A 20‐year study of melt processes over Larsen C Ice Shelf using a high‐resolution regional atmospheric model: Part 2, Drivers of surface melting

Quantifying the relative importance of the atmospheric drivers of surface melting on the Larsen C ice shelf is critical in the context of recent and future climate change. Here, we present analysis of a new multi-decadal, high-resolution model hindcast using the Met Office Unified Model (MetUM), described in part 1 of this study. We evaluate the contribution of various atmospheric conditions in order to identify and rank, for the first time, the most significant causes of melting over the recent past. We find the primary driver of surface melting on Larsen C is solar radiation. Foehn events are the second most important contributor to surface melting, especially in non-summer seasons when less solar radiation is received at the surface of the ice shelf. Thirdly, cloud influences surface melting via its impact on the surface energy balance (SEB); when the surface temperature is warm enough, cloud can initiate or prolong periods of melting. Lastly, large-scale circulation patterns such as the Southern Annular Mode (SAM), El Niño Southern Oscillation (ENSO) and Amundsen Sea Low (ASL) control surface melting on Larsen C by influencing the local meteorological conditions and SEB. These drivers of melting interact and overlap, for example, the SAM influences the frequency of foehn, commonly associated with leeside cloud clearances and sunnier conditions. Ultimately, these drivers matter because sustained surface melting on Larsen C could destabilise the ice shelf via hydrofracturing, which would have consequences for the fate of the ice shelf and sea levels worldwide

Antarctic clouds, supercooled liquid water and mixed phase, investigated with DARDAR: geographical and seasonal variations

Antarctic tropospheric clouds are investigated using the DARDAR (raDAR/liDAR)-MASK products between 60 and 82∘&thinsp;S. The cloud fraction (occurrence frequency) is divided into the supercooled liquid-water-containing cloud (SLC) fraction and its complementary part called the all-ice cloud fraction. A further distinction is made between SLC involving ice (mixed-phase clouds, MPC) or not (USLC, for unglaciated SLC). The low-level (&lt;3&thinsp;km above surface level) SLC fraction is larger over seas (20&thinsp;%–60&thinsp;%), where it varies according to sea ice fraction, than over continental regions (0&thinsp;%–35&thinsp;%). The total SLC fraction is much larger over West Antarctica (10&thinsp;%–40&thinsp;%) than it is over the Antarctic Plateau (0&thinsp;%–10&thinsp;%). In East Antarctica the total SLC fraction – in summer for instance – decreases sharply polewards with increasing surface height (decreasing temperatures) from 40 % at the coast to &lt;5% at 82∘&thinsp;S on the plateau. The geographical distribution of the continental total all-ice fraction is shaped by the interaction of the main low-pressure systems surrounding the continent and the orography, with little association with the sea ice fraction. Opportunistic comparisons with published ground-based supercooled liquid-water observations at the South Pole in 2009 are made with our SLC fractions at 82∘&thinsp;S in terms of seasonal variability, showing good agreement. We demonstrate that the largest impact of sea ice on the low-level SLC fraction (and mostly through the MPC) occurs in autumn and winter (22&thinsp;% and 18&thinsp;% absolute decrease in the fraction between open water and sea ice-covered regions, respectively), while it is almost null in summer and intermediate in spring (11&thinsp;%). Monthly variability of the MPC fraction over seas shows a maximum at the end of summer and a minimum in winter. Conversely, the USLC fraction has a maximum at the beginning of summer. However, monthly evolutions of MPC and USLC fractions do not differ on the continent. This suggests a seasonality in the glaciation process in marine liquid-bearing clouds. From the literature, we identify the pattern of the monthly evolution of the MPC fraction as being similar to that of the aerosols in coastal regions, which is related to marine biological activity. Marine bioaerosols are known to be efficient ice-nucleating particles (INPs). The emission of these INPs into the atmosphere from open waters would add to the temperature and sea ice fraction seasonalities as factors explaining the MPC fraction monthly evolution.</p

Can Recurrence Quantification Analysis Be Useful in the Interpretation of Airborne Turbulence Measurements?

In airborne data or model outputs, clouds are often defined using information about Liquid Water Content (LWC). Unfortunately LWC is not enough to retrieve information about the dynamical boundary of the cloud, that is, volume of turbulent air around the cloud. In this work, we propose an algorithmic approach to this problem based on a method used in time series analysis of dynamical systems, namely Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA). We construct RPs using time series of turbulence kinetic energy, vertical velocity and temperature fluctuations as variables important for cloud dynamics. Then, by studying time series of laminarity (LAM), a variable which is calculated using RPs, we distinguish between turbulent and non-turbulent segments along a horizontal flight leg. By selecting a single threshold of this quantity, we are able to reduce the number of subjective variables and their thresholds used in the definition of the dynamical cloud boundary

Observations and comparisons of cloud microphysical properties in spring and summertime Arctic stratocumulus clouds during the ACCACIA campaign

Measurements from four case studies in spring and summer-time Arctic stratocumulus clouds during the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) campaign are presented. We compare microphysics observations between cases and with previous measurements made in the Arctic and Antarctic. During ACCACIA, stratocumulus clouds were observed to consist of liquid at cloud tops, often at distinct temperature inversions. The cloud top regions precipitated low concentrations of ice into the cloud below. During the spring cases median ice number concentrations (~ 0.5 L−1) were found to be lower by about a factor of 5 than observations from the summer campaign (~ 3 L−1). Cloud layers in the summer spanned a warmer temperature regime than in the spring and enhancement of ice concentrations in these cases was found to be due to secondary ice production through the Hallett–Mossop (H–M) process. Aerosol concentrations during spring ranged from ~ 300–400 cm−3 in one case to lower values of ~ 50–100 cm−3 in the other. The concentration of aerosol with sizes Dp > 0.5 μm was used in a primary ice nucleus (IN) prediction scheme (DeMott et al., 2010). Predicted IN values varied depending on aerosol measurement periods but were generally greater than maximum observed median values of ice crystal concentrations in the spring cases, and less than the observed ice concentrations in the summer due to the influence of secondary ice production. Comparison with recent cloud observations in the Antarctic summer (Grosvenor et al., 2012), reveals lower ice concentrations in Antarctic clouds in comparable seasons. An enhancement of ice crystal number concentrations (when compared with predicted IN numbers) was also found in Antarctic stratocumulus clouds spanning the H–M temperature zone; however, concentrations were about an order of magnitude lower than those observed in the Arctic summer cases but were similar to the peak values observed in the colder Arctic spring cases, where the H–M mechanism did not operate

In-situ aircraft observations of ice concentrations within clouds over the Antarctic Peninsula and Larsen Ice Shelf

In-situ aircraft observations of ice crystal concentrations in Antarctic clouds are presented for the first time. Orographic, layer and wave clouds around the Antarctic Peninsula and Larsen Ice shelf regions were penetrated by the British Antarctic Survey's Twin Otter aircraft, which was equipped with modern cloud physics probes. The clouds studied were mostly in the free troposphere and hence ice crystals blown from the surface are unlikely to have been a major source for the ice phase. The temperature range covered by the experiments was 0 to −21 °C. The clouds were found to contain supercooled liquid water in most regions and at heterogeneous ice formation temperatures ice crystal concentrations (60 s averages) were often less than 0.07 l−1, although values up to 0.22 l−1 were observed. Estimates of observed aerosol concentrations were used as input into the DeMott et al. (2010) ice nuclei (IN) parameterisation. The observed ice crystal number concentrations were generally in broad agreement with the IN predictions, although on the whole the predicted values were higher. Possible reasons for this are discussed and include the lack of IN observations in this region with which to characterise the parameterisation, and/or problems in relating ice concentration measurements to IN concentrations. Other IN parameterisations significantly overestimated the number of ice particles. Generally ice particle concentrations were much lower than found in clouds in middle latitudes for a given temperature. Higher ice crystal concentrations were sometimes observed at temperatures warmer than −9 °C, with values of several per litre reached. These were attributable to secondary ice particle production by the Hallett Mossop process. Even in this temperature range it was observed that there were regions with little or no ice that were dominated by supercooled liquid water. It is likely that in some cases this was due to a lack of seeding ice crystals to act as rimers to initiate secondary ice particle production. This highlights the chaotic and spatially inhomogeneous nature of this process and indicates that the accurate representation of it in global models is likely to represent a challenge. However, the contrast between Hallett Mossop zone ice concentrations and the fairly low concentrations of heterogeneously nucleated ice suggests that the Hallet Mossop process has the potential to be very important in remote, pristine regions such as around the Antarctic coast

Growth and mass wasting of volcanic centers in the northern South Sandwich arc, South Atlantic, revealed by new multibeam mapping

New multibeam (swath) bathymetric sonar data acquired using an EM120 system on the RRS James Clark Ross, supplemented by sub-bottom profiling, reveals the underwater morphology of a not, vert, similar 12,000 km2 area in the northern part of the mainly submarine South Sandwich volcanic arc. The new data extend between 55° 45′S and 57° 20′S and include Protector Shoal and the areas around Zavodovski, Visokoi and the Candlemas islands groups. Each of these areas is a discrete volcanic center. The entirely submarine Protector Shoal area, close to the northern limit of the arc, forms a 55 km long east–west-trending seamount chain that is at least partly of silicic composition. The seamounts are comparable to small subaerial stratovolcanoes in size, with volumes up to 83 km3, indicating that they are the product of multiple eruptions over extended periods. Zavodovski, Visokoi and the Candlemas island group are the summits of three 3–3.5 km high volcanic edifices. The bathymetric data show evidence for relationships between constructional volcanic features, including migrating volcanic centers, structurally controlled constructional ridges, satellite lava flows and domes, and mass wasting of the edifices. Mass wasting takes place mainly by strong erosion at sea level, and dispersal of this material along chutes, probably as turbidity currents and other mass flows that deposit in extensive sediment wave fields. Large scale mass wasting structures include movement of unconsolidated debris in slides, slumps and debris avalanches. Volcanism is migrating westward relative to the underlying plate and major volcanoes are asymmetrical, being steep with abundant recent volcanism on their western flanks, and gently sloping with extinct, eroded volcanic sequences to their east. This is consistent with the calculated rate of subduction erosion of the fore-arc

The hemispheric contrast in cloud microphysical properties constrains aerosol forcing

The change in planetary albedo due to aerosol−cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol−cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm−3 and 24 cm−3. By extension, the radiative forcing since 1850 from aerosol−cloud interactions is constrained to be −1.2 W⋅m−2 to −0.6 W⋅m−2. The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol−cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models

An evaluation of surface meteorology and fluxes over the Iceland and Greenland Seas in ERA5 reanalysis: The impact of sea ice distribution

The Iceland and Greenland Seas are a crucial region for the climate system, being the headwaters of the lower limb of the Atlantic Meridional Overturning Circulation. Investigating the atmosphere–ocean–ice processes in this region often necessitates the use of meteorological reanalyses—a representation of the atmospheric state based on the assimilation of observations into a numerical weather prediction system. Knowing the quality of reanalysis products is vital for their proper use. Here we evaluate the surface‐layer meteorology and surface turbulent fluxes in winter and spring for the latest reanalysis from the European Centre for Medium‐Range Weather Forecasts, i.e., ERA5. In situ observations from a meteorological buoy, a research vessel, and a research aircraft during the Iceland–Greenland Seas Project provide unparalleled coverage of this climatically important region. The observations are independent of ERA5. They allow a comprehensive evaluation of the surface meteorology and fluxes of these subpolar seas and, for the first time, a specific focus on the marginal ice zone. Over the ice‐free ocean, ERA5 generally compares well to the observations of surface‐layer meteorology and turbulent fluxes. However, over the marginal ice zone, the correspondence is noticeably less accurate: for example, the root‐mean‐square errors are significantly higher for surface temperature, wind speed, and surface sensible heat flux. The primary reason for the difference in reanalysis quality is an overly smooth sea‐ice distribution in the surface boundary conditions used in ERA5. Particularly over the marginal ice zone, unrepresented variability and uncertainties in how to parameterize surface exchange compromise the quality of the reanalyses. A parallel evaluation of higher‐resolution forecast fields from the Met Office's Unified Model corroborates these findings

Aircraft-based mass balance estimate of methane emissions from offshore gas facilities in the southern North Sea

Atmospheric methane (CH4) concentrations have more than doubled since the beginning of the industrial age, making CH4 the second most important anthropogenic greenhouse gas after carbon dioxide (CO2). The oil and gas sector represents one of the major anthropogenic CH4 emitters as it is estimated to account for 22 % of global anthropogenic CH4 emissions. An airborne field campaign was conducted in April–May 2019 to study CH4 emissions from offshore gas facilities in the southern North Sea with the aim of deriving emission estimates using a top-down (measurement-led) approach. We present CH4 fluxes for six UK and five Dutch offshore platforms or platform complexes using the well-established mass balance flux method. We identify specific gas production emissions and emission processes (venting and fugitive or flaring and combustion) using observations of co-emitted ethane (C2H6) and CO2. We compare our top-down estimated fluxes with a ship-based top-down study in the Dutch sector and with bottom-up estimates from a globally gridded annual inventory, UK national annual point-source inventories, and operator-based reporting for individual Dutch facilities. In this study, we find that all the inventories, except for the operator-based facility-level reporting, underestimate measured emissions, with the largest discrepancy observed with the globally gridded inventory. Individual facility reporting, as available for Dutch sites for the specific survey date, shows better agreement with our measurement-based estimates. For all the sampled Dutch installations together, we find that our estimated flux of (122.9 ± 36.8) kg h−1 deviates by a factor of 0.64 (0.33–12) from reported values (192.8 kg h−1). Comparisons with aircraft observations in two other offshore regions (the Norwegian Sea and the Gulf of Mexico) show that measured, absolute facility-level emission rates agree with the general distribution found in other offshore basins despite different production types (oil, gas) and gas production rates, which vary by 2 orders of magnitude. Therefore, mitigation is warranted equally across geographies.</p

The Iceland Greenland Seas Project

A coordinated atmosphere-ocean research project, centered on a rare wintertime field campaign to the Iceland and Greenland Seas, seeks to determine the location and causes of dense water formation by cold-air outbreaks. The Iceland Greenland Seas Project (IGP) is a coordinated atmosphere-ocean research program investigating climate processes in the source region of the densest waters of the Atlantic Meridional Overturning Circulation. During February and March 2018, a field campaign was executed over the Iceland and southern Greenland Seas that utilized a range of observing platforms to investigate critical processes in the region – including a research vessel, a research aircraft, moorings, sea gliders, floats and a meteorological buoy. A remarkable feature of the field campaign was the highly-coordinated deployment of the observing platforms, whereby the research vessel and aircraft tracks were planned in concert to allow simultaneous sampling of the atmosphere, the ocean and their interactions. This joint planning was supported by tailor-made convection-permitting weather forecasts and novel diagnostics from an ensemble prediction system. The scientific aims of the IGP are to characterize the atmospheric forcing and the ocean response of coupled processes; in particular, cold-air outbreaks in the vicinity of the marginal-ice zone and their triggering of oceanic heat loss, and the role of freshwater in the generation of dense water masses. The campaign observed the lifecycle of a long-lasting cold-air outbreak over the Iceland Sea and the development of a cold-air outbreak over the Greenland Sea. Repeated profiling revealed the immediate impact on the ocean, while a comprehensive hydrographic survey provided a rare picture of these subpolar seas in winter. A joint atmosphere-ocean approach is also being used in the analysis phase, with coupled observational analysis and coordinated numerical modelling activities underway