Slide 1 Retrospective analysis of ozone at ECMWF Rossana Dragani ECMWF Acknowledgements to: D. Tan, A. Inness, E. Hólm, and D. Dee R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 2 Overview Reanalysis activity at ECMWF Science application for reanalysis data Suitability to assess long-term variability Ozone analyses in ERA-40 and ERA-Interim Conclusions and remarks R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 3 Reanalysis at ECMWF: Reanalysis is based on analysis methods developed to provide initial states for numerical weather prediction. It applies a fixed, modern data assimilation system to multi-year sets of observations of different types, resulting in more uniform analysis quality. R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 ERA-15: 1979 – 1993 ERA-40: 1957 – mid-2002 ERA-Interim:1989 onwards ORA-S3:1959 onwards MACC:2003 – 2010 ERA-CLIM: European Reanalysis of Global Climate Observations An EU project to help prepare the next ECMWF reanalysis ERA-20C:1900 onwards

Slide 4 The ERA data base: ERA-Interim reanalysis is continuing in near real-time Products are updated monthly R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 5 Science applications that rely on reanalysis data “Observations” for verification and diagnosis -Development of forecast model, climate model; calibration of seasonal forecasting systems; use of data assimilation increments for identifying model errors Input data for model applications -for smaller-scales (global  regional; regional  local), ocean circulation, chemical transport, … Study of short-term atmospheric processes and influences -Polar vortex dynamics,… Providing climatologies Assessment of the observing system -providing feedback on observational quality, bias corrections and a basis for homogenization studies; contributing to data reprocessing activities Study of long-term climate variability and trends R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 6 ERA-40 + ERA-Interim  Mid 1957 to the present Analyses are physically coherent and consistent with observations  A realistic model can propagate information in poorly observed areas as well as moved forward in time. R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 Suitability to assess long-term changes: Geer, Planet Earth Autumn 2004, MIPAS O 3 retrievals: 23 Sep 2002 © ESA, Ozone fc: D+6 from 17 Sep 2002 Ozone analysis: 12Z 23 Sep 2002

Slide 7 Reanalysis uses a modern, stable and invariant forecasting /assimilation system to reprocess (re-analyse) past observations:  Remove artificial changes introduced by model upgrades Changes in the observing system and their error characteristics can also produce shifts and spurious trends. - ERA-Interim used VarBC for all radiances - ERA-Clim will also use VarBC for all L2 products (O 3, WV) R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 Suitability to assess long-term changes: From archived weather analyses: From a retrospective analysis:

Slide 8 Ozone reanalyses at ECMWF: Ozone has been reanalysed in ERA-40 and ERA-Interim -MACC also reanalysed ozone for the period Long record: ERA-40 until Dec ERA-Interim from Jan Main differences between ERA-40 (Cy23r4) and ERA-Interim (Cy31r2) : -Assimilation scheme: ERA-40 used 3D-Var; ERA-Interim used 4D-Var -Data usage -Variarional Bias Correction (VarBC) for radiances -Upgrades in the ozone model -Horizontal resolution: T159 (125km, ERA-40)  T255 (80 km, ERA-Interim) ERA-40 ozone analyses validated by Dethof and Hólm (2004, QJ). ERA-Interim ozone analyses validated by Dragani (2011, QJ submitted). R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 ERA-Interim ERA-40

Slide 9 Ozone in ERA-40: R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 Dethof & Hólm (2004) Hohenpeissenberg ( ) JFMJJA Neumayer ( ) SO Sonde ERA-40 Hilo ( ) JFMJJA

Slide 10 Ozone in ERA-Interim: R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 TCO validated against  OMI TCO (TOMS-like);  TCO from Dobson Spectrometers (WOUDC);  TCO climatology created as a 5-year running mean from the NASA merged satellite. 3D O 3 analyses (Jan 89-Dec 08) validated against  WOUDC sondes;  Satellite data. V6.2 V5 V2.2 V19 V4V6POAM: HALOE: MLS: SAGE:

Slide 11 Validation of the 3D O 3 reanalyses: R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF hPa Matching criteria:   t 3 hrs.  ERA-Interim analyses were interpolated at the obs locations. GOME SAGE HALOE MLS POAM Sat-An Sat

Slide 12 Summary of the comparisons: LatLev (hPa)SAGEHALOEMLSPOAM N 5±5%[0,+5]% [-25,+5]% 10[-10,+5]%[-5,+1]%[-5,+3]%[-25,+10]% 30[0,+20]%[-2,+20]%[0,+20]%[-10,+20]% 65[-20,+10]%[-2,+10]%[-20,+10]% 30S - 30N 5±5% 10±10%±5%[-5,+8]% 30[0,+20]% 65[-20,+30]%[-5,+20]%[+5,+25]% S 5[-8,+5]%±2%[-8,+5]%[-40,+1]% 10[-10,+5]%[-8,+1]%[-10,+3]%[-40,-5]% 30[0,+20]% [-20,+10]% 65[-20,+20]%[-2,+20]%[0,+20]%[-20,+20]% R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 13 ERA-40 vs. ERA-Interim: Jan Aug N 30 S-30 N S HALOEUARS MLS R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 14 ERA-40 vs. ERA-Interim: Comparisons with ozone sondes R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011 Pre-GOME ( ) GOME ( ) 60 – 90 S Sep-Oct 30N – 30S Jan-Feb-Mar ERA-Interim ERA soundings 942 soundings 238 soundings227 soundings

Slide 15 Conclusions ERA-40 O 3 analyses showed general good agreement with observations, but some problems were seen e.g. at mid-latitudes in winter, and at high latitudes in the SH in spring. ERA-Interim O 3 analyses compare well with observations, and show departures from SAGE, MLS and HALOE ≤10% in the middle stratosphere, and ≤ 20% in the lower stratosphere. ERA-Interim better than ERA-40 in the UTLS region before 1996, and over the whole stratosphere afterwards (GOME assimilation). Planned improvements on ozone: -Use the Variational Bias Correction (VarBC) scheme with L2 data. -Revise/improve the assimilation of ozone profiles (e.g. SBUV data) -Assimilate ozone information from different data type/instruments (ozone-sensitive radiances, L2 ozone data not yet used, e.g. those used for validation). R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011

Slide 16 Remarks: What can reanalysis deliver? -Consistent with observations -Physically coherent -Complete, with no gaps -Comprehensive -Accurate variability and trends -Meaningful information about uncertainties Progress towards climate quality requires open access to all input data. Progress is iterative and needs regular re-processing. R. Dragani, SPARC/IOC/IGACO, Geneva, Jan 2011 © ECMWF 2011