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Abstract
An algorithm that parameterizes the topographically induced subgrid variability in wind speed, snow transport, and blowing-snow sublimation was designed for use within macroscale hydrology models and other large-scale land surface schemes (LSSs). The algorithm is intended to provide consistent estimates of the relative influence of sublimation from blowing snow for continental-scale river basins, while balancing the land surface water and energy budgets. In addition to the standard LSS inputs, the model requires specification of the standard deviation of terrain slope, the mean fetch, and the lag-1 autocorrelation of terrain gradients. Sublimation fluxes are solved for each vegetation class, for each model grid cell. Model results are compared to observed snow water equivalent (SWE) and simulated estimates of sublimation from blowing snow for two small tundra watersheds: Imnavait Creek, Alaska, and Trail Valley Creek, Northwest Territories, Canada, produced by two different small-scale distributed blowing-snow algorithms. The macroscale algorithm reproduced most aspects of the variability between years and between vegetation types predicted by the more detailed models. The macroscale model was subsequently used to estimate sublimation from blowing snow and the snowpack for the 8000-km2 Kuparuk River watershed in northern Alaska. Annual average sublimation from blowing snow predicted by the model for this region varies from 47 mm in the foothills of the Brooks Range to approximately 31 mm on the Arctic coastal plain; sublimation was primarily controlled by topographic limitations on fetch in the foothills and by precipitation and vapor pressure on the coastal plain.
Abstract
An algorithm that parameterizes the topographically induced subgrid variability in wind speed, snow transport, and blowing-snow sublimation was designed for use within macroscale hydrology models and other large-scale land surface schemes (LSSs). The algorithm is intended to provide consistent estimates of the relative influence of sublimation from blowing snow for continental-scale river basins, while balancing the land surface water and energy budgets. In addition to the standard LSS inputs, the model requires specification of the standard deviation of terrain slope, the mean fetch, and the lag-1 autocorrelation of terrain gradients. Sublimation fluxes are solved for each vegetation class, for each model grid cell. Model results are compared to observed snow water equivalent (SWE) and simulated estimates of sublimation from blowing snow for two small tundra watersheds: Imnavait Creek, Alaska, and Trail Valley Creek, Northwest Territories, Canada, produced by two different small-scale distributed blowing-snow algorithms. The macroscale algorithm reproduced most aspects of the variability between years and between vegetation types predicted by the more detailed models. The macroscale model was subsequently used to estimate sublimation from blowing snow and the snowpack for the 8000-km2 Kuparuk River watershed in northern Alaska. Annual average sublimation from blowing snow predicted by the model for this region varies from 47 mm in the foothills of the Brooks Range to approximately 31 mm on the Arctic coastal plain; sublimation was primarily controlled by topographic limitations on fetch in the foothills and by precipitation and vapor pressure on the coastal plain.
Abstract
Seasonal snow water equivalent (SWE) accumulation in California’s Sierra Nevada is primarily governed by a few orographically enhanced snowstorms. However, as air temperatures gradually rise, resulting in a shift from snow to rain, the governing processes determining SWE accumulation versus ablation become ambiguous. Using a network of 28 snow pillow measurements to represent an elevational and latitudinal gradient across the Sierra Nevada, we identify distributions of critical temperatures and corresponding storm and snowpack properties that describe how SWE accumulation varies across the range at an hourly time scale for water years 2010–19. We also describe antecedent and prevailing conditions governing whether SWE accumulates or ablates during warm storms. Results show that atmospheric moisture regulates a temperature dependence of SWE accumulation. Conditions balancing precipitable water and snow formation requirements produce the most seasonal SWE, which was observed in the (low-elevation) northern and (middle-elevation) central Sierra Nevada. The high southern Sierra Nevada conservatively accumulates SWE with colder, drier air, resulting in less midwinter ablation. These differences explain a tendency for deep, low-density snowpacks to accumulate rather than ablate SWE during warm storms (having median temperatures exceeding 1.0°C), reflecting counteracting liquid storage and internal energy deficits. The storm events themselves in these cases are brief with modest moisture supplies or are otherwise followed immediately by ablation.
Abstract
Seasonal snow water equivalent (SWE) accumulation in California’s Sierra Nevada is primarily governed by a few orographically enhanced snowstorms. However, as air temperatures gradually rise, resulting in a shift from snow to rain, the governing processes determining SWE accumulation versus ablation become ambiguous. Using a network of 28 snow pillow measurements to represent an elevational and latitudinal gradient across the Sierra Nevada, we identify distributions of critical temperatures and corresponding storm and snowpack properties that describe how SWE accumulation varies across the range at an hourly time scale for water years 2010–19. We also describe antecedent and prevailing conditions governing whether SWE accumulates or ablates during warm storms. Results show that atmospheric moisture regulates a temperature dependence of SWE accumulation. Conditions balancing precipitable water and snow formation requirements produce the most seasonal SWE, which was observed in the (low-elevation) northern and (middle-elevation) central Sierra Nevada. The high southern Sierra Nevada conservatively accumulates SWE with colder, drier air, resulting in less midwinter ablation. These differences explain a tendency for deep, low-density snowpacks to accumulate rather than ablate SWE during warm storms (having median temperatures exceeding 1.0°C), reflecting counteracting liquid storage and internal energy deficits. The storm events themselves in these cases are brief with modest moisture supplies or are otherwise followed immediately by ablation.
Abstract
Using observation-driven simulations of global terrestrial hydrology and a cluster algorithm that searches for spatially connected regions of soil moisture, the authors identified 296 large-scale drought events (greater than 500 000 km2 and longer than 3 months) globally for 1950–2000. The drought events were subjected to a severity–area–duration (SAD) analysis to identify and characterize the most severe events for each continent and globally at various durations and spatial extents. An analysis of the variation of large-scale drought with SSTs revealed connections at interannual and possibly decadal time scales. Three metrics of large-scale drought (global average soil moisture, contiguous area in drought, and number of drought events shorter than 2 years) are shown to covary with ENSO SST anomalies. At longer time scales, the number of 12-month and longer duration droughts follows the smoothed variation in northern Pacific and Atlantic SSTs. Globally, the mid-1950s showed the highest drought activity and the mid-1970s to mid-1980s the lowest activity. This physically based and probabilistic approach confirms well-known droughts, such as the 1980s in the Sahel region of Africa, but also reveals many severe droughts (e.g., at high latitudes and early in the time period) that have received relatively little attention in the scientific and popular literature.
Abstract
Using observation-driven simulations of global terrestrial hydrology and a cluster algorithm that searches for spatially connected regions of soil moisture, the authors identified 296 large-scale drought events (greater than 500 000 km2 and longer than 3 months) globally for 1950–2000. The drought events were subjected to a severity–area–duration (SAD) analysis to identify and characterize the most severe events for each continent and globally at various durations and spatial extents. An analysis of the variation of large-scale drought with SSTs revealed connections at interannual and possibly decadal time scales. Three metrics of large-scale drought (global average soil moisture, contiguous area in drought, and number of drought events shorter than 2 years) are shown to covary with ENSO SST anomalies. At longer time scales, the number of 12-month and longer duration droughts follows the smoothed variation in northern Pacific and Atlantic SSTs. Globally, the mid-1950s showed the highest drought activity and the mid-1970s to mid-1980s the lowest activity. This physically based and probabilistic approach confirms well-known droughts, such as the 1980s in the Sahel region of Africa, but also reveals many severe droughts (e.g., at high latitudes and early in the time period) that have received relatively little attention in the scientific and popular literature.
Abstract
Diagnostic studies of offline, global-scale Variable Infiltration Capacity (VIC) model simulations of terrestrial water budgets and simulations of the climate of the twenty-first century using the parallel climate model (PCM) are used to estimate the time required to detect plausible changes in precipitation (P), evaporation (E), and discharge (Q) if the global water cycle intensifies in response to global warming. Given the annual variability in these continental hydrological cycle components, several decades to perhaps more than a century of observations are needed to detect water cycle changes on the order of magnitude predicted by many global climate model studies simulating global warming scenarios. Global increases in precipitation, evaporation, and runoff of 0.6, 0.4, and 0.2 mm yr−1 require approximately 30–45, 25–35, and 50–60 yr, respectively, to detect with high confidence. These conservative detection time estimates are based on statistical error criteria (α = 0.05, β = 0.10) that are associated with high statistical confidence, 1 − α (accept hypothesis of intensification when true, i.e., intensification is occurring), and high statistical power, 1 − β (reject hypothesis of intensification when false, i.e., intensification is not occurring). If one is willing to accept a higher degree of risk in making a statistical error, the detection time estimates can be reduced substantially. Owing in part to greater variability, detection time of changes in continental P, E, and Q are longer than those for the globe. Similar calculations performed for three Global Energy and Water Experiment (GEWEX) basins reveal that minimum detection time for some of these basins may be longer than that for the corresponding continent as a whole, thereby calling into question the appropriateness of using continental-scale basins alone for rapid detection of changes in continental water cycles. A case is made for implementing networks of small-scale indicator basins, which collectively mimic the variability in continental P, E, and Q, to detect acceleration in the global water cycle.
Abstract
Diagnostic studies of offline, global-scale Variable Infiltration Capacity (VIC) model simulations of terrestrial water budgets and simulations of the climate of the twenty-first century using the parallel climate model (PCM) are used to estimate the time required to detect plausible changes in precipitation (P), evaporation (E), and discharge (Q) if the global water cycle intensifies in response to global warming. Given the annual variability in these continental hydrological cycle components, several decades to perhaps more than a century of observations are needed to detect water cycle changes on the order of magnitude predicted by many global climate model studies simulating global warming scenarios. Global increases in precipitation, evaporation, and runoff of 0.6, 0.4, and 0.2 mm yr−1 require approximately 30–45, 25–35, and 50–60 yr, respectively, to detect with high confidence. These conservative detection time estimates are based on statistical error criteria (α = 0.05, β = 0.10) that are associated with high statistical confidence, 1 − α (accept hypothesis of intensification when true, i.e., intensification is occurring), and high statistical power, 1 − β (reject hypothesis of intensification when false, i.e., intensification is not occurring). If one is willing to accept a higher degree of risk in making a statistical error, the detection time estimates can be reduced substantially. Owing in part to greater variability, detection time of changes in continental P, E, and Q are longer than those for the globe. Similar calculations performed for three Global Energy and Water Experiment (GEWEX) basins reveal that minimum detection time for some of these basins may be longer than that for the corresponding continent as a whole, thereby calling into question the appropriateness of using continental-scale basins alone for rapid detection of changes in continental water cycles. A case is made for implementing networks of small-scale indicator basins, which collectively mimic the variability in continental P, E, and Q, to detect acceleration in the global water cycle.
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
Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological datasets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded datasets over the conterminous United States. Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5–2.5 day increase in mean wet spell length, 30% low bias in 20-yr return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation dataset is developed that omits the time adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new dataset is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate change applications across the CONUS.
Abstract
Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological datasets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded datasets over the conterminous United States. Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5–2.5 day increase in mean wet spell length, 30% low bias in 20-yr return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation dataset is developed that omits the time adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new dataset is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate change applications across the CONUS.
Abstract
Retrospectively simulated soil moisture from an ensemble of six land surface/hydrological models was used to reconstruct drought events over the continental United States for the period 1920–2003. The simulations were performed at one-half-degree spatial resolution, using a common set of atmospheric forcing data and model-specific soil and vegetation parameters. Monthly simulated soil moisture was converted to percentiles using Weibull plotting position statistics, and the percentiles were then used to represent drought severities and durations. An ensemble method, based on an inverse mapping of the average of the individual model’s soil moisture percentiles, was also used to combine all models’ simulations. Major results are 1) all models and the ensemble reconstruct the known severe drought events during the last century. The spatial extents and severities of drought are plausible for the individual models although substantial among-model disparities exist. 2) The simulations are in more agreement with each other over the eastern than over the western United States. 3) Most of the models show that soil moisture memory is much longer over the western than over the eastern United States. The results provide some insights into how a hydrological nowcast system can be developed, and also early results from a test application within the University of Washington’s real-time national Surface Water Monitor and a review of the multimodel nowcasts during the southeastern drought beginning in summer 2007 are included.
Abstract
Retrospectively simulated soil moisture from an ensemble of six land surface/hydrological models was used to reconstruct drought events over the continental United States for the period 1920–2003. The simulations were performed at one-half-degree spatial resolution, using a common set of atmospheric forcing data and model-specific soil and vegetation parameters. Monthly simulated soil moisture was converted to percentiles using Weibull plotting position statistics, and the percentiles were then used to represent drought severities and durations. An ensemble method, based on an inverse mapping of the average of the individual model’s soil moisture percentiles, was also used to combine all models’ simulations. Major results are 1) all models and the ensemble reconstruct the known severe drought events during the last century. The spatial extents and severities of drought are plausible for the individual models although substantial among-model disparities exist. 2) The simulations are in more agreement with each other over the eastern than over the western United States. 3) Most of the models show that soil moisture memory is much longer over the western than over the eastern United States. The results provide some insights into how a hydrological nowcast system can be developed, and also early results from a test application within the University of Washington’s real-time national Surface Water Monitor and a review of the multimodel nowcasts during the southeastern drought beginning in summer 2007 are included.
Abstract
To provide ground validation data for satellite precipitation products derived from the Global Precipitation Measurement (GPM) mission, such as IMERG, in cold seasons and where orographic factors exert strong controls on precipitation, the Olympic Mountain Experiment (OLYMPEX) was conducted during winter 2015/16. By utilizing multiple observational resources from OLYMPEX, estimates of daily and finer-scale precipitation are constructed at 1/32° spatial resolution over the OLYMPEX domain. The estimates are based on NOAA WSR-88D and gauge estimates as incorporated in NOAA’s National Severe Storms Laboratory (NSSL) Q3GC product, augmented with an additional 120 gauges available during OLYMPEX. Few stations are located in the interior of the Olympic Peninsula at elevations higher than about 500 m, and in this part of the domain the Variable Infiltration Capacity (VIC) hydrology model is used to invert the snow water equivalent (SWE) estimates, derived from two NASA JPL Airborne Snow Observatory (ASO) snow depth maps on 8–9 February 2016 and 29–30 March 2016, for precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. In comparison with this composite product, both IMERG (version 04A) and its Japanese counterpart GSMaP’s (version 04B) satellite-only products tend to underestimate winter precipitation, by 41% and 28%, respectively, over the entire domain from 1 October 2015 to 30 April 2016. The underestimation is more pronounced for the orographically enhanced mountainous interior of the OLYMPEX domain, by 57% and 48%, respectively. In contrast, IMERG and GSMaP storm interarrival time statistics are quite similar to those estimated from gridded observations.
Abstract
To provide ground validation data for satellite precipitation products derived from the Global Precipitation Measurement (GPM) mission, such as IMERG, in cold seasons and where orographic factors exert strong controls on precipitation, the Olympic Mountain Experiment (OLYMPEX) was conducted during winter 2015/16. By utilizing multiple observational resources from OLYMPEX, estimates of daily and finer-scale precipitation are constructed at 1/32° spatial resolution over the OLYMPEX domain. The estimates are based on NOAA WSR-88D and gauge estimates as incorporated in NOAA’s National Severe Storms Laboratory (NSSL) Q3GC product, augmented with an additional 120 gauges available during OLYMPEX. Few stations are located in the interior of the Olympic Peninsula at elevations higher than about 500 m, and in this part of the domain the Variable Infiltration Capacity (VIC) hydrology model is used to invert the snow water equivalent (SWE) estimates, derived from two NASA JPL Airborne Snow Observatory (ASO) snow depth maps on 8–9 February 2016 and 29–30 March 2016, for precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. In comparison with this composite product, both IMERG (version 04A) and its Japanese counterpart GSMaP’s (version 04B) satellite-only products tend to underestimate winter precipitation, by 41% and 28%, respectively, over the entire domain from 1 October 2015 to 30 April 2016. The underestimation is more pronounced for the orographically enhanced mountainous interior of the OLYMPEX domain, by 57% and 48%, respectively. In contrast, IMERG and GSMaP storm interarrival time statistics are quite similar to those estimated from gridded observations.
Abstract
A frequently encountered difficulty in assessing model-predicted land–atmosphere exchanges of moisture and energy is the absence of comprehensive observations to which model predictions can be compared at the spatial and temporal resolutions at which the models operate. Various methods have been used to evaluate the land surface schemes in coupled models, including comparisons of model-predicted evapotranspiration with values derived from atmospheric water balances, comparison of model-predicted energy and radiative fluxes with tower measurements during periods of intensive observations, comparison of model-predicted runoff with observed streamflow, and comparison of model predictions of soil moisture with spatial averages of point observations. While these approaches have provided useful model diagnostic information, the observation-based products used in the comparisons typically are inconsistent with the model variables with which they are compared—for example, observations are for points or areas much smaller than the model spatial resolution, comparisons are restricted to temporal averages, or the spatial scale is large compared to that resolved by the model. Furthermore, none of the datasets available at present allow an evaluation of the interaction of the water balance components over large regions for long periods. In this study, a model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950–2000, and is at a 3-h time step with a spatial resolution of ⅛ degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins. On this basis, and given the physically based model parameterizations, it is argued that other terms in the surface water balance (e.g., soil moisture and evapotranspiration) are well represented, at least for the purposes of diagnostic studies such as those in which atmospheric model reanalysis products have been widely used. These characteristics make this dataset useful for a variety of studies, especially where ground observations are lacking.
Abstract
A frequently encountered difficulty in assessing model-predicted land–atmosphere exchanges of moisture and energy is the absence of comprehensive observations to which model predictions can be compared at the spatial and temporal resolutions at which the models operate. Various methods have been used to evaluate the land surface schemes in coupled models, including comparisons of model-predicted evapotranspiration with values derived from atmospheric water balances, comparison of model-predicted energy and radiative fluxes with tower measurements during periods of intensive observations, comparison of model-predicted runoff with observed streamflow, and comparison of model predictions of soil moisture with spatial averages of point observations. While these approaches have provided useful model diagnostic information, the observation-based products used in the comparisons typically are inconsistent with the model variables with which they are compared—for example, observations are for points or areas much smaller than the model spatial resolution, comparisons are restricted to temporal averages, or the spatial scale is large compared to that resolved by the model. Furthermore, none of the datasets available at present allow an evaluation of the interaction of the water balance components over large regions for long periods. In this study, a model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950–2000, and is at a 3-h time step with a spatial resolution of ⅛ degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins. On this basis, and given the physically based model parameterizations, it is argued that other terms in the surface water balance (e.g., soil moisture and evapotranspiration) are well represented, at least for the purposes of diagnostic studies such as those in which atmospheric model reanalysis products have been widely used. These characteristics make this dataset useful for a variety of studies, especially where ground observations are lacking.
Abstract
An important goal of the Climate Variability and Predictability (CLIVAR) research on the American monsoon systems is to determine the sources and limits of predictability of warm season precipitation, with emphasis on weekly to interannual time scales. This paper reviews recent progress in the understanding of the American monsoon systems and identifies some of the future challenges that remain to improve warm season climate prediction. Much of the recent progress is derived from complementary international programs in North and South America, namely, the North American Monsoon Experiment (NAME) and the Monsoon Experiment South America (MESA), with the following common objectives: 1) to understand the key components of the American monsoon systems and their variability, 2) to determine the role of these systems in the global water cycle, 3) to improve observational datasets, and 4) to improve simulation and monthly-to-seasonal prediction of the monsoons and regional water resources. Among the recent observational advances highlighted in this paper are new insights into moisture transport processes, description of the structure and variability of the South American low-level jet, and resolution of the diurnal cycle of precipitation in the core monsoon regions. NAME and MESA are also driving major efforts in model development and hydrologic applications. Incorporated into the postfield phases of these projects are assessments of atmosphere–land surface interactions and model-based climate predictability experiments. As CLIVAR research on American monsoon systems evolves, a unified view of the climatic processes modulating continental warm season precipitation is beginning to emerge.
Abstract
An important goal of the Climate Variability and Predictability (CLIVAR) research on the American monsoon systems is to determine the sources and limits of predictability of warm season precipitation, with emphasis on weekly to interannual time scales. This paper reviews recent progress in the understanding of the American monsoon systems and identifies some of the future challenges that remain to improve warm season climate prediction. Much of the recent progress is derived from complementary international programs in North and South America, namely, the North American Monsoon Experiment (NAME) and the Monsoon Experiment South America (MESA), with the following common objectives: 1) to understand the key components of the American monsoon systems and their variability, 2) to determine the role of these systems in the global water cycle, 3) to improve observational datasets, and 4) to improve simulation and monthly-to-seasonal prediction of the monsoons and regional water resources. Among the recent observational advances highlighted in this paper are new insights into moisture transport processes, description of the structure and variability of the South American low-level jet, and resolution of the diurnal cycle of precipitation in the core monsoon regions. NAME and MESA are also driving major efforts in model development and hydrologic applications. Incorporated into the postfield phases of these projects are assessments of atmosphere–land surface interactions and model-based climate predictability experiments. As CLIVAR research on American monsoon systems evolves, a unified view of the climatic processes modulating continental warm season precipitation is beginning to emerge.
