GF4ACE -- Data from: Reanalysis-based global radiative response to sea surface temperature patterns: Evaluating the Ai2 climate emulator

The sensitivity of the radiative flux at the top of the atmosphere to
surface temperature perturbations cannot be directly observed. The
relationship between sea surface temperature and top-of-atmosphere
radiation can be estimated with Green's function simulations by
locally perturbing the sea surface temperature boundary conditions in
atmospheric climate models. We perform such simulations with the Ai2
Climate Emulator (ACE), a machine learning-based emulator trained on ERA5
reanalysis data (ACE2-ERA5). This produces a sensitivity map of the
top-of-atmosphere radiative response to surface warming that aligns with
our physical understanding of radiative feedback. However, ACE2-ERA5
likely underestimates the radiative response to historical warming. We
argue that Green's function experiments can be used to evaluate the
performance and limitations of machine learning-based climate emulators by
examining if causal physical relationships are correctly represented and
testing their capability for out-of-distribution predictions.