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Abstract
Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model—and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed precipitation data. The major challenge is the generation of a rainfall ensemble given one satellite rainfall map. An acceptable rainfall ensemble must contain a proper multiscale spatial correlation structure, and each ensemble member presents a realistic rainfall process in both space and time. A pattern-based sampling approach is proposed, in which random samples are drawn from a historical rainfall database according to the pattern of the satellite rainfall and then a cumulative distribution function matching procedure is applied to ensure the proper statistics for the pixel-level rainfall intensity. The assimilation system is applied using Tropical Rainfall Measuring Mission real-time satellite rainfall over the Red–Arkansas River basin. Results show that the ensembles so generated satisfy the requirements for spatial correlation and realism and the multiscale assimilation works reasonably well. A number of limitations also exist in applying this generation method, mainly stemming from the high dimensionality of the problem and the lack of historical records.
Abstract
Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model—and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed precipitation data. The major challenge is the generation of a rainfall ensemble given one satellite rainfall map. An acceptable rainfall ensemble must contain a proper multiscale spatial correlation structure, and each ensemble member presents a realistic rainfall process in both space and time. A pattern-based sampling approach is proposed, in which random samples are drawn from a historical rainfall database according to the pattern of the satellite rainfall and then a cumulative distribution function matching procedure is applied to ensure the proper statistics for the pixel-level rainfall intensity. The assimilation system is applied using Tropical Rainfall Measuring Mission real-time satellite rainfall over the Red–Arkansas River basin. Results show that the ensembles so generated satisfy the requirements for spatial correlation and realism and the multiscale assimilation works reasonably well. A number of limitations also exist in applying this generation method, mainly stemming from the high dimensionality of the problem and the lack of historical records.
Abstract
A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km2 domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (VIC) land surface model. The water balance was applied at the domain scale, and estimates of the water balance components for the domain are updated from the data assimilation step so as to assure closure.
Abstract
A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km2 domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (VIC) land surface model. The water balance was applied at the domain scale, and estimates of the water balance components for the domain are updated from the data assimilation step so as to assure closure.
Abstract
Land surface water and energy fluxes from the ensemble mean of the Atmospheric Model Intercomparison Project (AMIP) simulations of a Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model (AM2.5) were evaluated using offline simulations of a calibrated land surface model [Princeton Global Forcing (PGF)/VIC] and intercompared with three reanalysis datasets: MERRA-Land, ERA-Interim/Land, and CFSR. Using PGF/VIC as the reference, the AM2.5 precipitation, evapotranspiration, and runoff showed a global positive bias of ~0.44, ~0.27, and ~0.15 mm day−1, respectively. For the energy budget, while the AM2.5 net radiation agreed very well with the PGF/VIC, the AM2.5 improperly partitioned the net radiation, with the latent heat showing positive bias and sensible heat showing negative bias. The AM2.5 net radiation, latent heat, and sensible heat relative to the PGF/VIC had a global negative bias of ~1.42 W m−2, positive bias of ~7.8 W m−2, and negative bias of ~8.7 W m−2, respectively. The three reanalyses show greater biases in net radiation, likely due to the deficiencies in cloud parameterizations. At a regional scale, the biases of the AM2.5 water and energy budget components are mostly comparable to the three reanalyses and PGF/VIC. While the AM2.5 well simulated the actual values of water and energy fluxes, the temporal anomaly correlations of the three reanalyses with PGF/VIC were mostly greater than the AM2.5, partly due to the ensemble mean of the AM2.5 members averaging out the intrinsic variability of the land surface fluxes. The discrepancies among land surface model simulations, reanalyses, and high-resolution climate model simulations demonstrate the challenges in estimating and evaluating land surface hydrologic fluxes at regional-to-global scales.
Abstract
Land surface water and energy fluxes from the ensemble mean of the Atmospheric Model Intercomparison Project (AMIP) simulations of a Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model (AM2.5) were evaluated using offline simulations of a calibrated land surface model [Princeton Global Forcing (PGF)/VIC] and intercompared with three reanalysis datasets: MERRA-Land, ERA-Interim/Land, and CFSR. Using PGF/VIC as the reference, the AM2.5 precipitation, evapotranspiration, and runoff showed a global positive bias of ~0.44, ~0.27, and ~0.15 mm day−1, respectively. For the energy budget, while the AM2.5 net radiation agreed very well with the PGF/VIC, the AM2.5 improperly partitioned the net radiation, with the latent heat showing positive bias and sensible heat showing negative bias. The AM2.5 net radiation, latent heat, and sensible heat relative to the PGF/VIC had a global negative bias of ~1.42 W m−2, positive bias of ~7.8 W m−2, and negative bias of ~8.7 W m−2, respectively. The three reanalyses show greater biases in net radiation, likely due to the deficiencies in cloud parameterizations. At a regional scale, the biases of the AM2.5 water and energy budget components are mostly comparable to the three reanalyses and PGF/VIC. While the AM2.5 well simulated the actual values of water and energy fluxes, the temporal anomaly correlations of the three reanalyses with PGF/VIC were mostly greater than the AM2.5, partly due to the ensemble mean of the AM2.5 members averaging out the intrinsic variability of the land surface fluxes. The discrepancies among land surface model simulations, reanalyses, and high-resolution climate model simulations demonstrate the challenges in estimating and evaluating land surface hydrologic fluxes at regional-to-global scales.
Abstract
The interaction between synoptic eddy and low-frequency flow (SELF) has been recognized for decades to play an important role in the dynamics of the low-frequency variability of the atmospheric circulation. In this three-part study a linear framework with a stochastic basic flow capturing both the climatological mean flow and climatological measures of the synoptic eddy flow is proposed. Based on this linear framework, a set of linear dynamic equations is derived for the ensemble-mean eddy forcing that is generated by anomalous time-mean flows. By assuming that such dynamically determined eddy-forcing anomalies approximately represent the time-mean anomalies of the synoptic eddy forcing and by using a quasi-equilibrium approximation, an analytical nonlocal dynamical closure is obtained for the two-way SELF feedback. This linear closure, directly relating time-mean anomalies of the synoptic eddy forcing to the anomalous time–mean flow, becomes an internal part of a new linear dynamic system for anomalous time–mean flow that is referred to as the low-frequency variability of the atmospheric circulation in this paper.
In Part I, the basic approach for the SELF closure is illustrated using a barotropic model. The SELF closure is tested through the comparison of the observed eddy-forcing patterns associated with the leading low-frequency modes with those derived using the SELF feedback closure. Examples are also given to illustrate an important role played by the SELF feedback in regulating the atmospheric responses to remote forcing. Further applications of the closure for understanding the dynamics of low-frequency modes as well as the extension of the closure to a multilevel primitive equation model will be given in Parts II and III, respectively.
Abstract
The interaction between synoptic eddy and low-frequency flow (SELF) has been recognized for decades to play an important role in the dynamics of the low-frequency variability of the atmospheric circulation. In this three-part study a linear framework with a stochastic basic flow capturing both the climatological mean flow and climatological measures of the synoptic eddy flow is proposed. Based on this linear framework, a set of linear dynamic equations is derived for the ensemble-mean eddy forcing that is generated by anomalous time-mean flows. By assuming that such dynamically determined eddy-forcing anomalies approximately represent the time-mean anomalies of the synoptic eddy forcing and by using a quasi-equilibrium approximation, an analytical nonlocal dynamical closure is obtained for the two-way SELF feedback. This linear closure, directly relating time-mean anomalies of the synoptic eddy forcing to the anomalous time–mean flow, becomes an internal part of a new linear dynamic system for anomalous time–mean flow that is referred to as the low-frequency variability of the atmospheric circulation in this paper.
In Part I, the basic approach for the SELF closure is illustrated using a barotropic model. The SELF closure is tested through the comparison of the observed eddy-forcing patterns associated with the leading low-frequency modes with those derived using the SELF feedback closure. Examples are also given to illustrate an important role played by the SELF feedback in regulating the atmospheric responses to remote forcing. Further applications of the closure for understanding the dynamics of low-frequency modes as well as the extension of the closure to a multilevel primitive equation model will be given in Parts II and III, respectively.
Abstract
Amidst stormy atmospheric circulation, there are prominent recurrent patterns of variability in the planetary circulation, such as the Antarctic Oscillation (AAO), Arctic Oscillation (AO) or North Atlantic Oscillation (NAO), and the Pacific–North America (PNA) pattern. The role of the synoptic eddy and low-frequency flow (SELF) feedback in the formation of these dominant low-frequency modes is investigated in this paper using the linear barotropic model with the SELF feedback proposed in Part I. It is found that the AO-like and AAO-like leading singular modes of the linear dynamical system emerge from the stormy background flow as the result of a positive SELF feedback. This SELF feedback also prefers a PNA-like singular vector as well among other modes under the climatological conditions of northern winters.
A model with idealized conditions of basic mean flow and activity of synoptic eddy flow and a prototype model are also used to illustrate that there is a natural scale selection for the AAO- and AO-like modes through the positive SELF feedback. The zonal scale of the localized features in the Atlantic (southern Indian Ocean) for AO (AAO) is largely related to the zonal extent of the enhanced storm track activity in the region. The meridional dipole structures of AO- and AAO-like low-frequency modes are favored because of the scale-selective positive SELF feedback, which can be heuristically understood by the tilted-trough mechanism.
Abstract
Amidst stormy atmospheric circulation, there are prominent recurrent patterns of variability in the planetary circulation, such as the Antarctic Oscillation (AAO), Arctic Oscillation (AO) or North Atlantic Oscillation (NAO), and the Pacific–North America (PNA) pattern. The role of the synoptic eddy and low-frequency flow (SELF) feedback in the formation of these dominant low-frequency modes is investigated in this paper using the linear barotropic model with the SELF feedback proposed in Part I. It is found that the AO-like and AAO-like leading singular modes of the linear dynamical system emerge from the stormy background flow as the result of a positive SELF feedback. This SELF feedback also prefers a PNA-like singular vector as well among other modes under the climatological conditions of northern winters.
A model with idealized conditions of basic mean flow and activity of synoptic eddy flow and a prototype model are also used to illustrate that there is a natural scale selection for the AAO- and AO-like modes through the positive SELF feedback. The zonal scale of the localized features in the Atlantic (southern Indian Ocean) for AO (AAO) is largely related to the zonal extent of the enhanced storm track activity in the region. The meridional dipole structures of AO- and AAO-like low-frequency modes are favored because of the scale-selective positive SELF feedback, which can be heuristically understood by the tilted-trough mechanism.
Abstract
In this three-part study, a linear closure has been developed for the synoptic eddy and low-frequency flow (SELF) interaction and demonstrated that internal dynamics plays an important role in generating the leading low-frequency modes in the extratropical circulation anomalies during cold seasons.
In Part III, a new linearized primitive equation system is first derived for time-mean flow anomalies. The dynamical operator of the system includes a traditional part depending on the observed climatological mean state and an additional part from the SELF feedback closure utilizing the observed climatological properties of synoptic eddy activity. The latter part relates nonlocally all the anomalous eddy-forcing terms in equations of momentum, temperature, and surface pressure to the time-mean flow anomalies. Using the observational data, the closure was validated with reasonable success, and it was found that terms of the SELF feedback in the momentum and pressure equations tend to reinforce the low-frequency modes, whereas those in the thermodynamic equation tends to damp the temperature anomalies to make the leading modes equivalent barotropic. Through singular vector analysis of the linear dynamical operator, it is highlighted that the leading modes of the system resemble the observed patterns of the Arctic Oscillation, Antarctic Oscillation, and Pacific–North American pattern, in which the SELF feedback plays an essential role, consistent with the finding of the barotropic model study in Part II.
Abstract
In this three-part study, a linear closure has been developed for the synoptic eddy and low-frequency flow (SELF) interaction and demonstrated that internal dynamics plays an important role in generating the leading low-frequency modes in the extratropical circulation anomalies during cold seasons.
In Part III, a new linearized primitive equation system is first derived for time-mean flow anomalies. The dynamical operator of the system includes a traditional part depending on the observed climatological mean state and an additional part from the SELF feedback closure utilizing the observed climatological properties of synoptic eddy activity. The latter part relates nonlocally all the anomalous eddy-forcing terms in equations of momentum, temperature, and surface pressure to the time-mean flow anomalies. Using the observational data, the closure was validated with reasonable success, and it was found that terms of the SELF feedback in the momentum and pressure equations tend to reinforce the low-frequency modes, whereas those in the thermodynamic equation tends to damp the temperature anomalies to make the leading modes equivalent barotropic. Through singular vector analysis of the linear dynamical operator, it is highlighted that the leading modes of the system resemble the observed patterns of the Arctic Oscillation, Antarctic Oscillation, and Pacific–North American pattern, in which the SELF feedback plays an essential role, consistent with the finding of the barotropic model study in Part II.
Abstract
Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.
Abstract
Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.
Abstract
There is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R 2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R 2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R 2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.
Abstract
There is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R 2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R 2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R 2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.
Abstract
Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth’s surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.
Abstract
Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth’s surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.
