Download Baltic Sea region

5 December 2016 - "Expert Forums on Atmospheric Chemistry" of VDI, DECHEMA and GDCh on "New and emerging technologies: Impact on air quality and climate", Frankfurt
Hans von Storch:
Deconstruction of anthropogenic climate change:
Manifestation, detection, attribution
Based upon work done with Klaus Hasselmann, Eduardo Zorita, Armineh Barkhordarian, and Jonas Bhend
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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 talk, 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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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.
Michael Schrenk, © von Storch, HZG
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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
Michael Schrenk, © von Storch, HZG
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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
… 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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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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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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Michael Schrenk, © von Storch, HZG
Michael Schrenk, © von Storch, HZG
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“
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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Projected GS signal
patterns (RCMs)
Observed trend
patterns (CRU)
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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).
Michael Schrenk, © von Storch, HZG
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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