Developing a Sequential Deep Learning Pipeline to Model Alaskan Permafrost Thaw Under Climate Change

• URL: http://arxiv.org/abs/2510.06258v1
• Date: Sun, 05 Oct 2025 01:08:47 GMT
• Title: Developing a Sequential Deep Learning Pipeline to Model Alaskan Permafrost Thaw Under Climate Change
• Authors: Addina Rahaman,
• Abstract summary: Changing climate conditions threaten the natural permafrost thaw-freeze cycle, leading to year-round soil temperatures above 0degC.<n>In Alaska, the warming of the topmost permafrost layer, known as the active layer, signals elevated greenhouse gas release due to high carbon storage.<n>This study presents a proof-of-concept latitude-based deep learning pipeline for modeling yearly soil temperatures across multiple depths.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Changing climate conditions threaten the natural permafrost thaw-freeze cycle, leading to year-round soil temperatures above 0{\deg}C. In Alaska, the warming of the topmost permafrost layer, known as the active layer, signals elevated greenhouse gas release due to high carbon storage. Accurate soil temperature prediction is therefore essential for risk mitigation and stability assessment; however, many existing approaches overlook the numerous factors driving soil thermal dynamics. This study presents a proof-of-concept latitude-based deep learning pipeline for modeling yearly soil temperatures across multiple depths. The framework employs dynamic reanalysis feature data from the ERA5-Land dataset, static geologic and lithological features, sliding-window sequences for seasonal context, a derived scenario signal feature for long-term climate forcing, and latitude band embeddings for spatial sensitivity. Five deep learning models were tested: a Temporal Convolutional Network (TCN), a Transformer, a 1-Dimensional Convolutional Long-Short Term Memory (Conv1DLSTM), a Gated-Recurrent Unit (GRU), and a Bidirectional Long-Short Term Memory (BiLSTM). Results showed solid recognition of latitudinal and depth-wise temperature discrepancies, with the GRU performing best in sequential temperature pattern detection. Bias-corrected CMIP5 RCP data enabled recognition of sinusoidal temperature trends, though limited divergence between scenarios were observed. This study establishes an end-to-end framework for adopting deep learning in active layer temperature modeling, offering seasonal, spatial, and vertical temperature context without intrinsic restrictions on feature selection.

Related papers

• Four decades of circumpolar super-resolved satellite land surface temperature data [19.704680245752094]
  This paper presents a new 42 years pan-Arctic LST dataset, downscaled from AVHRR GAC to 1 km with a super-resolution algorithm.<n>The resulting dataset provides twice-daily, 1 km LST observations for the entire pan-Arctic region over four decades.<n>It enables improved modelling of permafrost, reconstruction of near-surface air temperature, and assessment of surface mass balance of the Greenland Ice Sheet.
  arXiv  Detail & Related papers  (2025-11-21T10:53:19Z)
• LUCIE-3D: A three-dimensional climate emulator for forced responses [2.2017231371066672]
  We introduce LUCIE-3D, a lightweight three-dimensional climate emulator.<n>It is designed to capture the vertical structure of the atmosphere, respond to climate change forcings, and maintain computational efficiency with long-term stability.
  arXiv  Detail & Related papers  (2025-09-02T07:59:23Z)
• Causal Representation Learning in Temporal Data via Single-Parent Decoding [66.34294989334728]
  Scientific research often seeks to understand the causal structure underlying high-level variables in a system. Scientists typically collect low-level measurements, such as geographically distributed temperature readings. We propose a differentiable method, Causal Discovery with Single-parent Decoding, that simultaneously learns the underlying latents and a causal graph over them.
  arXiv  Detail & Related papers  (2024-10-09T15:57:50Z)
• ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution [6.919867656801517]
  ClimDetect is a standardized dataset comprising 1.17M daily climate snapshots paired with target climate change indicator variables.<n>The dataset is curated from both CMIP6 climate model simulations and real-world observation-assimilated reanalysis datasets.<n>We also explore the application of vision transformers (ViT) to climate data -- a novel approach that, to our knowledge, has not been attempted before for climate change detection tasks.
  arXiv  Detail & Related papers  (2024-08-28T17:58:53Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw Cycle [2.539420625905208]
  Estimating landscape and soil freeze-thaw dynamics in the Northern Hemisphere is crucial for understanding permafrost response to global warming. New framework is presented for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network.
  arXiv  Detail & Related papers  (2024-07-04T18:40:50Z)
• Global Vegetation Modeling with Pre-Trained Weather Transformers [2.238622204691961]
  We adapt the publicly available pre-trained FourCastNet to model vegetation activity. We investigate how the learned global representation of the atmosphere's state can be transferred to model the normalized difference vegetation index (NDVI) We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch.
  arXiv  Detail & Related papers  (2024-03-27T10:45:16Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for localized surface temperature forecasting in an urban environment [29.94873599943544]
  This research proposed a framework based on Long Short-Term Memory (LSTM) deep learning network to generate day-ahead hourly temperature forecast with high spatial resolution. A case study is shown which uses historical in-situ observations and Internet of Things (IoT) observations for New York City, USA.
  arXiv  Detail & Related papers  (2021-02-04T21:21:21Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

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.