The Arctic sea ice plays a significant role in climate-related processes and has a considerable effect on humans, however accurately predicting the Arctic sea ice concentration is still challenging. Recently, with the rise and development of artificial intelligence, big data technology, machine learning has been widely used in the field of sea ice prediction. In this study, we utilized a sea ice concentration dataset obtained from satellite remote sensing and applied the k-nearest-neighbors (Ice-kNN) machine learning model to forecast the summer Arctic sea ice concentration and extent on 122 days prediction. Based on the physical characteristics of summer sea ice, different algorithms are employed to optimize the prediction model. A drift-ice correction algorithm is designed to address the unrealistic drift ice around the sea ice edge, and a distance function combined with the spatial pattern is proposed to enhance similarity detection. Deseasonalized and detrended sea ice datasets and an expanded training library are also utilized to improve model performance. Furthermore, sensitivity analysis reveals a positive impact of net surface heat flux on sea ice prediction. The modified Ice-kNN model outperforms climatological and anomaly persistence predictions, demonstrating its applicability to predicting summer Arctic sea ice. The September sea ice extent hindcasts of the modified Ice-kNN model are compared to a variety of models submitted to the Sea Ice Prediction Network, underscoring its potential to improve predictive skill for Arctic sea ice.

Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction

Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv2) for the IOD prediction during the period 1982–2014 is evaluated. The results indicate that the model performance for the IOD prediction is the worst in spring among the four seasons, which is manifested in the fact that a skilful prediction of spring IOD event is limited to a lead time of only about 1–2 months. To improve the forecast of spring IOD events, a physical‐empirical (PE) model and a convolutional neural network (CNN) model are established in the present study. The IOD in April–May–June (AMJ) is taken as the predictand, and the CFSv2‐predicted sea surface height (SSH) in AMJ and the observed Laptev sea ice in the preceding December are used as the two predictors. The original CFSv2‐predicted IOD time series has an insignificant correlation with the observed IOD time series with a temporal correlation coefficient (TCC) of 0.03; the PE model (CNN model) can largely improve the IOD prediction with a TCC of 0.74 (0.77) between the PE‐model‐predicted (CNN‐model‐predicted) IOD and the observed IOD during AMJ. Thus, the PE model and the CNN model developed in the present study can be applied to improve the IOD predictions from numerical models in the future.

Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach

Abstract The dominant mode of the interannual variability in the frequency of extreme high-temperature events (FEHE) during summer over eastern China showed a dipole mode with reversed anomalies of FEHE over northeastern and southern China. This study found that the interannual variability of this dipole mode underwent an interdecadal increase after the early 1990s. The anomalous atmospheric circulation responsible for the FEHE dipole mode was associated with the air–sea interaction over the western tropical Pacific and North Atlantic. Due to the weakened correlation between the SST in the tropical Pacific and in the Indian Ocean after the early 1990s, a meridional atmospheric wave train induced by the anomalous SST around the Maritime Continent (MCSST) was intensified during 1994–2013, which was also contributed by the increased interannual variability of MCSST. However, under the influence of the anomalous SST in the Indian Ocean concurrent with the anomalous MCSST, the meridional wave train was weakened and contributed less to the dipole mode during 1972–93. In addition, the dipole mode was associated with the atmospheric wave trains at middle to high latitudes, which were different during the two periods and related to different air–sea interaction in the North Atlantic. The interannual variability of the dipole mode induced by the associated SST anomalies in the North Atlantic during 1994–2013 was significantly larger than that during 1972–93. Therefore, the interannual variability of the dipole mode was increased after the early 1990s. 

/pdf/increased-interannual-variability-in-the-dipole-mode-of-2i9vq1b4.pdf

Increased Interannual Variability in the Dipole Mode of Extreme High-Temperature Events over East China during Summer after the Early 1990s and Associated Mechanisms

Hybrid Seasonal Prediction of Meridional Temperature Gradient Associated with “Warm Arctic-Cold Eurasia”

. Annual reductions in Arctic sea ice extent (SIE) due to global warming. According to International Panel on Climate Change (IPCC) climate model projections, the summer Arctic will be nearly sea ice free in the 50s of the 21st century, resulting in sea level rise and thus affecting human life. Therefore, it is important to predict SIE accurately. For the most current studies, the majority of deep learning-based SIE prediction models focus on single-step prediction, and they not only have short lead times 5 but also have limited forecasting skills. In addition, these models often lack interpretability. In this study paper, we construct the Ice Temporal Fusion Transformer (IceTFT) model, which consists mainly of the variable selection network (VSN), the long short-term memory (LSTM) encoder, and multi-headed attention mechanism. Then we select 11 predictors for IceTFT model, including SIE, atmospheric, and ocean variables according to the physical mechanisms influencing sea ice development. And the VSN in IceTFT can automatically adjust the weights of predictors and filter spuriously correlated variables. We also 10 evaluate the IceTFT model from the division of the training set, the slicing methods of input data, and the length of input. The IceTFT model directly generates 12-month SIE with average monthly prediction errors of less than 0.21 10 6 km 2 . And it predicts the September SIE nine months in advance with prediction error of less than 0.1 10 6 km 2 , which is superior to the models from Sea Ice Outlook (SIO). Furthermore, we analyze the sensitivity of the selected predictors to the SIE prediction. It verifies that the IceTFT model has some physical interpretability. And the variable sensitivities also provide some reference 15 for understanding the mechanisms governing sea ice development and selecting the assimilation variables in dynamic models

IceTFT v 1.0.0: Interpretable Long-Term Prediction of Arctic Sea Ice Extent with Deep Learning

Subseasonal to seasonal Arctic sea-ice prediction: A grand challenge of climate science

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Subseasonal to seasonal Arctic sea-ice prediction: A grand challenge of climate science