EUCAARI

1. EUCAARI European Integrated Project on Aerosol - Climate - Air Quality Interactions

2. Background of EUCAARI • Estimates on climate effect of aerosols have large uncertainity • Physical, dynamical parameterizations do not give consistent results to climate forcing • Future climate effect to parameterizations done for current climate are often poorly known • What will be the natural sources of aerosols in changing climate? • Current and future and controls of anthropogenic primary aerosol and precursor emissions • Do they have a feedback to climate?

3. Objectives of EUCAARI 1.Reduction of the current uncertainty of the impact of aerosol particles on climate by 50% and quantification of the relationship between anthropogenic aerosol particles and regional air quality. To achieve this objective EUCAARI will concentrate on the areas of greatest uncertainties and will: • Identify and quantify the processes and sources governing global and regional aerosol concentrations • Quantify the physico-chemical properties of atmospheric aerosols • Quantify the feedback processes that link climate change and atmospheric aerosol concentrations with emphasis on the production and loading of natural aerosols and their precursors 2. Quantification of the side effects of European air quality directives on global and regional climate, and provide tools for future quantifications for different stakeholders. 1a 1b 2 1c

4. AUTHORITIES POLICY MAKERS AUTHORITIES POLICY MAKERS INSTRUMENT INSTRUMENT *Recommendations *Recommendations *Recommendations *Recommendations DESIGN DESIGN EUCAARI - SERVICE EUCAARI - SERVICE *Test environment for *Test environment for *Test environment *Test environment * Data * Data different climate different climate for different climate for different climate 1.Observation Databank 1.Observation Databank 1.Observation Databank 1.Observation Databank scenarios scenarios scenarios scenarios - - - - Harmonized Existing Harmonized Existing Harmonized Existing Harmonized Existing data data data data RESEARCH - - EUCAARI data EUCAARI data RESEARCH - - EUCAARI data EUCAARI data INDUSTRY INDUSTRY ESA – EU ESA – EU 2.EUCAARI Model & 2.EUCAARI Model & 2.EUCAARI Model & 2.EUCAARI Model & COMMUNITY COMMUNITY *Recommendations *Recommendations GMES GMES Submodels sources Submodels sources Submodels sources Submodels sources *Submodels *Submodels *Test environment for *Test environment for * Validated * Validated 3. Simulation data 3. Simulation data * data 3. Simulation data 3. Simulation data * data different air quality different air quality measurements measurements * education module * education module scenarios scenarios AUTHORITIES AUTHORITIES POLICY MAKERS POLICY MAKERS AUTHORITIES AUTHORITIES POLICY MAKERS POLICY MAKERS INSTRUMENT INSTRUMENT INSTRUMENT INSTRUMENT *Recommendations *Recommendations *Recommendations *Recommendations *Recommendations *Recommendations *Recommendations *Recommendations DESIGN DESIGN DESIGN DESIGN EUCAARI EUCAARI EUCAARI EUCAARI - - - - SERVICE SERVICE SERVICE SERVICE *Test environment for *Test environment for *Test environment *Test environment *Test environment for *Test environment for *Test environment *Test environment * Data * Data * Data * Data different climate different climate different climate different climate for different climate for different climate for different climate for different climate 1.Observation Databank 1.Observation Databank 1.Observation Databank 1.Observation Databank scenarios scenarios scenarios scenarios scenarios scenarios scenarios scenarios - - - - Harmonized Existing Harmonized Existing Harmonized Existing Harmonized Existing USER INTERFACE FOR USER INTERFACE FOR USER INTERFACE FOR USER INTERFACE FOR data data data data STAKEHOLDERS STAKEHOLDERS STAKEHOLDERS STAKEHOLDERS - - - - EUCAARI data EUCAARI data EUCAARI data EUCAARI data RESEARCH RESEARCH RESEARCH RESEARCH INDUSTRY INDUSTRY INDUSTRY INDUSTRY ESA ESA ESA ESA – – – – EU EU EU EU 2.EUCAARI Model & 2.EUCAARI Model & 2.EUCAARI Model & 2.EUCAARI Model & COMMUNITY COMMUNITY COMMUNITY COMMUNITY *Recommendations *Recommendations *Recommendations *Recommendations GMES GMES GMES GMES Submodels sources Submodels sources Submodels sources Submodels sources *Submodels *Submodels *Submodels *Submodels *Test environment for *Test environment for *Test environment for *Test environment for * Validated * Validated * Validated * Validated 3. Simulation data 3. Simulation data 3. Simulation data 3. Simulation data * data * data * data * data different air quality different air quality different air quality different air quality measurements measurements measurements measurements * education module * education module * education module * education module scenarios scenarios scenarios scenarios Methods to reach Objectives Experiments: • Laboratory experiments • Field experiments • Lagrangian experiments • Instrumentation development • Satellite Retrievals Modelling • Process model simulations • Regional / Global aerosol models • Global climate models and Integrated assesment models • ”Network of models” – Using more accurate, but smaller scale models to provide parameterizations for larger models Integration • Integration between different scales of data and modelling • Dissemination through EUCAARI-portal • During project to partners • After project to end-users • Simulation- and data bank • End User information

5. Organization of Work • The project is divided to elements which coordinate studies of different parts of aerosol life-cycle • Cross-disiplinarity • Multi-scale and –method approach • Each Element has Work Packages, which in turn are divided into Tasks

6. Organization

7. WP3.2, i.e. us! Management PROJECT OFFICE ELEMENT LEADERS WORK PACKAGES Work Packages describe a single problem or methodology. Each WP has one or more Milestones to deliver. They are subdivided into tasks with different Deliverables Element managers lead the Elements and provide information of element progress and results to other elements and to Project coordination Project office handles the day-to-day project wide coordination, IP issues and reporting

8. Project Timelime • Project is organized in phases • Experiments and infrastructure building are concentrated on start of the project • Global climate and policy modelling in the end of the project WP3.2 involvement

9. Organization of WP3.2

10. OD CDNC Cloud optical depth A OD WP3.2 Assessment and quantification of the Indirect Aerosol Effect though Observations and LES modelling Warm cloud CDNC / size Cloud albedo CDNC CCN F A CCN CCN Mass Earth Radiation Budget Aerosol mass emission Aerosol mass Surface

11. Tasks of WP3.2

12. 3.2.1: IOP preparation and operation 15 April – 1 June 2008 Contributors : KNMI, FMI, CESAR consortium , CNRM, IFT, IGFUW, CNRS – LAMP Tools: CESAR and airborne measurements

14. 3.2.2: 9 months cloud and aerosol observations at CESAR 1 Jan 2008 – 1 October 2008 Contributors: the CESAR – partners, FMI, other parties

15. Remote Sensing site at CESAR / Cabauw

16. Earth Radiation Budget F A MSG+BSRN Cloud albedo A OD MSG+Surface Remote sensing + aircraft data Cloud optical depth OD CDNC Warm cloud CDNC / size CDNC CCN TowerMeasurements CCN CCN Mass Tower+Surface Measurements Aerosol mass Aerosol mass emission CESAR Surface and Remote Sensing Observations of Aerosol, Clouds and Radiation integrated with Airborne and Satellite observations

18. 3.2.3: Data analysis, case study simulations, after June 2008 Contributors: IGFUW, CNRM, IFT, KNMI, TNO, CNRS - LAMP Tools: Data analysis. Model simulations, LES with microphysics, aircraft data analysis tools Integrated Profiling techniques Integrated Profiling at CESAR, an advanced method to synthesize CESAR columnal observations to be used for climate, weather and process studies

19. Integrated Profiling Technique (IPTRL) 14 HATPRO brightness temperatures (TB) dabs profiles • optimized profiles of • temperature (T) • humidity (q) • LWC (on variable radar resolution) Radar-Lidar Ratio Bayesian Retrieval a priori LWC profile (mod. adiabatic) a priori T und q profiles (nearest-by radiosonde) measurement-consistent with respect to error covariances

21. LES Nudging over Cabauw • Simulate cloudy boundary layer at Cabauw on a daily basis with LES. • Problematic because large and unknown LS forcings • Integrating Profiling Technique (IPT) is the way out (Lohnert) • Use relaxation to IPT profiles: Correct mean state is provided by the IPT while the variability on a scale of 50m~10km is provides by LES.

23. 3.2.4: Assessment of aerosol impact on cloud life cycle, After June 2008 Contributors: CNRM, KNMI, IGFUW, CNRS - LAMP Tools: Data analysis, LES models

24. Boundary Layer Clouds 1st and 2nd A=0.50 Entrainment-Mixing Radiative Transfer rl=0.2 g kg-1 Microphysics Onset of Précipitation CCN Activation rv=20 g kg-1 Précipitation Evaporation Turbulent Fluxes A=0.25 Aerosol Indirect Effect rv T

26. 3.2.5: Impact of clouds, aerosols on SW irradiance Contributors: KNMI, FMI, CNRS - LAMP Tools: Radiative transfer calculations, data analysis

27. Baseline Surface Radiation Network

28. Observations: Surface and TOA irradiances

30. Aims for the coming two days 1. Get the science right, know who is who 2. Get the flight plans and observations to serve the science 3. Decide on necessary and sufficient instruments 4. Decide on the right weather conditions 5. Enjoy ourselves (!)

31. Outputs to this meeting: All ppt files to be collected by Gerd-Jan v Zadelhoff Summary of meeting with: Decisions on instruments Decisions on flight plans • Decisions on ground-based operations