What is it about?
The Intergovernmental Panel on Climate Change (IPCC) has asked the scientific community to determine new scenario projections to assist in future climate change assessments. This review explored the use of satellite microwave sounder observations to monitor climate change and the uncertainties associated with these observations. The article also discusses the challenges of optimising deep learning models for precipitation models using categorical binary metrics and presents an alternative formulation for these metrics. An assessment of the historical runs of Integrated Assessment Models (IAMs) reveals that all model runs express inconsistent global warming compared to remote–sensing observations in the lower and middle troposphere, both in the tropics and globally. The study concludes with an upward bias in climate model warming responses in the tropical troposphere, which has worsened in the latest generation of climate models.
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Why is it important?
The scientific community has been tasked by the Intergovernmental Panel on Climate Change (IPCC) to develop new scenario projections for future climate change assessments. This study examined the utilisation of satellite microwave sounder data to track climate change and the associated uncertainties. Additionally, the paper addresses the difficulties in optimising deep learning models for precipitation forecasting using categorical binary metrics, proposing an alternative approach. An evaluation of historical Integrated Assessment Models (IAMs) simulations indicates that all model runs show inconsistent global warming compared to remote-sensing observations in the lower and middle troposphere, both tropically and globally. The research concludes by highlighting an increasing upward bias in climate model warming responses within the tropical troposphere, which has become more pronounced in the latest generation of climate models.
Perspectives
The Intergovernmental Panel on Climate Change (IPCC) has charged researchers with creating new scenario projections for upcoming climate change assessments. This research investigated the application of satellite microwave sounder data in monitoring climate change and its associated uncertainties. Furthermore, the paper explores the challenges of optimising deep learning models for precipitation forecasting using categorical binary metrics, suggesting an alternative method. An analysis of historical Integrated Assessment Models (IAMs) simulations reveals that all model runs exhibit inconsistent global warming compared to remote-sensing observations in the lower and middle troposphere, both in tropical regions and globally. The study concludes by noting a growing upward bias in climate model warming responses within the tropical troposphere, which has become more evident in the most recent generation of climate models.
Soumyajit Koley
Indian Institute of Technology Kanpur
Read the Original
This page is a summary of: Augmenting Efficacy of Global Climate Model Forecasts: Machine Learning Appraisal of Remote Sensing Data, International Journal of Engineering Trends and Technology, June 2024, Seventh Sense Research Group Journals,
DOI: 10.14445/22315381/ijett-v72i6p139.
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