This work examines how climate projections are affected by using different subsets of available climate models. The researchers find several variables need to be optimised independently to get the best results.
The CLEX Career Development Award for Women and Underrepresented groups is offered every year to promote the leadership development amongst our women and other underrepresented groups within CLEX.
This paper proposes a standard categorisation and naming framework for marine heatwaves that works in a similar way to schemes used for tropical cyclones or earthquakes.
An improved Tropical Pacific Observing System that is responsive to user needs will provide for better understanding and prediction of the climate system, which will reduce climate uncertainty for society.
Weather time scales will be needed for models to accurately simulate the dynamic contribution to future precipitation changes with global warming. This will better reproduce spatial patterns and reduce regional uncertainties, especially in the tropics.
Weather and climate extremes occur on a wide range of time and space scales. Weather extremes occur on shorter timescales and are regionally or locally specific while climate extremes tend to be on longer timescales and can impact a region through to the whole globe. This note provides a statement on what we know about how weather and climate extremes might change in the future.
This research shows how to optimise the observational uncertainty description for data assimilation schemes in the special case of state dependent observational uncertainty.
New data assimilation method leads to large improvements in forecast accuracy when satellite observations of electromagnetic radiation emanating from the Earth were used to inform the data assimilation scheme.
Two papers by Centre of Excellence researchers are the first to systematically investigate and document hybrid cyclones in the Australian region.
Calibration errors in the widely used Global Inventory Monitoring and Modeling System Version 3 NDVI (GIMMSv3.0g) dataset caused significant errors in the trends over some of Australia’s dryland regions. Though identified over Australia, the problematic calibration in the GIMMSv3.0g dataset may have effected dryland NDVI values globally. These errors have been addressed in the updated GIMMSv3.1g which is strongly recommended for use in future studies.