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