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Yuna Mao, Guocan Wu, Guangzhi Xu, and Kaicun Wang

Abstract

Changes in precipitation seasonality or the distribution of precipitation have important impacts on hydrological extremes (e.g., floods or droughts). Precipitation extremes have been widely reported to increase with global warming; however, the variability and mechanism of precipitation seasonality have not been well quantified in China. Here, we explore the multiscale variability in precipitation seasonality from 1960 to 2018 in China. A seasonality index of precipitation is defined to quantify the precipitation seasonality with a lower value indicating a more even distribution throughout a year. The seasonality index increases from southeastern to northwestern China, with a decrease in the annual mean precipitation, a later timing of the wet season, and a shorter wet season duration. The seasonality index decreases from 1960 to 2018 in China, accompanied by the increasing duration of wet season, especially in northern climate-sensitive basins, such as the Northwest River, Hai River, and Songliao River basins. In the Northwest River basin, for example, the observed significant decrease in the seasonality index (~0.02 decade−1) from 1960 to 2018 is consistent with a significant decrease in the ratio of annual maximum 10-day precipitation to annual precipitation, which is confirmed by their significant positive correlation (R = 0.72; p = 0). El Niño–Southern Oscillation (ENSO) dominates interannual fluctuations and spatial patterns of precipitation seasonality in China. In El Niño years, the precipitation seasonality index decreases across China except for the Yangtze River basin, with broad increases in annual precipitation.

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Xinhua Liu, Kanghui Zhou, Yu Lan, Xu Mao, and Robert J. Trapp

Abstract

It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.

Open access
Cheng-Dong Xu, Jin-Feng Wang, Mao-Gui Hu, and Qing-Xiang Li

Abstract

A probabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty. The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971–2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.

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Jiaqing Xue, Cheng Sun, Jianping Li, Jiangyu Mao, Hisashi Nakamura, Takafumi Miyasaka, and Yidan Xu

Abstract

Global sea surface temperature (SST) evolution exhibits an antiphase variation between the two hemispheres that is referred to as the SST interhemispheric dipole (SSTID) mode. The impacts of the SSTID on extratropical atmospheric circulation in boreal winter are explored by both regression analysis and SST-forced numerical simulations. The responses of extratropical circulation to SSTID thermal forcing bear an equivalent barotropic structure. For the Southern Hemisphere (SH), positive SSTID events lead to a meridional dipolar perturbation in sea level pressure (SLP), similar in pattern to the positive southern annular mode (SAM). Although SSTID-forced SLP anomalies over the Northern Hemisphere (NH) do not exhibit a zonally symmetric pattern as is the case over the SH, they still show signs of a meridional dipole opposite to the SH over the oceans. Divergent circulation responses to SSTID forcing between the two hemispheres are suggested to be associated with contrasting storm-track variations. Positive SSTID events weaken oceanic fronts in both the North Atlantic and North Pacific, and thus lead to the decline of NH storm-track activity by decreasing atmospheric baroclinicity. In the SH, positive SSTID events correspond to the enhancement of SH transients by intensifying the Antarctic polar-frontal zone. Additionally, local baroclinic energy conversions are diagnosed to explain the SSTID-related storm-track variations over both hemispheres. Finally, an investigation of transient eddy feedback indicates that the SSTID mode modulates extratropical atmospheric circulation, primarily by regulating storm tracks and changing the corresponding eddy feedback.

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Xinhua Liu, Kanghui Zhou, Yu Lan, Xu Mao, and Robert J. Trapp

Abstract

It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.

Open access
Ke Xu, Riyu Lu, Baek-Jo Kim, Jiangyu Mao, and Jong-Kil Park

Abstract

The break of the western North Pacific (WNP) summer monsoon (WNPSM) occurs climatologically in early August and is accompanied by a remarkable suppression of convection over the ocean east of the Mariana Islands (10°–20°N, 140°–160°E). This suppression of convection is sandwiched between two convection peaks in late July and mid-August. Two types of monsoon break are identified in the interannual variation of the WNPSM break in the period 1979–2015, exhibiting a distinct subseasonal evolution of convection that is either in phase or out of phase with the climatological evolution. The preceding SST anomalies in the tropical WNP during early and mid-July are responsible for the interannual variation of the monsoon break. Warm (cold) SST anomalies induce an advanced (delayed) evolution of the WNPSM, with the establishment of strong convection in late July (early August) followed by a monsoon break in early August (mid-August). The subseasonal evolution of convection is therefore in phase (out of phase) with that of the climatological mean. The above SST anomalies mainly result from the local wind–evaporation–SST positive feedback during spring and summer. This local air–sea interaction is still robust after the linear regression components related to the variability of ENSO are excluded from the original fields, indicating that it is, to a large extent, independent of ENSO. The ENSO decaying phases have a secondary role in modulating the SST anomalies related to the WNPSM break.

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Ke Xu, Riyu Lu, Ying Na, Baek-Jo Kim, Jiangyu Mao, Jae-Young Byon, and Bo Pang

Abstract

This study indicates a significant variation of humidity on extreme heat (EH) days over South Korea and southern–central Japan during the period 1979–2018. EH is therefore classified into three categories: type-A wet EH, type-B wet EH, and dry EH. Their statistical characteristics and formation mechanisms are investigated and compared. Our results suggest that the type-A wet EH is the most destructive, with the highest intensity, longest duration, and broadest spatial scale covering most of midlatitude East Asia. By contrast, type-B wet EH and dry EH are weaker, shorter, and mostly confined to northeast Asia. Despite these differences in characteristics, both types of wet EH are caused by the poleward advance of tropical warm and humid air masses as a result of the northward displacement of the Asian westerly jet. By contrast, dry EH is primarily induced by an increase in adiabatic heating and solar radiation resulting from anomalous subsidence. The three types of EH are associated with distinct large-scale teleconnections over Eurasia. A stable and persistent tripole wave pattern is responsible for type-A wet EH. The activity of atmospheric blocking over northern Europe, where the pattern originates, plays a crucial role in maintaining this pattern. By contrast, type-B wet EH and dry EH are related to a quadrupole pattern and a Silk Road pattern–like teleconnection, respectively, both lasting for a shorter time. These results highlight the diversity of EH, which suggests that multiple local and large-scale circulations should be considered to improve the forecast skills for EH.

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Cheng-Dong Xu, Jin-Feng Wang, Mao-Gui Hu, and Qing-Xiang Li

Abstract

Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.

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Zhenzhong Zeng, Shilong Piao, Laurent Z. X. Li, Tao Wang, Philippe Ciais, Xu Lian, Yuting Yang, Jiafu Mao, Xiaoying Shi, and Ranga B. Myneni

Abstract

Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.

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Ke Xu, Riyu Lu, Baek-Jo Kim, Jong-Kil Park, Jiangyu Mao, Jae-Young Byon, Ruidan Chen, and Eun-Byul Kim

Abstract

The large-scale circulation anomalies associated with extreme heat (EH) in South Korea and southern–central Japan are examined using data during the time period 1979–2016. Statistical analysis indicates that EH days in these two regions are concentrated in July and August and tend to occur simultaneously. These EH days are therefore combined to explore the physical mechanisms leading to their occurrence. The composite results indicate that the anomalous atmospheric warming during EH days is dominantly caused by a significant subsidence anomaly, which is associated with a deep anomalous anticyclone over East Asia. Further investigation of the evolution of circulation anomalies suggests that the anomalous anticyclone over East Asia related to EH is primarily initiated by wave trains originating from upstream regions, which propagate eastward along the Asian westerly jet in the upper troposphere. These wave trains can be categorized into two types that are characterized by the precursor anticyclonic and cyclonic anomalies, respectively, over central Asia. The distinction between these two types of wave train can be explained by the wavenumbers of the Rossby waves, which are modulated by both the intensity and the shape of the Asian westerly jet as the background basic flow.

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