climate change

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%

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Site-specific predicted values of each GCM model (IPCC, 2007) for principal bioclimatic variables

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Site-specific monthly coefficient of variation using 14 GCM models (IPCC, 2007) for precipitation and temperature

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Colombia y el mundo en cambio climático

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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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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

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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

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COFFEE SUITABILITY

COFFEE: 14.3% of GDP in Nicaragua, 2006.

COFFEE SUITABILITY

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COFFEE ACIDITY SUITABILITY

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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.)