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This study investigates the land-atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)-Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land-atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land-Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land-atmosphere coupling, with soil moisture-temperature coupling being stronger than soil moisture-precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1-15-day lead, the added forecast skill reflects predictability (or land-atmosphere coupling strength) indicating that that model can capture the realistic land-atmosphere coupling at a short time scale. However, at a 16-30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land-atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1-15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16-30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies. [ABSTRACT FROM AUTHOR]

This study examines the land-atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)-Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)-Community Land Model, version 3 (CLM3) and CAM4-CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land-atmosphere coupling; 2) soil moisture-precipitation coupling is weaker than soil moisture-surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4-CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4-CLM4 is weaker than in CAM3-CLM3, which is further supported by Global Land-Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM-CLM models do not seem to overestimate the land-atmosphere coupling strength. [ABSTRACT FROM AUTHOR]

This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST-precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture-precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture-precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation. [ABSTRACT FROM AUTHOR]

Admixture mapping (AM) is a promising method for the identification of genetic risk factors for complex traits and diseases showing prevalence differences among populations. Efficient application of this method requires the use of a genomewide panel of ancestry-informative markers (AIMs) to infer the population of origin of chromosomal regions in admixed individuals. Genomewide AM panels with markers showing high frequency differences between West African and European populations are already available for disease-gene discovery in African Americans. However, no such a map is yet available for Hispanic/Latino populations, which are the result of two-way admixture between Native American and European populations or of three-way admixture of Native American, European, andWest African populations. Here, we report a genomewide AM panel with 2,120 AIMs showing high frequency differences between Native American and European populations. The average intermarker genetic distance is ∼1.7 cM. The panel was identified by genotyping, with the Affymetrix GeneChip Human Mapping 500K array, a population samplewith European ancestry, a Mesoamerican sample comprising Maya and Nahua from Mexico, and a South American sample comprising Aymara/Quechua from Bolivia and Quechua from Peru. The main criteria for marker selection were both high information content for Native American/European ancestry (measured as the standardized variance of the allele frequencies, also known as ''f value") and small frequency differences between the Mesoamerican and South American samples. This genomewide AM panel will make it possible to apply AM approaches in many admixed populations throughout the Americas. [ABSTRACT FROM AUTHOR]

Recent studies have used dense markers to examine the human genome in ancestrally homogeneous populations for hallmarks of selection. No genomewide studies have focused on recently admixed groups--populations that have experienced admixing among continentally divided ancestral populations within the past 200-500 years. New World admixed populations are unique in that they represent the sudden confluence of geographically diverged genomes with novel environmental challenges. Here, we present a novel approach for studying selection by examining the genomewide distribution of ancestry in the genetically admixed Puerto Ricans. We find strong statistical evidence of recent selection in three chromosomal regions, including the human leukocyte antigen region on chromosome 6p, chromosome 8q, and chromosome 11q. Two of these regions harbor genes for olfactory receptors. Interestingly, all three regions exhibit deficiencies in the European-ancestry proportion.

Latinos are the largest minority population in the United States. Although usually classified as a single ethnic group by researchers, Latinos are heterogeneous from cultural, socioeconomic, and genetic perspectives. From a cultural and social perspective, Latinos represent a wide variety of national origins and ethnic and cultural groups, with a full spectrum of social class. From a genetic perspective, Latinos are descended from indigenous American, European, and African populations. We review the historical events that led to the formation of contemporary Latino populations and use results from recent genetic and clinical studies to illustrate the unique opportunity Latino groups offer for studying the interaction between racial, genetic, and environmental contributions to disease occurrence and drug response.