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Precipitation over the contiguous United States exhibits large multi-decadal oscillations since the early twentieth century, and they often lead to dry (e.g., 1946-1976 and 1999-present) and wet (e.g., 1977-1998) periods and apparent precipitation trends (e.g., from the 1950s to 1990s) over most of the western and central US. The exact cause of these inter-decadal variations is not fully understood. Using observational and reanalysis data and model simulations, this paper examines the influence of the Inter-decadal Pacific Oscillation (IPO) on US precipitation. The IPO is a leading mode of sea surface temperatures (SSTs) seen mostly in the Pacific Ocean. It is found that decadal precipitation variations over much of the West and Central US, especially the Southwest, closely follow the evolution of the IPO ( r = 0.85 during 1923-2010 for the Southwest US), and the dry and wet periods are associated, respectively, with the cold and warm phases of the IPO. In particular, the apparent upward trend from the 1950s-1990s and the dry decade thereafter in precipitation over much of the West and Central US are largely caused by the IPO cycles, which switched to a warm phase around 1977 and back to a cold phase around 1999. An atmospheric model forced with observed SSTs reproduces much of this association of US precipitation with the IPO ( r = 0.95 between smoothed observed and simulated Southwest US precipitation during 1950-2009 and r = 0.88 between the simulated Southwest US precipitation and the IPO). Atmospheric reanalysis and model data both show a strong high (low) pressure center and anti-cyclonic (cyclonic) anomaly circulation over the North Pacific in the lower troposphere during cold (warm) phases of the IPO, which lead to dry and cold northwesterly and northerly winds and below-normal precipitation over much of the West US during IPO cold periods. The IPO induced changes are most pronounced during the boreal cold season. The results reinforce the notion that tropical Pacific SSTs (and the accompanying SST anomalies in the North Pacific) have large impacts on US precipitation and highlight the need to understand and simulate the IPO for decadal prediction of US precipitation. [ABSTRACT FROM AUTHOR]

An ensemble of future climate projections performed with the regional climate model RegCM4 is used to assess changes in inter-annual variability of precipitation over Southern Mexico and Central America (SMECAM). Two different Global Climate Models (GCMs) from the coupled model intercomparison project phase 5 are used to provide boundary conditions for two different RegCM4 configurations. This results in four regional climate projections extending from 1970 to 2100 for the greenhouse gas representative concentration pathway RCP8.5. The precipitation variability over the SMECAM region and its dependence on the gradient between Atlantic and Pacific sea surface temperature (SST) anomalies are verified by reproducing SST anomaly patterns during wettest and driest years similar to those seen in observational datasets. RegCM4 does a comparably better job than the driving GCMs. This strong relationship between precipitation and SST anomalies does not appear to change substantially under future climate conditions. For the rainy season, June to September, we find a future change in inter-annual variability of precipitation towards a much greater occurrence of very dry seasons over the SMECAM region, with this change being more pronounced in the regional than in the global model projections. A greater warming of the Tropical Northeastern Pacific (TNP) compared to the Tropical North Atlantic (TNA), which causes stronger wind fluxes from the TNA to the TNP through the Caribbean Low Level Jet, is identified as the main process responsible for these drier conditions. [ABSTRACT FROM AUTHOR]

This study evaluates the UCLA-ETA regional model's dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement. [ABSTRACT FROM AUTHOR]

This study examines the potential impact of vegetation feedback on changes in summer climate aridity over the contiguous United States (US) due to the doubling of atmospheric CO concentration using a set of 100-year-long climate simulations made by a global climate model interactively coupled with a dynamic vegetation model. The Thornthwaite moisture index ( I), which quantifies climate aridity on the basis of atmospheric water supply (i.e., precipitation) and atmospheric water demand (i.e., potential evapotranspiration, PET), is used to measure climate aridity. Warmer atmosphere and drier surface resulting from increased CO concentration increase climate aridity over most of the contiguous US. This phenomenon is due to larger increments in PET than in precipitation, regardless of the presence or absence of vegetation feedback. Compared to simulations without active dynamic vegetation feedback, the presence of vegetation feedback significantly alleviates the increase in aridity. This vegetation-feedback effect is most noticeable in the subhumid regions such as southern, midwestern and northwestern US, primarily by the increasing vegetation greenness. In these regions, the greening in response to warmer temperatures enhances moisture transfer from soil to atmosphere by evapotranspiration (ET). The increased ET and subsequent moistening over land areas result in weaker surface warming (1-2 K) and PET (3-10 mm month), and greater precipitation (4-10 mm month). Collectively, they result in moderate increases in I. Our results suggest that moistening by enhanced vegetation feedback may prevent aridification under climatic warming especially in areas vulnerable to climate change, with consequent implications for mitigation strategies. [ABSTRACT FROM AUTHOR]

We investigate the ability of a global atmospheric general circulation model (AGCM) to reproduce observed 20 year return values of the annual maximum daily precipitation totals over the continental United States as a function of horizontal resolution. We find that at the high resolutions enabled by contemporary supercomputers, the AGCM can produce values of comparable magnitude to high quality observations. However, at the resolutions typical of the coupled general circulation models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, the precipitation return values are severely underestimated. [ABSTRACT FROM AUTHOR]