A machine learning correction model of the winter clear-sky temperature bias over the Arctic Sea ice in atmospheric reanalyses

They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses.

These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the exist-ing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration at-tempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism re-sponsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic.