Climate Modeling, Global Warming and Weather Prediction, Introduction to

2011 ◽  
pp. 66-67
Author(s):  
Hartmut Grassl
Keyword(s):  
Global Warming ◽  
Climate Modeling ◽  
Weather Prediction

Jean-François Geleyn, professeur à l'université de Gand

2021 ◽  
pp. 094
Author(s):  
Steven Caluwaerts ◽  
Daan Degrauwe ◽  
Piet Termonia
Keyword(s):  
Numerical Weather Prediction ◽  
Driving Forces ◽  
Climate Modeling ◽  
Weather Prediction ◽  
Academic Program ◽  
Atmospheric Physics ◽  
Numerical Weather ◽  
Strong Link ◽  
The Creation ◽  
Meteorological Institute

Jean-François Geleyn avait un lien fort avec la Belgique à travers les activités du consortium Aladin. Lorsque l'université de Gand a lancé un programme de troisième cycle sur la météorologie et la prévision numérique du temps en 2007, il est devenu professeur invité et l'un des principaux moteurs de la création de ce programme universitaire. Plus tard, ce programme a été étendu à la modélisation du climat. De nombreux experts de l'Institut royal météorologique de Belgique ont suivi ses cours. Récemment, les activités de l'université de Gand ont servi de base à la création de l'Unité de physique de l'atmosphère dans le Département de physique et d'astronomie. Jean-François Geleyn had a strong link with Belgium through the activities of the ALADIN consortium. When Ghent university started a postgraduate program on meteorology and numerical weather prediction in 2007 he became a guest professor and one of the main driving forces of the creation of this academic program. Later the curriculum of this program was extented to include climate modeling. Many experts at the Royal Meteorological Institute of Belgium followed his courses. Recently, the activities at Ghent university formed the basis for the creation of the Atmospheric Physics Unit within the Department of Physics and Astronomy.

Total Column Water Vapour Retrieval from S-5P/TROPOMI in the Visible Blue Spectral Range

2020 ◽  
Author(s):  
Christian Borger ◽  
Steffen Beirle ◽  
Steffen Dörner ◽  
Holger Sihler ◽  
Thomas Wagner
Keyword(s):  
Water Vapour ◽  
Spectral Range ◽  
Climate Modeling ◽  
Weather Prediction ◽  
A Priori ◽  
Data Sets ◽  
Vertical Column ◽  
Linearized Scheme ◽  
The Impact ◽  
Total Column

<div> <p>Atmospheric water plays a key role for the Earth’s energy budget and temperature distribution via radiative effects (clouds and vapour) and latent heat transport. Thus, the distribution and transport of water vapour are closely linked to atmospheric dynamics on different spatio-temporal scales. In this context, global monitoring of the water vapour distribution is essential for numerical weather prediction, climate modeling and a better understanding of climate feedbacks.</p> </div><div> <p>Here, we present a total column water vapour (TCWV) retrieval using the absorption structures of water vapour in the visible blue spectral range. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme. Then, the retrieved slant column densities are converted to vertical column densities (VCDs) using an iterative scheme for the water vapour a priori profile shape which is based on an empirical parameterization of the water vapour scale height.  </p> </div><div> <p>We apply this novel retrieval to measurements of the TROPOspheric Monitoring Instrument (TROPOMI) onboard ESA‘s Sentinel-5P satellite and compare our retrieved H<sub>2</sub>O VCDs to a variety of different reference data sets. Furthermore we present a detailed characterization of this retrieval including theoretical error estimations for different observation conditions. In addition we investigate the impact of different input data sets (e.g. surface albedo) on the retrieved H<sub>2</sub>O VCDs.  </p> </div>

scholarly journals Method-uncertainty is essential for reliable confidence statements of precipitation projections

2020 ◽  
pp. 1-45
Author(s):  
Peter Uhe ◽  
Dann Mitchell ◽  
Paul D. Bates ◽  
Myles R. Allen ◽  
Richard A. Betts ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Modeling ◽  
Full Range ◽  
Climate Impact ◽  
Climate Projections ◽  
Present Climate ◽  
Multiple Sources ◽  
Multi Method Approach ◽  
Precipitation Projections

AbstractPrecipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding ‘method-uncertainty’ is rarely explicitly calculated in climate impact studies and major reports, but can substantially change estimated precipitation changes. A comparison across five commonly-used modeling activities shows that for changes in mean precipitation, less than half the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities capture the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multi-method approach. Our analysis highlights the risk of over-reliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this ‘method-uncertainty’. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates compared to using single-method projections to make decisions.

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