Scenario-based assessment
of climate change impacts
on agriculture
Gerald C. Nelson
Professor Emeritus, University of Illi...
… or how we used the SSPs
(and an RCP) in a model
intercomparison exercise
… or should we do any more
integrated assessments until
we fix the Lamppost problem
Policy questions that need
scenario answers
 What is the future of agricultural prices?
 How will agricultural productio...
Alternate perspectives on future prices with no
climate change, 2000-2050 in 2011
IMPACT – big price increases (e.g. 80%
i...
Why do the results differ?
 Differing perspectives on
 Today‟s unknowns that we should know
 Future unknowns
 Economic...
We had no idea which is most
important
7
Scenario harmonization:
Common values for key drivers
 Harmonized four key drivers
 Population from SSP2 and 3
 GDP fro...
SSPs:
What did we use and why?
 What?
 SSP2 and SSP3, version 0.5
 GDP, OECD version
 Population
 What not?
 Storyli...
SSP per capita income
Per capita incomes in 2010;
SSP2 and SSP3 in 2050
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000...
The modeling chain:
From biophysical to socioeconomic
Nelson, et al., PNAS 2013
Results: price changes
between 2005 and 2050 are
smaller, but…
Maximum price changes between 2005 and 2050 reduced.
Large differences remain.
General equilibrium Partial equilibrium
2050 price effects from the
socioeconomic
scenarios, with no climate
change
14
Model differ dramatically in their responses
to the negative future of SSP3
Price
declines
Price
increases
Price not
affec...
Climate change and
economic responses
Quantity
Price
Demand
Supply
Climate
change shifts
supply to the
left
Initial shorta...
Results from the climate
change scenarios
Comparisons are differences in 2050
outcomes
Mean -17% -11% +11% -2% - 1% -3% +20%
Climate change reduces 2050 yields.
Producers, consumers, & trade partially
compensa...
How do the model distribute their 2050
responses to climate change?
Mostly
area
Mostly
yield
Demand Area Yield
Demand/
Sup...
How „plausible‟ are the
ensemble results? Two views
 They are too pessimistic
 GHG concentration pathway with the greate...
And then there is the
Lamppost Problem
What is missing in our climate
change results?
 The models don‟t include effects of
 Increasing pest and disease pressur...
What to do about the
Lamppost Problem?
23
Merge the silos!
24
It‟s not enough for each community to
work together. We need a community
of communities, old and new.
...
Open the black boxes so they
aren‟t reinvented
25
• Share code within each community
• Identify „best‟ versions
• „Central...
Develop 21st century
modeling environment
 Approach depends on topic
 Crop modeling
 Code hierarchy with modular constr...
Conclusions
 Substance
 RCP8.5 results in lower yields
 Adaptation reduces some of those effects across the
supply and ...
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  • OECD comparison
  • Image source: http://www.flickr.com/photos/morville/4273477501/.
  • Ncar global econ g nelson

    1. 1. Scenario-based assessment of climate change impacts on agriculture Gerald C. Nelson Professor Emeritus, University of Illinois, Urbana-Champaign Presentation at the AAAS session “New Scenarios for Assessing Future Climate Change”, 26 February 2014
    2. 2. … or how we used the SSPs (and an RCP) in a model intercomparison exercise
    3. 3. … or should we do any more integrated assessments until we fix the Lamppost problem
    4. 4. Policy questions that need scenario answers  What is the future of agricultural prices?  How will agricultural production evolve?  How will climate change alter …  Prices  Land use  Trade  Undernourishment  Do we have the tools to answer these questions?
    5. 5. Alternate perspectives on future prices with no climate change, 2000-2050 in 2011 IMPACT – big price increases (e.g. 80% increase in coarse grains price)
    6. 6. Why do the results differ?  Differing perspectives on  Today‟s unknowns that we should know  Future unknowns  Economic development  Population growth  Climate change  Natural resource availability  Technological advance  Differing economic modeling approaches  CGE models more „flexible‟ (?)  Functional forms determine outcomes (e.g., Armington assumption, demand parameters; ref Bennett‟s Law) 6
    7. 7. We had no idea which is most important 7
    8. 8. Scenario harmonization: Common values for key drivers  Harmonized four key drivers  Population from SSP2 and 3  GDP from SSP2 and 3  Exogenous component of agricultural yield growth  Climate change effects on yield growth  No harmonization on other important drivers  Three „orthogonal‟ comparisons in 2050  Socioeconomics – SSP2 versus SSP3  Bioenergy policies – not covered here  Climate change – no climate change versus RCP 8.5 with no CO2 fertilization
    9. 9. SSPs: What did we use and why?  What?  SSP2 and SSP3, version 0.5  GDP, OECD version  Population  What not?  Storylines  Population makeup  Urbanization  Why?
    10. 10. SSP per capita income Per capita incomes in 2010; SSP2 and SSP3 in 2050 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 World Developing East Asia South Asia Europe & Central Asia WB definitions Middle East & North Africa Sub-Saharan Africa Latin America & Caribbean High Income 2010 SSP2 SSP3
    11. 11. The modeling chain: From biophysical to socioeconomic Nelson, et al., PNAS 2013
    12. 12. Results: price changes between 2005 and 2050 are smaller, but…
    13. 13. Maximum price changes between 2005 and 2050 reduced. Large differences remain. General equilibrium Partial equilibrium
    14. 14. 2050 price effects from the socioeconomic scenarios, with no climate change 14
    15. 15. Model differ dramatically in their responses to the negative future of SSP3 Price declines Price increases Price not affected
    16. 16. Climate change and economic responses Quantity Price Demand Supply Climate change shifts supply to the left Initial shortage caused by climate change Initial shortage = big price increase Final quantity Finalprice Model choices - Supply response? - Area - Yield - Demand response? - Where? - How much trade?
    17. 17. Results from the climate change scenarios Comparisons are differences in 2050 outcomes
    18. 18. Mean -17% -11% +11% -2% - 1% -3% +20% Climate change reduces 2050 yields. Producers, consumers, & trade partially compensate Nelson, et al., PNAS 2013 Final yield Biophysical effect
    19. 19. How do the model distribute their 2050 responses to climate change? Mostly area Mostly yield Demand Area Yield Demand/ Supply Model Type AIM 0.12 0.90 -0.02 0.14 CGE ENVISAGE 0.17 0.32 0.51 0.2 CGE FARM 0.04 0.67 0.29 0.04 CGE GTEM 0.06 0.29 0.65 0.06 CGE MAGNET 0.1 1.39 -0.49 0.11 CGE GCAM 0.22 0.78 0 0.28 PE GLOBIOM 0.49 0.12 0.38 0.98 PE IMPACT 0.38 0.53 0.09 0.61 PE MAgPIE -0.01 -0.08 1.09 -0.01 PE AVERAGE 0.17 0.53 0.3 0.21 Large demand Nelson, et al., Agricultural Economics 2014
    20. 20. How „plausible‟ are the ensemble results? Two views  They are too pessimistic  GHG concentration pathway with the greatest forcing (RCP 8.5)  Crop models assumed constant CO2 concentrations throughout the period  They are not too bad  Actual GHG concentrations similar to RCP 8.5 so far  Field results from FACE experiments suggest CO2 fertilization effect in the field is less than in the lab
    21. 21. And then there is the Lamppost Problem
    22. 22. What is missing in our climate change results?  The models don‟t include effects of  Increasing pest and disease pressure  Increasing extreme events  Increasing ozone  Effects on nutrition  Models for most crops don‟t exist  These could swamp the negative effects already quantified
    23. 23. What to do about the Lamppost Problem? 23
    24. 24. Merge the silos! 24 It‟s not enough for each community to work together. We need a community of communities, old and new. • Standard data protocols, developed together • Commonly agreed aggregation methods • „Centrally‟ managed data storage
    25. 25. Open the black boxes so they aren‟t reinvented 25 • Share code within each community • Identify „best‟ versions • „Centrally‟ manage code storage and dissemination • Identify data needs and develop new sources
    26. 26. Develop 21st century modeling environment  Approach depends on topic  Crop modeling  Code hierarchy with modular construction  Plant-level functions (e.g. photosynthesis)  Species-level functions  Variety-level functions  Design with modularity in mind  Identify critical parameters and data needs at each level  Talk to the computational biology folks  Facilitates the needed bulk development of models for fruits and vegetables 26
    27. 27. Conclusions  Substance  RCP8.5 results in lower yields  Adaptation reduces some of those effects across the supply and demand side  Economic models allocate response differently between supply (area and yield) and demand  Process  Existing models/methods are underestimating the effects of climate change on food security. We urgently need to address the Lamppost Problem before we do more assessments. 27

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