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- Author or Editor: Kingtse C. Mo x
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Abstract
The NCEP–NCAR reanalysis together with the outgoing longwave radiation anomalies (OLRAs) and a gridded daily precipitation over the United States were used to analyze precipitation over California on intraseasonal timescales. The intraseasonal (10–90 days) filtered OLRAs were subjected to singular spectrum analysis, which identifies nonlinear oscillations in noisy time series. There are two dominant oscillatory modes associated with California rainfall with periods near 36–40 and 20–25 days.
The 36–40-day mode is related to the Madden–Julian Oscillation (MJO) in the Tropics. Enhanced tropical convection propagates from the western Pacific to the central Pacific. A three-cell pattern with negative OLRAs in California and positive anomalies in the eastern Pacific and the Pacific Northwest starts to develop 4 days later and rainfall starts in California.
Anomalies associated with the 20–25-day mode are responsible for alternating wet and dry episodes over California with periods shorter than the timescales of the MJO. The 20–25-day mode is the leading mode in the 7–30-day band and is related to tropical convection in the Pacific. In the extratropics, cloud bands propagate northward along the west coast of North America from the eastern Pacific just north of the ITCZ through California to the Pacific Northwest. The 200-hPa streamfunction anomaly composites associated with the 20–25-day mode reveal a westward propagating wave train dominated by a zonal wavenumber 2. This mode has a spatial structure similar to the traveling pattern described by Branstator.
Abstract
The NCEP–NCAR reanalysis together with the outgoing longwave radiation anomalies (OLRAs) and a gridded daily precipitation over the United States were used to analyze precipitation over California on intraseasonal timescales. The intraseasonal (10–90 days) filtered OLRAs were subjected to singular spectrum analysis, which identifies nonlinear oscillations in noisy time series. There are two dominant oscillatory modes associated with California rainfall with periods near 36–40 and 20–25 days.
The 36–40-day mode is related to the Madden–Julian Oscillation (MJO) in the Tropics. Enhanced tropical convection propagates from the western Pacific to the central Pacific. A three-cell pattern with negative OLRAs in California and positive anomalies in the eastern Pacific and the Pacific Northwest starts to develop 4 days later and rainfall starts in California.
Anomalies associated with the 20–25-day mode are responsible for alternating wet and dry episodes over California with periods shorter than the timescales of the MJO. The 20–25-day mode is the leading mode in the 7–30-day band and is related to tropical convection in the Pacific. In the extratropics, cloud bands propagate northward along the west coast of North America from the eastern Pacific just north of the ITCZ through California to the Pacific Northwest. The 200-hPa streamfunction anomaly composites associated with the 20–25-day mode reveal a westward propagating wave train dominated by a zonal wavenumber 2. This mode has a spatial structure similar to the traveling pattern described by Branstator.
Abstract
Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian.
Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events.
The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap.
Abstract
Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian.
Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events.
The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap.
Abstract
The intraseasonal rainfall variability over North America is examined using singular spectrum analysis (SSA) and composites of outgoing longwave radiation anomalies (OLRAs), 200-hPa divergence and a gridded rainfall dataset over the United States. The evolution of the Arizona and New Mexico (AZNM) monsoon based on composites indicates that rainfall anomalies propagate eastward from the North Pacific through AZNM, the Great Plains, to the eastern United States. During summer, the wet and dry periods of the AZNM monsoon are modulated by an oscillatory mode with a period of 22–25 days (22-day mode). This is also the dominant mode associated with rainfall events over the Great Plains. The influence of the Madden–Julian Oscillation (MJO) on the AZNM monsoon is secondary. The strongest impact of the MJO is on precipitation over Mexico. SSA performed on the 200-hPa divergence and OLRAs averaged over Mexico show only one oscillatory mode with a period of about 36–40 days.
The 22–25-day mode also exists in the vertically integrated moisture fluxes over the Great Plains. During the wet periods of the AZNM monsoon, more moisture is transported from both the Gulf of Mexico and the Gulf of California to AZNM. The situation reverses when the oscillation reaches the other phase. The 22-day mode is linked to tropical convection. When rainfall associated with the 22-day mode travels eastward from AZNM to the Great Plains, the OLRA composites show westward propagating waves just north of the equator. When enhanced convection reaches the western Pacific, rainfall diminishes over AZNM. When convection in the western Pacific is suppressed and enhanced convection is located in the central Pacific, rainfall intensifies over AZNM.
Abstract
The intraseasonal rainfall variability over North America is examined using singular spectrum analysis (SSA) and composites of outgoing longwave radiation anomalies (OLRAs), 200-hPa divergence and a gridded rainfall dataset over the United States. The evolution of the Arizona and New Mexico (AZNM) monsoon based on composites indicates that rainfall anomalies propagate eastward from the North Pacific through AZNM, the Great Plains, to the eastern United States. During summer, the wet and dry periods of the AZNM monsoon are modulated by an oscillatory mode with a period of 22–25 days (22-day mode). This is also the dominant mode associated with rainfall events over the Great Plains. The influence of the Madden–Julian Oscillation (MJO) on the AZNM monsoon is secondary. The strongest impact of the MJO is on precipitation over Mexico. SSA performed on the 200-hPa divergence and OLRAs averaged over Mexico show only one oscillatory mode with a period of about 36–40 days.
The 22–25-day mode also exists in the vertically integrated moisture fluxes over the Great Plains. During the wet periods of the AZNM monsoon, more moisture is transported from both the Gulf of Mexico and the Gulf of California to AZNM. The situation reverses when the oscillation reaches the other phase. The 22-day mode is linked to tropical convection. When rainfall associated with the 22-day mode travels eastward from AZNM to the Great Plains, the OLRA composites show westward propagating waves just north of the equator. When enhanced convection reaches the western Pacific, rainfall diminishes over AZNM. When convection in the western Pacific is suppressed and enhanced convection is located in the central Pacific, rainfall intensifies over AZNM.
Abstract
A statistical model based on the combination of singular spectrum analysis (SSA) and the maximum entropy method (MEM) is applied to monitor and forecast outgoing longwave radiation anomalies (OLRAs) in the intraseasonal band over the Indian–Pacific sector and in the pan-American region. SSA is related to empirical orthogonal function analysis (EOF) but is applied to time series. The leading SSA modes (T-EOFs) are orthogonal and they are determined from the training period before filtering. The OLRA time series can be projected onto T-EOFs to obtain the principal components (T-PCs). To obtain fluctuations in any frequency band, one can partially sum up a chosen subset of T-EOFs and the related T-PCs in that band. The filter based on the SSA modes is data adaptive and there is no loss of end points. It is well suited for real-time monitoring of intraseasonal oscillations.
In the Pacific and the pan-American region, there are three leading modes (T-EOFs) of oscillations with periods near 40, 22, and 18 days. The T-PCs associated with these modes are quasiperiodic and they can be modeled by an autoregressive process. To perform forecasts, the MEM is used to determine the autoregressive coefficients from the training period. These coefficients are used to advance T-PCs. The summation of T-EOFs and T-PCs related to three preferred modes gives the predicted OLRAs. For 5-day mean OLRAs, the averaged correlation between the predicted and the observed anomalies is 0.65 at the lead times of four pentads (20 days). The SSA–MEM method is effective for any time series containing large oscillatory components. The deficiency of this method is that the forecasted magnitudes of anomalies are usually weaker than observations.
Abstract
A statistical model based on the combination of singular spectrum analysis (SSA) and the maximum entropy method (MEM) is applied to monitor and forecast outgoing longwave radiation anomalies (OLRAs) in the intraseasonal band over the Indian–Pacific sector and in the pan-American region. SSA is related to empirical orthogonal function analysis (EOF) but is applied to time series. The leading SSA modes (T-EOFs) are orthogonal and they are determined from the training period before filtering. The OLRA time series can be projected onto T-EOFs to obtain the principal components (T-PCs). To obtain fluctuations in any frequency band, one can partially sum up a chosen subset of T-EOFs and the related T-PCs in that band. The filter based on the SSA modes is data adaptive and there is no loss of end points. It is well suited for real-time monitoring of intraseasonal oscillations.
In the Pacific and the pan-American region, there are three leading modes (T-EOFs) of oscillations with periods near 40, 22, and 18 days. The T-PCs associated with these modes are quasiperiodic and they can be modeled by an autoregressive process. To perform forecasts, the MEM is used to determine the autoregressive coefficients from the training period. These coefficients are used to advance T-PCs. The summation of T-EOFs and T-PCs related to three preferred modes gives the predicted OLRAs. For 5-day mean OLRAs, the averaged correlation between the predicted and the observed anomalies is 0.65 at the lead times of four pentads (20 days). The SSA–MEM method is effective for any time series containing large oscillatory components. The deficiency of this method is that the forecasted magnitudes of anomalies are usually weaker than observations.
Abstract
Tropical intraseasonal variations in the Pacific are related to the tropical storm activity in the Atlantic basin using outgoing longwave radiation anomalies (OLRAs) and circulation anomalies from the NCEP–NCAR reanalysis. Tropical storms are most likely to develop and maintain in the Atlantic, when enhanced convection associated with the tropical intraseasonal oscillations (TIOs) is located over the Indian Ocean and convection in the Pacific is suppressed. Tropical storm activity decreases when the TIO shifts to the opposite phase.
The dominant signal associated with the TIO is the Madden–Julian oscillation. The atmospheric response in the Tropics is a dipole pattern in the 200-hPa streamfunction anomalies just north of the equator. Positive OLRA propagates eastward from the Indian Ocean to the central Pacific. The dipole moves eastward in concert with OLRAs. When enhanced convection is located in the Indian Ocean and convection in the Pacific is suppressed, positive 200-hPa streamfunction anomalies as a part of the dipole extend from Central America to the central Atlantic. There are more upper-tropospheric easterly wind anomalies over the Caribbeans and the tropical Atlantic. The vertical wind shear decreases. These conditions are favorable for tropical storms to development and enhance. When the TIO shifts to the opposite phase with enhanced convection in the Pacific, the wind shear in the tropical Atlantic increases and the occurrence of tropical storms decreases.
Abstract
Tropical intraseasonal variations in the Pacific are related to the tropical storm activity in the Atlantic basin using outgoing longwave radiation anomalies (OLRAs) and circulation anomalies from the NCEP–NCAR reanalysis. Tropical storms are most likely to develop and maintain in the Atlantic, when enhanced convection associated with the tropical intraseasonal oscillations (TIOs) is located over the Indian Ocean and convection in the Pacific is suppressed. Tropical storm activity decreases when the TIO shifts to the opposite phase.
The dominant signal associated with the TIO is the Madden–Julian oscillation. The atmospheric response in the Tropics is a dipole pattern in the 200-hPa streamfunction anomalies just north of the equator. Positive OLRA propagates eastward from the Indian Ocean to the central Pacific. The dipole moves eastward in concert with OLRAs. When enhanced convection is located in the Indian Ocean and convection in the Pacific is suppressed, positive 200-hPa streamfunction anomalies as a part of the dipole extend from Central America to the central Atlantic. There are more upper-tropospheric easterly wind anomalies over the Caribbeans and the tropical Atlantic. The vertical wind shear decreases. These conditions are favorable for tropical storms to development and enhance. When the TIO shifts to the opposite phase with enhanced convection in the Pacific, the wind shear in the tropical Atlantic increases and the occurrence of tropical storms decreases.
Abstract
No abstract available.
Abstract
No abstract available.
Abstract
Long-term trends and interannual variations of circulation anomalies in the Southern Hemisphere are examined using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis from 1949 to 1998. The changes in planetary circulation regimes are linked to global sea surface temperature anomalies (SSTAs).
Empirical orthogonal function (EOF) analysis was performed on 500-hPa height anomalies. The leading mode EOF1 shows a strong zonal symmetry with a phase reversal between height anomalies in high and midlatitudes. Apart from zonal symmetry, a zonal wavenumber 3 is evident with three centers located in three southern oceans. In the low-frequency band with fluctuations longer than 60 months, EOF1 is associated with the second rotated EOF mode of SSTAs with positive loadings over three southern oceans and negative loadings in the North Pacific and the North Atlantic.
The next two modes are the Pacific–South American (PSA) patterns. They depict wave-3 patterns in quadrature with each other and a well-defined wave train from the tropical Pacific to Argentina with large amplitudes in the Pacific–South American sector. On decadal timescales, the abrupt warming over the central and eastern Pacific is related to the strengthening of PSA1. In the interannual band, PSA1 is associated with the low-frequency part of El Niño–Southern Oscillation (ENSO) variability with the dominant period of 40–48 months. PSA2 is associated with the quasi-biennial component of ENSO variability with a period of 26 months.
Abstract
Long-term trends and interannual variations of circulation anomalies in the Southern Hemisphere are examined using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis from 1949 to 1998. The changes in planetary circulation regimes are linked to global sea surface temperature anomalies (SSTAs).
Empirical orthogonal function (EOF) analysis was performed on 500-hPa height anomalies. The leading mode EOF1 shows a strong zonal symmetry with a phase reversal between height anomalies in high and midlatitudes. Apart from zonal symmetry, a zonal wavenumber 3 is evident with three centers located in three southern oceans. In the low-frequency band with fluctuations longer than 60 months, EOF1 is associated with the second rotated EOF mode of SSTAs with positive loadings over three southern oceans and negative loadings in the North Pacific and the North Atlantic.
The next two modes are the Pacific–South American (PSA) patterns. They depict wave-3 patterns in quadrature with each other and a well-defined wave train from the tropical Pacific to Argentina with large amplitudes in the Pacific–South American sector. On decadal timescales, the abrupt warming over the central and eastern Pacific is related to the strengthening of PSA1. In the interannual band, PSA1 is associated with the low-frequency part of El Niño–Southern Oscillation (ENSO) variability with the dominant period of 40–48 months. PSA2 is associated with the quasi-biennial component of ENSO variability with a period of 26 months.
Abstract
The ensemble canonical correlation (ECC) prediction method is used to predict summer (July–September) and winter (January–March) seasonal mean surface temperature (T surf) over the United States. The predictors are the global sea surface temperature (SST), sea level pressure over the Northern Hemisphere T surf, and soil moisture over the United States from one to two seasons lead, as well as the model outputs from the NCEP seasonal forecast model. The canonical correlation analysis (CCA) prediction is performed for each variable separately. The predicted T surf fields form an ensemble. The ensemble forecast is the weighted average of its members. Both the simple ensemble forecast and the superensemble forecast are tested. The simple ensemble mean is the equally weighted average of its members. The weighting function for the superensemble forecast is determined by linear regression analysis.
Overall, both ensemble forecasts improve skill. On average, the superensemble gives the best performance. For summer, both ensemble forecasts improve skill substantially in comparison with the CCA forecasts based on the SST alone. Different variables recognize different forcing. They have forecast skills over different regions of the United States. Therefore, the ensemble forecasts are skillful.
For summer, the leading SST modes that contribute to the sources of skill are associated with the long-term decadal trends, ENSO, and variability in the North Atlantic. In addition to SSTs, soil moisture in March–May also plays an important role in forecasting T surf in summer. For winter, SSTs in the tropical Pacific associated with the decadal and ENSO variability dominate the contribution.
Abstract
The ensemble canonical correlation (ECC) prediction method is used to predict summer (July–September) and winter (January–March) seasonal mean surface temperature (T surf) over the United States. The predictors are the global sea surface temperature (SST), sea level pressure over the Northern Hemisphere T surf, and soil moisture over the United States from one to two seasons lead, as well as the model outputs from the NCEP seasonal forecast model. The canonical correlation analysis (CCA) prediction is performed for each variable separately. The predicted T surf fields form an ensemble. The ensemble forecast is the weighted average of its members. Both the simple ensemble forecast and the superensemble forecast are tested. The simple ensemble mean is the equally weighted average of its members. The weighting function for the superensemble forecast is determined by linear regression analysis.
Overall, both ensemble forecasts improve skill. On average, the superensemble gives the best performance. For summer, both ensemble forecasts improve skill substantially in comparison with the CCA forecasts based on the SST alone. Different variables recognize different forcing. They have forecast skills over different regions of the United States. Therefore, the ensemble forecasts are skillful.
For summer, the leading SST modes that contribute to the sources of skill are associated with the long-term decadal trends, ENSO, and variability in the North Atlantic. In addition to SSTs, soil moisture in March–May also plays an important role in forecasting T surf in summer. For winter, SSTs in the tropical Pacific associated with the decadal and ENSO variability dominate the contribution.
Abstract
Data from observations and the Intergovernmental Panel on Climate Change (IPCC) twentieth-century climate change model [phase 3 of the Coupled Model Intercomparison Project (CMIP3)] simulations were analyzed to examine the decadal changes of the impact of ENSO on air temperature T air and precipitation P over the United States. The comparison of composites for the early period (1915–60) and the recent period (1962–2006) indicates that cooling (warming) over the south and warming (cooling) over the north during ENSO warm (cold) winters have been weakening. The ENSO influence on winter P over the Southwest is strengthening, while the impact on P over the Ohio Valley is weakening for the recent decades. These differences are not due to the long-term trends in T air or P; they are attributed to the occurrence of the central Pacific (CPAC) ENSO events in the recent years. The CPAC ENSO differs from the canonical eastern Pacific (EPAC) ENSO. The EPAC ENSO has a sea surface temperature anomaly (SSTA) maximum in the eastern Pacific. Enhanced convection extends from the date line to the eastern Pacific, with negative anomalies in the western Pacific. The atmospheric responses resemble a tropical Northern Hemisphere pattern. The wave train is consistent with the north–south T air contrast over North America during the EPAC ENSO winters. The CPAC ENSO has enhanced convection in the central Pacific. The atmospheric responses show a Pacific–North American pattern. It is consistent with west–east contrast in T air and more rainfall over the Southwest during the CPAC ENSO winters.
Abstract
Data from observations and the Intergovernmental Panel on Climate Change (IPCC) twentieth-century climate change model [phase 3 of the Coupled Model Intercomparison Project (CMIP3)] simulations were analyzed to examine the decadal changes of the impact of ENSO on air temperature T air and precipitation P over the United States. The comparison of composites for the early period (1915–60) and the recent period (1962–2006) indicates that cooling (warming) over the south and warming (cooling) over the north during ENSO warm (cold) winters have been weakening. The ENSO influence on winter P over the Southwest is strengthening, while the impact on P over the Ohio Valley is weakening for the recent decades. These differences are not due to the long-term trends in T air or P; they are attributed to the occurrence of the central Pacific (CPAC) ENSO events in the recent years. The CPAC ENSO differs from the canonical eastern Pacific (EPAC) ENSO. The EPAC ENSO has a sea surface temperature anomaly (SSTA) maximum in the eastern Pacific. Enhanced convection extends from the date line to the eastern Pacific, with negative anomalies in the western Pacific. The atmospheric responses resemble a tropical Northern Hemisphere pattern. The wave train is consistent with the north–south T air contrast over North America during the EPAC ENSO winters. The CPAC ENSO has enhanced convection in the central Pacific. The atmospheric responses show a Pacific–North American pattern. It is consistent with west–east contrast in T air and more rainfall over the Southwest during the CPAC ENSO winters.
Abstract
No abstract available.
Abstract
No abstract available.
