Climate2015, 3(1), 193-209; doi:10.3390/cli3010193 (registering DOI) - published 4 March 2015 Show/Hide Abstract
Abstract: Using 1 km satellite remote sensing observations, this paper examines the clouds, aerosols, water vapor and surface skin temperature over Beijing to understand the possible urban system contributions to the extreme rainfall event on 21 July 2012 (i.e., 721 event). Remote sensing measurements, with the advantage of high spatial resolution and coverage, reveal three key urban-related mechanisms: (a) the urban heat island effect (UHI) resulted in strong surface convection and high level cloud cover over Beijing; (b) urban aerosol amount peaked before the rainfall, which “seeded” the clouds and invigorated precipitation; and (c) urban tall buildings provided additional lift for the air mass and provided heat at the underlying boundary to keep the rainfall system alive for a long duration precipitation (>10 hours). With the existing rainfall system moving from the northwest and abundant water vapor was transported from the southeast into Beijing, the urban canyon-lifting, aerosol, and UHI effects all enhanced this extreme rainfall event. This work proves that urban system is responsible, at least partly, for urban rainfall extremes and thus should be considered for urban extreme rainfall prediction in the future.
Climate2015, 3(1), 168-192; doi:10.3390/cli3010168 - published 17 February 2015 Show/Hide Abstract
Abstract: This study analyzed the trends of extreme daily rainfall indices over the Indochina Peninsula from 1960 to 2007. The trends were obtained from high-resolution gridded daily rainfall data compiled by APHRODITE with coordinates of 4°N–25°N and 90E°–112°E. The indices were selected from the list of climate change indices recommended by ETCCDI, which is a joint group of WMO CCl, CLIVAR and JCOMM. The indices are based on the number of heavy rainfall days (≥10 mm), number of very heavy rainfall days (≥20 mm), number of extremely heavy rainfall days (≥25 mm), consecutive dry days (<1 mm), consecutive wet days (≥1 mm), daily maximum rainfall, five-day maximum rainfall, annual wet-day rainfall total, Simple Daily Intensity Index, very wet days, and extremely wet days. The indices were simulated by calculating different extreme characteristics according to wet and dry conditions, frequency, and intensity. Linear trends were calculated by using a least squares fit and significant or non-significant trends were identified using the Mann–Kendall test. The results of this study revealed contrasting trends in extreme rainfall in eastern and western Indochina Peninsula. The changes in extreme rainfall events in the east primarily indicate positive trends in the number of heavy rainfall days, very heavy rainfall days, extremely heavy rainfall days, consecutive wet days and annual wet-day rainfall total, with significant trends at times. These events correlated with the northeastern monsoon that influences the Indochina Peninsula from October to February annually. The results in the west primarily indicate negative trends in consecutive wet days, where significant trends were correlated with decreasing number of annual wet-day rainfall total, heavy rainfall days, very heavy rainfall days, and extremely heavy rainfall days. Daily maximum rainfall, five-day maximum rainfall, very wet days, and extremely wet days show random positive (negative) significant (non-significant) trends, while the simple daily intensity index shows positive trends that dominate the southern part of the Indochina Peninsula, with some grids show significant trends.
Climate2015, 3(1), 150-167; doi:10.3390/cli3010150 - published 20 January 2015 Show/Hide Abstract
Abstract: Changes in annual rainfall in five sub-regions of the Argentine Pampa Region (Rolling, Central, Mesopotamian, Flooding and Southern) were examined for the period 1941 to 2010 using data from representative locations in each sub-region. Dubious series were adjusted by means of a homogeneity test and changes in mean value were evaluated using a hydrometeorological time series segmentation method. In addition, an association was sought between shifts in mean annual rainfall and changes in large-scale atmospheric pressure systems, as measured by the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI). The results indicate that the Western Pampas (Central and Southern) are more vulnerable to abrupt changes in average annual rainfall than the Eastern Pampas (Mesopotamian, Rolling and Flooding). Their vulnerability is further increased by their having the lowest average rainfall. The AMO showed significant negative correlations with all sub-regions, while the PDO and SOI showed significant positive and negative correlations respectively with the Central, Flooding and Southern Pampa. The fact that the PDO and AMO are going through the phases of their cycles that tend to reduce rainfall in much of the Pampas helps explain the lower rainfall recorded in the Western Pampas sub-regions in recent years. This has had a significant impact on agriculture and the environment.
Climate2015, 3(1), 135-149; doi:10.3390/cli3010135 - published 13 January 2015 Show/Hide Abstract
Abstract: Predicting the future climate and its impacts on the global environment is model based, presenting a level of uncertainty. Alternative robust approaches of analyzing high volume climate data to reveal underlying regional and local trends are increasingly incorporating satellite data. This study uses a centered log-ratio (clr) transformation approach and robust principal component analysis (PCA), on a long-term Normalized Difference Vegetation Index (NDVI) dataset to test its applicability in analyzing large multi-temporal data, and potential to recognize important trends and patterns in regional climate. Twenty five years of NDVI data derived by Global Inventory Modeling and Mapping Studies (GIMMS) from 1982 to 2006 were extracted for 88 subwatersheds in central Kenya and statistically analyzed. Untransformed (raw) and clr transformed NDVI data were evaluated using robust PCA. The robust PCA compositional biplots of the clr transformed long-term NDVI data demonstrated the finest spatial-temporal display of observations identifying climate related events that impacted vegetation activity and observed variations in greenness. The responses were interpreted as normal conditions, El Niño Southern Oscillation (ENSO) events of El Niño and La Niña, and drought events known to influence the moisture level and precipitation patterns (high, low, normal) and therefore the level of vegetation greenness (NDVI value). More drought events (4) were observed between 1990 and 2006, a finding corroborated by several authors and linked to increasing climate variability. Results are remarkable, emphasizing the need for appropriate data transformation prior to PCA, dealing with huge complex datasets, to enhance pattern recognition and meaningful interpretation of results. Through improved analysis of past data, uncertainty is decreased in modeling future trends.
Climate2015, 3(1), 118-132; doi:10.3390/cli3010118 - published 7 January 2015 Show/Hide Abstract
Abstract: Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM) problematic. We present a method to construct pseudo-observational precipitation data bymerging a large scale constrained RCMreanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models.