E3.5 Modeling soluble iron in E3SM

                    

Poster TitleModeling soluble iron in E3SM
AuthorsDouglas Hamilton (Unlicensed) mahowald@cornell.edu (Unlicensed) Rachel Scanza (Unlicensed)
First AuthorDouglas Hamilton (Unlicensed)
Session TypeE3SM Session
Session IDE3
Submission TypePoster
GroupAtmosphere
ExperimentBGC
Poster Link




Abstract

Atmospheric deposition of bioavailable iron contained in aerosols is an important source of new iron in the open ocean. The solubility of this iron is considered a key component in modulating its bioavailability, and the recent increase in efforts to model this parameter reflects its importance in understanding biogeochemical cycles and how human activity may be perturbing them.

Here we show initial modeling results of the atmospheric component of the iron cycle for the E3SM model. Two main sources of atmospheric iron and its soluble component are modeled: mineral iron and combustion iron (including wild fires and anthropogenic activity). During model transport atmospheric processing of insoluble to soluble iron is parametrized by an acidic interstitial reaction and a separate in-cloud reaction scheme based on an assumed oxalate concentration. Initial results compare very well with an extensive dataset of total iron observations, but less so with its soluble fraction which is much more variable in space and time.

We also explore the sensitivity of the magnitude of iron deposition and its soluble fraction to assumptions about iron emissions, the rate of the two dissolution reactions in the atmosphere and finally its deposition rates (both dry and wet). We perturb five parameter sets twelve times, split into two components: eight simulations using a fractional factorial design to explore the extremes and a further four simulations using a Latin hypercube space filling design to cover some of the intermediate parameter space.  Our goal is to determine the parameters which are most important for uncertainty in soluble iron deposition which will 1) enable the ‘tuning’ of these parameters to match the observations and 2) focus future research efforts on these uncertain parameters.