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Arthur J. Miller


Midlatitude ocean-atmosphere interactions are studied in simulations from a simplified coupled model that includes synoptic-scale atmospheric variability, ocean current advection of sea surface temperature (SST), and air-sea heat exchange. Although theoretical dynamical (“identical twin”) predictions using this model have shown that the SST anomalies in this model indeed influence the atmosphere, we find here that standard cross-correlation and empirical orthogonal function analyses of monthly mean model output yield the standard result, familiar from observational studies, that the atmosphere forces the ocean with little or no feedback. Therefore, these analyses are inconclusive and leave open the question of whether anomalous SST is influencing the atmosphere. In contrast, we find that compositing strong warm events of model SST is a useful indicator of ocean forcing the atmosphere. We present additional evidence for oceanic influence on the atmosphere, namely, that ocean current advection appears to enhance the persistence of model SST anomalies through a feedback effect that is absent when only heat flux is allowed to influence SST anomaly evolution. Models with more complete physics must ultimately be used to conclusively demonstrate these results.

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Aneesh C. Subramanian, Ibrahim Hoteit, Bruce Cornuelle, Arthur J. Miller, and Hajoon Song


This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.

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