Transcript PIAMDDI Renewal Research Projects

PIAMDDI Renewal Research Projects
A.
Integrated Climate Change Impacts Assessment
A.1.
A.2
A.3
A.4.
B.
Integrated Assessment model component emulation and response surfaces
B.1
B.2
C.
Providing global teleconnection based climate change predictions to the IAM framework
Implementing an improved representation of seal-level rise and potential threshold responses into IAMs
Characterizing/representing uncertainty in IAMs
C.1
C.2
C.3
C.4
D.
Water
Agriculture
Energy
Energy-Water-Land Nexus
Sequential decision making under uncertainty
CESM connections to IA and climate impacts: sea-level rise and energy demands
Integrated assessment of climate risk: refinement, testing and application of a reduced-form statistical
GCM emulator
Project on model comparison of uncertainties
Model diagnostics, including hind casting
D.1
D.2
D.3
How do we characterize the controlling uncertainties, assumptions, and parameterizations for complex
IAMs?
Looking back to move forward: evaluating global agricultural land use in integrated assessment models
Proposal for a full system hind casting experiment program
A. Integrated Climate Change Impacts Assessment
A.1 Water
A.1.1
Connecting IAMs to water balance models—Frolking, Grogan,
Lammers, Fisher-Vanden, Sue Wing, Forest, Reed, Shortle
• How can we link detailed water process model information with IAMs?
A.1.2.
Climate change adaptation and water resource management—
Olmstead, Fisher-Vanden, Frolking, Grogan, Lammers, Reed, Shortle,
Sue Wing
• Adaptation measures cannot easily be incorporated into IAMs without an
understanding of the institutions (most of them non-market) that determine
water allocation, pricing, and infrastructure investments
• And how these institutions may evolve in response to changing conditions
due to climate.
A. Integrated Climate Change Impacts Assessment
(continued)
A.2 Agriculture
A.2.1
Comparison of process models and empirical reduced-form models for
incorporation into IAMs—Schlenker, Frolking, Grogan, Lammers,
Hertel, Fisher-Vanden, Sue Wing
• How consistent are these alternative approaches to assessing the impacts of
climate change on agriculture?
• What are the trade-offs between these alternative approaches?
A.2.2.
Improving the empirical basis of agricultural impacts and adaptation in
IAMs—Schlenker, Diffenbaugh, Hertel, Sue Wing, Fisher-Vanden
• Update previous empirical work to include more recent climate data
• Generate new empirical estimates of farmer adaptation, both anticipated
and unanticipated
• Extend to rest of world and build into global IAM.
A.2.3.
Endogenizing risk-mitigating innovation: the case of agriculture—Popp,
Schlenker, Diffenbaugh, Hertel, Sue Wing, Fisher-Vanden
• Previous work explored link between natural disasters and technological
change.
• Incorporate endogenous technological change for adaptation into IAM.
A. Integrated Climate Change Impacts Assessment
(continued)
A.3 Energy
A.3.1
Climate change impacts on energy demand—Sue Wing, PNNL/JGCRI,
Mansur
• Incorporate new energy data
• Estimate cross-country energy demand responses to weather shocks
• Incorporate into IAMs (e.g., GCAM, Phoenix)
A.3.2.
Extensive versus intensive energy demand in response to climate
change—Mansur, Sue Wing, Popp
• Use household-level energy consumption data to identify how consumers
respond to weather shocks—e.g., more use of existing A/Cs? Or greater
adoption of A/C?
A.3.3.
Climate change impacts on energy supply—Sue Wing, UNH, JGCRI
• Construct demand functions for cooling water withdrawals that can be
incorporated into IAMs
• Combine these demand functions with hydropower supply functions in IAM
to simulate effects of climate on hydroelectricity capacity
A.3.4.
Advanced energy technology assessment—Benson, Weyant
• Decompose technology choice differences between models into structural
and parameter value differences
• Compare the assumptions made with historical experience and expert
elicitations
A. Integrated Climate Change Impacts Assessment
(continued)
A.4 Energy-Water-Land Nexus
A.4.1
The interplay of mitigation, impacts, and adaptation through the
bioenergy-land-water nexus—Hertel, Diffenbaugh, Schlenker
• Will bring together two stands of previous research and expand to global
scale:
1. How climate mitigation policy (in the form of bioenergy expansion)
interacts with physical water scarcity to determine land use change and
terrestrial carbon fluxes
2. Interaction between climate change and biofuels mandates, and
implications for commodity markets
B. Integrated assessment model component
emulation and response surfaces
B.1
Providing Global Teleconnection based climate change predictions to the
IAM framework—Forest, Li, Collins, Sue Wing, Fisher-Vanden, Reed
• Build statistical spatial-field emulators of key climate variables based on GTOs
for use in IAMs
• Estimate the GTO for the atmospheric model component of the iESM
B.2
Implementing an improved representation of sea-level rise and potential
threshold response into IAMs—Keller, Forest, Sriver, Nicholas, JGCRI/PNNL
• Implement a spatially-resolved module for sea-level rise into an IAM (e.g.,
GCAM, FUND, DICE) and into HECTOR.
• Leverage results from ensemble analyses using CESM to examine the relative
of contribution of natural variability vs. anthropogenic forcing to sea-level
rise.
C. Characterizing/representing uncertainty in IAMs
C.1
Sequential decision making under uncertainty—Webster, JGCRI, MIT
Joint Program, Fisher-Vanden, Sue Wing
• Incorporate sequential decision making under uncertainty in IAMs by
incorporating Approximate Dynamic Programming (ADP) into models such as
GCAM and EPPA.
C.2
CESM connections to integrated assessment and climate impacts: sealevel rise and energy demands—Sriver, Keller, Forest, Nicholas, Sue
Wing
• Utilize the ongoing CESM ensemble to analyze the effect of model
uncertainties on spatial variations in sea-level changes and trends.
C.3
Integrated assessment of climate risk: refinement, testing and
application of a reduced-form statistical GCM emulator—Sue Wing,
Keller, Nicholas, JGCRI
• Update existing emulator to incorporate recently-generated CMIP5 GCM
output
• Utilize emulator to conduct Monte Carlo realizations of future climates to
obtain gridded temp and precip to be used to manipulate crop yields and
energy demand in an IAM
C.4
Project on model comparison of uncertainties—Nordhaus, Gillingham
• Multi-model exercise to understand the key uncertainties affecting the
projections of major variables and projections of IAMs.
D. Model diagnostics, including hind casting
D.1
How do we characterize the controlling uncertainties, assumptions,
and parameterizations for complex IAMs?—Reed, JGCRI, FisherVanden, Keller
• Will collaborate with JGCRI to generalize our model diagnostics framework to
GCAM.
• Will provide a mechanism for understanding key mitigation and adaptation
uncertainties using rich spatial and temporal visualization of model controls.
D.2
Looking back to move forward: Evaluating global agricultural land use
in integrated assessment models—Hertel, Diffenbaugh, Weyant
• Will assist land-based IAMs in evaluating and improving model performance
by looking back at historical experience.
D.3
A full system hind casting experiment program—Santer, Weyant,
Fisher-Vanden, Taylor, Smith, Clarke
• Expand existing hind casting efforts to include more tests of the US-based
models and coordinate with the European efforts in this area.
• Already a plan in place to expand the initial GCAM experiments over longer
time horizons and more detailed inputs and outputs
• Discussion underway with the MIT/EPPA and the EPRI/MERGE modeling
groups to design similar hind casting protocols.
The End
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