Bayesian space–time model to analyse frost risk for agriculture in Southeast Australia. (5th August 2014)

• Record Type: Journal Article
• Authors: Crimp, Steven; Bakar, Khandoker Shuvo; Kokic, Philip; Jin, Huidong; Nicholls, Neville; Howden, Mark
• Is Part Of: International journal of climatology. Volume 35:Number 8(2015)
• Page Start: 2092
• Page End: 2108
• Publication Date: 2014-08-05
• Subjects: Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.4109
• Languages: English
• ISSNs: 0899-8418

Abstract

<abstract abstract-type="main" id="joc4109-abs-0001"> <title>ABSTRACT</title> <p id="joc4109-para-0001">Despite a broad pattern of warming in minimum temperatures over the past 50 years, regions of southeastern Australia have experienced increases in frost frequency in recent decades, and more broadly across southern Australia, an extension of the frost window due to an earlier onset and later cessation. Consistent across southern Australia is a later cessation of frosts, with some areas of southeastern Australia experiencing the last frost an average 4 weeks later than in the 1960s (i.e. mean date of last frost for the period 1960–1970 was 19 September <italic>versus</italic> 22 October for the period 2000–2009). We seek to model the spatial changes in frosts for a region exhibiting the strongest individual station trends, i.e. northern Victoria and southern New South Wales. We identify statistically significant trends at low‐lying stations for the month of August and construct and validate a Bayesian space–time model of minimum temperatures, using rates of greenhouse gas (GHG) emissions, as well as other well‐understood causal factors including solar radiation, the El Niño Southern Oscillation (ENSO 3.4) and times series data relating to the position (STRP) and intensity (STRI) of subtropical highs and blocking high pressure systems. We assess the performance of this modelling approach against observational records as well as against additive and linear regression modelling approaches using root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) as well as false alarm and hit rate metrics. The spatiotemporal modelling approach demonstrated considerably better predictive skill than the others, with enhanced performance across all the metrics analysed. This enhanced performance was consistent across each decade and for temperature extremes below 2 °C.</p> </abstract>