Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images

Many forest cover maps have been generated by using optical and/or microwave images, but these forest cover maps have large area and spatial discrepancies. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: canopy height and canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and the time series images from the Moderate Resolution Imaging Spectroradiometer, using the forest definition of the Food and Agriculture Organization of the United Nations (&gt;5 m tree height and &gt;10 % canopy coverage) as the reference. We used the canopy height and canopy coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 to assess annual forest cover maps from 2007 to 2010 and annual evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest cover and evergreen forest cover maps. These annual forest cover maps and annual evergreen forest cover maps provide data support for the analyses of the causes, processes, and consequences of forest cover changes in the Brazilian Amazon (https://doi.org/10.6084/m9.figshare.21445626; Qin and Xiao, 2022a; https://doi.org/10.6084/m9.figshare.21445590; Qin and Xiao, 2022b).</p

Proposal for a new meteotsunami intensity index

Atmospherically generated coastal waves labelled as meteotsunami are known to cause destruction, injury, and fatality due to their rapid onset and unexpected nature. Unlike other coastal hazards such as tsunami, there exist no standardised means of quantifying this phenomenon, which is crucial to understand shoreline impacts and to enable researchers to establish a shared language and framework for meteotsunami analysis and comparison. In this study, we present a new five-level Lewis Meteotsunami Intensity Index (LMTI) trialled in the United Kingdom (UK) but designed for global applicability. A comprehensive dataset of meteotsunami events recorded in the UK was utilised, and the index's effectiveness was evaluated, with intensity level and spatial distribution of meteotsunami occurrence derived. Results revealed a predominant occurrence of Level 2 moderate intensity meteotsunami (69 %) in the UK, with distinct hotspots identified in south-western England and Scotland. Further trial implementation of the LMTI in a global capacity revealed its potential adaptability to other meteotsunami-prone regions, facilitating the comparison of events and promoting standardisation of assessment methodologies.</p

An improved representation of aerosol acidity in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12

The atmospheric composition forecasting system used to produce the CAMS forecasts of global aerosol and trace gases distributions, IFS-COMPO, undergoes periodic upgrades. In this paper we describe the development of the future operational cycle 49R1, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12 for describing gas-aerosol partitioning processes for nitrate and ammonium and for providing diagnostic aerosol, cloud and precipitation pH values at global scale. This information on aerosol acidity influences tropospheric chemistry processes associated with aqueous phase chemistry and wet deposition. The other updates to cycle 49R1 include modifications to the description of Desert Dust, Sea-salt aerosols, Carbonaceous aerosols and the size description for the calculation of aerosol optics. The implementation of EQSAM4Clim significantly improves the partitioning of reactive nitrogen compounds decreasing surface concentrations of both nitrate and ammonium, which reduces PM2.5 biases for Europe, U.S. and China, especially during summertime. For aerosol optical depth there is generally a decrease in the simulated biases for wintertime, and for some regions an increase in the bias for summertime. Improvements in the simulated &Aring;ngstr&ouml;m exponent is noted for almost all regions, resulting in generally a good agreement with observations. The diagnostic aerosol and precipitation pH calculated by EQSAM4Clim have been compared against results from previous simulations (for aerosol pH) and against ground observations (for precipitation pH), with the temporal distribution in the annual mean values showing good agreement against the regional observational datasets. The use of aerosol acidity only has a relatively smaller impact on the aqueous-phase production of sulphate when compared to the changes in gas-to-particle partitioning brought by the use of EQSAM4Clim

Investigating long-term changes in polar stratospheric clouds above Antarctica: A temperature-based approach using spaceborne lidar detections

Polar stratospheric clouds play a significant role in the seasonal thinning of the ozone layer by facilitating the activation of stable chlorine and bromine reservoirs into reactive radicals, as well as prolonging the ozone depletion by removing HNO3 and H2O of the stratosphere by sedimentation. In a context of climate change, the cooling of the lower polar stratosphere could enhance the PSC formation and by consequence cause more ozone depletion. There is thus a need to document the evolution of the PSC cover to better understand its impact on the ozone layer. In this article we present a statistical model based on the analysis of the CALIPSO PSC product from 2006 to 2020. The model predicts the daily regionally-averaged PSC density by pressure level derived from stratospheric temperatures. Applying our model to stratospheric temperatures from the CALIPSO PSC product over the 2006&ndash;2020 period shows it is robust in the stratosphere between 10 and 150 hPa, reproducing well PSC variations over daily timescales and seasonal differences (2006&ndash;2020). The model reproduces well the PSC seasonal progression, even during disruptive events like stratospheric sudden warmings, except for years characterized by volcanic eruptions. We apply our model to gridded stratospheric temperatures from reanalyses over the complete south pole domain to evaluate changes in PSC seasons over the 1980&ndash;2021 period. We find two distinct periods in the evolution of the PSC season duration. Between 1980 and 2000, the PSC season increased by 15 days at 10&ndash;20 hPa with an increasing lengthening as we descend in altitudes to reach 30 days at 100&ndash;150 hPa. This lengthening is in possible relation with major eruptions occurring over this period. After 2000, a temporary drop mostly visible at high (10&ndash;20 hPa) and lower altitude (100&ndash;150 hPa) is followed by a progressive increase in PSC season duration. Over the 1980&ndash;2020 period, the PSC season increased by 20 days between 30&ndash;100 hPa. These changes are altitude-dependent and statistically significant. We discuss the impact of non-temperature stratospheric changes on the variations of PSC seasons

Sensitivity of the polar boundary layer to transient phenomena

Numerical weather prediction and climate models encounter challenges in accurately representing flow regimes in the stably stratified atmospheric boundary layer and the transitions between them, leading to an inadequate depiction of regime occupation statistics. As a consequence, existing models exhibit significant biases in near-surface temperatures at high latitudes. To explore inherent uncertainties in modeling regime transitions, the response of the near-surface temperature inversion to transient small-scale phenomena is analyzed based on a stochastic modeling approach. A sensitivity analysis is conducted by augmenting a conceptual model for near-surface temperature inversions with randomizations that account for different types of model uncertainty. The stochastic conceptual model serves as a tool to systematically investigate which types of unsteady flow features may trigger abrupt transitions in the mean boundary layer state. The findings show that the incorporation of enhanced mixing, a common practice in numerical weather prediction models, blurs the two regime characteristic of the stably stratified atmospheric boundary layer. Simulating intermittent turbulence is shown to provide a potential workaround for this issue. Including key uncertainty in models could lead to a better statistical representation of the regimes in long-term climate simulation. This would help to improve our understanding and the forecasting of climate change in high-latitude regions.</p

An assessment of potential improvements in social capital, risk awareness, and preparedness from digital technologies

Contributions to social capital, risk awareness, and preparedness constitute the parameters against which applications of digital technologies in the field of disaster risk management should be tested. We propose here an evaluation of four of these: mobile positioning data, social media crowdsourcing, drones, and satellite imaging, with an additional focus on acceptability and feasibility. The assessment is carried out through a survey disseminated among stakeholders. The frame of the analysis also grants the opportunity to investigate to what extent different methodologies to aggregate and evaluate the results, i.e., the Criteria Importance Through Criteria Correlation (CRITIC) model, the (Euclidean)-distance Criteria Importance Through Criteria Correlation (dCRITIC) model, the entropy model, the mean weight model, and the standard deviation model, may influence the preference of one technology over the others. We find that the different assumptions on which these methodologies rely deliver diverging results. We therefore recommend that future research adopt a sensitivity analysis that considers multiple and alternatives methods to evaluate survey results.</p

Grand Challenges in Social Aspects of Wind Energy Development

Social aspects are gaining traction in wind energy research. Increasing local opposition to wind energy projects is just one symptom of deeper-rooted challenges in the further expansion of the technology. A recent publication by Kirkegaard et al. (2023a) lays out the grand challenges related to the complex interactions between society and wind energy technology and outlines a research agenda for wind energy research from a socio-technical perspective. This article discusses these challenges in the context of a more technologically focused research audience. We begin by describing the role of social sciences in wind energy research, arguing for the diverse set of insights, research topics, and value that they can add, going beyond outdated concepts of social acceptance (such as NIMBY), and providing solutions for public engagement and planning processes, just ownership structures and value-based design. We discuss social grand challenges in five areas: (1) Project planning &amp; spatial relations, (2) Wind turbine design &amp; scalability, (3) Grid integration, roles &amp; responsibilities, (4) General public perception of the technology, (5) Energy policy to support system transformation. We conclude by reflecting how social sciences and technical sciences can be better integrated to jointly advance wind energy research into a new interdisciplinary era that is able to provide holistic solutions for a transition to carbon-neutrality

Technical note: Isotopic fractionation of evaporating waters: effect of sub-daily atmospheric variations and eventual depletion of heavy isotopes

Isotopic fractionation of evaporating waters has been studied constantly in recent decades, particularly because it enables calculation of both the volume of water evaporated from a water body and the isotopic composition of its source water. We studied the stable water isotopic composition of an artificial pan filled with water and subject to total evaporation in a sub-humid environment, in order to put into practice an operational method for estimating the time since disconnection of riverine pools when these are sampled for the quality of aquatic life. Results indicate that (i) when about 70 % of pan water had evaporated and its isotopic composition had become enriched in heavy isotopes, some subsequent periods of depletion instead of enrichment happened; and (ii) the customary application of isotopic fractionation equations to determine the isotopic composition of the water in the pan using weekly averaged atmospheric conditions (temperature and relative humidity) strongly underestimated the changes observed but predicted an early depletion of heavy isotopes. The first result, rarely reported in the literature, was found to be fully consistent with the early studies of the isotopic composition of evaporating waters. The second one could be attributed to the fact that weekly averages of temperature and relative humidity strongly overestimated air relative humidity during daylight periods of active evaporation. However, when the fractionation equations were parameterized using temperature and relative humidity weighted by potential evapotranspiration at sub-hourly time steps, they adequately reproduced the observed isotopic composition of the water in the pan, including the late periods of heavy isotope depletion. We demonstrate how weekly increases in air relative humidity when the pan water was already enriched in heavy isotopes led to their depletion. We also analyse the errors that can be incurred if time averages are used instead of flux-weighted meteorological data for model parameterization and if unidentified periods of heavy isotope depletion occur. Our results should be taken into account when applying fractionation equations, particularly in conditions or areas with high air relative humidity.</p

Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model

Fires are among the most influential disturbances affecting ecosystem structure and biogeochemical cycles in Siberia. Therefore, precise fire modeling via dynamic global vegetation models is important for predicting greenhouse gas emissions and other burning biomass emissions to understand changes in biogeochemical cycles. In this study, we integrated the widely used SPread and InTensity of FIRE (SPITFIRE) fire module into the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to improve the accuracy of fire predictions and then simulated future fire regimes to better understand their impacts. Under the Representative Concentration Pathways 8.5 climate scenario, we estimated that the CO2, CO, PM2.5, total particulate matter (TPM), and total particulate carbon (TPC) emissions in Siberia will continue to increase annually until 2100 by an average of 214.4, 17.16, 2.8, 2.1, and 1.47 Gg species year-1, respectively. Under the same scenario and period, 185 trees ha-1 year-1 are estimated to be killed by wildfires, resulting in a 319.3 g C m-2 year-1 loss of net primary production (NPP). These findings show that Siberia faces an increasing frequency of extreme fire events due to changing climate conditions. Our study offers insights into future fire regimes and provides helpful information for development strategies for enhancing regional resilience and for mitigating the broader environmental consequences of heightened fire activity in Siberia

Air mass history linked to the development of Arctic mixed-phase clouds

Clouds formed during marine cold-air outbreaks (MCAOs) exhibit a distinct transition from stratocumulus decks near the ice edge to broken cumuliform fields further downwind. The mechanisms associated with ice formation are believed to be crucial in driving this transition, yet the factors influencing such formation remain unclear. Through Lagrangian trajectories co-located with satellite data, this study investigates into the development of mixed-phase clouds using these outbreaks. Cloud formed in MCAOs are characterized by a swift shift from liquid to ice-containing states, contrasting with non-MCAO clouds also moving off the ice edge. These mixed-phase clouds are predominantly observed at temperatures below -20 &deg;C near the ice edge. However, further into the outbreak, they become the dominant at temperatures as high as -13 &deg;C. This shift is consistent with the influence of biological ice nucleating particles (INPs), which become more prevalent as the air mass ages over the ocean. The evolution of these clouds is closely linked to the history of the air mass, especially the length of time it spends over snow- and ice-covered surfaces, terrains may that be deficient in INPs. This connection also accounts for the observed seasonal variations in the development of Arctic clouds, both within and outside of MCAO events. The findings highlight the importance of understanding both local marine aerosol sources near the ice edge and the overarching INP distribution in the Arctic for modelling of cloud phase in the region