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Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [ABSTRACT FROM AUTHOR]

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of "tail dependence" arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data. [ABSTRACT FROM AUTHOR]

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]

Drivers of alpine plant community composition have been observed to vary with scale. Diversity of alpine tundra across four regions of the Rocky Mountains and among plots within one region was examined relative to temperature and precipitation variables. For regional scale analyses, averages of three metrics of plot-level species diversity relative to environmental variables and regional gamma diversity were examined for a subset of 60 plots from a stratified random sample in each region. For local scale analyses, additional soil and climate variables were included at 96 plots from one of the four regions. Correlations and visual examination of bivariate plots elucidated possible controls of cold temperatures and gamma diversity on average diversity metrics among the four regions and of precipitation and/or location on plot-level metrics within the single region. For the latter, the bivariate graph indicated a triangular distribution in which all levels of diversity exist at low precipitation but only low diversity at higher precipitation. We propose that change in drivers with scale is a general result of the relative importance of temperature and water in seed production (temperature > water) and seedling establishment (vice versa), and the logical priority of seed production over seedling establishment. [ABSTRACT FROM AUTHOR]

Freshwater delivery is an important factor determining estuarine character and health and may be influenced by large-scale climate oscillations. Variability in freshwater delivery (precipitation and discharge) to the Altamaha River estuary (GA, USA) was examined in relation to indices for several climate signals: the Bermuda High Index (BHI), the Southern Oscillation Index (SOI), the Improved El Niño Modoki Index (IEMI), the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Pacific/North American Pattern (PNA). Discharge to this estuary has been linked to key ecosystem properties (e.g., salinity regime, water residence time, nutrient inputs, and marsh processes), so understanding how climate patterns affect precipitation and river discharge will help elucidate how the estuarine ecosystem may respond to climate changes. Precipitation patterns in the Altamaha River watershed were described using empirical orthogonal functions (EOFs) of the combined multidecadal time series of precipitation at 14 stations. The first EOF (67 % of the variance) was spatially uniform, the second EOF (11 %) showed a spatial gradient along the long axis of the watershed (NW-SE), and the third EOF (6 %) showed a NE-SW pattern. We compared the principal components (PCs) associated with these EOFs, monthly standardized anomalies of Altamaha River discharge at the gauge closest to the estuary, and the climate indices. Complex, seasonally alternating patterns emerged. The BHI was correlated with June-January discharge and precipitation PC 1. The SOI was correlated with January-April discharge and precipitation PC 2, and also weakly correlated with PC 1 in November-December. The AMO was correlated with river discharge and precipitation PC 3 mainly in December-February and June. The correlation patterns of precipitation PCs with PDO and PNA were similar to those with SOI, but weaker. There were no consistent relationships with two NAO indices or IEMI. Connections between climate signals and estimates of nutrient loading were consistent with the connections to discharge. The occurrence of tropical storms in the region was strongly related to the BHI but not to the other climate indices, possibly representing the influence of storm tracking more than the rate of storm formation. Comparison with the literature suggests that the patterns found may be typical of southeastern USA estuaries but are likely to be different from those outside the region. [ABSTRACT FROM AUTHOR]

Ensemble regional model simulations over the central US with 30-km resolution are analyzed to investigate the physical processes of projected precipitation changes in the mid-twenty-first century under greenhouse gas forcing. An atmospheric moisture balance is constructed, and changes in the diurnal cycle are evaluated. Wetter conditions over the central US in April and May occur most strongly in the afternoon and evening, supported primarily by moisture convergence by transient eddy activity, indicating enhanced daytime convection. In June, increased rainfall over the northern Great Plains is strongest from 0000 to 0600 LT. It is supported by positive changes in stationary meridional moisture convergence related to a strengthening of the GPLLJ accompanied by an intensification of the western extension of the North Atlantic subtropical high. In the Midwest, decreased rainfall is strongest at 1500 LT and 0000 LT. Both a suppression of daytime convection as well as changes in the zonal flow in the GPLLJ exit region are important. Future drying over the northern Great Plains in summer is triggered by weakened daytime convection, and persists throughout August and September when a deficit in soil moisture develops and land-atmosphere feedbacks become increasingly important. [ABSTRACT FROM AUTHOR]

The effects of horizontal resolution and the treatment of convection on simulation of the diurnal cycle of precipitation during boreal summer are analyzed in several innovative weather and climate model integrations. The simulations include: season-long integrations of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) with explicit clouds and convection; year-long integrations of the operational Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts at three resolutions (125, 39 and 16 km); seasonal simulations of the same model at 10 km resolution; and seasonal simulations of the National Center for Atmospheric Research (NCAR) low-resolution climate model with and without an embedded two-dimensional cloud-resolving model in each grid box. NICAM with explicit convection simulates best the phase of the diurnal cycle, as well as many regional features such as rainfall triggered by advancing sea breezes or high topography. However, NICAM greatly overestimates mean rainfall and the magnitude of the diurnal cycle. Introduction of an embedded cloud model within the NCAR model significantly improves global statistics of the seasonal mean and diurnal cycle of rainfall, as well as many regional features. However, errors often remain larger than for the other higher-resolution models. Increasing resolution alone has little impact on the timing of daily rainfall in IFS with parameterized convection, yet the amplitude of the diurnal cycle does improve along with the representation of mean rainfall. Variations during the day in atmospheric prognostic fields appear quite similar among models, suggesting that the distinctive treatments of model physics account for the differences in representing the diurnal cycle of precipitation. [ABSTRACT FROM AUTHOR]

Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region. [ABSTRACT FROM AUTHOR]

The possible impact of El Niño-Southern Oscillation (ENSO) and macrocirculation patterns (CPs) on local precipitation are examined and analyzed here under climate change conditions. First the relationship between the input and output variables under present conditions is established using two models, a fuzzy rule-based model (FRBM) and a multivariate linear regression model (MLRM), then this historical relationship is extended under climate change conditions. The input variables for these models consist of lagged ENSO-data (represented by the Southern Oscillation Index, SOI) and 500 hPa height data clustered into macrocirculation patterns over the western United States, while the output is an estimate of monthly local precipitation at selected Arizona stations. To overcome the lack of SOI data under climate change, several scenarios are constructed by perturbing the historical SOI data in a design of experiments framework. The results of the experimental design show that, in general, the precipitation amount seems to decrease under climate change. While the stations and months have differences, as expected, the perturbed scenarios do not show significant differences. [ABSTRACT FROM AUTHOR]

Wet deposition (WD), throughfall (TF) and stemflow (SF) measurements undertaken in a deciduous forest show 85% of the WD liquid flux is observed as TF and approximately 2% as SF. TF and SF were observed to be enriched in base cations and accordingly had an average pH of 6.1 and 5.9, respectively relative to a WD pH of 5.1. The seasonal variability of TF pH below the deciduous canopies was more pronounced than that of WD though both exhibited a growing season maximum, and there is evidence that the seasonal variability of TF pH below the pines is inverted relative to the deciduous canopies likely due to enhanced dust capture and buffering by calcium carbonate. TF ion concentrations differed significantly between deciduous and pine canopies during the growing season, and there is some evidence that variation in sky view factor of 0.18–0.22 is sufficient to manifest statistically differing TF composition below sugar maples. The total atmospheric flux of inorganic nitrogen to the forest is approximately 14–18 kg-N ha−1 yr−1 with approximately half taken up by the canopy. Associated experiments designed to quantify uncertainties in the nutrient fluxes included laboratory tests of the Aerochem automated wet-dry sampler. These experiments indicate the delay in initiating sample collection is less than half a minute for rainfall rates above 1 cm h−1, but may increase substantially for lower precipitation rates. [ABSTRACT FROM AUTHOR]

Temperature and precipitation records from 1949 to 1998 were examined for 25 stations throughout the State of Alaska. Mean, maxima, and minima temperatures, diurnal temperature range, and total precipitation were analyzed for linear trends using least squares regressions. Annual and seasonal mean temperature increases were found throughout the entire state, and the majority were found to be statistically significant at the 95% level or better. The highest increases were found in winter in the Interior region (2.2 deg. C) for the 50 year period of record. Decreases in annual and seasonal mean diurnal temperature range were also found, of which only about half were statistically significant. A state-wide decrease in annual mean diurnal temperature range was found to be 0.3 deg. C, with substantially higher decreasesin the South/Southeastern region in winter. Increases were found in total precipitation for 3 of the 4 seasons throughout most of Alaska,while summer precipitation showed decreases at many stations. Few ofthe precipitation trends were found to be statistically significant,due to high interannual variability. Barrow, our only station in theArctic region, shows statistically significant decreases in annual and winter total precipitation. These findings are largely in agreement with existing literature, although they do contradict some of the precipitation trends predicted by the CO2-doubling GCM's. [ABSTRACT FROM AUTHOR]