Change in vegetation growth and C balance Change in vegetation growth and C balance

in the Tibetan Plateauin the Tibetan Plateau

Shilong Piao, Kun Tan, Nan Cong, Xuhui Wang

Peking University

slpiao@pku.edu.cn

Motivation Rapid climate warming

y = 0.0397x - 1.032

R2 = 0.74

y = 0.0226x - 0.5767

R2 = 0.7407

-2.00

-1.00

0.00

1.00

2.00

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Ano

mal

y of

MA

T (o

C)

Global

QZ

线性 (Global)

The Tibetan Plateau is one

of the most critical and

sensitive regions in the

earth’s climate system.

During the past five decade

s, the mean annual tempera

ture of the plateau has incre

ased by 0.4 oC per decade,

a faster rate than the mean

temperature trend over glob

al land surface.

Data from CRU

Motivation Precipitation change

ObjectivesRising CO2 concentration

How do vegetation growth and carbon storage change

in response to change in climate and rising CO2?

Glboal warming Precipitation change

1. Change in spring phenology

Outline

2. Change in carbon balance

Vegetation growth change

Dataset

NDVI defined as the ratio of the difference between near-infrared

reflectance and red visible reflectance to their sum, is an

indicator of vegetation greenness. The NDVI data used in this

study were from the GIMMS (Global Inventory Monitoring and

Modeling Study) group derived from NOAA/AVHRR land dataset,

with 8 km resolution for each 15 days from 1982 to 2006.

Vegetation growth change

Climate Dataset

Monthly climate data

recorded from 50

meteorological stations

over the Plateau.

Spring phenology change

1 2 3 4 5 6 7 8 9 10 11 120.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

ND

VI

1 2 3 4 5 6 7 8 9 10 11 12-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Relative N

DV

I Change

Month

1 2 3 4 5 6 7 8 9 10 11 120.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Month

ND

VI

Define NDVI threshold

Maximum NDVI changing rate

Apply NDVI threshold

Onset day

Method to detect changes in the vegetation green-up date

Firstly, we calculate the averaged annual NDVI

time series curve during 1982-2006 to

determine the NDVI threshold of vegetation

green-up in each pixel.

The threshold over 1982-2006 is defined as

the NDVI value with the highest positive

relative NDVI seasonal change;

We performed a least square regression

analysis between NDVI data and the

corresponding day of year (Julian day)

Finally, the annual green-up date is calculated

as the day when interpolated daily NDVI

crosses the corresponding threshold upwards .Piao et al., GCB (2006); AFM(2011)

Spring phenology changeSpatial patterns of spring vegetation green-up date

The green-up date increases from east

to west;

In the most inland part of the plateau,

green-up starts by early June

The southwest has the latest green-up

dates, typically by the end of June.

Spring phenology change

2750 3150 3550 3950 4350 4750 5150 5550120

130

140

150

160

170

180

Altitude (m)

Ons

et d

ate

of g

reen

-up

(Jul

ian

day) All: Slope=0.0078 R2=0.81 P<0.001

Below 3600m: Slope=-0.0066 R2=0.45 P=0.035Above 3600m: Slope=0.0113 R2=0.97 P<0.001

Spring vegetation green-up date vs. altitude

Spatial patterns of spring vegetation green up date closely linked with

altitude.

Across the Plateau, in response to increase in elevation by 100m, the

green-up date delays by 0.8 days.

Spring phenology changeTemperature vs. altitude

y = -0.004x + 19.61

R2 = 0.5579

-5

0

5

10

15

2000 2500 3000 3500 4000 4500 5000 5500

Altitude (m)

Tem

pera

ture

(oC

)

y = -0.0032x + 14.266

R2 = 0.3642

-5

0

5

10

15

2000 2500 3000 3500 4000 4500 5000 5500

Altitude (m)

Tem

pera

ture

(oC

)

Annual

Spring

Such a significant increase in

green-up date with increasing

altitude is coincident with

decreasing temperature;

Both annual and spring

temperature is negatively

correlated with altitude by 0.3

and 0.4 oC/100m, respectively.

Eurasia: -0.4 days/yr

North America: -0.43 days/yr

Spring phenology change

1982 1986 1990 1994 1998 2002 2006125

130

135

140

145

150

155

160

165

On

set d

ay

of g

ree

n-u

p (

Julia

n d

ay)

Year

1982-1999: y=-0.884x+1907.5 R2=0.56 P<0.001

1999-2006: y=2.211x+-4276.1 R2=0.44 P=0.074

1982-2006: y=0.013x+122.0 R2=0.00 P=0.945

The vegetation green-up significantly

advanced by 0.9 days yr-1 from 1982 to

1999 (R2=0.56, P<0.001);

From 1999 to 2006, the green-up date

marginally delayed with an overall rate of

2.2 days yr-1.

Spring phenology change

1982 1986 1990 1994 1998 2002 20063

3.5

4

4.5

5

5.5

6

6.5

7

Sp

rin

g te

mp

era

ture

(oC

)

Year

1982-1999: y=0.092x+-178.8 R2=0.44 P=0.003

1999-2006: y=-0.075x+156.3 R2=0.11 P=0.426

1982-2006: y=0.047x+-88.8 R2=0.27 P=0.007

Temporal change in spring temperature

The different trends in green-up dates b

efore and after 1999 are comparable wit

h the difference of spring temperature ch

ange between two periods (1982-1999 v

s. 1999-2006).

Spring temperature averaged across all

the 50 metrological stations in the Qingh

ai-Xizang Plateau showed a clear increa

sing trend during the period 1982-1999,

followed up by a decreasing trend from

1999 to 2006 .

Spring phenology change

Slope: 0.76, R2 = 0.54, P<0.001

0

4

8

12

16

20

-10 -5 0 5 10 15

Mean annual temperature (oC)

Tem

pera

ture

thre

shol

d (o

C)

The temperature threshold of green-u

p is significantly and positively correl

ated with MAT;

Vegetation in warmer environments r

equires a higher temperature thresho

ld to green up than in colder areas, b

ecause vegetation acclimate to high t

empeature.

The relationship between temperature at the date of green-up and mean annual temperature across all climate stations

Spring phenology changeTrend in spring phenology and temperature

Before 1999

After 1999

Vegetation green-up significantly a

dvanced in 29% of vegetated area,

particularly in the southwestern par

ts;

In contrast, during 1999-2006, the

green-up date delayed (positive tre

nds) in more than 75% of Qinghai-

Xizang Plateau.

Spring phenology change

Slope:-0.0003 R2 = 0.59, P<0.001

-3

-2

-1

0

1

2

2750 3150 3550 3950 4350 4750 5150 5550

Altitude (m)

Tre

nd in

ons

et o

f gre

en-u

p (d

ays/

yr)

Slope:0.0019 R2 = 0.89, P<0.001

-4

-2

0

2

4

6

8

2750 3150 3550 3950 4350 4750 5150 5550

Altitude (m)

Tre

nd in

ons

et o

f gre

en-u

p (d

ays/

yr)

Before 1999, along the elevation g

radient, higher advancing rate of s

pring phenology in the regions with

higher elevation;

The phenomenon was reversed du

ring 1999-2006 with higher delayin

g trend of spring phenology in high

elevation, particularly at elevation

higher than ≈ 4000 m .

Before 1999

After 1999

Green-up trends in relation to elevation

1. Change in spring phenology

Outline

2. Change in carbon balance

Atmosphere

Data: NOAA, CDIAC; Le Quéré et al. 2009, Nature-geoscience

CO

2 Par

titi

onin

g (P

gC y

-1)

1960 20101970 1990 20001980

10

8

6

4

2

Motivation

Total CO2 emissions

Fate of Anthropogenic CO2 Emissions (2000-2009)

Global Carbon Project 2010; Updated from Le Quéré et al. 2009, Nature Geoscience; Canadell et al. 2007, PNAS

Motivation

Most evidence from forests, and knowleadge on the role of

grassland in global carbon cycle is very limted;

Extensive grassland is covered in Tibetan Plateau, with 1.4 × 106 km2, roughly 44% of the total grassland area of China and 6%

of the worldwide grassland area ;

A large amount of soil organic carbon (SOC) related to slow de

composition due to low temperature are known to be sensitive

under global warming.

Why do we need C budget estimates for the Tibet grasslands?

Change in carbon balance

Change in carbon balance

The parameterizations of ORCHIDEE were improved and calibrate

d against multiple time-scale and spatial-scale observations includi

ng

(1) Eddy-covariance CO2 fluxes at Haibei alpine meadow site;

(2) Soil temperature collocated with 30 meteorological stations;

(3) Satellite leaf area index (LAI) data;

(4) Soil organic carbon (SOC) density profiles from China’s second na

tional soil survey.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 31 61 91 121 151 181 211 241 271 301 331 361

LA

I

FAPAR (average 2002-2004)

FAPAR (2002)

Modeled (2002)

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

FAPAR (average 2002-2004)

FAPAR (2003)

Modeled (2003)

1 31 61 91 121 151 181 211 241 271 301 331 361

FAPAR (average 2002-2004)

FAPAR (2004)

Modeled (2004)

-2

0

2

4

6

8

10

1 31 61 91 121 151 181 211 241 271 301 331 361

TE

R (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

TER(modeled)TER(measured)

-6

-4

-2

0

2

4

1 31 61 91 121 151 181 211 241 271 301 331 361

NE

E (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

NEE (modeled)NEE(measured)

-2

0

2

4

6

8

10

12

14

1 31 61 91 121 151 181 211 241 271 301 331 361

GP

P (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

-20

-15

-10

-5

0

5

10

15

20

T (

°C)

GPP(modeled)

GPP(measured)T (°C)

• LAI• GPP• TER• NEE

Before validation

Change in carbon balance

Ecosystem gross GPP,

TER fluxes, and

absolute values of NEE

are all overestimated.

Tan et al., GBC (2010)

After validation

-2

0

2

4

6

8

10

1 31 61 91 121 151 181 211 241 271 301 331 361

GP

P (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

GPP(modeled)

GPP(measured)

-6

-4

-2

0

2

4

1 31 61 91 121 151 181 211 241 271 301 331 361

NE

E (

gC/d

ay/m

2)

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

NEE(modeled)NEE(measured)

-2

0

2

4

6

8

1 31 61 91 121 151 181 211 241 271 301 331 361

TE

R (

gC/d

ay/m

2)

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

RE(modeled)RE(measured)

0.0

0.5

1.0

1.5

2.0

2.5

1 31 61 91 121 151 181 211 241 271 301 331 361

LA

I

FAPAR (average 2002-2004)

FAPAR (2002)

Modeled (2002)

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

FAPAR (average 2002-2004)

FAPAR (2003)

Modeled (2003)

1 31 61 91 121 151 181 211 241 271 301 331 361

FAPAR (average 2002-2004)

FAPAR (2004)

Modeled (2004)

• LAI• GPP• TER• NEE

After calibration,

ORCHIDEE can

successfully capture the

seasonal change of C

flux as well as the LAI.

Change in carbon balance

Tan et al., GBC (2010)

y = 1.07 x

R2 = 0.95, RMSE = 1.56

-15

-10

-5

0

5

10

15

20

25

-15 -10 -5 0 5 10 15 20 25

Modeled S-20cm Te (ºC)

Obs

erve

d S

-20c

mTe

(ºC

)

Spring (Apr. - May)

Summer (Jun. - Aug.)

Autumn (Sep. - Oct.)

Winter (Nov. - Mar.)

y = 0.96 x

R2 = 0.38, n=51RMSE = 4.20

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Modeled SOC (Kg C m-2

)

Ob

serv

ed S

OC

(Kg

C m

- 2)

(b)

OBSERVED

21

3

03

0

1

21

02 323

1

00

33

0,1,2,3

1.0

0.99

0.0

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Standard Deviation

Sta

nd

ard

Devia

tio

n

OBSERVEDGPPNEETERLAISOCGIMMS LAIS-TEarc系列1arc2系列12系列13系列14系列15系列16系列17系列18系列19系列20系列21系列22

Spatial patterns of

model estimated soil

temperature, SOC, and

LAI over Tibet grassland

are also comparable

with the observations.

Change in carbon balance

Tan et al., GBC (2010)

Change in carbon balance

Biomass and SOC in the Qinghai-Tibetan and the Tibetana

The total biomass C stocks in China’s grassland is about 1.05 Pg C [Piao et al., 2

007a], indicating that the Qinghai-Tibetan grasslands alone account for 34% of th

e whole-country grasslands biomass;

Wu et al. [2003], estimated that the total SOC stock of China’s grasslands is 20-2

4 Pg C, and Qinghai-Tibetan grasslands contribute about 50-60% .

Tan et al., GBC (2010)

Over the last five decades, both

GPP and TER of Qinghai-Tibetan

grasslands is significantly

increased;

But the increasing rate of GPP is

larger than that of TER;

Accordingly, NEP of Qinghai-

Tibetan grassland also shows a

significant increasing trend during

the study period, and the

magnitude of increase in NEP is

only 15% of that in GPP .

Change in total GPP, TER, and NEP

Change in carbon balance

y = 3.5223x + 301.79

R2 = 0.5718

200

300

400

500

600

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006

GP

P(T

g C

/yr)

y = 3.0222x + 302.49

R2 = 0.7375

200

300

400

500

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006

TER

(Tg

C/y

r)

y = 0.5001x - 0.6995

R2 = 0.0718

-50

0

50

100

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006

NE

P (T

g C

/yr)

Change in carbon balance over the European forests is mainly driven by TER rather than GPP

y = 0.2949x - 103.15

R2 = 0.5419

-50

-20

10

40

70

100

200 300 400 500 600

GPP (TgC/yr)

NE

P(T

gC/y

r)

y = 0.2355x - 77.233

R2 = 0.1973

-50

-20

10

40

70

100

200 250 300 350 400 450 500

TER (TgC/yr)

NE

P(T

gC/y

r)

Change in carbon balance over the Qinghai-Tibetan grasslands is mainly driven by GPP rather than TER

Change in carbon balance

C budget during 1980s-2000s (Tg C/yr)

Since 1980, Qinghai-Tibetan grassland

s are modeled to annually take up abou

t 18 Tg of carbon.

This is about 50-75% of the carbon sink

over the whole plateau (including other

ecosystems such as forest) or about 7-

10% of the carbon sink in China.

Change in carbon balance

Thanks !