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Xindong Peng, Feng Xiao, Takashi Yabe, and Keiji Tani

Abstract

A semi-Lagrangian-type advection scheme, cubic-interpolated pseudoparticle (CIP) method is implemented to the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5, version 3.4). A dimensional splitting CIP algorithm, with spatial third-order and temporal second-order accuracy, is derived to compute the advection in the MM5. The modified model is evaluated with ideal tests and real case studies in comparing with the leapfrog scheme, which was originally employed in the MM5. The CIP method appears remarkably superior to the leapfrog scheme in respect to both dissipative and dispersive errors, especially when discontinuities or large gradients exist in the advected quantity. Two real cases of severe mesoscale phenomena were simulated by using both the CIP scheme and the leapfrog scheme. In the advection dominant regions, the CIP shows remarkable advantages in capturing the detail structures of the predicted field. As computations with high resolution become more and more popular in experimental and/or operational modeling, implementing more accurate advection in numerical models, as is done in the present study, will be increasingly demanded.

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Yuzhang Che, Chungang Chen, Feng Xiao, Xingliang Li, and Xueshun Shen

Abstract

A new multimoment global shallow-water model on the cubed sphere is proposed by adopting a two-stage fourth-order Runge–Kutta time integration. Through calculating the values of predicted variables at half time step t = t n + (1/2)Δt by a second-order formulation, a fourth-order scheme can be derived using only two stages within one time step. This time integration method is implemented in our multimoment global shallow-water model to build and validate a new and more efficient numerical integration framework for dynamical cores. As the key task, the numerical formulation for evaluating the derivatives in time has been developed through the Cauchy–Kowalewski procedure and the spatial discretization of the multimoment finite-volume method, which ensures fourth-order accuracy in both time and space. Several major benchmark tests are used to verify the proposed numerical framework in comparison with the existing four-stage fourth-order Runge–Kutta method, which is based on the method of lines framework. The two-stage fourth-order scheme saves about 30% of the computational cost in comparison with the four-stage Runge–Kutta scheme for global advection and shallow-water models. The proposed two-stage fourth-order framework offers a new option to develop high-performance time marching strategy of practical significance in dynamical cores for atmospheric and oceanic models.

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Jian-Sheng Ye, Yan-Hong Gong, Feng Zhang, Jiao Ren, Xiao-Ke Bai, and Yang Zheng

Abstract

Intensifying climate extremes are one of the major concerns with climate change. Using 100-yr (1911–2010) daily temperature and precipitation records worldwide, 28 indices of extreme temperature and precipitation are calculated. A similarity percentage analysis is used to identify the key indices for distinguishing how extreme warm and cold years (annual temperature above the 90th and below the 10th percentile of the 100-yr distribution, respectively) differ from one another and from average years, and how extreme wet and dry years (annual precipitation above the 90th and below the 10th percentile of the 100-yr distribution, respectively) differ from each other and from average years. The analysis suggests that extreme warm years are primarily distinguished from average and extreme cold years by higher occurrence of warm nights (annual counts when night temperature >90th percentile), which occur about six more counts in extreme warm years compared with average years. Extreme wet years are mainly distinguished from average and extreme dry years by more occurrences of heavy precipitation events (events with ≥10 mm and ≥20 mm precipitation). Compared with average years, heavy events occur 60% more in extreme wet years and 50% less in extreme dry years. These indices consistently differ between extreme and average years across terrestrial ecoregions globally. These key indices need to be considered when analyzing climate model projections and designing climate change experiments that focus on ecosystem response to climate extremes.

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David M. W. Pritchard, Nathan Forsythe, Hayley J. Fowler, Greg M. O’Donnell, and Xiao-Feng Li

Abstract

Data paucity is a severe barrier to the characterization of Himalayan near-surface climates. Regional climate modeling can help to fill this gap, but the resulting data products need critical evaluation before use. This study aims to extend the appraisal of one such dataset, the High Asia Refined Analysis (HAR). Focusing on the upper Indus basin (UIB), the climatologies of variables needed for process-based hydrological and cryospheric modeling are evaluated, leading to three main conclusions. First, precipitation in the 10-km HAR product shows reasonable correspondence with most in situ measurements. It is also generally consistent with observed runoff, while additionally reproducing the UIB’s strong vertical precipitation gradients. Second, the HAR shows seasonally varying bias patterns. A cold bias in temperature peaks in spring but reduces in summer, at which time the high bias in relative humidity diminishes. These patterns are concurrent with summer overestimation (underestimation) of incoming shortwave (longwave) radiation. Finally, these seasonally varying biases are partly related to deficiencies in cloud, snow, and albedo representations. In particular, insufficient cloud cover in summer leads to the overestimation of incoming shortwave radiation. This contributes to the reduced cold bias in summer by enhancing surface warming. A persistent high bias in albedo also plays a critical role, particularly by suppressing surface heating in spring. Improving representations of cloud, snow cover, and albedo, and thus their coupling with seasonal climate transitions, would therefore help build upon the considerable potential shown by the HAR to fill a vital data gap in this immensely important basin.

Open access
Chang-Rong Liang, Xiao-Dong Shang, Yong-Feng Qi, Gui-Ying Chen, and Ling-Hui Yu

Abstract

Finescale parameterizations are of great importance to explore the turbulent mixing in the open ocean due to the difficulty of microstructure measurements. Studies based on finescale parameterizations have greatly aided our knowledge of the turbulent mixing in the open ocean. In this study, we introduce a modified finescale parameterization (MMG) based on shear/strain variance ratio R ω and compare it with three existing parameterizations, namely, the MacKinnon–Gregg (MG) parameterization, the Gregg–Henyey–Polzin (GHP) parameterization based on shear and strain variances, and the GHP parameterization based on strain variance. The result indicates that the prediction of MG parameterization is the best, followed by the MMG parameterization, then the shear-and-strain-based GHP parameterization, and finally the strain-based GHP parameterization. The strain-based GHP parameterization is less effective than the shear-and-strain-based GHP parameterization, which is mainly due to its excessive dependence on stratification. The predictions of the strain-based MMG parameterization can be comparable to that of the MG parameterization and better than that of the shear-and-strain-based GHP parameterization. Most importantly, MMG parameterization is even effective over rough topography where the GHP parameterization fails. This modified MMG parameterization with prescribed R ω can be applied to extensive CTD data. It would be a useful tool for researchers to explore the turbulent mixing in the open ocean.

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Elizabeth Lewis, Hayley Fowler, Lisa Alexander, Robert Dunn, Fergus McClean, Renaud Barbero, Selma Guerreiro, Xiao-Feng Li, and Stephen Blenkinsop

Abstract

Extreme short-duration rainfall can cause devastating flooding that puts lives, infrastructure, and natural ecosystems at risk. It is therefore essential to understand how this type of extreme rainfall will change in a warmer world. A significant barrier to answering this question is the lack of sub-daily rainfall data available at the global scale. To this end, a global sub-daily rainfall dataset based on gauged observations has been collated. The dataset is highly variable in its spatial coverage, record length, completeness and, in its raw form, quality. This presents significant difficulties for many types of analyses. The dataset currently comprises 23 687 gauges with an average record length of 13 years. Apart from a few exceptions, the earliest records begin in the 1950s. The Global Sub-Daily Rainfall Dataset (GSDR) has wide applications, including improving our understanding of the nature and drivers of sub-daily rainfall extremes, improving and validating of high-resolution climate models, and developing a high-resolution gridded sub-daily rainfall dataset of indices.

Open access