On the utility of individual tendency output: Revealing interactions between parameterised processes during a marine cold air outbreak

Forecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterisation schemes to represent sub-grid scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognised to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterisation schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic Seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterised shallow convection. The individual tendencies from several parameterisations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterise the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models.

North Atlantic Ocean–Atmosphere Driven Variations in Aerosol Evolution along Lagrangian Cold-Air Outbreak Trajectories

Atmospheric marine particle concentrations impact cloud properties, which strongly impact the amount of solar radiation reflected back into space or absorbed by the ocean surface. While satellites can provide a snapshot of current conditions at the overpass time, models are necessary to simulate temporal variations in both particle and cloud properties. However, poor model accuracy limits the reliability with which these tools can be used to predict future climate. Here, we leverage the comprehensive ocean ecosystem and atmospheric aerosol-cloud data set obtained during the third deployment of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES3). Airborne and ship-based measurements were collected in and around a cold-air outbreak during a three-day intensive operations period from September 17–19, 2017. Cold-air outbreaks are of keen interest for model validation because they are challenging to accurately simulate, which is due, in part, to the numerous feedbacks and sub-grid scale processes that influence aerosol and cloud evolution. The NAAMES observations are particularly valuable because the flight plans were tailored to lie along Lagrangian trajectories, making it possible to spatiotemporally connect upwind and downwind measurements with the state-of-the-art FLEXible PARTicle (FLEXPART) Lagrangian particle dispersion model and then calculate a rate of change in particle properties. Initial aerosol conditions spanning an east-west, closed-cell cloudy to clear air transition region of the cold-air outbreak indicate similar particle concentrations and properties. However, despite the similarities in the aerosol fields, the cloud properties downwind of each region evolved quite differently. One trajectory carried particles through a cold-air outbreak, resulting in a decrease in accumulation mode particle concentration (−42 %) and cloud droplet concentrations, while the other remained outside of the cold-air outbreak and experienced an increase in accumulation mode particle concentrations (+62 %). The variable meteorological conditions between these two adjacent trajectories result from differences in the local sea surface temperature altering stability of the marine atmospheric boundary layer because of the location of the Labrador Current. Further comparisons of historical satellite observations indicate that the observed pattern occurs annually in the region, making it an ideal location for future airborne Lagrangian studies tracking the evolution of aerosols and clouds over time under cold air outbreak conditions.

Retrieving H₂O/HDO columns over cloudy and clear-sky scenes from the Tropospheric Monitoring Instrument (TROPOMI)

This paper presents an extension of the scientific HDO/H2O column data product from the Tropospheric Monitoring Instrument (TROPOMI) including clear-sky and cloudy scenes. The retrieval employs a forward model which accounts for scattering, and the algorithm infers the trace gas column information, surface properties and effective cloud parameters from the observations. The extension to cloudy scenes greatly enhances coverage, particularly enabling data over oceans. The data set is validated against co-located ground-based Fourier transform infrared (FTIR) observations by the Total Carbon Column Observing Network (TCCON). The median bias for clear-sky scenes is 1.4 × 1021 molec cm−2 (2.9 %) in H2O columns and 1.1 × 1017 molec cm−2 (−0.3 %) in HDO columns, which corresponds to −17 ‰ (9.9 %) in a posteriori δD. The bias for cloudy scenes is 4.9 × 1021 molec cm−2 (11 %) in H2O, 1.1 × 1017 molec cm−2 (7.9 %) in HDO, and −20 ‰ (9.7 %) in a posteriori δD. At low-altitude stations in low and middle latitudes the bias is small, and has a larger value at high latitude stations. At high altitude stations, an altitude correction is required to compensate for different partial columns seen by the station and the satellite. The bias in a posteriori δD after altitude correction depends on sensitivity due to shielding by clouds, and on realistic prior profile shapes for both isotopologues. Cloudy scenes generally involve low sensitivity below the clouds, and since the information is filled up by the prior, it plays an important role in these cases. Over oceans, aircraft measurements with the Water Isotope System for Precipitation and Entrainment Research (WISPER) instrument from a field campaign in 2018 are used for validation, yielding a bias of −3.9 % in H2O and −3 ‰ in δD over clouds. To demonstrate the added value of the new data set, a short case study of a cold air outbreak over the Atlantic Ocean in January 2020 is presented, showing the daily evolution of the event with single overpass results.

The role of a tropopause polar vortex in the generation of the January 2019 extreme Arctic outbreak

An extreme Arctic cold air outbreak took place across the Midwest, Great Lakes, and Northeast during 29 January to 1 February 2019. The event broke numerous long-standing records with wide-reaching and detrimental societal impacts. This study found that this rare and dangerous cold air out-break (CAO) was a direct consequence of a tropopause polar vortex (TPV) originating at high latitudes and subsequently tracking southward into the United States. The tropopause depression at the center of this TPV extended nearly to the surface. Simulations using the atmospheric component of the Model for Prediction Across Scales (MPAS) were conducted revealing excellent predictability at 6-7 days lead times with the strength, timing, and location of the CAO linked to the earlier characteristics of the TPV over the Arctic. Within the middle latitudes, the TPV subsequently developed a tilt with height. Warming and the destruction of potential vorticity also took place as the TPV passed over the Great Lakes initiating a lake effect snow storm. The climatological investigation of CAOs suggests that TPVs frequently play a role in CAOs over North America with a TPV located within 1000 km of a CAO 85% of the time. These TPVs tended to originate in the Northern Canadian Arctic and are ejected equatorward into the Great Lakes/Upper-midwest and then to the northeast over Labrador. This study also provides insight into how the impact of Arctic circulations on middle latitudes may vary within the framework of a rapidly changing Arctic.

The impact of Secondary Ice Processes on a stratocumulus-to-cumulus transition during a Cold-Air Outbreak

<p>The representation of boundary layer clouds during marine Cold-Air Outbreaks (CAO) remains a great challenge for weather prediction models. Recent studies have shown that the representation of the transition from stratocumulus clouds to convective cumulus open cells largely depends on microphysical and precipitation processes, while Abel et al. (2017) further suggested that Secondary Ice Processes (SIP) may play a crucial role in the evolution of the cloud fields. In this study we use the Weather Research Forecasting model to investigate the impact of the most well-known SIP mechanisms (rime-splintering or Hallet-Mossop, mechanical break-up upon collisions between ice particles and drop-shattering) on a CAO case observed north of UK in 2013. While Hallet-Mossop is the only SIP process extensively implemented in atmospheric models, our results indicate that collisional break-up is also important in these conditions.</p><p> </p><p>Abel, S. J., Boutle, I. A., Waite, K., Fox, S., Brown, P. R. A., Cotton, R., Lloyd, G., Choularton, T. W., & Bower, K. N. (2017). The Role of Precipitation in Controlling the Transition from Stratocumulus to Cumulus Clouds in a Northern Hemisphere Cold-Air Outbreak, Journal of the Atmospheric Sciences, 74(7), 2293-2314. Retrieved Jan 9, 2021, from https://journals.ametsoc.org/view/journals/atsc/74/7/jas-d-16-0362.1.xml</p>