Merging CO 2 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 CO 2 across many AmeriFlux/Fluxnet sites. + Link observations of interannual variability in tower fluxes with the global CO 2 flask network. = Understand the mechanisms that govern interannual changes in the atmospheric CO 2 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, (R e =Ae BT ). 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 R 2 T WL B R 2 T HV B R 2 T WB B R 2 T Spring Summer Autumn Winter

Climate variables and NEE at various time scales (NB, WB, WL, HF) NEE of CO 2 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

Summary 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 exists Water stress is not critical Soil 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 is the second year of drought 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 sites Average 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 diurnal pattern of NEE with climate 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 = , 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 = ) 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 T In 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

Light response of ecosystem CO2 exchange R=canopy dark respiration, or total ecosystem respiration F   canopy assimilation rate at saturating light  =Apparent quantum yield Hypothesis Hypothesis: R, F   and  depend on temperature. Method Method: Nonparametric statistical method. Data Data: Multi-year daytime flux data in growing season.

What climate domain is favorable for more CO2 uptake?

Isopleths 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  on R, F , 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.

Summary 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: CO 2 mixing ratios from flux towers Application: What can we learn from a single site? –Advection matters –CO 2 advection occurs with weather –ABL budget method is promising for regional fluxes –Joint analyses of CO 2 – H 2 O 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 Time rate of change (e.g. CO 2 ) Mean transport Turbulent transport (flux) Source in the atmosphere Average over the depth of the atmosphere (or the ABL): F 0 C encompasses all surface exchange: Oceans, deforestation, terrestrial uptake, fossil fuel emissions. Inversion study: Observe C, model U, derive F Flux study: Observe F directly

Methods for determining NEE of CO 2 Methods for bridging the gap Upscale via ecosystem models and networks of towers. Move towards regional inverse modeling

Methodology: How can we use flux towers to gather worthwhile CO 2 mixing ratio measurements? Calibrate! Bakwin et al, Zhao et al, Use midday data - very small vertical gradients. Midday surface layer CO 2 data resolves synoptic, seasonal and annual spatial and temporal trends.

Chequamegon Ecosystem-Atmosphere Study (ChEAS) WLEF tall tower (447m) CO 2 flux measurements at: 30, 122 and 396 m CO 2 mixing ratio measurements at: 11, 30, 76, 122, 244 and 396 m Forest stand towers: Mature upland deciduous Deciduous wetland Mixed old growth All have both CO 2 flux and high precision mixing ratio measurements.

Coniferous Mixed deciduous/coniferous Wetland Open water Shrubland General Agriculture Willow Creek WLEF Lost Creek Landcover key North Upland, wetland, and very tall flux tower. Old growth tower to the NE. High-precision CO 2 profile at each site. Mini-mesonet, 15-20km spacing between towers.

View from 396m above Wisconsin: WLEF TV tower

Diurnal cycle of CO 2 in the ABL

Surface layer flux towers can be used to monitor continental CO 2 !

Synoptic variability in CO 2

The seasonal amplitude of the gradient in CO 2 between the continental ABL and the marine boundary layer is large. Surface layer - mid-ABL difference (1 to 2 ppmv) does not overwhelm this signal.

CO 2 gradients vs. the surface-layer - ABL bias at WLEF (all values in ppm CO 2 ) 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) 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 CO 2 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 CO 2 budget. Relative importance changes across the continent.

Net ecosystem-atmosphere exchange of CO 2 in northern Wisconsin

Monthly average ABL budgets from three towers. HForest is the least “1-D”. BOREAS NSA is the most “1-D”. Advection is related to the continent – marine CO 2 gradient. Credits to Harvard group.

Applications CO 2 transport happens during “weather”

Cold frontal passage and CO 2 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 CO 2 – H 2 O analyses may help.

ABL budget (why? Flux ‘fetch’ of ~100’s of km!) To estimate surface flux: Observe first term, observe or neglect second term, observe or avoid F z, solve for F 0. 0?

Flux 1: surface flux using vertical advection, storage flux, and turbulent flux Flux 2: surface flux using storage flux and turbulent flux Flux 3: surface flux using an ABL budget based on PPC data ABL budgets - regional inverse studies

1-D ABL budget vs eddy covariance fluxes Credit: Marc Fischer, LBNL

Regional fluxes from H 2 O - CO 2 similarity From Helliker and Berry, poster.

Multiple-site syntheses Interannual variability in NEE of CO 2 is driven by climate(?). Multiple flux towers experience the same climatic anomalies.(?) Large flux anomalies can be detected by atmospheric CO 2 data.(?) Flux tower network and CO 2 measurements, therefore, observe the same phenomenon! (The gap is bridged!)

Early leaf-out, 1998, Wisconsin

Impact on atmospheric [CO 2 ]

Spatial coherence of seasonal flux anomalies A similar pattern is seen at several flux towers in N. America and Europe. Three sites have high-quality [CO 2 ] measurements + data at 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 flask sites have slightly higher [CO 2 ] in the spring of 98. Mace Head, Ireland shows a depression of [CO 2 ] in the spring of 98. Potential exists to link flux towers with seasonal inverse studies.

North American Carbon Plan (NACP)

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 between NEE 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 between NEE 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 between NEE 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 between NEE 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  on R, F , and  R(T) is expected The 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 o C at WL. At higher temperature,  is different between AM and PM.

Comparison of the light response model with observations. Global---all daytime data was used. Morning---only morning data was used. Afternoon---only afternoon data was used.