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FloeNet: A mass-conserving global sea ice emulator that generalizes across climates

William Gregory, Mitchell Bushuk, James Duncan, Elynn Wu, Adam Subel, Spencer K. Clark, Bill Hurlin, Oliver Watt-Meyer, Alistair Adcroft, Chris Bretherton, Laure Zanna

Abstract

We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass-conserving model, emulating 6-hour mass and area budget tendencies related to sea ice and snow-on-sea-ice growth, melt, and advection. We train FloeNet using simulated data from a reanalysis-forced ice-ocean simulation and test its ability to generalize to pre-industrial control and 1% CO2 climates. FloeNet outperforms a non-conservative model at reproducing sea ice and snow-on-sea-ice mean state, trends, and inter-annual variability, with volume anomaly correlations above 0.96 in the Antarctic and 0.76 in the Arctic, across all forcings. FloeNet also produces the correct thermodynamic vs dynamic response to forcing, enabling physical interpretability of emulator output. Finally, we show that FloeNet outputs high-fidelity coupling-related variables, including ice-surface skin temperature, ice-to-ocean salt flux, and melting energy fluxes. We hypothesize that FloeNet will improve polar climate processes within existing atmosphere and ocean emulators.

FloeNet: A mass-conserving global sea ice emulator that generalizes across climates

Abstract

We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass-conserving model, emulating 6-hour mass and area budget tendencies related to sea ice and snow-on-sea-ice growth, melt, and advection. We train FloeNet using simulated data from a reanalysis-forced ice-ocean simulation and test its ability to generalize to pre-industrial control and 1% CO2 climates. FloeNet outperforms a non-conservative model at reproducing sea ice and snow-on-sea-ice mean state, trends, and inter-annual variability, with volume anomaly correlations above 0.96 in the Antarctic and 0.76 in the Arctic, across all forcings. FloeNet also produces the correct thermodynamic vs dynamic response to forcing, enabling physical interpretability of emulator output. Finally, we show that FloeNet outputs high-fidelity coupling-related variables, including ice-surface skin temperature, ice-to-ocean salt flux, and melting energy fluxes. We hypothesize that FloeNet will improve polar climate processes within existing atmosphere and ocean emulators.
Paper Structure (11 sections, 8 equations, 22 figures, 2 tables)

This paper contains 11 sections, 8 equations, 22 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Data and methods
  3. Training and validation data
  4. FloeNet architecture
  5. Results
  6. Closing the mass and area budget
  7. Generalization of mean state, trends, and variability
  8. Potential to improve atmosphere and ocean emulators
  9. Discussion and conclusions
  10. FloeNet architecture
  11. Sensitivity tests

Figures (22)

  • Figure 1: FloeNet mass and area budget anomaly climatologies (relative to Jan 1 00:00:00) for the period 2006--2022. (A) Arctic sea ice mass tendencies due to source terms (LSRCi), sink terms (LSNKi), and transport convergence (XPRTi). The sum of these terms gives the total sea ice mass (simass). (B) Same as (A) but for Antarctic. (C) The global sea ice mass budget conservation error, computed as the difference between the prognostic ice mass and the mass computed through a budget reconstruction. (D--F) Same as (A--C) but for snow mass. (G--I) Same as (A--C) but for sea ice concentration.
  • Figure 2: Arctic sea ice thickness and volume mean state and trends. (A) Time-mean sea ice thickness climatology from OM4, computed over 2006--2022. (B) Same as (A) but for a FloeNet rollout with OM4 forcing. (C) Same as (A) but for a full-state model rollout with OM4 forcing. (D) Same as (A) but for CM4 under piControl forcing. (E--F) Same as (B--C) but with CM4 piControl forcing. (G--I) Same as (D--F) but with 1% CO2 forcing. (J) Seasonal climatology of Arctic sea ice volume, computed over 2006-2022. (K) Annual-mean Arctic sea ice volume between 1969--2022. (L) Same as (J) but for piControl and 1% forcing runs, computed over 211--350. (M) Same as (K) but for piControl and 1% forcing between 211--350.
  • Figure 3: Arctic sea ice volume anomalies under different forcings. (A) Anomaly correlation coefficient (ACC) of annual-mean sea ice volume anomalies between OM4 and FloeNet, computed at each grid cell over the period 1969--2022. (B) Same as (A) but ACC computed between OM4 and the full-state model. (C) Central Arctic annual-mean sea ice volume anomalies over the period 1969--2022. (D--F) Same as (A--C) but for piControl forcing. (G--I) Same as (A--C) but for 1% CO2 forcing. (J) Same as (C) but now decomposing FloeNet and OM4 volume anomalies into thermodynamic and dynamic contributions. All central Arctic anomaly time series are computed as averages over the region shown by the contours in the spatial plots.
  • Figure 4: Same as Fig. 3, but for Antarctic. The contour in the spatial maps corresponds to the latitude 63$^\circ$S.
  • Figure S1: Same as Fig. 2 of main article, but for Arctic sea ice concentration and extent.
  • ...and 17 more figures