1 Hans von Storch: Recent climate change in the Baltic Sea region - manifestation, detection and attribution Based upon: Work done with Klaus Hasselmann,

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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

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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“

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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

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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

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Methodical issues

• Randomness• Significant trends?

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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.

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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.

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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

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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

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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

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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,

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… 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)

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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.

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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.

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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.

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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).

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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

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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).

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.

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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