EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching
Authors: Kirien Whan, Nikolaj T. Mücke, Karin van der Wiel
Organizations: KNMI, Utrechtseweg 297, De Bilt, 3731GA, The Netherlands · Scientific Computing, Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, 1098XG, The Netherlands · Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands · Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on transient simulations from EC-Earth3 (1950-2166, SSP2-4.5) to predict the day ahead temperature field from the previous days temperature as well as annual mean temperature. Predictions are made auto-regressively with rollout periods of between a month and an extended season. Using only this variable of interest, we are able to reproduce the daily variability, spatial patterns, annual cycle and long-term trend from EC-Earth3 at a substantially lower computational cost than the physical model. We demonstrate that EC-EarthFlow is stable for long inference periods, and that it can learn the physical relationships as simulated in EC-Earth3.
Figures & tables
Figure 1: The architecture of EC-EarthFlow showing that pseudo-time is concatenated with contextual inputs and fed into the neural network model to predict the flow matching vector field.
Figure 2: An illustration of the generative flow matching process. The process transforms noise to the target distribution by integrating an ODE over pseudo-time.
Figure 3: The auto-regressive rollout used at inference. The context information is combined with noise to produce a prediction for the next day. These predictions are then added to the context window for the following prediction. We produce an ensemble of predictions by using different noise samples.
Figure 4: Daily temperature at two locations (De Bilt, The Netherlands (top), and Sydney, Australia (bottom)) from May - November in 1950 (left), 2050 (middle) and 2150 (right). Predictions are made using a rollout length of 245 steps from only 5 EC-Earth member states at the end of April (member 16). For comparison the full EC-Earth simulation is shown in purple. The green shading indicates the ensemble range from nine EC-EarthFlow members, and the green line shows predictions from a single random EC-EarthFlow member. The gray line indicates a daily temperature value of 15 ∘ C as reference. The histograms show counts of the rank that the EC-Earth3 member is in the 10-member EC-EarthFlow ensemble.
Figure 5: Rank histograms of all grid points in 1950 (left), 2050 (middle) and 2150 (right). EC-EarthFlow predictions are made using the long (top) and short (bottom) rollout length from May in each year and inputs from a single EC-Earth member (member 16).
Figure 6: Global mean surface temperature with short rollout, starting in each month of the extended northern Hemisphere summer period (May - October). Each panel shows projections from a different year (1950, 2050, 2150). Inputs are from a single test member of EC-Earth3 (member 16, purple line). The green shading indicates the ensemble range from nine EC-EarthFlow members, and the green line shows predictions from a single random EC-EarthFlow member. The gray line indicates a GMST of 10 ∘ C as reference.
Figure 7: Global mean surface temperature (long rollout) (May - November). Columns show projections in select years (1950, 2050, 2150) made using EC-Earth3 members 16 and 15 as inputs (purple lines). The green shading indicates the ensemble range from all EC-EarthFlow members, and the green line shows predictions from a single random EC-EarthFlow member. The green dots show the EC-EarthFlow ensemble range from projections generated from EC-Earth3 member 16. The gray line indicates a GMST of 13 ∘ C as reference.
Figure 8: Global mean surface temperature projections from May - November with a short (top row, i.e., consecutive 30-day rollouts) and long (bottom row, i.e., a single rollout from May to November) lengths in 1950, 2050, and 2150. The black line is the ‘truth’ from EC-Earth3. The green lines are ensemble members created by the flow matching model. The red line indicates a GMST of 10 ∘ C as reference.
Figure 9: Box-plots of the grid point wise trajectory mean temperature differences between EC-Earth3 members (purple) and between predictions from EC-EarthFlow and the EC-Earth3 member used to generate them (green) in 2050. Similar results are seen for other years and other EC-EarthFlow members.
Figure 10: The correlation between temperature in De Bilt (left) or Sydney (right) and the rest of the world in EC-Earth3 (top) and EC-EarthFlow (bottom). We use a high-pass filter to remove variability and then take the correlation over all days.
ARCHES, INRIA, Paris, France · UMR LOCEAN, IPSL, Sorbonne Universit´e, IRD, CNRS, MNHN, Paris, France · Department of Computer Science, University of Colorado Boulder, USA
Allen Institute for Artificial Intelligence (Ai2), Seattle, WA, USA · Pacific Northwest National Laboratory, Richland, WA, USA · Los Alamos National Laboratory, Los Alamos, NM, USA +2