In Australia, meteorological measurements from air quality monitoring networks are an overlooked source of data for urban climatology and meteorology research and operations. The reasons for this are not clear but may include uncertainty in the quality of the observations. Here I compare over 1 million 1-min near surface air temperature (n = 516,334) and relative humidity (n = 516,717) measurements from two distinct observational stations at Camden Airport in New South Wales (NSW), Australia – the Bureau of Meteorology automatic weather station (AWS) and the NSW Department of Climate Change, Energy, the Environment and Water’s air quality monitoring station (AQMS). Annual mean bias in the AQMS temperature measurements was −0.14°C. There were seasonal and diurnal variations in temperature bias, with monthly mean bias varying from −0.27 to +0.06°C and mean hourly bias varying between −0.39 and +0.11°C. Annual mean bias in AQMS humidity measurements was −0.37%, monthly mean bias varied from −2.21 to +1.44% and bias in mean hourly measurements varied between −2.64 and +2.66%. Temperature and humidity mean biases were both within the range of the combined instrument uncertainties. The seasonal and diurnal signal in the bias is likely due to differences in instrument siting and the different radiation shields (Stevenson and multi-plate). This analysis suggests that temperature and humidity measurements from the NSW AQMS are of high quality. The performance of the AQMS measurements matches the AWS measurements and, for most circumstances, the temperature and humidity measurements are comparable. Urban climatologists and meteorologists should consider data from air quality networks in their research and can use this data with confidence.

Biomass burning is a significant particulate matter (PM) source, substantially contributing to elevated PM2.5 levels. Exposure to PM2.5 has been associated with various severe chronic illnesses. Therefore, it is crucial to address biomass burning occurrences, mitigate their impacts, and manage their consequences effectively. A key strategy for managing biomass burning haze involves identifying its sources, which facilitates the implementation of fire prevention and suppression measures. This study explores the sources and impacts of PM2.5 emissions from forest fires in Central Kalimantan in October 2023 using an integrated approach. We employed the WRF-HYSPLIT (Weather Research and Forecasting model coupled with the Hybrid Single Particle Lagrangian Integrated Trajectory model) and satellite instruments, including the GEMS (Geostationary Environment Monitoring Spectrometer), MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), to identify PM2.5 sources and analyse their spatial distribution. Palangka Raya experienced substantial impacts from multiple hotspot occurrences on 4 October 2023, particularly from the south-eastern and eastern regions of Central Kalimantan and South Kalimantan. Conversely, Pangkalan Bun showed relatively lower PM2.5 concentrations on 2 October 2024 owing to prevailing sea winds. Most PM2.5 in Palangka Raya originated from the south-east. Geopotential height and topography analyses with wind plots suggested stable atmospheric conditions in Palangka Raya, whereas GEMS satellite data revealed high aerosol optical depth values, indicating elevated PM2.5 concentrations. These findings underscore the importance of understanding local meteorological conditions and hotspot distributions for effective management and mitigation of forest fire impacts on air quality in Central Kalimantan.

Understanding carbon dioxide (CO2) surface fluxes is essential in the context of a changing climate. In particular, agriculture significantly contributes to CO2 fluxes. Recently, some studies have focused on understanding how synoptic-scale variability modulates CO2 fluxes associated with vegetation and agriculture, finding that frontal passages and precipitation events exert a strong influence on these fluxes. This variability is particularly relevant in the Argentinean Pampas, where large CO2 fluxes associated with extensive agriculture combine with strong synoptic variability. Numerical modelling provides a valuable tool for investigating surface CO2 fluxes and their variability, despite the uncertainties associated with the model’s limitations. In this work, we investigate simulated CO2 fluxes in the Argentinean Pampas using the Weather Research and Forecasting Model (WRF) coupled with the Vegetation, Respiration and Photosynthesis Model (VPRM) over three case studies representing different synoptic-scale conditions. In addition, we estimate the uncertainty in the simulations by comparing simulated CO2 fluxes using various WRF configurations and the ERA5 reanalysis. We found that the synoptic-scale conditions have a considerable impact on the magnitude of fluxes as well as the simulation uncertainty. Uncertainties in simulated CO2 fluxes can be as high as 60%, being larger at sunrise and sunset. Also, the largest uncertainty is associated with a case with a cold frontal passage and widespread precipitation. These results allow a more accurate estimation of CO2 flux uncertainty, which is key to understanding the effects of climate change.

A newly discovered erosional scar on the Nullarbor Plain in southern Australia was analysed and hypothesised to be a consequence of a tornado event. The scar, identified using satellite imagery, stretches ~11 km in length and varies between 160 and 250 m in width, with notable cycloidal marks indicating activity of suction vortexes. The timing of the scar formation, constrained by Landsat and Sentinel imagery, is between 16 and 18 November 2022, coinciding with a significant weather event on 17 November 2022. The scar’s characteristics and the associated weather patterns strongly suggest it was formed by a tornado. Based on the scar’s dimensions and the pattern of cycloidal marks, the tornado’s strength is estimated to be within the F2 or even F3 category on the Fujita scale, with wind speeds likely exceeding 200 km h–1, moving in an eastward direction and swirling clockwise.

This is a summary of the southern hemisphere atmospheric circulation patterns and meteorological indices for 2019–20; an account of rainfall and temperature for the Australian region is also provided. The second half of 2019 was dominated by a strong positive Indian Ocean Dipole, before a return to more normal conditions from early 2020. The Indian Ocean Dipole, along with generally negative phases of the Southern Annular Mode associated with a marked high-latitude sudden stratospheric warming, contributed to very dry conditions in Australia in the second half of 2019, with exceptional fire weather conditions and associated severe fires. The sudden stratospheric warming was also a major contributor to a well below average Antarctic ozone hole in spring 2019. It was also fairly dry over most other southern hemisphere continental areas, and over the Maritime Continent in the second half of 2019, with the most significant wet anomalies in 2019–20 being in equatorial east Africa. Temperatures were well above average in 2019–20 for both Australia and the southern hemisphere as a whole.

Austral autumn 2020, inclusive of the months March, April and May, followed a period of hot and dry conditions over summer that led to high bushfire risk in Australia due to a strong positive Indian Ocean Dipole. The autumn period was also warmer and drier than average overall with strong differences across the states. Notable exceptions were rainfall across New South Wales, Tasmania and Victoria, and rainfall associated with Tropical Cyclone Esther near the Pilbara and the Northern Territory Top End. Overall, Australia maximum mean temperatures were +0.65°C above average, with Western Australia in the top 10 recorded temperatures at +1.53°C above average. South-east Australian rainfall led to flooding and extinguished the remaining fires of the 2019–2020 bushfire season. The rainfall did not reach coastal regions of south-east Queensland where severe rainfall deficiencies persisted. Autumn 2020 was classified as neutral in terms of the El Niño–Southern Oscillation (ENSO) and marks the second time that the Great Barrier Reef has experienced mass coral bleaching due to elevated ocean temperatures in an ENSO neutral year. Elevated sea surface temperatures in the Coral Sea are largely attributed to a general warming trend in the region. Antarctic sea ice extent went from below average to close to average by the end of May 2020, aided by a positive Southern Annular Mode. At the end of autumn 2020, there were early signs of a La Niña event building, with negative temperature anomalies in the subsurface and cooling of sea surface temperatures along equatorial Pacific.

Low pressure systems are an important source of rainfall in southern Australia, particularly deep lows that extend from the surface to at least 500 hPa. This paper uses multiple reanalyses to assess long-term trends in lows over the period 1959–2023, and identifies statistically significant decreasing trends in the number of surface low pressure systems near southern Australia during May–October, linked to a decrease in cyclogenesis near south-western Western Australia. Trends in lows at 500 hPa are also negative but weaker than at the surface, and are less consistent between reanalyses owing to less consistent observations through time. The spatial pattern of observed declines during the cool season is consistent with trends using eight CMIP6 models, but global climate models systematically underestimate the magnitude of the observed decline in surface lows. Trends in rainfall associated with lows are also shown, including assessing the sensitivity of trends to the specific years used. Despite well above average numbers of lows and enhanced rainfall during recent La Niña years 2020–2022, total rainfall from low pressure systems is declining during the cool season in south-east Australia. Trends in rainfall from lows are largest on the east coast, where they explain more than 70% of observed rainfall changes since the 1960s.

The rise of pathways-based approaches to coastal adaptation in Australia has changed user requirements for coastal flood hazard information to support decision-making. This study identifies and addresses three aspects not considered in the existing Australia-specific scientific guidance for planning adaptation to sea-level rise. First, changes in the frequency of present-day extreme sea levels are compared between locations. Second, extreme sea levels are related to impact-based thresholds associated with past flood events. Third, the potential for chronic flooding emerging is assessed. This complements global studies that provide some Australian results on these topics. We survey these to identify the methods most suitable for our application and apply the chosen methods to the reference dataset for monitoring Australian coastal sea-level change. This yields a water-level frequency dataset covering daily to centennial water levels for 37 Australian tide gauges. We analyse the dataset to provide a national picture of how sea-level rise is expected to influence the future frequencies of coastal floods in Australia. For example, 85% of Australian locations expect present-day centennial extremes to occur 30 days per year with less than 1-m sea-level rise. The locations with the largest increases in the future frequency of these extremes have the smallest present-day sea-level extreme magnitudes relative to mean sea level, and lower flood thresholds relative to these extremes. We demonstrate three further potential applications of our dataset and methods using local case studies: impact-based forecasting, climate risk services and identifying the required sea-level rise for adaptation triggers and thresholds to be reached.

The planetary boundary layer height (PBLH) is an important meteorological feature defining the boundary between surface processes and the free troposphere. The PBLH plays a key role in cloud formation and the vertical extent of aerosols and air pollutants. Measurements of PBLH were made by meteorological sensors mounted to a multi-copter drone over the southern Great Barrier Reef, Australia. We then compared these drone-based measurements to remote-sensed PBLH observations, using a Mini-Micropulse (MP) LiDAR system. Across the measurement campaign (1 March–2 April 2023), the mean PBLH value using the drones was 801 ± 203 m. Using the gradient method for MP LiDAR normalised relative backscatter profiles, the mean PBLH was 811 ± 260 m. Using an ideal MP LiDAR profile fitting method the mean was 912 ± 202 m and using a wavelet covariance transform method the mean was 862 ± 298 m. The boundary layer was consistently well mixed, without convective instability or a strong diurnal PBLH cycle. The three MP LiDAR methods compared well to the drone measurements overall with Pearson’s R correlation coefficients >0.60; however, estimates from the MP LiDAR were typically ~10% higher than from the drone. These results indicate congruence between the backscatter- and thermodynamically derived PBLH at One Tree Island, which is robust to variations in sampling conditions and the choice of MP LiDAR PBLH retrieval method.

South-west Western Australia (SWWA) is home to a world class grains industry that is significantly affected by periods of drought. Previous research has shown a link between the Southern Annular Mode (SAM) and rainfall in SWWA, especially during winter months. Hence, the predictability of the SAM and its relationship to SWWA rainfall can potentially improve forecasts of SWWA drought, which would provide valuable information for farmers. In this paper, focusing on the 0-month lead time forecast, we assess the bias and skill of ACCESS-S2, the Australian Bureau of Meteorology’s current operational sub-seasonal to seasonal forecasting system, in simulating seasonal rainfall for SWWA during the growing season (May–October). We then analyse the relationship between the SAM and SWWA precipitation and how well this is captured in ACCESS-S2 as well as how well ACCESS-S2 forecasts the monthly SAM index. Finally, ACCESS-S2 rainfall forecasts and the simulation of SAM are assessed for a case study of extreme drought in 2010. Our results show that forecasts tend to have greater skill in the earlier part of the season (May–July). ACCESS-S2 captures the significant inverse SAM–rainfall relationship but underestimates its strength. The model also shows overall skill in forecasting the monthly SAM index and simulating the MSLP and 850-hPa wind anomaly patterns associated with positive and negative SAM phases. However, for the 2010 drought case study, ACCESS-S2 does not indicate strong likelihoods of the upcoming dry conditions, particularly for later in the growing season, despite predicting a positive (although weaker than observed) SAM index. Although ACCESS-S2 is shown to skillfully depict the SAM–SWWA rainfall relationship and generally forecast the SAM index well, the seasonal rainfall forecasts still show limited skill. Hence it is likely that model errors unrelated to the SAM are contributing to limited skill in seasonal rainfall forecasts for SWWA, as well as the generally low seasonal-timescale predictability for the region.

A new convection scheme, ‘CoMorph-A’, has been introduced into the latest UK Met Office coupled (GC4) and atmosphere-only (GA8) models. In this study, the impact of CoMorph-A is assessed in atmosphere-only Atmospheric Model Intercomparison Project simulations, as well as in sets of initialised 28-day forecasts with both the coupled and uncoupled models. Initial results show improvements over the Indo-Pacific and northern Australian regions, as well as improvements in the rainfall bias, Madden–Julian Oscillation simulation and prediction, tropical cyclone forecasts and the diurnal cycle of rainfall over the Maritime Continent. The improvements are mostly consistent across the initialised forecasts and the climate simulations, indicating the effectiveness of the new scheme across applications. The use of this new convection scheme is promising for future model configurations, and for improving the simulation and prediction of Australian weather and climate. The UK Met Office is continuing to develop CoMorph and will soon release version B.

Perth Airport is located on a coastal plain in the south-west of Australia, with the Indian Ocean to the west and the Darling Scarp running approximately parallel to the coast to the east. On average, there are approximately nine fog events per year at the airport, typically occurring during the cooler months in the early morning hours. Onshore winds bringing moisture from the Indian Ocean can combine with nocturnal cooling in stable atmospheres to encourage fog formation. A previous climatological study of fog at Perth Airport found that the majority of events had north to north-easterly 10-m winds at fog onset time. Two case studies are presented to gain a better understanding of the physical processes associated with the north to north-easterly near-surface flow and their influence on the development of fog. The hypothesis is that the escarpment is blocking the moist environmental flow, resulting in light northerly near-surface winds. This was tested through numerical experiments including altered terrain. The main finding from the case studies was that the northerly winds stem from a blocking of the airmass in the lower level of the atmosphere by the Darling Scarp in moderate wind situations. During calm or very light wind occasions, the winds below the surface inversion level can tend northerly regardless of topography. The trapped airmass and light winds in the near surface layer in combination with nocturnal surface cooling and moisture from the environmental flow, create conditions favourable for the development of fog at Perth Airport.

The areal extent of rainfall remains one of the most challenging meteorological variables to model accurately due to its high spatial and temporal variability. Weather radar is a remote sensing instrument that is increasingly used to estimate rainfall by providing unique observations of precipitation events at fine spatial and temporal resolutions, which are difficult to obtain using conventional rain gauge networks. Dense rain gauge networks combined with operational weather radars are widely considered as the most reliable source of rainfall depth estimates. This paper compares the various sources of rainfall data available and explores the benefits of merging radar data with rain gauge data by reviewing the outcomes of a case study of a major agricultural cropping and pasture region. Comparison is made of rainfall measurements obtained from a dense rain gauge network covered by the output from a weather radar installation. We conclude that merging radar data with rain gauge data provides improved resolution of the spatial variability of rainfall, resulting in a significantly improved data source for agricultural water management and hydrological modelling. However, the use of weather radar merged with rain gauge data is generally underrated as a management tool.

Hailstorms develop over the La Plata Basin, in south-eastern South America, more often during later winter and early austral spring, between September and October. These systems have significant socioeconomic impacts over the region. Thus, a better understanding of how atmospheric drivers modulate the formation of hailstorms is important to improve the forecast of such phenomena. In this study, we selected a hailstorm event observed over the eastern La Plata Basin during 14–15 July 2016 to evaluate the performance of the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS) model. The ability of the model in simulating cloud microphysical properties was evaluated by comparing simulations driven by different global forcings against in situ and remote sensing observations. The model results showed good skill in capturing the basic characteristics of the thunderstorm, particularly in terms of the spatial distribution of hydrometeors. The simulated spatial distribution of hail covers locations where hail fall was reported. The BRAMS simulations suggest that, despite relatively low values of the convective available potential energy (CAPE) (700–1000 J kg−1), environments with strong 0–8-km bulk shear (60–70 kt, ~30.9–36.0 m s–1) can promote the formation of ice clouds and hail fall over the eastern La Plata Basin. To be more conclusive, however, further research is needed to understand how different combinations of CAPE and shear affect hail formation over the region.

Subseasonal to seasonal forecasts of ocean temperatures, including extreme events such as marine heatwaves, have demonstrated utility in informing operational decision-making by marine end users and managing climate risk. Verification is critical for effective communication and uptake of forecast information, together with understanding ocean temperature predictability. The forecast skill of surface and subsurface ocean temperature forecasts from the Bureau of Meteorology’s new ACCESS-S2 seasonal prediction system are assessed here over an extended 38-year hindcast period, from 2 weeks to 6 months into the future. Forecasts of sea surface temperature (SST), heat content down to 300 m (HC300), bottom temperatures on continental shelves, and mixed layer depth are compared to both satellite observations and ocean reanalyses for the globe and the Australian region, using a variety of skill metrics. ACCESS-S2 demonstrates increased SST skill over its predecessor ACCESS-S1 at subseasonal timescales for all variables assessed. Heat content skill is particularly high in the tropics but reduced in subtropical regions especially when compared to persistence. Forecast skill for ocean temperature is higher in the austral summer months than winter at lead times up to 2 months in the Western Pacific region. Mixed layer depth is poorly predicted at all lead times, with only limited areas of skill around Australia and in the south-west Pacific region. Probability of exceedance forecasts for the 90th percentile as an indicator for marine heatwave conditions, shows adequate skill for SST, HC300 and bottom temperatures, especially near shelf regions at shorter lead times. This work will underpin the future development of an operational marine heatwave forecast service, which will provide early warning of these events and thus valuable preparation windows for marine stakeholders.

This paper describes projected climate evolution and outcomes simulated by the Australian Community Climate and Earth System Simulator (ACCESS) to varying future scenarios, including of socio-ecological and technological development, and land-use and land-cover change. Contributions to the Coupled Model Intercomparison Project Phase 6 (CMIP6) from the climate model version, ACCESS-CM2, and the fully coupled Earth System Model version, ACCESS-ESM1.5, are presented for the near-future (2020–2050), 21st Century (2000–2100) and longer-term (2100–2300). Scenario differentiation in the near future is aided by high-density sampling in large-ensemble ACCESS-ESM1.5, more clearly illustrating projected 2020–2050 global changes in temperature, precipitation and aerosol optical depth. Over the 21st Century, the heightened equilibrium climate sensitivity of ACCESS-CM2 relative to ACCESS-ESM1.5 results in persistently greater surface air temperature increases and larger amplified polar warming, leading to more rapid sea ice decline. Although weakening of the Atlantic meridional overturning circulation (AMOC) occurs in both models, 21st Century recovery under aggressive-mitigation and overshoot scenarios only occurs in ACCESS-ESM1.5; AMOC weakening continues under all scenarios in ACCESS-CM2 through to 2100. Longer-term climate response from simulations extending to 2300 depict opposing hemispheric responses of polar surface air temperatures and sea ice in both models under scenarios based on aggressive mitigation action, leading to a resurgence of surface ocean warming and Antarctic sea ice decline. Under a future scenario where development is driven by continued fossil fuel use, both AMOC and Antarctic Bottom Water Formation continue to weaken across 2200–2300 in both models, reaching such low levels in ACCESS-CM2 that these pivotal components of global meridional overturning circulation could be considered essentially to have ceased.

According to the Australian Bureau of Meteorology, the northern Australian wet season extends through to April, which also formally marks the end of Australia’s tropical cyclone season. Mid-autumn is when the tropical dry season transition period begins, when crop farmers prepare land for annual crops or pasture–fodder harvest, or when beef cattle producers make decisions regarding stock numbers and feed rationing. Potentially knowing if the last rains of the wet season will be later or earlier than normal would be valuable information for northern sectors such as agriculture, infrastructure and tourism. The Bureau of Meteorology provides seasonal forecasts of the Northern Rainfall Onset – the date when a location has accumulated 50 mm of rain from 1 September – yet there is currently no prediction of the rainy season retreat (the Northern Rainfall Retreat, NRR). In this study, we draw on three different NRR definitions and investigate how they vary with the El Niño–Southern Oscillation and the Madden–Julian Oscillation (MJO). In general, retreats occur ~1 week later than normal across the far northern tropics following La Niña events, but little change from normal occurs for El Niño. Although most retreats occur when the MJO is weak, if the MJO is active, retreats are mostly observed in phases 6 and 7, when convection is passing through the western Pacific. Utilising the Bureau of Meteorology’s sub-seasonal to seasonal forecast system, ACCESS-S2, we show that the model has some skill in forecasting the NRR across the far northern regions at a lead time of ~2.5 months, but poor skill in the subtropics and arid locations. Verification of the 2023 NRR forecasts, highlights the challenges of predicting the timing and magnitude of daily rainfall at such a long lead time.

The Australian Bureau of Meteorology (The Bureau) has been involved in the package testing and assessment process of the UK Met Office Global Coupled Model Version 5.0 (GC5) configuration. GC5 will underpin the Met Office’s next seasonal prediction system, global coupled numerical weather prediction (NWP) system and Earth System Model. It will also likely be the next version of The Bureau’s seasonal prediction system, and the version to replace the global atmosphere-only NWP system to be the first global coupled NWP system at The Bureau. The GC5 configuration includes a new sea-ice model and substantial updates to almost all areas of model physics. We have evaluated the present-day climate simulation, and compared it to observations and with previous versions GC4 and GC2. Our assessment focuses on the climate mean state and variabilities relevant to Australian seasonal prediction, including the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode and the Madden–Julian Oscillation. Notably, in comparison to its predecessor (GC4), GC5 shows significant improvements in the eastern Pacific mean state but a slight degradation in the Indian Ocean in terms of the mean state and variability. These and other results provide us with early insights of the potential performance of the next sub-seasonal or seasonal forecast system. Longstanding issues in the seasonal prediction system associated with the equatorial eastern Indian Ocean biases and an overactive ENSO and IOD will likely remain; however, improvements over the eastern equatorial Pacific in GC5 hold promise of improved prediction skill of ENSO and its teleconnections.

In 2019 there were forest fires in Sumatra, Indonesia, which co-occurred with a strong positive IOD. The forest fire disaster caused the thick smoke containing dusts such as particulate matter with a size of 2.5 μm (PM2.5). In this study, a model simulation was conducted to predict the distribution of PM2.5 using the WRF–Chem model when forest fires in 2019 occurred. The prediction was produced by adding the latest version of fire inventory from NCAR (FINN) input data, version 2.5. The prediction result was verified and compared with ground station data from the Ministry of Environment and Forestry (KLHK) station and NASA’s EOSDIS satellite imagery. There are three ground stations that were used for verification: the Jambi, Palembang and Pekanbaru stations. Of the three stations, the prediction results at the Palembang station are the best in correlation and RMSE value. Spatially, the distribution of PM2.5 from the model result is similar and can follow the pattern of smoke distribution from satellite imagery observations. Even though, generally the WRF–Chem model equipped with the latest FINN data still cannot produce an accurate prediction for the 2019 forest fires event yet in Sumatra region.

The Hunter Valley is well known for the strong westerly winds in winter and elevated fire danger arising from hot and dry north-westerly winds in summer. These hazards are closely related to the valley channelling in the region, and the connection between the two has been an interest to weather forecasters, emergency service personnel, and the aviation industry. In this paper, the climatology of valley winds is constructed to identify the dominant types of channelling in the Hunter Valley and their preferred directions using the 10-year data of Automatic Weather Station observations, upper air sounding and ERA5 reanalysis data between July 2010 and June 2020. Particular attention is given to the conceptual model of pressure-driven channelling of westerlies in winter and its mechanics, as the climatology shows that it is the main cause of wind-warning conditions in the Hunter.

An analysis of the South Australian severe thunderstorm and tornado outbreak of 28 September 2016, which produced at least seven tornadoes and contributed to a state-wide power outage, is presented here. Although challenging, prediction and understanding of tornadoes and other hazards associated with severe thunderstorms is very important to forecasters and to community and emergency services preplanning and preparedness. High-resolution deterministic and ensemble simulations of the event are conducted using the Australian Community Climate and Earth-System Simulator (ACCESS) model and the simulations are compared to radar and satellite observations. The deterministic simulation and two of the ensemble members show that the overall structure, orientation, intensity and timing of simulated thunderstorms is in good agreement with the observations. In the deterministic simulation, a hook-echo feature in the simulated reflectivity, indicating the presence of a mesocyclone, appeared at the time and location of one of the observed tornadoes. Two diagnostics were found to have good value for identifying tornado-formation risk. Updraft helicity successfully identified the potential for mesocyclone development, and the Okubo–Weiss parameter identified model-resolved mesocyclone rotation. The ensemble simulations show a wide range of outcomes for intensity, timing and structure of the event, as well as differences in potential for tornado formation. This emphasises the importance of ensemble simulations in forecasting severe weather and associated hazards, as ensembles identify a range of possible scenarios and the uncertainty, leading to improved guidance for forecasters and emergency services.

Studies of the spatial and temporal variability of precipitation concentration are necessary. This variability is a significant climate element and also a critical socioeconomic factor. This study aims to contribute to the knowledge of rainfall in Argentina using records of monthly precipitation for 64 stations (period 1991–2021) to calculate the monthly precipitation concentration index (PCI). Precipitation in Argentina, given its vast territory, presents a great variability with a wide range of rainfall regimes; the range of PCI values is 10.6–27.3. Values of PCI range from uniform distribution (less than 10) to strong irregularity of precipitation distribution (greater than 20). The north-west of the country shows a high concentration and the south has a low concentration. Analysis shows that the majority of the stations have positive trends in PCI (although not significant), and this increase in concentration of precipitation could become a risk depending on the evolution of the associated rain.

Monthly humidity (represented as dew point temperature, DWPT) data from 22 land and 5 island Australian upper-air sites were analysed, with trends estimated over the 1965–2017 period at four pressure levels. Humidity data were selected to ensure that data collected under consistent sampling conditions were used (‘modified data’). The quality control process involved examining station metadata and applying an objective statistical test that detected discontinuities in the data series. At each station and pressure level, modified data series were adjusted (homogenised) on a monthly timescale when discontinuities were identified. Analysis of the homogenised (adjusted) modified DWPT data indicates that, over the 1965–2017 period, linear trends are mostly positive and smaller compared to unadjusted modified data. The all-Australian time-series show positive trends at the 850–400-hPa levels. The total increases in DWPT since 1965 at 850-, 700-, 500- and 400-hPa levels are ~0.5, ~1.2, ~1.3 and ~0.8°C respectively. The increase in humidity in the lower and middle troposphere is in accordance with the expectation that, as the troposphere warms, the amount of moisture in it should increase (at a differential rate of ~7% °C–1 at low altitudes globally, following Clausius–Clapeyron scaling) due to increasing surface evaporation and moisture-holding capacity of the air. However, changes in atmospheric dynamics also influence the magnitude and distribution of humidity trends. The homogenised modified Australian radiosonde data for the 850-hPa level show that the amount of moisture at this level increased ~8.8% °C–1 during 1965–2015