Impact of Sea Surface Temperatures,
Climate and Management on
Plant Production and GHG fluxes in
Asia and the Great Plains
William Parton
Maosi Chen
Melannie Hartman
Steve Del Grosso
Dennis Ojima

Outline
• Linking DayCent soil C and nutrient cycling
model to UCLA land surface model
• Predicting the impact of land use practices on
greenhouse gas fluxes in Asia
• Impact of sea surface temperatures on spring
AET and grassland plant production in the Great
Plains
• Global patterns in plant production and soil
decomposition from 1900 to 2015
• Conclusions

Progress Linking SSiB and
DayCent
 Completed: Point version of DayCent SOM cycling model with SSiB
I/O
• Reads daily SSiB drivers (plant litter inputs, potential NPP, plant N demand,
soil moisture, soil temperature, precipitation, radiation, AET, snow cover)
from a text file
• Updates SOM pools, inorganic N pools
• Returns fraction of plant N demand that can be met, soil respiration, NOx,
CH4, N2O
• This fraction is used by SSiB to downscale daily NPP
• Tested for all 7 SSiB Land Cover Types
 In progress: Interface for SSiB and DayCent
• Enables DayCent to be run on a grid across space before time
• For each day, for each grid cell
1. DayCent retrieves SSiB drivers and the state of the grid cell from the previous day
2. DayCent completes the simulation for the day and sends its results to the interface
3. DayCent saves the state of the grid cell to the interface

SSiB
DayCent
Retrieve grid cell state
from previous time step
for each grid cell
If time=0, initialize grid
cell from spinup
Receive inputs from SSiB
Save grid cell state to
global data structure
Send results to SSiB
SSiB/DayCent
Interface

Use of Agricultural Best
Management Practices in
China
30% reduction in inorganic
fertilizer
Use of no-tillage cultivation
practices
Addition of straw and manure
Drainage of flooded rice fields

Cheng, K. S.M. Ogle, W.J. Parton, and G. Pan. 2014. Simulating greenhouse gas mitigation potentials for Chinese Croplands using the DAYCENT
ecosystem model. Global Change Biology 20: 948-962.

Cheng, K. S.M. Ogle, W.J. Parton, and G. Pan. 2014. Simulating greenhouse gas mitigation potentials for Chinese Croplands using the DAYCENT
ecosystem model. Global Change Biology 20: 948-962.

Cheng, K. S.M. Ogle, W.J. Parton, and G. Pan. 2014. Simulating greenhouse gas mitigation potentials for Chinese Croplands using the DAYCENT
ecosystem model. Global Change Biology 20: 948-962.
BMP1: 30% N Fertilizer Reduction and Flooding
BMP2: Reduced Tillage with Straw Return
BMP3: 30% N Fertilizer Reduction and Manure Application
BMP4: 30% N Fertilizer Reduction, Flooding, Straw Return and Manure Application
Zhangye Xigaze

N2O Emissions for Maize (2015-
2020)
g N2O-N m-2 yr-1
Business As Usual Auto-fertilization of N

AET ratio
0.63 - 0.75
0.76 - 0.80
0.81 - 0.85
0.86 - 0.90
0.91 - 0.95
0.96 - 1.00
1.01 - 1.05
1.06 - 1.10
1.11 - 1.15
1.16 - 1.43
DayCent Model Satellite Derived (NDVI)
Mean Spring Evapotranspiration Cool PDO
(1998-2014)/ Warm PDO (1978-1997)
Mean Annual Plant Production Cool PDO
(1998-2014)/ Warm PDO (1982-1997)
Mean Values

DayCent Model Satellite Derived (NDVI)
Annual Spring Evapotranspiration Variability Cool
PDO (1998-2014)/ Warm PDO (1978-1997)
Annual Plant Production Variability Cool PDO
(1998-2014)/ Warm PDO (1982-1997)
AET variability
0.43 - 0.70
0.71 - 0.80
0.81 - 0.90
0.91 - 1.10
1.11 - 1.20
1.21 - 1.30
1.31 - 1.40
1.41 - 1.50
1.51 - 1.60
1.61 - 2.86
Variability

Shortgrass Steppe

Shortgrass Steppe
COLD PDO
Nino 3 Average
AET
% < 14
< -.75 14.89 44.44%
-.25 to -.75 18.39 14.29%
.25 to -.25 18.69 27.27%
> .25 20.39 0.00%
WARM PDO
Nino 3 Average
AET
% < 14
< -.25 16.56 26.67%
.25 to -.25 20.05 7.14%
> .25 21.42 0.00%

Conclusions
• Making progress in linking DayCent to UCLA GCM
• Using best land use practice in agriculture can
greatly reduce GHG fluxes
• PDO, AMO, and ENSO SST’s are correlated to
drought frequency and plant production in the
Great Plains
• Plant production and soil decomposition rates
have been increasing rapidly from 1980 to 2015
for the tundra and boreal systems
• Soil decomposition is increasing more rapidly