Machine learning suggests climate and seasonal definitions should change under global warming
Extreme and unseasonal temperature and precipitation events have increased worldwide. The greater frequency and variability of floods, heatwaves, and droughts challenge traditional definitions of climate periods as 30-year means. Machine learning (ML) studies, focusing on southern Austral...
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Academia.edu Journals
2024-11-01
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| Series: | Academia Environmental Sciences and Sustainability |
| Online Access: | https://www.academia.edu/125737465/Machine_learning_suggests_climate_and_seasonal_definitions_should_be_changed_under_global_warming |
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| author | Milton Speer Lance Leslie |
| author_facet | Milton Speer Lance Leslie |
| author_sort | Milton Speer |
| collection | DOAJ |
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Extreme and unseasonal temperature and precipitation events have increased worldwide. The greater frequency and variability of floods, heatwaves, and droughts challenge traditional definitions of climate periods as 30-year means. Machine learning (ML) studies, focusing on southern Australia, identified the dominant attributes of these precipitation and temperature events. The attributes are both local and remote climate drivers, amplified by global warming. Their impacts include longer, hotter warm seasons and shorter, drier wet seasons in Australia’s southern Mediterranean climate regions. In contrast, flooding has increased in coastal eastern Australia. The poleward contraction of mid-latitude westerly winds is a readily identifiable contributor. Improvements in climate models are expected to more accurately predict future phases of climate drivers. Because global warming is not uniform across the Earth’s surface, the revised definitions of climate will vary by region. In this work, we chose one clear example that supports the need for re-considering climate periods. |
| format | Article |
| id | doaj-art-2501467d386945a88ebca5845e58fbd5 |
| institution | Kabale University |
| issn | 2997-6006 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Academia.edu Journals |
| record_format | Article |
| series | Academia Environmental Sciences and Sustainability |
| spelling | doaj-art-2501467d386945a88ebca5845e58fbd52025-02-10T23:04:46ZengAcademia.edu JournalsAcademia Environmental Sciences and Sustainability2997-60062024-11-011310.20935/AcadEnvSci7419Machine learning suggests climate and seasonal definitions should change under global warmingMilton Speer0Lance Leslie1School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia. Extreme and unseasonal temperature and precipitation events have increased worldwide. The greater frequency and variability of floods, heatwaves, and droughts challenge traditional definitions of climate periods as 30-year means. Machine learning (ML) studies, focusing on southern Australia, identified the dominant attributes of these precipitation and temperature events. The attributes are both local and remote climate drivers, amplified by global warming. Their impacts include longer, hotter warm seasons and shorter, drier wet seasons in Australia’s southern Mediterranean climate regions. In contrast, flooding has increased in coastal eastern Australia. The poleward contraction of mid-latitude westerly winds is a readily identifiable contributor. Improvements in climate models are expected to more accurately predict future phases of climate drivers. Because global warming is not uniform across the Earth’s surface, the revised definitions of climate will vary by region. In this work, we chose one clear example that supports the need for re-considering climate periods.https://www.academia.edu/125737465/Machine_learning_suggests_climate_and_seasonal_definitions_should_be_changed_under_global_warming |
| spellingShingle | Milton Speer Lance Leslie Machine learning suggests climate and seasonal definitions should change under global warming Academia Environmental Sciences and Sustainability |
| title | Machine learning suggests climate and seasonal definitions should change under global warming |
| title_full | Machine learning suggests climate and seasonal definitions should change under global warming |
| title_fullStr | Machine learning suggests climate and seasonal definitions should change under global warming |
| title_full_unstemmed | Machine learning suggests climate and seasonal definitions should change under global warming |
| title_short | Machine learning suggests climate and seasonal definitions should change under global warming |
| title_sort | machine learning suggests climate and seasonal definitions should change under global warming |
| url | https://www.academia.edu/125737465/Machine_learning_suggests_climate_and_seasonal_definitions_should_be_changed_under_global_warming |
| work_keys_str_mv | AT miltonspeer machinelearningsuggestsclimateandseasonaldefinitionsshouldchangeunderglobalwarming AT lanceleslie machinelearningsuggestsclimateandseasonaldefinitionsshouldchangeunderglobalwarming |