The CM-SAF expections on EURO4M

1. The CM-SAF expections on EURO4M R.W. Mueller, J. Lennhardt, C.Träger, J. Trentmann DWD EURO4M kick off meeting

2. Introduction • Increase accuracy and climate quality of Essential Climate Variables (ECV), in order to improve our understanding of the climate system and the climate change.

3. Example Trend Analysis Basic trend analysis of solar incoming surface radiation using Helioat data set (1995 – 2005: Data of Univ. of Oldenburg) • Substantial spatial variability of ‘solar brightening’ in Europe. • Range of values (up to2 Wm-2yr-1) consistent with surface observations (e.g., Wild et al., JGR, 2009). • Significant increase of energy uptake in Baltic sea.

4. Methods to improve ECVs • Increase accuracy and climate quality of Essential Climate Variables (ECV), in order to improve our understanding of the climate system and the climate change. • Data fusion: Combine exisiting data sources in order to benefit from the strength and eliminate the weakness of the individual data sources (satellite, ground based, reanalysis) -> a unique selling proposition

5. Methods to improve ECVs • Increase accuracy and climate quality of Essential Climate Variables (ECV), in order to improve our understanding of the climate system and the climate change. • Support of reanalysis improvement by verification as one basis for needed model system improvements and clarification of climate application areas (trend, anomalies) and associated analysis uncertainties. -> This in turn is a basis for a reasonable data fusion, an example !

6. Evaluation of SDL Evaluation with BSRN stations (SDL): Main error quantities The evaluation provide a clear indication that accuracy and precision of satellite based SDL products is not higher than that of ERA-interim ! CM-SAF, ISCCP & GEWEX uses beside satellite NWP information !

7. Evaluation of SIS • Retrieval: RTM based hybrid eigenvector approach (R. Mueller et al., 2009, RSE). No need for NWP model information. CM-SAF SIS has ignificantly higher accuracy and precision.

8. DWD - EURO4M Philosophy Reanalysis data is based on assimilation of a large and increasing amount of satellite data. Reanalysis provides a wide set of parameters including surface radiation. • Satellite products should focus on: - ECVs with a higher accuracy than reanalysis products. - ECVs with “equal” accuracy without or at least only 2nd order NWP model dependency.

9. Data Fusion Example • CM-SAF Solar Incoming Surface (SIS) products has a higher • accuracy than ERA-Interim but thermal products have not. • CM-SAF will focus on the retrieval of SIS and SAL • and cloud albedo for EURO4M. • However, the user will be able to get the complete Surface • Radiation Budget (SRB) from EURO4M. • SOL, SDL reanalysis data will be used as basis. The data will • be evaluated and afterwards improved by topography and • bias correction. • -> SRB example for data fusion. • Expection: Focus of work should be the benefit of the user • and not the interests of individual partner.

10. General Expections • Establish a European Network for Climate monitoring based • on reanalysis, satellite and ground based data. • Three different communities come together we should use • The opportunity to improve the cooperation between this • communities • -> Indolent in the development and improvement of the • reanalysis system. • Close user interaction. • Focus of work should be the benefit of the user and not the I • interests of individual partner. • Support decision makers and scientists with valuable • information about climate change (outcome of data analysis).

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12. Product Example: Full disk SIS Monthly mean 200908:(15x15 km²). SIS is based on the MAGIC retrieval algorithm applied to GERB/SEVIRI (R. Mueller et al, RSE 2009, algorithm is also applied to AVHRR)

13. Accuracy of Heliosat • Data provided by the University of Oldenburg has been used for first validation study. Data, hence validation results only for Europe, 1995-2005 (other validation results for globe or full MSG disk respectively). -> Finally, some first trend studies

14. R Trend Analysis Basic trend analysis of solar incoming surface radiation using Helioat data set (1995 – 2005: Data of Univ. of Oldenburg) • Substantial spatial variability of ‘solar brightening’ in Europe • Range of values (up to2 Wm-2yr-1) consistent with surface observations (e.g., Wild et al., JGR, 2009). • Significant increase of energy uptake in Baltic sea.

15. Conclusions-II • 11 year period is not long enough to draw final • conclusions (limited amount of samples). • Longer time series needed to proof long term behaviour of • the trends and analyse reasons for trends. • . CI is a measure of cloud albedo Data of CM-SAF DimmingBrithening

16. Conclusions-II • 11 year period is not long enough to draw final • conclusions. Longer time series needed to proof • and analyse the trends. • However, first results demonstrate the importance of cloud • albedo monitoring and analysis. • CDR of cloud albedo enables the seperation of clear sky • (AOD, H20) and cloud effects supporting the analyse • of the dimming and brightening sources. • Regional trends up to 2W/m²/yr has been found. This • indicates that trends in cloud albedo could lead to a • significantly higher radiative forcing than that resulting • from increase of greenhouse gases and could therefore • significantly “confuse” the observation of “greenhouse” • warming.

17. Verification of ERA-interim with BSRN Incoming thermal radiation at the surface

18. Verification of ERA-interim with BSRN

19. Shortwave radiation (SIS) ECHAM5 HAM model simulations Consistent with CMSAF-Heliosat data set for Europe, satellite-based trends in Africa will be investigated starting in spring 2010! Wild, JGR, 2009