greening of the earth and its drivers - ipsl
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Greening of the Earth and
its drivers
Shilong Piao, Zaichun Zhu
Sep 22, 2015
Changes in CO2
(Keeling et al., 1996. Nature)
Changes in vegetation index in northern high lattitudes
(Myneni et al., 1997. Nature)
Pioneer research:
- Inferred from changes in seasonal cycle of atmospheric CO2 concentration
- Direct evidence from satellite observations.
Background
Relationship between vegetation index and temperature
(Zhou et al., 2001)
Changes in global NPP during 1982-1999
(Piao et al., 2006)
Following research:
- Statistical analyses between vegetation and environmental factors (Zhou et al., 2001)
- Linking satellite observations and ecosystem models (Lucht et al., 2001;Piao et al.,
2006)
Background
Relationship between the constructed vegetation index
and observation (Los et al., 2013)
Dominant driving factors in LAI
(Mao et al., 2013)
Recent research:
- Attribution of global vegetation based on statistical method (Nemani et al.,
2003; Los et al., 2013)
- Attribution of global vegetation trends based on ecosystem models (Mao
et al., 2013)
Background
Main limitations:
Statistical methods – assume factors are independent
Ecosystem models – large model uncertainties
Uncertainties in satellite observations
Lack of nitrogen deposition and land cover change
processes
Background
Main data
Models S1 S2 S3 S4
CLM4.5 yes yes no yes
LPJ yes yes no yes
LPJ-GUESS yes yes no yes
LPX-Bern yes yes no yes
OCN yes yes no yes
ORCHIDEE yes yes no yes
VISIT yes yes no yes
CLM4 yes yes yes no
CALBE yes yes yes no
VEGAS yes yes no no
Simulations: S1: varying CO2
S2: varying CO2 and climate
S3: varying CO2, climate and nitrogen deposition
S4: varying CO2, climate and land cover change
CO2 fertilization: S1
Climate change: S2-S1
Nitrogen deposition: S3-S2
Land cover change: S4-S2
Satellite observations:
GIMMS LAI
GLOBMAP LAI
GLASS LAI
Freeze/Thaw data
Study period:
1982-2009
Schematic diagram of growing season
AVHRR GIMMS LAI3g - Global coverage
- 8 km
- 15 days
- 1982 – 2009
FT-ESDR(Freeze/Thaw data) - Global
- 25 km
- daily
- 1982 – 2009
Time line Start date (SG,20%) End date (SG,20%)
Thawed Periods
Growing Season
……
Note: - Evergreen needle
leaf forests
Definition of growing season
Growing Season:
Overlapping period of thawed period and SG based growing season.
Comparison between growing season
integrated leaf area index (LAI) and GPP
product from Beer et al. (2010) for 83
terrestrial eco-regions of the world (Olsen,
2001).
Definition of growing season
Trend Analysis
Spatial distribution of greening:
GIMMS LAI: ~25%
GLOBMAP LAI: ~43%
GLASS LAI: ~50%
Browning:
<4%
Optimal Fingerprints
Models CTL
years (chunks)
ACCESS1-0 500(17)
ACCESS1-3 500(17)
bcc-csm1-1 500(17)
bcc-csm1-1-m 400(14)
CCSM4 1051(37)
CESM1-BGC 500(17)
CESM1-CAM5 319(11)
CESM-FASTCHEM 222(7)
CESM1-WACCM 200(7)
GFDL-CM3 500(17)
GFDL-ESM2G 500(17)
GFDL-ESM2M 500(17)
HadGEM2-CC 240(8)
HadGEM2-ES 577(20)
Inmcm4 500(17)
MPI-ESM-LR 1000(35)
MPI-ESM-MR 1000(35)
MPI-ESM-P 1156(41)
Y = 𝛽𝑖𝑥𝑖 + 𝜀
𝑛
𝑖=1
Internal variability (18 models)
CO2 fertilization: S1
Climate change: S2-S1
Nitrogen deposition: S3-S2
Land cover change: S4-S2
GIMMS LAI3g
GLOBMAP LAI
GLASS LAI
Satellite observations
Model simulated signals
Attribution
CO2 fertilization:
70.1±29.4%, 0.048±0.020 m2m-2yr-1
Climate change:
8.1±20.6%, 0.006±0.014 m2m-2yr-1
Nitrogen deposition:
8.8±11.8%, 0.006±0.008 m2m-2yr-1
Land cover change:
3.7±14.7%, 0.003±0.010 m2m-2yr-1
Global scale:
Simple Conceptual Model
W =𝐴
𝐸=𝐶𝑎1.6𝑣1 −𝐶𝑖𝐶𝑎
𝑑𝑊
𝑊=𝑑𝐴
𝐴−𝑑𝐸
𝐸=𝑑𝐶𝑎𝐶𝑎−𝑑𝑣
𝑣+𝑑 1 −
𝐶𝑖𝐶𝑎
1 −𝐶𝑖𝐶𝑎
𝑑𝑊
𝑊=𝑑𝐴
𝐴−𝑑𝐸
𝐸=𝑑𝐶𝑎𝐶𝑎−1
2
𝑑𝑣
𝑣
The water use efficiency of photosynthesis is defined as the ratio of assimilation (A)
and transpiration (E) rates per unit of leaf area:
Here, v, Ci and Ca refer to leaf-to-air water vapor pressure deficit, intercellular and atmospheric
CO2 concentrations, respectively.
The quantity 1 − 𝐶𝑖 𝐶𝑎 has been modeled and observed as being proportional to 𝑣 (𝐹𝑎𝑟𝑞𝑢ℎ𝑎𝑟 𝑒𝑡 𝑎𝑙. 1993, 𝑀𝑒𝑑𝑙𝑦𝑛 𝑒𝑡 𝑎𝑙. 2011). Thus,
Simple Conceptual Model
E = 1.6𝑔𝑠𝑣
𝑑𝐸
𝐸=𝑑𝑔𝑠𝑔𝑠+𝑑𝑣
𝑣
𝑑𝑊
𝑊=𝑑𝐴
𝐴−𝑑𝑔𝑠𝑔𝑠−𝑑𝑣
𝑣=𝑑𝐶𝑎𝐶𝑎−1
2
𝑑𝑣
𝑣
𝑑𝐴
𝐴=𝑑𝐶𝑎𝐶𝑎+1
2
𝑑𝑣
𝑣+𝑑𝑔𝑠𝑔𝑠
E can be written as
The relative effects of changes in gs and v can be expressed as:
With that, Equation 3 can be written as
And,
Simple Conceptual Model
𝑑𝐴
𝐴=𝑑𝐶𝑎𝐶𝑎+1
2
𝑑𝑣
𝑣+𝑑𝑔𝑠𝑔𝑠
341ppm to 387ppm (~46 ppm) (Tans 2015), i.e. 𝑑𝐶𝑎
𝐶𝑎=13.5%
Climate Research Unit (CRU), we calculated that 𝑑𝑣
𝑣=2.25%
Experiment measurements suggests that 𝑑𝑔𝑠
𝑔𝑠= -5.0 ~ -3.0%
We can estimate: 𝑑𝐴
𝐴=9.7~11.7%
or 𝑑𝐴𝑐𝑜2
𝐴𝑐𝑜2=8.5~10.5%
FACE experiment: 𝑑𝑁𝑃𝑃
𝑁𝑃𝑃= 6.1~9.4%
𝑑𝐿𝐴𝐼
𝐿𝐴𝐼= 0.3~11.1%
Model simulation: 𝑑𝐺𝑃𝑃
𝐺𝑃𝑃=5.2~8.3%
𝑑𝑁𝑃𝑃
𝑁𝑃𝑃=5.2~9.0%
𝑑𝐿𝐴𝐼
𝐿𝐴𝐼=4.7~9.5%
The generally comparable relative changes of global vegetation growth estimated
from the simple conceptual models, the models, and the FACE experiments lend
credibility to our estimates of the response of global vegetation to elevated Ca
during 1982-2009.
Attribution
CO2 CLI NDE LUC
CLM4.5 0.054** -0.004 -- 0.007**
LPX-Bern 0.044** 0.003 -- 0.009
OCN 0.040** 0.016** -- -0.004**
LPJ 0.069** -0.002 -- -0.008
LPJ-GUESS 0.047** -0.010** -- 0.005
ORCHIDEE 0.036** -0.006 -- -0.008**
VISIT 0.089** 0.026** -- 0.018**
CLM4 0.034** 0.025** 0.011** --
CABLE 0.053** -0.006** 0.001** --
VEGAS 0.013** 0.013** -- --
MMEM 0.048**±0.020 0.006±0.014 0.006**±0.008 0.002±0.010
Individual Model Results:
Attribution
Continental scale: Africa, Asia
Attribution
Continental scale: Europe, Oceania
Attribution
Continental scale: South America, North America
CO2 fertilization effects
Generally uniformly distributed
The largest magnitudes of LAI trends
due to CO2 is found in the tropics
Relative change in LAI trends due to
CO2 is posited over semiarid regions
Global climate regions
Pixel scale
Climate change effects
Strong heterogeneity
Over-sensitive to decrease in precipitation (Piao et al, 2013)
NDE and LCC effects
Strong NDE effects were observed in
Southeast Asia
Slight decrease in Europe
Large uncertainties - only two models
were available to assess the NDE effects
Strong LCC effects were observed in
Southeast China and Southeast U.S.
Slight decrease in tropics
Large discrepancies among models,
sometimes differ in sign
Dominant driving factors
Climate change dominates 28% of the
vegetated area, followed by CO2(23%),
LCC(10%) and NDE(1%)
Other factors (OF) dominates 25%,
mainly found in regions influenced by
intensive ecosystem management, such
as northeast China, India and Europe.
Conclusion
We show a persistent and widespread greening over 25 to 50% of
the global vegetated area, whereas less than 4% of the globe shows
browning.
CO2 fertilization effects explains 70% of the observed greening
trend, followed by nitrogen deposition (9%), climate change (8%)
and land cover change (LCC) (4%).
CO2 fertilization and climate change effects explain most of the
greening trends in the tropics and the high latitudes, respectively.
LCC explains the regional browning observed in Southeast China
and Eastern United States.
Effects of non-modeled factors suggest further research is required
to reduce uncertainties in ecosystem models, especially their
modeling of climate change and land management.
Acknowledgements
This study was supported by the National Basic Research Program of China (Grant No.
2013CB956303), National Natural Science Foundation of China (41125004), Chinese
Ministry of Environmental Protection Grant (201209031), the 111 Project(B14001), and
the European Research Council Synergy grant ERC-SyG-610028 IMBALANCE-P.
We thank all people and institutions who provide data used in this study, in particular, the
TRENDY modelling group.
RBM is funded by NASA Earth Science. JGC thanks the support from the Australian
Climate Change Science Program. AA and TMP acknowledge support through EC FP7
grant LUC4C (grant 603542) and the Helmholtz Association ATMO programme.
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