1. Pan-Eurasian Experiment (PEEX)
Tuukka Petäjä, Hanna K. Lappalainen and Markku Kulmala
PEEX-HQ-HELSINKI, University of Helsinki / FMI, FINLAND
GEOCRI meeting, St. Petersburg, Russia

2. PEEX – RESEARCH COMMUNITY
A network of 400 researchers
Research communities from 20 different countries
PEEX Initiative Kick Off in October 2012 Helsinki meeting
PEEX Meetings: Helsinki (1), Moscow (2), Hyytiälä (3), St. Petersburg (4)
China PEEX Kick off meeting Nov 2013, Beijing
1st PEEX Science Conference/Meeting in 2015, Helsinki (5)
2nd PEEX Science Conference/Meeting in 2016, Beijing (6)
3rd PEEX Science Conference in Moscow in Sep.2017
Active in many scientific forums in Europe, Russia and in China;

3. PEEX – SCIENCE APPROACH
Research agenda & large scale research questions: Science Plan
PEEX Special issue in Atmospheric Chemistry and Physics
Themes: e.g. climate change, air quality, biodiversity loss, use of natural resources, industry, energy production and transport
Editors V.-M. Kerminen, M. Heimann, D. Spracklen, T. Laurila, A. Ding, and I. Salma.
PEEX papers in J. Digital Earth / J. Geography, Environment Sustainability
More information:

4. PEEX – addresses global grand challenges
1) Multidisciplinary, multiscale science (Focus-1)
2) Comprehensive observations (Focus-2)
3) Science based services and impacts (Focus-3)
4) Knowledge transfer, multidisciplinary education (Focus-4)
GLOBAL
ENVIRONMENTAL
CHALLENGES
Refs. Kulmala et al. ACP 2016, Lappalainen et al. ACP 2016, Hari et al. ACP 2015, PEEX Science Plan

5. PEEX – RESEARCH INFRASTRUCTURE
THREE COMPONENTS TO IMPLEMENT THE PEEX RESEARCH AGENDA
Satellites provide context
Ground-based provides details
Models complete the picture
PEEX-Russia:
AEROCOSMOS
PEEX-China:
RADI
Univ Helsinki
FMI
Ground-based
Satellites
CURRENT STATE
• Initial Conditions
• Assimilation
4D targeted chemical &
microphysical detail point-location
time series
frequent, global
snapshots;
For example,
aerosol amount &
aerosol type maps,
plume & layer heights
Model Validation
• Parameterizations
• Climate Sensitivity
• Underlying mechanisms
Models
space-time interpolation,
calculation and prediction
Ref. Laj, Kulmala, Petäjä

6. SMEAR - stations SMEAR II-station 20 years of operation
Prof. Markku Kulmala, UHEL
Global flagship for Finnish Climate research measuring Ecosystem-Atmosphere relations
Continuous, comprehensive observations enables a unique information on climate change e.g. on aerosols and ecosystem-atmosphere interactions
10/08/15

7. WMO GAW Aerosol Network
Global SMEAR – a network of high quality observations
“filling the in-situ observational gaps in the world with a station network to provide comprehensive policy relevant data”
WMO GAW Aerosol Network
We welcome more collaborations, especially in Central and South America, Africa, Central and South Asia
10/08/15
PEEX preliminary work on existing in-situ observations

8. PEEX RESEARCH INFRASTRUCTURE (Focus-2)
Tools: multi-scale modelling & observation approach
(i) RI STRUCTURE
(ii)In-situ + satellite + models
(iii) BACKBONE FOR
PEEX IN-SITU: SMEAR
(iv) PEEX modeling platform: multi-scale approach

9. PEEX – NEXT STEPS
Finland Chairmanship of the Arctic Council starting in 2017
circumpolar collaboration
active participation in GEOSS CRI, SAON and AMAP WGs
active information exchange and collaboration together with Finnish stakeholders; Arctic Ambassador, Ministry of Foreign Affairs, Prime minister’s Office
promoting flagship concept in Russia & in China (global SMEAR)
contributing to the negotiations of new stations
continuing the metadata collection and opening the discussion on “open data” together with Russian and Chinese partners
PEEX Science Conference in Moscow Sep.2017
PEEX Modelling Platform concept
Consolidating PEEX’s role in China, in collaboration with DBAR

10. THANK YOU www.atm.helsinki.fi/peex/ peex-hq@helsinki.fi

11. 1-FROM MEASUREMENTS TO SCIENTIFIC UNDERSTANDING
PEEX-View-Tool: Visualize and analyze PEEX big data products
visualizes the time series for the modeled data vs. observed data (aerosol number concentration). 
modeled data based on Global Aerosol model (ECHAM-HAM)
Trajectories based on (NILU pre-calculated product) and satellite products (AOD, MODIS data)
Observations from Hyytiälä and Pallas as an example.
In the future expand dataset & the analysis for other parameters
demo software by
A. Malkamäki & R. Makkonen, Univ.Helsinki AtmSciences