ALBERT

Beschreibung: The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2022 is an update of the previous version, GLODAPv2.2021 (Lauvset et al., 2021). The major changes are as follows: data from 96 new cruises were added, data coverage was extended until 2021, and for the first time we performed secondary quality control on all sulphur hexafluoride (SF6) data. In addition, a number of changes were made to data included in GLODAPv2.2021. These changes affect specifically the SF6 data, which are now subjected to secondary quality control, and carbon data measured onboard the RV Knorr in the Indian Ocean in 1994–1995 which are now adjusted using CRM measurements made at the time. GLODAPv2.2022 includes measurements from almost 1.4 million water samples from the global oceans collected on 1085 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, CCl4, and SF6) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 96 new cruises were derived by comparing those data with the data from the 989 quality controlled cruises in the GLODAPv2.2021 data product using crossover analysis. SF6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO2) chemistry comparisons to estimates based on empirical algorithms provided additional context for adjustment decisions. The adjustments that we applied are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 μmol kg-1 in dissolved inorganic carbon, 4 μmol kg-1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments.

Beschreibung: Perturbations in stratospheric aerosol due to explosive volcanic eruptions are a primary contributor to natural climate variability. Observations of stratospheric aerosol are available for the past decades, and information from ice cores has been used to derive estimates of stratospheric sulfur injections and aerosol optical depth over the Holocene (approximately 10 000 BP to present) and into the last glacial period, extending back to 60 000 BP. Tephra records of past volcanism, compared to ice cores, are less complete but extend much further into the past. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present here an ensemble reconstruction of volcanic stratospheric sulfur injection (VSSI) over the last 140 000 years that is based primarily on terrestrial and marine tephra records. VSSI values are computed as a simple function of eruption magnitude based on VSSI estimates from ice cores and satellite observations for identified eruptions. To correct for the incompleteness of the tephra record, we include stochastically generated synthetic eruptions assuming a constant background eruption frequency from the ice core Holocene record. While the reconstruction often differs from ice core estimates for specific eruptions due to uncertainties in the data used and reconstruction method, it shows good agreement with an ice-core-based VSSI reconstruction in terms of millennial-scale cumulative VSSI variations over the Holocene. The PalVol reconstruction provides a new basis to test the contributions of forced vs. unforced natural variability to the spectrum of climate and the mechanisms leading to abrupt transitions in the palaeoclimate record with low- to high-complexity climate models. The PalVol volcanic forcing reconstruction is available at https://doi.org/10.26050/WDCC/PalVolv1 (Toohey and Schindlbeck-Belo, 2023).

Beschreibung: The importance of Antarctic sea ice and Southern Ocean warming has come into the focus of polar research during the last couple of decades. Especially around West Antarctica, where warm water masses approach the continent and where sea ice has declined, the distribution and evolution of sea ice play a critical role in the stability of nearby ice shelves. Organic geochemical analyses of marine seafloor surface sediments from the Antarctic continental margin allow an evaluation of the applicability of biomarker-based sea-ice and ocean temperature reconstructions in these climate-sensitive areas. We analysed highly branched isoprenoids (HBIs), such as the sea-ice proxy IPSO25 and phytoplankton-derived HBI-trienes, as well as phytosterols and isoprenoidal glycerol dialkyl glycerol tetraethers (GDGTs), which are established tools for the assessment of primary productivity and ocean temperatures respectively. The combination of IPSO25 with a phytoplankton marker (i.e. the PIPSO25 index) permits semi-quantitative sea-ice reconstructions and avoids misleading over- or underestimations of sea-ice cover. Comparisons of the PIPSO25-based sea-ice distribution patterns and TEX86L- and RI-OH′-derived ocean temperatures with (1) sea-ice concentrations obtained from satellite observations and (2) instrument measurements of sea surface and subsurface temperatures corroborate the general capability of these proxies to determine oceanic key variables properly. This is further supported by model data. We also highlight specific aspects and limitations that need to be taken into account for the interpretation of such biomarker data and discuss the potential of IPSO25 as an indicator for the former occurrence of platelet ice and/or the export of ice-shelf water.

Beschreibung: Forest fires modify soil organic carbon and suppress soil respiration for many decades after the initial disturbance. The associated changes in soil autotrophic and heterotrophic respiration from the time of the forest fire, however, are less well characterized. The FireResp model predicts soil autotrophic and heterotrophic respiration parameterized with a novel dataset across a fire chronosequence in the Yukon and Northwest Territories of Canada. The dataset consisted of soil incubation experiments and field measurements of soil respiration and soil carbon stocks. The FireResp model contains submodels that consider a Q10 (exponential) model of respiration compared to models of heterotrophic respiration using Michaelis–Menten kinetics parameterized with soil microbial carbon. For model evaluation we applied the Akaike information criterion and compared predicted patterns in components of soil respiration across the chronosequence. Parameters estimated with data from the 5 cm soil depth had better model–data comparisons than parameters estimated with data from the 10 cm soil depth. The model–data fit was improved by including parameters estimated from soil incubation experiments. Models that incorporated microbial carbon with Michaelis–Menten kinetics reproduced patterns in autotrophic and heterotrophic soil respiration components across the chronosequence. Autotrophic respiration was associated with aboveground tree biomass at more recently burned sites, but this association was less robust at older sites in the chronosequence. Our results provide support for more structured soil respiration models than standard Q10 exponential models.

Beschreibung: Aeromagnetic exploration is an important method of geophysical exploration. We study the compensation method of the towed bird system and establish the towed bird interference model. Due to the geomagnetic gradient changing greatly, the geomagnetic gradient is considered in the towed bird interference model. In this paper, we model the geomagnetic field gradient and analyze the influence of the towed bird system on the aeromagnetic compensation results. Finally, we apply the ridge regression method to solve the problem. We verify the feasibility of this compensation method through actual flight tests and further improve the data quality of the towed bird interference.

Beschreibung: The Arctic is exposed to even faster temperature changes than most other areas on Earth. Constantly increasing temperature will lead to thawing permafrost and changes in the methane (CH4) emissions from wetlands. One of the places exposed to those changes is the Abisko–Stordalen Mire in northern Sweden, where climate and vegetation studies have been conducted since the 1970s. In our study, we analyzed field-scale methane emissions measured by the eddy covariance method at Abisko–Stordalen Mire for 3 years (2014–2016). The site is a subarctic mire mosaic of palsas, thawing palsas, fully thawed fens, and open water bodies. A bimodal wind pattern prevalent at the site provides an ideal opportunity to measure mire patches with different permafrost status with one flux measurement system. The flux footprint for westerly winds was dominated by elevated palsa plateaus, while the footprint was almost equally distributed between palsas and thawing bog-like areas for easterly winds. As these patches are exposed to the same climatic and weather conditions, we analyzed the differences in the responses of their methane emission for environmental parameters. The methane fluxes followed a similar annual cycle over the 3 study years, with a gentle rise during spring and a decrease during autumn, without emission bursts at either end of the ice-free season. The peak emission during the ice-free season differed significantly for the two mire areas with different permafrost status: the palsa mire emitted 19 mg-C m−2 d−1 and the thawing wet sector 40 mg-C m−2 d−1. Factors controlling the methane emission were analyzed using generalized linear models. The main driver for methane fluxes was peat temperature for both wind sectors. Soil water content above the water table emerged as an explanatory variable for the 3 years for western sectors and the year 2016 in the eastern sector. The water table level showed a significant correlation with methane emission for the year 2016 as well. Gross primary production, however, did not show a significant correlation with methane emissions. Annual methane emissions were estimated based on four different gap-filing methods. The different methods generally resulted in very similar annual emissions. The mean annual emission based on all models was 3.1 ± 0.3 g-C m−2 a−1 for the western sector and 5.5 ± 0.5 g-C m−2 a−1 for the eastern sector. The average annual emissions, derived from these data and a footprint climatology, were 2.7 ± 0.5 and 8.2 ± 1.5 g-C m−2 a−1 for the palsa and thawing surfaces, respectively. Winter fluxes were relatively high, contributing 27 %–45 % to the annual emissions.

Beschreibung: This work presents the integration of a gas-phase and particulate atmospheric emission inventory (AEI) for Argentina in high spatial resolution (0.025∘×0.025∘; approx. 2.5 km×2.5 km) considering monthly variability from 1995 to 2020. The new inventory, called GEAA-AEIv3.0M, includes the following activities: energy production, fugitive emissions from oil and gas production, industrial fuel consumption and production, transport (road, maritime, and air), agriculture, livestock production, manufacturing, residential, commercial, and biomass and agricultural waste burning. The following species, grouped by atmospheric reactivity, are considered: (i) greenhouse gases (GHGs) – CO2, CH4, and N2O; (ii) ozone precursors – CO, NOx (NO+NO2), and non-methane volatile organic compounds (NMVOCs); (iii) acidifying gases – NH3 and SO2; and (iv) particulate matter (PM) – PM10, PM2.5, total suspended particles (TSPs), and black carbon (BC). The main objective of the GEAA-AEIv3.0M high-resolution emission inventory is to provide temporally resolved emission maps to support air quality and climate modeling oriented to evaluate pollutant mitigation strategies by local governments. This is of major concern, especially in countries where air quality monitoring networks are scarce, and the development of regional and seasonal emissions inventories would result in remarkable improvements in the time and space chemical prediction achieved by air quality models. Despite distinguishing among different sectoral and activity databases as well as introducing a novel spatial distribution approach based on census radii, our high-resolution GEAA-AEIv3.0M shows equivalent national-wide total emissions compared to the Third National Communication of Argentina (TNCA), which compiles annual GHG emissions from 1990 through 2014 (agreement within ±7.5 %). However, the GEAA-AEIv3.0M includes acidifying gases and PM species not considered in TNCA. Temporal comparisons were also performed against two international databases: Community Emissions Data System (CEDS) and EDGAR HTAPv5.0 for several pollutants; for EDGAR it also includes a spatial comparison. The agreement was acceptable within less than 30 % for most of the pollutants and activities, although a 〉90 % discrepancy was obtained for methane from fuel production and fugitive emissions and 〉120 % for biomass burning. Finally, the updated seasonal series clearly showed the pollution reduction due to the COVID-19 lockdown during the first quarter of year 2020 with respect to same months in previous years. Through an open-access data repository, we present the GEAA-AEIv3.0M inventory as the largest and more detailed spatial resolution dataset for the Argentine Republic, which includes monthly gridded emissions for 12 species and 15 stors between 1995 and 2020. The datasets are available at https://doi.org/10.17632/d6xrhpmzdp.2 (Puliafito et al., 2021), under a CC-BY 4 license.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.