climate change
DESCRIPTION
Presentation by Andy Jarvis for the CIAT KSW 2009TRANSCRIPT
Climate change
Knowledge Sharing Week
May 2009
Contents
• Climate change in 3 short minutes
• The data we have in CIAT
• Analysing impacts in agriculture– Our commodities– Others
• Adaptation, adaptation, adaptation but what does it really mean?
El clima esta cambiando, no me diga que no
Sources of Agricultural Greenhouse Gasesexcluding land use change Mt CO2-eq
Source: Cool farming: Climate impacts of agriculture and mitigation potential, Greenpeace, 07 January 2008
El pasado y el presente – El “hockey stick”
El arctico esta descongelando
Modelos GCM : “Global Climate Models”
• 21 “global climate models” (GCMs) basados en ciencias atmosféricas, química, física, biología, y, dependiendo de las creencias, algo de astrología
• Se corre desde el pasado hasta el futuro• Hay diferentes escenarios de emisiones de gases
Y que dicen los modelos?
What do the 21 models say?
The world gets warmer
….and wetter, but not everywhere
The Data We have in CIAT
• First, data from Stanford (Lobell)
• Second, data downloaded from IPCC
• Now….strategic partnership with the Tyndell Centre in UK who will provide us with the latest projections (7 GCM models, 4 emissions scenarios) through their AVOID project
Climate change data• Statistically downscaled from 18 GCM models
Originating Group(s) Country MODEL ID OUR ID GRID YearBjerknes Centre for Climate Research Norway BCCR-BCM2.0 BCCR_BCM2 128x64 2050Canadian Centre for Climate Modelling & Analysis Canada CGCM2.0 CCCMA_CGCM2 96x48 2020-2050Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T47) CCCMA_CGCM3_1 96x48 2050Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T63) CCCMA_CGCM3_1_T63 128x64 2050Météo-FranceCentre National de Recherches Météorologiques
France CNRM-CM3 CNRM_CM3 128x642050
CSIRO Atmospheric Research Australia CSIRO-MK2.0 CSIRO_MK2 64x32 2020CSIRO Atmospheric Research Australia CSIRO-Mk3.0 CSIRO_MK3 192x96 2050Max Planck Institute for Meteorology Germany ECHAM5/MPI-OM MPI_ECHAM5 N/A 2050Meteorological Institute of the University of BonnMeteorological Research Institute of KMA
GermanyKorea
ECHO-G MIUB_ECHO_G 96x482050
LASG / Institute of Atmospheric Physics China FGOALS-g1.0 IAP_FGOALS_1_0_G 128x60 2050US Dept. of CommerceNOAAGeophysical Fluid Dynamics Laboratory
USA GFDL-CM2.0 GFDL_CM2_0 144x902050
US Dept. of CommerceNOAAGeophysical Fluid Dynamics Laboratory
USA GFDL-CM2.0 GFDL_CM2_1 144x902050
NASA / Goddard Institute for Space Studies USA GISS-AOM GISS_AOM 90x60 2050Institut Pierre Simon Laplace France IPSL-CM4 IPSL_CM4 96x72 2050Center for Climate System ResearchNational Institute for Environmental StudiesFrontier Research Center for Global Change (JAMSTEC)
Japan MIROC3.2(hires) MIROC3_2_HIRES 320x1602050
Center for Climate System ResearchNational Institute for Environmental StudiesFrontier Research Center for Global Change (JAMSTEC)
Japan MIROC3.2(medres) MIROC3_2_MEDRES 128x642050
Meteorological Research Institute Japan MRI-CGCM2.3.2 MRI_CGCM2_3_2a N/A 2050National Center for Atmospheric Research USA PCM NCAR_PCM1 128x64 2050Hadley Centre for Climate Prediction and ResearchMet Office
UK UKMO-HadCM3 HCCPR_HADCM3 96x732020-2050
Center for Climate System Research (CCSR)National Institute for Environmental Studies (NIES) Japan NIES-99 NIES-99 64x32 2020
Climate Seasonality
General climate change description
The maximum temperature of the year increases from 30.81 ºC to 33.97 ºC while the warmest quarter gets hotter by 2.58 ºC in 2050The minimum temperature of the year increases from 19.08 ºC to 21.16 ºC while the coldest quarter gets hotter by 2.4 ºC in 2050The wettest month gets wetter with 358.48 millimeters instead of 353.03 millimeters, while the wettest quarter gets wetter by 5.17 mm in 2050
The rainfall increases from 2672.92 millimeters to 2739.29 millimeters in 2050 passing through 2613.89 in 2020Temperatures increase and the average increase is 2.45 ºC passing through an increment of 0.94 ºC in 2020
Average Climate Change Trends of Colombia
The mean daily temperature range increases from 9.52 ºC to 9.69 ºC in 2050
General climate
characteristics
Extreme conditions
Variability between models
Overall this climate becomes more seasonal in terms of variability through the year in temperature and less seasonal in precipitation
The driest month gets wetter with 96.32 millimeters instead of 84.75 millimeters while the driest quarter gets wetter by 45.47 mm in 2050
The maximum number of cumulative dry months keeps constant in 2 months
Precipitation predictions were uniform between models and thus no outliers were detected
Temperature predictions were uniform between models and thus no outliers were detectedThe coefficient of variation of temperature predictions between models is 3.62%
These results are based on the 2050 climate compared with the 1960-2000 climate. Future climate data is derived from 18 GCM models from the 3th (2001) and the 4th (2007) IPCC assessment, run under the A2a scenario (business as usual). Further information please check the website http://www.ipcc-data.org
The coefficient of variation of precipitation predictions between models is 5.72%
0
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1 2 3 4 5 6 7 8 9 10 11 12Month
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Tem
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ºC)
Current precipitationPrecipitation 2050Precipitation 2020Mean temperature 2020Mean temperature 2050Current mean temperatureMaximum temperature 2020Maximum temperature 2050Current maximum temperatureMinimum temperature 2020Minimum temperature 2050Current minimum temperature
Incertidumbre
0
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3500B
CC
R B
CM
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CC
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MIU
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AM
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NC
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Pre
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mm
)
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Te
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(ºC
)
Total annual precipitation (bio 12) Annual mean temperature (bio 1)
Annual maximum temperature (bio 5) Annual minimum temperature (bio 6)
Site-specific predicted values of each GCM model (IPCC, 2007) for principal bioclimatic variables
Incertidumbre
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1 2 3 4 5 6 7 8 9 10 11 12Month
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%)
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Precipitation Mean temperature Maximum temperature Minimum temperature
Site-specific monthly coefficient of variation using 14 GCM models (IPCC, 2007) for precipitation and temperature
23.0
23.5
24.0
24.5
25.0
25.5
26.0
26.5
27.0
27.5
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090Año
Tem
per
atu
ra m
edia
an
ual
(ºC
)
Temperatura media anual (ºC)
Tendencia temporal
Intervalo de confianza (95%)
2500
2550
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2750
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2850
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2950
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090
Año
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cip
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to
tal a
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mm
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Precipitación total anual (mm)Tendencia temporalIntervalo de confianza (95%)
Colombia y el mundo en cambio climático
Colombia
650
670
690
710
730
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770
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810
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090
Año
Pre
cip
itac
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to
tal a
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mm
)
Precipitación total anual (mm)Tendencia temporalIntervalo de confianza (95%)
6.0
7.0
8.0
9.0
10.0
11.0
12.0
1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090Año
Tem
per
atu
ra m
edia
an
ual
(ºC
)
Temperatura media anual (ºC)
Tendencia temporal
Intervalo de confianza (95%)
Mundo +4.5ºC+14%
+3.1ºC+8.1%
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
-200.0 -100.0 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0
Precipitation
Tem
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atu
re
India Myanmar Burma Mexico Dominican Republic Rwanda Brazil Uganda Korea Guatemala United States Colombia
1870 Baseline
Region DepartamentoCambio en
Precipitacion
Cambio en Temperatura
media
Cambio en estacionalidad de
precipitacion
Cambio en meses
consecutivos secos
Incertidumbre entre modelos (StDev prec)
Amazonas Amazonas 12 2.9 1.4 0 135Amazonas Caqueta 138 2.7 -1.3 0 193Amazonas Guania 55 2.9 -3.2 0 271Amazonas Guaviare 72 2.8 -2.9 -1 209Amazonas Putumayo 117 2.6 0.6 0 170Andina Antioquia 18 2.1 1.3 0 129Andina Boyaca 50 2.7 -3.9 -1 144Andina Cundinamarca 152 2.6 -2.6 0 170Andina Huila 51 2.4 1.0 0 144Andina Norte de santander 73 2.8 -0.4 0 216Andina Santander 51 2.7 -2.4 0 158Andina Tolima 86 2.4 -3.1 0 148Caribe Atlantico -74 2.2 -2.9 2 135Caribe Bolivar 90 2.5 -1.8 0 242Caribe Cesar -119 2.6 -1.3 0 160Caribe Cordoba -11 2.3 -3.8 0 160Caribe Guajira -69 2.2 -1.8 0 86Caribe Magdalena -158 2.4 -1.8 0 153Caribe Sucre 10 2.4 -4.1 -1 207Eje Cafetero Caldas 252 2.4 -4.2 -1 174Eje Cafetero Quindio 153 2.3 -4.1 -1 145Eje Cafetero Risaralda 158 2.4 -3.5 -1 141Llanos Arauca -13 2.9 -6.4 -1 188Llanos Casanare 163 2.8 -5.7 -1 229Llanos Meta 10 2.7 -5.4 -1 180Llanos Vaupes 46 2.8 -1.4 0 192Llanos Vichada 59 2.6 -2.6 0 152Pacifico Choco -157 2.2 -1.2 0 148Sur Occidente Cauca 172 2.3 -1.6 0 168Sur Occidente Narino 155 2.2 -1.4 0 126Sur Occidente Valle del Cauca 275 2.3 -5.1 -1 166
Estimating Likely Impacts in Agriculture
• Three modelling approaches:– Niche-based– Empirical– Mechanistic model- based
Ecocrop approach
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-5 0 5 10 15 20 25 30 35 40Temperature (ºC)
Pre
cip
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(m
m)
Optimumconditions
Marginalconditions
Death
Notsuitable
conditions
Pros and cons of the approach
• Simple to use and apply• Available for “minor” crops which are important
components of food and nutritional security• Captures the broad niche of the crop, including
within crop genetic diversity• Fails to capture complex physiological responses
of within season climate• Only provides index of suitability – not
productivity• Inferior model to those available for the “big”
crops
PR
OS
CO
NS
Current Cassava Suitability
Gmin: 150, Gmax: 365. KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45. Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.
Gmin: 150, Gmax: 365. KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45. Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.
Future Cassava Suitability (2020)
Gmin: 150, Gmax: 365. KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45. Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.
Change in Cassava Suitability
TECHNOLOGY OPTION:
+2-3oC HEAT TOLERANCE
Only India benefits from heat tolerance. This is a national strategy for technology development. CIAT’s strategy probably better placed in pests/diseases.
Changes in adaptability in
Green Mite 2020
Change in adaptability of Whitefly 2020
Arachis pintoi Krap.& Greg.
Gmin: 180, Gmax: 300 KTmp: 0, Tmin: 12, TOPmn: 22, TOPmx: 28,
Tmax: 30Rmax: 1600, ROPmn: 1800, ROPmx:2000,
Rmax: 3000
Clitoria ternatea L.
Gmin: 50, Gmax: 365 KTmp: -2, Tmin: 15, TOPmn: 19, TOPmx: 28,
Tmax: 32 Rmax: 400, ROPmn: 1200, ROPmx:1800,
Rmax: 4300
Leucaena leucocephala (La.)
Gmin: 180, Gmax: 365 KTmp: 0, Tmin: 10, TOPmn: 20, TOPmx: 32,
Tmax: 42 Rmax: 250, ROPmn: 600, ROPmx: 3000,
Rmax: 5000
Brachearia x hybrid
Gmin: 120, Gmax: 365 KTmp: 0, Tmin: 20, TOPmn: 24, TOPmx: 30,
Tmax: 35 Rmax: 800, ROPmn: 1200, ROPmx:1800,
Rmax: 3000
The geography of crop suitability
Crop SpeciesArea
Harvested (k Ha)
Alfalfa Medicago sativa L. 15214Apple Malus sylvestris Mill. 4786Banana Musa acuminata Colla 4180Barley Hordeum vulgare L. 55517Common Bean Phaseolus vulgaris L. 26540Common buckwheat Fagopyrum esculentum Moench 2743Cabbage Brassica oleracea L.v capi. 3138Cashew nuts Anacardium occidentale L. 3387Cassava Manihot esculenta Crantz. 18608Chick pea Cicer arietinum L. 10672Clover Trifolium repens L. 2629Cocoa bean Theobroma cacao L. 7567Coconut Cocos nucifera L. 10616Coffee Coffea arabica L. 10203Cotton Gossypium hirsutum L. 34733Cow peas Vigna unguiculata unguic. L 10176Grapes Vitis vinifera L. 7400Groundnut Arachis hypogaea L. 22232Lentil Lens culinaris Medikus 3848Linseed Linum usitatissimum L. 3017Maize Zea mays L. s. mays 144376Mango Mangifera indica L. 4155Millet Panicum miliaceum L. 32846Natural rubber Hevea brasiliensis (Willd.) 8259
Natural rubber Hevea brasiliensis (Willd.) 8259Oats Avena sativa L. 11284Oil palm Elaeis guineensis Jacq. 13277Olive Olea europaea L. 8894Onion Allium cepa L. v cepa 3341Oranges Citrus sinensis (L.) Osbeck 3618Pea Pisum sativum L. 6730Pigeon pea Cajanus cajan (L.) Mill ssp 4683Plantain bananas Musa balbisiana Colla 5439Potato Solanum tuberosum L. 18830Rapeseed Brassica napus L. 27796Rice Oryza sativa L. s. japonica 154324Rye Secale cereale L. 5994Perennial reygrass Lolium perenne L. 5516Sesame seed Sesamum indicum L. 7539Sorghum Sorghum bicolor (L.) Moench 41500Perennial soybean Glycine wightii Arn. 92989Sugar beet Beta vulgaris L. v vulgaris 5447Sugarcane Saccharum robustum Brandes 20399Sunflower Helianthus annuus L v macro 23700Sweet potato Ipomoea batatas (L.) Lam. 8996Tea Camellia sinensis (L) O.K. 2717Tobacco Nicotiana tabacum L. 3897Tomato Lycopersicon esculentum M. 4597Watermelon Citrullus lanatus (T) Mansf 3785Wheat Triticum aestivum L. 216100Yams Dioscorea rotundata Poir. 4591
Change in global suitability
Number of crops that lose out
Number of crops that gain
Cassava and maize in Africa and India – not all bad news
Differential response in maize
-80
-60
-40
-20
0
20
40
60
80
Angola
cass
Angola
maiz
Congo c
ass
Congo m
aiz
Ghana c
ass
Ghana m
aiz
India
cass
India
maiz
Mala
wi cass
Mala
wi m
aiz
Mozam
biq
ue c
ass
Mozam
biq
ue m
aiz
Tanzania
cass
Tanzania
maiz
Nig
eria c
ass
Nig
eria m
aiz
Uganda c
ass
Uganda m
aiz
Cro
p a
dap
tab
ilit
y a
no
maly
COFFEE SUITABILITY
COFFEE: 14.3% of GDP in Nicaragua, 2006.
COFFEE SUITABILITY
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Altitude (masl)
Su
itab
ility
current2050
COFFEE ACIDITY SUITABILITY
COFFEE ACIDITY SUITABILITY
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Altitude (masl)
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itab
ility
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2050
ADAPTATION STRATEGIES
1. Areas not anymore suitable for coffee (alternatives)
2. Areas only suitable with adapted management (varieties, irrigation, shade, etc)3. New potential areas (expand where viable
and possible)
IMPLICATIONS ON SUPPLY CHAIN
1. Shortage of commodity coffee and high value coffee
2. Possible increase in prices and income for supply chain actors
3. Change in sourcing areas and channels4. Possible loss of product reputation (Denomination of Origin in Veracruz)
Adaptation, adaptation, adaptation
• We see four types of agriculture within the context of climate change:– Traditional staple, short-cycle– Short-cycle low investment cash crop– Short-cycle high investment cash crop– Perennial long-term high investment system
Different strategies
• Traditional staple, short-cycle– Technology development, community-based adaptation
strategies (first practices)• Short-cycle low investment cash crop
– Technology development, crop substitution• Short-cycle high investment cash crop
– Careful planning, supply chain level adaptation strategies
• Perennial long-term high investment system– Technology development, entire supply-chain level
adaptation strategies (The Anchor)
Short-cycle high investment cash crops
Perennial long-term high investment system
Adaptive management across the supply chain
FARM
MARKET
Conclusions
• In multiple CIAT crops no panaceae in breeding strategies – we need to do more work on this
• Regional-level challenges at both all ends of spectrum (heat, drought, excess water)
• We need to strengthen the analysis of economic and social implications of climate change
• Technology development today for 2020• Scientific gap in understanding of crop
substitution (how common, implications, nutrition etc.)