The relationships between tropical Atlantic sea level variability and major climate indices

Relationships were examined between variability in tropical Atlantic sea level and major climate indices with the use of TOPEX/POSEIDON altimeter and island tide gauge data with the aim of learning more about the external influences on the variability of the tropical Atlantic ocean. Possible important connections were found between indices related to the El Niño–Southern Oscillation (ENSO) and the sea levels in all three tropical regions (north, equatorial, and south), although the existence of only one major ENSO event within the decade of available altimetry means that a more complete investigation of the ENSO-dependence of Atlantic sea level changes has to await for the compilation of longer data sets. An additional link was found with the Indian Ocean Dipole (IOD) in the equatorial region, this perhaps surprising observation is probably an artifact of the similarity between IOD and ENSO time series in the 1990s. No evidence was obtained for significant correlations between tropical Atlantic sea level and North Atlantic Oscillation or Antarctic Oscillation Index. The most intriguing relationship observed was between the Quasi-Biennial Oscillation and sea level in a band centered approximately on 10°S. A plausible explanation for the relationship is lacking, but possibilities for further research are suggested.     

Assessment of regional drought risk under climate change using bivariate frequency analysis

Since water supply failure is one of the primary impacts of drought, drought risk should be quantified in the context of a lack of available water. To assess the drought risk, water supply system performance indices such as reliability, resiliency, and vulnerability are usually introduced as they correspond to primary drought characteristics, i.e., frequency, duration, and magnitude. In this study, we developed a drought risk index (DRI) through weighted averaging the performance indices derived using bivariate drought frequency analysis. We suggested two types of DRI: observed DRI (DRI_O) and designed DRI (DRI_D). DRI_O was calculated using an observed (or synthesized) time series of water shortages. DRI_D was estimated from the bivariate drought frequency curves, which are the probabilistic magnitudes of water shortages corresponding to a particular duration. The historical maximum drought event that represents the maximum DRI_O has generally been used as the target security level. However, we could establish a practically applicable target security level considering that the future water supply failure risk is represented by DRI_D. We defined regional drought safety criteria in this study by comparing DRI_O and DRI_D. Application of the criteria to the Nakdong river basin in South Korea showed that W1 (Byeongseongcheon) and W4 (Hyeongsangang) had the lowest and highest drought risk, respectively, and the drought safety criteria showed an average range of 5–20 years.     

A drought frequency formula

Abstract

The important elements of a drought phenomenon are the longest duration and the largest severity for a desired return period. These elements form a basis for designing water storage systems to cope with droughts. At times, a third element, drought intensity, is also used and is defined as the ratio of severity to duration. The commonly available statistics for the causative drought variables such as annual rainfall or runoff sequences are the mean, the coefficient of variation and the lag one serial correlation coefficient, and occasionally some indication of the probability distribution function (pdf) of the sequences. The extremal values of the duration and severity are modelled in the present paper using information on the aforesaid parameters at the truncation level equal to the mean of the drought sequence, which is generally taken as the truncation level in the analysis of droughts. The drought severity has been modelled as the product of the duration and intensity with the assumption of independence between them. An estimate of drought intensity has been realized from the concept of the truncated normal distribution of the standardized form of the drought sequences in the normalized domain. A formula in terms of the extremal severity and the T-year return period has been suggested similar to the flood frequency formulae, commonly cited in hydrological texts.     

Regional applicability of seven meteorological drought indices in China

The definition of a drought index is the foundation of drought research. However, because of the complexity of drought, there is no a unified drought index appropriate for different drought types and objects at the same time. Therefore, it is crucial to determine the regional applicability of various drought indices. Using terrestrial water storage obtained from the Gravity Recovery And Climate Experiment, and the observed soil moisture and streamflow in China, we evaluated the regional applicability of seven meteorological drought indices: the Palmer Drought Severity Index (PDSI), modified PDSI (PDSI_CN) based on observations in China, self-calibrating PDSI (scPDSI), Surface Wetness Index (SWI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and soil moisture simulations conducted using the community land model driven by observed atmospheric forcing (CLM3.5/ObsFC). The results showed that the scPDSI is most appropriate for China. However, it should be noted that the scPDSI reduces the value range slightly compared with the PDSI and PDSI_CN; thus, the classification of dry and wet conditions should be adjusted accordingly. Some problems might exist when using the PDSI and PDSI_CN in humid and arid areas because of the unsuitability of empiricalparameters. The SPI and SPEI are more appropriate for humid areas than arid and semiarid areas. This is because contributions of temperature variation to drought are neglected in the SPI, but overestimated in the SPEI, when potential evapotranspiration is estimated by the Thornthwaite method in these areas. Consequently, the SPI and SPEI tend to induce wetter and drier results, respectively. The CLM3.5/ObsFC is suitable for China before 2000, but not for arid and semiarid areas after 2000. Consistent with other drought indices, the SWI shows similar interannual and decadal change characteristics in detecting annual dry/wet variations. Although the long-term trends of drought areas in China detected by these seven drought indices during 1961–2013 are consistent, obvious differences exist among the values of drought areas, which might be attributable to the definitions of the drought indices in addition to climatic change.     

Soil moisture drought detection and multi-temporal variability across China

Soil moisture droughts can trigger abnormal changes of material and energy cycles in the soil-vegetation-atmosphere system,leading to important effects on local ecosystem,weather,and climate.Drought detection and understanding benefit disaster alleviation,as well as weather and climate predictions based on the understanding the land-atmosphere interactions.We thus simulated soil moisture using land surface model CLM3.5 driven with observed climate in China,and corrected wet bias in soil moisture simulations via introducing soil porosity parameter into soil water parameterization scheme.Then we defined soil moisture drought to quantify spatiotemporal variability of droughts.Over the period from 1951 to 2008,40%of months(to the sum of 12×58)underwent droughts,with the average area of 54.6%of total land area of Mainland China.The annual monthly drought numbers presented a significant decrease in arid regions,but a significant increase in semi-arid and semi-humid regions,a decrease in humid regions but not significant.The Mainland as a whole experienced an increasing drought trend,with77.3%of areal ratio of decrease to increase.The monthly droughts in winter were the strongest but the weakest in summer,impacting 54.3%and 8.4%total area of the Mainland,respectively.The drought lasting three months or more occurred mainly in the semi-arid and semi-humid regions,with probability51.7%,even77.6%,whereas those lasting 6 and 12 months or more impacted mainly across arid and semi-arid regions.     

Global upper ocean heat content and climate variability

Observational data from the Global Temperature and Salinity Profile Program were used to calculate the upper ocean heat content (OHC) anomaly. The thickness of the upper layer is taken as 300 m for the Pacific/Atlantic Ocean and 150 m for the Indian Ocean since the Indian Ocean has shallower thermoclines. First, the optimal spectral decomposition scheme was used to build up monthly synoptic temperature and salinity dataset for January 1990 to December 2009 on 1° × 1° grids and the same 33 vertical levels as the World Ocean Atlas. Then, the monthly varying upper layer OHC field (H) was obtained. Second, a composite analysis was conducted to obtain the total-time mean OHC field ([`([`(H)])] \bar{\bar{H}} ) and the monthly mean OHC variability ( [(\textH)\tilde] \widetilde{\text{H}} ), which is found an order of magnitude smaller than [^(\textH)] \widehat{\text{H}} . Third, an empirical orthogonal function (EOF) method is conducted on the residue data ( [^(\textH)] \widehat{\text{H}} ), deviating from [(\textH)\tilde] \widetilde{\text{H}}  +  [(\textH)\tilde] \widetilde{\text{H}} , in order to obtain interannual variations of the OHC fields for the three oceans. In the Pacific Ocean, the first two EOF modes account for 51.46% and 13.71% of the variance, representing canonical El Nino/La Nina (EOF-1) and pseudo-El Nino/La Nina (i.e., El Nino Modoki; EOF-2) events. In the Indian Ocean, the first two EOF modes account for 24.27% and 20.94% of the variance, representing basin-scale cooling/warming (EOF-1) and Indian Ocean Dipole (EOF-2) events. In the Atlantic Ocean, the first EOF mode accounts for 49.26% of the variance, representing a basin-scale cooling/warming (EOF-1) event. The second EOF mode accounts for 8.83% of the variance. Different from the Pacific and Indian Oceans, there is no zonal dipole mode in the tropical Atlantic Ocean. Fourth, evident lag correlation coefficients are found between the first principal component of the Pacific Ocean and the Southern Oscillation Index with a maximum correlation coefficient (0.68) at 1-month lead of the EOF-1 and between the second principal component of the Indian Ocean and the Dipole Mode Index with maximum values (around 0.53) at 1–2-month advance of the EOF-2. It implies that OHC anomaly contains climate variability signals.     

Investigating effect of climate change on drought propagation from meteorological to hydrological drought using multi-model ensemble projections

Stochastic Environmental Research and Risk Assessment - Climate change is a main driving force that affects the hydrological cycle, leading to an increase in natural hazards. Among these natural...     

Correlation functions and variability in an oscillatory climate model

Abstract

The correlation functions of a stochastically forced climate model describing the coupling between the mean ocean surface temperature and the extent of sea-ice are computed in the Gaussian approximation. A diagnostic relation between the variability of the surface temperature and that of the ice extent is obtained. Information on the variance of the random forces acting on the system is also deduced.     

Evaluating climate variability and pumping effects in statistical analyses

As development of ground water resources reaches the limits of sustainability, it is likely that even small changes in inflow, outflow, or storage will have economic or environmental consequences. Anthropogenic impacts of concern may be on the scale of natural variability, making it difficult to distinguish between the two. Under these circumstances, we believe that it is important to account for effects from both ground water development and climate variability. We use several statistical methods, including trend analysis, cluster analysis, and time series analysis with seasonal decomposition, to identify climate and anthropogenic effects in regional ground water levels and spring discharge in southern Nevada. We discuss the parameterization of climate and suggest that the relative importance of various measures of climate provides information about the aquifer system response to climate. In our system, which may be characteristic of much of the arid southwestern United States, ground water levels are much more responsive to wet years than to dry years, based on the importance of selected climate parameters in the regression. Using cluster analysis and time series seasonal decomposition, we relate differences in amplitude and phase in the seasonal signal to two major forcings—climate and pumping—and distinguish between a regional recharge response to an extremely wet year and a seasonal pumping/evapotranspiration response that decays with distance from the pumping center. The observed spring discharge data support our hypothesis that regional spring discharge, particularly at higher elevation springs, is sensitive to relatively small ground water level changes.     

Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data

This study aims to model the joint probability distribution of periodic hydrologic data using meta-elliptical copulas. Monthly precipitation data from a gauging station (410120) in Texas, US, was used to illustrate parameter estimation and goodness-of-fit for univariate drought distributions using chi-square test, Kolmogorov–Smirnov test, Cramer-von Mises statistic, Anderson-Darling statistic, modified weighted Watson statistic, and Liao and Shimokawa statistic. Pearson’s classical correlation coefficient r _n , Spearman’s ρ _n, Kendall’s τ, Chi-Plots, and K-Plots were employed to assess the dependence of drought variables. Several meta-elliptical copulas and Gumbel-Hougaard, Ali-Mikhail-Haq, Frank and Clayton copulas were tested to determine the best-fit copula. Based on the root mean square error and the Akaike information criterion, meta-Gaussian and t copulas gave a better fit. A bootstrap version based on Rosenblatt’s transformation was employed to test the goodness-of-fit for meta-Gaussian and t copulas. It was found that none of meta-Gaussian and t copulas considered could be rejected at the given significance level. The meta-Gaussian copula was employed to model the dependence, and these results were found satisfactory.     

关于日地关系主要元素与各种指数——综述及展望

本文着眼于日地空间物理之间的因果联系,从源头即太阳发生扰动时输出的各种形式的能量如电磁辐射、粒子辐射和电磁场等到行星际空间的表现和演化.到地球空间环境包括：磁层-电离层-中高层大气等对太阳事件的响应变化,论述了日地关系中的主要物理、化学过程及其之间的相互关系.同时,对日地系统发生变化时的各种表征参量,包括地磁指数、太阳辐射指数和太阳风-磁层耦合函数等,以及各参量对整体的日地空间环境预报中可能的应用前景进行了综述和展望.     

Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions

ABSTRACT

In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions.     

Modes of low-frequency climate variability and their relationships with land precipitation and surface temperature: application to the Northern Hemisphere winter climate

Variations in the Earth's climate have had considerable impact on society sectors such as energy, agriculture, fisheries, water resources, and environmental quality. This natural climate variability must be documented and understood in order to assess its potential impacts, its predictability and relationships with human-induced changes. Understanding the mechanisms responsible for natural variability proceeds through a strategy based on the use of a hierarchy of climate models and careful data analysis. In this paper, we examine primarily climate fluctuations on interannual-to-decadal time scales and their climate signature in terms of precipitation and temperature. First, space and time characteristics of two of the major variability modes, the Southern Oscillation (and its associated teleconnection patterns) and the North Atlantic Oscillation, are documented with a focus onto the midlatitudes of the Northern Hemisphere. Then, the current hypothesis regarding the nature of these modes (forced, coupled or internal) are reviewed based on both simulation results and statistical data analyses. Next, we address the potential predictability of seasonal surface temperature and land precipitation using an ensemble of atmospheric model simulations forced by observed sea surface temperatures. Finally, we review the relationships between the atmospheric variability modes and the recent low-frequency trends and suggest a possible influence of anthropogenic effects on these low-frequency variations.     

Teleconnection between low flows and large-scale climate indices in Texas River basins

Understanding low flow variability is critical for assessing water quality and health of riverine ecosystems in a river basin. Low flows are dependent on human water abstraction as well as the climate variability. This paper investigates the changing nature of low flows and their association with large-scale climate variability for different watersheds in the State of Texas, USA. For this purpose, we employed trend, wavelet analysis and linear as well as nonlinear correlations to identify important changes in low flow characteristics for three stream-gauging stations selected from different (i.e. Brazos, Colorado and Trinity) river basins located in Texas for the time period of 1916–1959 and 1960–2003. We also investigated the teleconnections between low flow variables and the large-scale climate indices (NINO 3.4, SOI and PDO) using cross wavelet analysis as well as their linear and non-linear correlation relationship. Our results indicated that the low flow magnitudes have shown considerable different characteristics for selected river basins during two separate time periods (1916–1959 and 1960–2003). Based on cross wavelet analysis, we identified that the low flows in selected stations of Colorado and Trinity River basins are likely to be influenced by all three large-scale climate indices. In addition to that, we identified that low flows are more nonlinearly associated with climate indices. Among the selected River basins, the stronger association between low flows and large scale climate indices are observed for Trinity River basin. The results from this study can help in better understanding of low flow hydrology and their potential relationship with large scale indices.     

Hydrologic model-based Palmer indices for drought characterization in the Yellow River basin,China

The Palmer indices (PIs) that have been most widely used for drought monitoring and assessment are criticized for two main drawbacks: coarse hydrological accounting processes with a simplified two-stage bucket soil water balance model and arbitrary rules for defining drought properties and standardizing index values through limited calibration and comparison. In this study, we introduce a new proposal of the VIC hydrologic model-based Palmer drought scheme, where traditional PIs (e.g. PDSI) can readily be calculated on the basis of distributed finescale hydrologic simulations. Moreover, recent variants of PI (i.e., SPDI and SPDI-JDI) also provide a preferable standardization strategy that allows probabilistic invariability and better spatio-temporal comparability of computed drought indices. Using gridded meteorological forcing, soil and vegetation data to drive the three-layer VIC model, both non-VIC and VIC-based PIs are investigated to examine their performances for drought characterization and detection. Results indicate that VIC hydrologic model would allow for adjustments in statistical properties of computed PDSI and VIC-based SPDI is also preferable to PDSI for better statistical robustness and spatio-temporal consistency/comparability. Moreover, the joint SPDI-JDI has the strength of integrating multi-scale probabilistic properties and drought information released by individual SPDI, providing overall drought conditions that take into account the onset, persistence and termination of droughts. At proposed 0.25° grid scale, the VIC-based SPDI-JDI indicates high frequency and long total time of drought condition in the Yellow River basin (YRB), China. Although no significant temporal trends are found in identified drought duration and severity, both the seasonal and annual drought index values demonstrate a downward trend (higher drought intensity) for considerable proportions of the YRB. These findings imply high drought risk and potential drying stress for this region. The new framework of hydrologic model-based PIs can help to strengthen our knowledge and/or practices in regional drought monitoring and assessment.     

Links between global CO_2 variability and climate anomalies of biomes

The global rate of fossil fuel combustion continues to rise, but the amount of CO2 accumulating in the atmosphere has not increased accordingly. The causes for this discrepancy are widely debated. Par- ticularly, the location and drivers for the interannual variability of atmospheric CO2 are highly uncertain. Here we examine links between global atmospheric CO2 growth rate (CGR) and the climate anomalies of biomes based on (1986―1995) global climate data of ten years and accompanying satellite data sets. Our results show that four biomes, the tropical rainforest, tropical savanna, C4 grassland and boreal forest, and their responses to climate anomalies, are the major climate-sensitive CO2 sinks/sources that control the CGR. The nature and magnitude by which these biomes respond to climate anomalies are generally not the same. However, one common influence did emerge from our analysis; the ex- tremely high CGR observed for the one extreme El Nio year was caused by the response of the tropical biomes (rainforest, savanna and C4 grassland) to temperature.     

Phase-coherent oscillatory modes in solar and geomagnetic activity and climate variability

Oscillatory modes with the period of approximately 7–8 yr were detected in monthly time series of sunspot numbers, geomagnetic activity aa index, NAO (North Atlantic Oscillation) index and near-surface air temperature from several mid-latitude European locations. Instantaneous phases of the modes underwent synchronization analysis and their statistically significant phase coherence, beginning from 1950s, has been observed. Thus the statistical evidence for a coupling between solar/geomagnetic activity and climate variability has been obtained from continuous monthly data, independent of the season, however, confined to the temporal scale related to oscillatory periods about 7–8 yr.     

A method for finding the optimal predictor indices for local wave climate conditions

In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage (n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.