Time-Varying Causal Treatment for Quantifying the Causal Effect of Short-Term Variations on Arctic Sea Ice Dynamics

• URL: http://arxiv.org/abs/2601.17647v1
• Date: Sun, 25 Jan 2026 01:44:55 GMT
• Title: Time-Varying Causal Treatment for Quantifying the Causal Effect of Short-Term Variations on Arctic Sea Ice Dynamics
• Authors: Akila Sampath, Vandana Janeja, Jianwu Wang,
• Abstract summary: Quantifying the causal relationship between ice melt and freshwater distribution is critical, as these complex interactions manifest as regional fluctuations in sea surface height.<n>We propose the Knowledge-Guided Variational Autoencoder (KGCM-VAE) to quantify causal mechanisms between sea ice thickness and SSH.<n> Experimental results on both synthetic and real-world Arctic datasets demonstrate that KGCM-VAE superior PEHE compared to state-of-the-art benchmarks.
• Score: 0.16206783799607727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantifying the causal relationship between ice melt and freshwater distribution is critical, as these complex interactions manifest as regional fluctuations in sea surface height (SSH). Leveraging SSH as a proxy for sea ice dynamics enables improved understanding of the feedback mechanisms driving polar climate change and global sea-level rise. However, conventional deep learning models often struggle with reliable treatment effect estimation in spatiotemporal settings due to unobserved confounders and the absence of physical constraints. To address these challenges, we propose the Knowledge-Guided Causal Model Variational Autoencoder (KGCM-VAE) to quantify causal mechanisms between sea ice thickness and SSH. The proposed framework integrates a velocity modulation scheme in which smoothed velocity signals are dynamically amplified via a sigmoid function governed by SSH transitions to generate physically grounded causal treatments. In addition, the model incorporates Maximum Mean Discrepancy (MMD) to balance treated and control covariate distributions in the latent space, along with a causal adjacency-constrained decoder to ensure alignment with established physical structures. Experimental results on both synthetic and real-world Arctic datasets demonstrate that KGCM-VAE achieves superior PEHE compared to state-of-the-art benchmarks. Ablation studies further confirm the effectiveness of the approach, showing that the joint application of MMD and causal adjacency constraints yields a 1.88\% reduction in estimation error.

Related papers

• Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model [9.985969370583426]
  Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions.<n>Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings.<n>We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs).
  arXiv  Detail & Related papers  (2026-02-11T19:34:50Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction [2.1141584811533645]
  We introduce a causality-aware deep learning framework for causal feature selection within a hybrid neural architecture.<n>The proposed method identifies causally influential predictors, prioritizes direct causes of SIE dynamics, reduces unnecessary features, and enhances computational efficiency.<n> Experimental results show that incorporating causal inputs leads to improved prediction accuracy and interpretability across varying lead times.
  arXiv  Detail & Related papers  (2025-09-11T03:54:39Z)
• Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature [44.99833362998488]
  High-resolution projections of the Greenland ice sheet's surface mass balance (SMB) and surface temperature are essential for understanding future sea-level rise.<n>Here, we introduce a novel physics-constrained generative modeling framework based on a consistency model (CM) to downscale low-resolution SMB and surface temperature fields.<n>Our approach delivers realistic, high-resolution climate forcing for ice-sheet simulations with fast inference.
  arXiv  Detail & Related papers  (2025-07-30T08:43:48Z)
• Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model [7.617560936972677]
  We introduce IceMamba, a deep learning architecture that integrates sophisticated attention mechanisms within the state space model.<n>IceMamba outperforms all tested models regarding average RMSE and anomaly correlation coefficient (ACC) and ranks second in Integrated Ice Edge Error (IIEE)<n>This innovative approach enhances our ability to foresee and alleviate the effects of sea ice variability, offering essential insights for strategies aimed at climate adaptation.
  arXiv  Detail & Related papers  (2025-05-15T19:15:00Z)
• Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction [3.868211565468035]
  We propose a causality-driven deep learning framework that integrates causal discovery algorithms with a hybrid deep learning architecture.<n>Our approach identifies causally significant factors, prioritizes features with direct influence, reduces feature overhead, and improves computational efficiency.<n> Experiments demonstrate that integrating causal features enhances the deep learning model's predictive accuracy and interpretability across multiple lead times.
  arXiv  Detail & Related papers  (2025-03-03T22:24:14Z)
• Emergence of fluctuating hydrodynamics in chaotic quantum systems [47.187609203210705]
  macroscopic fluctuation theory (MFT) was recently developed to model the hydrodynamics of fluctuations. We perform large-scale quantum simulations that monitor the full counting statistics of particle-number fluctuations in boson ladders. Our results suggest that large-scale fluctuations of isolated quantum systems display emergent hydrodynamic behavior.
  arXiv  Detail & Related papers  (2023-06-20T11:26:30Z)
• Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference [2.0672522722098683]
  We propose TCINet - time-series causal inference model to infer causation under continuous treatment. We show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt.
  arXiv  Detail & Related papers  (2023-02-22T19:35:28Z)
• Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing [42.20480089840438]
  Single image dehazing models (SIDM) based on atmospheric scattering model (ASM) have achieved remarkable results. In this study, a new fully non-homogeneous atmospheric scattering model (FNH-ASM) is proposed for well modeling the hazy images. Two new cost sensitive loss functions, beta-Loss and D-Loss, are innovatively developed for limiting the parameter bias of sensitive positions.
  arXiv  Detail & Related papers  (2021-08-25T15:27:44Z)
• SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model [72.3183990520267]
  We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
  arXiv  Detail & Related papers  (2021-06-24T17:29:42Z)
• Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference [0.0]
  Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland.
  arXiv  Detail & Related papers  (2020-06-22T16:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.