Abstract

Given a biased coupled model and the atmospheric and oceanic observing system, maintaining a balanced and coherent climate estimation is of critical importance for producing accurate climate analysis and prediction initialization. However, because of limitations of the observing system (e.g., mos... Show moreGiven a biased coupled model and the atmospheric and oceanic observing system, maintaining a balanced and coherent climate estimation is of critical importance for producing accurate climate analysis and prediction initialization. However, because of limitations of the observing system (e.g., most of the oceanic measurements are only available for the upper ocean), directly evaluating climate estimation with real observations is difficult. With two coupled models that are biased with respect to each other, a biased twin experiment is designed to simulate the problem. To do that, the atmospheric and oceanic observations drawn from one model based on the modern climate observing system are assimilated into the other. The model that produces observations serves as the truth and the degree by which an assimilation recovers the truth steadily and coherently is an assessment of the impact of the data constraint scheme on climate estimation. Given the assimilation model bias of warmer atmosphere and colder ocean, where the atmospheric-only (oceanic only) data constraint produces an overcooling (overwarming) ocean through the atmosphere-ocean interaction, the constraints with both atmospheric and oceanic data create a balanced and coherent ocean estimate as the observational model. Moreover, the consistent atmosphere-ocean constraint produces the most accurate estimate for North Atlantic Deep Water (NADW), whereas NADW is too strong (weak) if the system is only constrained by atmospheric (oceanic) data. These twin experiment results provide insights that consistent data constraints of multiple components are very important when a coupled model is combined with the climate observing system for climate estimation and prediction initialization. Show less