Daily Papers

While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have been trained to emulate the sensitivity of climate to substantial changes in CO_2 or other greenhouse gases. As an initial step we couple the Ai2 Climate Emulator version 2 to a slab ocean model (hereafter ACE2-SOM) and train it on output from a collection of equilibrium-climate physics-based reference simulations with varying levels of CO_2. We test it in equilibrium and non-equilibrium climate scenarios with CO_2 concentrations seen and unseen in training. ACE2-SOM performs well in equilibrium-climate inference with both in-sample and out-of-sample CO_2 concentrations, accurately reproducing the emergent time-mean spatial patterns of surface temperature and precipitation change with CO_2 doubling, tripling, or quadrupling. In addition, the vertical profile of atmospheric warming and change in extreme precipitation rates with increased CO_2 closely agree with the reference model. Non-equilibrium-climate inference is more challenging. With CO_2 increasing gradually at a rate of 2% year^{-1}, ACE2-SOM can accurately emulate the global annual mean trends of surface and lower-to-middle atmosphere fields but produces unphysical jumps in stratospheric fields. With an abrupt quadrupling of CO_2, ML-controlled fields transition unrealistically quickly to the 4xCO_2 regime. In doing so they violate global energy conservation and exhibit unphysical sensitivities of and surface and top of atmosphere radiative fluxes to instantaneous changes in CO_2. Future emulator development needed to address these issues should improve its generalizability to diverse climate change scenarios.

While previous versions of the Ai2 Climate Emulator (ACE) have been trained with CO_2 as a forcing, they are only accurate within a narrow range of scenarios, for example climate over the last 80 years forced by observed sea surface temperature (SST), sea ice, and CO_2 (AMIP), or equilibrium or near-equilibrium climates with CO_2 concentrations ranging from 1x to 4x that of the present day. Attempting to simulate climate forced by AMIP SST perturbed by +4 K or the response to an abrupt quadrupling of CO_2, results in unphysical behavior. We attribute this to these models being trained on datasets where the SST and CO_2 are correlated, limiting their ability to accurately learn their separate effects. In this study we introduce a new class of "random-CO_2" reference simulations where the SST and CO_2 are prescribed to vary independently. Trained on a balance of AMIP, equilibrium-climate, and random-CO_2 data, and including a total energy conservation constraint for improved interpretability, we present a more data-efficient model that not only accurately emulates its reference model in scenarios in which previous models excelled, but also scenarios like AMIP +4 K and slab-ocean-coupled abrupt 4xCO_2 where they did not. Limitations are that it has simplified or prescribed representations of other Earth system components like the ocean, land, and sea ice; does not expose other known climate drivers as forcings; and relies solely on physics-based model output for training data, inheriting the biases relative to observations thereof. Each of these represent opportunities for future work.