Impact of Sea Surface Temperatures, Climate and
Management onPlant Production and GHG fluxes
in Asia and the Great Plains
William PartonMaosi Chen
Melannie HartmanSteve 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 day2. DayCent completes the simulation for the day and sends its results to the interface3. 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/DayCentInterface
Use of Agricultural Best Management Practices in China
30% reduction in inorganic fertilizer
Use of no-tillage cultivation practices
Addition of straw and manureDrainage 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 FloodingBMP2: Reduced Tillage with Straw ReturnBMP3: 30% N Fertilizer Reduction and Manure ApplicationBMP4: 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 ratio0.63 - 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 variability0.43 - 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 SteppeCOLD 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