In 2024, the mean temperature deviation in Australia was 1.46 degrees Celsius higher than the reference value for that year, indicating a positive anomaly. Over the course of the last century, mean temperature anomaly measurements in Australia have exhibited an overall increasing trend. Temperature trending upwards Global land temperature anomalies have been fluctuating since the start of their measurement but show an overall upward tendency. Australian mean temperatures have followed this trend and continued to rise as well. Considered the driest inhabited continent on earth, this has severe consequences for the country. In particular, the south of Australia is predicted to become susceptible to drought, which could lead to an increase in bushfires as well. The highest temperatures recorded in Australia as of 2022 were measured in South Australia and Western Australia, both exceeding 50 degrees. The 2019/2020 bushfire season Already prone to wildfires due to its dry climate, the change in temperature has made Australia even more vulnerable to an increase in bushfires. One of the worst wildfires in Australia, and on a global level as well, happened during the 2019/2020 bushfire season. The combination of the hottest days and the lowest annual mean rainfall in 20 years resulted in a destruction of 12.5 million acres. New South Wales was the region with the largest area burned by bushfires in that year, a major part of which was conservation land.

Temperature in Australia increased to 22.32 celsius in 2023 from 21.93 celsius in 2022. This dataset includes a chart with historical data for Australia Average Temperature.

As of December 2022, the highest recorded temperature in Australia was at Onslow Airport in Western Australia, where the temperature was **** degrees Celsius. This was matched by the highest temperature recorded at Oodnadatta Airport, South Australia, in 1960. What is causing increasing temperatures? The annual mean temperature deviation in the country has increased over the past century. In 2024, the annual national mean temperature was **** degrees Celsius above average. Climate experts agree that the major climate driver responsible for the heat experienced in Australia was a positive Indian Ocean Dipole (IOD). This is where sea surface temperatures are cooler in the eastern half of the Indian Ocean than the western half. The discrepancy in temperatures led to drier, warmer conditions across Australia. Global warming due to greenhouse gas emissions has been linked to the warming of sea surface temperatures and the IOD. Social change While the topic of global warming is undoubtedly controversial, many people perceive global warming as influencing Australia’s climate. In 2023, around ** percent of Australians believed climate change was occurring. Furthermore, around **** of Australians agreed that their government was not doing enough in terms of climate change action.

In 2023, the observed annual average mean temperature in Australia reached 22.32 degrees Celsius. Overall, the annual average temperature had increased compared to the temperature reported for 1901. Impact of climate change The rising temperatures in Australia are a prime example of global climate change. As a dry country, peak temperatures and drought pose significant environmental threats to Australia, leading to water shortages and an increase in bushfires. Western and South Australia reported the highest temperatures measured in the country, with record high temperatures of over 50°C in 2022. Australia’s emission sources While Australia has pledged its commitment to the Paris Climate Agreement, it still relies economically on a few high greenhouse gas emitting sectors, such as the mining and energy sectors. Australia’s current leading source of greenhouse gas emissions is the generation of electricity, and black coal is still a dominant source for its total energy production. One of the future challenges of the country will thus be to find a balance between economic security and the mitigation of environmental impact.

In 2023, the observed annual average maximum temperature in Australia reached 29.67 degrees Celsius. Overall, the annual average maximum temperature had increased compared to the temperature reported for 1901.

Australian Bureau of Meteorology assembled this dataset of 191 Australian rainfall stations for the purpose of climate change monitoring and assessment. These stations were selected because they are believed to be the highest quality and most reliable long-term rainfall stations in Australia. The longest period of record is August 1840 to December 1990, but the actual periods vary by individual station. Each data record in the dataset contains at least a monthly precipitation total, and most records also have daily data as well.

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Köppen's scheme to classify world climates was devised in 1918 by Dr Wladimir Köppen of the University of Graz in Austria. Over the decades it has achieved wide acceptance amongst climatologists. However, the scheme has also had its share of critics, who have challenged the scheme's validity on a number of grounds. For example, Köppen's rigid boundary criteria often lead to large discrepancies between climatic subdivisions and features of the natural landscape. Furthermore, whilst some of his boundaries have been chosen largely with natural landscape features in mind, other boundaries have been chosen largely with human experience of climatic features in mind. The present paper presents a modification of Köppen's classification that addresses some of the concerns and illustrates this modification with its application to Australia.

A modification of the Köppen classification of world climates has been presented. The extension has been illustrated by its application to Australian climates. Even with the additional complexity, the final classification contains some surprising homogeneity. For example, there is a common classification between the coastal areas of both southern Victoria and southern New South Wales. There is also the identical classification of western and eastern Tasmania. This arises due to the classification not identifying every climate variation because a compromise has to be reached between sacrificing either detail or simplicity. For example, regions with only a slight annual cycle in rainfall distribution do not have that variation so specified in the classification. Similarly, regions with only slightly different mean annual temperatures are sometimes classified as being of the same climate.

The classification descriptions need to be concise, for ease of reference. As a result, the descriptions are not always complete. For example, the word "hot" is used in reference to those deserts with the highest annual average temperatures, even though winter nights, even in hot desert climates, can't realistically be described as "hot".

In conclusion, the authors see the classification assisting in the selection of new station networks. There is also the potential for undertaking subsequent studies that examine climate change in the terms of shifts in climate classification boundaries by using data from different historical periods, and by using different characteristics to define climate type such as "inter-annual variability of precipitation". In the future, it is planned to prepare climate classification maps on a global scale, as well as on a regional-Australian scale.

TABLE 1

Köppen's original scheme New scheme

Tropical group Divided into equatorial & tropical groups

Monsoon subdivision Becomes rainforest (monsoonal) subdivision

Dry group Divided into desert & grassland groups

Summer/winter drought subdivisions Now requires 30+mm in wettest month

Temperate group Divided into subtropical & temperate groups

Cold-snowy-forest group Cold group

Dry summer/winter subdivisions Moderately dry winter subdivision added

Polar group Maritime subdivision added

Frequent fog subdivision Applies now only to the desert group

Frequent fog subdivision Becomes high humidity subdivision

High-sun dry season subdivision Absorbed into other subdivisions

Autumn rainfall max subdivision Absorbed into other subdivisions

Other minor subdivisions Absorbed into other subdivisions

This dataset has been provided to the BA Programme for use within the programme only. For copyright information go to http://www.bom.gov.au/other/copyright.shtml. Information on how to request a copy of data can be found at www.bom.gov.au/climate/data.

Dataset History

Trewartha (1943) notes that Köppen's classification has been criticised from "various points of view" (Thornthwaite 1931, Jones 1932, Ackerman, 1941). Rigid boundary criteria often lead to large discrepancies between climatic subdivisions and features of the natural landscape. Some boundaries have been chosen largely with natural landscape features in mind (for example, "rainforest"), whilst other boundaries have been chosen largely with human experience of climatic features in mind (for example, "monsoon"). Trewartha (1943) acknowledges the validity of these criticisms when he writes that "climatic boundaries, as seen on a map, even when precisely defined, are neither better nor worse than the human judgements that selected them, and the wisdom of those selections is always open to debate". He emphasises, however, that such boundaries are always subject to change "with revision of boundary conditions ... (and that) ... such revisions have been made by Köppen himself and by other climatologists as well".

Nevertheless, the telling evidence that the Köppen classification's merits outweigh its deficiencies lies in its wide acceptance. Trewartha (1943) observes that "its individual climatic formulas are almost a common language among climatologists and geographers throughout the world ... (and that) ... its basic principles have been ... widely copied (even) by those who have insisted upon making their own empirical classifications". Trewartha's (1943) comments are as relevant today as they were half a century ago (see, for example, Müller (1982); Löhmann et al. (1993)).

For the above reasons, in modifying the Köppen classification (Figures 1 and 2), the authors have chosen to depart only slightly from the original. Nevertheless, the additional division of some of the Köppen climates and some recombining of other Köppen climates may better reflect human experience of significant features. In recognition of this, the following changes, which are also summarised in Table 1, have been adopted in this work:

The former tropical group is now divided into two new groups, an equatorial group and a new tropical group. The equatorial group corresponds to the former tropical group's isothermal subdivision. The new tropical group corresponds to that remaining of the former tropical group. This is done to distinguish strongly between those climates with a significant annual temperature cycle from those climates without one (although this feature is not as marked in the Australian context, as elsewhere in the world). Under this definition some climates, distant from the equator, are classified as equatorial. This is considered acceptable as that characteristic is typical of climates close to the equator. Figure 1 shows that, in Australia, equatorial climates are confined to the Queensland's Cape York Peninsula and the far north of the Northern Territory.

The equatorial and tropical group monsoon subdivisions are re-named as rainforest (monsoonal) subdivisions. This is done because, in these subdivisions, the dry season is so short, and the total rainfall is so great, that the ground remains sufficiently wet throughout the year to support rainforest. Figure 2 shows that, in Australia, rainforest subdivisions are found along parts of the northern part of Queensland's east coast.

The former dry group is now divided into two new groups, a desert group and a grassland group. The new groups correspond to the former subdivisions of the dry group with the same name. This is believed necessary because of the significant differences between the types of vegetation found in deserts and grasslands. That there is a part of central Australia covered by the grassland group of climates (Figure 1) is a consequence of the higher rainfall due to the ranges in that region.

The new desert and grassland winter drought (summer drought) subdivisions now require the additional criterion that there is more than 30 mm in the wettest summer month (winter month) to be so classified. This change is carried out because drought conditions may be said to prevail throughout the year in climates without at least a few relatively wet months. It should be noted that the original set of Köppen climates employed the phrases "winter drought" and "summer drought" to respectively describe climates that are seasonally dry. Figure 2 shows that the summer drought subdivisions are found in the southern half of the country, whilst the winter drought subdivisions are found in the northern half of the country.

The former temperate group is divided into two new groups, a temperate group and a subtropical group. The new subtropical group corresponds to that part of the former temperate group with a mean annual temperature of at least 18°C. The new temperate group corresponds to that part of the former temperate group remaining. This is done because of the significant differences in the vegetation found in areas characterised by the two new groups, and in order that there is continuity in the boundary between the hot and warm desert and grassland climates where they adjoin rainy climates. Figure 1 shows that a large region, covering much of southeast Queensland and some elevated areas further north, is now characterised as subtropical.

For simplicity, the former Köppen cold snowy forest group of climates is re-named as the cold group. Figure 1 shows that this climate is not found on the Australian mainland or in Tasmania.

For the temperate, subtropical, and the cold groups, the distinctly dry winter subdivision requires the additional criterion of no more than 30 mm in the driest winter month to be so classified. In order that there be consistency between the criteria for the distinctly dry winter and the distinctly dry summer subdivisions, this is thought to be a worthwhile change. Figure 2 shows that, whereas that part of Western Australia characterised as subtropical has a distinctly dry summer, much of subtropical southeast

This statistic displays the average minimum and maximum temperatures in Australia in 2015. According to the source, in Queensland, the hottest temperature was 30.94 degrees on average in 2015.

1) Data Introduction • The Rain in Australia Dataset is a tabular weather forecasting dataset, including daily weather information collected for approximately 10 years from various weather stations across Australia, and next-day precipitation (more than 1 mm, RainTomorrow).

2) Data Utilization (1) Rain in Australia Dataset has characteristics that: • Each row contains a variety of daily weather variables and target variables (RainTomorrow: Next Day RainTomorrow) such as date, region, highest/lowest temperature, precipitation, humidity, wind speed, and air pressure. • The data reflect multiple regions and various weather conditions, making them suitable for time series and spatial weather pattern analysis and the development of binary classification prediction models. (2) Rain in Australia Dataset can be used to: • Development of precipitation prediction models: Machine learning-based next-day precipitation prediction (whether an umbrella is required) models can be built using various weather variables and RainTomorrow labels. • Weather Patterns and Regional Analysis: By analyzing regional and seasonal weather variables and precipitation patterns, it can be used to establish customized weather strategies for each industry, such as climate change research and agriculture and tourism.

The Bureau of Meteorology provides the Australian and international maritime communities with weather forecasts, warnings and observations for coastal waters areas and high seas around Australia. …Show full descriptionThe Bureau of Meteorology provides the Australian and international maritime communities with weather forecasts, warnings and observations for coastal waters areas and high seas around Australia. Generally most of these services are provided routinely throughout the day, while marine weather warnings may be issued at any time when the need becomes apparent. Because of the complex nature of the sea, the Bureau of Meteorology uses advanced computer models to predict the physical characteristics of the ocean. These computer forecasts are used by meteorologists in the preparation of marine forecasts and warnings. The forecasts include wind, weather, sea and swell and are intended to describe the average conditions over specified areas. Marine forecasts have been enhanced by the inclusion of ocean currents and sea-surface temperature forecasts through the BLUElink ocean forecasting initiative. The Sea Surface Temperature Browse Service provides access to browse images (1:5 resolution) of satellite derived Daily Sea Surface Temperature data available from 30 December 1998. The Bureau currently uses measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) series of polar orbiting satellites to derive SSTs for the Australian region. The data is calibrated and quality controlled against SST data collected from ships and drifting buoys. The SSTs are used in real time operations and also archived as the data as part of Australia's National Climate Record. This record also provides links to BOM Ocean Analysis data including Daily/Weekly/Monthly records of Australian and Global Sea Surface and Subsurface Temperatures.

Overview The report is a quarterly report with a consistent and regular assessment of crop prospects for major field crops, forecasts of area, yield and production and a summary of seasonal conditions on a state by state basis. Key issues • Condition of crops at the start of spring varied considerably between the states because of highly varied seasonal conditions over autumn and winter. ◦ Crops in Western Australia are generally in good to excellent condition with high yield prospects after a timely seasonal break and above average winter rainfall. ◦ Seasonal conditions in Victoria and South Australia were mixed and while crop prospects in some major growing regions are generally good, there are regions where crop prospects are generally below average. ◦ Seasonal conditions were very unfavourable in most cropping regions in New South Wales and Queensland and winter crop production in these states is forecast to be very much below average. • Winter crop production will be heavily dependent on seasonal conditions during spring in regions in the eastern states (including South Australia) where soil moisture levels are low. • According to the latest three-month rainfall outlook (September to November), issued by the Bureau of Meteorology on 30 August 2018, spring rainfall will likely be below average in most cropping regions. Warmer than average temperatures in September are likely in Western Australia and some parts of Queensland. Temperatures in October are likely to be above average in most cropping regions in Australia. • Total winter crop production is forecast to decrease by 12% in 2018-19 to 33.2 million tonnes. • Winter crop production in 2018-19 is forecast to be 9% below the twenty-year average to 2017-18 but forecast production is 91% above the lowest production level during this period. Production in Queensland and New South Wales is forecast to be 38% and 46% below 2017-18 while production in Western Australia is forecast to be 12% above. • For the major winter crops, wheat production is forecast to decrease by 10% to 19.1 million tonnes, barley production is forecast to fall by 7% to around 8.3 million tonnes, and canola production is forecast to fall by 24% to around 2.8 million tonnes. • Area planted to summer crops is forecast to fall by 20% in 2018-19 to 1.1 million hectares, driven by forecast falls in area planted to rice and cotton. Area planted to grain sorghum is forecast to increase by 7% in response to favourable prices. Total summer crop production is forecast to fall by 16% to 3.5 million tonnes.

Overview The report is a quarterly report with a consistent and regular assessment of crop prospects for major field crops, forecasts of area, yield and production and a summary of seasonal …Show full descriptionOverview The report is a quarterly report with a consistent and regular assessment of crop prospects for major field crops, forecasts of area, yield and production and a summary of seasonal conditions on a state by state basis. Key issues • Condition of crops at the start of spring varied considerably between the states because of highly varied seasonal conditions over autumn and winter. ◦ Crops in Western Australia are generally in good to excellent condition with high yield prospects after a timely seasonal break and above average winter rainfall. ◦ Seasonal conditions in Victoria and South Australia were mixed and while crop prospects in some major growing regions are generally good, there are regions where crop prospects are generally below average. ◦ Seasonal conditions were very unfavourable in most cropping regions in New South Wales and Queensland and winter crop production in these states is forecast to be very much below average. • Winter crop production will be heavily dependent on seasonal conditions during spring in regions in the eastern states (including South Australia) where soil moisture levels are low. • According to the latest three-month rainfall outlook (September to November), issued by the Bureau of Meteorology on 30 August 2018, spring rainfall will likely be below average in most cropping regions. Warmer than average temperatures in September are likely in Western Australia and some parts of Queensland. Temperatures in October are likely to be above average in most cropping regions in Australia. • Total winter crop production is forecast to decrease by 12% in 2018-19 to 33.2 million tonnes. • Winter crop production in 2018-19 is forecast to be 9% below the twenty-year average to 2017-18 but forecast production is 91% above the lowest production level during this period. Production in Queensland and New South Wales is forecast to be 38% and 46% below 2017-18 while production in Western Australia is forecast to be 12% above. • For the major winter crops, wheat production is forecast to decrease by 10% to 19.1 million tonnes, barley production is forecast to fall by 7% to around 8.3 million tonnes, and canola production is forecast to fall by 24% to around 2.8 million tonnes. • Area planted to summer crops is forecast to fall by 20% in 2018-19 to 1.1 million hectares, driven by forecast falls in area planted to rice and cotton. Area planted to grain sorghum is forecast to increase by 7% in response to favourable prices. Total summer crop production is forecast to fall by 16% to 3.5 million tonnes.

This dataset contains time series for monthly precipitation over six sites (Blackheath, Braidwood, Darkes Forest, Goulburn, Lithgow and Moss Vale) in the Sydney Catchment Area (SCA) and monthly mean maximum and mean minimum temperature for three sites (Goulburn, Lithgow, and Moss Vale) in the SCA. This data was used in the study Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning. The data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (8% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper:

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia's largest city. Increasing population, a warming climate, land surface changes, and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney's water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis is applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, SAM, DMI and measures of global warming such as the Tasman Sea Sea Surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insight into whether the season would be wet or dry compared to the climatology.

Global warming is one of the most significant and widespread effects of climate change. While early life stages are particularly vulnerable to increasing temperatures, little is known about the molecular processes that underpin their capacity to adapt to temperature change during early development. Using a quantitative proteomics approach, we investigated the effects of thermal stress on octopus embryos. We exposed Octopus berrima embryos to different temperature treatments (control 19 °C, current summer temperature 22 °C, or future projected summer temperature 25 °C) until hatching. By comparing their protein expression levels, we found that future projected temperatures significantly reduced levels of key eye proteins such as S-crystallin and retinol dehydrogenase 12, suggesting the embryonic octopuses had impaired vision at elevated temperature. We also found that this was coupled with a cellular stress response that included a significant elevation of proteins involved in molecular chaperoning and redox regulation. Energy resources were also redirected away from non-essential processes such as growth and digestion. These findings, taken together with the high embryonic mortality observed under the highest temperature, identify critical physiological functions of embryonic octopuses that may be impaired under future warming conditions. Our findings demonstrate the severity of the thermal impacts on the early life stages of octopuses as demonstrated by quantitative proteome changes that affect vision, protein chaperoning, redox regulation, and energy metabolism as critical physiological functions that underlie the responses to thermal stress. Methods Female Octopus berrima (Stranks & Norman 1992) (n = 9) were obtained in October 2021 (austral spring) from an artisanal octopus fishery at Venus Bay, South Australia using unbaited octopus pots. Unlike other merobenthic octopus species that produce small planktonic hatchlings, Octopus berrima is a holobenthic species that produce large, well-developed hatchlings (Hua, Nande, Doubleday, & Gillanders, 2023). Following capture, octopuses were transported in individual, 12 L aerated buckets of local seawater (15 °C) kept in insulated bags. Dens in the form of sectioned PVC pipes (65 mm diameter, 20 cm length) were provided for each octopus during transport. Octopuses were transported to the South Australian Research and Development Institute (SARDI) in Adelaide, where all experiments were conducted in a controlled environment room. All octopuses remained in their respective dens during transport and were transferred in their dens from the buckets into separate glass tanks (50 cm × 25 cm × 30 cm) with filtered (0.5 μm) flow-through seawater, and a constant photoperiod (12:12 h). Adults were pre-acclimated at 16 ± 1 °C (mean ± SD) and were fed three live shore crabs (Grapsidae) daily supplemented with occasional live mussels (Mytilidae) and oysters (Ostreidae). All tanks were cleaned daily and covered with shade cloth to reduce excessive light and to induce spawning. All females spawned in their dens between two and 66 days after being transported to the facility and stopped feeding following spawning. We expected different individuals to begin spawning at different times due to natural intra-female variation. To ensure that no females (and embryos) were exposed to the treatment temperatures longer than others pre-spawning, we only exposed them to their respective temperatures once eggs were laid. Females were pre-acclimated at 16 ± 1 °C (mean ± SD) until spawning, after which temperatures were raised by 1.4 ± 0.8 °C per day until respective temperature treatments were reached. Temperature treatments were: 19.3 ± 0.6 °C (control; equivalent to the lower end of current summer average temperature in South Australia, hereafter referred to as control), 22 ± 0.1 °C (higher end of current summer average temperature in South Australia, hereafter referred to as current temperature) and 24.6 ± 0.2 °C (higher end of future projected summer average temperature based on projections from IPCC (2022), hereafter referred to as future temperature) (Table 1). Eggs received maternal care for the entire embryonic duration (average 62 ± 7 days; Table 1) until all eggs had hatched. Tanks were checked daily for hatchlings. Embryos were deemed viable or non-viable by checking the state of the embryos by visual inspection (e.g. heart palpitation and continued development). One-day-old hatchlings were euthanised by immersion in 1.5% magnesium chloride (MgCl2) for 10 min and then in 3.5% MgCl2 for 30 min. Hatchlings were then lightly dried before their wet weights were measured using a micro-balance. Each specimen was placed in a cryogenic vial and snap-frozen in liquid nitrogen before storing at -80°C until subsequent analyses.

These apparent temperature maps show the average (mean) annual and average monthly indoor apparent temperature distribution across Australia, over the period 1976 to 2005. Indoor apparent temperature describes the combined effect of temperature and humidity on the typical human; sometimes, wind and radiation are taken into account (outdoor apparent temperature). The apparent temperature values displayed here are the Steadman Indoor Apparent Temperatures and do not take into account the effect of sun or wind (Steadman 1994). For example, under Australian conditions the effect of full sun produces a maximum increase in the outdoor apparent temperature of about 8°C when the sun is at its highest elevation in the sky. For more information about thermal comfort observations, including apparent temperature, see : [ http://www.bom.gov.au/info/thermal_stress/index.shtml ].

The data set derived from this project consists of the extraction of unusually cold days at Melbourne and Perth. (The basic source was the Bureau of Meteorology daily data records.) Another part of the data set is the points along the trajectories taken by the air to reach the cities as cold events.

From the abstracts of the referenced papers:

Cold air outbreaks, characterised by unseasonably low maximum temperatures, occurring over Melbourne between May 1972 and June 1991 have been identified and examined using an air parcel trajectory model and data from observations during the period of the outbreak events. Using a definition based on the long-term climatology of the region, thirteen outbreaks were identified during the study period.

The cold air pool source regions for each outbreak were examined via the use of the air parcel trajectory model using the assumption of travel along isobaric surfaces. Mean sea-level pressure patterns, the temporal behaviour of the maximum temperature surrounding an outbreak, three-hourly basic observational data and the determined isobaric trajectories were used to analyse the nature of each Melbourne outbreak.

It has emerged that air of recent Antarctic origin is not a feature common to the majority of outbreaks examined. It is also apparent that characteristic synoptic patterns are associated with cold outbreaks over the Melbourne region. These have been grouped into three categories, 'classic', warm front, and blocking anti-cyclone type. In the mean there is identifiable atmospheric organisation around the Antarctic continent associated with the events.

Unseasonably cold weather episodes have the potential to cause dislocation to many aspects of society, regardless of the season in which they occur. In this work we devise a method for quantitatively identifying extreme cold events in such a way that it is not biased to the winter season (as is usual in most other studies). We have applied this method to the daily maximum temperatures (over the period January 1972 to June 1991) in the southern Australian cities of Melbourne and Perth. We identify 10 cold events in winter and summer for the cities. Analyses were performed to determine the synoptic environment in which these events occurred. The most common synoptic type in these samples was the 'classic', which is characterised by, amongst other factors, the passage of a cold front over the city on the day of the outbreak, and the transport of air from subantarctic latitudes. Melbourne recorded five such events in summer and six in winter, while seven and eight occurred in the two seasons for Perth. The circulation features and characteristics of other synoptic types identified with these episodes is also examined.

The mean synoptic anomalies which are coincident with these cold events are analysed. For both cities and seasons there is a 'high-low' anomalous dipole in the regional MSLP pattern, with the high located in the 'upstream' quadrant from the anomalous cyclone. Having said this, the relative importance of the two features of the dipole in being associated with the cold event strongly depended on the city and season under consideration. The research shows that the regional structures associated with cold events in Melbourne and Perth bear some similarity, but also display a number of significant differences. These differences are associated partly with the different climatological and synoptic settings in which these cities find themselves, and the nature of their seasonality.

Overview \r \r The report is a quarterly report with a consistent and regular assessment of crop prospects for major field crops, forecasts of area, yield and production and a summary of seasonal conditions on a state by state basis. \r \r Key issues • Condition of crops at the start of spring varied considerably between the states because of highly varied seasonal conditions over autumn and winter. ◦ Crops in Western Australia are generally in good to excellent condition with high yield prospects after a timely seasonal break and above average winter rainfall. \r ◦ Seasonal conditions in Victoria and South Australia were mixed and while crop prospects in some major growing regions are generally good, there are regions where crop prospects are generally below average. \r ◦ Seasonal conditions were very unfavourable in most cropping regions in New South Wales and Queensland and winter crop production in these states is forecast to be very much below average. \r \r • Winter crop production will be heavily dependent on seasonal conditions during spring in regions in the eastern states (including South Australia) where soil moisture levels are low. \r • According to the latest three-month rainfall outlook (September to November), issued by the Bureau of Meteorology on 30 August 2018, spring rainfall will likely be below average in most cropping regions. Warmer than average temperatures in September are likely in Western Australia and some parts of Queensland. Temperatures in October are likely to be above average in most cropping regions in Australia. \r • Total winter crop production is forecast to decrease by 12% in 2018-19 to 33.2 million tonnes. \r • Winter crop production in 2018-19 is forecast to be 9% below the twenty-year average to 2017-18 but forecast production is 91% above the lowest production level during this period. Production in Queensland and New South Wales is forecast to be 38% and 46% below 2017-18 while production in Western Australia is forecast to be 12% above. \r • For the major winter crops, wheat production is forecast to decrease by 10% to 19.1 million tonnes, barley production is forecast to fall by 7% to around 8.3 million tonnes, and canola production is forecast to fall by 24% to around 2.8 million tonnes. \r • Area planted to summer crops is forecast to fall by 20% in 2018-19 to 1.1 million hectares, driven by forecast falls in area planted to rice and cotton. Area planted to grain sorghum is forecast to increase by 7% in response to favourable prices. Total summer crop production is forecast to fall by 16% to 3.5 million tonnes. \r

Surface weather observations are recorded half hourly, primarily from aerodromes with some additional data coming from unmanned automatic weather stations. In special conditions, observations may be …Show full descriptionSurface weather observations are recorded half hourly, primarily from aerodromes with some additional data coming from unmanned automatic weather stations. In special conditions, observations may be made earlier and for cost savings, some stations may only report hourly. The following data are recorded: datetime, id_num (WMO index number, normally a unique id, but can be missing), id_name (abbreviated name, used to identify the observing site), date, time, wdir (wind direction, degrees from N), wspd (wind speed, knots), t_db (temperature dry bulb, degree C), dp (dew point, degree C), qnh (aircraft altimeter setting, hPa), rf9am (rainfall since 9am, mm), rf10m (rainfall last 10 minutes, mm), vic (visibility, m), avis (automatically measured visibility, m), gust (maximum wind gust last 10 minutes, knots), wx1int (first (most important) present weather intensity), wx1dsc (first (most important) present weather qualifier), wx1wx1 (first (most important) present weather type), wx1wx2 (additional weather type for mixed precipitation), wx1wx3 (additional weather type for mixed precipitation), wx2int (second (less important) present weather intensity), wx2dsc (second (less important) present weather qualifier), wx2wx1 (second (less important) present weather type), wx2wx2 (additional weather type for mixed precipitation), wx2wx3 (additional weather type for mixed precipitation), cld1amt (lowest cloud layer amount), cld1typ (lowest cloud layer type), cld1typ (lowest cloud layer base, m), cld2amt (second cloud layer amount), cld1typ (second cloud layer type), cld1base (second cloud layer base, m), cld3amt (third cloud layer amount), cld3typ (third cloud layer type), cld3base (lowest cloud layer base, m), cld4amt (fourth cloud amount), cld4typ (fourth cloud layer type), cld4base (fourth cloud layer base, m), ceil1amt (lowest cloud layer amount measured by ceilometer), ceil1base (lowest cloud layer base measured by ceilometer, m), ceil2amt (second cloud layer amount measured by ceilometer), ceil2base (second cloud layer base measured by ceilometer, m), ceil3amt (third cloud layer amount measured by ceilometer), ceil1base (third cloud layer base measured by ceilometer, m), rotation (required for rotation of wind barbs in MapServer), rh (relative humidity, %), stn_name (full station name). A record of the last 24 hours is available for each station. Information about codes can be found at [ http://www.bom.gov.au/weather-services/about/IDY03100.doc ].

Surface weather observations are recorded half hourly, primarily from aerodromes with some additional data coming from unmanned automatic weather stations. In special conditions, observations may be made earlier and for cost savings, some stations may only report hourly. The data show the latest reading at each site over the last 60 minutes.

  The following data are recorded:
  datetime, id_num (WMO index number, normally a unique id, but can be missing), id_name (abbreviated name, used to identify the observing site), date, time, wdir (wind direction, degrees from N), wspd (wind speed, knots), t_db (temperature dry bulb, degree C), dp (dew point, degree C), qnh (aircraft altimeter setting, hPa), rf9am (rainfall since 9am, mm), rf10m (rainfall last 10 minutes, mm), vic (visibility, m), avis (automatically measured visibility, m), gust (maximum wind gust last 10 minutes, knots), wx1int (first (most important) present weather intensity), wx1dsc (first (most important) present weather qualifier), wx1wx1 (first (most important) present weather type), wx1wx2 (additional weather type for mixed precipitation), wx1wx3 (additional weather type for mixed precipitation), wx2int (second (less important) present weather intensity), wx2dsc (second (less important) present weather qualifier), wx2wx1 (second (less important) present weather type), wx2wx2 (additional weather type for mixed precipitation), wx2wx3 (additional weather type for mixed precipitation), cld1amt (lowest cloud layer amount), cld1typ (lowest cloud layer type), cld1typ (lowest cloud layer base, m), cld2amt (second cloud layer amount), cld1typ (second cloud layer type), cld1base (second cloud layer base, m), cld3amt (third cloud layer amount), cld3typ (third cloud layer type), cld3base (lowest cloud layer base, m), cld4amt (fourth cloud amount), cld4typ (fourth cloud layer type), cld4base (fourth cloud layer base, m), ceil1amt (lowest cloud layer amount measured by ceilometer), ceil1base (lowest cloud layer base measured by ceilometer, m), ceil2amt (second cloud layer amount measured by ceilometer), ceil2base (second cloud layer base measured by ceilometer, m), ceil3amt (third cloud layer amount measured by ceilometer), ceil1base (third cloud layer base measured by ceilometer, m), rotation (required for rotation of wind barbs in MapServer), rh (relative humidity, %), stn_name (full station name).

  Information about codes can be found at http://www.bom.gov.au/weather-services/about/IDY03100.doc.

The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages ~46.7°C (Tcrit) 1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. We found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers, and remote sensing (ECOSTRESS) have midday-peak temperatures of ~34°C during dry periods, with a long high-temperature tail that can exceed 40°C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper-canopy leaves exceed Tcrit 0.01% of the time. Further, upper-canopy leaf warming experiments (+2, 3, and 4°C in Brazil, Puerto Rico, and Australia) increased leaf temperatures non-linearly with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% >43.5°C, 0.3% >49.9°C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential collapse in metabolic function, but the remaining uncertainty in our understanding of Tcrit could reduce this to 2.6 ± 0.6°C. The 4.0°C estimate is within the “worst case scenario” (RCP-8.5) of climate change predictions2 for tropical forests and therefore it is still within our power to decide (e.g., by not taking the RCP 8.5 route) the fate of these critical realms of carbon, water, and biodiversity 3,4. Methods Field Data - We estimate canopy temperature at the km 83 eddy covariance tower in the Tapajos region of Brazil 1–3 using a pyrgeometer (Kipp and Zonen, Delft, Netherlands) mounted at 64 m to measure upwelling longwave radiation (L↑ in W m-2) with an estimated radiative-flux footprint of 8,000 m2 4. Data were collected every 2 seconds and averaged over 30-minute intervals between August 2001 and March 2004. We estimated canopy temperature with the following equation: Eq 1 – Canopy temperature (°C) = (L↑/(E*5.67e-8))0.25-273.15 We chose an emissivity value (E) of 0.98 for the tower data, as this was the most common value used in the ECOSTRESS data (SDS_Emis1-5 (ECO2LSTE.001) and the broader literature for tropical forests 5. We compared canopy temperature derived from the pyrgeometer to eddy covariance derived latent heat fluxes (flux footprint ~1 km2), air temperature at 40 m, which is the approximate canopy height (model 076B, Met One, Oregon, USA; and model 107, Campbell Scientific, Logan, Utah, USA) and soil moisture at depths of 40 cm (model CS615, Campbell Scientific, Logan, Utah, USA). Further details on instrumentation and eddy covariance processing can be found in 1,3. This site was selectively logged, which had a minor overall impact on the forest 6, but did not affect any trees near the tower. Leaf thermocouple data - We measured canopy leaf temperature at a 30 m canopy walk-up tower between July to December of 2004 and July to December of 2005 at the same site. We initially placed 50 thermocouples on canopy-exposed leaves of Sextonia rubra, Micropholis sp., Lecythis lurida) (originally published in Doughty and Goulden 2008). Fine wire thermocouples (copper constantan 0.005 Omega, Stamford, CT) were attached to the underside of leaves by threading the wire through the leaf and inserting the end of the thermocouple into the abaxial surface. The thermocouples were wired into a multiplexer attached to a data logger (models AM25T and 23X, Campbell Scientific, Logan, UT, USA) and the data were recorded at 1 Hz. Additional upper-canopy leaf thermocouple data from Brazil7, Puerto Rico8, Panama9, Atlantic forest Brazil10 and Australia 11, were generally collected in a similar manner.
Satellite data - ECOSTRESS data (ECO2LSTE.001) – The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission is a thermal infrared (TIR) multispectral scanner with five spectral bands at 8.28, 8.63, 9.07, 10.6, and 12.05 µm. The sensor has a native spatial resolution of 38 m x 68 m, resampled to 70 m x 70 m, and a swath width of 402 km (53°). Data are collected from an average altitude of 400 ± 25 km on the International Space Station (ISS). ECOSTRESS is an improvement over other thermal sensors because no other sensors provide TIR data with sufficient spatial, temporal, and spectral resolution to reliably estimate LST at the local-to-global scale for a diurnal cycle 12. To ensure the highest quality data, we used ECOSTRESS quality flag 3520, which identifies the best quality pixels (no cloud detected), a minimum-maximum difference (MMD) indicative of vegetation or water (Kealy and Hook 1993), and nominal atmospheric opacity. We accessed ECOSTRESS LST data through the AppEEARS website (https://lpdaac.usgs.gov/tools/appeears/) for the following products and periods: SDS_LST (ECO2LSTE.001) from a long longitudinal swath of the Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a red box) and then a larger area of the western Amazon for 18 September to 29 September 2019 (SI Fig 1a green box), Central Africa for 1 August to 30 August 2019 (SI Fig 1b), and SE Asia for 15 January to 30 February 2020 (SI Fig. 1c). The dates were chosen as all ECOSTRESS data available at the start of the study for the smaller regions and for warm periods with low soil moisture for the larger areas. We calculated “peak median,” which is defined as the average of the highest three medians of each granule (i.e., for the Amazon SI Fig. 1a, there were 934 granules) for each hour period. Comparison of LST data – We compared ECOSTRESS LST to VIIRS LST (VNP21A1D.001) and MODIS LST (MYD11A1.006). A more detailed comparison and description of these sensors can be found in Hulley et al 202113. Details for the sensors and quality flags used are given in Table S1. Broadly, G1 for ECOSTRESS and VIIRS is classified as vegetation (using emissivity) and of medium quality. G2 is classified as vegetation, but of the highest quality. MODIS landcover classifies this region as almost entirely broadleaf evergreen vegetation, but using MMD (emissivity) only 18% (VIIRS) and 12% (ECOSTRESS) of the data are classified as vegetation, rather than as soils and rocks (Table S2). Therefore, we use the vegetation classification (from MMD) as a very conservative estimate of complete forest canopy cover and not farms, urban, or degraded forest where rocks or soils are more likely to appear to satellites. SMAP data – To estimate pantropical soil moisture, we use the Soil Moisture Active Passive (SMAP) sensor and the product Geophysical_Data_sm_rootzone (SPL4SMGP.005). SMAP measurements provide remote sensing of soil moisture in the top 5 cm of the soil 14 and the L4 products combine SMAP observations and complementary information from a variety of sources. We accessed SMAP data from the AppEEARS website for the following products and periods: Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a), Central Africa for 25 December 2019 to 20 July 2020 (SI Fig 1b), and Borneo for 25 December 2018 to 20 July 2020 (SI Fig 1c). Warming experiments – For model validation, we used the results of three upper-canopy leaf and branch warming experiments of 2°C (Brazil), 3°C (Puerto Rico), and 4°C (Australia). The first experiment (Brazil), was 4 individual leaf-resistant heaters on each of 6 different upper-canopy species at the Floresta National (FLONA) do Tapajos as part of the Large-Scale Biosphere–Atmosphere Ecology Program (LBA-ECO) in Santarem, Brazil14. On the same six species, black plastic passively heated branches by an average ~2°C. Initially, heat balance sap flow sensors and the passive heaters were added to 40 branches, but we had confidence in the data from 9 heated and 4 control in the final analysis. The second experiment (Puerto Rico) had two species (Ocotea sintenisii (Mez) Alain and Guarea guidonia (L.) Sleumer where leaves were heated by 3 °C at the Tropical Responses to Altered Climate Experiment (TRACE) canopy tower site at Sabana Field Research Station, Luquillo, Puerto Rico8. The final experiment (Australia), which increased leaf temperatures by 4 °C, was conducted at Daintree Rainforest Observatory (DRO) in Cape Tribulation, Far North Queensland, Australia. Leaf heaters were installed using a pair of 30-gauge copper-constantan thermocouples, one reference leaf, and one heated with a target temperature differential of 4°C. There were two pairs in the upper canopy of each tree crown installed in 2–3 individuals across four species with the thermocouples installed on the underside of the leaves. Two absolute 36-gauge copper-constantan thermocouples were installed in each species to measure the leaf temperatures of the reference leaves. Thermocouple wires connected into an AM25T multiplexer from Campbell Scientific connected to a CR1000 Campbell datalogger. More details about the experiment and sensors can be found in 11. Model – We created a model of individual leaves on a tree (100 by 100 grid where each leaf is a pixel) to estimate the upper limit of tropical canopy temperatures with projected changes in climate. At the start of the simulation, we randomly applied the measured distribution (ambient Fig 1c) of canopy leaf temperatures >31.2 °C (chosen to give a mean canopy temperature of 33.2 ± 0.4 °C, matching the canopy average Fig 1b) to the entire grid. Each year we increased the mean air temperatures by 0.03°C to simulate a warming planet. As air temperatures reached +2, 3, and 4°C, we applied the leaf temperature distributions (but subtracted out the air temperature increases) from the different warming experiments (+2°C (Brazil), +3°C (Puerto Rico), and +4°C (Australia), respectively (Fig S7)). We ran the model at a daily time step with leaves flushing once a year (all dead leaves reset to living each year).
In