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- Author or Editor: Camille Risi x
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Abstract
The concept of the “Asian monsoon” masks the existence of two separate summer rainfall régimes: convective storms over India, Bangladesh, and Nepal (the South Asian monsoon) and frontal rainfall over China, Japan, and the Korean Peninsula (the East Asian monsoon). In addition, the Himalayas and other orography, including the Arakan Mountains, Ghats, and Yunnan Plateau, create smaller precipitation domains with abrupt boundaries. A mode of continental precipitation variability is identified that spans both South and East Asia during July and August. Point-to-point correlations and EOF analysis with Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of the Water Resources (APHRODITE), a 57-yr rain gauge record, show that a dipole between the Himalayan foothills (+) and the “monsoon zone” (central India, −) dominates July–August interannual variability in South Asia, and is also associated in East Asia with a tripole between the Yangtze corridor (+) and northern and southern China (−). July–August storm tracks, as shown by lag–lead correlation of rainfall, remain mostly constant between years and do not explain this mode. Instead, it is proposed that interannual change in the strength of moisture transport from the Bay of Bengal to the Yangtze corridor across the northern Yunnan Plateau induces widespread precipitation anomalies. Abundant moisture transport along this route requires both cyclonic monsoon circulation over India and a sufficiently warm Bay of Bengal, which coincide only in July and August. Preliminary results from the LMDZ version 5 (LMDZ5) model, run with a zoomed grid over Asia and circulation nudged toward the ECMWF reanalysis, support this hypothesis. Improved understanding of this coupling may help to project twenty-first-century precipitation changes in East and South Asia, home to over three billion people.
Abstract
The concept of the “Asian monsoon” masks the existence of two separate summer rainfall régimes: convective storms over India, Bangladesh, and Nepal (the South Asian monsoon) and frontal rainfall over China, Japan, and the Korean Peninsula (the East Asian monsoon). In addition, the Himalayas and other orography, including the Arakan Mountains, Ghats, and Yunnan Plateau, create smaller precipitation domains with abrupt boundaries. A mode of continental precipitation variability is identified that spans both South and East Asia during July and August. Point-to-point correlations and EOF analysis with Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of the Water Resources (APHRODITE), a 57-yr rain gauge record, show that a dipole between the Himalayan foothills (+) and the “monsoon zone” (central India, −) dominates July–August interannual variability in South Asia, and is also associated in East Asia with a tripole between the Yangtze corridor (+) and northern and southern China (−). July–August storm tracks, as shown by lag–lead correlation of rainfall, remain mostly constant between years and do not explain this mode. Instead, it is proposed that interannual change in the strength of moisture transport from the Bay of Bengal to the Yangtze corridor across the northern Yunnan Plateau induces widespread precipitation anomalies. Abundant moisture transport along this route requires both cyclonic monsoon circulation over India and a sufficiently warm Bay of Bengal, which coincide only in July and August. Preliminary results from the LMDZ version 5 (LMDZ5) model, run with a zoomed grid over Asia and circulation nudged toward the ECMWF reanalysis, support this hypothesis. Improved understanding of this coupling may help to project twenty-first-century precipitation changes in East and South Asia, home to over three billion people.
Hurricanes are lethal and costly phenomena, and it is therefore of great importance to assess the long-term risk they pose to society. Among the greatest threats are those associated with high winds and related phenomena, such as storm surges. Here we assess the probability that hurricane winds will affect any given point in space by combining an estimate of the probability that a hurricane will pass within some given radius of the point in question with an estimate of the spatial probability density of storm winds.
To assess the probability that storms will pass close enough to a point of interest to affect it, we apply two largely independent techniques for generating large numbers of synthetic hurricane tracks. The first treats each track as a Markov chain, using statistics derived from observed hurricane-track data. The second technique begins by generating a large class of synthetic, time-varying wind fields at 850 and 250 hPa whose variance, covariance, and monthly means match NCEP–NCAR reanalysis data and whose kinetic energy follows an ω −3 geostrophic turbulence spectral frequency distribution. Hurricanes are assumed to move with a weighted mean of the 850- and 250-hPa flow plus a “beta drift” correction, after originating at points determined from historical genesis data. The statistical characteristics of tracks generated by these two means are compared.
For a given point in space, many (~104) synthetic tracks are generated that pass within a specified distance of a point of interest, using both track generation methods. For each of these tracks, a deterministic, coupled, numerical simulation of the storm's intensity is carried out, using monthly mean upper-ocean and potential intensity climatologies, together with time-varying vertical wind shear generated from the synthetic time series of 850- and 250-hPa winds, as described above. For the case in which the tracks are generated using the synthetic environmental flow, the tracks and the shear are generated using the same wind fields and are therefore mutually consistent.
The track and intensity data are finally used together with a vortex structure model to construct probability distributions of wind speed at fixed points in space. These are compared to similar estimates based directly on historical hurricane data for two coastal cities.
Hurricanes are lethal and costly phenomena, and it is therefore of great importance to assess the long-term risk they pose to society. Among the greatest threats are those associated with high winds and related phenomena, such as storm surges. Here we assess the probability that hurricane winds will affect any given point in space by combining an estimate of the probability that a hurricane will pass within some given radius of the point in question with an estimate of the spatial probability density of storm winds.
To assess the probability that storms will pass close enough to a point of interest to affect it, we apply two largely independent techniques for generating large numbers of synthetic hurricane tracks. The first treats each track as a Markov chain, using statistics derived from observed hurricane-track data. The second technique begins by generating a large class of synthetic, time-varying wind fields at 850 and 250 hPa whose variance, covariance, and monthly means match NCEP–NCAR reanalysis data and whose kinetic energy follows an ω −3 geostrophic turbulence spectral frequency distribution. Hurricanes are assumed to move with a weighted mean of the 850- and 250-hPa flow plus a “beta drift” correction, after originating at points determined from historical genesis data. The statistical characteristics of tracks generated by these two means are compared.
For a given point in space, many (~104) synthetic tracks are generated that pass within a specified distance of a point of interest, using both track generation methods. For each of these tracks, a deterministic, coupled, numerical simulation of the storm's intensity is carried out, using monthly mean upper-ocean and potential intensity climatologies, together with time-varying vertical wind shear generated from the synthetic time series of 850- and 250-hPa winds, as described above. For the case in which the tracks are generated using the synthetic environmental flow, the tracks and the shear are generated using the same wind fields and are therefore mutually consistent.
The track and intensity data are finally used together with a vortex structure model to construct probability distributions of wind speed at fixed points in space. These are compared to similar estimates based directly on historical hurricane data for two coastal cities.
