Cirrus Clouds and Climate Engineering: New Findings on Ice Nucleation and Theoretical Basis

Mitchell, David L.; Mishra, Subhashree; Lawson, R. Paul

doi:10.5772/24664

Cited by 6 publications

(9 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(4). The former parameter is especially critical as it controls the steepness of the PSD towards small particles and strongly varies depending on the dominating nucleation processes (Mitchell, 1991). The normalization approach by D05 should, in principle, account for such variations in the PSD shape by adjusting the scaling parameters N * 0 and D m , but large deviations from the assumed α value due to unusual conditions could still have consequences on subsequent N i estimates.…”

Section: A2 Propagation Of Dardar Operational Errorsmentioning

confidence: 99%

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

Sourdeval

Gryspeerdt

Krämer³

et al. 2018

Atmos. Chem. Phys.

106

View full text Add to dashboard Cite

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 regarding the ice crystal number concentration (Ni). This study investigates how combined lidar–radar measurements can be used to provide satellite estimates of Ni, 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, which focuses on ice clouds with temperatures Tc<-30 ∘C. Theoretical considerations demonstrate the capability for accurate retrievals of Ni, apart from a possible bias in the concentration in small crystals when Tc≳−50 ∘C, due to the assumption of a monomodal PSD shape in the current method. This is verified via a comparison of satellite estimates to coincident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar–radar observations to Ni. Following these results, satellite estimates of Ni are evaluated in the context of a case study and a preliminary climatological analysis based on 10 years of global data. Despite a lack of other large-scale references, this evaluation shows a reasonable physical consistency in Ni spatial distribution patterns. Notably, increases in Ni are found towards cold temperatures and, more significantly, in the presence of strong updrafts, such as those related to convective or orographic uplifts. Further evaluation and improvement of this method are necessary, although these results already constitute a first encouraging step towards large-scale observational constraints for Ni. Part 2 of this series uses this new dataset to examine the controls on Ni.

show abstract

Section: A2 Propagation Of Dardar Operational Errorsmentioning

confidence: 99%

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

Sourdeval

Gryspeerdt

Krämer³

et al. 2018

Atmos. Chem. Phys.

106

View full text Add to dashboard Cite

show abstract

“…Mitchell et al, 2011;Mishra et al, 2014;Luebke et al, 2016). In the occurrence of a bi-modal shape in in situ measurements, the D05 parameterization naturally tends to reproduce the concentration in large particles due to their strong weight on D m and IWC.…”

Section: Reproducibility Of Psds By D05mentioning

confidence: 99%

“…Mitchell et al, 2011;. Nevertheless, D05 and Field et al (2005) showed that two-moment normalization methods can be used to reasonably approximate a wide range of measured size distributions to a single shape function, noted F , referred to as a "universal" or "normalized" PSD.…”

mentioning

confidence: 99%

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

Sourdeval¹,

Gryspeerdt²,

Krämer³

et al. 2018

Preprint

View full text Add to dashboard Cite

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 .

show abstract

“…Such features and temperature dependency of PSD shapes have already been widely reported in the literature (e.g. Mitchell et al, 2011;Mishra et al, 2014;Luebke et al, 2016). In the occurrence of a bi-modal shape in in situ measurements, the D05 parameterization naturally tends to reproduce the concentration in large particles due to their strong weight on D m and IWC.…”

mentioning

confidence: 53%

“…Parameterizing PSDs is a challenging task due to the large variability of their shapes on a global scale or even within a cloud layer (e.g. Mitchell et al, 2011;Krämer et al, 2016). Nevertheless, D05 and Field et al (2005) showed that two-moment normalization methods can be used to reasonably approximate a wide range of measured size distributions to a single shape function, noted F , referred to as a "universal" or "normalized" PSD.…”

Section: Representation Of the Size Distributionmentioning

confidence: 99%

Response to RC1

Sourdeval¹

2018

Preprint

View full text Add to dashboard Cite

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, 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 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 dataset to examine the controls on N i. 1 Introduction Clouds play a major role in the climate system and are essential components of the Earth-atmosphere radiation balance (Stephens, 2005). A precise understanding of their properties and processes therefore is necessary to properly address current uncertainties of climate change estimates (Boucher et al., 2013). In particular, the impact of ice clouds on the Earth's radiation budget is recognized as being substantial (e.g. Liou, 1986; Stephens et al., 1990) but still remains difficult to quantify

show abstract

Cirrus Clouds and Climate Engineering: New Findings on Ice Nucleation and Theoretical Basis

Cited by 6 publications

References 62 publications

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

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

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

Response to RC1

Contact Info

Product

Resources

About