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Abstract
Most meteorological soil moisture budgets do not account for soil moisture stress changes in the drying cycle or for changes in ground cover or expanding root system. A simple modulated technique is described which considers these factors. Soil moisture stress determined by this technique has a significantly higher correlation with wheat yield than does moisture stress determined by a common method.
Abstract
Most meteorological soil moisture budgets do not account for soil moisture stress changes in the drying cycle or for changes in ground cover or expanding root system. A simple modulated technique is described which considers these factors. Soil moisture stress determined by this technique has a significantly higher correlation with wheat yield than does moisture stress determined by a common method.
Abstract
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Abstract
Ensemble simulations and forecasts provide probabilistic information about the inherently uncertain climate system. Counting the number of ensemble members in a category is a simple nonparametric method of using an ensemble to assign categorical probabilities. Parametric methods of assigning quantile-based categorical probabilities include distribution fitting and generalized linear regression. Here the accuracy of counting and parametric estimates of tercile category probabilities is compared. The methods are first compared in an idealized setting where analytical results show how ensemble size and level of predictability control the accuracy of both methods. The authors also show how categorical probability estimate errors degrade the rank probability skill score. The analytical results provide a good description of the behavior of the methods applied to seasonal precipitation from a 53-yr, 79-member ensemble of general circulation model simulations. Parametric estimates of seasonal precipitation tercile category probabilities are generally more accurate than the counting estimate. In addition to determining the relative accuracies of the different methods, the analysis quantifies the relative importance of the ensemble mean and variance in determining tercile probabilities. Ensemble variance is shown to be a weak factor in determining seasonal precipitation probabilities, meaning that differences between the tercile probabilities and the equal-odds probabilities are due mainly to shifts of the forecast mean away from its climatological value.
Abstract
Ensemble simulations and forecasts provide probabilistic information about the inherently uncertain climate system. Counting the number of ensemble members in a category is a simple nonparametric method of using an ensemble to assign categorical probabilities. Parametric methods of assigning quantile-based categorical probabilities include distribution fitting and generalized linear regression. Here the accuracy of counting and parametric estimates of tercile category probabilities is compared. The methods are first compared in an idealized setting where analytical results show how ensemble size and level of predictability control the accuracy of both methods. The authors also show how categorical probability estimate errors degrade the rank probability skill score. The analytical results provide a good description of the behavior of the methods applied to seasonal precipitation from a 53-yr, 79-member ensemble of general circulation model simulations. Parametric estimates of seasonal precipitation tercile category probabilities are generally more accurate than the counting estimate. In addition to determining the relative accuracies of the different methods, the analysis quantifies the relative importance of the ensemble mean and variance in determining tercile probabilities. Ensemble variance is shown to be a weak factor in determining seasonal precipitation probabilities, meaning that differences between the tercile probabilities and the equal-odds probabilities are due mainly to shifts of the forecast mean away from its climatological value.
Abstract
Although El Niño events each have distinct evolutionary character, they typically provide systematic large-scale forcing for warming and increased drought frequency across the tropical continents. We assess this response in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis and in a 10-member-model Atmospheric Model Intercomparison Project (AMIP) ensemble. The lagged response (3–4 months) of mean tropical land temperature to El Niño warming in the Pacific Ocean is well represented. MERRA-2 reproduces the patterns of precipitation in the tropical regions, and the AMIP ensemble reproduces some regional responses that are similar to those observed and some regions that are not simulating the response well. Model skill is dependent on event forcing strength and temporal proximity to the peak of the sea surface warming. A composite approach centered on maximum Niño-3.4 SSTs and lag relationships to energy fluxes and transports is used to identify mechanisms supporting tropical land warming. The composite necessarily moderates weather-scale variability of the individual events while retaining the systematic features across all events. We find that reduced continental upward motions lead to reduced cloudiness and more shortwave radiation at the surface, as well as reduced precipitation. The increased shortwave heating at the land surface, along with reduced soil moisture, leads to warmer surface temperature, more sensible heating, and warming of the lower troposphere. The composite provides a broad picture of the mechanisms governing the hydrologic response to El Niño forcing, but the regional and temporal responses can vary substantially for any given event. The 2015/16 El Niño, one of the strongest events, demonstrates some of the forced response noted in the composite, but with shifts in the evolution that depart from the composite, demonstrating the limitations of the composite and individuality of El Niño.
Abstract
Although El Niño events each have distinct evolutionary character, they typically provide systematic large-scale forcing for warming and increased drought frequency across the tropical continents. We assess this response in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis and in a 10-member-model Atmospheric Model Intercomparison Project (AMIP) ensemble. The lagged response (3–4 months) of mean tropical land temperature to El Niño warming in the Pacific Ocean is well represented. MERRA-2 reproduces the patterns of precipitation in the tropical regions, and the AMIP ensemble reproduces some regional responses that are similar to those observed and some regions that are not simulating the response well. Model skill is dependent on event forcing strength and temporal proximity to the peak of the sea surface warming. A composite approach centered on maximum Niño-3.4 SSTs and lag relationships to energy fluxes and transports is used to identify mechanisms supporting tropical land warming. The composite necessarily moderates weather-scale variability of the individual events while retaining the systematic features across all events. We find that reduced continental upward motions lead to reduced cloudiness and more shortwave radiation at the surface, as well as reduced precipitation. The increased shortwave heating at the land surface, along with reduced soil moisture, leads to warmer surface temperature, more sensible heating, and warming of the lower troposphere. The composite provides a broad picture of the mechanisms governing the hydrologic response to El Niño forcing, but the regional and temporal responses can vary substantially for any given event. The 2015/16 El Niño, one of the strongest events, demonstrates some of the forced response noted in the composite, but with shifts in the evolution that depart from the composite, demonstrating the limitations of the composite and individuality of El Niño.
Abstract
The physical mechanisms and predictability associated with extreme daily rainfall in southeastern South America (SESA) are investigated for the December–February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This represents a basic set of daily circulation regimes related to the continental and oceanic phases of the South Atlantic convergence zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere polar jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different mesoscale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th and 99th percentiles) is associated with two of these weather types (WTs), which are characterized by moisture advection intrusions from lower latitudes and the Pacific Ocean; another three WTs, characterized by above-normal moisture advection toward lower latitudes or the Andes, are associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross–time scale interaction between the different climate drivers improves the predictive skill of extreme precipitation in the region.
Abstract
The physical mechanisms and predictability associated with extreme daily rainfall in southeastern South America (SESA) are investigated for the December–February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This represents a basic set of daily circulation regimes related to the continental and oceanic phases of the South Atlantic convergence zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere polar jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different mesoscale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th and 99th percentiles) is associated with two of these weather types (WTs), which are characterized by moisture advection intrusions from lower latitudes and the Pacific Ocean; another three WTs, characterized by above-normal moisture advection toward lower latitudes or the Andes, are associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross–time scale interaction between the different climate drivers improves the predictive skill of extreme precipitation in the region.
Abstract
This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple time scales. To illustrate the approach, three sets of 32-yr-long simulations are analyzed for northeastern North America and for the March–May season using the Geophysical Fluid Dynamics Laboratory’s Low Ocean–Atmosphere Resolution (LOAR) and Forecast-Oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach and are associated with a misrepresentation of troughs centered north of the Great Lakes and deep coastal troughs. Moreover, the intraseasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, interexperiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple time scales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.
Abstract
This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple time scales. To illustrate the approach, three sets of 32-yr-long simulations are analyzed for northeastern North America and for the March–May season using the Geophysical Fluid Dynamics Laboratory’s Low Ocean–Atmosphere Resolution (LOAR) and Forecast-Oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach and are associated with a misrepresentation of troughs centered north of the Great Lakes and deep coastal troughs. Moreover, the intraseasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, interexperiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple time scales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.
Abstract
Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December–February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross–time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden–Julian oscillation, and combinations of both). Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur.
Abstract
Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December–February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross–time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden–Julian oscillation, and combinations of both). Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur.
The present lack of a lower atmosphere research satellite program for the 1980s has prompted consideration of the Space Shuttle/Spacelab system as a means of flying sensor complements geared toward specific research problems, as well as continued instrument development. Three specific examples of possible science questions related to precipitation are discussed: 1) spatial structure of mesoscale cloud and precipitation systems, 2) lightning and storm development, and 3) cyclone intensification over oceanic regions. Examples of space sensors available to provide measurements needed in addressing these questions are also presented. Distinctive aspects of low-earth orbit experiments would be high resolution, multispectral sensing of atmospheric phenomena by complements of instruments, and more efficient sensor development through reflights of specific hardware packages.
The present lack of a lower atmosphere research satellite program for the 1980s has prompted consideration of the Space Shuttle/Spacelab system as a means of flying sensor complements geared toward specific research problems, as well as continued instrument development. Three specific examples of possible science questions related to precipitation are discussed: 1) spatial structure of mesoscale cloud and precipitation systems, 2) lightning and storm development, and 3) cyclone intensification over oceanic regions. Examples of space sensors available to provide measurements needed in addressing these questions are also presented. Distinctive aspects of low-earth orbit experiments would be high resolution, multispectral sensing of atmospheric phenomena by complements of instruments, and more efficient sensor development through reflights of specific hardware packages.
Abstract
Motivated by the question of whether recent interannual to decadal climate variability and a possible “climate shift” may have affected the global water balance, we examine precipitation minus evaporation (P – E) variability integrated over the global oceans and global land for the period 1979–2010 from three points of view—remotely sensed retrievals and syntheses over the oceans, reanalysis vertically integrated moisture flux convergence (VMFC) over land, and land surface models (LSMs) forced with observations-based precipitation, radiation, and near-surface meteorology.
Over land, reanalysis VMFC and P − evapotranspiration (ET) from observationally forced LSMs agree on interannual variations (e.g., El Niño/La Niña events); however, reanalyses exhibit upward VMFC trends 3–4 times larger than P − ET trends of the LSMs. Experiments with other reanalyses using reduced observations show that upward VMFC trends in the full reanalyses are due largely to observing system changes interacting with assimilation model physics. The much smaller P − ET trend in the LSMs appears due to changes in frequency and amplitude of warm events after the 1997/98 El Niño, a result consistent with coolness in the eastern tropical Pacific sea surface temperature (SST) after that date.
When integrated over the global oceans, E and especially P variations show consistent signals of El Niño/La Niña events. However, at scales longer than interannual there is considerable uncertainty especially in E. This results from differences among datasets in near-surface atmospheric specific humidity and wind speed used in bulk aerodynamic retrievals. The P variations, all relying substantially on passive microwave retrievals over ocean, also have uncertainties in decadal variability, but to a smaller degree.
Abstract
Motivated by the question of whether recent interannual to decadal climate variability and a possible “climate shift” may have affected the global water balance, we examine precipitation minus evaporation (P – E) variability integrated over the global oceans and global land for the period 1979–2010 from three points of view—remotely sensed retrievals and syntheses over the oceans, reanalysis vertically integrated moisture flux convergence (VMFC) over land, and land surface models (LSMs) forced with observations-based precipitation, radiation, and near-surface meteorology.
Over land, reanalysis VMFC and P − evapotranspiration (ET) from observationally forced LSMs agree on interannual variations (e.g., El Niño/La Niña events); however, reanalyses exhibit upward VMFC trends 3–4 times larger than P − ET trends of the LSMs. Experiments with other reanalyses using reduced observations show that upward VMFC trends in the full reanalyses are due largely to observing system changes interacting with assimilation model physics. The much smaller P − ET trend in the LSMs appears due to changes in frequency and amplitude of warm events after the 1997/98 El Niño, a result consistent with coolness in the eastern tropical Pacific sea surface temperature (SST) after that date.
When integrated over the global oceans, E and especially P variations show consistent signals of El Niño/La Niña events. However, at scales longer than interannual there is considerable uncertainty especially in E. This results from differences among datasets in near-surface atmospheric specific humidity and wind speed used in bulk aerodynamic retrievals. The P variations, all relying substantially on passive microwave retrievals over ocean, also have uncertainties in decadal variability, but to a smaller degree.
Abstract
A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal Experiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each grid point using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of week-3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, sub-Saharan Africa and Southeast Asia, and over subtropical South America, Africa, and Australia. Near-surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America, and East Africa in all seasons. An example real-time week-3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden–Julian oscillation event as well as weak–moderate ongoing La Niña event.
Significance Statement
This paper develops a system for forecasting of precipitation and temperatures globally over land, several weeks in advance, with a focus on biweekly averaged conditions between three and four weeks ahead. The system provides the likelihood of biweekly and weekly conditions being below, near, or above their long-term averages, as well the probability of exceeding (or not exceeding) any threshold value. Using historical data, the precipitation forecasts are demonstrated to have skill in many tropical regions, and the temperature forecasts are more widely skillful. While weather and seasonal range forecasts have become quite generally available, this is one of the first examples of a publicly available, calibrated multimodel probabilistic real-time forecasting system for the subseasonal range.
Abstract
A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal Experiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each grid point using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of week-3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, sub-Saharan Africa and Southeast Asia, and over subtropical South America, Africa, and Australia. Near-surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America, and East Africa in all seasons. An example real-time week-3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden–Julian oscillation event as well as weak–moderate ongoing La Niña event.
Significance Statement
This paper develops a system for forecasting of precipitation and temperatures globally over land, several weeks in advance, with a focus on biweekly averaged conditions between three and four weeks ahead. The system provides the likelihood of biweekly and weekly conditions being below, near, or above their long-term averages, as well the probability of exceeding (or not exceeding) any threshold value. Using historical data, the precipitation forecasts are demonstrated to have skill in many tropical regions, and the temperature forecasts are more widely skillful. While weather and seasonal range forecasts have become quite generally available, this is one of the first examples of a publicly available, calibrated multimodel probabilistic real-time forecasting system for the subseasonal range.
