Flux Estimates Research Articles

Analysis of Radon Near-Surface Measurements, Using Co-Located Ozone Data, Radio-Sounding Vertical Profiles, Sensible Heat Flux and Back-Trajectory Calculation

Simultaneous and co-located observations of near-surface Radon-222, ozone and meteorological parameters in a central Italy observation site operated by the University of L’Aquila (Italy), are used to study the physical drivers of the radon abundance during night-time hours. The knowledge of the potential temperature vertical gradient in the surface layer of nocturnal thermal inversion is made possible using co-located radio-sounding vertical profiles of pressure and temperature, thus making possible to indirectly infer the local surface flux of atmospheric radon (16 ± 6 mBq m−2 s−1). The dynamical removal due to turbulent convective motions is found to be the dominant controlling process, determining large differences in the near-surface radon abundance between stable and unstable conditions of the nocturnal Planetary Boundary Layer (PBL). Usual unstable PBL conditions during daytime hours induce an effective dynamical vertical dilution of surface radon, which rapidly reaches a quasi-steady-state abundance during mid-day and afternoon hours, with very low concentration values (5.1 ± 2.0 Bq m−3). Using back-trajectory reanalyses, estimates of local radon fluxes and vertical mixing efficiencies inside the PBL along the air mass latitudinal-longitudinal path and finally the irreversible radon loss due to radioactive decay, we have explored the fraction of daytime radon attributable to long-range advection in the continental near-mountain measurement site of L’Aquila (44 ± 18%).

Carbon Emissions From Chinese Inland Waters: Current Progress and Future Challenges

AbstractInland waters are significant emitters of greenhouse gases for the atmosphere and play an important role in the global carbon cycle. With a vast land area in East Asia spanning a broad range of climatic conditions, China has a large number of natural and human‐made water bodies. These inland water systems are of global importance because of their high carbon emission fluxes. Over the past decades, China has experienced unprecedented environmental changes driven by rapid economic development, which have profoundly modified its inland water carbon biogeochemistry and associated emissions. This review focuses on carbon dioxide (CO2) and methane (CH4) emission dynamics from China's inland waters in response to global change. Major drivers of CO2 and CH4 emissions, including aquatic metabolism, hydrological and climatic factors, and prevailing human impacts, are examined. To advance our understanding of carbon emissions from China's inland waters, we further identify several critical knowledge gaps, such as inadequate research in headwater streams and the climate‐sensitive Tibetan Plateau aquatic ecosystems. Furthermore, insufficient understanding of carbon emissions from inland waters undergoing extensive human interventions (e.g., damming, flow regulation, pollution, and farming practices in aquaculture ponds) is highlighted. We suggest that future efforts should be made to better capture the spatiotemporal heterogeneity in dissolved CO2 and CH4 concentrations and fluxes across China as well as their long‐term trends. To overcome uncertainties in carbon sources and current flux estimates, future research to mechanistically understand carbon transport and transformation in Chinese inland waters and their underlying processes is particularly needed.

Improving Monte Carlo radiative transfer in the regime of high optical depths: The minimum scattering order

Radiative transfer simulations are a powerful tool that enables the calculation of synthetic images of a wide range of astrophysical objects. These simulations are often based on the Monte Carlo method, as it provides the needed versatility that allows the consideration of the diverse and often complex conditions found in those objects. However, this method faces fundamental problems in the regime of high optical depths which may result in overly noisy images and severely underestimated flux values. In this study, we propose an advanced Monte Carlo radiative transfer method, namely, an enforced minimum scattering order that is aimed at providing a minimum quality of determined flux estimates. For that purpose, we extended our investigations of the scattering order problem and derived an analytic expression for the minimum number of interactions that depends on the albedo and optical depth of the system, which needs to be considered during a simulation to achieve a certain coverage of the scattering order distribution. The method is based on the utilization of this estimated minimum scattering order and enforces the consideration of a sufficient number of interactions during a Monte Carlo radiative transfer simulation. Moreover, we identified two notably distinct cases that shape the kind of complexity that arises in such simulations: the albedo-dominated case and the optical depth-dominated case. Based on that, we analyzed the implications related to the best usage of a stretching method as a means to alleviate the scattering order problem. We find that its most suitable application requires taking into account the value of the albedo as well as the optical depth. Furthermore, we argue that the derived minimum scattering order can be used to assess the performance of a stretching method with regard to the scattering orders its usage promotes. Finally, we stress the need for developing advanced pathfinding techniques to fully solve the problem of Monte Carlo radiative transfer simulations in the regime of high optical depths.

Exceptionally low mercury concentrations and fluxes from the 2021 and 2022 eruptions of Fagradalsfjall volcano, Iceland

Mercury (Hg) is naturally released by volcanoes and geothermal systems, but the global flux from these natural sources is highly uncertain due to a lack of direct measurements and uncertainties with upscaling Hg/SO2 mass ratios to estimate Hg fluxes. The 2021 and 2022 eruptions of Fagradalsfjall volcano, southwest Iceland, provided an opportunity to measure Hg concentrations and fluxes from a hotspot/rift system using modern analytical techniques. We measured gaseous Hg and SO2 concentrations in the volcanic plume by near-source drone-based sampling and simultaneous downwind ground-based sampling. Mean Hg/SO2 was an order of magnitude higher at the downwind locations relative to near-source data. This was attributed to the elevated local background Hg at ground level (4.0 ng m−3) likely due to emissions from outgassing lava fields. The background-corrected plume Hg/SO2 mass ratio (5.6 × 10−8) therefore appeared conservative from the near-source to several hundred meters distant, which has important implications for the upscaling of volcanic Hg fluxes based on SO2 measurements. Using this ratio and the total SO2 flux from both eruptions, we estimate the total mass of gaseous Hg released from the 2021 and 2022 Fagradalsfjall eruptions was 46 ± 33 kg, equivalent to a flux of 0.23 ± 0.17 kg d−1. This is the lowest Hg flux estimate in the literature for active open-conduit volcanoes, which range from 0.6 to 12 kg d−1 for other hotspot/rift volcanoes, and 0.5–110 kg d−1 for arc volcanoes. Our results suggest that Icelandic volcanic systems are fed from an especially Hg-poor mantle. Furthermore, we demonstrate that the aerial near-source plume Hg measurement is feasible with a drone-based active sampling configuration that captures all gaseous and particulate Hg species, and recommend this as the preferred method for quantifying volcanic Hg emissions going forward.

Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth

The tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Improved atmospheric constraints on Southern Ocean CO2 exchange

We present improved estimates of air-sea CO2 exchange over three latitude bands of the Southern Ocean using atmospheric CO2 measurements from global airborne campaigns and an atmospheric 4-box inverse model based on a mass-indexed isentropic coordinate (Mθe). These flux estimates show two features not clearly resolved in previous estimates based on inverting surface CO2 measurements: a weak winter-time outgassing in the polar region and a sharp phase transition of the seasonal flux cycles between polar/subpolar and subtropical regions. The estimates suggest much stronger summer-time uptake in the polar/subpolar regions than estimates derived through neural-network interpolation of pCO2 data obtained with profiling floats but somewhat weaker uptake than a recent study by Long et al. [Science 374, 1275-1280 (2021)], who used the same airborne data and multiple atmospheric transport models (ATMs) to constrain surface fluxes. Our study also uses moist static energy (MSE) budgets from reanalyses to show that most ATMs tend to have excessive diabatic mixing (transport across moist isentrope, θe, or Mθe surfaces) at high southern latitudes in the austral summer, which leads to biases in estimates of air-sea CO2 exchange. Furthermore, we show that the MSE-based constraint is consistent with an independent constraint on atmospheric mixing based on combining airborne and surface CO2 observations.

Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry

Abstract. The reduction of CO2 emissions and the enhancement of CO2 removals related to land use are considered essential for future pathways towards net-zero emissions and mitigating climate change. With the growing pressure under global climate treaties, country-level land-use CO2 flux data are becoming increasingly important. So far, country-level estimates are mainly available through official country reports, such as the greenhouse gas inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Recently, different modelling approaches, namely dynamic global vegetation models (DGVMs) and bookkeeping models, have moved to higher spatial resolutions, which makes it possible to obtain model-based country-level estimates that are globally consistent in their methodology. To progress towards a largely independent assessment of country reports using models, we analyse the robustness of country-level CO2 flux estimates from different modelling approaches in the period 1950–2021 and compare them with estimates from country reports. Our results highlight the general ability of modelling approaches to estimate land-use CO2 fluxes at the country level and at higher spatial resolution. Modelled land-use CO2 flux estimates generally agree well, but the investigation of multiple DGVMs and bookkeeping models reveals that the robustness of their estimates strongly varies across countries, and substantial uncertainties remain, even for top emitters. Similarly, modelled land-use CO2 flux estimates and country-report-based estimates agree reasonably well in many countries once their differing definitions are accounted for, although differences remain in some other countries. A separate analysis of CO2 emissions and removals from land use using bookkeeping models also shows that historical peaks in net fluxes stem from emission peaks in most countries, whereas the long-term trends are more connected to removal dynamics. The ratio of the net flux to the sum of CO2 emissions and removals from land use (the net-to-gross flux ratio) underlines the spatio-temporal heterogeneity in the drivers of net land-use CO2 flux trends. In many tropical regions, net-to-gross flux ratios of about 50 % are due to much larger emissions than removals; in many temperate countries, ratios close to zero show that emissions and removals largely offset each other. Considering only the net flux thus potentially masks large emissions and removals and the different timescales upon which they act, particularly if averaged over countries or larger regions, highlighting the need for future studies to focus more on the gross fluxes. Data from this study are openly available via the Zenodo portal at https://doi.org/10.5281/zenodo.8144174 (Obermeier et al., 2023).

The Umbilical Cord Creatine Flux and Time Course of Human Milk Creatine across Lactation.

(1) Background: The aim of the present paper was to study fetal and infant creatine (Cr) supply to improve nutrition and neuroprotection in term and especially in preterm infants. The primary outcomes were the placental Cr flux at the end of pregnancy and the time course of human milk (HM) Cr. (2) Methods: The estimation of placental Cr flux was based on umbilical arterial and venous cord blood Cr in 10 term infants after elective caesarian section. HM Cr, creatinine (Crn), and macronutrients were measured longitudinally in 10 mothers across the first 6 months of breastfeeding. (3) Results: At the end of pregnancy, the mean fetal Cr flux was negative (-2.07 mmol/min). HM Cr was highest in colostrum, decreased significantly within the first 2 weeks of breastfeeding (p < 0.05), and did not change significantly thereafter. HM Cr was not correlated with HM Crn or macronutrient composition. (4) Conclusions: The present data suggest that fetal endogenous Cr synthesis covers the needs at the end of pregnancy. However, high colostrum Cr and HM Cr levels, independent of macronutrient composition, suggest that there may be a critical Cr demand immediately after birth that needs to be covered by enteral supply.

Gross primary production of Mediterranean watersheds: Using isotope mass balance approach to improve estimations

AbstractGlobal‐scale estimates of carbon fluxes from satellite data‐driven models are constrained by considerable uncertainties regarding Gross Primary Production (GPP) and the lack of the watershed‐scale measurements required for model calibration. Recently conducted global modelling efforts indicate that semiarid ecosystems dominate the increasing trends and inter‐annual variation of net CO2 exchange with the atmosphere, but semi‐arid regions have received little attention with regard to GPP estimation. In this study, we used the distinct isotope effect of transpiration and evaporation to calculate transpiration losses and subsequently CO2 uptake by terrestrial vegetation through the water and carbon cycle using the water use efficiency of plants. By studying two Mediterranean watersheds with contrasted environmental conditions over several hydrological years, we found a strong dependence of GPP on annual and seasonal water availability. The results demonstrated that when compared to GPP values obtained in worldwide biomes using biological methods, our isotope approach was validated, highlighting the limitations of satellite‐data‐driven models like MODIS in capturing the impact of water stress on photosynthesis and GPP estimates. These results encourage investigation of GPP by the isotope mass balance approach where direct carbon flux measurements are rare or absent in order to help to substantiate, modify or shed doubt on interpolated GPP for those regions and achieve consensus on global GPP estimates. Given the relevant role of semi‐arid ecosystems in the global carbon balance as well as the limitation of existing data sets, our improved method based on the isotope mass balance approach helps to obtain rapid and affordable estimates of GPP for semi‐arid ecosystems.

Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020

The Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances and climate change have impacted the Amazon carbon balance. Here we conduct a comprehensive synthesis of existing state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon using a set of bottom-up methods (i.e., dynamic vegetation models and bookkeeping models) and a top-down inversion (atmospheric inversion model) over the Brazilian Amazon and the whole Biogeographical Amazon domain. Over the whole biogeographical Amazon region bottom-up methodologies suggest a small average carbon sink over 2010-2020, in contrast to a small carbon source simulated by top-down inversion (2010-2018). However, these estimates are not significantly different from one another when accounting for their large individual uncertainties, highlighting remaining knowledge gaps, and the urgent need to reduce such uncertainties. Nevertheless, both methodologies agreed that the Brazilian Amazon has been a net carbon source during recent climate extremes and that the south-eastern Amazon was a net land carbon source over the whole study period (2010-2020). Overall, our results point to increasing human-induced disturbances (deforestation and forest degradation by wildfires) and reduction in the old-growth forest sink during drought.

Effect of sampling bias on global estimates of ocean carbon export

Shipboard sampling of ocean biogeochemical properties is necessarily limited by logistical and practical constraints. As a result, the majority of observations are obtained for the spring/summer period and in regions relatively accessible from a major port. This limitation may bias the conceptual understanding we have of the spatial and seasonal variability in important components of the Earth system. Here we examine the influence of sampling bias on global estimates of carbon export flux by sub-sampling a biogeochemical model to simulate real, realistic and random sampling. We find that both the sparseness and the ‘clumpy’ character of shipboard flux observations generate errors in estimates of globally extrapolated export flux of up to ∼ ± 20%. The use of autonomous technologies, such as the Biogeochemical-Argo network, will reduce the uncertainty in global flux estimates to ∼ ± 3% by both increasing the sample size and reducing clumpiness in the spatial distribution of observations. Nevertheless, determining the climate change-driven trend in global export flux may be hampered due to the uncertainty introduced by interannual variability in sampling patterns.

Temporal variability of air-water gas exchange of carbon dioxide in clam and fish aquaculture ponds

The growing trend of land-based aquaculture has heightened the significance of comprehensively assessing air-water carbon dioxide (CO2) gas exchange in these inland waters, given their potential impact on carbon neutral strategies. However, temporal variations of partial pressure of CO2 (pCO2) and CO2 flux in clam and fish aquaculture ponds were barely investigated. We assessed the water surface pCO2 in one to five months intervals by deploying a lab-made buoy in three clam ponds and three fishponds located in tropical and subtropical climates. Measurements were conducted over a 24 h period each time, spanning from April 2021 to June 2022, covering the stocking, middle, and harvesting stages of the culture cycle. Diurnal pCO2 variations were dominantly controlled by biologically driven changes in dissolved inorganic carbon and total alkalinity (~97 %), while temperature and salinity effects were minor (~3 %). Clam ponds acted as a sink of atmospheric CO2 during stocking stages and transitioned to a source during middle to harvesting stages. In contrast, fishponds acted as a source of atmospheric CO2 throughout culture cycles and CO2 flux strengthened when reaching harvesting stages. Overall, clam ponds acted as a weak sink for atmospheric CO2 (−2.8 ± 17.3 mmol m−2 d−1), whereas fishponds acted as a source (16.8 ± 21.7 mmol m−2 d−1). CO2 emission was stronger during daytime coinciding with higher windspeeds compared to nighttime in fishponds. We suggest incorporating high temporal resolution measurements to account for diurnal and culture-stage variations, enabling more accurate estimates of air-water CO2 flux in aquaculture ponds. Moreover, the findings of this study highlight the importance of feeding, aeration, and biological activities (photosynthesis, remineralization, and calcification) in controlling the air-water CO2 flux in aquaculture ponds and such information can be used in implementing better strategies to achieve carbon neutral goals.

A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020

Abstract. In recent years, advancements in machine learning based interpolation methods have enabled the production of high-resolution maps of sea surface partial pressure of CO2 (pCO2) derived from observations extracted from databases such as the Surface Ocean CO2 Atlas (SOCAT). These pCO2-products now allow quantifying the oceanic air–sea CO2 exchange based on observations. However, most of them do not yet explicitly include the coastal ocean. Instead, they simply extend the open ocean values onto the nearshore shallow waters, or their spatial resolution is simply so coarse that they do not accurately capture the highly heterogeneous spatiotemporal pCO2 dynamics of coastal zones. Until today, only one global pCO2-product has been specifically designed for the coastal ocean (Laruelle et al., 2017). This product, however, has shortcomings because it only provides a climatology covering a relatively short period (1998–2015), thus hindering its application to the evaluation of the interannual variability, decadal changes and the long-term trends of the coastal air–sea CO2 exchange, a temporal evolution that is still poorly understood and highly debated. Here we aim at closing this knowledge gap and update the coastal product of Laruelle et al. (2017) to investigate the longest global monthly time series available for the coastal ocean from 1982 to 2020. The method remains based on a two-step Self-Organizing Maps and Feed-Forward Network method adapted for coastal regions, but we include additional environmental predictors and use a larger pool of training and validation data with ∼18 million direct observations extracted from the latest release of the SOCAT database. Our study reveals that the coastal ocean has been acting as an atmospheric CO2 sink of −0.40 Pg C yr−1 (−0.18 Pg C yr−1 with a narrower coastal domain) on average since 1982, and the intensity of this sink has increased at a rate of 0.06 Pg C yr−1 decade−1 (0.02 Pg C yr−1 decade−1 with a narrower coastal domain) over time. Our results also show that the temporal changes in the air–sea pCO2 gradient plays a significant role in the long-term evolution of the coastal CO2 sink, along with wind speed and sea-ice coverage changes that can also play an important role in some regions, particularly at high latitudes. This new reconstructed coastal pCO2-product (https://doi.org/10.25921/4sde-p068; Roobaert et al., 2023) allows us to establish regional carbon budgets requiring high-resolution coastal flux estimates and provides new constraints for closing the global carbon cycle.

Dissolved Major and Trace Elements in the Largest Eurasian Arctic Rivers: Ob, Yenisey, Lena, and Kolyma

In contrast to fairly good knowledge of dissolved carbon and major elements in great Arctic rivers, seasonally resolved concentrations of many trace elements remain poorly characterized, hindering assessment of the current status and possible future changes in the hydrochemistry of the Eurasian Arctic. To fill this gap, here we present results for a broad suite of trace elements in the largest rivers of the Russian Arctic (Ob, Yenisey, Lena, and Kolyma). For context, we also present results for major elements that are more routinely measured in these rivers. Water samples for this study were collected during an international campaign called PARTNERS from 2004 through 2006. A comparison of element concentrations obtained for Arctic rivers in this study with average concentrations in the world’s rivers shows that most elements in the Arctic rivers are similar to or significantly lower than the world average. The mineral content of the three greatest rivers (Ob, Yenisey, and Lena) varies within a narrow range (from 107 mg/L for Yenisey to 123 mg/L for Ob). The Kolyma’s mineral content is significantly lower (52.4 mg/L). Fluxes of all major and trace elements were calculated using average concentrations and average water discharge for the 2004–2006 period. Based on these flux estimates, specific export (i.e., t/km2/y) for most of the elements was greatest for the Lena, followed by the Yenisey, Ob, and Kolyma in decreasing order. Element pairwise correlation analysis identified several distinct groups of elements depending on their sources and relative mobility in the river water. There was a negative correlation between Fe and DOC concentration in the Ob River, which could be linked to different sources of these components in this river. The annual yields of major and trace elements calculated for each river were generally consistent with values assessed for other mid-size and small rivers of the Eurasian subarctic.

Kinetic modeling of the plasma–wall interaction in the DTT divertor region

A precise estimate of the local energy fluxes and erosion profiles at the divertor monoblocks of a fusion reactor requires a kinetic modeling of the plasma–wall interaction. Here, a two-dimensional Particle-in-Cell code is used to quantify the particle and energy fluxes and ion impact distribution functions across the divertor monoblocks of the ‘Divertor Tokamak Test’ reactor, focusing on poloidal gaps with toroidal bevelling. The considered critical locations are close to the strike points at both Inner and Outer Vertical Targets. A worst-case scenario for particle fluxes corresponding to attached plasma conditions and featuring a single-null magnetic configuration is assumed. The separate and cumulative effects of including electron wall emission and ions/electrons collisions with a background neutral gas (recycled at the walls) are also assessed. It is found that a non-negligible energy flux affects the shadowed regions of the monoblocks, especially when accounting for collisions, and that the ion impact distribution functions are strongly influenced by the considered kinetic effects, with important implications on the induced sputtering yield.

On the importance of the humidity flux for the surface mass balance in the accumulation zone of the Greenland Ice Sheet

Abstract. It is highly uncertain how the humidity flux between the snow surface and the atmosphere contributes to the surface mass balance (SMB) of the interior Greenland Ice Sheet (GrIS). Due to sparse observations, evaluations of the simulated humidity flux are limited. Model-based estimates of the humidity flux contribution to the SMB are, therefore, unconstrained and even disagree in magnitude and sign. In this study, we evaluate the regional climate model MAR at the EGRIP (East Greenland Ice-Core Project) site in the accumulation zone of the GrIS. We use a combined dataset of continuous one-level bulk estimates of the humidity flux covering the period of May 2016–August 2019 and eddy-resolving eddy-covariance humidity flux measurements from all four summer seasons. In summer, we document a bias of too little sublimation (−1.3 W m−2, −1.65 mm w.e.) caused by a cold bias in both air and surface temperature, leading to a reduced humidity gradient. In winter, MAR overestimates vapor deposition by about 1 order of magnitude. This is a consequence of an overestimated temperature gradient in too stable atmospheric conditions compared to observations. Both systematic errors cause a large discrepancy in the annual net humidity flux between the model and observations of −9 mm w.e. yr−1. Remarkably, the simulated net annual humidity flux contributes positively to the SMB, contrary to observations documenting a net sublimation flux. We correct the systematic errors by applying a simple but effective correction function to the simulated latent heat flux. Using this correction, we find that 5.1 % of the annual mass gain at the EGRIP site sublimates again, and 4.3 % of the total mass gain is deposited vapor from the near-surface air. The estimated net humidity flux contribution to the annual SMB is about −1 % (net sublimation) compared to +5.6 % for the uncorrected simulation. In summer, the corrected MAR simulation shows that vapor deposition accounts for 9.6 % of the total mass gain and that 31 % of the total mass gain at the EGRIP site sublimates again. The net fluxes contribute to −32 % of the summer SMB. These results demonstrate that the humidity flux is a major driver of the summer SMB in the accumulation zone of the GrIS and highlight that even small changes could increase its importance for the annual SMB in a warming climate.

Long-Term Patterns in Forest Soil CO2 Flux in a Pacific Northwest Temperate Rainforest

Soil CO2 efflux (Fs) plays an important role in forest carbon cycling yet estimates of Fs can remain unconstrained in many systems due to the difficulty in measuring Fs over long time scales in natural systems. It is important to quantify seasonal patterns in Fs through long-term datasets because individual years may show patterns that are not reflective of long-term averages. Additionally, determining predictability of net patterns in soil carbon flux based on environmental factors, such as moisture and temperature, is critical for appropriately modeling forest carbon flux. Ecosystems in moderate climates may have strong CO2 efflux even during winter, and so continuous quantification of annual variability is especially important. Here, we used a 2008–2023 dataset in a lowland temperate forest ecosystem to address two main questions: (1) What are the seasonal patterns in Fs in a highly productive temperate rainforest? (2) How is average Fs across our study area predicted by average coincident temperature, soil moisture and precipitation totals? Data showed clear seasonality where Fs values are higher in summer. We also find Fs across our measurement network was predicted by variation in abiotic factors, but the interaction between precipitation/moisture and temperature resulted in greater complexity. Specifically, in spring a relatively strong relationship between air temperature and Fs was present, while in summer the relationship between temperature and Fs was flat. Winter and autumn seasons showed weak positive relationships. Meanwhile, a negative relationship between precipitation and Fs was present in only some seasons because most precipitation falls outside the normal growing season in our study system. Our data help constrain estimates of soil CO2 fluxes in a temperate rainforest ecosystem at ~14–20 kg C ha−1 day−1 in summer and autumn, and 6.5–10.5 kg C ha−1 day−1 in winter and spring seasons. Together, estimates suggest this highly productive temperate rainforest has annual soil-to-atmosphere fluxes of CO2 that amount to greater than 4.5 Mg C ha−1 year−1. Sensitivity of such fluxes to regional climate change will depend on the balance of Fs determined by autotrophic phenological responses versus heterotrophic temperature and moisture sensitivity. Relatively strong seasonal variation coupled with comparatively weak responses to abiotic variables suggest Fs may be driven largely by seasonal trends in autotrophic respiration. Accordingly, plant and tree responses to climate may have a stronger effect on Fs in the context of climate change than temperature or moisture changes alone.

Large-eddy-model closure and simulation of turbulent flux patterns over oasis surface

Abstract. Large-eddy simulations (LESs) have been increasingly used for studying atmosphere and land surface interactions over heterogeneous areas. However, parameterizations based on Monin–Obukhov Similarity Theory (MOST) often violate the basic assumptions of the very theory, generate inconsistencies with the LES turbulence closures, and produce surface flux estimates depending on LES model resolutions. Here, we propose a novel scheme for turbulent flux estimates in LES models. It computes the fluxes locally using the LES subgrid closure, which is then constrained on the macroscopic scale using MOST. Compared with several other schemes, the new scheme performs better for the various types of land surfaces tested. We validate our scheme by comparing surface flux estimates with field measurements obtained over an oasis surface at various height levels. Additionally, we scrutinize other quantities related to the surface energy balance, including net radiation, ground heat flux, and surface skin temperature, all of which align well with observational data. Our sensitivity experiments, focusing on model horizontal resolution, underscore the robustness of our scheme, as it maintains its corrective efficacy despite changes in horizontal grid spacing. We find that the macroscopic constraint imposed by MOST on LES-estimated fluxes strengthens as the horizontal grid spacing decreases, with a more pronounced influence on sensible than latent heat fluxes. These findings collectively highlight the promise and adaptability of our scheme for improved surface flux estimates in LES models.

Inverse estimation of heat flux and temperature field for a nonlinear heat transfer system using step-renewed two-stage Kalman filter

Inverse estimation of the thermal input and states is a useful tool for the monitoring of a thermal system. Nonlinear heat transfer is a quite common issue in this kind of problem. As the thermal properties are temperature-dependent, it is difficult to inversely estimate the input and temperature field. To deal with this problem, a step-renewed scheme is adopted into the weighted optimal two-stage Kalman filter (WOTSKF), forming step-renewed WOTSKF (SR-WOTSKF). This strategy is a marching method, without solving the Jacobian matrix in extended Kalman filter. The feasibility of dealing with the nonlinear inverse heat transfer problem is implemented by comparing WOTSKF and SR-WOTSKF. Then, the weighting factor, initial guess of the input heat flux and measurement noise are changed to investigate their effects on the simultaneous estimation results of heat flux and temperature field. It is found that when the weighting factor is 0.95, the relative error of the estimated heat flux using SR-WOTSKF is approximately 2.73% which is much lower than that using WOTSKF. Results using SR-WOTSKF demonstrate that the estimation results are independent of the initial guess and the estimated heat flux can track the exact one in a short period. An incorrect initial guess causes the initial estimation error of the temperature and heat flux and it makes no sense to following period. When the standard deviations of the measurement noise are adopted as 0.01, 0.10, 0.50 and 1.00, the mean relative error of the heat flux is 2.73%, 4.79%, 7.71% and 9.43%, demonstrating the strong robustness of SR-WOTSKF to resist the measurement noise.

Soft Computing Model for Inverse Prediction of Surface Heat Flux from Temperature Responses in Short-Duration Heat Transfer Experiments

Abstract Aerodynamic experiments in high speed flow domain mainly rely on precise measurement transient surface temperatures and subsequent quantification of heat flux. These experiments are mainly simulated in high enthalpy short-duration facilities for which test flow duration are in the order of few milliseconds and the thermal loads resemble the nature of step/impulse. This study focuses on a specially designed fast-response coaxial surface junction thermal probe (CSTP) with capability of capturing transient temperature signals. The short-duration calibration experiments are realized to mimic the simulated flow conditions of high enthalpy test facilities. The classical one-dimensional heat conduction modelling has been used to deduce surface heat flux from the acquired temperature responses. It demonstrates a commendable accuracy of 2.5% when compared with known heat loads of calibration experiment. An advanced soft computing technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS), is introduced for short-duration heat flux predictions. This methodology successfully recovers known (step or ramp) heat loads within a specific experimental time frame (0.2s). The results exhibit excellent agreement in prediction of trend and magnitude, carrying uncertainties of 3% for radiative and 5% for convective experiments. Consequently, the CSTP appears as a rapidly responsive transient heat flux sensor; for real-time short-duration experiments. The soft computing approach (ANFIS) offers an alternative means of heat flux estimation from temperature history irrespective of mode of heat transfer and nature of heat load, marked by its prediction accuracy, diminished mathematical intricacies, and reduced numerical requisites.