With the development and continuous improvements to the Global Surface Temperature Dataset (merged with global land surface air temperatures and sea surface temperatures), the consistency of estimations of global/regional surface temperature series and warming trends (including recent short-term climate change trends) has been strengthened since 1880. However, based on the 5th IPCC Assessment Report, it is believed that there are considerable uncertainties in many Essential Climate Variables (ECVs) at multiple timescales, which have been used to monitor climate change and global warming of the Earth. Therefore there is still a long way to go in the study of global climate change in both observations and uncertainty estimates.
This Research Topic aims to gather studies from meteorologists, climatologists, hydrologists, and oceanographers and will focus on observational studies of high-quality global (land, ocean, and upper atmosphere) and regional data products, with attention to climate change facts, impacts, and causes in daily to long-term scales. This is especially with regard to ECVs (Including their extreme climate events) related to global/regional warming and in light of the carbon and water cycles and radiation balance. We will focus on the evaluation of the uncertainty level of various variables at different spatial and temporal scales. In addition, the Topic also intends to encourage and promote the exchange and application of innovative methods in global/regional climate change research.
We welcome studies related to all topics stated above, and in particular, aspects such as the below:
• Quality control, homogenization, and validation of high quality climatic data sets
• Gridding, merging, and other temporal and spatial analysis of the comprehensive observations
• Climate change observation in different temporal scales and the uncertainty evaluation in land, ocean, and upper air data
• Inter-comparisons between in situ observation, remote sensing, reanalysis, and model outputs
• Observed historic global climate (including extreme climate events) change facts, impacts, causes, and drivers
• Development of algorithms, methods, or statistical models in global warming detection, fitting, and separation of the climate change components.
With the development and continuous improvements to the Global Surface Temperature Dataset (merged with global land surface air temperatures and sea surface temperatures), the consistency of estimations of global/regional surface temperature series and warming trends (including recent short-term climate change trends) has been strengthened since 1880. However, based on the 5th IPCC Assessment Report, it is believed that there are considerable uncertainties in many Essential Climate Variables (ECVs) at multiple timescales, which have been used to monitor climate change and global warming of the Earth. Therefore there is still a long way to go in the study of global climate change in both observations and uncertainty estimates.
This Research Topic aims to gather studies from meteorologists, climatologists, hydrologists, and oceanographers and will focus on observational studies of high-quality global (land, ocean, and upper atmosphere) and regional data products, with attention to climate change facts, impacts, and causes in daily to long-term scales. This is especially with regard to ECVs (Including their extreme climate events) related to global/regional warming and in light of the carbon and water cycles and radiation balance. We will focus on the evaluation of the uncertainty level of various variables at different spatial and temporal scales. In addition, the Topic also intends to encourage and promote the exchange and application of innovative methods in global/regional climate change research.
We welcome studies related to all topics stated above, and in particular, aspects such as the below:
• Quality control, homogenization, and validation of high quality climatic data sets
• Gridding, merging, and other temporal and spatial analysis of the comprehensive observations
• Climate change observation in different temporal scales and the uncertainty evaluation in land, ocean, and upper air data
• Inter-comparisons between in situ observation, remote sensing, reanalysis, and model outputs
• Observed historic global climate (including extreme climate events) change facts, impacts, causes, and drivers
• Development of algorithms, methods, or statistical models in global warming detection, fitting, and separation of the climate change components.
