Carbon dynamics: seasonality, interannual variability, and the future under climate change

1
Carbon dynamics seasonality, interannual
variability, and the future under climate change
what we are learning from remote sensing,
surface measurements, and modeling
NASA Carbon Cycle Ecosystems Workshop University
of Maryland, April 28 to May 2, 2008
MODIS sensor on Terra satellite
Amazon Eddy flux tower
2
Carbon dynamics seasonality, interannual
variability, and the future under climate change
Scott Saleska, University of Arizona
Mike Behrenfeld, Sangram Ganguly, Mike Goulden,
Kamel Didan, Mark Friedl, Scott Goetz, Alfredo
Huete, Ranga Myneni, Piyachat Ratana, Natalia
Restrepo-Coupe, Joellen Russell, Humberto da
Rocha, Yosio Shimabukuro, Xiaoyang Zhang
MODIS sensor on Terra satellite
Amazon Eddy flux tower
3
Outline

• 1. Terrestrial Systems
• a. High latitude trends with climate change
• b. Tropical seasonality and response to drought
• 2. Ocean Systems
• 3. Summary, Outstanding questions, and future work

4
What is the effect of climate trends on
vegetation seasonality and productivity?
5
What is the effect of climate trends on
vegetation seasonality and productivity?
Answer 10 years ago (Myneni et al., 1997)
Consistent INCREASE in seasonal amplitude of
satellite-derived vegetation greenness in
Northern Hemisphere (NDVI from AVHRR) (earlier
spring green-up ? bigger NDVI amplitude)
Seasonal NDVI amplitude
6
What is the effect of climate trends on
vegetation seasonality and productivity?
Answer 10 years ago (Myneni et al., 1997)
Consistent INCREASE in seasonal amplitude of
satellite-derived vegetation greenness in
Northern Hemisphere (NDVI from AVHRR) (earlier
spring green-up ? bigger NDVI amplitude)
Seasonal NDVI amplitude
Also was consistent with increasing amplitude of
atmospheric CO2 oscillation
7
What happened since 1991?
8
Unburned Areas Photosynthesis Trends
Goetz et al. PNAS 2005
See also Zhou et al., 2001 Angert et al.,
2005 Ganguly et al., in review
NDVI Changes in Unburned Areas, 1982-2003
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Unburned Areas Photosynthesis Trends
Seasonal NDVI
Goetz et al. PNAS 2005
See also Zhou et al., 2001 Angert et al.,
2005 Ganguly et al., in review
NDVI Changes in Unburned Areas, 1982-2003
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Unburned Areas Photosynthesis Trends
Seasonal NDVI
Pinatubo cooling?
Goetz et al. PNAS 2005
See also Zhou et al., 2001 Angert et al.,
2005 Ganguly et al., in review
NDVI Changes in Unburned Areas, 1982-2003
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Unburned Areas Photosynthesis Trends
Seasonal NDVI
Goetz et al. PNAS 2005
12
Drier summers cancel out the CO2 uptake
enhancement induced by warmer springs
Angert, et al. (2005), PNAS.
1.5
0
Trend toward earlier spring uptake with warming
continues post-Pinatubo
-1.5
Anomaly (ppm/yr or C/yr)
1985
1990
1995
2000
1.5
0
-1.5
1985
1990
1995
2000
13
Drier summers cancel out the CO2 uptake
enhancement induced by warmer springs
Angert, et al. (2005), PNAS.
Trend toward earlier spring uptake with warming
continues post-Pinatubo
Anomaly (ppm/yr or C/yr)
1985
1990
1995
2000
But trend towards increased CO2 uptake over
whole-growing season decouples from warming.
14
Effects of 2003 European Heatwave reveal
mechanisms consistent with long-term trends
(Jolly et al., 2005)
2003 MODIS summer FPAR (relative to long-term
mean)
15
Effects of 2003 European Heatwave reveal
mechanisms consistent with long-term trends
(Jolly et al., 2005)
2003 MODIS summer FPAR (relative to long-term
mean)
16
Effects of 2003 European Heatwave reveal
mechanisms consistent with long-term trends
(Jolly et al., 2005)
2003 MODIS summer FPAR (relative to long-term
mean)
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B. Tropics What is the seasonality of ecosystem
metabolism in Amazônia?
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B. Tropics What is the seasonality of ecosystem
metabolism in Amazônia?

• Previous consensus answer photosynthesis and/or
  transpiration decline in dry seasons

Dickenson Henderson-Sellars (1988)Nobre et al.
(1991) Tian et al. (1998) TEMBotta et al.
(2002) IBISWerth Avissar (2002) GISS GCM
Lee et al. (2005) NCAR CLM
Climate and/or ecosystem models
But see Potter et al. (1998) modeling study (CASA
model)
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B. Tropics What is the seasonality of ecosystem
metabolism in Amazônia?

• Previous consensus answer photosynthesis and/or
  transpiration decline in dry seasons
• LBA-Eco produced a suite of evidence suggesting a
  different picture

Dickenson Henderson-Sellars (1988)Nobre et al.
(1991) Tian et al. (1998) TEMBotta et al.
(2002) IBISWerth Avissar (2002) GISS GCM
Lee et al. (2005) NCAR CLM
Climate and/or ecosystem models
Amazonian ecosystems are not water-limited (at
least over seasonal timescales) but are driven by
available energy and sunlight
Results partly anticipated by Potter et al.
(1998) modeling study (CASA model)
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Measurements across the basin
Eddy Flux towers measuring photosynthesis (GPP)
C. Caxiuana
B. Santarém (km67)
A. Manaus, km34
GPP
(gCm-2 d-1)
PAR (?mol m-2 s-1)
precip (mm mo-1)
Restrepo-Coupe, in prep.(and Araujo et al.
(2002) Manaus)
See poster
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Measurements across the basin
Remote Sensing(MODIS EVI)
Eddy Flux towers
Huete et al. (2006)
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Measurements across the basin
Remote Sensing(MODIS EVI)
Eddy Flux towers
Huete et al. (2006)
Also see parallel results in LAI
seasonality(Myneni et al., 2007)
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The seasonality of forest metabolism is it
linked to the future of the forest under climate
change?
Forest? ...
or Savanna?
24
Model-simulated responses of Amazon forest to
drought
(U.K. Hadley Center model)
Long-term drought(Climate change)
Changes in broadleaf tree-cover
By 2080 Widespread loss of Amazon forest (Betts
et al. 2004)
?cover (fraction)
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Model-simulated responses of Amazon forest to
drought
(U.K. Hadley Center model)
Long-term drought(Climate change)
Short-term drought(e.g. El Nino)
Changes in broadleaf tree-cover
Hadley model-predicted GPP precip in central
Amazonia in years relative to El Nino drought
El Nino Drought
Forest Photosynthesis (Mg C ha-1 yr-1)
By 2080 Widespread loss of Amazon forest (Betts
et al. 2004)
(Jones et al., 2001)
?cover (fraction)
Years -3 -2 -1 0 1 2 3 4
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Model-simulated responses of Amazon forest to
drought
(U.K. Hadley Center model)
? This prediction is testable!
Long-term drought(Climate change)
Short-term drought(e.g. El Nino)
Changes in broadleaf tree-cover
Hadley model-predicted GPP precip in central
Amazonia in years relative to El Nino drought
El Nino Drought
Forest Photosynthesis (Mg C ha-1 yr-1)
By 2080 Widespread loss of Amazon forest (Betts
et al. 2004)
(Jones et al., 2001)
?cover (fraction)
Years -3 -2 -1 0 1 2 3 4
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Observed response to 2005 Amazon drought
precipitation anomaly
Units number of standard deviations in 2005
from the long-term mean for the July/Aug/Sept
(JAS) quarter. I.e., for each pixel
Saleska, Didan, Huete, Rocha (2007), Science
28
Observed response to 2005 Amazon drought
precipitation anomaly
vegetation greenness anomaly
Units number of standard deviations in 2005
from the long-term mean for the July/Aug/Sept
(JAS) quarter. I.e., for each pixel
Saleska, Didan, Huete, Rocha (2007), Science
29
Observed response to 2005 Amazon drought
precipitation anomaly
vegetation greenness anomaly
Short term drought, contrary to model
predictions, does not cause photosynthetic
slow-down forests may be adapted to drought, to
take advantage of extra sunlight
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2. Oceans How do changes in climate affect
ocean productivity?
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Climate change will alter ocean phytoplankton

• Stratified Oceans (low latitude)
• Perpetual growing season
• Nutrient impoverished surface layer
• Inverse relationship b/w temperature
• and phytoplankton chlorophyll

Surface warming enhanced nutrient stress
decreases growth rates and biomass, shallower
mixing increases growth irradiance all of which
decrease chlorophyll levels
low nutrient high light mixed layer
restricted vertical exchange
low light high nutrient deep layer
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Climate change will alter ocean phytoplankton

• Seasonal Seas (high latitude)
• Variable growing season
• Light seasonally limiting
• Nutrients seasonally limiting
• Positive relationship b/w
• temperature and chlorophyll

• Stratified Oceans (low latitude)
• Perpetual growing season
• Nutrient impoverished surface layer
• Inverse relationship b/w temperature
• and phytoplankton chlorophyll

Surface warming enhanced nutrient stress
decreases growth rates and biomass, shallower
mixing increases growth irradiance all of which
decrease chlorophyll levels
Surface warming enhanced stratification increases
growing season, chlorophyll increases with
improved growth rates
low nutrient high light mixed layer
nutrient charged low light mixed layer
restricted vertical exchange
enhanced vertical exchange
low light high nutrient deep layer
low light high nutrient deep layer
33
Model-based predictions
Primary Productivity change (Pg C deg-1 y-1)
low-lat decreases (stratified ocean)
34
Model-based predictions
Primary Productivity change (Pg C deg-1 y-1)
High-lat increases
low-lat decreases (stratified ocean)
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Satellite-based (SeaWiFS) observations
Stratified Oceans 1997 - 2007
I

• Chlorophyll and temperature are
• inversely related
• - i.e., chlorophyll decreases as
• temperature increases
• Temperature-effect not direct
• Temperature related to stratification
• Stratification influences nutrients
• light, which directly effect
• phytoplankton

Decrease
Increase
Temp. anomaly (oC)
El Nino warmth
Chlorophyll anomaly (Tg C month-1)
Stratification anomaly
This Region
Decrease
Increase
Behrenfeld et al. (2006)
36
Satellite-based (SeaWiFS) observations
High Latitudes 1997 - 2007
Decrease
Increase

• Chlorophyll changes in high-latitude
• north larger than the south
• Clear relationships between chlorophyll
• and temperature
• In both high latitude regions, overall
• pattern is decreasing chlorophyll with
• increasing temperature this is the
• opposite of what models predict

High-latitude North
Chlorophyll anomaly (Tg C month-1)
Temp. anomaly (oC)
These Regions
High-latitude South
Decrease
Behrenfeld et al. (2006)
Increase
37
3. Summary, Outstanding Science Questions, and
Research Needs
38
Summary Outstanding Science Questions

• In Northern high latitude terrestrial systems--
  1980s earlier springs/more vegetation
  activity-- 1990-2000s differential response
  drought reduces vegetation activity

39
Summary Outstanding Science Questions

• In Northern high latitude terrestrial systems--
  1980s earlier springs/more vegetation
  activity-- 1990-2000s differential response
  drought reduces vegetation activity
• ? Questions -- what caused the slow-down in
  atmospheric CO2 after Pinatubo?

40
Summary Outstanding Science Questions

• In Northern high latitude terrestrial systems--
  1980s earlier springs/more vegetation
  activity-- 1990-2000s differential response
  drought reduces vegetation activity
• ? Questions -- what caused the slow-down in
  atmospheric CO2 after Pinatubo?
• 2. In tropical Amazon forests -- seasonality
  of ecosystem metabolism driven by available
  sunlight-- 2005 drought suggests Amazon forests
  are resilient

41
Summary Outstanding Science Questions

• In Northern high latitude terrestrial systems--
  1980s earlier springs/more vegetation
  activity-- 1990-2000s differential response
  drought reduces vegetation activity
• ? Questions -- what caused the slow-down in
  atmospheric CO2 after Pinatubo?
• 2. In tropical Amazon forests -- seasonality
  of ecosystem metabolism driven by available
  sunlight-- 2005 drought suggests Amazon forests
  are resilient
• ? Question what are the limits of forest
  tolerance of drought?

42
Summary Outstanding Science Questions

• In Northern high latitude terrestrial systems--
  1980s earlier springs/more vegetation
  activity-- 1990-2000s differential response
  drought reduces vegetation activity
• ? Questions -- what caused the slow-down in
  atmospheric CO2 after Pinatubo?
• 2. In tropical Amazon forests -- seasonality
  of ecosystem metabolism driven by available
  sunlight-- 2005 drought suggests Amazon forests
  are resilient
• ? Question what are the limits of forest
  tolerance of drought?
• 3. Ocean declines in productivity (chlorophyl)
  with increasing temperature, in both low latitude
  (stratified) and high latitude (seasonal) seas.
• ? Question Why ?

43
Future Research with a Comprehensive Earth
Observation system
Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Long-term ground observation network (e.g.
FluxNet plus)
44
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Long-term ground observation network (e.g.
FluxNet plus)
45
Example Does NDVI detect changes in vegetation
phenology or in snow cover?
(discussed by Shabonov, et al., 2002 and Dye
Tucker, 2003)
46
Example Does NDVI detect changes in vegetation
phenology or in snow cover?
(discussed by Shabonov, et al., 2002 and Dye
Tucker, 2003)
Flux-defined growing season
Canadian Boreal Forest
Daytime maximum uptake?mol CO2 m-2 s-1
NDVI
NDVI
Snow-cover defined season
MacMillan Goulden (in press)
47
Example Does NDVI detect changes in vegetation
phenology or in snow cover?
(discussed by Shabonov, et al., 2002 and Dye
Tucker, 2003)
Flux-defined growing season
Canadian Boreal Forest
Daytime maximum uptake?mol CO2 m-2 s-1
NDVI
NDVI
Snow-cover defined season

• Cautious interpretation needed for understanding
  springtime NDVI increases
• Emerging long-term (decadal) flux datasets will
  help

MacMillan Goulden (in press)
48
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Long-term ground observation network (e.g.
FluxNet plus)
49
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends
• Better understanding of what satellite vs.
  surface observations measure

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Long-term ground observation network (e.g.
FluxNet plus)
50
Example What satellite index best compares to
eddy flux-derived GPP?
MaeKlong Tropical Forest, Thailand
3500
)
0.8
3000
-1
mo
2500
-1
0.6
MODIS indices
2000
(kg C ha
1500
0.4
Tower GPP
1000
GPP
0.2
500
Jul
Jan
Feb
Apr
Jun
Oct
Mar
Aug
Sep
Nov
Dec
May
Month
Huete et al., (in press) Multiple site tower flux
and remote sensing comparisons of tropical forest
dynamics in monsoon Asia, Ag. For. Met.
51
Example What satellite index best compares to
eddy flux-derived GPP?
MaeKlong Tropical Forest, Thailand
3500
)
0.8
3000
-1
mo
2500
-1
0.6
MODIS indices
2000
(kg C ha
1500
0.4
Tower GPP
(R20.07)
MODIS GPP
1000
GPP
0.2
500
Jul
Jan
Feb
Apr
Jun
Oct
Mar
Aug
Sep
Nov
Dec
May
Month
Huete et al., (in press) Multiple site tower flux
and remote sensing comparisons of tropical forest
dynamics in monsoon Asia, Ag. For. Met.
52
Example What satellite index best compares to
eddy flux-derived GPP?
MaeKlong Tropical Forest, Thailand
3500
)
0.8
3000
-1
mo
2500
-1
0.6
MODIS indices
2000
(kg C ha
1500
0.4
Tower GPP
(R20.07)
MODIS GPP
1000
(R20.01)
GPP
MODIS FPAR
0.2
500
Jul
Jan
Feb
Apr
Jun
Oct
Mar
Aug
Sep
Nov
Dec
May
Month
Huete et al., (in press) Multiple site tower flux
and remote sensing comparisons of tropical forest
dynamics in monsoon Asia, Ag. For. Met.
53
Example What satellite index best compares to
eddy flux-derived GPP?
MaeKlong Tropical Forest, Thailand
3500
)
0.8
3000
-1
mo
2500
-1
0.6
MODIS indices
2000
(kg C ha
1500
0.4
(R20.07)
1000
(R20.01)
GPP
(R20.88)
0.2
500
Jul
Jan
Feb
Apr
Jun
Oct
Mar
Aug
Sep
Nov
Dec
May
Month
Huete et al., (in press) Multiple site tower flux
and remote sensing comparisons of tropical forest
dynamics in monsoon Asia, Ag. For. Met.
54
Example What satellite index best compares to
eddy flux-derived GPP?
South East Asia Tropical forests Monthly tower
GPP vs MODIS EVIacross three sites
GPP (kgC/ha/mo)
Regression lines from other studies
55
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends
• Better understanding of what satellite vs.
  surface observations measure

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Long-term ground observation network (e.g.
FluxNet plus)
56
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends
• Better understanding of what satellite vs.
  surface observations measure

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Hyperspectral(high resolution, direct
biophysical observations)
Hyperspectral
Hyperspectral
Long-term ground observation network (e.g.
FluxNet plus)
57
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends
• Better understanding of what satellite vs.
  surface observations measure
• Long-term inter-comparability of datasets for
  trend analysis MODIS vs. AVHRR (looking
  backwards) and MODIS vs. VIIRS (looking forward)

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Hyperspectral(high resolution, direct
biophysical observations)
Hyperspectral
Hyperspectral
Long-term ground observation network (e.g.
FluxNet plus)
58
MEASURES project Vegetation Phenology and
Vegetation Index Products from Multiple Long Term
Satellite Data Records An improved global time
series in support of CCE science

• Kamel Didan (PI), Jeff Czapla, Mark Friedl,
  Alfredo Huete, Calli Jenkerson, Willem van
  Leeuwen, Thomas Maiersperger, Tomoaki Miura,
  Xiaoyang Zhang

http//phenology.arizona.edu summer 08 See
poster
59
Future Research with a Comprehensive Earth
Observation system

• Equally long records of satellite and surface for
  understanding trends
• Better understanding of what satellite vs.
  surface observations measure
• Long-term inter-comparability of datasets for
  trend analysis MODIS vs. AVHRR (looking
  backwards) and MODIS vs. VIIRS (looking forward)

Moderate-Resolution remote sensing (AVHRR, MODIS,
VIIRS) for comprehensive spatial and temporal
coverage
Hyperspectral(high resolution, direct
biophysical observations)
Hyperspectral
Hyperspectral
Long-term ground observation network (e.g.
FluxNet plus)
60
Thanks!
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Increase in CO2 anomaly
Nemani et al. (2003)
63
Does NAO set the pace for the biosphere and for
growing season CO2 drawdown?
(Joellen Russell Mike Wallace, 2004)
Data series 1980-2000 (Climate from NCEP, CO2
from NOAA, NDVI from AVHRR)
64
Does NAO set the pace for the biosphere and for
growing season CO2 drawdown?
(Joellen Russell Mike Wallace, 2004)
The North Atlantic Oscillation (NAO)
(Jan-Mar Sea Level Pressure field)
Negative anomaly
Positive anomaly
Regression Sea-Level Pressure field with
growing-season CO2 drawdown
High CO2 draw-down is associated with high NAO
index
Data series 1980-2000 (Climate from NCEP, CO2
from NOAA, NDVI from AVHRR)
65
Does NAO set the pace for the biosphere and for
growing season CO2 drawdown?
(Joellen Russell Mike Wallace, 2004)
The North Atlantic Oscillation (NAO)
Regression air temperature field vs. NAO Index
(Jan-Mar Sea Level Pressure field)
Negative anomaly
(when NAO is high, tempera-ture anomalies look
like this
Positive anomaly
Regression Sea-Level Pressure field with
growing-season CO2 drawdown
Regression growing season NDVI vs. NAO Index
and NDVI anomalies look like this
High CO2 draw-down is associated with high NAO
index
Data series 1980-2000 (Climate from NCEP, CO2
from NOAA, NDVI from AVHRR)