detection and attribution, forced changes, natural variability, signal … · 2018. 3. 15. · reto...

Detection and attribution, forced changes, natural variability, signal and noise, ensembles

Reto Knutti, IAC ETH

ETH Zurich | Reto Knutti

Reto Knutti / IAC ETH Zurich

What’s wrong with this presentation?

“For the next two decades, a warming of about 0.2°C per decade is projected for a range of SRES emission scenarios.” (IPCC SPM 2007)


Why were temperatures not rising much 1998 to 2013 even though CO2 was increasing?

(http://www.cru.uea.ac.uk/cru/data/temperature/)


Das Klima ändert – was nun? (OCCC, 2008)


Klimaszenarien CH2011

Daten: MeteoSchweiz, CH2011; Visualisierung: ETH

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Niederschlagsänderung Sommer [%] 1

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A2 A1B RCP3PD

Reto Knutti / IAC ETH Zurich 7

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Cascade Jun/Jul

Montana Jun/Jul

US Jun/JulUS National Annual MeanGlobal Annual Mean

Year

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0.66°F/century; r**2 = 0.011

0.87°F/century; r**2= 0.070

1.0°F/century; r**2 = 0.143

0.92°F/century; r**2 = 0.595

0.33°F/century; r**2 = 0.002

US Jun/Jul

Montana Jun/Jul

Cascade, MTJun/Jul

Surface temperature trends: regional to local


Variability in a the 40 member CCSM ensemble

(Deser et al., 2012)


Toy model example

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Forced signal, predictable,modeled deterministically

Noise, some predictability from memory,modeled probabilistically

Cause 1 Cause 2


Signal and noise in Atlantic Storms Reconstruction of Atlantic tropical storms (1878 to current) with

adjustments during the pre-satellite era (1878-1965) based on weather reporting ship track density in the Atlantic. Blue curve shows the adjustment for estimated number of missing storms.

(Vecchi and Knutson, 2008)


Global ocean heat content

What is the signal and what is noise?

Other problems of instruments, interpolation, coverage?

(Levitus et al. 2000)


From ‘something is happening’ to ‘why’

Understand the timescales, spatial scales Understand the process Build a quantitative model for the process or system Quantify variability (from observations or from a model) Understand data limitations Understand model limitations Determine all important driving factors


Global ocean heat content

Models can help separating signal and noise, but models can also have difficulties in representing variability

(Domingues et al. 2008)

Models without volcanoes Models with volcanoes


Assessing model variability

Short record prevents estimate of longer timescale variability

No unperturbed control state One realization with a

combination of forced and unforced response

(IPCC 2007)


Internal variability in global temperature

(Stott et al. 2010)

Internal variability often has to be estimated from long control integrations of models (constant boundary conditions, no forcing, little drift)


Separation of signal and noise

Smoothing, filters Ensembles: Combinations of multiple simulations with a) different

initial conditions (e.g. different sections of control run), b) perturbed initial conditions/forcing, c) perturbed physics ensembles, d) multiple models


Separation of signal and noise Averaging ensembles can improve signal to noise and

isolate different drivers Ensembles are expensive Averaging can create unrealistic/unphysical results

ObservedSingle modelsModel average


Sources of uncertainty

(Hawkins and Sutton, 2009)

Internal variability: very little predictabilityModel uncertainty: potentially reducible, true value exists but is unknownScenario uncertainty: difficult to predict, choice rather than intrinsic uncertainty


Fitting modelsTwo different statistical models of Atlantic hurricane activity vs sea surface temperature (SST). The upper panel statistically models hurricane activity based on "local" tropical Atlantic SST, while the bottom panel statistically models hurricane activity based on tropical Atlantic SST relative to SST averaged over the remainder of the tropics.

(Knutson et al. 2010)


Summary part 1 What we observe is a combination of forced change and unforced

(largely unpredictable) variations. Separating those two is critical. Signal to noise depends on variable, spatial scale, temporal scale,

quantity (mean vs. variability vs. trend), other confounding factors Problems: Short observation, interpolation/sampling errors,

instrument biases, instrument changes, homogenization/calibration issues. We can’t go back and measure again.

Multiple simulations or multiple models can improve signal to noise. In contrast to models, there is only one realization of the observed world.

Models have limitations. Using them to estimate variability is often difficult.

We are using the same observations to develop, calibrate and evaluate models, and then the models to explain the observations…


When is the Arctic ice free in those two models?


Detection and attribution: Determining cause and effect


Determining cause and effect

(Gavin Schmidt, http://www.realclimate.org/index.php/archives/2007/05/fun-with-correlations/)


Fun with correlations

With correlation, lags, filters, detrending, data homogenization, regime shifts almost anything can be explained.

“Anything is possible if you want long enough.”

“Give me four parameters, and I can fit an elephant. Give me five, and I can wiggle its trunk.“ (John von Neumann)

(Gavin Schmidt, http://www.realclimate.org/index.php/archives/2007/05/fun-with-correlations/)


“…an example of how studies based on popular belief and unsubstantiated theory, seconded by low quality references and supported by coincidental statistical association could lead to apparent scientific endorsement.”

(Höfer et al. 2004)


Detection and attribution (the classic IPCC D&A)

(Stott et al., 2010, IPCC 2007)

Detection is the process of demonstrating that an observed change is significantly different than can be explained by natural internal variability. Detection of a change does not necessarily imply that its causes are understood.

Attribution is the process of establishing a causal between a forcing and the detected changes.


Detection and attribution

Observed changes are unlikely to be due to internal variability (detection);

Observed changes are consistent with the calculated responses from best-guess estimates of anthropogenic and natural forcing (attribution)

Observed changes are not consistent with alternative, physically plausible explanations of recent climate

The difference between the observations and the attribution patterns, i.e., the part of the observed signal which is not explained by the assumed forcing, must be consistent with internal unforced climate variability.

Because the uncertainty in the radiative forcing is large, attribution of the observed warming does not rest very securely on the straightforward argument that a significantly positive anthropogenic radiative forcing caused the observed warming. Rather, attribution is demonstrated indirectly by the following arguments.


NOT attribution The conclusion that most of the observed warming over

the last fifty years is due to human influence is not just based on the timeseries of global mean surface temperature.

ObservedSingle modelsModel average


The human “fingerprint” Detection and attribution is

successful because the spatial and temporal patterns of change are different for each forcing.

Detection and attribution is now possible for global surface temperature, vertical atmospheric temperature profiles, ocean temperature, zonal precipitation, continental temperature changes, changes in the tropopause height, atmospheric humidity, sea ice trends, extreme events, etc.


Attribution in IPCC reports SAR 1996: “The balance of evidence

suggests that there is a discernible human influence on global climate”

TAR 2001: “There is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities.In the light of new evidence and taking into account the remaining uncertainties, most of the observed warming over the last 50 years is “likely” [>66% probability] to have been due to the increase in greenhouse gas concentrations.”


Attribution in IPCC AR4

Climate models can only reproduce the observed spatial and temporal patterns of warming when they include anthropogenic and natural forcings.

(IPCC 2007)


Attribution in IPCC AR4“Most of the observed increase in global average temperatures since the mid-20th century is “very likely” [>90% probability] due to the observed increase in anthropogenic greenhouse gas concentrations.* [ ] Discernible human influences now extend to other aspects of climate, including ocean warming, continental-average temperatures, temperature extremes and wind patterns.”

*Consideration of remaining uncertainty is based on current methodologies.

(IPCC 2007)


Attribution in IPCC AR5“Human influence has been detected in warming of the atmosphere and the ocean, in changes in the global water cycle, in reductions in snow and ice, in global mean sea level rise, and in changes in some climate extremes (see Figure SPM.6 and Table SPM.1). This evidence for human influence has grown since AR4.It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century.”

(IPCC 2013)


The attribution recipe Run model with certain forcing (e.g. greenhouse gases) and project

observations on the model response, calculate scaling factor β, estimate uncertainty in β from model control runs.

Detection: β inconsistent with zero at given significance level Attribution: β consistent with unity

Greenhouse gases (G), Sulphate (S), Indirect (I), Solar (So), Volcanic (V)


Two pattern attribution Clear GHG and sulphate signal since ~1950 Difficult separation of GHG and sulphate before 1950 Some evidence for solar signal before 1950, but large uncertainties

and evidence for models underestimating solar signal

(Stott et al. 2001)


(Barnett et al. 2005)

ObservedSimulated range from different ensemble members

Attribution of anthropogenic ocean warming


(Zhang et al. 2007)

Attribution of precipitation trends Attribution impossible on global precipitation trends, on regional

scales, but possible on zonal averages Observed trends about twice as large as modeled


Attribution of human influence on drivers We can attribute trends in sea

surface temperature in the cyclogenesis region to human influence.

We can‘t formally attribute changes in hurricane intensity or frequency to human influence.

Other factors are relevant and poorly observed, understood, or it‘s unclear how they will change in the future.

(Santer et al. 2006)


Single event attribution: 2003 heat wave “It is an ill-posed question whether

the 2003 heatwave was caused, in a simple deterministic sense, by modification of the external influences on climate – for example, increasing concentrations of greenhouse gases in the atmosphere – because almost any such weather event might have occurred by chance in an unmodified climate.”

“We estimate it is very likely (confidence level >90%) that human influence has at least doubled the risk of a heatwave exceeding this threshold magnitude.”

(Stott et al. 2004)


Mostly natural or mostly anthropogenic? There has been a controversy on whether recent extreme events (e.g.

the Russian heat wave) are caused predominantly by human influence or whether they are natural. Both statements are correct , but one is about the magnitude of the event, the other about the frequency.

External influence makes the extreme event three times more likely.

The externally driven change in the magnitude is small compared to the natural component, i.e. most of the event magnitude is natural.


Lack of formal attribution does not imply that nothing is happening or that a particular driver is not relevant. The signal may just be too weak, or there may be too many confounding factors.

In the case of climate extremes and rare events, for example, it may not always be possible to reliably estimate from observations whether there has been a change in frequency or intensity of a given type of event.

Nevertheless, it may still be possible to make a multi-step attribution assessment of an indirectly estimated change in the likelihood of such an event, if there is a detectable change in climatic conditions that are tightly linked to the probability of that event.

For example, a change in the frequency of rare heatwaves may not be detectable, but detectable changes in mean temperatures would lead to an expectation of a change in the frequency of heatwaves.

Multi step attribution

(IPCC detection and attribution guidance paper, 2010)


Associative Pattern AttributionAnthropogenic Influence on Physical and Biological SystemsSpatial pattern of observed impacts are compared with observed climate trends using statistical pattern-comparison measures.Changes are consistent with known responses to regional temperature change and a functional understanding of the systems and unlikely to have been substantially influenced byother driving forces.

(Rosenzweig et al. 2008, IPCC detection and attribution guidance paper, 2010)



Issues to consider Data biases and gaps. Spatial scale and temporal resolution or coverage of data (for

example, season) should be matched to the variable of interest. Estimates of the variability internally generated within the climate

system or climate impact system are needed to establish if observed changes are detectable.

When downscaling tools are used, a separate assessment is needed of the performance of these tools.

Confounding factors.



Issues to consider To avoid selection bias in studies, it

is vital that the data are not preselected based on observed responses, but instead chosen to represent regions / phenomena / timelines in which responses are expected, based on process-understanding.

Statistical analysis methods should be chosen appropriately, taking account of temporal and spatial autocorrelation, sampling changes, observer bias.



Issues to consider: confounding factors A confounding factor is one that affects the variable or system of

interest but is not explicitly accounted for in the design of a study. Examples of possible confounding factors for attribution studies include pervasive biases and errors in instrumental records; model errors and uncertainties; improper or missing representation of forcings in climate and impact models; structural differences in methodological techniques; uncertain or unaccounted for internal variability; and nonlinear interactions between forcings and responses.


Summary part 2 Detection and attribution is successful if the spatial and temporal

patterns of change or characteristics (the fingerprints) are different for each driving forcing and different from noise.

The unexplained part of the signal must be consistent with noise. Process understanding is essential. Detection and attribution is very often limited by data (length,

coverage, accuracy, signal/noise). Single climate events can never be attributed in a deterministic

way but a fraction of attributable risk can be defined in a probabilistic sense.

Separating the effect of an external driver from other drivers and noise is a common problem. Detection and attribution can be powerful but (like any other statistical method) must be applied carefully.

detection and attribution, forced changes, natural variability, signal … · 2018. 3. 15. · reto...

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