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Recent work has shown that bulk-Richardson (Rib) parameterizations for friction velocity, sensible heat flux, and latent heat flux have similar, and in some instances better, performance than long-standing parameterizations from Monin–Obukhov similarity theory (MOST). In this work, we expanded upon new Rib parameterizations and developed parameterizations of turbulence statistics, i.e., standard deviations in the 30-min u (horizontal), υ (meridional), and w (vertical) wind components (i.e., σu, συ, and σw, respectively), which allowed us to derive Rib-based parameterizations of turbulent kinetic energy (e), and standard deviations in the 30-min temperature and moisture measurements (σθ and σq, respectively). We used datasets from three 10-m micrometeorological towers installed during the Land Atmosphere Feedback Experiment (LAFE) conducted in Oklahoma from 1 to 31 August 2017 and evaluated the new parameterizations by comparing them against parameterizations from MOST. We used the LAFE datasets and fully independent datasets obtained from two micrometeorological towers installed in Alabama between February 2016 and April 2017 to evaluate the performance of the parameterizations. Based on the slope of the relationship between the observed and parameterized turbulence statistics (mb) and the coefficient of correlation (r), we found that the Rib relationships generally performed better than MOST at parameterizing συ, σw, σθ, and σq, and the Rib relationships performed better at low wind speeds than at high wind speeds. These results, coupled with recent developments of Rib parameterizations for surface-layer momentum, heat, and moisture fluxes, provide further evidence to consider using Rib-based parameterizations in weather forecasting models. Significance Statement Deficiencies in Monin–Obukhov similarity theory (MOST) are well known, yet MOST forms the basis in weather forecasting models for describing heat, moisture, and momentum transfer between the land surface and atmosphere. We expanded upon previous work suggesting a MOST alternative called the bulk-Richardson approach. We used data collected from meteorological towers installed in Oklahoma and compared the bulk-Richardson approach with MOST. We evaluated these two approaches using data from meteorological towers installed in Oklahoma and Alabama and found that, overall, the bulk-Richardson approach performed better than MOST in determining the 30-min variability in temperature, moisture, and wind. This result provides additional motivation to use a bulk-Richardson approach in weather forecasting models because doing so will likely yield improved forecasts.

The ability of high-resolution mesoscale models to simulate near-surface and sub-surface meteorological processes is critical for representing land-atmosphere feedback processes. The High Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous U.S. from 1 January 2021 through 31 December 2021. We compared the 01-, 03-, 06-, 12-, 18-, 24-, 30-, and 48-hour forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well-simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the Western U.S. and negative MBE over the Eastern U.S. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models.

Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Rib) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Rib parameterizations to stable regimes and developed parameterizations for the friction velocity (u*), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Rib parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Rib parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Rib parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange.

Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol. 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models’ lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth’s land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.

The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR’s performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR’s initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R2 ≈ 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic.

Dryland ecosystems occupy ~40% of the land surface and are thought to dominate the inter-annual variability (IAV) and long-term trend of the global carbon (C) cycle. Therefore, it is imperative tha...

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA&rsquo, s Air Resources Laboratory (ARL)&rsquo, s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of &lt, 0.23 &deg, C, and root mean square error (RMSE) of &lt, 0.36 &deg, C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 &deg, C and RMSE = 0.67 &deg, C) than with LST(LWR) (bias &gt, 1.1 and RMSE &gt, 1.46 &deg, C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were &lt, 0.47 &deg, C, whereas nighttime AT explained &gt, 81% of the variance in LST(IRT) with a bias of 2.64 &deg, C and RMSE of 3.6 &deg, C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was &lt, 0.7 &deg, C, except at the semiarid grassland (1.5 &deg, C), whereas the absolute bias between AT and LST at the four sites were &lt, 2 &deg, C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 &deg, C (5 &deg, C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

The objective of this study was to evaluate the performance of two commercial‐grade electromagnetic sensors in measuring soil water content for the U.S. Climate Reference Network (USCRN). One sensor was a 50‐MHz coaxial impedance dielectric sensor (model HydraProbe, Stevens Water Monitoring Systems). The second sensor was a 1‐GHz time domain reflectometry (TDR) sensor (model TDR‐315L, Acclima). There was no substantial difference between both probes in daily mean volumetric soil water content and soil temperature measurements at a 0.1‐m depth in a customized uniform loamy soil testbed in Oak Ridge, TN, from 2016 to 2018, and at select USCRN stations with a variety of soils. However, the TDR‐315L provided more representative soil water content measurements for stations with high‐clay‐content soils than the HydraProbe. These results confirm the necessity of using site‐specific soil properties for soil sensors like the HydraProbe for estimating soil water content in different soil environments. They also suggest that TDR sensors may provide an opportunity to improve USCRN soil moisture measurements in higher‐clay‐content soils.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4-Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn.

Adjustments for the undercatch of solid precipitation caused by wind were developed for different weighing gauge/wind shield combinations tested in WMO-SPICE. These include several different manufacturer-provided unshielded and single-Alter shielded weighing gauges, a MRW500 precipitation gauge within a small, manufacturer-provided shield, and host-provided precipitation gauges within double-Alter, Belfort double-Alter, and small Double-Fence Intercomparison Reference (SDFIR) shields. Previously-derived adjustments were also tested on measurements from each weighing gauge/wind shield combination. The transfer functions developed specifically for each of the different types of unshielded and single-Alter shielded weighing gauges did not perform significantly better than the more generic and universal transfer functions developed previously using measurements from eight different WMO-SPICE sites. This indicates that wind shield type (or lack thereof) is more important in determining the magnitude of wind-induced undercatch than the type of weighing precipitation gauge. It also demonstrates the potential for widespread use of the previously-developed, multi-site single-Alter shielded and unshielded transfer functions. In addition, corrections for the lower-porosity Belfort double-Alter shield and a standard double-Alter shield were developed and tested using measurements from two separate sites for the first time. Among all of the manufacturer-provided shields tested, with an average undercatch of about 5 %, the Belfort double Alter shield required the least amount of correction, and caught ~ 80 % of the reference amount of precipitation even in snowy conditions with wind speeds greater than 5 m s−1. The SDFIR-shielded gauge accumulated 98 % of the Double-Fence Automated Reference (DFAR) precipitation amount on average, accumulated 90 % of the DFAR accumulation in high winds, and was almost indistinguishable from the full-sized DFAR used as a reference. In general, the more effective wind shields, that were associated with smaller unadjusted errors, also produced more accurate measurements after adjustment.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions. © 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

Global-scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink. However, such analyses are poorly constrained by measured CO2 exchange in drylands. Here we address this observation gap with eddy covariance data from 25 sites in the water-limited Southwest region of North America with observed ranges in annual precipitation of 100-1000 mm, annual temperatures of 2-25°C, and records of 3-10 years (150 site-years in total). Annual fluxes were integrated using site-specific ecohydrologic years to group precipitation with resulting ecosystem exchanges. We found a wide range of carbon sink/source function, with mean annual net ecosystem production (NEP) varying from -350 to +330 gCm-2 across sites with diverse vegetation types, contrasting with the more constant sink typically measured in mesic ecosystems. In this region, only forest-dominated sites were consistent carbon sinks. Interannual variability of NEP, gross ecosystem production (GEP), and ecosystem respiration (Reco ) was larger than for mesic regions, and half the sites switched between functioning as C sinks/C sources in wet/dry years. The sites demonstrated coherent responses of GEP and NEP to anomalies in annual evapotranspiration (ET), used here as a proxy for annually available water after hydrologic losses. Notably, GEP and Reco were negatively related to temperature, both interannually within site and spatially across sites, in contrast to positive temperature effects commonly reported for mesic ecosystems. Models based on MODIS satellite observations matched the cross-site spatial pattern in mean annual GEP but consistently underestimated mean annual ET by ~50%. Importantly, the MODIS-based models captured only 20-30% of interannual variation magnitude. These results suggest the contribution of this dryland region to variability of regional to global CO2 exchange may be up to 3-5 times larger than current estimates.

Soil drainage is a widely used agricultural practice in the midwest USA to remove excess soil water to potentially improve the crop yield. Research shows an increasing trend in baseflow and streamflow in the midwest over the last 60 years, which may be related to artificial drainage. Subsurface drainage (i.e., tile) in particular may have strongly contributed to the increase in these flows, because of its extensive use and recent gain in the popularity as a yield-enhancement practice. However, how evapotranspiration (ET) is impacted by tile drainage on a regional level is not well-documented. To explore spatial and temporal ET patterns and their relationship to tile drainage, we applied an energy balance-based multisensor data fusion method to estimate daily 30-m ET over an intensively tile-drained area in South Dakota, USA, from 2005 to 2013. Results suggest that tile drainage slightly decreases the annual cumulative ET, particularly during the early growing season. However, higher mid-season crop water use suppresses the extent of the decrease of the annual cumulative ET that might be anticipated from widespread drainage. The regional water balance analysis during the growing season demonstrates good closure, with the average residual from 2005 to 2012 as low as -3 mm. As an independent check of the simulated ET at the regional scale, the water balance analysis lends additional confidence to the study. The results of this study improve our understanding of the influence of agricultural drainage practices on regional ET, and can affect future decision making regarding tile drainage systems.

Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt – Mead, NE, Ames, IA and Champaign, IL – to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010 to 2017, which included both drought and pluvial conditions as well as a broad range in yield values. Detrended yield anomalies for corn and soybean were correlated with ESI computed using annual ET curves temporally aligned based on (1) calendar date, (2) crop emergence date, and (3) a growing degree day (GDD) scaled time axis. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improve the correlation, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. Regression equations derived at the time of peak correlation were used to estimate yields at county scale using a leave-one-out cross-validation strategy. The ESI-based yield estimates agree well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.79 to 0.93 and percent root-mean-square errors of 5–8%. These results demonstrate that remotely sensed ET at high spatiotemporal resolution can convey valuable water stress information for forecasting crop yields across the Corn Belt if interannual phenological variability is considered.

Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For optimal utility in agricultural water management applications, frequent thermal imaging (

Core Ideas This study quantifies the variability of soils at US Climate Reference Network sites. Soil properties were determined from the analysis of soil core samples. Soil properties displayed large variability with depth and location within and among sites. Variability of soil properties helped determine and interpret soil moisture variability. The objective of this study was to provide direct measurements of soil properties for 70 of the 114 US Climate Reference Network (USCRN) sites across the continental United States. Soil properties determined from the analysis of soil core samples include the particle size distribution (PSD, consisting of sand, silt, and clay contents), soil texture classifications, bulk density (BD), and the soil moisture content at water potentials of 33 kPa (field capacity, FC) and 1500 kPa (wilting point, WP). Sand, silt, and clay contents of the 70 sites indicated about 10 soil texture classifications as follows: three sites with loamy sand, 15 with sandy loam, two with clay, 11 with silt loam, five with clay loam, 10 with loam, seven with sand, eight with silty clay loam, four with sandy clay, and three with silty clay. The comparison of soil properties among soil depths and pits indicated considerable variability, with the silt, clay, and sand contents varying more with soil depth than with location at individual sites. The silt content tended to decrease with soil depth, clay tended to increase, and sand tended to vary randomly with depth. Regression lines fitted to values of FC and WP between the pits indicated a slope > 0.8, R2 > 0.88, and RMSE ranging from 2.7 to 4%. Compared with FC and WP, BD was less consistent among the pits, with slope = 0.6, R2 = 0.4, and RMSE of about 0.2 g cm−3.

Corrigendum to “Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands” published in Biogeosciences, 6, 969–986, 2009 E. Lokupitiya1, S. Denning1, K. Paustian2,3, I. Baker1, K. Schaefer4, S. Verma5, T. Meyers6, C. J. Bernacchi7, A. Suyker5, and M. Fischer8 1Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA 2Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA 3Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA 4National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309, USA 5School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0978, USA 6NOAA/ARL/ATDD, Oak Ridge, TN 37830-2456, USA 7Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA 8Lawrence Berkeley National Laboratory, Environmental Energy Technologies Division, Atmospheric Science Department, Berkeley, CA 94720, USA

Measurements taken by an automated national meteorological monitoring network during the 2017 total solar eclipse illuminate how the land and atmosphere respond to a sudden loss of sunlight.

Land surface temperature (LST) is a key variable for studying the energy and water vapor exchange at the biosphere–atmosphere interface. In an effort to better quantify the spatial variability and overall representativeness of single-point LST measurements being recorded at NOAA's Climate Reference Network (CRN) sites and to improve the accuracy of satellite LST measurements, airborne flight campaigns were conducted over a CRN site in Crossville, Tennessee, USA during 2010 to 2011. Multiple measurements of LST were made using infrared temperature sensors at micrometeorological tower sites and onboard an instrumented Piper Navajo airborne research aircraft. In addition to this, coincident LST products from the moderate resolution imaging spectroradiometer (MODIS) instruments (Collection 5), onboard NASA Terra and Aqua Earth Observing System satellites were used. In this paper the comparison of LST measurements made from multiple platforms are presented. Our study showed that the temporal and spatial variability of surface temperature as indicated by the standard deviation of the brightness temperature (Tb) during the flight periods were

Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR(canopy)) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR(chl)) or green leaves (FPAR(green)) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency CLUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m(-2) day(-1) (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 232, 3.16 and 3.90 g C m(-2) day-1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions. (C) 2015 Elsevier Inc All rights reserved.

Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant inaccuracies. Solid precipitation is particularly difficult to measure accurately, and differences between winter-time precipitation measurements from different measurement networks or different regions can exceed 100 %. Using precipitation gauge results from the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, crystal habit, and wind were quantified. The methods used to calculate gauge catch efficiency and correct known biases are described. Adjustments, in the form of transfer functions that describe catch efficiency as a function of air temperature and wind speed, were derived using measurements from eight separate WMO-SPICE sites for both unshielded and single-Alter shielded weighing precipitation gauges. The use of multiple sites to derive such adjustments makes these results unique and more broadly applicable to other sites with various climatic conditions. In addition, errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites were estimated.

The Multilayer Model (MLM) has been used for many years to infer dry deposition fluxes from measured trace species concentrations and standard meteorological measurements for national networks in the U.S., including the U.S. Environmental Protection Agency's Clean Air Status and Trends Network (CASTNet). MLM utilizes a resistance analogy to calculate deposition velocities appropriate for whole vegetative canopies, while employing a multilayer integration to account for vertically varying meteorology, canopy morphology and radiative transfer within the canopy. However, the MLM formulation, as it was originally presented and as it has been subsequently employed, contains a non-physical representation related to the leaf-level quasi-laminar boundary layer resistance that affects the calculation of the total canopy resistance. In this note, the non-physical representation of the canopy resistance as originally formulated in MLM is discussed and a revised, physically consistent, formulation is suggested as a replacement. The revised canopy resistance formulation reduces estimates of HNO3 deposition velocities by as much as 38% during mid-day as compared to values generated by the original formulation. Inferred deposition velocities for SO2 and O3 are not significantly altered by the change in formulation (

Net CO₂ exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic light-response function method. The analyses produced net CO₂ exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO₂ exchange rates and ecophysiological parameters of legumes were lower than those of maize (Zea mays L.) but higher than for wheat (Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO₂ m–² yr–¹ to sources of –2100 g CO₂ m–² yr–¹, with an average of –330 g CO₂ m–² yr–¹, indicating overall moderate CO₂–source activity related to a shorter period of photosynthetic uptake and metabolic costs of N₂ fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO₂, with an average net ecosystem production of 980 (range 550–1200) g CO₂ m–² yr–¹.

More accurate estimation of the carbon dioxide flux depends on the improved scientific understanding of the terrestrial carbon cycle. Remote-sensing-based approaches to continental-scale estimation of net ecosystem exchange (NEE) have been developed but coarse spatial resolution is a source of errors. Here we demonstrate a satellite-based method of estimating NEE using Landsat TM/ETM + data and an upscaling framework. The upscaling framework contains flux-footprint climatology modeling, modified regression tree (MRT) analysis and image fusion. By scaling NEE measured at flux towers to landscape and regional scales, this satellite-based method can improve NEE estimation at high spatial-temporal resolution at the landscape scale relative to methods based on MODIS data with coarser spatial-temporal resolution. This method was applied to sixteen flux sites from the Canadian Carbon Program and AmeriFlux networks located in North America, covering forest, grass, and cropland biomes. Compared to a similar method using MODIS data, our estimation is more effective for diagnosing landscape NEE with the same temporal resolution and higher spatial resolution (30 m versus 1 km) (r(2) = 0.7548 vs. 0.5868, RMSE = 1.3979 vs. 1.7497 g C m-(2) day(-1), average error = 0.8950 vs. 1.0178 g C m(-2) day(-1), relative error = 0.47 vs. 0.54 for fused Landsat and MODIS imagery, respectively). We also compared the regional NEE estimations using Carbon Tracker, our method and eddy-covariance observations. This study demonstrates that the data-driven satellite-based NEE diagnosed model can be used to upscale eddy-flux observations to landscape scales with high spatial-temporal resolutions. (C) 2013 Elsevier Inc. All rights reserved.

The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.

Recent incorporation of coupled soil biogeochemical and bi-directional NH3 air–surface exchange algorithms into regional air quality models holds promise for further reducing uncertainty in estimates of NH3 emissions from fertilized soils. While this represents a significant advancement over previous approaches, the evaluation and improvement of such modeling systems for fertilized crops requires process-level field measurements over extended periods of time that capture the range of soil, vegetation, and atmospheric conditions that drive short-term (i.e., post-fertilization) and total growing season NH3 fluxes. This study examines the processes of NH3 air–surface exchange in a fertilized corn (Zea mays) canopy over the majority of a growing season to characterize soil emissions after fertilization and investigate soil–canopy interactions. Micrometeorological flux measurements above the canopy, measurements of soil, leaf apoplast and dew/guttation chemistry, and a combination of in-canopy measurements, inverse source/sink, and resistance modeling were employed. Over a period of approximately 10 weeks following fertilization, daily mean and median net canopy-scale fluxes yielded cumulative total N losses of 8.4% and 6.1%, respectively, of the 134 kg N ha−1 surface applied to the soil as urea ammonium nitrate (UAN). During the first month after fertilization, daily mean emission fluxes were positively correlated with soil temperature and soil volumetric water. Diurnally, maximum hourly average fluxes of ≈ 700 ng N m−2 s−1 occurred near mid-day, coincident with the daily maximum in friction velocity. Net emission was still observed 5 to 10 weeks after fertilization, although mid-day peak fluxes had declined to ≈ 125 ng N m−2 s−1. A key finding of the surface chemistry measurements was the observation of high pH (7.0–8.5) in leaf dew/guttation, which reduced the ability of the canopy to recapture soil emissions during wet periods. In-canopy measurements near peak leaf area index (LAI) indicated that the concentration of NH3 just above the soil surface was highly positively correlated with soil volumetric water, which likely reflects the influence of soil moisture on resistance to gaseous diffusion through the soil profile and hydrolysis of remaining urea. Inverse source/sink and resistance modeling indicated that the canopy recaptured ≈ 76% of soil emissions near peak LAI. Stomatal uptake may account for 12–34% of total uptake by foliage during the day compared to 66–88% deposited to the cuticle. Future process-level NH3 studies in fertilized cropping systems should focus on the temporal dynamics of net emission to the atmosphere from fertilization to peak LAI and improvement of soil and cuticular resistance parameterizations.

a b s t r a c t We analyzed net CO2 exchange data from 13 flux tower sites with 27 site-years of measurements over maize and wheat fields across midcontinent North America. A numerically robust "light-soil temperature- VPD"-based method was used to partition the data into photosynthetic assimilation and ecosystem respiration components. Year-round ecosystem-scale ecophysiological parameters of apparent quantum yield, photosynthetic capacity, convexity of the light response, respiration rate parameters, ecological light-use efficiency, and the curvature of the VPD-response of photosynthesis for maize and wheat crops were numerically identified and interpolated/extrapolated. This allowed us to gap-fill CO2 exchange com- ponents and calculate annual totals and budgets. VPD-limitation of photosynthesis was systematically observed in grain crops of the region (occurring from 20 to 120 days during the growing season, depend- ing on site and year), determined by the VPD regime and the numerical value of the curvature parameter of the photosynthesis-VPD-response, � VPD. In 78% of the 27 site-years of observations, annual gross pho- tosynthesis in these crops significantly exceeded ecosystem respiration, resulting in a net ecosystem production of up to 2100 g CO2 m−2 year−1. The measurement-based photosynthesis, respiration, and net ecosystem production data, as well as the estimates of the ecophysiological parameters, provide an empirical basis for parameterization and validation of mechanistic models of grain crop production in

Global-scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO

Hydrologic measurements are becoming increasingly important for both the short and long term management of water resources. Of all the terms in the hydrologic budget, precipitation is the typically most important input. However, measurements of precipitation are still subject to large errors and biases. For example, a high-quality but unshielded weighing precipitation gauge can collect less than 50 % of the actual amount of solid precipitation when wind speeds exceed 5 ms−1. Using results from two different precipitation testbeds, such errors have been assessed for unshielded weighing gauges and for four of the most common windshields currently in use. Functions used to correct wind-induced undercatch were developed and tested. In addition, corrections for the single Altar weighing gauge were developed using the combined results of two separate sites, one of which was in Norway and other in the US. In general the results indicate that corrections described as a function of air temperature and wind speed effectively remove the undercatch bias that affects such precipitation measurements. In addition, a single ‘universal’ function developed for the single Altar gauges effectively removed the bias at both sites, with the bias at the US site improved from −12 % to 0 %, and the bias at the Norwegian site improved from −27 % to −3 %. These correction functions require only wind speed and air temperature, and were developed for use in national and local precipitation networks, hydrological monitoring, roadway and airport safety work, and climate change research. The techniques used to develop and test these transfer functions at more than one site can also be used for other more comprehensive studies, such as the WMO Solid Precipitation Intercomparison Experiment.

Subsurface tile drainage is a widely used agricultural practice in Midwestern USA to remove excess water to improve crop yield. Research shows an increasing trend of baseflow over the last 60 years and may be due to this artificial drainage activity. The influence of tile drainage on streamflow has been studied using in situ measurements and hydrological models. Evapotranspiration (ET) is an important component of hydrologic cycle. However, the impact of tile drainage on ET, or on other components of the water budget, is not well documented. In this study, we applied an energy balance based multi-sensor (GOES, MODIS and Landsat) data fusion method to estimate daily 30 m ET over an intensively drained area in South Dakota, USA to assess the model performance and further explore the spatial and temporal ET patterns through 2004–2013 and their relationship to drain installation.

In Kochendorfer et al. (Boundary-Layer Meteorol 145:383–398, 2012, hereafter K2012) the vertical wind speed \((w)\) measured by a non-orthogonal three-dimensional sonic anemometer was shown to be underestimated by \(\approx \)12 %. Turbulent statistics and eddy-covariance fluxes estimated using \(w\) were also affected by this underestimate in \(w\). Methodologies used in K2012 are clarified here in response to Mauder’s comment. In addition, further analysis of the K2012 study is presented to help address questions raised in the comment. Specific responses are accompanied with examples of time series, calculated correlation coefficients, and additional explanation of the K2012 methods and assumptions. The discussion and analysis included in the comment and in this response do not affect the validity of the methods or conclusions presented in K2012.

Studies of global hydrologic cycles, carbon cycles and climate change are greatly facilitated when. global estimates of evapotranspiration (E) are available. We have developed an air-relative-humidity-based two-source (ARTS) E model that simulates the surface energy balance, soil water balance, and environmental constraints on E. It uses remotely sensed leaf area index (L-ai) and surface meteorological data to estimate E by: 1) introducing a simple biophysical model for canopy conductance (G(c)), defined as a constant maximum stomatal conductance g(smax) of 12.2 mm s(-1) multiplied by air relative humidity (R-h) and L-ai (G(c) = g(srnax) x R-h X L-ai); 2) calculating canopy transpiration with the G(c)-based Penman-Monteith (PM) E model; 3) calculating soil evaporation from an air-relative-humidity-based model of evapotranspiration (Yan & Shugart, 2010); 4) calculating total E (E-0) as the sum of the canopy transpiration and soil evaporation, assuming the absence of soil water stress; and 5) correcting E-0 for soil water stress using a soil water balance model. This physiological ARTS E model requires no calibration. Evaluation against eddy covariance measurements at 19 flux sites, representing a wide variety of climate and vegetation types, indicates that daily estimated E had a root mean square error = 0.77 mm d(-1). bias = -0.14 mm d(-1), and coefficient of determination, R-2 = 0.69. Global, monthly, 0.5 degrees-gridded ARTS E simulations from 1984 to 1998, which were forced using Advanced Very High Resolution Radiometer Lai data, Climate Research Unit climate data, and surface radiation budget data, predicted a mean annual land E of 58.4 x 10(3) km(3). This falls within the range (58 x 10(3)-85 x 10(3) km(3)) estimated by the Second Global Soil Wetness Project (GSWP-2: Dirmeyer et al., 2006). The ARTS E spatial pattern agrees well with that of the global E estimated by GSWP-2. The global annual ARTS E increased by 15.5 mm per decade from 1984 to 1998, comparable to an increase of 9.9 mm per decade from the model tree ensemble approach (Jung et al., 2010). These comparisons confirm the effectivity of the ARTS E model to simulate the spatial. pattern and climate response of global E. This model is the first of its kind among remote-sensing-based PM E models to provide global land E estimation with consideration of the soil water balance. (C) 2012 Elsevier Inc. All rights reserved.

a b s t r a c t The Atmosphere-Land Exchange Surface Energy (ALEX) balance model is an analytical formulation of the energy and mass transport within the soil and the vegetation canopy used for simulating energy, evapotranspiration, and CO2 fluxes in a wide range of vegetation environments. The objective of this study was to evaluate the ability of ALEX to simulate the effect of soil-surface leaf litter residue on soil heat conduction (G), sensible heat (H), evapotranspiration (ET) (or latent heat (LE) when expressed as rate of energy loss) and CO2 fluxes in a deciduous forest. The model was evaluated in a deciduous forest in Oak Ridge, Tennessee where about 550 g m −2 of dry weight of slow decomposing leaf litter is produced annually during the fall season. Incorporating an explicit formulation of water and energy exchanges within the residue layer in ALEX improved the performance of the model against eddy covariance and G measurements. The discrepancies between model simulations made with and without leaf litter residue were largest during the spring and fall, when soil contributions dominated the energy budget of the forest. During these periods, particularly during the spring, without the inclusion of the residue layer the model overpredicted LE, G, soil temperature and soil moisture, and underpredicted H. The model showed no differences in simulating above-canopy net radiation (RN), with a slight difference in the above-canopy CO2 flux. The largest model improvement for residue effects was in the simulation of G, with the slope of the regression line between predicted and measured values reduced from 2.28 for the model without residue effects to 1.07 when the residue effect was considered. © 2012 Elsevier B.V. All rights reserved.

Sonic anemometers are capable of measuring the wind speed in all three dimen- sions at high frequencies (10-50 Hz), and are relied upon to estimate eddy-covariance-based fluxes of mass and energy over a wide variety of surfaces and ecosystems. In this study, wind-velocity measurement errors from a three-dimensional sonic anemometer with a non- orthogonal transducer orientation were estimated for over 100 combinations of angle-of- attack and wind direction using a novel technique to measure the true angle-of-attack and wind speed within the turbulent atmospheric surface layer. Corrections to the vertical wind speed varied from −5 to 37% for all angles-of-attack and wind directions examined. When applied to eddy-covariance data from three NOAA flux sites, the wind-velocity corrections increased the magnitude of CO2 fluxes, sensible heat fluxes, and latent heat fluxes by ≈11%, with the actual magnitude of flux corrections dependent upon sonic anemometer, surface type, and scalar. A sonic anemometer that uses vertically aligned transducers to measure the vertical wind speed was also tested at four angles-of-attack, and corrections to the ver- tical wind speed measured using this anemometer were within ±1% of zero. Sensible heat fluxes over a forest canopy measured using this anemometer were 15% greater than sensible heat fluxes measured using a sonic anemometer with a non-orthogonal transducer orienta- tion. These results indicate that sensors with a non-orthogonal transducer orientation, which includes the majority of the research-grade three-dimensional sonic anemometers currently in use, should be redesigned to minimize sine errors by measuring the vertical wind speed using one pair of vertically aligned transducers.

article i nfo An accurate and synopticquantification of gross primary production (GPP) incrops isessential for studies of car- bon budgets at regional and global scales. In this study, we tested a model, relating crop GPP to a product of total canopy chlorophyll (Chl) content and potential incident photosynthetically active radiation (PARpotential). The approach isbased on remotely sensed data;specifically, vegetation indices (VI) thatare proxiesfor total Chlcon- tent and PARpotential, which is incident PAR under a condition of minimal atmospheric aerosol loading. Using VI retrieved from surface reflectance Landsat data, we found that the model is capable of accurately estimating GPP in maize, with coefficient of variation (CV) below 23%, and in soybean with CV below 30%. The algorithms established and calibrated over three Mead, Nebraska AmeriFlux sites were able to estimate maize and soybean GPP at tower flux sites in Minnesota, Iowa and Illinois with acceptable accuracy.

This paper presents recent efforts to understand the relative accuracies of different instrumentation and gauges with various windshield configurations to measure snowfall. Results from the National Center for Atmospheric Research (NCAR) Marshall Field Site will be highlighted. This site hosts a test bed to assess various solid precipitation measurement techniques and is a joint collaboration between the National Oceanic and Atmospheric Administration (NOAA), NCAR, the National Weather Service (NWS), and Federal Aviation Administration (FAA). The collaboration involves testing new gauges and other solid precipitation measurement techniques in comparison with World Meteorological Organization (WMO) reference snowfall measurements. This assessment is critical for any ongoing studies and applications, such as climate monitoring and aircraft deicing, that rely on accurate and consistent precipitation measurements.

Deposition of trace gases, such as sulfur dioxide (SO 2 ), can affect plant and soil chemistry in different ecosystems. Measurements over a complex ecosystem, like a forest clearing, are necessary to determine more accurate deposition rates that can be used to improve parameterizations and models. The flux-gradient technique was used to determine SO 2 fluxes over grass in a clearing at Duke Forest, North Carolina, USA on 25 June – 2 July 2008. The mean flux was −0.037 ± 0.024 μg m −2 s −1 . Dew on the canopy enhanced the uptake of SO 2 , which increased deposition rates. Deposition velocities ( V d ) fluctuated greatly with a mean of 1.00 ± 0.48 cm s −1 . The large variation in V d was not fully captured by estimates determined from a multilayer model (MLM) and a big-leaf model (BLM). Mean deposition velocities derived from the MLM and BLM were 1.25 ± 0.21 cm s −1 and 0.63 ± 0.12 cm s −1 , respectively. The model estimations of V d in this study were probably affected by uncertainties associated with canopy resistance, particularly with stomatal and non-stomatal processes.

The seasonal and interannual variability in surface energy exchange and evapotranspiration ( E ) of two temperate semi-arid grasslands in southeastern Arizona, USA, were investigated using multi-year (2004–2007) eddy covariance measurements. The study sites, a post-fire site (AG) and an unburned site (KG), received 43–87% of the annual precipitation ( P ) during the North American Monsoon season (July–September), with the lowest values in the drought years of 2004 and 2005. Irrespective of the differences in temperature, surface albedo, vegetation cover and soil characteristics both sites responded similarly to changes in environmental conditions. The seasonal and interannual variations in the partitioning of net radiation to turbulent fluxes were mainly controlled by P through the changes in soil water content ( θ ) and vegetation growth. Drastic changes in albedo, vegetation growth and energy fluxes occurred following the onset of the monsoon season in July. During the dry or cool periods of autumn, winter and spring, the sensible heat flux was the largest component of the energy balance, whereas latent heat flux dominated during the warm and wet periods of summer. The dry-foliage Priestley–Taylor coefficient ( α ) declined when θ in the 0–15 cm soil layer dropped below 0.08 m 3 m −3 at AG, and 0.09 m 3 m −3 at KG, respectively. The July–September average of dry-foliage surface conductance, α and E , reached their lowest values in 2004 at AG and in 2005 at KG. During July–September, monthly E was linearly correlated to the monthly mean θ and the broadband normalized difference vegetation index (NDVI), whereas during May–June the relationship between NDVI and E was not significant. Annual E varied from 264 to 322 mm at AG and from 196 to 284 mm at KG with the lowest value during the severe drought year at the site. July–September E had positive correlation with total P , the mean NDVI and the number of growing season days during that period. Annual P explained more than 80% of the variance in annual E .

A fundamental equation of eddy covariance (FQEC) is derived that allows the net ecosystem exchange (NEE) Ns¯ of a specified atmospheric constituent s to be measured with the constraint of conservation of any other atmospheric constituent (e.g. N2, argon, or dry air). It is shown that if the condition Ns¯≫χs¯NCO2¯ is true, the conservation of mass can be applied with the assumption of no net ecosystem source or sink of dry air and the FQEC is reduced to the following equation and its approximation for horizontally homogeneous mass fluxes: Ns¯=cd¯w′χ′s¯h+∫0hcd¯(z)∂χs∂t¯ dz+∫0h[χs¯(z)−χs¯(h)]∂cd∂t¯ dz≈cd¯(h)w′χ′s¯h+∫0h∂χs∂t¯ dz. Here w is vertical velocity, c molar density, t time, h eddy flux measurement height, z vertical distance and χs = cs/cd molar mixing ratio relative to dry air. Subscripts s, d and CO2 are for the specified constituent, dry air and carbon dioxide, respectively. Primes and overbars refer to turbulent fluctuations and time averages, respectively. This equation and its approximation are derived for non-steady state conditions that build on the steady-state theory of Webb, Pearman and Leuning (WPL; Webb et al., 1980. Quart. J. R. Meteorol. Soc. 106, 85–100), theory that is widely used to calculate the eddy fluxes of CO2 and other trace gases. The original WPL constraint of no vertical flux of dry air across the EC measurement plane, which is valid only for steady-state conditions, is replaced with the requirement of no net ecosystem source or sink of dry air for non-steady state conditions. This replacement does not affect the ‘eddy flux ’ term cd¯w′χ′s¯ but requires the change in storage to be calculated as the ‘effective change in storage’ as follows: ∫0h∂cs∂t¯ dz−χs¯(h)∫0h∂cd∂t¯ dz=∫0hcd¯(z)∂χs∂t¯ dz+∫0h[χs¯(z)−χs¯(h)]∂cd∂t¯ dz≈cd¯(h)∫0h∂χs∂t¯ dz. Without doing so, significant diurnal and seasonal biases may occur. We demonstrate that the effective change in storage can be estimated accurately with a properly designed profile of mixing ratio measurements made at multiple heights. However further simplification by using a single measurement at the EC instrumentation height is shown to produce substantial biases. It is emphasized that an adequately designed profile system for measuring the effective change in storage in proper units is as important as the eddy flux term for determining NEE. When the EC instrumentation measures densities rather than mixing ratios, it is necessary to use: Ns¯≈w′c′s¯h+χs¯w′c′v¯+c¯w′T′¯T¯h+cd¯(h)∫0h∂χs∂t¯ dz. Here T is temperature and cv and c are the molar densities of water vapor and moist air, respectively. For some atmospheric gas species such as N2 and O2, the condition Ns¯≫χs¯NCO2¯ is not satisfied and additional information is needed in order to apply the EC technique with the constraint of conservation of dry air.

Deforestation in mid-to high latitudes is hypothesized to have the potential to cool the Earth's surface by altering biophysical processes. In climate models of continental-scale land clearing, the cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85±0.44K (mean±one standard deviation) northwards of 45°N and 0.21±0.53K southwards. Below 35°N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models. © 2011 Macmillan Publishers Limited. All rights reserved.