Many of the clouds in the atmosphere contain ice particles. These ice particles play an important role in the climate system, because high-altitude cirrus clouds cover around 30% of the globe at any one time, and act to warm the planet. Ice particles are also important for the development of precipitation, and not only in cold polar climates: even in mid-latitudes (like the UK), over three quarters of the precipitation that falls originates as snowflakes aloft - it is just that most of it melts before arriving at the surface. Ice particles, both in clouds and in snowfall at the surface, precipitate. In other words, they are able to grow large enough to fall through the air. This has several implications. The most obvious of these is that the rate at which the particles fall out controls the transport of water vertically through the atmosphere and to the surface. More subtly, the movement of each ice particle through the air directly influences the rate at which the particle grows, evaporates and melts. For example, if the air is humid enough, water molecules will diffuse to the ice crystal's surface and deposit there, leading to growth. If the particle is stationary, this growth occurs steadily but slowly, because the growing ice crystal depletes the vapour around it, leading to a shallow gradient in the concentration of molecules. If the particle is falling, this growth can occur much faster, because the ice crystal is constantly falling into fresh, humid air, leading to steep concentration gradients. The quantitative details of exactly how fast an ice particle of a given size and shape falls, and how much the growth rates are enhanced by, is determined by the airflow around the ice particle, or its aerodynamics. Unfortunately, this is an area of cloud physics where our understanding is extremely limited. The aerodynamics of simple shapes like spheres, spheroids and discs is well studied. However it is clear from observation of natural ice particles that they are not simple in their geometry. Instead the particles are often complex and irregular in their shape. We have almost no high-quality data on the aerodynamics of such particles. As a result, even state-of-the-art microphysical models are forced to approximate the aerodynamical effects on ice processes as though these complex irregular particles were spheres or spheroids, hoping that this is an adequate approximation. To solve this problem, experimental data is needed for the aerodynamics of particles with the complex shapes that we observe in the atmosphere. The stumbling block is that making suitable observations of natural ice particles in free-fall is extremely challenging. In snowfall at the surface the particles are small, fragile, easily blown by the wind, and likely to melt or evaporate if not handled with great care. Direct sampling of falling particles in cirrus clouds is impossible. In neither case is it possible to directly determine the airflow around the particle or the influence of that flow on the microphysical process rates. In this project we overcome these problems with the use of analogues. Using 3D printing techniques we will create plastic particles with the same complex geometry as natural ice particles. By dropping the particles in tanks of liquids, and through air in the laboratory and a vertical wind tunnel, we can determine how the fall speed of the particles is controlled by their size and geometry. Exploiting recent developments in tomographic particle imaging velocimetry we can measure the airflow around the falling analogues. From this we can directly determine how the airflow enhances the particle growth, evaporation and melting rates.

SUMMARY The Amazon is the most important biome of South America, harbouring extraordinarily high levels of biodiversity and providing important ecosystems services. This biome is particularly notable for evolving independently from fire and in a moist, warm climate. In recent decades, altered fire regimes and an increasingly hotter and drier climate has pushed this key biome towards ecological thresholds that will likely lead to major losses in biodiversity and ecosystem services. Similarly, the ecotonal forests at the Amazon-Cerrado transition are unique ecosystems in terms of form and function, but they may be the first to suffer large-scale tree mortality and species loss due to the combined effects of increased anthropogenic disturbance, altered fire regimes and a drier climate. Vulnerability of fire and droughts are closely intertwined in Amazonian and transitional forests because fires in this region only occur when there is water stress and a human ignition source. Thus, drought increases vulnerability to fire, but we do not yet understand the magnitude and spatial variation of these vulnerabilities. Once a forest burns there is immediate tree mortality, but recent evidence also shows a significant time-lagged mortality that can last for decades, becoming an important carbon source. However, the mechanistic processes that lead to time-lagged tree mortality in this myriad of forest ecosystems encompassing the Amazon biome and the Amazon-Cerrado transition are still poorly understood. We also lack knowledge on how these processes might vary spatially across the biome and its transition. A better understanding of the mechanisms that lead to tree mortality after fires and droughts is needed to design future policies that emphasise nature-based solutions including restoration and natural regeneration. This proposal presents a multi-level approach that aims at deciphering the mechanisms that underly vulnerability to fire and time-lagged post-fire mortality across the tropical forests in Amazon and Amazon-Cerrado transition. To achieve this aim, we will quantify fire vulnerability at three different scales and link them through an upscaling approach. First, we will identify the ecological mechanisms, reflected through functional traits, that explain why individuals and species die after fires occur. For this, we will focus on poorly understood traits that can be related to fire and/or hydraulic functioning. Second, at the community scale, we will examine how vegetation structure, community traits and microclimate affect the probability to burn, through an intensive characterisation of different vegetation types with multispectral and light detection and ranging (LIDAR) imagery. Third, we will use our our unique ground-dataset on functional traits, vegetation structure and moisture dynamics, and the latest state-of-art remotely sensed information on structure and water stress to predict the vulnerability of the Amazon forests and Amazon-Cerrado transitional forests. This information will be directly applicable for the detection of sensitive hotspots (areas particularly vulnerable to fire) through satellite products. We will deliver quantifiable early-warning metrics of ecosystem vulnerability to fire that can be mapped and incorporated into fire management policies. This is a revised version of a NERC proposal that was rejected with a score of 7 by the NERC Panel in July 2020, and we have carefully addressed the Panel's comments. Specifically, we have clarified the methodology and we have reformulated the hypotheses, so they address vulnerability to fire and not drought fire-interactions.