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Characterization of vertical cloud variability over Europe using spatial lidar observations and regional simulation

Meriem Chakroun 1, * Sophie Bastin 1 Marjolaine Chiriaco 1 Hélène Chepfer 2
* Corresponding author
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
Abstract : In this paper we characterize the seasonal and inter-annual variabilities of cloud fraction profiles in both observations and simulation since they are critical to better assess the impact of clouds on climate variability. The spaceborne lidar onboard CALIPSO, providing cloud vertical profiles since 2006, is used together with a 23-year WRF simulation at 20 km resolution. A lidar simulator helps to compare consistently model with observations. The bias in observations due to the satellite under-sampling is first estimated. Then we examine the vertical variability of both occurrence and properties of clouds. It results that observations indicate a similar occurrence of low and high clouds over continent, and more high than low clouds over the sea except in summer. The simulation shows an overestimate (underestimate) of high (low) clouds comparing to observations, especially in summer. However the seasonal variability of cloud vertical profiles is well captured by WRF. Concerning inter-annual variability, observations show that in winter, those of high clouds is twice the low clouds one, an order of magnitude that is is well simulated. In summer, the observed inter-annual variability is vertically more homogeneous while the model still simulates more variability for high clouds than for low clouds. The good behavior of the simulation in winter allows us to use the 23 years of simulation and 8 years of observations to estimate the time period required to characterize the natural variability of the cloud fraction profile in winter, i.e. the time period required to detect significant anomalies and trends.
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Meriem Chakroun, Sophie Bastin, Marjolaine Chiriaco, Hélène Chepfer. Characterization of vertical cloud variability over Europe using spatial lidar observations and regional simulation. Climate Dynamics, Springer Verlag, 2018, 51 (3), pp.813-835. ⟨10.1007/s00382-016-3037-3⟩. ⟨insu-01282729⟩



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