Improving Global and Regional Projections of Climate Impacts on Agriculture and Food Security

Cynthia Rosenzweig1, Jim Jones2, Jerry Hatfield3, Alex Ruane1, and Sonali McDermid1

1NASA GISS, 2University of Florida, 3USDA-Ames

AAAS February 15, 2013

Why AgMIP?

•  Agricultural risks growing, including climate change –  Climate extremes affecting major agricultural regions –  Regional and world food crises driven by multiple environmental and economic stresses –  Decision-makers demanding improved information for risk management

•  Consistent approach needed to enable agricultural sector analysis across relevant scales and disciplines

–  Difficult to compare climate impact studies across regions and models –  Lack of a transdisciplinary community connecting climate scientists, crop modelers, economists, and IT specialists –  Need for improved climate assessment based on multi-model capabilities and better defined uncertainties

•  Long-term process lacking for rigorous agricultural model testing, improvement, and assessment

–  Agricultural model evaluation and assessment lagging behind climate model intercomparisons and projections –  Need to make better use of available data and methods – There is a need for a continuing process i.e., AgMIP 1 à AgMIP2 . . .

Track 1: Model Improvement and Intercomparison Track 2: Climate Change Multi-Model Assessment Cross-Cutting Themes Uncertainty, Aggregation and Scaling, Representative Agricultural Pathways Regional and Global Scales

AgMIP Sentinel Sites

Silver

Gold

Platinum

Two-Track Science Approach

Rosenzweig et al., 2013 AgForMet

Worldwide Learning Community

1st Global Oct 2010

South America

2nd Global Oct 2011

Rice

Wheat

Sub-Saharan Africa South Asia

3rd Global Oct 2012

~500 listserve members

Maize

Capacity Building and Decision Making • Regional vulnerability • Adaptation strategies • Trade policy instruments • Technology exchange

Climate Team

Crop Modeling Team

Economics Team

Information Technology

Team

Improvements and Intercomparisons

•  Crop models •  Agricultural economic models •  Scenario construction •  Aggregation methodologies

Cross-Cutting Themes

• Uncertainty

• Aggregation and Scaling

• Representative Agricultural Pathways

Assessments • Regional • Global • Crop-specific

Work Groups

• Soils

• Water Resources

• Livestock and Grasslands

• Pests and Diseases

Teams, Linkages and Outcomes

Rosenzweig et al., 2013

New Methods for Regional Integrated Assessment

(ω)

0

Map of a heterogeneous region

Distribution of gains and losses (e.g., due to CC or adaptation) = v1 – v2 = losses from CC v1 = present income v2 = future income

Losses > 0

Gains < 0

= losses

Antle et al., 2013

Regional  Integrated  Assessments    

AgMIP Handbook on Regional Integrated Assessment

Interdisciplinary teams link climate scenarios, crop modeling, and economic modeling in distributional approach

Representative Agricultural Pathways RCPs, SSPs, RAPs

Physical & economic heterogeneity

Land allocation

Farm & household size

Non-farm income Crop, fertilizer and fuel prices

Crop & livestock productivity

Mitigation policy

Infrastructure SSP

RAP

RCP

Global GDP

Population

Trade policy

Antle, 2011; Arnell and Kram, 2011

Representa)ve  Agricultural  Pathways  •  RAPs  needed  for  crop  and  economic  

modeling  scenarios  •  Similar  scenarios  may  be  useful  for  

other  impacts  sectors  

John Antle, Oregon State University

Crosscutting theme

Benefits include - Improved capacity for climate, crop and economic modeling to identify and prioritize adaptation strategies - Consistent protocols, scenarios and data access - Improved regional assessments of climate impacts - Facilitated transdisciplinary collaboration and active partnerships - Contributions to National Adaptation Plans

= Wheat

= Maize

= Rice

0˚

0˚ 90˚ -90˚

45˚

-45˚ = Sugarcane

Ames

Morogoro

Wongan Hills

Delhi

Ludhiana

Ayr

Los Baños

Piracicaba

Shizukuishi

Rio Verde

La Mercy

Haarweg

Lusignan

Balcarce

Nanjing

AgMIP Sentinel Sites

Regions and Crop Model Pilots

North America

South America

Sub-Saharan Africa

Europe

South Asia

Asia*

Australia*

*In Development

Rosenzweig et al., 2013

Maize Crop Pilot Preliminary Results

Model Behavior Maize Crop Pilot Sensitivity Analysis

Low input information

Response to Temperature (6 models)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

-­‐5 0 5 10

yield  ratio

Temperature  increase  (°C)

Morogoro  (Tanzania)

00,20,40,60,81

1,21,41,61,8

-­‐5 0 5 10

Yield  ratio

T  increase

Ames  (Us)Ames (US)

Wheat Pilot

Uncertainties in simulating wheat yields under climate change In Review: Nature Climate Change

S. Asseng, F. Ewert, C. Rosenzweig, J.W. Jones, J.L. Hatfield, A. Ruane, K.J. Boote, P. Thorburn, R.P. Rötter, D. Cammarano, N. Brisson#, B. Basso, P. Martre, P.K. Aggarwal, C. Angulo, P. Bertuzzi, C. Biernath, A.J. Challinor, J. Doltra, S. Gayler, R. Goldberg, R. Grant, L. Heng, J. Hooker, L.A. Hunt, J. Ingwersen, R.C. Izaurralde, K.C. Kersebaum, C. Müller, S. Naresh Kumar, C. Nendel, G. O’Leary, J.E. Olesen, T. M. Osborne, T. Palosuo, E. Priesack, D. Ripoche, M.A. Semenov, I. Shcherbak, P. Steduto, C. Stöckle, P. Stratonovitch, T. Streck, I. Supit, F. Tao, M. Travasso, K. Waha, D. Wallach, J.W. White, J.R. Williams and J. Wolf 51 authors, 4 sites, 27 wheat models

27 wheat models

Crop Model Improvement

• Maize Model Improvement Work Group – Leaders M.Tollenaar, S. Kumudini, K. Boote, J. Jones

• Experimentalists and modelers interacting to use data on CO2, temperature, water, and nitrogen responses

– Leaders J. Hatfield, K. Boote First workshop held in Ames, Iowa - US, Sep 2012

• AgMIP Crop Experiment (ACE) database and interfaces for multiple crop models (DSSAT, APSIM, STICS completed)

– Led by C. Porter, S. Janssen, C. Villalobos

• AgMIP Crop Model Output (ACMO) database for input to economic models and analysis/visualization

– Led by C. Porter, S. Janssen, C. Villalobos

Global Scale Assessment

Potential Sources of Uncertainty

• Data collection and analysis • Scenarios

• Model ensembles • Unresolved processes

• Model interactions • Methodological choices

Climate Models

Crop and Livestock Models

Aggregation and Scaling

Global Economic Models

Aggregate Outputs Equilibrium Prices

RAPs

Regional Economic Models

RCPs

Regional and Global Model

Intercomparisons

SSPs

Rosenzweig et al., 2013

Daniel Wallach (INRA) Mike Rivington

(James Hutton Institute, Scotland) and Linda Mearns (NCAR)

Uncertainty Crosscutting Theme

Global Gridded Crop Models

Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison

Submitted to: Proceedings of the National Academy of Science Cynthia Rosenzweig, Joshua Elliott, Delphine Deryng, Alex C. Ruane, Almut Arneth, Kenneth J. Boote, Christian Folberth, Michael Glotter, Christoph Müller, Kathleen Neumann, Franziska Piontek, Thomas Pugh, Erwin Schmid,

Elke Stehfest, and James W. Jones

7 GGCMs 5 GCMs 4 RCPs

w and w/o CO2 effects w and w/o irrigation

Site-based – field scale

dynamics and management

Ecosystem – C and N dynamics at global scale

6 PNAS papers, ~135 authors part of ISI-MIP

Economics Team - Global

The effects of climate change on agricultural prices (S3-S6 results in 2050 relative to S1 results in 2050)

Source: AgMIP model runs, December 2012.

Nelson, Gerald C., Dominique van der Mensbrugghe, Tomoko Hasegawa, Kiyoshi Takahashi, Ronald D. Sands, Page Kyle, Katherine Calvin, et al. 2013 submitted. “Agriculture and Climate Change in Global Scenarios: Why Don’t the Models Agree.” Agricultural Economics.

9 global economic

models

Launch of Coordinated Climate-Crop Modeling Project

C3MP

To mobilize the international community of crop modelers for a coordinated investigation of climate vulnerability and climate change impacts for AgMIP. Crop modelers are invited to run a set of common climate experiments at sites where their models are already-calibrated and then submit results to enable coordinated analysis and data products.

Limits of CTW Space Sensitivity tests in CTW Space

C3MP Results Emulator

From Ruane et al., forthcoming

[CO2] T

P T

[CO2] P

= baseline

Figure 1: Cross Sections of Hypercube Emulator for relative changes in 30-year mean Peanut yield (kg/ha)

Submitted results are used to fit emulators to estimate impacts response surfaces

The response surfaces help to efficiently assess

crop responses to CMIP5 climate scenarios probabilistically

Change in Temperature [-1 to +8]

Figure 2: 20 CMIP5 GCM Projections over Henry County, FL

%C

hang

e in

Pre

cipi

tatio

n [-5

0% to

50%

]

For more information, please contact Alex Ruane, Sonali McDermid, and Cynthia

Rosenzweig at c3mp@agmip.org

18

What have we learned so far

•  Tremendous interest in agricultural research community in interdisciplinary multi-model research and assessment.

•  Exciting methodological advances being made.

•  Global and regional scales need to be linked and trajectories of population, growth, technology, etc. need to be taken into account.

•  Crop model uncertainty is large and generally greater than climate model uncertainty; model geneaology matters.

•  Crop responses to CO2 and temperature are key sources of uncertainty.

•  Economic model uncertainties are also large.

•  Intercomparisons must lead to model improvement. Serious investment in agricultural models and data needed.

AgMIP is becoming a global program

Upcoming –  South America: Brasilia, March, 2013

–  East Asia: In development, China, 2013

–  Fourth Global Workshop: New York, 2013

–  Climate Methods: In development; ICTP, Trieste, early 2014

Next Steps and Upcoming Events

For protocols, up-to-date events and news, and to join AgMIP listserve – www.agmip.org

Contacts

Cynthia Rosenzweig cynthia.rosenzweig@nasa.gov Alex Ruane Alexander.C.Ruane@nasa.gov

Carolyn Mutter czm2001@columbia.edu

To be part of C3MP – www.agmip.org/c3mp