matrix approach to facilitate land carbon modeling: case
TRANSCRIPT
Matrix approach to facilitate land carbon modeling:Case studies with CLM45 and ORCHIDEE-MICT
Yuanyuan Huang1,2,3, Xingjie Lu1,2, Zheng Shi1,2, David Lawrence4, Charles Koven5, Jianyang Xia6,7, Philippe ciais3, Yiqi Luo1,2,8
Contact: [email protected]
Introduction
S0 S1 S2 S3 S4 S5
I I0 Ie Ie Ie Ie Ie
B B0 B0 Be Be Be Be
N N0 N0 N0 Ne Ne Ne
ɛ ɛ0 ɛ0 ɛ0 ɛ0 ɛe ɛe
V V0 V0 V0 V0 V0 Ve
Matrix Simulations Using MATLAB
CLM Default Run 2eCO2(560ppm)
CLM Default Run 1CO2(280ppm)
The terrestrial carbon (C) cycle has been commonly
represented by a series of C balance equations to track C
influxes into and effluxes out of individual pools in earth
system models (ESMs). This representation matches our
understanding of C cycle processes well but makes it difficult
to track model behaviors. It is also computationally
expensive, especially for soil organic carbon (SOC) spin-up.
To overcome these challenges, we have developed a matrix
approach, which reorganizes C balance equations in the
original ESM into one matrix equation without changing any
modeled C cycle processes. We applied the matrix approach
to the Community Land Model (CLM4.5) and ORCHIDEE-
MICT with vertically resolved biogeochemistry.
1. The matrix equation exactly reproduces litter and SOC of the standard CLM45 and
SOC of ORCHIDEE-MICT.
2. The matrix analytical solution can potentially accelerate model spin-up and alleviate
the constraint from high computational requirement.
3. Easy diagnosis of system properties such as C residence time and C storage potential,
and traceability analysis.
4. Broad scientific applications through effective manipulation of matrix components
such as attribution of global change impacts.
𝑿 : 70x1I : 70x1 𝑨 : 70x70
𝑿 : 100x1I : 100x1𝑨 : 100x100
𝜺 : 70x70 𝒌 : 70x70𝑽 : 70x70
𝜺 : 100x100 𝒌 : 100x100 𝑽 : 100x100
ORCHIDEE-MICT
CLM45Figure 1. The matrix equation (up) and structure of ORCHIDEE-MICT
(left), CLM45 (right) litter and SOC processes.
Figure 2. C pools simulated
from the matrix equation (left
column) and the 1:1 line
spanned by matrix vs. default
CLM45 simulations at a Brazil
site.
Figure 3. C pools simulated from the
matrix equation (left column) and
the difference between the matrix
and default CLM45 after 10 years
simulation starting from 1850.
Figure 4. The same as Figure
2, but for ORCHIDEE-MICT
at a high latitude site.
Figure 5. The same as Figure 3, but
for ORCHIDEE-MICT after 150
years simulation starting from cold
start.
CLM45
ORCHIDEE-MICT
Figure 6. C storages (fast, slow and passive ) after
150 + 200,000+100 years default ORCHIDEE-MICT
simulation (left column) and C storage capacity
diagnosed from 398 years matrix calculation (right
column).
Figure 7. Ecosystem C residence time (a), C input (b) and C storages (c) diagnosed from the
CLM45 matrix simulation at an Alaska site.
3-dimentional parameter space:
Ecosystem C input
Ecosystem C residence time
Ecosystem C storage potential
1. C storage capacity captures the
maximum C an ecosystem can store (C
input x C residence time).
2. C storage potential tracks transient C
dynamics and is the difference between
C storage capacity and storage.
3. C residence time diagnosed from the
matrix equation is different from the
common practice of dividing stocks by
fluxes at non-steady state conditions.
CO2 fertilization affects litter and
SOC through C input, allocation
of external C into different C
pools, nitrogen regulation, altered
soil environmental conditions, and
vertical mixing along the soil
profile in CLM45.
Figure 8. Simulation protocol.
Figure 9. Total CO2 fertilization effect (topleft) and relative contributions from various processes.
1. Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
2. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
3. Laboratoire des Sciences du Climat et de l’Environnement, 91191 Gif-sur-Yvette, France
4. Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO, USA
5. Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
6. Tiantong National Forest Ecosystem Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200062, China.
7. Research Center for Global Change and Ecological Forecasting, East China Normal University, Shanghai 200062
8. Department of Earth System Science, Tsinghua University, Beijing, China,
Model Structure and the Matrix Equation
Application 1: Model Spin-up
Application 2: 3-dimentional diagnostics
Summary
Authors and Reference
Luo Y, Shi Z, Lu X et al. (2017) Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications. Biogeosciences, 14, 145-161.
Application 3: Attribution of Response to Global Change
The relative contribution from each of
these processes are quantified through a
series of matrix simulations which
sequentially plug in relevant factors
under 580 ppm CO2 conditions
compared to 280 ppm CO2. As shown in
Figure 9, the largest contribution comes
from altered litter input.
Matrix vs. Default Simulations
Global Total SOC
ORCHIDEE-MICT Default: 3328 GtCORCHIDEE-MICT Matrix: 3304 GtC