Abstract. In models of the global carbon cycle, the pCO 2 of the atmosphere is more sensitive to the chemistry of the high-latitude surface ocean than the tropical ocean. Because sea-surface nutrient concentrations are generally high in the high latitudes, pCO 2 sensitivity to high-latitude forcing also determines pCO 2 sensitivity to the biological pump globally. We diagnose highlatitude sensitivity of a range of ocean models using atmospheric pCO 2 above an abiotic ocean; cold high-latitude waters pull abiotic pCO 2 to low values. Box models are very high-latitude sensitive, while most global circulation models are considerably less so, including a twodimensional overturning model, two primitive equation models, the Hamburg class of large scale geostrophic (LSG) general circulation models (GCMs), and the MICOM isopycnic GCM. Highlatitude forcing becomes more important in a depth-coordinate GCM when lateral diffusion is oriented along isopycnal surfaces, rather than horizontally, following Redi [1982]. In two different GCMs (a primitive equation model and LSG), addition of the Gent and McWillams [ 1990] isopycnal thickness diffusion scheme had only minor impact on high-latitude sensitivity. Using a simplified box model, we show that high-latitude sensitivity depends on a high-latitude monopoly on deep water formation. In an attempt to bridge the gap between box models and GCMs, we constructed a simple slab overturning model with an imposed stream function which can be discretized at arbitrary resolution from box model to GCM scale. High-latitude sensitivity is independent of model resolution but very sensitive to vertical diffusion. Diffusion acts to break the high-latitude monopoly, decreasing high-latitude sensitivity. In the isopycnal GCM MICOM, however, high-latitude sensitivity is relatively insensitive to diapycnal diffusion of tracers such as CO2. This would imply that flow pathways in MICOM take the place of vertical diffusion in the slab model. The two nominally most sophisticated ocean models in the comparison are the isopycnal model MICOM and the depth-coordinate GCM with Redi [ 1982] and Gent and McWilliams [1990] mixing. Unfortunately, these two models disagree in their abiotic CO2 behavior; the depth-coordinate isopycnal mixing GCM is high-latitude sensitive, in accord with box models, while MICOM is less so. The rest of the GCMs, which have historically seen the most use in geochemical studies, are even less high-latitude sensitive than MICOM. This discrepancy needs to be resolved. In the meantime, the implication of the MICOM/traditional GCM result would be that box models overestimate high-latitude sensitivity of the real ocean.
The performance of several state-of-the-art climate model ensembles, including two multi-model ensembles (MMEs) and four structurally different (perturbed parameter) single model ensembles (SMEs), are investigated for the first time using the rank histogram approach. In this method, the reliability of a model ensemble is evaluated from the point of view of whether the observations can be regarded as being sampled from the ensemble. Our analysis reveals that, in the MMEs, the climate variables we investigated are broadly reliable on the global scale, with a tendency towards overdispersion. On the other hand, in the SMEs, the reliability differs depending on the ensemble and variable field considered. In general, the mean state and historical trend of surface air temperature, and mean state of precipitation are reliable in the SMEs.However, variables such as sea level pressure or top-ofatmosphere clear-sky shortwave radiation do not cover a sufficiently wide range in some. It is not possible to assess whether this is a fundamental feature of SMEs generated with particular model, or a consequence of the algorithm used to select and perturb the values of the parameters. As under-dispersion is a potentially more serious issue when using ensembles to make projections, we recommend the application of rank histograms to assess reliability when designing and running perturbed physics SMEs.
Abstract. The Fast Ocean Atmosphere Model (FOAM) is a climate system model intended for application to climate science questions that require long simulations. FOAM is a distributed-memory parallel climate model consisting of parallel general circulation models of the atmosphere and ocean with complete physics paramaterizations as well as sea-ice, land surface, and river transport models. FOAM's coupling strategy was chosen for high throughput (simulated years per day). A new coupler was written for FOAM and some modifications were required of the component models. Performance data for FOAM on the IBM SP3 and SGI Origin2000 demonstrates that it can simulate over thirty years per day on modest numbers of processors.
The ensemble Kalman filter (EnKF) (Evensen, 2009a) has proven effective in quantifying uncertainty in a number of challenging dynamic, state estimation, or data assimilation, problems such as weather forecasting and ocean modeling. In these problems a high-dimensional state parameter is successively updated based on recurring physical observations, with the aid of a computationally demanding forward model that propagates the state from one time step to the next. More recently, the EnKF has proven effective in history matching in the petroleum engineering community (Evensen, 2009b;Oliver and Chen, 2010). Such applications typically involve estimating large numbers of parameters, describing an oil reservoir, using data from production history that accumulate over time. Such history matching problems are especially challenging examples of computer model calibration since they involve a large number of model parameters as well as a computationally demanding forward model. More generally, computer model calibration combines physical observations with a computational model -a computer model -to estimate unknown parameters in the computer model. This paper explores how the EnKF can be used in computer model calibration problems, comparing it to other more common approaches, considering applications in climate and cosmology.
We report here on a project that expands the applicability of dynamic climate modeling to very long time scales. The Fast Ocean_Atmosphere Model (FOAM) is a coupled ocean-atmosphere model that incorporates physics of interest in understanding decade to century time scale variability. It addresses the high computational cost of this endeavor with a combination of improved ocean model formulation, low atmosphere resolution, and efficient coupling. It also uses message-passing parallel processing techniques, allowing for the use of cost-effective distributed memory platforms. The resulting model runs over 6000 times faster than real time with good fidelity and has yielded significant results.
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