Model Dev News Today

Aquifers contain the largest store of unfrozen freshwater, making groundwater critical for life on Earth. Surprisingly little is known about how groundwater responds to surface warming across spatial and temporal scales. Focusing on diffusive heat transport, we simulate current and projected groundwater temperatures at the global scale. We show that groundwater at the depth of the water table (excluding permafrost regions) is conservatively projected to warm on average by 2.1 °C betwe...

The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO2) and other important greenhouse gases are changing in response to human influences. These changes need to be quantified to understand climate change and its impacts, but this is difficult to do because natural fluxes occur over large spatial and temporal scales and cannot be directly observed. Flux inversion is a technique that estimates the spatiotemporal distribution of a gas’ fluxes using observations of the gas�...

Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1–5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled da...

Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but much more difficult to write. Julia is a programming language that aims to be the best of both worlds – a development and production language at the same time. To test Julias utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, the Model for Predi...

Taking stock of global progress towards achieving the Paris Agreement requires consistently measuring aggregate national actions and pledges against modelled mitigation pathways1. However, national greenhouse gas inventories (NGHGIs) and scientific assessments of anthropogenic emissions follow different accounting conventions for land-based carbon fluxes resulting in a large difference in the present emission estimates2,3, a gap that will evolve over time. Using state-of-the-art methodologies4 a...

High Mountain Asia (HMA) has experienced a spatial imbalance in water resources in recent decades, partly because of a dipolar pattern of precipitation changes known as South Drying–North Wetting1. These changes can be influenced by both human activities and internal climate variability2,3. Although climate projections indicate a future widespread wetting trend over HMA1,4, the timing and mechanism of the transition from a dipolar to a monopolar pattern remain unknown. Here we demonstrate ...

Degrowth advocates argue for structural transformations in how economies and societies prioritize material wealth accumulation to reduce the negative effects of future anthropogenic climate change. Degrowth proponents argue that human economic activity could be lessened, and societies transformed to prioritize improved wellbeing, reducing the threat of climate change. This paper explores implications of alternative patterns of economic growth with transformational policy pathways (i.e.,...

Wildfires are thought to be increasing in severity and frequency as a result of climate change1–5. Air pollution from landscape fires can negatively affect human health4–6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale7–23. Here, we estimate global daily LFS outdoor fine particulate matter (PM2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the peri...

California has experienced enhanced extreme wildfire behaviour in recent years1–3, leading to substantial loss of life and property4,5. Some portion of the change in wildfire behaviour is attributable to anthropogenic climate warming, but formally quantifying this contribution is difficult because of numerous confounding factors6,7 and because wildfires are below the grid scale of global climate models. Here we use machine learning to quantify empirical relationships between temperature (a...

Earth system models and various climate proxy sources indicate global warming is unprecedented during at least the Common Era1. However, tree-ring proxies often estimate temperatures during the Medieval Climate Anomaly (950–1250 ce) that are similar to, or exceed, those recorded for the past century2,3, in contrast to simulation experiments at regional scales4. This not only calls into question the reliability of models and proxies but also contributes to uncertainty in f...