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Joseph A. Santanello Jr., Mark A. Friedl, and Michael B. Ek

Abstract

The convective planetary boundary layer (PBL) integrates surface fluxes and conditions over regional and diurnal scales. As a result, the structure and evolution of the PBL contains information directly related to land surface states. To examine the nature and magnitude of land–atmosphere coupling and the interactions and feedbacks controlling PBL development, the authors used a large sample of radiosonde observations collected at the southern Atmospheric Research Measurement Program–Great Plains Cloud and Radiation Testbed (ARM-CART) site in association with simulations of mixed-layer growth from a single-column PBL/land surface model. The model accurately predicts PBL evolution and realistically simulates thermodynamics associated with two key controls on PBL growth: atmospheric stability and soil moisture. The information content of these variables and their influence on PBL height and screen-level temperature can be characterized using statistical methods to describe PBL–land surface coupling over a wide range of conditions. Results also show that the first-order effects of land–atmosphere coupling are manifested in the control of soil moisture and stability on atmospheric demand for evapotranspiration and on the surface energy balance. Two principal land–atmosphere feedback regimes observed during soil moisture drydown periods are identified that complicate direct relationships between PBL and land surface properties, and, as a result, limit the accuracy of uncoupled land surface and traditional PBL growth models. In particular, treatments for entrainment and the role of the residual mixed layer are critical to quantifying diurnal land–atmosphere interactions.

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Robert J. Zamora, Edward P. Clark, Eric Rogers, Michael B. Ek, and Timothy M. Lahmers

Abstract

The NOAA Hydrometeorology Testbed (HMT) program has deployed a soil moisture observing network in the Babocomari River basin located in southeastern Arizona. The Babocomari River is a major tributary of the San Pedro River. At 0000 UTC 23 July 2008, the second-highest flow during the period of record was measured just upstream of the location where the Babocomari River joins the main channel of the San Pedro River.

Upper-air and surface meteorological observations and Special Sensor Microwave Imager (SSM/I) satellite images of integrated water vapor were used to establish the synoptic and mesoscale conditions that existed before the flood occurred. The analysis indicates that a weak Gulf of California surge initiated by Hurricane Fausto transported a warm moist tropical air mass into the lower troposphere over southern Arizona, setting the stage for the intense, deep convection that initiated the flooding on the Babocomari River. Observations of soil moisture and precipitation at five locations in the basin and streamflow measured at two river gauging stations enabled the documentation of the hydrometeorological conditions that existed before the flooding occurred. The observations suggest that soil moisture conditions as a function of depth, the location of semi-impermeable layers of sedimentary rock known as caliche, and the spatial distribution of convective precipitation in the basin confined the flooding to the lower part of the basin. Finally, the HMT soil moisture observations are compared with soil moisture products from the NOAA/NWS/NCEP Noah land surface model.

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Youlong Xia, Michael B. Ek, Yihua Wu, Trent Ford, and Steven M. Quiring

Abstract

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.

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Youlong Xia, Michael B. Ek, Yihua Wu, Trent Ford, and Steven M. Quiring

Abstract

In this second part of a two-part paper, the impacts of soil texture and vegetation type misclassification and their combined effect on soil moisture, evapotranspiration, and total runoff simulation are investigated using the Noah model. The results show that these impacts are significant for most regions and soil layers, although they vary depending on soil texture classification, vegetation type, and season. The use of site-observed soil texture classification and vegetation type in the model does not necessarily improve anomaly correlations and reduce mean absolute error for soil moisture simulations. Instead, results are mixed when examining all regions and soil layers. This is attributed to the compensation effects (e.g., effect of ill-calibrated model parameters), as Noah has been more or less calibrated with model-specified soil texture classification and vegetation type. The site-based analysis shows that Noah can reasonably simulate the variation of daily evapotranspiration, soil moisture, and total runoff when soil texture classification (vegetation type) is corrected from loam (forest) to clay (grasslands) or vice versa. This suggests that the performance of Noah can be further improved by tuning model parameters when site-observed soil texture and vegetation type are used.

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Ben Livneh, Youlong Xia, Kenneth E. Mitchell, Michael B. Ek, and Dennis P. Lettenmaier

Abstract

A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales.

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Youlong Xia, David Mocko, Maoyi Huang, Bailing Li, Matthew Rodell, Kenneth E. Mitchell, Xitian Cai, and Michael B. Ek

Abstract

To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

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Youlong Xia, Michael B. Ek, David Mocko, Christa D. Peters-Lidard, Justin Sheffield, Jiarui Dong, and Eric F. Wood

Abstract

This study analyzed uncertainties and correlations over the United States among four ensemble-mean North American Land Data Assimilation System (NLDAS) percentile-based drought indices derived from monthly mean evapotranspiration ET, total runoff Q, top 1-m soil moisture SM1, and total column soil moisture SMT. The results show that the uncertainty is smallest for SM1, largest for SMT, and moderate for ET and Q. The strongest correlation is between SM1 and SMT, and the weakest correlation is between ET and Q. The correlation between ET and SM1 (SMT) is strongest in arid–semiarid regions, and the correlation between Q and SM1 (SMT) is strongest in more humid regions in the Pacific Northwest and the Southeast. Drought frequency analysis shows that SM1 has the most frequent drought occurrence, followed by SMT, Q, and ET. The study compared the NLDAS drought indices (a research product) with the U.S. Drought Monitor (USDM; an operational product) in terms of drought area percentage derived from each product. It proposes an optimal blend of NLDAS drought indices by searching for weights for each index that minimizes the RMSE between NLDAS and USDM drought area percentage for a 10-yr period (2000–09) with a cross validation. It reconstructed a 30-yr (1980–2009) Objective Blended NLDAS Drought Index (OBNDI) and monthly drought percentage. Overall, the OBNDI performs the best with the smallest RMSE, followed by SM1 and SMT. It should be noted that the contribution to OBNDI from different variables varies with region. So a single formula is probably not the best representation of a blended index. The representation of a blended index using the multiple formulas will be addressed in a future study.

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Youlong Xia, David M. Mocko, Shugong Wang, Ming Pan, Sujay V. Kumar, Christa D. Peters-Lidard, Helin Wei, Dagang Wang, and Michael B. Ek

Abstract

Since the second phase of the North American Land Data Assimilation System (NLDAS-2) was operationally implemented at NOAA/NCEP as part of the production suite in August 2014, developing the next phase of NLDAS has been a key focus of the NCEP and NASA NLDAS teams. The Variable Infiltration Capacity (VIC) model is one of the four land surface models of the NLDAS system. The current operational NLDAS-2 uses version 4.0.3 (VIC403), the research NLDAS-2 used version 4.0.5 (VIC405), and the NASA Land Information System (LIS)-based NLDAS uses version 4.1.2.l (VIC412). The purpose of this study is to evaluate VIC403 and VIC412 and check if the latter version has better performance for the next phase of NLDAS. Toward this, a comprehensive evaluation was conducted, targeting multiple variables and using multiple metrics to assess the performance of different model versions. The evaluation results show large and significant improvements in VIC412 over the southeastern United States when compared with VIC403 and VIC405. In other regions, there are very limited improvements or even deterioration to some degree. This is partially due to 1) the sparseness of USGS streamflow observations for model parameter calibration and 2) a deterioration of VIC model performance in the Great Plains (GP) region after a model upgrade to a newer version. Overall, the model upgrade enhances model performance and skill scores for most parts of the continental United States; exceptions include the GP and western mountainous regions, as well as the daily soil moisture simulation skill, suggesting that VIC model development is on the right path. Further efforts are needed for scientific understanding of land surface physical processes in the GP, and a recalibration of VIC412 using reasonable reference datasets is recommended.

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Paul A. Dirmeyer, Liang Chen, Jiexia Wu, Chul-Su Shin, Bohua Huang, Benjamin A. Cash, Michael G. Bosilovich, Sarith Mahanama, Randal D. Koster, Joseph A. Santanello, Michael B. Ek, Gianpaolo Balsamo, Emanuel Dutra, and David M. Lawrence

Abstract

This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.

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Paul A. Dirmeyer, Jiexia Wu, Holly E. Norton, Wouter A. Dorigo, Steven M. Quiring, Trenton W. Ford, Joseph A. Santanello Jr., Michael G. Bosilovich, Michael B. Ek, Randal D. Koster, Gianpaolo Balsamo, and David M. Lawrence

Abstract

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

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