Tropical cyclones (TCs) cause severe coastal flooding in the middle latitudes. While the IPCC Fifth Assessment Reports (AR5, 2013) have focused on mean sea-level rise, recent advances (e.g., IPCC Sixth Assessment Reports, AR6, 2021) have shown the importance of storm surges and wave changes in extreme water levels causing coastal flooding. Both TC intensity and track changes are linked to future changes in extreme storm surges and wave climates in middle latitudes. This brief review summarizes historical and future long-term changes in extreme water levels, the contribution of increased storm surges, and wave height by the monitoring data and climate projections. In addition, several examples of impact assessment of storm surges and extreme wave changes are presented.

This article uses historical-ecological insights for a re-reading of two little-known mid-twentieth-century Australian plays, Oriel Gray’s The Torrents and Eunice Hanger’s Flood, which highlight developments relevant to the environmental disasters of today. In particular, the article focuses on the significance of key cultural assumptions embedded in the texts – and a revival of The Torrents in 2019 – including those to do with land use in a period of accelerating development. This approach offers new insights into the dominance of mining, irrigation, and dam-building activities within the Australian ethos, landscape, and economy. One of these insights is the framing of development as progressive. The article thus also examines how development projected as progressive takes place amid the continuing denial of prior occupation of the land by First Nations peoples and of knowledge systems developed over thousands of years. The intersectional settler-colonialist-ecocritical approach here seeks to capture the compounding ecosystem that is modern Australian theatre and its critique. The intention is not to apply revisionist critiques of 1950s plays but to explore the historical relationship between humans, colonialism, and the physical environment over time. Denise Varney is Professor of Theatre Studies in the School of Culture and Communication at the University of Melbourne. Her research is in modern and contemporary theatre and performance, with published work in the areas of ecocriticism, feminism, and Australian theatre. Her most recent book is Patrick White’s Theatre: Australian Modernism on Stage 1960–2018 (Sydney University Press, 2021).

An accomplished dancer, acrobat, and physical theatre performer, Hanna Cormick became ill in 2014 with a trifecta of rare genetic conditions that make her severely allergic to pollutants in the air — smoke, detergents, and food particles — and her bones and internal organs prone to dislocation. In January 2020, during Australia’s summer of unprecedented bushfires, Cormick staged The Mermaid, risking her life to make a performance about the climate emergency and how we are all vulnerable bodies at risk in a changing environment.

Paleontological remains retrieved from permafrost represent the most informative records of Pleistocene ecosystems. Different levels of past microbial activity affecting fossil material preservation are presented for two selected bone samples—an almost intact Bison sp. metacarpus (45.0 ± 5.0 14C ka BP) and a weathered Equus sp. metacarpus (37.8 ± 1.7 14C ka BP) from the recently exposed cryogenic geo-contexts in the Yana River basin, NE Yakutia. Diagenetic changes in bone porosity and chemical composition as a result of the past microbial activity were investigated by multiple analytical methods. In the bison bone, which was permafrost-sealed shortly after death of the animal and conserved for ca. 45 ka in a frozen state in a cryolithic formation, only superficial microbial degradation processes were detected. Progressive microbial attacks characterize the horse bone, which was exposed to MIS 3 sub-aerial biogenic decay and modern surficial weathering. This is evidenced by extensive bacterial micro-boring with the typical focal destructions, an increase in microbial porosity, and de-mineralized osseous zones due to waterlogged and poorly oxygenated past depositional conditions. New information contributes to better understanding of the diagenesis particularities and the associated chemical and biological agents of the fossil osteological assemblages with respect to their taphonomic and paleoenvironmental implications.

Sustaining productivity of the rice-based cropping systems in the Eastern Indo-Gangetic Plain (EIGP) requires practices to reverse declining soil fertility resulting from excessive tillage and crop residue removal, while decreasing production costs and increasing farm profits. We hypothesize that the adoption of conservation agriculture (CA), involving minimum tillage, crop residue retention and crop rotation, can address most of these challenges. Therefore, the effects of crop establishment methods – strip planting (SP), bed planting (BP) and conventional tillage (CT); and levels of crop residue retention – high residue (HR) and low residue (LR) on individual crop yield, system yield and profitability were evaluated in a split-plot design over three cropping seasons in two field experiments (Alipur and Digram sites) with contrasting crops and soil types in the EIGP. The SP and BP of non-rice crops were rotated with non-puddled rice establishment; CT of non-rice crops was rotated with puddled transplanted rice. In the legume-dominated system (rice-lentil-mung bean), lentil yields were similar in SP and CT, while lower in BP in crop season 1. A positive effect of high residue over low residue was apparent by crop season 2 and persisted in crop season 3. In crop season 3, the lentil yield increased by 18–23% in SP and BP compared to CT. In the cereal-dominated system (rice-wheat-mung bean), significant yield increases of wheat in SP and BP (7–10%) over CT, and of HR (1–3%) over LR, were detected by crop season 3 but not before. Rice yields under CA practices (non-puddled and HR) were comparable with CT (puddled and LR) in both systems. Improved yield of lentil and wheat with CA was correlated with higher soil water content. The net income of SP increased by 25–28% for dry season crops as compared to CT and was equal with CT for rice cropping systems. Conservation agriculture practices provide opportunities for enhancing crop yield and profitability in intensive rice-based systems of the EIGP of Bangladesh.

The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.

Residential energy efficiency programs play an important role in combating climate change. More precise quantification of the magnitude and timing of energy savings would bring large system benefits, allowing closer integration of energy efficiency into resource adequacy planning and balancing variable renewable electricity. However, it is often difficult to quantify the efficacy of an energy efficiency intervention, because doing so requires consideration of a hypothetical counterfactual case in which there was no intervention, and randomized control trials are often implausible. Although quasi-experimental econometric evaluation sometimes works well, we find that for a set of energy efficiency rebate programs in Northern California, a naïve interpretation of econometric measurement finds that rebate participation is associated with an average increase in electricity consumption of 7.2% [4.5%, 10.1%], varying in magnitude and sign depending on the type of appliance or service covered by the rebate. A subsequent household survey on appliance purchasing behavior and analysis of utility customer outreach data suggest that this regression approach is likely measuring the gross impact of buying a new appliance but fails to adequately capture a counterfactual comparison. Indeed, it is unclear whether it is even possible to construct a suitable counterfactual for econometric analyses of these rebate programs using data generally available to electric utilities. We view these results as an illustration of a limitation of econometric methods of program evaluation and the importance of weighing engineering modeling and other imperfect methods against one another when attempting to provide useful evaluations of real-world policy interventions.

Modeling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extreme value theory is often used to model spatial extremes. However, most commonly used approaches require strong assumptions and are either too simplistic or over-parameterized. From a machine learning perspective, generative adversarial networks (GANs) are a powerful tool to model dependencies in high-dimensional spaces. Yet in the standard setting, GANs do not well represent dependencies in the extremes. Here we combine GANs with extreme value theory (evtGAN) to model spatial dependencies in summer maxima of temperature and winter maxima in precipitation over a large part of western Europe. We use data from a stationary 2000-year climate model simulation to validate the approach and explore its sensitivity to small sample sizes. Our results show that evtGAN outperforms classical GANs and standard statistical approaches to model spatial extremes. Already with about 50 years of data, which corresponds to commonly available climate records, we obtain reasonably good performance. In general, dependencies between temperature extremes are better captured than dependencies between precipitation extremes due to the high spatial coherence in temperature fields. Our approach can be applied to other climate variables and can be used to emulate climate models when running very long simulations to determine dependencies in the extremes is deemed infeasible.

Given the growing use of Artificial intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help reduce climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.