Deterministic and stochastic tendency adjustments derived from data assimilation and nudging

We develop and compare model-error representation schemes derived from data assimilation increments and nudging tendencies in multidecadal simulations of the Community Atmosphere Model, version 6. Each scheme applies a bias correction during simulation runtime to the zonal and meridional winds. We quantify the extent to which such online adjustment schemes improve the model climatology and variability on daily to seasonal timescales. Generally, we observe about a 30% improvement to annual upper-level zonal winds, with largest improvements in boreal spring (around 35%) and winter (around 47%). Despite only adjusting the wind fields, we additionally observe around 20% improvement to annual precipitation over land, with the largest improvements in boreal fall (around 36%) and winter (around 25%), and around 50% improvement to annual sea-level pressure, globally. With mean-state adjustments alone, the dominant pattern of boreal low-frequency variability over the Atlantic (the North Atlantic Oscillation) is significantly improved. Additional stochasticity increases the modal explained variances further, which brings the variability closer to the observed value. A streamfunction tendency decomposition reveals that the improvement is due to an adjustment to the high- and low-frequency eddy–eddy interaction terms. In the Pacific, the mean-state adjustment alone led to an erroneous deepening of the Aleutian low, but this was remedied with the addition of stochastically selected tendencies. Finally, from a practical standpoint, we discuss the performance of using data assimilation increments versus nudging tendencies for an online model-error representation.