The impact of El Niño–Southern Oscillation (ENSO) on the South Atlantic subtropical dipole mode (SASD) is investigated using both observations and model simulations. The SASD is the dominant mode of coupled ocean–atmosphere variability in the South Atlantic. This study focuses on austral summer, when both ENSO and SASD peak. It is shown that negative SASD events are associated with central Pacific El Niño events by triggering the Pacific–South American wave train (PSA). The latter resembles the third leading mode of atmospheric variability in the Southern Hemisphere (PSA2) and causes a weakening and meridional shift of the South Atlantic subtropical high, which then generates the negative SASD events. On the other hand, a strengthening of the South Atlantic subtropical high related to central La Niña teleconnections causes positive SASD events. The results herein show that the PSA2, triggered by central Pacific ENSO events, connects the tropical Pacific to the Atlantic. This connection is absent from eastern Pacific ENSO events, which appear to initiate the second leading mode of atmospheric variability in the Southern Hemisphere (PSA1). It is for this reason that previous studies have found weak correlations between ENSO and SASD. These findings can improve the climate prediction of southeastern South America and southern Africa since these regions are affected by sea surface temperature anomalies of both the Pacific and Atlantic Oceans.

Available climatological information of Distrito Federal does not satisfy the requirements for detailed climate diagnosis, as they do not provide the necessary spatial resolution for water resources management purposes. Annual and seasonal climatology (1971–2000) of precipitation from 6 meteorological stations and 54 rain gauges from Central Brazil were used to test eight different spatial interpolation methods. Geographical factors (i.e., altitude, longitude and latitude) explain a large portion of precipitation in the region, and therefore, multivariate models were included. The performance of estimations was assessed through independent validation using mean square error, correlation coefficient and Nash–Sutcliffe efficiency criterion. Inverse distance weighting (IDW), ordinary kriging (OK) and the multivariate regression with interpolation of residuals by IDW (MRegIDW) and OK (MRegOK) have performed the lowest errors and the highest correlation and Nash–Sutcliffe efficiency criterion. In general, interpolation methods provide similar spatial distributions of rainfall wherever observation network is dense. However, the inclusion of geographical variables to the interpolation method should improve estimates in areas where the observation network density is low. Nevertheless, the assessment of uncertainties using a geostatistical method provides supplementary and qualitative information which should be considered when interpreting the spatial distribution of rainfall.

Available climatological information of Distrito Federal does not satisfy the requirements for detailed climate diagnosis, as they do not provide the necessary spatial resolution for water resources management purposes. Annual and seasonal climatology (1971–2000) of precipitation from 6 meteorological stations and 54 rain gauges from Central Brazil were used to test eight different spatial interpolation methods. Geographical factors (i.e., altitude, longitude and latitude) explain a large portion of precipitation in the region, and therefore, multivariate models were included. The performance of estimations was assessed through independent validation using mean square error, correlation coefficient and Nash–Sutcliffe efficiency criterion. Inverse distance weighting (IDW), ordinary kriging (OK) and the multivariate regression with interpolation of residuals by IDW (MRegIDW) and OK (MRegOK) have performed the lowest errors and the highest correlation and Nash–Sutcliffe efficiency criterion. In general, interpolation methods provide similar spatial distributions of rainfall wherever observation network is dense. However, the inclusion of geographical variables to the interpolation method should improve estimates in areas where the observation network density is low. Nevertheless, the assessment of uncertainties using a geostatistical method provides supplementary and qualitative information which should be considered when interpreting the spatial distribution of rainfall.

A key challenge for climate projection science is to serve the growing needs of impact assessments in an environment with substantial differences in the projections of climate models and an increasing number of relevant climate model results. In order to assist the assessment of water resources impacts under future climate change, this work provides a synthesis of the simulations of General Circulation Models (GCMs) for the region of Distrito Federal, Brazil. The work analyzes projections of mean surface air temperature and precipitation of 22 GCMs, as well as seven extreme indices of 10 GCMs. Trends of the multi-model ensemble median, as well as their significance, were calculated. The consistency in the sign of change was assessed through the percentage of agreement of simulations with the median. Finally, the probability density function of the multi-model ensemble provides valuable information about the uncertainties of projections. Investigations were performed for annual and seasonal temporal scales for the period 2011-2050. The main results here identified are: (a) a consensus of the multi-model ensemble and median to increasing temperature; (b) a slightly, but less consistent, decrease of precipitation in the dry season; and (c) increase of heat waves and droughts events, although changes in precipitation extremes are much less coherent than for temperature. The approach used gives a comprehensive assessment of the possible future climate until the middle of the twenty-first century, as well as the uncertainties involved in the multi-model ensemble projections.

Smallholder farming is among the most vulnerable sectors due to its great social and economic sensitivity. Despite future climate change, current climate variability is already an issue of concern that justifies adaptation efforts. In Brazil, the Semi-Arid Region is a climate hotspot, well known for both historic socioeconomic setbacks, and agriculture failures caused by dry spells and severe droughts. In 2010, the Brazilian government enacted the National Policy on Climate Change, which states as one of its key goals the identification of vulnerabilities and the adoption of adequate measures of adaptation to climate change. The improvement of vulnerability assessment tools is a response to the growing demand of decision makers for regular information and indicators with high spatial and temporal resolution. This article aims at undertaking a comparative assessment of smallholder farming’s vulnerability to droughts. An integrated assessment system has been developed and applied to seven municipalities located in the Brazilian Semi-Arid Region (within the State of Ceará). Results show regional vulnerability contrasts driven by institutional and socioeconomic factors, beyond climatic stressors.

A key challenge for climate projection science is to serve the growing needs of impact assessments in an environment with substantial differences in the projections of climate models and an increasing number of relevant climate model results. In order to assist the assessment of water resources impacts under future climate change, this work provides a synthesis of the simulations of General Circulation Models (GCMs) for the region of Distrito Federal, Brazil. The work analyzes projections of mean surface air temperature and precipitation of 22 GCMs, as well as seven extreme indices of 10 GCMs. Trends of the multi-model ensemble median, as well as their significance, were calculated. The consistency in the sign of change was assessed through the percentage of agreement of simulations with the median. Finally, the probability density function of the multi-model ensemble provides valuable information about the uncertainties of projections. Investigations were performed for annual and seasonal temporal scales for the period 2011–2050. The main results here identified are: (a) a consensus of the multi-model ensemble and median to increasing temperature; (b) a slightly, but less consistent, decrease of precipitation in the dry season; and (c) increase of heat waves and droughts events, although changes in precipitation extremes are much less coherent than for temperature. The approach used gives a comprehensive assessment of the possible future climate until the middle of the twenty-first century, as well as the uncertainties involved in the multi-model ensemble projections.

The Brazilian Northeast (NE) is strongly affected by El Niño–Southern Oscillation (ENSO). During La Niña events, the precipitation over the NE is generally above average. However, during the last La Niña event in 2011–2012, the NE went through its worst drought in the last 30 years. In this study, observations and numerical simulations are used to determine what made the 2011–2012 event different from other events. We find that eastern Pacific (canonical) La Niña events cause a cooling of the tropical North Atlantic and warming of the tropical South Atlantic that lead to a southward migration of the Intertropical Convergence Zone, which in turn brings rain to the NE. On the other hand, La Niña events with the cooling concentrated in the central Pacific cause the opposite meridional sea surface temperature (SST) gradient in the tropical Atlantic, leading to droughts over the NE. The 2011–2012 event was of the latter type. This study also shows that it is possible to predict the sign of the NE rainfall anomaly during ENSO events using a simple SST index.

It has recently been found that the frequency distribution of remotely sensed tree cover in the tropics has three distinct modes, which seem to correspond to forest, savanna, and treeless states. This pattern has been suggested to imply that these states represent alternative attractors, and that the response of these systems to climate change would be characterized by critical transitions and hysteresis. Here, we show how this inference is contingent upon mechanisms at play. We present a simple dynamical model that can generate three alternative tree cover states (forest, savanna, and a treeless state), based on known mechanisms, and use this model to simulate patterns of tree cover under different scenarios. We use these synthetic data to show that the hysteresis inferred from remotely sensed tree cover patterns will be inflated by spatial heterogeneity of environmental conditions. On the other hand, we show that the hysteresis inferred from satellite data may actually underestimate real hysteresis in response to climate change if there exists a positive feedback between regional tree cover and precipitation. Our results also indicate that such positive feedback between vegetation and climate should cause direct shifts between forest and a treeless state (rather than through an intermediate savanna state) to become more likely. Finally, we show how directionality of historical change in conditions may bias the observed relationship between tree cover and environmental conditions.