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Correlated Extremes for
Agriculture and Food Security ApplicationsAlex Ruane, Climate Impacts Group
NASA Goddard Institute for Space Studies, New York
Columbia University Center for Climate Systems Research
Correlated Extremes WorkshopMay 30, 2019
Presentation represents author, not necessarily NASA
National Aeronautics and Space Administration Goddard Institute for Space Studies
Goddard Space Flight Center Sciences and Exploration Directorate
Earth Sciences Division
clJ COLUMBIA
https://ntrs.nasa.gov/search.jsp?R=20190026592 2020-08-03T13:31:22+00:00Z
Outline2
Agriculture and Food Security are vulnerable to
correlated extremes
Compound Extremes
Concurrent Extremes
Sequential Extremes
Complex nature of food systems can exacerbate or
buffer ‘shocks’
Designing models and simulations to explore extreme
responses
Bonus opportunities for contribution (IPCC and GRAF)
Correlated Extremes in the Agricultural Sector
and Broader Food System
4
5
The Complex Food System
6http://www.nourishlife.org/teach/food-system-tools/
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IIOLOGICAL SYSTEM
N.da,,l
SOCIAL SYSTEM
Socia\
ECONOMIC SYSTEM
E 0 C: 0 c..,
Lu
7
Maize Production (1000s of kg)
Top Regions Accounting for 90% of World Maize Production
Tracking a Global Commodity
Data from Monfreda et al., 2002
0 5 10 15 20 25 30 0 5 10 15 20 25 30
Correlated Extremes for Food Systems
8
Compound Extremes:
Hot/wet: Livestock and labor productivity; Disease pressure
Hot/dry: Crop production, water resources
Hot/Ozone: Crop production
Concurrent Extremes:
Multi-breadbasket failure
Crop production loss
and interruption of market access
Sequential Extremes:
Early warm spell followed by frost
Early season wet spell followed by
late season drought
Food Security:
Availability, Access, Utility, Stability
The Complex Food System
9
Assumption of perpetually declining food prices
now called into question.
Spikes due to: drought, heat waves, and wildfires causing poor production in some regions;
energy price spikes, declining food stocks, trade policy, expansion of biofuels
Consequences: increased number of malnourished, shift in diets, reduced spending on
other essentials, gender-based outcomes, social unrest, migration
FAQ Food Price Index in nominal and real terms
2002-2004= 100
250
Food price spikes can exacerbate instability
10
Vulnerability:Urbanconsumers
280
260
240
220
X Q)
"tJ 200 C
Q) u '-c..
"tJ 180 0 0
I.I.
160
140
120
2004
Algeria (8), Saudi Arabia {15) 0 (2) M (l0) H ·t · ( ) E t (3) Mauritania {3), Suda n (2), Jordan (4) 1 ma(n35) •8 horo.cco(98)
a1 1 5, gyp . Yemen (2 000+ / raq , a rain Cote d' Ivoire (1) Somali~ (5) ' / / Syria (20,000+)
1
) Tuni sia (1) Egypt (800+) 1
// Uganda (5), Ira n {12) Sudan (3)i ( Libya (30,000+) ,..,.- I~ Georgia (2), Israel (30)
Cameroon (40) T . . (300 ) : : - Kenya (2), Malawi {18) Yemen (12) l unisia + - ( :: ·-- soma lia (10)
Mozambique {6) / :: India (1) ~::: Sudan (1) ~:::
Burundi (1)
2006
... , Mozambique (13) ~:::
Mauritania (2) , •••• ~-·· India (4) :
Somalia (5) : : I
I I I I
I I I I I I I I
I I I I I I I I I I
I I
I I
I I I I
I I I I
I I I I I I I I
I I
2008
.. , •.. , ~-11 .. , •••• ~-·· ••• ... , ~-·· .. , ... , ~-11 .. , ... , ~-·· ••• .. , ~-·· .. , ... , ~-·· .. , ... , ~-·· .. , .. , ~-·· .. , ••••
2010 2012 2014
Red dashed lines correspond to t he beginn ing dates of"food riots'' and protests in North Africa and the Midd le East between 2004 and 2011. The overall death toll is indicated in parent heses next to each country.
Source: Lagi, Bertand, Bar-Ya m 2011.
11
The Agricultural Model
Intercomparison and
Improvement Project (AgMIP)
12
Modified from Rosenzweig et al., 2013 AgForMet
AgMIP Enables Evaluation of Agricultural Risk
and Testing of Risk Reduction Strategies
The Agricultural Model Intercomparison and Improvement Project (AgMIP)
was created in 2010 to provide a community for systematic improvement
and application of multi-disciplinary, multi-model, multi-scale frameworks
for agricultural development and food security.
Historical
Climate
and Policy
Future
Climate
and Policy
Agricultural ModelsPhysical, biophysical,
and socio-economics
Ag M I P The Agricultural Model lntercomparison and Improvement Project
Track 1
Track 2
Mod el calibration and improvement
Adaptation, mitigation, and extensions
Evaluation and
iratertcomparison
Future agricultural production, trade, and food security
Ozone and Air Pollution
Climate Scenarios
AgMERRA
Impacts Explorer
Seasonal Forecasting
Soy
Current AgMIP ActivitiesBuilding an integrated framework across scales, disciplines, and models
13
AgMIP is an international community
of 1000+ climate scientists,
agronomists, economists,
and IT experts working to improve
assessments of future food security
Visit www.agmip.org
for more information
Process-based Cropping Systems Models Capable
of Compounding Impacts Across Time and Hazards
CO2Light Temperature
Rainfall /
Irrigation
Crop Management
Carter 2013
SoilTime (daily steps)
Ozone
Cultivar Agronomy
Breeding
Slide courtesy of Senthold Asseng, UFlorida
15
Applications Across Time HorizonsCan we find/generate/respond to correlated extremes?
Ruane et al., in prep
Ag M I P The Agricultural Model lntercomparison and Improvement Project
Cyclones Pests & Diseases Air Pollution
Detection and Attribution
Cou nterfactua I Management
Historical
Retrospective Analysis
& Extreme Storms
"::.<:'"'- , , , .. ,,, ,
::,O}.,., ... ~ , , ~
p , ~ ~~ o:: :.o.. - • • • • Temperature Extremes Rainfall Extremes
Real-time and Seasonal Outlook
Monitoring Forecasting
• Climate
' ' . Change
- Market -~ ~::,, Influences ~
>,_ Policy Change
Socioeconomic Change
'...Jit. Environmental 111111 Sustainability
Long-term Outlook
Projections
Lead Time
Designing Experiments to Explore
Unobserved Agro-Climate Conditions
16
The AgMIP
Global Gridded Crop Model
Intercomparison
(GGCMI Phase 2) examines
CO2-temperature-water-
nitrogen-adaptation
sensitivity tests across
multiple crops, models, and
farm systems
Impacts on yield, water use,
and nutritional quality
[CO2] = 360, 510, 660, 810 ppm
ΔT = -1 +0 +1 +2 +3 +4 +6 ⁰C
ΔW = -50% -30% -10% +0% +10% +30%
N = 10, 60, 200 kg N/ha
A = Fixed cultivars,
Cultivars selected to restore growing season length
[CO2]
ΔT
Mean Yield response (% change) from 106
rain-fed maize sites around the world
Mean Temperature Change (⁰C)
[CO
2]
(pp
m)
Colors = emulated mean yield changes
Gray contours = uncertainty (lighter is more uncertain)
= current conditions
GG
CM
I P
ha
se
2
17
1988 Drought
pDSSAT
crop model
Ag MIP
. , .. ,.
' ~ .. .
The Agricultural Model lntercomparison and Improvement Project
. ...
.. ... .....
-50 -40 -30
~ -.
. ... . .... .........•
-20 -10 0 10 20
percent change from C36O, TO, WO, N2OO 30 40
··• . .... .
r, . ···• ·, ....
50
18
Plausible Events and
Climate Change-driven shifts in Risk Profile
19881988 – 1ºC 1988 + 1ºC
1988 + 10% rain
1988 – 10% rain
How would 1988 drought
have been different if it were:
Wetter
Warmer
pDSSAT
crop model
19
Many individual years evaluatedMaize Yield by Climate Anomaly in Corn Belt, USA
Corn
Belt M
aiz
e P
roduction A
nom
aly
(%
)
Models uniquely
capable of exploring
unobserved
climate extremes,
field environments,
genetics, and
farm management
to characterize
particular
vulnerabilities
Filled contours show smooth fit to seasonal production anomaly as function of mean T, P
Dots represent individual years influenced by correlated extremes and phenological timing
The Intergovernmental Panel on
Climate Change Sixth
Assessment Report (IPCC AR6)
and
The UNDRR Global Risk
Assessment Framework
IPCC Sixth Assessment Report – Enhanced Focus on Risk
21
Working Group 1:
‘Regional Chapters’
Chapter 10: Linking global to regional climate change
Chapter 11: Weather and climate extreme events in a changing climate
Chapter 12: Climate change information for regional impact and for risk
assessment
• Risk = hazard x vulnerability x exposure
• Impact = a specific realization of a risk
• A climate value becomes a climate hazard when it connects to exposure
and/or vulnerability
• Hazards can change in their magnitude, frequency, duration, timing, and/or
spatial extent
Submit by December 31st, 2019!
UNDRR Global Risk Assessment Framework
22
The UNDRR GRAF
aims to provide a
curated space for
disaster systems
assessment, providing
an infrastructure to
foster improved risk
information for
stakeholders
Target: 2030
Check out GAR19
Summary
23
Vulnerabilities to correlated extremes are
increasingly recognized within the agricultural
sector and broader food system
Compound, concurrent, and sequential extremes
pose unique challenges, particularly when coupled
with field management and markets that span
many spatial scales and time horizons
AgMIP is systematically connecting climate to crop,
livestock, economics, food security, and nutrition
using a coherent modeling framework that captures
more complex behaviors of correlated extremes
Experiments designed to explore historical (as well
as hypothetical) extremes aid in big data analysis
and planning for resilience and broader risk
management
Opportunities to better connect extremes, hazards,
disasters and climate through IPCC and GRAF
Thanks! alexander.c.ruane@nasa.gov
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