Climate variability, trends and scenarios for Mexico and Argentina

Climate variability, trends Climate variability, trends and and scenariosscenarios for Mexico and for Mexico and

ArgentinaArgentina..Cecilia Conde, Marta Vinocur, Carlos Cecilia Conde, Marta Vinocur, Carlos

Gay, Roberto Seiler. Gay, Roberto Seiler.

AIACC LA-29AIACC LA-29Integrated Assessment of Social Vulnerability and

Adaptation to Climate Variability and Change Among Farmers in Mexico and Argentina

Uribe, 2002.

Douglas, 1993.

Douglas, 1993.

Palma, 2004

4.5

5.5

6.5

7.5

8.5

9.5

10.5

11.5

Pcp

(mm

/da

y)

1 4 71013161922252831343740434649525558616467707376798285889194

year

Observed Pcp. Veracruz.JJA. 1901- 1995http://ipcc-ddc.cru.uea.ac.uk/

wr 1961-1990

17

17.5

18

18.5

19

19.5

20

20.5

21

21.5

22

22.5

23 T

(°C

)

1 4 7 1013161922252831343740434649525558616467707376798285889194

year

Observed T. Veracruz. JJA. 1901- 1995http://ipcc-ddc.cru.uea.ac.uk/

wr 1961-1990

Central Region Veracruz12 Events > +1 std

3 events <-1std

1 event > +1 std

8 events <-1std

7 events > +1 std 1 event > +1 std

6 events<-1std7 events<-1std

O n c a t i v o

L a b o u la y e

R í o C u a r t o

M a r c o J u á r e z

ArgentinaArgentina

Study areaStudy area

Córdoba Córdoba ProvinceProvince

IPCC vs Observed data (3 stations)IPCC vs Observed data (3 stations)Southern CórdobaSouthern Córdoba

1961 1967 1973 1979 1985 1991 1997 2003

160

250

340

430

520

610

DJF

pre

cipi

tatio

n (m

m)

Years

pcp_djf

avr_djf

r = 0.86 Pcp. Obs. DEF.

Pcp Laboulaye, 1961 -2003Pcp Laboulaye, 1961 -2003

1961 1967 1973 1979 1985 1991 1997 2003

-1.8

-0.9

0.0

0.9

1.8

2.7

ISP

3 -

Feb

ruar

y

Years

base

ISP3_lb

p_is3lb_m

p_is3lb_p

Risk spaceRisk space. Veracruz. JJA. Veracruz. JJA

-110

-80

-50

-20

10

40

70

100

An

om

. P

CP

(%

)

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 Anom. T (°C)

61

6263

64 6566

6768

69

70

71

72

73

74

75

76

77

78

79

80

81

8283

84

85

86

8788

89 90

91 92

93

9495

96

9798

99

00

LAS VIGAS, VER.JJA. 1961-2000

PCP vs. TMAX

N

NN N

N

N

N

N N

Na

Na

Na

Na

Na

Na

Na Na

-60

-40

-20

0

20

40

60

80

100

Pcp a

nom

alies (

%)

-3 -2 -1 0 1 2 3 4

Tmax anomalies (ºC)

61+62+63+

64+

65+

66+

67

68-69

70

71-

7273+

74

75-76+

77+

78 79-

80

81+82

83+

84-

85

86

87+

8889-

9091+

92+

93+

94+

95+

96-

97+

98

99+

00+

Laboulaye, Córdoba. DEF

Risk spaceRisk space. Laboulaye, Cba. . Laboulaye, Cba.

• Relation between climate – specific crops• Allows us to differentiate seasonal climatic

impacts from other stressors • Relation to current governmental programs

(example: FAPRACC, Mexico). • Helps communication. Decision makers and

regional experts. • Helps to decide between climate change

scenarios.

Some advantages of these Climatic Some advantages of these Climatic Risk SpacesRisk Spaces

Uncertainties Uncertainties

• Spatial: Regional, local?

• Temporal: annual, seasonal, monthly, daily data (frost, hail, strong winds)? Future?

• “Risk” to whom? to what? Different crop sensitivity

Climate Change scenariosClimate Change scenarios

• Magicc /ScengenMagicc /Scengen outputs– SRES: A2 and B2– Medium and High Sensitivity– Echam, Hadley, GFDL– 2020, 2050 (monthly and seasonal)– Temperature and Precipitation

• Simple interpolation in 1ºx1º grid (Mexico).• For study sites: scatter plots (simple interpolation)• Downscaling techniques for Veracruz (Mexico).

No SRES. 2xCO2

C. Conde, A. Tejeda, C. Gay, O. Sánchez*, R. Araujo, B. Palma, Vinocur.

Selected GCMsSelected GCMs

• ECHAMECHAM model: model: Lowest differences with observed data. México (Magaña, 2003;Conde, 2003).

• GFDL (and CC) models: GFDL (and CC) models: used in Country Study: Mexico (1994 – 1996)

• HADLEY model: HADLEY model: used in LA

• These models are used also for Córdoba, Córdoba, Argentina, Argentina, as suggested by LA-26

Downscaling. JJA. GFDL Downscaling. JJA. GFDL

• T = F(Z).

(Used for electricity rates)

• T0corr= - k1 – k2 Z + k3 T1Model

• r = 0.966; r2=93.4

Tcorr = b1 T

Temperature Base Scenario

Palma, B. 2004

Examples for Mexico.Examples for Mexico.

ECHAM98. A2 MES. 2020.

PRECIPITATION. JULY

(-8,-2)

(12%,- 8%)

(16%, 8%)

“user friendly”

Sánchez, Araujo, Conde

MEXICO. Temperature Climate Change MEXICO. Temperature Climate Change Scenarios. A2, B2. 2020, 2050. 3 GCMs. JulyScenarios. A2, B2. 2020, 2050. 3 GCMs. July

0.5

1

1.5

2

2.5

3

Cha

ng

es in

Te

mp

era

ture

(ºC

)

2020 2050

hadcm3 a2

gfdl30 a2echam4 a2hadcm3b2

echam4 b2

gfdl30 b2

T(ºC) Change Scenarios.2020,2050

Central Region. Veracruz. July

0

0.4

0.8

1.2

1.6

2020 2050

ecA2

ecB2

gfA2

gfB2

hdA2

hdB2

T(ºC) Change Scenarios.2020,2050Central-Southern Region. Cordoba. JAN

ARGENTINA. Temperature Climate Change ARGENTINA. Temperature Climate Change Scenarios. A2, B2. 2020, 2050. 3 GCMs. Jan.Scenarios. A2, B2. 2020, 2050. 3 GCMs. Jan.

MEXICO. Precipitation Climate Change MEXICO. Precipitation Climate Change Scenarios. A2, B2. 2020, 2050. 3 GCMs. JulyScenarios. A2, B2. 2020, 2050. 3 GCMs. July

-40

-20

0

20

40

60

Pre

cip

itation c

hange (

%)

2020 2050

hadcm3 a2

echam4 a2

gfdl30 a2

hadcm3b2

echam4 b2

gfdl30 b2

Pcp Change Scenarios. JulyCentral Region. Veracruz. 2020,2050

Argentina. Precipitation Climate Change Argentina. Precipitation Climate Change

Scenarios.Scenarios. A2, B2. 2020, 2050. 3 GCMs. A2, B2. 2020, 2050. 3 GCMs. Jan.Jan.

-2

0

2

4

6

8

changes in p

cp (

%)

2020 2050

ECA2

ECB2GFA2

GFB2HDA2HDB2

Precipitation Climate Change ScenariosJanuary. Central -Southern Córdoba

Decisions?Decisions?

-40

-20

0

20

40

60

Pre

cip

ita

tio

n c

ha

nge

(%

)

2020 2050

hadcm3 a2

echam4 a2

gfdl30 a2

hadcm3b2

echam4 b2

gfdl30 b2

Pcp Change Scenarios. JulyCentral Region. Veracruz. 2020,2050

Pcp: -35% to +40%

T: 1.5ºC to 3.8ºC

Which of the multiple

combinations represent

future climatic risk?

Or an opportunity?

““Risk Space”. Veracruz. 2020Risk Space”. Veracruz. 2020

-30

-20

-10

0

10

20

0.9 1 1.1 1.2 1.3 1.4 1.5 1.6

hadcm3 a2

hadcm3b2

echam4 a2echam4 b2

gfdl30 a2

gfdl30 b2

Scenarios A2,B2. 2020. 3 GCMs

July

Summer Temperature 1969-2050E=Echam, H=Hadley, sm=Clim Sen. Med., sa=Clim.

Sen. High, trend=tendency (aleatory numeric generator).

What about changes in variability?What about changes in variability?

Gay,C., F. Estrada, C. Conde, 2004

ConclusionsConclusions• Regional climatic variability and trends analysis

helps defining climatic risk• Climatic “risk spaces” can be use as a tool to

communicate risk, related to crops and defining other stressors.

• Regional climate change scenarios can be compared to “risk spaces” to define future climatic risk and/or opportunities.

• Changes in climate variability are fundamental for agriculture