ALBERT

Description: Warming of the Arctic led to an increase in permafrost temperatures by about 0.3 �C during the last decade. Permafrost warming is associated with increasing sediment water content, permeability, and diffusivity and could in the long term alter microbial community composition and abundance even before permafrost thaws. We studied the long-term effect (up to 2500 years) of submarine permafrost warming on microbial communities along an onshore–offshore transect on the Siberian Arctic Shelf displaying a natural temperature gradient of more than 10 �C. We analysed the in situ development of bacterial abundance and community composition through total cell counts (TCCs), quantitative PCR of bacterial gene abundance, and amplicon sequencing and correlated the microbial community data with temperature, pore water chemistry, and sediment physicochemical parameters. On timescales of centuries, permafrost warming coincided with an overall decreasing microbial abundance, whereas millennia after warming microbial abundance was similar to cold onshore permafrost. In addition, the dissolved organic carbon content of all cores was lowest in submarine permafrost after millennial-scale warming. Based on correlation analysis, TCC, unlike bacterial gene abundance, showed a significant rank-based negative correlation with increasing temperature, while bacterial gene copy numbers showed a strong negative correlation with salinity. Bacterial community composition correlated only weakly with temperature but strongly with the pore water stable isotopes �18O and �D, as well as with depth. The bacterial community showed substantial spatial variation and an overall dominance of Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonadetes, and Proteobacteria, which are amongst the microbial taxa that were also found to be active in other frozen permafrost environments. We suggest that, millennia after permafrost warming by over 10 �C, microbial community composition and abundance show some indications for proliferation but mainly reflect the sedimentation history and paleoenvironment and not a direct effect through warming.

Description: Due to its dryness, the subtropical free troposphere plays a critical role in the radiative balance of the Earth's climate system. But the complex interactions of the dynamical and physical processes controlling the variability in the moisture budget of this sensitive region of the subtropical atmosphere are still not fully understood. Stable water isotopes can provide important information about several of the latter processes, namely subsidence drying, turbulent mixing, and dry and moist convective moistening. In this study, we use high-resolution simulations of the isotope-enabled version of the regional weather and climate prediction model of the Consortium for Small-Scale Modelling (COSMOiso) to investigate predominant moisture transport pathways in the Canary Islands region in the eastern subtropical North Atlantic. Comparison of the simulated isotope signals with multi-platform isotope observations (aircraft, ground- and space-based remote sensing) from a field campaign in summer 2013 shows that COSMOiso can reproduce the observed variability of stable water vapour isotopes on timescales of hours to days, thus allowing us to study the mechanisms that control the subtropical free-tropospheric humidity. Changes in isotopic signals along backward trajectories from the Canary Islands region reveal the physical processes behind the synoptic-scale isotope variability. We identify four predominant moisture transport pathways of mid-tropospheric air, each with distinct isotopic signatures: - air parcels originating from the convective boundary layer of the Saharan heat low (SHL) – these are characterised by a homogeneous isotopic composition with a particularly high δD (median mid-tropospheric δD=−122‰), which results from dry convective mixing of low-level moisture of diverse origin advected into the SHL; - air parcels originating from the free troposphere above the SHL – although experiencing the largest changes in humidity and δD during their subsidence over West Africa, these air parcels typically have lower δD values (median δD=−148‰) than air parcels originating from the boundary layer of the SHL; - air parcels originating from outside the SHL region, typically descending from tropical upper levels south of the SHL, which are often affected by moist convective injections from mesoscale convective systems in the Sahel – their isotopic composition is much less enriched in heavy isotopes (median δD=−175‰) than those from the SHL region; - air parcels subsiding from the upper-level extratropical North Atlantic – this pathway leads to the driest and most depleted conditions (median δD=−255‰) in the middle troposphere near the Canary Islands. The alternation of these transport pathways explains the observed high variability in humidity and δD on synoptic timescales to a large degree. We further show that the four different transport pathways are related to specific large-scale flow conditions. In particular, distinct differences in the location of the North African mid-level anticyclone and of extratropical Rossby wave patterns occur between the four transport pathways. Overall, this study demonstrates that the adopted Lagrangian isotope perspective enhances our understanding of air mass transport and mixing and offers a sound interpretation of the free-tropospheric variability of specific humidity and isotope composition on timescales of hours to days in contrasting atmospheric conditions over the eastern subtropical North Atlantic.

Description: Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of the non-Gaussianity of forecast error distributions at 1 km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (local ensemble transform Kalman filter) assimilating phased array radar observations every 30 s. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 min to 30 s, particularly for vertical velocity and radar reflectivity.

Description: Two feature-based verification methods, thus far only used for the diagnostic evaluation of atmospheric models, have been applied to compare ∼7 km resolution pre-operational analyses of chlorophyll-a (Chl-a) concentrations to a 1 km gridded satellite-derived Chl-a concentration product. The aim of this study was to assess the value of applying such methods to ocean models. Chl-a bloom objects were identified in both data sets for the 2019 bloom season (1 March to 31 July). These bloom objects were analysed as discrete (2-D) spatial features, but also as space–time (3-D) features, providing the means of defining the onset, duration and demise of distinct bloom episodes and the season as a whole. The new feature-based verification methods help reveal that the model analyses are not able to represent small coastal bloom objects, given the coarser definition of the coastline, also wrongly producing more bloom objects in deeper Atlantic waters. Model analyses' concentrations are somewhat higher overall. The bias manifests itself in the size of the model analysis bloom objects, which tend to be larger than the satellite-derived bloom objects. The onset of the bloom season is delayed by 26 d in the model analyses, but the season also persists for another month beyond the diagnosed end. The season was diagnosed to be 119 d long in the model analyses, compared to 117 d from the satellite product. Geographically, the model analyses and satellite-derived bloom objects do not necessarily exist in a specific location at the same time and only overlap occasionally.

Description: The observing system design of multidisciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increases in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel numerical simulation–environmental response function (NS–ERF) approach to observing system simulation experiments that aids surface–atmosphere synthesis at the interface of mesoscale and microscale meteorology. In a case study we demonstrate application of the NS–ERF approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered the placement of 20 eddy covariance flux towers, operations for 72 h of low-altitude flux aircraft measurements, and integration of various remote sensing data products. A 2 h high-resolution large eddy simulation created a cloud-free virtual atmosphere for surface and meteorological conditions characteristic of the field campaign domain and period. To explore two specific design hypotheses we super-sampled this virtual atmosphere as observed by 13 different yet simultaneous observing system designs consisting of virtual ground, airborne, and satellite observations. We then analyzed these virtual observations through ERFs to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19's potential to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its modular extensibility, NS–ERF lends itself to optimizing observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection, and multi-species applications, among other use cases.

Description: This paper presents a new technique to derive thermospheric temperature from space-based disk observations of far ultraviolet airglow. The technique, guided by findings from principal component analysis of synthetic daytime Lyman–Birge–Hopfield (LBH) disk emissions, uses a ratio of the emissions in two spectral channels that together span the LBH (2,0) band to determine the change in band shape with respect to a change in the rotational temperature of N2. The two-channel-ratio approach limits representativeness and measurement error by only requiring measurement of the relative magnitudes between two spectral channels and not radiometrically calibrated intensities, simplifying the forward model from a full radiative transfer model to a vibrational–rotational band model. It is shown that the derived temperature should be interpreted as a column-integrated property as opposed to a temperature at a specified altitude without utilization of a priori information of the thermospheric temperature profile. The two-channel-ratio approach is demonstrated using NASA GOLD Level 1C disk emission data for the period of 2–8 November 2018 during which a moderate geomagnetic storm has occurred. Due to the lack of independent thermospheric temperature observations, the efficacy of the approach is validated through comparisons of the column-integrated temperature derived from GOLD Level 1C data with the GOLD Level 2 temperature product as well as temperatures from first principle and empirical models. The storm-time thermospheric response manifested in the column-integrated temperature is also shown to corroborate well with hemispherically integrated Joule heating rates, ESA SWARM mass density at 460 km, and GOLD Level 2 column O/N2 ratio.

Description: In this paper, we present a new version of the chemistry–climate model SOCOL-AERv2 supplemented by an iodine chemistry module. We perform three 20-year ensemble experiments to assess the validity of the modeled iodine and to quantify the effects of iodine on ozone. The iodine distributions obtained with SOCOL-AERv2-I agree well with AMAX-DOAS observations and with CAM-chem model simulations. For the present-day atmosphere, the model suggests that the iodine-induced chemistry leads to a 3 %–4 % reduction in the ozone column, which is greatest at high latitudes. The model indicates the strongest influence of iodine in the lower stratosphere with 30 ppbv less ozone at low latitudes and up to 100 ppbv less at high latitudes. In the troposphere, the account of the iodine chemistry reduces the tropospheric ozone concentration by 5 %–10 % depending on geographical location. In the lower troposphere, 75 % of the modeled ozone reduction originates from inorganic sources of iodine, 25 % from organic sources of iodine. At 50 hPa, the results show that the impacts of iodine from both sources are comparable. Finally, we determine the sensitivity of ozone to iodine by applying a 2-fold increase in iodine emissions, as it might be representative for iodine by the end of this century. This reduces the ozone column globally by an additional 1.5 %–2.5 %. Our results demonstrate the sensitivity of atmospheric ozone to iodine chemistry for present and future conditions, but uncertainties remain high due to the paucity of observational data of iodine species.

Description: China has experienced dramatic changes in emissions since 2010, which accelerated following the implementation of the Clean Air Action program in 2013. These changes have resulted in significant air quality improvements that are reflected in observations from both surface networks and satellite observations. Air pollutants, such as PM2.5, surface ozone, and their precursors, have long enough lifetimes in the troposphere to be easily transported downwind. Emission changes in China will thus not only change the domestic air quality but will also affect the air quality in other regions. In this study, we use a global chemistry transport model (CAM-chem) to simulate the influence of Chinese emission changes from 2010 to 2017 on both domestic and foreign air quality. We then quantify the changes in air-pollution-associated (including both PM2.5 and O3) premature mortality burdens at regional and global scales. Within our simulation period, the population-weighted annual PM2.5 concentration in China peaks in 2011 (94.1 µg m−3) and decreases to 69.8 µg m−3 by 2017. These estimated national PM2.5 concentration changes in China are comparable with previous studies using fine-resolution regional models, though our model tends to overestimate PM2.5 from 2013 to 2017 when evaluated with surface observations. Relative to 2010, emission changes in China increased the global PM2.5-associated premature mortality burdens through 2013, among which a majority of the changes (∼ 93 %) occurred in China. The sharp emission decreases after 2013 generated significant benefits for human health. By 2017, emission changes in China reduced premature deaths associated with PM2.5 by 108 800 (92 800–124 800) deaths per year globally, relative to 2010, among which 92 % were realized in China. In contrast, the population-weighted, annually averaged maximum daily 8 h ozone concentration peaked in 2014 and did not reach 2010 levels by 2017. As such, O3 generated nearly 8500 (6500–9900) more premature deaths per year in 2017 compared to 2010. Downwind regions, such as South Korea, Japan, and the United States, generally experienced O3 improvements following 2013 due to the decreased export of ozone and its precursors. Overall, we conclude that the sharp emission reductions in China over the past decade have generated substantial benefits for air quality that have reduced premature deaths associated with air pollution at a global scale.

Description: Icequakes are the result of processes occurring within the ice mass or between the ice and its environment. Studying icequakes provides a unique view on ice dynamics, specifically on the basal conditions. Changes in conditions due to environmental or climate changes are reflected in icequakes. Counting and characterizing icequakes is thus essential to monitor them. Most of the icequakes recorded by the seismic station at the Belgian Princess Elisabeth Antarctica Station (PE) have small amplitudes corresponding to maximal displacements of a few nanometres. Their detection threshold is highly variable because of the rapid and strong changes in the local seismic noise level. Therefore, we evaluated the influence of katabatic winds on the noise measured by the well-protected PE surface seismometer. Our purpose is to identify whether the lack of icequake detection during some periods could be associated with variations in the processes generating them or simply with a stronger seismic noise linked to stronger wind conditions. We observed that the wind mainly influences seismic noise at frequencies greater than 1 Hz. The seismic noise power exhibits a bilinear correlation with the wind velocity, with two different slopes at a wind velocity lower and greater than 6 m s−1 and with, for example at a period of 0.26 s, a respective variation of 0.4 dB (m −1 s) and 1.4 dB (m −1 s). These results allowed a synthetic frequency and wind-speed-dependent noise model to be presented that explains the behaviour of the wind-induced seismic noise at PE, which shows that seismic noise amplitude increases exponentially with increasing wind speed. This model enables us to study the influence of the wind on the original seismic dataset, which improves the observation of cryoseismic activity near the PE station.