Andy J Escenarios De Cambio Climatico Para Colombia

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)

http://en.wikipedia.org/wiki/Image:Global_Atmospheric_Model.jpg

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

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



2050A1B



2020A1B



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 )

0

1 2 0 8 4

A n n u a l p r e c i p i t a t i o n ( m m )

A

B

C

~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

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12Month

Pre

cip

itat

ion

(m

m)

0

5

10

15

20

25

30

35

40

Tem

per

atu

re (

ºC)

Current precipitation

Future precipitation

Future mean temperature

Current mean temperature

Future maximum temperature

Current maximum temperature

Future minimum temperature

Current minimum temperature

Incertidumbre

0

500

1000

1500

2000

2500

3000

3500B

CC

R B

CM

2 0

CC

CM

A C

GC

M3

1

CC

CM

A C

GC

M3

1 T

63

CN

RM

CM

3

CS

IRO

MK

3 0

GF

DL

CM

2

GF

DL

CM

2 1

MIR

OC

3 2

HIR

ES

MIR

OC

3 2

ME

DR

ES

MIU

B E

CH

O G

MP

I E

CH

AM

5

NC

AR

PC

M 1

CC

CM

A C

GC

M2

HC

CP

R H

AD

CM

3

Pre

cip

ita

tio

n (

mm

)

0

5

10

15

20

25

30

35

40

45

50

Te

mp

era

ture

(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

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12Month

Pre

cip

itat

ion

co

eff

icie

nt

of

va

ria

tio

n (

%)

0

5

10

15

20

25

30

35

Te

mp

era

ture

co

eff

icie

nt

of

va

riat

ion

(%

)

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)

http://www.lisamcpherson.org/protest/3-8-03/images/anarchy_jpg.jpg

Andy J Escenarios De Cambio Climatico Para Colombia

Technology

Transcript of Andy J Escenarios De Cambio Climatico Para Colombia