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Abstract
Mesoscale water vapor heterogeneities in the boundary layer are studied within the context of the International H2O Project (IHOP_2002). A significant portion of the water vapor variability in the IHOP_2002 occurs at the mesoscale, with the spatial pattern and the magnitude of the variability changing from day to day. On 14 June 2002, an atypical mesoscale gradient is observed, which is the reverse of the climatological gradient over this area. The factors causing this water vapor variability are investigated using complementary platforms (e.g., aircraft, satellite, and in situ) and models. The impact of surface flux heterogeneities and atmospheric variability are evaluated separately using a 1D boundary layer model, which uses surface fluxes from the High-Resolution Land Data Assimilation System (HRLDAS) and early-morning atmospheric temperature and moisture profiles from a mesoscale model. This methodology, based on the use of robust modeling components, allows the authors to tackle the question of the nature of the observed mesoscale variability. The impact of horizontal advection is inferred from a careful analysis of available observations. By isolating the individual contributions to mesoscale water vapor variability, it is shown that the observed moisture variability cannot be explained by a single process, but rather involves a combination of different factors: the boundary layer height, which is strongly controlled by the surface buoyancy flux, the surface latent heat flux, the early-morning heterogeneity of the atmosphere, horizontal advection, and the radiative impact of clouds.
Abstract
Mesoscale water vapor heterogeneities in the boundary layer are studied within the context of the International H2O Project (IHOP_2002). A significant portion of the water vapor variability in the IHOP_2002 occurs at the mesoscale, with the spatial pattern and the magnitude of the variability changing from day to day. On 14 June 2002, an atypical mesoscale gradient is observed, which is the reverse of the climatological gradient over this area. The factors causing this water vapor variability are investigated using complementary platforms (e.g., aircraft, satellite, and in situ) and models. The impact of surface flux heterogeneities and atmospheric variability are evaluated separately using a 1D boundary layer model, which uses surface fluxes from the High-Resolution Land Data Assimilation System (HRLDAS) and early-morning atmospheric temperature and moisture profiles from a mesoscale model. This methodology, based on the use of robust modeling components, allows the authors to tackle the question of the nature of the observed mesoscale variability. The impact of horizontal advection is inferred from a careful analysis of available observations. By isolating the individual contributions to mesoscale water vapor variability, it is shown that the observed moisture variability cannot be explained by a single process, but rather involves a combination of different factors: the boundary layer height, which is strongly controlled by the surface buoyancy flux, the surface latent heat flux, the early-morning heterogeneity of the atmosphere, horizontal advection, and the radiative impact of clouds.
Abstract
In the Sahel very high temperatures prevail in spring, but little is known about heat waves in this region at that time of year. This study documents Sahelian heat waves with a new methodology that allows selecting heat waves at specific spatiotemporal scales and can be used in other parts of the world. It is applied separately to daily maximum and minimum temperatures, as they lead to the identification of distinct events. Synoptic–intraseasonal Sahelian heat waves are characterized from March to July over the period 1950–2012 with the Berkeley Earth Surface Temperature (BEST) gridded dataset. Morphological and temperature-related characteristics of the selected heat waves are presented. From March to July, the further into the season, the shorter and the less frequent the heat waves become. From 1950 to 2012, these synoptic–intraseasonal heat waves do not tend to be more frequent; however, they become warmer, and this trend follows the Sahelian climatic trend. Compared to other commonly used indices, the present index tends to select heat waves with more uniform intensities. This comparison of indices also underlined the importance of the heat index definition on the estimated climatic heat wave trends in a changing climate. Finally, heat waves were identified with data from three meteorological reanalyses: ERA-Interim, MERRA, and NCEP-2. The spreads in temperature variabilities, seasonal cycles, and trends among reanalyses lead to differences in the characteristics, interannual variability, and climatic trends of heat waves, with fewer departures from BEST for ERA-Interim.
Abstract
In the Sahel very high temperatures prevail in spring, but little is known about heat waves in this region at that time of year. This study documents Sahelian heat waves with a new methodology that allows selecting heat waves at specific spatiotemporal scales and can be used in other parts of the world. It is applied separately to daily maximum and minimum temperatures, as they lead to the identification of distinct events. Synoptic–intraseasonal Sahelian heat waves are characterized from March to July over the period 1950–2012 with the Berkeley Earth Surface Temperature (BEST) gridded dataset. Morphological and temperature-related characteristics of the selected heat waves are presented. From March to July, the further into the season, the shorter and the less frequent the heat waves become. From 1950 to 2012, these synoptic–intraseasonal heat waves do not tend to be more frequent; however, they become warmer, and this trend follows the Sahelian climatic trend. Compared to other commonly used indices, the present index tends to select heat waves with more uniform intensities. This comparison of indices also underlined the importance of the heat index definition on the estimated climatic heat wave trends in a changing climate. Finally, heat waves were identified with data from three meteorological reanalyses: ERA-Interim, MERRA, and NCEP-2. The spreads in temperature variabilities, seasonal cycles, and trends among reanalyses lead to differences in the characteristics, interannual variability, and climatic trends of heat waves, with fewer departures from BEST for ERA-Interim.
