How do the multiple large-scale climate oscillations trigger extreme precipitation?
TL;DR: In this article, a probabilistic analysis approach by means of a state-of-the-art Copula-based joint probability distribution is developed to characterize the aggregated behaviors for large-scale climate patterns and their connections to precipitation.
About: This article is published in Global and Planetary Change.The article was published on 2017-10-01. It has received 31 citations till now. The article focuses on the topics: Climate oscillation & Climate pattern.
Citations
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TL;DR: In this paper, the uncertainty and limitations of greenhouse gas measurement from Chinese freshwater bodies based on available data are reviewed and the challenges in estimating and predicting greenhouse gas emissions from hydropower reservoirs along with suggested mitigation measures.
75 citations
TL;DR: In this paper, a modified version of the HBV-D model, with stronger physics basis in snow/glacier module and higher spatial resolution, is used to simulate daily streamflow processes and is capable of reproducing the variations of glacier area and glacier volume during the historical period.
29 citations
TL;DR: Wang et al. as discussed by the authors proposed a new hybrid model (HBNN) integrating HBV hydrological model, Bayesian neural network (BNN) and uncertainty analysis to improve precision.
Abstract: Statistical methods have been widely used to build different streamflow prediction models; however, lacking of physical mechanism prevents precise streamflow prediction in alpine regions dominated by rainfall, snow and glacier. To improve precision, a new hybrid model (HBNN) integrating HBV hydrological model, Bayesian neural network (BNN) and uncertainty analysis is proposed. In this approach, the HBV is mainly used to generate initial snow-melt and glacier-melt runoffs that are regarded as new inputs of BNN for precision improvement. To examine model reliability, a hybrid deterministic model called HLSSVM incorporating the HBV model and least-square support vector machine is also developed and compared with HBNN in a typical region, the Yarkant River basin in Central Asia. The findings suggest that the HBNN model is a robust streamflow prediction model for alpine regions and capable of combining strengths of both the BNN statistical model and the HBV hydrological model, providing not only more precise streamflow prediction but also more reasonable uncertainty intervals than competitors particularly at high flows. It can be used in predicting streamflow for similar regions worldwide.
29 citations
Cites background from "How do the multiple large-scale cli..."
...In recent years, global warming has accelerated glacier ablation in the last decade (Bolch 2007) and caused extensive retreat of glaciers at high-elevation mountainous regions (Shi et al. 2017; Wang et al. 2011a)....
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TL;DR: In this paper, the spatiotemporal variability of temperature and precipitation on monthly, seasonal, and annual scales were analyzed on nine meteorological stations in the Upper Indus Basin (UIB), and the wavelet analysis illustrated sporadic interannual covariance of seasonal Tmax, Tmin and precipitation with ENSO, NAO, IOD and PDO.
Abstract: Having an extreme topography and heterogeneous climate, the Upper Indus Basin (UIB) is more likely to be affected by climate change and it is a crucial area for climatological studies. Based on the monthly minimum temperature (Tmin), maximum temperature (Tmax) and precipitation from nine meteorological stations, the spatiotemporal variability of temperature and precipitation were analyzed on monthly, seasonal, and annual scales. Results show a widespread significant increasing trend of 0.14 °C/decade for Tmax, but a significant decreasing trend of −0.08 °C/decade for Tmin annually, during 1955–2016 for the UIB. Seasonally, warming in Tmax is stronger in winter and spring, while the cooling in Tmin is greater in summer and autumn. Results of seasonal Tmax indicate increasing trends in winter, spring and autumn at rates of 0.38, 0.35 and 0.05 °C/decade, respectively, while decreasing in summer with −0.14 °C/decade. Moreover, seasonal Tmin results indicate increasing trends in winter and spring at rates of 0.09 and 0.08 °C/decade, respectively, while decreasing significantly in summer and autumn at rates of −0.21 and −0.22 °C/decade respectively for the whole the UIB. Precipitation exhibits an increasing trend of 2.74 mm/decade annually, while, increasing in winter, summer and autumn at rates of 1.18, 2.06 and 0.62 mm/decade respectively. The warming in Tmax and an increase in precipitation have been more distinct since the mid-1990s, while the cooling in Tmin is observed in the UIB since the mid-1980s. Warming in the middle and higher altitude (1500–2800 m and >2800 m) are much stronger, and the increase is more obvious in regions with elevation >2800 m. The wavelet analysis illustrated sporadic inter-annual covariance of seasonal Tmax, Tmin and precipitation with ENSO, NAO, IOD and PDO in the UIB. The periodicities were usually constant over short timescales and discontinuous over longer timescales. This study offers a better understanding of the local climate characteristics and provides a scientific basis for government policymakers.
26 citations
01 Jun 2018
TL;DR: In this article, a probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia.
Abstract: Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.
24 citations
References
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TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Abstract: The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.
47,133 citations
"How do the multiple large-scale cli..." refers background in this paper
...Small values of the two measures suggest good fit (Akaike, 1974)....
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Book•
01 Jan 1999
TL;DR: This book discusses the fundamental properties of copulas and some of their primary applications, which include the study of dependence and measures of association, and the construction of families of bivariate distributions.
Abstract: The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. This book is suitable as a text or for self-study.
8,626 citations
01 Jan 1993
TL;DR: The definition of drought has continually been a stumbling block for drought monitoring and analysis as mentioned in this paper, mainly related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.
Abstract: 1.0 INTRODUCTION Five practical issues become important in any analysis of drought. These include: 1) time scale, 2) probability, 3) precipitation deficit, 4) application of the definition to precipitation and to the five water supply variables, and 5) the relationship of the definition to the impacts of drought. Frequency, duration and intensity of drought all become functions that depend on the implicitly or explicitly established time scales. Our experience in providing drought information to a collection of decision makers in Colorado is that they have a need for current conditions expressed in terms of probability, water deficit, and water supply as a percent of average using recent climatic history (the last 30 to 100 years) as the basis for comparison. No single drought definition or analysis method has emerged that addresses all these issues well. Of the variety of definitions and drought monitoring methods used in the past, by far the most widely used in the United States is the Palmer Drought Index (Palmer, 1965), but its weaknesses (Alley, 1984) frequently limit its wise application. For example, time scale is not defined for the Palmer Index but does inherently exist. The definition of drought has continually been a stumbling block for drought monitoring and analysis. Wilhite and Glantz (1985) completed a thorough review of dozens of drought definitions and identified six overall categories: meteorological, climatological, atmospheric, agricultural, hydrologic and water management. Dracup et al. (1980) also reviewed definitions. All points of view seem to agree that drought is a condition of insufficient moisture caused by a deficit in precipitation over some time period. Difficulties are primarily related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.
6,514 citations
"How do the multiple large-scale cli..." refers background or methods in this paper
...InfInt can be interpreted by the moisture classification shown in Table 1 (Kim et al., 2006; Mckee et al., 1993; National Climate Center, China)....
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...Detailed information on algorithm of SPI calculation can be found in McKee et al. (1993)....
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...Among these indices, SPI has zero mean and unit standard deviation and provides a measure of the precipitation frequency distribution (McKee et al., 1993; Edwards and Mckee, 1997)....
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...The SPI and corresponding wetness categories (McKee et al., 1993; Kim et al., 2006; National Climate Center, China) are shown in Table 1....
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...The SPI quantifies observed precipitation as a standardized departure from a selected probability distribution function that models the raw precipitation data (McKee et al., 1993)....
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TL;DR: The Atlantic Multidecadal Oscillation (AMO) as mentioned in this paper is a 65-80 year cycle with a 0.4 C range, referred to as the AMO by Kerr (2000).
Abstract: North Atlantic sea surface temperatures for 1856-1999 contain a 65-80 year cycle with a 0.4 C range, referred to as the Atlantic Multidecadal Oscillation (AMO) by Kerr (2000). AMO warm phases occurred during 1860- 1880 and 1940-1960, and cool phases during 1905-1925 and 1970-1990. The signal is global in scope, with a posi- tively correlated co-oscillation in parts of the North Pa- cic, but it is most intense in the North Atlantic and cov- ers the entire basin there. During AMO warmings most of the United States sees less than normal rainfall, including Midwest droughts in the 1930s and 1950s. Between AMO warm and cool phases, Mississippi River outflow varies by 10% while the inflow to Lake Okeechobee, Florida varies by 40%. The geographical pattern of variability is influenced mainly by changes in summer rainfall. The winter patterns of interannual rainfall variability associated with El Ni~no- Southern Oscillation are also signicantly changed between AMO phases.
2,582 citations
"How do the multiple large-scale cli..." refers background in this paper
...It is defined as the detrended, regionally averaged, summer sea surface temperatures (SSTs) anomalies over the North Atlantic Ocean (Enfield et al., 2001)....
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TL;DR: In this paper, a review of the evidence so far, it is argued that certain events or an increase in their frequency can be linked with confidence to the human influence on climate.
Abstract: It has been widely debated whether recent extreme weather events are related to global warming. Now, from a review of the evidence so far, it is argued that certain events or an increase in their frequency can be linked with confidence to the human influence on climate.
1,772 citations
"How do the multiple large-scale cli..." refers background in this paper
...La Niña usually has an opposite effect compared to El Niño....
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...For example, the record-breaking high global temperature and devastating floods worldwide in 1998 could be partly attributed to El Niño (Lean and Rind, 2008; Foster and Rahmstorf, 2011), and the 2010 Pakistan flood was linked to a strong La Niña (Coumou and Rahmstorf, 2012)....
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...We find that high phases of ENSO (La Niña event) pose wet effect during the joint phases (i.e. LAHS, HAHSLP and LAHSLP)....
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...Goodrich (2007) investigated the influence of PDO on winter precipitation and droughts during years of neutral ENSO in the western United States, and found that the resulting winter precipitation patterns were spatially similar to those occur during years of La Niña-cold PDO and, to a lesser extent, years of El Niño-warm PDO....
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...El Niño is the warm phase of ENSO, and it is often followed by a cold phase called La Niña....
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