NSF Decision Making Under Uncertainty Collaborative Groups

A Center for

Robust Decision Making on Climate and Energy Policy

Climate Change, Agriculture, and Human Health

Modeling the Health Risks of Climate Change, NAS, November 3rd, 2014

Joshua Elliott University of Chicago Computation Institute

Argonne National Laboratory Math and Computer Science Division Columbia University Center for Climate Systems Research

London School of Economics Center for Analysis of Time-Series

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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, …

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/

Climate change: what we know

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Climate change: what we know

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• 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

─ Lower latitudes are more vulnerable to climate change ─ [CO2] effects key to understanding future impacts and uncertainty ─ Models that incorporate realistic nitrogen see significantly less

gains from [CO2] effects at present-day fertilizer levels 7

Impacts: Global agricultural productivity

End-of-century (2070-2099) climate impact. Median of 7 GGCMs

and 5 GCMs. Hatched areas indicate model

agreement in sign

Rosenzweig, C (2014) PNAS, 111(9): 3268-3273.

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.

Compare ensembles of 11 global hydrological models (GHMs) and 6 global gridded crop models (GGCMs) driven by climate change scenarios from 5 GCMs under RCP8.5.

• Freshwater limits imply reversion of 20-60 Mha of irrigated cropland to rainfed by 2100, and a further loss of 600-2900 Pcal

• Net globally, surplus water could support a net increase in irrigation with significant investment

Impacts: [CO2] Effects on Crop Water Use

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• W/ CO2, global CWP increases 6 (±27) to 17 (±20)% • W/o CO2, global CWP decreases −14 (±17) to −28 (±14)%

• 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. Disentangling uncertainties in future crop water productivity under climate change. In preparation.

• 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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Data from ISI-MIP1 and Myers-2014. Reproduced from Müller, Elliott, and Levermann. "Food security: Fertilizing hidden hunger." Nature Climate Change 4.7 (2014): 540-541.

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: 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.

Climate change is not happening in a vacuum • Regional heterogeneity

– Household systems, poverty and vulnerability

• Large-scale drought and extremes • Irrigation and groundwater

– Technology, irrigation, and adaptation

• Population growth, urbanization and food prices – Urban consumers and political stability

• ….

Vulnerability

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Vulnerability: AgMIP Regional Assessments

5-year project, DFID funded 8 regional teams, 18 countries, ≈ 200 scientists Data, models, scenarios designed & implemented by multi-disciplinary teams & stakeholders

Forthcoming in Hillel, D. and C. Rosenzweig, eds. Handbook of Climate Change and Agroecosystems, 2014

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Vulnerability: AgMIP Regional Assessments

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

System-level sensitivity and adaptation

We perform counterfactuals, exchanging the weather in 1988 with the weather in 2012, all else equal. • If the 2012 drought had hit the US

with 1988 technology, yields would have been 29% below trend.

• 1988 drought in 2012, losses would average only 18%.

Elliott, J., M. Glotter, N. Best, A.C. Ruane, K.J. Boote, J.W. Jones, J.L. Hatfield, C. Rosenzweig, L.A. Smith and I. Foster. Predicting and adapting to the agricultural impacts of large-scale drought (in preparation).

Figure reproduced from: N. Savage. Modelling: Predictive yield. Nature, 501:S10-S11, September 2013.

~25% reduction in drought sensitivity over ~25 years

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• Groundwater accounts for 60% of irrigation and 40% of drinking water in the US

• Warming, management, and adaptation pressures are expected to increase demand for irrigation

• Increased CO2 is expected to decrease H2O demand, but serious questions remain how well this will translate to the field (especially for C4 crops)

Vulnerability: Groundwater depletion

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Scanlon et al 2012.

• In the absence of climate change, tech/management trends imply earlier planting and longer growing seasons.

• These drive increase irrigation demand even in the absence of climate change.

Vulnerability: Technology, irrigation, and adaptation

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Vulnerability: Population and food prices

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Vulnerability: Urban consumers

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• The AgMIP Global Gridded Crop Model Intercomparison (GGCMI)

• ISI-MIP 2.0 • National and global extreme scenarios and stress-

tests • Agriculture, food and nutrition security, and disease • …

What’s next

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• Phase 1: Global/0.5 degrees/1948-2012/9 climate forcing products/ harmonized on fertilizer, sowing date, growing-season length, [CO2], etc.

• Results from 12-15 models from 11 countries, each for 4-16 crops. • Many outputs expected (and many more unexpected)

– Ensemble evaluation and model validation – Special focus on characterizing and comparing extreme events – Special focus on model representations of evapotranspiration

What’s next: The Global Gridded Crop Model Intercomparison

Examples of large-scale extremes, 1948-2012

Sheffield et al. 2011 http://www.agmip.org/ag-grid/ggcmi/

• 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

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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• Stress-testing the global food system – Vulnerability of the global food

system to extreme shocks to one or more major food-producing region

• The next US dust bowl – Using the droughts of the 1930s as

analogs for future severe extremes to stress-test US agriculture

What’s next: Stress-testing with extremes

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• Modeling nutrient concentrations in crops

• The role of food security in public health

• The connection of agriculture to disease vectors

• …

What’s next: Agriculture and health

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Thanks!

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• Summary of datasets, with and without bias-correction.

Historical climate forcings datasets

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Climate, irrigation, and adaptation: Water availability and constraints

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Climate, irrigation, and adaptation: Summary of conclusions

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Open source model analysis pipeline

• All processing scripts available in Git: https://github.com/RDCEP/ggcmi

• Quality control: http://users.rcc.uchicago.edu/~joshuaelliott/ggcmi-summaries/

• Aggregation: – Produces aggregated versions of all data for GADM-0 (countries) and GADM-1

(~states), Koeppen-Geiger climate regions, river basins, FPUs, continents, etc.

• Removing trends and bias – Uses 4 different trend removal techniques (linear, quadratic, moving-mean, and

linear-trend-removed fractional first difference) – Produce retrended/rescaled gridded sims to compare to Iizumi and Ray datasets.

• Multi-metric validation – Create multi-dimensional arrays of validation metrics for many different metrics

such as time-series correlation, RMSE, extreme hit rates, etc. • Construct multi-model ensembles

– Develop many different weighted and unweighted ensembles using different target metrics.

– Test performance of ensembles versus individual models.

• No model performs best in all regions

• Skill-weighted ensembles improve skill (shocking!)

• Much more work needed (and to come)

Preliminary results

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• Models based on DSSAT, APSIM and EPIC are providing extra simulations with multiple representations of ET within the same model

• Methods considered: – Penman-Monteith, Priestly-Taylor, Hargreaves, Baier-

Robertson, and transpiration efficiency

• By testing how these different methods perform in different environmental conditions, we can better inform modeling choices.

Model improvement: Evapotranspiration

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Overview of the agriculture sector

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Updating the state of knowledge from AR-4

• AR-4 state of knowledge based on synthesis of small number of point based simulation experiments in uncoordinated locations (orange)

• Major low-lat yield declines • Major distinction between models

that consider explicit nitrogen stress (pDSSAT, EPIC, GEPIC, and PEGASUS) and those that do not (LPJmL, LPJ-GUESS, IMAGE)

Rosenzweig, C., J. Elliott, et al (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. PNAS, 111 (9): 3268-3273.

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Modeling drought impacts to crops

• 1988 was comparable to 2012 (probably less severe) • But it had a more severe impact on US maize (25% vs. 22% below trend) • Developed semi-empirical parameterizations of tech and management

in five-year steps based on data and expert elicitation • Planting dates, cultivar choice, planting density, distribution,

irrigation, genetic characteristics, etc. • Results aggregated to county and validated against NASS survey

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Climate, irrigation, and adaptation

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Compare within and among ensembles of water supply and demand (and yield with/without irrigation) projections from 11 global hydrological models (GHMs) and 6 global gridded crop models (GGCMs) driven by climate change scenarios from 5 GCMs under RCP8.5.

GHMs show large increase in irrigation use, GGCMs with [CO2] effects show reduction in overall demand.

Elliott et al. (2014). Constraints and potentials of future irrigation water availability on global agricultural production under climate change. PNAS, 111 (9): 3239-3244.