Figure 6 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
The seasonal dependence of median De (color legend at center is in µm) for temperature T ≤ 235 K for the years 2008 and 2013, based on the first formulation (SPARTICUS in situ data with N(D)1 included) of the CALIPSO retrieval.
Source publication
There are two fundamental mechanisms through which cirrus clouds form; homo- and heterogeneous ice nucleation (henceforth hom and het). The relative contribution of each mechanism to ice crystal production often determines the microphysical and radiative properties of a cirrus cloud. This study attempts to estimate the radiative contribution of hom...
Contexts in source publication
Context 1
... relative to CALCAL. However, the mid-level changes are greater than the UT changes (both in magnitude and vertical extent), which acts to reduce FLNTC more in HET. This appears to be the main cause of the difference between the CRE 5 changes in Table 3 and the TOA net flux changes in Table 4. HET -CALCAL differences in TOA net flux are shown in Fig. 16 based on annual means and for winter verses summer. As with the CRE differences, there is a strong seasonal dependence. The RH radiative effect is strongest in the Polar Regions, especially during the N. ...
Context 2
... that, outside the tropics, De tends to be largest and N lowest during the summer season as shown in Figs. 6 and 7. The seasonal behavior of De and N shown in Fig. 6 and 7 is probably not sufficiently realized in climate models. This study attempts to estimate the radiative impact of this seasonal ...
Context 3
... that, outside the tropics, De tends to be largest and N lowest during the summer season as shown in Figs. 6 and 7. The seasonal behavior of De and N shown in Fig. 6 and 7 is probably not sufficiently realized in climate models. This study attempts to estimate the radiative impact of this seasonal ...
Similar publications
Cloud segmentation plays a crucial role in image analysis for climate modeling. Manually labeling the training data for cloud segmentation is time-consuming and error-prone. We explore to train segmentation networks with synthetic data due to the natural acquisition of pixel-level labels. Nevertheless, the domain gap between synthetic and real imag...
How ice crystals form in the troposphere strongly affects cirrus cloud properties. Atmospheric ice formation is often initiated by aerosol particles that act as ice nucleating particles. The aerosol-cloud interactions of soot and associated feedbacks remain uncertain, in part because a coherent understanding of the ice nucleation mechanism and acti...
Citations
... See Lawson et al. (2019) for a review of cirrus cloud particle shape, including various m-D relationships. Mitchell et al. (2020) provide calculations of ice cloud properties for use in climate models. ...
Ice clouds pose crucial challenges in climate model simulations and remote sensing retrievals due to complicated mechanisms of ice cloud formation that span from micro scale to planetary wave scale. Despite numerous attempts to parameterize these processes, many questions remain unanswered. This technical note provides a summary of the most common and recent studies on ice clouds and compiles important equations in a simple yet applicable manner to relate key microphysical properties of cirrus and ice clouds such as ice particle effective diameter, ice water path, cloud optical depth, particle number concentration, fall velocity, and particle size distribution. Additionally, it introduces relationships between ice particle mass (m) and dimension (D), as well as ice particle projected area (A) and D, which are essential for parameterizing the diverse shapes of ice particles in ice clouds. The equations provided in this technical note are valuable for the accurate representation of cirrus and ice clouds in remote sensing and climate modeling.
... Ice particles form through either homogeneous or heterogeneous ice nucleation and then grow/decay with time. Different form types depend on many factors, such as ice nucleating particle existence, temperature, supersaturation, updraft strength, etc. [30]. The homogeneous-dominated cirrus is more abundant during the winter at midlatitudes and over mountainous terrain [31][32][33]. ...
We present an improved remote sensing technique to infer an optimal habit/shape model for ice particles in cirrus clouds using multi-angle polarimetric measurements at 865 nm made by the Airborne Multi-angle SpectroPolarimeter Imager (AirMSPI) instrument. The common method of ice model inference is based on intensity (total reflectivity) measurements, which is generally not applicable to optically thin ice clouds (i.e., cirrus clouds) where single scattering dominates. The new approach is able to infer an ice model in clouds with optical thicknesses smaller than 5. The improvement is made by first assuming the optical thickness retrieved using total reflectivity. Subsequently, the polarized reflectivity is calculated based on look-up tables generated from simulated polarized reflectances computed for cirrus clouds in conjunction with eight ice particle models. The ice particle model that leads to the closest fit to the measurements is regarded as the optimal ice particle model. Additionally, an alternative method is applied that does not consider polarized reflectivity. These two methods are applied to a data sample as a proof-of-concept study where AirMSPI observed a single cirrus layer. In this case study, the hexagonal column aggregate model works for most pixels both with and without considering polarized reflectivities. The retrieval cost function is high when the camera pairs with large zenith angles are included in the retrievals. This result suggests that further studies will be necessary to have a better understanding of all eight selected ice particle models at scattering angles smaller than 100°.
Warm Air Intrusions (WAI) are responsible for the transportation of warm and moist airmasses from the mid-latitudes into the high arctic (>70° N). In this work, we study cirrus clouds that form during WAI events (WAI cirrus) and during undisturbed arctic conditions (AC cirrus) and investigate possible differences between the two cloud types based on their macrophysical and optical properties with a focus on Relative Humidity over ice (RHi). We use airborne measurements from the combined high spectral resolution and differential absorption lidar, WALES, performed during the HALO-(AC)3 campaign. We classify each research flight and the measured clouds as either AC or WAI, based on the ambient conditions and study the macrophysical, geometrical and optical characteristics for each cirrus group. As our main parameter we choose the Relative Humidity over ice (RHi) which we calculate RHi by combining the lidar water vapor measurements with model temperatures. RHi is affected by and can be used as an indication of the nucleation process and the structure of cirrus clouds. We find that during WAI events the arctic is warmer and moister and WAI cirrus are both geometrically and optically thicker compared to AC cirrus. WAI cirrus and the layer directly surrounding them, are more frequently supersaturated, also at high supersaturations over the threshold for homogeneous ice nucleation (HOM). AC cirrus have a supersaturation dominated cloud top and a subsaturated cloud base. WAI cirrus also have high supersaturations at cloud top, but also at cloud base.
While it is clear that climate change is affecting many species including humans, it is not clear whether humankind could or should try to remedy Earth’s warming climate. The situation requires cross-disciplinary, out-of-the-box thinking. Here we describe the use of a novel collaborative approach to developing creative hypotheses related to climate change. In most scientific explorations, the scientific method is used to explore a topic of interest. In contrast, in this case report, we used a topic of interest to us (intuition) to explore new ways of inspiring science. We did not test a hypothesis about intuition or try to prove that a psychic aspect of intuition exists. Instead, we explored how using an intuition-guided research approach might help address several questions pertinent to climate science. Seven intuitives who were not climate scientists were asked to use their intuition to address questions they would be shown in the future, most of which currently do not have clear answers. The content of the questions, which we called “objectives,” was informed by a collaboration between the project leads and two atmospheric scientists. To respond to the unknown objectives, the intuitives used a technical form of psychic functioning called remote viewing. This project was strictly intended to spark creativity and new ideas in climate science, so we did not attempt to prove that the intuitives were using psychic abilities in their work. Instead, we used an interpretation-heavy process, similar to Rorschach blot interpretation, that nonetheless offered insights, ideas, hypotheses, and motivations to examine new directions in climate science. Based on what we learned within this project, we provide very tentative suggestions for understanding climate change and its mitigation as well as suggestions for those considering using an intuition-guided research approach to stimulate new scientific ideas.
Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version 4 (V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and water path estimates for ice and liquid clouds. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 (V3) as a result of the significant changes implemented in the V4 algorithms, which are presented in a companion paper (Part I). The analysis of the three-channel IIR observations (08.65, 10.6, and 12.05 µm) is informed by the scene classification provided in the V4 CALIOP 5 km cloud layer and aerosol layer products. Thanks to the reduction of inter-channel effective emissivity biases in semi-transparent (ST) clouds when the oceanic background radiance is derived from model computations, the number of unbiased emissivity retrievals is increased by a factor of 3 in V4. In V3, these biases caused inconsistencies between the effective diameters retrieved from the 12/10 (βeff12/10 = τa,12/τa,10) and 12/08 (βeff12/08 = τa,12/τa,08) pairs of channels at emissivities smaller than 0.5. In V4, microphysical retrievals in ST ice clouds are possible in more than 80 % of the pixels down to effective emissivities of 0.05 (or visible optical depth ∼0.1). For the month of January 2008, which was chosen to illustrate the results, median ice De and ice water path (IWP) are, respectively, 38 µm and 3 g m−2 in ST clouds, with random uncertainty estimates of 50 %. The relationship between the V4 IIR 12/10 and 12/08 microphysical indices is in better agreement with the “severely roughened single column” ice habit model than with the “severely roughened eight-element aggregate” model for 80 % of the pixels in the coldest clouds (230 K). Retrievals in opaque ice clouds are improved in V4, especially at night and for 12/10 pair of channels, due to corrections of the V3 radiative temperature estimates derived from CALIOP geometric altitudes. Median ice De and IWP are 58 µm and 97 g m−2 at night in opaque clouds, with again random uncertainty estimates of 50 %. Comparisons of ice retrievals with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua in the tropics show a better agreement of IIR De with MODIS visible–3.7 µm than with MODIS visible–2.1 µm in the coldest ST clouds and the opposite for opaque clouds. In prevailingly supercooled liquid water clouds with centroid altitudes above 4 km, retrieved median De and liquid water path are 13 µm and 3.4 g m−2 in ST clouds, with estimated random uncertainties of 45 % and 35 %, respectively. In opaque liquid clouds, these values are 18 µm and 31 g m−2 at night, with estimated uncertainties of 50 %. IIR De in opaque liquid clouds is smaller than MODIS visible–2.1 µm and visible–3.7 µm by 8 and 3 µm, respectively.
