Diurnal cycles of fossil fuel CO 2 : Comparison of model results with observations at Heidelberg and Schauinsland Felix Vogel 1 including work of: I. Levin 1, U. Karstens 2, C. Rödenbeck 2, M. Krol 3, S. Houweling 3, P. Peylin 4, P. Bousquet 4, C. Aulagnier 4, C. Geels 5, A. Vermeulen 6 1 Institut für Umweltphysik, Universität Heidelberg 2 Max-Planck-Institute for Biogeochemistry, Jena 3 National Institute for Space Research Utrecht 4 Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette 5 National Environmental Research Institute Roskilde 6 Energy research Center of the Netherlands, Petten 5 th CarboEurope-IP Integrated Project Meeting, Poznań 2007

 Introduction  Methods - Calculating FFCO 2 from observations - Statistics  Comparison - Heidelberg - Schauinsland  Summery & Outlook Outline

 Introduction  Methods - Calculating FFCO 2 from observations - Statistics  Comparison - Heidelberg - Schauinsland  Summery & Outlook Outline

Calculating FFCO 2 from observations weekly excess  14 C weekly excess CO 2 continuous excess CO calculated continuous excess FFCO 2 weekly excess ratio FFCO 2 /CO X = weekly excess CO weekly excess FFCO 2 [Levin et al GRL Vol.30/23]

Mean diurnal cycle - Heidelberg 2002 Central European Time Calculating FFCO 2 from observations

 Introduction  Methods - Calculating FFCO 2 from observations - Statistics  Comparison - Heidelberg - Schauinsland  Summery & Outlook Outline

Methods - Statistics Wrong phasing of the diurnal cycle significantly decreases the correlation coefficient ! Mean diurnal cycle - Heidelberg summer 2002

R² < 0.2 R² = 0.63 R² = 1  Find maximal correlation coefficient  Determine time shifts in model data  Possibly validate diurnal cycle of the emission inventories  No measure for variability! (Amplitude x 2) Methods - Statistics Time shift = 2h Time shift = 1hTime shift = 0h

Methods - Statistics Why diurnal analysis and not pure comparison of the time series? Advantages:  Less sensitive to pollution events  Reduction of uncertainties  Less computational effort Implicit assumptions:  Similar emission statistics for each season  Diurnal cycle is significant compared to the noise

 Motivation  Methods - Calculating FFCO 2 from observations - Statistics  Comparison - Heidelberg - Schauinsland  Summery & Outlook Outline

Results - Heidelberg Excess FFCO 2 diurnal cycle – Heidelberg Winter 2002 unshifted

Results - Heidelberg Excess FFCO 2 diurnal cycle – Heidelberg Winter FFCO 2, corr = FFCO 2,mod x Rn mod Rn meas Amplitude shifted

Results - Heidelberg Excess FFCO 2 diurnal cycle – Heidelberg Winter Amplitude shifted

Results - Heidelberg Excess FFCO 2 diurnal cycle – Heidelberg Summer shifted Amplitude

Results - Heidelberg Excess FFCO 2 diurnal cycle – Heidelberg summer 2002

 Introduction  Methods - Calculating FFCO 2 from observations - Statistics  Comparison - Heidelberg - Schauinsland  Summery & Outlook Outline

Results - Schauinsland Excess FFCO 2 diurnal cycle - Schauinsland Winter 2002 unshifted

Results - Schauinsland Excess FFCO 2 diurnal cycle - Schauinsland Winter 2002 shifted

Results - Schauinsland Excess FFCO 2 diurnal cycle - Schauinsland Winter 2002

 Motivation  Methods - Calculating FFCO 2 from observations - Statistics  Results - Schauinsland - Heidelberg  Summery & Outlook Outline

 Differences between IER and EDGAR are not significant but IER seems to perform better in Heidelberg  Large spread in models  Ensemble analysis shows that the significant shifts in the diurnal cycle are not likely due to the inventories  Winter better than summer - less variable boundary layer height in winter - vertical mixing in summer is still a problem Summery

 Analysis: - Extensive variability analysis - Running R² on continuous records  Methods: - Studies on CO diurnal cycle - Include Δ 14 C bio - Further studies on the radon source - Measurements at more representative sites Outlook