Transcript Consistency analysis

Consistency of observed trends
in northern Europe with regional
climate change projections
Jonas Bhend1 and Hans von Storch12
1Institute
for Coastal Research, GKSS Research Center, Geesthacht, Germany
2Meteorological
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Institute, Hamburg University, Hamburg, Germany
10 IMSC, 20-24 August 2007, Beijing
The Baltic Sea Catchment Climate Change Assessment: BACC
An effort to establish which
knowledge about anthropogenic
climate change is available for
the Baltic Sea catchment.
Working group BACC of GEWEX
program BALTEX.
Approximately 80 scientist from
10 countries have documented
and assessed the published
knowledge.
Assessment has been accepted
by intergovernmental HELCOM
Commission as a basis for its
future deliberations.
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The Baltic Sea Catchment Climate Change Assessment: BACC
Summary of BACC Results
Baltic Area Climate Change Assessment
• Presently a warming is going on in the Baltic Sea region.
• No formal detection and attribution studies available.
• BACC considers it plausible that this warming is at least partly related
to anthropogenic factors.
• So far, and in the next few decades, the signal is limited to temperature
and directly related variables, such as ice conditions.
• Later, changes in the water cycle are expected to become obvious.
• This regional warming will have a variety of effects on terrestrial and
marine ecosystems – some predictable such as the changes in the
phenology others so far hardly predictable.
BACC Group: Assessment of climate change for the
Baltic Sea basin, Springer-Verlag, in press
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Options
• Detection:
“Is the observed change different from what we
expect due to internal variability alone?” – not
doable at this time.
• Trends – are there significant trends? – no useful
results.
• Consistency:
“Are the observed changes similar to what we
expect from anthropogenic forcing?”
Doable: Plausibility argument using an a priori
known forcing.
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„Significant“ trends
Often, an anthropogenic influence is assumed to be in operation
when trends are found to be „significant“.
• In many cases, the tests for assessing the significance of a
trend are false as they fail to take into account serial
correlation.
• If the null-hypothesis is correctly rejected, then the
conclusion to be drawn is – if the data collection exercise would
be repeated, then we may expect to see again a similar trend.
• Example: N European warming trend April – July as part of the
seasonal cycle.
• It does not imply that the trend will continue into the future
(beyond the time scale of serial correlation).
• Example. Usually September is cooler than July.
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„Significant“ trends
Establishing the statistical significance of a trend is a
necessary condition for claiming that the trend would
represent evidence of anthropogenic influence.
Claims of a continuing trend require that the dynamical
cause for the present trend is identified, and that the
driver causing the trend itself is continuing to operate.
Thus, claims for extension of present trends into the
future require
- empirical evidence for ongoing trend, and
- theoretical reasoning for driver-response dynamics, and
- forecasts of future driver behavior.
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Consistency analysis: attribution without detection
The check of consistency of recent and ongoing trends
with predictions from dynamical (or other) models
represents a kind of „attribution without detection“.
This is in particular useful, when time series of
insufficient length are available or the signal-to-noise
level is too low.
The idea is to estimate the driver-related change E from
a (series of) model scenarios (or predictions), and to
compare this “expected change” E with the recent trend
R.
If R  E, then we may conclude that the recent change is
not due to the suspected driver, at least not completely.
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10 IMSC, 20-24 August 2007, Beijing
DJF mean precip in the Baltic Sea catchment
Example:
Recent 30-year
trend R
Trend of DJF precip
according to
different data
sources.
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Consistency analysis
Expected signals E
• six simulations with regional coupled atmosphere-Baltic
Sea regional climate model RCAO (Rossby-Center,
Sweden)
• three simulations run with HadCM3 global scenarios,
three with ECHAM4 global scenarios; 2071-2100
• two simulation exposed to A2 emission scenario, two
simulations exposed to B2 scenario; 2071-2100
• two simulations with present day GHG-levels; 1961-90
• Regional climate change in the four scenarios relatively
similar.
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Consistency analysis
R
E
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Δ=0.05%
Regional DJF precipitation
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Consistency analysis
Patterns correlations between “observed” (CRU) trends in DJF
seasonal precipitation in the Baltic Sea catchment and “expected”
signals derived from scaled RCM changes.
Global
model
scenario
Pattern
correlations
Pattern correlations
without NAO
A2
0.83
0.75
B2
0.75
0.64
A2
0.85
0.75
B2
0.84
0.74
HadAM
ECHAM
The pattern correlations are all significantly larger than pattern
correlations between random combinations of trends.
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10 IMSC, 20-24 August 2007, Beijing
Consistency analysis
Ratio of intensities between “observed” (CRU) trends in DJF seasonal
precipitation in the Baltic Sea catchment and “expected” signals
derived from scaled RCM changes.
All model predictions
result in too large
trends for the past
years.
When taking out the
NAO the situation
slightly improves.
Global
model
scenario
Intensity-ratio
R/E
Intensity-ratio
without NAO
A2
2.96
2.53
B2
4.50
3.98
A2
1.94
1.57
B2
2.50
2.07
HadAM
ECHAM
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Regional JJA temperatures
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Consistency analysis
All seasons: RCAO-ECHAM B2 scenario
Pattern correlation
Intensities
precipitation
temperature
precipitation
temperature
DJF
0.84* (0.74*)
0.95* (0.73)
2.50 (2.07)
1.33 (0.66)
MAM
0.72* (0.69*)
0.83 (0.79)
3.21 (2.86)
1.15 (1.06)
JJA
-0.28
0.95*
4.42
1.85
SON
-0.59
0.60
2.23
0.71
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Consistency analysis: Baltic Sea catchment
1.
We suggest a consistency analysis to compare model outlooks with
recent trends.
2. Consistency of the patterns of model “predictions” and recent trends
is found in most seasons.
3. A major exceptation is precip in JJA and SON.
4. Removing the NAO-signal changes improves consistency slightly.
5. The observed trends in precip are stronger than the anthropogenic
signal suggested by the models.
6. Possible causes:
- scenarios inappropriate (false)
- drivers other than CO2 at work (industrial aerosols?)
- natural variability much larger than signal (signal-to-noise ratio 
0.2-0.5).
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