14 research outputs found
Machine-learned climate model corrections from a global storm-resolving model
Due to computational constraints, running global climate models (GCMs) for
many years requires a lower spatial grid resolution ( km) than is
optimal for accurately resolving important physical processes. Such processes
are approximated in GCMs via subgrid parameterizations, which contribute
significantly to the uncertainty in GCM predictions. One approach to improving
the accuracy of a coarse-grid global climate model is to add machine-learned
state-dependent corrections at each simulation timestep, such that the climate
model evolves more like a high-resolution global storm-resolving model (GSRM).
We train neural networks to learn the state-dependent temperature, humidity,
and radiative flux corrections needed to nudge a 200 km coarse-grid climate
model to the evolution of a 3~km fine-grid GSRM. When these corrective ML
models are coupled to a year-long coarse-grid climate simulation, the time-mean
spatial pattern errors are reduced by 6-25% for land surface temperature and
9-25% for land surface precipitation with respect to a no-ML baseline
simulation. The ML-corrected simulations develop other biases in climate and
circulation that differ from, but have comparable amplitude to, the baseline
simulation
Emulating Fast Processes in Climate Models
Cloud microphysical parameterizations in atmospheric models describe the
formation and evolution of clouds and precipitation, a central weather and
climate process. Cloud-associated latent heating is a primary driver of large
and small-scale circulations throughout the global atmosphere, and clouds have
important interactions with atmospheric radiation. Clouds are ubiquitous,
diverse, and can change rapidly. In this work, we build the first emulator of
an entire cloud microphysical parameterization, including fast phase changes.
The emulator performs well in offline and online (i.e. when coupled to the rest
of the atmospheric model) tests, but shows some developing biases in
Antarctica. Sensitivity tests demonstrate that these successes require careful
modeling of the mixed discrete-continuous output as well as the input-output
structure of the underlying code and physical process.Comment: Accepted at the Machine Learning and the Physical Sciences Workshop
at the 36th conference on Neural Information Processing Systems (NeurIPS)
December 3, 202
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society
Neural Network Parameterization of SubgridâScale Physics From a Realistic Geography Global StormâResolving Simulation
Abstract Parameterization of subgridâscale processes is a major source of uncertainty in global atmospheric model simulations. Global stormâresolving simulations use a finer grid (less than 5Â km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarseâgrid (200Â km) global atmosphere model, using training data obtained by spatially coarseâgraining a 40âday realistic geography global stormâresolving simulation. The training targets are the threeâdimensional fields of effective heating and moistening rates, including the effect of gridâscale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the timeâdependent heating and moistening rates in each grid column using the coarseâgrained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, landâsea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35âday simulations, with metrics of skill such as the timeâmean pattern of nearâsurface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of humanâdesigned parameterizations with dataâdriven ones in a realistic setting