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 Amazon Eddy flux tower MODIS sensor on Terra satellite

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 Amazon Eddy flux tower MODIS sensor on Terra satellite

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

What is the effect of climate trends on vegetation seasonality and productivity?

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)

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) Also was consistent with increasing amplitude of atmospheric CO2 oscillation

What happened since 1991?

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

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

Unburned Areas – Photosynthesis Trends

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

Unburned Areas – Photosynthesis Trends

Trend

Negative Near Zero Positive

Tundra m ha (%)

2.7 ( 4%) 45.1 (62%) 24.5 (34%)

Unburned Forest

25.8 (22%) 87.9 (74%) 5.1 ( 4%) Goetz

et al.

PNAS 2005

1.5

0 -1.5

1985 “Drier summers cancel out the CO2 uptake enhancement induced by warmer springs” Angert, et al. (2005), PNAS.

1990 1995 2000 Trend toward earlier spring uptake with warming continues post Pinatubo… 1.5

0 -1.5

1985 1990 1995 2000

1.5

0 -1.5

1985 “Drier summers cancel out the CO2 uptake enhancement induced by warmer springs” Angert, et al. (2005), PNAS.

1990 1995 2000 Trend toward earlier spring uptake with warming continues post Pinatubo… 1.5

0 -1.5

1985 1990 1995 2000 But trend towards increased CO2 uptake over whole-growing season decouples from warming.

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)

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)

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)

B. Tropics: What is the seasonality of ecosystem metabolism in Amazônia?

B. Tropics: What is the seasonality of ecosystem metabolism in Amazônia?

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

Climate and/or ecosystem models Dickenson & Henderson-Sellars (1988) Nobre et al. (1991) Tian et al. (1998) [TEM] Botta et al. (2002) Werth & Avissar (2002) Lee et al. (2005) [ IBIS] [ GISS GCM ] [ NCAR CLM] But see Potter et al. (1998) modeling study (CASA model)

B. Tropics: What is the seasonality of ecosystem metabolism in Amazônia?

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

Climate and/or ecosystem models Dickenson & Henderson-Sellars (1988) Nobre et al. (1991) Tian et al. (1998) [TEM] Botta et al. (2002) Werth & Avissar (2002) Lee et al. (2005) [ IBIS] [ GISS GCM ] [ NCAR CLM]

• LBA-Eco produced a suite of evidence suggesting a different picture:

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)

Measurements across the basin

Eddy Flux towers measuring photosynthesis (GPP)…

9 8 5 4 7 6

A. Manaus, km34

11 10 9 8

B. Santarém (km67)

12 10 8 6

C. Caxiuana

400 300 200 100 0 J F M A M J J A S O N D J F M A M J J A S O N D Restrepo-Coupe, in prep.

(and Araujo et al. (2002) Manaus) J F M A M J J A S O N D 900 800 700 600 See poster

Measurements across the basin

9 8 5 4 7 6 Eddy Flux towers…

A. Manaus, km34

11

B. Santarém (km67)

10 9 8 12

C. Caxiuana

10 8 6 400 300 200 100 0 J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 900 800 700 600 Remote Sensing (MODIS EVI) Huete et al. (2006)

Measurements across the basin

9 8 5 4 7 6 Eddy Flux towers…

A. Manaus, km34

11

B. Santarém (km67)

10 9 8 12

C. Caxiuana

10 8 6 Remote Sensing (MODIS EVI) 400 300 200 100 0 J F M A M J J A S O N D J F M A M J J A S O N D 900 800 700 600 Huete et al. (2006) J F M A M J J A S O N D Also see parallel results in LAI seasonality (Myneni et al., 2007)

The seasonality of forest metabolism: is it linked to the future of the forest under climate change?

Forest? ... or Savanna?

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)

Model-simulated responses of Amazon forest to drought: (U.K. Hadley Center model) Short-term drought (e.g. El Nino) Hadley model-predicted GPP & precip in central Amazonia in years relative to El Nino drought 30 El Nino Drought Long-term drought (Climate change) Changes in broadleaf tree-cover 20 10 2 3 (Jones et al., 5 1 2001) 6 2 7 3 8 4 200 100 0 By 2080: Widespread loss of Amazon forest (Betts et al. 2004) 

cover

(fraction)

 Model-simulated responses of Amazon forest to drought: (U.K. Hadley Center model) This prediction is testable!

Short-term drought (e.g. El Nino) Long-term drought (Climate change) Changes in broadleaf tree-cover Hadley model-predicted GPP & precip in central Amazonia in years relative to El Nino drought 30 El Nino Drought 20 10 2 3 (Jones et al., 5 1 2001) 6 2 7 3 8 4 200 100 0 By 2080: Widespread loss of Amazon forest (Betts et al. 2004) 

cover

(fraction)

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:

Anomaly

2005,

JAS



x

2005,

JAS



JAS



x JAS

Saleska, Didan, Huete, Rocha (2007),

Science

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:

Anomaly

2005,

JAS



x

2005,

JAS



JAS



x JAS

Saleska, Didan, Huete, Rocha (2007),

Science

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

2. Oceans: How do changes in climate affect ocean productivity?

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 low nutrient high light mixed layer low light high nutrient deep layer

restricted vertical exchange

Surface warming enhanced nutrient stress decreases growth rates and biomass, shallower mixing increases growth irradiance – all of which decrease chlorophyll levels

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 low nutrient high light mixed layer low light high nutrient deep layer

restricted vertical exchange

Surface warming enhanced nutrient stress decreases growth rates and biomass, shallower mixing increases growth irradiance – all of which decrease chlorophyll levels Seasonal Seas (high latitude) • • Variable growing season • Light seasonally limiting • Nutrients seasonally limiting Positive relationship b/w temperature and chlorophyll nutrient charged low light mixed layer

enhanced vertical exchange

Surface warming enhanced stratification increases growing season, chlorophyll increases with improved growth rates low light high nutrient deep layer

Model-based predictions

Details

• •

Six different coupled climate models Ocean biological responses to climate warming from industrial revolution to 2050

•

Expansion of low production permanently stratified ocean by 4% (N) to 9.4% (S)

•

Significant shifts in community composition Primary Productivity change (Pg C deg -1 y -1 )

low-lat decreases (stratified ocean)

Model-based predictions

Details

• •

Six different coupled climate models Ocean biological responses to climate warming from industrial revolution to 2050

•

Expansion of low production permanently stratified ocean by 4% (N) to 9.4% (S)

•

Significant shifts in community composition Primary Productivity change (Pg C deg -1 y -1 )

High-lat increases low-lat decreases (stratified ocean)

Satellite-based (SeaWiFS) observations Stratified Oceans: 1997 - 2007

Decrease

Δ chlorophyll Δ SST • Chlorophyll and temperature are inversely related - i.e., chlorophyll decreases as temperature increases • Temperature-effect not direct 170 120 70 20 -30 -80 -130 -180 -230

Decrease

• Temperature related to stratification El Nino warmth Δ chlorophyll Δ stratification -0.15

-0.1

-0.05

0 0.05

0.1

0.15

• Stratification influences nutrients & light, which directly effect phytoplankton

‘98 ‘00 ‘02 ‘04 ‘06 Year Increase

Behrenfeld et al. (2006)

Satellite-based (SeaWiFS) observations High Latitudes: 1997 - 2007

Increase

Δ chlorophyll Δ SST

Decrease

• Chlorophyll changes in high-latitude north larger than the south

High-latitude North

• 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

Δ chlorophyll Δ SST

Decrease High-latitude South Increase

Behrenfeld et al. (2006)

3. Summary, Outstanding Science Questions, and Research Needs

Summary & Outstanding Science Questions

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

Summary & Outstanding Science Questions

1. 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 CO 2 after Pinatubo?

Summary & Outstanding Science Questions

1. 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 CO 2 after Pinatubo?

2. In tropical Amazon forests: -- seasonality of ecosystem metabolism driven by available sunlight -- 2005 drought suggests Amazon forests are resilient

Summary & Outstanding Science Questions

1. 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 CO 2 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?

Summary & Outstanding Science Questions

1. 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 CO 2 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 ?

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)

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)

Example: Does NDVI detect changes in vegetation phenology – or in snow cover?

(discussed by Shabonov, et al., 2002; and Dye & Tucker, 2003)

Example: Does NDVI detect changes in vegetation phenology – or in snow cover?

(discussed by Shabonov, et al., 2002; and Dye & Tucker, 2003) Canadian Boreal Forest Flux-defined growing season NDVI Snow-cover defined season MacMillan & Goulden (in press)

Example: Does NDVI detect changes in vegetation phenology – or in snow cover?

(discussed by Shabonov, et al., 2002; and Dye & Tucker, 2003) Canadian Boreal Forest Flux-defined growing season 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)

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)

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)

Example: What satellite index best compares to eddy flux-derived GPP?

MaeKlong Tropical Forest, Thailand

3500 3000 0.8

2500 2000 1500 1000 500

Tower GPP

0.6

0.4

0.2

Month

Huete et al., (in press) Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in monsoon Asia,

Ag. For. Met.

Example: What satellite index best compares to eddy flux-derived GPP?

MaeKlong Tropical Forest, Thailand

3500 3000 0.8

2500 2000 1500 1000 500

Tower GPP MODIS GPP

(R 2 =0.07) 0.6

0.4

0.2

Month

Huete et al., (in press) Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in monsoon Asia,

Ag. For. Met.

Example: What satellite index best compares to eddy flux-derived GPP?

MaeKlong Tropical Forest, Thailand

3500 3000 0.8

2500 2000 1500 1000 500

Tower GPP MODIS GPP MODIS FPAR

(R 2 =0.07) (R 2 =0.01) 0.6

0.4

0.2

Month

Huete et al., (in press) Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in monsoon Asia,

Ag. For. Met.

Example: What satellite index best compares to eddy flux-derived GPP?

MaeKlong Tropical Forest, Thailand

3500 3000 0.8

2500 2000 1500 1000 500

Tower GPP MODIS GPP MODIS FPAR MODIS EVI

(R 2 =0.07) (R (R 2 2 =0.01) =0.88) 0.6

0.4

0.2

Month

Huete et al., (in press) Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in monsoon Asia,

Ag. For. Met.

Example: What satellite index best compares to eddy flux-derived GPP?

South East Asia Tropical forests: Monthly tower GPP vs MODIS EVI across three sites 4000 MaeKlong Sakaerat Bukit Soeharto 3000 2000 Regression lines from other studies: 1000 0 0.2

0.3

0.4

0.5

MODIS EVI

y = 8282x - 2118 R 2 = 0.74

0.6

0.7

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)

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

Long-term ground observation network (e.g. FluxNet plus)

Hyperspectral

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

Long-term ground observation network (e.g. FluxNet plus)

Hyperspectral

MEASURES project Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite Data Records An improved global time series in support of CC&E 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

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

Long-term ground observation network (e.g. FluxNet plus)

Hyperspectral

Thanks!

Nemani et al. (2003) Increase in CO2 anomaly

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, CO 2 from NOAA, NDVI from AVHRR)

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, CO 2 from NOAA, NDVI from AVHRR)

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) Regression: air temperature Negative field vs. NAO Index anomaly (when NAO is high, tempera ture anomalies look like this… Positive anomaly Regression: Sea-Level Pressure field with growing-season CO2 drawdown High CO2 draw-down is associated with high NAO index Regression: growing season … and NDVI anomalies look like this: NDVI vs. NAO Index Data series 1980-2000 (Climate from NCEP, CO 2 from NOAA, NDVI from AVHRR)