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Transcript
How do we know that human
influence is changing (regional)
climate?
Hans von Storch12 and Jonas Bhend1
1Institute
for Coastal Research, GKSS Research Center, Geesthacht
2Meteorological
Page 1
Institute, Hamburg University
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Questions about ongoing non-natural change
• Global anthropogenic change An argument for efforts to mitigate climate change
by diminishing global drivers (“political asset”)
• Regional anthropogenic change–
need to discriminate between global and regional drivers.
An argument for efforts to mitigate regional change by
diminishing regional drivers (“political asset”), AND
an argument to implement adaptive measures to deal
with changing risks and opportunities (“information for
stakeholders”)
Page 2
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Detection and attribution of ongoing change
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5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Detection and attribution of non-natural ongoing change
• Detection of the presence of non-natural signals: rejection of null
hypothesis that recent trends are drawn from the distribution of
trends given by the historical record. Statistical proof.
• Different definition: „Detection is the process of demonstrating than an observed
change is significantly different (in a statistical sense) than can be explained by natural
internal variability“ (IPCC, TAR, 2001; see also IDAG, 2005)
• Attribution of cause(s): Non-rejection of the null hypothesis that
the observed change is made up of a sum of given signals. Plausibility
argument.
History:
Hasselmann, K., 1979: On the signal-to-noise problem in atmospheric response studies. Meteorology over the tropical
oceans (B.D.Shaw ed.), pp 251-259, Royal Met. Soc., Bracknell, Berkshire, England.
Hasselmann, K., 1993: Optimal fingerprints for the detection of time dependent climate change. J. Climate 6, 1957 1971
Hasselmann, K., 1998: Conventional and Bayesian approach to climate change detection and attribution. Quart. J. R.
Meteor. Soc. 124: 2541-2565
IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J.
Climate 18, 1291-1314
Page 4
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Global
Page 5
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Cases of Global Climate Change Detection Studies
… of strong, well documented signals
Examples: 1) Rybski et al. (2006)
2) Counting recent extremes
… of weak, not well documented signals.
Example: Near-globally distributed air temperature
IDAG (2005), Hegerl et al. (1996), Zwiers (1999)
Rybski, D., A. Bunde, S. Havlin,and H. von Storch, 2006: Long-term persistence in climate and the detection problem.
Geophys. Res. Lett. 33, L06718, doi:10.1029/2005GL025591
IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J.
Climate 18, 1291-1314
Hegerl, G.C., H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, P.D. Jones, 1996: Detecting anthropogenic climate
change with an optimal fingerprint method. J. Climate 9, 2281-2306
Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch and G. Flöser (Eds.): Anthropogenic Climate
Change. Springer Verlag, 163-209, ISBN 3-540-65033-4
Page 6
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
The Rybski-et al. study
Page 7
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Rybski, D., A. Bunde, S. Havlin,and H. von Storch, 2006: Long-term
persistence in climate and the detection problem. Geophys. Res.
Lett. 33, L06718, doi:10.1029/2005GL025591
Page 8
- Statistics of ΔT(m,L) which is the
difference of two m-year NH
temperature means, separated by L years.
- Temperature variations are modeled as
Gaussian long-memory process, fitted to
5th Study Conference on BALTEX
the
various
reconstructions.
Kuressaare, Saaremaa,
Estonia,
4-8 June
2007
Among the last 16 years, 19912006, there were the 12
warmest years since 1881 (i.e.,
in 126 samples) – how probable
is such an event if the time
series were stationary?
Monte-Carlo simulations taking
into account serial correlation,
either AR(1) (with lag-1
correlation ) or long-term
memory process (with Hurst
parameter H=0.5+d).
Best guesses
  0.8
H = 0.5 + d  0.5+0.3 (??)
Page 9
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Joint unpublished work by Zorita, Stocker and von Storch, 2007
Counting extremely warm years
Regional analysis by
Eduardo Zorita.
Giorgi-regions
Top: AR(1)-memory
Bottom: Number N of
years in 1991-2006 with
annual temperature T
larger than maximum
prior to 1991 (different
time series lengths in
different regions!)
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5th Study Conference on BALTEX
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Zorita, pers. comm
5%-significant
Success of detection a blending of
strength of signal + length of record + strength of memory
Page 11
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Kuressaare, Saaremaa, Estonia, 4-8 June 2007
How do we determine the control climate?
In general, the data base for the
“control”/undisturbed climate is not good:
• With the help of the limited empirical evidence
from instrumental observations, possibly after
suitable extraction of the suspected „non-natural“
signal.
• By projection of the signal on a proxy data space,
and by determining the stats of the latter from
geoscience indirect evidence (e.g., tree rings).
• By accessing long „control runs“ done with quasirealistic climate models
Page 12
5th Study Conference on BALTEX
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Trend in air
temperature
1965-1994
1916-1945
Page 13
Hegerl, G.C., H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, P.D.
Jones, 1996: Detecting anthropogenic climate change with an optimal
fingerprint method. J. Climate 9, 2281-2306
Signal or noise?
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Reducing the degrees of freededom
Specific problem in climate applications: usually very many
(>103) degrees of freedom, but the signal of change
resides in a few of these degrees of freedom.
Example:
Signal = (2, 0, 0, ...0) with all
components independent.
Power of detecting the signal,
depends on degrees of freedom.
Thus, the dimension of the problem must be reduced
before doing anything further. Usually, only very few
components are selected, such as 1 or 2.
Page 14
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
“Guess patterns”
The reduction of degrees of
freedom is done by projecting
the full signal S on one or a
few several “guess patterns”
Gk, which are assumed to
describe the effect of a
driver.
S = k k Gk + n
with n = undescribed part.
Example: guess pattern
supposedly representative of
increased CO2 levels
When Gk orthonormal then k
= STGk.
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5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Hegerl, G.C., H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, P.D.
Jones, 1996: Detecting anthropogenic climate change with an optimal
fingerprint method. J. Climate 9, 2281-2306
The attribution problem
Attribution is considered to be obtained, when
1) the suspected link between forcing and response is
theoretically established, and
2) the data do not contradict that k=1 in the assumed
representation S = k k Gk + n.
A contradiction prevails if the null hypothesis “k=1” is
rejected.
Thus, a non-contradiction is a plausibility-argument. It
may be due to a too small data base.
Page 17
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Attribution
2-patterns problem
guess patterns for climate change mechanisms taken as first EOFs of a
climate change simulation on that mechanism.
• only CO2 increase
• increase of CO2 and industrial aerosols as well.
• orthogonalisation of the two patterns
• estimation of natural variability through GCM control simulations done
• (Hegerl et al., 1997)
Page 18
5th Study Conference on BALTEX
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163-209, ISBN 3-540-65033-4
Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch
and G. Flöser (Eds.): Anthropogenic Climate Change. Springer Verlag,
Example: Attribution
Page 19
Attribution
diagram for
observed 50year trends in
JJA mean
temperature.
The ellipsoids enclose non-rejection regions for testing the null hypothesis
that the 2-dimensional vector of signal amplitudes estimated from
observations has the same distribution as the corresponding signal
amplitudes estimated from the simulated 1946-95 trends in the greenhouse
gas, greenhouse gas plus aerosol and solar forcing experiments.
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Attribution - plausibility
From:
Hadley
Center,
IPCC TAR,
2001
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5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
Regional:
the Baltic Sea
catchment
Page 21
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
The Baltic Sea Catchment 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 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
Page 23
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
„Significant“ trends
Often,an anthropogenic influence is assumed to be found 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.
Page 24
5th Study Conference on BALTEX
Kuressaare, Saaremaa, Estonia, 4-8 June 2007
„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 change.
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.
Page 25
5th Study Conference on BALTEX
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Overall summary
How do we know that human influence is
changing (regional) climate?
-Statistical comparison of ongoing change with
distribution of “naturally” occurring changes – Detection:
statistical proof.
Attribution: statistical plausibility
- ok für global and continental scale temp (IDAG, 2005).
2/3 of warming is very likely man-made.
- Consistency of continental temp change with change in
regions such as Baltic Sea catchment (temp and related
variables; see next presentation by Jonas Bhend), but
only part of regional change can be explained
consistently by GHG effects.
Page 26
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