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International audience; AbstractClimate strongly influences agricultural profitability. Climate risks on agriculture can be managed by moving production to other areas, but this may be costly and reduce profitability if climatic conditions are dynamic. Here, we test whether integrating spatial diversification and climate information could minimize climate risk, while not sacrificing profitability. We use 27 years of farm business profit and climate data (1991–2018) from four of Australia’s most climatically diverse regions where grazing underpins socioeconomic activity. We show that spatial diversification coupled with seasonal climate information from El Niño-Southern Oscillation (ENSO) provides better estimates of optimized risk-profit tradeoffs in different ENSO years compared to estimates without climate information. Conditional Value-at-Risk (CVaR) (a measure of financial risk) is lower when climate information is used in El Niño (1.23 $/ha), La Niña (1.19 $/ha), and Neutral years (1.22 $/ha), compared to when no climate information (1.57 $/ha) is used. When targeting high profits, CVaR is reduced by 15, 86, and 22% in El Niño, La Niña, and Neutral years, respectively, compared to a 5% reduction when no climate information is used. When aiming to minimize CVaR in drought (El Niño), profit is higher using climate-informed spatial diversification (expected profits of 2.71 $/ha), relative to when it is not (expected profits of 2.30 $/ha). Climate-informed spatial diversification also provides options to graziers to achieve much higher gains (expected profits of up to ~ 4.13 $/ha) under La Niña versus 2.62 $/ha when no climate information is used. Here, we show for the first time that seasonal climate information coupled with spatial diversification can provide strategies to help graziers reduce risk and increase profitability. Our approach is applicable to other parts of the world and could be used to decrease climate risk and increase profitability for other agricultural sectors exposed to variable climatic conditions.

From late January to early February 2019, a quasi-stationary monsoon depression situated over northeast Australia caused devastating floods, killing an estimated 625,000 head of cattle in northwest Queensland, and inundating over 3 000 homes in the coastal city of Townsville. The monsoon depression lasted ~10 days, driving daily rainfall accumulations exceeding 200 mm/day, maximum temperatures 8–10 °C below normal, and wind gusts above 70 km/h. In this study, the atmospheric conditions during the event and its predictability on the weekly to subseasonal range are investigated. Results show that during the event, the tropical convective signal of the Madden-Julian Oscillation was over the western Pacific, and likely contributed to the heavy rainfall, however the El Niño-Southern Oscillation was not in the usual phase for increased rainfall over Queensland. Over the northern Tasman Sea, an anticyclone helped maintain a positive phase of the Southern Annular Mode and promote onshore easterly flow. Somewhat consistent with these climate drivers, the monthly rainfall outlook for February issued by the Australian Bureau of Meteorology on 31 January provided no indication of the event, yet forecasts, not available to the public, of weekly-averaged conditions by the Bureau's dynamical subseasonal-to-seasonal (S2S) prediction system were more successful. For the week of 31 January to 6 February the prediction system forecast a more than doubling of the probability of extreme (highest quintile) weekly rainfall a week prior to the event, along with increased probabilities of extremely low (lowest quintile) maximum temperatures and extreme (highest quintile) wind speeds. Ensemble-mean weekly rainfall amounts, however, were considerably underestimated by the prediction system, even in forecasts initialised at the start of the peak flooding week, consistent with other state-of-the-art dynamical S2S prediction systems. Despite this, one of the individual ensemble members of the Bureau's prediction system did manage to forecast close to 85% of the magnitude of the rainfall across the most heavily impacted region of northwest Queensland a week before the event. Predicting this exceptional event beyond two weeks appears beyond our current capability despite the dynamical system forecasts showing good skill in forecasting the broad-scale atmospheric conditions north of Australia a week prior. Keywords: Extreme, Rainfall, Temperature, Flood, Forecast, Skill, Madden-Julian Oscillation, Subseasonal