•Interannual variability in net carbon exchange is large relative to its mean.•Many biophysical variables explain this variability, and differ by region.•Ecosystem photosynthesis modulated net carbon ...exchange more than respiration.•A few ecosystems are on the verge of switching from a carbon sink to sources.
As the lifetime of regional flux networks approach twenty years, there is a growing number of papers that have published long term records (5 years or more) of net carbon fluxes between ecosystems and the atmosphere. Unanswered questions from this body of work are: 1) how variable are carbon fluxes on a year to year basis?; 2) what are the biophysical factors that may cause interannual variability and/or temporal trends in carbon fluxes?; and 3) how does the biophysical control on this carbon flux variability differ by climate and ecological spaces? To address these questions, we surveyed published data from 59 sites that reported on five or more years of continuous measurements, yielding 544 site-years of data.
We found that the standard deviation of the interannual variability in net ecosystem carbon exchange (162gCm−2y−1) is large relative to its population mean (−200gCm−2 y−1). Broad-leaved evergreen forests and crops experienced the greatest absolute variability in interannual net carbon exchange (greater than ±300gCm−2y−1) and boreal evergreen forests and maritime wetlands were among the least variable (less than ±40 gCm−2y−1).
A disproportionate fraction of the yearly variability in net ecosystem exchange was associated with biophysical factors that modulated ecosystem photosynthesis rather than ecosystem respiration. Yet, there was appreciable and statistically significant covariance between ecosystem photosynthesis and respiration. Consequently, biophysical conditions that conspired to increase ecosystem photosynthesis to from one year to the next were associated with an increase in ecosystem respiration, and vice versa; on average, the year to year change in respiration was 40% as large as the year to year change in photosynthesis. The analysis also identified sets of ecosystems that are on the verge of switching from being carbon sinks to carbon sources. These include sites in the Arctic tundra, the evergreen forests in the Pacific northwest and some grasslands, where year to year changes in respiration are outpacing those in photosynthesis.
While a select set of climatic and ecological factors (e.g. light, rainfall, temperature, phenology) played direct and indirect roles on this variability, their impact differed conditionally, as well as by climate and ecological spaces. For example, rainfall had both positive and negative effects. Deficient rainfall caused a physiological decline in photosynthesis in temperate and semi-arid regions. Too much rain, in the humid tropics, limited photosynthesis by limiting light. In peatlands and tundra, excess precipitation limited ecosystem respiration when it raised the water table to the surface. For deciduous forests, warmer temperatures lengthened the growing season, increasing photosynthesis, but this effect also increased soil respiration.
Finally, statistical analysis was performed to evaluate the detection limit of trends; we computed the confidence intervals of trends in multi-year carbon fluxes that need to be resolved to conclude whether the differences are to be attributed to randomness or biophysical forcings. Future studies and reports on interannual variations need to consider the role of the duration of the time series on random errors when quantifying potential trends and extreme events.
FLUXNET, the global network of eddy covariance flux towers, provides the largest synthesized data set of CO2, H2O, and energy fluxes. To achieve the ultimate goal of providing flux information ...“everywhere and all of the time,” studies have attempted to address the representativeness issue, i.e., whether measurements taken in a set of given locations and measurement periods can be extrapolated to a space‐ and time‐explicit extent (e.g., terrestrial globe, 1982–2013 climatological baseline). This study focuses on the temporal representativeness of FLUXNET and tests whether site‐specific measurement periods are sufficient to capture the natural variability of climatological and biological conditions. FLUXNET is unevenly representative across sites in terms of the measurement lengths and potentials of extrapolation in time. Similarity of driver conditions among years generally enables the extrapolation of flux information beyond measurement periods. Yet such extrapolation potentials are further constrained by site‐specific variability of driver conditions. Several driver variables such as air temperature, diurnal temperature range, potential evapotranspiration, and normalized difference vegetation index had detectable trends and/or breakpoints within the baseline period, and flux measurements generally covered similar and biased conditions in those drivers. About 38% and 60% of FLUXNET sites adequately sampled the mean conditions and interannual variability of all driver conditions, respectively. For long‐record sites (≥15 years) the percentages increased to 59% and 69%, respectively. However, the justification of temporal representativeness should not rely solely on the lengths of measurements. Whenever possible, site‐specific consideration (e.g., trend, breakpoint, and interannual variability in drivers) should be taken into account.
Key Points
FLUXNET is unevenly representative across sites in terms of the measurement lengths and potentials of extrapolation in time
Several drivers have trends or breakpoints in the baseline period and are underrepresented by FLUXNET measurement periods
Justification of site temporal representativeness should consider the natural variability of climatological and biological conditions
•Daily WUE ceased to increase when Ta reached a threshold for both wet and dry years.•High precipitation decreased GEP and WUE when LAI was still small in spring.•Combined effect of seasonal drought ...and Rn significantly decreased WUE in summer.•The monthly model was able to capture 78% of growing-season variation in WUE.•Spring LAI and P, summer Rn and Ta and autumn VPD and Rn were main variables of WUE.
The impacts of extreme weather events on water–carbon (C) coupling and ecosystem-scale water use efficiency (WUE) over a long term are poorly understood. We analyzed the changes in ecosystem water use efficiency (WUE) from 10 years of eddy-covariance measurements (2004–2013) over an oak-dominated temperate forest in Ohio, USA. The aim was to investigate the long-term response of ecosystem WUE to measured changes in site-biophysical conditions and ecosystem attributes. The oak forest produced new plant biomass of 2.5±0.2gCkg−1 of water loss annually. Monthly evapotranspiration (ET) and gross ecosystem production (GEP) were tightly coupled over the 10-year study period (R2=0.94). Daily WUE had a linear relationship with air temperature (Ta) in low-temperature months and a unimodal relationship with Ta in high-temperature months during the growing season. On average, daily WUE ceased to increase when Ta exceeded 22°C in warm months for both wet and dry years. Monthly WUE had a strong positive linear relationship with leaf area index (LAI), net radiation (Rn), and Ta and weak logarithmic relationship with water vapor pressure deficit (VPD) and precipitation (P) on a growing-season basis. When exploring the regulatory mechanisms on WUE within each season, spring LAI and P, summer Rn and Ta, and autumnal VPD and Rn were found to be the main explanatory variables for seasonal variation in WUE. The model developed in this study was able to capture 78% of growing-season variation in WUE on a monthly basis. The negative correlation between WUE and P in spring was mainly due to the high precipitation amounts in spring, decreasing GEP and WUE when LAI was still small, adding ET being observed to increase with high levels of evaporation as a result of high SWC in spring. Summer WUE had a significant decreasing trend across the 10 years mainly due to the combined effect of seasonal drought and increasing potential and available energy increasing ET, but decreasing GEP in summer. We concluded that seasonal dynamics of the interchange between precipitation and drought status of the system was an important variable in controlling seasonal WUE in wet years. In contrast, despite the negative impacts of unfavorable warming, available groundwater and an early start of the growing season were important contributing variables in high seasonal GEP, and thus, high seasonal WUE in dry years.
Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common ...phenomena in many eutrophic lakes and can cause many environmental stresses, yet their effects on the net exchange of CO2 (FCO2) at large spatial scales have not been adequately addressed. We integrated remote sensing and Eddy Covariance (EC) technologies to investigate the effects that algal blooms have on FCO2 in the western basin of Lake Erie-a large lake infamous for these blooms. Three years of long-term EC data (2012-2014) at two sites were analyzed. We found that at both sites: (1) daily FCO2 significantly correlated with daily temperature, light, and wind speed during the algal bloom periods; (2) monthly FCO2 was negatively correlated with chlorophyll-a concentration; and (3) the year with larger algal blooms was always associated with lower carbon emissions. We concluded that large algal blooms could reduce carbon emissions in the western basin of Lake Erie. However, considering the complexity of processes within large lakes, the weak relationship we found, and the potential uncertainties that remain in our estimations of FCO2 and chlorophyll-a, we argue that additional data and analyses are needed to validate our conclusion and examine the underlying regulatory mechanisms.
Net ecosystem carbon dioxide (FCO2) and methane (FCH4) exchanges were measured by using the eddy covariance method to quantify the atmospheric carbon budget at a Typha‐ and Nymphaea‐dominated ...freshwater marsh (March 2011 to March 2013) and a soybean cropland (May 2011 to May 2012) in northwestern Ohio, USA. Two year average annual FCH4 (49.7 g C‐CH4 m−2 yr−1) from the marsh was high and compatible with its net annual CO2 uptake (FCO2: −21.0 g C‐CO2 m−2 yr−1). In contrast, FCH4 was small (2.3 g C‐CH4 m−2 yr−1) and accounted for a minor portion of the atmospheric carbon budget (FCO2: −151.8 g C‐CO2 m−2 yr−1) at the cropland. At the seasonal scale, soil temperature associated with methane (CH4) production provided the dominant regulator of FCH4 at the marsh (R2 = 0.86). At the diurnal scale, plant‐modulated gas flow was the major pathway for CH4 outgassing in the growing season at the marsh. Diffusion and ebullition became the major pathways in the nongrowing season and were regulated by friction velocity. Our findings highlight the importance of freshwater marshes for their efficiency in turning over and releasing newly fixed carbon as CH4. Despite marshes accounting for only ~4% of area in the agriculture‐dominated landscape, their high FCH4 should be carefully addressed in the regional carbon budget.
Key Points
Two year average CH4 flux from a marsh was compatible with its net CO2 uptake
CH4 flux was regulated by different factors at diurnal and seasonal scales
Plant‐modulated gas flow and inundation led to high CH4 flux from the marsh
Evaporation (E) is a critical component of the water and energy budget in lake systems yet is challenging to quantify directly and continuously. We examined the magnitude and changes of E and its ...drivers over Lake Erie—the shallowest and most southern lake of the Laurentian Great Lakes. We deployed two eddy‐covariance tower sites in the western Lake Erie Basin—one located nearshore (CB) and one offshore (LI)—from September 2011 through May 2016. Monthly E varied from 5 to 120 mm, with maximum E occurring in August–October. The annual E was 635 ± 42 (±SD) mm at CB and 604 ± 32 mm at LI. Mean winter (October–March) E was 189 ± 61 mm at CB and 178 ± 25 mm at LI, accounting for 29.8% and 29.4% of annual E. Mean daily E was 1.8 mm during the coldest month (January) and 7.4 mm in the warmest month (July). Monthly E exhibited a strong positive linear relationship to the product of wind speed and vapor pressure deficit. Pronounced seasonal patterns in surface energy fluxes were observed with a 2‐month lag in E from Rn, due to the lake's heat storage. This lag was shorter than reports regarding other Great Lakes. Difference in E between the offshore and nearshore sites reflected within‐lake spatial heterogeneity, likely attributable to climatic and bathymetric differences between them. These findings suggest that predictive models need to consider lake‐specific heat storage and spatial heterogeneity in order to accurately simulate lake E and its seasonal dynamics.
Key Points
The maximum lake evaporation occurred in August–October, with one third in winter over the annual evaporation
Monthly evaporation showed a positive linear relationship with vapor pressure deficit and temperature but not correlated with wind speed
Pronounced seasonal patterns in the surface energy fluxes were observed with a 2‐month lag in evaporation from net radiation
Abstract
AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth ...science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.
As the lifetime of regional flux networks approach twenty years, there is a growing number of papers that have published long term records (5 years or more) of net carbon fluxes between ecosystems ...and the atmosphere. Unanswered questions from this body of work are: 1) how variable are carbon fluxes on a year to year basis?; 2) what are the biophysical factors that may cause interannual variability and/or temporal trends in carbon fluxes?; and 3) how does the biophysical control on this carbon flux variability differ by climate and ecological spaces? To address these questions, we surveyed published data from 59 sites that reported on five or more years of continuous measurements, yielding 544 site-years of data. We found that the standard deviation of the interannual variability in net ecosystem carbon exchange (162 gC m-2 y-1) is large relative to its population mean (-200 gC m-2 y-1). Broad-leaved evergreen forests and crops experienced the greatest absolute variability in interannual net carbon exchange (greater than ±300 gC m-2 y-1) and boreal evergreen forests and maritime wetlands were among the least variable (less than ±40 gC m-2 y-1). A disproportionate fraction of the yearly variability in net ecosystem exchange was associated with biophysical factors that modulated ecosystem photosynthesis rather than ecosystem respiration. Yet, there was appreciable and statistically significant covariance between ecosystem photosynthesis and respiration. Consequently, biophysical conditions that conspired to increase ecosystem photosynthesis to from one year to the next were associated with an increase in ecosystem respiration, and vice versa; on average, the year to year change in respiration was 40% as large as the year to year change in photosynthesis. The analysis also identified sets of ecosystems that are on the verge of switching from being carbon sinks to carbon sources. These include sites in the Arctic tundra, the evergreen forests in the Pacific northwest and some grasslands, where year to year changes in respiration are outpacing those in photosynthesis. While a select set of climatic and ecological factors (e.g. light, rainfall, temperature, phenology) played direct and indirect roles on this variability, their impact differed conditionally, as well as by climate and ecological spaces. For example, rainfall had both positive and negative effects. Deficient rainfall caused a physiological decline in photosynthesis in temperate and semi-arid regions. Too much rain, in the humid tropics, limited photosynthesis by limiting light. In peatlands and tundra, excess precipitation limited ecosystem respiration when it raised the water table to the surface. For deciduous forests, warmer temperatures lengthened the growing season, increasing photosynthesis, but this effect also increased soil respiration. Finally, statistical analysis was performed to evaluate the detection limit of trends; we computed the confidence intervals of trends in multi-year carbon fluxes that need to be resolved to conclude whether the differences are to be attributed to randomness or biophysical forcings. Ongoing studies and reports on interannual variations need to consider the role of the duration of the time series on random errors when quantifying potential trends and extreme events.
•Large-scale eddy-covariance flux datasets need to be used with footprint-awareness•Using a fixed-extent target area across sites can bias model-data integration•Most sites do not represent the ...dominant land-cover type at a larger spatial extent•A representativeness index provides general guidance for site selection and data use
Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
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