Abstract. The net ecosystem exchange of CO 2 (NEE) varies at time scales from seconds to years and longer via the response of its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), to physical and biological drivers. Quantifying the relationship between flux and climate at multiple time scales is necessary for a comprehensive understanding of the role of climate in the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the strength of the interactions between gappy eddy covariance flux and micrometeorological measurements at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the response of terrestrial carbon flux to climatic variability. The variability of NEE, GEP and RE, and their co-variability with dominant climatic drivers, are explored with nearly one thousand site-years of data from the FLUXNET global dataset consisting of 253 eddy coCorrespondence to: P. C. Stoy (paul.stoy@ed.ac.uk) variance research sites. The NEE and GEP wavelet spectra were similar among plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP were relatively less variable than climate. The RE spectra rarely differed among PFT across time scales as expected. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. CANOAK ecosystem model simulations demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics, rather than direct ecosystem response to climate, provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for low frequency patterns in ecosystem CO 2 exchange.

Abstract. The biosphere-atmosphere flux of CO2 responds to climatic variability at time scales from seconds to years and longer. Quantifying the strength of the interaction between the flux and climate variables at multiple frequencies is necessary to begin understanding the climatic controls on the dynamics of the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the interaction between flux and microclimate at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the measured climate-flux interaction. The variability of the net ecosystem exchange of CO2 (NEE), gross ecosystem productivity (GEP) and ecosystem respiration (RE), and their co-variability with dominant climatic drivers, are explored with a global dataset consisting of 253 eddy covariance research sites. Results demonstrate that the NEE and GEP wavelet spectra are similar amongst plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP are relatively less variable than climate. The RE spectra rarely differ among PFT across time scales. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. The low frequency Fourier coefficients of eight sites with more than eight years of data were compared against CANOAK ecosystem model simulations. Both measurements and theory demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for these patterns.

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[1] The Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project has been using the eddy covariance technique since 1998 to monitor energy, water, and carbon surface fluxes over Amazonia. The results obtained up-to-date indicate high level of uncertainties, especially regarding the role of the Amazonian ecosystem to the global carbon budget. Besides the problems related to the eddy covariance measuring system (systematic error and nighttime stable conditions), an extremely important factor is associated with the averaging time scale or ''time window'' used by the scientific community to determine the surface fluxes. This work presents initial efforts to determine the turbulence time scale for long-term carbon and energy surface fluxes over the Amazon rain forest. A total of 198 nights and 218 days during 2006 were analyzed. The multiresolution decomposition technique was applied to project the signal into several time scales and determine when the spectral and cospectral gap occurred. This technique permitted evaluating and separating the real contribution from turbulent and mesoscale fluxes to the total surface fluxes at both diurnal and nocturnal periods. The average turbulence time scale was below 200 and 1200 s for all scalars at nighttime and daytime, respectively. In all cases, there is seasonal dependence. This result shows that the time scale commonly used to calculate nocturnal surface fluxes (30 min) includes a good portion of mesoscale flux in the estimates. The role of these mesoscale fluxes, in terms of seasonal dependence and the uncertainties they add to the estimates, is then analyzed.

Nesta comunicação apresentamos uma pesquisa desenvolvida com uma turma do ensino médio integrado de uma escola da rede federal de ensino da cidade de Manaus. O obetivo da pesquisa foi de avaliar qual o papal exercido pelos conhecimentos prévios para elaboração de hipóteses em questõs abertas no ensino de física. Constatamos que o conhecimento prévio é uma condição necessária mas não suficiente, pois possibilitou ao alunado formularem as hipóteses embora, em sua maioria, elas não estivessem adequadas ao que é posto pela física. Isto indica que o senso comum foi o conhecimento mais utilizado para formulá-las ao invés do conhecimento científico, como esperado.