Urban areas are currently responsible for ~70% of the global energy-related carbon dioxide (CO2) emissions, and rapid ongoing global urbanization is increasing the number and size of cities. Thus, understanding city-scale CO2 emissions and how they vary between cities with different urban densities is a critical task.

Aviation emissions have been found to cause 5% of global anthropogenic radiative forcing and ~16 000 premature deaths annually due to impaired air quality. When aiming to reduce these impacts, decision makers often face trade-offs between different emission species or impacts in different times and locations.

Surface water floods (SWFs) that lead to household losses are mainly localized phenomena. Research on describing the associated precipitation characteristics has previously been based on case studies and on the derivation of local rainfall thresholds, but no approaches have yet been presented on the national scale. Here, we propose a new way to overcome this scaling problem.

Changes in precipitation totals and extremes are among the most relevant consequences of climate change, but in particular regional changes remain uncertain. While aggregating over larger regions reduces the noise in time series and typically shows increases in the intensity of precipitation extremes, it has been argued that this may not be the case in water-limited regions.

Human activities threaten the effectiveness of protected areas (PAs) in achieving their conservation goals across the globe. In this study, we contrast the influence of human and macro-environmental factors driving fire activity inside and outside PAs. Using area burned between 1984 and 2014 for 11 ecoregions in Canada and the United States, we built and compared statistical models of fire likelihood using the MaxEnt software and a set of 11 key anthropogenic, climatic, and physical variables. Overall, the full model (i.e.

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system.

In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models.

During austral summer (DJF) 2017/18, the New Zealand region experienced an unprecedented coupled ocean-atmosphere heatwave, covering an area of 4 million km2. Regional average air temperature anomalies over land were +2.2 °C, and sea surface temperature anomalies reached +3.7 °C in the eastern Tasman Sea.

The tropics have suffered substantial forest loss, and elevated deforestation rates have been closely linked to large-scale land acquisitions(LSLA).

Each year wildland fires kill and injure trees on millions of forested hectares globally, affecting plant and animal biodiversity, carbon storage, hydrologic processes, and ecosystem services. The underlying mechanisms of fire-caused tree mortality remain poorly understood, however, limiting the ability to accurately predict