The role of inertial instability in cross-hemispheric coupling

Recent studies suggest linkages between anomalously warm temperatures in the winter stratosphere, and the high-latitude summer mesopause. The summer temperature anomaly is manifested in the decline of polar mesospheric clouds. The two-day wave is a strong-amplitude and transient summer feature that interacts with the background state so as to warm the high-latitude summer mesopause. This wave has been linked to a low-latitude phenomenon called inertial instability, that is organized by breaking planetary waves in the winter stratosphere. Hence, inertial instability has been identified as a possible nexus between the disturbed winter stratosphere, and summer mesopause warming. We investigate a sustained occurrence of inertial instability during July 19-August 8, 2014. During this period, stratospheric winter temperatures warmed by about 10 K, while a steep decline in polar mesospheric clouds was reported between July 26–August 6. We present, for the first time, wave driving associated with observed inertial instability. The effect of inertial instability is to export eastward momentum from the winter hemisphere across the equator into the summer hemisphere. Using a primitive equation model, we demonstrate that the wave stresses destabilize the stratopause summer easterly jet. The reconfigured wind profile excites the wavenumber 4 component of the two-day wave, leading to enhanced warming of the summer mesopause. This work supports previous numerical investigations that identified planetary wave-driven inertial instability as a source of the two-day wave.

A technical description of the Balloon Lidar Experiment (BOLIDE)

The Balloon Lidar Experiment (BOLIDE) was the first high-power lidar flown and operated successfully on board a balloon platform. As part of the PMC Turbo payload, the instrument acquired high-resolution backscatter profiles of polar mesospheric clouds (PMCs) from an altitude of ∼ 38 km during its maiden ∼ 6 d flight from Esrange, Sweden, to northern Canada in July 2018. We describe the BOLIDE instrument and its development and report on the predicted and actual in-flight performance. Although the instrument suffered from excessively high background noise, we were able to detect PMCs with a volume backscatter coefficient as low as 0.6×10-10 m−1 sr−1 at a vertical resolution of 100 m and a time resolution of 30 s.

A technical description of the Balloon Lidar Experiment BOLIDE

The Balloon Lidar Experiment (BOLIDE) was the first high-power lidar flown and operated successfully onboard a balloon platform. As part of the PMC Turbo payload, the instrument acquired high resolution backscatter profiles of Polar Mesospheric Clouds (PMCs) from an altitude of ∼38 km during its maiden ∼6 day flight from Esrange, Sweden, to Northern Canada in July 2018. We describe the BOLIDE instrument and its development and report on the predicted and actual in-flight performance. Although the instrument suffered from excessively high background noise, we were able to detect PMCs with a volume backscatter coefficient as low as 0.6 × 10−10 m−1 sr−1 at a vertical resolution of 100 m and a time resolution of 30 s.

Common volume satellite studies of polar mesospheric clouds with Odin/OSIRIS tomography and AIM/CIPS nadir imaging

Two important approaches for satellite studies of polar mesospheric clouds (PMCs) are nadir measurements adapting phase function analysis and limb measurements adapting spectroscopic analysis. Combining both approaches enables new studies of cloud structures and microphysical processes but is complicated by differences in scattering conditions, observation geometry and sensitivity. In this study, we compare common volume PMC observations from the nadir-viewing Cloud Imaging and Particle Size (CIPS) instrument on the Aeronomy of Ice in the Mesosphere (AIM) satellite and a special set of tomographic limb observations from the Optical Spectrograph and InfraRed Imager System (OSIRIS) on the Odin satellite performed over 18 d for the years 2010 and 2011 and the latitude range 78 to 80∘ N. While CIPS provides preeminent horizontal resolution, the OSIRIS tomographic analysis provides combined horizontal and vertical PMC information. This first direct comparison is an important step towards co-analysing CIPS and OSIRIS data, aiming at unprecedented insights into horizontal and vertical cloud processes. Important scientific questions on how the PMC life cycle is affected by changes in humidity and temperature due to atmospheric gravity waves, planetary waves and tides can be addressed by combining PMC observations in multiple dimensions. Two- and three-dimensional cloud structures simultaneously observed by CIPS and tomographic OSIRIS provide a useful tool for studies of cloud growth and sublimation. Moreover, the combined CIPS/tomographic OSIRIS dataset can be used for studies of even more fundamental character, such as the question of the assumption of the PMC particle size distribution. We perform the first thorough error characterization of OSIRIS tomographic cloud brightness and cloud ice water content (IWC). We establish a consistent method for comparing cloud properties from limb tomography and nadir observations, accounting for differences in scattering conditions, resolution and sensitivity. Based on an extensive common volume and a temporal coincidence criterion of only 5 min, our method enables a detailed comparison of PMC regions of varying brightness and IWC. However, since the dataset is limited to 18 d of observations this study does not include a comparison of cloud frequency. The cloud properties of the OSIRIS tomographic dataset are vertically resolved, while the cloud properties of the CIPS dataset is vertically integrated. To make these different quantities comparable, the OSIRIS tomographic cloud properties cloud scattering coefficient and ice mass density (IMD) have been integrated over the vertical extent of the cloud to form cloud albedo and IWC of the same quantity as CIPS cloud products. We find that the OSIRIS albedo (obtained from the vertical integration of the primary OSIRIS tomography product, cloud scattering coefficient) shows very good agreement with the primary CIPS product, cloud albedo, with a correlation coefficient of 0.96. However, OSIRIS systematically reports brighter clouds than CIPS and the bias between the instruments (OSIRIS – CIPS) is 3.4×10-6 sr−1 (±2.9×10-6 sr−1) on average. The OSIRIS tomography IWC (obtained from the vertical integration of IMD) agrees well with the CIPS IWC, with a correlation coefficient of 0.91. However, the IWC reported by OSIRIS is lower than CIPS, and we quantify the bias to −22 g km−2 (±14 g km−2) on average.

A new description of probability density distributions of polar mesospheric clouds

In this paper we present a new description of statistical probability density functions (pdfs) of polar mesospheric clouds (PMCs). The analysis is based on observations of maximum backscatter, ice mass density, ice particle radius, and number density of ice particles measured by the ALOMAR Rayleigh–Mie–Raman lidar for all PMC seasons from 2002 to 2016. From this data set we derive a new class of pdfs that describe the statistics of PMC events that is different from previous statistical methods using the approach of an exponential distribution commonly named the g distribution. The new analysis describes successfully the probability distributions of ALOMAR lidar data. It turns out that the former g-function description is a special case of our new approach. In general the new statistical function can be applied to many kinds of different PMC parameters, e.g., maximum backscatter, integrated backscatter, ice mass density, ice water content, ice particle radius, ice particle number density, or albedo measured by satellites. As a main advantage the new method allows us to connect different observational PMC distributions of lidar and satellite data, and also to compare with distributions from ice model studies. In particular, the statistical distributions of different ice parameters can be compared with each other on the basis of a common assessment that facilitates, for example, trend analysis of PMC.