Total Column Research Articles

Climatology and trends of atmospheric water vapour transport in New Zealand

Atmospheric moisture transport is crucial for understanding New Zealand’s climate dynamics, particularly with respect to extreme precipitation events. While the majority of previous studies have focussed on Atmospheric Rivers (ARs), this study examines the entire spectrum of water vapour transport and its link to extreme precipitation using 40 years (1981–2020) of Integrated Water Vapour Transport (IVT) data over the region. Although ARs are important drivers of extreme precipitation, they are infrequent as they account for less than 10% of total moisture transport at most coastal locations. Extreme water vapour transport (defined by the 90th percentile IVT threshold) corresponds more closely with precipitation extremes than ARs alone, even using an expanded AR detection range. Here, IVT is classified into strength categories from weak to strong. Over the study period, all but the weakest category of IVT has increased in frequency of occurrence over most of the South Island, while decreasing in northern North Island. Similarly, monthly IVT anomaly trends show a positive trend in the South Island and negative trend in the northern North Island during warmer months. Separate analysis of moisture weighted wind speeds (UV) and total column water vapour (TCWV) revealed that even though the dynamic component of IVT has decreased in many locations, the increase in TCWV across New Zealand is the driving factor underpinning the IVT trends. Correspondingly, these findings indicate the importance of analysis both dynamic and thermodynamic factors in seeking to understand hydrometeorological variation and when investigating the responses to climate change.

Improving Error Estimates for Evaluating Satellite-Based Atmospheric CO2 Measurement Concepts through Numerical Simulations

To assess the accuracy of satellite monitoring of anthropogenic CO2 emissions, inversions of satellite data in SWIR are usually combined with the assimilation of the total CO2 column into a Kalman filter that reconstructs the sources and sinks of atmospheric CO2. To provide error estimates of the total CO2 column for multi-month assimilation experiments of simulated satellite data, we parametrise these errors using linear regressions. These regression are obtained from a database that links meteorological situations, albedos, and aerosols to the errors in the inversion of the total CO2 column based on simulated satellite data for those conditions. The errors in this database are explicitly computed using the Bayesian estimation formalism, and the linear regressions are optimised by selecting appropriate predictors and predictants. For different levels of measurement noise, error simulations are performed over a period of several months using the albedo and aerosol data from MODIS.

Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements

Abstract. The increase in greenhouse gas concentrations, particularly CO2, has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges, particularly in terms of computational efficiency in atmospheric CO2 retrieval and analysis. To address these challenges, this study focuses on improving the speed of retrieving the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using spectral data from the Orbiting Carbon Observatory-2 (OCO-2) satellite while still maintaining retrieval accuracy. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrievals. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of the operational optimization model, which is released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion performance and prediction performance of the machine learning model for XCO2 are compared, analyzed, and discussed for the observed region over east Asia. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model not only achieves rapid predictions (<1 ms) with good accuracy (1.8 ppm or approximately 0.45 %) but also effectively captures sudden increases in XCO2 plumes near industrial emission sources. The accuracy of the machine learning model retrieval results is validated against reliable data from Total Carbon Column Observing Network (TCCON) sites, demonstrating its ability to effectively capture CO2 seasonal variations and annual growth trends.

The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars

AbstractOzone simulated in Mars Global Climate Models (MGCMs) is used to assess the underlying chemistry occurring in the atmosphere. Currently, ozone total column abundance (TCA) is under‐predicted in MGCMs by up to 120%, implying missing or inaccurate chemistry in models. Heterogeneous reactions of hydroxyl radicals (HOX) have been offered as an explanation for some of this bias, because they cause ozone to increase at locations where it's currently under‐predicted. We use four simulations to compare modeled ozone TCA with observations from the UVIS spectrometer aboard the ExoMars Trace Gas Orbiter to improve the representation of heterogeneous processes and their impact on ozone. We use a gas‐phase only run, a dissociative scheme, an adsorbed HOX retention scheme, and a hybrid scheme that combines the dissociative mechanism with the retention of HOX on water ice. We find retention of HOX is dependent on water ice sublimation, and ozone abundance increases when water ice persists for longer periods (1–20 sols). Over time, the loss of HOX causes a depletion in H2O2 concentration (HOX reservoir), and thus allows ozone concentration to increase. When adsorbed HOX are desorbed and dissociate into other by‐products, HOX are not immediately available to destroy ozone. This results in larger ozone concentrations than if desorbed HOX are released directly back into their gaseous states. When using the hybrid scheme, ozone TCA is increased up to 50% where the ozone deficit is greatest, demonstrating the best agreement with observations, and implying that HOX radicals are both retained when adsorbed and dissociate.

Evaluating TROPOMI δD Column Retrievals With In Situ Airborne Field Campaign Measurements Using Expanded Collocation Criterion

AbstractSatellite observations of column‐averaged water isotopes are relatively new retrieval products that are in need of further in situ evaluation. Such evaluation studies are generally difficult to perform due to the wide mismatch in temporal and spatial scales between the satellite observations based on instantaneous pixel averages during an overpass and airborne in situ measurements ranging up to several hours over a km‐scale. In addition, topography, weather conditions and in particular cloudiness impose severe constraints on an exact collocation between satellite and airborne in situ measurement platforms. Here we present a new method that allows a comparison between in situ measurements and satellite observations of δD on a broader statistical basis. We use regional isotope‐enabled model simulations as intermediate information to identify the area for best comparisons. Applying our methodology to TROPOMI total column δD retrievals for the L‐WAIVE campaign in Annecy, France, during June 2019 increases the number of satellite pixels for comparison despite widespread cloudiness on average by a factor of 20. In addition, the comparison of simulated and observed δD revealed a dependency of the satellite evaluation on the structure of the middle and upper troposphere. We conclude that our method provides a more robust statistic basis for in situ evaluation of δD satellite retrievals. The method will thus be useful in planning and executing forthcoming validation and evaluation campaigns, and can potentially be used for the evaluation of other satellite products.

Machine learning models application for spatiotemporal patterns of particulate matter prediction and forecasting over Morocco in north of Africa

Atmospheric air pollution exposure raises morbidity and mortality rates and is a major cause of the world's illness burden. In this context, we explored spatial and temporal trends in particulate matter PM10 from 2003 to 2020 over Morocco to assess air pollution exposure. We use the capabilities of ML models to study PM10 trends using 26 predictor variables, including meteorological parameters, volatile organic compounds, atmospheric oxidants, and aerosol optical depth data from the Copernicus Atmosphere Monitoring Service (CAMS). For this purpose, three ML models were built: Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Generalized Additive Model (GAM). To match and optimize these models, a set of ML algorithms has been coupled with each model. The results show all these models are highly accurate in predicting and forecasting PM10 total column trends. Cross-validation showed that GAM had better prediction ability for the PM10 total column with R2 = 0.994 and a very low root mean squared error (RMSE) not exceeding 0.046 × 10−16 kg/m2. The GAM model showed much higher predictive ability and lower bias than the other models. This finding can be explained by the advantages of GAMs, including their ability to capture complex and non-linear patterns in the data, making them particularly useful when relationships are not easily represented by linear models. This study has presented a comprehensive methodology for predicting the spatiotemporal variability of PM10. The proposed methodology holds potential applicability across all regions, facilitating the generation of high-resolution PM10 monitoring and the establishment of systems for the early detection of air pollution incidents in Morocco. Furthermore, the developed models exhibit versatility, enabling their application for estimating future trends of individual pollutants or making real-time predictions of air quality levels. This research contributes to advancing the understanding and proactive management of air quality in the context of Morocco, offering valuable insights for pollution control efforts.

A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system

Abstract. Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations in carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 and −2.32 ± 0.18 Pg C yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 Pg C yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 Pg C yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with Total Carbon Column Observing Network (TCCON) retrievals as well as Observation Package (ObsPack) surface flask observations and aircraft observations. Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error (RMSE) in posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at https://doi.org/10.5281/zenodo.8368846 (Jin et al., 2023a).

Measured and Modeled Trends of Seven Tropospheric Pollutants in the High Arctic From 1999 to 2022

AbstractThe long‐term trends and seasonality of many tropospheric pollutants are not well characterized in the high Arctic due to a dearth of trace‐gas measurements in this remote region. In this study, the inter‐ and intra‐annual variabilities of carbon monoxide (CO), acetylene (C2H2), ethane (C2H6), methanol (CH3OH), formaldehyde (H2CO), formic acid (HCOOH), and peroxyacetyl nitrate (PAN) in the high Arctic region were derived from the total column time‐series of ground‐based Fourier transform infrared (FTIR) measurements at Eureka, Nunavut (80.05°N, 86.42°W, 2006–2020) and Thule, Greenland (76.53°N, 68.74°W, 1999–2022). Consistent seasonal cycles were observed in the FTIR measurements at both sites for all species. Negative trends were observed for CO, C2H2, and CH3OH at both sites, and for HCOOH at Eureka. Positive trends were detected for C2H6 and H2CO at both sites, and for PAN at Eureka. Additionally, a 19‐year simulation was performed using the novel GEOS‐Chem High Performance model v14.1.1 for the period of 2003–2021. The model was able to reproduce the observed seasonality of all gases, but all species showed negative biases relative to observations, and CH3OH was found to have a particularly large bias of approximately −70% relative to the FTIR measurements. The GEOS‐Chem modeled trends broadly agreed with observations for all species except C2H6, H2CO, and PAN, which were found to have opposite trends in the model. For some species, the measurement‐model differences are suspected to be the result of errors or underestimations in the emissions inventories used in the simulation.

Global agricultural N2O emission reduction strategies deliver climate benefits with minimal impact on stratospheric O3 recovery

Agricultural nitrous oxide (N2O) emission reduction strategies are required given the potency of N2O as a greenhouse gas. However, the growing influence of N2O on stratospheric ozone (O3) with declining stratospheric chlorine means the wider atmospheric impact of N2O reductions requires investigation. We calculate a N2O emission reduction of 1.35 TgN2O yr-1 (~5% of 2020 emissions) using spatially separate deployment of nitrification inhibitors ($70–113 tCO2e−1) and crushed basalt (no-cost co-benefit) which also sequesters CO2. In Earth System model simulations for 2025–2075 under high (SSP3-7.0) and low (SSP1-2.6) surface warming scenarios, this N2O mitigation reduces NOx-driven O3 destruction, driving regional stratospheric O3 increases but with minimal impact on total O3 column recovery. By 2075, the radiative forcing of the combined N2O and CO2 reductions equates to a beneficial 9–11 ppm CO2 removal. Our results support targeted agricultural N2O emission reductions for helping nations reach net-zero without hindering O3 recovery.

Global expansion of tropical cyclone precipitation footprint

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

Observations, Remote Sensing, and Model Simulation to Analyze Southern Brazil Antarctic Ozone Hole Influence

This paper presents the observational, remote sensing, and model simulation used to analyze southern Brazil Antarctic ozone hole influence (SBAOHI) events that occurred between 2005 and 2014. To analyze it, we use total ozone column (TOC) data provided by a Brewer spectrophotometer (BS) and the OMI (Ozone Monitoring Instrument). In addition to the AURA/MLS (Microwave Limb Sounder) instrument, satellite ozone profiles were utilized with DYBAL (Dynamical Barrier Localization) code in the MIMOSA (Modélisation Isentrope du Transport Mésoéchelle de l’Ozone Stratosphérique par Advection) model Potential Vorticity (PV) fields. TOC has 7.0 ± 2.9 DU reductions average in 62 events. October has more events (30.7%). Polar tongue events are 19.3% in total, being more frequently observed in October (50% of cases), with medium intensity (58.2%), and in the stratosphere medium levels (55.0%). Already, polar filament events (80.7%) are more frequent in September (32.0%), with medium intensity (42.0%), and stratosphere medium levels (40.7%).

Data-driven modeling of equatorial atmospheric waves: The role of moisture and nonlinearity on global-scale instabilities and propagation speeds.

The equatorial region of the Earth's atmosphere serves as both a significant locus for phenomena, including the Madden-Julian Oscillation (MJO), and a source of formidable complexity. This complexity arises from the intricate interplay between nonlinearity and thermodynamic processes, particularly those involving moisture. In this study, we employ a normal mode decomposition of atmospheric reanalysis ERA-5 datasets to investigate the influence of nonlinearity and moisture on amplitude growth, propagation speed, and mode coupling associated with equatorially trapped waves. We focus our analysis on global-scale baroclinic Kelvin and Rossby waves, recognized as crucial components contributing to the variability of the MJO. We examine the dependence of wave amplitudes on the background moisture field in the equatorial region, as measured by total column water vapor. Our analysis demonstrates the crucial role of moisture in exciting these waves. We further investigate the dependence of the propagation speed of the waves on their amplitudes and the background moisture field. Our analysis reveals a robust correlation between the phase speed of the normal modes and their corresponding amplitude, whereas a weaker correlation is found between the eigenmodes' phase speed and the moisture field. Hence, our findings suggest that moisture plays a role in exciting the global-scale Rossby-Kelvin structure of the MJO. In this context, the propagation speed of the eigenmodes is mainly influenced by their amplitudes, underscoring the significant role of nonlinearity in wave propagation.

The outflowing ionised gas of I Zw 1 observed by HST COS

Aims. We present an analysis of the Hubble Space Telescope Cosmic Origins Spectrograph spectrum of I Zw 1 aiming to probe the absorbing medium associated with the active galactic nucleus (AGN). Methods. We fitted the emission spectrum and performed spectral analysis of the identified absorption features to derive the corresponding ionic column densities and covering fractions of the associated outflows. We employed photoionisation modelling to constrain the total column density and the ionisation parameter of four detected kinematic components. By investigating the implications of the results together with the observed kinematic properties of both emission and absorption features, we derived constraints on the structure and geometry of the absorbing medium in the AGN environment. Results. We find and characterise absorption line systems from outflowing ionised gas in four distinct kinematic components, located at −60, −280, −1950, and −2900 km s−1 with respect to the source rest frame. While the two slower outflows are consistent with a full covering of the underlying radiation source, the well-constrained doublet line ratios of the faster two, higher column density, outflows suggest partial covering, with a covering fraction of Cf ∼ 0.4. The faster outflows show also line-locking in the N V doublet, a signature of acceleration via line absorption. This makes I Zw 1 possibly the closest object that shows evidence for hosting line-driven winds. The observed −1950 km s−1 absorption is likely due to the same gas as an X-ray warm absorber. Furthermore, the behaviour in UV and X-ray bands implies that this outflow has a clumpy structure. We find that the highly asymmetric broad emission lines in I Zw 1, indicative of a collimated, outflowing broad line region, are covered by the absorbing gas. Finally, the strongest UV–X-ray absorber may be connected to some of the blueshifted line emission, indicative of a more spatially extended structure of this ionised medium.

Synoptic Variability in the Tropical Oceanic Moist Margin

AbstractRecent research has described a ‘moist margin’ in the tropics, defined through a total column water vapor (TCWV) value of 48 kg m−2, that encloses most of the rainfall over the tropical oceans. Diagnosing the moist margin in the ERA5 reanalysis reveals that it varies particularly on synoptic time scales, which this study aims to quantify. We define ‘wet and dry perturbation’ objects based on the margin's movement relative to its seasonal climatology. These perturbations are associated with a variety of synoptic weather systems. Wet (dry) perturbations produce substantially more (less) rainfall compared to the seasonal average, confirming the clear link between moisture and precipitation. On synoptic scales we suggest that mid‐tropospheric humidity plays a key role in creating these perturbations, while sea surface temperatures (SSTs) are relatively unimportant.

Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University

Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO2, CH4, and N2O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO2, CH4, and N2O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr−1 for CO2; (0.46 ± 0.02) % yr−1 for CH4; (0.28 ± 0.01) % yr−1 for N2O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH4 and N2O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory.

Drivers of late Holocene ice core chemistry in Dronning Maud Land: the context for the ISOL-ICE project

Abstract. Within the framework of the Isotopic Constraints on Past Ozone Layer in Polar Ice (ISOL-ICE) project, we present initial ice core results from the new ISOL-ICE ice core covering the last millennium from high-elevation Dronning Maud Land (DML) and discuss the implications for interpreting the stable isotopic composition of nitrogen in ice core nitrate (δ15N(NO3-)) as a surface ultra-violet radiation (UV) and total column ozone (TCO) proxy. In the quest to derive TCO using δ15N(NO3-), an understanding of past snow accumulation changes, as well as aerosol source regions and present-day drivers of their variability, is required. We therefore report here the ice core age–depth model, the snow accumulation and ice chemistry records, and correlation analysis of these records with climate variables over the observational era (1979–2016). The ISOL-ICE ice core covers the last 1349 years from 668 to 2017 CE ± 3 years, extending previous ice core records from the region by 2 decades towards the present and shows excellent reproducibility with those records. The extended ISOL-ICE record of last 2 decades showed a continuation of the methane sulfonate (MSA−) increase from ∼ 1800 to present while there were less frequent large deposition events of sea salts relative to the last millennium. While our chemical data do not allow us to distinguish the ultimate (sea ice or the open ocean) source of sea salt aerosols in DML winter aerosol, our correlation analysis clearly suggests that it is mainly the variability in atmospheric transport and not the sea ice extent that explains the interannual variability in sea salt concentrations in DML. Correlation of the snow accumulation record with climate variables over the observational era showed that precipitation at ISOL-ICE is predominately derived from the South Atlantic with onshore winds delivering marine air masses to the site. The snow accumulation rate was stable over the last millennium with no notable trends over the last 2 decades relative to the last millennium. Interannual variability in the accumulation record, ranging between 2 and 20 cm a−1 (w.e.), would influence the ice core δ15N(NO3-) record. The mean snow accumulation rate of 6.5±2.4 cm a−1 (w.e.) falls within the range suitable for reconstructing surface mass balance from ice core δ15N(NO3-), highlighting that the ISOL-ICE ice core δ15N(NO3-) can be used to reconstruct either the surface mass balance or surface UV if the ice core δ15N(NO3-) is corrected for the snow accumulation influence, thereby leaving the UV imprint in the δ15N(NO3-) ice core record to quantify natural ozone variability.

A multi-resolution ensemble model of three decision-tree-based algorithms to predict daily NO2 concentration in France 2005–2022

Understanding and managing the health effects of Nitrogen Dioxide (NO2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO2 health effects in epidemiological studies.

Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and reanalysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, captures the nuanced dynamics of Cyprus’s atmospheric conditions. With a temporal resolution of 15 min and a spatial of 0.05° × 0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models’ accuracy but also highlights its proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), ranging from −2 to −4% per decade, have been found in autumn, especially marked in the island’s northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of −2 to −3% per decade, which were found in spring, do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the day light integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2

Abstract. Solar radiation modification by a sustained deliberate source of SO2 into the stratosphere (strat-SRM) has been proposed as an option for climate intervention. Global interactive aerosol–chemistry–climate models are often used to investigate the potential cooling efficiencies and associated side effects of hypothesized strat-SRM scenarios. A recent model intercomparison study for composition–climate models with interactive stratospheric aerosol suggests that the modeled climate response to a particular assumed injection strategy depends on the type of aerosol microphysical scheme used (e.g., modal or sectional representation) alongside host model resolution and transport. Compared to short-duration volcanic SO2 emissions, the continuous SO2 injections in strat-SRM scenarios may pose a greater challenge to the numerical implementation of microphysical processes such as nucleation, condensation, and coagulation. This study explores how changing the time steps and sequencing of microphysical processes in the sectional aerosol–chemistry–climate model SOCOL-AERv2 (40 mass bins) affects model-predicted climate and ozone layer impacts considering strat-SRM by SO2 injections of 5 and 25 Tg(S) yr−1 at 20 km altitude between 30° S and 30° N. The model experiments consider the year 2040 to be the boundary conditions for ozone-depleting substances and greenhouse gases (GHGs). We focus on the length of the microphysical time step and the call sequence of nucleation and condensation, the two competing sink processes for gaseous H2SO4. Under stratospheric background conditions, we find no effect of the microphysical setup on the simulated aerosol properties. However, at the high sulfur loadings reached in the scenarios injecting 25 Tg(S) yr−1 of SO2 with a default microphysical time step of 6 min, changing the call sequence from the default “condensation first” to “nucleation first” leads to a massive increase in the number densities of particles in the nucleation mode (R<0.01 µm) and a small decrease in coarse-mode particles (R>1 µm). As expected, the influence of the call sequence becomes negligible when the microphysical time step is reduced to a few seconds, with the model solutions converging to a size distribution with a pronounced nucleation mode. While the main features and spatial patterns of climate forcing by SO2 injections are not strongly affected by the microphysical configuration, the absolute numbers vary considerably. For the extreme injection with 25 Tg(S) yr−1, the simulated net global radiative forcing ranges from −2.3 to −5.3 W m−2, depending on the microphysical configuration. Nucleation first shifts the size distribution towards radii better suited for solar scattering (0.3 µm <R< 0.4 µm), enhancing the intervention efficiency. The size distribution shift, however, generates more ultrafine aerosol particles, increasing the surface area density and resulting in 10 DU (Dobson units) less ozone (about 3 % of the total column) in the northern mid-latitudes and 20 DU less ozone (6 %) over the polar caps compared to the condensation first approach. Our results suggest that a reasonably short microphysical time step of 2 min or less must be applied to accurately capture the magnitude of the H2SO4 supersaturation resulting from SO2 injection scenarios or volcanic eruptions. Taken together, these results underscore how structural aspects of model representation of aerosol microphysical processes become important under conditions of elevated stratospheric sulfur in determining atmospheric chemistry and climate impacts.

Study of UV Index and Total Ozone Climatology Over Bhairahawa, Pokhara, and Lumle in Nepal

Recent studies on solar ultraviolet (UV) radiation have been pivotal in unveiling its detrimental impacts on humans and other living organisms on Earth. The Ultraviolet Index (UVI) is a crucial tool to ascertain the level of solar ultraviolet radiation at any given location, utilizing data from diverse sources. This study aims to examine the UV Index and Total Ozone Climatology over Bhairahawa (27.52°N, 83.43°E, 109m asl), Pokhara (28.22°N, 83.32°E, 850m asl), and Lumle (28.30°N, 83.80°E, 1740m asl), situated in the mid of Nepal, using OMI/Aura satellite data. The average minimum and maximum UV index recorded are 3.11±1.01 and 7.77±2.25 at Bhairahawa, 4.31±1.36 and 12.90±2.28 at Lumle, and 4.03±1.34 and 10.71±3.38 at Pokhara respectively. Additionally, the minimum Total Ozone Column (TOC) value is observed to be 251.4-288.5DU in December, with the maximum value reaching 291-285.3DU in April across all sites. This study concludes that the solar UV index increases with an increase in altitude, among other influential factors. The exploration of UVI and ozone data from the OMI/AURA satellite over specific locations in Nepal aims to enrich the existing pool of knowledge on solar ultraviolet radiation and its varying intensity, thereby contributing to the comprehension of its harmful implications on living entities.