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Tropical–extratropical cloud bands are common in South America (SAm), contributing significantly to the total rainy season precipitation. Thus, it is fundamental that climate and weather forecast models correctly represent them and their associated dynamic aspects. Adopting an event‐based framework, we evaluate the performance of two global models in simulating the observed cloud bands over SAm: the Brazilian Global Atmospheric Model version 1.2 (BAM‐1.2) and the Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3‐GC3.1). Both models reproduce the main characteristics of cloud bands and the dynamical aspects leading to their development and persistence. Nonetheless, the biases in precipitation during simulated cloud bands contribute more than 50% of the bias in total precipitation in some regions. BAM‐1.2 simulates fewer but more persistent cloud bands than observed; HadGEM3‐GC3.1 simulates weaker cloud band activity during early summer and more persistent events after January than observed. In all models, the biases in cloud band events arise from the interaction between biases in the basic state and the synoptic‐scale regional circulation. In the basic state, stronger upper level westerlies over the midlatitude South Pacific support the propagation of longer and slower Rossby waves towards subtropical SAm, increasing the duration of the cloud band events. This bias interacts with negative biases in the upper level westerlies over subtropical SAm, increasing the wind shear, hindering the propagation of synoptic‐scale Rossby waves into lower latitudes, and resulting in biases in the cloud band location, intensity, and seasonality. The application in this study of an event‐based framework robust to differences in model resolution and complexity enables the identification of small but critical biases in circulation. These biases are linked to synoptic‐scale rainfall system biases and help to explain the season total rainfall model biases. [ABSTRACT FROM AUTHOR]

The features of monsoon systems in the Northern and Southern Hemispheres are analysed in climate simulations of two atmospheric models: the Brazilian Global Atmospheric Model version 1.2 (BAM‐v1.2) and the UK Met Office Hadley Centre Global Environment Model version 3 (HadGEM3). The results are compared to GPCP precipitation and ERA5 datasets. Although they have different configurations and parameterizations, the purpose is to evaluate their ability in representing key features of the global monsoon system. The spatial extent of the monsoon domains is well simulated by the models, as well as the main characteristics of the monsoons, although precipitation biases are noticed in the regions affected by the systems, consistent with vertical motion and moisture flux biases. The largest precipitation biases are found in the West Pacific Monsoon Region, extended to the east, and in the Australia Monsoon Region extended to the Maritime continent. Deficiencies in precipitation can be related to inaccuracy of vertical motion and humidity flux, as well as to the lack of air–sea interaction. However, the atmospheric circulation features at low and high levels are well represented in all monsoon regions, as well as the annual cycle of precipitation in those regions by both models. The divergence at high levels and convergence at low levels associated with ascending air movement and precipitation in monsoon regions are well represented by the models. An analysis of two monsoon indices at eight monsoon regions showed the models are generally able to simulate the relationship between precipitation and circulation features. In the majority of years, the signs of indices from the models agree with observations. Correlations of precipitation and circulation indices between models and observations show statistically significant values for some monsoon regions. The results obtained contribute to improving knowledge about global monsoon features and their representation in the two models. [ABSTRACT FROM AUTHOR]

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a "knowledge-value" gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development--demonstrating both skill and utility across sectors--this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale. [ABSTRACT FROM AUTHOR]

The International Verification Methods Workshop was held online in November 2020 and included sessions on physical error characterization using process diagnostics and error tracking techniques; exploitation of data assimilation techniques in verification practices, e.g., to address representativeness issues and observation uncertainty; spatial verification methods and the Model Evaluation Tools, as unified reference verification software; and meta-verification and best practices for scores computation. The workshop reached out to diverse research communities working in the areas of high-impact weather, subseasonal to seasonal prediction, polar prediction, and sea ice and ocean prediction. This article summarizes the major outcomes of the workshop and outlines future strategic directions for verification research. [ABSTRACT FROM AUTHOR]

Models participating in the North American Multi Model Ensemble project were calibrated and combined to produce reliable precipitation probabilistic forecast over South America. Ensemble Regression method (EREG) was chosen as it is computationally affordable and uses all the information from the ensemble. Two different approaches based on EREG were applied to combine forecasts while different ways to weight the relative contribution of each model to the ensemble were used. All the consolidated forecast obtained were confronted against the simple multi-model ensemble. This work assessed the performance of the predictions initialized in November to forecast the austral summer (December–January–February) for the period 1982–2010 using different probabilistic measures. Results show that the consolidated forecasts produce more skillful forecast than the simple multi-model ensemble, although no major differences were found between the combination and weighting approaches considered. The regions that presented better results are well-known to be impacted by El Niño Southern Oscillation. [ABSTRACT FROM AUTHOR]

Precipitation predictions in the sub-seasonal timescale are very important for several sectors such as agriculture and energy in regions of South America that are very much affected by precipitation extremes, both excess and lack of rain. The aim of the present study is to investigate the ability of two S2S project models (ECMWF and NCEP) to detect the Southern Hemisphere teleconnections in model hindcasts and the associated anomalous precipitation over South America. The period of analyses is 1999-2010 for the austral summer season (December-January-February). Both models represented adequately the Southern Annular Mode (SAM) pattern in predictions up to 4 weeks ahead and the Pacific South America (PSA) pattern up to 3 weeks. Atmospheric variables of observed extreme cases of SAM were well predicted by the two models, 2 and 3 weeks in advance. The models predicted well atmospheric variables in observed extreme cases of PSA, 2 weeks in advance and with less intensity in the third week. Precipitation anomaly signals associated with these modes were well predicted 2 weeks in advance, although with different intensities. The good ability of the models hindcast in predicting teleconnection patterns and precipitation anomalies over South America provides more confidence to use predictions at sub-seasonal timescale. [ABSTRACT FROM AUTHOR]

Verification of forecasts and warnings of high-impact weather is needed by the meteorological centres, but how to perform it still presents many open questions, starting from which data are suitable as reference. This paper reviews new observations which can be considered for the verification of high-impact weather and provides advice for their usage in objective verification. Two high-impact weather phenomena are considered: thunderstorm and fog. First, a framework for the verification of high-impact weather is proposed, including the definition of forecast and observations in this context and creation of a verification set. Then, new observations showing a potential for the detection and quantification of high-impact weather are reviewed, including remote sensing datasets, products developed for nowcasting, datasets derived from telecommunication systems, data collected from citizens, reports of impacts and claim/damage reports from insurance companies. The observation characteristics which are relevant for their usage in forecast verification are also discussed. Examples of forecast evaluation and verification are then presented, highlighting the methods which can be adopted to address the issues posed by the usage of these non-conventional observations and objectively quantify the skill of a high-impact weather forecast. [ABSTRACT FROM AUTHOR]

In South America, land–atmosphere interactions have an important impact on climate, particularly the regional hydrological cycle, but detailed evaluation of these processes in global climate models has been limited. Focusing on the satellite-era period of 2003–14, we assess land–atmosphere interactions on annual to seasonal time scales over South America in satellite products, a novel reanalysis (ERA5-Land), and two global climate models: the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) and the U.K. Hadley Centre Global Environment Model version 3 (HadGEM3). We identify key features of South American land–atmosphere interactions represented in satellite and model datasets, including seasonal variation in coupling strength, large-scale spatial variation in the sensitivity of evapotranspiration to surface moisture, and a dipole in evaporative regime across the continent. Differences between products are also identified, with ERA5-Land, HadGEM3, and BAM-1.2 showing opposite interactions to satellites over parts of the Amazon and the Cerrado and stronger land–atmosphere coupling along the North Atlantic coast. Where models and satellites disagree on the strength and direction of land–atmosphere interactions, precipitation biases and misrepresentation of processes controlling surface soil moisture are implicated as likely drivers. These results show where improvement of model processes could reduce uncertainty in the modeled climate response to land-use change, and highlight where model biases could unrealistically amplify drying or wetting trends in future climate projections. Finally, HadGEM3 and BAM-1.2 are consistent with the median response of an ensemble of nine CMIP6 models, showing they are broadly representative of the latest generation of climate models. [ABSTRACT FROM AUTHOR]

Skillful and reliable predictions of week-to-week rainfall variations in South America, two to three weeks ahead, are essential to protect lives, livelihoods, and ecosystems. We evaluate forecast performance for weekly rainfall in extended austral summer (November-March) in four contemporary subseasonal systems, including a new Brazilian model, at 1-5-week leads for 1999-2010. We measure performance by the correlation coefficient (in time) between predicted and observed rainfall; we measure skill by the Brier skill score for rainfall terciles against a climatological reference forecast. We assess unconditional performance (i.e., regardless of initial condition) and conditional performance based on the initial phase of the Madden-Julian oscillation (MJO) and El Niño-Southern Oscillation (ENSO). All models display substantial mean rainfall biases, including dry biases in Amazonia and wet biases near the Andes, which are established by week 1 and vary little thereafter. Unconditional performance extends to week 2 in all regions except for Amazonia and the Andes, but to week 3 only over northern, northeastern, and southeastern South America. Skill for upper- and lower-tercile rainfall extends only to week 1. Conditional performance is not systematically or significantly higher than unconditional performance; ENSO and MJO events provide limited ''windows of opportunity" for improved S2S predictions that are region and model dependent. Conditional performance may be degraded by errors in predicted ENSO and MJO teleconnections to regional rainfall, even at short lead times. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of climate simulations produced by the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975–2017 period at two spatial resolutions, corresponding to ~ 180 and ~ 100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model's ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Niño-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model. Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features. [ABSTRACT FROM AUTHOR]

Verification of high-impact weather is needed by the Meteorological Centres, but how to perform it still presents many open questions, starting from which data are suitable as reference. This paper reviews new observations which can be considered for the verification of high-impact weather, and provides advice for their usage in objective verification. Two high-impact weather phenomena are considered: Thunderstorm and fog. First, a framework for the verification of high-impact weather is proposed, including the definition of forecast and observations in this context and creation of a verification set. Then, new observations showing a potential for the detection and quantification of high-impact weather are reviewed, including remote sensing datasets, products developed for nowcasting, datasets derived from telecommunication systems, data collected from citizens, reports of impacts and claim/damage reports from insurance companies. The observation characteristics which are relevant for their usage in forecast verification are also discussed. Examples of forecast evaluation and verification are then presented, highlighting the methods which can be adopted to address the issues posed by the usage of these non-conventional observations and objectively quantify the skill of a high-impact weather forecast. [ABSTRACT FROM AUTHOR]

We applied the Goddard Earth Observing System for subseasonal to seasonal climate prediction to assess the impact of inclusion biomass burning (BB) aerosols over South America (SA) during the austral winter. We also evaluated the model sensitivity to the BB emissions prescription using no emissions, monthly climatological, and daily emissions. Each hindcast consisted of four members running from June to November of each year between 2000 and 2015. Our results indicated that interactive BB aerosols improve the seasonal climate prediction performance over SA. More realistic daily based emissions significantly further improve the performance in comparison with the climatological ones. Therefore, improvements in the BB emissions representation are urged to represent the aerosol impacts on seasonal climate prediction performance adequately. Plain Language Summary: Vegetation fires severely affect tropical forest and savannah‐type biomes in South America (SA) during winter in Southern Hemisphere. Biomass burning (BB) aerosols are important agents changing energy budget and clouds. This study focused on assessing whether including aerosol‐radiation‐cloud interaction in a climate model, particularly the contribution of BB aerosols, can provide additional information for improving seasonal climate predictions. This study has two primary outcomes. First, that including BB aerosols does improve the model's ability to predicted precipitation and near‐surface temperature in SA. Second, it proved it is indeed essential to improve BB emissions representation to further elevate seasonal climate prediction performance. Key Points: This study documents the impacts of biomass burning aerosols on South America seasonal climate predictions in a coupled modeling systemUse of interactive biomass burning aerosols improves seasonal prediction performance for the austral winter over South AmericaPrescribing daily emission estimates provides better performance in comparison with prescribing monthly climatological mean emissions [ABSTRACT FROM AUTHOR]

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis. [ABSTRACT FROM AUTHOR]

This article presents the Centre for Weather Forecast and Climate Studies (CPTEC) developments for configuring a global sub‐seasonal prediction system and assessing its ability in producing retrospective predictions (hindcasts) for meteorological conditions of the following 4 weeks. Six Brazilian Global Atmospheric Model version 1.2 (BAM‐1.2) configurations were tested in terms of vertical resolution, deep convection and boundary‐layer parametrizations, as well as soil moisture initialization. The aim was to identify the configuration with best performance when predicting weekly accumulated precipitation, weekly mean 2 m temperature (T2M) and the Madden–Julian Oscillation (MJO) daily evolution. Hindcasts assessment was performed for 12 extended austral summers (November–March, 1999/2000– 2010/2011) with two start dates for each month for the six configurations and two ensemble approaches. The first approach, referred to as Multiple Configurations Ensemble (MCEN), was formed of one ensemble member from each of the six configurations. The second, referred to as Initial Condition Ensemble (ICEN), was composed of six ensemble members produced with the chosen configuration as the best using an empirical orthogonal function (EOF) perturbation methodology. The chosen configuration presented high correlation and low root‐mean‐squared error (RMSE) for precipitation and T2M anomaly predictions at the first week and these indices degraded as lead time increased, maintaining moderate performance up to week‐4 over the tropical Pacific and northern South America. For MJO predictions, this configuration crossed the 0.5 bivariate correlation threshold in 18 days. The ensemble approaches improved the correlation and RMSE of precipitation and T2M anomalies. ICEN improved precipitation and T2M predictions performance over eastern South America at week‐3 and over northern South America at week‐4. Improvements were also noticed for MJO predictions. The time to cross the above‐mentioned threshold increased to 21 days for MCEN and to 20 days for ICEN. [ABSTRACT FROM AUTHOR]

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often. [ABSTRACT FROM AUTHOR]

The development of novel diagnostic tools for enhancing climate monitoring capabilities is being increasingly demanded by several important societal sectors, such as agriculture, energy production, and industry, among others. This study aims to contribute to such developments by characterizing the observed daily precipitation conditions within the climatic context for the austral summer months over the southeast region of the state of São Paulo, Brazil. The characterization was designed to quantify the climatological features of daily precipitation and of the number of rainy (or wet) days. The objective is to provide complementary information to the traditionally available monthly and annual precipitation monitoring. Such a diagnostic characterization can assist answering the following questions: How did daily precipitation behave in southeast São Paulo during the last two decades? What is the typical distribution of summer precipitation in southeast São Paulo? How many wet summer days are typically observed in southeast São Paulo? How many wet summer days were observed during the last two decades? What is the typical distribution of wet summer days in southeast São Paulo? How have the latest years been in terms of wet summer days? The daily precipitation and wet day characterization was performed using historical data for the 1998/1999 to 2017/2018 period. The representativeness of these characteristics was assessed using a four times longer time series from a meteorological station located within the investigated region. The analysis indicated that for the southeast region of the state of São Paulo, the 2013/2014 summer period presented values ​well below those previously recorded in association with exceptionally dry conditions established over this region, corroborating previous findings but now from the perspective of daily precipitation information. [ABSTRACT FROM AUTHOR]

This study assessed subseasonal global precipitation hindcast quality from all Subseasonal to Seasonal (S2S) prediction project models. Deterministic forecast quality of weekly accumulated precipitation was verified using different metrics and hindcast data considering lead times up to 4 weeks. The correlation scores were found to be higher during the first week and dropped as lead time increased, confining meaningful signals in the tropics mostly due to El Niño–Southern Oscillation and Madden–Julian Oscillation-related effects. The contribution of these two phenomena to hindcast quality was assessed by removing their regressed precipitation patterns from predicted fields. The model's rank showed ECMWF, UKMO, and KMA as the top scoring models even when using a single control member instead of the mean of all ensemble members. The lowest correlation was shared by CMA, ISAC, and HMCR for most weeks. Models with larger ensemble sizes presented noticeable reduction in correlation when subsampled to fewer perturbed members, showing the value of ensemble prediction. Systematic errors were measured through bias and variance ratio revealing in general large positive (negative) biases and variance overestimation (underestimation) over the tropical oceans (continents and/or extratropics). The atmospheric circulation hindcast quality was also examined suggesting the importance of using a relatively finer spatial resolution and a coupled model for resolving the tropical circulation dynamics, particularly for simulating tropical precipitation variability. The extratropical circulation hindcast quality was found to be low after the second week likely due to the inherent unpredictability of the extratropical variability and errors associated with model deficiencies in representing teleconnections. [ABSTRACT FROM AUTHOR]

ABSTRACT: The State of São Paulo in Southeast Brazil experienced during the 2012/2013 summer one of the most severe electrical storm events in recent years, causing various impacts to society, e.g. fires and fatalities. Thus, the objective of this work is to understand which mechanisms and climatic variability modes are associated with the lightning incidence during this summer in this region. The study includes a statistical‐temporal assessment of cloud‐to‐ground (CG) lightning occurrence comprising the 16 years period from 1999 to 2014. The results showed that, for this period of analysis, the lightning incidence was associated with climatic patterns connecting the tropical and extratropical region, through a wave train, from the Indian Ocean to South America, favouring the formation and development of convective storms over Southeast Brazil. [ABSTRACT FROM AUTHOR]

The article focuses on the use of a multimethod attribution analysis about the prolonged hydrometeorological drought in northeast Brazil from 2012-2016. It determines possible changes in the hydrometeorological hazard, which comprise the accumulated precipitation, the difference between precipitation and evaporation as well as its potential impact on two basin reservoirs inflows.

This study investigates the feasibility and presents an assessment of probabilistic rainy season onset forecasts for São Paulo, Brazil, located in a region with a well-defined wet season from mid-austral spring (October) to austral autumn (March/April). The probabilistic forecasts were produced with (1) a simple empirical Cox-regression model using July Niño-3 index as predictor, (2) the dynamical coupled atmosphere-land-surface-ocean-sea-ice model used in the UK Met Office Global Seasonal Forecast System (GloSea5) and (3) a procedure that combines the empirical and dynamical model onset probabilistic forecasts. The probabilistic forecast assessment was performed over the 1996–2009 retrospective forecast period for the event rainy season onset earlier (or later) than the historical (mean) onset date. The three investigated approaches resulted in similar discrimination ability of around 80%, which represents the probability of the probabilistic forecasts correctly distinguishing earlier from a later than mean onsets, suggesting good potential for rainy season onset forecasts for São Paulo. The robustness of this assessment for an extended period (longer than 1996–2009) and for a region (20°S, 25°S, 45°W, 55°W) that includes the city of São Paulo was checked, reinforcing the validity of the obtained results at both local and regional scales. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the feasibility and presents an assessment of probabilistic rainy season onset forecasts for São Paulo, Brazil, located in a region with a well-defined wet season from mid-austral spring (October) to austral autumn (March/April). The probabilistic forecasts were produced with (1) a simple empirical Cox-regression model using July Niño-3 index as predictor, (2) the dynamical coupled atmosphere-land-surface-ocean-sea-ice model used in the UK Met Office Global Seasonal Forecast System ( GloSea5) and (3) a procedure that combines the empirical and dynamical model onset probabilistic forecasts. The probabilistic forecast assessment was performed over the 1996-2009 retrospective forecast period for the event rainy season onset earlier (or later) than the historical (mean) onset date. The three investigated approaches resulted in similar discrimination ability of around 80%, which represents the probability of the probabilistic forecasts correctly distinguishing earlier from a later than mean onsets, suggesting good potential for rainy season onset forecasts for São Paulo. The robustness of this assessment for an extended period (longer than 1996-2009) and for a region (20°S, 25°S, 45°W, 55°W) that includes the city of São Paulo was checked, reinforcing the validity of the obtained results at both local and regional scales. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates an alternative modelling approach for empirical seasonal temperature forecasts over South America. Seasonal average temperatures are found to be non-stationary at most parts of South America over the 1949-2012 period. Simple persistence and lagged regression methods have considerable correlation skill in forecasting next season temperature using previous season temperature as predictor. However, the presence of trends in both predictor and predictand temperature variables can affect correlation skill. Models that can account for non-stationarity in these variables may do better in modelling and forecasting seasonal temperatures known to have trends. A novel method (cointegration), introduced here for empirical seasonal climate forecasting, is found to perform better than the traditional persistence and regression forecasts for places where the predictor and predictand temperatures have stochastic trends. Potential skill pairwise comparisons between temperature forecasts produced with cointegration and those produced using persistence and lagged regression have shown that the alternative cointegration method performs significantly better than the other two. One of the main reasons for the better performance of cointegration method is that the modelling procedure accounts for the existing non-stationarity in the process, and thus enables the estimated model to predict out of the range as efficiently as possible. Overall, this method appears to be ideal for modelling and predicting climate under the current global warming scenario. This is because most of the climatic variables including temperature in particular cannot be assumed to be stationary through time under such warming scenario. [ABSTRACT FROM AUTHOR]

The article examines the influence climate change on dry conditions and water shortage in southeast Brazil during the period from January 2014 to February 2015. Topics covered include the effect of the South Atlantic convergence zone, analysis of hydrometeorological hazard defined by accumulated precipitation and the difference between precipitation and evaporation and the effect of anthropogenic climate change on water consumption.

ABSTRACT This study aimed to perform a comparative skill assessment of consensus and physically based tercile probability seasonal precipitation forecasts for Brazil produced during the last decade. Two fundamental forecast attributes have been examined: discrimination and reliability. The discrimination assessment revealed that forecast quality is seasonally dependent and that consensus and physically based forecasts are complementary. During spring and summer consensus forecasts were generally found to have better discrimination ability than physically based forecasts. During autumn and winter physically based forecasts were found to have better discrimination ability than consensus forecasts. However, discrimination is a necessary but not sufficient forecast skill attribute, and therefore only provides indication of potential forecast quality provided forecasts are reliable (i.e. well calibrated). The analysis of tendency diagrams has revealed that both consensus and physically based forecasts suffer from systematic errors (biases) for the three forecast categories. Both forecasts under-forecasted the below-normal category and over-forecasted the above normal category. This over-forecasting feature was stronger for physically based forecasts when compared to consensus forecasts. The normal category was more severely over-forecast for consensus forecasts when compared to physically based forecasts. The assessment through the computation of the reliability component of the Brier Score has revealed that consensus forecasts are better calibrated than CPTEC/ AGCM physically based forecasts. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The Amazon has a well-defined wet austral summer monsoon and dry winter monsoon precipitation regime and experienced a sequence of drought events in the last 13 years. This study performs a comparative assessment of observed and predicted climate conditions during the three most recent drought events in the Amazon, in 1997-1998, 2004-2005 and 2009-2010, with emphasis on how these events affected the regional monsoon-like precipitation regime. A century long Negro River level time series at Manaus is investigated, applying extreme values theory for estimating return periods of these major drought events. Possible teleconnections of river levels at Manaus and sea surface temperature at remote regions are explored. Large scale oceanic and atmospheric conditions are investigated to highlight the mechanisms associated with the observed drought conditions, particularly during the dry monsoon season. Satellite estimates are used for diagnosing biomass burning aerosol and discuss possible contributions to the observed precipitation deficits in the 2005 and 2010 drought events during the dry monsoon season. The study is concluded with an analysis of the performance of seasonal precipitation predictions for the dry monsoon seasons of July to September 1998, 2005 and 2010 produced with the operational seasonal prediction system used at the Center for Weather Forecasts and Climate Studies (CPTEC) of the Brazilian National Institute for Space Research (INPE). This system was capable of producing 1 month in advance drought warning for the three investigated events, relevant for helping the government and local population make decisions for reducing drought impacts in the Amazon region. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

El Niño brings widespread drought (i.e., precipitation deficit) to the tropics. Stronger or more frequent El Niño events in the future and/or their intersection with local changes in the mean climate toward a future with reduced precipitation would exacerbate drought risk in highly vulnerable tropical areas. Projected changes in El Niño characteristics and associated teleconnections are investigated between the twentieth and twenty-first centuries. For climate change models that reproduce realistic oceanic variability of the El Niño–Southern Oscillation (ENSO) phenomenon, results suggest no robust changes in the strength or frequency of El Niño events. These models exhibit realistic patterns, magnitude, and spatial extent of El Niño–induced drought patterns in the twentieth century, and the teleconnections are not projected to change in the twenty-first century, although a possible slight reduction in the spatial extent of droughts is indicated over the tropics as a whole. All model groups investigated show similar changes in mean precipitation for the end of the twenty-first century, with increased precipitation projected between 10°S and 10°N, independent of the ability of the models to replicate ENSO variability. These results suggest separability between climate change and ENSO-like climate variability in the tropics. As El Niño–induced precipitation drought patterns are not projected to change, the observed twentieth-century variability is used in combination with model-projected changes in mean precipitation for assessing year-to-year drought risk in the twenty-first century. Results suggest more locally consistent changes in regional drought risk among models with good fidelity in reproducing ENSO variability. [ABSTRACT FROM AUTHOR]

The seasonal predictability of cold spring seasons (March–May) in Europe from hindcasts/forecasts of three operational coupled general circulation models (CGCMs) is investigated. The models used in the investigation are the Met Office Global Seasonal Forecast System (GloSea), the ECMWF System-2 (S2), and the NCEP Climate Forecast System (CFS). Using the relative operating characteristic score and the Brier skill score the long-term prediction skill for spring 2-m temperature in the lower quintile (20%) is assessed. Over much of central and eastern Europe the predictive skill is found to be high. The skill of the Met Office GloSea and ECMWF S2 models significantly surpasses that of damped persistence over much of Europe but the NCEP CFS model outperforms this reference forecast only over a small area. The higher potential predictability of cold spring seasons in eastern relative to southwestern Europe can be attributed to snow effects as areas of high skill closely correspond with the climatological snow line, and snow is shown in this paper to be linked to cold spring 2-m temperatures in eastern Europe. The ability of the models to represent snow cover during the melt season is also investigated. The Met Office GloSea and the ECMWF S2 models are able to accurately mimic the observed pattern of monthly snow-cover interannual variability, but the NCEP CFS model predicts too short a snow season. Improvements in the snow analysis and land surface parameterizations could increase the skill of seasonal forecasts for cold spring temperatures. [ABSTRACT FROM AUTHOR]