Positive water vapour feedback in climate models confirmed by satellite data

Nature ◽  
1991 ◽  
Vol 349 (6309) ◽  
pp. 500-503 ◽  
Author(s):  
D. Rind ◽  
E.-W. Chiou ◽  
W. Chu ◽  
J. Larsen ◽  
S. Oltmans ◽  
...  
1993 ◽  
Vol 46 (1) ◽  
pp. 149 ◽  
Author(s):  
Graeme L Stephens ◽  
Stephen A Tjemkes

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.


Author(s):  
J. David Neelin ◽  
Ole Peters ◽  
Johnny W.-B Lin ◽  
Katrina Hales ◽  
Christopher E Holloway

Convective quasi-equilibrium (QE) has for several decades stood as a key postulate for parametrization of the impacts of moist convection at small scales upon the large-scale flow. Departures from QE have motivated stochastic convective parametrization, which in its early stages may be viewed as a sensitivity study. Introducing plausible stochastic terms to modify the existing convective parametrizations can have substantial impact, but, as for so many aspects of convective parametrization, the results are sensitive to details of the assumed processes. We present observational results aimed at helping to constrain convection schemes, with implications for each of conventional, stochastic or ‘superparametrization’ schemes. The original vision of QE due to Arakawa fares well as a leading approximation, but with a number of updates. Some, like the imperfect connection between the boundary layer and the free troposphere, and the importance of free-tropospheric moisture to buoyancy, are quantitatively important but lie within the framework of ensemble-average convection slaved to the large scale. Observations of critical phenomena associated with a continuous phase transition for precipitation as a function of water vapour and temperature suggest a more substantial revision. While the system's attraction to the critical point is predicted by QE, several fundamental properties of the transition, including high precipitation variance in the critical region, need to be added to the theory. Long-range correlations imply that this variance does not reduce quickly under spatial averaging; scaling associated with this spatial averaging has potential implications for superparametrization. Long tails of the distribution of water vapour create relatively frequent excursions above criticality with associated strong precipitation events.


Author(s):  
Houaria Namaoui ◽  
Salem Kahlouche ◽  
Ahmed Hafidh Belbachir

Remote sensing of atmospheric water vapour using GNSS and Satellite data has become an efficient tool in meteorology and climate research. Many satellite data have been increasingly used to measure the content of water vapour in the atmosphere and to characterize its temporal and spatial variations. In this paper, we have used observations from radiosonde data collected from three stations (Algiers, Bechar and Tamanrasset) in Algeria from January to December 2012 to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water vapour (PWV) products. Results show strong agreement between the total precipitable water contents estimated based on radiosondes observations and the ones measured by the sensor MODIS with the correlation coefficients in the range 0.69 to 0.95 and a mean bias, which does not exceed 1.5.  


Cirrus ◽  
2002 ◽  
Author(s):  
Patrick Minnis

The determination of cirrus properties over large spatial and temporal scales will, in most instances, require the use of satellite data. Global coverage at resolutions as fine as several meters are attainable with instruments on Landsat, and temporal coverage at 1-min intervals is now available with the latest Geostationary Operational Environmental Satellite (GOES) imagers. Extracting information about cirrus clouds from these satellite data sets is often difficult because of variations in background, similarities to other cloud types, and the frequently semitransparent nature of cirrus clouds. From the surface, cirrus clouds are readily discerned by the human observer via the patterns of scattered visible radiation from the sun, moon, and stars. The relatively uniform background presented by the sky facilitates cloud detection and the familiar textures, structures, and apparent altitude of cirrus distinguish it from other cloud types. From satellites, cirrus can also be detected from scattered visible radiation, but the demands of accurate identification for different surface backgrounds over the entire diurnal cycle and quantification of the cirrus properties require the analysis of radiances scattered or emitted over a wide range of the electromagnetic spectrum. Many of these spectra and high-resolution satellite data can be used to understand certain aspects of cirrus clouds in particular situations. Intensive study of well-measured cases can yield a wealth of information about cirrus properties on fine scales (e.g., Minnis et al. 1990; Westphal et al. 1996). Production of a global climatology of cirrus clouds, however, requires compromises in spatial, temporal, and spectral coverage (e.g., Schiffer and Rossow 1983). This chapter summarizes both the state of the art and the potential for future passive remote sensing systems to aid the understanding of cirrus processes and to acquire sufficient statistics for constraining and refining weather and climate models. Theoretically, many different aspects of cirrus can be determined from passive sensing systems. A limited number of quantities are the focus of most efforts to describe cirrus clouds. These include the areal coverage, top and base altitude or pressure, thickness, top and base temperatures, optical depth, effective particle size and shape, vertical ice water path, and size, shape and spacing of the cloud cells.


2018 ◽  
Vol 18 (11) ◽  
pp. 8331-8351 ◽  
Author(s):  
Stefan Lossow ◽  
Dale F. Hurst ◽  
Karen H. Rosenlof ◽  
Gabriele P. Stiller ◽  
Thomas von Clarmann ◽  
...  

Abstract. Trend estimates with different signs are reported in the literature for lower stratospheric water vapour considering the time period between the late 1980s and 2010. The NOAA (National Oceanic and Atmospheric Administration) frost point hygrometer (FPH) observations at Boulder (Colorado, 40.0° N, 105.2° W) indicate positive trends (about 0.1 to 0.45 ppmv decade−1). On the contrary, negative trends (approximately −0.2 to −0.1 ppmv decade−1) are derived from a merged zonal mean satellite data set for a latitude band around the Boulder latitude. Overall, the trend differences between the two data sets range from about 0.3 to 0.5 ppmv decade−1, depending on altitude. It has been proposed that a possible explanation for these discrepancies is a different temporal behaviour at Boulder and the zonal mean. In this work we investigate trend differences between Boulder and the zonal mean using primarily simulations from ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg/Modular Earth Submodel System) Atmospheric Chemistry (EMAC), WACCM (Whole Atmosphere Community Climate Model), CMAM (Canadian Middle Atmosphere Model) and CLaMS (Chemical Lagrangian Model of the Stratosphere). On shorter timescales we address this aspect also based on satellite observations from UARS/HALOE (Upper Atmosphere Research Satellite/Halogen Occultation Experiment), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) and Aura/MLS (Microwave Limb Sounder). Overall, both the simulations and observations exhibit trend differences between Boulder and the zonal mean. The differences are dependent on altitude and the time period considered. The model simulations indicate only small trend differences between Boulder and the zonal mean for the time period between the late 1980s and 2010. These are clearly not sufficient to explain the discrepancies between the trend estimates derived from the FPH observations and the merged zonal mean satellite data set. Unless the simulations underrepresent variability or the trend differences originate from smaller spatial and temporal scales than resolved by the model simulations, trends at Boulder for this time period should also be quite representative for the zonal mean and even other latitude bands. Trend differences for a decade of data are larger and need to be kept in mind when comparing results for Boulder and the zonal mean on this timescale. Beyond that, we find that the trend estimates for the time period between the late 1980s and 2010 also significantly differ among the simulations. They are larger than those derived from the merged satellite data set and smaller than the trend estimates derived from the FPH observations.


2018 ◽  
Author(s):  
Laura Thölix ◽  
Alexey Karpechko ◽  
Leif Backman ◽  
Rigel Kivi

Abstract. Stratospheric water vapor plays a key role in radiative and chemical processes, it e.g. influences the chemical ozone loss via controlling the polar stratospheric cloud formation in the polar stratosphere. The amount of water entering the stratosphere through the tropical tropopause differs substantially between chemistry-climate models. This is because the present-day models have difficulties in capturing the whole complexity of processes that control the water transport across the tropopause. As a result there are large differences in the stratospheric water vapour between the models. In this study we investigate the sensitivity of simulated Arctic ozone loss to the amount of water, which enters the stratosphere through the tropical tropopause. We used a chemical transport model, FinROSE-CTM, forced by ERA-Interim meteorology. The water vapour concentration in the tropical tropopause was varied between 0.5 and 1.6 times the concentration in ERA-Interim, which is similar to the range seen in chemistry climate models. The water vapour changes in the tropical tropopause led to about 1.5 and 2 ppm more water vapour in the Arctic polar vortex compared to the ERA-Interim, respectively. We found that the impact of water vapour changes on ozone loss in the Arctic polar vortex depend on the meteorological conditions. Polar stratospheric clouds form in the cold conditions within the Arctic vortex, and chlorine activation on their surface lead to ozone loss. If the cold conditions persist long enough (e.g. in 2010/11), the chlorine activation is nearly complete. In this case addition of water vapour to the stratosphere increased the formation of ICE clouds, but did not increase the chlorine activation and ozone destruction significantly. In the warm winter 2012/13 the impact of water vapour concentration on ozone loss was small, because the ozone loss was mainly NOx induced. In intermediately cold conditions, e.g. 2013/14, the effect of added water vapour was more prominent than in the other studied winters. The results show that the simulated water vapour concentration in the tropical tropopause has a significant impact on the Arctic ozone loss and deserves attention in order to improve future projections of ozone layer recovery.


2019 ◽  
Author(s):  
Øivind Hodnebrog ◽  
Gunnar Myhre ◽  
Bjørn H. Samset ◽  
Kari Alterskjær ◽  
Timothy Andrews ◽  
...  

Abstract. The relationship between changes in integrated water vapour (IWV) and precipitation can be characterized by quantifying changes in atmospheric water vapour lifetime. Precipitation isotope ratios correlate with this lifetime, a relationship that helps understand dynamical processes and may lead to improved climate projections. We investigate how water vapour and its lifetime respond to different drivers of climate change, such as greenhouse gases and aerosols. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. A lifetime increase from 8 to 10 days is projected between 1986–2005 and 2081–2100, under a business-as-usual pathway. By disentangling contributions from individual climate drivers, we present a physical understanding of how global warming slows down the hydrological cycle, due to longer lifetime, but still amplifies the cycle due to stronger precipitation/evaporation fluxes. The feedback response of IWV to surface temperature change differs somewhat between drivers. Fast responses amplify these differences and lead to net changes in IWV per degree surface warming ranging from 6.4±0.9 %/K for sulphate to 9.8±2 %/K for BC. While BC is the driver with the strongest increase in IWV per degree surface warming, it is also the only driver with a reduction in precipitation per degree surface warming. Consequently, increases in BC aerosol concentrations yield the strongest slowdown of the hydrological cycle among the climate drivers studied, with a change in water vapour lifetime per degree surface warming of 1.1±0.4 days/K, compared to less than 0.5 days/K for the other climate drivers (CO2, methane, solar irradiance, sulphate).


2020 ◽  
Author(s):  
Veronique Michot ◽  
Helene Brogniez ◽  
Mathieu Vrac ◽  
Soulivanh Thao ◽  
Helene Chepfer ◽  
...  

<p>The multi-scale interactions at the origin of the links between clouds and water vapour are essential for the Earth's energy balance and thus the climate, from local to global. Knowledge of the distribution and variability of water vapour in the troposphere is indeed a major issue for the understanding of the atmospheric water cycle. At present, these interactions are poorly known at regional and local scales, i.e. within 100km, and are therefore poorly represented in numerical climate models. This is why we have sought to predict cloud scale relative humidity profiles in the intertropical zone, using a non-parametric statistical downscaling method called quantile regression forest. The procedure includes co-located data from 3 satellites: CALIPSO lidar and CloudSat radar, used as predictors and providing cloud properties at 90m and 1.4km horizontal resolution respectively; SAPHIR data used as a predictor and providing relative humidity at an initial horizontal resolution of 10km. Quantile regression forests were used to predict relative humidity profiles at the CALIPSO and CloudSat scales. These predictions are able to reproduce a relative humidity variability consistent with the cloud profiles and are confirmed by values of coefficients of determination greater than 0.7, relative to observed relative humidity, and Continuous Rank Probability Skill Score between 0 and 1, relative to climatology. Lidar measurements from the NARVAL 1&2 campaigns and radiosondes from the EUREC4A campaigns were also used to compare Relative Humidity profiles at the SAPHIR scale and at the scale of forest regression prediction by quantile regression.</p>


1994 ◽  
Vol 18 (1) ◽  
pp. 1-15 ◽  
Author(s):  
David Greenland

Common types of satellite-derived measurements are reviewed with respect to how they are used to provide information on variables important to land surface climatology. The variables considered include solar radiation, surface albedo, surface temperature, outgoing longwave radiation, cloud cover, net radiation, soil moisture, latent and sensible heat flux, surface cover and leaf area index. A selection of land surface climate modelling schemes is identified and considered with a view to their practicality for use with satellite-derived data. Issues arising from the foregoing considerations include the absence from satellite data of some variables required by land surface climate models, the importance of extreme pixel values in model parameterization, the importance of matching spatial resolution in satellite data and climate model, and the need to have concurrent, independently observed, meteorological data in order to make full use of the satellite data.


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