Biological invasions are one of the main drivers of biodiversity losses. As threats from 39 biological invasions increase, one of the most urgent tasks is to identify areas of high 40 vulnerability. However, the lack of comprehensive information on the impacts of invasive 41 alien species (IAS) is especially a problem on islands, where most recorded extinctions 42 associated with IAS have occurred. Here we provide a global, network-oriented analysis of 43 IAS on islands. Using network analysis, we structured 27,081 islands and 437 threatened 44 vertebrates into 21 clusters, based on their profiles in term of invasiveness and shared 45 vulnerabilities. These islands are mainly located in the southern hemisphere and many are in 46 biodiversity hotspots. Some of them share similar characteristics regarding their connectivity 47 that could be useful in understanding their response to invasive species. The major invaders 48 found in these clusters of islands are feral cat, feral dog, pigs and rats. Our analyses reveal 49 those IAS that systematically act alone or in combination, and the pattern of shared IAS 50 among threatened species, providing new information to implement effective eradication 51 strategies. Combined with further local, contextual information this can contribute to global 52 strategies to deal with IAS. Islands harbour a significant portion of the Earth's species and have an unusually high rate of 64 endemism 1 . However, many species on islands are now recorded as threatened, and most 65 recorded extinctions of vertebrate species have occurred on islands 2 . Invasive alien species 66 (IAS) are considered the second most important driver of species extinctions on islands, and 67 are associated with nearly 25% of birds and amphibians currently threatened worldwide 3 . 68Island ecosystems are particularly vulnerable to biological invasions 2 . To date, there have 69 been over 700 attempted eradications of invasive alien mammals 4 , which have benefited to 70 600 local populations, leading to larger populations or increased distributional areas 4 . 71Specifically, 236 species have benefited from those eradication programs, including 62 72 species that are at risk of extinction, and four species had their extinction risk reduced as a 73 direct result of these eradications 4 . Despite these encouraging results, the threat posed by 74 invasive alien species (IAS) remains significant and widespread for native species 3 . Thus, 75 prioritization of research efforts and eradication strategies needs to be more effective 5 and 76 there are many more opportunities to decrease extinction risk for island species by eradicating 77 IAS. 78Because funding allocated to conservation is limited, it is important that these interventions 79 target islands where the conservation benefit will be highest. Efforts to prioritize research, 80 management, and policy for IAS have traditionally relied on expert judgments, and have been 81 limited to either single IAS 6 or subsets of islands 7,8 . In the absence of a systema...

Abstract:The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60 • N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m −2 , IWP > 0.24 kg·m −2 over land, and SIC > 57%, TPW > 5.1 kg·m −2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms.

In this study, we provide an insight to the role of deep convection (DC) and the warm conveyor belt (WCB) as leading processes to Mediterranean cyclones' heavy rainfall. To this end, we use reanalysis data, lighting and satellite observations to quantify the relative contribution of DC and the WCB to cyclone rainfall, as well as to analyse the spatial and temporal variability of these processes with respect to the cyclone centre and life cycle. Results for the period 2005-2015 show that the relationship between cyclone rainfall and intensity has high variability and demonstrate that even intense cyclones may produce low rainfall amounts. However, when considering rainfall averages for cyclone intensity bins, a linear relationship was found. We focus on the 500 most intense tracked cyclones (responsible for about 40-50% of the total 11-year Mediterranean rainfall) and distinguish between the ones producing high and low rainfall amounts. DC and the WCB are found to be the main cause of rainfall for the former (producing up to 70% of cyclone rainfall), while, for the latter, DC and the WCB play a secondary role (producing up to 50% of rainfall). Further analysis showed that rainfall due to DC tends to occur close to the cyclones' centre and to their eastern sides, while the WCBs tend to produce rainfall towards the northeast. In fact, about 30% of rainfall produced by DC overlaps with rainfall produced by WCBs but this represents only about 8% of rainfall produced by WCBs. This suggests that a considerable percentage of DC is associated with embedded convection in WCBs. Finally, DC was found to be able to produce higher rain rates than WCBs, exceeding 50 mm in 3-hourly accumulated rainfall compared to a maximum of the order of 40 mm for WCBs. Our results demonstrate in a climatological framework the relationship between cyclone intensity and processes that lead to heavy rainfall, one of the most prominent environmental risks in the Mediterranean. Therefore, we set perspectives for a deeper analysis of the favourable atmospheric conditions that yield high impact weather.

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10 −3 kg·m −2 ) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m −2 . Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70 • S-70 • N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

The Mediterranean basin occasionally hosts tropical‐like cyclones named “Medicanes”. Medicanes may have intensity comparable to hurricanes in terms of wind speeds along with an axisymmetric cloud structure. Although these events can be particularly violent, very few studies so far have investigated the distribution and temporal evolution of deep convection within these cyclones. In this study, the characteristics and lifetime of deep convection and lightning activity surrounding the core of the longest‐lasting and probably the most intense Medicane ever recorded in terms of wind speed (Rolf, November 2011) are presented by all available means of microwave and infrared satellite retrievals and a lightning detection system. Results showed that deep convective clouds penetrated the lowest stratosphere and were wrapped around the cyclone centre during the intensification period. Lightning activity was mostly active about a day before the maximum strength of the cyclone studied and it was not temporarily correlated with the most intense deep convection activity. Overall, this study reveals that spatial and temporal distribution of deep convection and lightning activity around the centre of Rolf show more similarities with Tropical Cyclones than intense Mediterranean cyclones.

In this study, we present a new module for the Snow retrievaL ALgorithm fOr gMi (SLALOM) that retrieves surface snowfall rate using Global Precipitation Measurement (GPM) Microwave Imager measurements together with humidity and temperature vertical profiles. This module, named Surface Snowfall Rate Module, is tuned using colocated surface snowfall observations of the Cloud Profiling Radar onboard CloudSat. Using this new module, the SLALOM algorithm is able to predict surface snowfall rate with a relative bias of −13%, a root‐mean‐square error of 0.08 mm/hr, and a correlation coefficient of 0.7. Surface Snowfall Rate Module is then used to retrieve snowfall rate for three case studies and to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation, and climatological analyses.

This study evaluates the potential use of the Microwave Humidity Sounder (MHS) for snowfall detection in the Arctic. Using two years of colocated MHS and CloudSat observations, we develop an algorithm that is able to detect up to 90% of the most intense snowfall events (snow water path ≥400 g m−2 and 50% of the weak snowfall rate events (snow water path ≤50 g m−2. The brightness temperatures at 190.3 GHz and 183.3 ± 3 GHz, the integrated water vapor, and the temperature at 2 m are identified as the most important variables for snowfall detection. The algorithm tends to underestimate the snowfall occurrence over Greenland and mountainous areas (by as much as −30%), likely due to the dryness of these areas, and to overestimate the snowfall occurrence over the northern part of the Atlantic (by up to 30%), likely due to the occurrence of mixed phase precipitation. An interpretation of the selection of the variables and their importance provides a better understanding of the snowfall detection algorithm. This work lays the foundation for the development of a snowfall rate quantification algorithm.

This study aims at understanding how deep convection is organized and contributes to the intensification of nine Mediterranean tropical-like cyclones which developed between 2005 and 2018. Through a multi-satellite approach, a combination of infrared and microwave diagnostics provides insights into the temporal and spatial evolution of deep convection. ERA5 reanalysis complements the remote-sensing observations and is used to compute the vertical wind shear and vortex tilt to investigate their interactions with deep convection. Results show that vertical wind shear and topography have an important impact on the organization of deep convection and the symmetry of the cyclones. Only a fraction of these cyclones experienced intense convective activity close to their centres and we show that persistent deep convection in the upshear quadrants led to intensification periods. Convective activity solely in the downshear quadrants was not linked to intensification periods, while short-lived hurricane-like structures develop only during symmetric convective activity, leading to cyclone intensification in some of the cases. Finally, a classification of the Mediterranean tropical-like cyclones is proposed based on the evolution of deep convection and their intensification periods.