Abstract

As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near-surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large-scale predictors to the target site is a reasonable alternative to predictors derived from grid-boxes. The validation metrics, measures (i.e. bias, root-mean-square error, and Pearson’s correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near-surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single-site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse-resolution projections. Its performance is higher than a quantile-mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near-surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.

This study investigates atmospheric blocking over eastern South America in austral summer for the period of 1979–2014. The results show that blocking over this area is a consequence of propagating Rossby waves that grow to large amplitudes and eventually break anticyclonically over subtropical South America (SSA). The SSA blocking can prevent the establishment of the South Atlantic convergence zone (SACZ). As such, years with more blocking days coincide with years with fewer SACZ days and reduced precipitation. Convection mainly over the Indian Ocean associated with Madden–Julian oscillation (MJO) phases 1 and 2 can trigger the wave train that leads to SSA blocking whereas convection over the western/central Pacific associated with phases 6 and 7 is more likely to lead to SACZ events. It is found that the MJO is a key source of long-term variability in SSA blocking frequency. The wave packets associated with SSA blocking and SACZ episodes differ not only in their origin but also in their phase and refraction pattern. The tropopause-based methodology used here is proven to reliably identify events that lead to extremes of surface temperature and precipitation over SSA. Up to 80% of warm surface air temperature extremes occur simultaneously with SSA blocking events. The frequency of SSA blocking days is highly anticorrelated with the rainfall over southeast Brazil. The worst droughts in this area, during the summers of 1984, 2001, and 2014, are linked to record high numbers of SSA blocking days. The persistence of these events is also important in generating the extreme impacts.

As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near-surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large-scale predictors to the target site is a reasonable alternative to predictors derived from grid-boxes. The validation metrics, measures (i.e. bias, root-mean-square error, and Pearson’s correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near-surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single-site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse-resolution projections. Its performance is higher than a quantile-mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near-surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct ‘savanna-like’ state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available.

Understanding the mechanisms controlling the distribution of biomes remains a challenge. Although tropical biome distribution has traditionally been explained by climate and soil, contrasting vegetation types often occur as mosaics with sharp boundaries under very similar environmental conditions. While evidence suggests that these biomes are alternative states, empirical broad-scale support to this hypothesis is still lacking. Using community-level field data and a novel resource-niche overlap approach, we show that, for a wide range of environmental conditions, fire feedbacks maintain savannas and forests as alternative biome states in both the Neotropics and the Afrotropics. In addition, wooded grasslands and savannas occurred as alternative grassy states in the Afrotropics, depending on the relative importance of fire and herbivory feedbacks. These results are consistent with landscape scale evidence and suggest that disturbance is a general factor driving and maintaining alternative biome states and vegetation mosaics in the tropics.

The south-eastern Amazon rainforest is subject to ongoing deforestation and is expected to become drier due to climate change. Recent analyses of the distribution of tree cover in the tropics show three modes that have been interpreted as representing alternative stable states: forest, savanna and treeless states. This situation implies that a change in environmental conditions, such as in the climate, could cause critical transitions from a forest towards a savanna ecosystem. Shifts to savanna might also occur if perturbations such as deforestation exceed a critical threshold. Recovering the forest would be difficult as the savanna will be stabilized by a feedback between tree cover and fire. Here we explore how environmental changes and perturbations affect the forest by using a simple model with alternative tree-cover states. We focus on the synergistic effects of precipitation reduction and deforestation on the probability of regime shifts in the south-eastern Amazon rainforest. The analysis indicated that in a large part of the south-eastern Amazon basin rainforest and savanna could be two alternative states, although massive forest dieback caused by mean-precipitation reduction alone is unlikely. However, combinations of deforestation and climate change triggered up to 6.6 times as many local regime shifts than the two did separately, causing large permanent forest losses in the studied region. The results emphasize the importance of reducing deforestation rates in order to prevent a climate-induced dieback of the south-eastern Amazon rainforest.

The atmospheric teleconnections associated with the Eastern Pacific El Niño and El Niño Modoki events onto the tropical Atlantic Ocean are investigated. The Eastern Pacific El Niños drive significant warming of the tropical North Atlantic basin during boreal spring after its peak via the atmospheric bridge and tropospheric temperature mechanisms. However, the tropical Atlantic does not show a robust response to El Niño Modoki events. Here our results suggest that the preconditioning of the tropical North Atlantic sea surface temperature (SST) anomalies in boreal winter plays an important role in the following season, not only during Eastern Pacific El Niños but also during El Niño Modoki events. Additionally, we examine three other factors that could explain potential differences in the tropical Atlantic teleconnections from El Niño Modoki and Eastern Pacific El Niño events: (1) The distant location of the maximum SST warming in the Pacific; (2) The weak warming associated with this pattern; and (3) The SST pattern including a cooling in the eastern Pacific. Using numerical experiments forced with idealised SST in the equatorial Pacific, we show that the location of the El Niño Modoki SST warming during its mature phase could be favourable for exciting atmospheric teleconnections in boreal winter but not in the following spring season due to the seasonal shift of the Inter-Tropical Convergence Zone that modulates deep convection over the anomalous SST. This demonstrates the importance of the mean seasonal atmospheric circulation in modulating the remote teleconnections from the central-western Pacific warming in the model. However, it is suggested here that the cooling in the eastern Pacific associated with El Niño Modoki counteracts the atmospheric response driven by the central western Pacific warming, generating a consequent weaker connection to the tropical Atlantic compared to the stronger link during Eastern Pacific El Niño events. Finally we show that the modeled Pacific–tropical Atlantic teleconnections to an eastern Pacific warming depends strongly on the underlying seasonal cycle of SST.

Land use and land cover change (LUCC) affects regional climate through modifications in the water balance and energy budget. These impacts are frequently expressed by: changes in the amount and frequency of precipitation and alteration of surface temperatures. In South America, most of the studies of the effects of LUCC on the local and regional climate have focused on the Amazon region (54 studies), whereas LUCC within non-Amazonian regions have been largely undermined regardless their potential importance in regulating the regional climate (19 studies). We estimated that 3.6 million km2 of the original natural vegetation cover in non-Amazonian South America were converted into other types of land use, which is about 4 times greater than the historical Amazon deforestation. Moreover, there is evidence showing that LUCC within such fairly neglected ecosystems cause significant reductions in precipitation and increases in surface temperatures, with occasional impacts affecting neighboring or remote areas. We explore the implications of these findings in the context of water security, climatic extremes and future research priorities.

The aim of this study is to characterize rainfall patterns in a vast transition zone
between the Amazon and the Cerrado Biomes. The analysis is focused on annual and seasonal
tendencies, mainly about the onset and offset of the rainy season, its length and shifts. More
than 200 Rain Gauges (RGs) were analyzed in the study area using Pettitt’s and MannKendall’s
non-parametric tests allied to a Linear Regression Analysis over the period 1971–
2010. The onset and offset dates of the rainy season and its duration are also identified for 89
RGs. Pettitt’s test indicates ruptures in 16 % of the rainfall time series while Mann-Kendall’s
monthly test indicates that 45 % of the RGs had negative trends, mainly in the transition seasons
(spring and austral autumn). Linear Regression Analysis indicates negative trends in 63 % of
the time series concomitant to the rainy season onset and offset analysis, which confirmes a
delay for the onset of the rainy season in 76 % of the RGs and a premature demise for 84 % of
the RGs. Identification of the tendencies for rainy season duration indicates that the rainy season
has become shorter at 88 % of the RGs. There were recurring patterns in the results displaying
drier conditions in RGs localized in deforested areas opposed to forested locations.

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.