Climate change predictions in Sub-Saharan Africa: impacts and adaptation CLIMAFRICA Climate change predictions in Sub-Saharan Africa: impacts and adaptations

Climate change predictions in Sub-Saharan Africa: impacts and adaptation

CLIMAFRICAClimate change predictions in Sub-Saharan Africa:

impacts andadaptations


CLIMAFRICAClimate change predictions in Sub-Saharan Africa: impacts and adaptations

European Commission – FP73.5 M€

48 months: 1 Oct 2010 – 30 Sep 2014

Key WordsClimate Predictions; Climate Impacts; Vulnerabilities; Adaptation; Case Studies; Agriculture and Water Resources;Socio-economic analysis


ClimAfrica RationaleUrgent international need for the most appropriate and up-to-date tools to better understand and predict climate change in Africa, assess its impact on African ecosystems and population, and develop the correct adaptation strategies.

ClimAfrica Objectives1- Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;2- Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;3- Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;4- Suggest and analyse new suited adaptation strategies;5- Develop a new concept of medium term monitoring and forecasting warning system (for food security, risk management, civil protection) 6- Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.


Acronym Participant organisation name Country1 CMCC CENTRO EURO-MEDITERRANEO per i CAMBIAMENTI CLIMATICI Italy2 ULUND LUNDS UNIVERSITET Sweden

3 CEA COMMISSARIAT A L’ENERGIE ATOMIQUE France

4 MPG MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN Germany5 VUA VERENIGING VOOR CHRISTELIJK HOGER ONDERWIJS WETENSCHAPPELIJK

ONDERZOEK EN PATIENTEZORGNetherlands

6 CTFC CENTRE TECNOLOGIC FORESTAL DE CATALUNYA Spain

7 PIC POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH Germany

8 CIRAD CENTRE DE COOPERATION INTERNATIONAL EN RECHERCHE AGRONOMIQUE POUR LE DEVELOPPEMENT

France

9 FAO – GTOS FOOD and AGRICULTURE ORGANISATION of the UNITED NATIONS

10 SOW – VU STICHTING ONDERZOEK WERELDVOEDSELVOORZIENING VAN DE VRIJE UNIVERSITEIT

Netherlands

11 UR2PI UNITE DE RECHERCHE SUR LA PRODUCTIVITE DES PLANTATIONS INDUSTRIELLES

Congo

12 UCT UNIVERSITY OF CAPE TOWN South Africa

13 BCA UNIVERSITY OF MALAWI Malawi

14 LBEV UNIVERSITY OF LOMÉ Togo

15 ARC AGRICULTURAL RESEARCH CORPORATION Sudan

16 ICPAC IGAD CENTRE FOR CLIMATE PREDICTION APPLICATION Kenya

17 CSIR-CRI COUNCIL FOR SCIENTIFIC INDUSTRIAL RESEARCH – CROPS RESEARCH INSTITUTE

Ghana

18 CERPINEDD CENTRE D’ETUDE DE RECHERCHE ET DE PRODUCTION EN INFORMATION POUR L’ENVIRONNEMENT ET LE DEVELOPPEMENT DURABLE

Burkina Faso

Partnership

18 institutions: 9 Europe8 Africa+ FAO

Project coordinator: CMCC – Italywww.cmcc-orgLocal case studies:

1)Burkina Faso2)Ghana3)Togo4)Sudan5)Ethiopia6)Congo7)Tanzania8)Kenya9)Malawi10) South Africa


WORK PACKAGESLead

PartnerWP1 Effects of past climate variability on ecosystem

productivity and water cycleMPG

WP2 Feedbacks between climate variability / changes and the land surface. Improving modelling seasonal to decadal climate predictions

CMCC

WP3 Analysis of climate impacts on key ecosystem services (water, agriculture)

LU

WP4 Medium-term of Forecasting food and water vulnerabilities and recommending relevant adaptation measures

FAO

WP5 Socio-economic implications of climate change impacts and adaptation measures in SSA

CMCC

WP6 Regional case studies in SSA CTFC

WP7 Project Management CMCC

WP8 Dissemination and exploitation of project results CMCC


WP2climate

predictability and forecasts

WP1past climate

variability

WP4 Medium-term

warning system

vulnerability, adaptation

WP3climate impacts

WP5 Socio- economic

implications

WP6case studies

WP

7 p

roje

ct

ma

na

ge

me

nt

WP

8 d

iss

em

ina

tio

n


WP1 - Africa is a hotspot of interannual variability of the global land carbon cycle

Jung et al.In press


WP1 – Past Climate VariabilityCollection and synthesis of various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades. The data streams range from ground based observations and satellite remote sensing to model simulations. WP1 aims at providing consolidated data to other WPs in ClimAfrica, and at analyzing the interactions between climate variability, water availability, and ecosystem productivity of Sub-Saharan Africa.

Recent trends in the water cycle of Africa

Jung et al. 2010


WP1 – data already available internally to the project (from Martin Jung and Uli Weber, MPI)

•daily meteorology from 1901-2010•soil texture•historical land cover/use from 1901 till 2007•albedo for 2000-2010 based on MODIS•latent and sensible heat fluxes derived from upscaling eddy covariancedata (1982-2008, monthly)•monthly FAPAR from 1982-2010 based on AVHRR, SeaWiFS, MERIS

Expected data (soon):•Land use data (by PIK)•digital elevation model•meteo data update until 2030 based on Echam5 A2•soil moisture data set (by VUA)•MODIS land surface temperatures and LAI

(all data are 0.5° and gap-filled if based on remote sensing)


WP2 - CMCC seasonal forecast and decadal prediction system

•Seasonal retrospective forecast for 22 years (1989-2010). Four six-month-forecasts per year, start dates 1st Feb, 1st May, 1st Aug, 1st Nov.•Decadal predictions. Twenty-year-simulations, start dates 1990-1995-2000-2005-2010, November 1st. •Simulations are performed by means of a global climate model initialized with the best observational products of ocean, land and atmosphere.

Outputs provided:•surface temperature•Precipitation•heat fluxes•winds•etc.

East Africa


Will the weather be favourablethis summer?

ColdandWet

ColdandWet

Hot and Dry Cool and Wet

Warm

Hot and Wet

WP2


Radiative forcingsGHGs & SO4

Land SurfaceSILVA

(Alessandri 2006, 2007)

AtmosphereECHAM5 (T63 ≈ 1.87°x1.87°)

(Roeckner et al 1996, 2003)

OceanOPA 8.2 (ORCA2)

(Madec et al, 1998)

Sea IceLIM (ORCA2)

(Timmerman et al, 2005)

CouplerOASIS3

(Valcke et al, 2000)

T & S - OI assimilationSOFA 3.0

(De Mey and Benkiran 2002)

Bellucci, Masina, Di Pietro & Navarra, 2007. MWR

Ocean initial condition production

Spectral & Time interpolationINTERA

(Kirchner, 2001)

Atmospheric IC from ERA-Interim Reanalysis

Coupling Daily No flux adjustment

Coupled Model component

Off line Initialization Tools

CMCC Seasonal Prediction System

WP2


WP2


WP2 - Observations and global model skill for Eastern AfricaRainfall is most important climate element to model and prediction in Sub-Saharan Africa (SSA) food security and water resources. Studies on rainfall patterns are already ongoing in many areas of Africa.

Seasonal rainfall as percentage of annual total amounts for some seasons within Eastern Africa(from ICPAC, Kenya).


WP2 - Observations and global model skill for Eastern Africa

Examples from several Global models: “Skill” basis for use of Global model in multi-model downscaling is quite high for East Africa sub-domain of ClimAfrica analysis and applications.

Regional model: Regional model skill is reasonably good also PRECIS model will be a reliable source of detailed future projections within time window 2010-2090s.


WP3 - Climate impacts on key ecosystem services Quantifying sensitivity of vegetation productivity and water resources to seasonal, interannual and decadal variability in weather and climate

Identify tradeoffs and areas of risk and vulnerability related to: a)water related hazardsb)agricultural and pastoral performancec)soil degradation

…using an agroDVM

Work in progress: Model development and input data processing.

Current models in combination with recently developed datasets of land use and climate (from WP2) will be used to simulate crop yield and water resources. Simulations using short-term scenarios of future climate change (5-10 years) will be used to identify regional differences in the climate sensitivity of crop production etc. Scenarios for the African agricultural/pastoral sectors will also be made using longer model runs.

Crop Model

CO2 emissions Climate Change Land Use Change

Crop yield Water use


a. Maize

b. Maniok

WP3 - Preliminary output

GUESS with crop module:Hadley A2 scenarioPotential NPP


WP4 - Medium-term of Forecasting food and water vulnerabilities and adaptation mesuresEstablish a monitoring and forecasting warning system (based on ClimAfrica data) that produces prospective analyses about food insecurity and water crisis for at least the next 10 years.

Fill the gap between seasonal scale predictions and long-term impact scenarios

Identify the future Areas of Concerns (AoCs) and likely hotspots of vulnerabilities and food insecurity


WP4 - Natural Resources and Food Security – Systems at Risk (NaF-SAR) Integrate and harmonize ClimAfrica data with existing data and information to be used as inputs to develop improved vulnerability assessment and optimal adaptation options. A Risk framework is being used to provide a strategic context for the data and tools generated. The framework consists of 4 key stages: 1- Physiographic data related to hazard or environmental pressure; 2- Socio-economic data (people) 3- Risk assessment hot spotting based on (1) & (2) and the development of scenarios 4- Decision support.


WP5 – Socio-economic implications

The social economic research group within CLIMAFRICA aims to provide a throughout economic assessment of agriculture and water sector, using, among others, a computable general equilibrium model, ICES, developed at the CMCC. The main advantage of this investigation approach is to depict the economy as a system where goods and factor markets interacts domestically and internationally. Price effects, competitiveness effects, demand and supply adjustments triggered by impacts on the agricultural sector can thus be properly captured.

OutputOutput

V.A. + Energy OtherInputs

Domestic Foreign

Region1 Regionn

Region...

NaturalResources Land Labour Capital

+Energy

Capital Energy

Non Electric Electric

Domestic Foreign

Reg1Regn

Reg..

CoalNon Coal

Domestic Foreign

Reg1 Regn

Reg..Gas

Oil

PetroleumProducts

Domestic Foreign

Reg1 Regn

Reg..

Domestic Foreign

Reg1 Regn

Reg..

Domestic Foreign

Reg1 Regn

Reg..

Nested tree Structurefor the supply sideICES model

UtilityUtility

Private Consumption

Savings

Domestic Foreign

Region1 Regionn

Region...

Item…Itemm

Public Consumption

Item1

Domestic Foreign

Region1 Regionn

Region...

Item…ItemmItem1

Nested tree Structurefor the demand sideICES model


WP5Number of people exposed to

climate-change induced water stress(IPCC AR4, 2007)

Climate Change impacts on crops’ yields.


Climate Change Impacts: Summary

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

1.2 °C 3.1 °C 1.2 °C 3.1 °C 1.2 °C 3.1 °C 1.2 °C 3.1 °C 1.2 °C 3.1 °C 1.2 °C 3.1 °C

Agriculture EnergyDemand

Health Sea LevelRise

Tourism All Impacts

Temperature increase

% o

f G

DP

USA

Med_Europe

North_Europe

East_Europe

FSU

KOSAU

CAJANZ

NAF

MDE

SSA

SASIA

CHINA

EASIA

LACA

World

WP5

Climate change impacts by category and region (Bosello et al. 2009)


WP6 – Case StudiesCharacterize the environmental and socio-economic conditions of 9 different Sub-Saharan African regions distributed along a wide climate gradient (Ghana, Burkina Faso, Togo, Malawi, Republic of Congo, Sudan, Kenya, Ethiopia and Tanzania).The studies carried out in these regions will provide field data to other work packages for empirical model development and mechanistic model parametrization. In addition, the synergies developed with the existing actors (managers and policy-makers, NGO’s, local farmer’s organizations, women’s associations, etc.) during these studies will allow to test and validate both the individual model outputs and the Medium Term Warning System in these regions.

Tchizalamou site, Congo


Case Studies

The countries to be considered for the choice of the case studies have been identified by an analysis of the Sub-Saharan region based on the following five data sets, as indicators of environmental, climatic, agricultural and socio-economic conditions:

•Global Ecological Zones (2001) - as indicator of ecologic conditions•Major farming systems of SSA(2001) (as indicator of farming systems)•GLC-2000 Based 1 km Global Land Cover - Africa (2004) - as indicator of land cover•Prevalence of stunting among children under five (2007) - as indicator of malnutrition, under nutrition, and poverty•Total renewable water per capita (actual) (1960-2007) - as indicator of water scarcity

The analysis has produced the following list of eligible countries:1) Burkina Faso, 2) Ghana, 3) Togo, 4) Sudan, 5) Ethiopia, 6) Congo, 7) Tanzania, 8) Kenya, 9) Malawi, 10) South Africa

Documents

Climate change predictions in Sub-Saharan Africa: impacts and adaptation CLIMAFRICA Climate change predictions in Sub-Saharan Africa: impacts and adaptations