Blocking Statistics in a Varying Climate: Lessons from a “Traffic Jam” Model with Pseudostochastic Forcing

Abstract

Abstract Recently Nakamura and Huang proposed a semiempirical, one-dimensional model of atmospheric blocking based on the observed budget of local wave activity in the boreal winter. The model dynamics is akin to that of traffic flow, wherein blocking manifests as traffic jams when the streamwise flux of local wave activity reaches capacity. Stationary waves modulate the jet stream’s capacity to transmit transient waves and thereby localize block formation. Since the model is inexpensive to run numerically, it is suited for computing blocking statistics as a function of climate variables from large-ensemble, parameter sweep experiments. We explore sensitivity of blocking statistics to (i) stationary wave amplitude, (ii) background jet speed, and (iii) transient eddy forcing, using frequency, persistence, and prevalence as metrics. For each combination of parameters we perform 240 runs of 180-day simulations with aperiodic transient eddy forcing, each time randomizing the phase relations in forcing. The model climate shifts rapidly from a block-free state to a block-dominant state as the stationary wave amplitude is increased and/or the jet speed is decreased. When eddy forcing is increased, prevalence increases similarly but frequency decreases as blocks merge and become more persistent. It is argued that the present-day climate lies close to the boundary of the two states and hence its blocking statistics are sensitive to climate perturbations. The result underscores the low confidence in GCM-based assessment of the future trend of blocking under a changing climate, while it also provides a theoretical basis for evaluating model biases and understanding trends in reanalysis data.