
Climate warming stimulates ecosystem respiration in tundra, leading to increased carbon release into the atmosphere, with the magnitude and persistence of this effect driven by local soil conditions and nutrient dynamics.


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Datasets from in situ warming experiments across 28 arctic and alpine tundra sites covering  a span of less than 1 year up to 25 years show the importance of local soil conditions and warming-induced changes&nbsp;therein for future climatic impacts on&nbsp;ecosystem respiration.

Environmental drivers of increased ecosystem respiration in a warming tundra - Nature

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Increased Ecosystem Respiration in Warming Tundra: Environmental Drivers and Implications


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Enhanced rock weathering (EW) is a simple technology that can simultaneously address climate change by removing carbon dioxide from the atmosphere and improve global food security by releasing essential minerals to fertilize the soil.


The Climate crisis & world hunger could be a thing of the past.

This Simple Technology Could Save The World In Two Ways

enhanced-rock-weathering-a-promising-solution-to-climate-change-and-global-hunger


Enhanced Rock Weathering: A Promising Solution to Climate Change and Global Hunger



Pre-trained weather models enhance global vegetation modeling, improving NDVI estimates.


global-vegetation-modeling-with-pre-trained-weather-transformers-at-iclr-2024-workshop


Global Vegetation Modeling with Pre-Trained Weather Transformers at ICLR 2024 Workshop



The author argues that drought conditions are a significant factor in promoting overnight burning, challenging traditional fire management practices and the diurnal fire cycle model.


By examining the hourly diurnal cycle of 23,557 fires in North America during 2017–2020, 1,095 overnight burning events were identified, mostly associated with extreme fires and&nbsp;driven by long-term drought conditions.

Drought triggers and sustains overnight fires in North America - Nature

unveiling-the-impact-of-drought-on-overnight-fires-in-north-america


Unveiling the Impact of Drought on Overnight Fires in North America



The author proposes a new methodological framework for extreme quantile regression using neural networks to capture complex non-linear relationships and improve predictive performance.


regression-modelling-of-extreme-u-s-wildfires-using-neural-networks


Regression Modelling of Extreme U.S. Wildfires Using Neural Networks



The author introduces Environmental Insights, an open-source Python package, to democratize access to air pollution concentration data and enable forecasting of future conditions through machine learning models.


democratizing-access-to-ambient-air-pollution-data-with-environmental-insights


Democratizing Access to Ambient Air Pollution Data with Environmental Insights



The author argues that rising sea levels and coastal subsidence pose a significant threat to major US coastal cities, with potential consequences for population and property.


High-resolution vertical land motion and elevation datasets combined with projections of sea-level rise of 32 major US coastal cities shows that a considerable amount of land area, population, and properties are threatened by relative sea-level rise by 2050.

Disappearing cities on US coasts - Nature

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Rising Sea Levels Threaten US Coastal Cities by 2050



The author argues that model uncertainty hinders the identification of a significant driver of soil carbon levels, emphasizing the importance of understanding microbial carbon use efficiency.


Reply to: Model uncertainty obscures major driver of soil carbon - Nature

unveiling-the-impact-of-model-uncertainty-on-soil-carbon-drivers


Unveiling the Impact of Model Uncertainty on Soil Carbon Drivers



The author argues that microbial carbon use efficiency plays a crucial role in promoting global soil carbon storage, highlighting the significance of understanding microbial community-level regulation.


Model uncertainty obscures major driver of soil carbon - Nature

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Model Uncertainty and Soil Carbon Storage



The author explores the use of machine learning techniques for time series predictions in unmonitored sites, highlighting the superiority of modern ML methods over traditional models and the challenges faced in transferring knowledge to new regions.


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Time Series Predictions in Unmonitored Sites: Machine Learning Techniques in Water Resources
