Multi-Model Ensemble Forecasts of Regional Air Quality in

Idir Bouarar, Guy P. Brasseur, Katinka Petersen Max Planck Institute for Meteorology, Hamburg, Germany With contribution from Bas Meijling (KNMI), Mikhail Sofiev and D. Rostislav (FMI) Outline qBackground qEnsemble Model Forecasting System qResults qImproving Model Performance qNext Steps Forecasting Air Quality in China: A Challenging Task

• Air quality forecast systems rely also on meteorological models which still contain some inaccuracies in the formulation of physical processes

• Chemical (and Physical) processes are not always carefully formulated: simplified assumptions are sometimes adopted

• Large uncertainties in current emissions inventories

• Urban environment “every where!”: - 9 megacities of 10-25 Million inhabitants - 37 Cities over 3 Million inhabitants

• The formation mechanisms leading to severe haze episodes remain uncertain

• Complex landscape and conditions in some cities: e.g. Beijing is prone to develop stagnant conditions, because of surrounding mountains to the north of the city

• Coupling interactions between complex meteorological conditions, pollution sources, and atmospheric transformation processes

• … An Ensemble of 7 Models to Forecast Air Quality in China

ECMWF 40km Institute Model Emissions Domain Res. KNMI (NL) CHIMERE MEIC E. China: 0.25 v2013 INTEX-B 18-50N, 102- MEGAN 132E ECMWF C-IFS MACCity China: 10-49N, 0.4 (UK) 75-135E SCUEM SCUEM WRF- MEIC2010 East China: 6km (CN) Chem 21-44N, 104- 6km 132E FMI (FI) SILAM MACCity E. Asia: 0.25 GFAS 7-54N, 67-147E MEGAN MPI-M WRF- HTAPv2 E. China: 0.2 (DE) Chem FINN 18-45N, 95-125E MPI-M MEGAN 60km Met.NO EMEP HTAP+MEI China: 0.125 20km (NO) C 15-55N, 90 (PanHam) TNO (NL) LOTOS- MEIC E. China: 0.125 Euros Edgar 20-45N, 105- 130E Selected cities

– Beijing – Harbin – Hefei – Shanghai – Suzhou – Changzhou – Guangzhou – Qingdao – Tangshan – Shenzhen – Jinan – Changsha – – – Xuzhou – Tianjin – – Wenzhou – Chengdu – Kunming – Guiyang – Nanjing – Wuxi – Ürümqi – Xi’an – Xiamen – Zibo – Wuhan – Changchun – Fuzhou – Shenyang – Ningbo – Shijiazhuang – Dongguan – Nanning – Chongqing – Taiyuan • Cities with over 3 million inhabitants (37), according to 2010 census • Most cities are covered by all models Surface Observations

• Currently, collected from websites (e.g. www.pm25.in): – 367 different cities – 1526 stations • Species: O3, NO2, PM2.5, PM10 (soon CO, NO, SO2?) • Hourly data automatically copied to KNMI database, starting from April 2015 • Ground value given is an area average, typically based on 5-12 stations Schematic Overview Data Flow

Courtesy B. Meijling

Forecast ... Forecast Retrieval hourly model #1 model #N station measurements

Postprocessing Postprocessing

Retrieval from local server

Central database (KNMI)

www.marcopolo-panda.eu/forecast

Calculation ensemble

Generation images (time series and maps)

Publication on webserver Operational Forecasting System at MPI-M with WRF-Chem (DKRZ, Hamburg, Germany)

KNMI 10pm (LT) 6pm (LT) www.marcopolo-penda.eu Meteo. WRF NCEP GFS Meteo Forecasts spinup

10pm (LT) 00:00 (LT): Chem. BC: 3d Forcasts ECMWF Dissemination CAMS

aqicn.org/forecast Chem. IC: Day -1 Forecast HTAPv2 FINN Emissions MarcoPolo-PANDA Forecasting Systems

www.marcopolo-panda.eu/forecast Results

q Cases: 1. All/Most Models Agree 2. All/Most Models Fail 3. Models Show Substantial Differences q Ensemble Model Performance Case 1: All/Most Models Agree

CHIMERE WRF-Chem/SCUEM

SILAM WRF-Chem /MPI Case 1

WRF-Chem

Clean air

CHIMERE

Polluted air Case 1

WRF-Chem PM2.5 Prediction

28Nov.-02Dec. 2015 Case 2: All/Most Models Fail Special Haze Events in N. China

PM2.5 Beijing 3 March 2016

WRF-Chem/SCUEM CHIMERE

WRF-Chem MPI C-IFS SILAM Case 3: Models Show Substantial Differences

CHIMERE C-IFS EMEP

Lotos-Euros WRF-Chem WRF-Chem MPI-M SCUEM

SILAM Mean Mediam

Courtesy K. Rostislav Case 3 PM10 14 Apr. 2016 X’ian

WRF-Chem

CHIMERE Case 3

Observations 13 April 2016 05-06 May 2016 WRF-Chem SILAM SCUEM CIFS CHIMERE

28Nov.-02Dec. 2015 15-18 March 2016 Clear N Clear PM2.5 Ice Sunny Beijing Haze Fog 9C N S N S -3C

NO2 Beijing Median Values

O3 Beijing CHIMERE C-IFS EMEP Courtesy K. Rostislav

O3 June 2016

LOTOS WRF/MPI WRF/SCUEM

Bias r CHIMERE 25.8 0.83 C-IFS 19.1 0.79 EMEP 25.1 0.71 SILAM Mean Median LOTOS 44.1 0.74 MPI 45.5 0.72 SCUEM 74.7 0.78 SILAM 36.1 0.73 Mean 30.5 0.84 Median 29.2 0.83 CHIMERE C-IFS EMEP Courtesy K. Rostislav

PM2.5 June 2016

LOTOS WRF/MPI WRF/SCUEM

Bias r CHIMERE 37 0.63 C-IFS 74 0.47 EMEP 41 0.53 SILAM Mean Median LOTOS 20.8 0.65 MPI 28.8 0.4 SCUEM 14.7 0.54 SILAM 41.6 0.63 Mean 33.3 0.67 Median 25 0.64 Other challenging cases

Beijing Coastal Cases X’ian Shanghai Chengdou Shanghai Guangzhou

Regional + Urban Dust Complex Terrain

X’ian Guangzhou Chinese new year! (Coincidence?)

PM2.5 in Beijing [ug/m3], 8 February 2016 Diurnal Variability NO2

• Significant differences among models

• Increased bias at nighttime

• Mean/median ensemble forecast affected by individual model skills O3 Nighttime Boundary Layer in Urban Environments Daytime Nighttime

NO2

Daytime O3 Nighttime

WRF-Chem Beijing Boundary Layer Effect in Urban Environments Sensitivity to Emissions

T1: CONTROL NOx T2: -50% NOx

T3: MACCity Emissions (0.5x0.5)

T4: Other PBL

O3 Diurnal Variability of Emissions

With diurnal variation Without diurnal variation 0.0800 0.0700 NOx 0.0600

0.0500 power 0.0400 industry 0.0300 residential 0.0200 transportation 0.0100

0.0000 0 2 4 6 8 10 12 14 16 18 20 22 hour

O3 CO Effect of Vertical Diffusion

CONTROL -50% NOx Kz >=10

CO NOx

O3 Beijing O3 forecast Before

After Perspectives

• the Systems : see, in long-term, how current measures to reduce emissions are efficient

• Consistent Scientific Evaluation: Model-Intercomparison Exercise

• Get Access to Monitoring Stations and Other Observations

• Provide Other Products/Information: AQI, Other Chemical Species, Visibility, Haze

• Further Investigate Boundary Layer Impact on Pollution in Urban Environment

• Improve key model processes (e.g. SOA formation, key Met. Parameters…)

• Update anthropogenic emissions (e.g. with satellite inversions)

• Downscale to City-level

• …… Improving Model Predictions Assimilation Meteorology Chemical IC : WRF-Chem/DART

CTM CAMS BC

Chemical Post-processing Analog-Based / Bias Correction BC

Delle Monache et al. 2013

Emissions Life must go on …