Results 61 to 70 of about 24,239 (287)
This study proposes a novel downscaling technique based on stacking ensemble machine learning (SEML) to predict rainfall under climate change. The SEML consists of two levels.
Mahdi Valikhan Anaraki +3 more
doaj +1 more source
Climate change and perennial crop production: Evidence of yield impact and adaptation in California
Abstract Perennial crops are economically important. They contribute to food security, providing essential nutrients that are often lacking in annual crops, and provide additional environmental benefits compared with annual crops. Despite their importance, empirical research on the impacts of climate change and adaptation on perennial crops remains ...
Yuanyuan Wen +2 more
wiley +1 more source
Abstract Premise Understanding the habitat requirements of imperiled flora is critical for informing ex situ conservation practices, designing effective reintroduction strategies, and understanding how climate change will impact such species, especially in montane regions with high levels of environmental heterogeneity. In southern Appalachia, USA, the
Nicholas J. Chang +6 more
wiley +1 more source
High‐elevation endemic plants predicted to lose habitat from changing climate in Washington State
Abstract Premise High‐elevation plants face unique challenges from potential climate change impacts that will likely require upslope migration into increasingly smaller suitable habitat. This situation is particularly acute for endemic species that by definition occupy small geographic ranges.
Nicholas L. Gjording +4 more
wiley +1 more source
CMIP6 Community Survey Results
Results from the CMIP6 Community Survey. The WCRP Working Group on Coupled Modelling (WGCM), the CMIP Panel and the WGCM Infrastructure Panel (WIP) would like to thank all those who responded to the “CMIP Community next steps survey” providing in-depth, insightful, and honest feedback that will help support the development of future phases of CMIP ...
openaire +1 more source
This study evaluates the COSMO‐CLM regional climate model over Italy under CMIP6 scenarios. Compared to its driving global model, COSMO‐CLM reduces temperature biases by 50%–75% and better represents precipitation and extremes, adding critical mesoscale detail.
Alejandro Vichot‐Llano +5 more
wiley +1 more source
Projected Evolution of Climatic Aridity in Spain: Robust Signals and Model Uncertainties
This study examines the projected evolution of climatic aridity in Spain throughout the 21st century, using the UNEP Aridity Index and CMIP6 simulations under different emission scenarios and global warming levels. Despite model biases, results show a general increase in aridity across the country, particularly in southern regions and the Canary ...
Víctor Trullenque‐Blanco +5 more
wiley +1 more source
Evaluating Causal Arctic‐Midlatitude Teleconnections in CMIP6
AbstractTo analyze links among key processes that contribute to Arctic‐midlatitude teleconnections we apply causal discovery based on graphical models known as causal graphs. First, we calculate the causal dependencies from observations during 1980–2021.
Galytska, Evgenia +6 more
openaire +2 more sources
Sampling Biases in Daily Average Temperatures From Greenland Climate Records
Biases are introduced in the calculation of daily average temperatures due to uneven sampling times for the investigated weather station network in Greenland. The figure shows the network, an example of a daily temperature cycle with daily averages based on all or only two observations and an overview of how the number of observations per day changes ...
Dina Rapp +4 more
wiley +1 more source
Dry‐Season Water Deficits in the Southwestern Amazon Under High Emissions
Projected climatic water deficit in the study region indicates a longer and more intense dry season, with delays in the onset of the wet season under higher emission scenarios. These changes, particularly, pronounced under SSP5‐8.5, suggest increased ecological vulnerability and greater seasonal water stress.
Débora J. Dutra +18 more
wiley +1 more source

