climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer

ArXi:2604.21085v1 Announce Type: cross Accurate representation of moist convective sub-grid-scale processes remains a major challenge in global climate models, as traditional parameterization schemes are both computationally expensive and difficult to scale. Neural network (NN) emulators offer a promising alternative by learning efficient mappings between atmospheric states and convective tendencies while retaining fidelity to the underlying physics.