OUP CORRECTED PROOF – FINALS, 07/03/2010, SPi

Plate 1 Comparison of observed continental- and global-scale changes in surface temperature with results simulated by climate models using natural and anthropogenic forcings. Decadal averages of observations are shown for 1906–2005 (black line) plotted against the centre of the decade and relative to the corresponding average for 1901–1950. Lines are dashed where spatial coverage is less than 50%. Blue shaded bands show the 5–95% range for 19 simulations from fi ve climate models using only the natural forcings due to solar activity and volcanoes. Red shaded bands show the 5–95% range for 58 simulations from 14 climate models using both natural and anthropogenic forcings. The fi gure is taken from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change WGI (Work Group I) Summary for Policymakers ( IPCC, 2007a ). See Figure 2.2 , page 12.

North America

Europe

Africa

Asia

Australia

South America

1.0

0.5

0.0

1.0

0.5

0.0

1.0

0.5

0.0

1.0

0.5

0.0

1.0

0.5

0.0

1.0

0.5

0.0

1.0

0.5

0.0

1900 1950 2000

1900 1950 2000

1900 1950 2000

1900 1950 2000

1900 1950 2000

1900 1950 2000

1900 1950 2000 1900 1950 2000 1900 1950 2000

Tem

per

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(ºC

)

Tem

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)T

emp

erat

ure

an

omal

y (º

C)

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(ºC

)

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(ºC

)

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)

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1.0

0.5

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Year

Year

Year

Year

Year

Year

Global Global land Global ocean

Year

Models using only natural forcings

Models using both natural and anthropogenic forcings

Year Year

Observations

150W

120W

90W

60W

30W

0

30E

(hPa)–3.5 –3 –2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5 2 2.5 3 3.5

60E

90E

120E

150E

180

Einter SLP (1981–2009) – (1951–1980)

Plate 2 Boreal winter (December–March) average Northern Hemisphere sea level pressure (SLP) anomalies (hPa) since 1981 expressed as departures from the 1951–1980 average values. The SLP data are from Trenberth and Paolino ( 1980 ) . See Figure 2.3 , page 16.

OUP CORRECTED PROOF – FINALS, 07/03/2010, SPi

Stan

dar

d d

evia

tion

s

Year

–0.9

–0.9–0.6–0.3 0 0.3 0.6 0.9

–0.93

2

1

0

1860 1890 1920 1950 1980 2010

–1

–2

–3

–0.6–0.3 0 0.3 0.6 0.9–0.6–0.3 0 0.3 0.6 0.9

Plate 3 Correlations with the Southern Oscillation index (SOI) ( Table 2.1 ) for annual (May–April) means for sea level pressures (SLP; top left) and surface temperature (top right) for 1958–2004, and estimates of global precipitation for 1979–2003 (bottom left), updated from Trenberth and Caron (2000) and IPCC ( 2007a ) . The Darwin-based SOI, in normalized units of standard deviation, from 1866 to 2009 (lower right) features monthly values with an 11-point low-pass fi lter, which effectively removes fl uctuations with periods of less than 8 months. The smooth black curve shows decadal variations. Red values indicate positive SLP anomalies at Darwin and thus El Niño conditions. See Figure 2.5 , page 19.

Plate 4 Changes in winter (December–March) surface pressure, temperature and precipitation corresponding to a unit deviation of the North Atlantic Oscillation (NAO) index over 1900–2009. Top left: Mean sea level pressure (0.1hPa). Top right: Land surface air and SST (0.1°C; contour increment 0.2°C): regions of insuffi cient data (e.g. over much of the Arctic) are not contoured. Bottom left: Precipitation for 1979–2009 based on global estimates (0.1mm/day; contour interval 0.6mm/day). Bottom right: Station-based index of winter NAO ( Table 2.1 ). The heavy solid line represents the index smoothed to remove fl uctuations with periods less than 4 years. The indicated year corresponds to the January of the winter season (e.g. 1990 is the winter of 1989/1990). Adapted and updated from Hurrell et al. ( 2003 ) and IPCC (2007a) . SLP, sea level pressure; Sfc T, surface temperature; Precip, precipitation; NAO, North Atlantic Oscillation;DJFM, December, January, February, March. See Figure 2.8 , page 23.

SLP Sfc T

90W

180

0

0

90E

0

–12 –10 –8 –6 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6–4 –2 0

0

180

90E90W

–2

–2

2

4

–6

Precip

NAO DJFM

6

4

2

0

1860 1890 1920 1950 1980 2010

–2

–4

–6

Year

0 2 4 6 8 10 12–12 –10 –8 –6 –4 –2

2 4 6 8 10 12

–4

–4

4

90W

0

90E

2

3

5

4

1

180

OUP CORRECTED PROOF – FINALS, 07/03/2010, SPi

Sfc T0

0

–1.8

1960

–4

–2

0

SAM

ind

ex (s

easo

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2

4

1970 1980 1990 2000 2010

–1.4 –1 –0.6 –0.2 0.2 0.6 1 1.4 1.8

Year

90W90E

850hPa m

–14–10 –6 –2 2

180

90W90E

60S

75S

6 10 14

180

20S

45S

Plate 5 Bottom: Seasonal values of the southern annular mode (SAM) index ( Table 2.1 ; updated from Marshall, 2003 ). The smooth black curve shows decadal variations. Top: The SAM geopotential height pattern as a regression based on the SAM time series for seasonal anomalies at 850 hPa (see also Thompson and Wallace, 2000 ). (Middle) The regression of changes in surface temperature (°C) over the 23-year period (1982–2004) corresponding to a unit change in the SAM index, plotted south of 60°S. Values exceeding about 0.4°C in magnitude are signifi cant at the 1% signifi cance level. Adapted from IPCC ( 2007a ) . Sfc T, surface temperature . See Figure 2.9 , page 25.

–10.5

–0.5

0

0.5

1

CS

0

Egg mass

Clu

tch

size

–1

Laying date0.50

–0.5

1–0.5

Plate 6 Visualization of the genetic variance–covariance matrix for three reproductive traits (laying date, clutch size, egg weight ) in female great tits at Wytham, UK, estimated under two time periods with contrasting trends in early spring temperature: 1965–1988 (no warming trend) and 1989–2004 (rapid increase in early spring temperature). Each ellipsoid represents the genetic variance and covariance for the three traits in each time period: the fi lled surface represents the years when warming occurred and the wire-framed surface the years with no warming trend. The ellipsoids can be thought of as bounding a space containing 95% of the expected breeding values in a hypothetical population. As the volumes largely overlap, there is little evidence that genetic variance and covariance for these characters changed as the environment changed. Reproduced, with permission, from Garant et al. ( 2008 ) . See Figure 12.2 , page 156.

Geographic space

T˚ of thecoldestmonth

Current conditions

1.0

0.8

0.6

0.4

0.2

0

1.0

0.8

0.6

0.4

0.2

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–20 –15 –10 –5 0 –5 –10

500 1000 1500 2000 2500

Future conditions

Observed distribution(EBCC)

Annual prec.

Geographic spaceEcological space

P.ann

Mtc

ModelsER ~ a + ÂbiXi

Plate 7 Diagram representing the concept of habitat suitability modelling. The observed distribution of a given species (e.g. from the European Bird Census Council atlas of European breeding birds, their distribution, and abundance) is related to relevant environmental data (e.g. climatic such as temperature (T°) of the coldest month and annual precipitation) using a statistical model (e.g. generalized additive model). Then, the niche of the species is projected back onto the geographic space to depict the potential current distribution (i.e. under current conditions) and the future potential suitable habitat (i.e. under future conditions). See Figure 8.1 , page 78.

OUP CORRECTED PROOF – FINALS, 07/03/2010, SPi

(b)

<-35%

-35 to -25%

-25 to -15%-15 to -5%-5 to +5%+5 to +15%

+15 to +25%+25 to +35%

>+35%

(a)

Plate 8 Predicted changes in species richness of migrant passerines in Africa under future climatic scenarios (by 2100). (a) Future-predicted variations in species’ richness under the hypothesis of full dispersal ability between predicted present and future ranges and (b) future-predicted variations in species richness under the hypothesis of null dispersal ability. Based on Barbet-Massin et al. ( 2009 ) . See Figure 18.1 , page 280.

Brazil

AtlanticOcean

High

Low

Argentina

Paraguay

Plate 9 Predicted cumulative probabilities of occurrence for the 38 Cerrado birds in Brazil for the future (2046–2060), as obtained with an ensemble-forecasting approach. The colours in the maps represent the cumulative probability of occurrence (climatic suitability) within a pixel, ranging from 0 to 1 (pale to dark green). Large (>30,000ha) reserves are represented by black polygons. Based on Marini et al. ( 2009 ) . See Figure 18.2 , page 281.