EURO4M kick off meeting

The CM-SAF expections on EURO4M

R.W. Mueller, J. Lennhardt, C.Träger, J. Trentmann

DWD

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

Introduction

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.

Example Trend Analysis

• 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

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 !

Methods to improve ECVs

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 !

Evaluation of SDL

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.

Evaluation of SIS

Reanalysis data is based on assimilation of a largeand increasing amount of satellite data. Reanalysis provides a wide set of parameters including surfaceradiation.

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.

DWD - EURO4M Philosophy

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 SurfaceRadiation 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.

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).

THE END

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)

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).

Accuracy of Heliosat

-> Finally, some first trend studies

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 EuropeRange 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.

R Trend Analysis

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..

Dimming BritheningCI is a measure of cloud albedo Data of CM-SAF

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 analyseof 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 asignificantly higher radiative forcing than that resulting from increase of greenhouse gases and could thereforesignificantly “confuse” the observation of “greenhouse” warming.

Verification of ERA-interim with BSRNIn

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Verification of ERA-interim with BSRN

Shortwave radiation (SIS)

Wild, JGR, 2009

ECHAM5 HAM model simulations

Consistent with CMSAF-Heliosat data set for Europe, satellite-based trends in Africa will be investigated starting in spring 2010!