Transcript Linking the global and the regional ‐ what means

Hans von Storch
Institute for Coastal Research, GKSS, Geesthacht
and KlimaCampus, Hamburg, Germany
Göteborg, 15 October 2010
Linking the global and the regional ‐
what means global warming regionally
in the Baltic Sea catchment?
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.
What is this?
The question if we „see something“ supporting the reality of a
human influence on climate – needs the adoption of a mathematical
language.
Determination of man-made climate change is not a matter of
theory, but of assessing data.
The framework is of statistical nature, and the results are probability
statements condition upon certain assumptions.
The whole process is called „detection and attribution“.
„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.
„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.
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
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 models.
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.
Trend in air temperature
1965-1994
Signal or noise?
1916-1945
Hegerl et al., 1996
“Guess patterns”
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 increased CO2 levels
Hegerl et al., 1996
How do we determine
the „natural climate variability“?
• With the help of the limited empirical evidence from
instrumental observations, possibly after suitable extraction
of the suspected „non-natural“ signal.
• By accessing long „control runs“ done with quasi-realistic
climate models.
Trends in a scenario
calculation until 2100
Trends in temp until 1995
Hegerl et al., 1996
4
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-
Attribution
diagram for
observed 50year trends in
JJA mean
temperature.
The ellipsoids enclose non-rejection regions for testing the null hypothesis that the 2dimensional 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
From:
Hadley Center,
IPCC TAR, 2001
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”
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.
Rybski et al., 2006
for m=5 and L=20 (top), and
for m=30 and L=100 years.
Counting extremely warm years
Among the last 17 years, 1990-2006,
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?
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).
  0.8
d  0.3 (very uncertain)
Zorita, et al 2009
Best guesses
Derived from Hadley Center/CRU data for „Giorgi bins“.
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
Regional:
Intention: Preparation and
design of measures to adapt
to expected adverse effects of
climate change.
Problems: high variability, little
knowledge about natural
variability; more humanrelated drivers (e.g. industrial
aerosols, urban effects)
Temperature development in Northern
Europe
•Observations
– Interpolated land station data
– Temperature: CRUTEM 3v
– Precipitation: GPCC v4
•Simulations: Global model data from
CMIP3
•ALL:
anthropogenic and natural
forcing
•ANT: anthropogenic forcing only
Jones and Moberg, 2003: Hemispheric and large-scale surface air temperature variations. Journal of Climate
Schneider et al. 2008: Global precipitation analysis products of the GPCC. Technical report, DWD
21
Meehl et al. 2007: The WCRP CMIP3 multimodel dataset - a new era in climate change research. BAMS
Bhend, 2009
Detection using optimal fingerprinting
Model response
is too weak
Model response
is consistent with
observed change
No detection
Bhend, 2009
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 from a (series of)
model scenarios (or predictions), and to compare this “expected
change” with the recent trend.
If change  expectation, then we may conclude that the recent
change is not due to the suspected driver, at least not completely.
Consistency analysis
Expected signals
six simulations with regional coupled atmosphere-Baltic Sea
regional climate model RCAO (Rossby-Center, Sweden)
• three simulations forced 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
• Regional climate change in the four scenarios relatively similar.
Consistency analysis
All seasons: RCAO-ECHAM B2 scenario
Pattern correlation
Intensities
precipitation
temperature
precipitation
temperature
DJF
0.84* (0.74*)
0.95* (0.73)
2.50 (2.07)
1.33 (0.66)
MAM
0.72* (0.69*)
0.83 (0.79)
3.21 (2.86)
1.15 (1.06)
JJA
-0.28
0.95*
4.42
1.85
SON
-0.59
0.60
2.23
0.71
Regional DJF precipitation
Regional JJA temperatures
Consistency analysis: Baltic Sea catchment
1. Consistency of the patterns of model “predictions” and recent
trends in terms of temperature and precipitation 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-to-noise ratio 
0.2-0.5).
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 for global and continental scale temperature.
- In the 1990s, advanced statistical analysis needed, today also
done with simpler methodology.
- Consistency of continental temperature change with change
in regions such as Baltic Sea catchment (temperature and
related variables); problem with precipitation.
The purpose
of BACC is to provide the scientific community and the
public with an assessment of ongoing and future climate
change in the Baltic Sea Basin. This is done by reviewing
and assessing published scientific knowledge on climate
change in the Basin.
An important element is the comparison with the historical
past (until about 1800) to provide a framework for the
severity and unusualness of the change.
The unique feature of BACC is the combination of evidence
on climate change and related impacts on marine,
freshwater and terrestrial ecosystems in the Baltic Sea
Basin.
It is the first systematic scientific effort for assessing
climate change in the Baltic Sea Basin.
No additional or external funding was needed.
The results have not been influenced by either
political or special interests.
Past and current climate change
 Air temperature increased by about 1.2 C since 1871
until 2004.
 Most pronounced warming in spring.
 Related observed changes in winter runoff, ice duration
and snow.
 More precipitation in the 2nd half of the 20th century
with major regional variations.
 No systematic change in windiness found.
 No clear long-term trends in Baltic Sea salinity.
Past and current climate change: Air temperature
Temperature anomaly ( C)
4
3
2
1
Baltic Sea basin land
surface spring air
temperature
1871-2004
0
-1
-2
-3
-4
-5
1870
1900
North Spring
1930
Filter
1960
South Spring
1990
Filter
Winter
Spring
Summer
Fall
Year
North
1,17
1,95
0,78
1,04
1,3
South
1,30
1,43
0,40
0.80
1,01
Linear temperature trends 1871 – 2004 for the northern (latitude > 60 °N) and
southern (latitude < 60 °N) Baltic Sea basin.
Anomaly time series of annual precipitation over Sweden,
1860-2004 (reference period 1961-90).
Precipitation
Past and current climate change: Wind
No changes in wind and storminess
Number of low pressure systems (p< 980 hPa) in Stockholm and Lund
Past and current climate change: Precip and ice
Volkhov-Volkhovo
Tornionjoki
9.6. 200
Ice cover
duration,days
30.5. 150
20.5.
10.5.
30.4.
100
50
0
19
46
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
Break-up (date)
19.6.
20.4.
1693
Years
1743
1793
1843
1893
1943
1993
Year
Ice ice
break
up duration
in Tornionjoki
River,
Finland.
Changes in river
cover
(Volkhov
river,
Russia).
salinity
Past and current climate change
Sea level change
Baltic Sea water level:
Post-glacial uplift versus eustatic sea level rise, Stockholm
Isostatic sea level change =
land uplift due to post-glacial rebound
Eustatic sea level =
water level rise due to global effects
Ongoing changes
in regional ecosystems
 Associated changes in terrestrial ecosystems include
- earlier spring phenological phase,
- northward species shift, and
- increased growth and vigour of vegetation.
 Robust assessments of changes in marine
ecosystems related to climate change are hardly
possible at this time. Further research is needed to
discriminate between climate change and other
anthropogenic drivers such as over-fishing,
euthrophication, air pollution and land use changes.
Past and current climate change: Terrestrial ecosystems
Mean rate of change (days/year) of date of
leaf unfolding in birch, 1958-2000
Marine Ecosystems:
Regime shift in about 1988?
Caveats
 Link to raising greenhouse gas concentrations is plausible, but no
robust regional attribution has been established. (On the global
scale this link has been established)
 Many conclusions relate to different time periods studied,
changes occur at different time scales: Variability versus trend
problem.
 Only few observational records span the entire recent 150 to 200
years.
 Changing observational techniques influence data homogeneity.
 “Detection and attribution” studies at the regional scale are
urgently needed to determine the influence of anthropogenic
factors in changing the regional climate.
Scenarios of future climate …
 … constructed by feeding assumed emissions of greenhouse
gases and aerosols into quasi-realistic models of the climate
system.
 Future emissions can not be predicted; only plausible and
consistent visions of the future (i.e., scenarios) are possible.
 Scenarios provide a frame for decision makers to explore the
range of policy options to deal with the reality of
anthropogenic climate change.
 Scenarios are no predictions.
Scenarios of future climate change
Global climate models (GCMs) project warming over the Baltic
Sea basin.
Regional scenarios are constructed from regional climate
modelling, which provides more geographical detail and is
broadly consistent with GCM projections.
Results from regional climate modelling do not fully reflect
model and scenario uncertainties.
Within these limits, these results give an indication of plausible
future changes by the end of the 21st century.
Projections of
future regional climate change
 Increasing temperatures very likely during the entire 21st
century, but size of the trend depends considerably on model.
 Projected mean precipitation increases, largest increase in
winter throughout the basin and decrease in summer in the
southern basin.
 No clear projection for wind speed and storms.
BACC projections: Winter precipitation
1
Regional climate model simulated precipitation changes in % for winter (DJF) between the periods
1961-1990 and 2071-2100 using the SRES-A2 emissions scenario. The upper plots show results from the
HIRHAM Model and the lower plots are from the RCAO Model. Plots on the left used GCM boundary
conditions from HadAM3H; plots on the right used ECHAM4/OPYC3.
BACC projections: Summer precipitation
1
Regional climate model simulated precipitation changes in % for summer (JJA) between the periods
1961-1990 and 2071-2100 using the SRES-A2 emissions scenario. The upper plots show results from the
HIRHAM Model and the lower plots are from the RCAO Model. Plots on the left used GCM boundary
conditions from HadAM3H; plots on the right used ECHAM4/OPYC3.
BACC projections: River runoff
Change of river flow to
Baltic Sea basins 2071-2100
BACC projections: Sea ice
Mean number of ice
days in a present day
simulation (right) and
two scenarios for 20702100 (bottom)
Projections of future climate impacts on
terrestrial ecosystems
The expected future
warming is associated to a
possibly accelerated
continuation of the present
trends in
- earlier spring
phenological phases,
- northward species
shifts and
- increased growth and
vigour of vegetation
changes in the relative cover of different
vegetation types in Northern Europe
Projections of future climate impacts
on marine ecosystems
 No detailed, comprehensive analysis available –projections
are more ad-hoc and uncertain.
 Effect of other changing influences hardly predictable.
 Possible Baltic Sea salinity decrease would have major effect
on marine fauna.
 Expected changes in precipitation and river runoff may have
additional detrimental effects on the problem of
eutrophication.
Past and current climate change
Impacts on marine ecosystems
… increase of temperature…
… decrease of salinity…
•
•
•
•
•
• Osmotic stress
• Shift in species composition (phyto– &
zooplankton)
• Egg survival
• Food quality for fish (growth rate)
• Distribution of benthos
• Reduction of fitness
• Invading species
Higher metabolic rates
Impact on acclimation capacity
Reduce the general fitness
Reduce enzyme activities
Shift in species composition
(phytoplankton)
• Enhanced cyanobacteria blooms
… reduction in sea ice…
• Ringed seal survival
Major findings
-a marked increase of mean surface air temperature of more than 0.7 C in the
region during the recent century;
- consistent changes in other variables such as extreme temperatures, increase of
winter runoff, shorter ice seasons and reduced ice thickness on rivers and lakes in
many areas;
- a spatially non-uniform pattern of upward and downward trends in precipitation,
which is difficult to be related to anthropogenic climate change;
- evidence on increasing Baltic Sea SST only significant for the 3 recent decades,
the century-long data records may have severe inhomogeneities;
- assessment of indications that at least part of the recent warming in the Baltic
Sea basin is related to the steadily increasing atmospheric concentrations of
greenhouse gases;
- for the future, projections indicate that increased winter precipitation may
emerge later in this century over the entire area, while summers may become
drier in the southern part – but this expectation is uncertain for the time
being;
-for the Baltic Sea, a tendency towards lower salinity and less ice coverage
could be expected;
-no clear signals, whether for the past or for future scenarios, are available
with regard to wind conditions;
- observed changes in past temperature have been associated with consistent
changes in terrestrial ecosystems, such as earlier spring phenological phases,
northward species shifts and increased growth and vigour of vegetation, these
changes are expected to continue and become more pronounced in the
future;
- an assessment for the marine ecosystem of the Baltic Sea is particularly
difficult because of the presence of strong non-climatic stressors such as
eutrophication, fishing, release of pollutants, related to human activities.
BACC @ Springer
Publication in January 2008:
More than 30 contributing institutions
More than 80 contributing authors from
13 countries
More than 475 pages
More than 2000 references (~150 non-English)
Ch1: Introduction and summary
Ch2: Past and current climate change
Ch3: Projections of future climate change
Ch4: Climate-related change in terrestrial and
freshwater ecosystems
Ch5: Climate-related change in marine ecosystems
Ch6: Annexes
www.baltex-research.eu/BACC
BACC and HELCOM
HELCOM Thematic Assessment published
May 2007
The report is based on the BACC material but
condensed to 59 pages with a focus of the
marine environment of the Baltic Sea. It has
been approved by the HELCOM contracting
governments of 9 countries and the European
Commission.
An unprecedented cooperation of a
climate-related research program and an
intergovernmental body
Thanks for your attention
When you want more to know:
http://coast.gkss.de/staff/storch
Contact: [email protected]