Search

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model. [ABSTRACT FROM AUTHOR]

Some teleconnection patterns characterized through various hydro‐climate indices (HCIs) have been shown to influence regional rainfall regimes over a range of time scales in Brazil. However, our current knowledge of how various HCI–rainfall relationships vary across regions in Brazil is still incomplete. In this study, simple correlation analysis is performed using sliding window sizes and lag times on the order of months to years to substantially reduce the effects of high‐frequency variability inherent in HCI and precipitation time series and reveal any lower‐frequency relationships that may exist. This analysis is applied to monthly rainfall and HCI data during the time‐span of 1961–2015. The strength and significance of each correlation were tested. HCIs considered in this analysis include those characterizing the El Niño–Southern Oscillation (ENSO), those characterizing the Madden–Julian Oscillation (MJO), as well as the Tropical Southern Atlantic index (TSA), the Caribbean index (CAR), and several others. A cluster analysis based on the k‐medoids algorithm was also applied to explore the relationship between local factors and the distribution of HCIs exhibiting maximum correlation at each site. A 60‐month sliding window and a 12‐month lag time were found to optimize the results of the correlation. The MJO was found to have a strong and significant link to annual rainfall throughout much of eastern Brazil, while other HCIs showing strong influence in other regions include ENSO, the CAR, and the TSA. The cluster analysis revealed a more spatially homogeneous response over the Amazon River basin and the southern regions than other regions of Brazil with complex topography, where the orographic effect attenuates the influence of the main atmospheric mechanisms at a local scale. In general, the rainfall–HCI relationship could be exploited for some operational applications (e.g., a regional drought early warning system), where the strongest correlations were observed. [ABSTRACT FROM AUTHOR]

Quantifying spatiotemporal influence of climate index on extreme precipitation will help to better understand the variability of extreme precipitation. The extreme precipitation is usually influenced by different climate indices, and mutual offset is unavoidable to occur, thus the rotated empirical orthogonal function was used to identify the different influences of climate indices on extreme precipitation in space and time. The variation of extreme precipitation in data‐scarce region is also concerned, hence, an improved spatiotemporal regional frequency analysis model was further developed, therein the identified spatiotemporal influences of climate indices on extreme precipitation were quantified using Bayesian hierarchical method. In this study, the in situ seasonal maximum one‐day precipitation amount (Rx1day) was used to represent seasonal precipitation extremes from 1957 to 2010 in the Poyang Lake basin, and spatiotemporal influences of El Niño‐Southern Oscillation (ENSO), North Atlantic oscillation (NAO) and Indian Ocean Dipole (IOD) on seasonal Rx1day were quantified. Results indicated that the seasonal Rx1day was influenced by different climate indices in the Poyang Lake basin, ENSO tends to affect spring and autumn Rx1day, IOD tends to affect summer Rx1day, and NAO tends to affect spring and winter Rx1day. The response of extreme precipitation on climate index is varied in different regions, and this was well distinguished and verified, such as negative ENSO (in the same year) events tends to cause spring Rx1day slight decrease in the southern part of the basin while increase about 15% in the northern part with center around the Poyang lake. [ABSTRACT FROM AUTHOR]

This study presents the first global‐scale comprehensive climatology of marine fog and is based on ICOADS ship present weather observations for the period 1950–2007. In general, the median marine fog occurrence away from the polar oceans is low (0.2%). Substantially greater marine fog occurrences are limited to four regions, not including the polar region. Fog occurrence maxima along the western side of the sub‐polar ocean gyre occur during the warm season and over the shelf, which includes one centred over the Northwest Pacific Kuril Islands (60%) and one over the Northwest Atlantic Grand Banks (45%), while a third lies over the Argentinean shelf break. Fog maxima over seven marginal seas occur over the Okhotsk Sea, Sea of Japan, Yellow Sea, South China Sea, Nova Scotia, North Sea, and Baltic Sea. Five wind‐driven coastal upwelling zone maxima comprise the California‐Oregon, Namibia‐South Africa, Peru, Morocco and Arabian regions. Maximum upwelling fog occurrence during the warm season was found to be inversely proportional to the minimum sea surface temperature (SST). Most fog maximum occurrence locations lie over SST minima in shallow water during the warm season and are capped by a stable lower atmosphere. Positive correlations (up to 0.79) were found between 5‐year moving averages of fog in the Kuril Islands and the North Pacific Oscillation. Five‐year moving averages of fog in the Grand Banks were positively and significantly correlated (up to 0.56), with the North Atlantic Oscillation represented by the sea level pressure difference between Gibraltar and Reykjavik. In contrast, 5‐year moving averages of fog in the Grand Banks and the Atlantic Multidecadal Oscillation index were negatively and significantly correlated (up to −0.75). [ABSTRACT FROM AUTHOR]

ABSTRACT The variability of occurrence rate of heavy precipitation ( HPOR) across China was analysed to examine if the change in heavy precipitation satisfied the stationarity assumption. HPOR obtained by peaks-over-threshold ( POT) sampling can be interpreted as a realisation of a point process, which can be represented as a Poisson process. A number of methods, including kernel density estimation method, Cox regression model, Poisson regression model and Generalized Additive Models for Location, Scale, and Shape ( GAMLSS), were used to evaluate the nonstationarity in HPOR. Nonstationarity was also assessed for HPOR defined by different thresholds which indicate different levels of severity of heavy precipitation. Our results indicated that: (1) HPOR in northwestern China, a typical arid region, is subject to the most uneven temporal distribution and a significantly increasing trend in terms of inter-annual variability. Moreover, this uneven temporal distribution is increasing for greater heavy precipitation events defined by higher percentiles, and the changing pattern is opposite in eastern, central and southwestern China; (2) the seasonal variability of HPOR is significantly related to climate indices during the year, which shows that the HPOR is not independent and exhibits nonstationary with seasonality. However, as the thresholds increase, the impacts of climate indices on HPOR are found to be weakening; (3) the annual number of greater heavy precipitation is subject to over-dispersion, however, under-dispersion can be identified for the annual number of heavy precipitation events defined by lower thresholds. Higher Southern Oscillation Index ( SOI) and Pacific Decadal Oscillation ( PDO) may trigger an increase in the frequency of heavy precipitation in northeastern and eastern China. These findings show the challenges of flood preventions and water resources management in a changing climate. [ABSTRACT FROM AUTHOR]

ABSTRACT Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter-annual wind variability. The objective of this paper is to examine the long-term variability of wind speed in the United Arab Emirates ( UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann-Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Niño Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step-wise multiple linear regression models were developed to select appropriate predictors among the various climate indices. [ABSTRACT FROM AUTHOR]

The article presents a seasonal forecast of regional climate in Korea from preceding large-scale climate indices. A seasonal forecasting model was developed based on a multivariate linear regression with an adaptive choice of predictors utilizing regularly updated climate indices. Available sets of predictors with a lead time of 2 months were applied to Korea on a monthly basis, and cross-validation was applied to acquire forecasting skill at the 1% significance level. Analysis of the monthly anomaly correlation coefficient skill for temperature and precipitation, as well as the coefficient for determination, is discussed.