Search for: All records

We investigate how effective surface length scales (Le f f ) and atmospheric boundary layer stability modulate surface-induced secondary circulations over a realistic heterogeneous sur- face. The evolution of the circulations and their impact on surface-atmosphere fluxes are studied using coupled large eddy simulations of the CHEESEHEAD19 field campaign. The heterogeneity-induced circulations were diagnosed using time and ensemble averaging of the atmospheric fields. Simulations were performed for summer (August) and autumn (Septem- ber) Intensive Observation Periods of the field campaign, characterised differently in terms of normalised surface length scales and ABL stability. Quasi-stationary and persistent cir- culations were diagnosed in the daytime ABL that span the entire mixed layer height (zi ). Their variation in time and space are presented. Homogeneous control runs were also per- formed to compare and contrast spatial organisation and validate the time-ensemble averaging operation. In the convective boundary layers simulated during the summer time simulations, wavelengths that scale as the effective surface heterogeneity length scales contribute the most to the heterogeneity-induced transport. Contributions from surface-induced circulations were lower in the simulated near-neutral BL for the autumn simulations. We find that both Le f f /zi and ABL static stability control the relative contribution of surface-induced circulations to the area averaged vertical transport. This scale analysis supports prior work over the study domain on scaling tower measured fluxes by including low frequency contribution. We believe that the conceptual framework presented here can be extended to include the effects of sub-grid land surface heterogeneity in numerical weather prediction and climate models and also to further explore scale-aware scaling methodologies for near surface-atmosphere exchanges. 

Abstract Dry deposition is the second largest tropospheric ozone (O₃) sink and occurs through stomatal and nonstomatal pathways. Current O₃uptake predictions are limited by the simplistic big‐leaf schemes commonly used in chemical transport models (CTMs) to parameterize deposition. Such schemes fail to reproduce observed O₃fluxes over terrestrial ecosystems, highlighting the need for more realistic treatment of surface‐atmosphere exchange in CTMs. We address this need by linking a resolved canopy model (1D Multi‐Layer Canopy CHemistry and Exchange Model, MLC‐CHEM) to the GEOS‐Chem CTM and use this new framework to simulate O₃fluxes over three north temperate forests. We compare results with in situ measurements from four field studies and with standalone, observationally constrained MLC‐CHEM runs to test current knowledge of O₃deposition and its drivers. We show that GEOS‐Chem overpredicts observed O₃fluxes across all four studies by up to 2×, whereas the resolved‐canopy models capture observed diel profiles of O₃deposition and in‐canopy concentrations to within 10%. Relative humidity and solar irradiance are strong O₃flux drivers over these forests, and uncertainties in those fields provide the largest remaining source of model deposition biases. Flux partitioning analysis shows that: (a) nonstomatal loss accounts for 60% of O₃deposition on average; (b) in‐canopy chemistry makes only a small contribution to total O₃fluxes; and (c) the CTM big‐leaf treatment overestimates O₃‐driven stomatal loss and plant phytotoxicity in these temperate forests by up to 7×. Results motivate the application of fully online vertically explicit canopy schemes in CTMs for improved O₃predictions. 

Abstract Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near‐real‐time for both cloudy and clear sky conditions at a five‐minute resolution. We compared two machine learning (ML) models, Long Short‐Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top‐of‐atmosphere observations from the Advanced Baseline Imager (ABI) on the GOES‐16 satellite against observations from hundreds of observation sites for a five‐year period. Long Short‐Term Memory outperformed GBR, especially at coarser resolutions and under challenging conditions, with a clear sky R²of 0.96 (RMSE 2.31K) and a cloudy sky R²of 0.83 (RMSE 4.10K) across CONUS, based on 10‐repeat Leave‐One‐Out Cross‐Validation (LOOCV). GBR maintained high accuracy and ran 5.3 times faster, with only a 0.01–0.02 R²drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized more time information in cloudy conditions. A comparative analysis against the physically based ABI_LSTproduct showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data‐driven models for LST estimation and suggests potential pathways for integrating ML models to enhance the accuracy and coverage of LST products. 

Abstract How convective boundary‐layer (CBL) processes modify fluxes of sensible (SH) and latent (LH) heat and CO₂(F_c) in the atmospheric surface layer (ASL) remains a recalcitrant problem. Here, large eddy simulations for the CBL show that whileSHin the ASL decreases linearly with height regardless of soil moisture conditions,LHandF_cdecrease linearly with height over wet soils but increase with height over dry soils. This varying flux divergence/convergence is regulated by changes in asymmetric flux transport between top‐down and bottom‐up processes. Such flux divergence and convergence indicate that turbulent fluxes measured in the ASL underestimate and overestimate the “true” surface interfacial fluxes, respectively. While the non‐closure of the surface energy balance persists across all soil moisture states, it improves over drier soils due to overestimatedLH. The non‐closure does not imply thatF_cis always underestimated;F_ccan be overestimated over dry soils despite the non‐closure issue. 

Single point eddy covariance measurements of the Earth’s surface energy budget frequently identify an imbalance between available energy and turbulent heat fluxes. While this imbalance lacks a definitive explanation, it is nevertheless a persistent finding from single-site measurements; one with implications for atmospheric and ecosystem models. This has led to a push for intensive field campaigns with temporally and spatially distributed sensors to help identify the causes of energy balance non-closure. Here we present results from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19)—an observational experiment designed to investigate how the Earth’s surface energy budget responds to scales of surface spatial heterogeneity over a forest ecosystem in northern Wisconsin. The campaign was conducted from June–October 2019, measuring eddy covariance (EC) surface energy fluxes using an array of 20 towers and a low-flying aircraft. Across the domain, energy balance residuals were found to be highest during the afternoon, coinciding with the period of surface heterogeneity-driven mesoscale motions. The magnitude of the residual varied across different sites in relation to the vegetation characteristics of each site. Both vegetation height and height variability showed positive relationships with the residual magnitude. During the seasonal transition from latent heat-dominated summer to sensible heat-dominated fall the magnitude of the energy balance residual steadily decreased, but the energy balance ratio remained constant at 0.8. This was due to the different components of the energy balance equation shifting proportionally, suggesting a common cause of non-closure across the two seasons. Additionally, we tested the effectiveness of measuring energy balance using spatial EC. Spatial EC, whereby the covariance is calculated based on deviations from spatial means, has been proposed as a potential way to reduce energy balance residuals by incorporating contributions from mesoscale motions better than single-site, temporal EC. Here we tested several variations of spatial EC with the CHEESEHEAD19 dataset but found little to no improvement to energy balance closure, which we attribute in part to the challenging measurement requirements of spatial EC. 

Abstract The water vapor transport associated with latent heat flux (LE) in the planetary boundary layer (PBL) is critical for the atmospheric hydrological cycle, radiation balance, and cloud formation. The spatiotemporal variability of LE and water vapor mixing ratio (r_v) are poorly understood due to the scale‐dependent and nonlinear atmospheric transport responses to land surface heterogeneity. Here, airborne in situ measurements with the wavelet technique are utilized to investigate scale‐dependent relationships among LE, vertical velocity (w) variance (), andr_vvariance () over a heterogeneous surface during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, , and at 100 m height, with a majority scale range of 120 m–4 km in LE, 32 m–2 km in , and 200 m–8 km in . The scales are classified into three scale ranges, the turbulent scale (8–200 m), large‐eddy scale (200 m–2 km), and mesoscale (2–8 km) to evaluate scale‐resolved LE contributed by and . The large‐eddy scale in PBL contributes over 70% of the monthly mean total LE with equal parts (50%) of contributions from and . The monthly temporal variations mainly come from the first two major contributing classified scales in LE, , and . These results confirm the dominant role of the large‐eddy scale in the PBL in the vertical moisture transport from the surface to the PBL, while the mesoscale is shown to contribute an additional ∼20%. This analysis complements published scale‐dependent LE variations, which lack detailed scale‐dependent vertical velocity and moisture information. 

Abstract Global warming increases ecosystem respiration (ER), creating a positive carbon-climate feedback. Thermal acclimation, the direct responses of biological communities to reduce the effects of temperature changes on respiration rates, is a critical mechanism that compensates for warming-induced ER increases and dampens this positive feedback. However, the extent and effects of this mechanism across diverse ecosystems remain unclear. By analyzing CO2 flux data from 93 eddy covariance sites worldwide, we observed thermal acclimation at 84 % of the sites. If sustained, thermal acclimation could reduce projected warming-induced nighttime ER increases by at least 25 % across most climate zones by 2041-2060. Strong thermal acclimation is particularly evident in ecosystems at high elevation, with low-carbon-content soils, and within tundra, semi-arid, and warm-summer Mediterranean climates, supporting the hypothesis that extreme environments favor the evolution of greater acclimation potential. Moreover, ecosystems with dense vegetation and high productivity such as humid tropical and subtropical forests generally exhibit strong thermal acclimation, suggesting that regions with substantial CO2 uptake may continue to serve as strong carbon sinks. Conversely, some ecosystems in cold continental climates show signs of enhancing thermal responses, the opposite of thermal acclimation, which could exacerbate carbon losses as climate warms. Our study underscores the widespread yet climate-specific patterns of thermal acclimation in global terrestrial ER, emphasizing the need to incorporate these patterns into Earth System Models for more accurate carbon-climate feedback projections. 

ABSTRACT AimTo quantify the intra‐community variability of leaf‐out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers and deduce its ecological consequences with regard to radiation received and exposure to late frost. LocationEastern North America (ENA) and Europe (EUR). Time Period2009–2022. Major Taxa StudiedTemperate deciduous forest trees. MethodsWe developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra‐community variability of the amount of radiation received and of exposure to late frost. ResultsLeaf‐out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf‐out to temperature was identical in both regions (−3.4 days per °C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species‐rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf‐out resulted in higher ICVLo. ICVLo resulted in ca. 8% difference of radiation received from leaf‐out to September among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts. Main ConclusionsWe conducted the first intercontinental analysis of the variability of leaf‐out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo (difference in terms of radiation received and exposure to late frost), which should be explored in the context of ongoing climate change, which affects trees differently according to their phenological niche.