Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil’s 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (Rt) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire fifteen-month period and several shorter, three-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and Rt. We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and specific humidity with increased transmission. However, the impact of meteorology, policy, and mobility on Rt varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.

Previous work has found that as the surface warms the large-scale tropical circulations weaken, convective anvil cloud fraction decreases, and atmospheric static stability increases. Circulation changes inevitably lead to changes in the humidity and cloud fields which influence the surface energetics. The exchange of mass between the boundary layer and the midtroposphere has also been shown to weaken in global climate models. What has remained less clear is how robust these changes in the circulation are to different representations of convection, clouds, and microphysics in numerical models. We use simulations from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) to investigate the interaction between overturning circulations, surface temperature, and atmospheric moisture. We analyze the underlying mechanisms of these relationships using a 21-member model ensemble that includes both general circulation models and cloud resolving models. We find a large spread in the change of intensity of the overturning circulation. Both the range of the circulation intensity, and its change with warming can be explained by the range of the mean upward vertical velocity. There is also a consistent decrease in the exchange of mass between the boundary layer and the midtroposphere. However, the magnitude of the decrease varies substantially due to the range of responses in both mean precipitation and mean precipitable water. This work implies that despite well understood thermodynamic constraints, there is still a considerable ability for the cloud fields and the precipitation efficiency to drive a substantial range of tropical convective responses to warming.

Robust and reliable projections of future streamflow are essential to create more resilient water resources, and such projections must first be bias corrected. Standard bias correction techniques are applied over calendar-based time windows and leverage statistical relations between observed and simulated data to adjust a given simulated datapoint. Motivated by a desire to connect the statistical process of bias correction to the underlying dynamics in hydrologic models, we introduce a novel windowing technique for projected streamflow wherein data are windowed based on hydrograph-relative time, rather than Julian day. We refer to this method as ‘seasonally anchored’. Four existing bias correction methods, each using both the standard day-of-year and the novel windowing technique, are applied to daily streamflow simulations driven by 10 global climate models across a diverse subset of six watersheds in California to investigate how these methods alter the model climate change signals. Among the methods, only PresRat preserves projected annual streamflow changes, and does so for both windowing techniques. The seasonally anchored window PresRat reduces the ensemble bias by a factor of two compared to quantile mapping (Qmap), cumulative distribution function transform (CDFt), and equidistant quantile matching (EDCDFm) methods. For wet season flows, PresRat with seasonally anchored windowing best preserves the original model change over the entire distribution, particularly at the highest quantiles, and the other three methods show improved performance using the novel windowing method. Concerning temporal shifts in seasonality, PresRat and CDFt preserve the original model signals with both the novel and standard windowing methods.

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detection with snow seasonality classifications to examine fire activity in California’s seasonal and ephemeral snow areas. We find a nearly tenfold increase in fire activity during 2020 and 2021 compared to 2001-2019 as measured by satellite data. Accumulation season snow albedo declined 17-77% in two burned sites as measured by in-situ data relative to un-burned conditions, with greater declines associated with increased soil burn severity. By enhancing snowpack susceptibility to melt, decreased snow albedo drove mid-winter melt during a multi-week midwinter dry spell in 2022. Despite similar meteorological conditions in 2013 and 2022, which we link to persistent high pressure weather regimes, minimal melt occurred in 2013. Post-fire differences are confirmed with satellite measurements. Our findings suggest larger areas of California’s snowpack will be increasingly impacted by the compounding effects of dry spells and wildfire.

The complex network is a method with a high flexibility and easy application. Complex Network allows extracting relevant information from the system, like its organization and dynamics, as well as different indices that allow obtaining particular characteristics. This work studies the communities present on the rain network in the Amazon basin for the austral summer. Summer was used due to the presence of the South American monsoon system (SAMS), since this is the greatest mechanism for modulating precipitation over South America. Once the communities were obtained, the minimum correlation value (MCV) was varied in order to verify the spatial variations of the communities. Where it was verified how certain communities are composed of subcommunities while others simply disappear. Finally, it is shown how the spatial distribution of the subcommunities shows a relationship with the presence of SAMS. However, more detailed analyzes are needed for each of these communities.

In this work, machine learning techniques were applied to detect clusters present in satellite and weather radar images. The technique used was the unsupervised clustering algorithm DBSCAN. This algorithm was used to extract the morphological characteristics of atmospheric systems that occurred between February 1 and March 30, 2014 (rainy season) and September 15 to October 15, 2014 (dry season). The morphological characteristics are extracted from different thresholds (235K, 220K and 210K) of cloud top brightness temperature observed in the infrared channel of GOES-13 satellite, and also the precipitation estimated at the reflectivity thresholds (20dBZ, 30dBZ and 40dBZ) of the SIPAM meteorological radar in the city of Manaus. The results present the number of clusters identified by the algorithm and described the characteristics of the clusters during the diurnal cycle and in both seasons.

Brazil is one of countries highest incidence of lightning in the world and the characterization of thus event can help in the development of public polices and decision-making by authorities to mitigate the socio-economic damage that may be caused. This work presents some analysis of spatio-temporal patterns of lightnings in Brazil in 2020, generated from Self-Organizing Map (SOM) technique. This analysis considers the activity of the lightning in the hourly, daily and monthly periods accumulated in the different Brazilian states. The seasonal variation of lightning was also evaluated, considering the four seasons of 2020. The results showed that the self-organizing maps were efficient in identifying spatio-temporal patterns of lightning, which are highly variability events. Thus, theses results can support the development of new tools or analysis in which the spatio-temporal information lightning is important, for example, in warning and forecasting systems.

Carbon fluxes from agroecosystems contribute to the variability in the carbon cycle and atmospheric [CO2]. In this study, we used the Integrated Science Assessment Model (ISAM) equipped with a spring wheat module to study carbon fluxes and their variability in spring wheat agroecosystems of India. First, ISAM was run in the site-scale mode to simulate the Gross Primary Production (GPP), Total Ecosystem Respiration (TER), and Net Ecosystem Production (NEP) over an experimental spring wheat site in the north India. Comparison with flux-tower observations showed that the spring wheat module in ISAM can match the observed flux patterns better than generic crop models. Next, regional-scale runs were conducted to simulate carbon fluxes across the country for the 1980-2016 period. Results showed that the fluxes vary widely, primarily due to variations in planting dates across regions. Fluxes peak earlier in the eastern and central parts of the country, where the crops are planted earlier. All fluxes show statistically significant increasing trends (p<.01) during the study period. The GPP, Net Primary Production (NPP), Autotrophic respiration (Ra), and Heterotrophic Respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC/yr2, respectively. Numerical experiments were conducted to study how natural forcings like changing temperature and [CO2] and agricultural management practices like nitrogen fertilization and water availability could contribute to the increasing trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and water added through irrigation contributed to the increase of carbon fluxes, with nitrogen fertilization having the strongest effect.

The emergence of COVID-19 brought panic and a sense of urgency causing governments to impose strict restrictions on human and vehicular movement. With anthropogenic emissions, especially traffic and industrial activities, said to be a significant contributor to ambient air pollution, this study assessed the impacts of the imposed restrictions on the atmospheric concentrations and size distribution of atmospheric aerosols and gaseous pollutants over West African sub-region and seven major COVID-19 epicenters in the sub-region. Satellite retrievals and reanalysis datasets were used to study the impact of the restrictions on Aerosol Optical Depth (AOD) and atmospheric concentrations NO2, SO2, CO and O3. These anomalies were computed for 2020 relative to 2017-2019 (the reference years). In 2020 relative to the reference years, there was a significant reduction of between 0.5±24.6 – 13.7±30.3% and 5.9±17.1% in area-averaged AOD levels at the epicenters and over the sub-region, respectively. The levels of NO2 and SO2 also reduced substantially at the epicenters, especially during the periods when the restrictions were highly enforced. However, the atmospheric levels of CO and ozone increased slightly in 2020 compared to the reference years. This study shows that “a one cap fits all” policy cannot reduced the level of air pollutants and that traffic and industrial processes are not the major sources of CO in major cities in the sub-region. Although not available, ground-based measurements would have given a clearer and better picture of the anomalies observed with the dataset used in this study which are on a coarser spatial resolution.

One of the most important components of an atmospheric radiation scheme is its treatment of gas optical properties, which determines not only the accuracy of its radiative forcing calculations fundamental to climate prediction, but also its computational cost. This paper describes a free software tool ‘ecCKD’ for generating fast gas-optics models by optimally dividing the spectrum into pseudo-monochromatic spectral intervals (known as k-terms) according to a user-specified error tolerance and the range of greenhouse-gas concentrations that needs to be simulated. The models generated use the correlated k-distribution method in user-specified bands, but can also generate accurate ‘full-spectrum correlated-k’ models that operate on the entire longwave or near-infrared parts of the spectrum. In the near-infrared, the large spectral variation in cloud absorption is represented by partitioning the parts of the spectrum where gases are optically thin into three or more sub-bands, while allowing k-terms for the optically thicker parts of the spectrum (where clouds and surface reflectance are less important) to span the entire near-infrared spectrum. Candidate models using only 16 and 32 k-terms in each of the shortwave and longwave are evaluated against line-by-line calculations on clear and cloudy profiles. The 32-term models are able to accurately capture the radiative forcing of varying greenhouse gases including CO2 concentrations spanning a factor of 12, and heating rates at pressures down to 1 Pa.

This study developed an ensemble four-dimensional variational (En4DVar) hybrid data assimilation (DA) system. Different from most of the available En4DVar systems that adopted ensemble Kalman Filter class or ensemble DA approaches to produce ensemble covariances for their hybrid background error covariances (BECs), it used a four-dimensional ensemble-variational (4DEnVar) system to obtain the ensemble covariance. The localization scheme for 4DEnVar applied orthogonal functions to decompose the correlation matrix so that it was implemented easily and rapidly. In terms of analysis quality and forecast skill, the En4DVar system was evaluated in the single-point observation experiments and observing system simulation experiments (OSSEs) with sounding and cloud-derived wind observations, using its standalone four-dimensional variational (4DVar) and 4DEnVar components as references. The single-point observation experiments visually verified the explicit flow-dependent characteristic of the BEC due to the introduction of the ensemble covariance from the 4DEnVar system. The OSSE-based sensitivity experiments revealed different contributions of the weight for the ensemble covariance in the En4DVar system to the forecasts in the Northern and Southern Extratropics and Tropics. A much higher weight for the ensemble covariance in a properly inflated hybrid covariance helped En4DVar produce the most reasonable analysis. The forecast initialized by En4DVar is overall better than by 4DVar and 4DEnVar, although the quality of En4DVar analysis is between those of 4DVar and 4DEnVar ensemble mean analyses. It indicates that the flow-dependent ensemble covariance provided by 4DEnVar dominantly contributes to the improvements in the En4DVar-initialized forecast, with certain but necessary constraint from the balanced climatological covariance.

Because of their limited spatial resolution, numerical weather prediction and climate models have to rely on parameterizations to represent atmospheric turbulence and convection. Historically, largely independent approaches have been used to represent boundary layer turbulence and convection, neglecting important interactions at the subgrid scale. Here we build on an eddy-diffusivity mass-flux (EDMF) scheme that represents all subgrid-scale mixing in a unified manner, partitioning subgrid-scale fluctuations into contributions from local diffusive mixing and coherent advective structures and allowing them to interact within a single framework. The EDMF scheme requires closures for the interaction between the turbulent environment and the plumes and for local mixing. A second-order equation for turbulence kinetic energy (TKE) provides one ingredient for the diffusive local mixing closure, leaving a mixing length to be parameterized. A new mixing length formulation is proposed, based on constraints derived from the TKE balance. It expresses local mixing in terms of the same physical processes in all regimes of boundary layer flow. The formulation is tested at a range of resolutions and across a wide range of boundary layer regimes, including a stably stratified boundary layer, a stratocumulus-topped marine boundary layer, and dry convection. Comparison with large eddy simulations (LES) shows that the EDMF scheme with this diffusive mixing parameterization accurately captures the structure of the boundary layer and clouds in all cases considered.

We demonstrate that an extended eddy-diffusivity mass-flux (EDMF) scheme can be used as a unified parameterization of subgrid-scale turbulence and convection across a range of dynamical regimes, from dry convective boundary layers, over shallow convection, to deep convection. Central to achieving this unified representation of subgrid-scale motions are entrainment and detrainment closures. We model entrainment and detrainment rates as a combination of turbulent and dynamical processes. Turbulent entrainment/detrainment is represented as downgradient diffusion between plumes and their environment. Dynamical entrainment/detrainment are proportional to a ratio of buoyancy difference and vertical velocity scale, partitioned based on buoyancy sorting approaches and modulated by a function of relative humidity difference in cloud layer to represent buoyancy loss owing to evaporation in mixing. We first evaluate the closures offline against entrainment and detrainment rates diagnosed from large-eddy simulations (LES) in which tracers are used to identify plumes, their turbulent environment, and mass and tracer exchanges between them. The LES are of canonical test cases of a dry convective boundary layer, shallow convection, and deep convection, thus spanning a broad range of regimes. We then compare the LES with the full EDMF scheme, including the new closures, in a single column model (SCM). The results show good agreement between the SCM and LES in quantities that are key for climate models, including thermodynamic profiles, cloud liquid water profiles, and profiles of higher moments of turbulent statistics. The SCM also captures well the diurnal cycle of convection and the onset of precipitation.

Some of the Earth system data products such as those from NASA airborne and field investigations (a.k.a. campaigns), are highly heterogeneous and cross-disciplinary, making the data extremely challenging to manage. For example, airborne and field campaign measurements tend to be sporadic over a period of time, with large gaps. Data products generated are of various processing levels and utilized for a wide range of inter- and cross-disciplinary research and applications. Data and derived products have been historically stored in a variety of domain-specific standard (and some non-standard) formats and in various locations such as NASA Distributed Active Archive Centers (DAACs), NASA airborne science facilities, field archives, or even individual scientists’ computer hard drives. As a result, airborne and field campaign data products have often been managed and represented differently, making it onerous for data users to find, access, and utilize campaign data. Some difficulties in discovering and accessing the campaign data originate from the incomplete data product and contextual metadata that may contain details relevant to the campaign (e.g. campaign acronym and instrument deployment locations), but tend to lack other significant information needed to understand conditions surrounding the data. Such details can be burdensome to locate after the conclusion of a campaign. Utilizing consistent terminology, essential for improved discovery and reuse, is also challenging due to the variety of involved disciplines. To help address the aforementioned challenges faced by many repositories and data managers handling airborne and field data, this presentation will describe stewardship practices developed by the Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) under the NASA’s Earth Science Data systems (ESDS) Program.

This study investigates trends in global tropical cyclone (TC) activity from 1990–2020, a period where observational platforms are mostly consistent. Several global TC metrics have decreased during this period, with significant decreases in hurricanes and Accumulated Cyclone Energy (ACE). Most of this decrease has been driven by significant downward trends in the western North Pacific. Globally, short-lived named storms, 24-hr intensification periods of >=50 kt day-1 and TC-related damage have increased significantly. The increase in short-lived named storms is likely due to technological improvements, while rapidly intensifying TC increases may be fueled by higher potential intensity. Damage increases are largely due to increased coastal assets. The decreasing trends in hurricane numbers and global ACE are likely due to the trend towards a more La Niña-like base state from 1990–2020, favoring TC activity in the North Atlantic and suppressing TC activity in the eastern and western North Pacific.

A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. We formulate the first supermodel framework for Earth System Models (ESMs) and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated from them. Discrepancies in grid and resolution are handled by constructing the synthetic pseudo-observations on a common grid. We compare the performance of two supermodel approaches to that of the NI for 1980—2006. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in locations where the ocean drives the climate variability. The time variability of the supermodel multi-model mean SST is reduced compared to the observed variability; most where synchronisation is not achieved and is bounded by NI. The damping is larger in EW than in SINGLE because EW yields additional damping of the variability in the individual models. Hence, under partial synchronisation, the part of variability that is not synchronised gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. The performance of a trained supermodel remains to be tested.

The autumn precipitation in the central region of China (APCC) can exert great influences to the production and people’s livelihood. With the use of reanalysis data from 1979−2020, we found a simultaneous relationship between the interannual variability of APCC and the second mode of the 200-hPa meridional wind field over the Eurasian continent, which featured a ‘+-+’ wave-like pattern in autumn (denoted by EC-a). When EC-a is in a positive phase, the coupling of the positive geopotential height with anticyclonic anomalies in the upper level and low sea level pressure over the central China provides a conducive moisture and dynamic condition for precipitation, which is reversed in the negative phase. As indicated by the diagnostic equation, the local vertical motion anomaly is mainly dependent on the temperature advection anomaly by the perturbed wind acting on mean temperature. The strengthened anticyclonic wind shear over East Asia reinforces the southeasterly, which induces warmer air to move northward, resulting in a positive temperature advection and hence enhancing local ascending motion. Moreover, wave flux analysis and numerical simulations show that the EC-a wave train could be triggered by an abnormal dipole pattern SST over the North Atlantic Ocean, which acts as a critical pacemaker on the variability of EC-a.

In response to north-south pressure gradients set by the annual march of the Sun, a cross-equatorial flow that turns to become a low-level Somali jet at around $10^{\circ}$ N is established in the lower troposphere over the Indian ocean. This flow plays a fundamental role in the Indian monsoon. A mechanistic understanding of drivers of this flow is lacking. Here we present a seasonal-mean analysis of the Kinetic Energy (KE) budget of the low-level flow using high spatiotemporal resolution ERA5 reanalysis to identify sources and sinks of KE. We find that the largest KE generation occurs around east African orography where the Somali jet forms while a significant KE is also generated over western Ghats and the Madagascar Island (‘hot spots’). These regions are distant from core monsoon precipitation regions, suggesting that local circulations driven by condensation do not directly produce the bulk of KE during monsoons. A unique KE balance supports the generation of Somali jet, with KE generation balanced by nonlinear KE advection as it forms. Over oceans, KE generation occurs mainly due to cross-isobaric meridional winds in the boundary layer. In contrast, over east African highlands and western Ghats KE generation maximizes just above the boundary layer and mainly occurs due to interaction of flow with orography. We propose a simple decomposition of lower tropospheric KE generation into contributions from surface pressure, orography and free-tropospheric gradients that corroborates the important role played by surface pressure gradients once adjusted for effects of orography.

The 20 km² Galabre catchment belongs to the French network of critical zone observatories. It is representative of the sedimentary geology and meteorological forcing found in Mediterranean and mountainous areas. Due to the presence of highly erodible and sloping badlands of various lithologies, the site was instrumented in 2007 to understand the dynamics of suspended sediments (SS) in such areas. Two meteorological stations including measurements of air temperature, wind speed and direction, air moisture, rainfall intensity, raindrop size and velocity distribution are installed both in the upper and lower part of the catchment. At the catchment outlet, a gauging station records the water level, temperature and the turbidity (10 min. time-step). Water and sediment samples are collected automatically to estimate SS concentration-turbidity relationships, providing SS fluxes quantifications with known uncertainties. The sediment samples are further characterized by measuring their particle size distributions (PSD) and by applying a low-cost sediment fingerprinting approach using spectrocolorimetric tracers. Thus, the contributions of badlands on different lithologies to total SS flux are quantified at a high temporal resolution providing the opportunity to better analyze the links between meteorological forcing variability and watershed hydrosedimentary response. The set of measurements was extended to the dissolved phase in 2017. Both the river electrical conductivity and its major ion concentrations are measured each week and every three hours during storm events. This allows progress in understanding both the origin of the water during the events and the partitioning between particulate and dissolved fluxes in the critical zone.

Preprint of paper Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses, published in IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 109-119, Jan. 2018. doi: 10.1109/TVCG.2017.2745178 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8019883&isnumber=8165924

Given the regional surface network of the Umbria region, a mountainous area located in central Italy, the observed hourly temperature time series from 2010 to 2017 have been analyzed applying  basic and extended quality control (QC) procedures following the World Meteorological Organization (WMO) standards. The validation procedure consisted of automatic QC, producing validated data with metadata subsequently recorded in NetCDF format. After these controls, data have been manually checked and an extended procedure has been applied to reconstruct the temperature time series for missing data. The spatiotemporal method used to reconstruct the data has been linear interpolation for 1-hour gaps, and the Empirical Orthogonal Function algorithm (EOF) for the 2-hour and longer gaps. The introduction of a complete and homogeneous data set of hourly reanalysis ERA5 (ECMWF) allowed the reconstruction of the longest gaps with statistical and physical consistency. The final product of this study is a continuous station time series of hourly temperatures that will be available to the public by the end of 2020; a daily version of the original time series is already available at the regional website (https://annali.regione.umbria.it/).Keywords --- Agrometeorology, Quality control, Missing data, ERA5, Empirical Orthogonal Function