Absence of 21st century warming on Antarctic Peninsula consistent with natural variability.

Since the 1950s, research stations on the Antarctic Peninsula have recorded some of the largest increases in near-surface air temperature in the Southern Hemisphere. This warming has contributed to the regional retreat of glaciers, disintegration of floating ice shelves and a 'greening' through the expansion in range of various flora. Several interlinked processes have been suggested as contributing to the warming, including stratospheric ozone depletion, local sea-ice loss, an increase in westerly winds, and changes in the strength and location of low-high-latitude atmospheric teleconnections. Here we use a stacked temperature record to show an absence of regional warming since the late 1990s. The annual mean temperature has decreased at a statistically significant rate, with the most rapid cooling during the Austral summer. Temperatures have decreased as a consequence of a greater frequency of cold, east-to-southeasterly winds, resulting from more cyclonic conditions in the northern Weddell Sea associated with a strengthening mid-latitude jet. These circulation changes have also increased the advection of sea ice towards the east coast of the peninsula, amplifying their effects. Our findings cover only 1% of the Antarctic continent and emphasize that decadal temperature changes in this region are not primarily associated with the drivers of global temperature change but, rather, reflect the extreme natural internal variability of the regional atmospheric circulation.

Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop.

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Recent changes in Antarctic Sea Ice.

In contrast to the Arctic, total sea ice extent (SIE) across the Southern Ocean has increased since the late 1970s, with the annual mean increasing at a rate of 186×10(3) km(2) per decade (1.5% per decade; p<0.01) for 1979-2013. However, this overall increase masks larger regional variations, most notably an increase (decrease) over the Ross (Amundsen-Bellingshausen) Sea. Sea ice variability results from changes in atmospheric and oceanic conditions, although the former is thought to be more significant, since there is a high correlation between anomalies in the ice concentration and the near-surface wind field. The Southern Ocean SIE trend is dominated by the increase in the Ross Sea sector, where the SIE is significantly correlated with the depth of the Amundsen Sea Low (ASL), which has deepened since 1979. The depth of the ASL is influenced by a number of external factors, including tropical sea surface temperatures, but the low also has a large locally driven intrinsic variability, suggesting that SIE in these areas is especially variable. Many of the current generation of coupled climate models have difficulty in simulating sea ice. However, output from the better-performing IPCC CMIP5 models suggests that the recent increase in Antarctic SIE may be within the bounds of intrinsic/internal variability.

The Time Machine framework: monitoring and prediction of biodiversity loss.

Transdisciplinary solutions are needed to achieve the sustainability of ecosystem services for future generations. We propose a framework to identify the causes of ecosystem function loss and to forecast the future of ecosystem services under different climate and pollution scenarios. The framework (i) applies an artificial intelligence (AI) time-series analysis to identify relationships among environmental change, biodiversity dynamics and ecosystem functions; (ii) validates relationships between loss of biodiversity and environmental change in fabricated ecosystems; and (iii) forecasts the likely future of ecosystem services and their socioeconomic impact under different pollution and climate scenarios. We illustrate the framework by applying it to watersheds, and provide system-level approaches that enable natural capital restoration by associating multidecadal biodiversity changes to chemical pollution.

Regional disparities and seasonal differences in climate risk to rice labour.

The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term (p ≪ 0.01, R 2 > 0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress.

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.