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- Author or Editor: Guiling Wang x
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Abstract
This paper presents a conceptual modeling study on the behaviors of terrestrial biosphere–atmosphere systems as they relate to multiple equilibrium states and climate variability, and emphasizes their implications for physically based climate modeling. The conceptual biosphere–atmosphere model consists of equilibrium responses of vegetation and precipitation to each other, dynamics of the vegetation system, and stochastic forcing of precipitation representing the impact of atmospheric internal variability. Using precipitation as the atmospheric variable in describing the biosphere–atmosphere interactions, this model pertains to regions where vegetation growth is limited by water. Low moisture convergence in the atmosphere combined with high sensitivity of the atmospheric climate to vegetation changes provides the most favorable condition for the existence of multiple equilibrium states. In a coupled biosphere–atmosphere system with multiple equilibria, experiments varying the stochastic forcing indicate that atmospheric internal variability is an important factor in the long-term variability of the model climate and in its sensitivity to initial conditions. Specifically, the enhancement of low-frequency rainfall variability by vegetation dynamics is most pronounced with a moderate magnitude of atmospheric internal variability and is less pronounced if internal variability is either too large or too small; detecting the existence of multiple equilibria by examining the sensitivity of the coupled model climate to initial conditions is not always reliable since too large an internal variability reduces or even eliminates the model sensitivity to initial conditions. Findings from the conceptual model are confirmed using results from a physically based, synchronously coupled biosphere–atmosphere model. This study provides guidance for interpreting and understanding the model dependence of biosphere–atmosphere interaction studies using complex climate system models.
Abstract
This paper presents a conceptual modeling study on the behaviors of terrestrial biosphere–atmosphere systems as they relate to multiple equilibrium states and climate variability, and emphasizes their implications for physically based climate modeling. The conceptual biosphere–atmosphere model consists of equilibrium responses of vegetation and precipitation to each other, dynamics of the vegetation system, and stochastic forcing of precipitation representing the impact of atmospheric internal variability. Using precipitation as the atmospheric variable in describing the biosphere–atmosphere interactions, this model pertains to regions where vegetation growth is limited by water. Low moisture convergence in the atmosphere combined with high sensitivity of the atmospheric climate to vegetation changes provides the most favorable condition for the existence of multiple equilibrium states. In a coupled biosphere–atmosphere system with multiple equilibria, experiments varying the stochastic forcing indicate that atmospheric internal variability is an important factor in the long-term variability of the model climate and in its sensitivity to initial conditions. Specifically, the enhancement of low-frequency rainfall variability by vegetation dynamics is most pronounced with a moderate magnitude of atmospheric internal variability and is less pronounced if internal variability is either too large or too small; detecting the existence of multiple equilibria by examining the sensitivity of the coupled model climate to initial conditions is not always reliable since too large an internal variability reduces or even eliminates the model sensitivity to initial conditions. Findings from the conceptual model are confirmed using results from a physically based, synchronously coupled biosphere–atmosphere model. This study provides guidance for interpreting and understanding the model dependence of biosphere–atmosphere interaction studies using complex climate system models.
Abstract
Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.
Abstract
Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.
Abstract
To investigate the impact of anomalous soil moisture conditions on subsequent precipitation over North America, a series of numerical experiments is performed using a modified version of the Community Atmosphere Model version 3 and the Community Land Model version 3 (CAM3–CLM3). First, the mechanisms underlying the impact of spring and summer soil moisture on subsequent precipitation are examined based on simulations starting on 1 April and 1 June, respectively. How the response of precipitation to initial soil moisture anomalies depends on the characteristics of such anomalies, including the timing, magnitude, spatial coverage, and vertical depth, is then investigated. There are five main findings. First, the impact of spring soil moisture anomalies is not evident until early summer although their impact on the large-scale circulation results in slight changes in precipitation during spring. Second, precipitation increases with initial soil moisture almost linearly within a certain range of soil moisture. Beyond this range, precipitation is less responsive. Third, during the first month following the onset of summer soil moisture anomalies, the precipitation response to wet anomalies is larger in magnitude than that to dry anomalies. However, the resulting wet anomalies in precipitation quickly dissipate within a month or so, while the resulting dry anomalies in precipitation remain at a considerable magnitude for a longer period. Consistently, wet spring anomalies are likely to be ameliorated before summer, and thus have a smaller impact (in magnitude) on summer precipitation than dry spring anomalies. Fourth, soil moisture anomalies of smaller spatial coverage lead to precipitation anomalies that are smaller and less persistent, compared to anomalies at the continental scale. Finally, anomalies in shallow soil can persist long enough to influence the subsequent precipitation at the seasonal time scale. Dry anomalies in deep soils last much longer than those in shallow soils.
Abstract
To investigate the impact of anomalous soil moisture conditions on subsequent precipitation over North America, a series of numerical experiments is performed using a modified version of the Community Atmosphere Model version 3 and the Community Land Model version 3 (CAM3–CLM3). First, the mechanisms underlying the impact of spring and summer soil moisture on subsequent precipitation are examined based on simulations starting on 1 April and 1 June, respectively. How the response of precipitation to initial soil moisture anomalies depends on the characteristics of such anomalies, including the timing, magnitude, spatial coverage, and vertical depth, is then investigated. There are five main findings. First, the impact of spring soil moisture anomalies is not evident until early summer although their impact on the large-scale circulation results in slight changes in precipitation during spring. Second, precipitation increases with initial soil moisture almost linearly within a certain range of soil moisture. Beyond this range, precipitation is less responsive. Third, during the first month following the onset of summer soil moisture anomalies, the precipitation response to wet anomalies is larger in magnitude than that to dry anomalies. However, the resulting wet anomalies in precipitation quickly dissipate within a month or so, while the resulting dry anomalies in precipitation remain at a considerable magnitude for a longer period. Consistently, wet spring anomalies are likely to be ameliorated before summer, and thus have a smaller impact (in magnitude) on summer precipitation than dry spring anomalies. Fourth, soil moisture anomalies of smaller spatial coverage lead to precipitation anomalies that are smaller and less persistent, compared to anomalies at the continental scale. Finally, anomalies in shallow soil can persist long enough to influence the subsequent precipitation at the seasonal time scale. Dry anomalies in deep soils last much longer than those in shallow soils.
Abstract
Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The modified CLM includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation, and precipitation at the seasonal time scale. The focus of this study is on how the impact of vegetation feedback varies with the timing and direction of initial soil moisture anomalies. During summer, wet soil moisture anomalies lead to increase in leaf area index and, consequently, increase in evapotranspiration and surface heating. Such increases tend to favor precipitation. Therefore, under wet summer soil moisture anomalies, the soil moisture–induced precipitation increase is reinforced when predictive phenology is included. That is, the vegetation feedback to precipitation is positive. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due probably to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, the leaf area index (LAI) response is delayed since LAI is still limited by cold temperature at that time of the year. During the summer following wet spring soil moisture anomalies, vegetation feedback is negative; that is, it tends to suppress the response of precipitation through the depletion of soil moisture by vegetation.
Abstract
Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The modified CLM includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation, and precipitation at the seasonal time scale. The focus of this study is on how the impact of vegetation feedback varies with the timing and direction of initial soil moisture anomalies. During summer, wet soil moisture anomalies lead to increase in leaf area index and, consequently, increase in evapotranspiration and surface heating. Such increases tend to favor precipitation. Therefore, under wet summer soil moisture anomalies, the soil moisture–induced precipitation increase is reinforced when predictive phenology is included. That is, the vegetation feedback to precipitation is positive. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due probably to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, the leaf area index (LAI) response is delayed since LAI is still limited by cold temperature at that time of the year. During the summer following wet spring soil moisture anomalies, vegetation feedback is negative; that is, it tends to suppress the response of precipitation through the depletion of soil moisture by vegetation.
Abstract
Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on subdaily data from observations, a reanalysis product, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variations over the ocean. We further identify the high temperature–induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. A large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves.
Significance Statement
Negative scaling, a decrease of extreme precipitation at high local temperature, is a poorly understood phenomenon. It was suggested that the negative scaling may be a reflection of precipitation’s influence on temperature. Here we show based on observational data, a reanalysis product, and climate models that the negative scaling results primarily from the impact of the high temperature–induced saturation deficit on precipitation over land and from storm-induced temperature variations over the ocean. In hot weather when moisture is limited (as is over land), a large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation, leading to a negative scaling. The same mechanism can also contribute to increased compound drought and heat events.
Abstract
Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on subdaily data from observations, a reanalysis product, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variations over the ocean. We further identify the high temperature–induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. A large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves.
Significance Statement
Negative scaling, a decrease of extreme precipitation at high local temperature, is a poorly understood phenomenon. It was suggested that the negative scaling may be a reflection of precipitation’s influence on temperature. Here we show based on observational data, a reanalysis product, and climate models that the negative scaling results primarily from the impact of the high temperature–induced saturation deficit on precipitation over land and from storm-induced temperature variations over the ocean. In hot weather when moisture is limited (as is over land), a large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation, leading to a negative scaling. The same mechanism can also contribute to increased compound drought and heat events.
Abstract
This study examines the land–atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)–Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) and CAM4–CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land–atmosphere coupling; 2) soil moisture–precipitation coupling is weaker than soil moisture–surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4–CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4–CLM4 is weaker than in CAM3–CLM3, which is further supported by Global Land–Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM–CLM models do not seem to overestimate the land–atmosphere coupling strength.
Abstract
This study examines the land–atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)–Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) and CAM4–CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land–atmosphere coupling; 2) soil moisture–precipitation coupling is weaker than soil moisture–surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4–CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4–CLM4 is weaker than in CAM3–CLM3, which is further supported by Global Land–Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM–CLM models do not seem to overestimate the land–atmosphere coupling strength.
Abstract
This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST–precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture–precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture–precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation.
Abstract
This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST–precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture–precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture–precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation.
Abstract
This paper presents a new index to quantify the strength of soil moisture–precipitation coupling in AGCMs and explores how the soil moisture–precipitation coupling in Community Atmosphere Model version 3 (CAM3)–Community Land Model version 3 (CAM3–CLM3) responds to parameterization-induced surface water budget changes. Specifically, this study (a) compares the regions of strong coupling identified by the newly proposed index and the index currently used in the Global Land–Atmosphere Coupling Experiment (GLACE); (b) examines how the surface water budget changes influence the strength of soil moisture–precipitation coupling as measured by the two indexes, respectively; and (c) examines how these changes influence the memory of the coupled land–atmosphere system as measured by the correlation between soil moisture and subsequent precipitation. The new index and the GLACE index are consistent in identifying central North America and West Africa as major regions of strong coupling during June–August (JJA). However, in some areas of western Europe and of subtropical South America where the GLACE index is low, the new index suggests a modest significant coupling during JJA. In response to the surface water budget changes that presumably favor a stronger soil moisture–precipitation coupling, the new index increases, but the GLACE index decreases in a majority of the regions of modest-to-strong coupling, although both show some mixed response. Changes in the land–atmosphere system memory suggest an increase of coupling strength, consistent with results from the new index. The strong dependence of the GLACE index on the relative importance of atmospheric internal variability is identified as a potential cause for the differences between the two indexes. The two indexes emphasize different aspects of soil moisture–precipitation coupling, and one might be more suitable than the other depending on the purpose of individual studies.
Abstract
This paper presents a new index to quantify the strength of soil moisture–precipitation coupling in AGCMs and explores how the soil moisture–precipitation coupling in Community Atmosphere Model version 3 (CAM3)–Community Land Model version 3 (CAM3–CLM3) responds to parameterization-induced surface water budget changes. Specifically, this study (a) compares the regions of strong coupling identified by the newly proposed index and the index currently used in the Global Land–Atmosphere Coupling Experiment (GLACE); (b) examines how the surface water budget changes influence the strength of soil moisture–precipitation coupling as measured by the two indexes, respectively; and (c) examines how these changes influence the memory of the coupled land–atmosphere system as measured by the correlation between soil moisture and subsequent precipitation. The new index and the GLACE index are consistent in identifying central North America and West Africa as major regions of strong coupling during June–August (JJA). However, in some areas of western Europe and of subtropical South America where the GLACE index is low, the new index suggests a modest significant coupling during JJA. In response to the surface water budget changes that presumably favor a stronger soil moisture–precipitation coupling, the new index increases, but the GLACE index decreases in a majority of the regions of modest-to-strong coupling, although both show some mixed response. Changes in the land–atmosphere system memory suggest an increase of coupling strength, consistent with results from the new index. The strong dependence of the GLACE index on the relative importance of atmospheric internal variability is identified as a potential cause for the differences between the two indexes. The two indexes emphasize different aspects of soil moisture–precipitation coupling, and one might be more suitable than the other depending on the purpose of individual studies.
Abstract
Subgrid variability in rainfall distribution has been widely recognized as an important factor to include in the representation of land surface hydrology within climate models. In this paper, using West Africa as a case study, the impact of the subgrid variability in rainfall interception on the modeling of the biosphere–atmosphere system is investigated. According to the authors’ results, when neglecting the rainfall spatial variability, even if the impact on the total evapotranspiration is negligible, significant errors may result in the representation of surface hydrological processes and surface energy balance. These findings are consistent with the results of previous studies. However, in this paper, this issue is further explored and it is demonstrated that the extent of the resulting errors is not limited to the land surface processes. They extend to the atmosphere via the low-level cloud feedback to impact solar radiation, boundary layer energy, atmospheric circulation, and the distribution of precipitation. The same errors also propagate into the biosphere through vegetation dynamics and can eventually lead to a significantly different biosphere–atmosphere equilibrium state. This study provides a good example for the need to have physical realism in modeling the subgrid variability and most other details of the complex biosphere–atmosphere–ocean system.
Abstract
Subgrid variability in rainfall distribution has been widely recognized as an important factor to include in the representation of land surface hydrology within climate models. In this paper, using West Africa as a case study, the impact of the subgrid variability in rainfall interception on the modeling of the biosphere–atmosphere system is investigated. According to the authors’ results, when neglecting the rainfall spatial variability, even if the impact on the total evapotranspiration is negligible, significant errors may result in the representation of surface hydrological processes and surface energy balance. These findings are consistent with the results of previous studies. However, in this paper, this issue is further explored and it is demonstrated that the extent of the resulting errors is not limited to the land surface processes. They extend to the atmosphere via the low-level cloud feedback to impact solar radiation, boundary layer energy, atmospheric circulation, and the distribution of precipitation. The same errors also propagate into the biosphere through vegetation dynamics and can eventually lead to a significantly different biosphere–atmosphere equilibrium state. This study provides a good example for the need to have physical realism in modeling the subgrid variability and most other details of the complex biosphere–atmosphere–ocean system.
Abstract
The natural variability in the annual flow of the Nile is significantly regulated by the El Niño–Southern Oscillation (ENSO). In this paper, several sources of information are combined, including ENSO, rainfall over Ethiopia, and the recent history of river flow in the Nile, in order to obtain accurate forecasts of the Nile flood at Aswan. The Bayesian theorem is used in developing the discriminant forecasting algorithm. Conditional categoric probabilities are used to describe the flood forecasts, and a synoptic index is defined to measure the forecasts’ skill. The results presented show that ENSO information is the only valuable predictor for the long-range forecasts (lead time longer than the hydrological response timescale, which is 2–3 months in this study). However, the incorporation of the rainfall and river flow information in addition to the ENSO information significantly improves the quality of the medium-range forecasts (lead time shorter than the hydrological response timescale).
Abstract
The natural variability in the annual flow of the Nile is significantly regulated by the El Niño–Southern Oscillation (ENSO). In this paper, several sources of information are combined, including ENSO, rainfall over Ethiopia, and the recent history of river flow in the Nile, in order to obtain accurate forecasts of the Nile flood at Aswan. The Bayesian theorem is used in developing the discriminant forecasting algorithm. Conditional categoric probabilities are used to describe the flood forecasts, and a synoptic index is defined to measure the forecasts’ skill. The results presented show that ENSO information is the only valuable predictor for the long-range forecasts (lead time longer than the hydrological response timescale, which is 2–3 months in this study). However, the incorporation of the rainfall and river flow information in addition to the ENSO information significantly improves the quality of the medium-range forecasts (lead time shorter than the hydrological response timescale).
