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Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

The Arctic region is experiencing notable warming as well as more lightning. Lightning is the dominant source of upper tropospheric nitrogen oxides (NOx), which are precursors for ozone and hydroxyl radicals. In this study, we combine the nitrogen dioxide (NO2) observations from the TROPOspheric Monitoring Instrument (TROPOMI) with Vaisala Global Lightning Dataset 360 to evaluate lightning NO2 (LNO2) production in the Arctic. By analyzing consecutive TROPOMI NO2 observations, we determine the lifetime and production efficiency of LNO2 during the summers of 2019-2021. Our results show that the LNO2 production efficiency over the ocean is ∼6 times higher than over continental regions. Additionally, we find that a higher LNO2 production efficiency is often correlated with lower lightning rates. The summertime lightning NOx emission in the Arctic (north of 70° N) is estimated to be 219 ± 116 Mg of N, which is equal to 5% of anthropogenic NOx emissions. However, for the span of a few hours, the Arctic LNO2 density can even be comparable to anthropogenic NO2 emissions in the region. These new findings suggest that LNO2 can play an important role in the upper-troposphere/lower-stratosphere atmospheric chemical processes in the Arctic, particularly during the summer.

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high-resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature-forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity-related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low- and high-resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere-only runs) and Hist-1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high-resolution HighResMIP model compared to the low-resolution HighResMIP model. © 2022 The Authors. International Journal of Climatology published by John Wiley Sons Ltd on behalf of Royal Meteorological Society.

Ongoing urbanization highlights the need for a better understanding and high resolution modelling of the urban climate. In this study, we combine rural observations by WMO surface stations, weather radar data and urban crowd-sourced observations with very fine-scale modelling efforts for Amsterdam, The Netherlands. As a model, we use the Weather Research and Forecasting (WRF) mesoscale model with 3D variational data assimilation at a 100-m resolution in the innermost model domain. In order to enable the assimilation of observations within the urban canopy, we develop a scheme to reduce urban temperature biases by adjusting urban fabric temperatures. The scheme is tested against independent urban observations for the summer month of July 2014 and specifically for a hot period and an extreme precipitation event. We find data assimilation reduces biases in temperature and wind speed. Within the city, the most significant improvement is the reduction of negative temperature biases during clear nights, which implies a better prediction of the Urban Heat Island (UHI). Concerning precipitation, the fractional skill score improves incrementally when additional observations are assimilated, and the largest impact is seen from the assimilation of weather radar observations.

Accurate forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore reliable forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly, missing crucial information on meteorological variability such as clouds. In this work we present a methodology to forecast minute-to-minute irradiance variability in terms of its probability density function (PDF) based on hourly NWP results, by applying statistical postprocessing using machine learning. The algorithm is tested using the 2.5 × 2.5 km2 HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 meteorological stations throughout the Netherlands used as a ground truth. The applicability of the algorithm to 31 × 31 km2 global-scale models is investigated using ERA5 reanalysis data, which yields comparable accuracies. We find that almost half of the inaccuracy of the postprocessed result is due to errors in the radiation forecast of the NWP model used as input. Finally, the proposed post-processing algorithm is compared to the next generation weather models based on high resolution Large Eddy Simulation (LES), at 75 m horizontal grid spacing, on a case study spanning four days. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of cloud and irradiance dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.

According to Jacobson et al. the energy transition from fossil fuels (Business as Usual, BAU) to energy using wind, water and sun (WWS) can be completed by the year 2050 at minimal costs and with a land occupation of less than 1% of the total land area. This claim relies on unrealistic assumptions and is therefore misleading. In their ambition to show the road to a better world without pollution from energy production, the authors stretched some basic underlying parameter values beyond what is physically possible. The authors wrongly excluded nuclear energy as an option.

Vegetation and atmosphere processes are coupled through a myriad of interactions linking plant transpiration, carbon dioxide assimilation, turbulent transport of moisture, heat and atmospheric constituents, aerosol formation, moist convection, and precipitation. Advances in our understanding are hampered by discipline barriers and challenges in understanding the role of small spatiotemporal scales. In this perspective, we propose to study the atmosphere-ecosystem interaction as a continuum by integrating leaf to regional scales (multiscale) and integrating biochemical and physical processes (multiprocesses). The challenges ahead are (1) How do clouds and canopies affect the transferring and in-canopy penetration of radiation, thereby impacting photosynthesis and biogenic chemical transformations? (2) How is the radiative energy spatially distributed and converted into turbulent fluxes of heat, moisture, carbon, and reactive compounds? (3) How do local (leaf-canopy-clouds, 1 m to kilometers) biochemical and physical processes interact with regional meteorology and atmospheric composition (kilometers to 100 km)? (4) How can we integrate the feedbacks between cloud radiative effects and plant physiology to reduce uncertainties in our climate projections driven by regional warming and enhanced carbon dioxide levels? Our methodology integrates fine-scale explicit simulations with new observational techniques to determine the role of unresolved small-scale spatiotemporal processes in weather and climate models.

Addressing complex sustainability issues in higher education requires the combination and integration of various disciplines, perspectives and approaches. Challenge-Based Learning (CBL) can support interdisciplinary collaboration on sustainability issues. It requires students to actively explore, discuss, reflect on and integrate information and methods from various disciplines. Online learning could enhance interdisciplinary collaboration since it is associated with greater geographical and educational flexibility and accessibility. Applying an active learning approach such as CBL in an online setting is believed to support interdisciplinary learning and collaboration. We present a case study that took place in a 10-week online interdisciplinary, inter-university undergraduate course on sustainability education. Our research is based on well-known online learning theories “Transactional distance” and “Community of Inquiry” (CoI). The aim of this study was to investigate how transactional distance, presence and (online) interdisciplinary learning are perceived by students. 23 undergraduate students from three universities were enrolled in the course. Quantitative survey data (N = 13) and qualitative data from student reflection papers and interviews (N = 15) were collected. Students perceived low levels of transactional distance and high levels of presence. Unexpectedly, a small increase in perceived distance between students was measured which could be explained by reported limitations of the course design. Students valued the open, interactive and creative character of the course and the online format was not perceived as hindering. Students reflected on interdisciplinary competences that they developed during the course. This study is a first step towards future national as well as international interdisciplinary, inter-university educational collaboration on sustainability issues.

This study presents a fine-scale simulation approach to assess the representativity of ammonia (NH3) measurements in the proximity of an emission source. Close proximity to emission sources (< 5 km) can introduce a bias in regionally representative measurements of the NH3 molar fraction and flux. Measurement sites should, therefore, be located a significant distance away from emission sources, but these requirements are poorly defined and can be difficult to meet in densely agricultural regions. This study presents a consistent criterion to assess the regional representativity of NH3 measurements in proximity to an emission source, calculating variables that quantify the NH3 plume dispersion using a series of numerical experiments at a fine resolution (20 m). Our fine-scale simulation framework with explicitly resolved turbulence enables us to distinguish between the background NH3 and the emission plume, including realistic representations of NH3 deposition and chemical gas–aerosol transformations. We introduce the concept of blending distance based on the calculation of turbulent fluctuations to systematically analyze the impact of the emission plume on simulated measurements, relative to this background NH3. We perform a suite of systematic numerical experiments for flat homogeneous grasslands, centered around the CESAR Observatory at Cabauw, to analyze the sensitivity of the blending distance, varying meteorological factors, emission/deposition and NH3 dependences. Considering these sensitivities, we find that NH3 measurements at this measurement site should be located at a minimum distance of 0.5–3.0 and 0.75–4.5 km from an emission source for NH3 molar fraction and flux measurements, respectively. The simulation framework presented here can easily be adapted to local conditions, and paves the way for future ammonia research to integrate simulations at high spatio-temporal resolutions with observations of NH3 concentrations and fluxes.

Estimating lake evaporation is a challenge due to both practical considerations and theoretical assumptions embedded in indirect methods. For the first time, we evaluated measurements from an optical microwave scintillometer (OMS) system over an open-water body under arid conditions. The OMS is a line-of-sight remote sensing technique that can be used to measure the sensible and latent heat fluxes over horizontal areas with pathlengths ranging from 0.5 to 10 km. We installed an OMS at a saline lake surrounded by a wet-salt crust in the Salar del Huasco, a heterogeneous desert landscape in the Atacama Desert. As a reference, we used eddy covariance systems installed over the two main surfaces in the OMS footprint. We performed a footprint analysis to reconstruct the surface contribution to the OMS measured fluxes (80% water and 20% wet salt). Furthermore, we investigated the applicability of the Monin–Obukhov similarity theory (MOST), which was needed to infer fluxes from the OMS-derived structure parameters to the fluxes. The OMS structure parameters and MOST were compromised, which we mitigated by fitting MOST coefficients to the site conditions. We argue that the MOST deviation from values found in the literature is due to the effects of the surface heterogeneity and the nonlocal processes induced by regional circulation. With the available dataset we were not able to rule out instrument issues, such as additional fluctuations to the scintillation signal due to absorption or the effect of vibration in high-wind conditions. The adjusted MOST coefficients lowered by a factor of 1.64 compared to using standard MOST coefficients. For H and LυE, we obtained zero-intercept linear regressions with correlations, R2, of 0.92 and 0.96, respectively. We conclude that advances in MOST are needed to successfully apply the OMS method in landscapes characterized by complex heterogeneity such as the Salar del Huasco.

Plastics and their degradation products, microplastics (MPs), are ubiquitous in the environment, and urban and industrial soils may be the most polluted soils by this type of pollution. There is scarce information addressing human exposure to MPs from urban and industrial soils. This study assesses MP pollution in urban and industrial soils of two county capital cities in Iran, i.e. Abadan and Khorramshahr. The concentration of MPs ranged from 17 MPs/100 g industrial soil to 347 MPs/100 g urban soil, with a mean abundance of 122 MPs/100 g soil. Residential areas were the most contaminated by MPs among all the sampling sites. There, microfiber MPs, red and black, were the most abundant MPs, and it was also the case across sampling sites. Polyethylene terephthalate and polypropylene were the most common polymers among the investigated MPs, while nylon and polystyrene MPs were also present. Regarding MP sizes, those in the fraction 250–500 µm prevailed across all investigated sites. Estimated exposures to MPs through contact with the urban and industrial soils, for a normal scenario according to US EPA parameters, would lead to a daily intake < 1 MP for both adult and children, and their respective yearly intake of MPs is estimated in 6–127 and 12–253 MPs. Further research is needed to assess the health impact of current MP types and levels found in urban soil.

We investigate how evaporation changes depending on the scales in the Altiplano region of the Atacama Desert. More specifically, we focus on the temporal evolution from the climatological to the sub-diurnal scales on a high-altitude saline lake ecosystem. We analyze the evaporation trends over 70 years (1950–2020) at a high-spatial resolution. The method is based on the downscaling of 30 km ERA5 reanalysis data at hourly resolution to 0.1 km spatial resolution data, using artificial neural networks to analyze the main drivers of evaporation. To this end, we use the Penman open-water evaporation equation, modified to compensate for the energy balance non-closure and the ice cover formation on the lake during the night. Our estimation of the hourly climatology of evaporation shows a consistent agreement with eddy-covariance (EC) measurements and reveals that evaporation is controlled by different drivers depending on the time scale. At the sub-diurnal scale, mechanical turbulence is the primary driver of evaporation, and at this scale, it is not radiation-limited. At the seasonal scale, more than 70 % of the evaporation variability is explained by the radiative contribution term. At the same scale, and using a large-scale moisture tracking model, we identify the main sources of moisture to the Chilean Altiplano. In all cases, our regime of precipitation is controlled by large-scale weather patterns closely linked to climatological fluctuations. Moreover, seasonal evaporation significantly influences the saline lake surface spatial changes. From an interannual scale perspective, evaporation increased by 2.1 mm yr−1 during the entire study period, according to global temperature increases. Finally, we find that yearly evaporation depends on the El Niño–Southern Oscillation (ENSO), where warm and cool ENSO phases are associated with higher evaporation and precipitation rates, respectively. Our results show that warm ENSO phases increase evaporation rates by 15 %, whereas cold phases decrease it by 2 %.

The exchange ratio (ER) between atmospheric O2 and CO2 is a useful tracer for better understanding the carbon budget on global and local scales. The variability of ER (in mol O2 per mol CO2) between terrestrial ecosystems is not well known, and there is no consensus on how to derive the ER signal of an ecosystem, as there are different approaches available, either based on concentration (ERatmos) or flux measurements (ERforest). In this study we measured atmospheric O2 and CO2 concentrations at two heights (23 and 125 m) above the boreal forest in Hyytiälä, Finland. Such measurements of O2 are unique and enable us to potentially identify which forest carbon loss and production mechanisms dominate over various hours of the day. We found that the ERatmos signal at 23 m not only represents the diurnal cycle of the forest exchange but also includes other factors, including entrainment of air masses in the atmospheric boundary layer before midday, with different thermodynamic and atmospheric composition characteristics. To derive ERforest, we infer O2 fluxes using multiple theoretical and observation-based micro-meteorological formulations to determine the most suitable approach. Our resulting ERforest shows a distinct difference in behaviour between daytime (0.92 ± 0.17 mol mol−1) and nighttime (1.03 ± 0.05 mol mol−1). These insights demonstrate the diurnal variability of different ER signals above a boreal forest, and we also confirmed that the signals of ERatmos and ERforest cannot be used interchangeably. Therefore, we recommend measurements on multiple vertical levels to derive O2 and CO2 fluxes for the ERforest signal instead of a single level time series of the concentrations for the ERatmos signal. We show that ERforest can be further split into specific signals for respiration (1.03 ± 0.05 mol mol−1) and photosynthesis (0.96 ± 0.12 mol mol−1). This estimation allows us to separate the net ecosystem exchange (NEE) into gross primary production (GPP) and total ecosystem respiration (TER), giving comparable results to the more commonly used eddy covariance approach. Our study shows the potential of using atmospheric O2 as an alternative and complementary method to gain new insights into the different CO2 signals that contribute to the forest carbon budget.

We investigate the diurnal variability in and above the Amazonia rainforest for a representative day during the dry season period. We combine high-resolution large-eddy simulations constrained and evaluated against a comprehensive observations gathered during the field experiment GOAMAZON14.Our findings quantified the large variability of the photosynthesis drivers in the canopy. This leads to a large scatter on the values of the leaf conductance with minimum and maximum values that vary more than 100% from the average value. The impact of turbulence on the fluxes of heat, moisture and carbon dioxide differs: at the canopy top, we found more strike structures related to wind at the canopy-atmosphere interface whereas at the canopy bottom the structure remind the ones of convective cells. In systematically comparing with the observations, we find that the agreement with observations depend very much on the variable. We find the best spatiotemporal agreement with variables related to wind. The heat distribution and fluxes compare also satisfactorily with the observations. The increasing of complexity on the biophysical processes, related to ecophysiology and soil and the atmospheric control, leads to the largest disagreement between observations and simulation results for evaporation, carbon dioxide plant assimilation and soil efflux. Though the model is able to capture the correct dependences, the magnitude still differ. We discuss here the need to revise and adjust the leaf and soil models as well as to set a more comprehensive observational strategy to advance our understanding at leaf and canopy levels, and their coupling with the atmosphere.

According to Jacobson et al. the energy transition from fossil fuels (Business as Usual, BAU) to energy using wind, water and sun (WWS) can be completed by the year 2050 at minimal costs and with a land occupation of less than 1% of the total land area. This claim relies on unrealistic assumptions and is therefore misleading. In their ambition to show the road to a better world without pollution from energy production, the authors stretched some basic underlying parameter values beyond what is physically possible. The authors wrongly excluded nuclear energy as an option.

Dry deposition of ammonia (NH3) is the largest contributor to the nitrogen deposition from the atmosphere to soil and vegetation in the Netherlands, causing eutrophication and loss of biodiversity; however, data sets of NH3 fluxes are sparse and in general have monthly resolution at best. An important reason for this is that measurement of the NH3 flux under dry conditions is notoriously difficult. There is no technique that can be considered as the gold standard for these measurements, which complicates the testing of new techniques. Here, we present the results of an intercomparison of two novel measurement set-ups aimed at measuring dry deposition of NH3 at half hourly resolution. Over a 5-week period, we operated two novel optical open-path techniques side by side at the Ruisdael station in Cabauw, the Netherlands: the RIVM-miniDOAS 2.2D using the aerodynamic gradient technique, and the commercial Healthy Photon HT8700E using the eddy covariance technique. These instruments are widely different in their measurement principle and approach to derive deposition values from measured concentrations; however, both techniques showed very similar results (r=0.87) and small differences in cumulative fluxes (∼ 10 %) as long as the upwind terrain was homogeneous and free of nearby obstacles. The observed fluxes varied from ∼ −80 to ∼ +140 ng NH3 m−2 s−1. Both the absolute flux values and the temporal patterns were highly similar, which substantiates that both instruments were able to measure NH3 fluxes at high temporal resolution. However, for wind directions with obstacles nearby, the correlations between the two techniques were weaker. The uptime of the miniDOAS system reached 100 % once operational, but regular intercalibration of the system was applied in this campaign (35 % of the 7-week uptime). Conversely, the HT8700E did not measure during and shortly after rain, and the coating of its mirrors tended to degrade (21 % data loss during the 5-week uptime). In addition, the NH3 concentrations measured by the HT8700E proved sensitive to air temperature, causing substantial differences (range: −15 to +6 µg m−3) between the two systems. To conclude, the miniDOAS system appears ready for long-term hands-off monitoring. The current HT8700E system, on the other hand, had a limited stand-alone operational time under the prevailing weather conditions. However, under relatively dry and low-dust conditions, the system can provide sound results, opening good prospects for future versions, also for monitoring applications. The new high temporal resolution data from these instruments can facilitate the study of processes behind NH3 dry deposition, allowing an improved understanding of these processes and better parameterisation in chemical transport models.

Numerical simulations of the tropical mesoscales often exhibit a self-reinforcing feedback between cumulus convection and shallow circulations, which leads to the self-aggregation of clouds into large clusters. We investigate whether this basic feedback can be adequately captured by large-eddy simulations (LESs). To do so, we simulate the non-precipitating, cumulus-topped boundary layer of the canonical “BOMEX” case over a range of numerical settings in two models. Since the energetic convective scales underpinning the self-aggregation are only slightly larger than typical LES grid spacings, aggregation timescales do not converge even at rather high resolutions (

Clouds cast shadows on the surface and locally enhance solar irradiance by absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface. Typical spatiotemporal scales and driving mechanisms of this intra-day irradiance variability are not well known, hence even one day ahead forecasts of variability are inaccurate. Here we use long term, high frequency solar irradiance observations combined with satellite imagery, numerical simulations, and conceptual modelling to show how irradiance variability is linked to the cloud size distribution. Cloud shadow sizes are distributed according to a power law over multiple orders of magnitude, deviating only from the cloud size distribution due to cloud edge transparency at scales below 750 meters. Locally cloud-enhanced irradiance occurs as frequently as shadows, and is similarly driven mostly by boundary layer clouds, but distributed over a smaller range of scales. We reconcile studies of solar irradiance variability with those on clouds, which brings fundamental understanding to what drives irradiance variability. Our findings have implications for not only for weather and climate modelling, but also for solar energy and photosynthesis by vegetation, where detailed knowledge of surface solar irradiance is essential., Implemented peer review feedback, with many textual clarifications, an extra figure, and improved discussion of results

Animations and graphs of the Cloud Botany ensemble of large eddy simulations The data is stored as a single zip file. When unpacked, the visualizations can be navigated in a web browser - start by opening index.html in the top directory. The simulations were performed with DALES, the Dutch Atmospheric Large Eddy Simulation, on Supercomputer Fugaku. For access to the data itself, see How To EUREC4A. Additional material: ERA5 data for the experiment region for the spring 2020 and JOANNE dropsonde data from the EUREC4A campaign, used to define the ranges for the Cloud Botany ensemble parameter space.

Meteorology and weather forecasting are crucial for water-limited agriculture. We evaluate the added value of downscaling seven-months global deterministic seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather, Research and Forecasting (WRF) model over Zimbabwe for ten growing seasons (2011–2021). Downscaling reduces the area of significant differences between forecasted and observed total seasonal rainfall. Downscaling also improves the score for droughts as measured through the standardized precipitation index and 3-class method. Yield forecasts by the WOrld FOod STudies (WOFOST) model reveal that downscaling improves the estimated growing season evolution and maize yield in all studied regions across the country. For the main maize production region Karoi, the bias, root mean square error, mean absolute error and mean absolute percentage error reduce by 33% (0.2 ton/ha), 27% (0.4 ton/ha), 31% (0.4 ton/ha) and 27% (8.3 %) respectively by downscaling. Hence we illustrate that downscaling the deterministic seasonal forecasts may assist in food security in a crucial area in southern Africa.

Ammonia deposition is a threat to many natural ecosystems, including coastal dune areas, because of eutrophication and acidification. Direct measurements of ammonia fluxes are nevertheless scarce. In this paper we present a full year of measurements to derive the ammonia dry deposition flux in a Dutch coastal dune ecosystem, based on the aerodynamic flux-gradient method (AGM). We found a mean ammonia flux of −7.1 ± 1.7 ng m−2 s−1, and an annual ammonia deposition flux of −132 ± 32 mol ha−1 yr−1 (equivalent to 1.8 ± 0.4 kg N ha−1 yr−1), which is at the low end of the range from estimates from literature made with inferential methods. Modeling the fluxes with the DEPAC module resulted in a mean flux of −17.0 ng m−2 s−1. The model overestimated the deposition fluxes, but diurnal variations of the fluxes derived from measurements were well captured by the model. We propose to change certain DEPAC parameters, like the leaf area index, to values more applicable for a dune ecosystem and show that this improves the agreement between model and measurements.

International audience; We present a comparison of atmospheric transport model (ATM) simulations for carbonyl sulfide (COS), within the framework of the atmospheric tracer transport model intercomparison project "TransCom-COS." Seven ATMs participated in the experiment and provided simulations of COS mixing ratios over the years 2010-2018, using state-of-the-art surface fluxes for various components of the COS budget: biospheric sink, oceanic source, sources from fire and industry. The main goal of TransCom-COS is to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of tropospheric COS mixing ratios. A control case with seasonal surface fluxes of COS was constructed. The results indicate that the COS mixing ratios are underestimated by at least 50 parts per trillion (ppt) in the tropics, pointing to a missing tropical source. In summer, the mixing ratios are overestimated by at least 50 ppt above 40°N, pointing to a likely missing sink in the high northern latitudes. Regarding the latitudinal profile, the model spread is greater than 60 ppt above 40°N in boreal summer. Regarding the seasonal amplitude, the model spread reaches 50 ppt at 6 out of 15 sites, compared to an observed seasonal amplitude of 100 ppt. All models simulated a too late minimum by at least 2-3 months at two high northern-latitude sites, likely owing to errors in the seasonal cycle in the ocean emissions. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. Plain Language Summary In this study, we evaluate the state-of-the-art fluxes for various components of the carbonyl sulfide (COS) budget: biospheric sink, oceanic source, sources from fire and industry. A control case with seasonal surface fluxes of COS was constructed. Seven atmospheric transport models provided simulations of COS mixing ratios. Then, the simulated mixing ratios were evaluated against atmospheric measurements at several surface sites. Results show that all models fail to capture the observed latitudinal distribution and that the model spread is small compared to the model-observation mismatch. In summer, the overestimated mixing ratios above 40°N point to a likely missing sink in the high northern latitudes. The underestimated mixing ratios in the tropics point to a missing tropical source. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. REMAUD ET AL.

Global reanalyses like ERA5 accurately capture atmospheric processes at spatial scales of O(10) km or larger. By downscaling ERA5 with large-eddy simulation (LES), LES can provide details about processes at spatio-temporal scales down to meters and seconds. Here, we present an open-source Python package named the “Large-eddy simulation and Single-column model - Large-Scale Dynamics”, or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly-periodic LES. A validation with observations, for several sensitivity experiments consisting of month-long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day-to-day variability in the weather is well captured by (LS)2D and LES, but the setup under-performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land-surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES-ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions.

Study region: Citarum river basin, West Java — Indonesia. Study focus: We look at the skill of Empirical Quantile Mapping corrected ECMWF SEAS5 (SEAS5 EQM bias-corrected) based streamflow forecasts in the Citarum river basin. We focus on July to October because these are agriculturally important months in Java. We use a high-resolution hydrologic model (wflow_sbm) with data for the period 1989–2009. New hydrological insights for the region: Water users and agricultural practitioners commonly need monthly to seasonal hydrological forecasts. The forecasts should be sufficiently skillful and provide information that is relevant to the decisions makers in order to have practical value to them. We assess if skilful SEAS5 EQM bias-corrected based seasonal forecasts are available with the purpose to support rice production. In this streamflow forecast calibration, we look at different aggregation days and different lead times. For the verification, we use the Continuous Ranked Probability Skill Score (CRPSS), Brier Skill Score (BSS), and Mean Average Error (MAE). We also look at the correlation, the Root Mean Square Error (RMSE), and the Receiver operating characteristic Skill (ROCS). The LT1 and LT2 forecast show higher skills than longer lead times. Meanwhile, streamflow based on the aggregated forecast at 30 to 60 days aggregation days is more skillful than larger aggregations. In Indonesia, this study is a study that initiates using a hydrological model with inputs from a seasonal rainfall forecast.

Observations in the Altiplano region of the Atacama Desert show that the atmospheric boundary layer (ABL) suddenly collapses at noon. This rapid decrease occurs simultaneously to the entrance of a thermally driven, regional flow that causes a rise in wind speed and a marked temperature decrease. We identify the main drivers that cause the observed ABL collapse by using a land–atmosphere model. The free atmosphere lapse rate and regional forcings, such as advection of mass and cold air as well as subsidence, are first estimated by combining observations from a comprehensive field campaign and a regional model. Then, to disentangle the ABL collapse, we perform a suite of numerical experiments with increasing level of complexity: from only considering local land–atmosphere interactions, to systematically including the regional contributions of mass advection, cold air advection, and subsidence. Our results show that non-local processes related to the arrival of the regional flow are the main factors explaining the boundary-layer collapse. The advection of a shallower boundary layer ($$\approx -250$$ ≈ - 250 m h$$^{-1}$$ - 1 at noon) causes an immediate decrease in the ABL height (h) at midday. This occurs simultaneously with the arrival of a cold air mass, which reaches a strength of $$\approx -4$$ ≈ - 4 K h$$^{-1}$$ - 1 at 1400 LT. These two external forcings become dominant over entrainment and surface processes that warm the atmosphere and increase h. As a consequence, the ABL growth is capped during the afternoon. Finally, a wind divergence of $$\approx 8 \times 10^{-5}$$ ≈ 8 × 10 - 5 s$$^{-1}$$ - 1 contributes to the collapse by causing subsidence motions over the ABL from 1200 LT onward. Our findings show the relevance of treating large and small-scale processes as a continuum to be able to understand the ABL dynamics.

Data and scripts for paper on simulation vs radiative accuracy in cloud-resolving large-eddy simulation (submitted)

Three consecutive dry summers in western Europe (2018–2019–2020) had widespread negative impacts on society and ecosystems, and started societal debate on (changing) drought vulnerability and adaptation measures. We investigate the occurrence of multi-year droughts in the Rhine basin, with a focus on event probability in the present and in future warmer climates. Additionally, we investigate the temporally compounding physical drivers of multi-year drought events. A combination of multiple reanalysis datasets and multi-model large ensemble climate model simulations was used to provide a robust analysis of the statistics and physical processes of these rare events. We identify two types of multi-year drought events (consecutive meteorological summer droughts and long-duration hydrological droughts), and show that these occur on average about twice in a 30 year period in the present climate, though natural variability is large (zero to five events can occur in a single 30 year period). Projected decreases in summer precipitation and increases in atmospheric evaporative demand, lead to a doubling of event probability at 1 $$^\circ$$ ∘ C additional global warming relative to present-day and an increase in the average length of events. Consecutive meteorological summer droughts are forced by two, seemingly independent, summers of lower than normal precipitation and higher than normal evaporative demand. The soil moisture response to this temporally compound meteorological forcing has a clear multi-year imprint, resulting in a relatively larger reduction of soil moisture content in the second year of drought, and potentially more severe drought impacts. Long-duration hydrological droughts start with a severe summer drought followed by lingering meteorologically dry conditions. This limits and slows down the hydrological recovery of soil moisture content, leading to long-lasting drought conditions. This initial exploration provides avenues for further investigation of multi-year drought hazard and vulnerability in the region, which is advised given the projected trends and vulnerability of society and ecosystems.

The interaction of aerosols and the planetary boundary layer (PBL) plays an important role in deteriorating urban air quality. Aerosols from different sources may have different effects on regulating PBL structures owing to their distinctive dominant compositions and vertical distributions. To characterize the complex feedback of aerosols on PBL over the Beijing megacity, multiple approaches, including in situ observations in the autumn and winter of 2016–2019, backward trajectory clusters, and large-eddy simulations, were adopted. The results revealed notable distinctions in aerosol properties, vertical distributions and thermal stratifications among three types of air masses from the West Siberian Plain (Type-1), Central Siberian Plateau (Type-2) and Mongolian Plateau (Type-3). Low loadings of 0.28 ± 0.26 and 0.15 ± 0.08 of aerosol optical depth (AOD) appeared in the Type-1 and Type-2, accompanied by cool and less stable stratification, with a large part (80%) of aerosols concentrated below 1500 m. For Type-3, the AOD and single scattering albedo (SSA) were as high as 0.75 ± 0.54 and 0.91 ± 0.05, demonstrating severe pollution levels of abundant scattering aerosols. Eighty percent of the aerosols were constrained within a lower height of 1150 m owing to the warmer and more stable environment. Large-eddy simulations revealed that aerosols consistently suppressed the daytime convective boundary layer regardless of their origins, with the PBL height (PBLH) decreasing from 1120 m (Type-1), 1160 m (Type-2) and 820 m (Type-3) in the ideal clean scenarios to 980 m, 1100 m and 600 m, respectively, under polluted conditions. Therefore, the promotion of absorbing aerosols below the residual layer on PBL could be greatly hindered by the suppression effects generated by both absorbing aerosols in the upper temperature inversion layer and scattering aerosols. Moreover, the results indicated the possible complexities of aerosol-PBL interactions under future emission-reduction scenarios and in other urban regions.

We present simulations of cumulus convection over land with shortwave radiation computed by a Monte Carlo ray tracer coupled to the flow solver. Ray tracing delivers very realistic in-cloud heating rates and global horizontal irradiance fields. The necessary performance of the ray tracer has been enabled by the raw power of graphics processing unit computing and from techniques for accelerating data lookup and ray tracer convergence. We used a case study over a grassland in the Netherlands to compare simulations with a coupled ray tracer to those with a conventional two-stream solver, and to test ray tracer convergence. We demonstrate that the simulated cloud evolution is insensitive to the convergence of the ray tracing across a wide range of samples per pixel per spectral quadrature point. Furthermore, simulations with a coupled ray tracer produce surface irradiance patterns that resemble observations and that strongly feed back to the evolution of clouds via locally enhanced surface heat fluxes.

Quantification and control of NOx and CO2 emissions are essential across the world to limit adverse climate change and improve air quality. We present a new top-down method, an improved superposition column model to estimate day-to-day NOx and CO2 emissions from the large city of Wuhan, China, located in a polluted background. The latest released version 2.3.1 TROPOMI (TROPOspheric Monitoring Instrument) NO2 columns and version 10r of the Orbiting Carbon Observatory-2 (OCO-2)-observed CO2 mixing ratio are employed. We quantified daily NOx and CO2 emissions from Wuhan between September 2019 and October 2020 with an uncertainty of 31 % and 43 %, compared to 39 % and 49 % with the earlier v1.3 TROPOMI data, respectively. Our estimated NOx and CO2 emissions are verified against bottom-up inventories with minor deviations (

Southern Africa has been identified as one of the hotspot areas of climate extremes increasing, at the same time many communities in the region are dependent on rain-fed agriculture, which is vulnerable to these rainfall and temperature extremes. The aim of this study is to understand changes in extreme indices during the agricultural season under climate change and how that affect the modeling of maize suitability in Southern Africa. We analyze the changes in rainfall and its extreme indices (consecutive dry days, heavy rain events and prolonged rainfall events), and temperature and its extreme indices (hot night temperatures, hot day temperatures and frequency of very hot days) from the past (1986–2014) to the future (2036–2064) and integrate these into a maize suitability model. Temperature extremes are projected to increase in both duration and intensity, particularly in the eastern parts of the region. Also, consecutive dry days are projected to increase over larger areas during the agricultural season, while rainfall will be less in sums, heavier in intensity and less prolonged in duration. Including extreme climate indices in maize suitability modeling improves the efficiency of the maize suitability model and shows more severe changes in maize suitability over Southern Africa than using season-long climatic variables. We conclude that changes in climate extremes will increase and complicate the livelihood-climate nexus in Southern Africa in the future, and therefore, a set of comprehensive adaptation options for the agricultural sector are needed. These include the use of heat, drought and high-intensity rainfall tolerant maize varieties, irrigation and/or soil water conservation techniques, and in some cases switching from maize to other crops.

Condensation in cumulus clouds plays a key role in structuring the mean, nonprecipitating trade wind boundary layer. Here, we summarize how this role also explains the spontaneous growth of mesoscale [>O(10) km] fluctuations in clouds and moisture around the mean state in a minimal-physics, large-eddy simulation of the undisturbed period during BOMEX on a large [O(100) km] domain. Small, spatial anomalies in condensation in cumulus clouds, which form on top of small moisture fluctuations, power circulations that transport moisture, but not heat, from dry to moist regions, and thus reinforce the condensation anomaly. We frame this positive feedback as a linear instability in mesoscale moisture fluctuations, whose time scale depends only on (i) a vertical velocity scale and (ii) the mean environment’s vertical structure. In our minimal-physics setting, we show both ingredients are provided by the shallow cumulus convection itself: it is intrinsically unstable to length scale growth. The upshot is that energy released by clouds at kilometer scales may play a more profound and direct role in shaping the mesoscale trade wind environment than is generally appreciated, motivating further research into the mechanism’s relevance.

Despite the consensus on the overall downward trend in Amazon forest loss in the previous decade, estimates of yearly carbon emissions from deforestation still vary widely. Estimated carbon emissions are currently often based on data from local logging activity reports, changes in remotely sensed biomass, and remote detection of fire hotspots and burned area. Here, we use 16 years of satellite-derived carbon monoxide (CO) columns to constrain fire CO emissions from the Amazon Basin between 2003 and 2018. Through data assimilation, we produce 3 d average maps of fire CO emissions over the Amazon, which we verified to be consistent with a long-term monitoring programme of aircraft CO profiles over five sites in the Amazon. Our new product independently confirms a long-term decrease of 54 % in deforestation-related CO emissions over the study period. Interannual variability is large, with known anomalously dry years showing a more than 4-fold increase in basin-wide fire emissions relative to wet years. At the level of individual Brazilian states, we find that both soil moisture anomalies and human ignitions determine fire activity, suggesting that future carbon release from fires depends on drought intensity as much as on continued forest protection. Our study shows that the atmospheric composition perspective on deforestation is a valuable additional monitoring instrument that complements existing bottom-up and remote sensing methods for land-use change. Extension of such a perspective to an operational framework is timely considering the observed increased fire intensity in the Amazon Basin between 2019 and 2021.

This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatiotemporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically-23 % to-37 % in clean to slightly polluted conditions but reaching values as high as-51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about-0:2 Pmolec cm-2, i.e. approx.-2 % in summer to-15 % in winter; and (iii) a bias ranging from zero to-50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm-2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm-2) but exceed those for the tropospheric column data (0.7 Pmolec cm-2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm-2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

Within the framework of the DACCIWA (Dynamics Aerosol Chemistry Cloud Interactions in West Africa) project and based on a field experiment conducted in June and July 2016, we analyze the daytime breakup of continental low-level stratiform clouds in southern West Africa. We use the observational data gathered during 22 precipitation-free occurrences at Savè, Benin. Our analysis, which starts from the stratiform cloud formation usually at night, focuses on the role played by the coupling between cloud and surface in the transition towards shallow convective clouds during daytime. It is based on several diagnostics, including the Richardson number and various cloud macrophysical properties. The distance between the cloud base height and lifting condensation level is used as a criterion of coupling. We also make an attempt to estimate the most predominant terms of the liquid water path budget in the early morning. When the nocturnal low-level stratiform cloud forms, it is decoupled from the surface except in one case. In the early morning, the cloud is found coupled with the surface in 9 cases and remains decoupled in the 13 other cases. The coupling, which occurs within the 4 h after cloud formation, is accompanied by cloud base lowering and near-neutral thermal stability in the subcloud layer. Further, at the initial stage of the transition, the stratiform cloud base is slightly cooler, wetter and more homogeneous in coupled cases. The moisture jump at the cloud top is usually found to be lower than 2 g kg-1 and the temperature jump within 1 5 K, which is significantly smaller than typical marine stratocumulus and explained by the monsoon flow environment in which the stratiform cloud develops over West Africa. No significant difference in liquid water path budget terms was found between coupled and decoupled cases. In agreement with previous numerical studies, we found that the stratiform cloud maintenance before sunrise results from the interplay between the predominant radiative cooling, entrainment and large-scale subsidence at its top. Three transition scenarios were observed depending on the state of coupling at the initial stage. In coupled cases, the low-level stratiform cloud remains coupled until its breakup. In five of the decoupled cases, the cloud couples with the surface as the lifting condensation level rises. In the eight remaining cases, the stratiform cloud remains hypothetically decoupled from the surface throughout its life cycle since the height of its base remains separated from the condensation level. In cases of coupling during the transition, the stratiform cloud base lifts with the growing convective boundary layer roughly between 06:30 and 08:00 UTC. The cloud deck breakup, occurring at 11:00 UTC or later, leads to the formation of shallow convective clouds. When the decoupling subsists, shallow cumulus clouds form below the stratiform cloud deck between 06:30 and 09:00 UTC. The breakup time in this scenario has a stronger variability and occurs before 11:00 UTC in most cases. Thus, we argue that the coupling with the surface during daytime hours has a crucial role in the low-level stratiform cloud maintenance and its transition towards shallow convective clouds.

This study aims at estimating errors to be accounted for in atmospheric inversions of methane (CH4) emissions at the European scale. Four types of errors are estimated in the concentration space over the model domain and at selected measurement sites. Furthermore, errors in emission inventories are estimated at country and source sector scales. A technically ready method is used, which is implemented by running a set of simulations of hourly CH4 mixing ratios for 2015 using two area-limited transport models at three horizontal resolutions with multiple data sets of emissions and boundary and initial conditions as inputs. The obtained error estimates provide insights into how these errors could be treated in an inverse modelling system for inverting CH4 emissions over Europe. The main results show that sources of transport errors may better be controlled alongside the emissions, which differs from usual inversion practices. The average total concentration error is estimated at 29 ppb. The assessed error of total CH4 emissions is 22% and emission errors are heterogeneous at sector (23–49%) and country scales (16–124%), with largest errors occurring in the waste sector due to uncertainties in activity data and emission factors and in Finland due to uncertainties in natural wetland emissions.

In this study, the stability dependence of turbulent Prandtl number ($$Pr_t$$ P r t ) is quantified via a novel and simple analytical approach. Based on the variance and flux budget equations, a hybrid length scale formulation is first proposed and its functional relationships to well-known length scales are established. Next, the ratios of these length scales are utilized to derive an explicit relationship between $$Pr_t$$ P r t and gradient Richardson number. In addition, theoretical predictions are made for several key turbulence variables (e.g., dissipation rates, normalized fluxes). The results from our proposed approach are compared against other competing formulations as well as published datasets. Overall, the agreement between the different approaches is rather good despite their different theoretical foundations and assumptions.

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990-2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011-2015, the CO2 land sources and sinks from NGHGI estimates report-90 Tg C yr-1 ± 30 Tg C yr-1 while all other BU approaches report a mean sink of-98 Tg C yr-1 (± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr-1 ± 400 Tg C yr-1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of "CO2 flux"obtained from different approaches. The referenced datasets related to figures are visualized.

Land surface modellers need measurable proxies to constrain the quantity of carbon dioxide (CO2) assimilated by continental plants through photosynthesis, known as gross primary production (GPP). Carbonyl sulfide (COS), which is taken up by leaves through their stomates and then hydrolysed by photosynthetic enzymes, is a candidate GPP proxy. A former study with the ORCHIDEE land surface model used a fixed ratio of COS uptake to CO2 uptake normalised to respective ambient concentrations for each vegetation type (leaf relative uptake, LRU) to compute vegetation COS fluxes from GPP. The LRU approach is known to have limited accuracy since the LRU ratio changes with variables such as photosynthetically active radiation (PAR): while CO2 uptake slows under low light, COS uptake is not light limited. However, the LRU approach has been popular for COS GPP proxy studies because of its ease of application and apparent low contribution to uncertainty for regional-scale applications. In this study we refined the COS GPP relationship and implemented in ORCHIDEE a mechanistic model that describes COS uptake by continental vegetation. We compared the simulated COS fluxes against measured hourly COS fluxes at two sites and studied the model behaviour and links with environmental drivers. We performed simulations at a global scale, and we estimated the global COS uptake by vegetation to be 756 Gg S yr1, in the middle range of former studies (490 to 1335 Gg S yr1). Based on monthly mean fluxes simulated by the mechanistic approach in ORCHIDEE, we derived new LRU values for the different vegetation types, ranging between 0.92 and 1.72, close to recently published averages for observed values of 1.21 for C4 and 1.68 for C3 plants. We transported the COS using the monthly vegetation COS fluxes derived from both the mechanistic and the LRU approaches, and we evaluated the simulated COS concentrations at NOAA sites. Although the mechanistic approach was more appropriate when comparing to high-Temporal-resolution COS flux measurements, both approaches gave similar results when transporting with monthly COS fluxes and evaluating COS concentrations at stations. In our study, uncertainties between these two approaches are of secondary importance compared to the uncertainties in the COS global budget, which are currently a limiting factor to the potential of COS concentrations to constrain GPP simulated by land surface models on the global scale.

We report about a new third-party observation, namely, wind measurements derived from hot-air balloon (HAB) tracks. We first compare the HAB winds with wind measurements from a meteorological tower and a radio acoustic wind profiler, both situated at the topographically flat observatory near Cabauw, the Netherlands. To explore the potential of this new type of wind observation in other topographies, we present an intriguing HAB flight in Austria with a spectacular mountain–valley circulation. Subsequently, we compare the HAB data with a numerical weather prediction (NWP) model during 2011–13 and the standard deviation of the wind speed is 2.3 m s−1. Finally, we show results from a data assimilation feasibility experiment that reveals that HAB wind information can have a positive impact on a hindcasted NWP trajectory.

This study presents the results from the Tropospheric Chemistry Reanalysis version 2 (TCR-2) for the period 2005–2018 at 1.1∘ horizontal resolution obtained from the assimilation of multiple updated satellite measurements of ozone, CO, NO2, HNO3, and SO2 from the OMI, SCIAMACHY, GOME-2, TES, MLS, and MOPITT satellite instruments. The reanalysis calculation was conducted using a global chemical transport model MIROC-CHASER and an ensemble Kalman filter technique that optimizes both chemical concentrations of various species and emissions of several precursors, which was efficient for the correction of the entire tropospheric profile of various species and its year-to-year variations. Comparisons against independent aircraft, satellite, and ozonesonde observations demonstrate the quality of the reanalysis fields for numerous key species on regional and global scales, as well as for seasonal, yearly, and decadal scales, from the surface to the lower stratosphere. The multi-constituent data assimilation brought the model vertical profiles and interhemispheric gradient of OH closer to observational estimates, which was important in improving the description of the oxidation capacity of the atmosphere and thus vertical profiles of various species. The evaluation results demonstrate the capability of the chemical reanalysis to improve understanding of the processes controlling variations in atmospheric composition, including long-term changes in near-surface air quality and emissions. The estimated emissions can be employed for the elucidation of detailed distributions of the anthropogenic and biomass burning emissions of co-emitted species (NOx, CO, SO2) in all major regions, as well as their seasonal and decadal variabilities. The data sets are available at https://doi.org/10.25966/9qgv-fe81 (Miyazaki et al., 2019a).

The CloudRoots field experiment was designed to obtain a comprehensive observational dataset that includes soil, plant, and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub-kilometre scale. Our findings demonstrate the need to include measurements at leaf level to better understand the relations between stomatal aperture and evapotranspiration (ET) during the growing season at the diurnal scale. Based on these observations, we obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the model reproduces the stomatal leaf conductance and the leaf-level photosynthesis satisfactorily. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From highly resolved vertical profile measurements of carbon dioxide (

An overview is given of 50-year Cabauw observations and research on the structure and dynamics of the atmospheric boundary layer. It is shown that over time this research site with its 200-m meteorological tower has grown into an atmospheric observatory with a comprehensive observational program encompassing almost all aspects of the atmospheric column including its boundary conditions. This is accomplished by the Cabauw Experimental Site for Atmospheric Research (CESAR) a consortium of research institutes. CESAR plays an important role in the educational programs of the CESAR universities. The current boundary-layer observational program is described in detail, and other parts of the CESAR observational program discussed more briefly. Due to an open data policy the CESAR datasets are used by researchers all over the world. Examples are given of the use of the long time series for model evaluation, satellite validation, and process studies. The role of tall towers is discussed in relation to the development of more and better ground-based remote sensing techniques. CESAR is now incorporated into the Ruisdael observatory, the large-scale atmospheric research infrastructure in the Netherlands. With Ruisdael the embedding of the Dutch atmospheric community in national policy landscape, and in the European atmospheric research infrastructures is assured for the coming decade.

Carbonyl sulfide (COS) flux measurements with the eddy covariance (EC) technique are becoming popular for estimating gross primary productivity. To compare COS flux measurements across sites, we need standardized protocols for data processing. In this study, we analyze how various data processing steps affect the calculated COS flux and how they differ from carbon dioxide (CO2) flux processing steps, and we provide a method for gap-filling COS fluxes. Different methods for determining the time lag between COS mixing ratio and the vertical wind velocity (w) resulted in a maximum of 15.9 % difference in the median COS flux over the whole measurement period. Due to limited COS measurement precision, small COS fluxes (below approximately 3 pmol m−2 s−1) could not be detected when the time lag was determined from maximizing the covariance between COS and w. The difference between two high-frequency spectral corrections was 2.7 % in COS flux calculations, whereas omitting the high-frequency spectral correction resulted in a 14.2 % lower median flux, and different detrending methods caused a spread of 6.2 %. Relative total uncertainty was more than 5 times higher for low COS fluxes (lower than ±3 pmol m−2 s−1) than for low CO2 fluxes (lower than ±1.5 µmol m−2 s−1), indicating a low signal-to-noise ratio of COS fluxes. Due to similarities in ecosystem COS and CO2 exchange, we recommend applying storage change flux correction and friction velocity filtering as usual in EC flux processing, but due to the low signal-to-noise ratio of COS fluxes, we recommend using CO2 data for time lag and high-frequency corrections of COS fluxes due to the higher signal-to-noise ratio of CO2 measurements.