Transcript Detection and attribution of climate change for the

Detection and attribution of
climate change
for the Baltic Sea Region
Hans von Storch, Institute of Coastal
Research, Geesthacht
and
Armineh Barkhordarian,
UCLA
16-19 June 2015, Baltic Sea Science Conference, Riga
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Detection and attribution of change
Based upon:
Work done with Klaus Hasselmann, Eduardo Zorita, Armin Bunde, Armineh
Barkhordarian, and Jonas Bhend
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The issue is
deconstructing a given record
with the intention to identify „predictable“ components.
„Predictable“
-- either natural processes, which are known of having limited life times,
-- or man-made processes, which are subject to decisions (e.g., GHG, urban effect)
Differently understood in different social and scientific quarters.
The issue is also to help to discriminate between culturally
supported claims and scientifically warranted claims
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What is behind this time serie?
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Detection and attribution
Detection:
Determination if observed variations are within the
limits of variability of a given climate regime. If this
regime is the undisturbed, this is internal variability (of
which ENSO, NAO etc. are part)
If not, then there must be an external (mix of)
cause(s) foreign to the considered regime.
Attribution:
In case of a positive detection: Determination of a mix
of plausible external forcing mechanisms that best
“explains” the detected deviations
Issues: Uniqueness, exclusiveness, completeness of
possible causes
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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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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?
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)
•
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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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
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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
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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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
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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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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“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“
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).
Projected GS signal
patterns (RCMs)
Observed trend
patterns (CRU)
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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, is the effect of changing regional aerosol
loads
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Discussion: Attribution
1. Attribution needs guess patterns describing the expected effect of different
drivers.
2. Non-attribution may be attained by detecting deviations from a given climate
regime (the case of the stagnation)
“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 (the case of the Baltic Sea Region)
4. Impact studies need guess patterns of other drivers, mostly socio-economic
drivers (the case of Hamburg storm surges and hurricane damages)
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.
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).
Precipitation (1979-2008)
Observed (CRU3, GPCC6, GPCP)
Projected GS signal (ENSEMBLES)
In winter (DJF) non of the 59
segments derived from 2,000 year
paleo-simulations yield a positive
trend of precipitation as strong as that
observed. There is less than 5%
probability that observed positive
trends in winter be due to natural
(internal + external) variability alone
(with less than 5% risk).
In spring (MAM), summer (JJA) and Annual trends externally forced changes are not detectable. However
observed trends lie within the range of changes described by 10 climate change scenarios, indicating that
also in the scenarios a systematic trend reflecting external forcing is not detectable (< 5% risk).
In autumn (SON) the observed negative trends of precipitation contradicts the upward trends suggested by
10 climate change scenarios, irrespective of the observed dataset used.
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Precipitation (over land of Mediterranean
Sea)
1966-2005, CMIP3
(Barkhordarian et al , Climate Dynamics 2013)
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Changes in Large-scale circulation (SON)
Mean Sea-level pressure
Projected GS signal
pattern (RCMs)
Observed trend pattern
(1978-2009)
 Observed trend pattern shows areas of decrease in SLP over the Med. Sea and areas
of increase in SLP over the northern Europe. Observed trend pattern of SLP in SON
contradicts regional climate projections.
 The mismatch between projected and observed precipitation in autumn is
already present in the atmospheric circulation.
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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 fo 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).
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