1. Technische Universität München In situ networks and measurements - European phenological networks Prof. Dr. Annette Menzel 1, Dr. This Rutishauser 2, Dr. Elisabeth Koch 3 1 Ökoklimatologie Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt Technische Universität München menzel@forst.tu-muenchen.de 2 University of Bern, Switzerland 3 ZAMG, Austria An International Workshop on the Validation of Satellite-based Land Surface Phenology Products, 18.6.2010

2. Technische Universität München Europe

3. Technische Universität München Historical networks in Europe Famous single site centential data series, such as Marsham record in the UK, grape harvest dates in Central Europe, Swiss phenological records.. Carl von Linné, the father of modern plant phenology, established the first phenological network in Sweden (1750-1752) Network of the Societas Meteorological Palatina (1781-1792) International Phenological Gardens since 1957 Many networks established by national meteorological and hydrological services, breakdown after world war II, partly recovered, new cuts after 1990

4. Technische Universität München Information about current networks Schwartz 2003 book EPN Phenological meta data base http://www.pik-potsdam.de/~rachimow/epn/html/frameok.html COST725 www.cost725.orgwww.cost725.org database at the ZAMG PEP 725

5. Technische Universität München PEP 725 5 years programme of EUMETNET and ZAMG D1 Development, operations and management of the PEP725 database D2 Development, operations and management of the PEP725 webportal Participants & Partners Swedish National Phenology Network SWE-NPN, Fondazione Edmund Mach-Instituto Agrario di San Michele all’Adige, GDR 29687 Observatoire Des Saisons, Finnish Forest Research Institute Muhos Research Unit, Lithuanian Arricultural institute, Skre Natur- og Miljøvurdering, Trinity College Dublin, Wageningen University national meteorological services from: ZAMG - Austria, RMI - Belgium, DHMZ – Croatia, FMI – Finland, DWD – Germany, OMSZ – Hungary, Met Èireann – Ireland, Met.no – Norway, IMGW – Poland, RHMSS – Serbia, EARS – Slovenia, AEMet – Spain, MeteoSwiss – Switzerland, CHMI - Czech Rep., NMAR – Romania, SHMÚ - Slovak Rep Pan European Phenology DB Zentralanstalt für Meteorologie und Geodynamik

6. Technische Universität München PEP coverage and observation scheme stations in 201005

7. Technische Universität München PEP database structure and quality control

8. Technische Universität München The footprint of climate change IPCC 2007, WG II, Ch 01 / Rosenzweig et al. Nature 2008 First to formally link observed global changes to human-induced climate change

9. Technische Universität München Agriculture - 0.4 days / decade Bud burst / flowering: - 2.5 days / decade Menzel et al. GCB 2006, IPCC AR4 WGII Ch.01 2007 57%  13% sig. 43%  6% sig. 75%  25% sig. 25%  3% sig. 48%  12% sig. 52%  15% sig. 78%  31% sig. 22%  3% sig. Fruit ripening: - 2.4 days / decade Autumn: + 0.2 days / decade n~120.000 1971-2000 Phenological response in Europe (COST725)

10. Technische Universität München Temperature response Menzel et al. 2006 Farmers activities Leafing, flowering Fruit ripening Leaf colouring

11. Technische Universität München Spring phases: leaf unfolding flowering migration R 2 = 47% Europe - Phenological change pattern matches climate change Menzel et al. GCB 2006

12. Technische Universität München Locations of significant changes in observations IPCC 2007, WGII, SPM “It is likely that warming caused by human activities has had a discernible impact on many physical and biological systems at the global level” “Many natural systems on all continents and some oceans are affected by regional climate change (rising temperatures)”

13. Technische Universität München Location and consistency of observed changes with warming Rosenzweig,.. Menzel,.. Estrella,.., Nature, et al. 2008

14. Technische Universität München The inherent problem Agriculture – Forestry Canopy – understory Subpixel mixing LULC Farm management....

15. Technische Universität München NOAA AVHRR captures snow drop & forsythia flowering

16. Technische Universität München NOAA AVHRR are more variable in autumn

17. Technische Universität München NOAA AVHRR growing season is too long...

18. Technische Universität München Plant phenological metrics

19. Technische Universität München Plant phenological metrics at a regional scale 2x2° Ground plant phenology Land Surface Phenology (NDVI) Temperature high correlation at large scale local scale? Rutishauser et al. 2007, JGR; Stöckli & Vidale 2004, IJRemSen; Studer et al. 2007, IJBiometeorol

20. Technische Universität München Solution: Plant phenological metrics by PCA Summary –Re-interpreting existing data sets –Define a green-up index Based existing multi-species data sets Statistical estimation for fill gaps Integrated view of the phenology of a landscape Applications –> 8’000 sites in Cost725 European phenological data base –Comparisons with LSP and model: ground truth and verification –Climatic impacts on green-up indices –Gap-free data set for e.g. extreme climatic event studies

21. Technische Universität München Comparison to modelled ground phenology 20-40 40-60 60-70 70-80 80-90 90-100 100-110 110-120 120-130 130-140 140-150 150-160 >160 Figure 3.3. Comparison of the SOS the ‘green wave’ (a) and simulation of leaf unfolding of Betula pendula (b) over 1982-1994: average date in DOY (a-b). a b

22. Technische Universität München Calibration of ground measures to which SOS estimate ? White et al. 2009

23. Technische Universität München Good correspondance to GPP

24. Technische Universität München “The story remains difficult … ” Summary Current phenological data are not online available (+1.5 years) Europe is most variable concerning networks, species, spatial coverage and density, availability despite COST 725 and PEP 725 efforts Many phenological observations to interpret satellite measures are lacking, e.g. second cropping in autumn, unusual management (irrigation,..) Phenological data requires quality control and spatial interpolation Similarly, SOS, EOS, LOS measures out of satellite products most variable