andy j escenarios de cambio climatico para colombia
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Impactos del cambio climático en Colombia: Modelos y metodos
Andy Jarvis, Julian Ramirez,
Contenido
• La demanda de la agricultura
• Un breve introduccion a los modelos
• Downscaling empirico• Downscaling con RCM• Perspectivas para el futuro
La demanda - resolucion
• Agricultura es una industria de nicho
• Entonces necesitamos datos de clima relevantes para caracterizar el nicho
• Escala: 1km, 90m?
La demanda - variables
• Necesitamos multiples variables
–Temperatura• Max, min, media
–Precipitacion– Humedad relativa– Radiacion solar– Vientos– …….
Men
os im
port
ante
s
Mas
cer
tidum
bre
La demanda - tiempos
• Necesitamos como minimo datos mensuales
• Para algunas aplicaciones detallados (ej. modelos mechanisticos) necesitamos datos diarios
• 2050 y 2080 son irrelevantes para la toma de decision en agricultura
• Estamos buscando pronosticos para variabilidad climatica (within season, seasonal, annual, Nino/Nina)
• Y para cambio en linea base: 2020-2030
La demanda - certidumbre
• Los cultivos son suprememente sensibles a sus condiciones climaticos
• Para adaptaciones especificos, necesitamos alta certidumbre
• Faltando certidumbre, trabajamos en resiliencia (pero es mas dificil)
Los modelos
• Empezo con los GCMs– Grillas grandes, muy complejos
• Vamos hacia los RCMs– Grillas mas pequenhas, igualmente complejos
Modelos GCM : “Global Climate Models”
• 21 “global climate models” (GCMs) basados en ciencias atmosféricas, química, física, biología etc.
• Se corre desde el pasado hasta el futuro• Hay diferentes escenarios de emisiones de gases
INCERTIDUMBRE POLITICO (EMISIONES), Y INCERTIDUMBRE CIENTIFICO (MODELOS)
MENSAJE 1
En la agricultura, las diferentes
escenarios de emisiones no son
importantes: de aqui a 2030 la diferencia entre escenarios es
minima
BCCR-BCM2.0 CCCMA-CGCM2CCCMA-CGCM3.1
T47 CCCMA-CGCM3.1-T63 CNRM-CM3 IAP-FGOALS-1.0G
GISS-AOM GFDL-CM2.1 GFDL-CM2.0 CSIRO-MK3.0 IPSL-CM4 MIROC3.2-HIRES
MIROC3.2-MEDRES MIUB-ECHO-G MPI-ECHAM5 MRI-CGCM2.3.2A NCAR-PCM1 UKMO-HADCM3
BCCR-BCM2.0 CCCMA-CGCM2CCCMA-CGCM3.1
T47 CCCMA-CGCM3.1-T63 CNRM-CM3 IAP-FGOALS-1.0G
GISS-AOM GFDL-CM2.1 GFDL-CM2.0 CSIRO-MK3.0 IPSL-CM4 MIROC3.2-HIRES
MIROC3.2-MEDRES MIUB-ECHO-G MPI-ECHAM5 MRI-CGCM2.3.2A NCAR-PCM1 UKMO-HADCM3
MENSAJE 2
La incertidumbre cientifico SI es relevante para la agricultura: tenemos
que tomar decisiones dentro de un contexto de incertidumbre
YDepender de un solo GCM es peligroso
Opciones para downscaling• Uso de GCMs de alta resolucion
– MRI es un GCM con 20km resolucion (Japones)
• Uso de una o multiples RCMs (dynamical)– PRECIS
• Downscaling empirico (statistical)– CLIMGEN con el Tyndell– Ramirez y Jarvis usando WorldClim
• Downscaling hybrid (RCM + empirico)
Mitchell TD and Osborn TJ (2005) ClimGen: a flexible tool for generating monthly climate data sets and scenarios. Tyndall Centre for Climate Change Research Working Paper.
Ghaffari et al., 2002. Climatic change
Tubiello et al., 2000. Eur. Jour. Agron.
Arnell and Osborn (2006)…
Datos de Tyndall Centre• A1B: 2020, 2030, 2040, 2050, 2060, 2070, 2080…• 7 modelos representativos:
– CCCMA-CGCM3.1– CSIRO-MK3.0– IPSL-CM4– MPI-ECHAM5– NCAR-CCSM3.0– UKMO-HADCM3– UKMO-HADGEM1
CCCMA-CGCM3.1 CSIRO-MK3.0 IPSL-CM4 MPI-ECHAM5
NCAR-CCSM3.0 UKMO-HADCM3 UKMO-HADGEM1
2020A1B
CCCMA-CGCM3.1 CSIRO-MK3.0 IPSL-CM4 MPI-ECHAM5
NCAR-CCSM3.0 UKMO-HADCM3 UKMO-HADGEM1
2050A1B
CCCMA-CGCM3.1 CSIRO-MK3.0 IPSL-CM4 MPI-ECHAM5
NCAR-CCSM3.0 UKMO-HADCM3 UKMO-HADGEM1
2020A1B
CCCMA-CGCM3.1 CSIRO-MK3.0 IPSL-CM4 MPI-ECHAM5
NCAR-CCSM3.0 UKMO-HADCM3 UKMO-HADGEM1
2050A1B
Downscaling a la Ramirez y Jarvis
• 50km no es suficiente para la agricultura
• Solucion: Downscaling empirico usando el metodo delta, basado en WorldClim
• Un supuesto: a nivel local, la distribucion espacial de clima no cambia, solo a nivel macro
WorldClim• Global high resolution 1km
monthly climate surfaces for precipitation, mean, max and min temperature
• Based on 47,554 precipitation stations, 24,542 mean temperature stations, 14,835 minimum and maximum temperature stations
• Interpolated using a thin-plate smoothing spline in the AnuClim software
Citado > 500 veces de 2005-2009
- 3 0 .1
3 0 .5
M e a n a n n u a lt e m p e r a t u r e ( º C )
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A n n u a l p r e c i p i t a t i o n ( m m )
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~1500 stations in Colombia
For precipitation, spline interpolation method uses elevation as a co-variable, and searches for local correlations to make an “informed” interpolation between points.
To illustrate, rainfall around Cali.
WorldClimClimate stations are not randomly distributed, but most dense in
populated regions.
• Average distance from a CIAT climbing bean collection to a WorldClim station:
• Precipitation : 11.2km
• Mean temperature : 30.7km
• Minimum/maximum temperature : 33.4km
• Average distance from a WCMC cloud forest site to a WorldClim station:
• Precipitation : 20.6km
• Mean temperature : 38.8km
• Minimum/maximum temperature : 52.6km
Bases de Datos
• 18 modelos GCM para 2050, 9 para 2020 (datos de Stanford) downscaled a 20km, 5km, 1km
• 7 GCMs con informacion decadal de Tyndell• Diferentes escenarios, A1b, B1, commit
http://gisweb.ciat.cgiar.org/GCMPage/
Region DepartamentoCambio en
Precipitacion
Cambio en Temperatura
media
Cambio en estacionalidad de
precipitacion
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
Climate characteristic
Climate Seasonality
The mean daily temperature range increases from 9.57 ºC to 9.85 ºC
The driest month gets wetter with 94.2 millimeters instead of 83.6 millimeters while the driest quarter gets wetter by 40.25 mm
Temperature predictions were uniform between models and thus no outliers were detected
Precipitation predictions were uniform between models and thus no outliers were detected
General climate change description
The maximum temperature of the year increases from 30.84 ºC to 34.36 ºC while the warmest quarter gets hotter by 2.81 ºC The minimum temperature of the year increases from 19.05 ºC to 21.23 ºC while the coldest quarter gets hotter by 2.6 ºC The wettest month gets wetter with 354.88 millimeters instead of 350.35 millimeters, while the wettest quarter gets wetter by 3.55 mm
The rainfall increases from 2645.89 millimeters to 2702.41 millimetersTemperatures increase and the average increase is 2.66 ºC
The coefficient of variation of temperature predictions between models is 3.7%
The maximum number of cumulative dry months keeps constant in 2 months
Average Climate Change Trends of Colombia
These results are based on the 2050 climate compared with the 1960-2000 climate. Future climate data is derived from 14 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%
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
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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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Current precipitation
Future precipitation
Future mean temperature
Current mean temperature
Future maximum temperature
Current maximum temperature
Future minimum temperature
Current minimum temperature
Incertidumbre
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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
Pre
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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
• MRI con 20km resolución, datos diarios para tres epocas, temp. minima, maxima y precipitacion (distribucion restringido por acuerdo con Japon)
• RCM: PRECIS, con boundary conditions de ECHAM4, ECHAM5, HADCM3 x 4
En camino
Blade
Arreglo de disco
Arreglo de disco
Alternate servers
Array disk
La demanda vs. la ofertaDemanda GCMs RCMs GCMs con
downscaling empirico
Alta resolucion No Moderado Si
Variables Si Si No
Frecuencia Si Si No
Certidumbre Moderado Baja Moderado
Entonces que hacemos frente todo esto?
• No hay una sola estrategia gana-gana• Necesitamos multiples acercamientos para mejorar la
base de informacion acerca de escenarios de cambio climatico– Desarollo de RCMs (multiples: PRECIS NO ES SUFICIENTE)– Downscaling empirico, metodos hybridos– Probamos diferentes metodologias
• Se requiere flujo de informacion (CCC): compartimos, comparemos, charlamos (chismoseamos)
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