the regional issue of detection and attribution hans von storch institute of coastal research...

The regional issue of detection and attribution

Hans von StorchInstitute of Coastal ResearchHelmholtz-Zentrum GeesthachtGermanywith help of Jonas Bhend,Armineh Barkhordarianand Michael Richter

20. September 2010 - Universitet Göteborg

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Observed temperature anomalies

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: is a stochastic system, which exhibits variations on all time scales without an external and identifiable “cause”. (Hasselmann’s “Stochastic Climate Model”)

First task: Describing change

Second task: “Detection” - Assessing change if consistent with natural variability (does the explanation need invoking external causes?)

Third task: “Attribution” – If the presence of a cause is “detected”, determining which mix of causes describes the present change best

Assessing change

Wind speed measurements

SYNOP Measuring net (DWD)

Coastal stations at the German Bight

Observation period: 1953-2005

First task: Example of inhomogeneous data

This and the next 3 transparencies: Janna Lindenberg, HZG

1.25 m/s

First task: Inhomogeneity of wind data

First task: Inhomogeneity of wind 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)

„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 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.

Detection and attribution of non-natural ongoing change

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Anthropogenic

Natural

Internalvariability

Detection and attribution

Attribution

Anthropogenic

Natural

Observations

External forcings

Climate system

Detection

Internalvariability

Dimension of D&A

• Purely scientific

• Stakeholder utility

• Attribution – the competitors

• Falsification

Purely scientific

• 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.

Dimension of D&A

Stakeholder utility

Evidence that anthropogenic warming is related to human drivers –

serving as an arguments to implement broad global mitigation

measures.

Evidence that recent change is part of an ongoing (predictable) pattern,

or not. Serving as information to guide regional and local adaptation

measures.

Dimension of D&A

This is what we need to know more about

„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 local environment, and which measures for mitigation are available,

and which levels of adaptation are required. – very little done.

Attribution – the competitors

Climate drivers – relatively easy; not too many drivers, such as

urbanization, aerosol, land-use.

Impact drivers – hardly dealt with.

Many drivers: eutrophication, pollution, overuse,

regulation, globalization, urbanization

Example: Baltic Sea ecosystems

Dimension of D&A

“Mini-IPCC” assessment on knowledge about climate change in the

Baltic Sea Basin

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

Average diurnal cycle of UHI (urban heat island) intensity for the whole year, winter months (DJF), spring months (MAM), summer months (JJA) and autumn months (SON) for 1996 to 2009

Urban Heat Island effect in Stockholm

• Mean UHI intensity 1.2 °C

• Maximum measured UHI intensity 12.9 °C

•Maximum temperature differences urban-rural in warm season

Michael Richter

Average monthly UHI intensities for 2001 to 2009, computed from each difference of monthly averages between inner-city station Rostock-Holbeinplatz (Ho) and Rostock-Stuthof, Rostock-Warnemünde and Gülzow stations

• Mean UHI intensity 0.3-0.6 °C for different stations

• Maximum measured UHI intensity 8.5 °C

• Maximum temperature differences urban-rural in warm season

Urban Heat Island effect in Rostock

Michael Richter

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Gill et al.,2007

Local change – another major driver: urban warming

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.

Suggestion: Have assessment done by scientists independent of those,

who formulated the hypotheses.

Dimension of D&A

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 signalsExamples: Rybski et al. (2006) Zorita et al. (2009)

Cases of Global Climate Change Detection Studies

Global 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

for m=5 and L=20 (top), andfor m=30 and L=100 years.

The thresholds R = 2, 2.5 and 3σ are given as dashed lines; they are derived from temperature variations are modelled as Gaussian long-memory processes fitted to various reconstructions of historical temperature (Moberg, Mann, McIntyre)

The Rybski et al-approach dealing with global mean temperature

Global detection

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)

Zo

rita

, et a

l., 2

009

Log-probability of the event E that the m largest values of 157 values occupy the last17 places in long-term autocorrelation synthetic series

Derived from Hadley Center/CRU data for „Giorgi bins“.

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Baltic Sea: Observations and simulations used

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

Jonas Bhend

Regional JJA temperatures

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Baltic Sea: Detection using optimal fingerprinting

Model response is too weak

Model response is consistent with observed change

No detection

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Detection with different models, 1943-1997

Temperature scaling

Model response is too weak

No detection

Consistency

Δ=

0.0

5%

Regional DJF precipitation

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Precipitation scaling

Model response is too weak

Detection with different models, 1943-1997

No detection

Consistency

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Consistency of observed trend with a B2 scenario

> Consistency not in all seasons

> Ppecip change too large compared to scenario

> NAO (*) has significant influence

Pattern correlation Ratio of area mean change

precipitation temperature precipitation temperature

DJF 0.84* (0.74*) 0.95* (0.73) 2.0 (1.5) 1.3 (0.5)

MAM 0.72* (0.69*) 0.83 (0.79) 2.2 (1.9) 0.9 (0.8)

JJA -0.28 0.95* -1.8 1.7

SON -0.59 0.60 -1.2 0.5

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-to-noise ratio 0.2-0.5).

BACC conclusion

•Detection and consistency within reach for Northern Europe

•But not really for attribution, since signals for changig aerosol emissions and land-use change are not known.

•Other signals?

•Falsification of detection and attribution an open problem

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Observed trend 1966-2005

Ensemble mean 22 models (A1B)

Ensemble mean 18 models (A2)

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 landMed Sea region: Precipitation over land

Observed seasonal and annual area mean changesObserved seasonal and annual area mean changes of 2m temperature over the period 1980-2009 in of 2m temperature over the period 1980-2009 in

comparison with GS signalscomparison 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 Less than 5% probability that observed warming can be attributed to natural internal variability alonenatural internal variability alone

Externally forced changes are detectable in all seasons except in winterExternally forced changes are detectable in all seasons except in winter

2m Temperature2m Temperature

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Rest - Armineh

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