weatherconditionsconducivetobeijingsevere t ......aﬀected 30 cities17–21, and the maximum daily...

In the format provided by the authors and unedited.LETTERS

PUBLISHED ONLINE: 20MARCH 2017 | DOI: 10.1038/NCLIMATE3249

Weather conditions conducive to Beijing severehaze more frequent under climate changeWenju Cai1,2, Ke Li3,4, Hong Liao5*, HuijunWang6 and LixinWu1

The frequency of Beijing winter severe haze episodes hasincreased substantially over the past decades1–4, and is com-monly attributed to increased pollutant emissions fromChina’srapid economic development5,6. During such episodes, levelsof fine particulate matter are harmful to human health andthe environment, and cause massive disruption to economicactivities3,4,7–16, as occurred in January 201317–21. Conduciveweather conditions are an important ingredient of severe hazeepisodes3,21, and include reduced surface winter northerlies3,21,weakened northwesterlies in the midtroposphere, and en-hanced thermal stability of the lower atmosphere1,3,16,21. Howsuch weather conditions may respond to climate change isnot clear. Here we project a 50% increase in the frequencyand an 80% increase in the persistence of conducive weatherconditions similar to those in January 2013, in response toclimate change. The frequency and persistence between thehistorical (1950–1999) and future (2050–2099) climate werecompared in 15 models under Representative ConcentrationPathway 8.5 (RCP8.5)22. The increased frequency is consistentwith large-scale circulation changes, including an ArcticOscillation upward trend23,24, weakening East Asian wintermonsoon25,26, and faster warming in the lower troposphere27,28.Thus, circulation changes induced by global greenhouse gasemissions can contribute to the increased Beijing severehaze frequency.

Beijing severe haze episodes are most frequent in borealwinter (December, January and February, DJF)3,29, due to highpollutant emissions, and a lack of rain (<11mm in winter, but>420mm in summer) to wash away pollutants. The Beijing wintercirculation is characterized by northwesterly winds near the surface(Supplementary Fig. 1a) associated with the East Asian wintermonsoon, and in the midtroposphere (Supplementary Fig. 1b)associated with the East Asia trough3,16,21,30. During severe haze,the surface and midtropospheric northwesterly winds weaken, oreven reverse. The concentration of fine particles with a diameterof 2.5 µm or smaller (PM2.5) increases dramatically. This leadsto a sharp decrease in visibility, affecting economic activities bycausing air and ground traffic hazards and disruptions3,4,16,21. Thesefine particles contain toxic substances that affect respiratory andcirculatory system, with detrimental impacts on the cardiovascular,immune and nervous systems, arguably increasing morbidityand mortality7–12.

The frequency of Beijing winter severe haze has increasedover past decades, culminating in events during January 2013,December 2015 and December 2016, when a high number

of episodes occurred16–21. In January 2013, severe haze eventsaffected 30 cities17–21, and the maximum daily PM2.5 averagenear Beijing reached 500 µgm−3. While the underlying causeis increased pollutant emissions, local weather conditions playa part3,4,16,21. Further, decadal variability and change, includingweakened northerly winds1,30, decreased relative humidity2, reducedArctic Sea ice4, and declined East Asian winter monsoon30, mayhave contributed. However, no long-term PM2.5 observations areavailable for attribution of the increased frequency.

In particular, how the conducive weather conditions mayrespond to climate change remains unclear. Climate models forcedunder the RCP8.5 (high) emission scenario22 simulated a generalincrease in occurrences of atmospheric stagnation27,28, but nochange over Beijing was projected28. Here we show that climatechange increases occurrences of weather conditions conducive toBeijing winter severe haze.

Correlation of available observed PM2.5 over 2009–2015 with arange of ambient daily anomalies reinforces the link between PM2.5and weather conditions. Strong correlations are found in lowertropospheric meridional flows, midtropospheric zonal flows, andvertical temperature profiles (Supplementary Fig. 2a–c). Defininga severe haze day as one when the concentration of PM2.5 >

150 µgm−3, we constructed composites of daily meteorologicalanomalies associated with severe haze, referenced to the dailyclimatology and normalized by the local standard deviation. Wechose the threshold of 150 µgm−3 for two reasons. Firstly, this isgreater than the observed winter average and median values of109.7 and 99.3 µgm−3, respectively. Secondly, the Beijing municipalgovernment issues a ‘red alert’ when the PM2.5 concentration isforecast to exceed 150 µgm−3 for 72 consecutive hours. In theJanuary 2013 episodes, the concentration above 150 µgm−3 lastedfor four to six days.

The composite vertical temperature profile features warmanomalies in the near-surface (around 850 hPa) preventingvertical dispersion of pollutants, accompanied by an anomalouscooling in the upper atmosphere3,21 (Fig. 1a). Guided by locationof maximum correlations (Supplementary Fig. 2), the intensityof this thermal structure is measured by a vertical difference(�T ) in raw temperature anomalies between the near-surface(850 hPa, over the area of 32.5–45◦ N, 112.5–132.5◦ E) and theupper atmosphere (250 hPa, 37.5–45◦ N, 122.5–137.5◦ E) (greenlines in Fig. 1a). These low-level atmosphere warm anomaliesare supported by a sea level pressure gradient between a low overChina and a high off the east coast3,16,21 (Supplementary Fig. 3).This leads to reduced seasonal prevailing surface cold northerlies,

1Physical Oceanography Laboratory/CIMSST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology,Yushan Road, Qingdao 266003, China. 2CSIRO Oceans and Atmosphere, Aspendale, Victoria 3195, Australia. 3Institute of Atmospheric Physics, ChineseAcademy of Sciences, Beijing 100029, China. 4University of Chinese Academy of Sciences, Beijing 100049, China. 5School of Environmental Science andEngineering/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology,Nanjing 210044, China. 6Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of MeteorologicalDisaster of Ministry of Education, Nanjing University for Information Science and Technology, Nanjing 210044, China. *e-mail: [email protected]

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LETTERSPUBLISHED ONLINE: 20MARCH 2017 | DOI: 10.1038/NCLIMATE3249

Weather conditions conducive to Beijing severehaze more frequent under climate changeWenju Cai1,2, Ke Li3,4, Hong Liao5*, HuijunWang6 and LixinWu1

The frequency of Beijing winter severe haze episodes hasincreased substantially over the past decades1–4, and is com-monly attributed to increased pollutant emissions fromChina’srapid economic development5,6. During such episodes, levelsof fine particulate matter are harmful to human health andthe environment, and cause massive disruption to economicactivities3,4,7–16, as occurred in January 201317–21. Conduciveweather conditions are an important ingredient of severe hazeepisodes3,21, and include reduced surface winter northerlies3,21,weakened northwesterlies in the midtroposphere, and en-hanced thermal stability of the lower atmosphere1,3,16,21. Howsuch weather conditions may respond to climate change isnot clear. Here we project a 50% increase in the frequencyand an 80% increase in the persistence of conducive weatherconditions similar to those in January 2013, in response toclimate change. The frequency and persistence between thehistorical (1950–1999) and future (2050–2099) climate werecompared in 15 models under Representative ConcentrationPathway 8.5 (RCP8.5)22. The increased frequency is consistentwith large-scale circulation changes, including an ArcticOscillation upward trend23,24, weakening East Asian wintermonsoon25,26, and faster warming in the lower troposphere27,28.Thus, circulation changes induced by global greenhouse gasemissions can contribute to the increased Beijing severehaze frequency.

Beijing severe haze episodes are most frequent in borealwinter (December, January and February, DJF)3,29, due to highpollutant emissions, and a lack of rain (<11mm in winter, but>420mm in summer) to wash away pollutants. The Beijing wintercirculation is characterized by northwesterly winds near the surface(Supplementary Fig. 1a) associated with the East Asian wintermonsoon, and in the midtroposphere (Supplementary Fig. 1b)associated with the East Asia trough3,16,21,30. During severe haze,the surface and midtropospheric northwesterly winds weaken, oreven reverse. The concentration of fine particles with a diameterof 2.5 µm or smaller (PM2.5) increases dramatically. This leadsto a sharp decrease in visibility, affecting economic activities bycausing air and ground traffic hazards and disruptions3,4,16,21. Thesefine particles contain toxic substances that affect respiratory andcirculatory system, with detrimental impacts on the cardiovascular,immune and nervous systems, arguably increasing morbidityand mortality7–12.

The frequency of Beijing winter severe haze has increasedover past decades, culminating in events during January 2013,December 2015 and December 2016, when a high number

of episodes occurred16–21. In January 2013, severe haze eventsaffected 30 cities17–21, and the maximum daily PM2.5 averagenear Beijing reached 500 µgm−3. While the underlying causeis increased pollutant emissions, local weather conditions playa part3,4,16,21. Further, decadal variability and change, includingweakened northerly winds1,30, decreased relative humidity2, reducedArctic Sea ice4, and declined East Asian winter monsoon30, mayhave contributed. However, no long-term PM2.5 observations areavailable for attribution of the increased frequency.

In particular, how the conducive weather conditions mayrespond to climate change remains unclear. Climate models forcedunder the RCP8.5 (high) emission scenario22 simulated a generalincrease in occurrences of atmospheric stagnation27,28, but nochange over Beijing was projected28. Here we show that climatechange increases occurrences of weather conditions conducive toBeijing winter severe haze.

Correlation of available observed PM2.5 over 2009–2015 with arange of ambient daily anomalies reinforces the link between PM2.5and weather conditions. Strong correlations are found in lowertropospheric meridional flows, midtropospheric zonal flows, andvertical temperature profiles (Supplementary Fig. 2a–c). Defininga severe haze day as one when the concentration of PM2.5 >

150 µgm−3, we constructed composites of daily meteorologicalanomalies associated with severe haze, referenced to the dailyclimatology and normalized by the local standard deviation. Wechose the threshold of 150 µgm−3 for two reasons. Firstly, this isgreater than the observed winter average and median values of109.7 and 99.3 µgm−3, respectively. Secondly, the Beijing municipalgovernment issues a ‘red alert’ when the PM2.5 concentration isforecast to exceed 150 µgm−3 for 72 consecutive hours. In theJanuary 2013 episodes, the concentration above 150 µgm−3 lastedfor four to six days.

The composite vertical temperature profile features warmanomalies in the near-surface (around 850 hPa) preventingvertical dispersion of pollutants, accompanied by an anomalouscooling in the upper atmosphere3,21 (Fig. 1a). Guided by locationof maximum correlations (Supplementary Fig. 2), the intensityof this thermal structure is measured by a vertical difference(�T ) in raw temperature anomalies between the near-surface(850 hPa, over the area of 32.5–45◦ N, 112.5–132.5◦ E) and theupper atmosphere (250 hPa, 37.5–45◦ N, 122.5–137.5◦ E) (greenlines in Fig. 1a). These low-level atmosphere warm anomaliesare supported by a sea level pressure gradient between a low overChina and a high off the east coast3,16,21 (Supplementary Fig. 3).This leads to reduced seasonal prevailing surface cold northerlies,

1Physical Oceanography Laboratory/CIMSST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology,Yushan Road, Qingdao 266003, China. 2CSIRO Oceans and Atmosphere, Aspendale, Victoria 3195, Australia. 3Institute of Atmospheric Physics, ChineseAcademy of Sciences, Beijing 100029, China. 4University of Chinese Academy of Sciences, Beijing 100049, China. 5School of Environmental Science andEngineering/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology,Nanjing 210044, China. 6Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of MeteorologicalDisaster of Ministry of Education, Nanjing University for Information Science and Technology, Nanjing 210044, China. *e-mail: [email protected]

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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE3249

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http://dx.doi.org/10.1038/nclimate3249

Supplementary Figure 1 | Contrast between boreal winter mean circulation and

the circulation anomaly during a month of frequent severe-haze events. a,

Surface winds (m s-1) and sea level pressure (hPa) averaged over 1986–2015, using

reanalysis data31. b, The same as a but for winds and geopotential height (m) at 500

hPa. c, d, The same as a, b, respectively, but for December 2015 during which several

severe-haze events occurred. The green dots denotes the location of Beijing.





Supplementary Figure 2 | Relationship of daily weather conditions with observed

boreal winter PM2.5 and the Haze Weather Index (HWI). Correlation of PM2.5

with a, temperatures and b, meridional flows in an east-west-height section along

40°N, and c, zonal flows at 500 hPa in a north-south-height section along 120°E. d, e,

f, The same as a, b, c, respectively, but with the HWI. Correlations are calculated

over 2009–2015, when PM2.5 observations are available.







with a, relative humidity, b, sea level pressure, and c, geopotential height. d, e, f, The

same as a, b, c, respectively, but with HWI. Correlations are calculated over 2009–

2015, when PM2.5 observations are available. The green dots denotes the location of

Beijing.







with a, surface wind speed, b, boundary layer heights, and c, K index. d, e, f, The

same as a, b, c, respectively, but with HWI. The K index is derived by the following

formula: K = (T850 – T500) + Td850 – (T –Td)700. Tj is air temperature at j level (hPa

surface), and (Td)j is dew point temperature at j level (hPa surface). The K index

indicates the stratification instability in the lower and middle atmosphere (see

Methods). Correlations are calculated over 2009–2015, when PM2.5 observations are

available. The green dots denotes the location of Beijing.





Supplementary Figure 5 | Histograms of observed boreal winter daily weather

conditions near Beijing. a, Haze weather index (HWI) for non-severe haze days

(blue) and severe haze days (red). Approximately 49% of the days with HWI>0

correspond to severe-haze days. b, c, d, The same as a, but for V850, ∆T, and U500,

respectively (see text for their definitions). Correlations are calculated over 2009–

2015, when PM2.5 observations are available.





Supplementary Figure 6 | Distribution of percentages of severe-haze days and

non-severe-haze days for a given threshold value range. a, Haze weather index

(HWI) for non-severe haze days (blue) and severe haze days (red). In a, all days with

-2.5<HWI<-2 are non-severe haze days and 0% are severe haze days; for 1<HWI<1.5

40% of days are non-severe haze days and 60% are severe haze days; for 1.5<HWI<2

17% of days are non-severe haze days and 83% are severe haze days. The

percentage increases with increasing HWI values. b, c, d, The same as a, but for

V850, ∆T, and U500, respectively (see text for their definitions). Correlations are

calculated over 2009–2015, when PM2.5 observations are available.





Supplementary Figure 7 | Histograms of observed boreal winter daily HWI near

Beijing for 1948–1981 and 1982–2015. a, Haze weather index (HWI) for 1948–1981

(blue) and 1982–2015 (red) constructed from reanalysis31. The two histograms in

each panel are statistically significantly different above the 95% confidence level as

indicated by indicated p-values. b, c, d, The same as a, but for V850, ∆T, and U500,

respectively. In all panels, there is a shift to the right, indicating an increased

frequency of weather conditions conducive to severe-haze events.





Supplementary Figure 8 | Difference between future (2050–2099) and historical

(1950–1999) winter severe-haze weather conditions near Beijing (116.4E,

39.9N). a, b, Differences in flow at 850 hPa and 500 hPa (future minus historical).

The green dots denotes the location of Beijing; green boxes denote the regions for

the calculation of V850 and U500. Shown is aggregated over 15 models.





Supplementary Figure 9 | Future changes of persistence of HWI>1 events. a,

Histogram of historical (blue bars, 1950–1999) and future (red bars, 2050–2099)

persistence of HWI>1 events, aggregated from 15 climate models under historical

emissions and future emission scenario RCP8.5. Averaged historical and future

persistence are 2.15, and 2.50 days per event, respectively.





Supplementary Figure 10 | Response of the Arctic Oscillation to greenhouse

warming. The Arctic Oscillation appears as the first EOF of boreal winter mean sea

level anomalies in all models over 1950–2100. a, b, Multi-model ensemble mean

EOF1 pattern and time series. c, Trend for individual models over the full period of

1950–2100 (decade-1). The multi-model ensemble (MME) average is shown in the

first bar.





Supplementary Table 1 | Comparison of frequency (days per season) between

historical (1950–1999) and future (2050–2099) climates in which boreal winter

HWI>0. Also shown are occurrences for contributing components V850, ∆T, and

U500>0. The results are from 15 climate models, for which daily outputs are available.

Numbers in red indicate decreased frequency. Multi-model ensemble (MME)

averages are presented in blue, and observed historical values are in green.

Days (>0) HWI V850 ∆T U500

Hist. Future Hist. Future Hist. Future Hist. Future

ACCESS1-0 46.3 52.7 44.4 41.7 46.7 63.2 46.8 51.6

ACCESS1-3 45.2 57.0 43.6 46.5 46.9 65.7 47.0 55.0

CanESM2 44.8 62.6 45.0 45.2 45.5 79.5 44.8 53.0

CMCC-CESM 46.2 56.0 44.0 46.9 46.5 63.7 46.3 54.2

CMCC-CM 45.3 56.1 42.8 46.3 46.5 62.7 46.2 55.7

CMCC-CMS 46.5 57.9 43.4 48.7 47.8 64.2 47.4 56.7

CNRM-CM5 45.5 41.8 44.8 39.8 45.4 48.0 45.6 38.6

CSIRO-Mk3-6-0 44.4 59.5 44.0 50.9 45.1 63.2 45.5 61.1

FGOALS-g2 45.2 51.2 45.1 48.7 45.6 57.2 46.2 48.8

GFDL-CM3 43.2 58.9 41.9 45.7 44.3 69.5 45.9 59.2

GFDL-ESM2G 45.5 47.5 43.9 39.7 45.7 55.2 47.1 46.6

GFDL-ESM2M 46.1 50.8 43.6 42.0 45.8 57.2 47.1 50.6

MIROC5 45.6 52.6 43.6 45.1 47.5 60.3 45.6 51.5

MPI-ESM-LR 45.5 53.5 43.4 46.2 46.6 60.0 46.0 51.3

MPI-ESM-MR 45.9 53.9 43.9 46.3 46.2 60.8 46.6 52.9

MME 45.4 54.1 43.8 45.3 46.1 62.0 46.3 52.5

Observed 45.5 44.1 46.4 46.1





Supplementary Table 2 | Comparison of the modelled and observed historical

(1950–1999) frequency (days per season) and standard deviation (STD, last

column) of boreal winter HWI. The results are from 15 climate models, for which

daily outputs are available. Multi-model ensemble (MME) averages are presented in

blue, and observed historical values are in green.

HWI >0 >0.5 >1 >1.5 >2 STD

ACCESS1-0 46.3 29.6 14.8 5.5 1.5 2.70

ACCESS1-3 45.2 28.8 15.1 5.7 1.6 2.70

CanESM2 44.8 28.6 14.3 6.0 1.9 2.74

CMCC-CESM 46.2 29.1 14.7 5.3 1.5 2.74

CMCC-CM 45.3 29.7 15.1 6.0 1.5 2.72

CMCC-CMS 46.5 29.6 14.6 5.6 1.4 2.70

CNRM-CM5 45.5 28.2 15.0 5.9 1.6 2.73

CSIRO-Mk3-6-0 44.4 28.8 15.5 6.6 1.6 2.73

FGOALS-g2 45.2 29.4 15.5 5.4 1.5 2.68

GFDL-CM3 43.2 27.6 15.1 6.9 2.2 2.71

GFDL-ESM2G 45.5 29.2 14.0 6.1 1.7 2.72

GFDL-ESM2M 46.1 29.2 14.3 5.8 1.6 2.73

MIROC5 45.6 29.4 15.3 6.2 1.4 2.67

MPI-ESM-LR 45.5 28.7 14.8 6.1 1.7 2.71

MPI-ESM-MR 45.9 28.7 15.0 5.8 1.8 2.68

MME 45.4 29.0 14.9 5.9 1.6 2.71

Observed 45.5 29.8 15.0 5.8 1.6 2.73





Supplementary Table 3 | Change in frequency for different boreal winter HWI

values. The second and third columns show frequency for historical (1950–1999) and

future (2050–2099) climates (days per season), respectively. The fourth column

shows the change in frequency and percentage.

HWI 1950-1999

(days season-1)

2050-2099

(days season-1)

Change

(days season-1) (%)

>0 45 54 9.0 (20%)

>0.25 37 46 9.0 (24%)

>0.5 29 38 9.0 (31%)

>0.75 21 30 9.0 (43%)

>1 15 23 8.0 (53%)

>1.25 10 16 6.0 (60%)

>1.5 5.9 11 5.1 (86%)

>1.75 3.3 6.5 3.2 (97%)

>2 1.6 3.7 2.1 (131%)





Supplementary Table 4 | Bootstrap test for HWI and individual meteorological

parameters at different values. Uncertainty around the mean of the historical (1950–

1999) and the future (2050–2099) climates is shown as the standard deviation

(STDhistorical, STDfuture, and their total) of the frequency in the inter-realizations. Also

shown are the mean changes of the frequency between the historical and future

climates. The smallest change is found in V850, but is still more than 2.5 times the

sum of the STDhistorical and STDfuture, i.e., greater than the 95% confidence level.

(days per season) >0 >0.5 >1 >2

HWI

STDhistorical 0.33 0.31 0.24 0.07

STDfuture 0.38 0.38 0.32 0.13

Total uncertainty 0.71 0.67 0.56 0.20

Mean changes 8.7 9.2 7.9 2.1

V850

STDhistorical 0.26 0.24 0.18 0.07

STDfuture 0.30 0.27 0.22 0.09


Mean changes 1.5 1.9 1.9 0.83

ΔT

STDhistorical 0.37 0.34 0.26 0.07

STDfuture 0.43 0.48 0.44 0.20


Mean changes 15.9 16.7 14.5 4.0

U500

STDhistorical 0.38 0.36 0.27 0.07

STDfuture 0.43 0.43 0.34 0.13


Mean changes 6.2 7.0 6.0 1.5





Supplementary Table 5 | Change in frequency for different boreal winter HWI

values when the K index is included in the HWI. Shown are results from 15 climate

models, for which daily outputs are available. The second and third columns show

frequency for historical (1950–1999) and future (2050–2099) climates (days per

season), respectively. The fourth column shows the change in frequency and

percentage.

HWI 1950-1999

(days season-1)

2050-2099

(days season-1)

Change

(days season-1) (%)

>0 46 56 10 (22%)

>0.25 37 48 11 (30%)

>0.5 29 40 11 (38%)

>0.75 21 32 11 (52%)

>1 15 24 9.0 (60%)

>1.25 9.6 18 8.4 (88%)

>1.5 5.8 12 6.2 (107%)

>1.75 3.2 7.4 4.2 (131%)

>2 1.5 4.3 2.8 (187%)





weatherconditionsconducivetobeijingsevere t ......aﬀected 30 cities17–21, and the maximum daily...

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