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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).

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.

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.

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.

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.

This paper describes the long-term effects of practicing ten years reduced tillage on crop yields in a conventional and organic farming system context. Data were collected from the BASIS field experiment, which was established in the Netherlands in 2009 to investigate the effects of reduced tillage on crop yield and soil properties in a controlled traffic farming system on a sandy loam soil. The experiment is unique in its focus on root crops grown on ridges, planted crops and small seeded crops, including cash crops such as potato (Solanum tuberosum), carrot (Daucus carota subsp. Sativus), onion (Allium cepa) and sugar beet (Beta vulgaris). The objective of the present paper was to analyse the long-term effects of reduced tillage on the marketable yields of these crops, grown in a conventional (4-year rotation) and organic farming system (6-year rotation). Tillage treatments were conventional deep inversion tillage (CT, mouldboard plough, 23–25 cm depth), reduced tillage with sub-soiling (RTS, chisel plough, 18–20 cm depth), and reduced tillage without sub-soiling (RT). Our key finding is that the two reduced tillage systems, over a 10-year period, provided similar or even higher marketable yields than conventional tillage, for 12 (RTS) and 11 (RT) of the 13 crops grown. Reduced tillage resulted in lower yields in two crops: carrot (−13.4% in RTS and −15.2% in RT) and cabbage (−5.2% in RT). In both cases, yield losses could be partly related to negative effects of crop residues from the preceding cover crop. Our results provide evidence that yield levels of crops grown in RT in our experiment can generally compete with crop yields in ploughed systems. However, crop residue management and seedbed preparation remain a challenge in reduced tillage systems, requiring further attention in research and dissemination.

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.

Nitrogen oxides (NO x ) play a central role in catalyzing tropospheric ozone formation. Nitrogen dioxide (NO 2 ) has recently reemerged as a key target for air pollution control measures, and observational evidence points toward a limited understanding of ozone in high-NO x environments. A complete understanding of the mechanisms controlling the rapid atmospheric cycling between ozone (O 3 )–nitric oxide (NO)–NO 2 in high-NO x regimes at the surface is therefore paramount but remains challenging because of competing dynamical and chemical effects. Here, we present long-term eddy covariance measurements of O 3 , NO, and NO 2 , over an urban area, that allow disentangling important physical and chemical processes. When generalized, our findings suggest that the depositional O 3 flux near the surface in urban environments is negligible compared to the flux caused by chemical conversion of O 3 . This leads to an underestimation of the Leighton ratio and is a key process for modulating urban NO 2 mixing ratios. As a consequence, primary NO 2 emissions have been significantly overestimated.

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.