What eddy-covariance measurements tell us about prior land flux errors in CO₂-flux inversion schemes

To guide the future development of CO₂-atmospheric inversion modeling systems, we analyzed the errors arising from prior information about terrestrial ecosystem fluxes. We compared the surface fluxes calculated by a process-based terrestrial ecosystem model with daily averages of CO₂ flux measurements at 156 sites across the world in the FLUXNET network. At the daily scale, the standard deviation of the model-data fit was 2.5 gC·m⁻²·d⁻¹; temporal autocorrelations were significant at the weekly scale (>0.3 for lags less than four weeks), while spatial correlations were confined to within the first few hundred kilometers (<0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6

What eddy-covariance measurements tell us about prior land flux errors in CO2-flux inversion schemes

0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6

Evaluating the performance of land surface model ORCHIDEE-CAN v1.0 on water and energy flux estimation with a single- and multi-layer energy budget scheme

Canopy structure is one of the most important vegetation characteristics for land-atmosphere interactions, as it determines the energy and scalar exchanges between the land surface and the overlying air mass. In this study we evaluated the performance of a newly developed multilayer energy budget in the ORCHIDEE-CAN v1.0 land surface model (Organising Carbon and Hydrology In Dynamic Ecosystems - CANopy), which simulates canopy structure and can be coupled to an atmospheric model using an implicit coupling procedure. We aim to provide a set of accept-able parameter values for a range of forest types. Top-canopy and sub-canopy flux observations from eight sites were collected in order to conduct this evaluation. The sites crossed climate zones from temperate to boreal and the vegetation types included deciduous, evergreen broad-leaved and evergreen needle-leaved forest with a maximum leaf area index (LAI; all-sided) ranging from 3.5 to 7.0. The parametrization approach proposed in this study was based on three selected physical processes - namely the diffusion, advection, and turbulent mixing within the canopy. Short-term sub-canopy observations and long-term surface fluxes were used to calibrate the parameters in the sub-canopy radiation, turbulence, and resistance modules with an automatic tuning process. The multi-layer model was found to capture the dynamics of sub-canopy turbulence, temperature, and energy fluxes. The performance of the new multi-layer model was further compared against the existing single-layer model. Although the multi-layer model simulation results showed few or no improvements to both the nighttime energy balance and energy partitioning during winter compared with a single-layer model simulation, the increased model complexity does provide a more detailed description of the canopy micrometeorology of various forest types. The multi-layer model links to potential future environmental and ecological studies such as the assessment of in-canopy species vulnerability to climate change, the climate effects of disturbance intensities and frequencies, and the consequences of biogenic volatile organic compound (BVOC) emissions from the terrestrial ecosystem.Peer reviewe

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms

It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystemclimate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.This is the publisher’s final pdf. The published article is copyrighted by New Phytologist Trust and can be found at: http://www.newphytologist.org/Keywords: Climate change, Temperature acclimation, Optimum temperature, Thermal optimality, Temperature adaptatio

Tropical tree growth driven by dry-season climate variability

Interannual variability in the global land carbon sink is strongly related to variations in tropical temperature and rainfall. This association suggests an important role for moisture-driven fluctuations in tropical vegetation productivity, but empirical evidence to quantify the responsible ecological processes is missing. Such evidence can be obtained from tree-ring data that quantify variability in a major vegetation productivity component: woody biomass growth. Here we compile a pantropical tree-ring network to show that annual woody biomass growth increases primarily with dry-season precipitation and decreases with dry-season maximum temperature. The strength of these dry-season climate responses varies among sites, as reflected in four robust and distinct climate response groups of tropical tree growth derived from clustering. Using cluster and regression analyses, we find that dry-season climate responses are amplified in regions that are drier, hotter and more climatically variable. These amplification patterns suggest that projected global warming will probably aggravate drought-induced declines in annual tropical vegetation productivity. Our study reveals a previously underappreciated role of dry-season climate variability in driving the dynamics of tropical vegetation productivity and consequently in influencing the land carbon sink.We acknowledge financial support to the co-authors provided by Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT 2014-2797) to M.E.F.; Alberta Mennega Stichting to P.G.; BBVA Foundation to H.A.M. and J.J.C.; Belspo BRAIN project: BR/143/A3/HERBAXYLAREDD to H.B.; Confederação da Agricultura e Pecuária do Brasil - CNA to C.F.; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES, Brazil (PDSE 15011/13-5 to M.A.P.; 88881.135931/2016-01 to C.F.; 88887.199858/2018-00 to G.A.-P.; Finance Code 001 for all Brazilian collaborators); Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, Brazil (ENV 42 to O.D.; 1009/4785031-2 to G.C.; 311874/2017-7 to J.S.); CONACYT-CB-2016-283134 to J.V.-D.; CONICET to F.A.R.; CUOMO FOUNDATION (IPCC scholarship) to M.M.; Deutsche Forschungsgemeinschaft - DFG (BR 1895/15-1 to A.B.; BR 1895/23-1 to A.B.; BR 1895/29-1 to A.B.; BR 1895/24-1 to M.M.); DGD-RMCA PilotMAB to B.T.; Dirección General de Asuntos del Personal Académico of the UNAM (Mexico) to R.B.; Elsa-Neumann-Scholarship of the Federal State of Berlin to F.S.; EMBRAPA Brazilian Agricultural Research Corporation to C.F.; Equatorian Dirección de Investigación UNL (21-DI-FARNR-2019) to D.P.-C.; São Paulo Research Foundation FAPESP (2009/53951-7 to M.T.-F.; 2012/50457-4 to G.C.; 2018/01847‐0 to P.G.; 2018/24514-7 to J.R.V.A.; 2019/08783-0 to G.M.L.; 2019/27110-7 to C.F.); FAPESP-NERC 18/50080-4 to G.C.; FAPITEC/SE/FUNTEC no. 01/2011 to M.A.P.; Fulbright Fellowship to B.J.E.; German Academic Exchange Service (DAAD) to M.I. and M.R.; German Ministry of Education, Science, Research, and Technology (FRG 0339638) to O.D.; ICRAF through the Forests, Trees, and Agroforestry research programme of the CGIAR to M.M.; Inter-American Institute for Global Change Research (IAI-SGP-CRA 2047) to J.V.-D.; International Foundation for Science (D/5466-1) to M.I.; Lamont Climate Center to B.M.B.; Miquelfonds to P.G.; National Geographic Global Exploration Fund (GEFNE80-13) to I.R.; USA’s National Science Foundation NSF (IBN-9801287 to A.J.L.; GER 9553623 and a postdoctoral fellowship to B.J.E.); NSF P2C2 (AGS-1501321) to A.C.B., D.G.-S. and G.A.-P.; NSF-FAPESP PIRE 2017/50085-3 to M.T.-F., G.C. and G.M.L.; NUFFIC-NICHE programme (HEART project) to B.K., E.M., J.H.S., J.N. and R. Vinya; Peru ‘s CONCYTEC and World Bank (043-2019-FONDECYT-BM-INC.INV.) to J.G.I.; Peru’s Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (FONDECYT-BM-INC.INV 039-2019) to E.J.R.-R. and M.E.F.; Programa Bosques Andinos - HELVETAS Swiss Intercooperation to M.E.F.; Programa Nacional de Becas y Crédito Educativo - PRONABEC to J.G.I.; Schlumberger Foundation Faculty for the Future to J.N.; Sigma Xi to A.J.L.; Smithsonian Tropical Research Institute to R. Alfaro-Sánchez.; Spanish Ministry of Foreign Affairs AECID (11-CAP2-1730) to H.A.M. and J.J.C.; UK NERC grant NE/K01353X/1 to E.G.Peer reviewe

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe

Nocturnal accumulation of CO2 underneath a tropical forest canopy along a topographical gradient

Flux measurements of carbon dioxide and water vapor above tropical rain forests are often difficult to interpret because the terrain is usually complex. This complexity induces heterogeneity in the surface but also affects lateral movement of carbon dioxide (CO2) not readily detected by the eddy covariance systems. This study describes such variability using measurements of CO2 along vertical profiles and along a toposequence in a tropical rain forest near Manaus, Brazil. Seasonal and diurnal variation was recorded, with atmospheric CO2 concentration maxima around dawn, generally higher CO2 build-up in the dry season and stronger daytime CO 2 drawdown in the wet season. This variation was reflected all along the toposequence, but the slope and valley bottom accumulated clearly more CO2 than the plateaus, depending on atmospheric stability. Particularly during stable nights, accumulation was along lines of equal altitude, suggesting that large amounts of CO2 are stored in the valleys of the landscape. Flushing of this store only occurs during mid-morning, when stored CO2 may well be partly transported back to the plateaus. It is clear that, for proper interpretation of tower fluxes in such complex and actively respiring terrain, the horizontal variability of storage needs to be taken into account not only during the night but also during the mornings. © 2008 by the Ecological Society of America