First description of the Minnesota Earth System Model for Ocean biogeochemistry (MESMO 1.0)

Abstract

Abstract.Here we describe the first version of the Minnesota Earth System Model for Ocean biogeochemistry (MESMO 1.0), an intermediate complexity model based on the Grid ENabled Integrated Earth system model (GENIE-1). As with GENIE-1, MESMO has a 3D dynamical ocean, energy-moisture balance atmosphere, dynamic and thermodynamic sea ice, and marine biogeochemistry. Main development goals of MESMO were to: (1) bring oceanic uptake of anthropogenic transient tracers within data constraints; (2) increase vertical resolution in the upper ocean to better represent near-surface biogeochemical processes; (3) calibrate the deep ocean ventilation with observed abundance of radiocarbon. We achieved all these goals through a combination of objective model optimization and subjective targeted tuning. An important new feature in MESMO that dramatically improved the uptake of CFC-11 and anthropogenic carbon is the depth dependent vertical diffusivity in the ocean, which is spatially uniform in GENIE-1. In MESMO, biological production occurs in the top two layers above the compensation depth of 100 m and is modified by additional parameters, for example, diagnosed mixed layer depth. In contrast, production in GENIE-1 occurs in a single layer with thickness of 175 m. These improvements make MESMO a wellcalibrated model of intermediate complexity suitable for investigations of the global marine carbon cycle requiring long integration time.

Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity

“…-ME: MESMO version 1 (Matsumoto et al, 2008) is based on the C-GOLDSTEIN ocean model (Edwards and Marsh, 2005). It consists of a frictional geostrophic 3-D ocean circulation model coupled to a dynamicthermodynamic sea ice model and atmospheric model of energy and moisture balance.…”

Biological production in the Indian Ocean upwelling zones –Part 1: refined estimation via the use of a variable compensation depth in ocean carbon models

“…The observations also provide vital insights into physical and biological interactions of the ecosystem (Naqvi et al, 2010) as well as the biophysical feedbacks , although limitations of sparse observations often force us to depend on models to examine the large spatio-temporal variability of the ecosystem (Valsala and Maksyutov, 2013). Simpleto intermediate-complexity marine ecosystem models have been employed in several of the previous studies (Sarmiento et al, 2000;Orr et al, 2001;Matsumoto et al, 2008). However, the representation of marine ecosystems with proper parameterisations in models has always been a daunting task.…”

“…At Zc, the NCP is zero and above Zc the net primary production (NPP) exceeds the community respiration and the ecosystem will grow (Smetacek and Passow, 1990;Gattuso et al, 2006;Sarmiento and Gruber, 2006;Regaudix-de-Gioux and Duarte, 2010;Marra et al, 2014). However, Zc was held constant in time and space in OCMIP-II models (Najjar and Orr, 1998;Matsumoto et al, 2008) because the OCMIP-II protocol takes a minimalist approach to biology and simplifies the model calculations with a very limited set of state variables suitable for long-term simulations when implemented in coarse-resolution models (Orr et al, 2005). However, in reality Zc varies in space and time (Najjar and Keeling, 1997) just as the euphotic zone depth does, as documented in ship measurements (Qasim, 1977(Qasim, , 1982.…”

“…a default value of Zc = 75 m in OCMIP-II protocol (Najjar and Orr, 1998) and Zc = 100 m in the Minnesota Earth System Model (Matsumoto et al, 2008). Depending on the latitude, Zc varies between 50 and 100 m in the real world (Najjar and Keeling, 1997).…”

Multi-objective optimization of GENIE Earth system models

“…Population-based multiobjective design search and optimization algorithms from the engineering domain have proved useful for parameter estimation in GENIE models. In particular, the non-dominated sorting genetic algorithm (NSGA-II) with surrogate modelling has been applied successfully for parameter estimation in a number of models from the GENIE framework (Price et al 2006;Matsumoto et al 2008). This optimization process involves the iterative evaluation of response surface models (RSMs) followed by the execution of multiple Earth system simulations.…”