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- Author or Editor: Gerald L. Potter x
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Abstract
In order to better identify and more fully understand the differences in sensitivity among climate models, two quite different models are systematically compared in terms of their seasonal response. The two-dimensional statistical dynamical model (SDM) developed at the Lawrence Livermore National Laboratory and the Oregon State University three-dimensional general circulation model (GCM) were integrated using as closely comparable boundary conditions and forcing as possible. Comparison of the seasonal anomaly (defined as the departure of the monthly zonal average from the zonal annual mean at each latitude) shows that the models agree quite well in terms of the seasonal phase and amplitude of net radiation simulated at the top of the atmosphere, the tropospheric average temperature and surface temperature and the precipitation. The models also resemble the observed seasonal anomalies of these variables to a reasonable degree, although there are significant errors in each formulation.
Abstract
In order to better identify and more fully understand the differences in sensitivity among climate models, two quite different models are systematically compared in terms of their seasonal response. The two-dimensional statistical dynamical model (SDM) developed at the Lawrence Livermore National Laboratory and the Oregon State University three-dimensional general circulation model (GCM) were integrated using as closely comparable boundary conditions and forcing as possible. Comparison of the seasonal anomaly (defined as the departure of the monthly zonal average from the zonal annual mean at each latitude) shows that the models agree quite well in terms of the seasonal phase and amplitude of net radiation simulated at the top of the atmosphere, the tropospheric average temperature and surface temperature and the precipitation. The models also resemble the observed seasonal anomalies of these variables to a reasonable degree, although there are significant errors in each formulation.
Abstract
The ability of the ECMWF model (cycle 33) to simulate the Indian and East Asian summer monsoons is evaluated at four different horizontal resolutions: T21, T42, T63, and T1O6. Generally, with respect to the large-scale features of the circulation, the largest differences among the simulations occur at T42 relative to T21. However, on regional scales, important differences among the high-frequency temporal variability serve as a further critical rest of the model's ability to simulate the monsoon.
T106 best captures both the spatial and temporal characteristics of the Indian and East Asian monsoons, whereas T42 fails to correctly simulate the sequence and development of synoptic-scale milestones that characterize the monsoon flow. In particular, T106 is superior at simulating the development and migration of the monsoon trough over the Bay of Bengal. In the T42 simulation, the development of the monsoon occurs one month earlier than typically observed. At this time the trough is incorrectly located adjacent to the east coast of India, which results in an underestimate of precipitation over the Burma-Thailand region. This early establishment of the monsoon trough affects the evolution of the East Asian monsoon and yields excessive preseason rainfall over the Mei-yu region. EOF analysis of precipitation over China indicates that T106 best simulates the Mei-yu mode of variability, which is associated with an oscillation of the rainband that gives rise to periods of enhanced rainfall over the Yangtze River valley. The coarse resolution of T21 precludes simulation of the aforementioned regional-scale monsoon flows.
Abstract
The ability of the ECMWF model (cycle 33) to simulate the Indian and East Asian summer monsoons is evaluated at four different horizontal resolutions: T21, T42, T63, and T1O6. Generally, with respect to the large-scale features of the circulation, the largest differences among the simulations occur at T42 relative to T21. However, on regional scales, important differences among the high-frequency temporal variability serve as a further critical rest of the model's ability to simulate the monsoon.
T106 best captures both the spatial and temporal characteristics of the Indian and East Asian monsoons, whereas T42 fails to correctly simulate the sequence and development of synoptic-scale milestones that characterize the monsoon flow. In particular, T106 is superior at simulating the development and migration of the monsoon trough over the Bay of Bengal. In the T42 simulation, the development of the monsoon occurs one month earlier than typically observed. At this time the trough is incorrectly located adjacent to the east coast of India, which results in an underestimate of precipitation over the Burma-Thailand region. This early establishment of the monsoon trough affects the evolution of the East Asian monsoon and yields excessive preseason rainfall over the Mei-yu region. EOF analysis of precipitation over China indicates that T106 best simulates the Mei-yu mode of variability, which is associated with an oscillation of the rainband that gives rise to periods of enhanced rainfall over the Yangtze River valley. The coarse resolution of T21 precludes simulation of the aforementioned regional-scale monsoon flows.
