change in vegetation growth and c balance in the tibetan plateau
DESCRIPTION
Change in vegetation growth and C balance in the Tibetan Plateau. Shilong Piao , Kun Tan, Nan Cong, Xuhui Wang. Peking University. [email protected]. Motivation. Rapid climate warming. The Tibetan Plateau is one of the most critical and sensitive regions in the earth ’s climate system . - PowerPoint PPT PresentationTRANSCRIPT
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
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 !