A brief description of GENIE-1 relevant for GENIE-M

Cours A brief description of GENIE-1 relevant for GENIE-M, tutoriel & guide de travaux pratiques en pdf.

Introduction

Earth system Models of lntermediate Complexity (EMICs) occupy a unique and important position within the hierarchy of climate models (Ciaussen et al., 2002). ln many 2o ways, EMICs represent a compromise between high resolution, comprehensive coupied models of atmospheric and oceanic circulation, which require significant computational resources, and conceptual (box) models, which are computationally very efficient but represent the climate system in a highly idealized manner. A critical difference between comprehensive coupled models and box models is the absence of dynamical 25 feedbacks in the latter. ln box models, large scale circulation is typically prescribed and not allowed to change over the course of a simulation. The lack of dynamical feedbacks makes box models unsuitable for realistic simulations of transient climate change. On the ether hand, comprehensive coupled models are so computationally intensive that their behavior within a given parameter space is difficult to tully explore. EMICs nicely till this gap by retaining important dynamics while remaining computationally efficient, 5 which is typically achieved by reducing spatial resolution and/or number of processes compared to high resolution coupled models. The effectiveness of EMICs is evident in the numerous publications that have successfully employed them in studying past, present, and future climates (Ganopolski and Rahmstorf, 2001; Ganopolski et al., 1998; Joos et al., 1999; Knutti et al., 2002; 10 Nusbaumer and Matsumoto, 2008; Platiner et al., 2001 ). Also, the important role that EMICs played in understanding the postindustrial carbon cycle changes is highlighted in the two recent IPCC science reports TAR (Houghton et al., 2001) and AR4 (IPCC, 2007). Here we document development of GENIE-M. a new version of an existing and suc15 cessful EMIC called GENIE-1. Our immediate motivation for this work is to possess a tool to investigate postindustrial changes in the natural ocean carbon cycle. Our efforts were thus geared toward improving representation of marine biogeochemistry and distributions of natural and anthropogenic transient tracers in the oceans. These improvements, combined with more highly resolved upper ocean and reasonable seasonal sea 20 ice formation, represent significant steps toward reaching our immediate objective and making GENIE-M useful for future investigations of the global ocean carbon cycle.

A brief description of GENIE-1 relevant for GENIE-M

GENIE is a new EMIC developed primarily in the UK (http://www,geni(U:te,uk/) with the goal of making it as modular as possible so that in theory one can choose to construct a 25 model with any permutation of the existing modules (e.g., slab or 3-D dynamical ocean module coupled to energy balance or 3-D dynamical atmospheric module). Following Ridgwell et al. (2007), we will refer to GENIE-1 as a model configuration that consists of the physical climate module C-GOLDSTEIN, a simple atmospheric chemistry module ATCHEM, and a marine biogeochemistry module BIOGEM. The starting point of our model development is this GENIE-1, which in terms of the actual code is Version 6 of CBS-GOLDSTEIN as distributed by A. Ridgwell in 2006. Unless noted otherwise, 5 GENIE-M is identical to Version 6. As described Edwards and Marsh (2005), C-GOLDSTEIN is itself a stand-alone, coarse gridded, efficient climate model that is comprised of a 3-dimensional circulation model of the world ocean, an energy and moisture balance model of the atmosphere, and a dynamic and thermodynamic model of sea ice. The ocean model is on a 36×36 10 equal-area horizontal grid with 10° increments in longitude and uniform in sine of latitude. There are 8 levels in the vertical with the top layer being 175 m thick. Ocean dynamics is based on the frictional geostrophic equations (Edwards et al., 1998) and includes the Gent-McWilliams (GM) eddy mixing parameterization according to Griffies (1998) that reduces excessive vertical mixing in coarse gridded models (Duffy et al., 15 1997; England and Rahmstorf, 1999). The momentum flux that drives the surface ocean circulation is based on the annual NCEP reanalysis wind stress and therefore has no seasonality. The atmospheric component of C-GOLDSTEIN is an energy and moisture balance model following Weaver et al. (2001 ). External forcing by shortwave solar radiation is 20 temporally constant, annually averaged and thus has no seasonality. Without explicit atmospheric dynamics, eddy diffusion coefficients of heat and moisture become important in determining the atmospheric distributions of temperature and humidity, both prognostic variables in the model. Edwards and Marsh (2005) identify twelve underconstrained yet critical parameters, 25 including the two eddy diffusion coefficients in the atmosphere, that largely determine the climate state of C-GOLDSTEIN (Table 1). Through a large ensemble of 2000-year C-GOLDSTEIN simulations where the values of the twelve parameters are randomly changed within specified ranges, they determined a set of parameter values that minimizes the misfit between observations and simulations of surface air temperature and humidity and ocean temperatures and salinities. This exercise gives a degree of objectivity to tuning and credibility to the madel. To this physical madel C-GOLDSTEIN, Ridgwell et al. (2007) coupled ATCHEM and BIOGEM, making GENIE-1 a global madel of carbon and climate. The production 5 schema of BIOGEM is based on Michaelis-Menton type phosphate (P04) uptake kinetics, modified by the availability of light and sea ice. The partitioning of uptake into dissolved and particulate organic phases follows the Ocean Carbon cycle Madel lntercomparison Project Phase 2 (OCMIP-2) BIOTIC protocol (Najjar et al., 2007; Najjar and Orr, 1999). An important advance made by Ridgwell et al. (2007) is the objective 1o calibration of BIOGEM through data assimilation of P04 and alkalinity (ALK). GENIE-1 thus benefited from having C-GOLDSTEIN and BIOGEM both calibrated objectively.

Rationale for improving GENIE-1

ln recent years, GENIE-1 is increasingly used in climate and carbon cycle studies of the present, future, and past (Lenton et al., 2007, 2006; Matsumoto, 2007; Ridgwell, 15 2007; Ridgwell et al., 2007). Also, the computational efficiency of GENIE-1 has allowed it to sweep entire parameter spaces with respect to the Atlantic meridional overturning circulation (MOC) so that the model’s MOC behavior is now fairly weil characterized (Marsh et al., 2004). We have been motivated to build on the success of GENIE-1 for a number of rea2o sons, especially when we begin to consider the transient response of marine biology to climate change. First, the 175 m top layer of the ocean madel is too thick compared to typical depths of the euphotic zone and mixed layer in the ocean. This prevents a more realistic representation of marine production for example, because nutrient uptake cannat be given a dependence on the mixed layer depth. The importance of this 25 dependance was shawn in a seminal work by Sverdrup (1953). Also, the large vertical gradients of nutrients observed today in the upper ocean are completely lost in the 175 m thick layer such that it is not clear what « surface » nutrient concentration in GENIE-1 really means.