Download Workshop-questions

How do we know that human
influence is changing (regional)
climate?
Hans von Storch12 and Jonas Bhend1
1Institute
for Coastal Research, GKSS Research Center, Geesthacht, Germany
and
2Meteorological
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Institute, Hamburg University, Hamburg, Germany
UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Person: Hans von Storch
Hans von Storch
• director of Institute for
Coastal Research @ GKSS
• professor for
Meteorology at the
Meteorological Institute
@ U Hamburg
• author of „Statistical
Analysis in Climate
Research“ @ Cambridge
U Press (with Francis
Zwiers)
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
The Institute for Coastal Research@gkss
An institute of the GKSS Research Center near Hamburg.
• Home of international project offices of LOICZ and BALTEX
• Shareholder of German Climate Computer Center (DKRZ)
Joint programs with
• Alfred Wegener-Institute for Polar and Marine Research (Bremerhaven etc.)
• Center for Marine and Atmospheric Sciences (ZMAW), which encompasses
Max-Planck Institute for Meteorology and geoscience institutes of the
University of Hamburg
Major focus: Analysis of past, ongoing and possible future regional climate
conditions and climate impact, specifically in Northern Europe and marine
environments. Mainly: WIND and related variables (storm surges, waves)
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Questions about ongoing non-natural change
• Global anthropogenic change An argument for efforts to mitigate climate change
by diminishing drivers (“political asset”)
• Regional anthropogenic change–
need to discriminate between global and regional drivers.
An argument for efforts to mitigate regional change by
diminishing regional drivers (“political asset”), or
an argument to implement adaptive measures to deal
with changing risks and opportunities (“information for
stakeholders”)
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Detection and attribution of ongoing change
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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.
• Different definition: „Detection is the process of demonstrating than an observed
change is significantly different (in a statistical sense) than can be explained by natural
internal variability“ (IPCC, TAR, 2001; see also IDAG, 2005)
• 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
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Global
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Cases of Global Climate Change Detection Studies
… of strong, well documented signals
Examples: Rybski et al. (2006)
Counting recent extremes
… of weak, not well documented signals.
Example: Near-globally distributed air temperature
IDAG (2005), Hegerl et al. (1996), Zwiers (1999)
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
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
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
The Rybski-et al. study
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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
- Statistics of ΔT(m,L) which is the
difference of two m-year NH
temperature means, separated by L years.
- Temperature variations are modeled as
Gaussian long-memory process, fitted to
the various reconstructions.
Among the last 16 years, 19912006, there were the 12
warmest years since 1881 (i.e.,
in 126 samples) – how probable
is such an event if the time
series were stationary?
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
  0.8
H = 0.5 + d  0.5+0.3 (??)
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Joint unpublished work by Zorita, Stocker and von Storch, 2007
Counting extremely warm years
How do we determine the control climate?
In general, the data base for the
“control”/undisturbed climate is not good:
• With the help of the limited empirical evidence
from instrumental observations, possibly after
suitable extraction of the suspected „non-natural“
signal.
• By projection of the signal on a proxy data space,
and by determining the stats of the latter from
geoscience indirect evidence (e.g., tree rings).
• By accessing long „control runs“ done with quasirealistic climate models
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Trend in air
temperature
1965-1994
1916-1945
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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
Signal or noise?
Reducing the degrees of freededom
Specific problem in climate applications: usually very many
(>103) degrees of freedom, but the signal of change
resides in a few of these degrees of freedom.
Example:
Signal = (2, 0, 0, ...0) with all
components independent.
Power of detecting the signal,
depends on degrees of freedom.
Thus, the dimension of the problem must be reduced
before doing anything further. Usually, only very few
components are selected, such as 1 or 2.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
“Guess patterns”
The reduction of degrees of
freedom is done by projecting
the full signal S one or a few
several “guess patterns” Gk,
which are assumed to
describe the effect of a
driver.
S = k k Gk + n
with n = undescribed part.
Example: guess pattern
supposedly representative of
increased CO2 levels
When Gk orthonormal then k
= STGk.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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
Optimization of the
expected signal to noise
ratio:
^
1
NN
Gk   Gk
with the inverse
covariance matrix of
the internal climate
variability.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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
Optimizing s/n ratio
The attribution problem
Attribution is considered to be obtained, when
1) the suspected link between forcing and response is
theoretically established, and
2) the data do not contradict that k=1 in the assumed
representation S = k k Gk + n.
3) A contradiction prevails if the null hypothesis “k=1”
is rejected.
4) Thus, a non-contradiction is a plausibility-argument.
It may be due to a too small data base.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Attribution
2-patterns problem (Hegerl et al. 1997)
• guess patterns for climate change mechanisms taken as first EOFs of
a climate change simulation on that mechanism.
• only CO2 increase
• increase of CO2 and industrial aerosols as well.
• orthogonalisation of the two patterns
• estimation of natural variability through GCM control simulations done
at MPI in Hamburg, GFDL in Princeton and HC in Bracknell.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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,
Example: Attribution
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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.
UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Attribution - plausibility
From:
Hadley
Center,
IPCC TAR,
2001
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Regional:
the Baltic Sea
catchment
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
The Baltic Sea Catchment Assessment: BACC
An effort to establish which
knowledge about anthropogenic
climate change is available for
the Baltic Sea catchment.
Working group BACC of GEWEX
program BALTEX.
Approximately 80 scientist from
10 countries have documented
and assessed the published
knowledge.
Assessment has been accepted
by intergovernmental HELCOM
commission as a basis for its
future deliberations.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
The Baltic Sea Catchment Assessment: BACC
Summary of BACC Results
Baltic Area Climate Change Assessment
• Presently a warming is going on in the Baltic Sea region.
• No formal detection and attribution studies available.
• BACC considers it plausible that this warming is at least partly related
to anthropogenic factors.
• So far, and in the next few decades, the signal is limited to temperature
and directly related variables, such as ice conditions.
• Later, changes in the water cycle are expected to become obvious.
• This regional warming will have a variety of effects on terrestrial and
marine ecosystems – some predictable such as the changes in the
phenology others so far hardly predictable.
BACC Group: Assessment of climate change for the
Baltic Sea basin, Springer-Verlag, in press
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
„Significant“ trends
Often,an anthropogenic influence is assumed to be found when trends
are found to be „significant“.
• In many cases, the tests for assessing the significance of a trend
are false as they fail to take into account serial correlation.
• 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 – 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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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
„Significant“ trends
Establishing the statistical significance of a trend is a
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 change.
Thus, claims for extension of present trends into the
future require
- empirical evidence for ingoing trend,
- theoretical reasoning for driver-response dynamics,
- forecasts of future driver behavior.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis:attribution without detection
The check of consistency of recent and ongoing trends
with predictions from dynamical (or other) models
represents a kind of „attribution without detection“.
This is in particular useful, when time series of
insufficient length are available or the signal-to-noise
level is too low.
The idea is to estimate the driver-related change E from
a (series of) model scenarios (or predictions), and to
compare this “expected change” E with the recent trend
R.
If R  E, then we may conclude that the recent change is
not due to the changing driver, at least not completely.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Measures of similarity
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis: Seasonal precip in
the Baltic Sea catchment
Example:
Changing DJFmean precipitation
in the Baltic Sea
catchment
Trend of precip
according to
different data
sources.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis
Expected signals E
• six simulations with regional coupled atmosphere-Baltic
Sea model RCAO (Rossby-Center, Sweden)
• three simulations run with HadCM3 global scenarios,
three with ECHAM4 global scenarios; 2071-2100
• two simulation exposed to A2 emission scenario, two
simulations exposed to B2 scenario; 2071-2100
• two simulations with present day GHG-levels; 1961-90
• Climate change in the four scenarios relatively similar.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis
Patterns correlations between “observed” (CRU) trends in DJF
seasonal precipitation in the Baltic Sea catchment and “expected”
signals derived from scaled RCM changes.
Global
model
scenario
Pattern
correlations
Pattern correlations
without NAO
A2
0.83
0.75
B2
0.75
0.64
A2
0.85
0.75
B2
0.84
0.74
HadAM
ECHAM
The pattern correlations are all significantly larger than pattern
correlations between random combinations of trends.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis
Ratio of intensities between “observed” (CRU) trends in DJF seasonal
precipitation in the Baltic Sea catchment and “expected” signals
derived from scaled RCM changes.
All model predictions
result in too large
trends for the past
years.
When taking out the
NAO the situation
slightly improves.
Global
model
scenario
Intensity-ratio
R/E
Intensity-ratio
without NAO
A2
2.96
2.53
B2
4.50
3.98
A2
1.94
1.57
B2
2.50
2.07
HadAM
ECHAM
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Consistency analysis: Baltic Sea catchment
1.
In DJF: patterns of trends in “observed” CRU data consistent with
patterns of trends predicted by RCAO-scenario.
2. Intensity of trends in 1976-2005 considerably larger than
predicted by RCAO scenarios.
3. When talking out the NAO, the dominant pattern of natural
variability, the over-prediction of the intensities is somewhat
smaller.
4. Possible causes:
- scenarios inappropriate (false)
- drivers other than CO2 at work (industrial aerosols?)
- natural variability much larger than signal (signal-to-noise
ratio  0.2-0.5).
5. Similar situation in spring.
6. No consistency in summer and fall.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Overall summary
How do we know that human influence is
changing (regional) climate?
-Statistical analysis of ongoing change with
distribution of “naturally” occurring changes –
detection, statistical proof.
- ok für global and continental scale temp.
- Consistency of continental temp change with
change in regions such as Baltic Sea catchment
(temp and related variables)
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Overall summary
How do we know that human influence is
changing (regional) climate?
- Attribution (of causal drivers) is a
plausibility argument: determine consistency
of ongoing change with expected changes.
- Done for global and continental scale temp
(and related) variables (see IDAG).
- First efforts on regional scales.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop questions
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: How do you define change (e.g. time scale, magnitude,
direction, significance of change, what signal to noise ratios are you
used to working with)?
„Change“ is any change of the statistics of any climate and climate
impact variable in the course of time.
We distinguish between natural change and anthropogenic change;
often, however, “change” is tacitly assumed to be anthropogenic –
but the IPCC is careful in its wording in this respect.
A risk of 5% is usually considered sufficiently small to allow for a
rejection of a null hypothesis.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: What are the most important changes being investigated in
your field and why - what is the motivation?
This depends on the view point.
Scientists interested in climate dynamics focus on variables which may
be considered as proxies for the global climate state.
Stakeholders interested in pushing for emission policies ask for changes
of global distributions of local weather phenomena (extremes).
Scientists interested in specific climate impact questions, such as
storm surge statistics, deal often with the infrequent but impactintense regional and local events.
Ditto stakeholders responsible for reduction of vulnerability and
adaptation in general.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: Does your science deal with changes in extremes or
changes in average behavior?
Both.
In general, large scale dynamics are more important than smaller
scale dynamics (downscaling paradigm).
Since extremes take place on smaller scales, they are usually less
important for climate dynamics.
For climate impact, for course, smaller scales and thus extremes
are very important.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: What are the most important positive and negative
feedback loops and what methods do you use to identify feedbacks?
Many.
They are explicitly modeled in GCMs.
Models of strongly reduced complexity are interesting and exciting
but fail to acknowledge the presence of ubiquitous noise.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: How do you treat non-linearities?
Even though the climate system is highly non-linear, “interesting”
nonlinearities, such as jumps, regime changes, and hysterises, are
strongly masked by the stochastic character (born from the
presence of ubiquitous smaller non-linearities).
In case of changes such as the presence or absence of sea ice or
ice-caps such non-linearities may become dominant.
The non-linearities are usually considered in the framework of
quasi-realistic models (GCMs) – model as may details as you can
afford.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: When does the delta change approach (or incremental
change approach) fail?
For instance, when zero/positive variables are involved, such as sea
ice cover.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: In approaching the problem - do you typically collect
more data, pool data, use simulation approaches, or some
combination thereof?
Pool existing data, simulation approaches with quasi-realistic
models;
Combining theoretical knowledge (models) with empirical evidence
(data)- data assimilation.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: When you get a final estimate of change, how do you
describe it? With what confidence?
… with the caveat that the estimate of natural variability may be
incomplete.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: How do you establish design criteria (e.g. high-tides
with a 100 year return period, wind loads on structures, etc.) in the
midst of change, if applicable?
Depends on life-time of investment.
Provide a range of perspectives for the relevant future, in the form
of scenarios.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
Workshop-questions
Question: How do you deal with model outputs from other fields,
if they are used (i.e. GCMs)?
We use the output of economic wisdom in the form of economic
scenarios. Usually, we just believe them as reasonable.
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA
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UNESCO.IHP workshop "Methods of detection and analysis of change
and feedback in the earth sciences", 26-28. March 2008, Tuscon, USA