Urban climate change – the story of several drivers.• Change!

• Detection and attribution

• Issues• No systematic results for urban

conglomerates known to me• Discussion

May 27, 2014 - URC 2014 Urban Regions under

Change: towards social-ecological resilience,

Hamburg

Hans von Storch

Which „signals“ make up these records?S

easo

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(mm

) in

HH

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(Data: Deutscher Wetterdienst, 2008; Source: Schlünzen et al., 2010)

y=36 mm/century

y=28 mm/century

y=-10 mm/century

y=8 mm/century

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.

Wind speed measurements

SYNOP Measuring net (DWD)

Coastal stations at the German

Bight

Observation period: 1953-2005

First task: Describing change

This and the next 3 transparencies:

Janna Lindenberg, HZG

1.25

m/s

Example: coastal wind data

First task: Inhomogeneity of data

The issue is

deconstructing a given recordwith 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” - 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

12

Anthropogenic

Natural

Internalvariability

Detection and attribution

Attribution

Anthropogenic

Natural

Observations

External forcings

Climate system

Detection

Internalvariability

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.

Detection

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 non-rejection of a null hypothesis.

tkk

kt NFS

Attribution

Detection and attribution

15

Storm surges in Hamburg

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.

Storm surges in the Elbe estuary

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

The spread of trends of 23 climate change projections

90% uncertainty range of observed trends, derived

from 10,000-year control simulations

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

2m Temperature in the Med Sea Region

Barkhordarian, 2013

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)

Med Sea region: Precipitation over land

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)

20

Bechtel and Schnmidt, 2011

21

Rostock

Seasonal cycle of urban heat differences in Rostock: Rostock-Holbeinplatz (Ho) vs.

Rostock-Stuthof (St), Rostock-Warnemünde (War) and Gülzow (Gü)

Richter et al., 2011

Stockholm

22

Diurnal cycle of the heat island effect in different seasons

Differences between Stockholm-Bromma and Tullinge-Air-port.

Richter et al., 2011

Increase of mean temperature

23

Mean temperatures in Rostock-Warnemünde and Stockholm

Richter et al., 2011

Warming due to urban

effects or global warming?

24

Gill et al.,2007

Local change

– another major driver: urban warming

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

26

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.

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.

Outlook: Urban change, detection and attribution

Which „signals“ make up these records?S

easo

nal p

reci

piita

tion

(mm

) in

HH

-Fuh

lsbü

ttel

(Data: Deutscher Wetterdienst, 2008; Source: Schlünzen et al., 2010)

y=36 mm/century

y=28 mm/century

y=-10 mm/century

y=8 mm/century

Which „signals“ make up these records?

I don‘t

know