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- Author or Editor: Adam A. Scaife x
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Abstract
North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.
Significance Statement
The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.
Abstract
North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.
Significance Statement
The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.
Abstract
The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.
Abstract
The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.
ABSTRACT
The impacts of winter weather on transport networks have been highlighted by various high-profile disruptions to road, rail, and air transport in the United Kingdom during recent winters. Recent advances in the predictability of the winter North Atlantic Oscillation (NAO) at seasonal time scales, using the Met Office Global Seasonal forecasting system, version 5 (GloSea5), present a timely opportunity for assessing the long-range predictability of a variety of winter-weather impacts on transport. This study examines the relationships between the observed and forecast NAO and a variety of U.K. winter impacts on transport in the road, rail, and aviation sectors. The results of this preliminary study show statistically significant relationships between both observed and forecast NAO index and quantities such as road-accident numbers in certain weather conditions, weather-related delays to flights leaving London Heathrow Airport, and weather-related incidents on the railway network. This supports the feasibility of the onward goal of this work, which is to investigate prototype seasonal forecasts of the relative risk of occurrence of particular impacts in a given winter for the United Kingdom, at lead times of 1–3 months. In addition, subject to the availability of relevant impacts data, there is scope for further work to make similar assessments for other parts of Europe and North America where the NAO has a strong effect on winter climate.
ABSTRACT
The impacts of winter weather on transport networks have been highlighted by various high-profile disruptions to road, rail, and air transport in the United Kingdom during recent winters. Recent advances in the predictability of the winter North Atlantic Oscillation (NAO) at seasonal time scales, using the Met Office Global Seasonal forecasting system, version 5 (GloSea5), present a timely opportunity for assessing the long-range predictability of a variety of winter-weather impacts on transport. This study examines the relationships between the observed and forecast NAO and a variety of U.K. winter impacts on transport in the road, rail, and aviation sectors. The results of this preliminary study show statistically significant relationships between both observed and forecast NAO index and quantities such as road-accident numbers in certain weather conditions, weather-related delays to flights leaving London Heathrow Airport, and weather-related incidents on the railway network. This supports the feasibility of the onward goal of this work, which is to investigate prototype seasonal forecasts of the relative risk of occurrence of particular impacts in a given winter for the United Kingdom, at lead times of 1–3 months. In addition, subject to the availability of relevant impacts data, there is scope for further work to make similar assessments for other parts of Europe and North America where the NAO has a strong effect on winter climate.
Abstract
The skill and reliability of forecasts of winter and summer temperature, wind speed, and irradiance over China are assessed using the Met Office Global Seasonal Forecast System, version 5 (GloSea5). Skill in such forecasts is important for the future development of seasonal climate services for the energy sector, allowing better estimates of forthcoming demand and renewable electricity supply. It was found that, although overall the skill from the direct model output is patchy, some high-skill regions of interest to the energy sector can be identified. In particular, winter mean wind speed is skillfully forecast around the coast of the South China Sea, related to skillful forecasts of the El Niño–Southern Oscillation. Such information could improve seasonal estimates of offshore wind-power generation. In a similar way, forecasts of winter irradiance have good skill in eastern central China, with possible use for solar-power estimation. Skill in predicting summer temperatures, which derives from an upward trend, is shown over much of China. The region around Beijing, however, retains this skill even when detrended. This temperature skill could be helpful in managing summer energy demand. While both the strengths and limitations of the results presented here will need to be considered when developing seasonal climate services in the future, the outlook for such service development in China is promising.
Abstract
The skill and reliability of forecasts of winter and summer temperature, wind speed, and irradiance over China are assessed using the Met Office Global Seasonal Forecast System, version 5 (GloSea5). Skill in such forecasts is important for the future development of seasonal climate services for the energy sector, allowing better estimates of forthcoming demand and renewable electricity supply. It was found that, although overall the skill from the direct model output is patchy, some high-skill regions of interest to the energy sector can be identified. In particular, winter mean wind speed is skillfully forecast around the coast of the South China Sea, related to skillful forecasts of the El Niño–Southern Oscillation. Such information could improve seasonal estimates of offshore wind-power generation. In a similar way, forecasts of winter irradiance have good skill in eastern central China, with possible use for solar-power estimation. Skill in predicting summer temperatures, which derives from an upward trend, is shown over much of China. The region around Beijing, however, retains this skill even when detrended. This temperature skill could be helpful in managing summer energy demand. While both the strengths and limitations of the results presented here will need to be considered when developing seasonal climate services in the future, the outlook for such service development in China is promising.
