The manifestations and impacts of global climate change are different in different geographical regions. Thus, regional and local information is required to assess risks and make important management decisions including adaptation actions. This regional information can be constructed or distilled from multiple lines of evidence. In order for the information constructed to be relevant and credible, the distillation process should cover a broad range of knowledge, involve a diverse group of actors (scientists, producers, and users), and engage the values and contextual knowledge of stakeholders. In addition to the values and context, knowing how the fitness for purpose guided the selection of the sources of information, facilitates decision making. It is well known that climate information by itself alone does not lead to optimal decision-making in relation to specific risks. Climate information is most likely to improve development outcomes when it is integrated with other information into a framework for decision-making in relation to specific sectors.
The generation and provision of the different kinds of data that are used to construct the information have been greatly influenced by the advancement in technology and science. For instance, high-resolution climate model simulations as well as large ensemble simulations, have relied on the advancement of High-Performance Computing while alternative techniques such as artificial intelligence and machine learning are being used to address other environmental challenges.
The provision and use of climate information for decision-making bring to bear the subject of climate services. Climate services contexts are diverse and complex. The service needs to have appropriate engagement from users and providers, be based on scientifically credible information and expertise, have an effective access mechanism, and meet the users’ needs. Predominantly, climate services are targeted at informing risk management in adaptation to climate variability and change.
The IPCC defines risk as the ‘potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with such systems.’ In the context of climate change, risks can arise from the impacts of climate change as well as from the potential human responses to climate change.
This Research Topic aims to produce a collection of articles that highlight how advancement in technology and science is influencing risk management at both regional and local scales through climate services.
The topics of interest include, but are not limited to the following:
• Methods to develop regional and local climate information.
• Innovations for analyzing and managing climate-related risks.
• Illustration of sector-specific research to understand the implications of climate change and its relationship with the sector concerned, and to improve sectoral decision making under climate uncertainty.
• Economic analysis of the value of climate services.
• Artificial Intelligence/Machine Learning, High-Performance Computing, or Quantum Computing in Environmental applications.
The manifestations and impacts of global climate change are different in different geographical regions. Thus, regional and local information is required to assess risks and make important management decisions including adaptation actions. This regional information can be constructed or distilled from multiple lines of evidence. In order for the information constructed to be relevant and credible, the distillation process should cover a broad range of knowledge, involve a diverse group of actors (scientists, producers, and users), and engage the values and contextual knowledge of stakeholders. In addition to the values and context, knowing how the fitness for purpose guided the selection of the sources of information, facilitates decision making. It is well known that climate information by itself alone does not lead to optimal decision-making in relation to specific risks. Climate information is most likely to improve development outcomes when it is integrated with other information into a framework for decision-making in relation to specific sectors.
The generation and provision of the different kinds of data that are used to construct the information have been greatly influenced by the advancement in technology and science. For instance, high-resolution climate model simulations as well as large ensemble simulations, have relied on the advancement of High-Performance Computing while alternative techniques such as artificial intelligence and machine learning are being used to address other environmental challenges.
The provision and use of climate information for decision-making bring to bear the subject of climate services. Climate services contexts are diverse and complex. The service needs to have appropriate engagement from users and providers, be based on scientifically credible information and expertise, have an effective access mechanism, and meet the users’ needs. Predominantly, climate services are targeted at informing risk management in adaptation to climate variability and change.
The IPCC defines risk as the ‘potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with such systems.’ In the context of climate change, risks can arise from the impacts of climate change as well as from the potential human responses to climate change.
This Research Topic aims to produce a collection of articles that highlight how advancement in technology and science is influencing risk management at both regional and local scales through climate services.
The topics of interest include, but are not limited to the following:
• Methods to develop regional and local climate information.
• Innovations for analyzing and managing climate-related risks.
• Illustration of sector-specific research to understand the implications of climate change and its relationship with the sector concerned, and to improve sectoral decision making under climate uncertainty.
• Economic analysis of the value of climate services.
• Artificial Intelligence/Machine Learning, High-Performance Computing, or Quantum Computing in Environmental applications.
