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Jing Zhang
,
Xinyu Guo
,
Liang Zhao
,
Yasumasa Miyazawa
, and
Qun Sun

Abstract

Onshore and offshore currents and the associated volume transport across three isobaths (50, 100, and 200 m) over the continental shelf of the East China Sea were examined using daily reanalysis data in 1993–2012. After being averaged along the isobaths, the velocities across 100 and 50 m are onshore in the bottom layer but offshore in the surface layer. In contrast, those across the 200-m isobath are onshore in the surface and bottom layers but without a clear direction in the midlayer, suggesting a three-layer structure. The surface offshore current across the 100-m isobath mainly arises from the Taiwan Strait Current, while the surface onshore current across the 200-m isobath mainly arises from the Kuroshio, both of which converge in the area between the 100- and 200-m isobaths and flow toward the Tsushima Strait. The control of bottom Ekman dynamics on the onshore bottom currents is important at the 100-m isobath, partly important at the 200-m isobath, and slightly important at the 50-m isobath. The seasonal variations of onshore and offshore currents in the surface layers across the three isobaths are likely caused by local winds, the Taiwan Strait Current, and the Changjiang discharge, while those in midlayer across the 200-m isobath demonstrate a strong geostrophic control and can be interpreted from a traditional viewpoint on the Kuroshio intrusion over the entire water column across the shelf slope. The close connection of bottom onshore currents across the three isobaths suggests that the bottom layer is an important pathway for water exchange of shelf water and the open sea.

Full access
Jie Feng
,
Jing Zhang
,
Zoltan Toth
,
Malaquias Peña
, and
Sai Ravela

Abstract

Ensemble prediction is a widely used tool in weather forecasting. In particular, the arithmetic mean (AM) of ensemble members is used to filter out unpredictable features from a forecast. AM is a pointwise statistical concept, providing the best sample-based estimate of the expected value of any single variable. The atmosphere, however, is a multivariate system with spatially coherent features characterized with strong correlations. Disregarding such correlations, the AM of an ensemble of forecasts removes not only unpredictable noise but also flattens features whose presence is still predictable, albeit with somewhat uncertain location. As a consequence, AM destroys the structure, and reduces the amplitude and variability associated with partially predictable features. Here we explore the use of an alternative concept of central tendency for the estimation of the expected feature (instead of single values) in atmospheric systems. Features that are coherent across ensemble members are first collocated to their mean position, before the AM of the aligned members is taken. Unlike earlier definitions based on complex variational minimization (field coalescence of Ravela and generalized ensemble mean of Purser), the proposed feature-oriented mean (FM) uses simple and computationally efficient vector operations. Though FM is still not a dynamically realizable state, a preliminary evaluation of ensemble geopotential height forecasts indicates that it retains more variance than AM, without a noticeable drop in skill. Beyond ensemble forecasting, possible future applications include a wide array of climate studies where the collocation of larger-scale features of interest may yield enhanced compositing results.

Free access
Jingzhuo Wang
,
Jing Chen
,
Hanbin Zhang
,
Hua Tian
, and
Yining Shi

Abstract

Ensemble forecasting is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by the ensemble transform Kalman filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread–skill relationship, a faster ensemble spread growth rate, and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

Open access
Wei Liu
,
Shaorou Dong
,
Jing Zheng
,
Chang Liu
,
Chunlin Wang
,
Wei Shangguan
,
Yajie Zhang
, and
Yu Zhang

Abstract

In this study, we used hourly observations to investigate the cooling effect of summer rainfall on surface air temperature (Ta) in a subtropical area, Guangdong province, South China. Data were categorized step-by-step by rainfall system (convection, monsoon, and typhoon), daily rainfall amount, and relative humidity (RH) level. Moreover, the average hourly Ta variation due to solar radiation was removed from all observations before statistical analysis. The results showed that the linear relationship between hourly Ta variation and rainfall intensity did not exist. However, the cooling effect of rainfall on Ta variation was dominant. In addition, convective rainfall does cause a greater temperature drop than the other two rainfall systems. After further partitioning all samples by RH level preceding the rainfall, the relationship between hourly Ta variation and rainfall intensity became distinctive. When RH was below 70%, rainfall-induced cooling became more substantial and scaled linearly with event intensity, but when RH exceeded 70%, the rainfall cooling effect was generally restrained by the RH increase. A strong correlation between hourly Ta variation and RH level preceding the rainfall suggests the importance of RH on the rainfall cooling effect.

Open access
Lu Zhang
,
Jing Li
,
Zhongjing Jiang
,
Yueming Dong
,
Tong Ying
, and
Zhenyu Zhang

Abstract

The direct perturbation of anthropogenic aerosols on Earth’s energy balance [i.e., direct aerosol radiative forcing (DARF)] remains uncertain in climate models. These uncertainties critically depend on aerosol optical properties, primarily aerosol optical depth (AOD), single scattering albedo (SSA), and the asymmetry factor g. In this study, we investigate the intermodel spread of DARF across 14 global models within phase 6 of the Coupled Model Intercomparison Project (CMIP6), using unified radiative transfer calculation and aerosol optical parameter assumptions. The global mean DARF for clear sky in 2014 with respect to 1850 is estimated as −0.77 ± 0.52 W m−2 assuming an externally mixed state and −0.68 ± 0.53 W m−2 for an internally mixed state. We further conduct a quantitative analysis and find that globally, for the external mixing assumption, AOD is the dominant factor, whose intermodel spread results in 36% of the total DARF uncertainty. For the internal mixing assumption, SSA becomes the major factor, which also leads to 36% DARF uncertainty. The g parameter and aerosol vertical distribution combined contribute to ∼30% of the DARF uncertainty. Regionally, DARF uncertainty is typically more sensitive to SSA where the absorbing aerosol fraction is high, such as South Asia and central Africa. Substantial differences between model-averaged and observed aerosol optical parameters are still noticed, with external mixing in general yielding closer agreement with observations. Our results highlight the importance of aerosol scattering and absorption properties in DARF estimation.

Full access
Lu Zhang
,
Jing Li
,
Zhongjing Jiang
,
Yueming Dong
,
Tong Ying
, and
Zhenyu Zhang

Abstract

The direct perturbation of anthropogenic aerosols on Earth’s energy balance [i.e., direct aerosol radiative forcing (DARF)] remains uncertain in climate models. In this study, we investigate the uncertainty of DARF associated with aerosol vertical distribution, using simulation results from 14 global models within phase 6 of the Coupled Model Intercomparison Project (CMIP6). The column mass loading for each aerosol species is first normalized to the multimodel average for each model, which is called the mass-normalization process. The unified radiative transfer model and aerosol optical parameter are used, so that the differences in the calculated DARF are solely attributed to the difference in aerosol vertical profiles. The global mean DARF values in 2014 with respect to 1850 before and after mass normalization are −0.77 ± 0.52 and −0.81 ± 0.12 W m−2 respectively, assuming external mixing, which indicates that the intermodel difference in aerosol vertical distribution accounts for ∼20% of the total DARF uncertainty. We further conduct two separate experiments by normalizing aerosol optical depth (AOD) and aerosol single scattering albedo (SSA) profiles, respectively, and find that the vertical distribution of SSA results in larger DARF uncertainty (0.17 W m−2) than that of AOD (0.10 W m−2). Finally, compared with CALIPSO observation, CMIP6 models tend to produce higher aerosol layers. The bias in modeled aerosol profile with respect to CALIPSO leads to stronger DARF, especially for land regions.

Full access
Jing Zhang
,
Fuhong Liu
,
Wei Tao
,
Jeremy Krieger
,
Martha Shulski
, and
Xiangdong Zhang

Abstract

The detailed mesoscale climatology of surface winds in the Chukchi–Beaufort Seas and adjacent Arctic Slope region is analyzed using the recently developed Chukchi–Beaufort High-Resolution Atmospheric Reanalysis (CBHAR). Within the study area, surface winds are mainly driven by the prevailing synoptic weather patterns of the Beaufort high and Aleutian low and are further modulated by local geographic features through thermodynamic and dynamic processes. Sea breezes, up- or downslope winds, and the mountain barrier jets are all clearly captured by CBHAR. Sea breezes emerge in June–September and last most of the day, with a maximum spatial extent 100 km inland and 50 km offshore and maximum speed around 1–3 m s−1 in the late afternoon [~1500 Alaska standard time (AKST)]. Thermodynamic impacts of mountains on the surface winds vary from time to time. Drainage flows begin to build at the mountaintop in September and reach the strongest during November–February, occupying the entire slope. Upslope winds demonstrate a clear diurnal cycle during summer, starting to build around 0900 local time, reaching the maximum strength around 1500 local time and continuing until 2100 local time. The mountain barrier jets (MBJs) are found to be most active around the Chukotka Mountains during cold seasons. Both sea breezes and MBJs are also subject to variations and changes in response to adjusted large-scale atmosphere circulation. Storm activities can inhibit the development of sea breezes. Different responses from the Beaufort high and Aleutian low to anomalies in large-scale circulations play a vital role in the variations of MBJ activities over the Chukotka Mountains.

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Xiangdong Zhang
,
John E. Walsh
,
Jing Zhang
,
Uma S. Bhatt
, and
Moto Ikeda

Abstract

Arctic cyclone activity is investigated in the context of climate change and variability by using a modified automated cyclone identification and tracking algorithm, which differs from previously used algorithms by single counting each cyclone. The investigation extends earlier studies by lengthening the time period to 55 yr (1948– 2002) with a 6-hourly time resolution, by documenting the seasonality and the dominant temporal modes of variability of cyclone activity, and by diagnosing regional activity as quantified by the cyclone activity index (CAI). The CAI integrates information on cyclone intensity, frequency, and duration into a comprehensive index of cyclone activity. Arctic cyclone activity has increased during the second half of the twentieth century, while midlatitude activity generally decreased from 1960 to the early 1990s, in agreement with previous studies. New findings include the following. 1) The number and intensity of cyclones entering the Arctic from the midlatitudes has increased, suggesting a shift of storm tracks into the Arctic, particularly in summer. 2) Positive tendencies of midlatitude cyclone activity before and after the 1960–93 period of decreasing activity correlate most strongly with variations of cyclone activity in the North Atlantic and Eurasian sectors. 3) Synchronized phase and amplitude variations in cyclone activity over the Arctic Ocean (70°–90°N) and the Arctic marginal zone (60°– 70°N) play a critical role in determining the variations of cyclone activity in the Arctic as a whole. 4) Arctic cyclone activity displays significant low-frequency variability, with a negative phase in the 1960s and a positive phase in the 1990s, upon which 7.8- and 4.1-yr oscillations are superimposed. The 7.8-yr signal generally corresponds to the alternation of the cyclonic and anticyclonic regimes of the Arctic sea ice and ocean motions.

Full access
Jing Zhang
,
Jie Feng
,
Hong Li
,
Yuejian Zhu
,
Xiefei Zhi
, and
Feng Zhang

Abstract

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged. We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Open access
Hua Zhang
,
Haibo Wang
,
Yangang Liu
,
Xianwen Jing
, and
Yi Liu

Abstract

Cloud albedo is expected to influence cloud radiative forcing in addition to cloud fraction, and inadequate description of the cloud overlapping effects on the cloud fraction may influence the simulated cloud fraction, and thus the relative cloud radiative forcing (RCRF) and cloud albedo. In this study, we first present a new formula by extending that presented previously to consider multilayer clouds directly in the relationship between cloud albedo, cloud fraction, and RCRF, and then quantitatively evaluate the effects of different cloud vertical overlapping structures, represented by the decorrelation length scales (L cf), on the simulated cloud albedos. We use the BCC_AGCM2.0_CUACE/Aero model with simultaneous validation by observations from the Clouds and the Earth’s Radiation Energy System (CERES) satellite. When L cf < 4 km (i.e., the cloud overlap is closer to the random overlap), the simulated cloud albedos are generally in good agreement with the satellite-based albedos for December–February and June–August; when L cf ≥ 4 km (i.e., the cloud vertical overlap is closer to the maximum overlap), the difference between simulated and observed cloud albedos became larger, due mainly to significant differences in cloud fractions and RCRF. Further quantitative analysis shows that the relative Euclidean distance, which represents the degree of overall model–observation disagreement, increases with the L cf for all three variables (cloud albedo, cloud fraction, and RCRF), indicating the importance of cloud vertical overlapping in determining the accuracy of the calculated cloud albedo for multilayer clouds.

Significance Statement

The purpose of this study is presenting a new formula to consider multilayer clouds directly in the relationship between cloud albedo, cloud fraction, and relative cloud radiative forcing (RCRF). This is important because the effects of different cloud vertical overlapping structures, represented by the decorrelation length scales (L cf), can affect the simulated cloud albedos. Our results provide a guide on the importance of cloud vertical overlapping in determining the accuracy of the calculated cloud albedo for multilayer clouds.

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