A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Australian Bureau of Meteorology using optimal interpolation (OI) on a global 0.25 degree grid. This BLUELink Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), and, the Advanced Microwave Scanning Radiometer-EOS (AMSRE), and in situ data from ships, and drifting and moored buoy from the Global Telecommunications System (GTS). In order to produce a foundation SST estimate, the AATSR skin SST data stream is converted to foundation SST using the Donlon et al. (2002) skin to foundation temperature conversion algorithms. These empirically-derived algorithms apply a small correction for the cool-skin effect depending on surface wind speed, and filter out SST values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 ms-1 during the day and less than 2 ms-1 during the night.

The Sea Surface Temperature (SST) sub-facility aims to enable accurate, quality controlled, SST data to be supplied in near real-time (within 24 hours) from SOOPs and research vessels in the …Show full descriptionThe Sea Surface Temperature (SST) sub-facility aims to enable accurate, quality controlled, SST data to be supplied in near real-time (within 24 hours) from SOOPs and research vessels in the Australian region. Remotely sensed sea surface temperature (SST) data is an important input to ocean, numerical weather prediction, seasonal and climate models. In order to improve calibration and validation of satellite SST in the Australian region, there is a need for high quality in situ SST observations with greater timeliness, spatial and temporal coverage than is currently available. Regions particularly lacking in moored or drifting buoy observations are the Western Pacific Tropical Warm Pool region (Indonesia), close to the Australian coast (including Bass Strait) and the Southern Ocean. Current ship SST observations from ships of opportunity (SOOP) are either of questionable accuracy or difficult to access in a timely manner. There are five vessels carrying automatic weather stations (AWS) that participate in the Australian Volunteer Observing Fleet (AVOF) program and two vessels equipped with a newly designed system for real-time SST data acquisition. Their routes include the Southern Ocean, coastal Australia (Queensland to South Australia), Bass Strait, Pacific Ocean, South-East Asia and the Tasman Sea. Four AVOF vessels with hull-mounted temperature sensors (Sea Bird SBE 48) and one with a digital oceanographic thermometer (Sea Bird SBE 38, RV L’Astrolabe) are supplying high-quality bulk SST data hourly. There is also one passenger ferry that is currently taking SST measurements using the high-accuracy SBE 38 sensor (MV SeaFlyte – Hillarys Harbour-Rottnest Island). In addition, there are now near real-time SST data streams available from two Australian research vessels (RV Southern Surveyor and RSV Aurora Australis), one New Zealand research vessel (RV Tangaroa), and a small research vessel operated by CSIRO near the south-east coast of Western Australia (RV Linnaeus). In total, twelve vessels send SST data in real-time. All SST data are being quality assured, placed on the Global Telecommunications System (GTS) and fed into the Bureau of Meteorology's near real-time satellite SST data validation system and operational regional and global SST analyses. Additionally, there are historical SST observations from four vessels which are not currently available in real-time: passenger ferry MV Fantasea, Whitsundays area, radiometer and bulk SST (November 2008 to March 2010); two AVOF vessels: MV Iron Yandi (February 2010 to January 2011) and PV Pacific Sun (December 2010 to July 2012); and one Voluntary Observing Ship (VOS), MV Pacific Celebes which has been carrying high-quality scientific equipment (January 2008 to March 2012).

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a merchant vessel (MV Portland - VNAH ) between Fremantle (Western Australia) and Melbourne (Victoria).Show full descriptionThis dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a merchant vessel (MV Portland - VNAH ) between Fremantle (Western Australia) and Melbourne (Victoria). The data have been quality controlled by the Bureau of Meteorology. Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time SST data (within 24 hours) from SOOPs and research vessels in the Australian region.

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.

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a merchant vessel (MV Highland Chief - VROB) between south-east of Australia (Melbourne) and Papua New Guinea, including New Zealand, New Caledonia and Solomon Islands.

The data have been quality controlled by the Bureau of Meteorology.

Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time SST data (within 24 hours) from SOOPs and research vessels in the Australian region.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of maximum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using trivariate splines (latitude, longitude and elevation) with partial dependence upon standardised day time MODIS land surface temperature. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) with partial dependence upon standardised day time MODIS land surface temperature. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. 2. Paget, MJ, King EA. 2008. MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research. Canberra, Australia. https://doi.org/10.4225/08/585c173339358 C) Model performance (3DS): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly maximum temperature RMSE = 0.48 °C Daily maximum temperature RMSE = 1.19 °C

Please refer to the linked manuscript for further details.

This data was sourced from the Bureau of Meteorology ACORN-SAT and SST from (ERSTTv4) details at http://www.bom.gov.au/climate/change/about/temp_timeseries.shtml http://www.bom.gov.au/climate/change/about/sst_timeseries.shtml .

Data used to produce figure ATM19 of the 2016 SoE. See; https://soe.environment.gov.au/theme/climate/topic/2016/temperature#climate-figure-ATM19

This record describes the Underway (UWY) data collected from the Marine National Facility (MNF) RV Investigator Voyage IN2019_V02, titled: "SOTS: Southern Ocean Time Series automated moorings for climate and carbon cycle studies southwest of Tasmania; Subantarctic Biogeochemistry of Carbon and Iron, Southern Ocean Time Series site." The voyage took place between March 14 and April 5, 2019 departing from Hobart (TAS) and arriving in Hobart.

Standard Underway data is continuously recorded, consisting of:

(1) Navigation data (NAV): Latitude, Longitude, Speed, Heading, Course Over Ground, Gyros, and Doppler Log (dual GPS instrument).

(2) Thermosalinograph (TSG): Water Salinity, Flow-Rate, Temperature, Fluorescence, pCO2 and Optode/Oxygen.

(3) Atmospheric (MET): Humidity, Wind Speed and Direction (vane and ultrasonic), Radiometer/Sea Surface Temperature, Pyranometer/Solar Radiation, Photosynthetically Active Radiation (PAR), Air Temperature, Air Pressure, Rain, Ozone and Trace Gases (port and starboard instruments).

The quality-controlled RV Investigator underway meteorological and SST data are supplied to the IMOS AODN where they are publicly available at the "Ships of Opportunity" Thredds server (http://thredds.aodn.org.au/thredds/catalog/IMOS/SOOP/SOOP-ASF/VLMJ_Investigator/catalog.html) for research. From there, they are downloaded by NOAA for their In Situ Quality Monitoring web site (iQUAM2) and made available for satellite SST validation.

The SBE 38 SST data are used within the Australian Bureau of Meteorology for real-time satellite SST validation and ingested into real-time SST analyses which are then used as the boundary condition for Numerical Weather Prediction models.

The meteorological and SST data are uploaded onto the Global Telecommunications System (GTS) for global dissemination to Meteorological Agencies, and separately supplied to the SAMOS (http://samos.coaps.fsu.edu/html/) Project for air-sea flux research.

The real-time SBE 38 SST data are currently (2017) used in the following data products (accessed via the GTS): • International Comprehensive Ocean-Atmosphere Data Set (ICOADS R3.0) (http://icoads.noaa.gov/) • NOAA NCEI Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) (https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4)
• Hadley Centre SST Data Set (HadSST3) (http://www.metoffice.gov.uk/hadobs/hadsst3/) • Global Ocean Surface Underway Data (GOSUD) Project (http://www.gosud.org/) • Bureau of Meteorology daily and weekly SST analyses (http://www.bom.gov.au/marine/sst.shtml) • NOAA NCEI “Reynolds” daily global OISST analysis (https://www.ncdc.noaa.gov/oisst) • NASA JPL “MUR” 1 km daily global SST analysis (https://mur.jpl.nasa.gov/) • UK Met Office “OSTIA” daily global SST analysis (http://ghrsst-pp.metoffice.com/pages/latest_analysis/ostia.html) • CMC daily global SST analysis • Ourocean “G1SST” global daily 1 km SST analysis (https://ourocean.jpl.nasa.gov/SST)

As part of the underway data gathering system, other datasets are produced (e.g., pCO2, ISAR SST) which are processed separately and have their own metadata records.

Data are recorded at 5 second intervals. Near real-time data are available via the link "Visualisation tool for Underway Data." This dataset will be processed and archived within the CSIRO Oceans & Atmosphere (O&A) Information and Data Centre (IDC) in Hobart (TAS). Data are available at time intervals of 5 sec (NetCDF format), 10 sec and 5 min (ASCII format). Additional information regarding this dataset is contained in the Voyage Summary and/or the Data Processing Report for this voyage.

This record describes the Underway (UWY) data collected from the Marine National Facility (MNF) RV Investigator Voyage IN2019_V04, titled: "Hotspot dynamics in the Coral Sea: connections between the Australian plate and deep Earth." The voyage took place between August 7 and September 3, 2019 departing from Cairns (QLD) and arriving in Brisbane (QLD).

Standard Underway data is continuously recorded, consisting of:

(1) Navigation data (NAV): Latitude, Longitude, Speed, Heading, Course Over Ground, Gyros, and Doppler Log (dual GPS instrument).

(2) Thermosalinograph (TSG): Water Salinity, Flow-Rate, Temperature, Fluorescence, pCO2 and Optode/Oxygen.

(3) Atmospheric (MET): Humidity, Wind Speed and Direction (vane and ultrasonic), Radiometer/Sea Surface Temperature, Pyranometer/Solar Radiation, Photosynthetically Active Radiation (PAR), Air Temperature, Air Pressure, Rain, Ozone and Trace Gases (port and starboard instruments).

The quality-controlled RV Investigator underway meteorological and SST data are supplied to the IMOS AODN where they are publicly available at the "Ships of Opportunity" Thredds server (http://thredds.aodn.org.au/thredds/catalog/IMOS/SOOP/SOOP-ASF/VLMJ_Investigator/catalog.html) for research. From there, they are downloaded by NOAA for their In Situ Quality Monitoring web site (iQUAM2) and made available for satellite SST validation.

The SBE 38 SST data are used within the Australian Bureau of Meteorology for real-time satellite SST validation and ingested into real-time SST analyses which are then used as the boundary condition for Numerical Weather Prediction models.

The meteorological and SST data are uploaded onto the Global Telecommunications System (GTS) for global dissemination to Meteorological Agencies, and separately supplied to the SAMOS (http://samos.coaps.fsu.edu/html/) Project for air-sea flux research.

The real-time SBE 38 SST data are currently (2017) used in the following data products (accessed via the GTS): • International Comprehensive Ocean-Atmosphere Data Set (ICOADS R3.0) (http://icoads.noaa.gov/) • NOAA NCEI Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) (https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4)
• Hadley Centre SST Data Set (HadSST3) (http://www.metoffice.gov.uk/hadobs/hadsst3/) • Global Ocean Surface Underway Data (GOSUD) Project (http://www.gosud.org/) • Bureau of Meteorology daily and weekly SST analyses (http://www.bom.gov.au/marine/sst.shtml) • NOAA NCEI “Reynolds” daily global OISST analysis (https://www.ncdc.noaa.gov/oisst) • NASA JPL “MUR” 1 km daily global SST analysis (https://mur.jpl.nasa.gov/) • UK Met Office “OSTIA” daily global SST analysis (http://ghrsst-pp.metoffice.com/pages/latest_analysis/ostia.html) • CMC daily global SST analysis • Ourocean “G1SST” global daily 1 km SST analysis (https://ourocean.jpl.nasa.gov/SST)

As part of the underway data gathering system, other datasets are produced (e.g., pCO2, ISAR SST) which are processed separately and have their own metadata records.

Data are recorded at 5 second intervals. Near real-time data are available via the link "Visualisation tool for Underway Data." This dataset will be processed and archived within the CSIRO Oceans & Atmosphere (O&A) Information and Data Centre (IDC) in Hobart (TAS). Data are available at time intervals of 5 sec (NetCDF format), 10 sec and 5 min (ASCII format). Additional information regarding this dataset is contained in the Voyage Summary and/or the Data Processing Report for this voyage.

This record describes the Underway (UWY) data collected from the Marine National Facility (MNF) RV Investigator Voyage IN2020_T01, titled: "Collaborative Australian Postgraduate Sea Training Alliance (CAPSTAN): Training the Next Generation of Marine Scientists." The voyage took place between March 13 and March 19, 2020 departing from Fremantle (WA) and arriving in Hobart (TAS).

Standard Underway data is continuously recorded, consisting of:

(1) Navigation data (NAV): Latitude, Longitude, Speed, Heading, Course Over Ground, Gyros, and Doppler Log (dual GPS instrument).

(2) Thermosalinograph (TSG): Water Salinity, Flow-Rate, Temperature, Fluorescence, pCO2 and Optode/Oxygen.

(3) Atmospheric (MET): Humidity, Wind Speed and Direction (vane and ultrasonic), Radiometer/Sea Surface Temperature, Pyranometer/Solar Radiation, Photosynthetically Active Radiation (PAR), Air Temperature, Air Pressure, Rain, Ozone and Trace Gases (port and starboard instruments).

The quality-controlled RV Investigator underway meteorological and SST data are supplied to the IMOS AODN where they are publicly available at the "Ships of Opportunity" Thredds server (http://thredds.aodn.org.au/thredds/catalog/IMOS/SOOP/SOOP-ASF/VLMJ_Investigator/catalog.html) for research. From there, they are downloaded by NOAA for their In Situ Quality Monitoring web site (iQUAM2) and made available for satellite SST validation.

The SBE 38 SST data are used within the Australian Bureau of Meteorology for real-time satellite SST validation and ingested into real-time SST analyses which are then used as the boundary condition for Numerical Weather Prediction models.

The meteorological and SST data are uploaded onto the Global Telecommunications System (GTS) for global dissemination to Meteorological Agencies, and separately supplied to the SAMOS (http://samos.coaps.fsu.edu/html/) Project for air-sea flux research.

The real-time SBE 38 SST data are currently (2017) used in the following data products (accessed via the GTS): • International Comprehensive Ocean-Atmosphere Data Set (ICOADS R3.0) (http://icoads.noaa.gov/) • NOAA NCEI Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) (https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4)
• Hadley Centre SST Data Set (HadSST3) (http://www.metoffice.gov.uk/hadobs/hadsst3/) • Global Ocean Surface Underway Data (GOSUD) Project (http://www.gosud.org/) • Bureau of Meteorology daily and weekly SST analyses (http://www.bom.gov.au/marine/sst.shtml) • NOAA NCEI “Reynolds” daily global OISST analysis (https://www.ncdc.noaa.gov/oisst) • NASA JPL “MUR” 1 km daily global SST analysis (https://mur.jpl.nasa.gov/) • UK Met Office “OSTIA” daily global SST analysis (http://ghrsst-pp.metoffice.com/pages/latest_analysis/ostia.html) • CMC daily global SST analysis • Ourocean “G1SST” global daily 1 km SST analysis (https://ourocean.jpl.nasa.gov/SST)

As part of the underway data gathering system, other datasets are produced (e.g., pCO2, ISAR SST) which are processed separately and have their own metadata records.

Data are recorded at 5 second intervals. Near real-time data are available via the link "Visualisation tool for Underway Data." This dataset will be processed and archived within the CSIRO Oceans & Atmosphere (O&A) Information and Data Centre (IDC) in Hobart (TAS). Data are available at time intervals of 5 sec (NetCDF format), 10 sec and 5 min (ASCII format). Additional information regarding this dataset is contained in the Voyage Summary and/or the Data Processing Report for this voyage.

IOCI3, a climate research collaboration between CSIRO, the Bureau of Meteorology (BoM) and the Western Australian Government, produced maps of mean hot spell intensity, frequency and duration for the 1958-2010 period using estimates derived from statistical models. They also produced maps of trends in hot spell intensity, frequency and duration for this time period. In addition they provided maps of mean hot spell thresholds, intensity, frequency and duration for the 1981-2010 period using estimates derived from statistical models, and projections of these characteristics for the 2070-2099 period under the A2 greenhouse gas (GHG) emissions scenario (described in the IPCC Special Report on Emissions Scenarios [SRES]), as well as the difference between these two periods." Results are provided in the JPEG file format. Lineage: High quality station data as well as quarter-degree gridded (0.25°× 0.25° resolution) daily maximum temperature data from BoM Australian Water Availability Project (AWAP) were used to produce these results. Hot spell temperature thresholds were selected using statistical methods. Hot spell occurrence (frequency) was modelled by a Poisson process, hot spell intensity by a generalized Pareto distribution, and hot spell duration through a geometric distribution. The Generalized Linear Model framework was used to estimate the parameters in the model for hot spells. This method was applied to daily maximum temperature data simulated from the CSIRO Cubic Conformal Atmospheric Model (CCAM) for both the present-day and possible future climate under the SRES A2 GHG emissions scenario. The CCAM was nested in the CSIRO Mk3.0 Global Climate Model host for the SRES A2 scenario. Caveats & limitations: The hot spell projections should be seen as initial estimates only, and they should not be used for making impact, vulnerability and risk assessments. They were made using only one climate model (CCAM); more work using an ensemble of global and regional climate model results is required to provide more robust projections of hot spells in Western Australia.

Extreme events are by definition rare, and analysis relies on partial (extreme) datasets (e.g., daily maximum temperatures higher 35 °C). In addition, estimating extremes necessitates extrapolating beyond such relatively small observed records. Consequently, the uncertainty associated with these projections of extremes is large, especially when extrapolating from a small dataset. To produce these projections we used AWAP data was used to overcome data shortages. However, the methods used to construct the AWAP dataset (interpolation) may smooth out some extreme values; this may lead to an underestimation of extremes in some cases. To these uncertainties are added the uncertainties inherent in the use of climate models.

The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) provides a large ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatio-temporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades.

Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are sub-sampled from the JRA55 atmospheric reanalysis. Strong coupling is implemented via explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system also produces a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

CAFE60v1 has been validated in comparison to empirical indices of the major climate teleconnections and blocking from various reanalysis products (ERA5, JRA55, NCEP NR1). Estimates of the large scale ocean structure and transports have been compared to those derived from gridded observational products (WOA18, HadISST, ERSSTv5) and climate model projections (CMIP). Sea ice (extent, concentration and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model (ERA5, CMIP) and observational (GPCP, AWAP, HadCRU4, GIOMAS, NSIDC, HadISST) products. This analysis shows that CAFE60v1 is a useful, comprehensive and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multi-year to decadal time scales.

The data and its spatio-temporal characteristics are described in the following publications in the American Meteorological Societies Journal of Climate.

CAFE60v1: A 60-year large ensemble climate reanalysis. Part I: System design, model configuration and data assimilation. Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Mark A. Collier, Russell Fiedler, Thomas S. Moore, Christopher C. Chapman, Bernadette M. Sloyan, and Richard J. Matear DOI: https://doi.org/10.1175/JCLI-D-20-0974.1 Published Online: 22 Mar 2021

CAFE60v1: A 60-year large ensemble climate reanalysis. Part II: Evaluation Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Dougal T. Squire, Mark A. Collier, Christopher C. Chapman, Russell Fiedler, Dylan Harries, Thomas S. Moore, Doug Richardson, James S. Risbey, Benjamin J. E. Schroeter, Serena Schroeter, Bernadette M. Sloyan, Carly Tozer, Ian G. Watterson, Amanda Black, Courtney Quinn, and Richard J. Matear DOI: https://doi.org/10.1175/JCLI-D-20-0518.1 Published Online: 22 Mar 2021 Lineage: CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multi-year climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to present.

The data was generated over a 12 month period at Australia's NCI facility by CSIRO scientists and in collaboration with Pavel Sakov at the Australian Bureau of Meteorology. The data produced is archived at CSIRO IM&T facilities and AWS.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from BILO Gridded Climate Data data provided by the CSIRO. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

Various climate variable summary for all 15 subregions. Including:

1. Time series mean annual Bureau of Meteorology Australian Water Availability Project (BAWAP) rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potential Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net Radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). All data used in this analysis came directly from James Risbey, CSIRO Marine and Atmospheric Research (CMAR), Hobart. As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was generated using various source data:

1. annual BAWAP rainfall

annual BAWAPrainfall

1. long term average BAWAP rainfall and Penman PET for each month

Monthly BAWAP rainfall

Monthly Penman PET

1. climate varaible trends

Monthly BAWAP rainfall

Monthly Penman PET

Monthly BAWAP Tair

Monthly BAWAP Tmax

Monthly BAWAP Tmin

Monthly VPD

Actual vapour measured at 9:00am, the saturated vapour is calculated from Tmax and Tmin.

Monthly Rn

Monthly Wind

This dataset is created by CLW Ecohydrological Time Series Remote Sensing Team. See http://www-data.iwis.csiro.au/ts/climate/wind/mcvicar_etal_grl2008/.

1. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers

Dataset Citation

Bioregional Assessment Programme (2013) Mean climate variables for all subregions. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/3f568840-0c77-4f74-bbf3-6f82d189a1fc.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of a ferry (MV Sea Flyte - VHW5167) between Hillarys Boat Harbour and Rottnest Island (Western Australia).

The trips take place several times a day. The data are averaged to 1-minute from the original 7 to 15 seconds sampling interval. The data have been quality controlled by the Bureau of Meteorology.

Data are made available through IMOS approx 24 hours after collection.

Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time SST data (within 24 hours) from SOOPs and research vessels in the Australian region.

This dataset contains Sea Surface Temperature (SST) underway observations collected on trips of the Merchant Vessel Fantasea Wonder (callsign: VJQ7467) between Shute Harbour and Hardy Reef. Data …Show full descriptionThis dataset contains Sea Surface Temperature (SST) underway observations collected on trips of the Merchant Vessel Fantasea Wonder (callsign: VJQ7467) between Shute Harbour and Hardy Reef. Data available from the vessel are skin and bulk SST from two types of sensors. Enhancement of Measurements on Ships of Opportunity (SOOP)-Sea Surface Temperature (SST) aims to supply near real-time SST data (within 24 hours) from SOOPs and research vessels in the Australian region. Observed data are retrieved from the vessel once a day around 6pm and quality controlled at the Bureau of Meteorology. A daily file of 1-minute averages of the observations is generated around 0900 UTC and provided to eMII.

Ocean wave hindcast, ongoing and updated monthly from 1979 to present. The 1979-2010 data was generated using the WaveWatch III v4.08 wave model forced with NCEP CFSR hourly winds and daily sea ice (see http://doi.org/10.4225/08/523168703DCC5). January 2011 - May 2013 was generated using the WaveWatch III v4.08 wave model forced with NCEP CFSv2 hourly winds and daily sea ice (see http://doi.org/10.4225/08/52817E2858340). June 2013 onward was generated using the WaveWatch III v4.18 wave model forced with NCEP CFSv2 hourly winds and daily sea ice. The dataset contains spectral wave output at 3683 points, as well as gridded outputs on a global 0.4 degree (24 arcminute) grid, with nested Australian and western Pacific subgrids of 10 and 4 arcminutes resolution. For further information, see Durrant, T., Greenslade, D., Hemer, M. and Trenham, C. 2014. A Global Wave Hindcast focussed on the Central and South Pacific CAWCR Technical Report No. 070. N.B. January 1979 is a "model spin-up" month and data from this month should not be used for research purposes. Spectral wave parameters output: time; station; longitude; latitude; frequency; frequency1; frequency2 (centre, upper and lower bands); direction; Efth (sea surface wave directional variance spectral density); depth; u10m; udir (wind speed and direction 10m above surface); curr; currdir (sea water speed and direction). Gridded parameters output: longitude; latitude; time; MAPSTA (status map) ; U10; V10 (Eastward and Northward wind); CI (sea ice area fraction) ; hs (significant wave height); wl (mean wave length) ; t02 (mean wave period Tm02); t (mean period Tm01); tm0m1(mean period Tm0-1); CgE (wave energy flux); fp (peak wave frequency); dir (mean wave direction); spr (directional spread); dp (peak direction); hs0; hs1; hs2; hs3 (significant wave height partitions); tp0; tp1; tp2; tp3 (peak period partitions); lp0; lp1; lp2; lp3 (mean wave length partitions); th0; th1; th2; th3 (mean wave direction partitions); si0; si1; si2; si3 (directional spread partitions); ws0; ws1; ws2; ws3 (wind sea fraction partitions); wsf (wind sea fraction); pnr (number of wave partitions); dtd (dynamic time step); uust; vust (eastward, northward friction velocities); cha (Charnock coefficient); faw (wind to wave energy flux); utaw; vtaw (eastward, northward wave supported wind stress); utwa; vtwa (eastward, northward wave to wind stress); wcc (whitecap coverage); Sxx; Syy; Sxy (radiation stress components); utwo; vtwo (eastward, northward wave to ocean stress); uuss; vuss (eastward, northward surface stokes drift). Please note that the licensee/user is required to acknowledge the source of this data on the following terms: 'Source: Bureau of Meteorology and CSIRO © 2013'. Apart from dealings under the Copyright Act 1968, the licensee shall not reproduce (electronically or otherwise), modify or supply (by sale or otherwise) this data without written permission. Please contact CSIRO CSIROEnquiries@csiro.au or BoM climatedata@bom.gov.au for more information. Lineage: The hindcast was performed using the WAVEWATCH III(TM) model, 1970 - May 2013 used version 4.08, June 2013 onward used version 4.18. The model was run on a 0.4 x 0.4° global grid with a series of nested grids of 10 arcminutes (~18km) down to 4 arcminutes (~7km) in the Western Pacific and Australian regions. Wave spectra were discretised over 29 frequencies exponentially spaced from 0.038 Hz to 0.5 Hz and 24 directions with a constant 15° directional resolution. For 1979 - 2010 all grids were forced with Climate Forecast System Reanalysis (CFSR) surface winds at 0.3° spatial and hourly temporal resolution. For 2011 onward all grids were forced with Climate Forecast System Reanalysis v.2 (CFSv2) surface winds at 0.2° spatial and hourly temporal resolution. Hourly sea ice concentrations from the CFSR and CFSv2 data sets for the respective time periods were also used to define the ice edge. Data output in NetCDF4 format.

CAWCR Wave Hindcast pre-June 2013 ERRATA Issued 21/09/2020. Wave data pre-June 2013 was created with an early release of WAVEWATCHIII (v4.08). Note that pre-June 2013 variable 't' should not be used. Pre-June 2013, Variable ‘t’, named mean wave period using the first spectral moment is a duplicate of variable ‘tm0m1’ mean wave period using the first inverse spectral moment. Post-June 2013 (inclusive), Variables ‘t01’ represent mean wave period using the first spectral moment, and ‘t0m1’ represents mean wave period using the first inverse spectral moment with no issues. Several other variable names changes took place during the upgrade.

This record describes the Underway (UWY) data collected from the Marine National Facility (MNF) RV Investigator Voyage IN2021_V02, titled "SOTS: Southern Ocean Time Series automated moorings for climate and carbon cycle studies southwest of Tasmania." The voyage took place between April 14 and April 27, 2021 departing from Hobart (TAS) and arriving in Hobart.

Standard Underway data is continuously recorded, consisting of:

(1) Navigation data (NAV): Latitude, Longitude, Speed, Heading, Course Over Ground, Gyros, and Doppler Log (dual GPS instrument).

(2) Thermosalinograph (TSG): Water Salinity, Flow-Rate, Temperature, Fluorescence, pCO2 and Optode/Oxygen.

(3) Atmospheric (MET): Humidity, Wind Speed and Direction (vane and ultrasonic), Radiometer/Sea Surface Temperature, Pyranometer/Solar Radiation, Photosynthetically Active Radiation (PAR), Air Temperature, Air Pressure, Rain, Ozone and Trace Gases (port and starboard instruments).

The quality-controlled RV Investigator underway meteorological and SST data are supplied to the IMOS AODN where they are publicly available at the "Ships of Opportunity" Thredds server (http://thredds.aodn.org.au/thredds/catalog/IMOS/SOOP/SOOP-ASF/VLMJ_Investigator/catalog.html) for research. From there, they are downloaded by NOAA for their In Situ Quality Monitoring web site (iQUAM2) and made available for satellite SST validation.

The SBE 38 SST data are used within the Australian Bureau of Meteorology for real-time satellite SST validation and ingested into real-time SST analyses which are then used as the boundary condition for Numerical Weather Prediction models.

The meteorological and SST data are uploaded onto the Global Telecommunications System (GTS) for global dissemination to Meteorological Agencies, and separately supplied to the SAMOS (http://samos.coaps.fsu.edu/html/) Project for air-sea flux research.

The real-time SBE 38 SST data are currently (2017) used in the following data products (accessed via the GTS): • International Comprehensive Ocean-Atmosphere Data Set (ICOADS R3.0) (http://icoads.noaa.gov/) • NOAA NCEI Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) (https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4)
• Hadley Centre SST Data Set (HadSST3) (http://www.metoffice.gov.uk/hadobs/hadsst3/) • Global Ocean Surface Underway Data (GOSUD) Project (http://www.gosud.org/) • Bureau of Meteorology daily and weekly SST analyses (http://www.bom.gov.au/marine/sst.shtml) • NOAA NCEI “Reynolds” daily global OISST analysis (https://www.ncdc.noaa.gov/oisst) • NASA JPL “MUR” 1 km daily global SST analysis (https://mur.jpl.nasa.gov/) • UK Met Office “OSTIA” daily global SST analysis (http://ghrsst-pp.metoffice.com/pages/latest_analysis/ostia.html) • CMC daily global SST analysis • Ourocean “G1SST” global daily 1 km SST analysis (https://ourocean.jpl.nasa.gov/SST)

As part of the underway data gathering system, other datasets are produced (e.g., pCO2, ISAR SST) which are processed separately and have their own metadata records.

Near real-time data are available via the link "Visualisation tool for Underway Data." This dataset is processed and archived within the CSIRO National Collections and Marine Infrastructure (NCMI) Information and Data Centre (IDC) in Hobart (TAS). Data are available at time intervals of 5 sec (NetCDF format), 5 sec, 10 sec, 1 min and 5 min (ASCII format). Additional information regarding this dataset is contained in the Voyage Summary and/or the Data Processing Report for this voyage.

The aim of the Satellite Remote Sensing (SRS) facility is to provide access to a range of satellite derived marine data products covering the Australian region.

The SRS has established a new X-Band reception facility at the Australian Institute of Marine Research near Townsville and upgraded the Tasmanian Earth Resource Satellite Station near Hobart.

These stations and facilities in Perth, Melbourne, Alice Springs and Darwin form a network supplying the SRS with near real-time data. These data are combined and processed to a number of products which are placed on disk storage systems in Melbourne, Canberra and Perth.

The Bureau of Meteorology has developed a sea surface temperature (SST) product in GHRSST-PP (www.ghrsst-pp.org) L3P format. The Bureau is developing some other SST products including daily and skin SSTs. These new products and some ¿ocean colour¿ products from MODIS will gradually become available.

Scientific users can access these data products are available through OPeNDAP and THREDDS supported technology and also through interfaces provided by eMII and the SRS (www.imos.org.au and www.imos.org.au/srs).