About this Research Topic
The atmosphere plays a crucial role in maintaining the weather and climate on the planet. The climate is composed of various components like temperature, precipitation, etc. which have the utmost interest among scientists and researchers to understand its variability, comparability, and uncertainties. Atmospheric trends and climate models play a critical role in understanding and mitigating the impacts of climate change. Variations in atmospheric composition can have significant impacts on various weather patterns.
One of the biggest global challenges we face today is climate change. Climate modelling methods play a crucial role in learning more about the climate system and how its elements interact. Hence, it is important to understand the science behind the Earth’s changing climate using different climate models. To better comprehend climate change, artificial intelligence (AI) can be applied to process-based climate models to identify patterns and generate predictions based on historical data. Therefore, this may lead to improved ability to predict future atmospheric and weather patterns accurately. By using process-based climate models and incorporating AI algorithms, this Research Topic aims to gain a better understanding of complex interactions between the atmosphere, ocean, land surface and other factors that influence climate.
Topics to be covered include but are not limited to the following:
• Earth's atmospheric patterns, extreme weather events
• Latest trends in atmospheric composition, including greenhouse gas concentrations and temperature variability; different statistical approaches to understanding climate change.
• Different coupled models and their application and advancement; assessment of climate models in simulating the observed trends in atmospheric composition; analysis of metrological parameters by using different climate models.
• Strengths and limitations of current climate models in representing the observed atmospheric trends; accuracy of climate models and a better understanding of the causes and impacts of atmospheric trends.
• Role and applications of machine learning and artificial intelligence in climate/weather predictions and projection.
One of the biggest global challenges we face today is climate change. Climate modelling methods play a crucial role in learning more about the climate system and how its elements interact. Hence, it is important to understand the science behind the Earth’s changing climate using different climate models. To better comprehend climate change, artificial intelligence (AI) can be applied to process-based climate models to identify patterns and generate predictions based on historical data. Therefore, this may lead to improved ability to predict future atmospheric and weather patterns accurately. By using process-based climate models and incorporating AI algorithms, this Research Topic aims to gain a better understanding of complex interactions between the atmosphere, ocean, land surface and other factors that influence climate.
Topics to be covered include but are not limited to the following:
• Earth's atmospheric patterns, extreme weather events
• Latest trends in atmospheric composition, including greenhouse gas concentrations and temperature variability; different statistical approaches to understanding climate change.
• Different coupled models and their application and advancement; assessment of climate models in simulating the observed trends in atmospheric composition; analysis of metrological parameters by using different climate models.
• Strengths and limitations of current climate models in representing the observed atmospheric trends; accuracy of climate models and a better understanding of the causes and impacts of atmospheric trends.
• Role and applications of machine learning and artificial intelligence in climate/weather predictions and projection.
Keywords: Climate Change, Climate Model, Climate Prediction, Climate Projection, Coupled model, Extreme Events, Machine learning, Meteorological Parameters, Weather pattern
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
