scholarly journals Water Vapour and Its Role in the Earth's Greenhouse

1993 ◽  
Vol 46 (1) ◽  
pp. 149 ◽  
Author(s):  
Graeme L Stephens ◽  
Stephen A Tjemkes
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Surface Temperature ◽  
Water Vapour ◽  
Satellite Data ◽  
Greenhouse Effect ◽  
Satellite Observations ◽  
Sea Surface ◽  
Water Vapour Feedback

This paper examines the role of water vapour as a greenhouse gas and discusses its role in the evolution of the atmospheres of Venus, Earth and Mars. The paper focuses on how the greenhouse effect operates on Earth and describes the feedback between temperature and water vapour that is thqught to play a key role in global warming induced by increasing concentrations of carbon dioxide. A method for analysing the contribution of water vapour to the greenhouse effect using satellite observations is discussed. It is shown how this contribution varies in a directly proportional way with the amount of water vapour vertically integrated through the column of the atmosphere. Based on the results obtained from the analyses of satellite data, it is established that the sensitivity of the greenhouse effect to changing sea surface temperature is not uniform over the globe and is significantly greater over warmer oceans. The relevance of the results to the water vapour feedback is discussed.

A convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations (DINCAE)

2020 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Estimates ◽  
Satellite Data ◽  
Cross Validation ◽  
Error Variance ◽  
Satellite Observations ◽  
Sea Surface

<p>A method to reconstruct missing data in satellite data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. However, it is unclear how to handle missing data (or data with variable accuracy) in a neural network when using incomplete satellite data in the training phase. The present work shows a consistent approach which uses essentially the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The approach is motivated by the way models and observations are combined in the frame of data assimilation. The neural network is trained by maximizing the likelihood of the observed value. The corresponding error variances are estimated during training and do not need to be known a priori. The approach, called DINCAE (Data-Interpolating Convolutional Auto-Encoder) is applied to a relatively long time-series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data based on an EOF decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error, while showing higher variability than the DINEOF reconstruction. The resulting error estimates are also validated using the cross-validation data and they follow closely the expected Gaussian distribution.</p>

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2019 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
World Ocean ◽  
Error Variance ◽  
Empirical Orthogonal Functions ◽  
Satellite Observations ◽  
Sea Surface

Abstract. A method to reconstruct missing data in satellite data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. However, it is unclear how to handle missing data (or data with variable accuracy) in a neural network when using incomplete satellite data in the training phase. The present work shows a consistent approach which uses essentially the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing likelihood of the observed value. The approach, called DINCAE (Data-Interpolating Convolutional Auto-Encoder) is applied to a relatively long time-series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data based on an EOF decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error, while showing higher variability than the DINEOF reconstruction.

Role of the Water Vapour Greenhouse Effect in the Forecasting Of Fog Occurrence

2003 ◽  
Vol 107 (2) ◽  
pp. 469-482 ◽  
Author(s):  
P. K. Pasricha ◽  
B. S. Gera ◽  
S. Shastri ◽  
H. K. Maini ◽  
T. John ◽  
...  
Keyword(s):  
Water Vapour ◽  
Greenhouse Effect

scholarly journals Role of sea surface temperature responses in simulation of the climatic effect of mineral dust aerosol

2011 ◽  
Vol 11 (12) ◽  
pp. 6049-6062 ◽  
Author(s):  
X. Yue ◽  
H. Liao ◽  
H. J. Wang ◽  
S. L. Li ◽  
J. P. Tang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Radiative Forcing ◽  
Mineral Dust ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climatic Effect ◽  
Radiative Effects ◽  
Climate Simulations

Abstract. Mineral dust aerosol can be transported over the nearby oceans and influence the energy balance at the sea surface. The role of dust-induced sea surface temperature (SST) responses in simulations of the climatic effect of dust is examined by using a general circulation model with online simulation of mineral dust and a coupled mixed-layer ocean model. Both the longwave and shortwave radiative effects of mineral dust aerosol are considered in climate simulations. The SST responses are found to be very influential on simulated dust-induced climate change, especially when climate simulations consider the two-way dust-climate coupling to account for the feedbacks. With prescribed SSTs and dust concentrations, we obtain an increase of 0.02 K in the global and annual mean surface air temperature (SAT) in response to dust radiative effects. In contrast, when SSTs are allowed to respond to radiative forcing of dust in the presence of the dust cycle-climate interactions, we obtain a global and annual mean cooling of 0.09 K in SAT by dust. The extra cooling simulated with the SST responses can be attributed to the following two factors: (1) The negative net (shortwave plus longwave) radiative forcing of dust at the surface reduces SST, which decreases latent heat fluxes and upward transport of water vapor, resulting in less warming in the atmosphere; (2) The positive feedback between SST responses and dust cycle. The dust-induced reductions in SST lead to reductions in precipitation (or wet deposition of dust) and hence increase the global burden of small dust particles. These small particles have strong scattering effects, which enhance the dust cooling at the surface and further reduce SSTs.

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