Transcript Earth System Model (ESM)

Earth System Model
Beyond the boundary
Model
•
A mathematical representation of the many
processes that make up our climate.
•
Requires:
–
–
–
–
Knowledge of the physical laws that govern
climate
Mathematical expressions for those laws
Numerical methods to solve the
mathematical expressions on a computer (if
needed)
A computer of adequate size to carry out the
calculations
Why?
Hypotheses
Observations
Numerical Simulations
• Understanding of cause and effect
• Predictive skill: our main tool to make predictions for the future
Evolution of Climate Science
Definition
There is no unique definition of which processes must be represented before a climate
model becomes an Earth System Model (ESM), but typically such models have at least
an interactive carbon cycle component. The development of this capability was
motivated by suggestions that the ability of terrestrial ecosystems and the ocean to
remove carbon dioxide from the atmosphere will be limited by future climate change
(e.g., Friedlingstein et al. 2006).
Climate-Carbon Feedback
[Friedlingstein et al. 2006]
Climate-Carbon Feedback
Positive feedback
if the warming leads to enhanced rates of decay of organic matter in soils, or a
reduction in oceanic carbon uptake, then the concentration of CO2 in the
atmosphere will rise more rapidly than it would in the absence of such (positive)
feedbacks, and the rate of warming will be greater as well.
Negative feedback
if increased CO2 in the atmosphere enhances photosynthesis and the storage of
carbon in plants and soils, then CO2 levels will rise less rapidly than in the absence
of this (negative) feedback, and climate change will also be slower as a result.
Earth System Model (ESM)
Atmospheric circulation and radiation
Climate Model
Sea Ice
Ocean circulation
Land physics
and hydrology
Atmospheric circulation and radiation
Allows Interactive CO2
Earth System Model
Sea Ice
Ocean ecology and
chemistry
Ocean circulation
Plant ecology, land use,
and Biogeochemistry
Land physics
and hydrology
Carbon cycle
CO2
CO2
Diagnostic
Prognostic
Global Climate Model
Earth System Model
Multi-disciplinary Science
Terrestrial ecosystems influence
climate through physical, chemical,
and biological processes that affect
planetary energetics, the hydrologic
cycle, and atmospheric composition
Earth system science spans
traditional disciplines
Three examples
 Anthropogenic land cover change
 Photosynthesis-transpiration
 Leaf area index
Bonan (2008) Ecological Climatology, 2nd ed (Cambridge Univ. Press)
11
History
Heterogenity
Dynamic Global Vegetation Model (DGVM)
BIOGEOPHYSICS (CLM)
CANOPY PHYSICS
Radiation transfer
Energy balance
Temperature
Aerodynamics
Water balance
CANOPY PHYSIOLOGY
Photosynthesis (GPP)
Stomatal conductance
SOIL/ICE/SNOW PHYSICS
Energy and water balance
Temperature
BIOGEOCHEMISTRY (LPJ)
autotrophic respiration
(RA)
Net Primary Production
(GPP- RA )
heterotrophic respiration
X
(RA)
not coupled
yet
PHENOLOGY (IBIS)
Daily Leaf Area Index
DAILY STATISTICS
10-day mean temperature
10-day mean photosynthesis
Growing degree-day accumulation
Fire probability
ECOPHYSIOLOGY
Allocation
Turnover
Mortality
COMPETITON
Light
Water
FIRE
SOIL
Occurrence (moisture, fuel load)
Litter
Mortality (fire resistance)
Soil organic matter
Combustion
ANNUAL STATISTICS
Fire season length
NPP,GPP and potential GPP
Minimum monthly temperature
Growing degree-days above 5℃
Precipitation
Growing degree-days above heat stress
Net CO2
(GPP-RA-RH)
At every time step (~20minute)
VEGETATION DYNAMICS (LPJ)
Daily
Multi-Time step
Yearly
ESTABLISHMENT
Potential rate
Canopy Gap
Frost tolerance
Heat stress
Winter chilling
Growing season warmth
Low precipitation
Plant Functional Type
Height
Plant Carbon
Litter and Soil
Carbon
Vegetation dynamics
Broadleaf Tree
Shrub
C3 Grass
Soil
Competition (10 days)
Plant functional type (PFT)
Deciduous, evergreen trees
Shrub
Grass
Crop
LAI (Model)
Vegetation activity
Phenology
Winter
Spring
Summer
Autumn
Winter
Time
Simulated Carbon
Annual cycle of LAI in ESMs
Observation (GIMMS New LAI)
Amplitude of LAI annual cycle
climatology (1982-2005)
[Jeong et al., in preparation]
Poor performance
Uncertainties in phenology
Net ecosystem productivity
4
CTR
EX1
EX2
EX3
EX4
EX5
EX6
EX84m
EX
EX4m
2
0
[Optimal parameterization]
[parameter]
[structure]
[hypothesis]
[species]
[DGVM group1]
[DGVM group2]
EX
5m
EX5m
-2
-4
-6
90
100
110
Budburst date
120
130
Carbon uptake commencement
Parameter:
-1.2 days
-1.0 days
Structure:
-0.5 days
- 0.0 days
Hypothesis:
-1.5 days
-2.0 days
Species:
-9.7 days
-11.5 days
DGVMs:
-9.2 days
-11.1 days
140
150
Day of year
[Jeong et al., 2012]
Potential solution
Species
25
B. papyrifera model
Q. rubra model
A. rubrum model
A. saccharum model
F. grandifolia model
RMS errors
[days]
20
15
10
5
0
B. papyrifera
Q. rubra
Early
Mid
A. rubrum
A. saccharum F. grandifolia
Late successional species
[Jeong et al., 2013b; Jeong and Medvigy, in review]
New paradigm
“ecological realism”
Managed ecosystem
Crop phenology
Phase 1
Phase 2
Phase 3
LAI
Grain Fill
Harvest
Planting
date
0
Leaf Emergence
Time
Green: climate, fertilization, and irrigation
Red: human-decision
Tradeoff between food benefit and climatic cost
1. Extensification (land use)
Global Climate Model
(one way)
2. Intensification (Irrigation, fertilization, practices)
1. Extensification (land use)
2. Intensification (Irrigation, fertilization, practices)
Earth System Model
(two way)
3. Interactive crop management (planting, harvesting)
Current problem
NCAR CESM 1.0 algorithm
Sacks et al., 2010
Wheat
Potential solution
[Jeong et al., 2013a]
Summary
We need more efforts to implement ecological realism in ESMs.
Human-managed phenology is the initial stage.
We need systematic analysis on phenology and atmospheric CO2 by integrating
satellite, ground, and Earth system model.
CO2 Concentration
Vegetation Activity
How will changes in phenology affect the variations in annual cycle of
atmospheric CO2?