Merging CO 2 flux and mixing ratio observations at synoptic, seasonal and interannual scales

Merging CO2 flux and mixing ratio observations at synoptic, seasonal

and interannual scales

Kenneth J. Davis, Chuixiang Yi,

Martha P. Butler, Michael D. Hurwitz and Daniel M. Ricciuto

The Pennsylvania State University

Peter S. Bakwin, NOAA CMDL

Major contributions from the Walker Branch Watershed, Harvard Forest,

Northern OBS and Little Washita sites, and the Fluxnet project

Goals

Obtain a mechanistic understanding of tower-scale interannual variability in NEE of CO2 across many

AmeriFlux/Fluxnet sites.+

Link observations of interannual variability in tower fluxes with the global CO2 flask network.

=

Understand the mechanisms that govern interannual changes in the atmospheric CO2 budget.

Outline

• Towards governing variables: (Yi et al)– Climatic factors and temporal variability in

NEE at various time scales. – Temperature, drought and the light response of

NEE.

• Bridging the gap in scales: (Davis et al)– Examples of merging flux and concentration

data at various time scales.

Part I: Climate variables and NEE at various time scales

(NB, WB, WL, HF)

Respiration and temperature

Correlation between nighttime tower flux and air temperature is very high on daily, monthly and seasonal time scales.

Correlation breaks down on interannual scales.

Respiration and temperature

Northern OBS tower (NB)

Manitoba, Canada

Wofsy, Munger et al.

Boreal black spruce forest

Respiration and temperature

WLEF TV tower (WL)

Northern Wisconsin, USA

Davis, Bakwin et al.

Mixed forest/wetland mosaic

Respiration and temperature

Harvard Forest (HF)

Massachusetts, USA

Wofsy, Munger et al.

Deciduous forest

Respiration and temperature

Walker Branch tower (WB)

Baldocchi, Wilson et al.

Tennessee, USA

Deciduous forest

Why does the temperature-respiration relationship

break down on annual time scales?

Hypotheses:

• Annual respiration is proportional to annual litter production which is a weak function of temperature?

• Temperature sensitivity is limited at the seasonal extremes (summer, winter).

Seasonal distribution of temperature sensitivity B, (Re=AeBT ).

Spring B is the largest except at the NB site.There is no correlation between T and respiration in winter except at the WB site.

Season

NB 

B R2 T

WL 

B R2 T

HV 

B R2 T

WB 

B R2 T

Spring 0.0351 0.47 -5.1 0.0844 0.50 5.2 0.0445 0.38 5.1 0.0632 0.60 13.3

Summer 0.0676 0.36 13.3 0.0526 0.23 16.2 0.0255 0.08 17.0 0.0283 0.10 22.3

Autumn 0.0603 0.83 -1.3 0.0801 0.45 7.8 0.0231 0.18 8.0 0.0547 0.66 14.1

Winter 0.0126 0.08 -21.8 0.0142 0.04 -7.0 0.0283 0.08 -3.3 0.0340 0.46 4.4

Climate variables and NEE at various time scales

(NB, WB, WL, HF)

NEE of CO2 and precipitation

Correlation between NEE and precipitation is very poor on daily, monthly and seasonal time scales.

Correlation becomes strong for interannual time scales.

NEE and precipitation

Northern OBS tower (NB)

Manitoba, Canada

Wofsy, Munger et al.

Boreal black spruce forest

NEE and precipitation

WLEF TV tower (WL)

Northern Wisconsin, USA

Davis, Bakwin et al.

Mixed forest/wetland mosaic

NEE and precipitation

Harvard Forest (HF)

Massachusetts, USA

Wofsy, Munger et al.

Deciduous forest

NEE and precipitation

Walker Branch tower (WB)

Baldocchi, Wilson et al.

Tennessee, USA

Deciduous forest

Climate variables and NEE at various time scales

(NB, WB, WL, HF)

NEE and net radiation

Correlation between NEE and net radiation is strong on all time scales.

NEE and net radiation

Northern OBS tower (NB)

Manitoba, Canada

Wofsy, Munger et al.

Boreal black spruce forest

NEE and net radiation

WLEF TV tower (WL)

Northern Wisconsin, USA

Davis, Bakwin et al.

Mixed forest/wetland mosaic

NEE and net radiation

Harvard Forest (HF)

Massachusetts, USA

Wofsy, Munger et al.

Deciduous forest

NEE and net radiation

Walker Branch tower (WB)

Baldocchi, Wilson et al.

Tennessee, USA

Deciduous forest

SummarySummary

• Dependence of NEE on climatic factors is not consistent across time scales.

• Net radiation and precipitation become more correlated with NEE on annual time scale.

• Dryness=Rn/(L*P) may be used as an annual controlling parameter on interannual variability of NEE of CO2.

Discontinuous permafrost existsWater stress is not criticalSoil thaw is critical; this depends on Rn

Drought leads to more release of CO2

With abundant soil moisture, available energy is critical for CO2 uptake.As dryness>0.95, water stress becomes critical.1998 is the second year of drought. 1999 is the third year of drought. (?)

Drought has strong effect on interannual variability in NEE at WB.

NB

WL

HV

WB

HV-Harvard Forest (US,92-99)

TH-Tharandt (Germany, 97-99)

WL-WLEF (US, 97-99)

WB-Walker Branch (US,95-98)

NO-Norunda (Sweden,96-97)

LW-Little Washita (US,97-98)

LO-Loobos (Netherlands,97-98)

HL-Howland (US, 96-97)

HE-Hesse (France, 98-99)

Across many sitesAverage per site over several years

Part II: Temperature, drought and light response of NEE

(LW, WB, WL, HF)

Drought and NEE

Drought stress is evident.

Diurnal asymmetry is intruiging.

How does drought stress modify the How does drought stress modify the diurnal pattern of NEE with climate diurnal pattern of NEE with climate

factors in the growing season?factors in the growing season?

• Use multi-year daytime data in growing season (June-July-August) for each site to make diurnal average for NEE and climate variables.

• Examine the relationship between NEE and climate variables.

• Dry years are shown in red, and wet years in blue on the plots.

Plus-Morning; Circle-Afternoon

Dotted line-AM; solid line-PM

F=NEE; Q=PAR

1) Drought stress effect (mean =1995-1998, red=1995, blue=1998)

2) Diurnal asymmetry?•Is AM different from PM?

-Plant experiences show: stomata opening is larger in AM, smaller in PM and near closed at midday.-Stomata open in the light or in response to a low concentration of CO2, close in darkness or when dehydration causes a loss of turgor.-Stomata open quickly and close slowly.-The time lag between transpiration and tree water uptake is as much as 3 hours.

Walker Branch

Grassland (LW,

1997, 1998, mean = 1996-1998)

In a very dry year, no photosynthesis.High T limits respiration.

Drought drives grass ecosystem from a carbon sink to a source

Water Use Efficiency

WUE = NEE/LE

In AM, wue decreases with TIn PM, wue is small and almost constant.

Drought reduces wue

Wue is much smaller at WL and LW than at WB and HV

Part II: Temperature, drought and light response of NEE

(LW, WB, WL, HF) Temperature and NEE

Light response factors are functions of temperature.

F=F(T, VPD, Q, Rn)

VPD=VPD(T)

Rn=Rn(Q)

F=F(T, Q)

F=NEE, Q=PAR

WB

y = 0.3997x - 41.604R2 = 0.9974

-100

0

100

200

300

400

500

600

0 500 1000 1500

Q (mol m-2 s-1)

Rn

(W m

-2)

WB

y = 0.0127x2 - 0.4125x + 3.394R2 = 0.9981

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

16 18 20 22 24 26 28

T (oC)

VP

D (

kPa)

Light response of ecosystem CO2 exchangeLight response of ecosystem CO2 exchange

( )*( , ) ( )

( ) / ( )

F T QF T Q R T

Q F T T

R=canopy dark respiration, or total ecosystem respiration Fcanopy assimilation rate at saturating light=Apparent quantum yield

HypothesisHypothesis: R, Fand depend on temperature.

MethodMethod: Nonparametric statistical method.

DataData: Multi-year daytime flux data in growing season.

What climate domain is favorable for more CO2 uptake?What climate domain is favorable for more CO2 uptake?

Isopleths of NEE in a (T, Q) planeIsopleths of NEE in a (T, Q) plane

Common features:Under high light conditions, temperature plays a key role in NEE and there is an optimal domain.

Difference:In low light conditions, temperature also has an important impact on NEE at WL and LW, a smaller impact at HV, but no effect at WB.

Functions of Functions of on R, F on R, F,,and and

R(T) is expected

F(T) at HV is different from WB.F(T) in AM is different from PM (WB).Higher T extremely reduces F at WB.F increases with T and saturates at higher T at HV.Water stress is critical at WB.Available energy is critical at HV.

Global is sensitive to T within specific range at WB. is quite different between AM and PM.

SummarySummary

• Diurnal asymmetry of relationship between NEE and climate variables is observed clearly.

• The light response of ecosystem CO2 exchange is affected by temperature and drought stress.

• Maximum assimilation rate and apparent quantum yield are temperature-dependent.

Part III: Bridging the gap across regions to continents

• Problem: Flux vs. mixing ratio observations – mismatch in scales.

• Method: CO2 mixing ratios from flux towers• Application: What can we learn from a single site?

– Advection matters– CO2 advection occurs with weather– ABL budget method is promising for regional fluxes– Joint analyses of CO2 – H2O may help.

• Application: How can we integrate multiple sites?– Continental and regional network ideas – Spatial coherence across many sites – spring anomaly

Atmospheric approaches to observing the terrestrial carbon cycle

Ci

i

i

i Sx

CU

x

CU

t

C

''

Time rate ofchange (e.g. CO2)

Mean transport

Turbulenttransport (flux)

Source in theatmosphere

Average over the depth of the atmosphere (or the ABL):

0C

zi

i

CC C

t x

FU

z

F

F0C encompasses all surface exchange: Oceans, deforestation,

terrestrial uptake, fossil fuel emissions.

Inversion study: Observe C, model U, derive FFlux study: Observe F directly

Methods for determining NEE of CO2 Methods for bridging the gap

Cha

mbe

r fl

ux

Tower flux

ABL budget Airborne flux

Forest inventory Inverse study

decade

year

month

hour

day

Tim

e S

cale

Spatial Scale

(1m)2 = 10-4ha

(1000km)2 = 108ha

(100km)2 = 106ha

(10km)2 = 104ha

(1km)2 = 102ha

Rearth

Upscale via ecosystem modelsand networks of towers.

Move towardsregional inversemodeling

Methodology: How can we use flux towers to gather worthwhile CO2 mixing

ratio measurements?• Calibrate! Bakwin et al, 1995. Zhao et al,

1997.• Use midday data - very small vertical

gradients.• Midday surface layer CO2 data resolves

synoptic, seasonal and annual spatial and temporal trends.

Chequamegon Ecosystem-Atmosphere Study (ChEAS) http://cheas.psu.edu

WLEF tall tower (447m)CO2 flux measurements at: 30, 122 and 396 mCO2 mixing ratio measurements at: 11, 30, 76, 122, 244 and 396 m

Forest stand towers: Mature upland deciduous Deciduous wetland Mixed old growthAll have both CO2 flux and high precision mixing ratio measurements.

WISCONSIN

Coniferous

Mixed deciduous/coniferous

Wetland

Open water

Shrubland

General Agriculture

Willow Creek

WLEF

Lost Creek

Landcover key

North

Upland, wetland, andvery tall flux tower. Oldgrowth tower to the NE.

High-precision CO2

profile at each site.

Mini-mesonet, 15-20kmspacing between towers.

View from 396m above Wisconsin: WLEF TV tower

Diurnal cycle of CO2 in the ABL

Surface layer flux towers can be used to monitor continental CO2!

1997 monthly mean CO2 mixing ratios from the WLEF tower.

Month

CO2 (ppm)at 11m,

early pm only

CO2 (ppm)at 396m,

early pm only

CO2 (ppm)11m – 396m,early pm only

CO2 (ppm)at 396m,entire day

CO2 (ppm)396m pm – 396m

entire day,1 371.4 370.3 1.1 369.7 0.62 371.4 371.2 0.2 371.1 0.13 371.4 371.0 0.4 371.0 0.04 370.4 370.4 0.0 370.4 0.05 368.1 368.2 -0.1 368.3 -0.16 355.5 357.3 -1.8 359.4 -2.17 348.0 350.2 -2.2 351.1 -0.98 346.1 348.1 -2.0 349.3 -1.29 354.9 356.2 -1.3 358.0 -1.8

10 365.8 365.6 0.2 366.0 -0.411 370.3 369.9 0.3 369.6 0.312 371.5 370.6 0.9 370.2 0.4

Annualmean 363.7 364.1 -0.4 364.5 -0.4

Annual mean at Mauna Loa, 1997, was 366.7 ppm.

Synoptic variability in CO2

The seasonal amplitudeof the gradient in CO2

between the continental ABL and the marine boundary layer is large.

Surface layer - mid-ABLdifference (1 to 2 ppmv)does not overwhelmthis signal.

CO2 gradients vs. the surface-layer - ABL bias at WLEF

(all values in ppm CO2)

Synoptic

(days, within continent)

Seasonal

(across time and the marine-continental boundary)

Annual

(marine-continental gradient)

Gradient

(can be used as input to derive fluxes)

5-20 4-15 2

Bias (midday surface layer to 24-hour mid-ABL)

1-4

(night-time data hard to interpret?)

1-4

(both peak in summer)

0.4/0.8

(about half day-night, half vertical)

Applications

• Advection and local fluxes are both important in the ABL CO2 budget.

• Relative importance changes across the continent.

• Advection can be huge with synoptic events.

• In between synoptic events, even 1-D ABL budgets do a fair job for flux estimates.

Applications

• Advection and local fluxes are both important in the ABL CO2 budget.

• Relative importance changes across the continent.

Net ecosystem-atmosphere exchange of CO2 in northern Wisconsin

Monthly averageABL budgetsfrom three towers.

HForest is the least “1-D”.

BOREAS NSAis the most “1-D”.

Advection isrelated to the continent – marineCO2 gradient.

Credits to Harvard group.

Applications

• CO2 transport happens during “weather”

Cold frontal passage and CO2 advection (14 July, 1998)

Applications

• ABL budget method is promising for regional fluxes

• In between synoptic events, even 1-D ABL budgets do a fair job for flux estimates.

• Joint CO2 – H2O analyses may help.

ABL budget(why? Flux ‘fetch’ of ~100’s of km!)

0C

zi

i

CC C

t x

FU

z

F

To estimate surface flux: Observe first term,

observe or neglect second term, observe or avoid Fz,

solve for F0.

0?

Fluxes at WLEF on May 18, 2001

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0 5 10 15 20 25

Hour (UTC)

CO2

Flux

(ppm

m s

-1)

Flux 1

Flux 2

Flux 3

Flux 1: surface flux using vertical advection, storage flux, and turbulent fluxFlux 2: surface flux using storage flux and turbulent fluxFlux 3: surface flux using an ABL budget based on PPC data

ABL budgets - regional inverse studies

1-D ABL budget vs eddy covariance fluxes

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Date

Su

rfa

ce

Flu

x (

pp

m*m

/s)

Predicted Tower BasedSurface FluxPredicted Aircraftbased surface flux

Mar 30Mar 26 May 18 Oct 7

Oct 9Credit:Marc Fischer,LBNL

Regional fluxes from H2O - CO2 similarity

FromHellikerand Berry,poster.

Multiple-site syntheses

• Interannual variability in NEE of CO2 is driven by climate(?).

• Multiple flux towers experience the same climatic anomalies.(?)

• Large flux anomalies can be detected by atmospheric CO2 data.(?)

• Flux tower network and CO2 measurements, therefore, observe the same phenomenon! (The gap is bridged!)

Early leaf-out, 1998, Wisconsin

Impact on atmospheric [CO2]

Spatial coherence of seasonal flux anomalies

A similar pattern isseen at several fluxtowers in N. Americaand Europe.

Three sites have high-quality [CO2] measurements + dataat Fluxnet (NOBS,HF, WLEF).

The spring 98 warm period and a later cloudy period appear at all 3 sites.

Detection of the spring 98 anomaly via oceanic flasks?

2 Alaskan flasksites have slightlyhigher [CO2] inthe spring of 98.

Mace Head, Irelandshows a depression of [CO2] in thespring of 98.

Potential exists to link flux towers with seasonal inverse studies.

North American Carbon Plan(NACP)

http://www.carboncyclescience.gov

Acknowledgements

• Funding and personnel support:– DoE – NIGEC – Midwest and Great Plains– NOAA CMDL– NASA – EOS Validation– DoE – TCP/TECO– NSF/NCAR– USDA-FS

Quit here.The rest is extra.

Respiration in autumn is much higher than in spring even though temperature is similar between these two seasons.

NEE and PAR

Correlation betweenNEE and PAR is good on short time scales but not on annual time scale.

Because respiration is not coupled with PAR

NEE and PAR

Correlation betweenNEE and PAR is good on short time scales but not on annual time scale.

Because respiration is not coupled with PAR

NEE and PAR

Correlation betweenNEE and PAR is good on short time scales but not on annual time scale.

Because respiration is not coupled with PAR

NEE and PAR

Correlation betweenNEE and PAR is good on short time scales but not on annual time scale.

Because respiration is not coupled with PAR

•Maximum F depends on temperature.

•Initial slope of light response curve varies with different temperature.

•WL seems to have double optimal temperature for CO2 uptake, the rest have single optimal T.

Functions of Functions of on R, F on R, F,,and and

R(T) is expectedThe wet year (97) data was only used for LW. The convergent result could not be obtained if we used the drier year (98) data. There may be no photosynthesis in dry year 98 at LW.

F(T) decreases with temperature at both sites and shows little sensitivity to time of day.Water stress is critical at both sites.

is almost constant at LW.is almost constant at T lower than about 20 oC at WL. At higher temperature, is different between AM and PM.

Comparison of the light response Comparison of the light response model with observations.model with observations.

Global---all daytime data was used.

Morning---only morning data was used.

Afternoon---only afternoon data was used.