NSF Decision Making Under Uncertainty Collaborative Groups

A Center for

Robust Decision Making on Climate and Energy Policy

Implications of global and regional climate change for agriculture

Joshua Elliott Christoph Müller

Our Common Future Under Climate Change UNESCO Paris, July 8th 2015

University of Chicago Potsdam Institute for Climate

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Acknowledgements

Collaborators RDCEP (rdcep.org)

AgMIP (agmip.org)

ISI-MIP (isi-mip.org)

GGCMI (agmip.org/ag-grid/ggcmi/)

….

Support from USDA, UK-AID (DFID), DOE, USAID, NSF, …

Climate change: what we know

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Climate change: what we know

• Global mean temp rose steadily over 20th C

Climate change: what we know

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• and that rise is expected to continue

Climate change: what we know

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• Not all warming is created equal

What about mitigation?

• Currently on worst emission path considered by IPCC (8.5)

• Expected to stay on path (maybe worse!) until ~2020

• Limited mitigation potential in near term (as much as ~80% of warming by 2050 already “baked in”)

As of 2013

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Climate impacts: what we know

• 2012 Fast-track project: ~40 model groups, 4 sectors (ag/econ, water, biomes, and health), 5 CMIP5 GCMs, 4 RCPs, lots of crops, …

• Key results published in a 11-paper special Issue of PNAS

1. Rosenzweig et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison

2. Elliott et al. Constraints and potentials of future irrigation water availability on global agricultural production under climate change

3. Nelson et al. Assessing uncertainty along the climate-crop-economy modeling chain

4. Piontek et al. Multi-sectoral climate impact hotspots in a warming world

Climate impacts: what we know

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All inputs and outputs of the project available through the ISI-MIP ESGF node, esgf-data.dkrz.de and through Globus endpoint isimipgo#us-archive

Impacts: Hydrology, irrigation, and adaptation

• With [CO2], direct losses of 400-1400 Pcal (8-24% of present)

• W/o [CO2] = 1400-2600 Pcal

Elliott et al (2014) PNAS, 111 (9): 3239-3244.

• Ensemble of 11 global hydro models & 6 global crop models

─ driven by RCP 8.5 climate from 5 GCMs

• Freshwater limits imply reversion of 20-60 Mha of irrigated cropland to rainfed by 2100,

─ further loss of 600-2900 Pcal

• CO2 effect on Crop Water Productivity at mid-century in RCP8.5, avg. of 30 GGCM×GCM combinations

Impacts: [CO2], more crop per drop

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maize wheat

soybean rice

Deryng, D. Regional disparities In the beneficial effects of rising CO2 Emissions On Crop Water Productivity. In review

Impacts: [CO2], more crop per drop

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• With CO2, global CWP increases 6 to 17% • Without CO2, global CWP decreases −14 to −28%

• Maize (1 study; Braunschweig, Germany) • Wheat (3 studies; Maricopa, Arizona, USA) • Rice (1 study; Iwate, Japan) • Soybean (1 study; Illinois, USA)

Multi-model range doubles when including CO2 effects

Deryng, D. Regional disparities In the beneficial effects of

rising CO2 Emissions On Crop Water Productivity. In review

• Nutrition effects of CO2 from FACE experiments (Myers et al. 2014).

• Caloric effects of climate change and CO2 (ISI-MIP).

• Zinc and iron decrease in all C3 crops studied.

• Protein decreases in all C3 crops without nitrogen fixing.

Impacts: Fertilizing hidden hunger

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Reproduced from Müller, Elliott, and Levermann. "Food

security: Fertilizing hidden hunger." Nature Climate Change

4.7 (2014): 540-541. Data from ISI-MIP1 and Myers-2014.

Atmospheric CO2 fertilization may go some way to compensating the negative

impact of climatic changes on crop yields, but it comes at the expense of a

deterioration of the current nutritional value of food.

Impacts: Climate mitigation

Müller et al.

under review

• Aggressive mitigation reduces (-) impacts, especially in the tropics

• But also reduces (+) effects of [CO2], esp. in high lats

Model evaluation & improvement

- Standardized open-source code-base

- Quality control

- Data processing/metrics

- Analysis and viz

- 24 (15) modeling groups

- 9 weather products

- Centralized processing

Elliott et al. 2015

• Lots of GGCMI Phase 1 papers coming soon:

– Historical model evaluation

– Aggregation and uncertainty

– Model parameterization and uncertainty

– Performance of different representations of ET

– Historical variability and extremes in agriculture

– ….

• Climate extremes and non-stationary risk in agriculture

• GGCMI Phase 2 protocols finalized tonight (over wine!)

• GGCMI Phase 3/ISI-MIP 2 (2016/17)

• Linked regional and global food security assessments

What’s next

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• Compare past and future distributions from ensembles of global crops models produced in AgMIP/ISI-MIP

– Variability and skewness of distributions generally increasing

– Extreme (-) percentiles of distribution generally getting worse

– Global 1-in-100 yr historical event occurs 1-in-30 in several decades

Non-stationary risk in agriculture

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Reproduced from Extreme weather and resilience of the global food system

Prepared for the UK-US Taskforce on Extreme Weather and Global Food System

Resilience

• Multi-dimensional sensitivity study of model response to carbon, temperature, water, and nitrogen

• Goals:

– Understand model processes and accelerate model improvement

– Develop lightweight statistical emulators to improve downstream model coupling and analyses

– Create additional ensemble with adaptation for those models that can participate

GGCMI Phase 2 (2015): CTWN

Reproduced from Ruane et al. 2014

• Focus on extreme events

• Adding many sectors, including forestry

• Improving cross-sector interactions and land-use change and water.

• Adding a regional focus at the watershed level

• Adaptation to climate

GGCMI Phase 3 and ISI-MIP2.0 (2016)

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Thanks!

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Context: RDCEP Web Apps for Climate and Economics

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WebDICE – An economic/

Integrated Assessment Modeling

tool for evaluating policy,

technology, and climate and

challenging our assumptions.

─ http://webdice.rdcep.org/

The Climate Emulator – A

lightweight statistical function

that allows you to explore future

climate scenarios from the entire

CMIP5 database and even test

out your own custom CO2

concentration pathways.

─ http://emulator.rdcep.org/

Impacts: Economics and uncertainty

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Effects of climate change on agro-economic variables (9 global econ models, 5 GGCMs, and 2 GCMs)

There is potential for large price increases with climate change, although uncertainty is also large

Nelson et al., Climate change effects on agriculture: Economic responses to biophysical shocks. PNAS, 111 (9): 3274-3279.

Vulnerability: Large-scale drought

• Large-scale drought and heat events accounted for 12% of all billion-dollar disaster events in the US from 1980-2011 but almost 25% of total monetary damages

• The 1988 US drought is estimated to have cost $79 billion (2013 USD), behind only Hurricane Katrina as the most costly weather-related disaster in US history.

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• Warming temperatures and shifting precipitation patterns may exacerbate the problem, increasing the frequency and/or severity of large-scale droughts in sensitive agricultural regions

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• A

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