Detection and Attribution of Climate Change

Why is detection and attribution work important?



	It is another form of model evaluation
	Successful simulation of historical changes in climate enhances confidence in projections of future climate change
	In an environment where there is still political debate regarding the reality of a human effect on global climate, it is imperative to have Òsound scienceÓ on the nature and causes of climate change

Structure of talk


	Introduction
	Charge to discussion leaders
		Where do you believe a consensus has formed?
		Where can consensus be expected in the near future?
		Where can statistical science provide further assistance to future research?
	Conclusions

Introduction: Definition of ÒconsensusÓ


	Con ¥ sen ¥ sus Noun.
	ÒA view or stance reached by a group as a whole or by majority will.Ó
	ÒGeneral agreement.Ó (American Heritage College Dictionary)
	From the Latin ÒconsentireÓ, to agree.

Introduction: Do the IPCC AR4 findings constitute a ÒconsensusÓ of climate science experts?


	152 Coordinating Lead Authors and Lead Authors
	Authors were from over 30 countries
	Drafts of Working Group I Report were subjected to two rounds of review and revision
	Report was reviewed by over 650 individual experts, as well as by governments and international organizations
	In total, over 30,000 written comments were received
	Summary for Policymakers was approved (line-by-line) by officials from 113 governments
	Report outline in Nov. 2003. Acceptance of SPM and underlying chapters in Feb. 2007

Structure of talk


	Introduction
	Charge to discussion leaders
		Where do you believe a consensus has formed?
		Where can consensus be expected in the near future?
		Where can statistical science provide further assistance to future research?
	Conclusions

Where do you believe a consensus has formed?



	Warming of the climate system is ÒunequivocalÓ
	There is compelling scientific evidence of a human ÒfingerprintÓ on global climate

ÒUnequivocalÓ warming of the climate system



	The oceans and land surface have warmed
	The troposphere has warmed
	Atmospheric water vapor has increased
	Sea level has risen
	Glaciers have retreated over most of the globe
	Snow and sea-ice extent have decreased in the Northern Hemisphere
	Individually, these changes are consistent with our scientific understanding of how the climate system should be responding to human influences

Where do you believe a consensus has formed?



	Warming of the climate system is ÒunequivocalÓ
	There is compelling scientific evidence of a human ÒfingerprintÓ on global climate

The scientific evidence for a human ÒfingerprintÓ on global climate has strengthened over time

Natural factors alone cannot explain the recent warming of the EarthÕs surface

What is Òclimate fingerprintingÓ?



	Strategy: Search for a computer model-predicted pattern of climate change (the ÒfingerprintÓ) in observed climate records
	Assumption: Each factor that influences climate has a different characteristic signature in climate records
	Method: Standard signal processing techniques
	Advantage: Fingerprinting allows researchers to make rigorous tests of competing hypotheses regarding the causes of recent climate change

Human-caused fingerprints have been identified in many different aspects of the climate system

We have made considerable progress in defining the ÒfingerprintsÓ of different forcings

Fingerprint detection explained pictoriallyÉ.

Estimating the ÒnoiseÓ of natural internal variability


	Model-based estimates of natural internal variability are an integral component of D&A research
	Why do we rely on models for these estimates?
		They can be used to perform the control experiments that we canÕt conduct in the real world
	Why is it difficult to estimate natural internal variability from observations?
		We want to estimate noise on multi-decadal to century timescales
		Most observational records are too short for this purpose
		Signal and noise are convolved – difficult to achieve unambiguous partitioning

Optimal fingerprinting: A brief example

D&A in a Òmulti-modelÓ framework: Use of multiple models to estimate fingerprints and noise

Estimating signal-to-noise ratios and Òdetection timesÓ

For water vapor, there is no evidence that Òclimate noiseÓ is systematically underestimated in IPCC AR4 models

Structure of talk


	Introduction
	Charge to discussion leaders
		Where do you believe a consensus has formed?
		Where can consensus be expected in the near future?
		Where can statistical science provide further assistance to future research?
	Conclusions

Where can consensus be expected in the near future?


	We will have some form of Òoperational attributionÓ capability
	D&A studies will routinely use information from large, multi-model ensembles (and will make more intelligent use of this information)
	Structural uncertainties in observations will become an integral part of D&A research
	We will have formally identified anthropogenic fingerprints
		At sub-continental spatial scales
		In variables more relevant to climate impacts
		In plant and animal distributions and abundances
	Fingerprinting will be feasible with increasingly shorter (< 30-year) observational records

Structure of talk


	Introduction
	Charge to discussion leaders
		Where do you believe a consensus has formed?
		Where can consensus be expected in the near future?
		Where can statistical science provide further assistance to future research?
	Conclusions

Where can statistical science provide further assistance to future D&A research?


	In assessing sensitivity of D&A results to Òmodel qualityÓ
	By contributing state-of-the-art space-time modeling approaches to Òfill in the gapsÓ in observational datasets with sparse, space- and time-varying coverage
	By helping to provide a better assessment of the Òtrade-offsÓ between ensemble size (for any individual model) and the number of models contributing to a multi-model average
	By contributing improved methods for assessing whether human influences have modulated the statistical behavior of existing modes of natural variability
	By bringing statistical rigor to regression-based predictions of hurricane activity
	Better constraining the Transient Climate Response obtained from D&A methods

Future research I: Sensitivity of D&A results to Òmodel qualityÓ



	A number of recent studies have attempted to weight model projections of future climate change (generally by model performance in simulating present-day climatological means)
	Thus far, no attempt to use any form of weighting in multi-model D&A work
	All multi-model D&A studies to date are Òone model, one voteÓ
	Are results from current multi-model D&A studies biased by inclusion of information from models with noticeable deficiencies in simulation of variability?

Future research I: Sensitivity of D&A results to Òmodel qualityÓ

Future research II: Improvement of observational datasets with sparse, space- and time-varying coverage


	Concerns have been expressed about the reliability of model-based estimates of the natural variability of ocean temperatures (e.g., Lyman et al., 2006)
	Casts doubt on reliability of D&A results obtained with ocean temperatures
	How do we address these concerns?
		Better quantification of uncertainties in observed variability estimates (e.g., AchutaRao et al., 2007). Involves use of both physical models (ocean data assimilation products) and statistical models
		Identify and adjust for the effects of instrumental biases in different ocean observing systems (Church et al., in preparation)
		Revisit ocean D&A studies with improved, bias-corrected observational data
		Use proxy data to obtain better constraints on model estimates of natural internal variability (e.g., multi-century SST reconstructions from corals)

Future research II: The ocean observing network has changed dramatically over time

Future research II: Do models systematically underestimate ÒobservedÓ ocean temperature variability?

Future research II: Sampling model data at locations of ocean observations improves model-data agreement

Future research II: Implications of observational uncertainty for D&A research

Conclusions



	We have identified human ÒfingerprintsÓ in a number of different aspects of the climate system:
		Temperature (land and ocean surface; stratosphere and troposphere; zonal-mean profiles through the atmosphere; upper 700 meters of the ocean; ocean heat content; height of thermal tropopause)
		Atmospheric circulation (mean sea-level pressure)
		Moisture-related variables (zonal-mean rainfall; surface specific humidity; total water vapor over oceans; continental runoff)
	The climate system is telling us a physically- and internally-consistent story
	From my own biased personal perspective, the collaboration between statisticians and climate scientists in the area of D&A research has been very successful. These interactions have been facilitated by:
		IDAG (International Detection and Attribution Group)
		IMSC (International Meetings on Statistical Climatology)

A brief history of D&A research: Some important milestones