Transcript Global Warming - Scientific Controversies in Climate Variability

Utility of
Detection and Attribution
Hans von Storch
Institute for Coastal Research
GKSS Research Center, Geesthacht, Germany
and CLISAP/KlimaCampus, Hamburg University
Page 1
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 (cf. Myles‘
„scepticism“)
Page 2
„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.
Page 3
„Significant“ trends
Establishing the statistical significance of a trend may be a
necessary condition for claiming that the trend would represent
evidence of anthropogenic influence.
Claims of a continuing trend require that the dynamical cause
for the present trend is identified, and that the driver causing the
trend itself is continuing to operate.
Thus, claims for extension of present trends into the future
require
- empirical evidence for an ongoing trend, and
- theoretical reasoning for driver-response dynamics, and
- forecasts of future driver behavior.
Page 4
Detection and attribution
of non-natural ongoing change
• Detection of the presence of non-natural signals: rejection of null
hypothesis that recent trends are drawn from the distribution of
trends given by the historical record. Statistical proof.
•Attribution of cause(s): Non-rejection of the null hypothesis that
the observed change is made up of a sum of given signals.
Plausibility argument.
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
Page 5
Global
The utility of global d&a is to
clarify that the concept of
GHG-related anthropogenic
climate change is real.
Conclusion from a successful
d&a:
The public is talking about a
real effect.
Page 6
Cases of Global Climate Change
Detection Studies
In the 1990s … weak, not well documented signals.
Example: Near-globally distributed air temperature
IDAG (2005), Hegerl et al. (1996), Zwiers (1999)
In the 2000s … strong, well documented signals
Examples: Rybski et al. (2006)
Zorita et al. (2009)
IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J. Climate 18, 1291-1314
Hegerl, G.C., H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, P.D. Jones, 1996: Detecting anthropogenic climate change with an
optimal fingerprint method. J. Climate 9, 2281-2306
Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch and G. Flöser (Eds.): Anthropogenic Climate Change. Springer
Verlag, 163-209, ISBN 3-540-65033-4
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
Zorita, E., T. Stocker and H. von Storch: How unusual is the recent series of warm years? Geophys. Res. Lett.
Page 7
The Rybski et al- approach
• Global mean air temperature
• Statistics of ΔTL,m, which is the
difference of two m-year
temperature means separated
by L years.
• Temperature variations are
modelled as Gaussian longmemory process, fitted to
various reconstructions of
historical temperature (Moberg,
Mann, McIntyre)
Historical Reconstructions – their significance for “detection”
Page 8
Historical Reconstructions – their significance for “detection”
Temporal development of
Ti(m,L) = Ti(m) – Ti-L(m)
divided by the standard
deviation of the m-year
mean reconstructed temp
record
The thresholds R = 2, 2.5
and 3σ are given as dashed
lines.
Page 9
Rybski et al., 2006
for m=5 and L=20 (top), and
for m=30 and L=100 years.
Counting extremely warm years
Monte-Carlo simulations taking
into account serial correlation,
either AR(1) (with lag-1
correlation ) or long-term
memory process (with Hurst
parameter H=0.5+d).
Best guesses
  0.85
d  0.45 (very uncertain)
Page 10
Zorita, et al 2009
Among the last 17 years, 19902006, there were the 13
warmest years since 1880 (i.e.,
in 127 samples) – how probable
is such an event if the time
series were stationary?
Regional:
Intention: Preparation and
design of measures to
mitigate expected adverse
effects of climate change.
Problems: high variability,
little knowledge about
natural variability; more
human-related drivers (e.g.
industrial aerosols, urban
effects)
Page 11
Derived from Hadley Center/CRU data for „Giorgi bins“.
Page 12
Zorita, et al., 2009
Log-probability of the event E that the m largest values of 157 values occupy the
last17 places in long-term autocorrelation synthetic series
For regional mean temperatures we have a
signal and attribute it to GHGs (see also Jonas‘
talk). What about precip?
This information may be relevant for a few
sectors, such as agriculture.
Page 13
Δ=0.05%
Regional DJF precipitation
Page 14
Regional JJA temperatures
Page 15
Consistency analysis: Baltic Sea catchment
1. Consistency of the patterns of model “predictions” and
recent trends is found in most seasons.
2. A major exception is precipitation in JJA and SON.
3. The observed trends in precipitation are stronger than
the anthropogenic signal suggested by the models.
4. Possible causes:
- scenarios inappropriate (false)
- drivers other than CO2 at work (industrial aerosols?)
- natural variability much larger than signal (signal-tonoise ratio  0.2-0.5).
Page 16
Local change – another major driver: urban warming
Gill et al.,2007
Page 17
17
Local Station data
Jones+Moberg
until 2000,
afterwards
NASA-GISS
Page 18
Conclusions
Based on my personal experience in interacting with public,
media and policymakers (German bias; all levels):
D&A is confronted with requests from different stakeholders, with
stakes at different geographical scales, woldviews and
perceptions.
Page 19
„Global clients“ want to have proof that the basic concept of
man-made global climate change is real. The best answer for this
client is an answer which is very robust and not critically
dependent on models. – Mostly done.
„Regional clients“ want to have best guesses of the foreseeable
future, in order to institute adaptive measures – on the scale of
medium-size catchment basins not many clear results.
„Local clients“ want know how global and local drivers shape the
future of the ocal environment, and which measures for
mitigation are available, and which levels of adaptation are
required. – very little done.
Page 20
Page 21
Storm surges in Hamburg
Sturmfluten in der Elbe
Vergangenheit
Differenz Scheitelhöhen Hamburg Cuxhaven
Sturmfluten in der Elbe
deutlich erhöht seit 1962 –
aufgrund wasserbaulicher
Maßnahmen, vor allem wegen
der Verkürzung der Deichlinie
Page 22