Transcript Richard Müller

The CM-SAF expections on EURO4M
R.W. Mueller, J. Lennhardt, C.Träger,
J. Trentmann
DWD
EURO4M kick off meeting
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.
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 to
2 Wm-2yr-1) consistent with
surface observations (e.g.,
Wild et al., JGR, 2009).
Significant increase of
energy uptake in Baltic sea.
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
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 !
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 !
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.
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.
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.
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)
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
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 to
2 Wm-2yr-1) consistent with
surface observations (e.g.,
Wild et al., JGR, 2009).
Significant increase of
energy uptake in Baltic sea.
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 Brithening
CI 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 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.
Incoming thermal radiation at the surface
Verification of ERA-interim with BSRN
Verification of ERA-interim with BSRN
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