sensitivity analysis of aerosol feedbacks on and … and climate at urban ... fast‐j scheme [wild...
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Sensitivity analysis of aerosol feedbacks on chemistry and climate at urban and regional
scales
Gregory R. Carmichael1, Man Yu1, Gao Meng1, Pablo Saide1, YafangCheng2, Scott Spak1, Pallavi Marrapu1, G. Beig4, Marcelo Mena3
1. Center for Global and Regional Environmental Research, University of Iowa
2. SKJ Laboratory of Environmental Simulation and Pollution Control, Peking University
3. Center for Sustainability Research, Universidad Andrés Bello, Chile4. IITM, Pune, India
Aerosol‐Chemistry‐Climate Interactions at Urban to Regional Scales
CMA GURME NRT‐PP
forecasts
•Critical to air quality & climate modeling, NWP and policy assessments.• Yet still highly uncertain, with large biases.We need to understand
‐ how contemporary models perform‐ how to improve them
Urban interactions
Exploring Multiscale Urban/Regional Air Quality –Weather Feedbacks
Current Applications: China, India, Chile, U.S.
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Fast‐J scheme [Wild et al., 2000] within CBM‐Z [Zaveri and Peters, 1999] to calculate photolysis rate
Aerosol direct effects (direct and semi‐direct): Optical properties calculated by Mie theory [Fast et al., 2006] and then passed into the Goddard short waveradiative scheme.
Aerosol indirect effects: Aerosol activationmodule to calculate activation of aerosols [Ghan and Easter, 2006, Abdul‐Razzak and Ghan, 2002], and then passed to Lin et al.Microphysics and Goddard short wave radiation modules
Exploring Multiscale Urban Air Quality – Weather Feedbacks
Current Applications: China, India, Chile, U.S.
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Sensitivity studies to explore importance of:
Urbanization (land use, anthro heat emissions)
Feedbacks (direct, indirect, role of absorption)
Emissions (new MICS)
Model resolution (80‐3km)(global (MACC) boundary conditions)
K PBL (m)
PM2.5 (g/m3)
Urban area
1. Impact of Land Use Change
Beijing NCP
Man et al., AE, 2013
August (daytime average)
Simulating Continental Precipitation Events
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2.5Pr
ecip
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ate
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/h)
Date (Local Time of Year 2008)
Observed Prate Run 6 Run 15 Run 14C06
Grell 3 cumulus schemeSensitivity runs with KF and GD cumulus schemes
WRF‐Chem captures thelocal daily time series of precipitation in Beijing.
Observations: Beijing Monitoring Center (Wang et al., 2010)
G3
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a. Satellite b. WRF‐Chem (colormap) vs WRF (contour)
c. Cloud Water & CAPE d. SO2
Aerosol Feedbacks (in this case) Lead To Better Precip Predictions
Aerosol mass, composition and number are important parameters in studying feedbacks. With no absorption cloud-system is even more organized with double the differences in the extreme rainfall accumulation
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a. Satellite b. WRF‐Chem (colormap) vs WRF (contour)
c. Cloud Water & CAPE d. SO2
Aerosol Feedbacks (in this case) Lead To Better Precip Predictions
Aerosol mass, composition and number are important parameters in studying feedbacks. With no absorption cloud-system is even more organized with double the differences in the extreme rainfall accumulation
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Δ 2‐m Temp.( C)
Δ PBL heights (m)
Δ CO(ppb)
Δ NOx(ppb)
Δ O3(ppb)
Δ PM 2.5 (ug/m3)
Day Night Day Night Day Night Day Night Day Night Day Night
Δ land‐cover(2004‐1992) 1.8 2.4 400 80 20 -40 -4 -10 16 6 16 5
Δ anthropogenic heat (NewLucy‐no heat) 0.8 1.2 300 100 -60 -160 -6 -18 3 18 -8 -12Δ pollution emission
(2010‐2006) 0.3 0.24 50 20 40 200 7 30 8 -16 15 40Δ aerosol feedbacks(with – without) -0.8 -0.6 -240 -80 100 160 0.8 7 7 -10 22 25
Δ horizontal resolutions (27km ‐81km) 1.9 4.5 190 90 80 -50 7.3 21 10 -10 -10 -37
Δ horizontal resolutions (9km ‐27km) -0.1 0.3 120 70 55 -45 4 30 0.5 -11 3 23
Δ horizontal resolutions (3km ‐9km) 0.1 0.4 50 30 16 -55 1.6 -2 -0.4 -0.02 0.1 -6
Δ vertical resolutions (27layers ‐9layers) 1.6 1.2 350 -40 75 300 4 40 6 -30 44 40
Δ vertical resolutions (54layers ‐27layers) 0.3 0.2 100 -20 10 100 2 30 -2 -8 10 20
base value 30.8 26.6 1810 258 476 1040 15.5 79.5 99 8.87 153 202
Challenge in including feedbacks
Systematic changes on order of up to > 20% for each of the different interactions
Moving Forward• Adding more physical “improvements” to weather‐AQ models can
result in significant (10‐30%) systematic changes in important parameters (T, PBL, PM2.5,…).
• Over time better representation of these processes (and supporting data sets) should lead to improved predictions.
• How can we accelerate prediction improvements and guidance into what processes/level of detail are needed for applications (and to transition to operations).– Need better understanding of processes and their
representation in models> Multimodel intercomparison studies and sensitivity analysisAQMEII, EuMetChem, MICS‐Asia, …..
‐ Need enhanced observing and assimilation systems to better constrain aerosol distributions to test the hypothesis “better aerosols predictions for air quality management AND improved weather prediction”
Developing WRF‐Chem Adjoint (TL) Buiding on WRFPLUS Capabilities for Sensitivities and Assimilation
So far AD and TL developed for:• Mix‐activate (Indirec t effects)• Goddard Short wave radiation scheme• Morrison Cloud microphysics• Optical properties code (AOD, SSA, backscattering) including water uptake for detailed aerosol
module (MOSAIC)• Gas phase chemistry (KPP)• Emissions (Anthropogenic and Biomass Burning ) • ACM2 PBL (Pleim et al. 2007) w/ integrated tracer transport (2013) (7)• Pleim‐Xiu and SFCLAY surface layer schemes (7,1)• Pleim‐Xiu and SLAB LSMs (7,1)• Wesely Dry Deposition• chem advection (similar to tracer advection by Xin Zhang in 2012)• Grell‐Freitas Convection (2013) [chemical tracers in development]• GOCART Aerosols
– BC aging– PM summation – Sulfate chemistry (pending testing)
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Aerosols Meteorology
Direct effects
Indirect effects
Improve aerosol predictions (Radiation)
(cloud formation, CCN)
But large uncertainties in prediction of aerosols
If well predicted improved
Saide et al. ACP, 2012
Half Soccer plot for 3 flights
Forecast
Daytime Nd after assimilation vs GOES and in‐situ aerosol22
• Large improvements during the first 2 days for all domain
• GOES Assimilation improves agreement with VOCALS‐Rex C130 aerosol number and mass observations
GOES10 OBS WRF-ChemGuess
WRF-Chem Assimilated
+22 hrs +22 hrs +22 hrs+5 hrs +5 hrs +5 hrs
MODIS NdAssimilation
Saide et al., PNAS, 2013
Important air‐quality forecasting improvements when adding new GOCI (Geostationary) data to assimilation
23 DUST SMOKEAnthroAnthro Anthro
GOCI AOD
Saide et al., in review 2014
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• Beginning to get a sense for modeled feedbacks at urban/regional scales.
• Feedbacks are real and can be large.• Lack of explicit treatment in NWP may be limiting forecast
skill (assimilation can only do so much).• Over time better representation of these processes (and
supporting data sets) should lead to improved predictions.
• Need to devise plans to accelerate prediction improvements and to provide guidance into what processes/level of detail to transition to operations.
Modeling Aerosol-Chemistry-Climate at urban/regional scales Closing Thoughts
Biomass burning smoke before and during the severe weather outbreak of April 27 and modeled impacts on tornado parameters. Left: 42 hour back trajectories from the beginning of violent tornado tracks, with circles marking 24 hour, observed AOD over ocean on 27 April, and fire
locations for the day before.Top‐right: Statistics of Significant Tornado Paramter (STP) used in tornado forecasting (Thompson et al., 2003) from WRF‐Chem simulations with fire emissions and data assimilation (blue) and without fire emissions (red). Statistics are computed for the mean near‐storm environment for each tornado, with numbers on top of each panel representing the number of tornadoes that go into the statistics and “*” showing significant differences at the 5% p‐value level. Bottom‐right: Map of mean STP differences for the outbreak period between the two simulations.
Application #5. Severe Storm (tornado) Prediction Saide et al., 2014 in review
FireOnoff
Application #5. Severe Storm (tornado) Prediction
• Smoke during this event leads tooptical thickening of shallow clouds• Soot within the smoke enhancesthe capping inversion throughradiation absorption.• These effects result in lower cloudbases and stronger low-level windshear in the warm sector of theextratropical cyclone generating thestorm outbreak, two indicators ofhigher probability of tornadogenesisand tornado intensity and longevity.•These mechanisms contribute totornado modulation by aerosols,further highlighting the need toincorporate aerosol feedbacks innumerical severe weatherforecasting.