Integrated Assessment Model Diagnostics – May 3, 2012 1
Land use, water, and carbon in the integrated Earth System Model (iESM)
WILLIAM COLLINS JAE EDMONDS, TONY JANETOS, ANDY JONES, ALLISON THOMPSON, AND PETER THORNTON
Lawrence Berkeley National Laboratory Pacific Northwest National Laboratory – Joint Global Change Research Institute Oak Ridge National Laboratory
Three Major Objectives
! Task 1: Create a first generation integrated Earth System Model (iESM) with both the human components of an IAM and a physical ESM
! Task 2: Further develop components and linkages within the iESM and apply the model to improve our understanding of the coupled physical, ecological, and human system
! Task 3: Add realistic hydrology, including freshwater demand, allocations and demands to hold stocks of water as well as representation of freshwater availability from surface water, ground water, and desalinization.
Why Is This So Important?
! In the present world emissions mitigation analysis is undertaken under the assumption that the climate is not changing. E.g. crop yields are unchanged, water resources are unchanged, and energy systems don’t have to cope with a changing climate.
! Climate impacts analysis is undertaken with the assumption that no resources are being diverted to address climate change. E.g. no land is being used for reforestation, no land is being used to grow bioenergy, and energy prices are unchanged.
! The changes in behavior of the coupled climate-energy-land modeling system are significantly different than in the un-coupled models. Modeling the system as a unit materially changes our understanding of the evolution of the physical climate system
! The development of an iESM means that fully consistent analysis of potential future climate change, emissions mitigation options, and impacts and adaptation options will be possible.
iESM Schematic
Four Modeling Systems: GCAM, GLM, CLM, & CCSM
1. GCAM (Human
Dimensions Elements only;
15 ghgs, aerosols, SLS; 14
geopolitical regions; 151 Ecoregions)
2. GLM (½ x ½ degree grid
land-use-land-cover.)
ESM1 (3. CLM & 4. CESM)
Fossil Fuel & Industrial Emissions (Gridded)
Land Use
LU-LC
1. GCAM(Human
Dimensions Elements only;
15 ghgs, aerosols, SLS; 14
geopolitical regions; 151 Ecoregions)
2. GLM(½ x ½ degree
grid land-use-land-cover.)
ESM1(3. CLM & 4. CCSM)
Fossil Fuel & Industrial Emissions (Gridded)
Land Use
LU-LC
Two Experiments
! Experiment 0: Compare two cases ! RCP 4.5 ! An RCP 4.5 replication Wm-2 scenario without terrestrial
mitigation. ! (Sneaker-net).
RCP 4.5 Climate
Rep 4.5 Climate
Two Experiments
RCP 4.5 Climate
Rep 4.5 Feedback
! Experiment 1: RCP 4.5, with feedback from the CLM-CCSM calculations ! 15-year time steps; ! Lagged adjustment: ! Sneaker-net.
Carbon Density and Productivity
1. GCAM(Human
Dimensions Elements only;
15 ghgs, aerosols, SLS; 14
geopolitical regions; 151 Ecoregions)
2. GLM(½ x ½ degree
grid land-use-land-cover.)
ESM1(3. CLM & 4. CCSM)
Fossil Fuel & Industrial Emissions (Gridded)
Land Use
LU-LC
iESM multi-phase coupling strategy
GCAM CLM/CESM GLM
Climate
C stocks, productivity
Up/down scaling (space and time)
Fossil fuel emissions
Atm CO2
iESM Experiment 0 (RCP 4.5)
iESM Experiment 1 Terrestrial Feedbacks
iESM Experiment 0: Bioenergy Scenarios with the Simplest One-Way Coupling
! GCAM to GLM to CESM ! Does the one-way pass of information replicate the
original RCP4.5 simulation done in CMIP5? ! If we now add a different policy scenario, but keep the
same concentration pathway, does the evolution of the climate system differ?
! What happens when we replicate the approach of the Wise et al. Science paper? ! RCP4.5 – carbon price on all carbon (UCT) ! RCP4.5 – carbon price ONLY on fossil carbon (FFCT)
9
GLM
GCAM scenario LULCC
information
Historical LULCC
information
Transition matrix dynpft file CLM
translator
Experiment 0 Work Flow
CLM / CCSM
Carbon fluxes and
[CO2]
Carbon stocks
Climate change
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990 2005 2020 2035 2050 2065 2080 2095
Other Unmanaged Land
Unmanaged Forests
Managed Forests
Pasture
Crops
Unmanaged Pasture
Desert
Bioenergy Crops
RCP 4.5 Land Use
No Policy
RCP4.5 (UCT) Rep 4.5 (FFICT)
RCP 4.5 and a Replication without terrestrial mitigation policy
Change in Landcover under the RCP4.5 FFICT Scenario
Jones et al, 2012
Effect of Land cover scenario on global mean temperature
Jones et al, 2012
Jones et al, 2012
Link between albedo and temperature
Jones et al, 2012 Jones et al, 2012
Linkage of water vapor and greenhouse effect
Jones et al, 2012
Jones et al, 2012
Major Findings of Experiment 0
! The two scenarios have the same radiative forcing from GHGs. ! Yet they are substantially different in the evolution of the climate:
the equivalent of 1.5 W/m2, or about 0.5°C global annual average. ! We can replicate RCP 4.5 with a one way pass of information. ! But it is also true that the actual policy chosen matters –
in this case the very large land-use change associated with FFICT. ! Radiative forcing by GHGs is not a complete metric for
evaluating the evolution of the climate system
Experiment 1: Simplest possible feedback from CESM(CLM) to GCAM
• Send maps of carbon density, by PFT, from CLM to GCAM
• GCAM updates internal carbon densities based on changes from CLM
• GCAM regenerates RCP, including new LULCC trajectory, based on updated carbon densities.
Experiment 1: A few other details…
• 15-year time step, first departure from control occurs in period 2020-2034.
• Feedback modifies spatial/temporal pattern of GCAM carbon density as integration proceeds, but doesn’t modify baseline carbon cycle.
• GLM has its own internal carbon density representation, which is not being modified in this experiment.
Transla'on of GCAM/GLM coupling response to CLM vegeta'on types Control (2020 to 2034) Expt 1 – Control (2020 to 2034)
Trees
Grass
Crop
Percent cover Percent cover change
Transla'on of GCAM/GLM coupling response to CLM harvest Control (2020 to 2034) Expt 1 – Control (2020 to 2034)
Harvest
Percent cover Percent cover change
Summary of coupling influence on CLM forcing: • Tree cover higher
• Grass cover higher
• Crop cover lower
• Pattern reversed for Sahel, India, southern boreal forest in Asia
• Regional modifications to harvest rate
CLM/CESM response to Expt 1 coupling (2020-‐2034)
No climate signal after first coupling time step
Expt1 Control
CLM/CESM response to Expt 1 coupling (2020-‐2034)
Clear signal in land cover change flux emerging signal in atmospheric CO2 concentration.
Expt1 Control
iESM Coupling Status
First Generation iESM
CESM
ATM (cam, datm)
LND (clm/iac, dlnd)
OCN (pop, docn)
ICE (cice, dice)
CLM IAC (giac, diac, siac)
GCAM GLM
CAM POP CICE
Driver Component Model Coupler
Status: • iESM code is written. • iESM code is running at NERSC. • Validation against sneaker-net version of
experiment 1.1 is underway.
Sneaker Net Implementation
run GCAM at JGCRI => regional land use change
run matlab script at JGCRI => ½ degree land use change
run GLM at JGCRI => ½ degree land cover change
run PL code at ORNL run mksurfdat at ORNL
=> clm surface dataset on clm grid run CESM at ORNL
=> carbon stocks on clm grid run 01_pft_to_grid_h1.ncl at JGCRI run sneaker.r at JGCRI
=> carbon stocks on gcam grid
Issues: • Diversity of languages
• Large amount of effort to conduct this relay race
• Human effort scales directly with coupling frequency.
Active Coupling Implementation
run CESM call lnd_run()
=> carbon stocks on gcam grid call gcam()
=> regional land use change call gcam2glm()
=> ½ degree land use change call glm()
=> ½ degree land cover change call glm2clm() ! (mksurfdt)
=> clm surface dataset on clm grid (to file) call clm()
=> carbon stocks on clm grid call clm2gcam
=> carbon stocks on gcam grid
iESM Coupling: Emulation of Sneaker-net Coupling
Status: • We emulate sneaker-net using 15-year timesteps.
Emulation of pasture distributions using iESM
gpast
longitude (degrees_east)
latit
ude
(deg
rees
_nor
th)
Range of gpast: 0 to 1 (null)Range of longitude: -179.75 to 179.75 degrees_eastRange of latitude: -89.75 to 89.75 degrees_northCurrent time: 0 day as %Y%m%d.%fFile glm_state_2020.orig.nc
jet T
hu A
pr 5
13:
50:0
8 20
12�
gpast
longitude (degrees_east)
latit
ude
(deg
rees
_nor
th)
Range of gpast: -5.00679e-06 to 2.39909e-05 (null)Range of longitude: -179.75 to 179.75 degrees_eastRange of latitude: -89.75 to 89.75 degrees_northCurrent time: 0 day as %Y%m%d.%fFile glm_state_diff_2020.nc
jet T
hu A
pr 5
13:
44:5
8 20
12�
Status: • We can reproduce the distributions of
pasture to 1 part in 100,000.
Overview of plans through 2014 ! Remainder of 2012
! Experiments ! Complete Experiment 1 ! Validate iESM code with Experiment 1 ! Meeting of working group on experiments to plan next phase
! Model development ! Integrate emissions coupling process into iESM ! Scoping of RTB integration into CESM
! Model testing ! Test CESM feedbacks to GCAM buildings sector
! Goals for 2013-2014 ! Integrate GCAM and CESM (RTB) water models ! Explore the consequences of the basic science uncertainties in CESM for
energy systems in GCAM ! Further develop and explore carbon cycle coupling with iESM ! Additional experiments with a wider range of climate, mitigation and
sustainability scenarios.
Acknowledgements to the Project Team
! PNNL – JGCRI: Ben Bond-Lamberty, Kate Calvin, Jae Edmonds, Mohamad Hejazi, Tony Janetos, Sonny Kim, Page Kyle, Pralit Patel, Allison Thomson, Marshall Wise, Yuyu Zhou
! PNNL- Richland: Maoyi Huang, Ruby Leung, Hongyi Li, Nathalie Voisin,
! ORNL: Peter Thornton, Marcia Branstetter, Jiafu Mao, Xiaoying Shi
! LBNL: Bill Collins, Andy Jones, John Truesdale, Tony Craig, Lisa Murphy, Alan Di Vittorio, Alan Sanstad, Margaret Torn
! UMD: George Hurtt, Louise Chini
30
iESM Project Briefing – April 12, 2012 31
Discussion
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