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Ankur Desai, Atmospheric & Oceanic Sci., UW-MadisonUniversity of Wisconsin Forest and Wildlife Ecology Seminar
April 20, 2011
Indirect and Direct Effects of Climate Change on Forest Carbon
Cycling
What observations and models tell us about
the future of land carbon dioxide uptake and
why it matters for future climate change
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Forests in the Earth System
• Climate system is driven by– Forcings that impact the energy budget, water
cycle, or trace gas and aerosol composition of atmosphere
– Feedbacks that reverse, limit, or enhance these forcings
• Forests have low albedo, moderate evapotranspiration rates, and high carbon stores. They also cover a significant area of the global land surface– Consequently, forcings and feedbacks imposed
by forests are worth considering!
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Radiation
LHF
SHF
Biogeophysical Mechanisms
Biogeochemical Mechanisms
CO2
CH4
Ozone, N20 ,Others
Forests in the Earth System
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Forests in the Earth System
Bonan et al., 2008
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Bonan et al., 2008
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Hypothesis
• The indirect sensitivity and feedbacks of forest carbon cycle to climate change may dwarf the direct sensitivity– Direct effects– Indirect effects
• Contemporary observations of forest carbon exchange can be used to evaluate and improve predictive simulation models
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What Do We Know?
IPCC, 4th AR, (2007)
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What Do We Know?
Source: Lüthi et al (2008), CDIAC, & Wikimedia Commons
Years Before Present
CO2 (
ppm
)
385 ppm(2008)
Ice ages
232 ppm
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What Do We Know?Since 1990:
•Global annual CO2 emissions grew 25% to27,000,000,000 tons of CO2
•CO2 in the atmosphere grew 10% to 385 ppm
•At current rates, CO2 is likely to exceed 500-950 ppm sometime this century
•But: Rate of atmospheric CO2 increase is about half the rate of emissions increase. Why?
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Where is the Carbon Going?
Houghton et al. (2007)
Ecosystem Carbon Sink
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Where is the Carbon Going?
Le Quére et al., 2009
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Where is the Carbon Going?
C. Williams, Clark U, NACP 2011
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What Don’t We Know?
• Sitch et al., 2008
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What Don’t We Know?
• Friedlingstein et al., 2006
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What Don’t We Know?
• Ricciuto et al., in prep
Ricciuto et al., PhD dissertation
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Is There Any Consistency to What We Don’t Know?
30 Models47 Flux Tower Sites
36 AmeriFlux11 Fluxnet Canada
24 submitted output10 runs per site
Num Model Num Model
1 Agro-IBIS 16 GTEC2 BEPS 17 ISAM3 Biome-BGC 18 ISOLSM4 Can-IBIS 19 LoTEC5 CLM-CASA' 20 LoTEC-DA6 CLM-CN 21 LPJ_wsl7 CN-CLASS 22 ORCHIDEE8 DAYCENT 23 ORCHIDEE-STICS9 DLEM 24 SiB310 DNDC 25 SiBCASA11 ecosys 26 SiBCrop12 ED2 27 SIPNET13 EDCM 28 SSiB214 EPIC 29 TECO15 GFDL LM3V 30 TRIPLEX-Flux
Schwalm et al., 2010
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Diurnal
Annual
Synoptic
Month
• Error peak at diurnal & annual time scales• Errors at synoptic & monthly time scales
Not Significant
Dietze et al., in review
A Little Bit
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N America is in Demographic Transition
Pan et al., 2011
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Forest age (years)
WE ARE HERE
EVEN-AGED(mostly aspen)
UNEVEN-AGED
(maple, oak, pine)ASPEN MORTALITY
natural senescence, pathogens, insects
Succession
Courtesy P. Curtis
N America is in Demographic Transition
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Disturbance Frequency is Poorly Constrained
• Fire: 40,000 km2/year• Harvest: 50,000 km2/year• Insects: larger• Storms/hurricanes: > 17,000 km2/year• Disease: ???
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CO2CO2 H2OH2O
HeatHeat VOCsVOCs
TemperatureTemperature PrecipitationPrecipitation Atm. Chem, O3Atm. Chem, O3
GHGsGHGs AerosolsAerosols
EcosystemsEcosystems
NOxNOx
Find the Surprise!
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Direct Effects
• Gross Primary Productivity (GPP)– PAR, VPD, T, Qsoil, [CO2], Navail
• Ecosystem Respiration (ER)– T, Qsoil, C:N
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Useful Towers
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The Value of Network Science
• Ecology is a “synthesis” scienceCarpenter et al., 2009
Dept of Energy, ORNL
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Temperature and Dryness Explain Most NEE Variation
Across Space
Yi et al., 2011, ERL
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Some Convergence of GPP
Baer et al., 2010, Science
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GPP Controls Are Understood?
Baer et al., 2010, Science
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Respiration Sensitivity Converges?
• Low-frequency component of respiration sensitivity to temperature is consistent across space
Mahecha et al., 2010, Science
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Indirect Effects
• Lagged or coupled responses of climate to carbon uptake– Temporal/spatial lags: Phenology, hydrology– Forest dynamics (recruitment, mortality,
growth): Successional trajectory– Disturbance frequency/intensity
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Phenology Explains GPP, too!
Richardson et al. (2009)
Onset of Spring Anomaly (Days)
Later springs lead to lower productivity in U.S. northeastern forests
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Models Overpredict Growing Season Length
• Early spring/late fall uptake means positive GPP bias
Richardson et al., submitted
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What About at the Regional Scale?
• Chequamegon Ecosystem-Atmosphere Study (ChEAS)
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Coherent Carbon Sinks Imply Climatic Forcing of Interannual
Variability
Desai et al., 2010
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Model-Data Assimilation Shows Predictive Skill with Phenology
Desai et al., 2010
Short-term onlyassimilation
Short and long termassimilation
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Even When Model is Forced to Maintain Coherent Phenology
Desai et al., 2010
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But Model Explains Coherent Flux Differently Depending on
Ecosystem
Desai et al., 2010
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Phenology is Not Simple!
• Niwot Ridge Ameriflux subalpine fir/spruce – 3050m elevation
Hu et al. (2010), Sacks et al. (2006)
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Moisture Matters
Hu et al. (2010)
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Snow Water Drives Productivity
Hu et al. (2010)
SnowmeltGroundwater
Soil 35 cm
Soil sfc
Rain
SNOW
WATER
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Speaking of Hydrology
Sulman et al. (2010)
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Do Models Get This?
• Six model intercomparison– Residuals = Modeled flux – Observed flux
Sulman et al., in prep
a) ER residuals
b) GPP residuals
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Water Table is a Critical Model Element
Sulman et al., in prep
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Disturbance Chronosequenceswith Annual NEP measured by eddy covariance
Fire = 4Harvest = 7+Insects = 3Hurricane Wilma
What About Longer Time Scales?
Amiro et al., 2010
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Age (years)
0 20 40 60 80 100
NE
P (
g C
m-2
y-1
)
-200
-100
100
200
300
0
Saskatchewan: PineManitoba: SpruceAlaska SpruceArizona: Pine
Amiro et al., 2010
Rapid Carbon Sink Recovery Post-Fire
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Consistent Ratio of GPP/ER With Age
Age (years)
0 20 40 60 80 100
GP
P/E
R
0.2
0.4
0.6
0.8
1.2
1.4
1.6
1.8
0.0
1.0
2.0
FireHarvest
Asymptote = 1.23
Ra = 0.55*GPP
Amiro et al., 2010
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Time since disturbance (years)
-2 -1 1 2 3 4 50
NE
P (
g C
m-2
y-1
)
-200
200
400
600
0
Mountain Pine BeetleForest Tent CaterpillarGypsy Moth
CFS web pageAmiro et al., 2010
Bugs Are Complicated!
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Extensive Bark Beetle Tree Mortality Suggests Large Impacts to C cycle…
Raffa et al., BioScience, 2008
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Usually a temporary phenomenon
Hicke et al. in revision
Growth Reduction Decreases NEP
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Hicke et al. in revision
Tree Mortality Decreases NEP
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Hicke et al. in revision
Mortality Recovery Drives Flux Response
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Where Do We Go From Here?
• More model intercomparison and benchmakring (MsTMIP, C-LAMB)
• Long-term carbon-cycle observatories (Fluxnet/Ameriflux, NEON, Inventory)
• Remote-sensing of disturbance (LEDAPS)• Large and small scale manipulative
experiments (FASET, ABoVE, MnSPRUCE)• Theoretical advancement• Vegetation dynamics in IPCC models:
Phenology, large-scale episodic disturbance, succession, wetland hydrology
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NEON, Inc.
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UMBS ForestCarbon CycleResearch Program
The Forest Accelerated Succession ExperimenT (FASET)
Conceptual model of NEP before, during, and following aspen and birch mortality. N availability will have an important effect on final NEP.
Year1998
20002002
20042006
20082010
20122014
20162018
NE
P (
Mg
C h
a-1
yr-
1 )
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5Hypothetical NEPHypothetical N availability
Girdling
succession
climateA
B
C
climate reco
verydisturbance
+
-
N a
vaila
ble
for
plan
t gro
wth
Courtesy of C. Gough, VCU
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UMBS ForestCarbon CycleResearch Program
Figure 1. Conceptual diagram of forest age and production. Recent data have called into question the extent of productivity decline in mature-to-senescing stands. Most ecosystem models are poorly equipped to simulate forests in this older age range.
Conventional theory suggests declining productivity and C storage in over-mature stands. Increasing biotic and structural complexity with age could alter this trajectory.
Courtesy of C. Gough, VCU
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Theoretical Development
Changes in productivity:
•Warming in cold climates•CO2 fertilization•Increased precipitation•Increased nitrogen deposition•Increased drought pressure
Changes in productivity:
•Warming in cold climates•CO2 fertilization•Increased precipitation•Increased nitrogen deposition•Increased drought pressure
Changes in disturbance rates:
•Severe storms•Logging and land use change•Insect outbreaks•Fire
Changes in disturbance rates:
•Severe storms•Logging and land use change•Insect outbreaks•Fire
Changes in decomposition
rates:
•Warming leads to faster decomposition rates•Increased drought pressure
Changes in decomposition
rates:
•Warming leads to faster decomposition rates•Increased drought pressure
Productivity multiplier
Productivity multiplier
Disturbance interval
Disturbance interval
Decay rate multiplierDecay rate multiplier
Clim
ate
chan
ge p
ress
ure
Mod
el
para
met
er
Sulman et al., in prep
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Most Complex Model Has Similar Sensitivities to All Three Effects
• Increased decay rates cause higher carbon uptake
• CO2 uptake has about the same sensitivity to changes in all three parameters
Combination effects of three parameters:
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Conclusions• Multi-year multi-site flux-tower observations provide
evidence for mechanisms that link phenology, hydrology, and biotic disturbance to carbon cycle
• Ecosystem models need continued “acid tests” to constrain and select optimal model structure and parameters
• Things I didn’t talk about:– Plant and microbial adaptation– Invasive species, herbivory, population dynamics– Rapid climate change– Nutrient cycling– Aquatic-terrestrial linkages– Coupled water/carbon cycle and boundary layer feedbacks– Lots of things!
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Thanks!• Desai Ecometeorology Lab (flux.aos.wisc.edu):
• Funding partners: UW Graduate school, NSF, UCAR, NOAA, USDA NRS, NASA, DOE, DOE NICCR, WI Focus on Energy
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Model Complexity Drives Disturbance Sensitivity
200-year modeled mean NEE for different parameter combinations
Ratios of sensitivity to the two parameters
Blue colors = higher C uptake
Negative numbers = C uptake