30 Search Results

The influence of thermal stratification on the turbulent kinetic energy balance has been widely studied; however, its influence on the turbulent stress remains less explored in the presence of tall vegetated canopies and less ideal micrometeorological conditions. Here, the impact of thermal stratification on turbulent momentum flux is considered in the roughness sublayer (RSL) and the atmospheric surface layer (ASL) using the Amazon Tall Tower Observatory (ATTO) in Brazil. A scalewise co‐spectral budget (CSB) model is developed using standard closure schemes for the pressure–velocity decorrelation. The CSB revealed that the co‐spectrum $${F}_{wu}\left({k}_x\right)$$ between longitudinal (u') and vertical (w') velocity fluctuationsmore » is impacted by the energy spectrum of the vertical velocity $${E}_{ww}\left({k}_x\right)$$ and the much less studied longitudinal heat‐flux co‐spectrum $${F}_{u{\theta}_{\mathrm{v}}}\left({k}_x\right)$$, where $${\theta}_{\mathrm{v}}^{\prime }$$ are temperature fluctuations and $${k}_x$$ is the longitudinal wavenumber. Under stable, very stable, and dynamic–convective conditions, the scaling exponent $${F}_{wu}\left({k}_x\right)$$ in for the inertial subrange (ISR) scales is dominated by $${F}_{u{\theta}_{\mathrm{v}}}\left({k}_x\right)$$ instead of $${E}_{ww}\left({k}_x\right)$$. A near $${k}_x^{-7/3}$$scaling in $${F}_{u{\theta}_{\mathrm{v}}}\left({k}_x\right)$$ robust to large variations in thermal stratification is found, whereas the Kolmogorov ISR scaling for $${E}_{ww}\left({k}_x\right)\sim {k}_x^{-5/3}$$ is not found. The scale‐dependent decorrelation time between u' and w' is dominated by $${\epsilon}^{-1/3}{k}_x^{-2/3}$$ in the ISR, but is nearly constant for eddies larger than the vertical velocity integral scale, regardless of stability. Implications of these findings for generalized stability correction functions that are based on the turbulent stress budget instead of the turbulent kinetic energy budget are discussed.« less

In inland water covering lakes, reservoirs, and ponds, the gas exchange of slightly soluble gases such as carbon dioxide, dimethyl sulfide, methane, or oxygen across a clean and nearly flat air‐water interface is routinely described using a water‐side mean gas transfer velocity $$\overline{k_{L}}$$, where overline indicates time or ensemble averaging. The micro‐eddy surface renewal model predicts $$\overline{k_{L}}$$ = α_oSc^-1/2 ($$v\bar{ϵ}$$)^1/4, where Sc is the molecular Schmidt number, $$v$$ is the water kinematic viscosity, and $$\bar{ϵ}$$ is the waterside mean turbulent kinetic energy dissipation rate at or near the interface. While α_o = 0.39 - 0.46 has been reported across amore » number of data sets, others report large scatter or variability around this value range. It is shown here that this scatter can be partly explained by high temporal variability in instantaneous ϵ around $$\bar{ϵ}$$, a mechanism that was not previously considered. As the coefficient of variation (CV_e) in ϵ increases, α_o must be adjusted by a multiplier (1 = CV_e²)^-3/32 that was derived from a log‐normal model for the probability density function of ϵ. Reported variations in α_o with a macro‐scale Reynolds number can also be partly attributed to intermittency effects in ϵ. Such intermittency is characterized by the long‐range (i.e., power‐law decay) spatial auto‐correlation function of ϵ. That α_o varies with a macro‐scale Reynolds number does not necessarily violate the micro‐eddy model. Instead, it points to a coordination between the macro‐ and micro‐scales arising from the transfer of energy across scales in the energy cascade.« less

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO₂ net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimatesmore » the global NEE at −5.75 ± 0.33 Pg C yr⁻¹ for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr⁻¹ for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×10³ ± 0.9×10³ km³ globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.« less

Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start_GPPand End_GPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO₂to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increasedmore » photosynthetic capacity in spring faster (1.95% d⁻¹) than ENF (1.10% d⁻¹), and their active season length (End_GPP–Start_GPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start_GPP, End_GPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start_GPP(1.3–2.5 days °C⁻¹) or later End_GPP(1.5–1.8 days °C⁻¹, depending on forest type and month). Finally, we tested whether existing phenological models could predict Start_GPPand End_GPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start_GPP, while a chilling‐degree‐day model was best for End_GPP. The root mean square errors (RMSE) between predicted and observed Start_GPPand End_GPPwere 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).« less

Tundra and boreal ecosystems encompass the northern circumpolar permafrost region and are experiencing rapid environmental change with important implications for the global carbon (C) budget. We analysed multi-decadal time series containing 302 annual estimates of carbon dioxide (CO₂) flux across 70 permafrost and non-permafrost ecosystems, and 672 estimates of summer CO₂ flux across 181 ecosystems. We find an increase in the annual CO₂ sink across non-permafrost ecosystems but not permafrost ecosystems, despite similar increases in summer uptake. Thus, recent non-growing-season CO₂ losses have substantially impacted the CO₂ balance of permafrost ecosystems. Furthermore, analysis of interannual variability reveals warmer summers amplifymore » the C cycle (increase productivity and respiration) at putatively nitrogen-limited sites and at sites less reliant on summer precipitation for water use. Our findings suggest that water and nutrient availability will be important predictors of the C-cycle response of these ecosystems to future warming.« less

Abstract Accounting for temporal changes in carbon dioxide (CO ₂ ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO ₂ based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO ₂ flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO ₂ . Annual CO ₂ emissions had an average of 25% (range 3%–58%) interannual variation. Similar tomore » studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO ₂ flux variability were delineated through mutual information analysis. Sample analysis of CO ₂ fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO ₂ fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.« less

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions.more » Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.« less

Wetlands are responsible for 20%–31% of global methane (CH₄) emissions and account for a large source of uncertainty in the global CH₄ budget. Data-driven upscaling of CH₄ fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH₄ emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH₄ flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH₄ fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ~0.52–0.63 and 0.53).more » UpCH4 estimated annual global wetland CH₄ emissions of 146 ± 43 TgCH₄ y^–1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH₄ y^–1) and overlaps with top-down atmospheric inversion models (155–200 TgCH₄ y^–1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH₄ fluxes has the potential to produce realistic extra-tropical wetland CH₄ emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions.« less

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (> 70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites inmore » the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (> 90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.« less