Download Recent climate change in the Baltic Sea region

Nordenskjöld Lecture,
Göteborgs Universitet, 16 November 2015
Hans von Storch:
Recent climate change in the Baltic Sea region
- manifestation, detection and attribution
Based upon:
Work done with Klaus Hasselmann, Eduardo Zorita, Armin Bunde, Armineh Barkhordarian, and Jonas Bhend
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Hans von Storch
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Climate researcher (in the field since 1971)
Coastal science; statistical analysis
Cooperation with social and cultural
scientists since 1992
Active in IPCC as Lead Author
in AR3 (2001) and AR5 (2013).
Co-Chair of the BACC-Assessment of Climate
Change in the Baltic Sea region, together with
Anders Omstedt.
Retired Director of the Institute of
Coastal Research of the Helmholtz
Zentrum Geesthacht, Germany
Professor at Universität Hamburg,
Meteorologcal Institute and Faculty of Social
and Economic Sciences
Guest-Professor at the Oean University of
China, Qingdao
Honorary doctorate of U Göteborg
BACC as „regional IPCC“
BALTEX Assessment of Climate Change for the
Baltic Sea basin - BACC
An effort to establish which scientifically
legitimized knowledge about climate change
and its impacts is available for the Baltic Sea
catchment.
Approximately 80 scientists from 12 countries
have documented and assessed the published
knowledge in 2008 in BACC 1;
In May 2015, BACC-2 came out, with
141 contributing authors.
The assessment has been accepted by the
intergovernmental HELCOM commission as a
basis for its judgment and recommendations.
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Change – a scientific challenge with societal significance
For the societal debate, at least in the west, there are several questions, which
need scientific answers, of significance:
a) Is there a change ? What are the dominant causes for such a chance, and
what are the expectations for the future?
b) Which consequences does this change have for people, society and
ecosystems?
In this lecture, I am dealing only with (a). We have three tasks
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Manifestation: The found change is real and not an artifact of the data and
data collection process (inhomogeneity)
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Detection: The found change is beyond what may be expected due to natural
(not externally caused) variations.
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Attribution: A change, which was found to be beyond the range of natural
variations, may plausibly and consistently be explained by a certain (mix of)
external cause(s).
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Klaus Hasselmann, the inventor of D&A
History:
Hasselmann, K., 1979: On the signal-tonoise 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
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Methodical issues
• Randomness
• Significant trends?
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Noise as
nuisance:
masking the
signal
The 300 hPa geopotential height fields in the Northern Hemisphere: the mean 1967-81 January field, the January
1971 field, which is closer to the mean field than most others, and the January 1981 field, which deviates
significantly from the mean field. Units: 10 m
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Where does the stochasticity come from?
Stochasticity is a mathematical construct to allow an efficient
description of the (simulated and observed) climate
variability.
Simulation data: internally generated by a very large
number of chaotic processes.
Dynamical “cause” for real world’s natural unforced
variability best explained as in simulation models.
Noise or deterministic
chaos?
Mathematical construct of
randomness – an adequate concept
for description of features resulting
from the presence of many chaotic
processes.
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„Significant“ trends
Often, an anthropogenic influence is assumed to be in operation
when trends are found to be „significant“.
• 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 to 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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Losses from Atlantic Hurricanes
Storm surges in Hamburg
Estimates of global mean temperature
increase
Quelle: http://www.dmi.dk/nyheder/arkiv/nyheder-2015/01/2014-er-klodens-varmeste-aar
Temperature
increase
in the Baltic Sea
Region
Baltic Sea region
(1982-2011)
Data: CRU & EOBS
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Losses from Atlantic
Hurricanes
Is the massive increase in
damages attributable to
extreme weather conditions?
Estimation of damage if presence of people
and values along the coast would have
been constant – the change is attributable
to socio-economic development
Pielke, Jr., R.A., Gratz, J., Landsea, C.W., Collins, D., Saunders,
M., and Musulin, R., 2008. Normalized Hurricane Damages in the
United States: 1900-2005. Natural Hazards Review
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Storm surges in Hamburg
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The Rybski et al-approach
Temporal development of Ti(m,L) =
Ti(m) – Ti-L(m) divided by the standard
deviation of the m-year mean
reconstructed temp record
for m=5 and L=20 (top), and
for m=30 and L=100 years.
The thresholds R = 2, 2.5 and 3σ are
given as dashed lines; they are derived
from temperature variations modelled as
Gaussian long-memory processes fitted to
various reconstructions of historical
temperature.
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
Clustering of warmest years
Counting of warmest years in the record of
thermometer-based estimates of global mean
surface air temperature:
In 2007, it was found that among the last 17
years (since 1990) there were the 13 warmest
years of all years since 1880 (127 years).
For both a short-memory world (𝛼 = 0.85) and
for a long-memory world (d = 0.45) the
probability for such an event would be less
than 10-3.
Thus, the data contradict the null hypothesis of
variations of internal stationary variability
Zorita, E., T. Stocker and H. von Storch, 2008: How unusual is the recent series of warm years?
Geophys. Res. Lett. 35, L24706, doi:10.1029/2008GL036228,
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… there is something to be explained
Thus, there is something going on in the global
mean air temperature record, which needs to be
explained by external factors.
IPCC AR5, SPM
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Zorita, et al., 2009
Regional clustering of warmest years
Observed temperature trends in the Baltic Sea
region (1982-2011)
Baltic Sea region
Observed CRU, EOBS (1982-2011)
95th-%tile of „non-GS“ variability,
derived from 2,000-year palaeo-simulations
Estimating natural variability:
2,000-year high-resolution regional climate
palaeo-simulation (Gómez-Navarro et al,
2013) is used to estimate natural (internal +
external) variability.
 An external cause is needed for explaining the recently observed annual and seasonal
warming over the Baltic Sea area, except for winter (with < 2.5% risk of error)
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Consistency of recent local change:
Storm surges in Hamburg
Difference betwenn peak heights of storm
surges in Cuxhaven and Hamburg
Main cause for recently elevated storm
surges in Hamburg is the modification of
the river Elbe – (coastal defense and
shipping channel deepening) and less so
because of changing storms or sea level.
von Storch, H. and K. Woth, 2008: Storm surges, perspectives and
options. Sustainability Science 3, 33-44
“Guess patterns”
When doing attribution, often “guess
patterns” are used, which supposedly
describe the fingerprint of the effect of a
possible cause.
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 given driver.
S = k k Gk + n
with n = undescribed part.
Example: guess pattern supposedly
representative of the impact of increased CO2
levels
Hegerl et al., 1996
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,
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.
Attribution: Can we describe the development of air temperature by
imposing realistic increasing greenhouse gas and aerosol loads on
climate models? Yes, we can.
Only natural
factors
Additional ly man
made factors
IPCC 2007
„observations“
Projected GS signal
patterns (RCMs)
Observed trend
patterns (CRU)
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Temperature change in the Baltic Sea Region
Guess patterns:
10 simulations of RCMs from ENSEMBLES project.
Forcing
 Boundary forcing of RCMs by global scenarios exposed to GS (greenhouse
gases and Sulfate aerosols) forcing
 RCMs are forced only by elevated GHG levels; the regional response to
changing aerosol presence is unaccounted for.
“Signal”
(2071-2100) minus (1961-1990); scaled to change per decade.
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Observed and projected temperature
trends (1982-2011)
Observed CRU, EOBS (1982-2011)
Projected GS signal, A1B scenario
10 simulations (ENSEMBLES)
 DJF and MAM changes can be explained by dominantly GHG driven scenarios
 None of the 10 RCM climate projections capture the observed annual and seasonal
warming in summer (JJA) and autumn (SON).
Solar surface irradiance
in the Baltic Sea Region
Observed 1984-2005 (MFG Satellites)
Projected GS signal (ENSEMBLES)
1880-2004 development of sulphur dioxide
emissions in Europe (Unit: Tg SO2). (after Vestreng
et al., 2007 in BACC-2 report, Sec 6.3 by HC
Hansson
 A possible candidate to explain the observed deviations of the trends in summer and
autumn, which are not captured by 10 RCMs, could be the effect of changing regional
aerosol emissions
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Michael Schrenk, © von Storch, HZG
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Climate Change in the Baltic Sea Region
• Temperature is rising since some decades.
• This increase is beyond the range of our estimate of natural variations. We need
an explanation by external (man-made) drivers.
• We can explain this increase in temperature in winter and spring by considering
elevated CO2 levels as sole external forcing.
• In summer and fall, however, the effect of elevated greenhouse gases is
insufficient to alone explain the warming. Thus, other drivers must be at work.
• A candidate would be the steady reduction of anthropogenic aerosol-generation
in Northern Europe since about 1980. Since aerosols tend to cool the atmosphere
in the warm season, a reduction of the aerosol load would go with an additional
warming.
• More work needed.
• A similar discrepancy between observed change and expected change is also
found for continental circulation and, consequently, precipitation amounts in
summer and fall (not shown).
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Dimension of D&A
Strength of the argument
• Statistical rigor (D) and plausibility (A).
• D depends on assumptions about “internal variability”
• A depends on model-based concepts.
Thus, remaining doubts exist beyond the specified.
How do we determine the „natural variability“?
• With the help of the limited empirical evidence from instrumental observations or
analyses, possibly after suitable extraction of the suspected „non-natural“ signal.
• By accessing long „control simulations“ done with quasi-realistic models.
• By projection of the signal on a proxy data space, and by determining the
statistics of the latter from geoscience indirect evidence (e.g., tree rings).
Discussion: Attribution
1. Attribution needs guess patterns describing the expected effect of different
drivers.
2. Non-attribution may be attained by detecting deviation from a given climate
regime. “Non-attribution” means only: considered factor is not sufficient to explain
change exclusively.
3. Regional and local climate studies need guess patterns (in space and time) of
more drivers, such as regional aerosol loads, land-use change including urban
effects
4. Impact studies need guess patterns of other drivers, mostly socio-economic
drivers
General: Consistency of change with a set of expected responses is a
demonstration of possibility and plausibility; but insufficient to claim exclusiveness.
Different sets of hypotheses need to be discussed before arriving at an attribution.
Change – a scientific challenge with societal significance
For the societal debate, at least in the west, there are several questions, which
need scientific answers, of significance:
a) Is there a change ? What are the dominant causes for such a chance, and
what are the expectations fo the future?
b) Which consequences does this change have for people, society and
ecosystems?
We have three tasks
•
Manifestation: The found change is real and not an artifact of the data and
data collection process (inhomogeneity)
•
Detection: The found change is beyond what may be expected due to natural
(not externally caused) variations.
•
Attribution: A change, which was found to be beyond the range of natural
variations, may plausibly and consistently be explained by a certain (mix of)
external cause(s).
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