Adam Lea - Academia.edu (original) (raw)
Papers by Adam Lea
A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions a... more A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August-September ENSO has a strong influence
intense hurricane landfalls on the U.S. and an estimated total damage bill approaching US $ 200bn... more intense hurricane landfalls on the U.S. and an estimated total damage bill approaching US $ 200bn rank as the most active and damaging consecutive hurricane years
hurricane year. Basin activity was at the 39th percentile and US landfalling activity at the 29th... more hurricane year. Basin activity was at the 39th percentile and US landfalling activity at the 29th percentile of years between 1950 and 2005. The TSR forecasts were unsuccessful this year, predicting that activity would be in the upper tercile historically. The poor forecasts appear to be due to the suppressing effect of dry air and Saharan dust in August and to the unexpected and rapid onset of El Niño conditions in September. The Tropical Storm Risk (TSR) consortium presents a validation of their seasonal probabilistic and deterministic forecasts for North Atlantic hurricane activity in 2006. These forecasts were issued monthly from the 6th December 2005 to the 4th August 2006. They include separate predictions for tropical storms, hurricanes, intense hurricanes and the ACE (Accumulated Cyclone Energy) index, each given for the following regions: North Atlantic basin, tropical North Atlantic, US landfalling and Caribbean Lesser Antilles landfalling. All forecasts greatly overpredic...
A year with near-average activity as defined by the basin ACE (Accumulated Cyclone Energy) index ... more A year with near-average activity as defined by the basin ACE (Accumulated Cyclone Energy) index but where the numbers of intense typhoons were in the upper tercile and the number of tropical storms were in the lower tercile. The TSR probabilistic forecasts with the exception of the early July forecast all correctly predicted the near-average ACE index activity. The TSR deterministic forecasts performed well for the ACE index but over-predicted the number of tropical storms and typhoons. The Tropical Storm Risk (TSR) consortium presents a validation of their seasonal probabilistic and deterministic forecasts for the NW Pacific basin ACE index, and deterministic forecasts for the numbers of intense typhoons, typhoons and tropical storms in 2006. These forecasts were
Hurricanes rank historically above earthquakes and floods as the major geophysical cause of prope... more Hurricanes rank historically above earthquakes and floods as the major geophysical cause of property damage in the United States. The annual mean damage bill and its standard deviation for hurricanes striking the continental US 1950-2002 is US $ 4.8 billion and US $ 7.7 billion respectively. Skillful seasonal prediction of US landfalling hurricane activity would benefit business, government and society by forewarning of damage and disruption. However, significant seasonal landfalling skill has not been reported to date. This contrasts with the demonstrated significant skill for the seasonal prediction of North Atlantic hurricane activity from 1 August. Here we show that seasonal US landfalling hurricane wind energy 1950-2002 is predictable from the 1 August start of the main Atlantic hurricane season with significant (p < 0.01) and useful skill. Predictability arises from a largescale pattern of North Atlantic tropospheric wind variability in July which establishes persistent ste...
An exceptionally active year with the third highest total ACE index since 1965 and four typhoon s... more An exceptionally active year with the third highest total ACE index since 1965 and four typhoon strikes on mainland Japan. The TSR probabilistic forecasts successfully predicted the above-average activity from June, the August update giving a 70% probability for the total ACE index in 2004 being in the upper tercile historically. The deterministic forecasts underpredicted the ACE index and intense typhoon numbers at all leads, but tropical storm and typhoon numbers were correctly forecast to within one standard error.
Earth and Space Science, 2020
The 2018 North Atlantic hurricane season was a destructive season with hurricanes Florence and Mi... more The 2018 North Atlantic hurricane season was a destructive season with hurricanes Florence and Michael causing significant damage in the southeastern United States. In keeping with most destructive hurricane seasons, basinwide tropical cyclone activity was above average in 2018-by~25% for named storm numbers, hurricane numbers, and Accumulated Cyclone Energy (ACE). In contrast to this above-normal activity, the August-September tropical environmental fields that explain~50% of the variance in Atlantic basin hurricane activity between 1950 and 2017 anticipated a well below-average 2018 hurricane season. The surprisingly large mismatch between the observed and replicated levels of hurricane activity in 2018 is an extreme example of the uncertainty inherent in seasonal hurricane outlooks and highlights the need for these outlooks to be issued in terms of probability of exceedance. Such probabilistic information would better clarify the uncertainty associated with hurricane outlooks to the benefit of users. With retrospective knowledge of the August-September 2018 key tropical environmental fields, the chance that the observed 2018 Atlantic hurricane activity would occur is about 5%. The reasons for the surprisingly high hurricane activity in 2018 are a hurricane outbreak in early September and, in particular, the occurrence of unusually high tropical cyclone activity in the subtropical North Atlantic. The hyperactive subtropical activity was not anticipated because contemporary statistical models of seasonal Atlantic hurricane activity lack skill in anticipating subtropical ACE compared to tropical ACE. Plain Language Summary Seasonal outlooks for North Atlantic hurricane activity contribute to the anticipation of risk for insurance companies, other weather-sensitive businesses, and local and national governments. However, the uncertainty associated with such forecasts is often unclear. This reduces their benefit and contributes to the perception of forecast "busts." The issue is highlighted by the destructive and surprising 25% above-average 2018 Atlantic hurricane season. Retrospective examination of the key August-September 2018 environmental fields that explain~50% of long-term Atlantic hurricane activity shows that all were consistent with a well below-average 2018 hurricane season. A below-normal season was also anticipated by hurricane outlooks issued in early August 2018. The large mismatch between the observed and replicated levels of hurricane activity in 2018 is an extreme example of the uncertainty inherent in hurricane outlooks. We show that this uncertainty may be properly clarified by expressing outlooks in terms of probability of exceedance. The likelihood that the observed 2018 hurricane activity would occur with retrospective knowledge of the key environmental fields is about 5%. Hyperactive storm activity in the subtropical North Atlantic contributed to the surprisingly active 2018 hurricane season. The unusual subtropical activity was not anticipated because statistical models lack skill in anticipating such activity.
American Journal of Physical Anthropology
Objectives: Human populations exhibit substantial geographical variability in body size and shape... more Objectives: Human populations exhibit substantial geographical variability in body size and shape. However, the ecological stresses underlying this morphological variability remain poorly understood. The prevailing evolutionary explanation, "Bergmann's rule" assumes that morphological variability represents an adaptive response to average thermal conditions. We hypothesized that other climate factors-annual average precipitation , a marker of ecological productivity and inter-annual temperature volatility, a marker of infectious disease spikes-may also contribute to variability in body composition. Materials and Methods: We explored this hypothesis by examining associations between these climate factors and geographic variability in body composition across 133 male and 105 female populations from nonindustrialized settings. We used monthly climate data over 113 years (1901-2013) to develop new climate indices for all worldwide land areas. We stratified our analyses by hot/cold setting (>/<20 C). Results: In hot environments, lean mass increased as predicted in association with ecological productivity, and decreased in association with ecological volatility. Conversely , levels of body fat increased in association with temperature volatility and precipitation. However, in cold settings, equivalent associations were only partially consistent with our hypotheses, and there was suggestive evidence of sex differences in these associations. Discussion: Beyond associations with mean annual temperature predicted by Ber-gmann's rule, variability in human body composition is also associated with mean annual temperature and inter-annual temperature volatility, with these associations further differing between hot and cold settings. Collectively, our results suggest that associations of human body composition with climate are complex for both physique (fat-free mass) and energy stores (adiposity).
Journal of Geophysical Research: Atmospheres
Statistical models can replicate annual North Atlantic hurricane activity from large-scale enviro... more Statistical models can replicate annual North Atlantic hurricane activity from large-scale environmental field data for August and September, the months of peak hurricane activity. We assess how well the six environmental fields used most often in contemporary statistical modeling of seasonal hurricane activity replicate North Atlantic hurricane numbers and Accumulated Cyclone Energy (ACE) over the 135 year period from 1878 to 2012. We find that these fields replicate historical hurricane activity surprisingly well, showing that contemporary statistical models and their seasonal physical links have long-term robustness. We find that August-September zonal trade wind speed over the Caribbean Sea and the tropical North Atlantic is the environmental field which individually replicates long-term hurricane activity the best and that trade wind speed combined with the difference in sea surface temperature between the tropical Atlantic and the tropical mean is the best multi-predictor model. Comparing the performance of the best single-predictor and best multi-predictor models shows that they exhibit little difference in hindcast skill for predicting long-term ACE but that the best multipredictor model offers improved skill for predicting long-term hurricane numbers. We examine whether replicated real-time prediction skill 1983-2012 increases as the model training period lengthens and find evidence that this happens slowly. We identify a dropout in hurricane replication centered on the 1940s and show that this is likely due to a decrease in data quality which affects all data sets but Atlantic sea surface temperatures in particular. Finally, we offer insights on the implications of our findings for seasonal hurricane prediction. Plain Language Summary Many universities, government agencies and private companies issue seasonal outlooks for North Atlantic hurricane activity. However, the longer-term historical robustness of these models and their skill is unknown. Clarity on this matter is desirable because current seasonal hurricane outlooks are built on data which extend back, at best, only to the 1950s, and because predictors which are identified from data which span only a few decades can sometimes later fail. Here we assess how well annual North Atlantic hurricane activity is replicated over an extended 135-year period from 1878 to 2012; this by using statistical models and the large-scale environmental fields used most often in contemporary statistical modeling of seasonal hurricane activity. We find that these environmental fields replicate historical hurricane activity surprisingly well, showing that contemporary statistical models and their seasonal physical links have long-term robustness. We find that trade wind speed over the Caribbean Sea and the tropical North Atlantic is the environmental field which individually replicates long-term hurricane activity the best. We identify a dropout in hurricane replication centered on the 1940s and show that this is likely due to a decrease in data quality which affects all data sets but Atlantic sea surface temperatures in particular.
Nature International Weekly Journal of Science, 2005
Seasonal forecasts of tropical storm activity in the North Atlantic have focused on predicting th... more Seasonal forecasts of tropical storm activity in the North Atlantic have focused on predicting the individual numbers of tropical storms, hurricanes and intense hurricanes; these numbers being indicative of the basin's overall seasonal activity. Recently NOAA have introduced the Accumulated Cyclone Energy (ACE) index as a measure which is arguably more appropriate for representing the overall wind energy and thus
A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions a... more A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August-September ENSO has a strong influence
intense hurricane landfalls on the U.S. and an estimated total damage bill approaching US $ 200bn... more intense hurricane landfalls on the U.S. and an estimated total damage bill approaching US $ 200bn rank as the most active and damaging consecutive hurricane years
hurricane year. Basin activity was at the 39th percentile and US landfalling activity at the 29th... more hurricane year. Basin activity was at the 39th percentile and US landfalling activity at the 29th percentile of years between 1950 and 2005. The TSR forecasts were unsuccessful this year, predicting that activity would be in the upper tercile historically. The poor forecasts appear to be due to the suppressing effect of dry air and Saharan dust in August and to the unexpected and rapid onset of El Niño conditions in September. The Tropical Storm Risk (TSR) consortium presents a validation of their seasonal probabilistic and deterministic forecasts for North Atlantic hurricane activity in 2006. These forecasts were issued monthly from the 6th December 2005 to the 4th August 2006. They include separate predictions for tropical storms, hurricanes, intense hurricanes and the ACE (Accumulated Cyclone Energy) index, each given for the following regions: North Atlantic basin, tropical North Atlantic, US landfalling and Caribbean Lesser Antilles landfalling. All forecasts greatly overpredic...
A year with near-average activity as defined by the basin ACE (Accumulated Cyclone Energy) index ... more A year with near-average activity as defined by the basin ACE (Accumulated Cyclone Energy) index but where the numbers of intense typhoons were in the upper tercile and the number of tropical storms were in the lower tercile. The TSR probabilistic forecasts with the exception of the early July forecast all correctly predicted the near-average ACE index activity. The TSR deterministic forecasts performed well for the ACE index but over-predicted the number of tropical storms and typhoons. The Tropical Storm Risk (TSR) consortium presents a validation of their seasonal probabilistic and deterministic forecasts for the NW Pacific basin ACE index, and deterministic forecasts for the numbers of intense typhoons, typhoons and tropical storms in 2006. These forecasts were
Hurricanes rank historically above earthquakes and floods as the major geophysical cause of prope... more Hurricanes rank historically above earthquakes and floods as the major geophysical cause of property damage in the United States. The annual mean damage bill and its standard deviation for hurricanes striking the continental US 1950-2002 is US $ 4.8 billion and US $ 7.7 billion respectively. Skillful seasonal prediction of US landfalling hurricane activity would benefit business, government and society by forewarning of damage and disruption. However, significant seasonal landfalling skill has not been reported to date. This contrasts with the demonstrated significant skill for the seasonal prediction of North Atlantic hurricane activity from 1 August. Here we show that seasonal US landfalling hurricane wind energy 1950-2002 is predictable from the 1 August start of the main Atlantic hurricane season with significant (p < 0.01) and useful skill. Predictability arises from a largescale pattern of North Atlantic tropospheric wind variability in July which establishes persistent ste...
An exceptionally active year with the third highest total ACE index since 1965 and four typhoon s... more An exceptionally active year with the third highest total ACE index since 1965 and four typhoon strikes on mainland Japan. The TSR probabilistic forecasts successfully predicted the above-average activity from June, the August update giving a 70% probability for the total ACE index in 2004 being in the upper tercile historically. The deterministic forecasts underpredicted the ACE index and intense typhoon numbers at all leads, but tropical storm and typhoon numbers were correctly forecast to within one standard error.
Earth and Space Science, 2020
The 2018 North Atlantic hurricane season was a destructive season with hurricanes Florence and Mi... more The 2018 North Atlantic hurricane season was a destructive season with hurricanes Florence and Michael causing significant damage in the southeastern United States. In keeping with most destructive hurricane seasons, basinwide tropical cyclone activity was above average in 2018-by~25% for named storm numbers, hurricane numbers, and Accumulated Cyclone Energy (ACE). In contrast to this above-normal activity, the August-September tropical environmental fields that explain~50% of the variance in Atlantic basin hurricane activity between 1950 and 2017 anticipated a well below-average 2018 hurricane season. The surprisingly large mismatch between the observed and replicated levels of hurricane activity in 2018 is an extreme example of the uncertainty inherent in seasonal hurricane outlooks and highlights the need for these outlooks to be issued in terms of probability of exceedance. Such probabilistic information would better clarify the uncertainty associated with hurricane outlooks to the benefit of users. With retrospective knowledge of the August-September 2018 key tropical environmental fields, the chance that the observed 2018 Atlantic hurricane activity would occur is about 5%. The reasons for the surprisingly high hurricane activity in 2018 are a hurricane outbreak in early September and, in particular, the occurrence of unusually high tropical cyclone activity in the subtropical North Atlantic. The hyperactive subtropical activity was not anticipated because contemporary statistical models of seasonal Atlantic hurricane activity lack skill in anticipating subtropical ACE compared to tropical ACE. Plain Language Summary Seasonal outlooks for North Atlantic hurricane activity contribute to the anticipation of risk for insurance companies, other weather-sensitive businesses, and local and national governments. However, the uncertainty associated with such forecasts is often unclear. This reduces their benefit and contributes to the perception of forecast "busts." The issue is highlighted by the destructive and surprising 25% above-average 2018 Atlantic hurricane season. Retrospective examination of the key August-September 2018 environmental fields that explain~50% of long-term Atlantic hurricane activity shows that all were consistent with a well below-average 2018 hurricane season. A below-normal season was also anticipated by hurricane outlooks issued in early August 2018. The large mismatch between the observed and replicated levels of hurricane activity in 2018 is an extreme example of the uncertainty inherent in hurricane outlooks. We show that this uncertainty may be properly clarified by expressing outlooks in terms of probability of exceedance. The likelihood that the observed 2018 hurricane activity would occur with retrospective knowledge of the key environmental fields is about 5%. Hyperactive storm activity in the subtropical North Atlantic contributed to the surprisingly active 2018 hurricane season. The unusual subtropical activity was not anticipated because statistical models lack skill in anticipating such activity.
American Journal of Physical Anthropology
Objectives: Human populations exhibit substantial geographical variability in body size and shape... more Objectives: Human populations exhibit substantial geographical variability in body size and shape. However, the ecological stresses underlying this morphological variability remain poorly understood. The prevailing evolutionary explanation, "Bergmann's rule" assumes that morphological variability represents an adaptive response to average thermal conditions. We hypothesized that other climate factors-annual average precipitation , a marker of ecological productivity and inter-annual temperature volatility, a marker of infectious disease spikes-may also contribute to variability in body composition. Materials and Methods: We explored this hypothesis by examining associations between these climate factors and geographic variability in body composition across 133 male and 105 female populations from nonindustrialized settings. We used monthly climate data over 113 years (1901-2013) to develop new climate indices for all worldwide land areas. We stratified our analyses by hot/cold setting (>/<20 C). Results: In hot environments, lean mass increased as predicted in association with ecological productivity, and decreased in association with ecological volatility. Conversely , levels of body fat increased in association with temperature volatility and precipitation. However, in cold settings, equivalent associations were only partially consistent with our hypotheses, and there was suggestive evidence of sex differences in these associations. Discussion: Beyond associations with mean annual temperature predicted by Ber-gmann's rule, variability in human body composition is also associated with mean annual temperature and inter-annual temperature volatility, with these associations further differing between hot and cold settings. Collectively, our results suggest that associations of human body composition with climate are complex for both physique (fat-free mass) and energy stores (adiposity).
Journal of Geophysical Research: Atmospheres
Statistical models can replicate annual North Atlantic hurricane activity from large-scale enviro... more Statistical models can replicate annual North Atlantic hurricane activity from large-scale environmental field data for August and September, the months of peak hurricane activity. We assess how well the six environmental fields used most often in contemporary statistical modeling of seasonal hurricane activity replicate North Atlantic hurricane numbers and Accumulated Cyclone Energy (ACE) over the 135 year period from 1878 to 2012. We find that these fields replicate historical hurricane activity surprisingly well, showing that contemporary statistical models and their seasonal physical links have long-term robustness. We find that August-September zonal trade wind speed over the Caribbean Sea and the tropical North Atlantic is the environmental field which individually replicates long-term hurricane activity the best and that trade wind speed combined with the difference in sea surface temperature between the tropical Atlantic and the tropical mean is the best multi-predictor model. Comparing the performance of the best single-predictor and best multi-predictor models shows that they exhibit little difference in hindcast skill for predicting long-term ACE but that the best multipredictor model offers improved skill for predicting long-term hurricane numbers. We examine whether replicated real-time prediction skill 1983-2012 increases as the model training period lengthens and find evidence that this happens slowly. We identify a dropout in hurricane replication centered on the 1940s and show that this is likely due to a decrease in data quality which affects all data sets but Atlantic sea surface temperatures in particular. Finally, we offer insights on the implications of our findings for seasonal hurricane prediction. Plain Language Summary Many universities, government agencies and private companies issue seasonal outlooks for North Atlantic hurricane activity. However, the longer-term historical robustness of these models and their skill is unknown. Clarity on this matter is desirable because current seasonal hurricane outlooks are built on data which extend back, at best, only to the 1950s, and because predictors which are identified from data which span only a few decades can sometimes later fail. Here we assess how well annual North Atlantic hurricane activity is replicated over an extended 135-year period from 1878 to 2012; this by using statistical models and the large-scale environmental fields used most often in contemporary statistical modeling of seasonal hurricane activity. We find that these environmental fields replicate historical hurricane activity surprisingly well, showing that contemporary statistical models and their seasonal physical links have long-term robustness. We find that trade wind speed over the Caribbean Sea and the tropical North Atlantic is the environmental field which individually replicates long-term hurricane activity the best. We identify a dropout in hurricane replication centered on the 1940s and show that this is likely due to a decrease in data quality which affects all data sets but Atlantic sea surface temperatures in particular.
Nature International Weekly Journal of Science, 2005
Seasonal forecasts of tropical storm activity in the North Atlantic have focused on predicting th... more Seasonal forecasts of tropical storm activity in the North Atlantic have focused on predicting the individual numbers of tropical storms, hurricanes and intense hurricanes; these numbers being indicative of the basin's overall seasonal activity. Recently NOAA have introduced the Accumulated Cyclone Energy (ACE) index as a measure which is arguably more appropriate for representing the overall wind energy and thus