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Deep convection overshooting the lowermost stratosphere is well known for its role in the local stratospheric water vapour (WV) budget. While it is seldom the case, local enhancement of WV associated with stratospheric overshoots is often published. Nevertheless, one debatable topic persists regarding the global impact of this event with respect to the temperature-driven dehydration of air parcels entering the stratosphere. As a first step, it is critical to quantify their role at a cloud-resolving scale before assessing their impact on a large scale in a climate model. It would lead to a nudging scheme for large-scale simulation of overshoots. This paper reports on the local enhancements of WV linked to stratospheric overshoots, observed during the TRO-Pico campaign conducted in March 2012 in Bauru, Brazil, using the BRAMS (Brazilian version of the Regional Atmospheric Modeling System; RAMS) mesoscale model. Since numerical simulations depend on the choice of several preferred parameters, each having its uncertainties, we vary the microphysics or the vertical resolution while simulating the overshoots. Thus, we produce a set of simulations illustrating the possible variations in representing the stratospheric overshoots. To better resolve the stratospheric hydration, we opt for simulations with the 800 m horizontal-grid-point presentation. Next, we validate these simulations against the Bauru S-band radar echo tops and the TRO-Pico balloon-borne observations of WV and particles. Two of the three simulations' setups yield results compatible with the TRO-Pico observations. From these two simulations, we determine approximately 333–2000 t of WV mass prevailing in the stratosphere due to an overshooting plume depending on the simulation setup. About 70 % of the ice mass remains between the 380 and 385 K isentropic levels. The overshooting top comprises pristine ice and snow, while aggregates only play a role just above the tropopause. Interestingly, the horizontal cross section of the overshooting top is about 450 km 2 at the 380 K isentrope, which is similar to the horizontal-grid-point resolution of a simulation that cannot compute overshoots explicitly. In a large-scale simulation, these findings could provide guidance for a nudging scheme of overshooting hydration or dehydration. [ABSTRACT FROM AUTHOR]

Numerical weather predictions (NWPs) are systematically subject to errors due to the deterministic solutions used by numerical models to simulate the atmosphere. Statistical postprocessing techniques are widely used nowadays for NWP calibration. However, time-varying bias is usually not accommodated by such models. The calibration performance is also sensitive to the temporal window used for training. This paper proposes space–time models that extend the main statistical postprocessing approaches to calibrate NWP model outputs. Trans-Gaussian random fields are considered to account for meteorological variables with asymmetric behavior. Data augmentation is used to account for the censoring of the response variable. The benefits of the proposed extensions are illustrated through the calibration of hourly 10-m height wind speed forecasts in Southeastern Brazil coming from the Eta model. [ABSTRACT FROM AUTHOR]

Investments in renewable energy sources are increasing in several countries, especially in wind energy, as a response to global climate change caused by the burning of fossil fuels for electricity generation. Thus, it is important to evaluate the Regional Climate Models that simulate wind speed and wind power density in promising areas for this type of energy generation with the least uncertainty in recent past, which is essential for the implementation of wind farms. Therefore, this research aims to calculate the wind power density from Regional Climate Models in areas at Northeast of Brazil from 1986 to 2005. Initially, the ECMWF-ERA5 reanalysis data was validated against observed data obtained from Xavier. The results were satisfactory, showing a strong correlation in areas of Ceará and Rio Grande do Norte (except during the SON season), and some differences in relation to the wind intensity registered by observed data, particularly during the JJA season. Then, the Regional Climate Models RegCM4.7, RCA4 and Remo2009 were validated against the ECMWF-ERA5 reanalysis data, with all models successfully representing the wind speed pattern, especially from December to May. Four specific areas in Northeast of Brazil were selected for further study. In these areas, the RCMs simulations were evaluated to identify the RCM with the best statistical indices and consequently the lowest associated uncertainty for each area. The selected RCMs were: RegCM4.7_HadGEM2 (northern coastal of Ceará and northern coastal of Rio Grande do Norte) and RCA4_Miroc (Borborema and Central Bahia). Finally, the wind power density was calculated from the selected RCM for each area. The northern regions of Rio Grande do Norte and Ceará exhibited the highest wind power density. [ABSTRACT FROM AUTHOR]

General circulation models generate climate simulations on grids with resolutions ranging from 50 to 600 km. The resulting coarse spatial resolution of the model outcomes requires post-processing routines to ensure reliable climate information for practical studies, prompting the widespread application of downscaling techniques. However, assessing the effectiveness of multiple downscaling techniques is essential, as their accuracy varies depending on the objectives of the analysis and the characteristics of the case study. In this context, this study aims to evaluate the performance of downscaling the daily precipitation series in the Metropolitan Region of Belo Horizonte (MRBH), Brazil, with the final scope of performing frequency analyses and estimating total precipitation and the number of rainy days per hydrological year at both annual and multiannual levels. To develop this study, 78 climate model simulations with a horizontal resolution of 100 km, which participated in the SSP1-2.6 and/or SSP5-8.5 scenarios of CMIP6, are employed. The results highlight that adjusting the simulations from the general circulation models by the delta method, quantile mapping and regression trees produces accurate results for estimating the total precipitation and number of rainy days. Finally, it is noted that employing downscaled precipitation series through quantile mapping and regression trees also yields promising results in terms of the frequency analyses. [ABSTRACT FROM AUTHOR]

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance. [ABSTRACT FROM AUTHOR]

Untangling the complex network of physical processes driving regional precipitation regimes in the present (1979–2014) and near-future climates (2020–2050) is fundamental to support a more robust scientific basis for decision making in the water-energy-food nexus. We propose a data-driven mechanistic approach to: (Goal 1) identify changes and variability of the regional precipitation mechanisms and (Goal 2) reduce the ensemble spread of future projections by weighting and filtering models that satisfactorily represent these drivers in present climate. Goal 1 is achieved by applying the Partial Least Squares (PLS) technique, a two-sided variant of principal component analysis (PCA), on a reanalysis dataset and 30 simulations of the future climate submitted to CMIP6 to discover the links between global sea-surface temperature (SST) and precipitation in Brazil. Goal 2 is achieved by selecting and weighting the future climate simulations from climate models that better represent the dominant modes discovered by the PLS in the present climate; with this subset of climate simulation, we produce precipitation change maps following IPCC's WG1 methodology. The main mechanistic link discovered by the technique is that the generalised warming of the oceans promotes a suppression of precipitation in Northeast and Southeast Brazil, possibly mediated by the intensification of the Hadley circulation. We show that this pattern of precipitation suppression is stronger in the near-future precipitation change maps produced using our methodology. This demonstrates that a reduction of epistemic uncertainty is achieved after we select models that skillfully represent these mechanisms in the present climate. Therefore, the approach is capable of supporting both a quantitative analysis of regional changes as well as the construction of storylines supported by mechanistic evidence. [ABSTRACT FROM AUTHOR]

The Cantareira Water Production System (CWPS), which supplies water to the most populous region in Brazil, is facing significant challenges due to hydroclimate change, thus threatening its water security. This research integrates data from climate models and field observations with hydrological modeling, aimed at quantifying trends in key variables of the hydrological cycle. The GFDL-CM4 climate model, the most suitable for the study area, was employed to generate runoff data under both current conditions and future scenarios (SSP2-4.5 and SSP5-8.5). Our analysis reveals an increasing trend in the frequency of dry hydrological years. The Standard Precipitation Index (SPI) and Drought Magnitude (DM) confirm an increase in both the occurrence and duration of droughts in future scenarios. The runoff in all basins was reduced, causing a substantial decrease in minimum flows of 16.9%, medium flows of 11.8%, and high flows of 9.2% for the SSP5-8.5 scenario. This research introduces an approach to hydroclimate impact assessment, combining rigorous data analysis with advanced modeling techniques. Our findings not only provide a comprehensive understanding of the challenges faced by the CWPS, but also offer critical quantitative insights essential for developing effective public policies and adaptive strategies for sustainable water resource management. [ABSTRACT FROM AUTHOR]

Oxygen isotope speleothems have been widely used to infer past climate changes over tropical South America (TSA). However, the spatial patterns of the millennial precipitation and precipitation δ18O (δ18Op) response have remained controversial, and their response mechanisms are unclear. In particular, it is not clear whether the regional precipitation represents the intensity of the millennial South American summer monsoon (SASM). Here, we study the TSA hydroclimate variability during the last deglaciation (20–11 ka ago) by combining transient simulations of an isotope-enabled Community Earth System Model (iCESM) and the speleothem records over the lowland TSA. Our model reasonably simulates the deglacial evolution of hydroclimate variables and water isotopes over the TSA, albeit underestimating the amplitude of variability. North Atlantic meltwater discharge is the leading factor driving the TSA’s millennial hydroclimate variability. The spatial pattern of both precipitation and δ18Op show a northwest–southeast dipole associated with the meridional migration of the intertropical convergence zone, instead of a continental-wide coherent change as inferred in many previous works on speleothem records. The dipole response is supported by multisource paleoclimate proxies. In response to increased meltwater forcing, the SASM weakened (characterized by a decreased low-level easterly wind) and consequently reduced rainfall in the western Amazon and increased rainfall in eastern Brazil. A similar dipole response is also generated by insolation, ice sheets, and greenhouse gases, suggesting an inherent stability of the spatial characteristics of the SASM regardless of the external forcing and time scales. Finally, we discuss the potential reasons for the model–proxy discrepancy and pose the necessity to build more paleoclimate proxy data in central-western Amazon. Significance Statement We want to reconcile the controversy on whether there is a coherent or heterogeneous response in millennial hydroclimate over tropical South America and to clearly understand the forcing mechanisms behind it. Our isotope-enabled transient simulations fill the gap in speleothem reconstructions to capture a complete picture of millennial precipitation/δ18Op and monsoon intensity change. We highlight a heterogeneous dipole response in precipitation and δ18Op on millennial and orbital time scales. Increased meltwater discharge shifts ITCZ southward and favors a wet condition in coastal Brazil. Meanwhile, the low-level easterly and the summer monsoon intensity reduced, causing a dry condition in the central-western Amazon. However, the millennial variability of hydroclimate response is underestimated in our model, together with the lack of direct paleoclimate proxies in the central-west Amazon, complicating the interpretation of changes in specific paleoclimate events and posing a challenge to constraining the spatial range of the dipole. Therefore, we emphasize the necessity to increase the source of proxies, enhance proxy interpretations, and improve climate model performance in the future. [ABSTRACT FROM AUTHOR]

This work presents a contribution to regional climate modeling studies with emphasis on seasonal variability of precipitation in the Amazon Basin (AMZ) and Northeast region of Brazil (NEB). In this sense, the present study aimed to analyze the sensitivity of the simulations carried out for the period 2001 to 2005 (5 years), using two different Planetary Boundary Layer parameterization schemes (Holtslag and UW-PBL) available in the regional climate model version 4 (RegCM4). As initial and large-scale boundary conditions, ERA15 reanalyses were used. Data from the Climate Prediction Center morphing technique (CMORPH) were used for the precipitation assessment. Statistical metrics were used to evaluate the simulations. The dominant feature in RegCM4 simulations is a dry bias with the largest average value (~ 6.0 mm d−1) in part of AMZ Basin, while the lowest mean bias precipitation (~ 1.0 mm d−1), mainly located in NEB region. It was found that both experiments are able to adequately represent the pattern of annual cycle of precipitation when compared to the CMORPH. The UW-PBL scheme experiment had the best performance in the northern sector of AMZ Basin in the austral summer season, with the highest correlation value (~ 0.6). Both experiments showed persistent dry bias in the southern sector of the AMZ Basin during the austral summer season. The Holtslag scheme experiment showed greater ability to reproduce climate variability in the NEB region, especially during austral winter season, with the highest correlation value (~ 0.8). This study can help RegCM users in choosing the suitable configuration of their experiments. In general, the results show that precipitation is better simulated by the Reg_UW-PBL experiment in part of AMZ Basin, while the Reg_Holtslag experiment has better performance in representing the precipitation in the NEB region. [ABSTRACT FROM AUTHOR]

Climate change in the coming decades could intensify extreme events such as severe droughts. Combined with the possible increase of water demands, these changes exert a great deal of pressure on the water systems. In order to confirm this assumption, a set of scenarios was proposed in this study to consider the combined impact of climate changes and the increase in water demand on the main multiple-use reservoirs of São Francisco River, Brazil. For this purpose, five CMIP6 climate models were used, considering two greenhouse gas emissions scenarios: the SSP2-4.5 and SSP5-8.5. The affluent natural flows and regulated flows were estimated to the adopted reservoirs considering all existing, new and projected demands. The combination of scenarios indicated an increase in Potential Evapotranspiration; possible significant reduction in water availability, with a decrease in precipitation with a magnitude of −15% in the most pessimistic scenarios and a decrease in flows with a magnitude varying between −5% and −40% in the Sobradinho and Três Marias reservoirs; growth in water demand, mainly for irrigation, with annual rates of 6.80%, 7.42%, 10.99% and 9.29% for Itaparica, Sobradinho, Três Marias and Retiro Baixo, respectively; and a substantial reduction in the performance of the evaluated reservoirs, mainly for the Retiro Baixo and Itaparica reservoirs, which showed a high vulnerability index and a low sustainability index. [ABSTRACT FROM AUTHOR]

The strategy for assessing simulations produced by climate models established as part of the Atmospheric Model Intercomparison Project (AMIP) delivers an outline for model analysis, verification/validation, and intercomparison. Numerical models are continuously being developed to find the best representation for the amount and distribution of precipitation in Brazil to improve the country's precipitation forecast. This article describes the key features of the Brazilian Global Atmospheric Model (BAM) (developed by the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE)) and analyses of its performance for annual rainfall climate simulations. This study considered the representation of the annual precipitation in Brazil mainly during the rainy season in the central part of Brazil by the BAM. The model was run over the 1990 to 2015 period using spectral Eulerian model dynamics with a 70-horizontal resolution of approximately 1.0 ∘ × 1.0 ∘ and 42 vertical sigma levels. The analysis was divided into two stages: the annual precipitation and the rainy season precipitation. Model precipitation analyses were performed using statistical methods, such as the mean and standard deviation, comparing modeled data with observed data from two datasets, data from the XAV (observed data from INMET, ANA, and DAEE), and the Climate Prediction Center (CPC). In general, the BAM model simulations reasonably replicated the configuration of the spatial distribution of precipitation in the Brazilian territory almost entirely, especially compared with the XAV. The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year − 1 in the extreme south to 1750 mm year − 1 in the north of this region. Average values of the BAM accumulated precipitation ranged from 1000 to 2000 mm year − 1 , within the expected average, compared to observed values of 750–1500 mm year − 1 (CPC and XAV, correspondingly). Although there was an underestimation of the accumulated precipitation by the model, the model reasonably reproduced the precipitation during the rainy season. The performed assessment identified model aspects that need to be improved. [ABSTRACT FROM AUTHOR]

The land–atmosphere interactions play an important role in modulating climate variability at different spatial and temporal scales. In South America, two recognized hot spots of soil moisture–atmosphere coupling are located in southeastern South America (SESA) and eastern Brazil. Soil moisture variability may not only alter the climate locally but may also have nonlocal impacts through changes in the regional circulation. Here we explore how these two local coupling hot spots interact with each other, how soil moisture variability modulates the regional circulation, and what is the consequent nonlocal impact on precipitation. To this end, we analyze numerical experiments, performed with a regional climate model for the period October–March of 1983–2012, that allow us to isolate the influence of the soil moisture interannual variability on the regional climate. When the soil moisture–atmosphere interaction is enabled, we find a nonlocal coupling mechanism that links both hot spots at different temporal scales, favoring precipitation in eastern Brazil to the detriment of the precipitation in SESA through shifts in the regional circulation, when compared with a simulation with constrained soil moisture–atmosphere interaction. In northeastern Argentina, a subregion of SESA located at the exit of the South American low-level jet, it was found that the amount of nighttime precipitation is modulated by the proposed nonlocal coupling mechanism. A better understanding of the variability of precipitation due to the influence of land–atmosphere interaction processes may contribute to improving the predictability of precipitation and the interpretation of climate projections. [ABSTRACT FROM AUTHOR]

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In this work we analyse the effects of climate change on the recharge and the groundwater flow of the Urucuia Aquifer System in western Bahia State, Brazil (UAS‐BA) in the period 2041–2060. We assessed possible changes and impacts on groundwater recharge with four climate models from the sixth phase of the Coupled Model Intercomparison Project Phase 6 (CMIP6): ACCESS‐ESM1‐5, CanESM5, EC‐Earth3 and MIROC6, under scenarios SSP1‐2.6 and SSP5‐8.5 (Shared Socioeconomic Pathways—SSP). Visual Balan 2.0 aquifer recharge assessment code, and the MODFLOW hydrogeological model were used to analyse the impact of future climate changes on the recharge. The projections of relative change in precipitation, under the SSP1‐2.6 scenario, show a precipitation reduction between 5% and 20%, using ACCESS‐ESM1‐5, CanESM5 and EC‐Earth3 models. Using SSP5‐8.5 scenario, these models behave differently, while the CanESM5 and EC‐Earth3 models indicate an even greater decrease in precipitation, with values up to 35%, the ACCESS‐ESM1‐5 projects an increase in precipitation of up to 25%. Using MIROC6, though, an increase of up to 10% in precipitation in the UAS‐BA is expected, under both scenarios. Regarding temperature, all the models agree with a warming in the future varying from 2 to 3°C. Recharge projections calculated using the Visual Balan 2.0 indicated in general a reduction between 8% and 43%. Using the MODFLOW model, the results show a reduction of recharge to the aquifer varying from 3% to 45% using CanESM5, EC‐Earth3 and MIROC6. The ACCESS‐ESM1‐5 has a 26% reduction (5% increase) under SSP1‐2.6 (SSP5‐8.5). Furthermore, projections indicate a downward trend in the water table in the northern portion of UAS‐BA, reaching a maximum drop of up to 12 m with the CanESM5, under SSP5‐8.5 scenario. [ABSTRACT FROM AUTHOR]

This study quantified hydrological impacts caused by climate and anthropogenic changes in nine basins in the Upper Grande River Basin (URGB) region and estimated potential future impacts on runoff based on climate model projections. The Budyko framework was used as a conceptual approach to estimate the causes of runoff variation by the decomposition method. Assuming that land use will not change in future, the runoff was estimated for 2006–2040, 2041–2070, and 2071–2099 time-slices using regionalized projections from four different climate models. The decomposition method indicated a strong influence of both climate and land-use changes in the hydrological regime in the URGB, with the expansion of the agricultural areas being the most important driver of anthropogenic impacts. Future climate projection multi-model ensembles indicated a reduction in average runoff. The Budyko framework performance on future projections presented similar trends to hydrological modeling estimations from previous studies in the same region, indicating the suitability of the method as an additional tool for assessing future climate change impacts on water availability. [ABSTRACT FROM AUTHOR]

Bioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production. [ABSTRACT FROM AUTHOR]

The onshore wind power is consolidated; the challenge is to reach the same level of maturity for offshore exploitation. Brazil has no offshore wind power plants and there are few studies in this direction. This paper aims to estimate the offshore wind resources in the State of Ceará, in Brazil. The investigation uses a mesoscale atmospheric computer model, the Regional Atmospheric Modeling System (RAMS), with horizontal resolution of 2 km, which estimates the offshore average wind speed, average wind direction, power density and turbulence taking into account the bathymetry data and navigation routes along the coast of Ceará. The wind potential was evaluated in three representative periods, La Niña, El Niño and Neutral year, analyzing the dry and rainy season for each period. The results indicate an average wind speed above 8 m/s and power density above 720 W/m 2 no matter the period evaluated, in the dry season. The predominant wind direction in the observed dry periods was from East to West and the turbulence intensity is smaller during dry season of El Niño. Besides, the bathymetry of the State of Ceará is shallow and the large ships route is far beyond the coast, offering no danger to future endeavors. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) and soil texture play a key role in meteorological models because they determine the vegetation and soil proprieties that interfere in the exchange of energy, moisture, and momentum between the land surface and the atmosphere. Additionally, LULC and soil texture are relevant input datasets in meteorological models affecting their results and future applicability as in weather researches and air quality modeling. Brazil has a complex and heterogeneous LU, and it has faced significant LULC changes in the past years. Therefore, this paper aims to update the LULC, using the national product MapBiomas, and soil texture data, by SoilGrids, to replace the default input data in the Weather Research and Forecast (WRF) model for São Paulo, Brazil. Aiming to evaluate the impact of those input data on WRF simulations, five cases were simulated using WRF v4.1.3 with 1 km of grid resolution, and combinations of "Default" and "Updated" input data. Sixty-days simulations from March 15th to May 15th of 2015, covering the transition of wet to dry season, were performed and evaluated with observational data over São Paulo State. The results showed significant differences in the classifications of LULC and soil texture in the entire domain between the default and updated data. The updated data is more realistic and coherent with local characteristics, being more representative, as an example over Santos city area being correctly classified as urban and built-in updated LULC and not water, as in the default. The comparison between the modeled results with observations data has shown a similar behavior for temperature and humidity for the five cases at the monitoring stations grid cells because the LULC changes were between classes with similar land parameters, such as albedo, roughness length, and soils moisture, although the Default classes are not accurate. However, the latent and sensible heat fluxes were ways more sensitive to the LULC/soil texture changes in the WRF model. Additionally, reasonable differences were observed over the entire modeling domain for these two variables. The updated land surface data provoked low temperatures at 9h and 17h UTC, less humidity at 9h UTC, and more humidity at 17h UTC, especially in the north part of the modeling domain, the area which has faced more LULC and soil changes. The PBL height was also affected by the updated data, probably caused by the impact at heat flux over the domain, causing a variation from 30% to 70% over the modeled grid cell, which may have a higher impact on air quality modeling. Thus, it is recommended to update the land surface data for Brazil to avoid misclassification of LULC and soil texture, even if the comparison at monitoring stations has shown similar behavior between the default and updated land surface data. Additionally, updates in the land parameter inside the model are required to represent each LULC/soil class better. • Land Use/Land Cover and soil texture play a key role in meteorological models. • The updated LULC and soil texture are more accurate than the default for Brazil. • Latent and sensible heat fluxes were very sensitive to LULC/soil texture changes in WRF. • Temperature and specific humidity were less sensitive to the LULC/soil texture changes at the monitoring stations sites. • Vegetation and soil parameters inside WRF need improvements, and some physical parameterizations are not suitable for Brazil. [ABSTRACT FROM AUTHOR]

The Madden Julian Oscillation (MJO) is the main source of intraseasonal variability in the tropics. MJO modulates rainfall in remote areas in the Southern Hemisphere by exciting tropical and extratropical wave trains. We use newly released reanalysis data to analyze how the Quasi‐Biennial Oscillation (QBO) can influence the MJO's effects over the Southern Hemisphere, focusing on precipitation anomalies over South America. The anomalies in the intensity of the South Atlantic Convergence Zone (SACZ) and precipitation over Southeastern South America during MJO phases 1 and 4 are intensified during the easterly QBO. The extratropical wave train excited by the anomalous convection over the maritime continent during MJO phase 4 is affected by the QBO, with a stronger, better‐defined pattern during westerly QBO. The conclusions are supported by a perturbation experiment of a case study, conducted using a high‐top atmospheric global climate model where the QBO and MJO are controlled. Plain Language Summary: Subseasonal to seasonal prediction remains challenging in the current forecast models. Previous studies have shown that the Quasi‐Biennial Oscillation (QBO) can influence the intensity and development of the Madden Julian Oscillation (MJO). Here, the consequences of this modulation on the remote effects of the MJO in the Southern Hemisphere are analyzed using reanalysis and perturbation experiments in an atmospheric global climate model. The rainfall anomalies related to the MJO in South America are more intense and widespread during easterly QBO over the South Atlantic Convergence Zone and southern Brazil than during westerly QBO. Thus, the inclusion of the state of the QBO in the estimate of the expected rainfall associated with the MJO is crucial to improve forecasts in the region. Key Points: The Quasi‐Biennial Oscillation (QBO) modulates the impacts of the Madden Julian Oscillation (MJO) in the Southern HemisphereMJO‐related rainfall anomalies in the South Atlantic Convergence Zone are intensified during easterly QBOEThe extratropical wave train forced by the anomalous convection in MJO phase 4 is intensified under QBOW [ABSTRACT FROM AUTHOR]

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall‐runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (IA) and baseflow index (IB). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (IA < 0.9) with low baseflow (IB < 0.4) were the easiest to model, with a single‐reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall‐runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (IB < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (IA < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features. Key Points: Baseflow and aridity help classify suitable model structures for different catchments in BrazilA single bucket model performs relatively well in wet catchments with low baseflow, mainly in southern BrazilIndependently of model structure, performance is generally lower in drier than in wetter catchments [ABSTRACT FROM AUTHOR]

The São Francisco River Basin plays a critical role in the hydroelectrical operational planning of Brazil. Understanding the hydroclimatic dynamic regime and, consequently, related climate changes is essential for decision-makers of the hydroelectrical sector. In this context, the Xingó hydropower plant and its drainage area are taken as a reference for the hydroclimatic features analyzed in this research. Observed rainfall in the drainage basin and streamflow measurements in the power plant between 1975 and 2016 are used to assess the climatology of the region and to identify trends in the time series. In addition, a methodological framework based on numerical modeling of the hydroclimatic variables is employed to examine the representation of the present climate (1961 to 1990) and to investigate the future projections (2011 to 2100). The outputs from the Eta regional climate model runs driven by two global models are adopted in this case under the RCP4.5 and RCP8.5 IPCC scenarios along with the SMAP rainfall-runoff model. In Xingó, the average annual precipitation is about 978 mm and the average annual streamflow is 2,534 m3 s−1. The size of the Xingó HPP drainage area must be considered, as the rainfall that occurs in a given month can influence the streamflow of the following month. There is no trend regarding precipitation, while the streamflow time series show a statistically significant decreasing trend in the present climate. Climate projections point to an elevation in air temperature and a reduction in rainfall and streamflow by 2100. Most scenarios indicate that the average annual streamflow could be extremely low for nearly half of the twenty-first century. The results shown in this work may contribute to scenarios for operation of the Xingó HPP and support with information for planning decisions on country's energy security in the present and future climates. [ABSTRACT FROM AUTHOR]

Studies that evaluate climate change projections over the whole of South America (SA) and including different seasons and models are scarce. In this context, the objective of this work is to assess climate projections for SA through the use of climatic indices, considering the entire continent, distinct seasons, and ensembles of models. Projections performed with the Eta regional climate model and its driving global climate models (GCMs) are analysed. From these projections, 19 climate indices based on daily precipitation and maximum and minimum temperature are computed. The results focus on two ensembles (GCMs and Eta), time slices (1980–2005 and 2050–2080), and scenarios (RCP4.5 and RCP8.5). In the validation of the present climate, it is shown that Eta adds value to GCMs. Future projections indicate, for both austral summer (DJF) and winter (JJA), an increase in the frequency and intensity of extreme events of daily rainfall over southeastern and extreme north of SA. Over the Amazon, during DJF, there is a statistically significant increase in the number of consecutive dry days and a decrease in the consecutive wet days. For northeastern Brazil, these features are more intense in JJA. The frequency of cold (warm) nights and days is projected to decrease (increase) over the whole continent and seasons. The climate change signal for the 19 climate indices is more intense under RCP8.5, and the regions more vulnerable to climate change are the Amazon, northeastern Brazil, and southeastern SA. Considering Brazil, the projections of precipitation and air temperature are also shown by biomes. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper compares forecasts of mean monthly water levels up to six months ahead at Ladário, on the Upper Paraguay River, Brazil, estimated from two long-range dependence models. In one of them, the marked seasonal cycle was removed and a fractionally differenced model was fitted to the transformed series. In the other, a seasonal fractionally differenced model was fitted to water levels without transformation. Forecasts from both models for periods up to six months ahead were compared with forecasts given by simpler "short-range dependence" Box-Jenkins models, one fitted to the transformed series, the other a seasonal autoregressive moving average (ARMA) model. Estimates of parameters in the four models (two "long-range dependence", two "short-range dependence") were updated at six-monthly intervals over a 20 year period, and forecasts were compared using root mean square errors (rmse) between water-level forecasts and observed levels. As judged by rmse, performances of the two long-range dependence models, and of the ARMA (1,1) short-range dependence model, were very similar; all three out-performed the seasonal short-range dependence ARMA model. There was evidence that all models performed better during recession periods, than on the hydrograph rising limb. [ABSTRACT FROM AUTHOR]

Abstract: This invited review paper tends to summarise the results based on the variability of occurrence of temperature extremes in South America. The first thing to note is that there is a geographical imbalance with respect to the number of published studies on temperature extremes. Most of the results come from the southern part of South America, east of the Andes, and a few from the northern part of the continent and for the Altiplano. The workshop organised by the ETCCDMI in Brazil was the first time to have the opportunity to collect information in a regional way and present trends in extreme daily temperatures. A better geographical picture enhanced with more data show significant geographical trends in warm (positive) and cold (negative) nights over Southern South America and over the northern South America coast. All other studies based on smaller regions also agree in finding the most significant trends in the evolution of the minimum temperature, with positive trends in almost all studies on the occurrence of warm nights (or hot extremes of minimum temperature) and negative trend in the cold extremes of the minimum. On the other hand, there is little agreement on the variability of maximum temperature. Generally the maximum temperature in southern South America has decreased, in opposition to the case of northern South America where it has increased. Strong decadal and interannual variability have been found in the occurrence of cold extremes. Reanalysis and climate models underestimate the intensity of extremes, mainly near the Andes. The studies trying to understand the dynamics of the circulation that leads to the occurrence of these extremes are analysed from its occurrence in almost all scales from the synoptic, intraseasonal, seasonal, annual, and multi-year linear trend with different methodologies, also, indentifying the local and remote forcing. A gap was found in studies that relate some specific local forcing (like changes in land use) and compare it with the remote ones. Different aspects of the occurrence of the temperature extremes are still missing in some regions of the continent. [Copyright &y& Elsevier]

Copyright of Brazilian Journal of Agricultural Sciences / Revista Brasileira de Ciências Agrárias is the property of Universidade Federal Rural de Pernambuco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, the use of adaptive filters for reducing forecast errors produced by a Regional Climate Model (RCM) is investigated. Seasonal forecasts are compared against the reanalysis data provided by the National Centers for Environmental Prediction. The reanalysis is used to train adaptive filters based on the Recursive Least Squares algorithm in order to reduce the forecast error. The K-means unsupervised learning algorithm is used to obtain the number of filters to employ from the climate variables. The proposed approach is applied to some climate variables such as the meridional wind, zonal wind, and the geopotential height. The forecast is produced by the Eta RCM at 40-km resolution in a domain covering most of Brazil. Results show that the proposed approach is capable of reducing the forecast errors, according to evaluation metrics such as normalized mean square error, maximum absolute error, and maximum normalized absolute error, thus improving the seasonal climate forecasts. [ABSTRACT FROM AUTHOR]

The present work aimed at building the hydrological model for the Paraíba do Sul River watershed, using the MOHID Land model, in order to study channel flows considering six different precipitation inputs. First, two scenarios were created with temporal variation of rain from the precipitation measured by 17 automatic stations of the Brazilian National Institute of Meteorology (INMET) located in the watershed, and interpolated with the FillMatrix tool, by the Inverse Distance Weighting (TIN) and Triangulated Irregular Networks (IDW) methods. The other four scenarios were created from four experiments simulated in the atmospheric model WRF-ARW, corresponding to the spatial and temporal variation of precipitation. The hydrological model considered the Manning coefficients, Curve Numbers and cross sections extracted from the database of the Brazilian National Water Agency (ANA) together with the operation curve of the reservoirs extracted from the situation room on the Geographical and Geoenvironmental Information System of the Paraíba do Sul River Watershed (SIGA-CEIVAP) website. The analysis period corresponds to the month of January 2019 (01/01/2019 00 UTC and 01/02/2019 00 UTC), with hourly evaluation. The measured channel flow data used were extracted from the ANA fluviometric station, in the Campos dos Goytacazes city, State of Rio de Janeiro, and they were used for the statistical indices calculation, BIAS, MAE, MAPE, RMSE and Pearson's Correlation (R), in order to evaluate the model. The results showed that once the physical parameterization of better performance in the atmospheric model (WRF - Weather Research and Forecasting) was established, the use of the modeled rain yields a superior performance in comparison to the interpolated (observed) rain for implementation in the hydrological model (MOHID - MODagem HIDrodinâmica), with the aim to study the channel flows. [ABSTRACT FROM AUTHOR]

There is an urgent need for more information about the climate change impact on health in order to strengthen the commitment to tackle climate change. However, few studies have quantified the health impact of climate change in Brazil and in the Latin America region. In this paper, we projected the impacts of temperature on cardiovascular (CVD) mortality according to two climate change scenarios and two regionalized climate model simulations in Brazilian cities. We estimated the temperature-CVD mortality relationship in 21 Brazilian cities, using distributed lag non-linear models in a two-stage time-series analysis. We combined the observed exposure-response functions with the daily temperature projected under two representative concentration pathways (RCP), RCP8.5 and RCP4.5, and two regionalized climate model simulations, Eta-HadGEM2-ES and Eta-MIROC5. We observed a trend of reduction in mortality related to low temperatures and a trend of increase in mortality related to high temperatures, according to all the investigated models and scenarios. In most places, the increase in mortality related to high temperatures outweighed the reduction in mortality related to low temperatures, causing a net increase in the excess temperature-related mortality. These trends were steeper according to the higher emission scenario, RCP8.5, and to the Eta-HadGEM2-ES model. According to RCP8.5, our projections suggested that the temperature-related mortality fractions in 2090-99 compared to 2010–2019 would increase by 8.6% and 1.7%, under Eta-HadGEM2-ES and Eta-MIROC5, respectively. According to RCP4.5, these values would be 0.7% and −0.6%. For the same climate model, we observed a greater increase trend in temperature-CVD mortality according to RCP8.5, highlighting a greater health impact associated with the higher emission scenario. Our results may be useful to support public policies and strategies for mitigation of and adaptation to climate change, particularly in the health sector. • We estimated climate change impacts on temperature-CVD mortality in Brazil. • The projections showed a reduction in CVD mortality related to low temperatures. • The projections showed an increase in CVD mortality related to high temperatures. • We found a net increase in temperature-related mortality in most cities. • A greater health impact was associated with the higher emission scenario. [ABSTRACT FROM AUTHOR]

There is little information about the availability of water in the Cerrado biome, the main agricultural frontier in Brazil, especially about groundwater, and this has compromised the region's economic and social development, as well as environmental sustainability. The reduction of rainfall in this region, indicated by numerous climate models, may reduce aquifer recharge and, consequently, groundwater availability and sustainable development of the Cerrado biome. This study aimed to evaluate the impact of global climate change on groundwater recharge in a Brazilian Savannah watershed. Rainfall and water table depth data were recorded between 2007 and 2015. Based on these data, equations were developed relating the average monthly depth of the water table with the accumulated average monthly rainfall. From these equations, monthly average recharges considering the future climate estimates made by climate models (Eta-HadGEM2-ES and Eta-MIROC5) and Representative Concentration Pathway (RCP) scenarios (4.5 and 8.5) were calculated. In a pessimistic scenario (RCP 8.5), the average monthly groundwater recharge is decreasing in the beginning and in the end of the rainy season, indicating that there may be an increase in the dry season and, consequently, a reduction in water availability in the Cerrado biome region. [ABSTRACT FROM AUTHOR]

This case study, using a regional climate model (RegCM-4.7) in high horizontal resolution, allowed to obtain information on the intensity of the average vertical wind profile over the Alcântara Launch Center (ALC), Brazil. In the literature, on the wind intensity, the lack of continuous monitoring of the existence of flows in the vertical profile of the wind at heights of 400 to 600 m and the measurement of its magnitude make it possible to have an estimate lower than what can occur in reality at a low level in the region for operational purposes for the rocket launch. Therefore, this works results points to an intraseasonal variability of the wind intensity with maximum winds of the order of 14 to 20 m·s-1 in the core wind intensity at heights of 600 m, corresponding to pressure levels of the order of 930 hPa, in August, September and October 2020. These intensity values should be further studied with the use of observation equipment such as sound detection and ranging (SODAR) in the continuation of this research, in moment future, as well; the global models of reanalysis have low resolution and are not suitable for comparisons. [ABSTRACT FROM AUTHOR]

Climate change effects can have significant impacts worldwide. Extreme events can modify water availability and agricultural production, making climate change planning an essential task. The National Institute for Space Research (INPE in Portuguese) in Brazil has made a large dataset of regional climate model outputs (simulations and projections) available, which opens up many possibilities of carrying out high-resolution climate change studies. However, there is still no performance evaluation of the model-derived rainfall output against high-resolution ground-based observation data considering the Brazilian biomes. This paper attempts to fill this gap and evaluates the simulated precipitation throughout Brazil. We used gridded observed precipitation data and historical climate simulations from the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) and from the Hadley Center Global Environment Model, version 2 (HadGEM2-ES), which were downscaled by the Eta RCM (Regional Climate Model). For the overlapping period (1980–2005), there is good agreement (PBIAS up to 10%) of downscaled annual simulations for the Amazon and Cerrado biomes and large biases (reaching 40%) in the Pampa biome, compared to the observations. Our results showed that HadGEM2-ES is capable of representing long-term mean monthly precipitation for large areas well, such as the Amazon and Cerrado. Furthermore, the Eta RCM has considerably improved the driving GCM MIROC5 simulations. In conclusion, we recommend using the HadGEM2-ES simulations for the Amazon, Eta/HadGEM2-ES for the Atlantic Forest, Cerrado, and Pampa, and Eta/MIROC5 for the Caatinga and Pantanal. Our study provides an overview of two downscaled simulation datasets in Brazil that may help verify the models' suitability for further climate change assessments. • We evaluated the so far most detailed precipitation projections for Brazil. • We compared the Eta RCM and driving GCMs against high-resolution observed dataset. • HadGEM2-ES GCM can represent the precipitation for large areas, such as the Amazon. • Eta RCM considerably improved the MIROC5 GCM precipitation simulations in all biomes. [ABSTRACT FROM AUTHOR]

We assess the effect of increasing horizontal resolution on simulated precipitation over South America in a climate model. We use atmosphere-only simulations, performed with HadGEM3-GC31 at three horizontal resolutions: N96 (∼130 km; 1.88∘×1.25∘), N216 (∼60 km; 0.83∘×0.56∘), and N512 (∼25 km; 0.35∘×0.23∘). We show that all simulations have systematic biases in annual mean and seasonal mean precipitation over South America (e.g. too wet over the Amazon and too dry in the northeast). Increasing horizontal resolution improves simulated precipitation over the Andes and northeast Brazil. Over the Andes, improvements from horizontal resolution continue to ∼25 km, while over northeast Brazil, there are no improvements beyond ∼60 km resolution. These changes are primarily related to changes in atmospheric dynamics and moisture flux convergence. Over the Amazon Basin, precipitation variability increases at higher resolution. We show that some spatial and temporal features of daily South American precipitation are improved at high resolution, including the intensity spectra of rainfall. Spatial scales of daily precipitation features are also better simulated, suggesting that higher resolution may improve the representation of South American mesoscale convective systems. [ABSTRACT FROM AUTHOR]

This study evaluated the performance of 25 earth system models (ESMs), statistically and dynamically downscaled to a high horizontal resolution (0.25° of latitude/longitude), in simulating extreme climate indices of temperature and precipitation for 1980–2005. Datasets analyzed include 21 statistically downscaled ESMs from the National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and dynamically downscaled Eta Regional Climate Model simulations driven by 4 ESMs generated by the Brazilian National Institute for Space Research (INPE). Downscaled outputs were evaluated against observational gridded datasets at 0.25° resolution over Brazil, quantifying the skill in simulating the observed spatial patterns and trends of climate extremes. Results show that the downscaled products are generally able to reproduce the observed climate indices, although most of them have poorest performance over the Amazon basin for annual and seasonal indices. We find larger discrepancies in the warm spell duration index for almost all downscaled ESMs. The overall ranking shows that three downscaled models (CNRM-CM5, CCSM4, and MRI-CGCM3) perform distinctively better than others. In general, the ensemble mean of the statistically downscaled models achieves better results than any individual models at the annual and seasonal scales. This work provides the largest and most comprehensive intercomparison of statistically and dynamically downscaled extreme climate indices over Brazil and provides a useful guide for researchers and developers to select the models or downscaling techniques that may be most suitable to their applications of interest over a given region. [ABSTRACT FROM AUTHOR]

Large domains in regional climate models are known to provide a better representation of the mesoscale circulation and hence the precipitation field. To evaluate the model performance in reproducing the tropical circulation as well as the severe drought event (2012–2016) in the northeastern Brazil, simulations for the 2012–2016 period were performed using the tropical band version of the Regional Climate Model (RegT-Band). The RegT-Band simulation were driven by the ERA-Interim data for the initial and lateral boundary conditions and sea surface temperature was obtained from NOAA_OISST_V2 reanalysis. The domain covers the entire tropical belt between 30°S and 30°N with grid spacing of about 30 km. Results showed that the general features of the tropical circulation and associated precipitation pattern over the entire domain are well captured by the model compared to the observations. However, systematic biases, mainly related to the double ITCZ bias are observed, which provide an unrealistic peak of double precipitation outside the equator and insufficient precipitation at the equator. In the NEB subdomain, RegT-Band shows weakness and significant model error to represent the subregion where the drought event was more severe. However, the model satisfactorily represents the precipitation anomalies in the driest years. Therefore, this study shows results encouraging the use of RegT-Band to study extreme events associated with droughts conditions in the Northeastern Brazil. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the operationally stored simulations of the RegCM4.7 model using simulations performed by the National Center for Environmental Prediction (NCEP) coupled forecast system model version 2 (CFSv2) in downscaling. The South American domain, however, seeks to observe prevailing weather systems that may affect the Alcântara Rocket Launch Center in Maranhão. One case is analyzed with three different global initializations of the CFSv2 model. On March 25, 2019, the occurrence of Cyclone Iba on the southern coast of Bahia. The RegCM4.7 Regional Model was able to simulate tropical cyclone three months in advance. This performance is very important because the influence of these anomalous systems affect wind intensity and rainfall behavior in the Alcântara region because it would affect the convective processes on the continent in northeastern Brazil. [ABSTRACT FROM AUTHOR]

The role of sea surface temperature (SST) in determining the predictability of monthly mean precipitation over the global land is assessed by analyzing the Atmospheric Model Intercomparison Project (AMIP)-like simulations forced by observed SST, which provides a benchmark for the impact of SST on the precipitation. The correlations of monthly mean precipitation anomalies between the ensemble mean of the AMIP simulations and observations are dominated by positive values with maxima around 0.3–0.4 in the tropical North Africa along 15° N and northeastern Brazil. The SST forcing for the precipitation variability is mainly associated with the El Niño-Southern Oscillation (ENSO) and in the tropical Indian Ocean. Statistically, positive and negative SST anomalies associated with an ENSO cycle have a comparable influence on precipitation variability over the land. In addition to the spatial variations, the precipitation responses to SST also vary with season and decade. Pattern correlations are larger in boreal winter than in boreal summer in the Northern Hemisphere, and relatively larger in April-June and September–November in the Southern Hemisphere. The global average of correlation is lower during 1957–1980 and 2000–2018, and higher in between. The interdecadal fluctuation of the pattern correlations is coherent with the interdecadal variation of the amplitude of ENSO. [ABSTRACT FROM AUTHOR]

Future changes of the aridity of South America (SA) are investigated. The projected changes of the Budyko and United Nations Environment Programme (UNEP) indices in the mid- and end of the twenty-first century from the ensemble mean RegCM4 simulations using the scenario RCP 8.5 are presented. The RegCM4 model driven by the global models of HadGEM2, MPI, and GFDL for the period 1970–2100 carries out the simulations. For both Budyko and UNEP indices, an aridity increase over SA is projected. Over Brazil, the higher changes were found in the Amazon, North Brazil, and Northeast Brazil (NEB). In the Amazon and North Brazil, an increase of the aridity of 33.8% and 36.9% (for the UNEP index) and 4.6% and 13.9% (for the Budyko index), respectively, are noted at the end of the twenty-first century suggesting an increase of the process of savannization in future climate. In NEB, a rise of 37.3% and 14.1%, respectively, for the UNEP and the Budyko indices, is found, indicating an expansion of areas of the dry land regime. [ABSTRACT FROM AUTHOR]

Local prediction of thunderstorms is one of the most challenging tasks in weather forecasting due to their high spatiotemporal variability. An improved understanding of such meteorological phenomena, therefore, requires high-frequency measurements along the vertical profile of the atmosphere of interest. In this context, the evaluation of thermodynamic and dynamic parameters obtained from radiosondes to identify atmospheric conditions favorable to thunderstorm and heavy-rainfall development emerges as a valuable tool for investigations of thunderstorms. In this context, four radiosondes were launched to collect a data set for the area of interest at the sub-daily scale (12 UTC, 16 UTC, 18 UTC, and 00 UTC). The collection period encompassed two dates—November 29 and December 12, 2016—chosen specifically due to the existence of heavy-rainfall warnings in the forecast for the Metropolitan Area of Rio de Janeiro, Brazil ("MARJ") for those days. However, heavy rainfall was registered only for December 12 and not for November 29 (which led us to explore this contrast with the announced rainfall forecasts). Sub-daily radiosonde data showed a clear decrease in atmospheric instability in the early afternoon on November 29. On the other hand, an opposite scenario occurred on December 12, which saw an expressive increase in thermodynamic instability during the day. The meteorological modeling approach used also revealed that the vertical coupling of low-level moisture flux convergence centers and upper-level mass flux divergence centers worked as a dynamic trigger for the thunderstorm and heavy-rainfall developments that took place on December 12, 2016. [ABSTRACT FROM AUTHOR]

Palythoa caribaeorum is a zoanthid often dominant in shallow rocky environments along the west coast of the Atlantic Ocean, from the tropics to the subtropics. This species has high environmental tolerance and is a good space competitor in reef environments. Considering current and future scenarios in the global climate regime, this study aimed to model and analyze the distribution of P. caribaeorum, generating maps of potential distribution for the present and the year 2100. The distribution was modeled using maximum entropy (Maxent) based on 327 occurrence sites retrieved from the literature. Calcite concentration, maximum chlorophyll-a concentration, salinity, pH, and temperature range yielded a model with the smallest Akaike information criterion (2649.8), and were used in the present and future distribution model. Data from the HadGEM2-ES climate model were used to generate the projections for the year 2100. The present distribution of P. caribaeorum shows that parts of the Brazilian coast, Caribbean Sea, and Florida are suitable regions for the species, as they are characterized by high salinity and pH and small temperature variation. An expansion of the species’ distribution was forecast northward under mild climate scenarios, while a decrease of suitable areas was forecast in the south. In the climate scenario with the most intense changes, P. caribaeorum would lose one-half of its suitable habitats, including the northernmost and southernmost areas of its distribution. The Caribbean Sea and northeastern Brazil, as well as other places under the influence of coastal upwellings, may serve as potential havens for this species. [ABSTRACT FROM AUTHOR]

One risk associated with rocket‐launching activities is the inhalation of exhaust gases, which can occur either in the vicinity of the launch pad or, depending on the way these gases are transported and dispersed through the atmosphere, at locations far from the launch pad. Immediately after the ignition of the rocket motors, a large, hot cloud is formed near the ground, composed of carbon monoxide (CO) and carbon dioxide (CO2), hydrogen chloride (HCl) and also particulate material composed of aluminium oxide (Al2O3) in the case of rockets driven by a solid propellant. The Weather Research and Forecasting Model (WRF), an atmospheric mesoscale model, was modified in the present study to simulate the transport, dispersion and chemistry mechanisms (with the addition of reactions involving HCl and other chlorinated compounds) of these gases released during a launch. Simulations of the dispersion of effluents were performed for different rainfall regimes (dry and rainy periods) as well as for atmospheric thermal stability (day‐ and night‐time conditions) for a specific rocket (the Veículo Lançador de Satélites, VLS) at Brazil's Alcântara Launch Center (Centro de Lançamento de Alcântara, CLA). The results show that the most critical levels of HCl and CO occurred on the launch pad (Setor de Preparação de Lançamento, SPL) from the time of the launch (t0) to 10 min. The village of Alcântara, which is 9 km from the launch pad, could be affected according to the wind direction. The concentration of HCl reached a level in the range of 2200–3800 ppmv for both dry and wet periods respectively, and these concentrations can be very hazardous to humans. A large cloud of toxic gas, such as the one shown, is formed at the surface level a few moments after the ignition of rockets. This hot cloud expands and rapidly covers the launch pad facility and can be transported to nearby cities. In order to predict the concentrations of gases after a launch, numerical air‐quality models need appropriate methods for handling rocket emissions in order to solve this problem. [ABSTRACT FROM AUTHOR]

The quantification of climate change impacts on several human activities depends on reliable weather data series, without gaps and long enough to build up future climate. Based on that, this study aimed to evaluate the performance of temperature-based models for estimating global solar radiation and gridded databases (AgCFSR, AgMERRA, NASA/POWER, and XAVIER) as alternative ways for filling gaps in historical weather series (1980–2009) in Brazil and to project climate change scenarios based on measured and gridded weather data. Projections for mid- and end-of-century periods (2040–2069 and 2070–2099), using seven global climate models from CMIP5 under intermediate (RCP4.5) and high (RCP8.5) emission scenarios, were performed. The Bristow–Campbell model was the one that best estimated solar radiation, whereas the XAVIER gridded database was the closest to observed weather data. Future climate projections, under RCP4.5 and RCP8.5 scenarios, as expected, showed warmer conditions for all scenarios over Brazil. On the contrary, rainfall projections are more uncertain. Despite that, the rainfall amounts will be reduced in the North-Northeast region and increased in Southern Brazil. No significant differences between projections using the observed and XAVIER gridded database were observed; therefore, such a database showed to be reliable for both to fill gaps and to generate climate change scenarios. [ABSTRACT FROM AUTHOR]

Palythoa caribaeorum is a zoanthid often dominant in shallow rocky environments along the west coast of the Atlantic Ocean, from the tropics to the subtropics. This species has high environmental tolerance and is a good space competitor in reef envi)ronments. Considering current and future scenarios in the global climate regime, this study aimed to model and analyze the distribution of P. caribaeorum, generating maps of potential distribution for the present and the year 2100. The distribution was modeled using maximum entropy (Maxent) based on 327 occurrence sites retrieved from the literature. Calcite concentration, maximum chlorophyll-a concentration, salinity, pH, and temperature range yielded a model with the smallest Akaike information criterion (2649.8), and were used in the present and future distribution model. Data from the HadGEM2-ES climate model were used to generate the projections for the year 2100. The present distribution of P. caribaeorum shows that parts of the Brazilian coast, Caribbean Sea, and Florida are suitable regions for the species, as they are characterized by high salinity and pH and small temperature variation. An expansion of the species’ distribution was forecast northward under mild climate scenarios, while a decrease of suitable areas was forecast in the south. In the climate scenario with the most intense changes, P. caribaeorum would lose one-half of its suitable habitats, including the northernmost and southernmost areas of its distribution. The Caribbean Sea and northeastern Brazil, as well as other places under the influence of coastal upwellings, may serve as potential havens for this species. [ABSTRACT FROM AUTHOR]

This study investigates the effects of Lake Sobradinho, a large reservoir in Northeastern Brazil, on the local near-surface atmospheric and boundary layer conditions. For this purpose, simulations with the regional climate model COSMO-CLM are compared for two different scenarios: (1) with the lake being replaced by the average normal native vegetation cover and (2) with the lake as it exists today, for two different two-month periods reflecting average and very dry conditions, respectively. The performance of the simulation is evaluated against data from surface meteorological stations as well as satellite data in order to ensure the model's ability to capture atmospheric conditions in the vicinity of Lake Sobradinho. The obtained results demonstrate that the lake affects the near-surface air temperature of the surrounding area as well as its humidity and wind patterns. Specifically, Lake Sobradinho cools down the air during the day and warms it up during the night by up to several °C depending on the large-scale meteorological conditions. Moreover, the humidity is significantly increased as a result of the lake's presence and causes a lake breeze. The observed effects on humidity and air temperature also extend over areas relatively far away from the lake. [ABSTRACT FROM AUTHOR]

A semi-distributed hydrological model is developed, calibrated and validated against unregulated river discharge from the Tocantins-Araguaia River Basin, northern Brazil. Climate change impacts are simulated using projections from the 41 Coupled Model Intercomparison Project Phase 5 climate models for the period 2071–2100 under the RCP4.5 scenario. Scenario results are compared to a 1971–2000 base line. Most climate models suggest declines in mean annual discharge although some predict increases. A large proportion suggest that the dry season experiences large declines in discharge, especially during the transition to the rising water period. Most models (>75%) suggest declines in annual minimum flows. This may have major implications for both current and planned hydropower schemes. There is greater uncertainty in projected changes in wet season and annual maximum discharges. Two techniques are investigated to reduce uncertainty in projections, but neither is able to provide more confidence in the simulated changes in discharge.Editor D. Koutsoyiannis Associate editor F. Hattermann [ABSTRACT FROM PUBLISHER]

Introduction: Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings: We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance: These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. Author Summary: Cutaneous leishmaniasis (CL) is a disease resulting from infection by the Leishmania parasites, which humans may acquire when bitten by an infected sandfly. From a public health standpoint, it is important to identify cases early and monitor patients' clinical outcomes because unsuccessfully-treated patients are at risk for severe complications. Since weather conditions affect survival and reproduction of sandflies that transmit Leishmania, routinely-gathered weather and climate data may be useful for anticipating CL outbreaks, bolstering clinical capacity for high-risk periods, and initiating interventions such as active case-finding during these periods to limit disease burden. Here we assessed whether the number of CL cases occurring per month in a rural region of Bahía, Brazil was associated temperature, humidity, precipitation, and El Niño sea surface temperature oscillation patterns observed during preceding seasons. We formulated models that improved accuracy of one, two, and three month-ahead CL predictions by accounting for weather. Forecasts of this nature can contribute to reducing CL burden by informing resource allocation and intervention planning in preparation for epidemics. [ABSTRACT FROM AUTHOR]

Reliable hydrometeorological forecasts are necessary to help reduce damages caused by extreme events. Coupling atmospheric and hydrological models can help improve those forecasts; therefore, the main objective of this work is to propose, apply, and evaluate a one-way coupled system involving an atmospheric and a hydrological model. The models were applied to a watershed in the Serra do Mar region, São Paulo State, Brazil. Because of the complex terrain and native tropical forest (Mata Atlântica), hydrometeorological modeling in this region requires specific studies and adequate models. The coupling was carried out between the atmospheric regional Eta model and the distributed hydrology soil vegetation model. The primary challenge in coupling hydrological and atmospheric models is the large difference in spatial and temporal scales in which these models operate. This study proposes a downscaling methodology to increase the spatial resolution of precipitation and temperature predicted by the atmospheric model from 10 to 2 km. The coupling was tested in three precipitation events that accumulated large amounts of precipitation. The results show that the quality of the predicted streamflows depends primarily on the quality of the predicted precipitation that is input to the hydrological model. The downscaling methodology applied does not significantly influence quality of the simulated and predicted basin outflow. [ABSTRACT FROM AUTHOR]

This study investigated the sensitivity of a dynamic downscaling atmospheric model system coupled with a rainfall-runoff model to hindcast an example of reservoir water management in the semi-arid region of Northeast Brazil (NEB). A regional atmospheric spectral model (RSM) is driven by the outputs of an atmospheric general circulation model (AGCM), itself forced by the observed sea surface temperature over the World Ocean. Daily precipitation simulated by the RSM was then used as the input to a hydrological rainfall-runoff model for the Upper Jaguaribe River Basin to estimate inflows at the Orós Reservoir in the state of Ceará. A hindcast analysis of precipitation was performed during the rainy season over NEB (January to June) from 1971 to 2000. The RSM captured the precipitation variability relatively well when a probability density function (PDF) was used to correct the numerical bias. Three hindcast series of inflow using (i) the observed rainfall, (ii) the simulated rainfall before the PDF correction, and (iii) the simulated rainfall after the PDF correction were performed during the study period and then compared to the series of observed inflow. The atmospheric-rainfall-runoff 'cascade' model efficiency was evaluated by comparing the Orós Reservoir release decisions from different scenarios based on observed, simulated (RSM, RSM-PDF), and mean historical reservoir inflows. The cascade model has the potential, relatively well balanced during dry, normal or wet years, to be a useful tool to correctly forecast the decision managements of reservoirs in the semi-arid region of NEB. Additional progress in the numerical simulation is however necessary to improve the performance. [ABSTRACT FROM AUTHOR]

The role of the Brazilian Plateau (BP) in maintaining the South Atlantic convergence zone (SACZ) has been examined by statistical analysis and numerical experiments. Statistical analysis using 27 years of data showed that the SACZ is most intense when it is over the BP. In this case, low-level cyclonic circulation appears over the southwestern part of the BP and forms westerly flow, which intensifies low-level convergence along the SACZ with northeasterly flow from the Amazon and northerly flow along the western edge of the South Atlantic subtropical high. A vorticity budget analysis indicates that precipitation over the BP that accompanies stretching maintains the cyclonic circulation. Sensitivity experiments using a regional atmospheric model for two different cases indicate that precipitation over the BP plays a dominant role as an atmospheric heat source in maintaining the cyclonic circulation and the SACZ. In model experiments in which rain was stopped around the BP but the topography was kept, the cyclonic circulation disappeared, and the SACZ shifted southward away from its original position. In comparison with a control run, precipitation over the BP was weakened and the SACZ shifted southward in experiments in which the BP was removed or its complex, multiple-valley terrain was smoothed out. The results of this study support the ideas suggested in previous studies (i.e., that the BP has an anchor effect on the SACZ): Precipitation is intensified over the complex terrain of the BP, and mechanisms of conditional instability of the second kind occur between the precipitation over the BP and the cyclonic circulation to its southwest, which intensifies moisture convergence over the plateau. [ABSTRACT FROM AUTHOR]

The southeastern coast of Brazil is frequently affected by meteorological disturbances such as cold fronts, which are sometimes associated with intense extratropical cyclones. These disturbances cause oscillations on the sea surface, generating low-frequency motions. The relationship of these meteorologically driven forces in low frequency to the storm-surge event is investigated in this work. A method to predict coastal sea level variations related to meteorological events that use a neural network model (NNM) is presented here. Pressure and wind values from NCEP–NCAR reanalysis data and tide gauge time series from the Cananéia reference station in São Paulo State, Brazil, were used to analyze the relationship between these variables and to use them as input to the model. Meteorological influences in the sea level fluctuations can be verified by filtering the astronomical tide frequencies for periods lower than tidal cycles (periods higher than 24 h). Thus, a low-pass filter was applied in the tide gauge and meteorological time series for periods lower than tides to identify more readily the interactions between coastal sea level response and atmospheric-driven forces. Statistical analyses on time and frequency domain were used. Maxima correlations and coherence between the low-frequency sea level and meteorological series could be defined using the time lag of the NNM input variables. The model was tested for 6-, 12-, 18-, and 24-hourly forecasts, and the results were compared with filtered sea level values. The results show that this model is able to capture the effects of atmospheric and oceanic interactions. It can be considered to be an efficient model for predicting the nontidal residuals and can effectively complement the standard constant harmonic analysis model. A case study of a storm that impacted coastal areas of southeastern Brazil in March 1998 was analyzed and indicates that the neural network model can be effectively utilized in the Cananéia region. [ABSTRACT FROM AUTHOR]