Climate Research

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2025 - 2507(1) - 2509(2)

Recent submissions

Any replacements are listed farther down

[3] ai.viXra.org:2509.0005 [pdf] submitted on 2025-09-02 07:15:24

Impact of Greenland's Ice Sheet Collapse on the Area Around the North Sea

Authors: Zhi Cheng
Comments: 11 Pages.

This study investigates the potential transatlantic impact of a large-scale ice sheet collapse from Southeast Greenland, specifically evaluating the tsunami hazard it poses to the North Sea region. Based on the scenario of a 30 km² ice sheet segment (500 m thick) collapsing into the ocean, the initial energy release is estimated at approximately 7.4 × 10¹u2076 J. While much of this energy dissipates locally, generating extreme near-field tsunamis, a significant portion is carried by low-frequency waves across the North Atlantic with minimal attenuation. Our analysis, incorporating AI-assisted literature synthesis and physical modeling, shows that these waves would be amplified by the shallow continental shelf of the North Sea, resulting in a background tsunami wave height of 1.5—3 meters along the open coast. Critically, the semi-enclosed basin of the North Sea is highly susceptible to resonance (Seiche effect), particularly in specific bays and harbors whose natural oscillation periods match the long-period components of the incoming tsunami. This resonance can amplify wave heights by a factor of 5 to 10, leading to localized catastrophic run-up heights exceeding 10 meters in vulnerable areas such as the Wadden Sea, the Moray Firth, and other funnel-shaped estuaries. Furthermore, the event's duration would be prolonged, with dangerous water level fluctuations and strong currents persisting for over 24 hours, far exceeding the typical duration of storm surges. This study concludes that a Greenland ice collapse represents a severe, under-appreciated risk for the North Sea coast, capable of triggering a prolonged, destructive, and highly localized tsunami disaster.
Category: Climate Research

[2] ai.viXra.org:2509.0001 [pdf] submitted on 2025-09-01 20:55:21

Comparative Study of ARIMA and ANN Models in Polar Motion Forecasting

Authors: Shufan Dong, Antonio Marsellos
Comments: 9 Pages.

Precise polar motion (PM) forecast is directly related to satellite navigation and climate studies. Traditional methodologies and machine learning (ML) have been poplar in modern climate modeling. This study addressed both approaches in comparison, investigating Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network’s (ANN) performance in seasonal geophysical signal forecast. We collected polar motion data from the International Earth Rotation and Reference Systems Service (IERS). We applied a Kolmogorov-Zurbenko (KZ) filter to isolate low-frequency trends. An ARIMA and an ANN were each implemented on training data for a 6-month signal prediction. Residual and statistical analysis showed that the ARIMA achieved superior accuracy than the ANN. The ANN’s recursive architecture has shown to overinterpret noise as signals, causing overfitting patterns and phase lags in forecasting. These results demonstrated that traditional methodologies can outperform neural networks in noncomplex, seasonal forecasts. This study can provide critical insights to space agencies and climate researchers, presenting comparative analysis on two popular methodologies and their corresponding performances in polar motion forecasting.
Category: Climate Research

[1] ai.viXra.org:2507.0118 [pdf] submitted on 2025-07-27 15:29:14

TInnovative Framework for High-Dimensional Error Modeling in Meteorological Data Assimilation: Fusion of TOENS-Q and Hierarchical Matrices with Spatial Error Correlation Optimization

Authors: Yueshui Lin
Comments: 4 Pages. (Note by ai.viXra.org Admin: Please cite listed scientific references)

High-dimensional modeling of spatially correlated observation errors in meteorological data assimi- lation faces computational complexity (O(n3 )) and precision loss (truncation noise >8 × 10³). This paper proposes an innovative framework integrating quaternion algebra (TOENS-Q) with hierarchi- cal matrices: (1) Spatial topology encoding via geographic quaternion Qobs = ϕ + λi + θj + hk ;(2) Precision error control with intensity parameter s achieving ε = 2- s error bound (δ < 10-9 when s > 1024); (3) Hierarchical acceleration reducing complexity to O(nlog n) with 16 × mem- ory compression. Experiments demonstrate 98% reduction in truncation noise and assimilation time compression from 42 minutes to 2.2 minutes for SEVIRI data assimilation.
Category: Climate Research

Replacements of recent Submissions

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