsely distributed ocean interior observations.…”

Atmospheric Processing of Iron-Bearing Mineral Dust Aerosol and Its Effect on Growth of a Marine Diatom, Cyclotella meneghiniana

“…Phytoplankton are the primary producers of most marine food webs and the major sequesterer of atmospheric carbon dioxide (CO 2 ) at a global scale. − However, the scarcity of bioavailable iron (Fe) limits the phytoplankton growth in nitrogen-rich oceans. − Iron is an essential micronutrient for phytoplankton due to its presence in iron–sulfur and cytochrome proteins involved in photosynthetic electron transport. − Despite these necessities, the dissolution of crustal Fe sources in sea-water is limited due to oxygenated basic ocean pH (∼8.2) . Hence, atmospheric acidic processing of iron-containing aerosols that are deposited with solubilized iron on surface water has been suggested as a major source of bioavailable Fe to oceans. , During these dust deposition events, the surface-ocean iron concentrations can rise from ∼0.2–2.0 to ∼8 nM. , The atmospheric processing under acidic conditions, and subsequent Fe solubility, depend on many factors such as particle size, nature of acid anions, pH of the deliquescence layer formed around the dust particle, available sunlight, relative humidity, temperature, and mineralogy. − …”

Measurement report: Cloud Processes and the Transport of Biological Emissions Regulate Southern Ocean Particle and Cloud Condensation Nuclei Concentrations

“…The marine boundary layer (MBL) over the Southern Ocean (SO) displays some of the most pristine conditions in the world, with few anthropogenic influences, making cloud properties and radiative forcing particularly sensitive to relatively small changes in aerosol source emissions (Downey et al, 1990;Fossum et al, 2018;Hudson et al, 1998;Li et al, 2018;McCoy et al, 2015;Murphy et al, 1998b;Pandis et al, 1994;Pierce and Adams, 2006;Pringle et al, 2009;Whittlestone and Zahorowski, 1998;Yoon and Brimblecombe, 2002). In spite of a growing number of studies, climate models still struggle to represent SO cloud radiative properties, partly because their representation of available cloud condensation nuclei (CCN) is not well constrained (Bodas-Salcedo et al, 2014;Brient et al, 2019;Efraim et al, 2020;Hyder et al, 2018;Lee et al, 2015;Mace and Protat, 2018;Mccoy et al, 2014;Ogunro et al, 2018;Schmale et al, 2019;Seinfeld et al, 2016;Trenberth and Fasullo, 2010).…”