ALBERT

Description: Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE, and GOCE (see appendices for a list of abbreviations) satellite missions, the geophysical community has identified the need for the continued monitoring of the time-variable component with the purpose of estimating the hydrological and glaciological yearly cycles and long-term trends. Currently, the GRACE-FO satellites are the sole dedicated provider of these data, while previously the GRACE mission fulfilled that role for 15 years. There is a data gap spanning from July 2017 to May 2018 between the end of the GRACE mission and start the of GRACE-FO, while the Swarm satellites have collected gravimetric data with their GPS receivers since December 2013. We present high-quality gravity field models (GFMs) from Swarm data that constitute an alternative and independent source of gravimetric data, which could help alleviate the consequences of the 10-month gap between GRACE and GRACE-FO, as well as the short gaps in the existing GRACE and GRACE-FO monthly time series. The geodetic community has realized that the combination of different gravity field solutions is superior to any individual model and set up the Combination Service of Time-variable Gravity Fields (COST-G) under the umbrella of the International Gravity Field Service (IGFS), part of the International Association of Geodesy (IAG). We exploit this fact and deliver the highest-quality monthly GFMs, resulting from the combination of four different gravity field estimation approaches. All solutions are unconstrained and estimated independently from month to month. We tested the added value of including kinematic baselines (KBs) in our estimation of GFMs and conclude that there is no significant improvement. The non-gravitational accelerations measured by the accelerometer on board Swarm C were also included in our processing to determine if this would improve the quality of the GFMs, but we observed that is only the case when the amplitude of the non-gravitational accelerations is higher than during the current quiet period in solar activity. Using GRACE data for comparison, we demonstrate that the geophysical signal in the Swarm GFMs is largely restricted to spherical harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations in Earth's gravity field over land areas since mid-2015 with roughly 4 cm equivalent water height (EWH) agreement with respect to GRACE. Over ocean areas, we illustrate that a more intense smoothing with 3000 km radius is necessary to resolve large-scale gravity variations, which agree with GRACE roughly at the level of 1 cm EWH, while at these spatial scales the GRACE observes variations with amplitudes between 0.3 and 1 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analysis is 0.91 cm, 0.76 cm yr−1, and 0.79 in terms of temporal mean, trend, and correlation coefficient, respectively. The Swarm monthly models are distributed on a quarterly basis at ESA's Earth Swarm Data Access (at https://swarm-diss.eo.esa.int/, last access: 5 June 2020, follow Level2longterm and then EGF) and at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm, last access: 5 June 2020), as well as identified with the DOI https://doi.org/10.5880/ICGEM.2019.006 (Encarnacao et al., 2019).

Description: Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20 % of all cloudy pixels as multilayer (decreasing to about 13 % if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

Description: In the context of the Arctic amplification of climate change affecting the regional atmospheric hydrological cycle, it is crucial to characterize the present-day moisture sources of the Arctic. The isotopic composition is an important tool to enhance our understanding of the drivers of the hydrological cycle due to the different molecular characteristics of water stable isotopes during phase change. This study introduces 2 years of continuous in situ water vapour and precipitation isotopic observations conducted since July 2015 in the eastern Siberian Lena delta at the research station on Samoylov Island. The vapour isotopic signals are dominated by variations at seasonal and synoptic timescales. Diurnal variations of the vapour isotopic signals are masked by synoptic variations, indicating low variations of the amplitude of local sources at the diurnal scale in winter, summer and autumn. Low-amplitude diurnal variations in spring may indicate exchange of moisture between the atmosphere and the snow-covered surface. Moisture source diagnostics based on semi-Lagrangian backward trajectories reveal that different air mass origins have contrasting contributions to the moisture budget of the Lena delta region. At the seasonal scale, the distance from the net moisture sources to the arrival site strongly varies. During the coldest months, no contribution from local secondary evaporation is observed. Variations of the vapour isotopic composition during the cold season on the synoptic timescale are strongly related to moisture source regions and variations in atmospheric transport: warm and isotopically enriched moist air is linked to fast transport from the Atlantic sector, while dry and cold air with isotopically depleted moisture is generally associated with air masses moving slowly over northern Eurasia.

Description: The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”. Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall 〉 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (〉 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (〉 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (〉 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; 〉 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).

Description: The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as finite difference (FD) modelling and spectral element methods (SEMs) are the most popular techniques for simulating seismic waves, but disadvantages such as their computational cost prohibit their use for many tasks. In this work, we investigate the potential of deep learning for aiding seismic simulation in the solid Earth sciences. We present two deep neural networks which are able to simulate the seismic response at multiple locations in horizontally layered and faulted 2-D acoustic media an order of magnitude faster than traditional finite difference modelling. The first network is able to simulate the seismic response in horizontally layered media and uses a WaveNet network architecture design. The second network is significantly more general than the first and is able to simulate the seismic response in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media, using a conditional autoencoder design. We test the sensitivity of the accuracy of both networks to different network hyperparameters and show that the WaveNet network can be retrained to carry out fast seismic inversion in the same media. We find that are there are challenges when extending our methods to more complex, elastic and 3-D Earth models; for example, the accuracy of both networks is reduced when they are tested on models outside of their training distribution. We discuss further research directions which could address these challenges and potentially yield useful tools for practical simulation tasks.

Description: Little is known about the climate evolution on the Kamchatka Peninsula during the last glacial–interglacial transition as existing climate records do not reach beyond 12 ka BP. In this study, a summer temperature record for the past 20 kyr is presented. Branched glycerol dialkyl glycerol tetraethers, terrigenous biomarkers suitable for continental air temperature reconstructions, were analyzed in a sediment core from the western continental margin off Kamchatka in the marginal northwest Pacific (NW Pacific). The record suggests that summer temperatures on Kamchatka during the Last Glacial Maximum (LGM) equaled modern temperatures. We suggest that strong southerly winds associated with a pronounced North Pacific High pressure system over the subarctic NW Pacific accounted for the warm conditions. A comparison with an Earth system model reveals discrepancies between model and proxy-based reconstructions for the LGM temperature and atmospheric circulation in the NW Pacific realm. The deglacial temperature development is characterized by abrupt millennial-scale temperature oscillations. The Bølling–Allerød warm phase and the Younger Dryas cold spell are pronounced events, suggesting a connection to North Atlantic climate variability.

Description: A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

Description: Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent (∼0.7 over continental sources and ∼0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m−2 over the SAO (20∘ S–10∘ N and 10∘ W–20∘ E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes (−25 W m−2) and surface land temperature (−1.5 ∘C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ∼200 m.

Description: We investigate stratospheric gravity wave observations by the Atmospheric InfraRed Sounder (AIRS) aboard NASA's Aqua satellite and the High Resolution Dynamics Limb Sounder (HIRDLS) aboard NASA's Aura satellite. AIRS operational temperature retrievals are typically not used for studies of gravity waves, because their vertical and horizontal resolution is rather limited. This study uses data of a high-resolution retrieval which provides stratospheric temperature profiles for each individual satellite footprint. Therefore the horizontal sampling of the high-resolution retrieval is 9 times better than that of the operational retrieval. HIRDLS provides 2-D spectral information of observed gravity waves in terms of along-track and vertical wavelengths. AIRS as a nadir sounder is more sensitive to short-horizontal-wavelength gravity waves, and HIRDLS as a limb sounder is more sensitive to short-vertical-wavelength gravity waves. Therefore HIRDLS is ideally suited to complement AIRS observations. A calculated momentum flux factor indicates that the waves seen by AIRS contribute significantly to momentum flux, even if the AIRS temperature variance may be small compared to HIRDLS. The stratospheric wave structures observed by AIRS and HIRDLS often agree very well. Case studies of a mountain wave event and a non-orographic wave event demonstrate that the observed phase structures of AIRS and HIRDLS are also similar. AIRS has a coarser vertical resolution, which results in an attenuation of the amplitude and coarser vertical wavelengths than for HIRDLS. However, AIRS has a much higher horizontal resolution, and the propagation direction of the waves can be clearly identified in geographical maps. The horizontal orientation of the phase fronts can be deduced from AIRS 3-D temperature fields. This is a restricting factor for gravity wave analyses of limb measurements. Additionally, temperature variances with respect to stratospheric gravity wave activity are compared on a statistical basis. The complete HIRDLS measurement period from January 2005 to March 2008 is covered. The seasonal and latitudinal distributions of gravity wave activity as observed by AIRS and HIRDLS agree well. A strong annual cycle at mid- and high latitudes is found in time series of gravity wave variances at 42 km, which has its maxima during wintertime and its minima during summertime. The variability is largest during austral wintertime at 60∘ S. Variations in the zonal winds at 2.5 hPa are associated with large variability in gravity wave variances. Altogether, gravity wave variances of AIRS and HIRDLS are complementary to each other. Large parts of the gravity wave spectrum are covered by joint observations. This opens up fascinating vistas for future gravity wave research.

Description: The composition of sediment organic matter (OM) exerts a strong control on biogeochemical processes in lakes, such as those involved in the fate of carbon, nutrients and trace metals. While between-lake spatial variability of OM quality is increasingly investigated, we explored in this study how the molecular composition of sediment OM varies spatially within a single lake and related this variability to physical parameters and elemental geochemistry. Surface sediment samples (0–10 cm) from 42 locations in Härsvatten – a small boreal forest lake with a complex basin morphometry – were analyzed for OM molecular composition using pyrolysis gas chromatography mass spectrometry for the contents of 23 major and trace elements and biogenic silica. We identified 162 organic compounds belonging to different biochemical classes of OM (e.g., carbohydrates, lignin and lipids). Close relationships were found between the spatial patterns of sediment OM molecular composition and elemental geochemistry. Differences in the source types of OM (i.e., terrestrial, aquatic plant and algal) were linked to the individual basin morphometries and chemical status of the lake. The variability in OM molecular composition was further driven by the degradation status of these different source pools, which appeared to be related to sedimentary physicochemical parameters (e.g., redox conditions) and to the molecular structure of the organic compounds. Given the high spatial variation in OM molecular composition within Härsvatten and its close relationship with elemental geochemistry, the potential for large spatial variability across lakes should be considered when studying biogeochemical processes involved in the cycling of carbon, nutrients and trace elements or when assessing lake budgets.