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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | https://arxiv.org/abs/2405.18391 |
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| _version_ | 1866914814967676928 |
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| author | Yadav, Alka Das, Sourish Chakraborti, Anirban |
| author_facet | Yadav, Alka Das, Sourish Chakraborti, Anirban |
| contents | In this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand climate systems, which serve as examples of "complex systems". We believe that this would offer valuable insights to comprehend the complexity of climate variability and pave the way for drafting policies for action against climate change, etc. Our basic aim is to analyse time-series data structures across diverse climate parameters, extract Fourier-transformed features to recognize and model the trends/seasonalities in the climate variables using standard methods like detrended residual series analyses, correlation structures among climate parameters, Granger causal models, and other statistical machine-learning techniques. We cite and briefly explain two case studies: (i) the relationship between the Standardised Precipitation Index (SPI) and specific climate variables including Sea Surface Temperature (SST), El Niño Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), uncovering temporal shifts in correlations between SPI and these variables, and reveal complex patterns that drive drought and wet climate conditions in South-West Australia; (ii) the complex interactions of North Atlantic Oscillation (NAO) index, with SST and sea ice extent (SIE), potentially arising from positive feedback loops. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Untangling Climate's Complexity: Methodological Insights Yadav, Alka Das, Sourish Chakraborti, Anirban Data Analysis, Statistics and Probability Atmospheric and Oceanic Physics Computational Physics In this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand climate systems, which serve as examples of "complex systems". We believe that this would offer valuable insights to comprehend the complexity of climate variability and pave the way for drafting policies for action against climate change, etc. Our basic aim is to analyse time-series data structures across diverse climate parameters, extract Fourier-transformed features to recognize and model the trends/seasonalities in the climate variables using standard methods like detrended residual series analyses, correlation structures among climate parameters, Granger causal models, and other statistical machine-learning techniques. We cite and briefly explain two case studies: (i) the relationship between the Standardised Precipitation Index (SPI) and specific climate variables including Sea Surface Temperature (SST), El Niño Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), uncovering temporal shifts in correlations between SPI and these variables, and reveal complex patterns that drive drought and wet climate conditions in South-West Australia; (ii) the complex interactions of North Atlantic Oscillation (NAO) index, with SST and sea ice extent (SIE), potentially arising from positive feedback loops. |
| title | Untangling Climate's Complexity: Methodological Insights |
| topic | Data Analysis, Statistics and Probability Atmospheric and Oceanic Physics Computational Physics |
| url | https://arxiv.org/abs/2405.18391 |