Transcript Climate variability and trends in Mexico and Argentina

Climate variability, trends and
scenarios for Mexico and
Argentina.
Cecilia Conde, Marta Vinocur, Carlos
Gay, Roberto Seiler.
AIACC LA-29
Integrated 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
Observed T. Veracruz. JJA. 1901- 1995
http://ipcc-ddc.cru.uea.ac.uk/
23
22.5
1 event > +1 std
22
12 Events > +1 std
21.5
21
T (°C )
20.5
20
19.5
19
18.5
18
wr 1961-1990
8 events <-1std
3 events <-1std
17.5
17
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94
year
Observed Pcp. Veracruz.JJA. 1901- 1995
http://ipcc-ddc.cru.uea.ac.uk/
11.5
P cp (m m / d a y)
10.5
1 event > +1 std
7 events > +1 std
9.5
8.5
7.5
6.5
5.5
4.5
wr 1961-1990
7 events<-1std
6 events<-1std
1 4 7 1013 16 19 22 25 28 31 3437 40 43 46 49 52 5558 61 64 67 70 73 7679 82 85 88 91 94
year
Central Region
Veracruz
Córdoba Province
Argentina
Study area
IPCC vs Observed data (3 stations)
Southern Córdoba
Pcp. Obs. DEF.
r = 0.86
610
DJF precipitation (mm)
520
430
pcp_djf
avr_djf
340
250
160
1961
1967
1973
1979
1985
Years
1991
1997
2003
Pcp Laboulaye, 1961 -2003
2.7
ISP3 - February
1.8
0.9
base
ISP3_lb
p_is3lb_m
0.0
p_is3lb_p
-0.9
-1.8
1961
1967
1973
1979
1985
Years
1991
1997
2003
Risk space. Veracruz. JJA
PCP vs. TMAX
LAS VIGAS, VER.
JJA. 1961-2000
81
A n o m . P C P (% )
100
70
72
40
61
69
-20
79
00
N
Na
Na
78
74 76
10
93
96
73
Na
7068
67
N
71
Na
64
95
84
Na
63
65
87
66
62 89
75
Na
Na
77
85
N
94
91 92
99
88
N
Na
8690
80
83
N
N
98
97
N
N
82
N
-50
-80
-110
-3 -2.5 -2 -1.5 -1 -0.5
0
0.5
1
Anom. T (°C)
1.5
2
2.5
3
3.5
Risk space. Laboulaye, Cba.
P c p a n o m a lie s (% )
Laboulaye, Córdoba. DEF
100
97+
80
92+77+
83+
60
76+
40
81+
20
94+
82
65+
74
72
99+
95+
86
73+ 70
79-
80
0
84-
78
93+
91+
9087+
-20
75-
85
62+63+
61+
66+
98
89-
6869
96-
64+
67
00+
88
-40
71-
-60
-3
-2
-1
0
1
2
Tmax anomalies (ºC)
3
4
Some advantages of these Climatic
Risk Spaces
• 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.
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 scenarios
• Magicc /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 GCMs
• ECHAM model: Lowest differences with
observed data. México (Magaña, 2003;Conde, 2003).
• GFDL (and CC) models: used in Country
Study: Mexico (1994 – 1996)
• HADLEY model: used in LA
• These models are used also for Córdoba,
Argentina, as suggested by LA-26
Downscaling. JJA. GFDL
Temperature Base Scenario
• T = F(Z).
(Used for electricity rates)
• T = - k1 – k2 Z + k3
T1Model
• r = 0.966; r2=93.4
0corr
• T
corr
= b1 T
Palma, B. 2004
Examples for Mexico.
(12%,- 8%)
(-8,-2)
(16%, 8%)
Sánchez, Araujo, Conde
ECHAM98. A2 MES. 2020.
PRECIPITATION. JULY
“user friendly”
C h a n g e s in T e m p e r a tu r e ( º C )
MEXICO. Temperature Climate Change
Scenarios. A2, B2. 2020, 2050. 3 GCMs. July
T(ºC) Change Scenarios.2020,2050
Central Region. Veracruz. July
3
2.5
2
hadcm3 a2
1.5
1
0.5
echam4
gfdl30 a2
b2
echam4 a2
hadcm3b2
gfdl30 b2
2020
2050
ARGENTINA. Temperature Climate Change
Scenarios. A2, B2. 2020, 2050. 3 GCMs. Jan.
T(ºC) Change Scenarios.2020,2050
Central-Southern Region. Cordoba. JAN
1.6
1.2
0.8
ecB2
gfB2
0.4
0
hdB2
ecA2
gfA2
hdA2
2020
2050
MEXICO. Precipitation Climate Change
Scenarios. A2, B2. 2020, 2050. 3 GCMs. July
Pcp Change Scenarios. July
P re c ip ita tio n c h a n g e (% )
Central Region. Veracruz. 2020,2050
60
40
20
gfdl30 a2
0
echam4 b2
a2
-20
hadcm3 a2
gfdl30 b2
hadcm3b2
-40
2020
2050
Argentina. Precipitation Climate Change
Scenarios. A2, B2. 2020, 2050. 3 GCMs.
Jan.
c h a n g e s in p c p ( % )
Precipitation Climate Change Scenarios
January. Central -Southern Córdoba
8
6
4
2
0
-2
GFB2
HDA2
HDB2
GFA2
ECB2
ECA2
2020
2050
Decisions?
Pcp Change Scenarios. July
P re c ip ita tio n c h a n g e (% )
Central Region. Veracruz. 2020,2050
60
40
20
gfdl30 a2
0
echam4 b2
a2
-20
hadcm3 a2
gfdl30 b2
hadcm3b2
-40
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. 2020
Scenarios A2,B2. 2020. 3 GCMs
July
20
gfdl30 a2
10
0
echam4
echam4 b2
a2
-10
hadcm3 a2
-20
gfdl30 b2
-30
0.9
hadcm3b2
1
1.1
1.2
1.3
1.4
1.5
1.6
What about changes in variability?
Summer Temperature 1969-2050
E=Echam, H=Hadley, sm=Clim Sen. Med., sa=Clim.
Sen. High,
trend=tendency (aleatory numeric generator).
Gay,C., F. Estrada, C. Conde, 2004
Conclusions
• 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