As the largest natural source of sulfur-containing gases into the atmosphere, ocean organism-derived dimethyl sulfide (DMS) has been considered to play a critical role in the Earth’s climate system. Yet there are great uncertainties in modeling the spatiotemporal variations of DMS and incomplete knowledge of influencing factors in different oceanic regions. Moreover, little is known about the future change of global DMS, which limits our understanding of the feedback of marine ecosystem to climate change. Here we develop an artificial neural network model and combine data mining approaches to address these issues. Phytoplankton biomass and salinity are currently predominant factors associated with DMS variability in the coastal and Arctic regions, respectively. In the mid- and low-latitude open oceans, nutrients and temperature are also crucial factors in addition to radiation and mixed layer depth, and their relationships with DMS show reversals when passing certain thresholds. Although the global average DMS concentration and emission slightly decline from 2005 to 2100, they may change considerably in specific regions. In contrast to the DMS decreases in the low-latitudes mainly related with phosphate reduction and temperature rise and in the North Atlantic subpolar gyre attributed to salinity decline, warming will cause DMS increase in the Southern Ocean and sea ice loss will dramatically enhance DMS emission in the Arctic. Although the global negative feedback loop between oceanic DMS and climate may not operate, the future spatial redistribution of DMS may lead to the change in cloud cover pattern and significantly affect regional climate.
We simulated the Nov 4, 2021 geomagnetic storm event penetrating electric field using the Multiscale Atmosphere-Geospace Environment (MAGE) and compared with the NASA ICON observation. The ICON observation showed enhancement of the vertical ion drift when the penetrating electric field arrived at the equatorial region. The simulated vertical ion drifts are consistent with ICON observation. Hence, we are able to verify the MAGE simulation with ICON observation. On the dusk side, the MAGE simulation showed strong pre-reversal enhancement (PRE), whereas the ICON observation did not display any sign of the PRE. The MAGE simulation did show that PRE amplitude decreases as altitude increase. Because the ICON orbital height is above the model upper boundary, it could be a factor for the discrepancy. Instrumental issue cannot be ruled out at this moment. GOLD UV image at the same time exhibits multiple plasma bubbles, which seem to suggest the existence of the PRE.
Searches for phosphine in Venus’ atmosphere have sparked a debate. Cordiner et al. 2022 analyse spectra from the Stratospheric Observatory For Infrared Astronomy (SOFIA) and infer <0.8 ppb of PH3. We noticed that spectral artefacts arose mainly from inessential calibration-load signals. By-passing these signals allows simpler post-processing, and 6.5σ detection of 1 ppb of PH3 at ~75 km altitude (just above the clouds). Compiling six phosphine results would suggest the abundance inverts: decreasing above the clouds but rising again in the mesosphere from some unexplained source. However, no such extra source is needed if phosphine is undergoing destruction by sunlight (photolysis), as it does on Earth. Low values/limits were found where the viewed part of the super-rotating Venusian atmosphere had passed through sunlight, while the high values are from views moving into sunlight. We suggest Venusian phosphine is indeed present, and so merits further work on models of its origins.
Dynamic influences on summertime seasonal United States rainfall variability are not well understood. A major cause of moisture transport is the Great Plains low-level jet (LLJ). Using observations and a dry atmospheric general circulation model, this study explored the distinct and combined impacts of two prominent atmospheric teleconnections - the East Asian monsoon (EAM) and North Atlantic subtropical high (NASH) - on the Great Plains LLJ in the summer. Separately, a strong EAM and strong western NASH are linked to a strengthened LLJ and positive rainfall anomalies in the Plains/ Midwest. Overall, NASH variability is more important for considering the LLJ impacts, but strong EAM events amplify western NASH-related Great Plains LLJ strengthening and associated rainfall signals. This occurs when the EAM-forced Rossby wave pattern over North America constructively interferes with low-level wind field, providing upper-level support for the LLJ and increasing mid- to upper-level divergence.
Within the Charlotte, North Carolina, to Atlanta, Georgia, megaregion (Charlanta), the Atlanta metropolitan area has been shown to augment proximal cloud-to-ground (CG) lightning occurrence. Although numerous studies have documented this “urban lightning effect” (ULE) with regard to CG lightning, relatively few have investigated urban effects on distributions of total lightning (TL). Moreover, there has yet to be a study of the ULE using TL observations from the Geostationary Lightning Mapper (GLM). In an effort to fill this gap, we investigated spatial distributions of TL around the cities of Atlanta, GA, Greenville, SC, and Charlotte, NC, using GLM data collected during the warm seasons of 2018–2021. Analyses reveal augmentation of total lightning intensity and frequency over the major cities of Atlanta and Charlotte, with a diminished urban signal over the smaller city of Greenville. This work also demonstrated the potential efficacy of the emerging satellite-based TL climatology in ULE studies.
The Hunga Tonga-Hunga Ha’apai (HTHH) volcanic eruption in January 2022 injected extreme amounts of water vapor (H2O) and a moderate amount of the aerosol precursor (SO2) into the Southern Hemisphere (SH) stratosphere. The H2O and aerosol perturbations have persisted and resulted in large-scale SH stratospheric cooling, equatorward shift of the Antarctic polar vortex, and slowing of the Brewer-Dobson circulation associated with a substantial ozone reduction in the SH winter midlatitudes. Chemistry-climate model simulations forced by realistic HTHH inputs of H2O and SO2 reproduce the observed stratospheric cooling and circulation effects, demonstrating the observed behavior is due to the volcanic influences. Furthermore, the combination of aerosol transport to polar latitudes and a cold polar vortex enhances springtime Antarctic ozone loss, consistent with observed polar ozone behavior in 2022.
Natural aerosols and their interactions with clouds remain an important uncertainty within climate models, especially at the poles. Here, we study the behavior of sea salt aerosols (SSaer) in the Arctic and Antarctic within 12 climate models from CMIP6. We investigate the driving factors that control SSaer abundances and show large differences based on the choice of the source function, and the representation of aerosol processes in the atmosphere. Close to the poles, the CMIP6 models do not match observed seasonal cycles of surface concentrations, likely due to the absence of wintertime SSaer sources such as blowing snow. Further away from the poles, simulated concentrations have the correct seasonality, but have a positive mean bias of up to one order of magnitude. SSaer optical depth is derived from the MODIS data and compared to modeled values, revealing good agreement, except for winter months. Better agreement for AOD than surface concentration may indicate a need for improving the vertical distribution, the size distribution and/or hygroscopicity of modeled polar SSaer. Source functions used in CMIP6 emit very different numbers of small SSaer, potentially exacerbating cloud-aerosol interaction uncertainties in these remote regions. For future climate scenarios SSP126 and SSP585, we show that SSaer concentrations increase at both poles at the end of the 21st century, with more than two times mid-20th century values in the Arctic. The pre-industrial climate CMIP6 experiments suggest there is a large uncertainty in the polar radiative budget due to SSaer.
In a context of climate change, the stakes surrounding water availability are getting higher. Decomposing and quantifying the effects of climate on discharge allows to better understand their impact on water resources. We propose a methodology to separate the effect of change in annual mean of climate variables from the effect of intra-annual distribution of precipitations. It combines the Budyko framework with outputs from a Land Surface Model (LSM). The LSM is used to reproduces the behavior of 2134 reconstructed watersheds over Europe between 1902 and 2010, with climate inputs as the only source of change. We fit to the LSM outputs a one parameter approximation to the Budyko framework. It accounts for the evolution of annual mean in precipitation (P) and potential evapotranspiration (PET). We introduce a time-varying parameter in the equation which represents the effect of long-term variations in the intra-annual distribution of P and PET. To better assess the effects of changes in annual means or in intra-annual distribution of P, we construct synthetic forcings fixing one or the other. The results over Europe show that the changes in discharge due to climate are dominated by the trends in the annual averages of P. The second main climate driver is PET, except over the Mediterranean area where changes in intra-annual variations of P have a higher impact on discharge than trends in PET. Therefore the effects of changes in intra-annual distribution of climate variables are not to be neglected when looking at changes in annual discharge.
Previous studies suggest that boreal summer intraseasonal variations along the subtropical westerly jet (SJ), featuring quasi-biweekly periodicity, frequently modulate downstream subseasonal variations over East Asia (EA). Based on subseasonal hindcasts from six dynamical models, this study discovered that the leading two-three-week prediction skills for surface air temperature (SAT) are improved significantly in summer when the SJ has strengthened intraseasonal signals, which are best demonstrated over the eastern Tibetan Plateau, Southwest Basin, and North China. The reasons are that the enhanced quasi-biweekly wave and the associated energy dispersion along the SJ cause more regular quasi-biweekly periodic variations of downstream SAT, which potentially increase regional predictability. This study suggests not only that intraseasonal variations along the SJ could provide a window of opportunity for achieving better subseasonal prediction over EA, but also that intraseasonal waves along the SJ are crucial for improving EA subseasonal prediction.
Predicting secondary organic aerosol (SOA) formation relies either on extremely detailed, numerically expensive models accounting for the condensation of individual species or on extremely simplified, numerically affordable models parameterizing SOA formation for large-scale simulations. In this work, we explore the possibility of creating a random forest to reproduce the behavior of a detailed atmospheric organic chemistry model at a fraction of the numerical cost. A comprehensive dataset was created based on thousands of individual detailed simulations, randomly initialized to account for the variety of atmospheric chemical environments. Recurrent random forests were trained to predict organic matter formation from dodecane and toluene precursors, and the partitioning between gas and particle phases. Validation tests show that the random forests perform well without any divergence over 10 days of simulations. The distribution of errors shows that the sampling of initial conditions for the training simulations needs to focus on chemical regimes where SOA production is the most sensitive. Sensitivity tests show that specializing multiple random forests for a specific chemical regime is not more efficient than training a single general random forest for the entire dataset. The most important predictors are those providing information about the chemical regime, oxidants levels and existing organic mass. The choice of predictors is crucial as using too many unimportant predictors reduces the performances of the random forests.
Changes in atmospheric iron (Fe) deposition to the open ocean affect net primary productivity, nitrogen fixation, and carbon uptake rates. We investigate the changes in soluble Fe (SFe) deposition from the pre-industrial period to the late 21st century using the EC-Earth3-Iron Earth System model, which stands out for its comprehensive representation of the atmospheric oxalate, sulfate, and Fe cycles. We show how anthropogenic activity has modified the magnitude and spatial distribution of SFe deposition by increasing combustion Fe emissions along with atmospheric acidity and oxalate levels. We find that SFe deposition has doubled since the early Industrial Era using the Coupled Model Intercomparison Project Phase 6 (CMIP6) emission inventory, with acidity being the main solubilization pathway for dust Fe, and ligand-promoted (oxalate) processing dominating the solubilization of combustion Fe. We project a global SFe deposition increase of 40% by the late 21st century relative to present day under Shared Socioeconomic Pathway (SSP) 3-7.0, which assumes weak climate change mitigation policies. In contrast, sustainable and business-as-usual SSPs (1-2.6 and 2-4.5) result in 35% and 10% global decreases, respectively. Despite these differences, SFe deposition consistently increases and decreases across SSPs over the (high nutrient low chlorophyl) equatorial Pacific and Southern Ocean (SO), respectively. Future changes in dust and wildfires with climate remains a key challenge for constraining SFe projections. We show that the equatorial Pacific and the SO would be sensitive not only to changes in Australian or South American dust emissions, but also to those in North Africa.
Convective cold pools (CPs) are known to mediate the interaction between convective rain cells and thereby help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has so far been hampered by the lack of relevant large-scale nearsurface data. Unlike numerical studies, where high-resolution near-surface fields of relevant quantities such as virtual temperature and winds are available and frequently used to detect cold pools, observational studies mainly identify CPs based on surface time series. Since research vessels or weather stations measure these time series locally, the characterization of cold pools from observations is limited to regional or station-based studies. To eventually enable studies on a global scale, we here develop and evaluate a methodology for the detection of CPs that relies only on data that (i) is globally available and (ii) has high spatio-temporal resolution. We trained convolutional neural networks to segment CPs in cloud and rainfall fields from high-resolution cloud resolving simulation output. Such data is not only available from simulations, but also from geostationary satellites that fulfill both (i) and (ii). The networks make use of a U-Net architecture, a common choice for image segmentation due to its strength in learning spatial correlations at different scales. Based on cloud and rainfall fields only, the trained networks systematically identify CP pixels in the simulation output. Our methodology may thus open for reliable global CP detection from space-borne sensors. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may offer new insight into the role of CPs in convective organization.
The imprint of marine atmospheric boundary layer (MABL) dynamical structures on sea surface roughness, as seen from Sentinel-1 Synthetic Aperture Radar (SAR) acquisitions, is investigated. We focus on February 13th, 2020, a case study of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) field campaign. For suppressed conditions, convective rolls imprint on sea surface roughness is confirmed through the intercomparison with MABL turbulent organization deduced from airborne measurements. A discretization of the SAR wide swath into 25 x 25 km$^2$ tiles then allows us to capture the spatial variability of the turbulence organization varying from rolls to cells. Secondly, we objectively detect cold pools within the SAR image and combine them with geostationary brightness temperature. The geometrical or physically-based metrics of cold pools are correlated to cloud properties. This provides a promising methodology to analyze the dynamics of convective systems as seen from below and above.
In this paper, we applied a variety of statistical methods to study gravity waves in the troposphere and lower stratosphere in the Brazilian sector, using an unprecedented large database from Instituto de Controle do Espaço Aéreo (ICEA) of radiosonde measurements carried out in 2014 on 32 locations in the Brazilian territory totaling 49,652 wind profiles. The average wind profiles were computed and classified by means of a hierarchical cluster analysis. The kinetic and potential energy densities of the gravity waves were determined using a detrending technique based on the least squares method and the Fast Fourier Transform. The time series of the energy densities were analyzed in detail and some persistent and seasonal behavior was found in some cases. A systematic search for quasi monochromatic waves was carried out and the main characteristics of such waves propagating in the troposphere and the lower stratosphere were found. The correlation analysis between the troposphere and the lower ionosphere based on parameters observed on both layers was used to investigate the wave coupling between the two layers. The results we found have implications in the so-called seeding problem of the equatorial ionospheric irregularities.
A month-long data assimilation experiment is carried out to assess the impact of CrIS and IASI Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3-h Weather Research and Forecasting (WRF) cycling system implemented over the central North Pacific Ocean. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses are used in the evaluation process. The results show that the assimilation of TRs, both alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800 to 300 hPa), and particularly in the lower portion (800 to 600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade-wind inversion.
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio-temporal resolution (~70 m, 1-5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio-temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature retrievals, we generated ECOSTRESS ET products over Europe and Africa using three structurally contrasting models, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non-parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites over 6 different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ~70 W m-2). However, the relatively complex TSEB model produced a higher RMSE of ~90 W m-2. Comparison between STIC ET estimate and the operational ECOSTRESS ET product from NASA PT-JPL model showed a difference in RMSE between the two ET products around 50 W m-2. Substantial overestimation (>80 W m-2) was noted in PT-JPL ET estimates over shrublands and savannas presumably due to the weak constraint of LST in the model. Overall, the EEH is promising to serve as a support to the Land Surface Temperature Monitoring (LSTM) mission.
On 12 October 2020, the NASA’s Global-scale Observations of the Limb and Disk (GOLD) mission observed three differently shaped EPBs within a 12o longitude range, near the subsatellite point. One is straight aligned to the magnetic field line, whereas the poleward extensions of the others are tilted eastward and westward from the magnetic field line resembling a C-shape and reversed C-shape structures. These EPBs were inside the GOLD imager’s field-of-view for a period of ~3 hours. This allowed us to compute their zonal motion and determine their drift velocities. EPBs’ drift velocities were derived from measuring their longitudinal shifts at the magnetic equator and at both EIA crests. This unique observation, showing three morphologies in a narrow longitude sector, indicates the occurrence of strong longitudinal gradients in the typical parameters associated with the dynamics of EPBs, like neutral winds, electric fields, or other parameters within such a narrow longitude range.