Abstract: The objective of this study was to determine the probability of occurrence of wet or dry season events, by means of estimating latitudinal profiles for the association between El Nino Southern-Oscillation and the rainfall along the west coast of Central and South America. The analysis was performed using multinomial linear regression and multinomial logit regression models. We used monthly time series of the Pacific equatorial sea surface temperature (SST), the Nino 3.4 index, a sea level pressure index (SOI) and rainfall anomalies over a 2.5 x 2.5 degrees grid along the west coast of Central and South America, for latitudes starting at 25 degrees N through 45 degrees S, from 1951 to 2011. We defined an ENSO index (NSO) as predictor and rainfall as response. Data was grouped into seasons and then categorized into terciles to construct 3 x 3 non-symmetrical three way contingency tables. As results, we generated latitudinal profiles of the predictability (association) of rainfall for the west coast of Central and South America, using the ENSO phases as predictor.

Abstract: Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Abstract: Although tropical cyclone rainfall (TCR) is common over Puerto Rico, the factors that cause this rain to vary from one storm to another have not been studied. The aim of this article is to understand how storm-specific characteristics including storm location, duration, storm centre proximity to land, intensity, horizontal translation speed (HTS) and environmental factors like moisture and vertical wind shear affect TCR variability over Puerto Rico. TCR was determined at rain gauge locations for days when storms were within a 500 km radius of Puerto Rico. The station data were then used to calculate an island-averaged total rainfall value for 86 storms during 1970�2010. The maximum observed rainfall was also examined. Correlation analyses of the individual predictors, principal component regression (PCR) procedures and Mann�Whitney U tests identified precipitable water, storm centre proximity to land, mid-level relative humidity (MRH), duration, HTS and longitude as the predictors with the strongest influence on rainfall. The PCR showed that a component comprised of precipitable water, MRH and longitude accounted for more than 60% in TCR variability. When an additional component comprised of storm duration, storm centre proximity to land and translation speed was considered, the PCR model explained 70% (52%) of the variability in mean (maximum) TCR. Key threshold values for high rainfall across Puerto Rico are a storm centre distance of 233 km or less and moisture exceeding 44.5 mm of precipitable water and 44.5% of relative humidity with forward speeds of 6.4 m s−1 or less. Extreme rainfall at a single location can occur when a TC's centre is over 450 km away.

Abstract: Fire is a primary driver of boreal forest dynamics. Intensifying fire regimes due to climate change may cause a shift in boreal forest composition toward reduced dominance of conifers and greater abundance of deciduous hardwoods, with potential biogeochemical and biophysical feedbacks to regional and global climate. This shift has already been observed in some North American boreal forests and has been attributed to changes in site conditions. However, it is unknown if the mechanisms controlling fire-induced changes in deciduous hardwood cover are similar among different boreal forests, which differ in the ecological traits of the dominant tree species. To better understand the consequences of intensifying fire regimes in boreal forests, we studied postfire regeneration in five burns in the Central Siberian dark taiga, a vast but poorly studied boreal region. We combined field measurements, dendrochronological analysis, and seed-source maps derived from high-resolution satellite images to quantify the importance of site conditions (e.g., organic layer depth) vs. seed availability in shaping postfire regeneration. We show that dispersal limitation of evergreen conifers was the main factor determining postfire regeneration composition and density. Site conditions had significant but weaker effects. We used information on postfire regeneration to develop a classification scheme for successional pathways, representing the dominance of deciduous hardwoods vs. evergreen conifers at different successional stages. We estimated the spatial distribution of different successional pathways under alternative fire regime scenarios. Under intensified fire regimes, dispersal limitation of evergreen conifers is predicted to become more severe, primarily due to reduced abundance of surviving seed sources within burned areas. Increased dispersal limitation of evergreen conifers, in turn, is predicted to increase the prevalence of successional pathways dominated by deciduous hardwoods. The likely fire-induced shift toward greater deciduous hardwood cover may affect climate-vegetation feedbacks via surface albedo, Bowen ratio, and carbon cycling.