Tower Observations Research Articles

Tall tower observations of a northward surging gust front in central Amazon and its role in the mesoscale transport of carbon dioxide

AbstractHigh‐frequency measurements obtained at two micrometeorological towers are investigated for a rare northward surging gust front that impacted the Amazon Tall Tower Observatory (ATTO), in central Amazon. The gust front originated from a decaying mesoscale convective system (MCS) during the morning hours of 27 December 2021 near Manaus, Amazonas state, northern Brazil, and surged north‐eastward towards the ATTO site. Large temperature drops and vigorous, persistent winds were observed at the towers which lasted for over 4 h despite the gust front being detached from its parent, decaying MCS. More importantly, the gust front was responsible for drastic increases of CO2 concentrations throughout the tower depths, which suggests that the gust front winds horizontally advected CO2‐rich air from a source upstream from the ATTO site. The CO2‐rich outflow is hypothesized to originate from downward transport and/or biomass burning from forest fires in southeastern Amazon, both ideas that are supported by large increases of aerosol concentrations measured at ATTO following the gust front passage. Our results stress the need for further investigations addressing the role played by mesoscale convective circulations in the redistribution of trace gases and aerosols in the Amazon.

Vertical distribution of ozone in spring based on two high tower observations over the Pearl River Delta, China

This study investigates the vertical distribution characteristics of ozone (O3) in the Pearl River Delta (PRD) region during spring, utilising observational data from two tall towers in urban and suburban areas of the PRD, i.e., the 600 m high Canton Tower and the 356 m high Shenzhen Meteorology Gradient Tower (SZMGT), between 2018 and 2020. The observations indicate that the peak times of O3 concentrations differ under polluted and clean conditions, with polluted conditions showing 1–3 h later peaks compared with clean conditions. The diurnal variation in the O3 concentration is more pronounced at the SZMGT than at the Canton Tower. The O3 vertical gradients differ between the two towers, with the Canton Tower showing larger daytime gradients and the SZMGT showing larger nighttime gradients under polluted conditions. The vertical O3 distribution patterns are categorised into three types: Polluted, Moderate, and Good levels. The O3 concentration initially rises with altitude and then decreases under polluted conditions. Elevated O3 is observed in the lower planetary boundary layer (PBL), approximately between 110 and 210 m in height, where the concentration is 1.1–1.4 times higher than the surface level. Both the Moderate and Good levels occur under clean conditions, in which the O3 of the Canton Tower increases with altitude, whereas the O3 of the SZMGT first increases and then decreases with altitude. The correlation between O3 and the meteorological conditions (temperature, relative humidity, wind direction, and wind speed) decreases with increasing altitude. The cluster analysis of backward trajectories identifies three main transport paths affecting the O3 levels: northeasterly continental, southerly oceanic, and easterly coastal paths. Combining the observations from O3 lidar in Guangzhou and Shenzhen and two additional cities within the PRD region, Foshan and Zhongshan, the analysis of a typical pollution case indicates that different cities have different local chemical generation and regional transport contributions to O3 pollution. A conceptual model reflecting the horizontal transport paths and vertical structures of O3 pollution in the spring over the PRD is proposed, which enhances our understanding of the O3 vertical distribution in different cities and contributes to the analysis of the O3 pollution mechanism in the PRD agglomeration.

Divergent response of energy exchange to heatwaves from flux tower observations among various vegetation types

Abstract The increasing frequency of European heatwaves and the associated impacts on ecosystems have raised widespread concern during the last two decades. The partitioning of surface energy between latent and sensible heat fluxes plays a pivotal role in regulating heat and water exchange between the land surface and the atmosphere. However, the responses of surface energy partitioning during heatwave events and the contributions of changes in energy partitioning to heatwave development have been underexplored. Here, we investigated the responses of surface energy exchange to temperature extremes during four devastating European heatwaves (2003, 2010, 2018, and 2022) based on long‒term observations from 31 flux towers. Our results demonstrated that the divergent responses of surface energy exchange to heatwaves were modulated by vegetation type and background climate in Europe. Forests maintained similar latent heat fluxes as the climatological mean but largely increased sensible heat under heat‒stressed conditions. While grasslands and croplands tended to increase sensible heat by suppressing latent heat during heatwaves, especially under water‒stressed conditions. Furthermore, the changes in surface energy partitioning strengthened positive land‒atmosphere feedbacks during the heatwave period, leading to unprecedented temperature extremes. This study highlights the importance of surface energy partitioning in land‒atmosphere interactions and heatwave developments.

The Decline in Summer Fallow in the Northern Great Plains Cooled Near‐Surface Climate but had Minimal Impacts on Precipitation

AbstractLand management can moderate or intensify the impacts of a warming atmosphere. Since the early 1980s, nearly 116,000 km2 of cropland that was once held in fallow during the summer is now planted in the northern North American Great Plains. To simulate the impacts of this substantial land cover change on regional climate processes, convection‐permitting model experiments using the Weather Research and Forecasting model were performed to simulate modern and historical amounts of summer fallow. The control simulation was extensively validated using multiple observational data products as well as eddy covariance tower observations. Results of these simulations show that the transition from summer fallow to modern land cover led to ∼1.5°C cooler temperatures and decreased vapor pressure deficit by ∼0.15 kPa during the growing season across the study region, which is consistent with observed cooling trends. The cooler and wetter land surface with vegetation led to a shallower planetary boundary layer and lower lifted condensation level, creating conditions more conducive to convective cloud formation and precipitation. Our model simulations however show little widespread evidence of land surface changes effects on precipitation. The observed precipitation increase in this region is more likely related to increased moisture transport by way of the Great Plains Low Level Jet as revealed by the ERA5 reanalysis. Our results demonstrate that land cover change is consistent with observed regional cooling in the northern North American Great Plains but changes in precipitation cannot be explained by land management alone.

Declined benefit of earlier spring greening on summer growth in northern ecosystems under future scenarios

Widespread spring warming and an earlier start of the growing season (SOS) compensated for spring vegetation productivity, simultaneously resulting in reduced retention of accessible nutrition and water resources in subsequent seasons. In this case, however, it is unknown whether the increased summer growth induced by earlier SOS will be sustained. Here, we analyzed the legacy effects of SOS on summer growth under historical and future scenarios, through combining remote sensing products, flux tower observations and model simulations. We observed that the background climate (summer temperature and summer precipitation) modulated the response of summer growth to SOS. In the past two decades, the advance of SOS promoted summer growth in most areas of the northern ecosystems, except for dry and hot regions. Moreover, the effects of SOS on plant growth reversed within the summer months, which may be related to the continued deterioration of moisture conditions. Models predicted a general shift from a negative to a positive correlation between SOS and summer growth under future warming and regionally uneven precipitation. In particular, under SSP370 and SSP585 scenarios, areas of positive correlation will exceed that of negative correlation by the end of the century. These results indicated the stimulation of summer growth by SOS advance may shift to inhibition in more regions in the future. Our findings highlighted the limited enhancement of plant carbon sink by altered spring phenology under future climate change.

Impact of the Sea Effect on Sudden Fog on the Western Coast of the Bohai Sea: A Case Study

The term “sea effect” generally refers to the process of air mass modification after cold air flows above a warm sea surface. Affected by the sea effect, small-scale and sudden fogs have occasionally been observed on the western coast of the Bohai Sea. A more in-depth study of this type of fog is crucial for ensuring the safety of maritime and aerial traffic routes in this region. This study investigated the formation mechanism of this specific type of fog on the morning of 17 October 2007, utilizing both meteorological stations and 255 m tower observations, combined with the results of the Weather Research and Forecasting model (WRF). It is demonstrated that Bohai Sea evaporation and the associated water vapor advection played crucial roles in the formation of fog along the west coast of the Bohai Sea. The cold return flow became more moist as it passed over the warm Bohai Sea, which was the primary contributor to triggering regional fog on the western coast. A moisture budget analysis revealed that water vapor from the Bohai Sea intruded into its western coast along an eastward trajectory, dominating the oscillations in the net moisture flux. The eastern water vapor flux significantly increased at 17:00 on the 16th (Local time, LST), reaching its peak at 21:00. Correspondingly, the fog water growth rate began to increase at 23:00 on the 16th, reaching its maximum at 03:00 on the 17th. A sensitivity experiment on evaporation further indicated that the Bohai sea effect played a decisive role in fog formation. With a tenfold reduction in evaporation from the Bohai Sea and subsequent significant weakening of water vapor advection, the simulated fog along the western coast of the Bohai Sea completely disappeared. Understanding the formation mechanism of this type of fog is beneficial for refining forecasting focal points, thereby enhancing forecast accuracy in a targeted manner.

Evaluation and Intercomparison of Small Uncrewed Aircraft Systems Used for Atmospheric Research

Abstract Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Simulation of gross primary productivity and impact of drought in Liulin watershed of Taihang mountains over 2000–2020

As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R2 = 0.94, RMSE = 0.47 g C m-2·d-1) and validation period (R2 = 0.91, RMSE = 0.60 g C m-2 d-1), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m-2 y-1 and an average value of 545.6 g C m-2 y-1 in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m-2 y-1 from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m-2 y-1 from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6–7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought.

Evaluation of Land–Atmosphere Coupling Processes and Climatological Bias in the UFS Global Coupled Model

Abstract This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS’s ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales.

Long-term, high-resolution GPP mapping in Qinghai using multi-source data and google earth engine

ABSTRACT The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern. However, there is still a lack of a high-resolution GPP dataset for Qinghai Province. To address this issue, we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE, and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model. We compared our GPP dataset with flux observations to verify its accuracy. The results showed that our GPP dataset had a high correlation with the flux tower observations, with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site, respectively, and each site had an RMSE of and 12.986 , respectively. There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types, with the average correlation coefficient ranging from 0.431 to 0.943. By comparing with the flux observations and the related analysis, we demonstrated that our GPP dataset features better accuracy, higher spatial resolution, and more temporal coverage than mainstream GPP datasets. This study offers the first long-term high-resolution GPP dataset for Qinghai Province, and we believe that this dataset has important implications for ecological management and climate research.

A novel approach for retrieving GPP of evergreen forest regions of India using random forest regression

Gross Primary Productivity (GPP) is a crucial variable of global carbon cycle for determining the ecosystem’s health. Various methods are devised to quantify GPP and upscale it in both time and space. The most common methods are physical model and eddy covariance-based estimation, which are very restricted to surrounding area of study, only. The alternative methods are empirical (e.g., LUE, CASA, SCARF, and MODIS) and Machine learning (ML) models that employ remote sensing satellite data and geographical factors. However, for using ML models, ground-based measurements of GPP is a very important factor, which is not available in most places. We propose an alternative and effective way of estimating the GPP using the ML model and data from various flux sites around the globe for a particular plant functional type (PFT). In the present study, RF is used as ML model, which is trained on global GPP data from evergreen forest and implemented in Indian region. The key findings indicated that ML-based GPP is highly accurate and hence, we generated 20 years of time series GPP dataset (2001–2020). We validated with ground-based flux tower observations during 2016–2018 for three sites (very limited datasets) in India and compared them with MODIS GPP. The coefficient of determination (R2) value of the ML-based model was 0.84 with root mean square error (RMSE) of 1.45 gC m−2 Day−1 and mean absolute error (MAE) of 0.838 gC m−2 Day−1. The proposed approach is highly accurate and far better than the MODIS-based GPP. Therefore, it can be further extended to other forest types for a holistic assessment of the carbon cycle of a region.

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO2), and methane flux (CH4)—with high precision and minimal loss (i.e. ALTRMSE: 1.327 cm [1969–2022]; CO2 RMSE: 0.697 µmolCO2m−2s−1 [2003–2021]; CH4 RMSE: 0.715 nmolCH4m−2s−1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

A framework for estimating actual evapotranspiration through spatial heterogeneity-based machine learning approaches

Actual evapotranspiration (ET) is a key variable controlling the exchange of energy and water in terrestrial ecosystems. The spatial heterogeneity of ET patterns poses a great challenge for regional ET estimation over heterogeneous landscapes. In this study, we proposed a framework for estimating ET through interactive detector for spatial associations (IDSA)-based machine learning (ML) approaches by combining data from remote sensing and four flux towers distributed in the Hai River Basin. The IDSA model was applied to explore individual and interactive determinants of ET over the Hai River Basin. In addition, the geographical regions of ET were determined according to the spatial heterogeneity. Then we simulated ET in each geographical region separately using three ML models, including random forest, gradient boosting decision tree, and Cubist. General ML models without considering spatial heterogeneity were used for comparison, and evaluations were conducted with the eddy covariance flux tower observations at the site scale, also with water balance ET at the basin scale. The results demonstrated that the spatial patterns of ET were difficult to be explained by individual environmental variables (64.9 %). The maximum interpretability was improved by about 11.2 % through the interaction of air temperature (T) and normalized different vegetation index (NDVI). The framework developed in this study had excellent performance with coefficient of determination (R2) ranging from 0.807 to 0.811, root mean square error (RMSE) ranging from 0.654 mm/day to 0.661 mm/day, and mean absolute error (MAE) ranging from 0.470 mm/day to 0.485 mm/day. It was superior to general ML models at the site and basin scales. Additionally, the ET simulated by IDSA-based ML models were in good agreement with the reference product, further illustrating its reliability. The proposed framework provides a theoretical basis for in-depth understanding of spatial heterogeneity of ET and a new perspective for ET prediction over large scales.

Sources and sinks of carbonyl sulfide inferred from tower and mobile atmospheric observations in the Netherlands

Abstract. Carbonyl sulfide (COS) is a promising tracer for the estimation of terrestrial ecosystem gross primary production (GPP). However, understanding its non-GPP-related sources and sinks, e.g., anthropogenic sources and soil sources and sinks, is also critical to the success of the approach. Here we infer the regional sources and sinks of COS using continuous in situ mole fraction profile measurements of COS along the 60 m tall Lutjewad tower (1 m a.s.l.; 53∘24′ N, 6∘21′ E) in the Netherlands. To identify potential sources that caused the observed enhancements of COS mole fractions at Lutjewad, both discrete flask samples and in situ measurements in the province of Groningen were made from a mobile van using a quantum cascade laser spectrometer (QCLS). We also simulated the COS mole fractions at Lutjewad using the Stochastic Time-Inverted Lagrangian Transport (STILT) model combined with emission inventories and plant uptake fluxes. We determined the nighttime COS fluxes to be -3.0±2.6 pmol m−2 s−1 using the radon-tracer correlation approach and Lutjewad observations. Furthermore, we identified and quantified several COS sources, including biodigesters, sugar production facilities and silicon carbide production facilities in the province of Groningen. Moreover, the simulation results show that the observed COS enhancements can be partially explained by known industrial sources of COS and CS2, in particular from the Ruhr Valley (51.5∘ N, 7.2∘ E) and Antwerp (51.2∘ N, 4.4∘ E) areas. The contribution of likely missing anthropogenic sources of COS and CS2 in the inventory may be significant. The impact of the identified sources in the province of Groningen is estimated to be negligible in terms of the observed COS enhancements. However, in specific conditions, these sources may influence the measurements in Lutjewad. These results are valuable for improving our understanding of the sources and sinks of COS, contributing to the use of COS as a tracer for GPP.

Assessment of global gridded transpiration products using the extended instrumental variable technique (EIVD)

Transpiration (T) constitutes a significant portion of total terrestrial evapotranspiration in ecosystems and functions as the connection between the water and energy cycle. Despite its importance, large-scale quantification of T remains a formidable challenge. Global gridded T datasets are essential for investigating and predicting the regulatory mechanisms of transpiration. However, the scarcity of reliable field references data limits direct validation efforts. This study uses collocation analysis methods, including triple collocation (TC) and extended double instrumental variable algorithm (EIVD), to assess three global T products at 0.25°-daily resolutions. Signal-to-noise ratios (SNR) and absolute error variances (σε2) obtained through EIVD serve as primary indicators. Our results show that both TC and EIVD approaches yield valuable insights into the accuracy and performance of the T products. Moreover, the analysis emphasizes the necessity of considering non-zero error cross-correlation when utilizing collocation analysis for error characterization. Overall, this study presents a comprehensive examination and comparison of collocation analysis for error characterization of three T products. The findings suggested that collocation analysis methods may function as reliable alternatives to tower observations at a global scale, offering potential benefits for data assimilation and merging.

Evapotranspiration regulates leaf temperature and respiration in dryland vegetation

Evapotranspiration regulates energy flux partitioning at the leaf surface, which in turn regulates leaf temperature. However, the mechanistic relationship between evapotranspiration and leaf temperature remains poorly constrained. In this study, we present a novel mechanistic model to predict leaf temperature as a linearized function of the evaporative fraction. The model is validated using measurements from infrared radiometers mounted on two flux towers in Arizona, USA, which measure canopies of Prosopis velutina with contrasting water availability. Both the observations and model predictions reveal that leaf temperature equilibrates with air temperature when latent heat flux consumes all of the energy incident on the leaf surface. Leaf temperature exceeds air temperature when there is a net input of energy into the leaf tissue. The flux tower observations revealed that evaporative cooling reduced canopy leaf temperature by ca. 1–5 °C, depending on water availability. Evaporative cooling also enhanced net carbon uptake by reducing leaf respiration by ca. 15% in the middle of the growing season. The regulation of leaf temperature by evapotranspiration and the resulting impact on net carbon uptake represents an important link between plant water and carbon cycles that has received little attention in literature. The model presented here provides a mechanistic framework to quantify leaf evaporative cooling and examine its impacts on plant physiological function.

Investigating the Diurnal Variation in Coastal Boundary Layer Winds on Hainan Island Using Three Tower Observations

This study analyzes wind structures up to 509 m in the atmospheric boundary layer in the coastal area of Hainan Island, using a dataset obtained from ultrasonic anemometers housed in three towers. The wind profile, consisting of the measurements from the three towers, followed logarithmic law. In a diurnal variation, the maximum wind speed occurred at night, with a greater component of northerly wind, while the minimum wind speed was observed at noon, with a greater component of easterly wind. The variation in wind speed suggests that the measurements were representative of the wind field in the upper part of the atmospheric boundary layer, and the variation in wind direction might be affected by sea and land breezes, which can be induced by the different thermal conditions of underlying surfaces. The diurnal variation in average wind speed ranged from 0.5 to 1.5 m s−1, and the diurnal variation in wind direction was 10–20 degrees. In our measurements, the diurnal trajectory of the wind vector was observed to be counterclockwise, which differs from previous studies conducted over uniform and flat underlying surfaces. This is partially due to the different thermodynamic conditions of the underlying land and sea surfaces. The impact of topographic relief on wind measurement is also discussed. The measurements suggest that wind speeds at altitudes above 50 m are less influenced by terrain. The height of the reversal layer, which is generated by the different diurnal variations in wind speed in the upper and lower parts of the boundary layer, was estimated to be around 300 m.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016

The value of leaf photosynthetic capacity (Vcmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such Vcmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr−1). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit Vcmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific Vcmax, the inclusion of the LCC-derived Vcmax improved the model estimates of GPP, with the coefficient of determination (R2) increased by 23% and the root mean square error (RMSE) decreased by 25%. Vcmax values are typically parameterized with PFT-specific Vcmax calibrated from flux tower observations or empirical Vcmax based on the TRY database (which includes 723 data points derived from Vcmax field measurements). On the regional scale, compared with GPP, using the LCC-derived Vcmax, the conventional method of fixing Vcmax using the calibrated Vcmax or TRY-based Vcmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr−1 or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr−2 or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr−1). Our study highlighted the urgent need to develop spatially and temporally consistent Vcmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change.

Site- and building height-dependent design extreme wind speed vertical profile of tropical cyclone

Tropical cyclone (TC) wind is one of the dominant loadings in the design of building structures. The radial distance-dependent vertical profile and prominent jet structure or super gradient of TC winds make the conventional terrain-dependent design profile inadequate for describing TC wind loadings along the height. Herein, a stochastic simulation-based algorithm is developed to determine the design extreme wind speed vertical profile of TC using a quasi-3D TC wind field model. The wind field model is first described and validated by comparing the vertical wind speed profiles with the upper-level dropsonde data and lower-level tower observation results. The evolution of the wind profile during the passage of historical real storms is reproduced by introducing optimal fitting results for the wind field parameters. Ten-thousand-year synthetic tracks are simulated to obtain the design extreme wind speeds for several coastal cities. The design extreme wind speed composite profile and structure height-dependent outer envelope profile (OEP) are proposed, which are applicable to a certain height of interest and buildings with different heights, respectively. These profiles also depend on terrain exposure, return period, and location of the site of interest. The code-suggested profiles are confirmed to underestimate the TC-induced wind loadings along the height, especially at sites with lower roughness lengths. The power law profile is still applicable, but larger power coefficients should be utilized, and the design extreme wind speed at 10m should be increased with an increase in the structure height. This study provides a forward step towards the rational estimation of TC-induced wind loadings for high-rise structures. • The radial distance-dependent vertical profile and prominent jet structure of tropical cyclone winds are reproduced. • The design extreme wind speed composite profile and structure height-dependent outer envelope profile (OEP) for tropical cyclone are proposed. • The inadequacy of current code-suggested wind speed vertical profiles to tropical cyclone winds is discussed. • The design extreme wind speed vertical profiles of tropical cyclone in several coastal cities are developed.