1 CIS oƒ HDGC Carnegie Mellon "A Globally Coherent Fingerprint of Climate Change Impacts across Natural Systems" Friday, January 31, 2003 GARY W. YOHECAMILLE

1CIS oƒ HDGC Carnegie Mellon

"A Globally Coherent Fingerprint of Climate Change Impacts across Natural Systems"

Friday, January 31, 2003

GARY W. YOHE CAMILLE PARMESAN

WESLEYAN UNIVERSITY UNIVERSITY OF TEXAS - AUSTIN

A Globally Coherent Fingerprint of Climate Change Impacts across Natural

Systems

Camille Parmesan & Gary Yohe

January 31, 2003

Center for Integrated Study of the Human Dimensions of Global Change,

CIS oƒ HDGC Carnegie Mellon


IPCC and Degrees of Confidence

Quantitative Scale:

»95% or greater Very High Confidence

»67-95% High Confidence

»33-67% Medium Confidence

»5 – 33% Low Confidence

»Less than 5% Very Low Confidence


IPCC and Degrees of Confidence

Qualitative Scale:

Well Established – Lots of evidence; high consensus

Established but Incomplete – high consensus on limited information

Competing Explanations – Lots of evidence; alternative explanations

Speculative – Little evidence and many plausible explanations


The IPCC Dynamic

“We have very high confidence that X might happen!”

“We have medium to low confidence that X will happen!”

Converged to the notion that the statements should speak to the “will” alternative for a baseline.


Observed Changes in Physical and Ecological Systems (from IPCC 2001)

hydrology / sea ice animals plants study covers study based onglaciers large area remote sensing


Key Conclusions from IPCC

Recent Regional Climate Changes, particularly Temperature Increases, have Already Affected Many Physical and Biological

Systems

(high confidence, or >67% sure)

Biotic change: 44 regional studies, 400 plants and animals, 20 to 50 years

Physical change: 16 regional studies, 100 processes, 20-150 yrs

» non-polar glacier retreat

» reduction in Arctic sea ice extent and thickness in summer

» earlier plant flowering and longer growing season in Europe

» poleward and upward (elevation) migration of plants, insects and animals

» earlier bird arrival and egg laying

» increased incidence of coral bleaching

» increased economic losses due to extreme weather events


1 2 3 4 5

Risks to unique & threatened systems

Risks to Some

Risks to Many

Increase Large increaseRisk of extreme weather events

Distribution of impacts

Negative for some regions

Negative formost regions

Aggregate impactsNet Negative in All MetricsPositive or Negative Monetary;Most People Adversely Affected

Very low HigherRisks of large scale discontinuities

Past Future

0

O b

s e

r v

a t

i o

n s

-0.7

Increase in Global Mean Temperature after 1990 (°C)

Figure 19-8-1: Summary of Lines of Evidence


The “Global Fingerprint” was a “Reason for Concern”

The degree of confidence issue was contentious.

Lisbon authors’ meeting:

Chapter 2 – Tools discussion – how to judge?

Chapter 5 – Ecosystems – Of course this is “very high” Confidence

Chapter 19 – Include in the burning ember, but with confidence “medium” at best or “very high” ???


A Schematic Portrait of the Problem

Study 1 Study 2 ……………… Study n

Climate ClimateClimate

Change ChangeChange

Impact Impact Impact

Non- Non- Non-

Climatic Climatic Climatic

Factors Factors Factors


A Thought Exercise

Let there be n separate studies.

Let n’ produce results that are contrarian with respect to predicted climate impacts.

Let p be the probability that there are explanations that compete with climate in any single study.

Let pi be the likelihood that climate was corrected attributed as the cause in any study.


Contours for “More Likely than Not”

0

0.25

0.5

0.75

1

0 0.2 0.4 0.6 0.8

(n'/n) Proportion

Min

imu

m P

rob

abili

ty

Medium Confidence;p = 0

Medium Confidence;p = 0.33

Medium Confidence;p = 0.67


Contours for Low and High Confidence

0

0.25

0.5

0.75

1

0 0.2 0.4 0.6 0.8

(n'/n) Proportion

Min

imu

m P

rob

abili

ty

Low Confidence;p = 0High Confidence;p = 0Low Confidence;p = 0.33High Confidence;p = 0.33Low Confidence;p = 0.67High Confidence;p = 0.67


Complications

• Common Drivers of Non-climatic Drivers – Raises the contours.

• Studies that Show Sign Changes – Lowers the contours.

• Publication Bias against Contrary or Insignificant results – Increases the (n’/n) ratio.


New Analyses of changes from literature

• Studies with > 20 years data

• Primarily multi-species

- Counteracts publishing bias highlighting only species which show significant change

• Mostly multi-site (moderate to large geographic coverage)

• Conducted in nature reserve or rural natural area

- minimizes chances of confounding factors (brings p closer to 1)


Biotic Changes are Systematically in accord with Climate Change Predictions

Type of AnalysisChanged as

predicted(n)

Changed oppositeto prediction

(n)P

PhenologicalN = 484 / (678)

87 % 13 % < .1 x10-12

Distributional changes: At poleward/upper range boundaries At equatorial/lower range boundaries

Community (abundance) changes: Cold-adapted species Warm-adapted species

N = 460 / (920)

81 %75 %

74 %91 %

81 %

19 %25 %

26 %9%

19 % < .1 x10-12

Meta-analysis Range-boundaries (n=99)

Phenologies (n=172)

6.1 km-m/decade northward/upward shift 2.3 d/decade advancement

.013

< 0.05

Diverse species of: trees, herbs, shrubs, reptiles, amphibians, fish, marine zooplankton & invertebrates, mammals, birds butterflies

(Parmesan & Yohe, Nature 2003)


Edith’s Checkerspot (Euphydryas editha)


Edith’s Checkerspot butterfly:

Patterns of Population Extinctions in natural areas (good habitat)

> 70 %

35 - 55 %

< 20 %

% extinctions

52° N

48° N

44° N

40° N

36° N

32° N


Climatic Connections: USA

• 0.7º C warming over Western USA climatic shift of

105 km North & 105 m up (Karl et al. 1996)

E. editha: mean location shifted

92 km North & 124 m up (Parmesan 1996)

• Both snowpack & E. editha extinction trends shift at 2400 m:

% snow/50 yrs % extinctions

» Below 2400 m 14 % less snowpack ; melt 7 d earlier 46 %

» Above 2400 m 8 % more snowpack ; no melt 14 %

(T. Johnson, 1998)


Climate and E. editha : literature

Ehrlich, Evolution ‘65

Singer, Ph.D. dissertation Stanford ‘71

Singer, Science ‘72,

Singer & Ehrlich, Fortschritte der Zoologie ‘79

Ehrlich et al., Oecologia ‘80

White & Levin, Amer. Midland Natur. ‘80

MacKay Ecology ‘85

MacKay, Res. Pop. Ecol. ‘85

Singer, Evolution ‘83

Murphy & White, Pan. Pacific Entomol. ‘84

Dobkin et al., Oecologia ‘87

Weiss et al, Oikos ‘87

Weiss et al, Ecology ‘88

Moore, Ecology ‘89

Parmesan, Ph.D dissertation ‘95

Boughton, Ecology ‘99

Boughton, Ph.D dissertation ‘99

Weiss et al, Oecologia ‘93

Foley et al.,

Singer & Thomas, American Naturalist ‘96

Thomas et al., American Naturalist ‘96

Parmesan, Nature ‘96

Hellman, book ch.

Fleishman et al. J. Res. Lep.

Singer, in press

McLaughlin et al. 2000


ATTRIBUTION by INFERENCE Example: Euphydras editha butterfly

• Correlational Patterns» Long-term patterns (100 years) --- range shift matches temperature

isotherm shift and matches patterns of snowpack dynamics (Parmesan 1996, Karl et al. 1996,

Johnson 1998)

» “natural experiments” (40 years) --- below 2400 m, population extinctions occur in drought years and following false springs (light snowpack). Above 2400m, booms occur with heavy snowpack

(Singer & Ehrlich 1979, Singer & Thomas 1996, McLaughlin et al. 2002)

• Field Manipulations» manipulating thermal environment (slope aspect, habitat type)

affects larval growth rates, pupal times, synchrony with host plant, and colonization success (Singer 1972, Weiss et al. 1988, 1993,, Boughton 1999)

• Laboratory Experiments» temperature increases larval growth rates (Weiss et al 1988, Hellmann 2000)


Thought Exercise revisited

Very high

High

Medium

Low

Confidence Regions

estimated confidencefromliterature review

0

0 . 2 5

0 . 5

0 . 7 5

1

0 0 . 2 0 . 4 0 . 6 0 . 8

Minimum Probability (š)

(n'/n) Proportion

p = Probability of competing explanations (confounding factors)π = Probability that observed change is really due to climate (mechanistic link)n’/n = Proportion of species going in opposite direction to climate change predictions Binomial probability model with each factor varying from 0 to 1

Here, p=0


Diagnostic Biological Fingerprint

• Temporal

- Advancement of timing or northward expansion in warm decades (1930s/40s & 1980s/'90s)

- Delay of timing or southward contraction in cool decades (1950s/'60s)

• Spatial

Different behaviors at extremes of range boundary during particular climate phase,

e.g. expansion at northern range boundary simultaneous with contraction at southern range boundary during warming period

• Community

Abundance changes have gone in opposite directions for cold-adapted vs. warm-adapted species.

e.g. lowland birds increasing and montane birds decreasing at mid-elevation site.


Diagnostic Biological Fingerprint

• “Sign-Switching” found for 294 species » 80% of abundance shifts in communities» 100% follow decadal trends in temperatures (up & down)» 100% show geographic contraction at equatorial

boundary coupled with expansion at poleward boundary of species range

• Increases confidence from either perspective

(Parmesan & Yohe 2003)


Conclusions

• We have high to very high confidence that regional climate changes (resulting from global warming) have had impacts on wild species

• Observed changes are typically small in magnitude, but are likely to be an important factor in long-term persistence of species and stability of ecosystems


More Conclusions from a Skeptical Perspective

• The thought exercise allows an approach that accommodates maximum skepticism.

• Even then, the Medium Confidence can be claimed.

• Indeed, Schneider’s “more likely than not” benchmark is satisfied.

• Adding sign-switching adds to the power and moves even a skeptical interpretation in the High Confidence range.

Documents

1 CIS oƒ HDGC Carnegie Mellon "A Globally Coherent Fingerprint of Climate Change Impacts across Natural Systems" Friday, January 31, 2003 GARY W. YOHECAMILLE