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ABSTRACT
The North American monsoon generates heavy rainfall across the southwestern United States between July and September, delivering beneficial moisture to the region and creating hazards that affect public and personal safety. The monsoon thus has the rapt attention of the public and science community, providing an opportunity to improve weather and climate literacy and public engagement in science. Engaging the public to forecast weather and climate phenomenon through contests offers an innovative way to reach diverse audiences and increase weather and climate literacy. We describe a “Monsoon Fantasy Forecasting” game conducted in 2021 with approximately 300 participants. The game that engaged the public in the forecasting of monthly rainfall at cities in Arizona, New Mexico, and Texas. We report on the game’s interactive design, results, and feedback. We show that the game attracted a diverse audience who was not the typical weather and climate enthusiast, and we provide suggestive results that the game may have influenced the players information-seeking behaviors. We argue that activities that provoke people to observe and think routinely about climate can help educate and build awareness about weather and climate issues.
ABSTRACT
The North American monsoon generates heavy rainfall across the southwestern United States between July and September, delivering beneficial moisture to the region and creating hazards that affect public and personal safety. The monsoon thus has the rapt attention of the public and science community, providing an opportunity to improve weather and climate literacy and public engagement in science. Engaging the public to forecast weather and climate phenomenon through contests offers an innovative way to reach diverse audiences and increase weather and climate literacy. We describe a “Monsoon Fantasy Forecasting” game conducted in 2021 with approximately 300 participants. The game that engaged the public in the forecasting of monthly rainfall at cities in Arizona, New Mexico, and Texas. We report on the game’s interactive design, results, and feedback. We show that the game attracted a diverse audience who was not the typical weather and climate enthusiast, and we provide suggestive results that the game may have influenced the players information-seeking behaviors. We argue that activities that provoke people to observe and think routinely about climate can help educate and build awareness about weather and climate issues.
Abstract
There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.
Abstract
There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.
Abstract
As the private sector becomes increasingly aware of the risks associated with climate change, climatologists have been engaging with companies to assess climate change risks and opportunities. Here, we outline how we have collaborated with a Fortune 500 company to assess the physical risks of climate change to their facilities. We provide a template for a climate change report card that we generated for >100 facilities globally. This report card is designed to communicate risk to company leadership and local facility managers. We believe that by sharing our experiences, climate scientists will be able to quantify and communicate climate risk more effectively to the private sector.
Abstract
As the private sector becomes increasingly aware of the risks associated with climate change, climatologists have been engaging with companies to assess climate change risks and opportunities. Here, we outline how we have collaborated with a Fortune 500 company to assess the physical risks of climate change to their facilities. We provide a template for a climate change report card that we generated for >100 facilities globally. This report card is designed to communicate risk to company leadership and local facility managers. We believe that by sharing our experiences, climate scientists will be able to quantify and communicate climate risk more effectively to the private sector.
Abstract
The predictability of the weather on Mount Everest’s upper slopes can be a matter of life or death for those trying to climb the world’s highest mountain, yet the performance of forecasts has been almost unknown due to a lack of surface observations. The extent to which climate change may be affecting this iconic location is also uncertain for the same reason. To address this data limitation, the National Geographic and Rolex Perpetual Planet Expedition installed the world’s highest weather station network (reaching within 420 m of the summit) on the Nepal side of Mount Everest in 2019. Its observations have already generated considerable advances in understanding the meteorological environment on the mountain’s upper slopes, but the network was compromised by damage to the highest stations in recent years. Here, we describe the expedition that upgraded the network and took it to new heights, focusing on the installation at the Bishop Rock (8,810 m MSL), just below the summit. Almost 70 years after Everest was first climbed successfully, we can now provide open access data to illuminate conditions at Earth’s highest climate frontier.
Abstract
The predictability of the weather on Mount Everest’s upper slopes can be a matter of life or death for those trying to climb the world’s highest mountain, yet the performance of forecasts has been almost unknown due to a lack of surface observations. The extent to which climate change may be affecting this iconic location is also uncertain for the same reason. To address this data limitation, the National Geographic and Rolex Perpetual Planet Expedition installed the world’s highest weather station network (reaching within 420 m of the summit) on the Nepal side of Mount Everest in 2019. Its observations have already generated considerable advances in understanding the meteorological environment on the mountain’s upper slopes, but the network was compromised by damage to the highest stations in recent years. Here, we describe the expedition that upgraded the network and took it to new heights, focusing on the installation at the Bishop Rock (8,810 m MSL), just below the summit. Almost 70 years after Everest was first climbed successfully, we can now provide open access data to illuminate conditions at Earth’s highest climate frontier.
Abstract
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
Abstract
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
Abstract
The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.
Abstract
The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.
Abstract
The accurate interpretation of hurricane risk graphics is expected to benefit public decision-making. To investigate public interpretation and suggest improvements to graphical designs, an interdisciplinary, mixed-methods approach is being undertaken. Drawing on a series of focus groups with Miami residents that focused on understanding interpretations of the National Hurricane Center’s (NHC) track forecast cone or “Cone of Uncertainty,” we developed an online survey targeting a much larger sample of Florida residents (n = 2,847). The findings from this survey are the primary focus of this short article. We attempt to answer three questions: 1) What are the most frequent and trusted sources of information that Florida residents use when they learn that a hurricane is coming their way? 2) How accurately are Florida residents able to interpret risk based on the NHC Cone of Uncertainty graphic? 3) What is the relationship, if any, between the number of correct interpretations and income, age, education, housing location, housing type, or “most trusted” sources of information? Unlike previous public surveys that focused more on evacuation decisions, forecast usage, and perception of hurricane risk, our approach specifically pays attention to the details of design elements of the forecast graphics with the long-term goal of minimizing misinterpretation of future graphics. Our analysis suggests that many residents have difficulty interpreting several aspects, suggesting a rethink on how to graphically communicate aspects such as uncertainty, the size of the storm, areas of likely damage, watches and warnings, and wind intensity categories. Graphical communication strategies need to be revised to better support the different ways in which people understand forecast products, and these strategies should be tested for validity in real world settings.
Abstract
The accurate interpretation of hurricane risk graphics is expected to benefit public decision-making. To investigate public interpretation and suggest improvements to graphical designs, an interdisciplinary, mixed-methods approach is being undertaken. Drawing on a series of focus groups with Miami residents that focused on understanding interpretations of the National Hurricane Center’s (NHC) track forecast cone or “Cone of Uncertainty,” we developed an online survey targeting a much larger sample of Florida residents (n = 2,847). The findings from this survey are the primary focus of this short article. We attempt to answer three questions: 1) What are the most frequent and trusted sources of information that Florida residents use when they learn that a hurricane is coming their way? 2) How accurately are Florida residents able to interpret risk based on the NHC Cone of Uncertainty graphic? 3) What is the relationship, if any, between the number of correct interpretations and income, age, education, housing location, housing type, or “most trusted” sources of information? Unlike previous public surveys that focused more on evacuation decisions, forecast usage, and perception of hurricane risk, our approach specifically pays attention to the details of design elements of the forecast graphics with the long-term goal of minimizing misinterpretation of future graphics. Our analysis suggests that many residents have difficulty interpreting several aspects, suggesting a rethink on how to graphically communicate aspects such as uncertainty, the size of the storm, areas of likely damage, watches and warnings, and wind intensity categories. Graphical communication strategies need to be revised to better support the different ways in which people understand forecast products, and these strategies should be tested for validity in real world settings.
Abstract
Air pollution is estimated to contribute to approximately 7 million premature deaths, of which around 4.5 million deaths are linked to ambient (outdoor) air pollution. The deaths attributed to air pollution rank the highest in the Asian region, and thus, the implementation of the stricter World Health Organization (WHO) Global Air Quality Guidelines (AQGs) released on 22 September 2021 will generate the greatest health benefits in the Asian region. Here we present some key messages and recommendations at national, regional, and global levels to promote the strategies for implementation of the ambitious WHO 2021 AQGs in the Asian region.
Abstract
Air pollution is estimated to contribute to approximately 7 million premature deaths, of which around 4.5 million deaths are linked to ambient (outdoor) air pollution. The deaths attributed to air pollution rank the highest in the Asian region, and thus, the implementation of the stricter World Health Organization (WHO) Global Air Quality Guidelines (AQGs) released on 22 September 2021 will generate the greatest health benefits in the Asian region. Here we present some key messages and recommendations at national, regional, and global levels to promote the strategies for implementation of the ambitious WHO 2021 AQGs in the Asian region.
Abstract
We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.
Abstract
We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.
Abstract
Weather, climate, and other Earth system models are growing in complexity as computing resources and technologies continue to evolve with time. Thus, models are and will remain a vital tool for scientific research. Exposure and education on the workings of such models can generate interest toward atmospheric science, and it can increase scientific literacy among the general public. Additionally, studies have suggested that early exposure to these models can affect the career trajectory of students. However, gaining exposure and experience remains difficult outside of internships, research settings, and other professional endeavors. Some of these barriers can include hardware and computing costs, curriculum structure, and access to instructors. As a means of addressing these barriers, the goal of this work is to utilize low-cost hardware and abstract away some of the complexities of running a numerical weather model without sacrificing fidelity. The approach is to create a graphical user interface (GUI) where users can quickly configure the model, run it, and analyze the output without knowledge of model configuration, system architecture, or navigation via a command line interface. The Pi-WRF application is packaged such that users can download and run the model within a matter of minutes. The application is designed to promote informal learning through hands-on experience. It is targeted toward lower secondary level students, but it can scale across grade levels, and it can be adapted for general audiences.
Abstract
Weather, climate, and other Earth system models are growing in complexity as computing resources and technologies continue to evolve with time. Thus, models are and will remain a vital tool for scientific research. Exposure and education on the workings of such models can generate interest toward atmospheric science, and it can increase scientific literacy among the general public. Additionally, studies have suggested that early exposure to these models can affect the career trajectory of students. However, gaining exposure and experience remains difficult outside of internships, research settings, and other professional endeavors. Some of these barriers can include hardware and computing costs, curriculum structure, and access to instructors. As a means of addressing these barriers, the goal of this work is to utilize low-cost hardware and abstract away some of the complexities of running a numerical weather model without sacrificing fidelity. The approach is to create a graphical user interface (GUI) where users can quickly configure the model, run it, and analyze the output without knowledge of model configuration, system architecture, or navigation via a command line interface. The Pi-WRF application is packaged such that users can download and run the model within a matter of minutes. The application is designed to promote informal learning through hands-on experience. It is targeted toward lower secondary level students, but it can scale across grade levels, and it can be adapted for general audiences.
