Last Glacial Maximum pattern effects reduce climate sensitivity estimates

Here, we show that the Last Glacial Maximum (LGM) provides a stronger constraint on equilibrium climate sensitivity (ECS), the global warming from increasing greenhouse gases, after accounting for temperature patterns. Feedbacks governing ECS depend on spatial patterns of surface temperature ("pattern effects"); hence, using the LGM to constrain future warming requires quantifying how temperature patterns produce different feedbacks during LGM cooling versus modern-day warming. Combining data assimilation reconstructions with atmospheric models, we show that the climate is more sensitive to LGM forcing because ice sheets amplify extratropical cooling where feedbacks are destabilizing. Accounting for LGM pattern effects yields a median modern-day ECS of 2.4°C, 66% range 1.7° to 3.5°C (1.4° to 5.0°C, 5 to 95%), from LGM evidence alone. Combining the LGM with other lines of evidence, the best estimate becomes 2.9°C, 66% range 2.4° to 3.5°C (2.1° to 4.1°C, 5 to 95%), substantially narrowing uncertainty compared to recent assessments.

Economic valuation of temperature-related mortality attributed to urban heat islands in European cities

As the climate warms, increasing heat-related health risks are expected, and can be exacerbated by the urban heat island (UHI) effect. UHIs can also offer protection against cold weather, but a clear quantification of their impacts on human health across diverse cities and seasons is still being explored. Here we provide a 500 m resolution assessment of mortality risks associated with UHIs for 85 European cities in 2015-2017. Acute impacts are found during heat extremes, with a 45% median increase in mortality risk associated with UHI, compared to a 7% decrease during cold extremes. However, protracted cold seasons result in greater integrated protective effects. On average, UHI-induced heat-/cold-related mortality is associated with economic impacts of €192/€ - 314 per adult urban inhabitant per year in Europe, comparable to air pollution and transit costs. These findings urge strategies aimed at designing healthier cities to consider the seasonality of UHI impacts, and to account for social costs, their controlling factors, and intra-urban variability.

Verification of a National Emission Inventory and Influence of On-road Vehicle Manufacturer-Level Emissions

Road vehicles make important contributions to a wide range of pollutant emissions from the street level to global scales. The quantification of emissions from road vehicles is, however, highly challenging given the number of individual sources involved and the myriad factors that influence emissions such as fuel type, emission standard, and driving behavior. In this work, we use highly detailed and comprehensive vehicle emission remote sensing measurements made under real driving conditions to develop new bottom-up inventories that can be compared to official national inventory totals. We find that the total UK passenger car and light-duty van emissions of nitrogen oxides (NO_x) are underestimated by 24-32%, and up to 47% in urban areas, compared with the UK national inventory, despite agreement within 1.5% for total fuel used. Emissions of NO_x at a country level are also shown to vary considerably depending on the mix of vehicle manufacturers in the fleet. Adopting the on-road mix of vehicle manufacturers for six European countries results in up to a 13.4% range in total emissions of NO_x. Accounting for the manufacturer-specific fleets at a country level could have a significant impact on emission estimates of NO_x and other pollutants across the European countries, which are not currently reflected in emission inventories.

A framework for probabilistic weather forecast post-processing across models and lead times using machine learning

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support: probabilities of future weather outcomes. First, we use quantile regression forests to learn the error profile of each numerical model, and use these to apply empirically derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20-35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

Has the red sheep tick, Haemaphysalis punctata, recently expanded its range in England?

The red sheep tick, Haemaphysalis punctata (Ixodida: Ixodidae), has been reported as present in the U.K. for more than a century; however, very little has been written about its distribution. In recent years, numbers of detections of this tick species reported to the Public Health England (PHE) Tick Surveillance Scheme have increased. This rise in the number of records may be attributable to increased tick surveillance activities or to the increased distribution of this species of tick in parts of England. This paper reviews published reports of H. punctata and all data held by the Biological Records Centre and PHE, and summarizes a number of field studies conducted by PHE and the Animal and Plant Health Agency over the past 8 years. It would appear from the evidence presented here that H. punctata may be expanding its range across the eastern part of the South Downs National Park, where there have also been reports of this tick species biting humans. It is possible that the movement of sheep between grassland sites is facilitating this spread. Further studies that better elucidate the ecology of this tick and its possible role as a vector of human and veterinary diseases are now warranted.

Global disparity in the supply of commercial weather and climate information services

Information about weather and climate is vital for many areas of decision-making, particularly under conditions of increasing vulnerability and uncertainty related to climate change. We have quantified the global commercial supply of weather and climate information services. Although government data are sometimes freely available, the interpretation and analysis of those data, alongside additional data collection, are required to formulate responses to specific challenges in areas such as health, agriculture, and the built environment. Using transactional data, we analyzed annual spending by private and public organizations on commercial weather and climate information in more than 180 countries by industrial sector, region, per capita, and percentage of GDP (gross domestic product) and against the country's climate and extreme weather risk. There are major imbalances regarding access to these essential services between different countries based on region and development status. There is also no relationship between the level of climate and weather risks that a country faces and the level of per capita spending on commercial weather and climate information in that country. At the international level, action is being taken to improve access to information services. With a better understanding of the flows of commercial weather and climate information, as explored in this study, it will be possible to tackle these regional and development-related disparities and thus to increase resilience to climate and weather risks.