We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1-31 January, 2005. We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.

We present the first water vapor profiles encompassing the upper mesosphere of Mars, 100–120 km, far exceeding the maximum altitudes where remote sensing has been able to observe water to date. Our results are based on solar occultation measurements by Atmospheric Chemistry Suite (ACS) onboard the ExoMars Trace Gas Orbiter (TGO). The observed wavelength range around 2.7 μm possesses strong CO2 and H2O absorption lines allowing sensitive temperature and density retrievals. We report a maximum H2O mixing ratio varying from 10 to 50 ppmv at 100–120 km during the global dust storm (GDS) of Martian Year (MY) 34 and around southern summer solstice of MY 34 and 35. During other seasons water remains persistently below ~2 ppmv. We claim that contributions of the MY34 GDS and perihelion periods into the projected hydrogen escape from Mars are nearly equivalent.

Horizontal winds from four low-latitude (+/-15o) specular meteor radars (SMRs) and the MIGHTI instrument on the ICON satellite, are combined to investigate quasi-2-day waves (Q2DWs) in early 2020. SMRs cover 80-100 km altitude whereas MIGHTI covers 95-300 km. Q2DWs are the largest dynamical feature of the summertime middle atmosphere. At the overlapping altitudes, comparisons between the derived Q2DWs exhibit excellent agreement. The SMR sensor array analyses show that the dominant zonal wavenumbers are s=+2 and +3, and help resolve ambiguities in MIGHTI results. We present the first Q2DW depiction for s=+3 up to 200 km and for $s=+2$ above 95 km, and show that their amplitudes are almost invariant between 80 and 100 km. Above 106 km, Q2DW amplitudes and phases present structures that might result from the superposition of Q2DWs and their aliased secondary waves.

Aura Microwave Limb Sounder (MLS) measurements show that chemical processing was critical to the observed record-low Arctic stratospheric ozone in spring 2020. The 16-year MLS record indicates more denitrification and dehydration in 2019/2020 than in any Arctic winter except 2015/2016. Chlorine activation and ozone depletion began earlier than in any previously observed winter, with evidence of chemical ozone loss starting in November. Active chlorine then persisted as late into spring as it did in 2011. Empirical estimates suggest maximum chemical ozone losses near 2.8 ppmv by late March in both 2011 and 2020. However, peak chlorine activation, and thus peak ozone loss, occurred at lower altitudes in 2020 than in 2011, leading to the lowest Arctic ozone values ever observed at potential temperature levels from ~400–480 K, with similar ozone values to those in 2011 at higher levels.

This study presents an improved method to estimate differential energy flux, auroral power and field-aligned current of electron precipitation from incoherent scatter radar data. The method is based on a newly developed data analysis technique that uses Bayesian filtering to fit altitude profiles of electron density, electron temperature, and ion temperature to observed incoherent scatter spectra with high time and range resolutions. The electron energy spectra are inverted from the electron density profiles. Previous high-time resolution fits have relied on the raw electron density, which is calculated from the backscattered power assuming that the ion and electron temperatures are equal. The improved technique is applied to one auroral event measured by the EISCAT UHF radar and it is demonstrated that the effect of electron heating on electron energy spectra, auroral power and upward field-aligned current can be significant at times. Using the fitted electron densities instead of the raw ones may lead to wider electron energy spectra and auroral power up to 75% larger. The largest differences take place for precipitation that produces enhanced electron heating in the upper E region, and in this study correspond to fluxes of electrons with peak energies from 3 to 5 keV. Finally, the auroral power estimates are verified by comparison to the 427.8 nm auroral emission intensity, which show good correlation. The improved method makes it possible to calculate unbiased estimates of electron energy spectra with high time resolution and thereby to study rapidly varying aurora.

We present an inversion methodology where acoustic observations of infrasound waves are used to update an atmospheric model. We sought a flexible parameterization that permits to incorporate physical and numerical constraints without the need to reformulate the inversion. On the other hand, the optimization conveys an explicit search over the solution space, making the solver computationally expensive. Nevertheless, through a parallel implementation and the use of tight constraints we demonstrate that the methodology is computationally tractable. Constraints to the solution space are derived from the spread (variance) of ERA5 ensemble reanalysis members, which summarize the best current knowledge of the atmosphere from assimilated measurements and physical models. Similarly, the initial model temperature and winds for the inversion are chosen to be the average of these parameters in the ensemble members. The performance of the inversion is demonstrated with the application to infrasound observations from an explosion generated by the destruction of ammunition at Hukkakero, Finland. The acoustic signals are recorded at an array station located at 178 km range, which is within the classical shadow zone distance. The observed returns are assumed to come from stratospheric reflections. Thus, the reflection altitude is also an inverted parameter.

Variability in the position and strength of the subtropical jet (STJ) and polar front jet (PFJ) streams has important implications for global and regional climate. Previous studies have related the position and strength of the STJ to tropical thermodynamic processes, whereas the position and strength of the PFJ are more associated with mid-latitude eddies. These conclusions have largely resulted from studies using idealized models. In this study, ERA-Interim reanalysis and CMIP6 global climate models are used to examine month-to-month and interannual variability of the wintertime Northern Hemisphere (NH) STJ and PFJ. This study particularly focuses on the regional characteristics of the jet variability, extending previous studies on zonal-mean jet streams. Consistent with idealized modeling studies, a close relationship is found between tropical outgoing longwave radiation (OLR) and the STJ, and between mid-latitude surface temperature gradients and the PFJ. Variations of both jets are also linked to well-known teleconnection patterns. Variations in tropical convection over the Pacific Ocean are associated with variations of the NH STJ at most longitudes, with different phases of the El Niño-Southern Oscillation (ENSO) associated with the shift and strengthening of the STJ in different regions. CMIP6 models generally capture these relationships, but the models’ tropical convection is often displaced westward when compared to observations, reflecting a climatological bias in OLR in the western tropical Pacific Ocean in many models. The displaced tropical convection in models excites different paths of Rossby wave propagation, resulting in different ENSO teleconnections on the STJ over North America and Europe.

An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely-structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2–10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.

Ionospheric perturbations induced by tsunamis and earthquakes can be used for tsunami early warning and remote sensing of earthquakes, provided the perturbations are characterized properly to distinguish them from the ones caused by other sources. The ionospheric perturbations are increasingly being obtained from Global Positioning System (GPS) based Total Electron Content (TEC) measurements sampled at uniform time intervals. However, the sampling is not uniform in space. The nonuniform spatial sampling along the GPS satellite tracks introduces aliasing if it is not accounted while computing the ionospheric perturbations. All the methods hitherto used to detect the co-seismic and tsunamigenic ionospheric perturbations did not account the nonuniform spatial sampling while computing these perturbations. In addition, the residual approach used to obtain the perturbations by detrending the TEC time series using high-order polynomial fit introduces artifacts. These aliasing and artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and frequency of ionospheric perturbations which are vital to distinguish the perturbations induced by tsunamis and earthquakes from the rest. We show that Spatio-Periodic Leveling Algorithm (SPLA) successfully removes such aliasing and artifacts. The efficiency of SPLA in removing the aliases and artifacts is validated under two simulated scenarios, and using GPS observations carried out during two natural disasters – the 2004 Indian Ocean tsunami and the 2015 Nepal-Gorkha earthquake. We, further, studied the severity of aliasing and artifacts on co-seismic and tsunamigenic perturbations by analyzing its characteristics employing SNR, spatiotemporal, and wavelet analyses. The results reveal that removal of aliasing and artifacts using SPLA i) increases the SNR up to ~149% compared to the residual method and ~39% compared to the differential method, ii) distinctly resolves signals from sharp static variations, and iii) detects 50% more co-seismic ionospheric perturbations and 25% more tsunami-induced ionospheric perturbations in the two events studied. Cross-correlation of the perturbation time series obtained using the residual method and SPLA reveals that aliasing and artifacts shift the time of occurrence by -7.64 minutes to +4.21 minutes. Further, the results show that the SPLA efficiently detects the ionospheric perturbations at low elevation angles, thereby removes the need of applying elevation cut-off and increases the area of ionospheric exploration of a GPS receiver.

Changes in the concentration of greenhouse gases within the atmosphere lead to changes in radiative fluxes throughout the atmosphere. The value of this change, called the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models, and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line-by-line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. The clear-sky instantaneous forcing by greenhouse gases to which the world has been subject is computed using a set of 100 profiles, selected from a re-analysis of present-day conditions, that represent the global annual mean forcing with sampling errors of less than 0.01 \si{\watt\per\square\meter}. Six contributing line-by-line models agree in their estimate of this forcing to within 0.025 \si{\watt\per\square\meter} while even recently-developed parameterizations have typical errors four or more times larger, suggesting both that the samples reveal true differences among line-by-line models and that parameterization error will be readily resolved. Agreement among line-by-line models is better in the longwave than in the shortwave where differing treatments of the water vapor vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are roughly estimated, as are adjustments due to stratospheric temperature change. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest non-linearity.

Previous studies have interpreted Last Interglacial (LIG; ~129-116 ka) sea-level estimates in multiple different ways to calibrate projections of future Antarctic ice-sheet (AIS) mass loss and associated sea-level rise. This study systematically explores the extent to which LIG constraints could inform future Antarctic contributions to sea-level rise. We develop a Gaussian process emulator of an ice-sheet model to produce continuous probabilistic projections of Antarctic sea-level contributions over the LIG and a future high-emissions scenario. We use a Bayesian approach conditioning emulator projections on a set of LIG constraints to find associated likelihoods of model parameterizations. LIG estimates inform both the probability of past and future ice-sheet instabilities and projections of future sea-level rise through 2150. Although best-available LIG estimates do not meaningfully constrain Antarctic mass loss projections or physical processes until 2060, they become increasingly informative over the next 130 years. Uncertainties of up to 50 cm remain in future projections even if LIG Antarctic mass loss is precisely known (+/-5 cm), indicating there is a limit to how informative the LIG could be for ice-sheet model future projections. The efficacy of LIG constraints on Antarctic mass loss also depends on assumptions about the Greenland ice sheet and LIG sea-level chronology. However, improved field measurements and understanding of LIG sea levels still have potential to improve future sea-level projections, highlighting the importance of continued observational efforts.

We demonstrate scintillation analysis from a network of geodetic Global Positioning System (GPS) receivers which provide data at 1-second resolution. We introduce proxy phase ($\sigma_{TEC}$) and amplitude ($SNR_4$) scintillation indices and validate them against the rate of change of TEC index (ROTI), and $S_4$. Additionally, we validate scintillation observations against a CASES scintillation receiver. We develop receiver dependent scintillation event thresholding using hardware-dependent noise variance. We analyze six-days adjacent to the 7-8 September 2017 geomagnetic storm, using 169 receivers covering magnetic latitudes between 15$^\circ$ and 65$^\circ$ in the American longitude sector. We leverage the available spatial sampling coverage to construct 2-D maps of scintillation and present episodic evolution of scintillation intensifications during the storm. We show that low-latitude and high-latitude scintillation morphology match well-established scintillation climatology patterns. At mid-latitudes, spatiotemporal evolution of scintillation partially agrees with known scintillation patterns. Additionally, the results reveal previously undocumented mid-latitude scintillation producing structures. The results provide an unprecedented view into the spatiotemporal development of scintillation-producing plasma irregularities and provide a resource to further exploit scintillation evolution at large spatial scales.

In this paper, we leverage open data and open-source software to develop flexible, probabilistic monthly and seasonal (three-month) precipitation forecasts for the Pacific region. We use data from a Multi-Model Ensemble (MME), i.e. a large ensemble of state-of-the-art General Circulation Models (GCMs) and make use of recent developments in the Python open-source software ecosystem allowing the processing of large datasets on standard consumer grade laptops or desktop computers, of particular relevance in the Pacific context. The validation of the deterministic MME forecasts against reanalysis and observational products shows good performance, and confirms that an MME outperforms even the best single GCM. We show that the MME’s forecast performance is modulated by the phases and characteristics of the El Nino Southern Oscillation (ENSO), with the longitude of the maximum Sea Surface Temperature anomalies playing a major role. We suggest that these findings could be used to provide additional confidence information along with the operational MME forecasts. Validation metrics for the probability of drought conditions, alternatively defined as seasonal rainfall accumulations below the climatological 1 tercile (percentile 33) or 1st quartile (percentile 25) show that the MME forecasts are reliable enough for most of the region. We provide an example of how this probabilistic forecast information can be integrated with real-time rainfall monitoring, in order to highlight areas in the tropical Pacific region which are at risk of water stress (i.e., where rainfall has recently been in deficit and forecasts indicate a high likelihood of dry conditions to persist or worsen).

To help understand and determine the driver of jovian auroral X-rays, we present the first statistical study to focus on the morphology and dynamics of the jovian northern hot spot (NHS) using Chandra data. The catalogue we explore dates from 18 December 2000 up to and including 8 September 2019. Using a numerical criterion, we characterize the typical and extreme behaviour of the concentrated NHS emissions across the catalogue. The mean power of the NHS is found to be 1.91 GW with a maximum brightness of 2.02 Rayleighs (R), representing by far the brightest parts of the jovian X-ray spectrum. We report a statistically significant region of emissions at the NHS center which is always present, the averaged hot spot nucleus (AHSNuc), with mean power of 0.57 GW and inferred average brightness of ∼ 1.2 R. We use a flux equivalence mapping model to link this distinct region of X-ray output to a likely source location and find that the majority of mappable NHS photons emanate from the pre-dusk to pre-midnight sector, coincident with the dusk flank boundary. A smaller cluster maps to the noon magnetopause boundary, dominated by the AHSNuc, suggesting that there may be multiple drivers of X-ray emissions. On application of timing analysis techniques (Rayleigh, Monte Carlo, Jackknife), we identify several instances of statistically significant quasi-periodic oscillations (QPOs) in the NHS photons ranging from ∼ 2.3-min to 36.4-min, suggesting possible links with ultra-low frequency activity on the magnetopause boundary (e.g. dayside reconnection, Kelvin-Helmholtz instabilities).

More recurrent heatwaves and extreme ozone episodes are likely to occur during the next decades and a key question is about the concurrence of those hazards, the atmospheric patterns behind their appearance and their joint effect on human health. In this work, we use surface maximum temperature and O3 observations during extended summers in two cities from Morocco: Casablanca and Marrakech, between 2010 and 2019. We assess the connection between these data and climate indexes (North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO) and Saharan Oscillation (SaOI)). We then identify concurrent heatwaves and ozone episodes, the weather type behind this concurrence and the combined health risks. Our findings show that the concurrence of heatwaves and O3 episodes depends both on the specific city and the large-scale atmospheric circulation. The likely identified synoptic pattern is when the country is under the combined influence of an anticyclonic area in the north and the Saharan trough extending the depression centered in the south. This pattern generates a warm flow and may foster photochemical pollution. Our study is the first step towards the establishment of an alert system. It will help to provide recommendations for coping with concurrent heatwaves and air pollution episodes.

Indian summer monsoon (ISM) has profound impact on water resources over the Asian Water Towers (AWTs) and surroundings. Stable isotopes in precipitation (δO and δD) are crucial tracers of ISM moisture transport processes. Here we presented spatiotemporal variations of stable isotopes in precipitation at three stations over Bangladesh in 2017-2018 to evaluate the influence of moisture sources and transport on intra-seasonal variations of stable isotopes in precipitation, combined with local meteorological data, ERA5 reanalysis data and HYSPLIT model. We found Bay of Bengal (BoB), tropical Indian Ocean (TIO) and Arabian Sea (AS) were the primary moisture suppliers throughout the year and moisture uptake process primarily occurred over BoB. The most enriched δO and δD values exist in the pre-monsoon season, associated with >50% contributions from BoB, and gradually decline throughout the monsoon and post-monsoon seasons due to increased contribution of moisture from AS (~30%) and IO (~40%), and reach to their lowest values by the end of the post-monsoon season when >25% contributed from BoB and ~20% from TIO. The strongly positive δO-OLR and negative δO-humidity relationships were found at all three stations showing a decreasing pattern from south to north.δO-temperature (δO-precipitation) relationship was only found at southern stations at local scale. Convective activities over the AS, BoB and northern IO primarily regulate the δO depletion, and a weak (strong) flux-δO relationship for northward (eastward) transport was found. This study could improve understanding of moisture transport by the ISM for our societies to promote the water resource management over AWTs.

Most countries around the world including the United States took actions to control COVID-19 spread that lead to an abrupt shift in human activity. On-road NOx emissions from light and heavy-duty vehicles decreased by 9% to 19% between February and March at the onset of the lockdown period in the middle of March in most of the US; between March and April, the on-road NOx emissions dropped further by 8% to 31% when lockdown measures were the most stringent. These precipitous drops in NOx emissions correlated well with tropospheric NO2 column amount observed by the Sentinel 5 Precursor TROPOspheric Monitoring Instrument (S5P TROPOMI). Furthermore, the changes in TROPOMI tropospheric NO2 across the continental U.S. between 2020 and 2019 correlated well with changes in on-road NOx emissions (r = 0.68) but correlated weakly with changes in emissions from the power plants (r = 0.35). At the height of lock-down related unemployment in the second quarter of 2020, the NO2 values decreased at the rate of 0.8 µmoles/m2 per unit percentage increase in the unemployment rate. Despite the lifting of lockdown measures, parts of the US continued to have ~20% below normal on-road NOx emissions. To achieve this new normal urban air quality in the US, continuing remote work policies that do not impede economic growth may become one of the many options.

Few studies have utilized machine learning techniques to predict or understand the Madden-Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here we present a simple framework for real-time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real-time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ~17 days in October-March and ~10 days in April-September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks is highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena.

This study investigates the energy spectrum of electron microbursts observed by the Focused Investigations of Relativistic Electron Burst Intensity, Range, and Dynamics II (FIREBIRD-II, henceforth FIREBIRD) CubeSats. FIREBIRD is a pair of CubeSats, launched in January 2015 into a low Earth orbit, that focus on studying electron microbursts. High resolution electron data from FIREBIRD-II consists of 5 differential energy channels between 200 keV and 1 MeV and a $>$1 MeV integral channel. This covers an energy range that has not been well studied from low Earth orbit with good energy and time resolution. This study aims to improve understanding of the scattering mechanism behind electron microbursts by investigating their spectral properties and their relationship to the equatorial electron population under different geomagnetic conditions. Microbursts are identified in the region of the North Atlantic where FIREBIRD only observes electrons in the bounce loss cone. The electron flux and exponential energy spectrum of each microburst is calculated using a FIREBIRD instrument response modeled in GEANT4 (GEometry ANd Tracking) and compared with the near equatorial electron spectra measured by the Van Allen Probes. Microbursts occurring when the AE index is enhanced tend to carry more electrons with relatively higher energies. The microburst scattering mechanism is more efficient at scattering electrons with lower energies, however the difference in scattering efficiency between low and high energy is reduced during periods of enhanced AE.

We present new measurements of the vertical density profile of the Earth’s atmosphere at altitudes between 70 and 200\,km, based on Earth occultations of the Crab Nebula observed with the X-ray Imaging Spectrometer onboard Suzaku and the Hard X-ray Imager onboard Hitomi. X-ray spectral variation due to the atmospheric absorption is used to derive tangential column densities of the absorbing species, i.e., N and O including atoms and molecules, along the line of sight. The tangential column densities are then inverted to obtain the atmospheric number density. The data from 219 occultation scans at low latitudes in both hemispheres from September 15, 2005 to March 26, 2016 are analyzed to generate a single, highly-averaged (in both space and time) vertical density profile. The density profile is in good agreement with the NRLMSISE-00 model, except for the altitude range of 70–110\,km, where the measured density is $\sim$50\% smaller than the model. Such a deviation is consistent with the recent measurement with the SABER aboard the TIMED satellite (Cheng et al. 2020). Given that the NRLMSISE-00 model was constructed some time ago, the density decline could be due to the radiative cooling/contracting of the upper atmosphere as a result of greenhouse warming in the troposphere. However, we cannot rule out a possibility that the NRL model is simply imperfect in this region. We also present future prospects for the upcoming Japan-US X-ray astronomy satellite, XRISM, which will allow us to measure atmospheric composition with unprecedented spectral resolution of $\Delta E \sim 5$\,eV in 0.3–12\,keV.

Atmospheric rivers (ARs) affect surface hydrometeorology in the US West Coast and Midwest. We systematically sought optimal AR indices for expressing surface precipitation impacts within the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) framework. We adopted a multifactorial approach. Four factors—moisture fields, climatological thresholds, shape criteria, and temporal thresholds—collectively generated 81 West Coast AR indices and 81 Midwest indices from January 1980 to June 2017. Two moisture fields were extracted from the MERRA-2 data for ARTMIP: integrated water vapor transport (IVT) and integrated water vapor (IWV). Metrics for precipitation effects included two-way summary statistics relating the concurrence of AR and that of precipitation, per-event averaged precipitation rate, and per-event precipitation accumulation. We found that an optimal AR index for precipitation depends on the types of impact to be addressed, associated physical mechanisms in the affected regions, timing, and duration. In West Coast and Midwest, IWV-based AR indices identified the most abundant AR event time steps, most accurately associated AR to days with precipitation, and represented the presence of precipitation the best. With a lower climatological threshold, they detected the most accumulated precipitation with the longest event duration. Longer duration thresholds also led to higher accumulated precipitation, holding other factors constant. IWV-based indices are the overall choice for Midwest ARs under varying seasonal precipitation drivers. IVT-based indices suitably capture the accumulation of intense orographic precipitation on the West Coast. Indices combining IVT and IWV identify the fewest, shortest, but most intense AR precipitation episodes.

Unusually long duration and heavy rainfall on 5-6 February 2021 causes devastating floods in Semarang. The heavy rainfall is produced by two mesoscale convective systems (MCSs). The first MCS develops at 13Z on 5 February 2021 over the southern coast of Sumatra and propagates towards Semarang. The second MCS develops over the north coast of Semarang at 18Z on 5 February 2021, which later led to the first peak of precipitation at 21Z on 5 February 2021. These two MCSs eventually merge into single MCS, producing the second peak of precipitation at 00Z on 6 February 2021. Analysis of moisture transport indicates that the strong and persistent northwesterly wind near the surface induced by CENS prior to and during the event, creates an intensive meridional (southward) tropospheric moisture transport from the South China Sea towards Semarang. In addition, the westerly flow induced by low-frequency variability associated with La-Nina and the tropical depression associated with tropical cyclone formation over the North of Australia, produces an intensive zonal (eastward) tropospheric moisture transport from the Indian Ocean towards Semarang. The combined effects of the zonal and meridional moisture transports provide favorable conditions for the development of MCSs, and hence, extreme rainfall over Semarang.

In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time, i.e., drought dynamics. Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small ‘artifact’ droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor (LAFDM) for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.

Downwelling longwave radiation (DLR) is an important part of the surface energy budget. Spectral trends in the DLR provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23-year downwelling longwave radiance record measured by the Atmospheric Emitted Radiance Interferometers (AERI) at the Southern Great Plains (SGP) site of the Atmospheric Radiation Program. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to characterize discrepancies between them due to undetected instrumentation errors. Using the 23-year record, we analyze the all-sky radiance trends in DLR, which reflects the associated surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near-surface air temperature variations. The changes in the sky fraction of clear-sky and thick cloudy-sky scenes offset the radiance changes in the window band. Our analysis shows that the radiance trend uncertainty in the DLR record to date mainly results from the climate internal variability rather than the measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.