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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
Nordenskjöld Lecture,
Göteborgs Universitet, 16 November 2015
Hans von Storch1. Climate researcher (in the field since 1971)2. Coastal science; statistical analysis3. Cooperation with social and cultural
scientists since 19924. Active in IPCC as Lead Author
in AR3 (2001) and AR5 (2013).5. Co-Chair of the BACC-Assessment of Climate Change
in the Baltic Sea region, together with Anders Omstedt.
6. Retired Director of the Institute of Coastal Research of the Helmholtz Zentrum Geesthacht, Germany
7. Professor at Universität Hamburg,Meteorologcal Institute and Faculty of Social and Economic Sciences
8. Guest-Professor at the Oean University of China, Qingdao
9. Honorary doctorate of U Göteborg
3
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.
BACC as „regional IPCC“
4
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
• 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).
Change – a scientific challenge with societal significance
5
Klaus Hasselmann, the inventor of D&A
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
6
Methodical issues
• Randomness• Significant trends?
7
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
Noise as nuisance:
masking the signal
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.
9
Noise or deterministic
chaos?
Mathematical construct of
randomness – an adequate concept
for description of features resulting
from the presence of many chaotic
processes.
„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.
11
Losses from Atlantic Hurricanes
Storm surges in Hamburg
Quelle: http://www.dmi.dk/nyheder/arkiv/nyheder-2015/01/2014-er-klodens-varmeste-aar
Estimates of global mean temperature increase
Temperature increase in the Baltic Sea Region
Baltic Sea region
(1982-2011)
Data: CRU & EOBS
16
17
Estimation of damage if presence of people
and values along the coast would have
been constant – the change is attributable to
socio-economic development
Is the massive increase in
damages attributable to
extreme weather conditions?Losses from Atlantic
Hurricanes
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
18
Storm surges in Hamburg
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.
The Rybski et al-approach
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
20
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 ( 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,
21
… there is something to be explained
IPCC AR5, SPM
Thus, there is something going on in the global
mean air temperature record, which needs to be
explained by external factors.
Zor
ita,
et a
l., 2
009
Regional clustering of warmest years
Observed temperature trends in the Baltic Sea region (1982-2011)
23
Observed CRU, EOBS (1982-2011)
95th-%tile of „non-GS“ variability,
derived from 2,000-year palaeo-simulations
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)
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.
Baltic Sea region
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
Consistency of recent local change:Storm surges in Hamburg
“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
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.
Zw
iers
, F.
W.,
199
9: T
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n: H
. vo
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and
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163
-209
, IS
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6503
3-4
Attribution
diagram for
observed 50-
year trends in
JJA mean
temperature.
IPCC 2007
Additional ly man-
made factors
Only natural
factors
„observations“
Attribution: Can we describe the development of air temperature by imposing realistic increasing greenhouse gas and aerosol loads on climate models? Yes, we can.
30
Projected GS signal
patterns (RCMs)
Observed trend
patterns (CRU)
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.
31
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).
34
Solar surface irradiance in the Baltic Sea Region
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
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
35Michael Schrenk, © von Storch, HZG
36
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).
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).
Dimension of D&A
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.
39
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).
Change – a scientific challenge with societal significance