Abstract

Abstract. To monitor the continental carbon cycle, a fully automated low maintenance measurement system is installed at the Zotino Tall Tower Observatory in Central Siberia (ZOTTO, 60°48' N, 89°21' E) since April 2009. A cavity ring-down spectroscopy (CRDS) analyzer continuously measures carbon dioxide (CO2) and methane (CH4) from six heights up to 301 m a.g.l. Buffer volumes in each air line remove short term CO2 and CH4 mixing ratio fluctuations associated with turbulence, and allow continuous, near-concurrent measurements from all tower levels. Instead of drying the air sample, the simultaneously measured water vapor is used to correct the dilution and pressure-broadening effects for the accurate determination of dry air CO2 and CH4 mixing ratios. The stability of the water vapor correction was demonstrated by repeated laboratory and field tests. The effect of molecular adsorption in the wet air lines was shown to be negligible. The low consumption of four calibration tanks that need recalibration only on decadal timescale further reduces maintenance. The measurement precision (accuracy) of 0.04 ppm (0.09 ppm) for CO2 and 0.3 ppb (1.5 ppb) for CH4 is compliant with the WMO recommendations. The data collected so far (until April 2010) reveals a seasonal cycle amplitude for CO2 of 30.4 ppm at the 301 m level.

Highlights

  • For the global climate, the most important greenhouse gases are water vapor (H2O), carbon dioxide (CO2) and methane (CH4) (Kiehl et al, 1997)

  • To monitor the continental carbon cycle, a fully automated low maintenance measurement system is installed at the Zotino Tall Tower Observatory in Central Siberia (ZOTTO, 60◦48 N, 89◦21 E) since April 2009

  • Buffer volumes in each air line remove short term CO2 and CH4 mixing ratio fluctuations associated with turbulence, and allow continuous, nearconcurrent measurements from all tower levels

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Summary

Introduction

The most important greenhouse gases are water vapor (H2O), carbon dioxide (CO2) and methane (CH4) (Kiehl et al, 1997). Atmospheric measurements from observational networks have been used to infer surface-atmosphere exchange fluxes using inverse models (Gurney et al, 2002; Rodenbeck et al, 2003; Peylin et al, 2005) This so-called top-down approach has a high potential for providing meaningful carbon budgets on regional to continental scales. The atmospheric signal has particular advantages compared to measurements on plot level (e.g. from eddy covariance measurements), because it integrates the heterogeneous carbon release due to natural (fire, pests, windstorms) and anthropogenic disturbances (forest harvesting) (Korner, 2003). These disturbances primarily influence the human footprint in the carbon cycle of temperate and boreal forests (Magnani et al, 2007)

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