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The coupling between soil, vegetation and atmosphere is thought to be crucial in the development and intensification of weather extremes, especially meteorological droughts, heat waves and severe storms. Therefore, understanding the evolution of the atmospheric boundary layer (ABL) and the role of land–atmosphere feedbacks is necessary for earlier warnings, better climate projection and timely societal adaptation. However, this understanding is hampered by the difficulties of attributing cause–effect relationships from complex coupled models and the irregular space–time distribution of in situ observations of the land–atmosphere system. As such, there is a need for simple deterministic appraisals that systematically discriminate land–atmosphere interactions from observed weather phenomena over large domains and climatological time spans. Here, we present a new interactive data platform to study the behavior of the ABL and land–atmosphere interactions based on worldwide weather balloon soundings and an ABL model. This software tool – referred to as CLASS4GL (http://class4gl.eu , last access: 27 May 2018) – is developed with the objectives of (a) mining appropriate global observational data from ∼15 million weather balloon soundings since 1981 and combining them with satellite and reanalysis data and (b) constraining and initializing a numerical model of the daytime evolution of the ABL that serves as a tool to interpret these observations mechanistically and deterministically. As a result, it fully automizes extensive global model experiments to assess the effects of land and atmospheric conditions on the ABL evolution as observed in different climate regions around the world. The suitability of the set of observations, model formulations and global parameters employed by CLASS4GL is extensively validated. In most cases, the framework is able to realistically reproduce the observed daytime response of the mixed-layer height, potential temperature and specific humidity from the balloon soundings. In this extensive global validation exercise, a bias of 10.1 m h -1 , -0.036 K h -1 and 0.06 g kg -1 h -1 is found for the morning-to-afternoon evolution of the mixed-layer height, potential temperature and specific humidity. The virtual tool is in continuous development and aims to foster a better process understanding of the drivers of the ABL evolution and their global distribution, particularly during the onset and amplification of weather extremes. Finally, it can also be used to scrutinize the representation of land–atmosphere feedbacks and ABL dynamics in Earth system models, numerical weather prediction models, atmospheric reanalysis and satellite retrievals, with the ultimate goal of improving local climate projections, providing earlier warning of extreme weather and fostering a more effective development of climate adaptation strategies. The tool can be easily downloaded via http://class4gl.eu (last access: 27 May 2018) and is open source. [ABSTRACT FROM AUTHOR]

The magnitude and sign of soil moisture--precipitation coupling (SMPC) is investigated using a probability-based approach and 10 years of daily microwave satellite data across North Africa at a 1° horizontal scale. Specifically, the co-existence and co-variability of spatial (i.e. using soil moisture gradients) and temporal (i.e. using soil moisture anomaly) soil moisture effects on afternoon rainfall is explored. The analysis shows that in the semi-arid environment of the Sahel, the negative spatial and the negative temporal coupling relationships do not only co-exist, but are also dependent on one another. Hence, if afternoon rain falls over temporally drier soils, it is likely to be surrounded by a wetter environment. Two regions are identified as SMPC "hot spots". These are the south-western part of the domain (7-15° N, 10°W-7° E), with the most robust negative SMPC signal, and the South Sudanese region (5-13° N, 24-34° E). The sign and significance of the coupling in the latter region is found to be largely modulated by the presence of wetlands and is susceptible to the number of long-lived propagating convective systems. The presence of wetlands and an irrigated land area is found to account for about 30% of strong and significant spatial SMPC in the North African domain. This study provides the first insight into regional variability of SMPC in North Africa, and supports the potential relevance of mechanisms associated with enhanced sensible heat flux and mesoscale variability in surface soil moisture for deep convection development. [ABSTRACT FROM AUTHOR]

The magnitude and sign of soil moisture - precipitation coupling (SMPC) is investigated using a probability-based approach and 10 years of daily microwave satellite data across North Africa at 1° horizontal resolution. Specifically, the co-existence and co-variability of spatial (i.e. using soil moisture gradients) and temporal (i.e. using soil moisture anomaly) soil moisture effects on afternoon rainfall is studied at 100 km scale. The analysis shows that in the semi-arid environment of the Sahel, the negative spatial and the negative temporal coupling relationships do not only co-exist, but are also dependent of one another. Hence, if afternoon rain falls over temporally drier soils, it is likely to be surrounded by a wetter environment. Two regions are identified as SMPC "hot spots". These are the south-western part of the domain (7-15° N, 10° W-7° E) with the most robust negative SMPC signal, and the South Sudanian region (5-13° N, 24-34° E). The sign and significance of the coupling in the latter region is found to be largely modulated by the presence of wetlands and is susceptible to the amount of long-lived propagating convective systems. The presence of wetlands and irrigated land areas is found to account for about 30 % of strong and significant spatial SMPC in North African domain. This study provides the first insight into regional variability of SMPC in North Africa, and supports potential relevance of mechanisms associated with enhanced sensible heat flux and meso-scale variability in surface soil moisture for deep convection development. [ABSTRACT FROM AUTHOR]

The coupling between soil, vegetation and atmosphere is thought to be crucial in the development and intensification of weather extremes, especially meteorological droughts, heat waves and severe storms. Therefore, understanding evolution of the atmospheric boundary layer (ABL) and the role of land–atmosphere feedbacks in that behaviour is necessary for earlier warnings, better climate projection and timely societal adaptation. However, this understanding is hampered by (a) the difficulties to attribute cause–effect relationships from complex coupled models, and (b) the irregular space-time distribution of in situ observations of the land–atmosphere system. As such, there is a need for simple deterministic appraisals that systematically discriminate land–atmosphere interactions from observed weather phenomena over large domains and climatological time spans. Here, we present a new interactive data platform to study the behaviour the ABL and land–atmosphere interactions based on global weather balloon soundings. This software tool, referred to as CLASS4GL, is developed with the objectives to (a) mine appropriate global observational data from over 2 million weather balloon soundings since 1981 together with satellite and reanalysis data into a single database, and (b) constrain and initialize a numerical model of the daytime evolution of the ABL that serves as a tool to interpret these observations mechanistically and deterministically. The suitability of the existing observations, model formulations, and surface parameter databases employed by CLASS4GL is extensively validated. In most cases, the framework is able to realistically reproduce the observed daytime response of the ABL height, potential temperature and specific humidity from the balloon soundings. The virtual tool is open-source and remains in continuous development. The software infrastructure allows the research community to easily employ extensive global sensitivity experiments for addressing the effect of land conditions and atmosphere dynamics on the ABL evolution. As such, it should foster a better process-understanding of the drivers of the ABL evolution and its global and climatological distribution, including the onset and amplification of weather extremes. Finally, it can be used to study the representation of land–atmosphere feedbacks and ABL dynamics in Earth System Models, reanalysis data and remote sensing products, with the ultimate goal to improve local climate projections, to provide earlier warnings of extreme weather, and to foster a more effective development of climate adaptation strategies. [ABSTRACT FROM AUTHOR]

Drought is arguably the climate and weather phenomenon that most strongly impacts societies worldwide, causing severe socioeconomic and ecologic damage. While models unanimously project an overall increase in aridity and drought occurrence in the future, observational evidence has so far been inconclusive. The discrepancies between the various drought definitions and drought indices has been a major factor contributing to our low confidence in observed global trends. In this study we investigate global trends in meteorological dry spells using a simple and intuitive diagnostic: the maximum annual number of consecutive dry days (CDDs). In contrast to popular drought indices, the number of CDDs is a direct measure of rainfall scarcity, easy to quantify based on rain data, free of parametrizations, and independent from other proxies. Previous analyses of trends in CDDs have been based on in situ rain gauges. While they may cover relatively long periods, they still suffer from spatial and temporal discontinuity, changes in accuracy in time, and various other quality-control issues. Because the time-span of available precipitation data records is constantly increasing, current satellite-based products are becoming an alternative to obtain spatially and temporally consistent global trends in CDDs. In particular, the Tropical Rainfall Measuring Mission (TRMM) has now been operational for over twenty years, thus offering a unique opportunity to analyze temporally-coherent single-platform precipitation data. Here, we use TRMM3B42 3-hourly data for 1998–2018, gap-filled with CMORPH-v1.0, to calculate and analyze the maximum annual number of CDDs worldwide. Preliminary results show that 30-–40% of the continental land monitored by TRMM experiences significant positive trends in the maximum annual number of CDDs. In depth analysis reveals that about 60% of this signal lies in regions that are affected by seasonal monsoons, and originates at most during the spring dry season. Further analyses relate the trends in CDDs to the intensity of monsoonal circulation and rainfall, and the role of land–atmospheric feedbacks. Our findings emphasize the vulnerability of global monsoon regions, where agriculture is mostly rainfed and population relies on rainfall availability and variability. An increasing length of dry spells as we progress into the future might lead to devastating socioeconomic and ecologic consequences in these regions. [ABSTRACT FROM AUTHOR]