• Energy Research

Climate projections indicate that dangerous fire weather will become more common over the coming century. We examine the potential of a network of temperature- and moisture-sensitive tree-ring sites in southeastern Australia to reconstruct the number of high fire-danger days for the January–March season. Using the Forest Fire Danger Index (FFDI), we show that modestly statistically skilful reconstructions for the far southeast of Australia (western Tasmania), where the majority of tree-ring predictors are located, can be developed. According to the averaged reconstructions for the 1590–2008 period, there have been 16 years prior to the start of the FFDI records (1950), and 7 years since 1950, with >48 (mean + 1σ) high fire-danger days in the 3-month season. The western Tasmanian reconstructions indicate extended relatively high fire-danger periods in the 1650s–1660s and 1880s–1890s. Fire danger has also been relatively high since 2000 CE. A persistent increase in the number of high fire-danger days over the past four decades has not been matched over the previous 390 years. This work indicates it is possible to produce statistically useful reconstructions of high seasonal fire danger – as opposed to fire occurrence – but that availability of local proxy records is key.

Abstract Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1–2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estimating true demand variability. We estimate daily energy demand between 2010 and 2019 for Australian states in the National Electricity Market using machine learning with reanalysis weather variables as predictors. We assess the performance of these models and examine their behaviour to identify which weather variables are most important for predicting demand. We then use the models to estimate demand for the period 1959–2022. We use this 64-year record to quantify how the probability of high demand days can change compared to individual 10-year periods and when conditioned by the phase of the El Ni n ~ o Southern Oscillation (ENSO). Energy demand can be accurately predicted with weather, with median errors of 2%–4% on years omitted from the training. We show that the probability of extreme demand over different 10-year periods can vary from half to twice as likely, depending on the decade. When further conditioned on ENSO phase, the probabilities can be up to 7 times higher than when using the 64-year period, implying a risk of overestimating weather-related energy demand if shorter records are used. We conclude that machine learning methods can accurately predict energy demand using only weather data, enabling us to estimate demand variability over longer time horizons than is possible with demand observations. These longer records are important when attempting to quantify tail risks of demand, and so can help to inform the design of energy systems.

AbstractSolar photovoltaic and wind power are central to Australia’s renewable energy future, implying an energy sector vulnerable to weather and climate variability. Alignment of weather systems and the influence of large-scale climate modes of variability risks widespread reductions in solar and wind resources, and could induce grid-wide impacts. We therefore systematically analyse the relationship between compound solar radiation and wind speed droughts with weather systems and climate modes of variability over multiple time scales. We find that compound solar and wind droughts occur most frequently in winter, affecting at least five significant energy-producing regions simultaneously on 10% of days. The associated weather systems vary by season and by drought type, although widespread cloud cover and anticyclonic circulation patterns are common features. Indices of major climate modes are not strong predictors of grid-wide droughts, and are typically within one standard deviation of the mean during seasons with the most widespread events. However, the spatial imprints of the teleconnections display strong regional variations, with drought frequencies varying by more than ten days per season between positive and negative phases of climate modes in some regions. The spatial variability of these teleconnection patterns suggests that droughts in one region may be offset by increased resource in another. Our work highlights the opportunity for minimising the impact of energy production variability by utilising weather and climate intelligence. Exploiting the spatial variability associated with daily weather systems and the seasonal influence of climate modes could help build a more climate-resilient renewables-dominated energy system.

Abstract. Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.