Transcript What can a regional observation network tell us about land-atmosphere exchange of carbon dioxide? Tall towers and tall tales in Wisconsin's Northwoods Ankur R Desai The Pennsylvania.

What can a
regional observation network
tell us about land-atmosphere
exchange of carbon dioxide?
Tall towers and tall tales
in Wisconsin's Northwoods
Ankur R Desai
The Pennsylvania State University
Department of Meteorology
15 December 2005
University of Wisconsin - Madison
Dept. of Atmospheric and Ocean Sciences
Outline
• Motivation
• Hypotheses & Assumptions
– aka tall tales
• Measurements & methods
– aka tall towers and not-so-tall towers
• Results & Implications
• Work in progress
• Future work
MOTIVATION
Why study carbon dioxide?
• Carbon dioxide and climate are closely linked in our
atmospheric system
• Atmospheric mixing ratios of CO2 exceed anything seen in
last 650,000 kyr
Brook, 2005, Science
Why study carbon dioxide?
• Carbon is the stuff of life
Sarmiento and Gruber, 2002, Physics Today
Why study carbon dioxide?
• Atmospheric CO2 growth rate is
not constant
– more variable than rate of
increase in fossil fuel use
• Land and ocean sources/sinks
– complex internal feedbacks
– also affected by external
episodic (e.g., volcano) and
oscillatory (e.g., ENSO) events
• Basic mechanisms understood
– specific processes in land and
ocean are not
– regional scale evaluation is
critically needed
NOAA ESRL, 2004
Why study the terrestrial carbon cycle?
• Responses between land and atmospheric CO2 are highly
variable and functions of:
– geography (e.g., N.H. land sink)
– land cover
– management (e.g., tropical deforestation)
– land-atmosphere feedbacks of carbon, water and energy
• Latest atmospheric data inversions and biogeochemical
models converge on terrestrial carbon cycle as primary
control on atmospheric CO2 growth rate variability (Peylin
et al, 2005, GBC)
• Measurements of atmospheric CO2 over land have, until
recently, been limited
Why study the terrestrial carbon cycle?
Why study the terrestrial carbon cycle?
Courtesy of A. Jacobson, Princeton Univ., 2005
Why study the terrestrial carbon cycle?
• Global scale remote sensing of photosynthesis captures
large scale gradients, but misses (important) details
Heinsch et al, 2006, IEEE TGRS, in press
Why study the terrestrial carbon cycle?
• Models diverge in coupled simulations of the future
IPCC, 2001
The land-atmosphere interface
• Fluxes of energy, momentum, water and CO2 controlled by:
– surface cover/vegetation type
– cover density, height, age, management
– soil and vegetation moisture, temperature, roughness
– air temperature, humidity/VPD, direct/diffuse SW/LW
radiation balances, precipitation, wind speed, stability,
turbulence, boundary layer depth and mixing
The land-atmosphere interface
• Desai et al, 2006, Boundary-Layer Meteorology, in press
- strong
coupling of soil
moisture to ABL
depth
- soil moisture –
surface energy
flux – boundary
layer depth
couplings may
force positive or
negative
feedbacks
The land-atmosphere interface
• Historic Europe-wide
heat wave and drought
led to > 35,000 deaths
– also caused major
reduction to plant net
photosynthetic
production
– probabilities of
extreme
heat/drought events
expected to increase
with warming climate
• Decline in carbon sink
exacerbates effects
– positive feedback
Ciais et al, 2005, Nature
Temp
Precip
NPP-summer
NPP
fpar-MODIS
fpar-model
Managed landscapes
• 83% of land surface and ecosystem services directly
impacted by human influence
– terrestrial carbon cycle is not immune to its effects
Sanderson et al, 2002, Bioscience
Regional field studies
• Why study regions?
– 1.) Global scale models and inversions of atmospheric
tracers are limited in process understanding of
sources/sinks
– 2.) Plot-level and stand scale studies of are limited by
how well spatial sampling footprint represents larger
region
Regional scale studies allow for integration and upscaling of
plot-level results and downscaling of global results to better
understand processes and variability of sources/sinks
Regional field studies
Courtesy of K. Davis, Penn State
ChEAS
• Chequamegon Ecosystem-Atmosphere Study
– http://cheas.psu.edu
– multi-investigator regional study in Northern
Wisconsin/Michigan focused on understanding regional
carbon and water cycle processes and scaling
ChEAS
• Centered on Park Falls, WI 447-m very tall tower - WLEF
– established by NOAA ESRL (CMDL) in Oct. 1994
Berger et al, 2001 JAOT
ChEAS
• A complex region!
ChEAS
• A complex region: Heavily forested, patchy wetland mosaic,
intensively managed, low population density
Courtesy of B. Cook, Univ. Minnesota
ChEAS
• History of forest harvest leaves imprint on cover types
observed in region
– heavy logging in late 19th, early 20th centuries
Clearcut
Mature Hardwood
Young Hardwood
Old-Growth
Courtesy of P. Bolstad, Univ. Minnesota
Sphagnum Bog
Lowland Conifers
Black Spruce wetland
Alder Fen
Courtesy of P. Bolstad, Univ. Minnesota
HYPOTHESES
&
ASSUMPTIONS
Tall tales
Assumptions
• Sampling of land-atmosphere fluxes in dominant ecosystems
is adequate for scaling of regional carbon flux and for
assimilating parameters into biogeochemical models
• Carbon / water flux parameters are insensitive to stand age
and anthropogenic disturbance
• Simple application of top-down atmospheric tracer budgets
and bottom-up flux scaling will converge on a regional flux
This study investigates these claims
Questions
• What density of stand-scale measurements is needed for
– scaling to regional flux estimates for a given uncertainty?
– understanding the scales of temporal coherence and
coupling to climate?
– parameterizing biogeochemical models to correctly
simulate regional fluxes?
• What precision and density of atmospheric trace gas
measurements and model complexity of transport
phenomena are required for regional tracer-transport flux
budgets and inversions?
• What do regional scale studies tell us about CO2, climate,
the future and potential policy alternatives for land CO2
source/sink management?
MEASUREMENTS
&
METHODS
Quantifying land-atmosphere exchange
• Multiple atmospheric and ecological methods can be used to
constrain and understand carbon flux in regions
• Regions can be used to
– test upscaling and downscaling
– quantify ecosystem and atmospheric processes
– identify ecosystem and regional “hotspots”
• Major methods include:
– direct carbon budgeting/accounting
– eddy covariance flux measurement and scaling
– ecophysiological chamber flux methods
– remote sensing
– biogeochemical & ecological modeling
– inverse methods and boundary layer budgets
Tall towers and less tall towers
• Eddy covariance takes advantage of atmospheric turbulent
mixing of trace gasses, heat and momentum to measure
surface-atmosphere flux
– spatial footprint ~10-100x measurement height
– requires high-frequency (5-20 Hz) measurement of
vertical velocity and trace gas
– typical instruments: ultrasonic anemometer (u, v, w, Ts),
and non-dispersive infrared gas analyzers (CO2, H2O)
• WLEF tall tower has fluxes at 30, 122 and 396 m
• Stand scale (10-40 m) towers
– our lab: mature hardwood, shrub wetland and old-growth
forest + roving, portable systems
– nearby investigators: aspen chronosequence, red pine
chronosequence, jack pine, shrub, mature hardwood
Tall towers and less tall towers
Eddy covariance flux network
• Emerging tower networks since early 1990s, 200+ towers
• Only several regional clusters with multiple towers in one
climate space
– ChEAS is one of few high density regional networks
– WLEF tall tower fluxes are globally unique
Eddy covariance method
• Yi et al, 2000, JGR; Wang et al, submitted, Ag. For. Met.
– ensemble-averaged turbulent scalar conservation eqn.
Eddy covariance method
Pitfalls with eddy covariance
• Major assumptions for using time-averaged flux as stand-in
for ensemble average (Reynolds’ “frozen field” hypothesis)
– flow is turbulent, above roughness sublayer, stationary
– signal spectral attenuation and instrument lags are
minimal and can be empirically corrected
– time period captures major scales of turbulence
Berger et al, 2001, JAOT
Pitfalls with eddy covariance
• Nocturnal stable boundary layer provides most challenging
conditions:
– nighttime NEE decline with u*
• suggests primary flow is not 1-D (e.g., advection)
• e.g., terrain slope induced “drainage” flow
• intermittent turbulence
– non-homogenous cover/terrain effects
Desai et al, 2005, Ag. For. Met
Cook et al, 2004, Ag. For. Met.
Pitfalls with eddy covariance
• Anomalous venting? – Cook et al, 2004, Ag. For. Met.
RESULTS
&
IMPLICATIONS
Terminology
• NEE = Net Ecosystem Exchange
– positive = source to atmosphere
– negative = biosphere sink
• NEP = Net Ecosystem Production = (-1) * NEE
• ER = RE = Ecosystem Respiration to atmosphere
– ER = Rh + Ra = Heterotrophic + Autotrophic respiration
• GEP = GPP = Gross Ecosystem / Primary Production
• NEE = ER – GEP
• NPP = Net Primary Production = GPP – Ra
• Units: mmol CO2 m-2 s-1 | gC m2 day-1 | tC ha-1 | Pg C
NEE decomposition and functional fits
• ER and GEP derived from NEE
• nighttime NEE is ER
• Desai-Cook model:
theoretically-robust movingwindow regression method to
find parameters
• stand age has significant
effect on parameters
Desai et al, 2005, Ag. For. Met
Fluxes across time
• Temporal coherence is observed in region for NEE, GEP
• But: tall tower is net source, stand scale towers are sinks
• Sub-continental scale events (e.g., drought) affect fluxes
across large regions (Butler et al., in prep)
• Primary controls: Temp/Moisture, phenology, insects
WLEF
tall tower
Willow Creek
hardwood
Lost Creek
wetland
Sylvania
old-growth
Fluxes across space
• Large variations in carbon flux with stand type and age,
primarily observed in growing season
Desai et al, 2006, Ag. For. Met, in press
Flux scaling: 1+1≠2
• Dominant stands (mature hardwood, wetland) cannot
explain flux observed at very tall tower
Flux across space: lots of towers
• Stand age since disturbance is primary control on long-term
carbon flux evolution in region
– by-product of forest harvest over the last century
Flux scaling: another approach
• Multi-tower synthesis aggregation during time period with
large number of towers (12) in same climate space
– towers mapped to cover/age types
– parameter optimization with minimal 2 equation model
Multi-tower upscaling results
• Multi-tower scaled NEP, ER and GEP over summer 2003
shows better agreement to tall tower, especially when tall
tower fluxes are decomposed and re-aggregated with
footprint decomposition method
Desai et al, 2006, Ag. For. Met,
in press
A word on footprints
• Flux “footprints” are measured of
– weighted average contribution from surface
– LaGrangian dispersion models parameterized as simple
functions of wind speed, direction, stability, MoninObhukov length
Wang et al, Ag. For. Met, submitted
wind
Biogeochemical models: ED
• We apply a height and age structured ecosystem model that
uses concepts of statistical mechanics to simulate the
dynamics of the mean-moment ensemble of forest patches
Moorcroft et al, 2001, Ecol. Mono.
Regional implementation of ED
• Region divided by into soil/topographic sub-sites:
– mesic upland (N hardwoods/hemlock)
– xeric upland (N pines/ash-oak)
– lowland (shrub and forested wetlands)
• Sub-sites aggregated by remotely sensed land cover
• Model parameters and variables constrained by:
– observed ecophysiological / biometric parameters
– historic / tower meteorology and atmospheric CO2
– land use/cover in 1800: Public land survey (Schulte,
2002), Hurtt et al. land use change, USFS FIA forest
harvest
Past Land use and current land cover
Impact of land use and CO2 on flux
• Evidence of interaction effect between CO2 fertilization and
forest harvest
Comparison to upscaling
Jun-Aug 2003
LEF = tall tower flux
LEF* = downscaled
regionally-integrated flux
Towers = multi-tower
upscaling
Model = ED Full Run
Age also affects water
• Age and disturbance also affects water
Mesic upland = black
Xeric upland = red
Wetland = blue
Implications
• ChEAS flux scaling and process studies reveal that:
– careful screening and footprint modeling of eddy
covariance can be used to assess regional carbon flux
• very tall tower might have footprint sampling bias
– large spatial variability in CO2 flux are driven by stand
age, disturbance and cover type
– temporal coherence exists over small and large regions
– interaction effects appear to occur between forest
harvest and carbon fertilization and lead to different
land cover and land-atmospheric sources/sinks of CO2
WORK IN PROGRESS
Atmospheric tracer budgets
• Tracer budgets provide additional constraints on fluxes
– can provide estimate of uncertainty
– ability to assess flux over larger region
• Three main methods for regional approach
– boundary layer volume budget
• tall tower only (Helliker et al, 2004, JGR; Bakwin et al,
2004, Tellus; Styles et al, submitted)
• multi-tower (Desai)
– parcel-following, LaGrangian dispersion models (STILT)
• COBRA: Gerbig et al, 2003, JGR; Lin et al, 2004, JGR
– tracer-transport regional inversion
• S. Denning, M. Uliasz, CSU; S. Richardson, N. Miles, PSU
• Each method has pros & cons
The ring of towers
• Regional low-cost, high-precision CO2 mixing ratio network
– experiments in summer 2003 and 2004
Courtesy of S. Richardson, PSU, 2003
= LI-820 sampling from 75m
above ground on
communication towers.
= 40m Sylvania flux tower
with high-quality standard
gases.
= 447m WLEF tower.
LI-820, CMDL
in situ and flask
measurements.
The ring of towers
Multi-tower mixing ratio budget
• Applicable to well-mixed convective boundary layer
– requires estimates of ABL depth, free troposphere mixing
ratio, ABL-FT entrainment, radial transport
– North American Regional Reanalysis (32 km) meteorology
– NOAA, COBRA, NASA INTEX-NA aircraft profiles
Budget issues: CO2 and the ABL
• Atmospheric CO2 over land covaries with ABL depth
Courtesy of P. Bakwin, NOAA, 2002
Budget issues: CO2 and synoptics
• Frontal mixing matters
• Why the jump?
– vertical mixing
– CO2 “blobs” and advection
– frontal “darkness”
Courtesy of S. Denning, CSU, 2005
Hurwitz et al, 2004, JAS
Budget issues: CO2 and synoptics
• Across the ring
400
Brule
380
400
[CO 2] (ppm)
Bayfield
380
400
WLEF
380
400
Fence
380
400
Wittenberg
380
0
3
6
9 12 15 18 21 24
Time (hr GMT)
Courtesy of N. Miles, PSU, 2005
Cold front at 1200 GMT
FUTURE WORK
Upcoming intensive regional studies
• Carbon-hydrologic cycle coupling ripe for research
• North American Carbon Program Mid-continent Intensive to
occur summer 2007
– relies on new (underfunded) NOAA tall tower network
Wetlands & methane
• Methane is 2nd most important
carbon greenhouse gas
• Warming potential is 23x CO2
• Emissions from natural
wetlands highly unconstrained
– largest natural source
– sensitive to water table
– wetland-upland transition
may be most sensitive
• Recent developments in fieldportable, lower-cost fast
response CH4 sensors
– eddy covariance
application
New sensors
• CalTech Total Column Carbon Observatory
– FTIR solar spectroscopy
– designed as evaluation for upcoming OCO satellite
– site at WLEF tall tower running since mid-2004
410
Courtesy of R. Washenfelder, CalTech, 2005
400
CO2 VMR (ppmv)
390
380
370
360
FTS Column Daytime Average
Tow er at 76-m (11:00 - 15:00 CDT)
Tow er at 396-m (11:00 - 15:00 CDT)
350
340
5/04
7/04
9/04
11/04
Date
1/05
3/05
5/05
Data assimilation in a data rich world
• Data assimilation methods for surface data less well
developed than for atmospheric data
• Large number of upcoming atmospheric and land remote
sensing technologies
– GOES-R – more spectral channels, higher resolution
– NPOESS – next generation MODIS
– OCO – Orbiting Carbon Observatory
– TCCON – Total Carbon Column Observing Network
– CO2 DIAL Lidar – NASA Langley, S. Ismail
• Regions serve as test bed for sensor evaluation, data
utility/quality assessment, parameter estimation &
assimilation and model evaluation
Where to go from here
• Work will be extended to better understand complex
regions, global models, other trace gasses and new sensors
• Integrated framework for regional carbon scaling needed
– many complementary ChEAS-like projects underway
– Regional hot spots and complex regions, 3 types:
• global impacts: Amazon – deforestation, Arctic – rapid
climate change
• complex regions: mountainous terrain – ACME, ChEAS
• understudied regions: sub-Saharan Africa, Siberia
• Connections to wide-range of existing atmospheric, carbon
cycle, remote sensing, ecological and environmental
research at U. Wisconsin
Acknowledgements
• Collaborators
–
–
–
–
–
–
–
–
–
–
Ken Davis lab, Penn State University
Paul Bolstad lab, University of Minnesota
Paul Moorcroft lab, Harvard University
Scott Denning lab, Colorado State University
Paul Wennberg lab, CalTech
Peter Bakwin / Arlyn Andrews, NOAA ESRL CCGG
U.S. Forest Service, North Central Forest Exp. Station, Rhinelander, WI
Larry Mahrt, Oregon State
Faith-Ann Heinsch and Steve Running, University of Montana
all other ChEAS investigators
• Funding
–
–
–
–
DOE Terrestrial Carbon Processes and NIGEC / NICCR
NSF Research Collaboration Network
NOAA ESRL
NASA Science Mission Directorate / PSU Space Grant
• Site owners, field technicians and crew
The End
CO2 in the boundary layer
• Seasonal covariances exist between boundary layer depth
and CO2 flux
Courtesy of S. Denning, CSU, 2002
CO2 in the boundary layer
Yi et al, 2001, JAS
Davis et al, 2003, Global Change Biol.
Land-atmosphere flux and age
Global inversions and models
Peylin et al, 2005, GBC