Modeling nighttime ecosystem respiration from measured CO2 concentration and air temperature profiles using inverse methods

Abstract

A major challenge for quantifying ecosystem carbon budgets from micrometeorological methods remains nighttime ecosystem respiration. An earlier study utilized a constrained source optimization (CSO) method using inverse Lagrangian dispersion theory to infer the two components of ecosystem respiration (aboveground and forest floor) from measured mean CO2 concentration profiles within the canopy. This method required measurements of within‐canopy mean velocity statistics and did not consider local thermal stratification. We propose a Eulerian version of the CSO method (CSOE) to account for local thermal stratification within the canopy for momentum and scalars using higher‐order closure principles. This method uses simultaneous mean CO2concentration and air temperature profiles within the canopy and velocity statistics above the canopy as inputs. The CSOE was tested at a maturing loblolly pine plantation over a 3‐year period with a mild drought (2001), a severe drought (2002), and a wet year (2003). Annual forest floor efflux modeled with CSOE averaged 111 g C m−2 less than that estimated using chambers during these years (2001: 1224 versus 1328 gCm−2; 2002: 1127 versus 1230 gCm−2; 2003: 1473 versus 1599 gCm−2). The modeled ecosystem respiration exceeded estimates from eddy covariance measurements (uncorrected for storage fluxes) by at least 25%, even at high friction velocities. Finally, we showed that the CSOEannual nighttime respiration values agree well with independent estimates derived from the intercept of the ecosystem light‐response curve from daytime eddy covariance CO2flux measurements.

Description

Keywords

Citation

Juang, Jehn-Yih, Katul, Gabriel G., Siqueira, Mario B. S., Stoy, Paul C., Palmroth, Sari, McCarthy, Heather R., Kim, Hyun-Seok, Oren, Ram (2006) Modeling nighttime ecosystem respiration from measured CO2 concentration and air temperature profiles using inverse methods. Journal of Geophysical Research 111: D8, D08S05. DOI: 10.1029/2005JD005976.

Collections

Endorsement

Review

Supplemented By

Referenced By

Copyright (c) 2002-2022, LYRASIS. All rights reserved.