This technical report introduces a Three-Dimensional Constrained Variational Analysis (3DCVA) (Tang and Zhang 2015) and its product of three-dimensional large-scale forcing data to drive single-column models (SCM), cloud-resolving models (CRM), and large-eddy simulation (LES) models, and to evaluate model results. The 3DCVA algorithm is an extension of the original 1D constrained variational analysis (1DCVA) (Zhang and Lin 1997, Zhang et al. 2001). The three-dimensional structure of the forcing data allows studies of spatial variation of the large-scale forcing fields and tests of physical parameterizations across scales. In the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the 3D forcing data are assigned the datastream name varanal3d. In this technical report, 3DCVA will be used to refer to the algorithm, while VARANAL3D will be used to refer to the data product.
Abstract Evapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets. Satellite‐based vegetation index approaches have used remotely sensed vegetation and reanalysis meteorological properties with surface energy balance models to estimate global ET (MOD16 ET). We reconstructed satellite retrievals using in situ meteorology (Argonne‐ET) and evaluated them using a dense network of surface turbulent heat flux measurements. Argonne‐ET resolves spatial heterogeneity of ET across the U.S. Southern Great Plains that is not well characterized by MOD16; MOD16 ET exhibits spatial autocorrelation (Moran's I significantly >0 in May–Aug.), whereas in situ and Argonne‐ET exhibit characteristics of a random spatial process (Moran's I not significantly different from 0). The skill in resolving ET temporal variability is not significantly different between MOD16 and Argonne‐ET (correlation coefficient = 0.75 and 0.72, respectively). However, the root‐mean‐square errors were significantly lower for Argonne‐ET (36 W/m2 ) than MOD16 (43 W/m2 ), and MOD16 exhibits substantial bias in annual ET relative to in situ measurements (−38%). This is attributed to overestimation in the dry canopy surface resistance ( r s ) parameterization. Using r s constrained to the range of typical measured values, Argonne‐ET substantially reduces the bias in the annual ET (+1%). The improved ET estimates are critical for regional water budget analyses. The methodology presented herein also demonstrates the ability to retrieve high temporally resolved (30 min; cf. 8‐day MOD16) ET that can be used for development of processed‐based diagnostics of model biases and to elucidate avenues to improve ET model parameterizations.
This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: (1) The method used to develop multi-year continuous forcing data (Xie et al. 2004), (2) The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and (3) Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products.
Small elevated experimental raceways have been used at many research sites in the United States to evaluate algal biomass productivity. This paper evaluates whether the productivities measured in these raceways are representative of large-scale commercial raceways. Open water surface evaporation and temperature models with shading algorithms were programmed in Python programming language and calibrated with temperature and evaporation data from the elevated experimental paddlewheel raceways in the Regional Algal Feedstock Testbed (RAFT) experiments at the University of Arizona. The final calibrated model for elevated experimental raceways was named the EERTEM (Elevated Experimental Raceway Temperature and Evaporation Model). The energy balance algorithms in the Biomass Assessment Tool (BAT) were added to the Python code and used to simulate temperature in standard commercial paddlewheel raceways. A comparison of BAT and EERTEM simulations indicated that elevated paddlewheel raceway temperature fluctuations are not representative of in ground commercial paddlewheel raceways, primarily due to the buffering effect of soil heat flux. The Huesemann Algae Biomass Growth (HABG) model simulated biomass productivities of three algae species with the BAT and EERTEM temperature profiles for the commercial and elevated experimental raceways, respectively. Differences in productivity were observed when the maximum daytime temperature of one raceway was in the optimal growth range but the temperature of the other raceway exceeded or was below the optimal growth range. If both raceway temperatures were in the optimal growth range for a given species, even if they had different temperatures, then there was minimal difference in growth. In summary, the EERTEM is not an accurate representation of commercial raceway productivity.
Many conventional General Circulation Models (GCMs) in the Global Land-Atmosphere Coupling Experiment (GLACE) tend to produce what is now recognized as overly strong land-atmosphere (L-A) coupling. We investigate the effects of cloud Superparameterization (SP) on L-A coupling on time-scales beyond diurnal where it has been recently shown to have a favorable muting effect hydrologically. Using the Community Atmosphere Model v3.5 (CAM3.5) and its Superparameterized counterpart SPCAM3.5, we conducted soil moisture interference experiments following the GLACE and Atmospheric Model Intercomparison Project (AMIP) protocols. The results show that, on weekly-to-subseasonal timescales, SP also mutes hydrologic L-A coupling. This is detectable globally, and happens through the evapotranspiration-precipitation segment. But on seasonal timescales, SP does not exhibit detectable effects on hydrologic L-A coupling. Two robust regional effects of SP on thermal L-A coupling have also been explored. Over the Arabian Peninsula, SP reduces thermal L-A coupling through a straightforward control by mean rainfall reduction. More counterintuitively, over the Southwestern US and Northern Mexico, SP enhances the thermal L-A coupling in a way that is independent of rainfall and soil moisture. This signal is associated with a systematic and previously unrecognized effect of SP that produces an amplified Bowen ratio, and is detectable in multiple SP model versions and experiment designs. In addition to amplifying the present-day Bowen ratio, SP is found to amplify the climate sensitivity of Bowen ratio as well, which likely plays a role in influencing climate change predictions at the L-A interface.
In May of 2015, a portable eddy covariance flux tower was installed by David Billesbach at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory E-32 extended facility west of Medford, Oklahoma. The goal of this deployment was to provide data sets that could be used to test land-atmosphere models and surface forcing from the SGP region. This site was chosen as being underrepresented in current data inventories. In January of 2016, a second portable flux system was installed (by Billesbach and Sebastien Biraud) in a field at the southeast corner of the intersection of Oklahoma highways 11 and 74 (also near Medford and designated as site 74). This site was chosen because it was to be planted in grain sorghum (milo) which is also an underrepresented crop. A secondary goal for this deployment was to refine the operational parameters of the newly rebuilt portable eddy correlation flux systems (ECOR), which incorporate several new sensors. These new measurements were designed to accommodate more advanced modeling and integration with remote-sensing products such as normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and diffuse solar radiation. Another secondary goal was to assess how well surface energy components—sensible heat flux (H) and latent heat flux (LE)—measured by ECOR and energy balance Bowen ratio (EBBR) instruments could be compared. Operations were terminated at the E-32 site in June of 2017 and Billesbach removed the equipment from the site. Operations at site 74 were terminated in October of 2017 and Billesbach (with assistance from SGP-Central Facility personnel) again removed the equipment from the field. The most notable event at the E-32 site was the retrenching of site power. This operation opened a wide gap in the normally grass-covered footprint of our optical sensors. We are still evaluating the effects that the bare soil had on our NDVI and PRI data. At site 74, we learned after germination that soy beans instead of grain sorghum had been planted for the second (2017) growing season.
The energy balance Bowen ratio (EBBR) system produces 30-minute estimates of the vertical fluxes of sensible and latent heat at the local surface. Flux estimates are calculated from observations of net radiation, soil surface heat flux, and the vertical gradients of temperature and relative humidity (RH). Meteorological data collected by the EBBR are used to calculate bulk aerodynamic fluxes, which are used in the Bulk Aerodynamic Technique (BA) EBBR value-added product (VAP) to replace sunrise and sunset spikes in the flux data. A unique aspect of the system is the automatic exchange mechanism (AEM), which helps to reduce errors from instrument offset drift.
Elucidating the causes for the energy imbalance, i.e. the phenomenon that eddy covariance latent and sensible heat fluxes fall short of available energy, is an outstanding problem in micrometeorology. This paper tests the hypothesis that the full energy balance, through incorporation of additional independent measurements which determine the driving forces of and resistances to energy transfer, provides further insights into the causes of the energy imbalance and additional constraints on energy balance closure options. Eddy covariance and auxiliary data from three different biomes were used to test five contrasting closure scenarios. The main result of our study is that except for nighttime, when fluxes were low and noisy, the full energy balance generally did not contain enough information to allow further insights into the causes of the imbalance and to constrain energy balance closure options. Up to four out of the five tested closure scenarios performed similarly and in up to 53% of all cases all ofthe tested closure scenarios resulted in plausible energy balance values. Our approach may though provide a sensible consistency check for eddy covariance energy flux measurements.