Stratospheric ozone depletion: a key driver of recent precipitation trends in South Eastern South America

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.     

Climate change predictions for South America

Some of the characteristics of predicted climate changes for South America are analysed for the years 2010 and 2050. These predictions are based on the results of three-dimensional Global Circulation Models (GCMs). The results differ between models (GISS, NCAR-CCMs and GFDL), particularly when applied to regional and sub-regional scales and to time scales of less than one-year intervals. It is concluded that these differences are due to the particular structure of each model as well as to the lack of sufficient basic data from the South American sub-continent. The dynamics of vegetation cover play an important role in future water balance changes. The changes in surface temperatures predicted by the GISS model are discussed in relation to changes in the climatic-dynamic base stemming from anthropogenic changes in the vegetation cover.     

An ensemble forecast of the South China Sea monsoon

1.IntroductionThispaperexploresanensembleforecaststrategyforthelarge--scaletropicalpredictionproblem.Thisisgeneralizedfromarecentstudyontheuseofempiricalorthogonalfunction(EOF)--basedperturbationsforhurricanetrackensembleforecasts,(ZhangandKrishnamur...     

Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties

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.     

Regional droughts in southern South America

From a regional inventory of monthly droughts, which was evaluated in six regions of southern South America, the seasonal occurrence of the phenomenon and its persistence together with the duration of monthly and annual sequences, extreme events, and other statistical estimates of the proposed index have been studied. In a primary analysis, it is possible to observe that regions present different behaviors regarding the duration of dry sequences, with more persistence in the Argentinean continental region. Temporal behaviors of the annual indexes have also been analyzed, in the attempt to determine any aspects of the impact of global warming. Through this analysis, the presence of long favorable tendencies regarding precipitations or the inverse of droughts occurrence are confirmed for the eastern Andes Mountains in Argentina (ARG) with its five sub-regions (Northwest Argentina-NWA, Northeast Argentina-NEA, Humid Pampa-HP, West-Centre Provinces-WC and Patagonia-PAT, and the inverse over the central region of Chile (CHI). Other climatic great-scale changes are interdecadales variations and variances explained according to ENSO.     

Extreme rainfalls in SE South America

Heavy rainfall trends in a region of south-eastern South America during 1959–2002 were discussed using daily data of 52 meteorological stations of Argentina, Brazil and Uruguay. Changes in intensity and frequency were both studied with different statistical tests and approaches to check the significance of trends of single and regional aggregated rainfall series. There were predominant positive trends in the annual maximum rainfalls, as well as a remarkable increment in the frequency of heavy rainfalls over thresholds ranging from 50 to 150 mm. However, significant positive trends were not shown in the series of annual maximums and shown only in 15% to 30% of the series of frequencies over thresholds. This lack of significance is due to the high variability of heavy rainfalls in space and time, which makes difficult their capture by single rain gauges. Thus, when the assessment of the heavy rainfall indicators of intensity and frequency were conducted at the regional and sub-regional level, it showed significant trends, both in intensity and frequency over thresholds, with a clearer signal in central and eastern Argentina between 30° and 40° S.     

Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Monthly-mean precipitation, mean (T_AVG), maximum (T_MAX) and minimum (T_MIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for T_AVG and T_MAX than for T_MIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.     

Upper-level cut-off lows in southern South America

Summary This paper presents a statistical study of the spatial and seasonal distribution and duration of cut-off low systems over the southern South American region based on the NCEP- NCAR reanalysis data for the period 1979–1988. Cut-off lows were first objectively determined as minimum geopotential values at the 250 hPa level and then subjectively imposing a cut-off circulation and a cold core. A total of 171 cut-off low events were detected, being more frequent in austral autumn followed by winter, spring and summer. There is a preferential region of occurrence in spring and autumn located between 68°–80° W and 30°–45° S. The Pacific area showed the greatest frequency of occurrence followed by the Atlantic and the continental areas. Most of the cut-off lows last 2 or 3 days (around 90% of the cases) though there is a tendency of the continental events to be longer. The cut-off low event developed upwind the Andes on 22–28 September 1986 was selected as a case study. Low-level cold air advection was the main forcing of the deepening of the upper level low system.     

Summer precipitation variability over Southeastern South America in a global warming scenario

December–January–February (DJF) rainfall variability in southeastern South America (SESA) is studied in 18 coupled general circulation models from the WCRP/CMIP3 dataset, for present climate and the SRES-A1B climate change scenario. The analysis is made in terms of properties of the first leading pattern of rainfall variability in the region, characterized by a dipole-like structure with centers of action in the SESA and South Atlantic Convergence Zone (SACZ) regions. The study was performed to address two issues: how rainfall variability in SESA would change in a future climate and how much of that change explains the projected increasing trends in the summer mean rainfall in SESA identified in previous works. Positive (negative) dipole events were identified as those DJF seasons with above (below) normal rainfall in SESA and below (above) normal rainfall in the SACZ region. Results obtained from the multi-model ensemble confirm that future rainfall variability in SESA has a strong projection on the changes of seasonal dipole pattern activity, associated with an increase of the frequency of the positive phase. In addition, the frequency increase of positive dipole phase in the twenty first century seems to be associated with an increase of both frequency and intensity of positive SST anomalies in the equatorial Pacific, and with a Rossby wave train-like anomaly pattern linking that ocean basin to South America, which regionally induces favorable conditions for moisture transport convergence and rainfall increase in SESA.     

A Re-Evaluation of Sulfur Budgets, Lifetimes, and Scavenging Ratios for Eastern North America

Lifetimes, scavenging ratios, andbudgets describe the cycling of atmosphericconstituents and are often used in formulating airpollution control strategies. Most previous studiesof sulfur lifetimes, budgets, and scavenging ratioshave been based on limited observational data or datafrom highly simplified models. The Regional AcidDeposition Model (RADM2.61) shows some skill inpredicting atmospheric mixing ratios of acidicmaterials and other related trace constituents andacid deposition patterns in North America, and so,analysis of its established, theoretical, databaseserves as a counterpoint to previous studies of sulfurbudgets, lifetimes, and scavenging ratios. The annualbudget shows that the net transport (outflow minusinflow) of sulfur compounds out of eastern NorthAmerica is equal to the total deposition within thedomain. Of the total deposition, 63% is from wetdeposition and 37% is from dry deposition. Theannual average lifetime of sulfur dioxide (38 hours),estimated by the turnover time, is limited by aqueousconversion, while that for sulfate aerosols (54 hours)is limited by their removal in precipitation. Theannual average lifetime of sulfur in this domain isslightly more than three days. Episodic lifetimes andbudgets, based on particular synoptic situations, showlarge variations around the annual values. Episodicprecipitation scavenging ratios exhibit similarvariability and are used to offer explanations ofseveral potential biases found in the wet sulfurdeposition amounts as predicted by the EMEP sulfurtransport model and other published results.     

Domain choice in an experimental nested modeling prediction system for South America

Summary The purposes of this paper are to evaluate the new version of the regional model, RegCM3, over South America for two test seasons, and to select a domain for use in an experimental nested prediction system, which incorporates RegCM3 and the European Community-Hamburg (ECHAM) general circulation model (GCM). To evaluate RegCM3, control experiments were completed with RegCM3 driven by both the NCEP/NCAR Reanalysis (NNRP) and ECHAM, using a small control domain (D-CTRL) and integration periods of January–March 1983 (El Ni?o) and January–March 1985 (La Ni?a). The new version of the regional model captures the primary circulation and rainfall differences between the two years over tropical and subtropical South America. Both the NNRP-driven and ECHAM-driven RegCM3 improve the simulation of the Atlantic intertropical convergence zone (ITCZ) compared to the GCM. However, there are some simulation errors. Irrespective of the driving fields, weak northeasterlies associated with reduced precipitation are observed over the Amazon. The simulation of the South Atlantic convergence zone is poor due to errors in the boundary condition forcing which appear to be amplified by the regional model. To select a domain for use in an experimental prediction system, sensitivity tests were performed for three domains, each of which includes important regional features and processes of the climate system. The domain sensitivity experiments were designed to determine how domain size and the location of the GCM boundary forcing affect the regional circulation, moisture transport, and rainfall in two years with different large scale conditions. First, the control domain was extended southward to include the exit region of the Andes low level jet (D-LLJ), then eastward to include the South Atlantic subtropical high (D-ATL), and finally westward to include the subsidence region of the South Pacific subtropical high and to permit the regional model more freedom to respond to the increased resolution of the Andes Mountains (D-PAC). In order to quantify differences between the domain experiments, measures of bias, root mean square error, and the spatial correlation pattern were calculated between the model results and the observed data for the seasonal average fields. The results show the GCM driving fields have remarkable control over the RegCM3 simulations. Although no single domain clearly outperforms the others in both seasons, the control domain, D-CTRL, compares most favorably with observations. Over the ITCZ region, the simulations were improved by including a large portion of the South Atlantic subtropical high (D-ATL). The methodology presented here provides a quantitative basis for evaluating domain choice in future studies.     

Downscaling extreme month-long anomalies in southern South America

We investigate the performance of one stretched-grid atmospheric global model, five different regional climate models and a statistical downscaling technique in simulating 3 months (January 1971, November 1986, July 1996) characterized by anomalous climate conditions in the southern La Plata Basin. Models were driven by reanalysis (ERA-40). The analysis has emphasized on the simulation of the precipitation over land and has provided a quantification of the biases of and scatter between the different regional simulations. Most but not all dynamical models underpredict precipitation amounts in south eastern South America during the three periods. Results suggest that models have regime dependence, performing better for some conditions than others. The models’ ensemble and the statistical technique succeed in reproducing the overall observed frequency of daily precipitation for all periods. But most models tend to underestimate the frequency of dry days and overestimate the amount of light rainfall days. The number of events with strong or heavy precipitation tends to be under simulated by the models.     

Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm

Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts.     

Low Frequency Variability of Rainfall in Southeastern South America

Summary The climatic variations of precipitation, within the period range of 10 and 100 years, in the Southeastern South America (SSA) region are investigated. A gaussian low-pass filter with a cut-off period at 30 years, was applied to annual precipitation records, in order to smooth high frequencies and to make evident the variability within the searched low frequency ranges (longer-than-30 years period). The principal components analysis (PCA) applied to filtered series allowed the determination of two climatic signals, defined by the first and second principal components (PC ₁ and PC ₂). Both signals reflect remarkable changes in the annual precipitation values (a sustained increase in precipitation), over a large region of SSA (great part of Argentine pampas and Uruguay). The PCA applied to the filtered precipitation records for each quarter, which we have associated to seasonal rainfall, allowed the determination of the incidence of PC ₁ and PC ₂ on the variability of the series. In particular, it is in the summer rainfall where the two first seasonal components, (PC1₁ and PC1₂), show a high degree of similarity (correlations >0.8) with the respective annual components PC ₁ and PC ₂. During the spring season only the first component, (PC4₁), shows high correlation with PC ₁; the rest of the seasonal components do not show correlations exceeding the established values of 0.8. Received May 5, 1997 Revised April 26, 1998     

Uncertainty assessments of climate change projections over South America

This paper assesses the uncertainties involved in the projections of seasonal temperature and precipitation changes over South America in the twenty-first century. Climate simulations generated by 24 general circulation models are weighted according to the reliability ensemble averaging (REA) approach. The results show that the REA mean temperature change is slightly smaller over South America compared to the simple ensemble mean. Higher reliability in the temperature projections is found over the La Plata basin, and a larger uncertainty range is located in the Amazon. A temperature increase exceeding 2 °C is found to have a very likely (>90 %) probability of occurrence for the entire South American continent in all seasons, and a more likely than not (>50 %) probability of exceeding 4 °C by the end of this century is found over northwest South America, the Amazon Basin, and Northeast Brazil. For precipitation, the projected changes have the same magnitude as the uncertainty range and are comparable to natural variability.     

NCEP集合预报系统在亚欧和北美区域的预报效果对比

使用NCEP集合预报系统（EPS）输出的500hPa位势高度场预报资料和相应的NCEP／NCAR再分析资料,针对集合平均预报和概率预报,采用多种预报效果检验评价方法,对该系统在亚欧和北美区域的预报效果进行全面的分析比较。总体而言,NCEP—EPS对亚欧区域的环流集合预报效果不亚于其对北关区域的预报效果。1）ACC检验表明,亚欧区域的集合平均预报效果在除冬季外的三个季节都明显优于北美区域,可用预报的时效相差达0．6～1d,且夏季的差别最大。RMSE检验表明,亚欧区域的预报效果在四个季节里均优于北美区域。2）集合概率预报可靠性的季节差别不明显,均为预报时效较短（长）时,北关（亚欧）区域的可靠性更好。系统对亚欧区域的事件识别范围相对较小,但其预报可靠性较高,北美区域则正好相反。3）夏季亚欧区域的集合概率预报效果明显优于北美区域,秋季和冬季北关区域的预报效果较好,春季在预报时效小于5d时北美区域占优,而其后则是亚欧区域的预报分辨能力更好。