Meteorological and Photochemical Modelling at the CSIR

Meteorological and Photochemical Modelling
at the CSIR
Mogesh Naidoo
Climate Studies, Modelling and Environmental Health
Natural Resources and the Environment
CSIR
Dialogue on Integrated Local and Regional Scale Air Quality Modelling
using the GAINS Model
14th February 2014
Knowledge Commons, CSIR, Pretoria.
www.csir.co.za
© CSIR 2010 Slide 1
Research at CSM&EH
• New group, seasoned scientists (atmospheric, environmental, health)
• Modelling = Atmospheric (Climate and Air Quality)
• Climate modelling output (present day + projections)
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Hydrological studies
Agriculture impact
Land-surface processes
Seasonal forecasting
Adaptation planning
Vulnerability studies
• Air quality modelling output (present day + projections)
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Environmental health
Regional tropospheric chemistry research
Industrial and municipal impact studies
© CSIR 2010 Slide 2
Climate modelling
• NWP and RCM based on the Conformal Cubic Atmospheric Model
(CCAM). Developed at CSIRO
• CCAM is a cube-based global model; semi-Lagrangian semi-implicit
solution of the hydrostatic primitive equations
© CSIR 2010 Slide 3
Climate modelling
• Can run in quasi-uniform (previous slide) or stretched grid mode
CCAM applied in stretched-grid mode
Modest stretching provides a resolution
of about 0.5 degrees over tropical and
southern Africa; decreases to about 4
degrees in the far-field. Options for
spectral nudging, gridpoint nudging or
no nudging from the host model
(atmospheric fields)
© CSIR 2010 Slide 4
Climate modelling
CGCMs: A2 SRES and RCP4.5 & 8.5
Simulation period: 1961 - 2100
Bias corrected SST and SIC
Global simulations,
quasi-uniform C192
resolution (~ 50 km)
SST, sea ice, atmospheric
nudging
User
applications
Very high-resolution
simulations over
areas of interest (~ 8
km).
Regrid to
lat/lon
© CSIR 2010 Slide 5
Climate modelling
• Model verification
Intra-annual cycle in rainfall and circulation
(Engelbrecht et al., 2009; IJC)
Closed-low tracks and extreme rainfall
events (Engelbrecht et al., 2012; IJC)
Inter-annual variability in AMIP-style runs
(Landman et al., 2010; WRC Report)
Accuracy and skill in short-range weather
forecasting (Potgieter 2006; Engelbrecht et
al., 2011; Water SA)
© CSIR 2010 Slide 6
Climate modelling
• Results used previously
CCAM ensemble: projected change in annual average
temperature for 2071-2100 vs 1961-1990
Under the A2 emission scenario, temperature increases of more
than 4oC are projected for the region, ~x2 the global rate.
This occurs in response to the strengthening of high-pressure
systems in the mid-troposphere over South Africa
© CSIR 2010 Slide 7
Climate modelling
• Results used previously
CCAM ensemble: projected change in the number of very hot
days (annual totals) for 2071-2100 vs 1961-1990
Under the A2 emission scenario, it is plausible that drastic
increases in the annual number of very hot days will occur over
the region – the number of such days is projected to increase by
90 to 120 over north-eastern South Africa
© CSIR 2010 Slide 8
Climate modelling
• Results used previously
CCAM ensemble: projected change in the number of extreme
rainfall events for 2071-2100 vs 1961-1990
A general increase in the frequency of occurrence of extreme
rainfall events (20 mm of rain falling within 24 hours over an area
of 50 km x 50 km) is projected for South Africa
© CSIR 2010 Slide 9
Climate modelling
• Results currently generated, e.g 8km Limpopo basin run
© CSIR 2010 Slide 10
Air Quality modelling
• A changing climate
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effect on air quality?
Rainfall, temperature, cloud cover, inversions, advection
• CCAM climate model forcing CAMx photochemical air quality model
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CAMx = ozone (NOx, VOC species, PM species)
• Model period 1989 – 2009 (20 years)
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Inter-annual variability
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20 years not enough (Large ENSO cycles)
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Earliest is 1989 due to input required (TOMS)
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 11
Air Quality modelling – CCAM-CAMx
NCEP
Reanalysis
(FNL)
CCAM
Meteorology
Deposition (wet
and dry)
Emissions data
(Spatial,
temporal,
speciated)
EPS
Surface and 3D
concentrations
Emissions
CAMx
Cape GAW
data
Initial /
Boundary
ICBC
Source
apportionment
Process analysis
TOMS / OMI
Total column
ozone
www.csir.co.za
NCAR TUV
© CSIR 2013
Photolysis
rates
© CSIR 2010 Slide 12
Air Quality modelling – Current domains
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 13
Air Quality modelling – Emissions inventory
• Previous research emission inventory (ozone formation over the Highveld)
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National at 12km resolution
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NOx, VOC, PM, SO2, CO and NH3
• Large industry (Sasol + Eskom + Coastal refineries)
• Small industry (Scheduled processes)
• Transport sector (SANRAL ADT + Arrive Alive)
• Residential fuel combustion (Census 2001)
• Biogenic emissions (GLOBEIS)
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 14
Air Quality modelling – Ancillary data
• Photolysis rates
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NCAR TUV radiative transfer model (results for CB4)
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TOMS/OMI total column ozone
• No total column ozone measurements for 1995/96
• Lateral boundary and initial conditions
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Cape Point GAW data
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Model initialize beginning of every month
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 15
Air Quality modelling – Camden monitoring station
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 16
Results – Camden comparison 2006 (Annual average diurnal)
60
Average surface ozone (ppb)
50
40
30
OBS
CAMx
20
10
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 17
Results – Camden comparison 2006 (Seasonal diurnal average)
60
60
SPRING
50
40
40
30
OBS
CAMx
Average surface ozone (ppb)
Average surface ozone (ppb)
WINTER
50
30
OBS
CAMx
20
20
10
10
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
Hour of day
60
13
14
15
16
17
18
19
20
21
22
23
24
60
SUMMER
50
AUTUMN
50
40
30
OBS
CAMx
Average surface ozone (ppb)
40
Average surface ozone (ppb)
12
Hour of day
30
OBS
CAMx
20
20
10
10
0
0
1
2
3
4
5
www.csir.co.za
6
7
8
9
10
11
12
13
Hour of day
14
15
16
17
18
© CSIR 2013
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20
21
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24
1
2
© CSIR 2010 Slide 18
3
4
5
6
7
8
9
10
11
12
13
Hour of day
14
15
16
17
18
19
20
21
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24
Results – Annual average surface ozone (1989-2009)
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 19
Results – Seasonal average surface ozone (1989-2009)
www.csir.co.za
SPRING
SUMMER
SPRING
AUTUMN
WINTER
© CSIR 2013
© CSIR 2010 Slide 20
Results – Annualar slope of linear regression (1989-2009)
aka “trend”
www.csir.co.za
© CSIR 2013
© CSIR 2010 Slide 21
Results – Seasonal slope of linear regression (1989-2009)
www.csir.co.za
SPRING
SUMMER
SPRING
AUTUMN
WINTER
© CSIR 2013
© CSIR 2010 Slide 22
To be completed…
• Complete selected 2010-2100 runs
• Correlate ozone trends to climate trends (establish mechanisms)
• Cloud cover (UV)
• Rainfall (Deposition/Chemistry)
• Temperature inversions (Transport)
• CAMx input optical depth
• ENSO (???) - AMT
© CSIR 2010 Slide 23
Thank you for your time
© CSIR 2010 Slide 24

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