Extreme heat can have devastating impacts on built and natural environments including crop losses, wildfire risk, infrastructure damage, and wildlife mortality (e.g.,

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Simultaneous heatwaves affecting multiple regions (referred to as concurrent heatwaves), pose compounding threats to various natural and societal systems, including global food chains, emergency response systems, and reinsurance industries. While anthropogenic climate change is increasing heatwave risks across most regions, the interactions between warming and circulation changes that yield concurrent heatwaves remain understudied. Here, we quantify historical (1979-2019) trends in concurrent heatwaves during the warm-season (May-September, MJJAS) across the Northern Hemisphere mid- to high-latitudes. We find a significant increase of ~46% in the mean spatial extent of concurrent heatwaves, ~17% increase in their maximum intensity, and ~6-fold increase in their frequency. Using Self-Organising Maps, we identify large-scale circulation patterns (300 hPa) associated with specific concurrent heatwave configurations across Northern Hemisphere regions. We show that observed changes in the frequency of specific circulation patterns preferentially increase the risk of concurrent heatwaves across particular regions. Patterns linking concurrent heatwaves across eastern North America, eastern and northern Europe, parts of Asia, and the Barents and Kara Seas, show the largest increases in frequency (~5.9 additional days per decade). We also quantify the relative contributions of circulation pattern changes and warming to overall observed concurrent heatwave day frequency trends. While warming has a predominant and positive influence on increasing concurrent heatwaves, circulation pattern changes have a varying influence and account for up to 0.8 additional concurrent heatwave days per decade. Identifying regions with an elevated risk of concurrent heatwaves and understanding their drivers is indispensable for evaluating projected climate risks on interconnected societal systems and fostering regional preparedness in a changing climate.

Abstract. Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmospherebiosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index-rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the IndianAustralian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño-Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

The impact of extreme heat on crop yields is an increasingly pressing issue given anthropogenic climate warming. However, some of the physical mechanisms involved in these impacts remain unclear, impeding adaptation-relevant insight and reliable projections of future climate impacts on crops. Here, using a multiple regression model based on observational data, we show that while extreme dry heat steeply reduced U.S. corn and soy yields, humid heat extremes had insignificant impacts and even boosted yields in some areas, despite having comparably high dry-bulb temperatures as their dry heat counterparts. This result suggests that conflating dry and humid heat extremes may lead to underestimated crop yield sensitivities to extreme dry heat. Rainfall tends to precede humid but not dry heat extremes, suggesting that multivariate weather sequences play a role in these crop responses. Our results provide evidence that extreme heat in recent years primarily affected yields by inducing moisture stress, and that the conflation of humid and dry heat extremes may lead to inaccuracy in projecting crop yield responses to warming and changing humidity.

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<div> <div> <p><span>Australian weather forecasts use Numerical Weather Prediction (NWP) model output. Forecast accuracy is improved by assimilating a range of observational data which includes Australian Bureau of Meteorology station data. The significant investment by the Bureau of Meteorology in the national observing network, and the constant evolution of observational technologies, requires an ongoing assessment of the scientific value of the network components. Examining an objective measure of the impact of each assimilated observing system on the quality of short-term NWP forecasts can potentially guide planning and investment decisions related to network efficiency and effectiveness.</span><span> </span></p> </div> <div> <p><span>Traditional techniques for assessing the impact of observations in NWP are inflexible (i.e. they require dedicated trials) and computationally expensive, but a widely used technique, known as adjoint-based Forecast Sensitivity to Observations (FSO), can provide forecast impact information continuously, flexibly, and in near real-time. We use archived FSO data to assess the relative forecast impact of in-situ data for different instruments and variables. We use two case studies to examine the impact of 1) three upper-air measurement instruments - radiosondes, aircraft, and a wind profiler - through the atmosphere at Sydney Airport, and 2) Automatic Weather Station surface observations along the Great Barrier Reef. These studies aim to provide network planners with information that can guide observations rationalisation decisions.</span><span> </span></p> </div> </div>