Study on GHG Spatial Distribution andClimate Change Impact in China

Wang Juanle, Yang Yaping,
Lv Ning, Feng Min, Chen Pengfei, Chen BaozhangInstitute of Geographic Sciences and Natural Resources
Research, Chinese Academy of Sciences
2012.2.18

Background Our Group leader, Prof. Sun Jiulin− Study the climate change on water resource

and agriculture yield more than 10 years.
− Expand the research area in near years, e.g.,
cover the GHG retrieval based on remotesensing, ecosystem effect simulation andagriculture adaption measurement faced toglobal change, etc.
 A new center was established in 2009. Many young scientists joined intoour group.
− Earth System Science Information Sharing Center, IGSNRR, CAS− Hosting 2 national platform for data archiving and sharing. One is Earth
System Science Data Sharing Platform under NSTI, Another is Data
Archiving Center for program research data under MOST.

Background
GHG retrieval and scenarios
simulation technologies
 Story 1:Greenhouse gas

concentration distribution
retrieval based on remotesensing Story 2:Simulating ecologicaleffects for future climate andLUCC scenarios Story 3:Winter wheat yield

effect analysis and its
adaptation measurement innorth-west area of Shandong
Province, China
NIES,2012

Story 1
Study on GHG concentration retrieving andits distribution in China

1. Retrieving greenhouse gases from AIRS observations
2378 Channels

Brightness Temperature /   1200

AIRS infrared spectrum

Wavenumber cm-1

3.7-15.4 m650-2665 cm-1

Inversion Solutions
Estimate methane concentration from Atmospheric Infrared Sounder (AIRS)
satellite data.
AIRS, an advanced sounder containing 2378 infrared channels and four visible/near-infrared channels,
aimed at obtaining highly accurate temperature profiles within the atmosphere plus a variety of additional
Earth/atmosphere products.
All raw-level AIRS data up to 40 TB have already been archived in Information Center, IGSNRR

 1. From Artificial Neural Network

To directly estimate the distribution ofmethane at the surface

Cloud screen to obtain clear-sky signal
Singular value decomposition to compress data
Feed-forward three-layer perception function
Bayesian regulation, Early stopping, Cost-function to optimize ANN model 2. From Radiative Transfer Model simulation

σ: is absorption coefficient;  ρ: atmospheric density profile;
V (v )   (v )  
I 0 (v) K1 a (v) o (v) ms(v)  exp  0  (v, z )q 0 ( z )( z ) M ( )dz   Σ: initial vertical mixing ratio; M:
air mass;
V (v)  C (v)  exp  0  (v, z )q 0 ( z )( z ) M ( )dz q: Scale factor.
V (v )
 (v )   (v )R (v ) ,  (v)  exp   (v, z )q 0 ( z )( z ) M ( )dz Line by Line model with 0 HITRAN 2008 for gas
V (v0 )  (v0 )  (v0 )absorptionW q  ( z )  ( z )dz  q
 ( z )  ( z )dz
CH 4



0CH 4CH 40CH 4



0CH 40Input Data for ANN model

The fixed stations that contribute data to theWorld Data Centre for Greenhouse Gases(WDCGG). The symbol " • " denotes thatthe data from the station has been updated in
the last 365 days.

The distribution of stations used in the ANN
model inversion.
These stations measure high frequency ambient
methane concentration per day by gas
chromatographic method, and provide
continuous, relative long-term observations.

Methane concentration over global land from AIRS –Our method (0.5°)

Ch4 Concentration by season in China

CO2 Concentration by month in China (2005)

2011.5-9, Ground truth

Qinghai-Tibet Plateau

Qinba Mountain

Dongting and Poyang Lake

Sampling Location

Qinghai-Tibet Plateau

Qinghai-Tibet Plateau

Qinba Mountain

Qinba Mountain

Dongting andPoyang Lake

Dongting andPoyang Lake

CO2CH4

RelativeError

RelativeError

WLG

2.1%

1.3%

NJS5.6%

2.0%

StationContrast with AIRS retrieval value

Story 2
Simulating ecological effects for future climate and
LUCC scenarios

Simulate land surface energy balances and evaporation for regions with different
dominating land cover types in China using the EASS (Ecosystem-AtmosphereSimulation Scheme) model

LUCC simulation

LUCC dynamics models

LUCC
scenarios

Data up-scaling

Climate models

Climatescenarios

Assess
impacts ofLUCC toclimatechanges

Simulation platform

Comprehen
sive
ecologicaleffects
simulation

Evaluation at
different scales

Chen, B., J. M. Chen, et al. (2007). "Remote sensing-based ecosystem–atmosphere simulation scheme (EASS)—Model formulation and test withmultiple-year data." Ecological Modelling 209: 277-300.

IPCC SRES scenarios and driving forcesMoreeconomic
A2

A1

More
regionalMoreglobal
B1

B2
More
environmentalPopulation, economy, technology
energy, agriculture, land use
Driving forces
Nakicenovic et al. (2000)

A General Circulation Model (GCM) is a mathematical model ofthe general circulation of a planetary atmosphere or ocean.

Down-scaling

Regional climatemodel（RCM）

A GCM can be used forsimulating climate elements
for different climate scenarios

B1

A1

A2

Temperature changes

Precipitation
distributionTemperature

Precipitation

Radiance

Wind speed

Relative humidity

115.05E 26.7333N

LUCC dynamics (LUCCD) model
Climate change
Land coverchange

Impacts of thecurrent LC tofuture land use
behaviors

LC Marginaltransformation
Behavior

UpdatesLC
simulation

Climatesimulation

Humanity elements
Socialvariables

DecisionmakingUpdates

Behavior

Land use balance
model

Current land use
driving the futureLC

Land usechange

Integrate models that simulating land cover changes derived by climate change, land
use change. Land use balance model has been adopted to estimate the interactions
between industry and agriculture regions.

Future land cover scenarios
 Simulated land cover scenarios

for 2010 ~ 2050
 3 scenarios： A2 (economic development) B2 (environmental development) GH (following the regional

development plans)

2020 （A2） Spatial resolution: 30km

2020 （B2）

Development and applications of integrated land
surface process modeling Develop and optimize theEASS model1

 Run EASS in the 4 selected

regions in China
 Simulate key land surface

24water heat flux parameters
 Team: B. Chen, M. Feng，S.

Fang, J. Yan, et al.

3

Test and optimize EASS for China
Preliminary evaluation results using in-situ observations at Qianyianzhou, Jiangxi, China (2005)

Integrated ecological effects simulation
Future scenarios

Other
computationalmodels
模式Data processing
model

Integrated
simulation

Derivates from remotesensing observations（LAI, LUCC, etc.）
In-situ observation（precipitation,temperature, etc.）

DeployInput

OutputFuture climate
scenarios

Future LUCC
scenarios

Spatial downscaling

Processing

Data &instructions

Input

EASS

Optimize
OutputObservations from flux Validation
tower, remote sensing（ET, Sensible heat,etc.）

Model adjustment and optimization

未来高时空模拟数据
未来高时空模拟数据
Futureclimate &环境（情景1）环境（情景1）
LUCC scenarios

ET、Energybalance

未来ET、能量平
未来ET、能量平
衡futureSimulated
衡（情景1）
ecologyeffects（情景1）

Model simulation results
 Simulated ecological effects in 4 regions with 3 land

cover scenarios（A2、B2、GH）for 2010~2050.
 ecological effects elements:
 Sensible heat
 Latent heat ET NPP GPP

2040
A2
cultivated land
woodlandgrasslandbuilt-up land
water area
wetlandnival area
desertbare rockdesertification land

Annual averaged
sensible heat

w/m2
45-10

Annualaveraged ET

mm/day1.502040
B2

Take the site as example for detailanalysis2040_B2(woodland）

2040_A2(cultivated land ）Sensible heat

ETThe site is located east to Guiyang. It is predicted as woodland in 2040 in B2 and remain
cultivated land in A2. Annual averaged sensible heat for B2 is 9.4% higher than A2; oncontrast, ET is 9.4% lower than A2.

Story 3
Winter wheat yield effect analysis and its adaptation
measurement study on north-west area of Shandong
Province, China

Objective
The purpose of this study is to analysis effect of climate change on
winter wheat yield and to give some appropriate adaptation strategies
for field management under A1B climate change scenario in
northwest of Shandong province, by combining regional climate
model and crop growth model.

Material and methods

Framework for studying on effect of climate change on winter wheat production

Material and methods

Framework for studying on field adaptation strategy with considering climate change

Results and analysis• Change of climate resources(under A1B)
— Heat resources

Averagetemperature#Maximum
temperature

Minimum
temperature

1961-1990

14.81a（0.04*） 20.75a（0.06） 8.86a（0.04）

2031-2060

18.24b（0.04）

24.24b（0.03） 12.23b（0.05）

2061-2090

19.84c（0.03）

25.79c（0.03） 13.88c（0.04）*：Brackets for the coefficient of variation;#:Different letters means significant at 0.05 level

Results and analysis

Temperature（°）• Change of climate resources(under A1B)
— Heat resources

MonthResults and analysis• Change of climate resources(under A1B)
— Precipitation

1961-1990
Averageprecipitation#

2031-2060

2061-2090

1.89a（0.21*） 1.98a（0.21）

2.06a（0.23）*：Brackets for the coefficient of variation;#:Different lettersmeans significant at 0.05 level

Results and analysis

Precipitation（mm）• Change of climate resources(under A1B)
— Precipitation

MonthResults and analysis• Change of climate resources(under A1B)
— Radiation

1961-1990
Averageradiation#

2031-2060

2061-2090

15.15a（0.02*） 15.05ab（0.02） 14.91b（0.02）*：Brackets for the coefficient of variation;#:Different lettersmeans significant at 0.05 level

Results and analysis

Radiation（MJ m-2 Day-1）• Change of climate resources(under A1B)
— Radiation

MonthResults and analysis

Predicted yield (kg ha-1)

Predicted growth stage (day after sowing)2）Results of growth optimization

Actual growth stage (day after sowing)

Actual yield (kg ha-1)

Comparison between predicted growth stage and actual growth stage,
and between predicted yield and actual yield in years 2005 to 2010

Results and analysis
3）Effects of climate change on winter wheat production
Table 1 Appeared time for different growth stage of winter wheat in BASELINE,2031-2060,2061-2090
GrowthstageBASELINE

2031-2060

2061-2090

Day after
planting/day

Coefficient ofvariation/%

Day after
planting/day

Coefficient ofvariation/%

Day after
planting/day

Coefficient ofvariation/%

Emergence

11a*

10.82

9b

10.93

9b

9.18

Jointing

167a

3.23

151b

4.05

141c

5.10

Flagging

201a

2.22

186b

2.61

176c

3.49

Flowering

214a

2.02

199b

2.68

190c

3.10

Mature

246a

1.72

231b

2.02

222c

2.64

Note: * In row, different letters means significant at 0.05 levelResults and analysis
3）Effects of climate change on winter wheat production

Potential Yield(kg ha-1): 6615a*
Coefficient of variation: 8.9%

Year

Potential Yield(kg ha-1): 6777ab
Coefficient of variation: 11.6%

Year

Potential Yield(kg ha-1): 7006b
Coefficient of variation: 10.8%

Year

Model simulated winter wheat yield in different period(*different letters in yield means significant at 0.05 level)

Results and analysis

Coefficient of variation(%)

Potential Yield(kg ha-1)

4) Adaptation strategies for winter wheat field management

Sowing day

Sowing day

Effects of sowing data on winter wheat yield4. Results and analysis4) Adaptation strategies for winter wheat field management
Table 2 The best irrigation schedule for different sowing day in BASELINE,20312060 and 2061-2090
Sowing day

Sep. 20th

Sep. 30th
Oct. 15th

Periods

Winterwater

Jointing
water

HeadingFlowering
water

Yield

2031-2060

180 mm

150 mm

100 mm

6900

2061-2090

180 mm

150 mm

100 mm6756

2031-2060

120 mm

150 mm

100 mm6890

2061-2090

120 mm

150 mm

100 mm6733

BASELINE

60mm

100 mm

100mm

7144

The situation now：
Sowing day：Between Oct. 7th to Oct. 20th
Winter water：120mm；Jointing water：150mm；Heading-Flowering water：150mm
Yield：7006 kg ha-1

5. Conclusion
─ After optimization, CERES- Wheat model can predict winter wheat growthaccurately.
─ Compared to BASELINE, Heat increases significantly, and radiation decreases,and precipitation does not change significantly in years 2031 to 2060 and years

2061 to 2090 under A1B greenhouse gas emission scenario.
─ Change of climate resource will results decline of winter wheat yield in north westof Shandong plain. That may caused by incompletely vernalization of wheat withincreased temperature in winter. Thus, Breading winter wheat cultivar, that have
reduced dependence of vernalization, will be the target of the local breeding─ Under the condition of local cultivar has not much improved, it is best to advancebroadcast day before 2 or 3 weeks, and keep local irrigation system with increasing
winter water amount, in order to maximize production and reduce inter-annualvariation.

Summary These research are based on State Key Basic Research Programand Environment Protection Public Welfare Project of China. Itis keep on going now.
− GHG’s distribution is focused in our research is not only juston quantity, but also its spatial distribution. These data can beretrieved automatically with high time sequence.
− More elements, such as land cover types, temperature,
precipitation, Sensible heat, Latent heat, ET, NPP, GPP, etc
are considered for ecological effects.
− Not only scenarios analysis, some feasible adaption
measurement are researched in small region.

Hope to cooperate in the near future, ……

Thanks!
Wang Juanle, Ph. D., Associate Prof.
Deputy Director, Earth System Science Information Sharing Center,IGSNRR, CAS
Datun Road A11, Chaoyang District, Beijing, China
E-mail: wangjl@igsnrr.ac.cn