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Interest in climatic change has never been greater and it is inevitable that the search for periodicities in the climatic record should be a popular means of attempting to unravel at least some of the complexities of the atmospheric system. This paper reviews aspects of the methodologies of spectral analysis and takes the example of the Barcelona precipitation series (1850–1991) to illustrate how both spectral analysis and other statistical analyses can provide information on climatic evolution as expressed through the medium of this one variable. Copyright © 1999 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

One major challenge in climate-related issues is the lack of accurate high-resolution precipitation data on the part of observations and climate model simulations, imposing profound constraints on various impact studies, for instance in the fields of hydrology, glaciology, agronomy and hazard assessment. Here, we present a physical model approach for orographic precipitation based on linear theory and combined with a stochastic weather generator which takes an intermediate position between computationally expensive high-resolution regional climate models and purely statistical downscaling techniques. The model is applied to a mountainous region in central Germany and run over a decade in a 0.005° resolution (≈500 m). The resulting precipitation fields are spatially and temporally compared with station data from a very dense meteorological network and with a 6 km regional reanalysis. The spatial correlation between local station data and corresponding model grid boxes is between 0.72 and 0.80. The presented linear model also agrees well with the observed spatial pattern of day-to-day variability, mean intensity, weak and extreme precipitation events. It is close to the performance of the high-resolution regional reanalysis COSMO-REA6, yet computationally far more efficient. Combined with the weather generator, the linear model matches the statistical properties of the station data almost perfectly and offers a consistent precipitation dataset at 0.005° resolution. The model can be applied to any larger-scale model or observational dataset, including climate change projections. [ABSTRACT FROM AUTHOR]

In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang-McFarlane (ZM) deterministic deep convective scheme. Its impacts on deep convection, shallow convection, large-scale precipitation and associated dynamic and thermodynamic fields are investigated. Results show that with the introduction of the PC stochastic parameterization, deep convection is decreased while shallow convection is enhanced. The decrease in deep convection is mainly caused by the stochastic process and the spatial averaging of input quantities for the PC scheme. More detrained liquid water associated with more shallow convection leads to significant increase in liquid water and ice water paths, which increases large-scale precipitation in tropical regions. Specific humidity, relative humidity, zonal wind in the tropics, and precipitable water are all improved. The simulation of shortwave cloud forcing (SWCF) is also improved. The PC stochastic parameterization decreases the global mean SWCF from −52.25 W/m2 in the standard CAM5 to −48.86 W/m2, close to −47.16 W/m2 in observations. The improvement in SWCF over the tropics is due to decreased low cloud fraction simulated by the stochastic scheme. Sensitivity tests of tuning parameters are also performed to investigate the sensitivity of simulated climatology to uncertain parameters in the stochastic deep convection scheme. [ABSTRACT FROM AUTHOR]

ABSTRACT Quantifying precipitation variability and trends is difficult and merits regular reassessment. In the present study, the seasonal homogenized precipitation series in Switzerland is investigated for the period 1901-2013 in terms of trends, inter-annual variability and the influence of large-scale flow. An objective clustering is applied, resulting in 32 distinct precipitation regions. Trends are analysed for all 32 regions with moving trend windows, and statistical significance ( p < 0.05) is assessed with Theil-Sen trend estimates and nonparametric Mann-Kendall trend tests. Inter-annual precipitation varies considerably regionally and seasonally. Of 2720 analysed trend windows of 33-113 years in length, 194 (7.1%) show a significantly positive trend and 10 (0.4%) a significantly negative trend. Most of the significantly positive trends are found for 50+ year series for winter, autumn and the year. 81% of the annual and 72% of the winter series show positive trends (0-3% per decade) for the 1901-2013 period. The trends are significant for 34% (22%) of the annual (winter) series. Long-term trends are generally in line with earlier estimates in literature considering somewhat shorter time periods. Significantly negative trends are only found for winter in the most recent decades. No significant trends are found for inter-annual variability and changes in the 10th and 90th percentile seasonal extreme precipitation thresholds. The influence of large-scale flow on precipitation variability is strongest in winter. The leading pattern for northern Switzerland ( nS) is the Euro-Atlantic blocking pattern. For southern Switzerland ( sS), the Eastern Atlantic (Scandinavian) pattern is dominant in winter (autumn). In nS, wet (dry) seasons are linked to south-westerly (north-easterly) flow. In sS, wet (dry) seasons are linked to southerly (northerly) flow. Strong evidence for imprints of atmospheric blocking on seasonal precipitation variability is found in winter and autumn. In spring, a high pressure anomaly stretching from the mid-Atlantic to central Europe is indicative of dryness in nS. [ABSTRACT FROM AUTHOR]

ABSTRACT This article addresses the regional impacts of climate change on precipitation and meteorological drought over Southern South America ( SSA) through a CMIP5 multi-model ensemble based on 15 General Circulation Models ( GCMs) forced under two different greenhouse gas concentration pathways ( RCP4.5 and RCP8.5). An assessment of the biases in the representation of the precipitation annual cycle was performed over the 1979-2008 period over five regions within SSA, based on a comparison between the GCMs precipitation outputs with the Global Precipitation Climatology Centre ( GPCC) dataset. The multi-model ensemble well reproduces the shape of the annual cycle of precipitation over most of SSA, although the monthly totals were overestimated (underestimated) over the North-West and South (North-East and Central-East) regions. Changes in precipitation and meteorological drought characteristics were identified by the difference for early (2011-2040) and late (2071-2100) 21st century values with respect to the 1979-2008 baseline, using the standardized precipitation index as a short- and long-term drought indicator. Future climate conditions are expected to modify the regional characteristics of meteorological droughts over SSA, but the range of uncertainty in the expected changes is high. A significant increase in the number of drought events in all the regions for most of the 21st century sub-periods is projected for the multi-model ensemble. The mean duration of drought events will be shorter, with no significant changes in the severity of droughts and the occurrence of multi-decadal changes in the number of critical dry months is likely, although the significance in the changes depends on the region, future time horizon and greenhouse gas concentration pathways. These results overlap with a projected increase in precipitation over most of the regions, which has a strong seasonality and, therefore, will have some implications upon the future meteorological drought developments and the agricultural and hydrological practices in SSA. [ABSTRACT FROM AUTHOR]

ABSTRACT An international programme dedicated to the study of the Third Pole Environment ( TPE) is now developing. The TPE region is centred on the Tibetan Plateau and concerns the interests of the surrounding countries and regions. To improve input for hydrological research, we collected precipitation data on 241 meteorological stations across the TPE region; these data were obtained from various countries, thus including various types of gauges. Employing the procedure recommended by the World Meteorological Organization ( WMO), a full version of bias adjustment was applied to the data, including adjustments for wind-induced error, wetting loss, evaporation loss and trace amount for each station. The results reveal that the average annual precipitation has increased considerably from a minimum of 4 mm to a maximum of 409 mm with an overall mean of 27% from the adjustment, the largest bias being found in the Chinese standard precipitation gauge ( CSPG) which was used in the central TPE region. In addition, the bias shows variable spatial and temporal patterns in different climate zones throughout this area. It is expected that this study and its results will be beneficial for hydrological and climatic studies over the TPE region. [ABSTRACT FROM AUTHOR]

ABSTRACT Changes observed in nine meteorological variables obtained from the Three Rivers Source Region ( TRSR) between 1960 and 2009 were investigated using a fitted linear model, Mann-Kendall test, moving t-test, and Morlet wavelet. Analysis of the regionally scaled annual series from 1960 to 2009 showed that minimum ( Tmin), maximum ( Tmax), mean ( Tmean) air temperature, precipitation ( P), potential evaporation ( Ep), and sunshine hours ( SH) increased while relative humidity ( RH) and wind speed ( W) decreased. Trends were significant at a 99% confidence level for air temperature and at a 95% confidence level for Ep and W. With the exception of SH, regional scale changes observed in all variables in the past decade (2000-2009) when compared to climate norms (means of all climatic variables) from 1961 to 1990 were consistent with their corresponding linear trends from 1960 to 2009. Tmax, Tmean, and drought index ( DI) exhibited one climate jump, Tmin and RH two, and SH, Ep, and W three at a significance level of α = 0.05. On a regional scale, the period from 1986 to 1997 experienced a warmer, drier climate due to higher than average air temperatures, lower P, and higher DI compared to means from 1960 to 2009. The majority of meteorological variables of the TRSR experienced significant ( α = 0.05) short periodical cycling between 2 and 5 years. In terms of spatial distribution, seven out of 12 meteorological stations underwent warmer and wetter periods from 1960 to 2009, whereas the other five situated in the southeastern section of the TRSR underwent warmer, drier periods. [ABSTRACT FROM AUTHOR]

Spatial‐resolution errors are inherent in gridded precipitation (P) fields—such as those produced by climate models and from satellite observations—and they can be sizeable when P is averaged spatially onto a coarse grid. They can also vary dramatically over space and time. In this paper, we illustrate the importance of evaluating resolution errors associated with gridded P fields by investigating the relationships between grid resolution and resolution error for monthly P within the Amazon Basin.Spatial‐resolution errors within gridded‐monthly and average‐monthly P fields over the Amazon Basin are evaluated for grid resolutions ranging from 0.1° to 5.0°. A resolution error occurs when P is estimated for a location of interest within a grid‐cell from the unbiased, grid‐cell average P. Graphs of January, July and annual resolution errors versus resolution show that, at the higher resolutions (<3° ), aggregation quickly increases resolution error. Resolution error then begins to level off as the grid becomes coarser. Within the Amazon Basin, the largest resolution errors occur during January (summer), but the largest percentage errors appear in July (winter). In January of 1980, e.g., resolution errors of 29, 52 and 65 mm—or 11, 19 and 24% of the grid‐cell means—were estimated at resolutions of 1.0°, 3.0° and 5.0°. In July of 1980, however, the percentage errors at these three resolutions were considerably larger, that is, 15%, 27% and 33% of the grid‐cell means. Copyright © 2005 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The Northern Hemisphere annular mode, also known as the Arctic Oscillation/North Atlantic Oscillation (AO/NAO) is a dominant atmospheric mode in the Northern Hemisphere winter that influences climate fluctuations from the eastern seaboard of the United States to Siberia and from the Arctic to the subtropical Atlantic. After almost a century of scientific investigation, the fundamental mechanisms determining the evolution of the AO/NAO are not yet completely understood. The ocean is favored as the most likely forcing of atmospheric variability, given the time scales of oceanic circulation and its large heat capacity. Our analyses of snow cover, soil temperatures, zonal winds, and geopotential heights identify the effect of land–atmosphere interaction over Eurasia on Northern Hemisphere atmospheric circulation, explaining the predictive signal that links fluctuations of April–October snow cover with the following winter AO/NAO phases. [ABSTRACT FROM AUTHOR]

The article presents a study which focuses on the effect of the unusual dry summer in 2002 in the three watersheds in north-eastern U.S. including Archer Creek in New York, Sleepers River in Vermont and Cone Pond in New Hampshire. It notes that the climatic change caused fluctuation in the precipitation level of the watersheds that affected its biogeochemical processes particularly its sulfur dynamics. Comparisons of the three watersheds in terms of chemical and isotopic responses to the weather conditions are also given.

The article presents a discussion on the use of a Support Vector Machine (SVM) in downscaling precipitation to river basin in India. It describes a methodology which uses predictor variables from the National Center for Environmental Protection (NCEP) and from the third Canadian general circulation model (CGCM3) for special report of emission scenarios (SRES). The data includes thermodynamic parameters related with precipitation. NCEP and CGCM3 data were used to study seasonal stratification. Using the model the study found that precipitation is expected to increase in several emission scenarios. An overview of the study is provided.

The article analyzes the relationships between sea surge and frequency of each weather regime at inter-annual to multi-decadal time scales. More than 75 percent of sea surges occur during both of the weather regimes associated with a negative North Atlantic Oscillation phase. The relationships between monthly/seasonal frequency of weather regime and 75th percentile of sea surge at Grau-de-la-Dent tend to strengthen during the 20th century. The correlation between seasonal frequencies of GA and 75th percentile of sea surge increases from 0.07 in 1905-1940 to 0.83 in 1974-2002.

The article discusses about the predictability of rainfall in Sri Lanka based on the El Niño-Southern Oscillation movements (ENSO). According to the article, the rainfall is predictable based on ENSO during the period of January-March, July-August and October-December. It was said that the El Niño brings wetter conditions on the months of October to December and drier conditions during the months of January-March. July to August are considered average. However, predictability in the mountainous region is decreased when orographic rainfall happens.