Carlos Navarro, Patricia Moreno, Julián Ramírez y Andy Jarvis,

CIAT experience in Climate& Crop Modelling

Maputo, Mozambique IIAM26/02/2013

Photo - Neil Palm

er

IIAM-CIAT Project: Managing climate related risks to improve livelihood resilience and adaptive capacity in agricultural ecosystems in Southern Mozambique

• Brief about climate & agricultre

• Climate data, availability, difficulties, options

• Our databases & portal of climate data

• Crop modelling and climate data

Contents

Why Mozambique?MOZ is particularly vulnerable to climate change due to:

• Confluence of many international rivers flowing into the Indian Ocean

• Land area that is below sea level; • High vulnerability to cyclones and storms• High temperatures• Aridity• Infertile soils• Lack of communications infrastructure• High population growth rate

• Poverty • High dependence on natural resources that

require predictable rain.• Many of the river basins suffer from saline

intrusion deep into river mouths

Managing climate related risks to

improve livelihood resilience and

adaptive capacity in agricultural

ecosystems in Southern

Mozambique

Output 1. Understand if and what kind of changes occurred in the agricultural practices, and the reasons for making them.

Output 2. A comprehensive analysis of the biophysical environment (climate, soil, water, vegetation, ecology and land use) and the impacts of predicted climate changes on agricultural suitability.

Output 3. Identification and testing of potential interventions that can build communities adaptive capacity to cope with climate change related risks.

Output 4. Lessons Learnt and Recommendations.

IIAM-CIAT Project:

How & why?

IIAM-CIAT Project:Where?

Chicualacuala

Xai Xai

-11 to + 4 %

Why Mozambique?

•Any agroecosystem respond to changes of anthropogenic factors, biotics, abiotics.•Weather and climate predictability is fairly limited. • The climate will change.• Each system is an specific case. •Crops are very sensitive to climatic conditions

The demand – CertaintyWe know…

Climate & Agriculture– Multiple variables– Very high spatial

resolution– Mid-high temporal (i.e.

monthly, daily) resolution– Accurate weather

forecasts and climate projections

– High certainty• Both for present and

future

–T°• Max,• Min, • Mean

–Prec–HR– Radiation– Wind– …….

Less

impo

rtan

ce

Mor

e ce

rtai

nty

The demand – Certainty

>> UNCERTAINTIES

We don’t know… What are the conditions in 30, 50, 100 years?

• How our system respond to these conditions?

• When, where and what type of change requiere to adapt?

• Who should plan? Who should leads the process ? Who should run?

The demand – Certainty

Reliable climatic data Gaps in representation of

the climate system

Inadequate climate models

Assessment of impacts of

climate change on agriculture

High degree of uncertainty

>> Uncertainty

Needs Limitations

The demand – Certainty

How to predict the future?

Economic

Environmental

Global Regional

Pessimistic“Bussiness as usual”

OptimisticPerfect World

IntermediateP

E

P

E

P

E

P

E

Emission Scenarios

In agriculture, the different emission scenarios are not important ... by

2030 the difference between the scenarios is

minimal

Key Message

GCMs are the only way we can predict the future

climate

Using the past to learn for the future

The ModelsGCM “Global Climate Model”

Variations of the Earth’s surface temperature: 1000 to 2100

What are saying the models?

Anthropogenic changes lead to changes in weather

Atmospheric concentrations

The Models

GCM - Limitations

Global scale Regional or local scale

Resolutions

• Horizontal resolution 100 to 300 km • 18 and 56 vertical levels

Model Country Atmosphere OceanBCCR-BCM2.0 Norway T63, L31 1.5x0.5, L35CCCMA-CGCM3.1 (T47) Canada T47 (3.75x3.75), L31 1.85x1.85, L29CCCMA-CGCM3.1 (T63) Canada T63 (2.8x2.8), L31 1.4x0.94, L29CNRM-CM3 France T63 (2.8x2.8), L45 1.875x(0.5-2), L31CSIRO-Mk3.0 Australia T63, L18 1.875x0.84, L31CSIRO-Mk3.5 Australia T63, L18 1.875x0.84, L31GFDL-CM2.0 USA 2.5x2.0, L24 1.0x(1/3-1), L50GFDL-CM2.1 USA 2.5x2.0, L24 1.0x(1/3-1), L50GISS-AOM USA 4x3, L12 4x3, L16GISS-MODEL-EH USA 5x4, L20 5x4, L13GISS-MODEL-ER USA 5x4, L20 5x4, L13IAP-FGOALS1.0-G China 2.8x2.8, L26 1x1, L16INGV-ECHAM4 Italy T42, L19 2x(0.5-2), L31INM-CM3.0 Russia 5x4, L21 2.5x2, L33IPSL-CM4 France 2.5x3.75, L19 2x(1-2), L30MIROC3.2-HIRES Japan T106, L56 0.28x0.19, L47MIROC3.2-MEDRES Japan T42, L20 1.4x(0.5-1.4), L43MIUB-ECHO-G Germany/Korea T30, L19 T42, L20MPI-ECHAM5 Germany T63, L32 1x1, L41MRI-CGCM2.3.2A Japan T42, L30 2.5x(0.5-2.0)NCAR-CCSM3.0 USA T85L26, 1.4x1.4 1x(0.27-1), L40NCAR-PCM1 USA T42 (2.8x2.8), L18 1x(0.27-1), L40UKMO-HADCM3 UK 3.75x2.5, L19 1.25x1.25, L20UKMO-HADGEM1 UK 1.875x1.25, L38 1.25x1.25, L20

Uncertainties!

GCM - Limitations

Difficulty 1. They differ on resolution

GCM - Limitations

• Difficulty 2. They differ in availability (via IPCC)WCRP CMIP3 A1B-P A1B-T A1B-Tx A1B-Tn A2-P A2-T A2-Tx A2-Tn B1-P B1-T B1-Tx B1-Tn

BCCR-BCM2.0 OK OK OK OK OK OK OK OK OK OK OK OKCCCMA-CGCM3.1-T63 OK OK NO NO NO NO NO NO OK OK NO NOCCCMA-CGCM3.1-T47 OK OK NO NO OK OK NO NO OK OK NO NOCNRM-CM3 OK OK NO NO OK OK NO NO OK OK NO NOCSIRO-MK3.0 OK OK OK OK OK OK OK OK OK OK OK OKCSIRO-MK3.5 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.0 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.1 OK OK OK OK OK OK OK OK OK OK OK OKGISS-AOM OK OK OK OK NO NO NO NO OK OK OK OKGISS-MODEL-EH OK OK NO NO NO NO NO NO NO NO NO NOGISS-MODEL-ER OK OK NO NO OK OK NO NO OK OK NO NOIAP-FGOALS1.0-G OK OK NO NO NO NO NO NO OK OK NO NOINGV-ECHAM4 OK OK NO NO OK OK NO NO NO NO NO NOINM-CM3.0 OK OK OK OK OK OK OK OK OK OK OK OKIPSL-CM4 OK OK NO NO OK OK NO NO OK OK NO NOMIROC3.2.3-HIRES OK OK OK OK NO NO NO NO OK OK OK OKMIROC3.2.3-MEDRES OK OK OK OK OK OK OK OK OK OK OK OKMIUB-ECHO-G OK OK NO NO OK OK NO NO OK OK NO NOMPI-ECHAM5 OK OK NO NO OK OK NO NO OK OK NO NOMRI-CGCM2.3.2A OK OK NO NO OK OK NO NO OK OK NO NONCAR-CCSM3.0 OK OK OK OK OK OK OK OK OK OK OK OKNCAR-PCM1 OK OK OK OK OK OK OK OK OK OK OK OKUKMO-HADCM3 OK OK NO NO OK OK NO NO OK OK NO NOUKMO-HADGEM1 OK OK NO NO OK OK NO NO NO NO NO NO

GCM - Limitations

Difficulty 3. Limited ability to represent present climates

Relying on a single GCM is dangerous!

GCM - Limitations

How I can use this information?

Problem

NeedsOptions

Downscaling by statistical or dynamical methods..

To increase resolution, uniformise, provide high resolution and contextualised dataEven the most

precise GCM is too coarse (~100km)

GCM - Limitations

The Delta Method

Options – Statistical Methods

• Use anomalies and discard baselines in GCMs– Climate baseline: WorldClim– Used in the majority of studies– Takes original GCM timeseries– Calculates averages over a baseline and

future periods (i.e. 2020s, 2050s)– Compute anomalies– Spline interpolation of anomalies– Sum anomalies to WorldClim

Options – Statistical Methods

Stations by variable:• 47,554

precipitation • 24,542

tmean • 14,835

tmax y tmin

- 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 )

What is WorldClim?

Sources:•GHCN•FAOCLIM•WMO•CIAT•R-Hydronet•Redes nacionales

– They use outputs of GCMs

– Area are limited .. Need boundary conditions.

– Performs calculations of atmospheric dynamics and solve equations for each grid.

– Daily data– Resolution varies between 25-

50km– More than 170 output variables

Options – Dynamical Methods

RCM PRECIS Providing REgional Climates for Impacts Studies

Options - Comparissons

Which one is the best?Method + -

Statistical downscaling

*Easy to implement* resolutions*Apply to all GCMs*Uniforme baseline

* Change variable only at big scale* Variables do not change their relations with time* variables

Dynamic downscaling

* Robust*Apply to GCMs if data available* variables

*Few platforms (PRECIS, CORDEX)*Many processes and stockages*Limited resolution (25-50km)*Missing development*Dificulty to quantify uncertainties

http://ccafs-climate.orgCCAFS Climate

CCAFS Climate - Users

GCMs

Effective adaptation options

MarkSim

DSSAT

Statistical Downscaling

Dynamical downscaling:Regional Climate Model

EcoCropStatistical Downscaling

MaxEnt

We need models to quantify the impacts and adaptation options for effective design

Based on niches

Prob

abili

ty

Environmental gradient

Based on process

Impacts

• Downscaling is inevitable.• Continuous improvements are

being done• Strong focus on uncertainty

analysis and improvement of baseline data

Conclusions

• We need multiple approaches to improve the information base on climate change scenariosDevelopment of RCMs (multiple: PRECIS not enough)Downscaling empirical, methods HybridsWe tested different methodologies

Thanks!Contactc.e.navarro@cgiar.org