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Xuefeng Zhang, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han

Abstract

Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean–atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean–atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.

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Guoyu Ren, Yaqing Zhou, Ziying Chu, Jiangxing Zhou, Aiying Zhang, Jun Guo, and Xuefeng Liu

Abstract

A dataset of 282 meteorological stations including all of the ordinary and national basic/reference surface stations of north China is used to analyze the urbanization effect on surface air temperature trends. These stations are classified into rural, small city, medium city, large city, and metropolis based on the updated information of total population and specific station locations. The significance of urban warming effects on regional average temperature trends is estimated using monthly mean temperature series of the station group datasets, which undergo inhomogeneity adjustment. The authors found that the largest effect of urbanization on annual mean surface air temperature trends occurs for the large-city station group, with the urban warming being 0.16°C (10 yr)−1, and the effect is the smallest for the small-city station group with urban warming being only 0.07°C (10 yr)−1. A similar assessment is made for the dataset of national basic/reference stations, which has been widely used in regional climate change analyses in China. The results indicate that the regional average annual mean temperature series, as calculated using the data from the national basic/reference stations, is significantly impacted by urban warming, and the trend of urban warming is estimated to be 0.11°C (10 yr)−1. The contribution of urban warming to total annual mean surface air temperature change as estimated with the national basic/reference station dataset reaches 37.9%. It is therefore obvious that, in the current regional average surface air temperature series in north China, or probably in the country as a whole, there still remain large effects from urban warming. The urban warming bias for the regional average temperature anomaly series is corrected. After that, the increasing rate of the regional annual mean temperature is brought down from 0.29°C (10 yr)−1 to 0.18°C (10 yr)−1, and the total change in temperature approaches 0.72°C for the period analyzed.

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Xuefeng Zhang, Peter C. Chu, Wei Li, Chang Liu, Lianxin Zhang, Caixia Shao, Xiaoshuang Zhang, Guofang Chao, and Yuxin Zhao

Abstract

Langmuir turbulence (LT) due to the Craik–Leibovich vortex force had a clear impact on the thermal response of the ocean mixed layer to Supertyphoon Haitang (2005) east of the Luzon Strait. This impact is investigated using a 3D wave–current coupled framework consisting of the Princeton Ocean Model with the generalized coordinate system (POMgcs) and the Simulating Waves Nearshore (SWAN) wave model. The Coriolis–Stokes forcing (CSF), the Craik–Leibovich vortex forcing (CLVF), and the second-moment closure model of LT developed by Harcourt are introduced into the circulation model. The coupled system is able to reproduce the upper-ocean temperature and surface mixed layer depth reasonably well during the forced stage of the supertyphoon. The typhoon-induced “cold suction” and “heat pump” processes are significantly affected by LT. Local LT mixing strengthened the sea surface cooling by more than 0.5°C in most typhoon-affected regions. Besides LT, Lagrangian advection of temperature also modulates the SST cooling, inducing a negative (positive) SST difference in the vicinity of the typhoon center (outside of the cooling region). In addition, CLVF has the same order of magnitude as the horizontal advection in the typhoon-induced strong-vorticity region. While the geostrophy is broken down during the forced stage of Haitang, CLVF can help establish and maintain typhoon-induced quasigeostrophy during and after the typhoon. Finally, the effect of LT on the countergradient turbulent flux under the supertyphoon is discussed.

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