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3 results for “Land-atmosphere feedback”

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zenodo40/100

Data from: River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon.

<p>The dataset contains post-processed output from two experiments performed&nbsp;for Indian Summer Monsoon&nbsp;(June-September) from 1991-2012 using WRF-CLM4: CTL&nbsp;and IRR.&nbsp;Here, CTL represents WRF-CLM4 simulation with irrigation currently practiced in India.&nbsp;We use a modified irrigation module in CLM4 that better represents the Indian practices of irrigation by incorporating groundwater withdrawal and flood irrigation practiced over paddy fields. The module can be found at&nbsp;<a href="https://github.com/IMMM-SFA/WRF_CLM4_Irrigation">https://github.com/IMMM-SFA/WRF_CLM4_Irrigation</a>&nbsp;and <a href="https://doi.org/10.1029/2019GL083875">https://doi.org/10.1029/2019GL083875</a>. IRR simulation adds additional irrigation to CTL by increasing the percentage of irrigated area to 80% in regions where India&#39;s river-interlinking projects target an increase in the culturable command area.</p> <p>The post-processed output contains the following variables:</p> <ol> <li>Mean Daily Temperature</li> <li>Daily Maximum Temperature</li> <li>Latent Heat Flux</li> <li>Sensible Heat Flux</li> <li>Relative Humidity</li> <li>U-Wind at Pressure levels</li> <li>V-Wind at Pressure levels</li> <li>Net-Solar Radiation on Land</li> <li>Soil Moisture</li> </ol> <p>Irrigation input files for CTL and IRR simulations of WRF-CLM4 are also included.</p>

opencc-by-4.0Aug 2023View details →
zenodo32/100

Data: Dryland self-expansion enabled by land-atmosphere feedbacks

<p>The repository contains the scripts and data required to reproduced the final figures in the article: Dryland self-expansion enabled by land-atmosphere feedbacks. Link to the article: <a href="https://doi.org/10.1126/science.adn6833">https://doi.org/10.1126/science.adn6833</a>.</p>

opencc-by-4.0Dec 2023View details →
zenodo32/100

Dataset for publication: The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback

<p>Here are zip files containing the data sets for the figures of the publication "The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback" by Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.</p><p>Data collected from the Halo Doppler lidar (DL), Atmospheric Raman Temperature and HUmidity Sounder ARTHUS, scanning differential absorption lidar (DIAL), Eddy-Covariance stations (EC) and the Water and Temperature Sensor Network (WaTSeN) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany. The data cover the temporal period as presented in the figures in Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.</p>

opencc-by-4.0Dec 2022View details →

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International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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OpenNeuro

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neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record