The impact of future atmospheric circulation changes over the Euro-Atlantic sector on urban PM_2.5 concentrations

Figures & data

Table 1. Summary of the two 30-year simulations performed with NorESM.

Name	Time period	Duration	Aerosol scenario	GHG scenario
Control	1976–2005	30 years	Historic	Historic
MFR	2037–2066	30 years	MFR	RCP4.5

Table 2. Root-mean-squared errors of the PM_2.5 downscaling relative to the pre-2005 AirBase measurements for the 2-level and 4-level approaches. The list of cities included in the different regions is provided in Figs. S1 and S2.

Region	RMS error 2-level	RMS error 4-level
Northwestern	5.2	4.5
Southern	11.8	7.8
Central/Eastern	5.5	5.5

Fig. 1. Schematic example of population scaling for four model grid cells. a) Fraction of urban area in model grid cells, red = urban, white = rural. b) Population density in model grid cells. The fraction of ρ_pop > 600/km² is the fraction of urban population. c) Model grid PM_2.5. d) Result of population scaling on PM_2.5 (image courtesy of Rita van Dingenen).

Fig. 2. Annual average (2000–2005) population-weighted anthropogenic PM_2.5, computed following the four-level downscaling approach described in Section 2.2.

Table 3. Urbanization classes and empirically defined limits imposed on urban enhancement and rural reduction factors.

Urbanisation class	Pop. density limits (km^−2)	Scaling factor limits
Urban	ρpop > 600	1 ≤ PFU ≤ 5.3
Urb. Centre	ρpop > 1000	1 ≤ PFUurb ≤ 1.5
Urb. Backg.	1000 > ρpop > 600	0.5 ≤ PFRurb ≤ 1
Rural	ρpop ≤ 600	0.5 ≤ PFR ≤ 1
Rur. Backg.	600 ≥ ρpop > 300	1 ≤ PFUrur ≤ 2
Nat. Backg.	300 ≥ ρpop	0.5 ≤ PFRrur ≤ 1

Table 4. Summary of the different thresholds associated with PM_2.5 guidelines.

Name	Threshold	Period	Applicability
WHO IT-1	35 μg/m^3	Annual	Control
WHO IT-1	75 μg/m^3	Daily	Control
WHO AQG	10 μg/m^3	Annual	MFR
WHO AQG	25 μg/m^3	Daily	MFR

Fig. 3. First EOF of sea level pressure for (a) the Control and (b) the MFR simulations, respectively. Circle (cross) markers indicate the Control (MFR) NACs. (c) Frequency distribution of the monthly NAOI for the Control (blue) and MFR (orange) simulations.

Fig. 4. (a) Climatological DJF blocking frequency (% of blocked days) for the Control simulation. (b) changes in blocking frequency for the MFR scenario relative to the Control simulation. Stippling denotes areas where the difference is not significant at the 95% confidence level under a 2-sample t-test. The boxes in (a) mark the domains used to compute the low-, mid- and high-latitude blocking indices.

Fig. 5. Frequency distributions of the daily DJF (a) jet latitude and (b) jet speed indices, for the Control (blue) and MFR (orange) simulations. Note that these are deviations from the seasonal cycle with the winter climatological means added back. (c) Displays the same distributions as (a) without the means added. The continuous curves are kernel estimations of the PDFs.

Fig. 6. Regression patterns (% of blocked days/month/standard deviation) of blocking frequency on (a, b) jet latitude and (c, d) jet speed time series for the Control (a, c) and MFR (b, d) simulations during DJF. Stippling denotes areas where the regression is not statistically significant at the 95% confidence level, based on the standard error of the regression.

Fig. 7. Regression patterns (µg/m³/standard deviation) of population-scaled urban anthropogenic PM_2.5 anomalies on (a, b) the NAOI, (c, d) jet speed and (e, f) jet latitude indices, for the (a, c, e) Control and (b, d, f) MFR simulations during DJF. Line contours display positive (blue) and negative (red) departures from the DJF climatological mean, with contour intervals of 5%, starting from 10%. For enhanced visibility, contours change line style as follows: dotted, dashed, dash-dot, solid. Stippling denotes areas where the regression is not significant at the 95% confidence level, based on the standard error of the regression. Note that the colour scale ranges in the left and right-hand columns differ.

Fig. 8. Regression patterns (µg/m³/standard deviation) of population-scaled urban anthropogenic PM_2.5 anomalies on (a, b) LLB, (c, d) MLB and (e, f) HLB indices, for the (a, c, e) Control and (b, d, f) MFR simulations during DJF. Note that the colour scale ranges in the left and right-hand columns differ. All markings and colours are as in Fig. 7.

Fig. 9. Climate penalty in PM_2.5 concentrations for (a, c) the control and (b, d) the MFR simulations during DJF. This is expressed in terms of (a, b) absolute concentration values and (c, d) fractional changes relative to the DJF climatology.

Fig. 10. Difference in the skewness of the population-scaled urban anthropogenic PM_2.5 between the Control and MFR simulations during DJF.

Fig. 11. Exceedances of (a, c) the IT-1 and (b, d) the AQG PM_2.5 aerosol limits in the control simulation. Results are expressed in terms of (a, b) the average number of days per year in which the daily targets were exceeded and (c, d) the number of years in which the yearly average targets were exceeded.

Fig. 12. Exceedances of (a, c) the IT-1 and (b, d) the AQG PM_2.5 aerosol limits in the MFR simulation. Results are expressed in terms of (a, b) the average number of days per year in which the daily targets were exceeded and (c, d) the number of years in which the yearly average targets were exceeded.