Abstract This study examines thermodynamic and microphysical relationships between in‐cloud updrafts and downdrafts within tropical deep convective anvil clouds, using aircraft in situ measurements from the Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil (ACRIDICON–CHUVA) field campaign. Our results extend current knowledge of in‐cloud drafts to higher altitudes and add detail regarding their microphysical characteristics. Key findings include observational evidence of supersaturated downdrafts and higher cloud particle number concentrations in downdrafts than in updrafts within optically thick anvil clouds. Mean draft diameters exhibit a broadening trend with altitude, while mean air‐mass fluxes decrease due to decreasing vertical velocity intensity and atmospheric density. Cloud water content is comparable in up‐ and downdrafts and increases with vertical velocity at all levels, without a clear trend with altitude. At upper levels (10–14 km), large negative vertical velocities are observed in supersaturated regions ; therefore, upper‐level downdrafts do not appear to be driven by latent cooling. Optically thick cloud regions exhibit higher median RH and ice water content (IWC) compared with optically thin clouds, with downdrafts () showing higher median and IWC than updrafts. Particle size distributions are similar for up‐ and downdrafts of comparable strength. With altitude, maximum particle size increases, and the number concentration of larger particles () increases more strongly in stronger drafts than in weaker drafts. Stronger drafts also exhibit higher concentrations of particles smaller than 100 m at upper levels. Optically thick regions exhibit higher particle number concentrations across all sizes than optically thin regions. Notably, within the optically thick regions, downdrafts show higher particle number concentrations than updrafts for all sizes. Overall, the characteristics of updrafts and downdrafts in anvil clouds exhibit strong similarities, suggesting the influence of processes, such as updraft–downdraft mixing and inertia‐driven downdrafts originating from older updraft cores, shaping microphysical and dynamical properties of anvil clouds.

Long-term carbon flux measurements at the FLUXNET site Loobos, a Pine forest in the Netherlands, reveal a counter-intuitive decline in total ecosystem respiration (TER) by tens of percents between 1997 and 2021. This trend cannot be explained by temperature variability or methodological changes alone. Instead, our findings point to a biogeochemical mechanism: despite a doubling of soil organic matter stocks, ecosystem respiration appears limited by decomposition rates rather than substrate availability. Soil incubation experiments indicate that microbial activity is limited by substrate quality and strongly acidic conditions (pH = 2.9), associated with large nitrogen deposition. Glucose addition experiments confirm the presence of an active microbiome, but its activity is suppressed under the present acidic soil conditions. These results raise concerns about ecosystem health under conditions of nitrogen deposition and the long-term sustainability of the observed carbon sink. Loobos may serve as an early indicator of broader ecosystem responses to environmental disturbances, as similar negative TER trends have been observed at other long-term FLUXNET sites. To advance understanding of the global carbon cycle, it is essential that observed flux trends are attributed and corroborated by changes in carbon and nitrogen stocks, and that models are continuously confronted with observational data. We therefore discuss the need of periodically measuring pH as soil acidification can be a limiting factor and suggest the need to introduce this variable in model representations of TER near regions sensitive to nitrification.

Atmospheric turbulence is a key factor when assessing the performance of earth-to-satellite optical communication links (Free Space Optical Communications, FSOC) as it impacts beam wander and intensity fluctuations of the laser beam. Models that allow to estimate vertical profiles of optical turbulence intensity, expressed as the structure parameters of the refractive index, Cn2, either require costly simulations or are based on crude, empirical relations. In this work, we propose an efficient, physics-based method for estimating Cn2 timeseries and vertical profiles in the convective boundary layer. We do this using the mixed layer column model CLASS (Chemistry Land-surface Atmosphere Soil Slab), which allows us to simulate surface fluxes of sensible and latent heat and the boundary layer height. Assuming linear profiles of the heat fluxes over the boundary layer, in line with mixed layer formulation, we derive profiles of CT2 and Cq2, the structure parameter of temperature and humidity respectively, which together define the Cn2 profile.We will validate our approach for a reference case at the Ruisdael Observatory of Cabauw, the Netherlands (https://ruisdael-observatory.nl/cabauw/) against measurements of the surface energy balance terms, Cn2 estimated from sonic anemometers and scintillometers, and the boundary layer height estimates from radiosondes and ceilometer. Next, we will present a sensitivity study of Cn2 to surface and boundary layer properties and from these identify some typical cases representative for extremely favourable or unfavourable conditions for FSOC. The main conclusions from this work is that our approach fills a niche in Cn2 profile modelling that better optimises model complexity versus model physics than the currently used approaches.

Abstract. East African March–May rainfall (MAM) remains difficult to predict despite its importance for agriculture, water resources, and disaster preparedness. This study identifies pre-season physical drivers of MAM rainfall and tests their value for probabilistic seasonal prediction. Predictor basins were derived from December and January sea surface temperature (SST), 2 m air temperature (T2), and sea-level pressure (SLP) anomalies relative to 1991–2020, using correlations with the leading mode of East African MAM rainfall and subsequent SHAP-based feature selection. The selected basin-derived indices were applied in Random Forest (RF) and Extreme Gradient Boosting (XGB) models. The dominant predictors appear to be the southern Indian Ocean T2 tendency, Australian and Eurasian T2 gradients, South Pacific and Antarctic T2 signals, Atlantic Niño tendency, and the Euro–African SLP gradient. T2-related predictors dominate both the January and December initialisations, showing that near-surface thermal gradients provide useful information in addition to SST memory. Walker-circulation diagnostics show that these drivers influence rainfall through pressure-gradient changes, tropical overturning, and upper-level wave-train development. For January initialisation, RF and XGB achieve spatially averaged Brier Skill Scores of 0.48 and 0.41, respectively, while the corresponding Area Under the Receiver Operating Characteristic Curve values amounting to 0.72 and 0.65. These results demonstrate that physically constrained machine learning provides promising probabilistic skill for East African MAM rainfall prediction.

The planetary boundary layer (PBL) is the atmospheric layer closest to Earth’s surface that is directly influenced by surface processes, where exchanges of momentum, heat, mass, and radiation regulate environmental conditions with direct societal relevance. Because the thermodynamic structure and dynamics of the PBL are tightly coupled to surface–atmosphere interactions, accurate characterization of these processes is essential for advancing understanding of Earth system feedbacks. However, despite their importance, significant observational gaps remain in capturing the coupled surface–PBL system across the spatial and temporal scales required for both scientific and operational applications. This perspective paper articulates the central role of surface–atmosphere interactions in PBL science and their importance for advancing multiplatform observing systems. We assess the key surface variables and their required spatiotemporal resolutions needed for accurate PBL characterization, evaluate the capabilities and limitations of current global observing systems and the Program of Record—including space-based, airborne, and ground-based assets—and review emerging technological and scientific efforts aimed at addressing these gaps. Building on this assessment, we argue that advancing toward a comprehensive, surfaceinformed PBL observing system is both a scientific and societal imperative. Such a system would overcome current observational limitations and unlock substantial benefits across a wide range of applications that depend critically on accurate PBL representation, yet remain underrecognized. By synthesizing current knowledge and defining clear observational priorities, this work aims to guide the design of future PBL global observing systems and its integration, as well as to mobilize the scientific community toward coordinated, multi-scale observations of surface–atmosphere interactions, ultimately advancing PBL science and its applications on a global scale. SIGNIFICANCE STATEMENT: The planetary boundary layer (PBL) is the lowest part of the 86

Abstract. Understanding the coupled exchange of H2O and CO2 between ecosystems and the atmosphere remains limited due to our inability to partition net fluxes into their individual source and sink components. For the Amazon rainforest, which plays an important role in the global balance of water and carbon, investigating these individual fluxes is critical given the environmental changes in recent years. Here, we apply a stable isotope-based approach to partition ecosystem-scale gas exchange from simultaneous eddy covariance measurements of H2O and CO2 isotopologues. During the 2022 CloudRoots-Amazon campaign at the Amazon Tall Tower Observatory, high-frequency isotopologue flux measurements from 57 m were used to derive multi-day composite diurnal cycles of δ fluxes and ecosystem source compositions. A steady-state midday interval, constrained with independent leaf and soil isotopic observations, allowed us to coherently link the H2O and CO2 isotopic states throughout the ecosystem (soil, canopy, leaf, atmosphere) using δ18O. Isotopic flux partitioning indicates that transpiration accounts for 95.5 % of the net evapotranspiration (ET) of water at 14:00 LT (all times are local time), with soil evaporation being responsible for 4.5 %. For CO2, δ18O-based partitioning indicates that the respiration flux from the soil equals −44 % of the net ecosystem exchange (NEE), where the photosynthetic assimilation flux in turn is 144 % of NEE. The partitioning of NEE was found to be strongly dependent on the leaf intercellular-to-atmospheric CO2 ratio (ci/ca) which determines the (apparent) isotopic composition associated with photosynthetic assimilation (δP). This underlines how important detailed leaf and soil level measurements of isotopic compositions and leaf characteristics are for ecosystem-scale flux partitioning.

Abstract. We investigated the characteristics of the nocturnal boundary layer (NBL) above and within the Amazon rainforest canopy during the 2022 dry season. The study aims to determine how NBL dynamics influence nocturnal CO2 and heat exchange across the canopy-atmosphere interface. Utilising observations from the CloudRoots-Amazon22 field campaign conducted at the Amazon Tall Tower Observatory, we distinguished between the strongly and weakly stable regimes to study the effect of radiative cooling and wind shear on CO2 and heat exchange. Our results reveal a distinct, stable layer above the canopy with an average height of 150 to 188 m, which develops due to strong radiative cooling of the canopy top. Below the canopy, the cooling marks the build-up of a well-mixed layer within the canopy. In the weakly stable regime, increased turbulence at the canopy top was observed, leading to a significant observed CO2 flux of 3.82 𝜇mol m−2 s−1 above the canopy. In contrast, in the strongly stable regime, turbulence was almost absent, and the observed flux was only 0.16 𝜇mol m−2 s−1, suggesting a decoupling of the canopy and the roughness sublayer. The decoupling was confirmed by the 2–3 times decreased vertical heat transport in the strongly stable regime. Even though our method includes typical observational uncertainties, our results show significant differences between CO2 and heat exchange between the two regimes, stressing the importance of correctly representing the nocturnal dynamics in tall canopies like the Amazon Rainforest.

Mesoscale convective systems (MCS) are organized clusters of deep convective cells that occur frequently in the tropical Amazon basin. Here, MCS constitute an important source of precipitation, contributing by ~40 – 60 % to the total. This makes MCS vital for the Amazonian water balance, which is dependent on the westward moisture transport from the Atlantic Ocean. During the transport, moisture is recycled in multiple precipitation-evaporation cycles, including the precipitation generated by the MCS. Nevertheless, extreme precipitation related to MCS systems can cause substantial economic damage through extreme rainfall, flash flooding, debris flows, severe winds and hail. Therefore, the effect of the warming climate and changing land-atmosphere interactions on MCS frequency and intensity will have important implications. However, research does not show consensus on the intensity changes in MCS in the Amazon rainforest, and whether MCSs will become more extreme due to changes in physical processes under warming climate conditions. The main shortcoming is that the current global models’ resolution is too coarse (>25 km) to explicitly resolve turbulence and moist convection. Because of this, the intensity of the changes is jeopardized by the sensitivity of these changes to the physical representation of moist deep convection. In the EU Next Generation Earth System models (NextGEMS) project, high-resolution (9 km) earth system experiments covering the Earth were performed. The novelty of these experiments is that they partly resolve the deep convection and capture the large-scale dynamics on a global scale. In this study, we analyze the Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM) of the NextGEMS project, to characterize the Amazonian MCS in the current climate (1990-2020) and project their future changes (2020-2050; SSP3-7). We do the MCS characterization in a systematic manner: To study the MCS systems, we track them based on brightness temperature and precipitation intensity with the Python FLEXible object TRacKeR (PyFLEXTKRK). Determining these MCS tracks provides insight into the current frequency, duration, and precipitation intensity of MCSs across regions and seasons. Once we have characterized the current climate, we do the same characterization for the projected future climate to determine the main climate change effects on MCS occurrence in the Amazon and their contribution to the water cycle. In the future characterizations, we emphasize extreme scenarios that produce large amounts of precipitation (95th percentile) because of their significant environmental impact.

The exchange of CO2 in the lower troposphere is governed by surface and atmospheric processes operating across a wide range of spatial and temporal scales. Over tropical regions such as the Amazon rainforest, daily occurring shallow-to-deep convective clouds are anticipated to significantly modulate the diurnal cycle and spatial variability of CO2. Yet, the physical processes controlling the exchange at the surface and across the interface of the boundary layer and free troposphere remain poorly understood and insufficiently quantified, limiting accurate estimates of CO2-exchange over the Amazon rainforest under a changing climate.To address this research gap, we examine the lower tropical troposphere CO2-exchange across shallow-to-deep convective regimes over the Amazon rainforest. More specifically, we develop an analytical framework that adopts well-mixed conditions, assuming vertical uniformity of state variables and greenhouse gases within the atmospheric boundary layer. Utilising this framework, we reconstruct and attribute the diurnal (from sunrise to sunset), day-to-day and spatial lower tropospheric CO2-exchange into seven physical components: (I) free tropospheric background mole fraction, (II) entrainment and detrainment during the night-to-day transition, (III) surface fluxes (including plant assimilation and soil respiration), (IV) entrainment and detrainment from convective boundary layer development as determined by the lapse rate of CO2, (V) boundary layer dilution associated with clouds, (VI) cloud mass flux and (VII) advection. Moreover, by inverting the expression we obtain a first-order, physics-based estimate of net ecosystem exchange that helps interpret the sensitivity of existing inverse modelling estimates. The framework is applied to the global storm-resolving Integrated Forecasting System (IFS) of the ECMWF across three horizontal resolutions (25 km, 9 km, and 4.4 km) and evaluated using comprehensive observations and turbulence and cloud resolving large-eddy simulations from the CloudRoots-Amazon22 campaign (dry season 2022).Our initial findings indicate that we satisfactorily reproduce and attribute the relative importance of each physical component to the diurnal CO2-exchange in the lower tropical troposphere under clear, shallow convective, and deep convective conditions in ECMWF-IFS. However, larger uncertainties persist in the early morning and late afternoon. Ongoing work aims to further disentangle the role of the physical processes in controlling the CO2-exchange over the Amazon rainforest across time, space and model resolutions.

Mounting evidence indicates concurrent changes in forest carbon uptake and cloudiness across tropical, temperate, and boreal biomes, pointing to emerging shifts in forest–atmosphere coupling with the potential to amplify climate feedbacks. At the same time, a growing body of research highlights the critical role of within-canopy microclimate, where forests generate strong vertical gradients in radiation, temperature, humidity, and turbulence. These gradients buffer climatic extremes, regulate ecosystem functioning, and control phenological responses that differ markedly between overstory and understory environments. Together, these findings demonstrate that forests actively regulate their internal microclimate while interacting dynamically with the atmosphere above.Despite these advances, forests and clouds are still largely studied and modelled as separate components, with land treated primarily as a lower boundary condition rather than as an active, three-dimensional driver of atmospheric dynamics. This conceptual separation limits our ability to understand and predict coupled carbon–cloud–climate feedbacks, particularly under ongoing climate change where both carbon uptake and cloud regimes are shifting.Here, we propose a conceptual and methodological shift towards treating forests and clouds as an integrated, dynamically coupled system. We outline a first-principles framework that bridges biological, chemical, and physical processes across spatiotemporal scales, explicitly linking radiative perturbations, stomatal responses, turbulence, atmospheric chemistry, and cloud formation. In the talk, I will present observations from comprehensive field campaigns, including LIAISE and CloudRoots-Amazon22, integrated with large-eddy simulations to resolve canopy–boundary layer–cloud interactions. This combined observational–modelling approach enables a process-based understanding of how forest structure and function feed back on atmospheric dynamics and cloud development.Embedding such observationally constrained, canopy-resolving representations into Earth System Models offers a pathway to reduce uncertainties in projections of carbon uptake, cloud dynamics, and their combined influence on climate, ultimately improving our capacity to predict biosphere–atmosphere feedbacks in a changing world.

Ecosystem productivity and availability of reactive nitrogen (Nr) are inevitably linked. Here, we study the link between ecosystem productivity and biosphere–atmosphere exchange of ammonia (NH3) and the influence of seasonal variability. We collected a year-round dataset of high temporal resolution measurements of NH3-fluxes (FNH3) between forest and atmosphere obtained with the eddy covariance method above a Dutch temperate Scots pine forest (Loobos). We observed a pronounced difference in NH3-exchange between the winter and summer season. During summer, we found a strong correlation between monthly gross primary productivity (GPP) and monthly FNH3, while during winter such a relation was absent. We hypothesized that this relation is a result of N-remobilization and N-assimilation, which scales with ecosystem activity. The high data-coverage, extensive meteorological observations, and deployment of additional leaf wetness sensors, also allowed us to study NH3-exchange in more detail. To that end, we divided the dataset into six categories to compare the efficiency of different exchange processes. We found exchange with wet leaf surfaces to be most efficient, even though limited by prevalent turbulent conditions. Finally, we compared the results of this study to measurements of FNH3 at the nearby forest site Speulderbos (Melman et al., 2025). Despite apparent similarities between the two sites, such as location, exposure to NH3 and forest type, they behave very differently in terms of their NH3 fluxes. We related these differences to the N-behavior of the different tree species, and N-availability in the ecosystem and GPP, which results in a low emission potential (hence large fluxes; Loobos) or a high emission potential, creating ’push-back’ of NH3 (hence smaller fluxes; Speulderbos).

Wildfires are increasingly showing pyro-cloud formation, thereby transitioning into a state with unpredictable fire behaviour. Although a multitude of factors determine the ability of any plume to create pyro-clouds, one of the essential components is the ability of dry convective plumes to reach altitudes where condensation can occur. Hence, to predict the onset of pyro-cloud formation, it is essential to understand what factors govern the plume top height of dry convective plumes.However, previous research predominantly focused on the injection height to improve smoke pollution predictions. Our research extends beyond the plume injection height, investigating the physics that govern the plume top height. As observations of convective wildfires are scarce due to the dangerous measurement conditions, we use MicroHH to create high-resolution (~20 m) large eddy simulations of convective wildfire plumes. Our preliminary analyses, in which we varied the fire intensity, revealed three distinct plume regimes dictated by the interaction between heating by the fire and atmospheric stratification:ABL-Plumes: plumes that cannot overshoot the atmospheric boundary layer (ABL), resulting in a plume top equal to the ABL top.Overshooting Plumes: Plumes that overshoot into the free troposphere, but with an injection height equal to the ABL top.Free Tropospheric Plumes: Plumes that both overshoot and inject into the free troposphere, meaning that both the plume top height and the injection height exceed the ABL top.With our study, we aim to answer two questions. First, how do the scaling relationships for maximum plume top height evolve as a plume transitions from an Overshooting to a Free Tropospheric plume? Second, for the Free Tropospheric plumes, do the plume top height and injection height share the same scaling relationships, or do distinct physical processes govern the plume top height?For example, we know from a previous study that the injection height of free tropospheric plumes scales with fire intensity to the power of 0.35. If both the plume top height and injection height follow the same scaling, this provides a unified physical explanation for plume rise. Alternatively, a different scaling suggests that additional physical processes govern the overshoot beyond the injection height. To explore these physics, we will vary the fire intensity across a range of realistic atmospheric conditions by modifying the boundary-layer height, ambient wind speed, capping inversion strength, and free-tropospheric lapse rate. Ultimately, our goal is to condense the plume dynamics derived from our 3D large eddy simulations into a simplified (adiabatic) parcel model to explain the scaling behaviours and regime transitions of dry convective wildfire plumes.

Clouds and aerosols can increase canopy photosynthesis relative to clear-sky values through changes in total and diffuse solar radiation: the diffuse fertilization effect (DFE). DFE varies across observational sites due to (a) inconsistent definitions and quantifications of DFE, (b) unexplored relationships between DFE and cloudiness type, and (c) insufficient knowledge of the effect of site characteristics. We showed that: DFE definitions vary, DFE quantifications do not connect to existing definitions or do not isolate the causal factor, and a systematic protocol to quantify DFE is lacking. A new theoretical framework served to clarify the relation between DFE definitions, and showed how DFE varies with cloudiness types and site characteristics. We proposed guidelines for a systematic DFE quantification across studies, and which aim to isolate the causal factor of DFE.Applying our framework to observations of canopy photosynthesis, solar radiation and cloudiness types we quantified DFE at daily and sub-daily time scales. We showed for the first time how DFE varies with cloudiness type, due to the varying trade-off between diffuse radiation and total solar radiation. Using an observation-driven canopy photosynthesis model, we showed that the DFE varies with site characteristics and time of day. The DFE responded strongly to leaf area index, canopy nitrogen distribution, leaf orientation and leaf transmittance, with leaf area index and leaf orientation driving DFE occurrences at our site. Our study emphasizes the importance of quantifying the DFE systematically and accurately across observational sites and highlights the need for information on cloudiness climatology and site characteristics.

Land-surface representations in weather and climate models simplify the characterization of vegetation as a single layer with bulk environmental conditions. This approach overlooks the vertical variability in leaf traits and environmental conditions within the canopy. This research explores the vertical variability of plant ecophysiology and environmental measurements within the Amazon tropical rainforest during daytime, specifically at the ATTO site, during the late dry season. To characterize the canopy and its vertical variability, we categorized the canopy into three layers: the top layer (approximately the upper third of the canopy, 18-27 m), the medium layer (approximately the medium third of the canopy, 9-18 m), and the low layer (approximately the lower third of the canopy, 0-9 m) where leaf gas exchange measurements were conducted. Utilizing these layers, we developed a multi-layer model representation that calculates water and CO2 fluxes based on within canopy on-site observations. We conducted sensitivity analyses of the rainforest multi-layer representation to discern the significance of capturing vertical variability in leaf traits and environmental conditions for deriving net fluxes of water and CO2 of the forest. Current results show that measured leaf traits exhibit vertical variation within the canopy, indicating larger productivity in the top layer compared to the medium and low layers. Environmental conditions, such as incoming radiation in the top layer, fluctuate due to cloud presence. Temperature peaks in the top layer and reaches a minimum at mid-canopy. This results in a non-uniform mixing of the canopy air, maintaining a stable layer within the forest canopy that can potentially affect the distribution of scalars within the canopy. Ongoing analyses explore the similarities and differences between the CO2 exchange between the multi-layer representation and CO2 fluxes from eddy covariance systems, as well as the sensitivity of the former to vertical variability in leaf traits and environmental conditions. By doing so, we aim to gain knowledge on the relevance (or irrelevance) of characterizing vertical variability in land-surface representations and on important processes that may not be well captured yet by land-surface representations.

Developing effective global strategies for climate mitigation requires an independent assessment of the greenhouse gas emission inventory at the urban scale. In the framework of the Dutch Ruisdael Observatory infrastructure project, we have enhanced the Dutch Atmospheric Large-Eddy-Simulation (DALES) model to simulate carbon dioxide (CO2) plume emission and three-dimensional dispersion within the turbulent boundary layer. The unique ability to explicitly resolve turbulent structures a the hectometer resolution (100 m) makes DALES particularly suitable for detailed realistic simulations of both singular high-emitting point sources and urban emissions, aligning with the goals of Ruisdael Observatory. The model setup involves a high-resolution simulation (100 m × 100 m) covering the main urban area of the Netherlands (51.5–52.5° N, 3.75–6.45° E). The model integrates meteorological forcing from the HARMONIE-AROME weather forecasting model, background CO2 levels from the CAMS reanalysis, and point source emissions and downscaled area emissions derived from the 1 km × 1 km emission inventory from the national registry. The latter are prepared using a sector-specific downscaling workflow, covering major emission categories. Biogenic CO2 exchanges from grasslands and forests are interactively included in the hectometer calculations within the heterogeneous land–surface model of DALES. Our evaluation strategy is twofold, comparing DALES simulations with (i) the state-of-the art LOTOS-EUROS model simulations and (ii) Ruisdael surface observations of the urban background in the Rotterdam area at Westmaas and Slufter and in situ rural Cabauw tower measurements. Our comprehensive statistical analysis confirmed the effectiveness of DALES at modeling the urban-scale CO2 emission distribution and plume dispersion under turbulent conditions but also revealed potential limitations and areas for further improvement. Thus, our new model framework provides valuable insights into the role of anthropogenic and biogenic contributions to local CO2 levels, as well as the transport and dispersion of CO2 emissions. This supports emission uncertainty reduction using atmospheric measurements and contributes to the development of effective regional climate mitigation strategies.

We study the diurnal variability in the atmospheric boundary layer (ABL) across spatial scales (between similar to 100 m and similar to 10 km) of irrigation-driven surface heterogeneity in the semi-arid landscape of the 2021 Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) experiment on the northeastern Iberian Peninsula. We combine observational analysis with explicit simulation of the ABL using observationally driven large-eddy simulation (LES) to better understand the physical mechanisms controlling ABL dynamics in heterogeneous regions. Our choice of spatial scales represents current and future single grid cells of global models, demonstrating how the sources and magnitude of subgrid-scale heterogeneity vary with model resolution.There is an observed positive buoyancy flux over the irrigated fields driven primarily by moisture fluxes, whereas, over the non-irrigated fields, there is a classical buoyancy profile driven by the surface sensible heat flux. The surface heterogeneity is felt most strongly near the surface; however, at approximately 1000 m above the surface, there appears to be a blending zone of mean scalars (i.e., potential temperature and specific humidity), indicating that the heterogeneity mixes into a new mean state of the atmosphere. There is a stable internal boundary layer (IBL; as defined as the first stable layer in individual radiosonde potential temperature profiles) up to approximately 500 m over the irrigated area. Taking advantage of the spatiotemporal extent of LES results, we perform spectral analyses to find that the ABL height had an integral length scale of similar to 800 m matching that of the imposed surface fluxes. Between the irrigated and non-irrigated areas, there is an adjustment of the ABL as it crosses the boundary up to 500 m upwind of the boundary. We observe a variable-dependent blending zone between scales in the middle of the ABL, but it is limited by the entrainment zone effectively introducing another source of heterogeneity driven by upper-atmosphere conditions.

During the 2022 CloudRoots-Amazonia field campaign high-frequency measurements of state variables and atmospheric composition were taken at the Amazon Tall Tower Observatory (ATTO) in Brazil. Specifically, we measured wind fields, radiation, H2O and CO2 mole fractions, and H2O and CO2 isotopic compositions at 4Hz or faster at 57m height. A main objective was to use these high-frequency measurements to investigate how the coherent canopy-atmosphere turbulent structures are influenced by the non-stationary passage of clouds. Novel in our investigation is the use of quadrant analysis in combination with high frequency isotopic composition measurements, as well as our approach to finding lag between cloud passages and turbulent variables. Using quadrant analysis to distinguish sweeping and ejection motions, we find that the passage of clouds influences the transport of scalars and energy. Preceding the passage of well-developed cumulus clouds, we see that the gust front forcefully ejects this subcanopy air into the atmospheric mixed layer. It seems that this is an effective upward transport mechanism for CO2 and other scalars emitted by the soil, plant roots, and understory. The understory separation was based on the qCO2’ > 0, qH2O’ > 0 quadrant. Keeling plots of this quadrant, made using the dD isotope of H2O, indicate strong midday depletion of understory water vapour (-20 ‰). This effect can only be explained by a major downwards moisture flux from the atmosphere into the soils through the process of condensation, even - or especially – when the air temperatures are highest. Finally, we show what the responses of the major fluxes (H, LE, FCO2) and their respective isotopologues are to the passage of clouds, including their lag times. Our study contributes to an improved quantification and understanding of the canopy-atmosphere fluxes influenced by the perpetual presence of clouds.

The Amazonian hydrological and carbon cycle are controlled by a complex, interconnected and interdependent myriad of surface and atmospheric processes. Improving our understanding and numerical representation of these cycles under a changing climate requires a deeper exploration of the biospheric-atmospheric coupling and the processes governing the formation and deepening of shallow cumulus clouds. Utilising a comprehensive set of surface and upper-air atmospheric measurements from the CloudRoots-Amazon22 campaign alongside an integrated hierarchy of models, we construct a numerical experiment to systematically study these processes throughout the dry season of 2022. The model hierarchy consists of a large eddy simulation resolving turbulence and shallow cumulus formation, a coupled rainforest-atmosphere mixed-layer model to map the sensitivity to surface and atmospheric observations and a moisture tracking model to identify and quantify moisture sources, sinks, and long-range transport. Individual days of observations were characterised into representative shallow convective and shallow-to-deep convective regimes. We accurately replicated the evolution of radiation and the asymmetrical exchange fluxes of energy, momentum, moisture, and carbon during the shallow convective regime. By analysing the diurnal variability of the state variables, we can determine how turbulent mixing controls the morning transition, from strong gradients to well-mixed conditions above the forest. Ongoing work involves improving the representation of in-canopy processes and simulating the shallow-to-deep convective regime by introducing thermodynamic forcings, such as moist air intrusion or increased wind sheared conditions, on the shallow convective experiment.

Diurnal temperature and carbon dioxide ranges are key metrics to quantify the impact of regional climate changes in forests. These ranges depend on biophysical processes, surface heat, water and carbon exchange, and boundary-layer dynamics. A crucial and elusive process is the entrainment of air from the free troposphere and residual air layers into the atmospheric boundary layer. Here we provide observational constraints on entrainment for two contrasting measurement sites: the Amazon Tall Tower Observatory (ATTO) in central Amazonia and the Loobos flux tower (NL-Loo) in a temperate forest in the Netherlands. We used radio soundings, air samples from tall towers and aircraft data in combination with surface air measurements and ecophysiological data. Fluxes and concentrations were measured for biophysical-process tracers CO2, O2/N2, δ13C, δ18O (in CO2) and δ18O (in water). These novel tracers are proposed to partition gross carbon and water fluxes and for estimating plant properties and we present a unique dataset with our interpretation. Our analysis enables us to unravel the role of entrainment on the diurnal ranges and how this is controlled by surface and entrainment fluxes. By means of a coupled forest-atmosphere model constrained by the comprehensive observations, we perform a sensitivity study on the surface flux partitioning (photosynthesis versus soil respiration; soil evaporation versus plant transpiration, sensible versus heat flux) under a wide range of leaf traits, surface and boundary-layer dynamic conditions. Our results are useful to assess the performance of carbon-climate models in tropical and temperate forests.