1. Daniel J. Jacob, Harvard University
Effect of climate change on winter haze pollution in Beijing: relative humidity as a critical variable
Daniel J. Jacob, Harvard University
Lu Shen
Drew Pendergrass

2. Relative humidity is a chemical driving factor of winter haze
Hourly winter 2014 observations in Beijing
Higher RH increases aqueous aerosol mass to drive sulfate and organic PM production, but this chemistry is missing from standard models ,
Song et al., ACPD 2018

3. High statistical correlation of Beijing wintertime PM2
High statistical correlation of Beijing wintertime PM2.5 with meridional wind velocity (V850) and relative humidity (RH)
Correlations for DJF monthly mean PM2.5 record from US embassy,
Even stronger correlation (R = 0.90) with principal component PC1 of V850 and RH:
PC1 = 0.5V850 (normalized) + 0.5RH (normalized)
PC1 provides meteorological proxy for Beijing haze: no significant trend 1970-present
Shen et al., ACPD 2018

4. Correlations of PC1 with Arctic sea ice cover and sea surface temperature are complicated and vary on multidecadal time scales
Correlation of PC1 with sea ice cover (SIC) in winter and previous fall,
The correlation is with a variable dipole – not very useful for future-climate projections
Shen et al., ACPD 2018

5. Effect of 21st century climate change on winter mean Beijing PM2
Effect of 21st century climate change on winter mean Beijing PM2.5 as projected from the proxy PC1(V850, RH)
vs changes in IPCC CMIP5 models for RCP 8.5 scenario
Mean change in V Mean change in RH Change in PM2.5
In individual models
75th
25th
current winter mean
PM2.5 = 110 μg m--3
Effect of climate change on Beijing haze is insignificant
Shen et al., ACPD 2018

6. What about extreme haze events?
Observed frequency distribution of wintertime 24-h PM2.5 in Beijing,
Apply extreme value theory
to fit probability of extreme events
to meteorological variables:
point process model
95th percentile
Consider ensemble of meteorological variables including V850, RH,
vertical temperature gradient (δT ), meridional gradient of 500hPa zonal wind (δU500)
Pendergrass et al., submitted to GRL

7. Best point process model fit of extreme haze events is to V850, RH
Contours: model probability
Dots: observed daily PM2.5
green: > 300 μg m-3
black: < 300 μg m-3
The same model correctly predicts probability for higher thresholds
Pendergrass et al., submitted to GRL

8. Implications for effect of climate change on extreme haze
Changes in (V850, RH) joint probability in CMIP5 models, vs
extreme haze
regime
Pendergrass et al., submitted to GRL

9. RCP8.5 future climate scenario
Changes in (V850, RH) joint probability in CMIP5 models, vs
extreme haze
regime
RCP8.5 scenario shows no change for the (V850, RH) range leading to extreme events
Pendergrass et al., submitted to GRL

10. RCP4.5 future climate scenario
Changes in (V850, RH) joint probability in CMIP5 models, vs
extreme haze
regime
RCP4.5 shows decreased probability of the (V850, RH) range leading to extreme events
RCP8.5 scenario shows no change for the (V850, RH) range leading to extreme events
Pendergrass et al., submitted to GRL

11. 2006-2015 to 2051-2060 change in probability of extreme haze
computed for each CMIP5 model by integrating over pdfs of meteorological variables
# extreme haze days per winter (CMIP5 ensemble)
RCP4.5
RCP8.5
point process model
function of V850 and RH only
(best model)
-10%
~ 0%
-10%
-10%
function of V850, RH, δT , δU500
(inferior model)
Number of extreme haze days per winter
+20%
+10%
function of V850, δT , δU500
(poor model, omitting RH )
Frequency of extreme haze events is most likely to decrease as result of climate change,
but not including RH as predictor variable would conclude to an increase
Pendergrass et al., submitted to GRL