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Precipitation at high latitudes and altitudes
In a warming climate, precipitation is expected to change not only in terms of du-ration, frequency and intensity (Trenberth et al., 2003; Stephens and Ellis, 2008; Behrangi et al., 2016), but also in terms of total precipitation amount (Trenberth et al., 2007; Behrangi et al., 2014, 2016). Under a scenario of rising greenhouse gas (GHG) emissions without a mitigation target, with an expected radiative forcing of 8.5 Wm 2 by the end of the 21st century (known as the representative concentra-tion pathway 8.5 scenario, RCP8.5) (IPCC, 2007; Riahi et al., 2011; Giorgi et al., 2014), most of the global precipitation model projections of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), show a progressive increase of annual precipitation over the current century, as a consequence of the increase of global surface evaporation due to the warming of the land and sea (Trenberth et al., 2007; Lau et al., 2013; Giorgi et al., 2019). At mid and high latitudes, the general pat-tern of precipitation is expected to increase signi cantly due to an increase in the moisture transported from the tropical troposphere to the poles under the RCP8.5 scenario, produced by a poleward shift of the storm track and the expansion of the Hadley cell, which in turn is caused by a maximum warming in the tropical region and a poleward shift in the location of the jet stream (IPCC, 2013; Giorgi et al., 2019).
Despite the general agreement among models on the increase in precipitation, they present a large uncertainty with a number of discrepancies on the regional and seasonal scales (Behrangi et al., 2016). Regarding the representation of the cur-rent state of global precipitation in models, large biases are present (Stephens et al., 2010), induced mainly by the large uncertainties in observational precipitation prod-ucts (Behrangi et al., 2012), especially in high-latitude and mountainous regions, where ground-based observations are scarce with poor spatial coverage (Adler et al., 2012). Moreover, as hydrology in high-altitude (e.g. Alps and Andes mountains during winter time) and high-latitude (e.g. Antarctica, Arctic) regions is dominated by precipitation in form of snow, the available gauge observations present signi – cant uncertainties, larger than 100% (Yang et al., 2005; Fuchs et al., 2001), due to di erent factors such as wind-induced under catch (Goodison et al., 1998), miss-detection of light snowfall and the presence of blowing snow events that spoil the measurements. Therefore, the accurate quanti cation of solid precipitation is key to understand its current state, in terms of amount and spatio/temporal distribution (Trenberth et al., 2007; Stephens et al., 2012; Behrangi et al., 2016), in order to evaluate the impacts on the hydrological cycle of high altitude and latitude regions (Andrews et al., 2009; Ye et al., 2014).
The phase of precipitation is also projected to change due to the increase in tem-perature, especially in mid-latitude mountainous regions, where a shortening of the period with temperatures below 0 C is expected, as well as a vertical shift of the limit between solid and liquid precipitation, leading to a reduction in the number of solid precipitation events (Diaz et al., 2003; IPCC, 2013; Wang et al., 2014). In the case of the Alpine regions, during the last decades the proportion between the number of snowfall days to the number of rainfall days has experienced a downward trend connected to increasing temperatures (Hantel and Hirtl-Wielke, 2007; Marty, 2008), with a stronger decrease in the lower elevation areas, close to the freezing level (Scherrer et al., 2004; Serquet et al., 2011). The change from solid to liquid precipitation leads to changes in the distribution of snow cover, mountain glacier areas (IPCC, 2013) and in freshwater discharge (Dyurgerov and Carter, 2006; Benis-ton and Sto el, 2016; Wurzer et al., 2016), favoring the reduction of the freshwater storage capacity and the increase of risks of winter and spring ooding (Knowles et al., 2001). These changes have serious implications in the regional hydrology, producing severe impacts at the socio-economic level (Fehlmann et al., 2018; Giorgi et al., 2019).
Concerning solid precipitation at high latitudes, Antarctica takes signi cant rel-evance. The Antarctic ice sheet is the single largest land water storage in the world (van Wessem et al., 2014, 2018), which may contribute in the long-term to an eu-static sea level change between 60 and 72 m, in the case that all southern polar ice melts (Drewry, 1992). Fortunately, such an extreme scenario is not happening in the near future, in fact IPCC (2013) reported no signi cant changes on surface melting on the Antarctic ice sheet during the current century, while snowfall is expected to increase, but with a low level of con dence. In general terms, this means that the surface mass balance (SMB, i.e. sum of precipitation, sublimation/evaporation, melt water, and blowing snow) of Antarctica is expected to increase (Palerme et al., 2017a), but with a strong dependence on the uncertainty associated with precipi-tation projections. As mentioned before, accurate observations of the current state of the precipitation are fundamental to the evaluation and validation of the cur-rent climate models and to determine the best future projections. This represents a great observational challenge in Antarctica, where the stations are scarce and scat-tered, moreover there is a reduced number of stations equipped with specialized instruments for monitoring precipitation in this region. Antarctica is a continent of di cult access and the environmental conditions are su ciently hostile to hinder the deployment of long-duration missions, thus, due to the lack of information and the di culties associated with its collection, there is a long way to go to understand the climatology of precipitation in this remote region of the world.
In the following subsections, more detailed information on precipitation, meth-ods of observation, previous investigations and major challenges in the context of Antarctica and the Alps is provided.
Antarctic context
The Antarctic continent is located south of the 60 S parallel, covering an area equivalent to 10% of the surface of the Earth. The Antarctic ice sheet (AIS) can be divided in three regions, the East Antarctic ice sheet (EAIS), West Antarctic ice sheet (WAIS) and the Antarctic Peninsula (see Figure 1). EAIS is the biggest region and it is dominated by the Antarctic Plateau, which reaches the highest average elevation in the continent, with altitudes higher than 4000 m above sea level and the coldest mean air temperatures in winter below -60 C (Bromwich and Stearns, 1993; King and Turner, 1997). AIS contains a volume of ice between 24 and 29 million km3 (Drewry, 1992) and during last decades it has contributed at a rate of 0.27 (0.16-0.38) mm yr{1 to the sea level rise, corresponding to 10% of the total sea level rise (i.e. sum of the contribution to sea level rise due to thermal expansion of the sea, glacier changes, Greenland ice sheet and other water storages) (IPCC, 2013).
Antarctica is a harsh environment for precipitation monitoring, due to the pres-ence of strong wind in the coastal regions and extremely cold temperature and very limited precipitation amount in the interior, and also to the remoteness and isolation that make operation on site expensive. In this environment, traditional snow gauges provide unreliable measurements (Turner et al., 1998; Genthon et al., 2003; Balsamo et al., 2015), hence the most commonly used monitoring methods have been carried out in an indirect way, such as snow pits and ice cores to estimate accumulation, which provided the rst approaches to obtain SMB maps in most of the Antarctic region (Vaughan et al., 1999). Although these local observations provide valuable information, they are strongly a ected by the wind-borne snow redistribution, due to the high frequency of moderate (e.g. Antarctic Plateau) to strong (e.g. Antarc-tic coast) winds, producing an underrepresentation of the spatial variability of the precipitation distribution (Braaten, 2000; King et al., 2004).
Traditionally, researchers have used coupled ocean-atmosphere climate models (e.g. models of CMIP5 and previous versions) and global reanalyses (e.g. the European Centre for Medium-range Weather Forecasts (ECMWF) Interim reanaly-sis (ERA Interim) and ERA-5, the National Centers for Environmental Prediction (NCEP), Climate Forecast System Reanalysis (CFSR)) to evaluate precipitation in Antarctica at regional scale (Genthon et al., 2009; Bromwich et al., 2011). However, these datasets contain di erent levels of uncertainty due the lack in observational data (Tang et al., 2018) and to the fact that certain microphysical properties of Antarctic precipitation are not well understood and represented (Listowski et al., 2019). The closest approach to obtain an observational product to evaluate precipi-tation in Antarctica, at the continental scale, is using satellite data. Genthon et al. (2003) analyzed the temporal variability of precipitation using reanalyses, models and the Global Precipitation Climatology Project (GPCP) monthly products, which combines passive microwaves and infrared data with gauge observations (Adler et al., 2012). GPCP products are known to have large errors in high latitude regions be-cause of the lack of in-situ observations and di culties of passive-microwave instru-ments to detect the complex scattering signal from falling snow. However, Genthon et al. (2003) found that GPCP satellite products are more reliable after 1988 and can be used to characterize Antarctic precipitation variability, using it in combination with other datasets.
Recently, new satellite missions have been launched with the objective of mon-itoring precipitation at large scale, using the advantage of active remote sensing to measure hydrometeor microwave scattering properties, such is the case of the Trop-ical Rainfall Measurement Mission (TRMM), the Global Precipitation Measuring (GPM) mission and the Cloud Satellite (CloudSat) mission (Tapiador, 2017; Wood, 2011). From these three di erent data sources of precipitation, only CloudSat can provide information in most of Antarctica, while TRMM and GPM only cover up to the latitudes 35 and 65 south, respectively. Palerme et al. (2014) performed the rst model-independent climatology of Antarctic precipitation using two snow-fall products derived from the Cloud Pro ling Radar (CPR) on board of CloudSat: 2C-PRECIP-COLUMN to assess precipitation phase (Haynes et al., 2009) and 2C-SNOW-PROFILE to obtain snowfall rate (Wood, 2011; Wood et al., 2013), covering most of the AIS (latitude < 82 S) with a spatial resolution of 1 of latitude by 2 of longitude. This new precipitation dataset has a great potential to evaluate current precipitation models as shown by Palerme et al. (2017a), but still presents a signif-icant uncertainty associated to the lack of in-situ observations and the assumptions about particle size distribution, microphysical and scattering properties of the hy-drometeors, used to establish the relationships between radar re ectivity factor (Z) and the snowfall rate (S). Additionally CloudSat observations near the surface are contaminated by ground clutter interference (1200 m closest to the surface), which leads to systematic errors with respect to ground precipitation, that are di cult to assess (Maahn et al., 2014).
PRECIPITATION IN HIGH LATITUDES AND ALTITUDES
Understanding cloud and precipitation properties is key to improve the satellite products and the representation of precipitation in regional and global models, in order to reduce biases in the current and future predictions (Gorodetskaya et al., 2015). Simulation of cloud cover is one of the main reasons of the large biases ob-served in the prediction of surface radiation derived from mesoscale high-resolution models (Bromwich et al., 2013), together with occurrence of supercooled liquid water (SLW) clouds (King et al., 2015; Listowski and Lachlan-Cope, 2017). It is funda-mental to understand the microphysical processes that govern the fraction of SLW with respect to ice particles within the mixed-phase clouds, in order to reduce sur-face radiative biases in high resolution models (Listowski et al., 2019). The presence of SLW in top of clouds is an important component in the solar radiation budget, representing between 27% and 38% of the total re ected solar radiation between the pallarel 40°and 70°S (Bodas-Salcedo et al., 2016). The monitoring of SLW bearing clouds takes great importance for the improvement of the cloud microphysics mod-elling in Antarctica. Lidar observations provide useful information to characterize cloud hydrometeors, based on light scattering properties (Listowski et al., 2019). In the case of lidar systems capable of polarization detection, it is possible to assess the phase and habit of the particles (Sassen, 1977). In this context, spaceborne lidar systems, as CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), on board of CALIPSO (Cloud-Aerosol Lidar and Infrared Path nder Satellite Observa-tion) represent a useful tool for cloud and precipitation monitoring (Stephens et al., 2002).Other optical instruments such as the Atmospheric Laser Doppler Instrument (Aladin), part of the resent Aeolus of the ESA (European Space Agency), and The ATmospheric LIDar (ATLID) on board of the coming mission EarthCARE (Earth Clouds, Aerosols and Radiation Explorer), represent important contributions to the research of cloud, aerosols and precipitation (Schillinger et al., 2003; Heliere et al., 2007, 2016).
The spaceborne products, derived from CloudSat and CALIPSO represent unique tools to study the vertical and horizontal structure of the precipitation in Antarctica, like none that has previously existed. However, both source of informa-tion present important limitations (Mace and Zhang, 2014) that have to be taken into account. The radar instrument on board of CloudSat cannot detect hydrome-teors in the lowest 1.2 km near the surface (Marchand et al., 2008; Maahn et al., 2014); small-scale clouds or precipitation processes may be potentially not captured by CPR due to the coarse spatial resolution (2 km in the track direction, 1 km in the cross-track and 480 m in the vertical); despite the high sensitivity of CPR (minimum detectable signal: -30 dBZ), very small non-precipitating liquid water clouds and optically thin and cold cirrus are not detected (Sassen et al., 2009; Mace and Zhang, 2014); in the case of CALIOP, it is strongly a ected by the attenuation of optically thick clouds and precipitation, losing the signal near the surface when it occurs. Both instruments presents problems due to power limitation, CPR can observed only during the daytime, since late 2009 a soft-short circuit in a power cell produced a lost of nighttime data for a full month and then the complete failure on the battery on April 2011 stopped all night observations (Nayak et al., 2012), which means that CPR cannot provide polar winter pro les (Souverijns et al., 2018b). On the other hand, the sensitivity of CALIOP is reduced during daylight (Mace and Zhang, 2014).
During the last years, long-term observations of precipitation and microphysical properties of clouds have been established to provide in-situ data for calibration and validation of models and satellite data, deploying di erent instruments in two sites in Antarctica. Observatories are deployed in Dumont d’Urville (DDU) station located on Adelie Land, and in Princess Elisabeth (PE) station located in Droming Maud Land, both in the EAIS. Observations at DDU are supported by the French project APRES3 (Antarctic Precipitation Remote Sensing from Surface, Grazioli et al. (2017a)) and and CALVA (Antartic eld data for CALibration and VAlida-tion of meteorological and climate models and satellite retrievals, Antarctic Coast to Dome C). The observations at PE ar supported by the Belgian project HY-DRANT (HYDRological cycle of ANTarctica, Gorodetskaya et al. (2015)) and its follow up AEROCLOUD project. Both sites were equipped with vertical-pointing K-band micro rain radars (MRR) that document the vertical structure of precip-itations. Moreover, a polarization lidar was implemented at DDU, that allowed a complementary observation of the composition of clouds.
The Alps are the most important mountain range located in Central Europe, with approximately 800 km of length, an average width of 200 km, and the highest el-evation located in the summit of the Mont Blanc, at 4810 meters above sea level (m a.s.l.) (Schar et al., 1998; Ravanel et al., 2013). In the valley areas, below 1000 m a.s.l., a tempered climate predominates, with short periods of snow falls in winter, which persist at the surface for a few weeks. In the Alpine areas, be-tween 1000 and 3000 m a.s.l., the seasonal snow cover predominates, starting from November with the rst snow falls, until the beginning of summer, when the snow cover has completely melted (Schar et al., 1998). Above the climatological snow line ( 3000 1100 m.a.s.l.), temperatures remain close to the freezing point and precipitation dominates in the form of snow throughout the year (Hantel et al., 2012).
Table of contents :
General Introduction
Precipitation in high latitudes and altitudes
Antarctic context
Alpine context
Objectives and outline
1 Instruments and datasets
1.1 Introduction
1.2 Radar remote sensing
1.2.1 Background
1.2.2 Scattering regime
1.2.3 Power and radar reectivity
1.2.4 Doppler radars
1.2.5 Estimation of snowfall with Ze S relationships
1.2.6 Micro rain radar (MRR)
1.3 Lidar remote sensing
1.3.1 Background
1.3.2 Elastic scattering
1.3.3 Lidar equation
1.3.4 Linear depolarization
1.4 Datasets
1.4.1 Sites
1.4.2 Instruments and data
2 Classification of cloud and precipitation
2.1 Introduction
2.2 Materials and Methods
2.2.1 Lidar
2.2.2 Micro Rain Radar
2.2.3 Radio soundings
2.3 Lidar Processing
2.3.1 Background correction
2.3.2 Signal-to-Noise ratio
2.3.3 Calibration parameters
2.4 Hydrometeor detection
2.5 Hydrometeor classification
2.6 Applications
2.6.1 Occurrence of clouds and SLW layers
2.6.2 SLW vertical distribution
2.6.3 Comparison of ground-based and satellite-derived classifications
2.7 Conclusion
3 Vertical structure of snowfall in Antarctica
3.1 Introduction
3.2 Material and methods
3.2.1 Study area
3.2.2 Ground-based MRR observations
3.2.3 MRR post-processing
3.2.4 Radio soundings
3.2.5 Statistics of vertical proles and temporal integration
3.2.6 Precipitation prole classication
3.3 Overall statistics
3.3.1 Vertical proles of reectivity
3.3.2 Vertical velocity proles
3.3.3 Vertical proles of spectral width
3.4 Surface precipitation and virga
3.5 Seasonal variability of vertical proles
3.5.1 Surface precipitation
3.5.2 Virga
3.6 Summary and conclusions
4 Vertical structure of precipitation in Alps
4.1 Introduction
4.2 Material and methods
4.2.1 Study area
4.2.2 Data
4.2.3 Method
4.3 Results
4.3.1 Comparison of snow-rain and virga-surface precipitation with disdrometer classication
4.3.2 Vertical variability of Doppler moments
4.3.3 Surface precipitation and virga occurrence
4.3.4 Temperature and relative humidity
4.4 Summary and conclusions
General Conclusions
Conclusions
Perspectives
