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- Author or Editor: V. Misra x
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Abstract
In this paper the concept of track integrated kinetic energy (TIKE) is introduced as a measure of seasonal Atlantic tropical cyclone activity and applied to seasonal variability in the Atlantic. It is similar in concept to the more commonly used accumulated cyclone energy (ACE) with an important difference that in TIKE the integrated kinetic energy (IKE) is accumulated for the life span of the Atlantic tropical cyclone. The IKE is, however, computed by volume integrating the 10-m level sustained winds of tropical strength or higher quadrant by quadrant, while ACE uses the maximum sustained winds only without accounting for the structure of the storm. In effect TIKE accounts for the intensity, duration, and size of the tropical cyclones. In this research, the authors have examined the seasonality and the interannual variations of the seasonal Atlantic TIKE over a period of 22 yr from 1990 to 2011. It is found that the Atlantic TIKE climatologically peaks in the month of September and the frequency of storms with the largest TIKE are highest in the eastern tropical Atlantic. The interannual variations of the Atlantic TIKE reveal that it is likely influenced by SST variations in the equatorial Pacific and in the Atlantic Oceans. The SST variations in the central equatorial Pacific are negatively correlated with the contemporaneous seasonal (June–November) TIKE. The size of the Atlantic warm pool (AWP) is positively correlated with seasonal TIKE.
Abstract
In this paper the concept of track integrated kinetic energy (TIKE) is introduced as a measure of seasonal Atlantic tropical cyclone activity and applied to seasonal variability in the Atlantic. It is similar in concept to the more commonly used accumulated cyclone energy (ACE) with an important difference that in TIKE the integrated kinetic energy (IKE) is accumulated for the life span of the Atlantic tropical cyclone. The IKE is, however, computed by volume integrating the 10-m level sustained winds of tropical strength or higher quadrant by quadrant, while ACE uses the maximum sustained winds only without accounting for the structure of the storm. In effect TIKE accounts for the intensity, duration, and size of the tropical cyclones. In this research, the authors have examined the seasonality and the interannual variations of the seasonal Atlantic TIKE over a period of 22 yr from 1990 to 2011. It is found that the Atlantic TIKE climatologically peaks in the month of September and the frequency of storms with the largest TIKE are highest in the eastern tropical Atlantic. The interannual variations of the Atlantic TIKE reveal that it is likely influenced by SST variations in the equatorial Pacific and in the Atlantic Oceans. The SST variations in the central equatorial Pacific are negatively correlated with the contemporaneous seasonal (June–November) TIKE. The size of the Atlantic warm pool (AWP) is positively correlated with seasonal TIKE.
Abstract
This paper diagnoses the temperature trends in maximum (T max) and minimum temperatures (T min) over a selection of 65 stations spread over the southeast United States (SEUS) from three observed datasets. They are the Cooperative Observer network program (COOP), the COOP data corrected for documented shifts in time of observation (COOP1), and the COOP data additionally corrected for documented changes in instrumentation (COOP2). These 65 stations have been isolated for having the three observed datasets for the same time period from 1948 to 2009. The authors’ comparisons suggest that COOP2 displays stronger warming (cooling) trends in T max (T min) compared with COOP1 in all four seasons. This is consistent with the expectation from the bias correction applied for the instrument change. In comparison, the differences between COOP and COOP2 are relatively larger. In the spring, summer, and fall seasons, the median T max trend is warming in COOP2 while it is cooling in COOP. In the winter season, the median trends of T max in the two datasets are positive, but their magnitudes are substantially different. Similarly, in the winter, summer, and fall seasons, the warming trend in T min in COOP is contrary to the cooling trend in COOP2. In the spring season, the median trend in T min is comparable between the two datasets. COOP2 shows the relationship of trends in T min, with the extent of urbanization in these 65 stations, to be statistically significant and to be consistent with expectations from theory in contrast to the COOP data.
Abstract
This paper diagnoses the temperature trends in maximum (T max) and minimum temperatures (T min) over a selection of 65 stations spread over the southeast United States (SEUS) from three observed datasets. They are the Cooperative Observer network program (COOP), the COOP data corrected for documented shifts in time of observation (COOP1), and the COOP data additionally corrected for documented changes in instrumentation (COOP2). These 65 stations have been isolated for having the three observed datasets for the same time period from 1948 to 2009. The authors’ comparisons suggest that COOP2 displays stronger warming (cooling) trends in T max (T min) compared with COOP1 in all four seasons. This is consistent with the expectation from the bias correction applied for the instrument change. In comparison, the differences between COOP and COOP2 are relatively larger. In the spring, summer, and fall seasons, the median T max trend is warming in COOP2 while it is cooling in COOP. In the winter season, the median trends of T max in the two datasets are positive, but their magnitudes are substantially different. Similarly, in the winter, summer, and fall seasons, the warming trend in T min in COOP is contrary to the cooling trend in COOP2. In the spring season, the median trend in T min is comparable between the two datasets. COOP2 shows the relationship of trends in T min, with the extent of urbanization in these 65 stations, to be statistically significant and to be consistent with expectations from theory in contrast to the COOP data.
Abstract
In this study, the authors contrast four century-long meteorological datasets comprising of two sets of observations [Climate Research Unit (CRU) and Parameter–Elevation Regressions on Independent Slopes Model (PRISM)] and two atmospheric reanalyses [Twentieth Century Reanalysis (20CR) and Florida Climate Institute–Florida State University Land–Atmosphere Regional Reanalysis version 1.0 (FLAReS1.0)] to diagnose the El Niño–Southern Oscillation (ENSO) forced variations on the streamflow in 28 watersheds spread across the southeastern United States (SEUS). The datasets are used to force three different lumped (calibrated) hydrological models with precipitation from these four sources of century-long datasets separately to obtain the median prediction from 1800 (=3 models × 600 simulations per model per watershed per season) multimodel estimates of seasonal mean streamflow across the 28 watersheds in the SEUS for each winter season from 1906 to 2005. The authors then compare and contrast the mean streamflow and its variability estimates from all three of the century-long climate forcings. The multimodel strategy of simulating the seasonal mean streamflow is to reduce the hydrological model uncertainty. The authors focus on the boreal winter season when ENSO influence on the SEUS climate variations is well known.
The authors find that the atmospheric reanalysis over the SEUS is able to reasonably capture the ENSO teleconnections as depicted in the CRU and PRISM precipitation datasets. Even the observed decadal modulation of this teleconnection by Atlantic multidecadal oscillation (AMO) is broadly captured. The streamflow in the 28 watersheds also show similar consistency across the four datasets in that the positive correlations of the boreal winter Niño-3.4 SST anomalies with corresponding anomalies of streamflow, the associated shift in the probability density function of the streamflow with the change in phase of ENSO, and the decadal modulation of the ENSO teleconnection by the AMO are sustained in the streamflow simulations forced by all four climate datasets (CRU, PRISM, 20CR, and FLAReS1.0). However, the ENSO signal in the streamflow is consistently much stronger in the southern watersheds (over Florida) of the SEUS across all four climate datasets. During the negative phase of the AMO, however, there is a clear shift of the ENSO teleconnections with streamflow, with winter streamflows in northern watersheds (over the Carolinas) exhibiting much stronger correlations with the ENSO Niño-3.4 index relative to the southern watersheds of the SEUS. This study clearly indicates that the proposed methodology using FLAReS1.0 serves as a viable alternative to reconstruct twentieth-century SEUS seasonal winter hydrology that captures the interannual variations of ENSO and associated decadal variations forced by the AMO. However, it is found that the FLAReS1.0 forced streamflow is far from adequate in simulating the streamflow dynamics of the watershed over the SEUS at a daily time scale.
Abstract
In this study, the authors contrast four century-long meteorological datasets comprising of two sets of observations [Climate Research Unit (CRU) and Parameter–Elevation Regressions on Independent Slopes Model (PRISM)] and two atmospheric reanalyses [Twentieth Century Reanalysis (20CR) and Florida Climate Institute–Florida State University Land–Atmosphere Regional Reanalysis version 1.0 (FLAReS1.0)] to diagnose the El Niño–Southern Oscillation (ENSO) forced variations on the streamflow in 28 watersheds spread across the southeastern United States (SEUS). The datasets are used to force three different lumped (calibrated) hydrological models with precipitation from these four sources of century-long datasets separately to obtain the median prediction from 1800 (=3 models × 600 simulations per model per watershed per season) multimodel estimates of seasonal mean streamflow across the 28 watersheds in the SEUS for each winter season from 1906 to 2005. The authors then compare and contrast the mean streamflow and its variability estimates from all three of the century-long climate forcings. The multimodel strategy of simulating the seasonal mean streamflow is to reduce the hydrological model uncertainty. The authors focus on the boreal winter season when ENSO influence on the SEUS climate variations is well known.
The authors find that the atmospheric reanalysis over the SEUS is able to reasonably capture the ENSO teleconnections as depicted in the CRU and PRISM precipitation datasets. Even the observed decadal modulation of this teleconnection by Atlantic multidecadal oscillation (AMO) is broadly captured. The streamflow in the 28 watersheds also show similar consistency across the four datasets in that the positive correlations of the boreal winter Niño-3.4 SST anomalies with corresponding anomalies of streamflow, the associated shift in the probability density function of the streamflow with the change in phase of ENSO, and the decadal modulation of the ENSO teleconnection by the AMO are sustained in the streamflow simulations forced by all four climate datasets (CRU, PRISM, 20CR, and FLAReS1.0). However, the ENSO signal in the streamflow is consistently much stronger in the southern watersheds (over Florida) of the SEUS across all four climate datasets. During the negative phase of the AMO, however, there is a clear shift of the ENSO teleconnections with streamflow, with winter streamflows in northern watersheds (over the Carolinas) exhibiting much stronger correlations with the ENSO Niño-3.4 index relative to the southern watersheds of the SEUS. This study clearly indicates that the proposed methodology using FLAReS1.0 serves as a viable alternative to reconstruct twentieth-century SEUS seasonal winter hydrology that captures the interannual variations of ENSO and associated decadal variations forced by the AMO. However, it is found that the FLAReS1.0 forced streamflow is far from adequate in simulating the streamflow dynamics of the watershed over the SEUS at a daily time scale.
Abstract
This study attempts to explain the considerable spatial heterogeneity in the observed linear trends of monthly mean maximum and minimum temperatures (T max and T min) from station observations in the southeastern (SE) United States (specifically Florida, Alabama, Georgia, South Carolina, and North Carolina). In a majority of these station sites, the warming trends in T min are stronger in urban areas relative to rural areas. The linear trends of T min in urban areas of the SE United States are approximately 7°F century−1 compared to about 5.5°F century−1 in rural areas. The trends in T max show weaker warming (or stronger cooling) trends with irrigation, while trends in T min show stronger warming trends. This functionality of the temperature trends with land features also shows seasonality, with the boreal summer season showing the most consistent relationship in the trends of both T max and T min. This study reveals that linear trends in T max in the boreal summer season show a cooling trend of about 0.5°F century−1 with irrigation, while the same observing stations on an average display warming trends in T min of about 3.5°F century−1. The seasonality and the physical consistency of these relationships with existing theories may suggest that urbanization and irrigation have a nonnegligible influence on the spatial heterogeneity of the surface temperature trends over the SE United States. The study also delineates the caveats and limitations of the conclusions reached herein due to the potential influence of perceived nonclimatic discontinuities (which incidentally could also have a seasonal cycle) that have not been taken into account.
Abstract
This study attempts to explain the considerable spatial heterogeneity in the observed linear trends of monthly mean maximum and minimum temperatures (T max and T min) from station observations in the southeastern (SE) United States (specifically Florida, Alabama, Georgia, South Carolina, and North Carolina). In a majority of these station sites, the warming trends in T min are stronger in urban areas relative to rural areas. The linear trends of T min in urban areas of the SE United States are approximately 7°F century−1 compared to about 5.5°F century−1 in rural areas. The trends in T max show weaker warming (or stronger cooling) trends with irrigation, while trends in T min show stronger warming trends. This functionality of the temperature trends with land features also shows seasonality, with the boreal summer season showing the most consistent relationship in the trends of both T max and T min. This study reveals that linear trends in T max in the boreal summer season show a cooling trend of about 0.5°F century−1 with irrigation, while the same observing stations on an average display warming trends in T min of about 3.5°F century−1. The seasonality and the physical consistency of these relationships with existing theories may suggest that urbanization and irrigation have a nonnegligible influence on the spatial heterogeneity of the surface temperature trends over the SE United States. The study also delineates the caveats and limitations of the conclusions reached herein due to the potential influence of perceived nonclimatic discontinuities (which incidentally could also have a seasonal cycle) that have not been taken into account.
