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David M. Mocko
and
William R. Cotton

Abstract

The Regional Atmospheric Modeling System (RAMS), developed at Colorado State University, was used to predict boundary-layer clouds and diagnose fractional cloudiness. The primary case study for this project occurred on 7 July 1987 off the coast of southern California. On this day, a transition in the type of boundary-layer cloud was observed from a clear area, to an area of small scattered cumulus, to an area of broken stratocumulus, and finally, to an area of solid stratocumulus. This case study occurred during the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment field study. RAMS was configured as a nested-grid mesoscale model with a fine grid having 5-km horizontal grid spacing covering the transition area.

Various fractional cloudiness schemes found in the literature were implemented into RAMS and tested against each other to determine which best represented observed conditions. The complexities of the parameterizations used to diagnose the fractional cloudiness varied from simple functions of relative humidity to a function of the model's subgrid variability. It was found that some of the simpler schemes identified the cloud transition better, while others performed poorly.

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David M. Mocko
and
Y. C. Sud

Abstract

Refinements to the snow-physics scheme of the Simplified Simple Biosphere Model (SSiB) are described and evaluated. The upgrades include a partial redesign of the conceptual architecture of snowpack to better simulate the diurnal temperature of the snow surface. For a deep snowpack, there are two separate prognostic temperature snow layers: the top layer responds to diurnal fluctuations in the surface forcing, while the deep layer exhibits a slowly varying response. In addition, the use of a very deep soil temperature and a treatment of snow aging with its influence on snow density is parameterized and evaluated. The upgraded snow scheme produces better timing of snowmelt in Global Soil Wetness Project (GSWP)-style simulations using International Satellite Land Surface Climatology Project (ISLSCP) Initiative I data for 1987–88 in the Russian Wheat Belt region.

To simulate more realistic runoff in regions with high orographic variability, additional improvements are made to SSiB's soil hydrology. These improvements include an orography-based surface runoff scheme as well as interaction with a water table below SSiB's three soil layers. The addition of these parameterizations further helps to simulate more realistic runoff and accompanying prognostic soil moisture fields in the GSWP-style simulations.

In intercomparisons of the performance of the new snow-physics SSiB with its earlier versions using an 18-yr single-site dataset from Valdai, Russia, the revised version of SSiB described in this paper again produces the earliest onset of snowmelt. Soil moisture and deep soil temperatures also compare favorably with observations.

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David M. Mocko
and
Y. C. Sud

Abstract

Four different methods of estimating land surface evapotranspiration are compared by forcing each scheme with near-surface atmospheric and soil- and vegetation-type forcing data available through International Satellite Land Surface Climatology Project Initiative I for a 2-yr period (1987–88). The three classical energy balance methods by Penman, by Priestley–Taylor, and by Thornthwaite are chosen; however, the Thornthwaite method is combined with a Mintz formulation of the relationship between actual and potential evapotranspiration. The fourth method uses the Simplified Simple Biosphere Model (SSiB), which is currently used in the climate version of the Goddard Earth Observing System II GCM. The goal of this study is to determine the benefit of using SSiB as opposed to one of the energy balance schemes for accurate simulation of surface fluxes and hydrology. Direct comparison of sensible and latent fluxes and ground temperature is not possible because such datasets are not available. However, the schemes are intercompared. The Penman and Priestley–Taylor schemes produce higher evapotranspiration than SSiB, while the Mintz–Thornthwaite scheme produces lower evapotranspiration than SSiB. Comparisons of model-derived soil moisture with observations show SSiB performs well in Illinois but performs poorly in central Russia. This later problem has been identified to be emanating from errors in the calculation of snowmelt and its infiltration. Overall, runoff in the energy balance schemes show less of a seasonal cycle than does SSiB, partly because a larger contribution of snowmelt in SSiB goes directly into runoff. However, basin- and continental-scale runoff values from SSiB validate better with observations as compared to each of the three energy balance methods. This implies a better evapotranspiration and hydrologic cycle simulation by SSiB as compared to the energy balance methods.

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Craig R. Ferguson
and
David M. Mocko

Abstract

While investigating linkages between afternoon peak rainfall amount and land–atmosphere coupling strength, a statistically significant trend in phase 2 of the North American Land Data Assimilation System (NLDAS-2) warm season (April–September) afternoon (1700–2259 UTC) precipitation was noted for a large fraction of the conterminous United States, namely, two-thirds of the area east of the Mississippi River, during the period from 1979 to 2015. To verify and better characterize this trend, a thorough statistical analysis is undertaken. The analysis focuses on three aspects of precipitation: amount, frequency, and intensity at 6-hourly time scale and for each calendar month separately. At the NLDAS-2 native resolution of 0.125° × 0.125°, Kendall’s tau and Sen’s slope estimators are used to detect and estimate trends and the Pettitt test is used to detect breakpoints. Parallel analyses are conducted on both NARR and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), subdaily precipitation estimates. Widespread breakpoints of field significance at the α = 0.05 level are detected in the NLDAS-2 frequency and intensity series for all months and 6-h periods that are absent from the analogous NARR and MERRA-2 datasets. These breakpoints are shown to correspond with a July 1996 NLDAS-2 transition away from hourly 2° × 2.5° NOAA/CPC precipitation estimates to hourly 4-km stage II Doppler radar precipitation estimates in the temporal disaggregation of CPC daily gauge analyses. While NLDAS-2 may provide the most realistic diurnal precipitation cycle overall, users should be aware of this discontinuity and its direct effect on long-term trends in subdaily precipitation and indirect effects on trends in modeled soil moisture, surface temperature, surface energy and water fluxes, snow cover, snow water equivalent, and runoff/streamflow.

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Sujay V. Kumar
,
Christa D. Peters-Lidard
,
David Mocko
, and
Yudong Tian

Abstract

The downwelling shortwave radiation on the earth’s land surface is affected by the terrain characteristics of slope and aspect. These adjustments, in turn, impact the evolution of snow over such terrain. This article presents a multiscale evaluation of the impact of terrain-based adjustments to incident shortwave radiation on snow simulations over two midlatitude regions using two versions of the Noah land surface model (LSM). The evaluation is performed by comparing the snow cover simulations against the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The model simulations are evaluated using categorical measures, such as the probability of detection of “yes” events (PODy), which measure the fraction of snow cover presence that was correctly simulated, and false alarm ratio (FAR), which measures the fraction of no-snow events that was incorrectly simulated. The results indicate that the terrain-based correction of radiation leads to systematic improvements in the snow cover estimates in both domains and in both LSM versions (with roughly 12% overall improvement in PODy and 5% improvement in FAR), with larger improvements observed during snow accumulation and melt periods. Increased contribution to PODy and FAR improvements is observed over the north- and south-facing slopes, when the overall improvements are stratified to the four cardinal aspect categories. A two-dimensional discrete Haar wavelet analysis for the two study areas indicates that the PODy improvements in snow cover estimation drop to below 10% at scales coarser than 16 km, whereas the FAR improvements are below 10% at scales coarser than 4 km.

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Michael G. Bosilovich
,
David Mocko
,
John O. Roads
, and
Alex Ruane

Abstract

A collection of eight operational global analyses over a 27-month period have been processed to common data structures to facilitate comparisons among the analyses and global observational datasets. The present study evaluated the global precipitation, outgoing longwave radiation (OLR) at the top of the atmosphere, and basin-scale precipitation over the United States. In addition, a multimodel ensemble was created from a linear average of the available data, as close to the analysis time as each system permitted. The results show that the monthly global precipitation and OLR from the multimodel ensemble compares generally better to the observations than any single analysis. Likewise, the daily precipitation from the ensemble exhibits better statistical comparison (in space and time) to gauge observations over the Mississippi River basin. However, the comparisons have seasonality, when the members of the ensemble exhibit generally more skill, during winter. There is notably higher skill of the summertime basin precipitation by the ensemble. Using the global precipitation and OLR, the sensitivity was tested to selectively choose the members with the best statistical comparisons to the reference data. Only small improvements in the statistics were found when comparing a selective ensemble to the full ensemble. Additionally, terms of the global energy budget were compared among the ensemble and to other estimates. The ensemble data and the variance of the ensemble should make a useful point of comparison for the development of model and assimilation components of global analyses.

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Soni Yatheendradas
,
David M. Mocko
,
Christa Peters-Lidard
, and
Sujay Kumar

Abstract

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Köppen climate zones. This reveals 1) agreement between some ratings and our MI values [high for example indicators like standardized precipitation evapotranspiration index (SPEI)]; 2) some divergences (e.g., soil moisture has high ratings but near-zero MIs for ESA Climate Change Initiative (CCI) soil moisture in the Western United States, indicating the need of another remotely sensed soil moisture source); and 3) new insights into the importance of variables such as snow water equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to 1) hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); 2) hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures, respectively); and 3) predictability (high for the California 2012–17 event, with longer-time scale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.

Significance Statement

Drought maps from the U.S. Drought Monitor and the Objective Short- and Long-Term Drought Indicator Blends and Blend Equivalents are integrated information sources of the different types of drought. Multiple indicators go into creation of these maps, yet it is usually not clear to both public and private stakeholders like local agencies and insurance companies about the importance of any indicator in any region and season to the drought maps. Our study provides such objective information to enable understanding the mechanism and type of drought occurring at a location, season, and possibly event of interest, as well as to potentially aid in better drought monitoring and forecasting using smaller custom sets of indicators.

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Grey S. Nearing
,
David M. Mocko
,
Christa D. Peters-Lidard
,
Sujay V. Kumar
, and
Youlong Xia

Abstract

Model benchmarking allows us to separate uncertainty in model predictions caused by model inputs from uncertainty due to model structural error. This method is extended with a “large sample” approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in 1) forcing data, 2) model parameters, and 3) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in phase 2 of the North American Land Data Assimilation System (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of NLDAS-2. In particular, continued work toward refining the parameter maps and lookup tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

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Sujay V. Kumar
,
David M. Mocko
,
Shugong Wang
,
Christa D. Peters-Lidard
, and
Jordan Borak

Abstract

Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

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Michael G. Bosilovich
,
Franklin R. Robertson
,
Lawrence Takacs
,
Andrea Molod
, and
David Mocko

Abstract

Closing and balancing Earth’s global water cycle remains a challenge for the climate community. Observations are limited in duration, global coverage, and frequency, and not all water cycle terms are adequately observed. Reanalyses aim to fill the gaps through the assimilation of as many atmospheric water vapor observations as possible. Former generations of reanalyses have demonstrated a number of systematic problems that have limited their use in climate studies, especially regarding low-frequency trends. This study characterizes the NASA Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) water cycle relative to contemporary reanalyses and observations. MERRA-2 includes measures intended to minimize the spurious global variations related to inhomogeneity in the observational record. The global balance and cycling of water from ocean to land is presented, with special attention given to the water vapor analysis increment and the effects of the changing observing system. While some systematic regional biases can be identified, MERRA-2 produces temporally consistent time series of total column water and transport of water from ocean to land. However, the interannual variability of ocean evaporation is affected by the changing surface-wind-observing system, and precipitation variability is closely related to the evaporation. The surface energy budget is also strongly influenced by the interannual variability of the ocean evaporation. Furthermore, evaluating the relationship of temperature and water vapor indicates that the variations of water vapor with temperature are weaker in satellite data reanalyses, not just MERRA-2, than determined by observations, atmospheric models, or reanalyses without water vapor assimilation.

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