Unraveling climate trends in the mediterranean - a hybrid machine learning and statistical approach

Aug 5, 2026

Upload by Maeve

This study performs a comprehensive spatiotemporal analysis of sea surface temperatures (SST) and surface air temperatures (TAS) across 15 Mediterranean coastal stations using centennial-scale observations. A three-phase pipeline combines data preprocessing, statistical change-point and trend analysis, and machine learning for climate-regime classification and cluster-specific projections. Pettitt testing and linear trends quantify breakpoint-driven variability, while K-means and tailored convolutional neural networks estimate future anomaly patterns. Findings highlight a strong anthropogenic warming signal, with heterogeneous shifts and larger SST increases in the northern Mediterranean.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Abstract

Introduction

Regional vulnerability and observed changes

Accelerated warming drivers and key indicators

Methodology and analytical framework

Data preprocessing and harmonization

Statistical modeling: change points and trends

Machine learning: clustering and CNN projections

Results and projected warming patterns

Heterogeneous shifts and hotspot behavior

2050 anomaly projections for SST and TAS

Implications for adaptation and climate dynamics

Modeling Earth Systems and Environment [https://doi.org/10.1007/s40808-024-02117-w](https://doi.org/10.1007/s40808-024-02117-w) ORIGINAL ARTICLE Unraveling climate trends in the mediterranean: a hybrid machine learning and statistical approach Mutaz AlShafeey1 Received: 30 June 2024 / Accepted: 25 July 2024 © The Author(s) 2024 Abstract This study presents a comprehensive spatiotemporal analysis of sea surface temperatures (SST) and surface air temperatures (TAS) across 15 Mediterranean coastal stations, leveraging centennial-scale data to analyze regional climate dynamics. The modeling framework integrates three sequential phases: data preprocessing, statistical analysis, and advanced machine learning techniques, creating a robust analytical pipeline. The data preprocessing phase harmonizes diverse datasets, addresses missing values, and applies transformations to ensure analytical consistency. The statistical modeling employs the Pettitt test for change point detection and linear trend analysis to unveil underlying patterns. The machine learning phase utilizes K-means clustering for climate regime classification and implements tailored Convolutional Neural Networks (CNNs) for cluster-specific future climate anomaly projections. Results unveil a marked anthropogenic climate signal, with contemporary observations consistently surpassing historical baselines. Breakpoint analyses and linear trend assessments reveal heterogeneous climatic shifts, with pronounced warming in the northern Mediterranean. Notably, Nice and Ajaccio exhibit the highest SST increases (0.0119 and 0.0113 °C/decade, respectively), contrasting with more modest trends in Alexandria (0.0052 °C/decade) and Antalya (0.0047 °C/decade) in the eastern Mediterranean. The application of clustering and CNN projections provides granular insights into differential warming trajectories. By 2050, cooler northwestern Mediterranean zones are projected to experience dramatic SST anomalies of approximately 3 °C above the average, with corresponding TAS increases of 2.5 °C. In contrast, warmer eastern and southern regions display more subdued warming patterns, with projected SST and TAS increases of 1.5–2.5 °C by mid-century. This research’s importance is highlighted by its potential to inform tailored adaptation strategies and contribute to the theoretical understanding of climate dynamics, advancing climate modeling and analysis efforts. Keywords Mediterranean climate change · Sea surface temperature · Surface air temperatures · Machine learning in climate analysis · Climate modeling Introduction In the realm of climate science, the Mediterranean region is a very well-known area due to its combination of subtropical arid and temperate environments, stemming from its unique location. However, this positioning makes it especially vulnerable to the effects of climate change, emphasizing its importance as a key climate change hotspot (Cramer et 􀀍 Mutaz AlShafeey [mutaz.alshafeey@uni-corvinus.hu](mutaz.alshafeey@uni-corvinus.hu) 1 Institute of Data Analytics and Information Systems, Corvinus University of Budapest, Fővám tér 13-15, Budapest H-1093, Hungary al. 2018; Lionello and Scarascia 2018) . This region, which includes a semi-enclosed sea and its surrounding lands, has experienced significant changes in climate patterns in the last few decades. These changes are supported by evidence in the Mediterranean region, manifested in altered temperature trends, shifting precipitation regimes, and an uptick in the frequency and severity of extreme weather events (Insua-Costa et al. 2022; Androulidakis et al. 2023) . The consequences of these climate changes go far beyond meteorological issues, severely affecting crucial socioeconomic sectors. Agriculture, water resource management, coastal zone planning, and the protection of fragile ecosystems all face considerable issues due to these changes (Raihan 2023; Noto et al. 2023) . As a result, there is an urgent n

FAQs

Which temperature variables and locations does the study analyze?

The study analyzes sea surface temperatures (SST) and surface air temperatures (TAS) across 15 Mediterranean coastal stations, using centennial-scale data to capture regional dynamics.

How does the research detect climate shifts and trends?

It uses the Pettitt test for change point detection and linear trend analysis to identify heterogeneous climatic shifts and breakpoints across regions.

What machine learning methods are used for future climate anomaly projections?

The approach applies K-means clustering to classify climate regimes, then uses tailored convolutional neural networks (CNNs) to project cluster-specific future SST and TAS anomalies.

Aug 5, 2026

Upload by Maeve