Ch. 10Detection and Attribution of

Climate Change:From Global to Regional

3 November 2014Brian Crow

Sam Atwood

Ch. 10Detection and Attribution of Climate

Change

Question:What are the causes of changes in climate reported in Ch 2-5?

Anthropogenic Forcing Natural Forcing Internal Climate Variability

Addresses data and observations from past to the present Last Century Last Millennium Future only addressed in terms of Equilibrium Climate Sensitivity

Uncertainties from AR4 addressed, e.g. Ocean temperature Observations inconsistent with Climate Models Detection and Attribution studies on Precipitation had not been done

Notes: Focus on explaining concepts to a wide audience

“To show how this fit is obtained in non-technical terms…” Lots of justification for all of the choices made

Detection and Attribution StudiesMethodology

What are D & A studies: Quantify causal links between external CC drivers and

observed changes in climate variables Central (though not only) line of evidence for statements:

“Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations”

Detection: Statistical change in a climate variable“The process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change.”

Attribution: The cause of the change“The process of evaluating the relative contributions of multiple causal factor to a change or event with an assignment of statistical confidence.”

Detection and Attribution StudiesMethodology

How do D & A studies work:Four Core Elements:

Observation of relevant Climate Variable Estimate of External Drivers and Confounding Influences (before

and during change) Quantitative Physical understanding of process Estimate of variability of Observed Climate Variable (random,

quasi-periodic, chaotic) not due to external forcing

Complex models often used Requires model of relationship between External Driver and

Observed Variable (physical, statistical… etc.) Should capture the statistics of variability (e.g. noise), not actual

observed values. New: D&A studies that capture change in probability (e.g. 1-in-100

year flood) attributed to CC.

Detection and Attribution StudiesFingerprint Studies

Simple Example Method:Compare model of (Natural + Anthropogenic) Forcings to Natural Forcings only.

Look for consistency with one and inconsistency with the other Attribution is warranted if this condition holds, provided that the model

correctly simulates all important processes and external drivers Big Assumption:

Most D&A studies instead assume the “shape” of their model response to external forcing is correct, rather than its magnitude.

Fingerprint D&A study Scaling Factors: Shape of model response (large-scale pattern in space

and/or time); can be scaled while remaining consistent with observations Model: Scaling Factor best estimate and uncertainty (e.g. mean and std

dev) Scaling Factor > 0: Response to a forcing is detectible in Obs. Scaling Factor = 1: Model response in consistent with Observations Scaling Factor > 0; 1: Unknown or unrepresented forcing; Or,

confounding factor

Detection and Attribution StudiesFingerprint Studies

Estimating Response to External Forcing Estimate separately – Errors may incorrectly affect response Estimate combined forcing response – Smaller uncertainty, but less

knowledge of response to each forcing Most studies estimate separately – Justify by use of spatial

information

Fit Model simulated response to observations Linear addition of forcing response by Scaling Factor

Good for: Large-scale temperature changes Not so good for: Precipitation, regional temperature changes

No non-additive (non-linear) approaches used to date

Internal Climate Variability (ICV) Primary estimate from Models Check these estimates against direct observations, paleoclimate

proxies and reconstructions

Detection and Attribution StudiesFingerprint Studies

Observations (dots) indicate observed warming

GMST response in CMIP3 & CMIP5 Orange: Anthropogenic GHG & aerosol forcing Blue: Natural solar and volcanic forcing Black: Best-fit linear fingerprint combination Residuals: Internal Climate Variability

Attribution of Observations to both fingerprints via the linear best-fit plane The scaling factors for each fingerprint are the

slope of the plane

Grey diamonds: Modulate Observations by ICV This uncertainty is mapped onto scaling factors

to include uncertainty

Separation of red cross and ellipse indicate ICV does not account for warming External influence on warming is indicated

Scaling Factors 1 Models underestimating the response to both

Forcings Anthropogenic within Confidence Interval Natural is not with Confidence Interval

Detection and Attribution StudiesFingerprinting GCMs

Real Observations are limited in time, space, and frequency Model runs “observe” at these data points for comparison

GCM Fingerprints improve understanding of Forcing and Response relationship

Result depends on accuracy of GCM response “Shape” Scaling of multiple factors may allow for contamination of

response by ICV

Optimal Fingerprinting Model simulated response and “Observations” are

generated Normalized by ICV to improve Signal/Noise ratio

Detection and Attribution StudiesAdditional Methods

Other methods generally agree with Fingerprint studies

Separation of Signal and Noise By spatial pattern and/or time scale

Econometric methods Estimate of Internal Climate Variability based on statistics of observations Still require a model, though don’t depend on errors in Climate Models

Time Series Methods e.g. Assume a single exponential decay time for a response to both External

Forcing, and Stochastic Fluctuations

Multi-Step Attribution For variables without a long and consistent set of observations or explicit model

relationship to External Forcing First-Step: Attribute a change in and variable to a Forcing (with uncertainty)

e.g. Large-Scale surface temperature change due to GHG increase Next-Steps: Add Physical or Statistical relationship to another variable

e.g. Regional surface temperature change associated with large-scale change Overall conclusion is as robust as the least certain link

Surface Air Temperature

All forcings

Natural forcing only

GHG forcing only

Surface Air Temperature

Attribution of Warming

Urbanization is unlikely to be responsible for more than ~10% of observed trend

Solar forcing contributed AT MOST 10% of warming since 1900

Observed warming trend still detectable even if magnitude of natural variability tripled

Anthropogenic GHGs very likely responsible for > 50% of observed warming

Global-Scale Temperature Trends

Regional Temperature Trends

ALLNaturalGHGHadley CentreRAOBCORERICH

Atmospheric Temperatures

1961-2010Lott et al. 2013

Regional changes far harder to attribute to human activity Higher variability, circulation changes, etc.

Each climate forcing produces different vertical and zonal warming patterns

Very likely that anthropogenic forcing has created observed warming troposphere/cooling stratosphere pattern since 1961

Regional Trends and Atmospheric Temperature Profile Trends

Medium confidence in anthropogenic contributions to observed water vapor increase

Detection & attribution of hydrologic variables is very difficult

Anthropogenic fingerprint has been detected in observed annual zonal-mean precip Increases at high latitudes, decreases in subtropics

Medium confidence in anthropogenic influences on streamflow and precipitation over land

Water Cycle

Hadley Cell Expansion

Latitude

DJF change in southern border of Hadley Cell, 1979-2005

Low confidence in any changes to ENSO, the Indian Ocean Dipole (IOD), PDO, or AMO

NAO had exhibited significant positive trend 1960s-1990s, but has since reversed

Southern Annular Mode (SAM) has significant positive trend over 1951-2011 Driven by GHGs, ODSs

Likely that humans have altered SLP patterns since 1951

Circulation Changes

Ocean Heat Content

Very likely that ocean heat content increases since 1970s have a significant anthropogenic contribution

High confidence that thermal expansion and non-Antarctic glacial melt explain 75% of SL rise since 1971 Large anthropogenic contributions to both factors

Very high confidence that anthropogenic CO2 has acidified ocean surface by -0.0015 to -0.0024 pH/year

Influence on Ocean Properties

Human influence on sea ice robustly detectable since 1990s

¾ of Arctic summer sea ice volume has been lost since 1980s

Low confidence in explaining Antarctic sea ice trends

High confidence that significant portion of glacier mass loss is likely anthropogenic

Cryosphere

Extremes

Very likely humans have contributed to observed changes in frequency & intensity of daily temperature extremes since 1950

Medium confidence anthropogenic forcing has contributed to intensification of heavy precipitation since 1950

Recent long-term droughts can’t yet be shown to be outside natural variability

Low confidence in attributing changes in drought

Low confidence in any long-term trends in tropical cyclone frequency

Extremes

Multi-Century to Millennia Pre 20th-Century Changes

Validate understanding of climate system Greater uncertainties in indirect or proxy datasets Periods of past NH mean temperature changes can be robustly identified

Natural Forcings seen in Seven centuries prior to 1950 10th Century: Can’t explain in some models Little Ice Age: External Forcing in cooling of NH temperatures 16th & 17th Centuries: Small drop in GHG contributed to cool conditions Solar Forcing: Not clear – But lower agreement for large solar forcing runs Volcanic Forcing: Evident

Internal Climate Variability – inter-decadal and longer scales Different for simulations with

vs without past External Forcings

Validates finding that “a large fraction of temperature variance in the last millennium has been externally driven.”

However, the past 50-60 year trend in Observations is far outside the range of past reconstructions of NH mean temperature in the last millennium

Multi-Century to Millennia Regional Temperature in

Europe Detectable fingerprint

response in temperature record

Forced Response + ICV reproduces MCA better than Force Only simulations

Volcanic fingerprint detectable in European summer temperatures

Similar fingerprint responses in other regions Volcanism in North America Decrease in SST following Volcanic Forcing Solar Influences on Regional Climate Reconstructions (possibly due to circulation changes)

Results Volcanic and GHG Forcings are most important for explaining past changes in NH temperature ICV + External Forcings provide consistent explanation of last millennium Very Unlikely that NH Temp from 1400 to 1850 can be explained by ICV alone Medium Confidence that external forcing contributed to NH Temp variability between 850 and

1400 Medium Confidence external forcing (Anthropogenic + Natural) contributed to European

Temperatures over last five centuries

Transient Climate Response

Magnitude of transient warming while climate system is equilibrating Short to Medium time scale response Better constrained by observations

than ECS TCR Definition:

Warming that has occurred at the time of CO2 doubling

~70 years for 1% yr-1 increasing CO2

TCR is a generic property that can be scaled against this number

Scaling Factors from D&A studies used to scale GHG and aerosol response against historical observations

Bounds on TCR: Likely within 1°C to 2.5°C Extremely Unlikely to be > 3°C Range is smaller than AR4 due to stronger observational constraints

Equilibrium Climate Sensitivity Long-term equilibrium warming response

Stable Atmospheric Conditions Doesn’t account for vegetation or ice sheet

changes Less constrained by Observations than TCR

ECS definition: Warming in response to sustained doubling

of CO2 Equilibrium for ocean-atmosphere system

ECS bounds based on: Recent Temperature Change TOA Radiative Balance Volcanic Forcing and ICV Paleoclimate

Low ECS estimates “problematic”

Overall Bounds on ECS: High Confidence that ECS is Extremely Unlikely < 1°C Medium Confidence that ECS is Likely between 1.5°C and 4.5°C Medium Confidence that ECS is Very Unlikely > 6°C Supports overall assessment in Ch 12

Synthesis – Global Climate Signals

Treatment of full Earth-System, rather than individual aspects Looking for consistent picture across sub-systems and climate

variables Multi-Variable Approach

Synthesis – Regional Climate Signals

FAQ: When will Human Influences on Climate Become Obvious on Local Scales?Surface Temperature Anomalies

CMIP5 forced by RCP8.5 emissions scenario First signs in Tropical Summer locations

Temperature increase needed for “Emergence from the envelope of early 20th century variability”

Conclusions

D&A studies focus on quantitative identification of the causes of observed change

Adds on to findings of AR4 – Focus on wide audience

Not Natural Forcing

Change in a number of climate variables is detectable and attributable to Human activity