energy, water and phenology controls on the annual carbon and

ENERGY, WATER AND
PHENOLOGY CONTROLS
ON THE ANNUAL
CARBON AND WATER
CYCLES
USING REMOTE SENSING TO UNDERSTAND
CLIMATE VARIABILITY
NATURE GEOSCIENCE
Julia Green– Columbia University, Pierre Gentine– Columbia University, Joe
Berry– Carnegie Institute, Jung-Eun Lee– Brown University,
Jana Kolassa– Columbia University
Introduction and Motivation
2
L.G.G. de Gonçalves et al. / Agricultural and Forest Meteorology 182–18
Discrepancies exist
between GCM results
and observations
¨  Certain processes
in the carbon cycle are
not well understood
¨ 
Fig. 1. Local-scale (A) evapotranspiration (ET) and (B) GPP as originally modeled by IBIS (brown line, Botta et al
L., et al.
"Overview ofshaded
the Large-Scale
Lee et al., 2005), and as observed (±SDGonçalves,
across years
2002-2004,
areas)Biosphere–Atmosphere
from eddy tower in Tapajos Nation
Experiment in Amazonia Data Model Intercomparison Project (LBAshow
dry-season
declines,
in
contrast
to observations from both satell
squares) is plotted with GPP in (B). Models
DMIP)" (2013). NASA Publications. Paper 133. http://digitalcommons.unl.edu/
nasapub/133from nearby (12 km distant) forest site in (B). (For interpretation
area (km 77 site) that has opposite seasonality
referred to the web version of the article.)
The experiments consisted of uncoupled land surface model
simulations forced by standardized atmospheric variables measured at eight sites across the Amazon region as shown in Fig. 2,
sites are in Brazilia
São Paulo was also
The evergreen
Introduction Cont.
3
¨ 
Potential options for improvement:
REMOTE
SENSING!!!
! In-situ
point leaf level measurements
! Flux tower measurements (canopy/ecosystem
scale)
M. Jung, M. Reichstein, A. Bondeau,
Towards global empirical upscaling of
FLUXNET eddy covariance observations:
Validation of a model tree ensemble
approach using a biosphere model.
Biogeosciences 6,2001 (2009).
Research Goals
4
¨ 
To improve our understanding of the variability
of land and atmospheric variables related to
the carbon and water cycles
! Temporally
(interannual, seasonal)
! Spatially (climatic conditions, ecosystems)
¨ 
To define spatially the control on the annual
carbon and water cycles
! Energy
! Water
! Phenology
Importance
5
Advance our understanding of how vegetation
responds to increases in atmospheric CO2
¨  Show us the effect of water stress on the CO2
cycle
¨  Improve the performance of General
Circulation, and Land Surface, and Vegetation
Models
¨  Allow us to more accurately make climate
change predictions and weather forecasts
¨ 
Remote Sensing Datasets
6
Parameter
Source
Net Radiation
Clouds and the Earth's Radiant Energy
System (CERES)
Global Precipitation Climatology
Project (GPCP)
Global Ozone Monitoring Experiment–
2 (GOME-2)
Moderate Resolution Imaging
Spectroradiometer (MODIS)/
Multiangle
GHCN_CAMS Gridded 2m
Temperature
Precipitation
Solar Induced Fluorescence
(SIF)
EVI
Temperature
Solar Induced Fluorescence (SIF)
7
¨ 
During photosynthesis a plant absorbs energy
through its chlorophyll
! %
used for ecosystem gross
primary production (GPP)
! % lost as heat
! % re-emitted (SIF)
¨ 
Relationship between GPP
and SIF is linear
Guanter, L., et al. " Global monitoring of terrestrial sun-induced
chlorophyll fluorescence from space." (2013). International Conference:
Towards a Global Carbon Observing System:Progresses and Challenges.
Climate Regimes
8
Mediterranean Climate
Monsoonal Climate
Tropical Climate
Mid-Latitude Climate
Mediterranean Climate
9
¨ 
In Mediterranean climates radiation and precipitation are out
of phase. Limited by light in winter and water in summer.
Interannual variability in GPP is due to the variability in precip.
Monsoonal Climate
10
¨ 
¨ 
Monsoonal climates have peak in radiation that drives the
precip., which then drives the GPP.
Variability in GPP due to interannual variability in precip.
Tropical Climate
11
¨ 
Tropical climates average annual cycles vary
greatly between regions
Mid-Latitude Climate
12
¨ 
Mid-latitude climates have radiation, GPP and EVI in phase(GPP
peaks slightly before radiation and decreases at conclusion of
phenological cycle). Largest interannual variability in precip.
13
Correlation between Radiation and
SIF
¨ 
Less strong in tropical and desert regions than in midlatitudes where radiation is driving GPP.
14
Correlation between Temperature
and SIF
¨ 
Similar to radiation but less highly correlated– opposite in
monsoonal regions (temperature drops during monsoon
season) and transitional zones.
Correlation between EVI and SIF
15
¨ 
Greenness typically has high correlations with SIF– but
not in the very wet tropical regions (EVI is constant year
round) and some desert regions (SIF is very minimal)
16
Correlation between Precipitation
and SIF
¨ 
Precipitation highly correlated with SIF in transition
zones. Regions with the most rain have lower correlation
due to the cloud coverage and constant EVI
Combining Correlations to RGB
Plot
Corr(SIF, Net Radiation)
R
Corr(SIF, Precipitation)
G
B
Corr(SIF, EVI)
18
Controls on Carbon and Water
Cycles
Corr(SIF, Net Radiation)
Corr(SIF, EVI)
Corr(SIF, Precipitation)
Conclusions
19
¨ 
¨ 
Globally defined each climatic regime in terms of
GPP as light, water or phenology controlled.
Learned wet tropical forests behave differently
(eg. Amazon vs. Congo Rainforest) in the
following ways:
! The
Amazon is more light limited than the Congo
Rainforest
! In the Amazon less precipitation (to a point) is
beneficial to photosynthesis
¨ 
Changes in the water cycle will therefore affect
distinct regions differently