Transcript Baltic Sea catchment

How do we know that human
influence is changing (regional)
climate?
Hans von Storch
Institute for Coastal Research, GKSS Research Center, Geesthachtand
Meteorological Institute, Hamburg University
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Detection and attribution of ongoing change
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Global
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
The Rybski-et al. study
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
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- 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
Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic
Basin,
24-25 Mayreconstructions.
2007, Göteborg
the
various
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 (??)
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Joint unpublished work by Zorita, Stocker and von Storch, 2007
Counting extremely warm years
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
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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?
Trend in air
temperature
1965-1994
1916-1945
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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.
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
“Guess patterns”
The reduction of degrees of
freedom is done by projecting
the full signal S 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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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
Optimization of the
expected signal to noise
ratio:
^
1
NN
Gk   Gk
with the inverse
covariance matrix of
the internal climate
variability.
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
Optimizing s/n ratio
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.
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attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Attribution
2-patterns problem (Hegerl et al. 1997)
• 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
at MPI in Hamburg, GFDL in Princeton and HC in Bracknell.
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
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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.
Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Attribution - plausibility
From:
Hadley
Center,
IPCC TAR,
2001
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Regional:
the Baltic Sea
catchment
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
„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.
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
„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.
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Overall summary
How do we know that human influence is
changing (regional) climate?
-Statistical analysis of ongoing change with
distribution of “naturally” occurring changes –
detection, statistical proof.
- ok für global and continental scale temp.
- Consistency of continental temp change with
change in regions such as Baltic Sea catchment
(temp and related variables; see Jonas’
presentation)
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
Overall summary
How do we know that human influence is
changing (regional) climate?
- Attribution (of causal drivers) is a
plausibility argument: determine consistency
of ongoing change with expected changes.
- Done for global and continental scale temp
(and related) variables (see IDAG).
- First efforts on regional scales (see JonasÄ’
presentation).
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg
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Workshop on added vales of regional climate models and detection and
attribution studies in the Baltic Basin, 24-25 May 2007, Göteborg