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The Andes Cordillera plays a role in driving moisture and heat from tropical onto subtropical South America. It forces the development of a lee-side trough that covers most of western Argentina and a low-level jet that maximizes over Paraguay, eastern Bolivia and northern Argentina and is tightly linked to precipitation variability over much of central and southeastern South America. Its steep slopes and the large zonal gradients in topography between the Equator and 40°S are misrepresented in climate simulations using Global Climate Models (GCM) with resolutions coarser than about 100 km, since they naturally have a poor representation of the Andes and related circulation features. This paper analyses the impact of varying artificially the altitude of the Andes Cordillera in a GCM as well as increasing the horizontal resolution to study how these variations determine moisture fluxes and precipitation over selected regions of South America. Results show that the height of the Andes is crucial in shaping moisture fluxes pathways onto subtropical South America all year long. In particular, the low-level jet is only simulated when the Andes heights are doubled. At the same time, the relationship between the Andes shape and the location of the Bolivian High in summer is also discussed. In terms of precipitation, the lowest bias in the simulations is achieved when the horizontal resolution is increased, while in particular near the Andes foothills the simulated annual rainfall is largely determined by the Mountains shape. [ABSTRACT FROM AUTHOR]

On a hemispheric scale, it is now well established that stratospheric ozone depletion has been the principal driver of externally forced atmospheric circulation changes south of the Equator in the last decades of the 20th Century. The impact of ozone depletion has been felt over the entire hemisphere, as reflected in the poleward drift of the midlatitude jet, the southward expansion of the summertime Hadley cell and accompanying precipitation trends deep into the subtropics. On a regional scale, however, surface impacts directly attributable to ozone depletion have yet to be identified. In this paper we focus on South Eastern South America (SESA), a region that has exhibited one of the largest wetting trends during the 20th Century. We study the impact of ozone depletion on SESA precipitation using output from 6 different climate models, spanning a wide range of complexity. In all cases we contrast pairs of model integrations with and without ozone depletion, but with all other forcings identically specified. This allows for unambiguous attribution of the computed precipitation trends. All 6 climate models consistently reveal that stratospheric ozone depletion results in a significant wetting of SESA over the period 1960-1999. Taken as a whole, these model results strongly suggest that the impact of ozone depletion on SESA precipitation has been as large as, and quite possibly larger than, the one caused by increasing greenhouse gases over the same period. [ABSTRACT FROM AUTHOR]

This paper identifies two sources of uncertainties in model projections of temperature and precipitation: internal and inter-model variability. Eight models of WCRP-CMIP3 and WCRP-CMIP5 were compared to identify improvements in the reliability of projections from new generation models. While no significant differences are observed between both datasets, some improvements were found in the new generation models. For example, in summer CMIP5 inter-model variability of temperature was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century. Reliability of temperature projections from both sets of models is high, with signal to noise ratio greater than 1 over most of the study region. Although no major differences were observed in both precipitation datasets, CMIP5 inter-model variability was lower over northern and eastern Brazil in summer (especially at the end of the 21st century). Reliability of precipitation projections was low in both datasets. However, the signal to noise ratio in new generation models was close to 1, and even greater than 1 over eastern Argentina, Uruguay and southern Brazil in some seasons. [ABSTRACT FROM AUTHOR]

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes. [ABSTRACT FROM AUTHOR]

The results of an ensemble of regional climate model (RCM) simulations over South America are presented. This is the first coordinated exercise of regional climate modelling studies over the continent, as part of the CLARIS-LPB EU FP7 project. The results of different future periods, with the main focus on (2071-2100) is shown, when forced by several global climate models, all using the A1B greenhouse gases emissions scenario. The analysis is focused on the mean climate conditions for both temperature and precipitation. The common climate change signals show an overall increase of temperature for all the seasons and regions, generally larger for the austral winter season. Future climate shows a precipitation decrease over the tropical region, and an increase over the subtropical areas. These climate change signals arise independently of the driving global model and the RCM. The internal variability of the driving global models introduces a very small level of uncertainty, compared with that due to the choice of the driving model and the RCM. Moreover, the level of uncertainty is larger for longer horizon projections for both temperature and precipitation. The uncertainty in the temperature changes is larger for the subtropical than for the tropical ones. The current analysis allows identification of the common climate change signals and their associated uncertainties for several subregions within the South American continent. [ABSTRACT FROM AUTHOR]

We investigate the temporal evolution of moisture divergence and its spatial clustering properties over South America. Our analysis focuses on dependencies on the phase of the El Niño Southern Oscillation (ENSO). Moisture divergence is computed from daily reanalysis data of vertically integrated moisture flux provided by Modern-Era Retrospective Analysis for Research and Applications for the time period from 1979 to 2010. We use a sliding-window approach to construct a sequence of complex networks, each obtained from synchronization of events of strong positive (negative) moisture divergence, which we interpret as strong evapotranspiration (precipitation) events. We make the following three key observations: (1) Moisture divergence values over the Amazon rainforest are typically higher during positive ENSO periods (El Niño events). (2) The spatial coherence of strong positive (upwelling) events assumes a characteristic pattern of reduced coherence in this area during El Niño conditions. This influence of ENSO on moisture divergence and its spatial coherence is dominated by the El Niño events of 1982, 1987, and 1997. (3) The clustering characteristics of the obtained climate networks qualitatively agree with the spatial distribution of connected regions with simultaneous events (i.e., events that occur at the same time), but provide a more detailed view on the spatial organization of strong atmospheric upwelling events. Interestingly, no comparable results are found for negative extremes of moisture divergence (strong precipitation events). [ABSTRACT FROM AUTHOR]

A dipole pattern in convection between the South Atlantic convergence zone and the subtropical plains of southeastern South America characterizes summer intraseasonal variability over the region. The dipole pattern presents two main bands of temporal variability, with periods between 10 and 30 days, and 30 and 90 days; each influenced by different large-scale dynamical forcings. The dipole activity on the 30-90-day band is related to an eastward traveling wavenumber-1 structure in both OLR and circulation anomalies in the tropics, similar to that associated with the Madden-Julian oscillation. The dipole is also related to a teleconnection pattern extended along the South Pacific between Australia and South America. Conversely, the dipole activity on the 10-30-day band does not seem to be associated with tropical convection anomalies. The corresponding circulation anomalies exhibit, in the extratropics, the structure of Rossby-like wave trains, although their sources are not completely clear. [ABSTRACT FROM AUTHOR]

This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric-ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well. [ABSTRACT FROM AUTHOR]

Intraseasonal (IS) variability in South America is analyzed during the cold season using 10-90 day bandpass filtered OLR anomalies (FOLR). IS variability explains a large percentage of variance with maximum values over Paraguay, northeastern Argentina, and southern Brazil. The leading pattern of FOLR, as isolated from an EOF analysis, (Cold Season IS pattern, CSIS), is characterized by a monopole centered over southeastern South America (SESA) with a northwest-southeast orientation. CSIS induces a large modulation on daily precipitation anomalies, especially on both wet spells and daily precipitation extremes, which are favored during positive (wet) CSIS phases. Large-Scale OLR anomalies over the tropical Indian and west Pacific Oceans associated with CSIS exhibit eastward propagation along tropical latitudes. In addition, circulation anomalies in the Southern Hemisphere reveal the presence of an anticyclonic anomaly over Antarctica with opposite-sign anomalies in middle latitudes 10 days before CSIS is maximum as well as evidence of Rossby wave-like patterns. Positive precipitation anomalies in SESA are favored during wet CSIS phases by the intensification of a cyclonic anomaly located further south, which is discernible over the southeastern Pacific for at least 14 days before CSIS peaks. The cyclonic anomaly evolution is accompanied by the intensification of an upstream anticyclonic anomaly, which remains quasi-stationary near the Antarctica Peninsula before the CSIS peak. We speculate that the stationary behavior of the anticyclonic center is favored by a hemispheric circulation anomaly pattern resembling that associated with a negative southern annular mode phase and a wavenumber 3-4 pattern at middle latitudes. [ABSTRACT FROM AUTHOR]

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990-2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the climate projections over South America using the Eta-CPTEC regional model driven by four members of an ensemble of the Met Office Hadley Centre Global Coupled climate model HadCM3. The global model ensemble was run over the twenty-first century according to the SRES A1B emissions scenario, but with each member having a different climate sensitivity. The four members selected to drive the Eta-CPTEC model span the sensitivity range in the global model ensemble. The Eta-CPTEC model nested in these lateral boundary conditions was configured with a 40-km grid size and was run over 1961-1990 to represent baseline climate, and 2011-2100 to simulate possible future changes. Results presented here focus on austral summer and winter climate of 2011-2040, 2041-2070 and 2071-2100 periods, for South America and for three major river basins in Brazil. Projections of changes in upper and low-level circulation and the mean sea level pressure (SLP) fields simulate a pattern of weakening of the tropical circulation and strengthening of the subtropical circulation, marked by intensification at the surface of the Chaco Low and the subtropical highs. Strong warming (4-6°C) of continental South America increases the temperature gradient between continental South America and the South Atlantic. This leads to stronger SLP gradients between continent and oceans, and to changes in moisture transport and rainfall. Large rainfall reductions are simulated in Amazonia and Northeast Brazil (reaching up to 40%), and rainfall increases around the northern coast of Peru and Ecuador and in southeastern South America, reaching up to 30% in northern Argentina. All changes are more intense after 2040. The Precipitation-Evaporation (P-E) difference in the A1B downscaled scenario suggest water deficits and river runoff reductions in the eastern Amazon and São Francisco Basin, making these regions susceptible to drier conditions and droughts in the future. [ABSTRACT FROM AUTHOR]

The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1°C, ice sheet topography, reduced CO2, and 21,000 BP orbital parameters. The high-resolution model captures modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1°C less than the control run, there are many lowland tropical land areas 4–6°C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age—consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent. [ABSTRACT FROM AUTHOR]