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Commentary on 'Physics-informed deep learning parameterization of ocean vertical mixing improves climate simulations' by Zhu et al.

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National Science Review 2022年第8期9卷 11-12页

作者： gustau camps-valls Image Processing Laboratory (IPL) Universitat de València

Climate models constitute an essential tool to understand our planet, as they implement the laws of physics describing the ocean, land and atmosphere dynamics. However, resolving processes at fine resolutions constitute an important computational botleneck. Parameterization(or closure) approximates such(e.g. turbulent) processes that cannot be resolved in climate models.

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Soil and vegetation water content identify the main terrestrial ecosystem changes

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National Science Review 2023年第5期10卷 167-179页

作者： Diego Bueso Maria Piles Philippe Ciais Jean-Pierre Wigneron álvaro Moreno-Martínez gustau camps-valls Image Processing Laboratory Universitat de València Laboratoire des Sciences du Climat et de l'Environnement Université Paris Saclay INRA Centre de Bordeaux Aquitaine Image Processing Laboratory Universitat deValència

Environmental change is a consequence of many interrelated factors.How vegetation responds to natural and human activity still needs to be well establishe d,quantified and understood.Re cent satellite missions providing hydrologic and ecological indicators enable better monitoring of Earth system changes,yet there is no automatic way to address this issue directly from observations.Here,we develop an observation-based methodology to capture evidence of changes in global terrestrial ecosystems and attribute these changes to natural or anthropogenic activity.We use the longest time record of global microwave L-band soil moisture and vegetation optical depth as satellite data and build spatially explicit maps of change in soil and vegetation water content and biomass reflecting large ecosystem changes during the last decade,2010-20.Regions of prominent trends(from-8% to 9% per year) are observed,especially in humid and semi-arid climates.We further combine such trends with land cover change maps,vegetation greenness and precipitation variability to assess their relationship with major documented ecosystem changes.Several regions emerge from our results.They cluster changes according to human activity drivers,including deforestation(Amazon,Central Africa) and wildfires(East Australia),artificial reforestation(South-East China),abandonment of farm fields(Central Russia) and climate shifts related to changes in precipitation variability(East Africa,North America and Central Argentina).Using the high sensitivity of soil and vegetation water content to ecosystem changes,microwave satellite observations enable us to quantify and attribute global vegetation responses to climate or anthropogenic activities as a direct measure of environmental changes and the mechanisms driving them.

关键词： soil moisture vegetation water content ecosystem trend remote sensing soil moisture and ocean salinity satellite

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A perspective on Gaussian processes for Earth observation

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National Science Review 2019年第4期6卷 616-618页

作者： gustau camps-valls Dino Sejdinovic Jakob Runge Markus Reichstein Image Processing Laboratory University of Valencia Department of Statistics University of Oxford German Aerospace Center Institute of Data Science Max Planck Institute for Biogeochemistry

INTRODUCTION Earth observation (EO) by airborne and satellite remote sensing and in situ observations plays a fundamental role in monitoring our planet. In the last decade, machine learning has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. Gaussian processes (GPs)[1], as flexible

关键词： Earth Gaussian A perspective on Gaussian processes for Earth observation

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