Transcript Change - hvonstorch.de

May 27, 2014 - URC 2014 Urban Regions under
Change: towards social-ecological resilience,
Hamburg
Hans von Storch
Urban climate
change – the story
of several drivers.
• Change!
• Detection and attribution
• Issues
• No systematic results for urban
conglomerates known to me
• Discussion
Seasoonal precipiitation
(mm) in HH-Fuhlsbüttel
Which „signals“ make up these records?
y=-10 mm/century
y=36 mm/century
y=28 mm/century
y=8 mm/century
(Data: Deutscher Wetterdienst, 2008; Source: Schlünzen et al., 2010)
Change !
Change is all over the place,
Change is ubiquitous.
What does it mean?
Anxiety; things become more extreme, more dangerous; our
environment is no longer predictable, no longer reliable.
Change is bad; change is a response to evil doings by egoistic social
forces. In these days, in particular: climate change caused by people
and greedy companies.
Change !
Change is all over the place,
Change is ubiquitous.
What does it mean?
There are other perceptions of change: it provides opportunities; it is
natural and integral part of the environmental system we live in.
The environmental system is a system with enormous many degrees
of freedom, many non-linearities – is short: a stochastic system,
which exhibits variations on all time scales without an external and
identifiable “cause”. (Hasselmann’s “Stochastic Climate Model”)
„Significant“ trends
Often, an anthropogenic influence is claimed 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.
First task:
Describing change
Wind speed measurements
 SYNOP Measuring net (DWD)
 Coastal stations at the German
Bight
 Observation period: 1953-2005
This and the next 3 transparencies:
Janna Lindenberg, HZG
Example: coastal wind data
1.25
m/s
First task: Inhomogeneity of data
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)
Detection and Attribution
“Detection” - Assessing change if consistent with natural
variability (does the explanation need invoking external
causes?)
“Attribution” – If the presence of a cause is “detected”,
determining which mix of causes describes the present
change best
• 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.
Detection and attribution
Detection
Climate system
Internal
variability
External forcings
Observations
Attribution
Anthropogenic
Natural
12
Detection
Test of the nullhypothesis:
„considered climate signal is consistent with natural climate
variability“
St ~ P[µo, ∑o]
with St representing the signal to be examined, whether it is
consistent with undisturbed statistics P[µo, ∑o]. The of the
distribution of the present climate is given by parameters µo
and ∑o.
Problem is to determine St and its distribution P.
Attribution
After we have found a signal to lie outside the range of natural
variations, the question arises whether this signal can be
causally related to an external factor.
Usually, there are many factors, but climatological theory
reduces the candidates to just a few (e.g., urban effects,
greenhouse gases, volcanic aerosols, solar effects).
Then, that mix of processes is attributed to the signal, which
fits best to the a-priori assumed link between cause and effect.
This may take the form of a best-fit or as the result of a nonrejection of a null hypothesis.
S t    k Fk  N t
k
Detection and attribution
15
Storm surges in Hamburg
Storm surges in the Elbe estuary
Difference in storm surge height
between Cuxhaven and Hamburg
Height massively increased since
1962 – after the 1962 event, the
shipping channel was deepened
and retention areas reduced.
2m Temperature in the Med Sea Region
Observed seasonal and annual area mean changes
of 2m temperature over the period 1980-2009 in
comparison with GS signals
Observed trends of 2m temperature (1980-2009)
Projected GS signal patterns (time slice experiment)
23 AOGCMs, A1B scenario derived from the CMIP3
90% uncertainty range of observed trends, derived
from 10,000-year control simulations
The spread of trends of 23 climate change projections
Less than 5% probability that observed warming can be attributed to natural
internal variability alone
Externally forced changes are detectable in all seasons except in winter
Barkhordarian, 2013
Med Sea region:
Precipitation over land
90% uncertainty range, 9000-year
control runs
Spread of trends of 22 GS signals
Spread of trend of 18 GS signal
Spread of trend of CRU3 and GPCC5
observed trends
There is less than 5% probability that
observed trends in DJF, JFM, FMA,
ASO, SON are due to
natural (internal) variability alone.
Externally forced changes are
significantly detectable in winter and
autumn intervals (at 5% level)
Barkhordarian, 2013
Climate Change in urban
conglomerates
A manifestation of three anthropogenic factors
1. Global warming (related to elevated greenhouse gas
concentrations)
2. Regional change (related to changing anthropogenic
aerosol loads)
3. Local change
(related to changing
urban size and
structure)
Bechtel and Schnmidt, 2011
20
Rostock
Richter et al., 2011
Seasonal cycle of urban heat differences in Rostock: Rostock-Holbeinplatz (Ho) vs.
Rostock-Stuthof (St), Rostock-Warnemünde (War) and Gülzow (Gü)
21
Stockholm
Richter et al., 2011
Diurnal cycle of the heat island effect in different seasons
Differences between Stockholm-Bromma and Tullinge-Air-port.
22
Increase of mean temperature
Richter et al., 2011
Mean temperatures in Rostock-Warnemünde and Stockholm
23
Local change
– another major driver: urban warming
Gill et al.,2007
24
Discussion
1. Climate is changing.
2. In cities there are at least three drivers – the local manifestation of
global (GH) and regional (aerosols) change, and the changing land
use in cities.
3. Many studies on the global effect exist, some on the urban effect, no
studies on the regional effect of reduced emissions of aerosols (in
Northern Europe)
4. Global and local effects seem to simply add.
5. No efforts are known to me to disentangle the effects on given
temperature records of cities.
6. In Hamburg the hat island effect is up to 1K and more, in Rostock up
to 0.5K and more and in Stockholm up to 1K and more.
25
Strategic issues
1.Since several factors affect urban climate, a combination of
mitigation measure may be available to reduce the impact of global
warming. Namely
- reducing of global emissions
- retracting previously formed urban heat islands
2.However, global growth together with global warming may
exacerbate the situation, when managing growth fails
3.What is needed of a scientific policy advice
- monitoring of urban climate change
- separation of effects, due to global, regional and local effects
- construction of realistic scenarios, which describe the effect of
possible future urban planning.
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Outlook: Urban change,
detection and attribution
Falsification
Which observations in the coming 5/10 (?) years would lead to reject present
attributions?
Suggestion: Formulate and freeze NOW falsifiable hypotheses, and test in
5/10 (?) years time – using the independent data of the additional years.
Seasonal precipiitation (mm)
in HH-Fuhlsbüttel
Which „signals“ make up these records?
I don‘t
know
y=-10 mm/century
y=36 mm/century
y=28 mm/century
y=8 mm/century
(Data: Deutscher Wetterdienst, 2008; Source: Schlünzen et al., 2010)