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Abstract
Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958–2010 period in the wet months of November–April were primarily associated with El Niño–Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May–October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May–October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001–10 decade, which is consistent with the observed weaker drying trend during this period.
Abstract
Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958–2010 period in the wet months of November–April were primarily associated with El Niño–Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May–October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May–October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001–10 decade, which is consistent with the observed weaker drying trend during this period.
Abstract
Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958–2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November–April) there is a positive relationship with the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May–October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS.
A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.
Abstract
Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958–2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November–April) there is a positive relationship with the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May–October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS.
A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.
Abstract
Tropical cyclone (TC) activity over the southeast Indian Ocean has been studied far less than other TC basins, such as the North Atlantic and northwest Pacific. The authors examine the interannual TC variability of the northwest Australian (NWAUS) subbasin (0°–35°S, 105°–135°E), using an Australian TC dataset for the 39-yr period of 1970–2008. Thirteen TC metrics are assessed, with emphasis on annual TC frequencies and total TC days.
Major findings are that for the NWAUS subbasin, there are annual means of 5.6 TCs and 42.4 TC days, with corresponding small standard deviations of 2.3 storms and 20.0 days. For intense TCs (WMO category 3 and higher), the annual mean TC frequency is 3.0, with a standard deviation of 1.6, and the annual average intense TC days is 7.6 days, with a standard deviation of 4.5 days. There are no significant linear trends in either mean annual TC frequencies or TC days. Notably, all 13 variability metrics show no trends over the 39-yr period and are less dependent upon standard El Niño–Southern Oscillation (ENSO) variables than many other TC basins, including the rest of the Australian region basin. The largest correlations with TC frequency were geopotential heights for June–August at 925 hPa over the South Atlantic Ocean (r = −0.65) and for April–June at 700 hPa over North America (−0.64). For TC days the largest correlations are geopotential heights for July–September at 1000 hPa over the South Atlantic Ocean (−0.7) and for April–June at 850 hPa over North America (−0.58). Last, wavelet analyses of annual TC frequencies and TC days reveal periodicities at ENSO and decadal time scales. However, the TC dataset is too short for conclusive evidence of multidecadal periodicities.
Given the large correlations revealed by this study, developing and testing of a multivariate seasonal TC prediction scheme has commenced, with lead times up to 6 months.
Abstract
Tropical cyclone (TC) activity over the southeast Indian Ocean has been studied far less than other TC basins, such as the North Atlantic and northwest Pacific. The authors examine the interannual TC variability of the northwest Australian (NWAUS) subbasin (0°–35°S, 105°–135°E), using an Australian TC dataset for the 39-yr period of 1970–2008. Thirteen TC metrics are assessed, with emphasis on annual TC frequencies and total TC days.
Major findings are that for the NWAUS subbasin, there are annual means of 5.6 TCs and 42.4 TC days, with corresponding small standard deviations of 2.3 storms and 20.0 days. For intense TCs (WMO category 3 and higher), the annual mean TC frequency is 3.0, with a standard deviation of 1.6, and the annual average intense TC days is 7.6 days, with a standard deviation of 4.5 days. There are no significant linear trends in either mean annual TC frequencies or TC days. Notably, all 13 variability metrics show no trends over the 39-yr period and are less dependent upon standard El Niño–Southern Oscillation (ENSO) variables than many other TC basins, including the rest of the Australian region basin. The largest correlations with TC frequency were geopotential heights for June–August at 925 hPa over the South Atlantic Ocean (r = −0.65) and for April–June at 700 hPa over North America (−0.64). For TC days the largest correlations are geopotential heights for July–September at 1000 hPa over the South Atlantic Ocean (−0.7) and for April–June at 850 hPa over North America (−0.58). Last, wavelet analyses of annual TC frequencies and TC days reveal periodicities at ENSO and decadal time scales. However, the TC dataset is too short for conclusive evidence of multidecadal periodicities.
Given the large correlations revealed by this study, developing and testing of a multivariate seasonal TC prediction scheme has commenced, with lead times up to 6 months.
Abstract
Horn of Africa rainfall varies on multiple time scales, but the underlying climate system controls on this variability have not been examined comprehensively. This study therefore investigates the linkages between June–September Horn of Africa (especially Ethiopian) rainfall and regional atmospheric circulation and global sea surface temperature (SST) variations on several key time scales. Wavelet analysis of 5-day average or monthly total rainfall for 1970–99 identifies the dominant coherent modes of rainfall variability. Several regional atmospheric variables and global SST are then identically wavelet filtered, based on the rainfall frequency bands. Regression, correlation, and composite analyses are subsequently used to identify the most important rainfall–climate system time-scale relationships.
The results show that Ethiopian monsoon rainfall variation is largely linked with annual time-scale atmospheric circulation patterns involving variability in the major components of the monsoon system. Although variability on the seasonal (75–210 days), quasi-biennial (QB; 1.42–3.04 yr), and El Niño–Southern Oscillation (ENSO; 3.04–4.60 yr) time scales accounts for much less variance than the annual mode (210 days–1.42 yr), they significantly affect Ethiopian rainfall by preferentially modulating the major regional monsoon components and remote teleconnection linkages. The seasonal time scale largely acts in phase with the annual mode, by enhancing or reducing the lower-tropospheric southwesterlies from the equatorial Atlantic during wet or dry periods. The wet QB phase strengthens the Azores and Saharan high and the tropical easterly jet (TEJ) over the Arabian Sea, while the wet ENSO phase enhances the Mascarene high, the TEJ, and the monsoon trough more locally. The effects of tropical SST on Ethiopian rainfall also are prominent on the QB and ENSO time scales. While rainfall–SST correlations for both the QB and ENSO modes are strongly positive (negative) over the equatorial western (eastern) Pacific, only ENSO exhibits widespread strong negative correlations over the Indian Ocean. Opposite QB and ENSO associations tend to characterize dry Ethiopian conditions. The relationships identified on individual time scales now are being used to develop and validate statistical prediction models for Ethiopia.
Abstract
Horn of Africa rainfall varies on multiple time scales, but the underlying climate system controls on this variability have not been examined comprehensively. This study therefore investigates the linkages between June–September Horn of Africa (especially Ethiopian) rainfall and regional atmospheric circulation and global sea surface temperature (SST) variations on several key time scales. Wavelet analysis of 5-day average or monthly total rainfall for 1970–99 identifies the dominant coherent modes of rainfall variability. Several regional atmospheric variables and global SST are then identically wavelet filtered, based on the rainfall frequency bands. Regression, correlation, and composite analyses are subsequently used to identify the most important rainfall–climate system time-scale relationships.
The results show that Ethiopian monsoon rainfall variation is largely linked with annual time-scale atmospheric circulation patterns involving variability in the major components of the monsoon system. Although variability on the seasonal (75–210 days), quasi-biennial (QB; 1.42–3.04 yr), and El Niño–Southern Oscillation (ENSO; 3.04–4.60 yr) time scales accounts for much less variance than the annual mode (210 days–1.42 yr), they significantly affect Ethiopian rainfall by preferentially modulating the major regional monsoon components and remote teleconnection linkages. The seasonal time scale largely acts in phase with the annual mode, by enhancing or reducing the lower-tropospheric southwesterlies from the equatorial Atlantic during wet or dry periods. The wet QB phase strengthens the Azores and Saharan high and the tropical easterly jet (TEJ) over the Arabian Sea, while the wet ENSO phase enhances the Mascarene high, the TEJ, and the monsoon trough more locally. The effects of tropical SST on Ethiopian rainfall also are prominent on the QB and ENSO time scales. While rainfall–SST correlations for both the QB and ENSO modes are strongly positive (negative) over the equatorial western (eastern) Pacific, only ENSO exhibits widespread strong negative correlations over the Indian Ocean. Opposite QB and ENSO associations tend to characterize dry Ethiopian conditions. The relationships identified on individual time scales now are being used to develop and validate statistical prediction models for Ethiopia.
Abstract
The recorded climatology of tropical cyclones that affect the Tasman Sea spans the period from 1911 to the present. This climatology is a subset of the much larger Australian Tropical Cyclone database, which is the official record of all tropical cyclones in the Australian area of responsibility. Such a long, detailed record should provide an excellent dataset for regional climate research. However, a detailed analysis of the database has revealed that it must be used with caution over the Tasman Sea, where statistically significant discontinuities are present, greatly reducing its quality and length for climate and climate change studies. Problems with the complete Australian Tropical Cyclone database have been identified and discussed earlier by a number of authors. This study is concerned with two statistically significant discontinuities that occurred in the Tasman Sea portion of the database in the mid-1950s and in 1977. The first discontinuity almost trebled the recorded frequency of tropical cyclones, whereas the second discontinuity exhibited an opposite trend, decreasing the recorded frequency of tropical cyclones by a factor of 8 from the previous period. Some possible explanations for the abrupt changes in this subset of one particular database are discussed. It is suggested here that the most likely explanation is the improved observing technology and the associated changes in interpretation of the new data. Finally, it is likely that other climate databases have been affected by similar problems and should be treated with the same degree of caution.
Abstract
The recorded climatology of tropical cyclones that affect the Tasman Sea spans the period from 1911 to the present. This climatology is a subset of the much larger Australian Tropical Cyclone database, which is the official record of all tropical cyclones in the Australian area of responsibility. Such a long, detailed record should provide an excellent dataset for regional climate research. However, a detailed analysis of the database has revealed that it must be used with caution over the Tasman Sea, where statistically significant discontinuities are present, greatly reducing its quality and length for climate and climate change studies. Problems with the complete Australian Tropical Cyclone database have been identified and discussed earlier by a number of authors. This study is concerned with two statistically significant discontinuities that occurred in the Tasman Sea portion of the database in the mid-1950s and in 1977. The first discontinuity almost trebled the recorded frequency of tropical cyclones, whereas the second discontinuity exhibited an opposite trend, decreasing the recorded frequency of tropical cyclones by a factor of 8 from the previous period. Some possible explanations for the abrupt changes in this subset of one particular database are discussed. It is suggested here that the most likely explanation is the improved observing technology and the associated changes in interpretation of the new data. Finally, it is likely that other climate databases have been affected by similar problems and should be treated with the same degree of caution.
Abstract
This study examines combining ENSO sea surface temperature (SST) regions for seasonal prediction of Coral Sea tropical cyclone (TC) frequency. The Coral Sea averages ~4 TCs per season, but is characterized by strong interannual variability, with 1–9 TCs per season, over the period 1977–2012. A wavelet analysis confirms that ENSO is a key contributor to Coral Sea TC count (TCC) variability. Motivated by the impact of El Niño Modoki on regional climate anomalies, a suite of 38 linear models is constructed and assessed on its ability to predict Coral Sea seasonal TCC. Seasonal predictions of TCC are generated by a leave-one-out cross validation (LOOCV). An important finding is that models made up of multiple tropical Pacific SST regions, such as those that comprise the El Niño Modoki Index (EMI) or the Trans-Niño Index (TNI), perform considerably better than models comprising only single regions, such as Niño-3.4 or Niño-4. Moreover, enhanced (suppressed) TC activity is expected in the Coral Sea when the central Pacific is anomalously cool (warm) and the eastern and western Pacific are anomalously warm (cool) during austral winter. The best cross-validated model has persistent and statistically significantly high correlations with TCC (r > 0.5) at lead times of ~6 months prior to the mean onset of the Coral Sea TC season, whereas correlations based heavily on the widely used Niño-3.4 region are not statistically significant or meaningful (r = 0.09) for the same lead times. Of the 38 models assessed, several optimized forms of the EMI and of the TNI perform best.
Abstract
This study examines combining ENSO sea surface temperature (SST) regions for seasonal prediction of Coral Sea tropical cyclone (TC) frequency. The Coral Sea averages ~4 TCs per season, but is characterized by strong interannual variability, with 1–9 TCs per season, over the period 1977–2012. A wavelet analysis confirms that ENSO is a key contributor to Coral Sea TC count (TCC) variability. Motivated by the impact of El Niño Modoki on regional climate anomalies, a suite of 38 linear models is constructed and assessed on its ability to predict Coral Sea seasonal TCC. Seasonal predictions of TCC are generated by a leave-one-out cross validation (LOOCV). An important finding is that models made up of multiple tropical Pacific SST regions, such as those that comprise the El Niño Modoki Index (EMI) or the Trans-Niño Index (TNI), perform considerably better than models comprising only single regions, such as Niño-3.4 or Niño-4. Moreover, enhanced (suppressed) TC activity is expected in the Coral Sea when the central Pacific is anomalously cool (warm) and the eastern and western Pacific are anomalously warm (cool) during austral winter. The best cross-validated model has persistent and statistically significantly high correlations with TCC (r > 0.5) at lead times of ~6 months prior to the mean onset of the Coral Sea TC season, whereas correlations based heavily on the widely used Niño-3.4 region are not statistically significant or meaningful (r = 0.09) for the same lead times. Of the 38 models assessed, several optimized forms of the EMI and of the TNI perform best.
Abstract
The major objective of this study is to re-evaluate the ocean–land transport of moisture for rainfall in West Africa using 1979–2008 NCEP–NCAR reanalysis data. The vertically integrated atmospheric water vapor flux for the surface–850 hPa is calculated to account for total low-level moisture flux contribution to rainfall over West Africa. Analysis of mean monthly total vapor fluxes shows a progressive penetration of the flux into West Africa from the south and west. During spring (April–June), the northward flux forms a “moisture river” transporting moisture current into the Gulf of Guinea coast. In the peak monsoon season (July–September), the southerly transport weakens, but westerly transport is enhanced and extends to 20°N owing to the strengthening West African jet off the west coast. Mean seasonal values of total water vapor flux components across boundaries indicate that the zonal component is the largest contributor to mean moisture transport into the Sahel, while the meridional transport contributes the most over the Guinea coast. For the wet years of the Sahel rainy season (July–September), active anomalies are displaced farther north compared to the long-term average. This includes the latitude of the intertropical front (ITF), the extent of moisture flux, and the zone of strong moisture flux convergence, with an enhanced westerly flow. For the dry Sahel years, the opposite patterns are observed. Statistically significant positive correlations between the zonal moisture fluxes and Sudan–Sahel rainfall totals are most pronounced when the zonal fluxes lead by 1–4 pentads. However, although weak, they still are statistically significant at lags 3 and 4 for meridional moisture fluxes.
Abstract
The major objective of this study is to re-evaluate the ocean–land transport of moisture for rainfall in West Africa using 1979–2008 NCEP–NCAR reanalysis data. The vertically integrated atmospheric water vapor flux for the surface–850 hPa is calculated to account for total low-level moisture flux contribution to rainfall over West Africa. Analysis of mean monthly total vapor fluxes shows a progressive penetration of the flux into West Africa from the south and west. During spring (April–June), the northward flux forms a “moisture river” transporting moisture current into the Gulf of Guinea coast. In the peak monsoon season (July–September), the southerly transport weakens, but westerly transport is enhanced and extends to 20°N owing to the strengthening West African jet off the west coast. Mean seasonal values of total water vapor flux components across boundaries indicate that the zonal component is the largest contributor to mean moisture transport into the Sahel, while the meridional transport contributes the most over the Guinea coast. For the wet years of the Sahel rainy season (July–September), active anomalies are displaced farther north compared to the long-term average. This includes the latitude of the intertropical front (ITF), the extent of moisture flux, and the zone of strong moisture flux convergence, with an enhanced westerly flow. For the dry Sahel years, the opposite patterns are observed. Statistically significant positive correlations between the zonal moisture fluxes and Sudan–Sahel rainfall totals are most pronounced when the zonal fluxes lead by 1–4 pentads. However, although weak, they still are statistically significant at lags 3 and 4 for meridional moisture fluxes.
Abstract
This study investigates the El Niño–Southern Oscillation (ENSO) contribution to Philippine tropical cyclone (TC) variability, for a range of quarterly TC metrics. Philippine TC activity is found to depend on both ENSO quarter and phase. TC counts during El Niño phases differ significantly from neutral phases in all quarters, whereas neutral and La Niña phases differ only in January–March and July–September. Differences in landfalls between neutral and El Niño phases are significant in January–March and October–December and in January–March for neutral and La Niña phases. El Niño and La Niña landfalls are significantly different in April–June and October–December. Philippine neutral and El Niño TC genesis cover broader longitude–latitude ranges with similar long tracks, originating farther east in the western North Pacific. In El Niño phases, the mean eastward displacement of genesis locations and more recurving TCs reduce Philippine TC frequencies. Proximity of La Niña TC genesis to the Philippines and straight-moving tracks in April–June and October–December increase TC frequencies and landfalls. Neutral and El Niño accumulated cyclone energy (ACE) values are above average, except in April–June of El Niño phases. Above-average quarterly ACE in neutral years is due to increased TC frequencies, days, and intensities, whereas above-average El Niño ACE in July–September is due to increased TC days and intensities. Below-average La Niña ACE results from fewer TCs and shorter life cycles. Longer TC durations produce slightly above-average TC days in July–September El Niño phases. Fewer TCs than neutral years, as well as shorter TC durations, imply less TC days in La Niña phases. However, above-average TC days occur in October–December as a result of higher TC frequencies.
Abstract
This study investigates the El Niño–Southern Oscillation (ENSO) contribution to Philippine tropical cyclone (TC) variability, for a range of quarterly TC metrics. Philippine TC activity is found to depend on both ENSO quarter and phase. TC counts during El Niño phases differ significantly from neutral phases in all quarters, whereas neutral and La Niña phases differ only in January–March and July–September. Differences in landfalls between neutral and El Niño phases are significant in January–March and October–December and in January–March for neutral and La Niña phases. El Niño and La Niña landfalls are significantly different in April–June and October–December. Philippine neutral and El Niño TC genesis cover broader longitude–latitude ranges with similar long tracks, originating farther east in the western North Pacific. In El Niño phases, the mean eastward displacement of genesis locations and more recurving TCs reduce Philippine TC frequencies. Proximity of La Niña TC genesis to the Philippines and straight-moving tracks in April–June and October–December increase TC frequencies and landfalls. Neutral and El Niño accumulated cyclone energy (ACE) values are above average, except in April–June of El Niño phases. Above-average quarterly ACE in neutral years is due to increased TC frequencies, days, and intensities, whereas above-average El Niño ACE in July–September is due to increased TC days and intensities. Below-average La Niña ACE results from fewer TCs and shorter life cycles. Longer TC durations produce slightly above-average TC days in July–September El Niño phases. Fewer TCs than neutral years, as well as shorter TC durations, imply less TC days in La Niña phases. However, above-average TC days occur in October–December as a result of higher TC frequencies.
Abstract
This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.
Abstract
This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.
Abstract
This study applies a multiphase, multiple-rheology, scalable, and extensible geofluid model to the Greenland Ice Sheet (GrIS). The model is driven by monthly atmospheric forcing from global climate model simulations. Novel features of the model, referred to as the scalable and extensible geofluid modeling system (SEGMENT-Ice), include using the full Navier–Stokes equations to account for nonlocal dynamic balance and its influence on ice flow, and a granular sliding layer between the bottom ice layer and the lithosphere layer to provide a mechanism for possible large-scale surges in a warmer future climate (granular basal layer is for certain specific regions, though). Monthly climate of SEGMENT-Ice allows an investigation of detailed features such as seasonal melt area extent (SME) over Greenland. The model reproduced reasonably well the annual maximum SME and total ice mass lost rate when compared observations from the Special Sensing Microwave Imager (SSM/I) and Gravity Recovery and Climate Experiment (GRACE) over the past few decades.
The SEGMENT-Ice simulations are driven by projections from two relatively high-resolution climate models, the NCAR Community Climate System Model, version 3 (CCSM3) and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)], under a realistic twenty-first-century greenhouse gas emission scenario. They suggest that the surface flow would be enhanced over the entire GrIS owing to a reduction of ice viscosity as the temperature increases, despite the small change in the ice surface topography over the interior of Greenland. With increased surface flow speed, strain heating induces more rapid heating in the ice at levels deeper than due to diffusion alone. Basal sliding, especially for granular sediments, provides an efficient mechanism for fast-glacier acceleration and enhanced mass loss. This mechanism, absent from other models, provides a rapid dynamic response to climate change. Net mass loss estimates from the new model should reach ~220 km3 yr−1 by 2100, significantly higher than estimates by the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 4 (AR4) of ~50–100 km3 yr−1. By 2100, the perennial frozen surface area decreases up to ~60%, to ~7 × 105 km2, indicating a massive expansion of the ablation zone. Ice mass change patterns, particularly along the periphery, are very similar between the two climate models.
Abstract
This study applies a multiphase, multiple-rheology, scalable, and extensible geofluid model to the Greenland Ice Sheet (GrIS). The model is driven by monthly atmospheric forcing from global climate model simulations. Novel features of the model, referred to as the scalable and extensible geofluid modeling system (SEGMENT-Ice), include using the full Navier–Stokes equations to account for nonlocal dynamic balance and its influence on ice flow, and a granular sliding layer between the bottom ice layer and the lithosphere layer to provide a mechanism for possible large-scale surges in a warmer future climate (granular basal layer is for certain specific regions, though). Monthly climate of SEGMENT-Ice allows an investigation of detailed features such as seasonal melt area extent (SME) over Greenland. The model reproduced reasonably well the annual maximum SME and total ice mass lost rate when compared observations from the Special Sensing Microwave Imager (SSM/I) and Gravity Recovery and Climate Experiment (GRACE) over the past few decades.
The SEGMENT-Ice simulations are driven by projections from two relatively high-resolution climate models, the NCAR Community Climate System Model, version 3 (CCSM3) and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)], under a realistic twenty-first-century greenhouse gas emission scenario. They suggest that the surface flow would be enhanced over the entire GrIS owing to a reduction of ice viscosity as the temperature increases, despite the small change in the ice surface topography over the interior of Greenland. With increased surface flow speed, strain heating induces more rapid heating in the ice at levels deeper than due to diffusion alone. Basal sliding, especially for granular sediments, provides an efficient mechanism for fast-glacier acceleration and enhanced mass loss. This mechanism, absent from other models, provides a rapid dynamic response to climate change. Net mass loss estimates from the new model should reach ~220 km3 yr−1 by 2100, significantly higher than estimates by the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 4 (AR4) of ~50–100 km3 yr−1. By 2100, the perennial frozen surface area decreases up to ~60%, to ~7 × 105 km2, indicating a massive expansion of the ablation zone. Ice mass change patterns, particularly along the periphery, are very similar between the two climate models.
