Climate change science and Victoria

Timbal, Bertrand; Ekström, Marie; Fiddes, Sonya L.; Grose, Michael; Kirono, Dewi; Lim, Eun‐Pa; Lucas, Chris; Wilson, Louise

doi:10.22499/4.0014

Cited by 15 publications

(19 citation statements)

References 60 publications

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“…Interestingly, these are also the regions where both CMIP3 (Fu et al , ) and CMIP5 GCMs (Timbal, ) sometimes struggle to simulate the annual rainfall cycle. This may be because tropical moisture penetrates too far south or because the north–south transect across the area is poorly simulated.…”

Section: Resultsmentioning

confidence: 96%

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Comparison of NCEP‐NCAR and ERA‐Interim over Australia

Charles

Timbal

et al. 2015

Intl Journal of Climatology

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This study compares observations of 22 climate variables [rainfall, minimum and maximum temperature, mean sea level pressure (MSLP), and air temperature, geopotential heights, relative and specific humidity, and u-wind and v-winds at 500, 700 and 850 hPa] over Australia with NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) and ERA-Interim, two most often used reanalysis products. The results indicate that both NCEP-NCAR and ERA-Interim generally reproduce the observed spatial patterns of long-term mean annual rainfall, daily maximum/minimum temperature and MSLP, the monthly distribution (annual cycle) of rainfall and temperature, although temperature is generally better simulated than rainfall and ERA-Interim shows an overall better performance than NCEP-NCAR in term of continental scale. In term of linear trends, both NCEP-NCAR and ERA-Interim simulate observed trend signs in some regions, but not in others, and the spatial distributions of temperature trends are generally not as well simulated as that of annual rainfall for both NCEP-NCAR and ERA-Interim. In addition, NCEP-NCAR and ERA-Interim show similar spatial patterns of annual mean air temperature, geopotential heights, humidity and winds. This implies that studies using NCEP-NCAR data could also make use of ERA-Interim to explore the range of uncertainties for the regions and climate variables where NCEP-NCAR and ERA-Interim show differences. This conclusion may also apply to regions other than Australia. For example, the different trend signs of MSLP between NCEP-NCAR and ERA-Interim could present a scientific challenging for climatic change studies using MSLP trends, and the split between large-scale and convective rainfall would also have merits globally to understand if the differences between the two reanalysis data sets are global or if we see convergence on key areas, such as tropices and frontal systems.

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Section: Resultsmentioning

confidence: 96%

“…It thus appears that biases in both the number of rainfall days and rainfall intensity contribute to the poorly simulated rainfall of both NCEP‐NCAR and ERA‐Interim. This is especially true for southeastern Australia and Tasmania, as CMIP3 and CMIP5 GCMs also have similar deficiencies in rainfall for these regions (Fu et al , ; Timbal, ).…”

Section: Resultsmentioning

confidence: 99%

Comparison of NCEP‐NCAR and ERA‐Interim over Australia

Charles

Timbal

et al. 2015

Intl Journal of Climatology

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“…The study area included the state of Victoria, south-eastern Australia (Figure 1). The state's climate is temperate with mean annual temperatures ranging from 12.6 • C in the South East region to 14.7 • C in the North and North West regions [33]. Annual mean precipitation in Victoria ranges from 300 mm to 2500 mm, and is distributed throughout the year [34].…”

Section: Study Area and Forest Typesmentioning

confidence: 99%

Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests

et al. 2018

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Spectral indices derived from optical remote sensing data have been widely used for fire-severity classification in forests from local to global scales. However, comparative analyses of multiple indices across diverse forest types are few. This represents an information gap for fire management agencies in areas like temperate south-eastern Australia, which is characterised by a diversity of natural forests that vary in structure, and in the fire-regeneration strategies of the dominant trees. We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in south-eastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 7.9M ha region’s forest area. Forest types were aggregated into six forest groups based on their fire-regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index performance was evaluated for each forest type and forest group by examining its sensitivity to four fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, separability, and optimality). For the best-performing indices, we calculated index-specific thresholds (by forest types and groups) to separate between the four severity classes, and evaluated the accuracy of fire-severity classification on independent samples. Our results indicated that the best-performing indices of fire severity varied with forest type and group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values ranged from 0.33 (fair agreement) to 0.77 (substantial agreement), depending on the forest group and index. Fire severity in resprouter open forests and woodlands was most accurately mapped using the delta Normalised Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference vegetation index) performed best for open forests with mixed fire responses (resprouters and seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate seeder closed forests. Our analysis highlighted the low sensitivity of all indices to fire impacts in Rainforest. We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes.

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“…For rainfall however, a number of studies have found that 30 years or less is not long enough to adequately represent the range of natural variability, especially when it is used as a predictive indicator of the conditions likely to be experienced in a given location. Victoria just experienced its driest cool season (April -October) rainfall for the last 30 years compared to any 30-year period in the historical record from 1900-2016 (Timbal et al 2016). Research undertaken during the South Eastern Australian Climate Initiative (CSIRO 2012) and the Victorian Climate Initiative have shown that the baseline climate is changing as the assumption of a stationary climate has been challenged by the recent persistently dry conditions.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Methods: Nowcasting to Climate Change - Abstracts of the Bureau of Meteorology Annual R and D Workshop, 26th November to 30th November 2018, Melbourne, Australia

Smith¹

2018

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The Bureau of Meteorology advise that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law and the Bureau of Meteorology (including each of its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

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Climate change science and Victoria

Cited by 15 publications

References 60 publications

Comparison of NCEP‐NCAR and ERA‐Interim over Australia

Comparison of NCEP‐NCAR and ERA‐Interim over Australia

Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests

Ensemble Methods: Nowcasting to Climate Change - Abstracts of the Bureau of Meteorology Annual R and D Workshop, 26th November to 30th November 2018, Melbourne, Australia

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