AEGIS: Differentiable Mars Climate Model with Neural Closures

Sameera S Kashyap; Victor Cruz; Angel Yepez; Razvan Marinescu

astro-ph.EPOct 2, 2026

AEGIS: Differentiable Mars Climate Model with Neural Closures

Authors: Sameera S Kashyap, Victor Cruz, Angel Yepez, Razvan Marinescu

Organizations: Department of Computer Science UC Santa Cruz Santa Cruz, CA 95064, USA

Abstract

General circulation models (GCMs) are the primary tool for simulating planetary atmospheres. They play a vital role in understanding Mars's atmosphere, as forecasting its unique weather is mission-critical for operations such as entry, descent, and landing. Mars poses unusual challenges for these models, as observations are sparse compared to Earth. In addition, a thin \co{} atmosphere alongside a radiatively active dust cycle creates a volatile atmosphere with large diurnal temperature swings and no true terrestrial analog for validation. Existing Mars GCMs, including the LMD PCM, the NASA Ames Mars GCM, and PlanetWRF, are mature and physically detailed but are implemented in legacy Fortran with finite-difference or finite-volume solvers, and they do not expose gradients for calibration or machine learning. Here we present AEGIS, a modular differentiable Mars climate model that couples Mars's unique atmospheric physics to the Dinosaur dynamical core, with interfaces for neural closures. We showcase stable ten-Mars-year simulations that reproduce the seasonal \co{} cycle while conserving the total \co{} inventory, capture realistic large-scale surface-temperature structure, and produce surface pressure that follows Mars Orbiter Laser Altimeter (MOLA) topography. Gradients through coupled trajectories agree with finite differences and support physical calibration and neural training. We compare with conventional GCMs, highlighting the framework's computational efficiency and differentiability.

Figures & tables

Figure 1: AEGIS architecture. Planetary constants and boundary data configure a shared GCM. Dinosaur supplies resolved dynamics; Mars physical operators supply column tendencies and surface evolution; neural components enter as bounded residuals or component replacements. The coupled time step advances atmosphere and surface reservoirs. Trajectory objectives connect the simulated fields to physical-parameter calibration and neural training through automatic differentiation. Dashed arrows denote this gradient-based update path.

Category	Objective	Horizon	Checks	Passed
Physical sensitivity	Final mean atmospheric temperature	1–148 steps	54	54/54
Physical calibration	Scalar fitting objective	task-dependent	9	9/9
Neural fitting	Multi-field trajectory objective	25 steps	3	3/3
Multi-diagnostic sensitivity	Mean surface $T$ , $p_{s}$ , and CO 2 frost	74–1480 steps	9	9/9

Table 1: Derivative checks through coupled AEGIS trajectories. All comparisons satisfy

\lvert g_{\mathrm{AD}}-g_{\mathrm{FD}}\rvert\leq 10^{-8}+10^{-4}\lvert g_{\mathrm{FD}}\rvert

Figure 2: Differentiable response of AEGIS to the CO 2 longwave opacity multiplier

\beta

\beta=1

, from a MY32,

L_{s}=45^{\circ}

window. Top: global-mean surface temperature, surface pressure, and surface CO 2 frost at rollout horizons of 0.25, 1, and 5 sols. Bottom: their derivatives with respect to

\beta

computed by forward-mode automatic differentiation through the full coupled rollout (lines) and by centered finite differences (crosses). The pressure and frost sensitivities are equal and opposite, reflecting conservation of the atmosphere–frost CO 2 inventory.

Configuration	Training	Validation	Test
Physical baseline	2.45	2.49	2.46
Calibrated physics	2.38	2.44	2.40
Coupled neural heating	2.42	2.49	2.46

Table 2: Twelve-window reanalysis pilot: atmospheric temperature RMSE (K) at a single lead of 7397.937 s. Each split contains four seasonally distributed windows, combined as the square root of their mean squared errors. The physical baseline is shared by the two fitting experiments.

Figure 3: MOLA terrain on the comparison grid (top), and seasonal fields at $L_{s}=135^{\circ}$ (bottom). The seasonal panels compare the physical model with NASA Ames, MCD 6.1, and ARCO-MACDA for surface temperature, pressure, near-surface wind speed, and surface CO 2 frost.

	Pressure		Temperature
Reference	RMSE (Pa)	Correlation	RMSE (K)	Correlation
NASA Ames	52.37	0.9864	20.64	0.7889
MCD 6.1	57.18	0.9775	21.58	0.7872
ARCO-MACDA	40.72	0.9879	22.27	0.7715

Table 3: Seasonal reference comparisons. Entries are arithmetic means of the four seasonal RMSE values and pattern correlations; they are not pooled spatial RMSE. Pressure and temperature refer to the surface. The high pressure correlations largely reflect common terrain structure.

Figure 4: Sensitivity of the T21/L12 Mars configuration. Bars show RMS differences from the paired full-physics trajectory over five saved times through 44 400 s; the right panel combines zonal and meridional wind RMSE by quadrature. All cases begin from the same state.

Training procedure	Interpolation	Higher dust
Initial local fit	0.620	1.799
Trajectory fine-tuning	0.566	1.653
Continued local fitting	0.416	0.979

Table 4: Atmospheric-temperature RMSE (K) after 48 coupled steps for learned radiation variants. All models use the same structured flux interface.

Figure 5: Archived neural-radiation experiment at a 48-step column rollout. Air-temperature RMSE is evaluated against the compact physical radiation kernel on held-out interpolation columns and higher-dust extrapolation columns (24 columns per split). The three bars compare the initial local fit, trajectory fine-tuning, and continued local fitting.

Figure 6: Physical inverse experiment from the neural-framework archive. Gradient-based optimization recovers a prescribed surface-exchange multiplier of 1.6 from three initial values over 120 updates. Each curve includes its initial value and the saved multiplier after every update. The parameter belongs to the controlled column model; the experiment tests the shared differentiable inference workflow.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Seasonal fields at $L_{s}=45^{\circ}$ . The panels compare the physical model with NASA Ames, MCD 6.1, and ARCO-MACDA for surface temperature, pressure, near-surface wind speed, and surface CO 2 frost, under the sampling conventions described in the main text.

Figure 8: Seasonal fields at $L_{s}=315^{\circ}$ . The panels compare the physical model with NASA Ames, MCD 6.1, and ARCO-MACDA for surface temperature, pressure, near-surface wind speed, and surface CO 2 frost, under the sampling conventions described in the main text.

Figure 9: Seasonal diagnostic comparison at $L_{s}=135^{\circ}$ , organized as a $4\times 4$ grid. Each row is one surface field—surface temperature (K), surface pressure (Pa), near-surface wind speed (m s -1 ), and surface CO 2 frost (Pa-equivalent)—and each column is one source: the AEGIS physical model (leftmost) alongside the NASA Ames, MCD 6.1, and ARCO-MACDA reference products. All panels are longitude–latitude maps ( $0$ – $360^{\circ}$ E, $90^{\circ}$ S– $90^{\circ}$ N) with the MOLA terrain contour overlaid for geographic reference, and the color scale is shared within each row so the model and the three references are directly comparable field by field. Reading across a row shows how closely the modeled field tracks each reference; reading down the leftmost column shows the modeled state across all four diagnostics. Table 3 summarizes the corresponding pattern correlations and RMSE.

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