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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08874 |
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| _version_ | 1866912271967453184 |
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| author | Li, Wantong Duveiller, Gregory Gans, Fabian Smits, Jeroen Kraemer, Guido Frank, Dorothea Mahecha, Miguel D. Weber, Ulrich Migliavacca, Mirco Ceglar, Andrej Keenan, Trevor F. Reichstein, Markus |
| author_facet | Li, Wantong Duveiller, Gregory Gans, Fabian Smits, Jeroen Kraemer, Guido Frank, Dorothea Mahecha, Miguel D. Weber, Ulrich Migliavacca, Mirco Ceglar, Andrej Keenan, Trevor F. Reichstein, Markus |
| contents | It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For example, in 2022, there are about 400 climate extremes and natural hazards worldwide, resulting in significant losses of lives and economic damage. Beyond these losses, comprehensive assessment on societal well-being, ecosystem services, and carbon dynamics are often understudied. The rapid expansion of geospatial, atmospheric, and socioeconomic data provides an unprecedented opportunity to develop systemic indices to account for a more comprehensive spectrum of Earth system change risks and to assess their socioeconomic impacts. We propose a novel approach based on the concept of syndromes that can integrate synchronized changes in biosphere, atmosphere, and socioeconomic trajectories into distinct co-evolving phenomena. While the syndrome concept was applied in policy related to environmental conservation, it has not been deciphered from systematic data-driven approaches capable of providing a more comprehensive diagnosis of anthropogenic impacts. By advocating interactive dimensionality reduction approaches, we can identify key interconnected socio-environmental changes as syndromes from big data. We recommend future research tailoring syndromes by incorporating granular data, particularly socio-economic, into dimensionality reduction at different spatio-temporal scales to better diagnose regional-to-global atmospheric and environmental changes that are relevant for socioeconomic changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diagnosing syndromes of biosphere-atmosphere-socioeconomic change Li, Wantong Duveiller, Gregory Gans, Fabian Smits, Jeroen Kraemer, Guido Frank, Dorothea Mahecha, Miguel D. Weber, Ulrich Migliavacca, Mirco Ceglar, Andrej Keenan, Trevor F. Reichstein, Markus Geophysics It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For example, in 2022, there are about 400 climate extremes and natural hazards worldwide, resulting in significant losses of lives and economic damage. Beyond these losses, comprehensive assessment on societal well-being, ecosystem services, and carbon dynamics are often understudied. The rapid expansion of geospatial, atmospheric, and socioeconomic data provides an unprecedented opportunity to develop systemic indices to account for a more comprehensive spectrum of Earth system change risks and to assess their socioeconomic impacts. We propose a novel approach based on the concept of syndromes that can integrate synchronized changes in biosphere, atmosphere, and socioeconomic trajectories into distinct co-evolving phenomena. While the syndrome concept was applied in policy related to environmental conservation, it has not been deciphered from systematic data-driven approaches capable of providing a more comprehensive diagnosis of anthropogenic impacts. By advocating interactive dimensionality reduction approaches, we can identify key interconnected socio-environmental changes as syndromes from big data. We recommend future research tailoring syndromes by incorporating granular data, particularly socio-economic, into dimensionality reduction at different spatio-temporal scales to better diagnose regional-to-global atmospheric and environmental changes that are relevant for socioeconomic changes. |
| title | Diagnosing syndromes of biosphere-atmosphere-socioeconomic change |
| topic | Geophysics |
| url | https://arxiv.org/abs/2503.08874 |