HTAP2/AQMEII 3 model evaluation

S. Galmarini, E. Solazzo, C. HogrefeAQMEII 3 modelling community

AQMEII 3: phase 3 of the Air Quality Model Evaluation International InitiativeCoordinated by EC/JRC and US-EPA

Regional program of the HTAP2 exercise (Hemispheric Trasp. Of Air Pollution)

Dealing with 2010 regional scale modelling over NA and EUEmissions: MACC and US EPA/Env Canada + Edgar v2 with embedded regional EI

BC: global models participating to hemispheric exercise, ECMWF

AQMEII 3 – delivery status

Institutegas phase

aerosolprecipchem

ozonesondes

met AERONETAERONET PROFILES

MOZAICgridded conc

gridded depgridded emissions

BASE CASE

US EPA x x x x x x x x x x x

ITU x x x x x x x x x x x

Aarhus x x x x x x x x x x x

Univ Murcia x x x x x x x x x x

Kings College x x x x x x

INERIS/CIEMAT x x x x x x x x

TNO x x x x x x x x x x

Univ L'Aquila x x x x x x x x x x

RSE x x x x x x x

Ricardo-AEA x x x x x x x

HZG x x x x x x

FMI x x x x x x x x x x

Univ Hertford x x x x x x x x x x

HZG-EDGAR x x x x x x

Environ x x

ECMWF x

In red-ish the contribution to North America, the others are all for Europe

TR1_MACC_bas WRF-CMAQ_1 30 km x 30 km, 184 x 156 cells

FI1_HTAP_basFI1_MACC_bas

ECMWF-SILAM_H0.25x0.25 deg

NL1_MACC_bas ECMWF-L.-EUROS0.5x0.25 deg

UK2_HTAP_bas WRF-CMAQ_2 30 km

IT2_MACC_bas WRF-WRF/Chem_1 23km

IT1_MACC_bas WRF-CAMx 265x220 grid cells at 23 km

DE1_HTAP_bas CCLM-CMAQ_H 0.22x0.22 deg

ES1_MACC_bas WRF-WRF/Chem_2 23km

UK3_MACC_bas WRF-CMAQ_3

DK1_HTAP_bas WRF-DEHM 50 km

FRES1_HTAP_bas ECMWF-Chimere_H 025x025 deg

UK1_MACC_bas WRF-CMAQ_4 15km

DE1_SMOKE_bas CCLM-CMAQ0.22x0.22 deg

US3_SMOKE_bas WRF-CMAQ_1 12 km, 459x299 cells

US1_SMOKE_bas WRF-CAMx 459x299 cells

29 February 20164

Spectral decomposition of time series of ozone derived from power spectrum analysis

Four components ID, DU, SY, LTID : intra-day (fast-acting < 12h)DU: diurnal (day/night [12h; 2.5d]SY : Synoptic (weather [2.5d;21d]LT : Long term (slow processes > 21d)

LT is the base line, the other components are obtained using the filter as band-pass and have zero mean

ID

SY

LT

DU

Spectral decomposition

mMSE: Portion of the variance not explainable by the model: either model too poor (under-fitting) (LT component) or measurements too noisy (DU component), possibly both.

ERROR

LT SY DU ID

bias variance mMSE

Solazzo & Galmarini, 2015: Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation, AE

The sub-regions have been identified by looking at the signal associativity by clustering analysis of the LT and SY components, following the procedure outlined by Solazzo and Galmarini (2015)

Ozone, RMSE by sub-region, season and component

FT: Full time series (no filter)DU: diurnal (day/night [12h; 2.5d]SY : Synoptic (weather [2.5d;21d]LT : Long term (slow processes > 21d)

The LT contains all the bias by construction

The signs indicate model underprediction (-) or overprediction (+) of bias and variance.

bias =(<mod>-<obs>)2

var = (σmod- r σobs)2

mMSE= 2(1-r) σmod σobs

PM10, RMSE by sub-region, season and component - EUROPE

FT: Full time series (no filter)DU: diurnal (day/night [12h; 2.5d]SY : Synoptic (weather [2.5d;21d]LT : Long term (slow processes > 21d)

The LT contains all the bias by construction

The signs indicate model underprediction (-) or overprediction (+) of bias and variance.

bias =(<mod>-<obs>)2

var = (σmod- r σobs)2

mMSE= 2(1-r) σmod σobs

Summary• A new approach to model evaluation is presented• It could be formally defined as a combination of operational

and diagnostic evaluation• The application to the spectral decomposition of mod and obs

signal provides a clear identification of the nature of the error, and the components of the signal where it is mainly appearing

• The spatial representation allows also to collect indication on the possible sources of the error

• Current work is underway ti further decompose the error into process specific components for a clearer identification of the origin

Air pollution impacts and the associated external costs over Europe and North America as calculated by a multi-model ensemble in frame of

AQMEII3

lead by Aarhus University

• the simulated surface concentrations of health related air pollutants from each modelling group will serve as input to the Economic Valuation of Air Pollution (EVA) model in order to calculate for the first time the impacts of these pollutants on human health and the associated external costs over the two continents based on a multi-model ensemble approach.

• In addition, the impacts of a 20% global emission reduction scenario on the human health and associated costs will be calculated.

• health impacts will be calculated using an ensemble of optimal set of models that will be determined by error and redundancy analyses in frame of the AQMEII3 project, in order to get a better understanding associated with the multi-model ensembles that would highly impact the policy applications.

• investigate the impact of global and regional emission reduction scenarios from HTAP on the concentration and deposition of air pollutants, focusing on O3, NO2, CO, SO2, PM10 and PM2.5 over Europe and North America.

• The analyses will be conducted for the annual mean and seasonal response of the regional surface levels and for the surface stations as well as the vertical levels over the ozonesonde and Aeronet stations.

• The stations close to the boundaries will be considered in particular. Individual model responses as well as the response from ensemble mean and median will be calculated in order to estimate the uncertainty among the different models and get a more quantitative measure of the impact of the perturbation scenarios.

Impact of global and regional emission perturbations on air pollutant levels and deposition over Europe and North America, Part I: Gaseous pollutants, Part II:

Particulate Matter.

lead by Aarhus University

Impact of Boundary Conditions and Emission Reductions

• HTAP Regions for the perturbation scenarios

NA EU

EA

Perturbation ScenariosPriority EU Modeling Domain NA Modeling Domain

1 GLO• Boundary conditions from the “GLO” scenario

(all anthropogenic emissions reduced by 20% globally)

• Reduced anthropogenic emissions within the modeling domain by 20%

GLO• Boundary conditions from the “GLO” scenario

(all anthropogenic emissions reduced by 20% globally)

• Reduced anthropogenic emissions within the modeling domain by 20%

2 NAM• Boundary conditions from the “NAM” scenario

(all anthropogenic emissions reduced by 20% in the HTAP “North America” region)

• Keep emissions the same as in the base case

EAS• Boundary conditions from the “EAS” scenario

(all anthropogenic emissions reduced by 20% in the HTAP “East Asia” region)

• Keep emissions the same as in the base case3 EUR

• Boundary conditions from the “EUR” scenario (all anthropogenic emissions reduced by 20% in the HTAP “Europe” region)

• Reduce anthropogenic emissions within the modeling domain by 20% for the portions of the modeling domain that fall within the HTAP “Europe” region

NAM• Boundary conditions from the “EAS” scenario

(all anthropogenic emissions reduced by 20% in the HTAP “North America” region)

• Reduce anthropogenic emissions within the modeling domain by 20% for the portions of the modeling domain that fall within the HTAP “North America” region

Participating Modelling Groups

– 11 groups from Europe modelling the European domain

– 2 North American and 2 European groupsmodelling the North American domain