Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Abstract

Abstract. The number concentration of cloud particles is a key quantity for understanding aerosol-cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist on the ice crystal number concentration (N i ). This study investigates how combined lidar-radar measurements can be used to provide satellite estimates of N i , using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR-raDAR (DARDAR) product serves as an existing base for this method, 5 which focuses on ice clouds with temperatures T c < -30°C.Theoretical considerations demonstrate the capability for accurate retrievals of N i , apart from a possible bias in the concentration in small crystals when T c -50°C, due to the assumption of a monomodal PSD shape in the current method. This is verified by comparing satellite estimates to co-incident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar-radar observations to N i . Following these results, satellite estimates of N i are evaluated in the context of a 10 case study and a preliminary climatological analysis based on 10 years of global data. Despite of a lack of other large-scale references, this evaluation shows a reasonable physical consistency in N i spatial distribution patterns. Notably, increases in N i are found towards cold temperatures and, more significantly, in the presence of strong updraughts, such as those related to convective or orographic uplifts. Further evaluation and improvements of this method are necessary but these results already constitute a first encouraging step towards large-scale observational constraints for N i . Part two of this series uses this new 15 dataset to examine the controls on N i .

Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53)

“…However, differently from Fig. 3e, observations do not present such a high peak of ICNC over Antarctica (Gryspeerdt et al, 2018b;Sourdeval et al, 2018). The annual global mean is about 53 L −1 , which means about one-quarter with respect to the ICNC global mean at 200 hPa.…”

“…Figure 3a also shows higher ICNCs around the edge of the Antarctic ice sheet and over those regions which experience a strong convective activity, i.e. the Intertropical Convergence Zone (ITCZ) and the Tropical Warm Pool (TWP), as observed in Sourdeval et al (2018). The annual global mean of ICNC at 200 hPa is about 200 L −1 (∼ 390 L −1 over land and ∼ 124 L −1 over ocean).…”

“…Besides the flight measurements, the recent ICNC estimates from lidar-radar satellite retrievals must be mentioned, e.g. Sourdeval et al (2018) and Gryspeerdt et al (2018b). In particular, Gryspeerdt et al (2018b) analysed the behaviour of ICNCs within clouds as a function of temperature.…”

“…As heterogeneous nucleation takes place at lower supersaturation than homogeneous nucleation, the available water vapour and the degree of supersaturation decrease, reducing or inhibiting the formation of ice crystals from homogeneous nucleation. This competition between homogeneous and heterogeneous nucleation for water vapour drastically affects the ICNC in the cirrus regime, even at low INP concentrations Spichtinger and Cziczo, 2010). On the other hand, both in the cirrus regime and in the mixed-phase regime, water vapour can also be reduced by depositional growth onto pre-existing ice crystals and ice crystals carried into the cloud via convective detrainment and advective transport, thus inhibiting ice nucleation.…”

Strong Ocean/Sea‐Ice Contrasts Observed in Satellite‐Derived Ice Crystal Number Concentrations in Arctic Ice Boundary‐Layer Clouds

“…The DARDAR‐Nice data used for the study of ice crystal number concentrations are available from the AERIS ICARE data center via https://doi.org/10.25326/09 and can be provided upon request (Sourdeval, Gryspeerdt, Krämer, Goren, Delanoë, Afchine, Hemmer & Quaas, 2018). The AMSR‐E and AMSR2 ASI Sea Ice Concentration datasets used here to distinguish the Arctic surface conditions are publicly available through PANGAEA—Data Publisher for Earth & Environmental Science via https://doi.org/10.1594/PANGAEA.919777 and https://doi.org/10.1594/PANGAEA.898399, respectively, (Melsheimer & Spreen, 2019, 2020).…”