Large Uncertainties Research Articles

Methane dynamics in vegetated habitats in inland waters: quantification, regulation, and global significance

Freshwater ecosystems, including lakes, wetlands, and running waters, are estimated to contribute over half the natural emissions of methane (CH4) globally, yet large uncertainties remain in the inland water CH4 budget. These are related to the highly heterogeneous nature and the complex regulation of the CH4 emission pathways, which involve diffusion, ebullition, and plant-associated transport. The latter, in particular, represents a major source of uncertainty in our understanding of inland water CH4 dynamics. Many freshwater ecosystems harbor habitats colonized by submerged and emergent plants, which transport highly variable amounts of CH4 to the atmosphere but whose presence may also profoundly influence local CH4 dynamics. Yet, CH4 dynamics of vegetated habitats and their potential contribution to emission budgets of inland waters remain understudied and poorly quantified. Here we present a synthesis of literature pertaining CH4 dynamics in vegetated habitats, and we (i) provide an overview of the different ways the presence of aquatic vegetation can influence CH4 dynamics (i.e., production, oxidation, and transport) in freshwater ecosystems, (ii) summarize the methods applied to study CH4 fluxes from vegetated habitats, and (iii) summarize the existing data on CH4 fluxes associated to different types of aquatic vegetation and vegetated habitats in inland waters. Finally, we discuss the implications of CH4 fluxes associated with aquatic vegetated habitats for current estimates of aquatic CH4 emissions at the global scale. The fluxes associated to different plant types and from vegetated areas varied widely, ranging from−8.6 to over 2835.8 mg CH4 m−2 d−1, but were on average high relative to fluxes in non-vegetated habitats. We conclude that, based on average vegetation coverage and average flux intensities of plant-associated fluxes, the exclusion of these habitats in lake CH4 balances may lead to a major underestimation of global lake CH4 emissions. This synthesis highlights the need to incorporate vegetated habitats into CH4 emission budgets from natural freshwater ecosystems and further identifies understudied research aspects and relevant future research directions.

Contribution of radioactive particles to the post-explosion exposure of atomic bomb survivors implied from their stable chromosome aberration rates.

Even today when nearly 80 years have passed after the atomic bomb (A-bomb) was dropped, there are still debates about the exact doses received by the A-bomb survivors. While initial airborne kerma radiation (or energy spectrum of emitted radiation) can be measured with sufficient accuracy to assess the radiation dose to A-bomb survivors, it is not easy to accurately assess the neutron dose including appropriate weighting of neutron absorbed dose. Particularly, possible post-explosion exposure due to the radioactive particles generated through neutron activation have been almost neglected so far, mainly because of a large uncertainty associated to the behavior of those particles. However, it has been supposed that contribution of such non-initial radiation exposure from the neutron-induced radioactive particles could be significant, according to the findings that the stable chromosomal aberration rates which indicate average whole-body radiation doses were found to be more than 30% higher for those exposed indoors than for those outdoors even at the same initial dose estimated for the Life Span Study. In this Mini Review article, the authors explain that such apparently controversial observations can be reasonably explained by assuming a higher production rate of neutron-induced radioactive particles in the indoor environment near the hypocenter.

Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget

Abstract. Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m−2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

Opinion: The importance of historical and paleoclimate aerosol radiative effects

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.

Towards a geodetic earthquake catalogue for Central America: detecting coseismic deformation in Costa Rica using Sentinel-1 InSAR

SUMMARY Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths $\le \, 20$ km and magnitudes Mw 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > Mw 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps.

The impacts of regional transport on anthropogenic source contributions of PM2.5 in a basin city, China

PM2.5 pollution events are often happened in urban agglomeration locates in mountain-basin regions due to the complex terra and intensive emissions. Source apportionment is essential for identifying the pollution sources and important for developing local mitigation strategies, however, it is influenced by regional transport. To understand how the regional transport influences the atmospheric environment of a basin, we connected the PM2.5 source contributions estimated by observation-based receptor source apportionment and the regional contributions estimated by a tagging technology in the comprehensive air quality model with extensions (CAMx) via an artificial neural network (ANNs). The result shows that the PM2.5 in Xi'an was from biomass burning, coal combustion, traffic related emissions, mineral dust, industrial emissions, secondary nitrate and sulfate. 48.8 % of the PM2.5 in study period was from Xi'an, then followed by the outside area of Guanzhong basin (28.2 %), Xianyang (14.6 %) and Weinan (5.8 %). Baoji and Tongchuan contributed trivial amount. The sensitivity analysis showed that the transported PM2.5 would lead to divergent results of source contributions at Xi'an. The transported PM2.5 from the outside has great a potential to alter the source contributions implying a large uncertainty of the source apportionment introduced when long-range transported pollutants arrived. It suggests that a full comprehension on the impacts of regional transport can lower the uncertainty of the local PM2.5 source apportionment and reginal collaborative actions can be of great use for pollution mitigation.

Accounting for hydroclimatic properties in flood frequency analysis procedures

Abstract. Flood hazard is typically evaluated by computing extreme flood probabilities from a flood frequency distribution following nationally defined procedures in which observed peak flow series are fit to a parametric probability distribution. These procedures, also known as flood frequency analysis, typically recommend only one probability distribution family for all watersheds within a country or region. However, large uncertainties associated with extreme flood probability estimates (>50-year flood or Q50) can be further biased when fit to an inappropriate distribution model because of differences in the tails between distribution families. Here, we demonstrate that hydroclimatic parameters can aid in the selection of a parametric flood frequency distribution. We use L-moment diagrams to visually show the fit of gaged annual maxima series across the United States, grouped by their Köppen climate classification and the precipitation intensities of the basin, to a general extreme value (GEV), log normal 3 (LN3), and Pearson 3 (P3) distribution. Our results show that in real space basic hydroclimatic properties of a basin exert a significant influence on the statistical distribution of the annual maxima. The best-fitted family distribution shifts from a GEV towards an LN3 distribution across a gradient from colder and wetter climates (Köppen group D, continental climates) towards more arid climates (Köppen group B, dry climates). Due to the diversity of hydrologic processes and flood-generating mechanisms among watersheds within large countries like the United States, we recommend that the selection of distribution model be guided by the hydroclimatic properties of the basin rather than relying on a single national distribution model.

Importance of stable mass transfer and stellar winds for the formation of gravitational wave sources

Abstract The large number of gravitational wave (GW) detections have revealed the properties of the merging black hole binary population, but how such systems are formed is still heavily debated. Understanding the imprint of stellar physics on the observable GW population will shed light on how we can use the gravitational wave data, along with other observations, to constrain the poorly understood evolution of massive binaries. We perform a parameter study on the classical isolated binary formation channel with the population synthesis code SeBa to investigate how sensitive the properties of the coalescing binary black hole population are on the uncertainties related to first phase of mass transfer and stellar winds. We vary five assumptions: 1 and 2) the mass transfer efficiency and the angular momentum loss during the first mass transfer phase, 3) the mass transfer stability criteria for giant donors with radiative envelopes, 4) the effective temperature at which an evolved star develops a deep convective envelope, and 5) the mass loss rates of stellar winds. We find that current uncertainties related to first phase of mass transfer have a huge impact on the relative importance of different dominant channels, while the observable demographics of GW sources are not significantly affected. Our varied parameters have a complex, interrelated effect on the population properties of GW sources. Therefore, inference of massive binary physics from GW data alone remains extremely challenging, given the large uncertainties in our current models.

Atmospheric Δ14C in the northern and southern hemispheres over the past two millennia: Role of production rate, southern hemisphere westerly winds and ocean circulation changes

The variations of atmospheric Δ14C over the past two millennia are classically attributed to changes in its production rate in the upper atmosphere, which in turn is related to changes in solar activity and the Earth's magnetic field. However, the potential contribution of atmospheric and oceanic circulation changes during this period has not been precisely quantified. This has been achieved here using a coupled climate model simulating explicitly the evolution of the different carbon isotopes, driven by a new estimate of 14C production derived from 10Be measurements, and using different production rates for each hemisphere. Our results confirm that changes in global and hemispheric atmospheric Δ14C are primarily driven by changes in production rate. The very good agreement between our results and observed atmospheric Δ14C also highlights the strong consistency of current interpretations of 10Be and 14C measurements. By contrast, the interhemispheric difference in atmospheric Δ14C is controlled by changes in the intensity of southern hemisphere westerly winds (SHWW) that impact atmosphere-ocean exchange and the upwelling of 14C depleted water masses in the Southern Ocean. Changes in deep water formation in both hemispheres or open ocean polynyas in the Southern Ocean only have a small impact on atmospheric Δ14C over that period. When driven by changes in the SHWW proportional to three existing reconstructions of the Southern Annular Mode (SAM) index, the model reproduces some key characteristics of the observed interhemispheric Δ14C gradient. These include the low values in the 15th century and the peak in the 16th century, but there are also clear differences between the experiments and Δ14C observations. This suggests that while SAM reconstructions capture some robust features in the centennial variability, large uncertainties remain.

Stability of Ice Shelves and Ice Cliffs in a Changing Climate

The largest uncertainty in future sea-level rise is loss of ice from the Greenland and Antarctic Ice Sheets. Ice shelves, freely floating platforms of ice that fringe the ice sheets, play a crucial role in restraining discharge of grounded ice into the ocean through buttressing. However, since the 1990s, several ice shelves have thinned, retreated, and collapsed. If this pattern continues, it could expose thick cliffs that become structurally unstable and collapse in a process called marine ice cliff instability (MICI). However, the feedbacks between calving, retreat, and other forcings are not well understood. Here we review observed modes of calving from ice shelves and marine-terminating glaciers, and their relation to environmental forces. We show that the primary driver of calving is long-term internal glaciological stress, but as ice shelves thin they may become more vulnerable to environmental forcing. This vulnerability—and the potential for MICI—comes from a combination of the distribution of preexisting flaws within the ice and regions where the stress is large enough to initiate fracture. Although significant progress has been made modeling these processes, theories must now be tested against a wide range of environmental and glaciological conditions in both modern and paleo conditions. ▪ Ice shelves, floating platforms of ice fed by ice sheets, shed mass in a near-instantaneous fashion through iceberg calving. ▪ Most ice shelves exhibit a stable cycle of calving front advance and retreat that is insensitive to small changes in environmental conditions. ▪ Some ice shelves have retreated or collapsed completely, and in the future this could expose thick cliffs that could become structurally unstable called ice cliff instability. ▪ The potential for ice shelf and ice cliff instability is controlled by the presence and evolution of flaws or fractures within the ice. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 52 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

How will drought evolve in global arid zones under different future emission scenarios?

Study regionGlobal aridity zones. Study focusEvaluating drought under the impact of climate change is crucial for ensuring sustainable development and ecological security, especially in global arid zones where the environment is vulnerable to global warming. However, there are large uncertainties in evaluating the future drought risk of arid zones due to uncertainties within climate projections and the use drought-related indices that may not be appropriate for all assessments. Therefore, under the precondition of bias correction applied to CMIP6 data, the drought detection capability of drought indices calculated from multimodel ensemble was evaluated. Based on the drought index with the strongest detection capability and the Reliability-Resilience-Vulnerability (RRV) framework, the future drought risk in global arid zones were predicted. New hydrological insights for the regionAmong the drought indices calculated from CMIP6 historical simulations, SPEI offered the best representation of drought patterns. According to the CMIP6 future projections, SSP1–2.6 can effectively mitigate the impact of future drought risk compared with the historical period. In scenarios with higher emissions, the equatorial to northern latitude 30° region will experience more severe drought. For SSP5–8.5, the areas in the arid zones where drought risk deteriorates significantly are several times larger than in other scenarios. To mitigate drought risks, most nations should support a greener, low-emission approach in line with sustainable development goals, reducing the risk of drought-related damage.

Dynamic Prediction with Statistical Uncertainty Evaluation of Phosphorus Content Based on Functional Relevance Vector Machine

In the steelmaking process, inaccurate composition of raw materials, experimental process parameter control, and other factors bring about large statistical uncertainty to the phosphorus content prediction of molten steel. Meanwhile, lots of models mainly predict the end‐point phosphorus content, but the composition change during the whole production process is still in the gray box condition, where the exact refining state is unknown. These two problems increase the difficulty in process parameter optimization and control. In order to improve the phosphorus content control in the steelmaking process, this work proposes the functional relevance vector machine (FRVM) method. This method smooths the time series of process parameters to continuous functions by functional data analysis to predict the phosphorus content change during the whole production process, and estimates the probabilistic distributions of regression weights based on the Bayesian framework to directly output the probabilistic confidence interval of the phosphorus content. The real‐world steelmaking dataset validates that the mean relative prediction error of phosphorus content by the FRVM method is less than 18.04% during the whole production process and the end‐point hit rate is 86.46% when the prediction error is within ±0.005 wt%.

The reduction of ill-posedness of the inverse heat transfer problem in rotating disc cavities using the wall temperature characteristics as the priori knowledge

Solving the heat conduction equation using surface measurement temperature as boundary conditions to obtain surface heat flux is an ill-posed inverse heat transfer problem, in which small temperature errors can create large uncertainties in heat flux. This paper proposes a RBF (Radial Basis Function) + BP (Back Propagation) neural network approximation method that uses the wall temperature characteristics as priori knowledge to reduce the ill-posedness of the inverse heat transfer problem in rotating disc cavities of aero-engines. The wall temperature characteristics are described using first-order derivative, which are discussed and obtained through analytical solution and fluid-thermal coupling numerical simulation. Bayesian inference theory is used to construct neural network training objective function with regularization term and the RBF + BP neural network approximation method with uncertain regularization coefficient is proposed. Numerical experiments were conducted based on numerical simulation data to verify the effectiveness of the proposed method. The results show that the method can effectively suppress the ill-posed nature of the inverse heat transfer problem and can reduce the heat flux relative error level by 1 ∼ 2 orders of magnitude compared with several traditional data fitting methods commonly used in rotating disc cavities. The method of densifying measurement points combined with priori knowledge revision near the air impact point can effectively capture the local heat flux characteristics caused by airflow impact. The RBF + BP approximation method can reduce the demand for measurement points by approximately 44 ∼ 60 % under the error of 3σ = 0 K ∼ 0.9 K compared with Bayesian inference method.

A Bayesian astrochronology for the Cambrian first occurrence of trilobites in West Gondwana (Morocco)

Abstract The first occurrence of trilobites at ca. 520 Ma is an iconic feature of the Cambrian Explosion. Developing a robust evolutionary view on early Cambrian life is generally hindered by large uncertainties in the ages of fossil finds and their global stratigraphic correlation. We developed an astrochronological interpretation for the Tiout section in Morocco that features some of the oldest trilobite fossils. Our novel approach to incorporating individual astronomical cycle durations in an integrated radioisotopic and astrochronological Bayesian age-depth model results in an age estimate of 519.62 Ma (519.70–519.54 Ma 95% highest posterior distribution) for the first occurrence of trilobites in West Gondwana. This level of precise age estimation is exceptional for biological events in deep time and demonstrates the power of our novel approach.

Genome reduction occurred in early Prochlorococcus with an unusually low effective population size.

In the oligotrophic sunlit ocean, the most abundant free-living planktonic bacterial lineages evolve convergently through genome reduction. The cyanobacterium Prochlorococcus responsible for 10% global oxygen production is a prominent example. The dominant theory known as "genome streamlining" posits that they have extremely large effective population sizes (Ne) such that selection for metabolic efficiency acts to drive genome reduction. Because genome reduction largely took place anciently, this theory builds on the assumption that their ancestors' Ne was similarly large. Constraining Ne for ancient ancestors is challenging because experimental measurements of extinct organisms are impossible and alternatively reconstructing ancestral Ne with phylogenetic models gives large uncertainties. Here, we develop a new strategy that leverages agent-based modeling to simulate the changes in the genome-wide ratio of radical to conservative nonsynonymous nucleotide substitution rate (dR/dC) in a possible range of Ne in ancestral populations. This proxy shows expected increases with decreases of Ne only when Ne falls to about 10k - 100k or lower, magnitudes characteristic of Ne of obligate endosymbiont species where drift drives genome reduction. Our simulations therefore strongly support a scenario where the primary force of Prochlorococcus genome reduction is drift rather than selection.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

Improving monthly mean land surface temperature estimation by merging four products using the generalized three-cornered hat method and maximum likelihood estimation

Land surface temperature (LST) is a critical parameter associated land surface energy and water balance at the land–atmosphere interface. Monthly mean LST (MMLST) is used not only as an input parameter for land surface models, but also as an indicator for general climate trend studies. Four MMLST products (i.e., NESDC, TPDC, ZENODO, and ERA5) were generated by different research teams and institutes using different algorithms. In this study, a new method was presented to improve MMLST estimation by merging these four products using the generalized three-cornered hat (TCH) method and maximum likelihood estimation (MLE). The generalized TCH method was used to simultaneously calculate the relative uncertainties of these four MMLST products during the period 2003–2019. In general, the smallest uncertainties are obtained for the TPDC MMLST product in most areas of the globe, whereas the largest uncertainties are achieved for the ERA5 MMLST product, especially in high-latitude regions. The MLE method was used to merge these four MMLST products and to generate a new MMLST product in consideration of the uncertainty of each individual MMLST product. In situ measurements at 25 stations from the AmeriFlux and SURFRAD networks during the period 2003–2019 were used to evaluate the accuracies of the individual and merged MMLST products. The largest root mean squared error (RMSE) of approximately 2.4 K is obtained for the TPDC MMLST product, whereas the smallest RMSE of around 1.6 K is reached for the merged MMLST product. The NESDC, ZENODO, and ERA5 MMLST products have similar RMSE of approximately 2 K. The results indicate that the merged MMLST product outperforms each individual MMLST product. The merged MMLST product shows promising prospects for climate- and global-scale studies and applications.

Development and validation of an analytical pyrolysis method for detection of airborne polystyrene nanoparticles

Microplastic is ubiquitous in the environment. Recently it was discovered that microplastic (MP, 1 μm−5 mm) contamination is present in the atmosphere where it can be transported over long distances and introduced to remote pristine environments. Sources, concentration levels, and transportation pathways of MP are still associated with large uncertainties. The abundance of atmospheric MP increases with decreasing particle size, suggesting that nanoplastics (NP, <1μm) could be of considerable atmospheric relevance. Only few analytical methods are available for detection of nanosized plastic particles. Thermoanalytical techniques are independent of particle size and are thus a powerful tool for MP and NP analysis. Here we develop a method for analysis of polystyrene on the nanogram scale using pyrolysis gas chromatography coupled to mass spectrometry. Pyrolysis was performed using a slow temperature ramp, and analytes were cryofocused prior to injection. The mass spectrometer was operated in selected ion monitoring (SIM) mode. A lower limit of detection of 1±1 ng and a lower limit of quantification of 2±2 ng were obtained (for the trimer peak). The method was validated with urban ▪ matrices of low (7 μg per sample) and high (53 μg per sample) aerosol mass loadings. The method performs well for low ▪ loadings, whereas high ▪ loadings seem to cause a matrix effect reducing the signal of polystyrene. This effect can be minimized by introducing a thermal desorption step prior to pyrolysis. The study provides a novel analysis method for qualitative and semi-quantitative analysis of PS on the nanogram scale in an aerosol matrix. Application of the method can be used to obtain concentration levels of polystyrene in atmospheric MP and NP. This is important in order to improve the understanding of the sources and sinks of MP and NP in the environment and thereby identify routes of exposure and uptake of this emerging contaminant.

Characterization of north Greenland polynyas with super-resolved passive microwave sea ice concentration

ABSTRACT Located at the easternmost sector of the Last Ice Area (LIA) with the oldest and thickest sea ice, the north of Greenland has witnessed some unprecedented polynyas since 2018, receiving considerable attention. Sea ice concentration (SIC) derived from passive microwave data can provide near daily observations of these polynyas. However, these SIC products are limited by the coarse spatial resolution of passive microwave data, with mixed pixels and large uncertainties at the ice–water divide. It is especially difficult in the case of newly formed polynyas in the form of leads with narrow openings, preventing accurate estimation of the regional energy budget and heat fluxes as well as the hindering causal analysis of the polynyas. In this paper, a novel state-of-the-art deep-learning-based super-resolution (SR) method is adopted, and passive microwave SR images, with spatial resolutions of up to 4 times the original, are generated and employed to derive super-resolved SIC (SR-SIC). Experimental results confirm the benefits of SR-SIC for finer monitoring of sea ice at both the polynya and lead scale compared to the original lower-resolution SIC, with an average 7.55% improvement in F1-Score for polynya extent and 31.17% for lead width. Using the SR-SIC, more accurate quantitative parameters of polynyas and leads are extracted to present more detailed spatial distributions at their different development stages. For example, SR-SIC with reduced mixed pixels can distinguish more SIC values below 40%, advantageous for detecting open water pixels within polynya areas and improving the accuracy of heat fluxes estimation and ice production calculation. Moreover, SR-SIC has the capability to identify leads with openings less than 3 km and to locate the finer boundaries of the polynyas. Finally, it is found that the lead development along the 2018 winter polynya is associated with regional wind, air temperature, and ice drift. The prominent summer polynya events in 2018, 2020, and 2021 may be in part caused by the thinning sea ice in the north of Greenland.