Abstract
AbstractThis study introduces a new flow‐dependent distribution sampling (FDDS) scheme for air–sea coupling. The FDDS scheme is implemented in a climate model and used to improve the simulated mean and variability of atmospheric and oceanic surface fields and thus air–sea fluxes. Most coupled circulation models use higher resolutions in the sea ice and ocean compared to the atmospheric model component, thereby explicitly simulating the atmospheric subgrid‐scale at the interface. However, the commonly applied averaging of surface fields and air–sea fluxes tends to smooth fine‐scale structures, such as oceanic fronts. The stochastic FDDS scheme samples the resolved spatial ocean (and sea ice) subgrid distribution that is usually not visible to a coarser‐resolution atmospheric model. Randomly drawn nodal ocean values are passed to the corresponding atmospheric boxes for the calculation of surface fluxes, aiming to enhance surface flux variability. The resulting surface field perturbations of the FDDS scheme are based on resolved dynamics, displaying pronounced seasonality with realistic magnitude. The AWI Climate Model is used to test the scheme on interannual time‐scales. Our set‐up features a high ocean‐to‐atmosphere resolution ratio in the Tropics, with grid‐point ratios of about 60:1. Compared to the default deterministic averaging, changes are largest in the Tropics leading to an improved spatial distribution of precipitation with bias reductions of up to 50%. Enhanced sea‐surface temperature variability in boreal winter further improves the seasonal phase locking of temperature anomalies associated with the El Niño–Southern Oscillation. Mean 2m temperature, sea ice thickness and concentration react with a contrasting dipole pattern between hemispheres but a joint increase of monthly and interannual variability. This first approach to implement a flow‐dependent stochastic coupling scheme shows considerable benefits for simulations of global climate, and various extensions and modifications of the scheme are possible.
Highlights
Current state-of-the-art climate and weather and seasonal forecast models represent the different climate subsystems, such as atmosphere, ocean, sea ice, and land surface, through a variety of modelling approaches
We investigate the impact of a new flow-dependent stochastic coupling scheme on the simulated climate of a global climate model (AWI-CM)
The new scheme is based on the fact that the ocean resolution in current CMIP5 and upcoming CMIP6 climate models is typically larger than the atmospheric resolution
Summary
Current state-of-the-art climate and weather and seasonal forecast models represent the different climate subsystems, such as atmosphere, ocean, sea ice, and land surface, through a variety of modelling approaches. Williams (2012) applied a stochastic approach, where he augmented the surface fluxes communicated between the atmospheric and oceanic model components by (multiplicative) white noise His results showed a systematic impact on the oceanic mean state and coupled El Niño–Southern Oscillation (ENSO) variability, the applied noise was symmetric. Similar to coarse-graining methods (Jung et al, 2014; Bessac et al, 2019; Christensen, 2019), this information can be used in a stochastic coupling scheme to better represent the statistical properties of the unresolved scales in the atmospheric model component Using this information can improve the representation of spatial variability of surface fluxes.
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