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Chen Li, Jing-Jia Luo, and Shuanglin Li

Abstract

The impacts of different types of El Niño–Southern Oscillation (ENSO) on the interannual negative correlation (seesaw) between the Somali cross-equatorial flow (CEF) and the Maritime Continent (MC) CEF during boreal summer (June–August) are investigated using the ECMWF twentieth-century reanalysis (ERA-20C) dataset and numerical experiments with a global atmospheric model [the Met Office Unified Model global atmosphere, version 6 (UM-GA6)]. The results suggest that ENSO plays a prominent role in governing the CEF-seesaw relation. A high positive correlation (0.86) exists between the MC CEF and Niño-3.4 index and also in the case of eastern Pacific (EP) El Niño, central Pacific (CP) El Niño, EP La Niña, and CP La Niña events. In contrast, a negative correlation (−0.35) exists between the Somali CEF and Niño-3.4 index, and this negative relation is significant only in the EP El Niño years. Further, the variation of the MC CEF is highly correlated with the local north–south sea surface temperature (SST) gradient, while the variation of the Somali CEF displays little relation with the local SST gradient. The Somali CEF may be remotely influenced by ENSO. The model results confirm that the EP El Niño plays a major role in causing the weakened Somali CEF via modifying the Walker cell. However, the impact of the EP El Niño on the Somali CEF differs with different seasonal background. It is also found that the interannual CEF seesaw displays a multidecadal change before and after the 1950s, which is linked with the multidecadal strengthening of the intensity of the EP ENSO.

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Zhizhen Xu, Jing Chen, Zheng Jin, Hongqi Li, and Fajing Chen

Abstract

To more comprehensively and accurately address model uncertainties in the East Asia monsoon region, a single-physics suite, where each ensemble member uses the same set of physics parameterizations as the control member in combination with multiple stochastic schemes, is developed to investigate if the multistochastic schemes that combine different stochastic schemes together can be an alternative to a multiphysics suite, where each ensemble member uses a different set of physics parameterizations (e.g., cumulus convection, boundary layer, surface layer, microphysics, and shortwave and longwave radiation). For this purpose, two experiments are performed for a summer monsoon month over China: one with a multiphysics suite and the other with a single-physics suite combined with multistochastic schemes. Three stochastic schemes are applied: the stochastically perturbed parameterizations (SPP) scheme, consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes; the stochastically perturbed parameterization tendencies (SPPT) scheme; and the stochastic kinetic energy backscatter (SKEB) scheme. The combination of the three stochastic schemes is compared with the multiphysics suite in the Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System with a horizontal grid spacing of 15 km. Verification results show that, overall, a single-physics suite that combines SPP, SPPT, and SKEB outperforms the multiphysics suite in precipitation verification and verification for upper-air weather variables, 10-m zonal wind, and 2-m temperature in the East Asian monsoon region. The indication is that a single-physics suite combining SPP, SPPT, and SKEB may be an appropriate alternative to a multiphysics suite. This finding lays a foundation for the development and design of future regional and global ensembles.

Open access
Yuxiao Chen, Jing Chen, Dehui Chen, Zhizhen Xu, Jie Sheng, and Fajing Chen

Abstract

The simulated radar reflectivity used by current mesoscale numerical weather prediction models can reflect the grid precipitation but cannot reflect the subgrid precipitation generated by a cumulus parameterization scheme. To solve this problem, this study developed a new simulated radar reflectivity calculation method to obtain the new radar reflectivity corresponding to the subgrid-scale and grid-scale precipitation based on the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model of the China Meteorological Administration. Based on this new method, two 15-day forecast experiments were carried out for two different time periods (11–25 April 2019 and 1–15 August 2019), and the radar reflectivity products obtained by the new method and previous method were compared. The results show that the radar reflectivity obtained by the new simulated radar reflectivity calculation method gives a clear indication of the subgrid-scale precipitation in the model. Verification results show that the threat scores of the improved experiments are better than those of the control experiments in general and that the reliability of the simulated radar reflectivity for the indication of precipitation is improved. It is concluded that the new simulated radar reflectivity calculation method is effective and significantly improves the reflectivity products. This method has good prospects for providing more information about forecasting precipitation and convective activity in operational models.

Open access
Mei Hong, Dong Wang, Ren Zhang, Xi Chen, Jing-Jing Ge, and Dandan Yu

Abstract

Abnormal activity of the western Pacific subtropical high (WPSH) may result in extreme weather events in East Asia. However, because the relationship between the WPSH and other components of the East Asian summer monsoon (EASM) system is unknown, it is still difficult to forecast such abnormal activity. The delay-relevant method is used to study 2010 data for abnormal weather and it is concluded that the Indian monsoon latent heat flux, the Somali low-level jet, and the Tibetan high activity index can significantly affect anomalies in the WPSH in the EASM system. By combining genetic algorithms and statistical–dynamical reconstruction theory, a nonlinear statistical–dynamical model of the WPSH and these three influencing factors was objectively reconstructed from actual 2010 data and a dynamically extended forecasting experiment was carried out. To further test the forecasting performance of the reconstructed model, further experiments using data from nine abnormal WPSH years and eight normal WPSH years were performed for comparison. All the results suggest that the forecasts of the subtropical high area index, the Indian monsoon latent heat flux, the Somali low-level jet, and the Tibetan high activity index all have good performance in the short and medium terms (<25 days). Not only is the forecasting trend accurate, but the mean absolute percentage error is ≤9%. This work suggests new areas of research into the association between the WPSH and EASM systems and provides a new method for the prediction of the WPSH area index.

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Yuqing Zhang, Qinglong You, Changchun Chen, Jing Ge, and Muhammad Adnan

Abstract

Compared to traditional drought events, flash droughts evolve rapidly during short-term extreme atmospheric conditions, with a lasting period of one pentad to several weeks. There are two main categories of flash droughts: the heat wave flash drought (HWFD), which is mainly caused by persistent high temperatures (heat waves), and the precipitation deficit flash drought (PDFD), which is mainly triggered by precipitation deficits. The authors’ previous research focused on the characteristics and causes of flash drought based on meteorological observations and Variable Infiltration Capacity (VIC) model simulations in a humid subtropical basin (Gan River basin, China). In this study, the authors evaluated the downscaled phase 5 of the Coupled Model Intercomparison Project (CMIP5) models’ simulations, coupled with the VIC model (CMIP5–VIC) in reproducing flash droughts in a humid subtropical basin in China. Most downscaled CMIP5–VIC simulations can reproduce the spatial patterns of flash droughts with respect to the benchmarks. The coupled models fail to readily replicate interannual variation (interannual pentad change), but most models can reflect the interannual variability (temporal standard deviation) and long-term average pentads of flash droughts. It is difficult to simultaneously depict both the spatial and temporal features of flash droughts within only one coupled model. The climatological patterns of the best multimodel ensemble mean are close to those of the all-model ensemble mean, but the best multimodel ensemble mean has a minimal bias range and relatively low computational burden.

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Baozhang Chen, Jing M. Chen, Gang Mo, Chiu-Wai Yuen, Hank Margolis, Kaz Higuchi, and Douglas Chan

Abstract

Land surface models (LSMs) need to be coupled with atmospheric general circulation models (GCMs) to adequately simulate the exchanges of energy, water, and carbon between the atmosphere and terrestrial surfaces. The heterogeneity of the land surface and its interaction with temporally and spatially varying meteorological conditions result in nonlinear effects on fluxes of energy, water, and carbon, making it challenging to scale these fluxes accurately. The issue of up-scaling remains one of the critical unsolved problems in the parameterization of subgrid-scale fluxes in coupled LSM and GCM models.

A new distributed LSM, the Ecosystem–Atmosphere Simulation Scheme (EASS) was developed and coupled with the atmospheric Global Environmental Multiscale model (GEM) to simulate energy, water, and carbon fluxes over Canada’s landmass through the use of remote sensing and ancillary data. Two approaches (lumped case and distributed case) for handling subgrid heterogeneity were used to evaluate the effect of land-cover heterogeneity on regional flux simulations based on remote sensing. Online runs for a week in August 2003 provided an opportunity to investigate model performance and spatial scaling issues.

Comparisons of simulated results with available tower observations (five sites) across an east–west transect over Canada’s southern forest regions indicate that the model is reasonably successful in capturing both the spatial and temporal variations in carbon and energy fluxes, although there were still some biases in estimates of latent and sensible heat fluxes between the simulations and the tower observations. Moreover, the latent and sensible heat fluxes were found to be better modeled in the coupled EASS–GEM system than in the uncoupled GEM. There are marked spatial variations in simulated fluxes over Canada’s landmass. These patterns of spatial variation closely follow vegetation-cover types as well as leaf area index, both of which are highly correlated with the underlying soil types, soil moisture conditions, and soil carbon pools. The surface fluxes modeled by the two up-scaling approaches (lumped and distributed cases) differ by 5%–15% on average and by up to 15%–25% in highly heterogeneous regions. This suggests that different ways of treating subgrid land surface heterogeneities could lead to noticeable biases in model output.

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Shaobo Sun, Baozhang Chen, Quanqin Shao, Jing Chen, Jiyuan Liu, Xue-jun Zhang, Huifang Zhang, and Xiaofeng Lin

Abstract

Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for long-term applications. Here, the Community Land Model, version 4.0 (CLM4.0); Dynamic Land Model (DLM); and Variable Infiltration Capacity model (VIC) were driven with observation-based forcing datasets, and a multiple-LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial–temporal variations were analyzed for the China landmass over the period 1979–2012. Evaluations against measurements from nine flux towers at site scale and surface water budget–based ET at regional scale showed that the LSMs-ET had good performance in most areas of China’s landmass. The intercomparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were fairly consistent patterns between each dataset. The LSMs-ET produced a mean annual ET of 351.24 ± 10.7 mm yr−1 over 1979–2012, and its spatial–temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr−1 (p < 0.01), and (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr−1, while only 6.41% of the area showed significant decreasing trends, ranging from −6.28 to −0.08 mm yr−1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

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I-Han Chen, Jing-Shan Hong, Ya-Ting Tsai, and Chin-Tzu Fong

Abstract

Recently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

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Junpeng Wen, Ji Chen, Wenshi Lin, Baolin Jiang, Suishan Xu, and Jing Lan

Abstract

This study investigated heavy frontal rainfall that occurred on 13–14 October 2011 over the Pearl River Delta (PRD) in China. The frontal rainstorm was simulated using the WRF-ARW Model (version 3.3), which included its urban canopy model. Although the model-simulated convection occurred 2 h early and the second precipitation peak was underestimated, the model represented the formation, development, and extinction of the frontal rainfall and captured the distribution of the peak value. In addition, the averaged value of 49.7 W m−2 was taken as the anthropogenic heat flux (AHF) of the PRD, and two land-use datasets were adopted: one for 1992 and the other for 2011. The simulation revealed that AHF and urban land-use change (ULUC) increased the total rainfall over the PRD by 6.3% and 7.4% and increased the maximum hourly rainfall intensity by 24.6% and 21.2%, respectively. Furthermore, to elucidate the mechanism of AHF and ULUC influence, the rainstorm structure, low-level jet (LLJ), and CAPE of the rainfall event were analyzed. It was found that AHF and ULUC enhanced two strong southward LLJs located over the urban areas, which carried abundant water vapor to the PRD and generated additional upper-level CAPE. This not only sustained steady ascent of the air, but it also created conditions favorable for downward motion, resulting in large persistent convective clouds and heavy frontal rainfall.

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Jingzhuo Wang, Jing Chen, Jun Du, Yutao Zhang, Yu Xia, and Guo Deng

Abstract

This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

Open access