Journal of Southern Hemisphere Earth Systems Science最新文献

{"title":"Synoptic-scale atmospheric cyclones in the South-East Tropical Indian Ocean (SETIO) and their relation to IOD variability","authors":"A. Kavi, J. Kämpf","doi":"10.1071/es22020","DOIUrl":"https://doi.org/10.1071/es22020","url":null,"abstract":"","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79923595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulations of the Waroona fire using the coupled atmosphere–fire model ACCESS-Fire","authors":"M. Peace, J. Greenslade, H. Ye, J. Kepert","doi":"10.1071/es22013","DOIUrl":"https://doi.org/10.1071/es22013","url":null,"abstract":"","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89465042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisions to the Australian tropical cyclone best track database","authors":"J. Courtney, Gary R. Foley, Johannes L. van Burgel, B. Trewin, Andrew D. Burton, J. Callaghan, N. Davidson","doi":"10.1071/es21011","DOIUrl":"https://doi.org/10.1071/es21011","url":null,"abstract":"\u0000The Australian tropical cyclone (TC) best track database (BT) maintained by the Bureau of Meteorology has records since 1909 of varying quality and completeness. Since 2005 a series of efforts to improve the database have included: removing internal inconsistencies, adding fixes, and identifying errors using comparisons with other datasets; upgrading intensity information since 1973 including adding maximum winds (Vm) prior to 1984–85, rederiving Dvorak Current Intensity numbers from archived material and accounting for different wind–pressure relationships used; a partial reanalysis of satellite imagery including microwave imagery using the HURSAT dataset since 1987; and considering an objective intensity dataset. The BT homogeneity is reviewed in the context of improvements in satellite technology, observational coverage, scientific developments, BT procedures and the subjective variation between analysts across time and offices. The scale of these variances is greatest in the early stages prior to 1981 in the absence of geostationary satellite imagery until 1978, satellite calibration issues from 1978–80 and prior to the introduction of the enhanced infra-red Dvorak technique in 1981. The current era since 2003 is considered to be the most accurate, comprehensive and homogeneous corresponding to the expansion of the TC database to include the current suite of fields; the application of microwave and scatterometry imagery; greater standardisation of BT practices and slight changes in the application of the Dvorak technique. These improvements have generated a more consistent dataset that could be used for weather and climate research and other TC-related work.\u0000","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79222482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verification of moist surface variables over northern Australia in a high-resolution reanalysis (BARRA)","authors":"P. May, B. Trewin, C. Su, B. Ostendorf","doi":"10.1071/es21007","DOIUrl":"https://doi.org/10.1071/es21007","url":null,"abstract":"\u0000Reanalyses are important tools for understanding past weather and climate variability, but detailed verification of near surface humidity variables have not been published. This is particularly concerning in tropical regions where humid conditions impact meteorology and human activities. In this study, we used screen level temperature and humidity data from a high-resolution atmospheric regional reanalysis, the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA), validated against automatic weather stations (AWS) data for 32 sites across northern Australia. Overall, the BARRA data was reliable, with the time series from the AWS and BARRA being very highly correlated, but there were some seasonal and diurnally varying biases. The variability of the differences also changed from location to location and as a function of time of day and season, but much less than the biases. This variability was less than the ‘weather signal’ as evidenced by the high correlations. In particular, the amplitude of the diurnal cycle was overestimated, particularly in the dry (winter) season. In general, the differences in temperature were larger than those of the dew point temperature, and the wet bulb temperature had the least uncertainty. Overall, this study contributes to a better understanding of the effectiveness of reanalyses for examining the impact of moist variables on tropical climate variability.\u0000","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89612560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future changes in stratospheric quasi-stationary wave-1 in the extratropical southern hemisphere spring and summer as simulated by ACCESS-CCM","authors":"K. Stone, A. Klekociuk, R. Schofield","doi":"10.1071/es21002","DOIUrl":"https://doi.org/10.1071/es21002","url":null,"abstract":"Seasonally dependent quasi-stationary planetary wave activity in the southern hemisphere influences the distribution of ozone within and near the equatorward edge of the stratospheric polar vortex. Accurate representation of this zonal asymmetry in ozone is important in the characterisation of stratospheric circulation and climate and their associated effects at the surface. In this study, we used the Australian Community and Climate Earth System Simulator-Chemistry Climate Model to investigate the influence of greenhouse gases (GHGs) and ozone depleting substances (ODSs) on the zonal asymmetry of total column ozone (TCO) and 10 hPa zonal wind between 50 and 70°S. Sensitivity simulations were used from 1960 to 2100 with fixed ODSs and GHGs at 1960 levels and a regression model that uses equivalent effective stratospheric chlorine and carbon dioxide equivalent radiative forcing as the regressors. The model simulates the spring and summer zonal wave-1 reasonably well, albeit with a slight bias in the phase and amplitude compared to observations. An eastward shift in the TCO and 10 hPa zonal wave-1 is associated with both decreasing ozone and increasing GHGs. Amplitude increases are associated with ozone decline and amplitude decreases with GHG increases. The influence of ODSs typically outweigh those by GHGs, partly due to the GHG influence on TCO phase at 50°S likely being hampered by the Andes. Therefore, over the 21st century, influence from ozone recovery causes a westward shift and a decrease in amplitude. An exception is at 70°S during spring, where the GHG influence is larger than that of ozone recovery, causing a continued eastward trend throughout the 21st century. Also, GHGs have the largest influence on the 10 hPa zonal wave-1 phase, but still only induce a small change in the wave-1 amplitude. Different local longitudes also experience different rates of ozone recovery due to the changes in phase of the zonal wave-1. The results from this study have important implications for understanding future ozone layer distribution in the Southern Hemisphere under changing GHG and ODS concentrations. Important future work would involve conducting a similar study using a large ensemble of models to gain more statistically significant results.","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90757302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Annual climate summary Australia (2016): strong El Niño gives way to strong negative IOD.","authors":"Skie Tobin, Phillip Reid, Elaine Miles","doi":"10.1071/es17008","DOIUrl":"https://doi.org/10.1071/es17008","url":null,"abstract":"Australian climate patterns and associated anomalies during 2016 are reviewed, with reference to relevant climate drivers for the Australian region. 2016 was the fourth-warmest year on record for Australia (annual anomaly of +0.87 °C), and the warmest year on record for the globe (the third year running that a new record has been set). Annual rainfall was above average for most of Australia, but below average for areas of the northern coasts between the Gascoyne in Western Australia and Townsville in Queensland, and pockets of coastal southeast Queensland and northeastern New South Wales.The very strong 2015–16 El Niño contributed to a very warm and dry first quarter. Autumn was the warmest on record nationally, with a significant nationwide heatwave occurring in late February to mid-March and bushfires at the start of the year in Victoria, Tasmania and Western Australia. In May the El Niño broke down and rainfall increased as a very strong negative Indian Ocean Dipole developed, lasting until November. While the central tropical Pacific approached La Niña thresholds during spring, a La Niña did not develop. The Southern Annual Mode commenced the year in a generally positive phase, was strongly positive in June and September, and was following by a strongly negative phase from late October until the end of the year.The period from May to September was record wet, relieving areas of drought in Queensland and southeastern Australia, but also causing flooding in multiple states. The last three months of the year saw a return to near-average rainfall and, while October and November were cooler than average for large areas, December was very warm for the eastern states.Ocean temperatures were also record warm for the Australian region during 2016, with an annual anomaly of +0.73 °C. Temperatures were particularly high during the first half of the year and resulted in widespread severe coral bleaching.","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia","authors":"Nadeeka Parana Manage, Natalie Lockart, Garry Willgoose, George Kuczera, Anthony S. Kiem, AFM Kamal Chowdhury, Lanying Zhang, Callum Twomey","doi":"10.1071/es16016","DOIUrl":"https://doi.org/10.1071/es16016","url":null,"abstract":"This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale.","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of TRMM Multi-satellite Precipitation Analysis during the passage of Tropical Cyclones over Fiji","authors":"Anil Deo, Kevin J. E. Walsh","doi":"10.1071/es16027","DOIUrl":"https://doi.org/10.1071/es16027","url":null,"abstract":"Fiji is prone to the devastating effects of heavy rainfall during the passage of tropical cyclones (TCs) and as such accurate measurement of rainfall during such events is urgent for effective disaster mitigation and risk analysis. Fiji, however, has a sparse distribution of rain gauges, thus there is a deficiency in the accurate measurement of rainfall. This gap could be filled by satellite-based rainfall estimates but before they are used, they need to be validated against a reference dataset for their accuracy and limitations. This study thus validates the TRMM based Multi-satellite Precipitation Analysis (TMPA) estimates over the island of Fiji. The study shows that TMPA has moderate skill in estimating rainfall during the passage of TCs over the island of Fiji. This skill is also highly variable as it decreases with an increase in rainfall intensity, increase in distance from the cyclone centre and increasing terrain elevation. The ability of TMPA also varies case by case but we report a general underestimation of rainfall by TMPA during the passage of TCs with a larger rainfall rate (defined in our case as those TCs with average daily rainfall greater than 25 mm day-1).","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying East Coast Lows with climate hazards on the eastern seaboard","authors":"Acacia S. Pepler, Agata Imielska, Aaron Coutts-Smith, Felicity Gamble, Martin Schweitzer","doi":"10.1071/es16010","DOIUrl":"https://doi.org/10.1071/es16010","url":null,"abstract":"East Coast Lows are an important weather system that can produce severe wind, wave and rainfall events along the eastern seaboard of Australia. While a number of databases of these systems have been produced, this information has historically not been readily accessible to potential users outside the research sec-tor. This paper details the development of a new product, Maps and Tables of Climate Hazards on the Eastern Seaboard (MATCHES), that bridges this gap. It combines a new database of East Coast Lows with weather impacts across the eastern seaboard. Through use of user-defined impacts thresholds and an intuitive front-end interface, this new tool provides an easy way to link East Coast Lows with their weather impacts.","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal climate summary southern hemisphere (spring 2015): El Niño nears its peak","authors":"David J. Martin","doi":"10.1071/es16017","DOIUrl":"https://doi.org/10.1071/es16017","url":null,"abstract":"Southern hemisphere circulation patterns and associated anomalies for the austral spring 2015 are reviewed, with an emphasis on Pacific climate indicators and Australian rainfall and temperature patterns. A strong El Niño persisted in the tropical Pacific Ocean with sea-surface temperature anomalies in excess of +2 °C in central and eastern parts, strongly negative outgoing longwave radiation near the Date Line, and the Southern Oscillation Index showing large negative departures. The positive Indian Ocean Dipole that had established in winter dissipated in late November, but was particularly influential on Australia's climate during the months of September and October.Australia’s spring rainfall was below average in the first two months, but improved later in the season: the northern half of Western Australia recorded above average November rainfall. Nevertheless, area-averaged rainfall in spring was below average for the country as a whole. For Australia, October was the warmest on record and had the highest mean temperature anomaly on record for any month since 1910. Spring temperatures were above average and Australia recorded its second-warmest spring on record, behind the record set in the previous year.","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}