Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: Laifang Li x
- Refine by Access: All Content x
Abstract
By modulating the moisture flux from ocean to adjacent land, the North Atlantic subtropical high (NASH) western ridge significantly influences summer-season total precipitation over the conterminous United States (CONUS). However, its influence on the frequency and intensity of daily rainfall events over the CONUS remains unclear. Here we introduce a Bayesian statistical model to investigate the impacts of the NASH western ridge position on key statistics of daily scale summer precipitation, including the intensity of rainfall events, the probability of precipitation occurrence, and the probability of extreme values. These statistical quantities play a key role in characterizing both the impact of wet extremes (e.g., the probability of floods) and dry extremes. By applying this model to historical rain gauge records (1948–2019) covering the entire CONUS, we find that the western ridge of the NASH influences the frequency of rainfall as well as the distribution of rainfall intensities over extended areas of the CONUS. In particular, we find that the NASH ridge also modulates the frequency of extreme rainfall, especially that over part of the Southeast and Upper Midwest. Our analysis underlines the importance of including the NASH western ridge position as a predictor for key statistical rainfall properties to be used for hydrological applications. This result is especially relevant for projecting future changes in daily rainfall regimes over the CONUS based on the predicted strengthening of the NASH in a warming climate.
Abstract
By modulating the moisture flux from ocean to adjacent land, the North Atlantic subtropical high (NASH) western ridge significantly influences summer-season total precipitation over the conterminous United States (CONUS). However, its influence on the frequency and intensity of daily rainfall events over the CONUS remains unclear. Here we introduce a Bayesian statistical model to investigate the impacts of the NASH western ridge position on key statistics of daily scale summer precipitation, including the intensity of rainfall events, the probability of precipitation occurrence, and the probability of extreme values. These statistical quantities play a key role in characterizing both the impact of wet extremes (e.g., the probability of floods) and dry extremes. By applying this model to historical rain gauge records (1948–2019) covering the entire CONUS, we find that the western ridge of the NASH influences the frequency of rainfall as well as the distribution of rainfall intensities over extended areas of the CONUS. In particular, we find that the NASH ridge also modulates the frequency of extreme rainfall, especially that over part of the Southeast and Upper Midwest. Our analysis underlines the importance of including the NASH western ridge position as a predictor for key statistical rainfall properties to be used for hydrological applications. This result is especially relevant for projecting future changes in daily rainfall regimes over the CONUS based on the predicted strengthening of the NASH in a warming climate.
Abstract
Tibetan Plateau (TP) vortices and the related 10–30-day intraseasonal oscillation in May–September 1998 are analyzed using the twice-daily 500-hPa synoptic weather maps, multiple reanalysis datasets, and satellite-retrieved brightness temperature. During the analysis period, distinctively active and suppressed periods of TP vortices genesis are noticed. In 1998, nine active periods of TP vortices occurred, which were largely clustered by the cyclonic circulations associated with the intraseasonal oscillation of 500-hPa relative vorticity. In addition to the well-recognized 30–60-day oscillation, the clustering of TP vorticity in the 1998 summer are more likely modulated by the 10–30-day oscillation, because all active periods of TP vortices fall into the positive phase of the 10–30-day oscillation in 1998. Even in the negative (i.e., anticyclonic) phases of the 30–60-day oscillation, the positive (i.e., cyclonic) 500-hPa 10–30-day oscillation can excite the clustering of TP vortices. This result indicates that the 10–30-day oscillation more directly modulates the activities of TP vortices by providing a favorable (unfavorable) cyclonic (anticyclonic) environment. The analysis of the 10–30-day atmospheric oscillation suggests that the westerly trough disturbances, in conjunction with convective instability due to low-level warm advection from the Indian monsoon region, are important in the clustering of TP vortex activities. In particular, the moisture flux from the southwest boundary of TP is essential to the accumulation of convective energy. Thus, a better understanding and prediction of the 10–30-day intraseasonal oscillation is needed to advance the extended-range forecasting of TP vortices and their downstream impacts on the weather and climate over East Asia.
Abstract
Tibetan Plateau (TP) vortices and the related 10–30-day intraseasonal oscillation in May–September 1998 are analyzed using the twice-daily 500-hPa synoptic weather maps, multiple reanalysis datasets, and satellite-retrieved brightness temperature. During the analysis period, distinctively active and suppressed periods of TP vortices genesis are noticed. In 1998, nine active periods of TP vortices occurred, which were largely clustered by the cyclonic circulations associated with the intraseasonal oscillation of 500-hPa relative vorticity. In addition to the well-recognized 30–60-day oscillation, the clustering of TP vorticity in the 1998 summer are more likely modulated by the 10–30-day oscillation, because all active periods of TP vortices fall into the positive phase of the 10–30-day oscillation in 1998. Even in the negative (i.e., anticyclonic) phases of the 30–60-day oscillation, the positive (i.e., cyclonic) 500-hPa 10–30-day oscillation can excite the clustering of TP vortices. This result indicates that the 10–30-day oscillation more directly modulates the activities of TP vortices by providing a favorable (unfavorable) cyclonic (anticyclonic) environment. The analysis of the 10–30-day atmospheric oscillation suggests that the westerly trough disturbances, in conjunction with convective instability due to low-level warm advection from the Indian monsoon region, are important in the clustering of TP vortex activities. In particular, the moisture flux from the southwest boundary of TP is essential to the accumulation of convective energy. Thus, a better understanding and prediction of the 10–30-day intraseasonal oscillation is needed to advance the extended-range forecasting of TP vortices and their downstream impacts on the weather and climate over East Asia.
Abstract
Recently Diem questioned the western ridge movement of the North Atlantic subtropical high (NASH) reported in a 2011 paper of Li et al. This reply shows more analysis that further strengthens the conclusions originally put forth by Li et al. Diem’’s analysis of the trend in the western ridge of the NASH was based on the data over a 30-yr period (1978–2007), whereas the main conclusions in Li et al. were drawn according to the data over a 60-yr period (1948–2007). Over the last 60 years, the NASH has shown a significant trend of westward movement, the meridional movement of the western ridge of the NASH has enhanced in the recent three decades, and the potential impact of global warming cannot be ruled out in an attempt to explain these changes of the NASH.
Abstract
Recently Diem questioned the western ridge movement of the North Atlantic subtropical high (NASH) reported in a 2011 paper of Li et al. This reply shows more analysis that further strengthens the conclusions originally put forth by Li et al. Diem’’s analysis of the trend in the western ridge of the NASH was based on the data over a 30-yr period (1978–2007), whereas the main conclusions in Li et al. were drawn according to the data over a 60-yr period (1948–2007). Over the last 60 years, the NASH has shown a significant trend of westward movement, the meridional movement of the western ridge of the NASH has enhanced in the recent three decades, and the potential impact of global warming cannot be ruled out in an attempt to explain these changes of the NASH.
Abstract
This study investigates the changes of the North Atlantic subtropical high (NASH) and its impact on summer precipitation over the southeastern (SE) United States using the 850-hPa geopotential height field in the National Centers for Environmental Prediction (NCEP) reanalysis, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), long-term rainfall data, and Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) model simulations during the past six decades (1948–2007). The results show that the NASH in the last 30 yr has become more intense, and its western ridge has displaced westward with an enhanced meridional movement compared to the previous 30 yr. When the NASH moved closer to the continental United States in the three most recent decades, the effect of the NASH on the interannual variation of SE U.S. precipitation is enhanced through the ridge’s north–south movement. The study’s attribution analysis suggested that the changes of the NASH are mainly due to anthropogenic warming. In the twenty-first century with an increase of the atmospheric CO2 concentration, the center of the NASH would be intensified and the western ridge of the NASH would shift farther westward. These changes would increase the likelihood of both strong anomalously wet and dry summers over the SE United States in the future, as suggested by the IPCC AR4 models.
Abstract
This study investigates the changes of the North Atlantic subtropical high (NASH) and its impact on summer precipitation over the southeastern (SE) United States using the 850-hPa geopotential height field in the National Centers for Environmental Prediction (NCEP) reanalysis, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), long-term rainfall data, and Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) model simulations during the past six decades (1948–2007). The results show that the NASH in the last 30 yr has become more intense, and its western ridge has displaced westward with an enhanced meridional movement compared to the previous 30 yr. When the NASH moved closer to the continental United States in the three most recent decades, the effect of the NASH on the interannual variation of SE U.S. precipitation is enhanced through the ridge’s north–south movement. The study’s attribution analysis suggested that the changes of the NASH are mainly due to anthropogenic warming. In the twenty-first century with an increase of the atmospheric CO2 concentration, the center of the NASH would be intensified and the western ridge of the NASH would shift farther westward. These changes would increase the likelihood of both strong anomalously wet and dry summers over the SE United States in the future, as suggested by the IPCC AR4 models.
Abstract
A mechanistic understanding of the Atlantic multidecadal variability (AMV) is highly desirable since it will considerably aid regional and global climate predictions. Although ocean dynamics have long been invoked to explain the AMV, recent studies have cast doubt on its influence. Here we evaluate the necessity of ocean dynamics for the AMV using an observationally based idealized model that isolates the contribution of atmospheric forcing to the AMV. By demonstrating that this model underestimates the magnitude of the observed sea surface temperature variability in the extratropical North Atlantic, we infer that ocean dynamics contribute significantly to the AMV in this region. This inference holds when we add anthropogenic aerosol forcing and the effects of mixed layer depth variability to the idealized model. Thus, our study suggests that ocean heat transport convergence is needed to explain sea surface temperature variability in the extratropical North Atlantic. Sustained ocean observing systems in the this region will help untangle the physical mechanisms involved.
Abstract
A mechanistic understanding of the Atlantic multidecadal variability (AMV) is highly desirable since it will considerably aid regional and global climate predictions. Although ocean dynamics have long been invoked to explain the AMV, recent studies have cast doubt on its influence. Here we evaluate the necessity of ocean dynamics for the AMV using an observationally based idealized model that isolates the contribution of atmospheric forcing to the AMV. By demonstrating that this model underestimates the magnitude of the observed sea surface temperature variability in the extratropical North Atlantic, we infer that ocean dynamics contribute significantly to the AMV in this region. This inference holds when we add anthropogenic aerosol forcing and the effects of mixed layer depth variability to the idealized model. Thus, our study suggests that ocean heat transport convergence is needed to explain sea surface temperature variability in the extratropical North Atlantic. Sustained ocean observing systems in the this region will help untangle the physical mechanisms involved.
Abstract
Understanding the extent to which Atlantic sea surface temperatures (SSTs) are predictable is important due to the strong climate impacts of Atlantic SST on Atlantic hurricanes and temperature and precipitation over adjacent landmasses. However, models differ substantially on the degree of predictability of Atlantic SST and upper-ocean heat content (UOHC). In this work, a lower bound on predictability time scales for SST and UOHC in the North Atlantic is estimated purely from gridded ocean observations using a measure of the decorrelation time scale based on the local autocorrelation. Decorrelation time scales for both wintertime SST and UOHC are longest in the subpolar gyre, with maximum time scales of about 4–6 years. Wintertime SST and UOHC generally have similar decorrelation time scales, except in regions with very deep mixed layers, such as the Labrador Sea, where time scales for UOHC are much larger. Spatial variations in the wintertime climatological mixed layer depth explain 51%–73% (range for three datasets analyzed) of the regional variations in decorrelation time scales for UOHC and 26%–40% (range for three datasets analyzed) of the regional variations in decorrelation time scales for wintertime SST in the extratropical North Atlantic. These results suggest that to leading order decorrelation time scales for UOHC are determined by the thermal memory of the ocean.
Abstract
Understanding the extent to which Atlantic sea surface temperatures (SSTs) are predictable is important due to the strong climate impacts of Atlantic SST on Atlantic hurricanes and temperature and precipitation over adjacent landmasses. However, models differ substantially on the degree of predictability of Atlantic SST and upper-ocean heat content (UOHC). In this work, a lower bound on predictability time scales for SST and UOHC in the North Atlantic is estimated purely from gridded ocean observations using a measure of the decorrelation time scale based on the local autocorrelation. Decorrelation time scales for both wintertime SST and UOHC are longest in the subpolar gyre, with maximum time scales of about 4–6 years. Wintertime SST and UOHC generally have similar decorrelation time scales, except in regions with very deep mixed layers, such as the Labrador Sea, where time scales for UOHC are much larger. Spatial variations in the wintertime climatological mixed layer depth explain 51%–73% (range for three datasets analyzed) of the regional variations in decorrelation time scales for UOHC and 26%–40% (range for three datasets analyzed) of the regional variations in decorrelation time scales for wintertime SST in the extratropical North Atlantic. These results suggest that to leading order decorrelation time scales for UOHC are determined by the thermal memory of the ocean.
Abstract
Mesoscale eddies, ubiquitous in the global ocean, play a key role in the climate system by stirring and mixing key tracers. Estimating, understanding, and predicting eddy diffusivity is of great significance for designing suitable eddy parameterization schemes for coarse-resolution climate models. This is because climate model results are sensitive to the choice of eddy diffusivity magnitudes. Using 24-yr satellite altimeter data and a Lagrangian approach, we estimate time-dependent global surface cross-stream eddy diffusivities. We found that eddy diffusivity has nonnegligible temporal variability, and the regionally averaged eddy diffusivity is significantly correlated with the climate indices, including the North Pacific Gyre Oscillation, Atlantic multidecadal oscillation, El Niño–Southern Oscillation, Pacific decadal oscillation, and dipole mode index. We also found that, compared to the suppressed mixing length theory, random forest (RF) is more effective in capturing the temporal variability of regionally averaged eddy diffusivity. Our results indicate the need for using time-dependent eddy mixing coefficients in climate models and demonstrate the advantage of RF in predicting mixing temporal variability.
Significance Statement
Mixing induced by ocean eddies can greatly modulate the ocean circulation and climate variability. Steady eddy mixing coefficients are often specified in coarse-resolution climate models. However, using satellite observations, we show that the eddy mixing rate has significant temporal variability at the global ocean surface. The regional temporal variability of eddy mixing is linked with large-scale climate variability (e.g., North Pacific Gyre Oscillation and Atlantic multidecadal oscillation). We found that random forest, a user-friendly machine learning algorithm, is a better tool to predict the mixing temporal variability than the conventional mixing theory. This study suggests the possibility of improving climate model performance by using time-dependent eddy mixing coefficients inferred from machine learning methods.
Abstract
Mesoscale eddies, ubiquitous in the global ocean, play a key role in the climate system by stirring and mixing key tracers. Estimating, understanding, and predicting eddy diffusivity is of great significance for designing suitable eddy parameterization schemes for coarse-resolution climate models. This is because climate model results are sensitive to the choice of eddy diffusivity magnitudes. Using 24-yr satellite altimeter data and a Lagrangian approach, we estimate time-dependent global surface cross-stream eddy diffusivities. We found that eddy diffusivity has nonnegligible temporal variability, and the regionally averaged eddy diffusivity is significantly correlated with the climate indices, including the North Pacific Gyre Oscillation, Atlantic multidecadal oscillation, El Niño–Southern Oscillation, Pacific decadal oscillation, and dipole mode index. We also found that, compared to the suppressed mixing length theory, random forest (RF) is more effective in capturing the temporal variability of regionally averaged eddy diffusivity. Our results indicate the need for using time-dependent eddy mixing coefficients in climate models and demonstrate the advantage of RF in predicting mixing temporal variability.
Significance Statement
Mixing induced by ocean eddies can greatly modulate the ocean circulation and climate variability. Steady eddy mixing coefficients are often specified in coarse-resolution climate models. However, using satellite observations, we show that the eddy mixing rate has significant temporal variability at the global ocean surface. The regional temporal variability of eddy mixing is linked with large-scale climate variability (e.g., North Pacific Gyre Oscillation and Atlantic multidecadal oscillation). We found that random forest, a user-friendly machine learning algorithm, is a better tool to predict the mixing temporal variability than the conventional mixing theory. This study suggests the possibility of improving climate model performance by using time-dependent eddy mixing coefficients inferred from machine learning methods.
Abstract
Moisture originating from the subtropical North Atlantic feeds precipitation throughout the Western Hemisphere. This ocean-to-land moisture transport leaves its imprint on sea surface salinity (SSS), enabling SSS over the subtropical oceans to be used as an indicator of terrestrial precipitation. This study demonstrates that springtime SSS over the northwestern portion of the subtropical North Atlantic significantly correlates with summertime precipitation over the U.S. Midwest. The linkage between springtime SSS and the Midwest summer precipitation is established through ocean-to-land moisture transport followed by a soil moisture feedback over the southern United States. In the spring, high SSS over the northwestern subtropical Atlantic coincides with a local increase in moisture flux divergence. The moisture flux is then directed toward and converges over the southern United States, which experiences increased precipitation and soil moisture. The increased soil moisture influences the regional water cycle both thermodynamically and dynamically, leading to excessive summer precipitation in the Midwest. Thermodynamically, the increased soil moisture tends to moisten the lower troposphere and enhances the meridional humidity gradient north of 36°N. Thus, more moisture will be transported and converged into the Midwest by the climatological low-level wind. Dynamically, the increases in soil moisture over the southern United States enhance the west–east soil moisture gradient eastward of the Rocky Mountains, which can help to intensify the Great Plains low-level jet in the summer, converging more moisture into the Midwest. Owing to these robust physical linkages, the springtime SSS outweighs the leading SST modes in predicting the Midwest summer precipitation and significantly improves rainfall prediction in this region.
Abstract
Moisture originating from the subtropical North Atlantic feeds precipitation throughout the Western Hemisphere. This ocean-to-land moisture transport leaves its imprint on sea surface salinity (SSS), enabling SSS over the subtropical oceans to be used as an indicator of terrestrial precipitation. This study demonstrates that springtime SSS over the northwestern portion of the subtropical North Atlantic significantly correlates with summertime precipitation over the U.S. Midwest. The linkage between springtime SSS and the Midwest summer precipitation is established through ocean-to-land moisture transport followed by a soil moisture feedback over the southern United States. In the spring, high SSS over the northwestern subtropical Atlantic coincides with a local increase in moisture flux divergence. The moisture flux is then directed toward and converges over the southern United States, which experiences increased precipitation and soil moisture. The increased soil moisture influences the regional water cycle both thermodynamically and dynamically, leading to excessive summer precipitation in the Midwest. Thermodynamically, the increased soil moisture tends to moisten the lower troposphere and enhances the meridional humidity gradient north of 36°N. Thus, more moisture will be transported and converged into the Midwest by the climatological low-level wind. Dynamically, the increases in soil moisture over the southern United States enhance the west–east soil moisture gradient eastward of the Rocky Mountains, which can help to intensify the Great Plains low-level jet in the summer, converging more moisture into the Midwest. Owing to these robust physical linkages, the springtime SSS outweighs the leading SST modes in predicting the Midwest summer precipitation and significantly improves rainfall prediction in this region.
