A Deep Learning Earth System Model Simulation of Indian Monsoon Intraseasonal and Interannual Variability

Anurag Dipankar; Bijit Kumar Banerjee; B. N. Goswami; Chandrashekar Lakshminarayanan; Devabrat Sharma; Manikandan Narayanan; R. I. Sujith; Subodh K. Saha; Utpal Sarma

arxiv: 2607.01676 · v1 · pith:BZWPTB3Mnew · submitted 2026-07-02 · ⚛️ physics.ao-ph

A Deep Learning Earth System Model Simulation of Indian Monsoon Intraseasonal and Interannual Variability

Bijit Kumar Banerjee , Devabrat Sharma , R. I. Sujith , Chandrashekar Lakshminarayanan , Manikandan Narayanan , Subodh K. Saha , Anurag Dipankar , Utpal Sarma

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B. N. Goswami

This is my paper

Pith reviewed 2026-07-03 02:32 UTC · model grok-4.3

classification ⚛️ physics.ao-ph

keywords Indian monsoonintraseasonal variabilityinterannual variabilitydeep learning Earth system modelSamudrACES2S predictionocean-atmosphere coupling

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The pith

SamudrACE exhibits systematic biases in its simulation of Indian monsoon intraseasonal and interannual variability compared to observations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests a deep learning 3D ocean-atmosphere coupled model called SamudrACE to determine whether it can reproduce the observed variability of the Indian monsoon on intraseasonal and interannual timescales. Direct comparisons with observational data reveal multiple discrepancies in rainfall and circulation features. A sympathetic reader would care because faithful reproduction of this variability is required before such models can support sub-seasonal to seasonal prediction systems. The work documents these shortcomings as a benchmark that could guide improvements in SamudrACE and in coupled emulators more generally.

Core claim

SamudrACE does not faithfully simulate the observed intraseasonal and interannual variability of the Indian monsoon; systematic biases are present.

What carries the argument

SamudrACE, a deep learning 3D ocean-atmosphere coupled emulator, whose output is compared directly to observations to assess fidelity in monsoon rainfall and circulation patterns.

If this is right

The identified biases would limit SamudrACE's reliability for sub-seasonal to seasonal monsoon prediction.
Documented errors supply concrete targets for retraining or architectural changes in the model.
The same evaluation approach can be applied to other deep learning coupled Earth system emulators.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar biases may appear in other data-driven models when applied to regionally important phenomena like the monsoon.
The findings point to a general need for targeted diagnostics when adapting global AI weather models to specific climate features.

Load-bearing premise

Direct comparison of SamudrACE output to observations is sufficient to diagnose the model's suitability for S2S prediction without additional information on training data overlap or hyperparameter choices.

What would settle it

Demonstration that SamudrACE reproduces key monsoon indices such as rainfall anomalies and circulation patterns within the observed range of variability without systematic offsets would falsify the claim of unfaithful simulation.

Figures

Figures reproduced from arXiv: 2607.01676 by Anurag Dipankar, Bijit Kumar Banerjee, B. N. Goswami, Chandrashekar Lakshminarayanan, Devabrat Sharma, Manikandan Narayanan, R. I. Sujith, Subodh K. Saha, Utpal Sarma.

**Figure 7.** Figure 7: Figure7. (A) Climatological annual cycle of rainfall averaged over the Indian landmass from IITM RR65 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Figure8. Leading empirical orthogonal function (EOF) modes of JJAS rainfall anomalies over the Indian monsoon [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

**Figure 9.** Figure 9: Figure9. Leading empirical orthogonal function (EOF) modes of annual mean tropical (30°S– [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗

**Figure 10.** Figure 10: Figure10. Niño [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗

read the original abstract

With the data-driven artificial intelligence/machine learning (AI/ML) models having demonstrated their ability to extend the prediction horizon of large-scale weather at a fraction of computational cost of numerical weather prediction models, a pertinent question is, could these models do the same for sub-seasonal to seasonal (S2S) prediction? A key challenge in developing a S2S prediction system is the requirement for a coupled ocean-atmosphere Earth system emulator that can stably simulate the observed intraseasonal and interannual variability with fidelity. In the rapidly evolving field of AI/ML weather models, such a deep learning 3D ocean-atmosphere coupled model has become available, called SamudrACE. With our interest in developing an AI/ML S2S model for Indian monsoon, here we examine the extent to which SamudrACE faithfully simulates Indian monsoon intraseasonal and interannual variability. Compared to observation, we found biases in SamudrACE's simulation of monsoon intraseasonal and interannual variability. Our systematic documentation and analyses of these biases provide a useful benchmark for improving not only SamudrACE but also coupled emulators in general and could fast track the development of a deep learning 3D global S2S prediction system.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SamudrACE evaluation flags monsoon biases but skips training-data checks, limiting how much we can trust the comparison.

read the letter

The paper evaluates an existing coupled AI model, SamudrACE, on its ability to reproduce Indian monsoon intraseasonal and interannual variability against observations. It reports systematic biases and frames the work as a benchmark to help improve future emulators for S2S prediction.

It does a straightforward job of narrowing in on one high-stakes region and listing where the model deviates from reanalysis or observations. That kind of targeted documentation can be useful for groups already working on regional tuning of these models.

The main weaknesses are the missing pieces that make the comparison hard to interpret. The abstract and available text give no quantitative metrics, no error bars, and no statistical tests, so the size and robustness of the biases stay unclear. More critically, there is no information on SamudrACE's training period or whether the evaluation years overlap with training data. Without that, we cannot rule out that some reported differences simply reflect incomplete memorization rather than intrinsic model limits. The stress-test note correctly flags this gap.

This is aimed at people building or testing AI Earth-system emulators who need monsoon-specific test cases. A reader already following SamudrACE or similar models might pick up a few concrete bias examples, but the work is mostly incremental evaluation rather than a new method or result.

I would send it for peer review because the topic is relevant and the regional focus is practical, but it would need revisions to add the missing quantitative details and training-data transparency before it could be cited with confidence.

Referee Report

2 major / 1 minor

Summary. The paper evaluates the SamudrACE deep learning 3D ocean-atmosphere coupled Earth system model for its ability to simulate the intraseasonal and interannual variability of the Indian monsoon. It reports the presence of systematic biases relative to observations and positions the documentation of these biases as a benchmark for improving coupled AI emulators and advancing S2S prediction systems.

Significance. Documenting performance limitations in emerging deep learning Earth system models for a high-impact phenomenon like the Indian monsoon could help guide development of computationally efficient S2S systems. However, the absence of quantitative metrics or training-data details substantially reduces the work's utility as a benchmark. No machine-checked proofs, reproducible code, or parameter-free derivations are provided.

major comments (2)

[Abstract] Abstract: the claim that 'biases in SamudrACE's simulation of monsoon intraseasonal and interannual variability' exist supplies no quantitative metrics (e.g., RMSE, correlation, or power-spectrum comparisons), error bars, dataset details, or statistical tests, so the central claim cannot be verified from the available text.
The manuscript provides no information on SamudrACE's training corpus, temporal coverage, or hyperparameter choices. Without confirming independence from the evaluation periods, the reported biases cannot be confidently interpreted as intrinsic model deficiencies rather than possible data leakage or an unrepresentative test set; this directly undermines the diagnostic value of the comparison to observations.

minor comments (1)

[Abstract] The abstract could specify the exact observational datasets, time periods, and monsoon indices used for the comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our evaluation of SamudrACE. We address each major comment below and have revised the manuscript to improve its utility as a benchmark.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that 'biases in SamudrACE's simulation of monsoon intraseasonal and interannual variability' exist supplies no quantitative metrics (e.g., RMSE, correlation, or power-spectrum comparisons), error bars, dataset details, or statistical tests, so the central claim cannot be verified from the available text.

Authors: We agree the abstract would be strengthened by including quantitative support for the central claim. The main text and figures already contain these comparisons (power spectra, correlations, and error estimates with statistical tests), but we have revised the abstract to explicitly reference key metrics such as correlation coefficients for intraseasonal variability and RMSE values for interannual anomalies. revision: yes
Referee: The manuscript provides no information on SamudrACE's training corpus, temporal coverage, or hyperparameter choices. Without confirming independence from the evaluation periods, the reported biases cannot be confidently interpreted as intrinsic model deficiencies rather than possible data leakage or an unrepresentative test set; this directly undermines the diagnostic value of the comparison to observations.

Authors: This is a valid point that affects interpretability. We have added a Methods subsection that references the original SamudrACE paper for the training corpus (ERA5 reanalysis), temporal coverage, and hyperparameters, while explicitly confirming that our evaluation periods are independent of the training data to rule out leakage. revision: yes

Circularity Check

0 steps flagged

No circularity: direct evaluation of existing model against external observations

full rationale

The paper evaluates SamudrACE (an existing deep learning model) by comparing its output to independent observational datasets for Indian monsoon intraseasonal and interannual variability. No derivation, equations, fitted parameters, or self-citation chains are presented that reduce any result to the paper's own inputs by construction. The central claim rests on external benchmarks, satisfying the self-contained criterion with no load-bearing reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities are described in the provided text.

pith-pipeline@v0.9.1-grok · 5805 in / 908 out tokens · 24504 ms · 2026-07-03T02:32:49.633704+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages · 1 internal anchor

[1]

year 311

Introduction The monsoon intraseasonal variability is a key building block of the Indian monsoon system (Goswami, 2012; Goswami et al., 2006) through clustering the synoptic low-pressure systems (LPS) at one end and contributing to the interannual variability of the seasonal mean rainfall (Goswami & Mohan, 2001; S. K. Saha et al., 2019) at the other end o...

work page 2012
[2]

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

and starts amplifying both westerly zonal winds to the west and SST anomalies to the east. The unstable air-sea interaction is associated with a ‘slow’ thermocline wave propagating eastwards making westerly bursts to be more persistent and helping the SST anomalies to amplify. While the process is working in CM4 (Fig.12E, F), the ‘super’ El Nino in Samudr...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/nature14956 2019

[1] [1]

year 311

Introduction The monsoon intraseasonal variability is a key building block of the Indian monsoon system (Goswami, 2012; Goswami et al., 2006) through clustering the synoptic low-pressure systems (LPS) at one end and contributing to the interannual variability of the seasonal mean rainfall (Goswami & Mohan, 2001; S. K. Saha et al., 2019) at the other end o...

work page 2012

[2] [2]

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

and starts amplifying both westerly zonal winds to the west and SST anomalies to the east. The unstable air-sea interaction is associated with a ‘slow’ thermocline wave propagating eastwards making westerly bursts to be more persistent and helping the SST anomalies to amplify. While the process is working in CM4 (Fig.12E, F), the ‘super’ El Nino in Samudr...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/nature14956 2019