Search Results
You are looking at 1 - 10 of 21 items for
- Author or Editor: David M. Mocko x
- Refine by Access: All Content x
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.
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.
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.
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.
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.
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.
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.
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.
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 Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., 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 2012-2017 event, with longer-timescale 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.
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 Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., 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 2012-2017 event, with longer-timescale 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.
Abstract
A quasi-isentropic, back-trajectory scheme is applied to output from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979–2005. The evaporative source patterns for any location and time period are effectively two-dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50%–400% larger than at monthly time scales. Significant differences suggest that moisture transport may be a key factor in precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.
Abstract
A quasi-isentropic, back-trajectory scheme is applied to output from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979–2005. The evaporative source patterns for any location and time period are effectively two-dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50%–400% larger than at monthly time scales. Significant differences suggest that moisture transport may be a key factor in precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.
Abstract
Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.
Abstract
Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.
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.
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.
Abstract
The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model or perhaps assimilating data from an inconsistent observing system. In the MERRA reanalysis, an area of long-term moisture flux divergence over land has been identified over the central United States. Here, the water vapor budget is evaluated in this region, taking advantage of two unique features of the MERRA diagnostic output: 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output dataset of the assimilated observations and their innovations (e.g., forecast departures).
In the central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRA’s Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 0600 and 1800 UTC analysis cycles, when radiosonde information is not prevalent. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSU-A (mainly window channels) and Atmospheric Infrared Sounder (AIRS). This effort also shows the complexities of the observing system and the reactions of the regional water budgets in reanalyses to the assimilated observations.
Abstract
The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model or perhaps assimilating data from an inconsistent observing system. In the MERRA reanalysis, an area of long-term moisture flux divergence over land has been identified over the central United States. Here, the water vapor budget is evaluated in this region, taking advantage of two unique features of the MERRA diagnostic output: 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output dataset of the assimilated observations and their innovations (e.g., forecast departures).
In the central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRA’s Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 0600 and 1800 UTC analysis cycles, when radiosonde information is not prevalent. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSU-A (mainly window channels) and Atmospheric Infrared Sounder (AIRS). This effort also shows the complexities of the observing system and the reactions of the regional water budgets in reanalyses to the assimilated observations.
Abstract
This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.
Abstract
This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.
