# MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting

arXiv: https://arxiv.org/abs/2308.04511 · HTML: https://mlsanity.com/paper/2308.04511/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Sea Ice Forecasting using Attention-based Ensemble LSTM](https://mlsanity.com/paper/2108.00853/) (2021)
- [Graph Neural Networks for Improved El Ni~no Forecasting](https://mlsanity.com/paper/2012.01598/) (2020)

## Built on by (most cited first, 5 of 5)

- [IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior](https://mlsanity.com/paper/2410.09111/) (2024)
- [SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting](https://mlsanity.com/paper/2410.14732/) (2024)
- [Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble](https://mlsanity.com/paper/2312.04330/) (2023)
- [IceBench-S2S: A Benchmark of Deep Learning for Challenging Subseasonal-to-Seasonal Daily Arctic Sea Ice Forecasting in Deep Latent Space](https://mlsanity.com/paper/2602.02567/) (2026)
- [Structured Neural Modeling of Daily Arctic Sea-Ice Concentration Evolution: Physical-Trajectory-Driven Learning and Forecast-Domain Adaptation](https://mlsanity.com/paper/2610.03743/) (2026)

## Abstract (arXiv)

Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations. Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major research question with fundamental challenges at play. In addition to physics-based Earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven approaches to study sea ice variations, we propose MT-IceNet - a UNet based spatial and multi-temporal (MT) deep learning model for forecasting Arctic sea ice concentration (SIC). The model uses an encoder-decoder architecture with skip connections and processes multi-temporal input streams to regenerate spatial maps at future timesteps. Using bi-monthly and monthly satellite retrieved sea ice data from NSIDC as well as atmospheric and oceanic variables from ERA5 reanalysis product during 1979-2021, we show that our proposed model provides promising predictive performance for per-pixel SIC forecasting with up to 60% decrease in prediction error for a lead time of 6 months as compared to its state-of-the-art counterparts.