Search Results
You are looking at 21 - 30 of 53 items for
- Author or Editor: Christian Jakob x
- Refine by Access: All Content x
Abstract
A cluster analysis is applied to the mesoscale convective systems (MCSs) that developed in northern Australia and the surrounding oceans during six wet seasons (September–April) from 1995/96 to 2000/01. During this period, 13 585 MCSs were identified and tracked using an infrared channel (IR1) on the Japanese Meteorological Agency Geostationary Meteorological Satellite 5 (GMS5). Based on the lifetimes of the MCSs, the area covered by cloud, the expansion rate of the cloud, the minimum cloud-top temperature, and their zonal direction of propagation, the MCSs are grouped objectively into four classes. One of the strengths of the analysis is that it objectively condenses a large dataset into a small number of classes, each with its own physical characteristics.
MCSs in class 1 (short) are relatively short lived, with 95% having lifetimes less than 5 h, and they are found most frequently over the oceans during the early and late parts of the wet season. MCSs in classes 2 and 3 [long and intermediate west (Int-West)] are longer lived and propagate to the west, developing over continental northwest Australia in deep easterly flow during breaks in the monsoon. These two classes are distinguished principally by their lifetime, with 95% of MCSs in the long class having lifetimes exceeding 4 h. Class 4 (Int-East) comprises MCSs that form over the subtropical latitudes of eastern Australia and in the deep westerly flow over northern parts of the continent during the monsoon and active phases of the MJO.
Abstract
A cluster analysis is applied to the mesoscale convective systems (MCSs) that developed in northern Australia and the surrounding oceans during six wet seasons (September–April) from 1995/96 to 2000/01. During this period, 13 585 MCSs were identified and tracked using an infrared channel (IR1) on the Japanese Meteorological Agency Geostationary Meteorological Satellite 5 (GMS5). Based on the lifetimes of the MCSs, the area covered by cloud, the expansion rate of the cloud, the minimum cloud-top temperature, and their zonal direction of propagation, the MCSs are grouped objectively into four classes. One of the strengths of the analysis is that it objectively condenses a large dataset into a small number of classes, each with its own physical characteristics.
MCSs in class 1 (short) are relatively short lived, with 95% having lifetimes less than 5 h, and they are found most frequently over the oceans during the early and late parts of the wet season. MCSs in classes 2 and 3 [long and intermediate west (Int-West)] are longer lived and propagate to the west, developing over continental northwest Australia in deep easterly flow during breaks in the monsoon. These two classes are distinguished principally by their lifetime, with 95% of MCSs in the long class having lifetimes exceeding 4 h. Class 4 (Int-East) comprises MCSs that form over the subtropical latitudes of eastern Australia and in the deep westerly flow over northern parts of the continent during the monsoon and active phases of the MJO.
Abstract
The variability of the north Australian wet season is examined by performing cluster analysis on the wind and thermodynamic information contained in the 2300 UTC radiosonde data at Darwin for 49 wet seasons (September–April) from 1957/58 to 2005/06. Five objectively derived regimes of the wet season are obtained and are found to differ significantly in their synoptic environment, cloud patterns, and rainfall distributions. One regime is primarily associated with the trade wind regime. Two regimes are associated with the lead up to and break periods of the monsoon at Darwin. A fourth regime is clearly identified with the active monsoon at Darwin and is offered as a definition of monsoon onset. This regime captures the active monsoon environment associated with significant widespread rainfall. The fifth regime is a mixed regime, with some days associated with the inactive monsoon, a period of westerly zonal winds at Darwin associated with relatively suppressed convection compared with the active monsoon. Other days for this regime are break period conditions with a low-level westerly flow below 900 hPa.
Abstract
The variability of the north Australian wet season is examined by performing cluster analysis on the wind and thermodynamic information contained in the 2300 UTC radiosonde data at Darwin for 49 wet seasons (September–April) from 1957/58 to 2005/06. Five objectively derived regimes of the wet season are obtained and are found to differ significantly in their synoptic environment, cloud patterns, and rainfall distributions. One regime is primarily associated with the trade wind regime. Two regimes are associated with the lead up to and break periods of the monsoon at Darwin. A fourth regime is clearly identified with the active monsoon at Darwin and is offered as a definition of monsoon onset. This regime captures the active monsoon environment associated with significant widespread rainfall. The fifth regime is a mixed regime, with some days associated with the inactive monsoon, a period of westerly zonal winds at Darwin associated with relatively suppressed convection compared with the active monsoon. Other days for this regime are break period conditions with a low-level westerly flow below 900 hPa.
Abstract
A climatology of clouds within marine cold air outbreaks, primarily using long-term satellite observations, is presented. Cloud properties between cold air outbreaks in different regions in both hemispheres are compared. In all regions marine cold air outbreak clouds tend to be low level with high cloud fraction and low-to-moderate optical thickness. Stronger cold air outbreaks have clouds that are optically thicker, but not geometrically thicker, than those in weaker cold air outbreaks. There is some evidence that clouds deepen and break up over the course of a cold air outbreak event. The top-of-the-atmosphere longwave cloud radiative effect in cold air outbreaks is small because the clouds have low tops. However, their surface longwave cloud radiative effect is considerably larger. The rarity of cold air outbreaks in summer limits their shortwave cloud radiative effect. They do not contribute substantially to global shortwave cloud radiative effect and are, therefore, unlikely to be a major source of shortwave cloud radiative effect errors in climate models.
Abstract
A climatology of clouds within marine cold air outbreaks, primarily using long-term satellite observations, is presented. Cloud properties between cold air outbreaks in different regions in both hemispheres are compared. In all regions marine cold air outbreak clouds tend to be low level with high cloud fraction and low-to-moderate optical thickness. Stronger cold air outbreaks have clouds that are optically thicker, but not geometrically thicker, than those in weaker cold air outbreaks. There is some evidence that clouds deepen and break up over the course of a cold air outbreak event. The top-of-the-atmosphere longwave cloud radiative effect in cold air outbreaks is small because the clouds have low tops. However, their surface longwave cloud radiative effect is considerably larger. The rarity of cold air outbreaks in summer limits their shortwave cloud radiative effect. They do not contribute substantially to global shortwave cloud radiative effect and are, therefore, unlikely to be a major source of shortwave cloud radiative effect errors in climate models.
Abstract
Of great importance for the simulation of climate using general circulation models is their ability to represent accurately the vertical distribution of fractional cloud amount. In this paper, a technique to derive cloud fraction as a function of height using ground-based radar and lidar is described. The relatively unattenuated radar detects clouds and precipitation throughout the whole depth of the troposphere, whereas the lidar is able to locate cloud base accurately in the presence of rain or drizzle. From a direct comparison of 3 months of cloud fraction observed at Chilbolton, England, with the values held at the nearest grid box of the European Centre for Medium-Range Forecasts (ECMWF) model it is found that, on average, the model tends to underpredict cloud fraction below 7 km and considerably overpredict it above. The difference below 7 km can in large part be explained by the fact that the model treats snow and ice cloud separately, with snow not contributing to cloud fraction. Modifying the model cloud fraction to include the contribution from snow (already present in the form of fluxes between levels) results in much better agreement in mean cloud fraction, frequency of occurrence, and amount when present between 1 and 7 km. This, together with the fact that both the lidar and the radar echoes tend to be stronger in the regions of ice clouds that the model regards as snow, indicates that snow should not be treated as radiatively inert by the model radiation scheme. Above 7 km, the difference between the model and the observations is partly due to some of the high clouds in the model being associated with very low values of ice water content that one would not expect the radar to detect. However, removal of these from the model still leaves an apparent overestimate of cloud fraction by up to a factor of 2. A tendency in the lowest kilometer for the model to simulate cloud features up to 3 h before they are observed is also found. Overall, this study demonstrates the considerable potential of active instruments for validating the representation of clouds in models.
Abstract
Of great importance for the simulation of climate using general circulation models is their ability to represent accurately the vertical distribution of fractional cloud amount. In this paper, a technique to derive cloud fraction as a function of height using ground-based radar and lidar is described. The relatively unattenuated radar detects clouds and precipitation throughout the whole depth of the troposphere, whereas the lidar is able to locate cloud base accurately in the presence of rain or drizzle. From a direct comparison of 3 months of cloud fraction observed at Chilbolton, England, with the values held at the nearest grid box of the European Centre for Medium-Range Forecasts (ECMWF) model it is found that, on average, the model tends to underpredict cloud fraction below 7 km and considerably overpredict it above. The difference below 7 km can in large part be explained by the fact that the model treats snow and ice cloud separately, with snow not contributing to cloud fraction. Modifying the model cloud fraction to include the contribution from snow (already present in the form of fluxes between levels) results in much better agreement in mean cloud fraction, frequency of occurrence, and amount when present between 1 and 7 km. This, together with the fact that both the lidar and the radar echoes tend to be stronger in the regions of ice clouds that the model regards as snow, indicates that snow should not be treated as radiatively inert by the model radiation scheme. Above 7 km, the difference between the model and the observations is partly due to some of the high clouds in the model being associated with very low values of ice water content that one would not expect the radar to detect. However, removal of these from the model still leaves an apparent overestimate of cloud fraction by up to a factor of 2. A tendency in the lowest kilometer for the model to simulate cloud features up to 3 h before they are observed is also found. Overall, this study demonstrates the considerable potential of active instruments for validating the representation of clouds in models.
Abstract
Some cumulus clouds with tops between 3 and 7 km (Cu3km–7km) remain in this height region throughout their lifetime (congestus) while others develop into deeper clouds (cumulonimbus). This study describes two techniques to identify the congestus and cumulonimbus cloud types using data from scanning weather radar and identifies the atmospheric conditions that regulate these two modes. A two-wet-season cumulus cloud database of the Darwin C-band polarimetric radar is analyzed and the two modes are identified by examining the 0-dBZ cloud-top height (CTH) of the Cu3km–7km cells over a sequence of radar scans. It is found that ~26% of the classified Cu3km–7km population grow into cumulonimbus clouds. The cumulonimbus cells exhibit reflectivities, rain rates, and drop sizes larger than the congestus cells. The occurrence frequency of cumulonimbus cells peak in the afternoon at ~1500 local time—a few hours after the peak in congestus cells. The analysis of Darwin International Airport radiosonde profiles associated with the two types of cells shows no noticeable difference in the thermal stability rates, but a significant difference in midtropospheric (5–10 km) relative humidity. Moister conditions are found in the hours preceding the cumulonimbus cells when compared with the congestus cells. Using a moisture budget dataset derived for the Darwin region, it is shown that the existence of cumulonimbus cells, and hence deep convection, is mainly determined by the presence of the midtroposphere large-scale upward motion and not merely by the presence of congestus clouds prior to deep convection. This contradicts the thermodynamic viewpoint that the midtroposphere moistening prior to deep convection is solely due to the preceding cumulus congestus cells.
Abstract
Some cumulus clouds with tops between 3 and 7 km (Cu3km–7km) remain in this height region throughout their lifetime (congestus) while others develop into deeper clouds (cumulonimbus). This study describes two techniques to identify the congestus and cumulonimbus cloud types using data from scanning weather radar and identifies the atmospheric conditions that regulate these two modes. A two-wet-season cumulus cloud database of the Darwin C-band polarimetric radar is analyzed and the two modes are identified by examining the 0-dBZ cloud-top height (CTH) of the Cu3km–7km cells over a sequence of radar scans. It is found that ~26% of the classified Cu3km–7km population grow into cumulonimbus clouds. The cumulonimbus cells exhibit reflectivities, rain rates, and drop sizes larger than the congestus cells. The occurrence frequency of cumulonimbus cells peak in the afternoon at ~1500 local time—a few hours after the peak in congestus cells. The analysis of Darwin International Airport radiosonde profiles associated with the two types of cells shows no noticeable difference in the thermal stability rates, but a significant difference in midtropospheric (5–10 km) relative humidity. Moister conditions are found in the hours preceding the cumulonimbus cells when compared with the congestus cells. Using a moisture budget dataset derived for the Darwin region, it is shown that the existence of cumulonimbus cells, and hence deep convection, is mainly determined by the presence of the midtroposphere large-scale upward motion and not merely by the presence of congestus clouds prior to deep convection. This contradicts the thermodynamic viewpoint that the midtroposphere moistening prior to deep convection is solely due to the preceding cumulus congestus cells.
Abstract
The aim for a more accurate representation of tropical convection in global circulation models is a long-standing issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state—an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.
Abstract
The aim for a more accurate representation of tropical convection in global circulation models is a long-standing issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state—an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.
Abstract
Coherent synoptic-scale weather systems within the Australian monsoon are identified and tracked in the isentropic potential vorticity (PV) field from the ECMWF Interim Reanalysis (ERA-Interim) dataset during the Southern Hemisphere summer. The resulting dataset is then used to compile statistics and synoptic composites of Australian monsoon disturbances. On average, a synoptic system is found in the region every 2.5 days. However, the time interval between consecutive events is highly variable, meaning that the synoptic activity in the Australian monsoon is not well represented by commonly employed spectral techniques. The analysis reveals that most synoptic systems originate within the Australian monsoon, but at the 315-K level (approximately 700 hPa) a significant proportion of systems are first detected near the east coast of the continent where extratropical Rossby waves are observed to frequently break.
The average Australian monsoon weather system propagates from east to west at approximately 6 m s−1 and has a characteristic length scale of 2000 km. Synoptic composite structures show some resemblance to African easterly waves; they move along a midtropospheric (approximately 700 hPa) easterly wind maximum and have peak meridional winds at this level. Composite rainfall shows that rainfall is significantly enhanced ahead (west) of the synoptic PV maximum and suppressed behind. It is estimated that in some parts of northwestern Australia 40%–50% of the summertime rainfall occurs with a tracked monsoon disturbance in the vicinity.
Abstract
Coherent synoptic-scale weather systems within the Australian monsoon are identified and tracked in the isentropic potential vorticity (PV) field from the ECMWF Interim Reanalysis (ERA-Interim) dataset during the Southern Hemisphere summer. The resulting dataset is then used to compile statistics and synoptic composites of Australian monsoon disturbances. On average, a synoptic system is found in the region every 2.5 days. However, the time interval between consecutive events is highly variable, meaning that the synoptic activity in the Australian monsoon is not well represented by commonly employed spectral techniques. The analysis reveals that most synoptic systems originate within the Australian monsoon, but at the 315-K level (approximately 700 hPa) a significant proportion of systems are first detected near the east coast of the continent where extratropical Rossby waves are observed to frequently break.
The average Australian monsoon weather system propagates from east to west at approximately 6 m s−1 and has a characteristic length scale of 2000 km. Synoptic composite structures show some resemblance to African easterly waves; they move along a midtropospheric (approximately 700 hPa) easterly wind maximum and have peak meridional winds at this level. Composite rainfall shows that rainfall is significantly enhanced ahead (west) of the synoptic PV maximum and suppressed behind. It is estimated that in some parts of northwestern Australia 40%–50% of the summertime rainfall occurs with a tracked monsoon disturbance in the vicinity.
Abstract
Since the 1970s, winter rainfall over coastal southwestern Australia (SWA) has decreased by 10%–20%, while summer rainfall has been increased by 40%–50% in the semiarid inland area. In this paper, a K-means algorithm is used to cluster rainfall patterns directly as opposed to the more conventional approach of clustering synoptic conditions (usually the mean sea level pressure) and inferring the associated rainfall. It is shown that the reduction in the coastal rainfall during winter is mainly due to fewer westerly fronts in June and July. The reduction in the frequency of strong fronts in June is responsible for half of the decreased rainfall in June–August (JJA), whereas the reduction in the frequency of weaker fronts in June and July accounts for a third of the total decrease. The increase in rainfall inland in December–February (DJF) is due to an increased frequency of easterly troughs in December and February. These rainfall patterns are linked to the southern annular mode (SAM) index and Southern Oscillation index (SOI). The reduction in coastal rainfall and the increase in rainfall inland are both related to the predominantly positive phase of SAM, especially when the phase of ENSO is neutral.
Abstract
Since the 1970s, winter rainfall over coastal southwestern Australia (SWA) has decreased by 10%–20%, while summer rainfall has been increased by 40%–50% in the semiarid inland area. In this paper, a K-means algorithm is used to cluster rainfall patterns directly as opposed to the more conventional approach of clustering synoptic conditions (usually the mean sea level pressure) and inferring the associated rainfall. It is shown that the reduction in the coastal rainfall during winter is mainly due to fewer westerly fronts in June and July. The reduction in the frequency of strong fronts in June is responsible for half of the decreased rainfall in June–August (JJA), whereas the reduction in the frequency of weaker fronts in June and July accounts for a third of the total decrease. The increase in rainfall inland in December–February (DJF) is due to an increased frequency of easterly troughs in December and February. These rainfall patterns are linked to the southern annular mode (SAM) index and Southern Oscillation index (SOI). The reduction in coastal rainfall and the increase in rainfall inland are both related to the predominantly positive phase of SAM, especially when the phase of ENSO is neutral.
Abstract
Observed regional rainfall characteristics can be analyzed by examining both the frequency and intensity of different categories of rainfall. A complementary approach is to consider rainfall characteristics associated with regional synoptic regimes. These two approaches are combined here to examine daily rainfall characteristics over the Australian region, providing a target for model simulations. Using gridded daily rainfall data for the period 1997–2007, rainfall at each grid point and averaged over several sites is decomposed into the frequency of rainfall events and the intensity of rainfall associated with each event. Daily sea level pressure is classified using a self-organizing map, and rainfall on corresponding days is assigned to the resulting synoptic regimes. This technique is then used to evaluate rainfall in the new Australian Community Climate and Earth-System Simulator (ACCESS) global climate model and separate the influence of large-scale circulation errors and errors due to the representation of subgrid-scale physical processes. The model exhibits similar biases to many other global climate models, simulating too frequent light rainfall and heavy rainfall of insufficient intensity. These errors are associated with particular synoptic regimes over different sectors of the Australian continent and surrounding oceans. The model simulates only weak convective rainfall over land during the summer monsoon, and heavy rainfall associated with frontal systems over southern Australia is also not simulated. As the model captures the structure and frequency of synoptic patterns, but not the associated rainfall intensity or frequency, it is likely that the source of the rainfall errors lies in model physical parameterizations rather than large-scale dynamics.
Abstract
Observed regional rainfall characteristics can be analyzed by examining both the frequency and intensity of different categories of rainfall. A complementary approach is to consider rainfall characteristics associated with regional synoptic regimes. These two approaches are combined here to examine daily rainfall characteristics over the Australian region, providing a target for model simulations. Using gridded daily rainfall data for the period 1997–2007, rainfall at each grid point and averaged over several sites is decomposed into the frequency of rainfall events and the intensity of rainfall associated with each event. Daily sea level pressure is classified using a self-organizing map, and rainfall on corresponding days is assigned to the resulting synoptic regimes. This technique is then used to evaluate rainfall in the new Australian Community Climate and Earth-System Simulator (ACCESS) global climate model and separate the influence of large-scale circulation errors and errors due to the representation of subgrid-scale physical processes. The model exhibits similar biases to many other global climate models, simulating too frequent light rainfall and heavy rainfall of insufficient intensity. These errors are associated with particular synoptic regimes over different sectors of the Australian continent and surrounding oceans. The model simulates only weak convective rainfall over land during the summer monsoon, and heavy rainfall associated with frontal systems over southern Australia is also not simulated. As the model captures the structure and frequency of synoptic patterns, but not the associated rainfall intensity or frequency, it is likely that the source of the rainfall errors lies in model physical parameterizations rather than large-scale dynamics.
