scholarly journals Comparison of the influence of two types of cold surge on haze dispersion in eastern China

2021 ◽  
Vol 21 (19) ◽  
pp. 15185-15197
Author(s):  
Shiyue Zhang ◽  
Gang Zeng ◽  
Xiaoye Yang ◽  
Ruixi Wu ◽  
Zhicong Yin
Keyword(s):  
Air Quality ◽  
Clustering Algorithm ◽  
Wave Train ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Horizontal Surface ◽  
Cold Surge ◽  
Thermal Inversion ◽  
Favorable Weather

Abstract. Cold surge (CS) is considered a favorable weather process to improve air quality and is widely recognized. However, there is no detailed study on the differences in the dispersion ability of different types of CSs in relation to haze days in eastern China (HDEC). This paper uses the hierarchical clustering algorithm to classify the cool-season (November to February of the following year) CSs across eastern China into blocking CSs and wave-train CSs and compares their influences on the number of HDEC from 1980 to 2017. Results show that the wave-train CSs can significantly improve the visibility in eastern China and generally improve air quality for about 2 d longer than the blocking CSs, which indicates that the blocking CSs have a weaker ability to dissipate HDEC compared with the wave-train CSs. The CSs affect the HDEC by changing meteorological elements like thermal inversion potential, horizontal surface wind, sea level pressure (SLP), and surface air temperature (SAT). A period of 4 d after the outbreak of CSs, the variations of thermal inversion potential and horizontal surface wind of two types of CSs tend to be consistent. However, the negative SAT anomalies and the positive SLP anomalies caused by the blocking CSs lasted shorter than those caused by the wave-train CSs, forming favorable conditions for the rapid growth of HDEC. Furthermore, results show that in recent years, especially after the 1990s, the frequency of wave-train CSs has decreased significantly, while the frequency of blocking CSs has slightly increased, indicating that the overall ability of CSs to dissipate HDEC has weakened in general. This work may provide reference for the future formulation of haze control policies in East Asia.

scholarly journals Comparison of influence between two types of cold surge on haze dispersion in Eastern China

2021 ◽  
Author(s):  
Shiyue Zhang ◽  
Gang Zeng ◽  
Xiaoye Yang ◽  
Ruixi Wu ◽  
Zhicong Yin
Keyword(s):  
Air Quality ◽  
Clustering Algorithm ◽  
Wave Train ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Horizontal Surface ◽  
Cold Surge ◽  
Thermal Inversion ◽  
Favorable Weather

Abstract. Cold surge (CS) is considered as a favorable weather process to improve air quality and is widely recognized. However, there is no detailed study on the differences in the dispersion ability of different types of CSs to haze days in eastern China (HDEC). This paper uses the hierarchical clustering algorithm to classify the cool season (November to February of the following year) CSs across eastern China into blocking and wave-train CSs and compares their influences on the number of HDEC from 1980 to 2017. Results show that the wave-train CS can significantly improve the visibility in eastern China and generally make the high air quality last for about 2 days longer than the blocking CS, which indicates that the blocking CS has a weaker ability to dissipate HDEC compared with the wave-train CS. The CSs affect the HDEC by changing these meteorological elements like thermal inversion potential, horizontal surface wind, sea level pressure (SLP), and surface air temperature (SAT). 4 days after the CSs outbreak, the variations of thermal inversion potential and horizontal surface wind of two types of CSs tend to be consistent. However, the negative SAT anomalies, and the positive SLP anomalies caused by the blocking CSs lasted shorter than those caused by the wave-train CSs, which forms favorable conditions for the rapid growth of HDEC. Furthermore, results show that in recent years, especially after the 1990s, the frequency of wave-train CSs has decreased significantly, while the frequency of blocking CSs has slightly increased, indicating that the overall ability of CSs to dissipate HDEC has weakened in general.

Subseasonal Dynamical Prediction of East Asian Cold Surges

2017 ◽  
Vol 32 (4) ◽  
pp. 1675-1694 ◽  
Author(s):  
Qiaoping Li ◽  
Song Yang ◽  
Tongwen Wu ◽  
Xiangwen Liu
Keyword(s):  
Geopotential Height ◽  
Lead Time ◽  
Large Scale ◽  
Wave Train ◽  
East Asian ◽  
Eastern China ◽  
Northwest Pacific ◽  
Cold Surge ◽  
Circulation Patterns ◽  
Cold Surges

Abstract Predictability of East Asian cold surges is studied using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2 (CFSv2). Prediction skills of the CFSv2 in forecasting cold surges, their annual variation, and their physical links to large-scale atmospheric circulation patterns are examined. Results show that the climatological characteristics of the East Asian winter monsoon can be reasonably reproduced by the CFSv2. The model can well capture the frequency, intensity, and location of cold surges at a lead time of about two weeks. Obviously, fewer-than-observed cold surge days are found in the predictions when the lead time is above 14 days. The spatiotemporal evolutions of high-, mid-, and low-level circulation patterns during cold surge occurrences are all accurately indicated in the CFSv2 prediction. Except for precipitation, the other variables associated with cold surges, such as geopotential height, wind, sea level pressure, and surface air temperature, exhibit higher skills. The lead time of skillful prediction of precipitation is limited to around 1 week, with systematic wet biases over the South China Sea, the Philippine Islands, and the northwest Pacific, but dry biases over India, the Indo-China Peninsula, and most high-latitude regions. Wave train–like patterns of geopotential height and wind differ distinguishably when cold surges occur in northern and southern regions (using 35°N as the dividing line), and the CFSv2 gives a consistent prediction to these anomalous patterns. A weaker-than-observed Siberian high and weaker northerly winds over eastern China are found in the predictions especially at longer lead times.

scholarly journals Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models

2010 ◽  
Vol 49 (10) ◽  
pp. 2077-2091 ◽  
Author(s):  
Scott Beaver ◽  
Saffet Tanrikulu ◽  
Ahmet Palazoglu ◽  
Angadh Singh ◽  
Su-Tzai Soong ◽  
...  
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
Model Evaluation ◽  
Clustering Algorithm ◽  
Surface Wind ◽  
Major Advance ◽  
Evaluation Technique ◽  
Meteorological Model ◽  
Quality Models ◽  
Weather Patterns

Abstract A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evaluation of AQM performance based on MM performance is a major advance over traditional evaluation methods. First, meteorological cluster analysis is used to assign the days of a historical measurement period among a small number of weather patterns having distinct air quality characteristics. The clustering algorithm groups days sharing similar empirical orthogonal function (EOF) representations of their measurements. In this study, EOF analysis is used to extract space–time patterns in the surface wind field reflecting both synoptic and mesoscale influences. Second, simulated wind fields are classified among the determined weather patterns using the measurement-derived EOFs. For a given period, the level of agreement between the observation-based clustering labels and the simulation-based classification labels is used to assess the validity of the simulation results. Mismatches occurring between the two sets of labels for a given period imply inaccurately simulated conditions. Moreover, the specific nature of a mismatch can help to diagnose the downstream effects of improperly simulated meteorological fields on AQM performance. This pattern-based model evaluation technique was applied to extended simulations of fine particulate matter (PM2.5) covering two winter seasons for the San Francisco Bay Area of California.

scholarly journals Spatial and Temporal Characteristics of Environmental Air Quality and Its Relationship with Seasonal Climatic Conditions in Eastern China during 2015–2018

2021 ◽  
Vol 18 (9) ◽  
pp. 4524
Author(s):  
Zhiyuan Wang ◽  
Xiaoyi Shi ◽  
Chunhua Pan ◽  
Sisi Wang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Large Scale ◽  
Asian Summer Monsoon ◽  
Eastern China ◽  
Climatic Conditions ◽  
Uniform Structure ◽  
Concentration Limit ◽  
Environmental Air

Exploring the relationship between environmental air quality (EAQ) and climatic conditions on a large scale can help better understand the main distribution characteristics and the mechanisms of EAQ in China, which is significant for the implementation of policies of joint prevention and control of regional air pollution. In this study, we used the concentrations of six conventional air pollutants, i.e., carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3), derived from about 1300 monitoring sites in eastern China (EC) from January 2015 to December 2018. Exploiting the grading concentration limit (GB3095-2012) of various pollutants in China, we also calculated the monthly average air quality index (AQI) in EC. The results show that, generally, the EAQ has improved in all seasons in EC from 2015 to 2018. In particular, the concentrations of conventional air pollutants, such as CO, SO2, and NO2, have been decreasing year by year. However, the concentrations of particulate matter, such as PM2.5 and PM10, have changed little, and the O3 concentration increased from 2015 to 2018. Empirical mode decomposition (EOF) was used to analyze the major patterns of AQI in EC. The first mode (EOF1) was characterized by a uniform structure in AQI over EC. These phenomena are due to the precipitation variability associated with the East Asian summer monsoon (EASM), referred to as the “summer–winter” pattern. The second EOF mode (EOF2) showed that the AQI over EC is a north–south dipole pattern, which is bound by the Qinling Mountains and Huaihe River (about 35° N). The EOF2 is mainly caused by seasonal variations of the mixed concentration of PM2.5 and O3. Associated with EOF2, the Mongolia–Siberian High influences the AQI variation over northern EC by dominating the low-level winds (10 m and 850 hPa) in autumn and winter, and precipitation affects the AQI variation over southern EC in spring and summer.

scholarly journals Contributors to linkage between Arctic warming and East Asian winter climate

2021 ◽  
Author(s):  
Xinping Xu ◽  
Shengping He ◽  
Yongqi Gao ◽  
Botao Zhou ◽  
Huijun Wang
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Initial Conditions ◽  
Wave Train ◽  
East Asian ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
The Arctic ◽  
Winter Climate ◽  
Arctic Warming

AbstractPrevious modelling and observational studies have shown discrepancies in the interannual relationship of winter surface air temperature (SAT) between Arctic and East Asia, stimulating the debate about whether Arctic change can influence midlatitude climate. This study uses two sets of coordinated experiments (EXP1 and EXP2) from six different atmospheric general circulation models. Both EXP1 and EXP2 consist of 130 ensemble members, each of which in EXP1 (EXP2) was forced by the same observed daily varying sea ice and daily varying (daily climatological) sea surface temperature (SST) for 1982–2014 but with different atmospheric initial conditions. Large spread exists among ensemble members in simulating the Arctic–East Asian SAT relationship. Only a fraction of ensemble members can reproduce the observed deep Arctic warming–cold continent pattern which extends from surface to upper troposphere, implying the important role of atmospheric internal variability. The mechanisms of deep Arctic warming and shallow Arctic warming are further distinguished. Arctic warming aloft is caused primarily by poleward moisture transport, which in conjunction with the surface warming coupled with sea ice melting constitutes the surface-amplified deep Arctic warming throughout the troposphere. These processes associated with the deep Arctic warming may be related to the forcing of remote SST when there is favorable atmospheric circulation such as Rossby wave train propagating from the North Atlantic into the Arctic.

scholarly journals The Influence of the Madden–Julian Oscillation on Canadian Wintertime Surface Air Temperature

2009 ◽  
Vol 137 (7) ◽  
pp. 2250-2262 ◽  
Author(s):  
Hai Lin ◽  
Gilbert Brunet
Keyword(s):  
Air Temperature ◽  
Wave Train ◽  
Winter Season ◽  
Surface Air Temperature ◽  
Madden Julian Oscillation ◽  
Eastern Canada ◽  
Central Pacific ◽  
Sea Level Pressure ◽  
The Indian Ocean ◽  
Rossby Wave Train

Using the homogenized Canadian historical daily surface air temperature (SAT) for 210 relatively evenly distributed stations across Canada, the lagged composites and probability of the above- and below-normal SAT in Canada for different phases of the Madden–Julian oscillation (MJO) in the winter season are analyzed. Significant positive SAT anomalies and high probability of above-normal events in the central and eastern Canada are found 5–15 days following MJO phase 3, which corresponds to an enhanced precipitation over the Indian Ocean and Maritime Continent and a reduced convective activity near the tropical central Pacific. On the other hand, a positive SAT anomaly appears over a large part of northern and northeastern Canada about 5–15 days after the MJO is detected in phase 7. An analysis of the evolution of the 500-hPa geopotential height and sea level pressure anomalies indicates that the Canadian SAT anomaly is a result of a Rossby wave train associated with the tropical convection anomaly of the MJO. Hence, the MJO phase provides useful information for the extended-range forecast of Canadian winter surface air temperature. This result also provides an important reference for numerical model verifications.

Numerical air quality forecasting over eastern China: An operational application of WRF-Chem

2017 ◽  
Vol 153 ◽  
pp. 94-108 ◽  
Author(s):  
Guangqiang Zhou ◽  
Jianming Xu ◽  
Ying Xie ◽  
Luyu Chang ◽  
Wei Gao ◽  
...  
Keyword(s):  
Air Quality ◽  
Eastern China ◽  
Operational Application ◽  
Air Quality Forecasting

scholarly journals Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models

2021 ◽  
Vol 18 (13) ◽  
pp. 6817
Author(s):  
Sungbo Shim ◽  
Hyunmin Sung ◽  
Sanghoon Kwon ◽  
Jisun Kim ◽  
Jaehee Lee ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
East Asia ◽  
Climate Model ◽  
Fine Particulate Matter ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Quality Controls ◽  
Quality Guidelines

This study investigates changes in fine particulate matter (PM2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM2.5 concentrations were comparable to satellite-derived data. Overall, the PM2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM2.5, it may also contribute to the warming of near- and mid-future climates.

scholarly journals Short‐Term Weather Patterns Modulate Air Quality in Eastern China During 2015–2016 Winter

2019 ◽  
Vol 124 (2) ◽  
pp. 986-1002 ◽  
Author(s):  
Shuyu Zhao ◽  
Tian Feng ◽  
Xuexi Tie ◽  
Wenting Dai ◽  
Jiamao Zhou ◽  
...  
Keyword(s):  
Air Quality ◽  
Eastern China ◽  
Short Term ◽  
Weather Patterns

scholarly journals Recent atmospheric changes and future projections along the Saudi Arabian Red Sea Coast

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Abdulhakim Bawadekji ◽  
Kareem Tonbol ◽  
Nejib Ghazouani ◽  
Nidhal Becheikh ◽  
Mohamed Shaltout
Keyword(s):  
Relative Humidity ◽  
Sea Level ◽  
Air Temperature ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Saudi Arabian ◽  
Sea Level Pressure ◽  
Ensemble Mean ◽  
Red Sea Coast ◽  
Sea Coast

AbstractRecent and future climate diagrams (surface air temperature, surface relative humidity, surface wind, and mean sea level pressure) for the Saudi Arabian Red Sea Coast are analysed based on hourly observations (2016–2020) and hourly ERA5 data (1979–2020) with daily GFDL mini-ensemble means (2006–2100). Moreover, GFDL mini-ensemble means are calculated based on the results of three GFDL simulations (GFDL-CM3, GFDL-ESM2M, and GFDL-ESM2G). Observation data are employed to describe the short-term current weather variability. However, ERA5 data are considered to study the long-term current weather variability after bias removal via a comparison to observations. Finally, a bias correction statistical model was developed by matching the cumulative distribution functions (CDFs) of corrected ERA5 and mini-ensemble mean data over 15 years (2006–2020). The obtained local statistic were used to statically downscale GFDL mini-ensemble means to study the future uncertainty in the atmospheric parameters studied. There occurred significant spatial variability across the study area, especially regarding the surface air temperature and relative humidity, based on monthly analysis of both observation and ERA5 data. Moreover, the results indicated that the ERA5 data suitably describe Tabuk, Jeddah and Jizan weather conditions with a marked spatial variability. The best performance of ERA5 surface air temperature and relative humidity (surface wind speed and sea level pressure) data was detected in Tabuk (Jeddah). These data for the Saudi Arabian Red Sea coast, 1979–2020, exhibit significant positive trends of the surface air temperature and surface wind speed and significant negative trends of the relative humidity and sea level pressure. The GFDL mini-ensemble mean projection result, up to 2100, contains a significant bias in the studied weather parameters. This is partly attributed to the coarse GFDL resolution (2° × 2°). After bias removal, the statistically downscaled simulations based on the GFDL mini-ensemble mean indicate that the climate in the study area will experience significant changes with a large range of uncertainty according to the considered scenario and regional variations.

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