Forecasting Air quality in China Using CAMS Boundary Conditions: the PANDA Project Guy P. Brasseur and Idir Bouarar June 206

The PANDA Project Coordinator: Guy Brasseur Deputy Coordinator: Prof. Xuemei Wang Period: Jan Dec Budget: 2 Millions Euros

The Different Elements of Prediction System

Some Elements of the AQ Downscaling System Boundary and Initial Conditions (CAMS C-IFS)) Emissions (natural, anthropogenic) Weather forecasts (IFS, NCEP, etc.) Representation of PBL processes Learning from daily diurnal predictions

Assimilation in CAMS WRF-Chem prediction 20x20km MACC forecast/reanalysis as IC & BC WRF-Chem 60x60km 20x20km Downscaling to Regional Scale in Asia 7 x 7 km Satellite data Air Quality Index (AQI) WRF-Chem prediction AQI

PANDA Methodology Global Model Ensemble of Regional Models 60 km Ensemble of Regional Models 60 km Local AQ Model Ensemble of Sub- regional Models 20 km Ensemble of Sub- regional Models 20 km Satellite Observations Assimilation CAMS operational validation Dissemination of AQ predictions validation

Do the Initial and Boundary Conditions Matter?

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS Do initial and boundary conditions matter? Monthly mean surface O3 concentrations for January 2010 simulated by WRF-Chem using MOZART (left) and MACC (right) initial and boundary conditions. MOZART ECMWF Idir Bouarar

Do the Emissions Matter?

Ratio between REAS and MACCITY NOx Emissions in Asia Analysis from the CAMS Group on Emissions

Do emissions matter? CO NOx WRF-Chem simulations (Jan. 2010) with: -HTAPv2 emissions (HTP) -REASv2 emissions (RAS) -MACCity emissions (MCT) With HTAPv2 Absolute differences with RAS and MCT simulations

O3 PM2.5 Do emissions matter?

Does the formulation of the Boundary Layer (PBL) Matter ?

Formulation of the PBL Surface concentrations depend critically on adopted PBL parameters. Height of the PBL (which decreases abruptly in early evening) Vertical mixing, specifically in the nighttime PBL. Needs adjustment in urban areas to account for heat island effects and mechanical turbulence generated by the buildings How well are the models doing?

PBL Height Diffusion Coef. At 18:00 Diffusion Coef. At 19:00 Below: Nightime Vertical exchange coefficient in rural areas In urban areas, this coefficient is set equal to 2 m 2 s -1 Rural nighttime PBL Daytime PBL

Effect of increasing diffusion in the urban area of Beijing CNTRL: Control run (2 m 2 s -1 ) TED: run with increased vertical diffusion from 2 to 10 m 2 s -2

Increased diffusion in urban areas leads to  decrease in NOx  increase in O 3 NOx O3O3

What Do we Learn from Daily Forecasts?

Ensemble Predictions for 37 Chinese Cities Using 7 Models

Predictions of NO 2 and O 3 Guangzhou 3 June 2016 CHIMERE C-IFS NO 2 O3O3

Predictions of NO 2 /O 3 Guangzhou 3 June 2016 WRF- Chem /HH SILAM

CHIMERE C-IFS SILAM WRF-Chem/HH EMEP LOTOS-EUROS PM2.5 Xi’An 3 June 2016

Conclusions CAMS predictions provide unique data used as boundary conditions for regional predictions in Asia. CAMS assimilates data and accounts for highly variable sources such as fire emissions and global meteorology. This allows regional predictions to be “relaxed” to space observations and to account for long- range influences.

Towards a Constellation of Similar Initiatives? North America South America Africa Europe Asia Russia Driven by the same global models and space observations, using similar methodologies, data bases, models, and providing the same type of products and services. MAP-AQ Initiative in support of WMO/GAW project

MAP-AQ Modeling, Analysis and Prediction of Air Quality To develop and implement a global air pollution monitoring, analysis and prediction system with downscaling capability in regions of the world (e.g., Asia, Latin America, Africa) that are affected by high levels of atmospheric pollutants.

Thank you! Questions?