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This paper describes the validation of the SimSphere SVAT model conducted at different ecosystem types in the USA and Australia. Specific focus was given to examining the models' ability in predicting Shortwave Incoming Solar Radiation (Rg), Net Radiation (Rnet), Latent Heat (LE), Sensible Heat (H), Air Temperature at 1.3m (Tair 1.3m) and Air Temperature at 50m (Tair 50m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from 8 sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation. The application of the model confirmed its high capability in representing the multifarious and complex interactions of the Earth system. Comparisons showed a good agreement between modelled and measured fluxes, especially for the days with smoothed daily flux trends. A good to excellent agreement between the model predictions and the in situ measurements was reported, particularly so for the LE, H, Tair 1.3m and Tair 50m parameters (RMSD= 39.47, 55.06 Wm-2, 3.23, 3.77°C respectively). A systematic underestimation of Rg and Rnet (RMSD=67.83, 58.69 Wm-2, MBE= 67.83, 58.69 Wm-2 respectively) was also found. Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. Very high values of the Nash-Sutcliffe efficiency index were also reported for all parameters ranging from 0.720 to 0.998, suggesting a very good model representation of the observations. To our knowledge, this study presents the first comprehensive validation of Sim- Sphere, particularly so in USA and Australian ecosystem types. Findings are important and timely, given the rapidly expanding use of this model worldwide both as an educational and research tool. This includes ongoing research by different Space Agencies examining its synergistic use with Earth Observation data towards the development of global operational products. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘ C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs). [ABSTRACT FROM AUTHOR]

The annular structure of Antarctic oscillation (AAO) is a research hotpot, but its asymmetry receives less attention. In this paper, the self‐organizing map method is employed to cluster the AAO patterns into symmetric and asymmetric modes in austral autumn. The asymmetry is mainly reflected in the Pacific‐Atlantic sector, and the AAO evolves toward asymmetric positive polarity, with the most pronounced asymmetry in May. Originating from near Australia, the asymmetry indicates a zonal wave train in the Pacific‐Atlantic sector. Both modeled and observed results demonstrate that the sea surface temperature anomaly in the equatorial western Pacific stimulates a local meridional circulation anomaly and induces anomalous Rossby wave sources near Australia subsequently. An anomalous wave train propagating toward the Antarctic Peninsula is formed, and the associated geopotential anomaly enhances the asymmetry of AAO. Asymmetric AAO is conducive to the Antarctic dipole, which modulates the air temperature and sea ice anomalies around Antarctica. Plain Language Summary: The zonal symmetry of the Antarctic oscillation (AAO; also named southern annular mode) is well known to researchers, while the zonal asymmetry has received less attention. We use a clustering method to cluster the AAO modes in austral autumn into symmetric modes and asymmetric modes. The asymmetry of AAO is mainly reflected in the Pacific‐Atlantic sector. The results show that the AAO mode evolves to a positive asymmetric mode, and the asymmetry is most obvious in May. Both simulations and observations suggest that this asymmetry stems from sea surface temperature anomaly in the equatorial western Pacific that stimulates an anomalous local meridional circulation near Australia, and in turn triggers an anomalous wave train propagating toward the Antarctic Peninsula. The associated geopotential anomalies enhance the asymmetry of AAO. The asymmetric AAO favors the Antarctic dipole, regulating the air temperature and sea ice anomalies around Antarctica. Key Points: Via a cluster method, we obtained the zonal asymmetric Antarctic oscillation (AAO) mode, and analyzed its trend and preferred monthThe source of its asymmetry is from the sea surface temperature anomaly in the tropical western PacificThe asymmetric AAO mode has a vital influence on the Antarctic dipole [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is changing the earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose to investigate future hazards. BARPA-R, a limited area model, has a 17 km horizontal grid-spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA-5 reanalysis. Here, an assessment of this evaluation experiment is provided. First, an examination of BARPA-R's representation of Australia's surface air temperature, rainfall and 10-m winds finds good performance overall, with biases including a 1K cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm/month in inland Australia. Recent trends in diurnal maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in rainfall show underestimated wetting in northern Australia. Rainfall and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10-metre winds are improved over ERA5 in six out of eight of the Australian regions considered. The second section of the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the SPCZ, Northwest Cloud-Bands and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extra-tropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 GCMs. [ABSTRACT FROM AUTHOR]

There is little concrete understanding of the energy consumption of refrigerating appliances during normal use or the main influences on their energy consumption. To date, no widely accepted method to disaggregate measured energy consumption measured in the home into its key components has been demonstrated. This paper examines the main external factors that impact on the energy consumption of existing refrigerating appliances in the home and how they respond to changing conditions, namely: room air temperature, defrosting behaviour and user interactions. Analysis of field data from 235 homes in Australia demonstrates that room air temperature is by far the largest factor accounting for typically around 75% of total energy consumption. Where present, energy used for defrosting is relatively small at around 10%, but this does vary by household and the type of defrost controller. User interactions typically account for 15% of total energy consumed by main household refrigerating appliances, but this varies from a few percent to as much as 45% in large households. The method set out in this paper provides a basis for more in depth analysis and a better understanding of energy consumption of household refrigerators in different regions. [ABSTRACT FROM AUTHOR]

Various human heat stress indices have been developed to relate atmospheric measures of extreme heat to human health impacts, but the usefulness of different indices across various health impacts and in different populations is poorly understood. This paper determines which heat stress indices best fit hospital admissions for sets of cardiovascular, respiratory, and renal diseases across five Australian cities. We hypothesized that the best indices would be largely dependent on location. We fit parent models to these counts in the summers (November-March) between 2001 and 2013 using negative binomial regression. We then added 15 heat stress indices to these models, ranking their goodness of fit using the Akaike information criterion. Admissions for each health outcome were nearly always higher in hot or humid conditions. Contrary to our hypothesis that location would determine the best-fitting heat stress index, we found that the best indices were related largely by health outcome of interest, rather than location as hypothesized. In particular, heatwave and temperature indices had the best fit to cardiovascular admissions, humidity indices had the best fit to respiratory admissions, and combined heat-humidity indices had the best fit to renal admissions. With a few exceptions, the results were similar across all five cities. The best-fitting heat stress indices appear to be useful across several Australian cities with differing climates, but they may have varying usefulness depending on the outcome of interest. These findings suggest that future research on heat and health impacts, and in particular hospital demand modeling, could better reflect reality if it avoided “all-cause” health outcomes and used heat stress indices appropriate to specific diseases and disease groups. [ABSTRACT FROM AUTHOR]

In this paper the authors dynamically downscale daily-averaged general circulation model (GCM) datasets over Australia using the Conformal Cubic Atmospheric Model (CCAM). The technique can take advantage of the wider range of Coupled Model Intercomparison Project phase 3 (CMIP3) daily-averaged GCM datasets than is available using 3-hourly datasets. The daily-averaged host GCM atmospheric data are fitted to a time interpolation formula and then differentiated in time to produce a first-order estimate of the atmosphere at 0000 UTC on each simulation day. The processed GCM data are forced into CCAM using a scale-selective filter with an 18°° radius. Since this procedure is unable to account for the diurnal cycle, the forcing data are only applied to winds and air temperatures once per day between 800 and 100 hPa. Lateral boundary conditions are not required since CCAM employs a variable-resolution global grid. The technique is evaluated by downscaling daily-averaged 2.5°° NCEP reanalyses over Australia at 60-km resolution from 1971 to 2000 and comparing the results to downscaling the 6-hourly reanalyses and to simulating with sea surface temperature (SST)-only forcing. The results show that the daily-averaged downscaling technique can simulate average seasonal maximum and minimum screen temperatures and rainfall similar to those obtained downscaling 6-hourly reanalyses. Some implications for regional climate projections are considered by downscaling four daily-averaged GCM datasets from the twentieth-century climate in coupled models (20C3M) experiment over Australia. [ABSTRACT FROM AUTHOR]

The global temperature trends provided by the Australian Bureau of Meteorology are artificially exaggerated due to subjective and unidirectional adjustments of recorded values. The present paper aims to promote the use of the raw stations' data corrected only for urban heat island formation. The longer temperature records of Australia exhibit significant oscillations with a strong quasi-60 years' signature of downward phases 1880 to 1910, 1940 to 1970 and 2000 to present, and upwards phases 1910 to 1940 and 1970 to 2000. A longer oscillation with downward phase until 1910 and an upwards phase afterwards is also detected. The warming since 1910 occurred at a nearly constant rate. Over the full length of the long Australian records since the end of the 1800s, there is no sign of warming or increased occurrence of extreme events. The monthly highest and mean maximum temperatures do not exhibit any positive trend. The differences between monthly highest and lowest, or monthly mean maximum and mean minimum temperatures, are all reducing because of urban heat island formation. [ABSTRACT FROM AUTHOR]

The anadromous Geotria australis , one of only three lamprey species representing the early agnathan (jawless) stage of vertebrate evolution in Australia and New Zealand, is declining in abundance. Its adults were caught soon after they had entered rivers on their non-trophic upstream migration and maintained in laboratory tanks for 13–15 months through to spawning. As adult G. australis are susceptible to haemorrhagic septicaemia, they were treated prophylactically and maintained in 3-m3 aquaria supplied with a flow-through charcoal filtration system and UV steriliser. Air temperature and the light : dark regime were constantly adjusted to parallel those in the environment. Males developed the very large suctorial disc and gular pouch characteristic of maturity and both sexes matured at the same time as in the wild. While males frequently showed aggressive behaviour towards each other, the same male and female mated on several occasions. The male coiled around the female and, with his urogenital papilla close to the female's cloaca, twisted and vibrated, leading to egg release. These eggs formed coagulated clusters as in the wild, with many progressing through to the eight-cell stage. Remarkably, numerous G. australis were still alive 95–392 days after the end of the short spawning period, and one male after a further 119 days. Postspawning survival would be facilitated inter alia by extensive proteolysis, reflected in a shortening of the body. The data in this paper emphasise that G. australis is a highly atypical lamprey and provides invaluable information for conserving this declining species. The lamprey Geotria australis , when provided with appropriate conditions, can be maintained in laboratory tanks for many months from the commencement of the upstream migration through to spawning. Successful maintenance requires, however, the provision of conditions that overcome the potential for this species to succumb to haemorrhagic septicaemia. Spawning, which occurs at the same time as in their natural environment, is described. The prolonged period during which G. australis survive after spawning parallels that recorded for this species in its riverine environment and is unique among lampreys. [ABSTRACT FROM AUTHOR]

This paper describes an extension of an operational consensus forecasting (OCF) scheme from site forecasts to gridded forecasts. OCF is a multimodel consensus scheme including bias correction and weighting. Bias correction and weighting are done on a scale common to almost all multimodel inputs (1.25°), which are then downscaled using a statistical approach to an approximately 5-km-resolution grid. Local and international numerical weather prediction model inputs are found to have coarse scale biases that respond to simple bias correction, with the weighted average consensus at 1.25° outperforming all models at that scale. Statistical downscaling is found to remove the systematic representativeness error when downscaling from 1.25° to 5 km, though it cannot resolve scale differences associated with transient small-scale weather. [ABSTRACT FROM AUTHOR]

The paper describes the development of a new metric to evaluate the thermal performance of residential houses for temperate climates, named the Adaptive Thermal Metric (ATM). The proposed ATM helps the evaluation of the thermal performance of an entire house by finding the internal air temperature of the building using Computational Fluid Dynamics (CFD) simulation then computes the fraction of time over which the internal air temperature of the house stayed within the 90% or 80% adaptive thermal comfort limits (designated ATM90 and ATM80 respectively). The adaptive thermal comfort approach allows inhabitants to choose an adequate range of internal air temperatures by implementing other adaptive practices such as changing clothes, opening windows, or using low energy solutions (e.g. fans) to adjust their thermal comfort level. The new metric uses temperature to assess a building's thermal performance. This differs from other commonly conventional approaches, which are established based on the amount of energy usage required to sustain an inhabitant's thermal comfort. To verify the modelling technique, the CFD simulated internal air temperatures were compared with the actual temperatures recorded for four full-scale housing test modules, each incorporating a different walling system. This comparison yielded an average accuracy of 93% at any given time for all simulated modules over the 12-month simulation period. Using the above CFD approach, the ATMs were calculated for each building test module over the studied period. The final results showed that the best thermal performance module was the Insulated Cavity Brick module (InsCB), followed by the Insulated Reverse Brick Veneer (InsRBV), Insulated Brick Veneer (InsBV) and Cavity Brick (CB) modules. These findings were accordant with both the house evaluation software used in Australia (i.e. AccuRate) and the earlier results on the walling systems research done by the University of Newcastle in Australia, which were used to assess the accuracy of the ATM. The results indicated that the ATM has the potential to be used as an alternative building evaluation technique to assess the overall building thermal performance in temperate climates. [ABSTRACT FROM AUTHOR]

Existing tourism-related climate information and evaluation are typically based on mean monthly conditions of air temperature and precipitation and do not include thermal perception and other climate parameters relevant for tourists. Here, we quantify climate based on the climate facets relevant to tourism (thermal, physical, aesthetical), and apply the results to the Climate-Tourism-Information-Scheme (CTIS). This paper presents bioclimatic and tourism climatological conditions in the Hunter Region-one of Australia's most popular tourist destinations. In the Hunter Region, generally, temperatures below 15°C occur from April through October, temperatures less than 25°C are expected throughout the whole year, while humidity sits around 50%. As expected, large differences between air temperature and physiologically equivalent temperature (PET) were clearly identified. The widest differences were seen in summer time rather than in the winter period. In addition, cold stress was observed less than 10% of the time in winter while around 40-60% of heat stress was observed in summer time. This correlates with the highest numbers of international visitors, who usually seek a warmer weather, at the beginning of summer time (November and December) and also to the number of domestic visitors, who tend to seek cooler places for recreation and leisure, in late summer (January-March). It was concluded that thermal bioclimate assessment such as PET and CTIS can be applied in the Hunter region, and that local governments and the tourism industry should take an integrated approach to providing more relevant weather and climate information for both domestic and international tourists in the near future. [ABSTRACT FROM AUTHOR]

Fires in urban areas can cause significant economic, physical and psychological damage. Despite this, there has been a comparative lack of research into the spatial and temporal analysis of fire incidence in urban contexts. In this paper, we redress this gap through an exploration of the association of fire incidence to weather, calendar events and socio-economic characteristics in South-East Queensland, Australia using innovative technique termed the quad plot. Analysing trends in five fire incident types, including malicious false alarms (hoax calls), residential buildings, secondary (outdoor), vehicle and suspicious fires, results suggest that risk associated with all is greatly increased during school holidays and during long weekends. For all fire types the lowest risk of incidence was found to occur between one and six a.m. It was also found that there was a higher fire incidence in socially disadvantaged neighbourhoods and there was some evidence to suggest that there may be a compounding impact of high temperatures in such areas. We suggest that these findings may be used to guide the operations of fire services through spatial and temporal targeting to better utilise finite resources, help mitigate risk and reduce casualties. [ABSTRACT FROM AUTHOR]

This paper presents an extension of the operational consensus forecast (OCF) method, which performs a statistical correction of model output at sites followed by weighted average consensus on a daily basis. Numerical weather prediction (NWP) model forecasts are received from international centers at various temporal resolutions. As such, in order to extend the OCF methodology to hourly temporal resolution, a method is described that blends multiple models regardless of their temporal resolution. The hourly OCF approach is used to generate forecasts of 2-m air temperature, dewpoint temperature, RH, mean sea level pressure derived from the barometric pressure at the station location (QNH), along with 10-m wind speed and direction for 283 Australian sites. In comparison to a finescale hourly regional model, the hourly OCF process results in reductions in average mean square error of 47% (air temperature), 40% (dewpoint temperature), 43% (RH), 29% (QNH), 42% (wind speed), and 11% (wind direction) during February–March with slightly higher reductions in May. As part of the development of the approach, the systematic and random natures of hourly NWP forecast errors are evaluated and found to vary with forecast hour, with a diurnal modulation overlaying the normal error growth with time. The site-based statistical correction of the model forecasts is found to include simple statistical downscaling. As such, the method is found to be most appropriate for meteorological variables that vary systematically with spatial resolution. [ABSTRACT FROM AUTHOR]

During 30 days in May and June 2003, the R/V Southern Surveyor was operating in the Gulf of Carpentaria, northern Australia. Measurements of sea surface temperature (SST) were made with an accurate single-channel infrared radiometer as well as with the ship’s thermosalinograph. These ship-based measurements have been used to assess the quality of the SST derived from nine satellite-borne instruments. The satellite dataset compiled during this period also allows the intercomparison of satellite-derived SST fields in areas not covered by the ship’s track. An assessment of the SST quality from each satellite instrument is presented, and suggestions for blending ground and satellite measurements into a single product are made. These suggestions are directly applicable to the international Global Ocean Data Assimilation Experiment (GODAE) High Resolution SST Pilot Project (GHRSST-PP) that is currently developing an operational system to provide 6-hourly global fields of SST at a spatial resolution close to 10 km. The paper demonstrates how the Diagnostic Datasets (DDSs) and Matchup Database (MDB) of the GHRSST-PP can be used to monitor the quality of individual and blended SST datasets. Recommendations for future satellite missions that are critical to the long-term generation of accurate blended SST datasets are included. [ABSTRACT FROM AUTHOR]

Teleconnections refer to long-range climate system linkages occurring over typically thousands of kilometers. Generally speaking, most teleconnections are attributed to the transmission of energy and propagation of waves although the physical complexity and characteristics behind these waves are not fully understood. To address this knowledge gap, we develop a climate network-based approach to reveal their directions and distribution patterns, evaluate the intensity of teleconnections, and identify sensitive regions using global daily surface air temperature data. Our results reveal a stable average intensity distribution pattern for teleconnections across a substantial spatiotemporal scale from 1948 to 2021, with the extent and intensity of teleconnection impacts increasing more prominently in the Southern Hemisphere over the past 37 years. Furthermore, we pinpoint climate-sensitive regions, such as southeastern Australia, which are likely to face increasing impacts due to global warming. Our proposed method offers new insights into the dynamics of global climate patterns and can inform strategies to address climate change and extreme events. [ABSTRACT FROM AUTHOR]

The incidence of pyro-cumulonimbus (pyroCb) caused by extreme wildfires has increased markedly in Australia over the last several decades. This increase can be associated with a dangerous escalation of wildfire risk and severe stratospheric pollution events. Atmospheric and fuel conditions are important influences on pyroCb occurrence, but the exact causal relationships are still not well understood. We used the Continuous Haines Index (C-Haines) to represent atmospheric instability and the Fuel Moisture Index (FMI) to represent fuel moisture to provide better insight into the effects of atmospheric and fuel conditions on pyroCb occurrence over southeast Australia. C-Haines and FMI were related to the probability of pyroCb occurrence by employing a logistic regression on data gathered between 1980 and 2020. Emphasis is placed on investigating the independent effects and combined effects of FMI and C-Haines, as well as assessing their potential to predict whether a pyroCb develops over a fire. The main findings of this study are: (1) high C-Haines and low FMI values are representative of favorable conditions for pyroCb occurrence, but C-Haines can offset the effect of FMI—the addition of C-Haines to the logistic model muted the significance of FMI; (2) among the components of C-Haines, air temperature lapse rate (CA) is a better predictor of pyroCb occurrence than the dryness component (CB); (3) there are important regional differences in the effect of C-Haines and FMI on pyroCb occurrence, as they have better predictive potential in New South Wales than in Victoria. [ABSTRACT FROM AUTHOR]

Tropical Cyclone Debbie (2017) made landfall near Airlie Beach on 28 March 2017 causing 14 fatalities and an estimated $2.67 billion (U.S. dollars) economic loss and was ranked as the most dangerous cyclone to hit Australia since TC Tracy in 1974. In addition to the extreme flooding as TC Debbie moved onshore and down the east coast of Australia, it intensified rapidly just offshore from category 2 to category 4 on the Australian TC intensity scale in under 18 h prior to making landfall. A high-resolution WRF simulation is used to analyze the inner-core structure and evolution during the offshore intensification period. Two stages are identified: a slow intensification (SI) stage characterized by an asymmetric eyewall contraction and a rapid intensification (RI) stage characterized by three eyewall breakdown and redevelopment events. Each round of breakdown and reestablishment brings high potential vorticity and equivalent potential temperature air back into the eyewall, reinvigorating eyewall convection activity and driving intensification. [ABSTRACT FROM AUTHOR]

Australia experienced record‐breaking wildfires during 2019–2020, which emitted large amounts of carbonaceous aerosols (CAs) to the atmosphere. In this study, we explored the atmospheric dynamics and thermodynamic mechanism of the long‐range transport of CAs during November 2019–February 2020. The results indicate that the emitted CAs had a ternary advective spreading pattern across the Pacific Ocean in the Southern Hemisphere (SH), influenced mainly by the westerlies and anticyclonic systems. Upward vertical motion over the convergence zone and heat released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs provided strong vertical lift as the predominant injection pathway of the CAs into the stratosphere in the central Pacific Ocean in the SH. The abundant tropospheric CAs induced atmospheric heating (and potentially surface cooling), with increasing air temperature and decreasing of surface temperature. The anomalous thermal structure led to changes in atmospheric circulation, which weakened the upward vertical motion. Plain Language Summary: During the summer of 2019–2020, Australia suffered unprecedented wildfires. These wildfires emitted large amounts of pollutants into the free atmosphere. The pollutants are mainly composed of carbonaceous aerosols (CAs). Under the effects of meteorological conditions, the CAs experienced a long‐range spread expanding from the western Pacific Ocean to the eastern Pacific Ocean even arriving at the Atlantic Ocean. During the transportation, the CAs were accumulated over the central Pacific Ocean under the trapping effect of the anticyclone and then exhibited significant lift owing to upward motion over the convergence zone between westerlies and anticyclone and heat buoyancy released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs. As a response, the strong absorption of CAs resulted in the warming of the atmosphere and cooling of the surface, which enhanced atmospheric stratification and constrained the development of atmospheric instability. Thus, the upward vertical motion was weakened. Key Points: The westerlies and anticyclonic system dominated the advective spread of carbonaceous aerosols (CAs)The upward vertical motion, heat released from smoke pyro‐cumulonimbus clouds, and strong CAs light absorption contributed to CAs liftThe CAs caused warming of air temperature and cooling of surface temperature, which weakened the upward vertical motion [ABSTRACT FROM AUTHOR]

This study quantifies some of the potential impacts of climate change and nutrient pollution to identify the most important factors on water quality changes and algal blooms in the study region. Three variables, air temperature and streamflow, representing climate change, and nutrient runoff, were varied in eight hypothetical scenarios to determine their impact on water quality and algal blooms by the calibrated and validated water quality model QUAL2K. Water quality was assessed by the concentrations of dissolved oxygen, total nitrogen, and phosphorus. Algal blooms were identified by phytoplankton concentration. An increase in air temperature of up to 2 °C resulted in an average increase of 3% in water temperature and 4.79% in phytoplankton concentration, and an average decrease of 0.48% in dissolved-oxygen concentration. Projected decreases in streamflow not only made the above phenomenon more significant but also significantly increased the concentration of total nitrogen, total phosphorus, and phytoplankton with the same pollution inputs. Under climate change, the biggest cause of concern for estuarine water quality is reduced streamflow due to decreases in rainfall. Water quality improvement is possible by regulating the concentration of non-point-source pollution discharge. By reducing nutrient runoff, the total nitrogen and phosphorus concentrations were also reduced, resulting in a significant increase in the dissolved oxygen concentration. This study highlights the most significant factors for managing water quality in estuaries subject to climate change. [ABSTRACT FROM AUTHOR]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

Abstract: The United Nations Environment Programme (UNEP) has begun a process of developing a legally binding instrument to manage emissions of mercury from anthropogenic sources. The UNEP Governing Council has concluded that there is sufficient evidence of significant global adverse impacts from mercury to warrant further international action; and that national, regional and global actions should be initiated as soon as possible to identify populations at risk and to reduce human generated releases. This paper describes the development of, and presents results from, a comprehensive, spatially and temporally resolved inventory of atmospheric mercury emissions from the Australian landmass. Results indicate that the best estimate of total anthropogenic emissions of mercury to the atmosphere in 2006 was 15 ± 5 tonnes. Three industrial sectors contribute substantially to Australian anthropogenic emissions: gold smelting (∼50%, essentially from a single site/operation), coal combustion in power plants (∼15%) and alumina production from bauxite (∼12%). A diverse range of other sectors contribute smaller proportions of the emitted mercury, but industrial emissions account for around 90% of total anthropogenic mercury emissions. The other sectors include other industrial sources (mining, smelting, and cement production) and the use of products containing mercury. It is difficult to determine historical trends in mercury emissions given the large uncertainties in the data. Estimates for natural and re-emitted emissions from soil, water, vegetation and fires are made using meteorological models, satellite observations of land cover and soil and vegetation type, fuel loading, fire scars and emission factors which account for the effects of temperature, insolation and other environmental variables. These natural and re-emitted sources comfortably exceed the anthropogenic emissions, and comprise 4–12 tonnes per year from vegetation, 70–210 tonnes per year from soils, and 21–63 tonnes per year from fires. [Copyright &y& Elsevier]

In this paper, we undertake an empirical investigation into the possibility of climate change in Australian centres using average monthly air temperatures for six sites around the country over the period 1901:1–1998:12. By estimating a multivariate structural time series model and carrying out the appropriate tests, it is concluded that the temperature series is I(1). A graphical inspection of the extracted trends reveals that temperature has an upward trend in many centres. Given that the data may involve measurement errors, the results should be treated with caution. [Copyright &y& Elsevier]

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%–75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (Topt). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary‐line analysis to eddy covariance flux observations to (a) derive ecosystem GPP–Ta relationships and Topt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP–Ta relationships within and among ecoregions; (c) examine the relationship between Topt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down‐welling short‐wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP–Ta relationship. GPP–Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem Topt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna—wet and dry seasons). The shape of GPP–Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between Topt and MDTa (Adjusted R2: 0.81; Slope: 1.08) with Topt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems. [ABSTRACT FROM AUTHOR]

Foundation species interact strongly with other species to profoundly influence communities, such as by providing food, refuge from predators or beneficial microclimates. We tested relative support for these mechanisms using spinifex grass (Triodia spp.), which is a foundation species of arid Australia that provides habitat for diverse lizard communities. We first compared the attributes of live and dead spinifex, bare ground and a structurally similar plant (Lomandra effusa), and then tested the relative strength of association of two spinifex specialist lizard species (Ctenophorus spinodomus and Ctenotus atlas) with spinifex using a mesocosm experiment. Temperatures were coolest within spinifex compared to bare ground and Lomandra. Invertebrate abundance and the threat of predation were indistinguishable between treatments, suggesting temperature attenuation may be a more important driver. Overall, the dragon C. spinodomus preferred live over dead spinifex, while the skink C. atlas preferred dead spinifex, particularly at warmer air temperatures. However, both species displayed individual variability in their use of available microhabitats, with some individuals rarely using spinifex. Our results provide an example of temperature attenuation by a foundation species driving niche use by ectothermic animals. [ABSTRACT FROM AUTHOR]

Energy-efficient building design needs an accurate way to estimate temperature inside the building which facilitates the calculation of heating and cooling energy requirements in order to achieve appropriate thermal comfort for occupants. Sky temperature is an important factor for any building assessment tool which needs to be precisely determined for accurate estimation of the energy requirement. Many building simulation tools have been used to calculate building thermal performance such as Autodesk Computational Fluid Dynamics (CFD) software, which can be used to calculate building internal air temperature but requires sky temperature as a key input factor for the simulation. Real data obtained from real-sized house modules located at University of Newcastle, Australia (southern hemisphere), were used to find the impact of different sky temperatures on the building's thermal performance using CFD simulation. Various sky temperatures were considered to determine the accurate response which aligns with a real trend of buildings' internal air temperature. It was found that the internal air temperature in a building keeps either rising or decreasing if higher or lower sky temperature is chosen. This significantly decreases the accuracy of the simulation. It was found that using the right sky temperature values for each module, Cavity Brick Module (CB) Insulated Cavity Brick Module (InsCB), Insulated Brick Veneer Module (InsBV) and Insulated Reverse Brick Veneer Module (InsRBV), will result in 6.5%, 7.1%, 6.2% and 6.4% error correspondingly compared with the real data. These errors mainly refer to the simulation error. On the other hand using higher sky temperatures by +10 °C will significantly increase the simulation error to 16.5%, 17.5%, 17.1% and 16.8% and lower sky temperature by +10 °C will also increase the error to 19.3%, 22.6%, 21.9% and 19.1% for CB, InsCB, InsBV and InsRBV modules, respectively. [ABSTRACT FROM AUTHOR]

A new version of the long‐term Australian temperature data set, known as ACORN‐SAT (Australian Climate Observations Reference Network—Surface Air Temperature), has been developed. ACORN‐SAT includes homogenized daily maximum and minimum temperature data from 112 locations across Australia, encompassing the period from 1910 to the present, with 60 of the locations having data for the full 1910–2018 period. Homogenization is achieved using a percentile‐matching methodology with a number of improvements beyond practices used in previous versions, including more effective detection and removal of potentially inhomogeneous reference stations and an enhanced breakpoint detection methodology. Explicit corrections have also been introduced for a change in instrument screen size, whilst an assessment has found that the transition from manual to automatic instruments and changes in effective response time of automatic instruments have had a negligible impact on the data. Adjustments associated with documented site moves from in‐town to out‐of‐town locations are predominantly negative, particularly for minimum temperature, with other adjustments showing no strong bias towards either positive or negative values. The new data set shows slightly stronger warming (0.12°C per decade in mean temperature over the 1910–2016 period) than either the previous ACORN‐SAT version (0.10°C) or the unhomogenized gridded data (0.08°C), primarily due to more effective treatment of systematic moves of sites out of towns and the removal of a rounding bias in the version 1 methodology. [ABSTRACT FROM AUTHOR]

This study uses statistical methods to analyse climatic variations of air surface temperatures in Australia between 1990 and 2012. Maximum monthly temperature data were obtained from the Australian Bureau of Meteorology. Monthly maximum temperatures, collected from 81 stations in Australia over a period of 23 years, were analysed to determine the variation trends. The data from each station were adjusted for seasonal effects, and then further autocorrelations were removed by using a second-order autoregressive process. In the next step, factor analysis was employed to account for spatial correlations and group all stations into five regions, namely south-east, central-north, central-east, central-west and central regions. A simple linear regression model was then fitted to the data within each region. The average maximum temperatures trended higher in the south-east and central-west regions, with temperatures increasing between 0.071 - 0.559°C per decade. In contrast, those in the central-north region decreased from 0.004 to 0.314°C per decade while the central and central-east regions showed insignificant increase in temperature. [ABSTRACT FROM AUTHOR]

Projection of extreme rainfall under climate change remains an area of considerable uncertainty. In the absence of geographically consistent simulations of extreme rainfall for the future, alternatives relying on physical relationships between a warmer atmosphere and its moisture carrying capacity are projected, scaling with a known atmospheric covariate. The most common atmospheric covariate adopted is surface air temperature, as it exhibits great consistency across climate model simulations into the future and, as per the Clausius‐Clapeyron relationship, has a well‐established link to atmospheric moisture capacity. However, empirical assessments of this relationship show that it varies with latitude, surface temperature, atmospheric temperature, and other factors, suggesting there may be more stable "global" atmospheric covariates that could be used instead. We argue that a better‐suited covariate would be one that captures the relationship between extreme rainfall and temperature but exhibits greater consistency in the relationship across regions as well as climatic zones. Our analysis identifies plausible atmospheric indicators of changes to future extreme rainfall, which now proliferate literature and compare their suitability based on the variability they exhibit across multiple geographical, topographic, and climatic zones within Australia. It is shown that surface air temperature exhibits a regionally inconsistent relationship with extreme rainfall and hence is not suitable for projecting to future conditions. The study identified integrated water vapor and surface dew point temperature as promising alternatives, with the former showing greater consistency in space but at the cost of reduced temporal coverage. Plain Language Summary: Studies of extreme rainfall sensitivity to temperature (termed scaling) improve our understanding of how extreme rainfall can be expected to change under global warming. The widespread and intuitive use of surface air temperature in rainfall scaling studies is likely the result of its observational availability and its apparent physical relationship with extreme rainfall. This study investigates the reliability of surface air temperature, integrated water vapor, surface dew point temperature, and 850 hPa atmospheric temperature as possible covariates for rainfall projection. Consistent with observed increases in extreme rainfall, a covariate that exhibits consistency across varying climatic conditions will result in more robust projections of future extreme rainfall. Here, we investigate the susceptibility of each covariate to varying conditions exhibited by the broad climates found across Australia. Integrated water vapor, retrieved from satellites, is found to be the most suitable scaling covariate for extreme rainfall projection. Key Points: Extreme rainfall does not scale consistently with surface air temperatureIntegrated water vapor is the most stable covariate when correlated to rainfallIt is suggested integrated water be used as a covariate for rainfall projection [ABSTRACT FROM AUTHOR]

This study uses statistical methods to analyse climatic variations of air surface temperatures in Australia between 1990 and 2012. Maximum monthly temperature data were obtained from the Australian Bureau of Meteorology. Monthly maximum temperatures, collected from 81 stations in Australia over a period of 23 years, were analysed to determine the variation trends. The data from each station were adjusted for seasonal effects, and then further autocorrelations were removed by using a second-order autoregressive process. In the next step, factor analysis was employed to account for spatial correlations and group all stations into five regions, namely south-east, central-north, central-east, central-west and central regions. A simple linear regression model was then fitted to the data within each region. The average maximum temperatures trended higher in the south-east and central-west regions, with temperatures increasing between 0.071 - 0.559 °C per decade. In contrast, those in the central-north region decreased from 0.004 to 0.314 °C per decade while the central and central-east regions showed insignificant increase in temperature. [ABSTRACT FROM AUTHOR]

Several regions of the world, including the east coast of Australia, are characterized by the occurrence of low‐pressure systems with a range of different dynamical structures, including tropical, extratropical, and hybrid cyclones. Future changes in the occurrence of cyclones are better understood if storms are classified according to their dynamical structure. Therefore, we apply a classification of cyclones according to their cold‐core or warm‐core structure to an ensemble of regional climate model simulations. First, we show that historical simulations reproduce well the reanalysis results in terms of cyclone classification. We then show that once cyclone classification is applied, projections of future cyclone activity become more robust, including a decrease in the occurrence of both cold‐core and warm‐core cyclones. Finally, we show that in a warmer climate warm‐core hybrid cyclone activity could increase close to the coast, while the associated rainfall and wind are projected to increase. Plain Language Summary: Cyclones in the tropics derive their energy from the temperature difference between warm ocean waters and the atmosphere and their interior is warmer than the environment (warm core), while cyclones in the midlatitudes derive their energy from differences in the atmospheric temperature and density at different locations and their interior is colder than the environment (cold core). In subtropical regions both types of cyclone can form. Also in those regions cyclones known as hybrid cyclones form, with mixed tropical‐extratropical features, such as a partial lower tropospheric warm core and a partial upper tropospheric cold core. This study is focused on cyclones along the eastern coast of Australia. Here we show that dividing cyclones in different classes according to their thermal structure provides a better framework to interpret changes in cyclone activity at subtropical latitudes. This study has two main results. First, classifying cyclones adds value to climate projection robustness. A large number of models agree on the decrease in the occurrence of both cold‐core and warm‐core cyclones. The study also indicates increased occurrence of hybrid cyclones close to the Australian coast. Second, the study shows evidence of future changes in cyclone‐related impacts, such as an increase in the associated rainfall. Key Points: A physically based classification of hybrid cyclones is applied to an ensemble of regional climate model simulationsThe cyclone classification method adds value to the projections of future cyclone activity, making them more robustResults indicate future changes (2060–2079) toward more intense impacts associated with hybrid cyclones [ABSTRACT FROM AUTHOR]

Satellite solar‐induced chlorophyll fluorescence (SIF) products from the Global Ozone Monitoring Experiment 2 (GOME‐2) and Orbiting Carbon Observatory 2 (OCO‐2) are used to investigate the responses of vegetation growth to the 2019 heat wave in Australia. Both satellite SIF data sets are more sensitive to water and heat stress than is the greenness‐based vegetation index (enhanced vegetation index). Moreover, the OCO‐2 SIF observations show a more significant reduction and earlier response to the heat stress than does GOME‐2 SIF, indicating that the two satellite SIF data sets differ in how they monitor the drought and heat wave event due to the different timing of observations. Eddy covariance measurements confirm the different responses of dryland vegetation to the 2019 heat wave at a subdaily time scale. The differences in the timing of the satellite SIF products can be used to assess different elements of the impact of heat and water stress on Australian dryland ecosystems. Plain Language Summary: An extreme heat wave occurred across Australia during the summer of 2019, and the high air temperatures and low precipitation affected the vegetation. We used two satellite solar‐induced chlorophyll fluorescence (SIF) data sets to investigate the response of vegetation growth to the extremely high temperatures in Australia. Vegetation growth was reduced during the 2019 summer, but SIF measurements allowed us to separate the impact of the heat wave on the vegetation in the morning and in the afternoon. Our work illustrates how the timing of satellite observations provides different information on how dryland vegetation responds to heat waves in Australia. Key Points: The impact of the 2019 heat wave on Australian dryland ecosystems is examinedSolar‐induced fluorescence (SIF) shows stronger reductions to heat and water stress than does vegetation indexSIF observed at noon responds to the heat stress earlier than does that measured in the morning in Australia [ABSTRACT FROM AUTHOR]

Urban parks can help to ameliorate the urban heat island effect. This study measured thermal environments in 18 urban parks in Gold Coast, a coastal subtropical city. In particular, this study categorized and investigated two types of planning factors that may affect the thermal environment in parks: internal factors, including park tree coverage and sky view factor, and external factors, such as distance to the sea. Through physical measurement and analysis of variance, it is found that that the park cooling intensity was affected by both internal and external factors, while their effects varied in different periods of the day. The effect of trees was most significant in the evening. An increase of park tree coverage and a decrease of sky view factor could reduce evening air temperature by around 2 ℃ and 2.5 ℃ respectively. The effect of the sea was more significant in the morning. The park with the shortest distance to the sea could decrease air temperature by more than 2 ℃ in the morning. This study identified the features of thermal environment in coastal subtropical areas and their influential factors; it also provided evidence and guidance for urban planning and design to improve urban thermal environment. [ABSTRACT FROM AUTHOR]

Many climate models have dry biases in tropical monsoon regions, but it is less clear how these errors can affect these model-simulated tropical–extratropical interactions and rainfall teleconnections. In this study, we evaluate the Australia and Asian (A–A) monsoon rainfall in two versions [Global Atmosphere version 6 (GA6), and version 7 (GA7)] of the UK Met Office Unified Model (UM) with uncoupled atmosphere-only simulations using two horizontal resolutions (N96 ~ 135 km and N216 ~ 60 km). Although UM can capture broad features of rainfall seasonal variations in the monsoon region, there are significant model errors in the locations of monsoon rainfall, its magnitudes and time evolutions, especially a climatological rainfall dry bias over the Indian subcontinent. This dry bias is progressively reduced by increased model resolutions from N96 to N216 and improved model physics from GA6 to GA7. Area-averaged peak monsoon rainfall over the Indian subcontinent (70°–90°E, 5°–25°N) increases by ~ 70% from 3 mm day−1 in GA6N216 to 5 mm day−1 in GA7N216, and rainfall dry bias averaged over a large tropical Asian monsoon domain is reduced by 2/3. In the uncoupled atmosphere-only experiment, the model rainfall is excessively sensitive to its underlying SST conditions, with positive local SST-rainfall correlations occurring over whole tropical oceans, while observations show negative correlations in regions dominated by atmospheric influence although such model performance is improved in its fully coupled runs. Most significant improvement of monsoon rainfall and ENSO (El Nino Southern Oscillations) relationship in GA7 occurred in the Australian continent, with GA7N216 better capturing the observed relationship than GA6N216. Although GA7N216 still suffers a dry bias in the Indian monsoon region, the increased monsoon rainfall substantially increases atmospheric diabatic heating over the region in the middle-upper troposphere, leading to more realistic extratropical circulation Gill-type responses to the heating. This results in improved monsoon-desert rainfall teleconnections, more realistic linkage between tropical Asian monsoon with its subtropical East Asian component, and its interhemispheric influence in Southern Hemisphere. Such results are further confirmed by the model nudging experiments in which the atmospheric wind and temperature are nudged towards observations over the Indian monsoon domain. Our study clearly demonstrates that uncertainty associated with monsoon simulations needs to be considered in future climate projections even outside the monsoon domain. [ABSTRACT FROM AUTHOR]

Future climate change predictions indicate that there will be an increase in ambient air temperature. Increases in ambient air temperature will result in a corresponding increase in soil temperature. The consequences of further increases in soil temperature will potentially be detrimental for the soil seed bank of plants in terms of length of dormancy and viability of seeds. This experiment investigated the effect of different exposure temperatures and duration of exposure on the germination of semi-aquatic plant species. Seeds of four species (Alternanthera denticulata, Juncus usitatus, Persicaria lapathifolia and Persicaria prostrata) were exposed to temperatures ranging from 25 to 100°C for durations between 1 and 14 days, before being germinated in an incubator for 6 weeks. Germination occurred in all four species after exposure to temperatures ranging from 25 to 60°C. These temperatures appeared to promote germination as the temperature and duration of exposure increased. However, in P. lapathifolia and P. prostrata, the number of seeds germinating declined when exposed to 70°C and there was no germination for temperatures exceeding this. In contrast, A. denticulata and J. usitatus only began to decline when exposed to 80°C, with no germination at higher temperatures. These results suggest that soil temperatures exceeding potential threshold temperatures of 70 and 80°C will result in a decline in the number of seeds germinating and may potentially see a change in species distributions. As such soil temperatures are already being experienced throughout Australia, some species may already be close to their thermal threshold. [ABSTRACT FROM AUTHOR]

At the global scale, reconstructions of cool season temperature over past centuries are relatively rare. Here we present 277-year reconstructions of cool season (July–October) temperatures for southern Australia based on three different data sets: a spatial field reconstruction based on highly resolved temperature data from the Australian Water Availability Product data; reconstructions for the four southeast Australian states based on the Berkeley Earth mean temperature data for each state; and reconstructions for individual stations in southeastern Australia from the Australian Bureau of Meteorology's Australian Climate Observations Reference Network–Surface Air Temperature data. Our reconstructions typically capture 25–50% of the variation over the late twentieth Century calibration period and are strongest for the southern state of Tasmania and the southeastern part of mainland Australia. All three use Tasmanian tree-rings sensitive to cool season temperatures and display similar variability. In the context of our reconstructions, the persistent warming in the observed record since ~ 1950 is unprecedented. While the low frequency variability of winter temperatures is generally in step with that in summer (December–February) temperatures, high frequency variability is not, illustrating the need for seasonal reconstructions to help improve understanding of variability in inter-seasonal dynamics and the historical importance of this on the environment. The reconstructions covary with changes in the Southern Annular Mode and may be useful for future reconstructions of this phenomenon. [ABSTRACT FROM AUTHOR]

In this study we assess impacts of land-surface initialization on sub-seasonal and seasonal forecast skills from a coupled model named ACCESS-S1 (a seasonal prediction version 1 of the Australian Community Climate and Earth-System Simulator). A series of sensitivity experiments is conducted to explore to what extent the model skill and mean bias can be affected by different land-surface initialisations. By focusing the analysis on the Australian continent, our study tries to address three questions: (1) how strong is soil moisture memory in the model and how realistic is that compared with some observational evidence; (2) how does the soil moisture memory affect surface fluxes; and (3) how these impacts on surface fluxes are translated into the impacts on forecasting rainfall, temperature and atmospheric circulation. Firstly, we run an offline experiment with the ACCESS-S1 land surface model JULES for the period of 1990–2012 using ERA-interim forcing data, with precipitation further adjusted by monthly observations. This produces 23-year soil moisture time series corresponding to "observed" meteorological forcing. Lagged correlations between soil moisture at different levels and at different months are compared with some in-situ observations over Murrumbidgee catchment. We show good agreement between modelled and observed soil moisture coupling between its top-layer and sub-surface root-zone in the southeast part of the continent, and notable soil moisture memory over these regions. We then use the JULES offline soil moisture data to initialize ACCESS-S1 in its 3-month hindcasts with start date of 1st May for the 23-year period. In contrast to the default ACCESS-S1 setup which uses climatological soil moisture in its land-surface initialisation, our results show significant improvements to ACCESS-S1 forecast skill of surface maximum temperature (Tmax) and evapotranspiration by initialising the model with JULES offline data. It also has moderate improvements of surface minimum temperature (Tmin) and precipitation forecasts. The skill gain is particularly evident over the eastern part of the continent where the modelled and observed soil moisture memory is strong. Our study demonstrates that in future forecast system development, we not only need to initialise the model with updated soil moisture anomalous conditions, but also need to make sure these anomalies are combined with correct and consistent soil moisture climatology for both hindcast and real-time forecast. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi‐model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables. The relative change of RMSE for different size of the CMIP5 subset compared with full number of GCMs using the random forest (RF) ensemble simulations. Our results demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the potential of applying stable oxygen isotopes (δ18O) from head capsules (HCs) of subfossil chironomids (subfamily Tanypodinae) to reconstruct past temperature changes from south‐eastern Australia. The study reports δ18O results from Tanypodinae HCs in nine lakes. The relationship between δ18O values of Tanypodinae HCs in lakes and summer (February) air temperature is robust (r = 0.84) supporting its potential to be applied as a temperature proxy in the Australian region. The comparison of these results with the δ18O values measured on Chironomus spp. HCs from the same lakes reveals differences between the two groups. δ18O values of Tanypodinae HCs have a stronger correlation with the δ18O of lake water, δ18O of precipitation and air temperature as compared with Chironomus δ18O values. This suggests that Tanypodinae HCs are superior targets to Chironomus spp. for temperature reconstructions. Our data indicate that the δ18O of Tanypodinae HCs could perform as well as the assemblage based chironomid transfer function from the Australian mainland. We recommend analysis of HCs from additional lakes to develop a more robust calibration curve relating Tanypodinae HC δ18O to temperature. [ABSTRACT FROM AUTHOR]

ABSTRACT We present a homogenized, monthly, upper-air temperature data set comprising 22 stations across the Australian continent, as well as five Australian remote island sites. The data set is based on 2300 UTC radiosonde soundings, with most records starting in 1958. The data are mostly complete since that time, with missing data more frequent at higher levels. The quality control process involved the examination of station metadata and an objective statistical test to detect discontinuities in the data, for which monthly temperature series were adjusted. Homogenized temperature data were analysed at standard levels. The all-Australian annual mean temperature shows statistically significant trends over the 1958-2011 period, larger than the trends in surface temperature. Over this period, the annual 850 and 700 hPa mean temperatures show positive trends of 0.21 and 0.16 °C decade−1, respectively, more than the surface temperature which shows positive trend of 0.12 °C decade−1. Mid-troposphere (the 500, 400 and 300 hPa levels) also shows positive trends of 0.13, 0.15 and 0.20 °C decade−1, respectively. Trends are small and of reverse sign at levels that intercept the tropopause: +0.08 °C decade−1 at 200 hPa and −0.14 °C decade−1 at 150 hPa. In the lower stratosphere (at 100 hPa), temperature trend is strongly negative, −0.39 °C decade−1. A similar vertical structure in the trends was found for the remote islands. The homogenized upper-air radiosonde records for the Australian region are an independent confirmation of the expected physical response to increased atmospheric greenhouse gas concentrations: (1) a general warming in the troposphere, (2) a cooling in the lower stratosphere and (3) a faster warming of the troposphere than the surface. [ABSTRACT FROM AUTHOR]

Daily rainfall records from seven stations in South Australia, with record lengths from 50 to 137 years and a common period of 36 years, are investigated for evidence of changes in the statistical distribution of annual total and annual average of monthly daily maxima. In addition, the monthly time series of monthly totals and monthly daily maxima are analysed for three stations for which records exceed 100 years. The monthly series show seasonality and provide evidence of a reduction in rainfall when the Southern Oscillation Index (SOI) is negative, which is modulated by the Pacific Decadal Oscillation (PDO). However, the monthly series do not provide any evidence of a consistent trend or of any changes in the seasonal pattern. Multivariate analyses, typically used in statistical quality control (SQC), are applied to time series of yearly totals and of averages of the 12 monthly daily maxima, during the common 36-year period. Although there are some outlying points in the charts, there is no evidence of any trend or step changes. However, some supplementary permutation tests do provide weak evidence of an increase of variability of rainfall measures. Furthermore, a factor analysis does provide some evidence of a change in the spatial structure of extremes. The variability of a factor which represents the difference between extremes in the Adelaide Hills and the plains increases in the second 18 years relative to the first 18 years. There is also some evidence that the mean of this factor has increased in absolute magnitude. [ABSTRACT FROM AUTHOR]

Quantifying reference evapotranspiration (ET) is essential in water resources management. Although, many methods have been developed with different level of accuracy, in this study, two new equations were developed and optimized for estimating ET using Honey-Bee Mating Optimization (HBMO) algorithm. The first eq. estimates ET from extraterrestrial radiation (R), relative humidity (RH) and mean daily temperature (T), while the second uses the same parameters except that mean daily temperatures is replaced with maximum daily air temperature (T). Both equations were developed using climatic data from eight weather stations in Western Australia and subsequently verified using data from ten sites across Australia. The estimated ET values from both equations versus the FAO56-Penman-Monteith have a coefficient of determination, R, of larger than 0.96. Moreover, the performance of six commonly used methods of estimating ET including Hargreaves-Samani, Thornthwaith, Hamon, Mc Guinness-Bordne, Irmak and Jensen-Haise were assessed and the Hargreaves-Samani method performed better than others. An attempt was made to calibrate the Hargreaves-Samani equation; however, its overall performance did not improved and the two newly proposed equations are suggested to be used in Australia. [ABSTRACT FROM AUTHOR]

Understanding environmental and climatic drivers of natural mortality of marine mammals is critical for managing populations effectively and for predicting responses to climate change. Here we use a 17-year dataset to demonstrate a clear relationship between environmental forcing and natural mortality of inshore marine mammals across a subtropical-tropical coastline spanning a latitudinal gradient of 13° (>2000 km of coastline). Peak mortality of inshore dolphins and dugongs followed sustained periods of elevated freshwater discharge (9 months) and low air temperature (3 months). At a regional scale, these results translated into a strong relationship between annual mortality and an index of El Niño-Southern Oscillation. The number of cyclones crossing the coastline had a comparatively weak effect on inshore marine mammal mortality, and only in the tropics. Natural mortality of offshore/migratory cetaceans was not predicted by freshwater discharge, but was related to lagged air temperature. These results represent the first quantitative link between environmental forcing and marine mammal mortality in the tropics, and form the basis of a predictive tool for managers to prepare responses to periods of elevated marine mammal mortality. [ABSTRACT FROM AUTHOR]

Despite their adverse impacts, definitions and measurements of heat waves are ambiguous and inconsistent, generally being endemic to only the group affected, or the respective study reporting the analysis. The present study addresses this issue by employing a set of three heat wave definitions, derived from surveying heat-related indices in the climate science literature. The definitions include three or more consecutive days above one of the following: the 90th percentile for maximum temperature, the 90th percentile for minimum temperature, and positive extreme heat factor (EHF) conditions. Additionally, each index is studied using a multiaspect framework measuring heat wave number, duration, participating days, and the peak and mean magnitudes. Observed climatologies and trends computed by Sen's Kendall slope estimator are presented for the Australian continent for two time periods (1951-2008 and 1971-2008). Trends in all aspects and definitions are smaller in magnitude but more significant for 1951-2008 than for 1971-2008. Considerable similarities exist in trends of the yearly number of days participating in a heat wave and yearly heat wave frequency, suggesting that the number of available heat wave days drives the number of events. Larger trends in the hottest part of a heat wave suggest that heat wave intensity is increasing faster than the mean magnitude. Although the direct results of this study cannot be inferred for other regions, the methodology has been designed as such that it is widely applicable. Furthermore, it includes a range of definitions that may be useful for a wide range of systems impacted by heat waves. [ABSTRACT FROM AUTHOR]

The projection of temperature extremes by climate models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) are examined regionally over Australia. Minimum and maximum temperature extremes are defined as the 20 year return value calculated using extreme value theory. Three measures of model evaluation, a means-based, a distribution-based [via probability density functions (PDFs)] and an extreme-based (via the tails of PDFs) method, are used to compare daily model data to observed daily data over various climatic regions for a 20 year period. Model ensembles consisting of the 'better' and 'poorer' models determined by each measure of skill are created for each region. These are compared with an all-model ensemble to examine the difference in more skilled ensemble projections of temperature extremes in the A2 (high emissions) scenario for 2046-2065 and 2081-2100. If either of the distribution-based evaluation methods were used to distinguish models, the higher skilled models projected smaller increases in the 20 year return values than the all-model ensemble for both maximum temperature and minimum temperature. For some regions, the 90% confidence intervals of the better and poorer ensemble ranges did not overlap, indicating that projections are statistically significantly different. We show that the means-based evaluation produces less consistent results to the two distribution-based evaluation methods. We conclude that specific AR4 models, shown to be relatively poor over most regions of Australia by different skill metrics, bias the projected increase in the 20 year temperature extremes towards higher values. We also suggest that performance in simulating the mean climate is an unreliable measure of climate model capacity used to select models for projecting changes in extremes over Australia. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Reviews of field studies of groundwater recharge have attempted to investigate how climate characteristics control recharge, but due to a lack of data have not been able to draw any strong conclusions beyond that rainfall is the major determinant. This study has used numerical modelling for a range of Köppen-Geiger climate types (tropical, arid and temperate) to investigate the effect of climate variables on recharge for different soil and vegetation types. For the majority of climate types, the correlation between the modelled recharge and total annual rainfall is weaker than the correlation between recharge and the annual rainfall parameters reflecting rainfall intensity. Under similar soil and vegetation conditions for the same annual rainfall, annual recharge in regions with winter-dominated rainfall is greater than in regions with summer-dominated rainfall. The importance of climate parameters other than rainfall in recharge estimation is highest in the tropical climate type. Mean annual values of solar radiation and vapour pressure deficit show a greater importance in recharge estimation than mean annual values of the daily mean temperature. Climate parameters have the lowest relative importance in recharge estimation in the arid climate type (with cold winters) and the temperate climate type. For 75% of all soil, vegetation and climate types investigated, recharge elasticity varies between 2 and 4 indicating a 20% to 40% change in recharge for a 10% change in annual rainfall. Understanding how climate controls recharge under the observed historical climate allows more informed choices of analogue sites if they are to be used for climate change impact assessments. [ABSTRACT FROM AUTHOR]

An operational surface analysis system for the continent of Australia is presented. The system is specifically designed to mitigate problems that arise when analyzing surface data with a highly inhomogeneous distribution. Hourly analyses of atmospheric pressure at mean sea level, potential temperature, 2-m dewpoint temperature, and 10-m wind components are generated on a ~4-km grid. The system employs a statistical interpolation technique using observations of pressure, temperature, dewpoint, and wind data. The problem of data gaps in space and time is addressed by introducing pseudo-observations. For stations missing a report at analysis time, estimates are reconstructed by interpolating off-time reports. Underobserved areas in the network are identified from precalculated, gridded observation densities for each analysis time, which also yield weights to combine preliminary analysis and first-guess data into pseudo-observations. A regression-based pressure reduction technique, consistent with local reductions at observing sites and devised specifically for this system, is used for accurate and fast conversion of pressure and, indirectly, temperature variables within the system. Analysis accuracy is verified by withholding observations for specific periods. Analyzed fields are shown to be significantly more accurate than the current operational numerical model fields used as a first guess for the high-resolution surface analysis. The system design and analysis accuracies are also assessed within this context and compared with similar overseas developments. [ABSTRACT FROM AUTHOR]

The pattern of evaporative sources and the direction of the large-scale circulation over the Indian Ocean during the boreal summer raises the question of whether atmospheric conditions in Australia could influence conditions over the Indian subcontinent, despite the long passage of air over the Indian Ocean. The authors propose that such an influence is sometimes possible when there is unusually low temperature over inland Australia during the austral winter, through the mechanism where such a temperature extreme enhances evaporation rate over the eastern tropical Indian Ocean and hence enhances rainfall over two regions in western India after 13-19 days. Results from trajectory calculations indicate that such an influence is mechanistically feasible, with air of Australian origin contributing 0.5-1.5% of the climatological net precipitation for monsoon seasonal rainfall over western India. Statistics performed on reanalysis, satellite and in situ data are consistent with such a mechanism. Since extreme winter temperature in Australia is often associated with cold-air outbreaks, the described mechanism may be an example of how southern hemispheric midlatitude weather can influence northern hemispheric monsoon rainfall. Further study is recommended through modelling and comparison with various known causes of atmospheric variability to confirm the existence of such a mechanism and determine the extent of its influence during specific low temperature episodes. [ABSTRACT FROM AUTHOR]

Links between southern Australian rainfall totals and tropical indices are investigated with the aim of identifying potential explanations for observed trends over the period 1950-2008. We find that, as has been noted in many previous studies, fluctuations in Indian Ocean sea surface temperature (SST) anomalies do not explain significantly more of interannual rainfall fluctuations than other indices which are dominated by El Nino Southern Oscillation events. It is proposed that a composite index, capturing SST variations in the Indian Ocean, the seas to the north of Australia, and the Pacific Ocean may be more useful in studying interannual variability. We also find that, while most indices can be significantly linked to rainfall fluctuations on interannual time scales, these links do not adequately describe the long-term trends. This suggests that the drivers of long-term trends in southern Australian rainfall do not involve the tropical oceans so much as changes in the large-scale atmospheric circulation. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]