Large-scale Climate Indices Research Articles

Concurrent changes in spatial distribution of the demersal community in response to climate variations in the southern Iberian coastal Large Marine Ecosystem

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 607:19-36 (2018) - DOI: https://doi.org/10.3354/meps12791 Concurrent changes in spatial distribution of the demersal community in response to climate variations in the southern Iberian coastal Large Marine Ecosystem Marina Delgado1,*, Manuel Hidalgo2,3, Patricia Puerta2, Ricardo Sánchez-Leal1, Lucía Rueda2, Ignacio Sobrino1 1Instituto Español de Oceanografía, Centro Oceanográfico de Cádiz, Muelle de Levante s/n, 11006 Cádiz, Spain 2Instituto Español de Oceanografía, Centre Oceanogràfic de les Balears, Moll de Ponent s/n, 07015 Palma de Mallorca, Spain 3Instituto Español de Oceanografía, Centro Oceanográfico de Málaga, Puerto Pesquero s/n, 29640 Fuengirola, Spain *Corresponding author: marina.delgado@ieo.es ABSTRACT: Species inhabiting transitional areas in large marine ecosystems are highly sensitive to climate variations and thus of paramount importance as indicators of ecosystem changes. The spatiotemporal distribution of representative species of the demersal community in the southern Iberian coastal Large Marine Ecosystem was investigated using time series data from trawl surveys conducted in the Gulf of Cádiz. The geographic and bathymetric variability in the population distribution was analyzed. We investigated the influence of hydroclimatic (chlorophyll a concentration, sea surface temperature, precipitation, runoff, oceanic wind and meso- and large-scale climate indices) and population drivers (density) on species depth and location. Distribution shifts were more evident over the longitudinal gradient with species approaching and moving away from the Strait of Gibraltar. This contrasting behavior was segregated by depth and the hydrographical boundary of the Guadalquivir River runoff plume. On the shallow shelf (<100 m), a southeastward movement towards the Strait of Gibraltar was clear. This geographical shift was also observed in species from the deeper strata, occasionally coupled with depth changes. These movements were mainly associated with climate variability, in particular the North Atlantic Oscillation, whose effects were clearly species specific. Additionally, longitudinal variations of the community at a regional scale were related to a local climatic index that captured a pattern of covariation in local surface wind, temperature and precipitation. This study focuses on responses of several species with contrasting life histories (from Sparidae to Elasmobranchs) to hydroclimatic variability in the southern Iberian coastal Large Marine Ecosystem. KEY WORDS: Demersal species · Spatial ecology · Species distribution · Geographic shifts · Climate indices · Southern Iberian coastal Large Marine Ecosystem Full text in pdf format PreviousNextCite this article as: Delgado M, Hidalgo M, Puerta P, Sánchez-Leal R, Rueda L, Sobrino I (2018) Concurrent changes in spatial distribution of the demersal community in response to climate variations in the southern Iberian coastal Large Marine Ecosystem. Mar Ecol Prog Ser 607:19-36. https://doi.org/10.3354/meps12791 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 607. Online publication date: December 06, 2018 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2018 Inter-Research.

Using novel spawning ground indices to analyze the effects of climate change on Pacific saury abundance

Abstract Pacific saury (Cololabis saira) are widely distributed in northwestern Pacific, migrating from their spawning ground in subtropical Kuroshio waters south of Japan to feeding grounds in Oyashio waters northeast of Japan. The abundance of Pacific saury is expected to be affected not only by environmental conditions in the fishing grounds of the Oyashio region, but also by recruitment processes influenced by environmental conditions in the Kuroshio region. In this study, we focus on the effects of environmental variations in the spawning ground on Pacific saury abundance, approximated by catch and catch per unit effort (CPUE) data. To examine interannual-decadal variability in the spawning ground, we developed a suite of spawning ground indices, including (1) average winter sea surface temperature (WSST), (2) the meridional position of 19 °C sea surface temperature (SST) isoline (MP19), and (3) SST-suitability-weighted size of spawning ground (WSSG), in the Kuroshio region. These spawning ground indices exhibited interannual-decadal variation patterns with regime shifts in 1962/63, 1976/77, 1987/88, 1997/98, and likely in the early 2010s, which corresponded well to data on catch and CPUE of Pacific saury. Large scale climatic indices such as Southern Oscillation Index (SOI) and Asian Monsoon Index (MOI) were negatively correlated with winter SST in most of the Kuroshio region, suggesting that large-scale climatic influences played important roles in the variability of SST within the Kuroshio region. Gradient forest analyses were used to quantify the importance of these spawning ground indices for explaining the variations of Pacific saury abundance and to identify shifts in catch and CPUE along the gradients of the spawning ground indices. MP19 with a 2-year lag (MP19_Lag2) was identified as the most important predictor of Pacific saury abundance in terms of CPUE, followed by WSST_Lag2, WSST, WSSG_Lag1, and WSSG. Spawning ground indices, particularly MP19_Lag2, were useful for rationalizing the dynamics of Pacific saury abundance, matching well the striking declines of catch both in the early 1960s and also in the most recent years.

Investigating the underlying structure of particulate matter concentrations: a functional exploratory data analysis study using California monitoring data

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

Predicting wintertime windy days in Beijing with the preceding autumn sea surface temperature and large-scale circulation patterns

In this study, we investigate the role of sea surface temperature and large-scale circulation patterns in the preceding autumn for the prediction of wintertime windy days in Beijing during the period of 1979–2016, based on a time-scale decomposition approach. A windy day is defined according to the threshold of 2 m·s−1, which can effectively remove or reduce air pollution of Beijing. Wintertime windy days in Beijing, preceding autumn sea surface temperature and large-scale climate indices are separated into the interannual and interdecadal components. For the interannual component, the preceding autumn sea surface temperature in central western Pacific Ocean, Polar/Eurasia pattern, and Scandinavia pattern are identified as predictors of wintertime windy days in Beijing. For the interdecadal component, the identified predictors include the preceding autumn sea surface temperature in northeastern part of North Atlantic Ocean, Arctic Oscillation, and North Atlantic Oscillation. We develop statistical models for the interannual and interdecadal components based on these preceding autumn predictors, and take them together to predict wintertime windy days in Beijing. The leave-one-out cross-validation for 1979–2016 time series shows that 76% of the total variance of observed wintertime windy days in Beijing can be predicted. Hindcast experiments for the period of 1999–2016 also yield the high prediction skills. The physical mechanisms responsible for links between these predictors and wintertime windy days in Beijing are also discussed. The statistical models proposed herein are expected to be applied to and aid to improve seasonal forecasting of wintertime windy days in Beijing.

Multiscale characteristics of Jing-Jin-Ji’s seasonal precipitation and their teleconnection with large-scale climate indices

Using wavelet theory, Mann-Kendall non-parametric test, principal component analysis, and correlation analysis, the study investigated the change characteristics and periodic variation of seasonal precipitation and their relationships with seven large-scale global climate indices including Nino 3.4, Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), Pacific North American Index (PNA), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Eastern Asia/Western Russia (EA/WR), for the period of 1959–2016 in Jing-Jin-Ji metropolitan region. The results showed the following: (1) The change characteristics of Jing-Jin-Ji’s precipitation varied for different seasons. Only JJA precipitation showed statistically significant decreasing trend at 5% significance level. Precipitation in other three seasons had increasing trends. The trend of annual precipitation was mainly dominated by JJA precipitation. The results of fractal characteristics of precipitation indicated that the future trend of JJA precipitation would likely increase, but the future trends of annual precipitation and precipitation in other three seasons would likely decrease. The inapparent periodic variation of seasonal precipitation was revealed using adjacent-average smoothing method. (2) Wavelet theory was used to investigate the periodic variation of seasonal precipitation and seven large-scale climate indices. In general, the oscillations varied for different seasons and periods. The real part of the Morlet wavelet amplitude exhibited similar cycles using wavelet power spectrum. However, it is possible to estimate the future trends of seasonal precipitation according to the nested periodic positive and negative cycles. The periods of seven large-scale climate indices were scattered. (3) The PC1 of seasonal precipitation and climate indices had isolated coherence, which varied for different climate indices and periods. The phases between them also varied for different periods. As for different climate indices, the EA/WR had strong positive impacts on the seasonal precipitation at relatively large period, such as 8–16 years. The effects of climate indices on the seasonal precipitation varied for different PCs and climate indices. However, the results of correlation analysis were consistent with those of wavelet coherence and phase difference. The corrections varied for different time lags between climate indices and seasonal precipitation. The results should be beneficial to reveal the historical and future trends of seasonal precipitation in Jing-Jin-Ji metropolitan region. The linear and nonlinear relationships between seasonal precipitation and climate indices indicated that it might be possible to predict the seasonal precipitation using the climate indices as potential predictors. However, the mechanisms of climate change impacts on the precipitation need to be further elucidated, which are recommended in our future study.

Variability, teleconnection, and predictability of Korean precipitation in relation to large scale climate indices

Spatiotemporal variability, teleconnection, and predictability of the Korean precipitation related to large scale climate indices were examined based on leading patterns of observed monthly Rx5day and total precipitation through an empirical orthogonal teleconnection (EOT). Cross-correlation and lag regression analyses for the leading modes and global atmospheric circulation dataset were employed on a monthly basis. The spatial pattern of the leading EOT modes for Rx5day and total precipitation represents a northern inland mode for boreal summer and a southern coastal mode in boreal winter. The temporal evolution of the leading EOT modes exhibits increasing trends during summer season and decadal variability for winter season. The leading EOT patterns of Rx5day precipitation show more widespread coherent patterns than those of total precipitation during warm and cold seasons, while the former explains less variance in precipitation variability than the latter. The tropical ENSO forcing has a coherent teleconnection with September and November-December precipitation patterns, while the Indian Ocean dipole is identified as a driver for precipitation variability in September and November. The monsoon circulation over the western North Pacific also exhibits a significant negative correlation with winter precipitation EOTs, while tropical cyclone indices are positively correlated with the fall precipitation EOTs. The leading patterns of the September and December Rx5day precipitation time series are predictable at up to six month lead time from the tropical Pacific sea surface temperatures (SSTs), while a somewhat weak predictable response from Indian Ocean SSTs was only detected at longer lead times. In addition, predictability from the Pacific SSTs for above normal precipitation is greater than that for below normal precipitation.

Patterns and drivers of phytoplankton phenology off SW Iberia: A phenoregion based perspective

Phytoplankton patterns, tightly linked to the dynamics of the ocean surface layer and its atmospheric forcing, have major impacts on ecosystem functioning and are valuable indicators of its response to environmental variability and change. Phytoplankton phenology and its underlying drivers are spatially variable, and the study of its patterns, particularly over heterogeneous regions, benefits from a delineation of regions with specific phenological properties, or phenoregions. The area Southwest off the Iberian Peninsula (SWIP, NE Atlantic) integrates a highly complex set of coastal and ocean domains that collectively challenge the understanding of regional phytoplankton phenology and related forcing mechanisms. This study aims to evaluate phytoplankton phenology patterns over the SWIP area, during an 18-year period (September 1997 – August 2015), using an objective, unsupervised partition strategy (Hierarchical Agglomerative Clustering – HAC) based on phenological indices derived from satellite ocean colour data. The partition is then used to describe region-specific phytoplankton phenological patterns related to bloom magnitude, frequency, duration and timing. Region-specific variability patterns in phenological indices and their linkages with environmental determinants, including local ocean physical-chemical variables, hydrodynamic variables and large scale climate indices, were explored using Generalized Additive Models (GAM). HAC analyses identified five coherent phenoregions over SWIP, with distinctive phytoplankton phenological properties: two open ocean and three coastal regions. Over the open ocean, a single, low magnitude and long bloom event per year, was regularly observed. Coastal phenoregions exhibited up to six short bloom events per year, and higher intra-annual and variability. GAM models explained 50–90% of the variance of all phenological indices except bloom initiation timing, and revealed that interannual patterns in phytoplankton phenology and their environmental drivers varied markedly among the five phenoregions. Over the oceanic phenoregions, large-scale climate indices (Eastern Atlantic Pattern, Atlantic Meridional Oscillation), mixed layer depth (MLD) and nitrate concentration preceding primary bloom events were influential predictors, reflecting the relevance of nutrient limitation. For the Coastal-Slope, a relatively more light-limited phenoregion, North Atlantic Oscillation and wind speed were more relevant, and bloom magnitude was also positively influenced by riverine discharge. This variable was a significant predictor of bloom frequency, magnitude and duration over the Riverine-influenced region. Over the Upwelling-influenced region, upwelling intensity and mean annual MLD showed stronger partial effects on phytoplankton phenology. Overall, our phenology-based unsupervised approach produced a biologically-relevant SWIP partition, providing an evaluation of the complexity of interactions between phytoplankton and multiple environmental forcing, particularly over coastal areas.

Global climate teleconnection with rainfall erosivity in South Korea

Rainfall Erosivity Index (REI) defined as the product of rainfall kinetic energy and rainfall intensity is a well-known hydrologic indicator of the potential risk of soil erosion. Global and regional scale climatic teleconnections with REI variability over South Korea are examined. We calculate leading patterns of observed monthly REIs using the Empirical Orthogonal Teleconnection (EOT) and Function (EOF) decomposition techniques. Also we used monthly statistical analyses using cross-correlation and lag regression for the leading modes and global atmospheric circulation measurement in the Pacific and Indian Ocean. As a result, the northern inland mode is applicable during summer season and the southern coastal mode applies to fall-winter season. The temporal evolution of REI exhibits mostly increasing and depends on interdecadal oscillation patterns. The leading EOT modes explain more variance in REI than the EOF modes during warm and cold seasons. The findings from this study illustrate that the tropical ENSO forcing has the coherent association with fall and winter REI patterns, and the Indian Ocean dipole is identified as a driver for REI variability in November. The monsoon circulations over western North Pacific also exhibit significant negative correlation with the December modes. The September leading modes also show a positive correlation with the tropical cyclone activity. Leading patterns in September and November have predictability up to five month lead time from the tropical Pacific Sea Surface Temperatures (SSTs). In addition, predictability from the Pacific SSTs for above normal extreme value of REI is greater than that for below normal value in winter. In conclusion, South Korea experiences climatic teleconnection between the large scale climate indices and mid-latitude hydrologic variables.

Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors

Soil moisture (SM) is a key component of the global energy cycle that regulates all domains of the natural environmental and the agricultural system. In this research, the challenge is to develop a low-cost data-intelligent SM forecasting model using climate dynamics (i.e., the climate indices, atmospheric and hydro-meteorological parameters) as the model inputs. A newly designed, multi-model ensemble committee machine learning approach based on the artificial neural network (ANN-CoM) is developed to forecast monthly upper layer (∼0.2 m from the surface) and the lower layer (∼0.2–1.5 m deep) SM at four agricultural sites in Australia’s Murray-Darling Basin. ANN-CoM model is validated with respect to non-tuned second-order Volterra, M5 model tree, random forest, and an extreme learning machine (ELM) models. To construct the ANN-CoM model, the input variables comprised of the hydro-meteorological data from the Australian Water Availability Project, large-scale climate indices and atmospheric parameters derived from the Interim ERA European Centre for Medium-Range Weather Forecasting ECMWF reanalysis fields leads to a total of 60 potential predictors used for SM forecasting. To reduce the model input data dimensionality for accurate forecasts, the Neighborhood Component Analysis (NCA) based feature selection algorithm for regression purposes (fsrnca) is applied to determine the relative feature weights related to the targeted variable. The optimal predictor variables are then screened with an ELM model as the fitness function of the fsrnca algorithm to identify the set of most pertinent model variables. Extensive performance evaluation using statistical score metrics with visual and diagnostic plots show that the ensemble committee based, ANN-CoM model is able to effectively capture the nonlinear dynamics involved in the modeling of monthly upper and lower layer SM levels. Therefore, the ANN-CoM multi-model ensemble-based approach can be considered to be a superior SM forecasting tool, portraying as an amicable, integrated (or ensemble) machine learning stratagem that can be explored for soil moisture modeling and applications in agriculture and other hydro-meteorological phenomena.

Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO.

Understanding the teleconnections between hydro-meteorological data and the El Niño–Southern Oscillation cycle (ENSO) is an important step towards developing flood early warning systems. In this study, the concept of mutual information (MI) was applied using marginal and joint information entropy to quantify the linear and non-linear relationship between annual streamflow, extreme precipitation indices over Mekong river basin, and ENSO. We primarily used Pearson correlation as a linear association metric for comparison with mutual information. The analysis was performed at four hydro-meteorological stations located on the mainstream Mekong river basin. It was observed that the nonlinear correlation information is comparatively higher between the large-scale climate index and local hydro-meteorology data in comparison to the traditional linear correlation information. The spatial analysis was carried out using all the grid points in the river basin, which suggests a spatial dependence structure between precipitation extremes and ENSO. Overall, this study suggests that mutual information approach can further detect more meaningful connections between large-scale climate indices and hydro-meteorological variables at different spatio-temporal scales. Application of nonlinear mutual information metric can be an efficient tool to better understand hydro-climatic variables dynamics resulting in improved climate-informed adaptation strategies.

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.

Local temperature and El Niño Southern Oscillation influence migration phenology of East Asian migratory waterbirds wintering in Poyang, China.

Temperature is a critical factor influencing avian phenology, due to its direct impact on food and water availability. Most previous studies have focused on the timing of spring migration and the arrival of birds at breeding grounds along the European and American flyways; little is known about migration ecology at the wintering sites along the Asian flyways. Using linear regression models, this study investigates how local temperature variation and EI Niño Southern Oscillation (ENSO) influences the arrival and departure timing of 9 waterbird species breeding in Mongolia or Siberia and overwintering in Poyang, China from 2002 to 2013. Birds mainly arrive at Poyang in October and depart for their breeding sites in March. Out of the 9 species, 6 show a strong negative relationship between departure time and overwintering temperature in Poyang. Departure dates also show a negative association with overwintering ENSO and March ENSO for two species. Both local and large-scale climate indices show no influence on the arrival timing of waterbirds. We suggest that birds react to the annual variation of overwintering temperature: an earlier departure of waterbirds is facilitated by a warmer overwintering period and vice versa. The long-term accumulated temperature effect is more pronounced than ENSO and the short-term local temperature effect. Our findings could help quantify the potential impact of global warming on waterbirds.

Unravelling region-specific environmental drivers of phytoplankton across a complex marine domain (off SW Iberia)

Phytoplankton, the dominant marine primary producers, are considered to be highly sensitive indicators of ecosystem condition and change. The southwest area off the Iberian Peninsula (SWIP, NE Atlantic) is located in a biogeographical transition zone between temperate and subtropical waters, and classified as being very vulnerable to climate change. SWIP includes a variety of oceanic and coastal domains, under the influence of topographic irregularities, coastal upwelling and continental freshwater outflows, that collectively challenge the understanding of phytoplankton dynamics and controls. This study aimed to evaluate patterns in seasonal and interannual variability in phytoplankton and underlying environmental determinants within specific regions of SWIP, during a 15-year period (1997–2012), and to assess whether climate variability affects the regions in different ways. Empirical Orthogonal Function (EOF) analysis of satellite-retrieved sea surface chlorophyll-a concentration (Chl-a), acquired from the Ocean Colour Climate Change Initiative (OC-CCI), 4-km, 16-day resolution, was used to regionalize the study area. Region-specific Chl-a variability patterns and their linkages with environmental determinants were explored using Generalized Additive Mixed Models (GAMM). A set of local physical-chemical variables, derived from satellite and model data, and large-scale climate indices, were used as environmental variables. EOF analysis of Chl-a variability over the heterogeneous SWIP area identified nine coherent regions, with distinctive variability patterns (4 coastals, 2 slopes and 3 open-ocean regions). Region-specific GAMM models explained between 32% and 82% of Chl-a variance, with higher explanatory power (>61%) for open ocean regions and coastal regions under increased riverine influence. Chl-a model predictors, as well as their effects, varied markedly among SWIP regions. However, climate-sensitive local environmental variables (sea surface temperature – SST and photosynthetically available radiation) emerged as the most influential general predictors overall, and large-scale climate indices showed significant but minor effects. Over oceanic SWIP regions, Chl-a (0.08–1.50μgL−1) showed a uni-modal annual cycle, with increases during the mixed-layer deepening and late-winter maxima, reflecting seasonal changes in SST and ocean stratification, and probably related to increased nutrient availability and/or decreased mortality. Over coastal regions south of 37°N, Chl-a (0.23–10μgL−1) also benefited from riverine discharges, mostly during winter, and upwelling induced by zonal westerly winds, stronger during summer. Over the Portuguese west coast region, Chl-a (0.26–2.20μgL−1) showed a uni-modal annual cycle, with summer maxima, associated with the stimulatory effects of meridional northerly winds and coastal upwelling that partially extended into slope waters. Chl-a interannual variability showed zonal differences within SWIP, with significant interannual patterns only for regions south of 37°N. Nonetheless, contrasting trends were detected in coastal (decline) and oceanic (increase) regions, possibly a consequence of between-region differences in the relative roles of nutrient and light limitation, corresponding to significant interannual increases in wind speed and mixed layer depth. Our study used a biologically-relevant objective regionalization of a heterogeneous area, to elucidate phytoplankton dynamics and controls. The region-specific associations observed between phytoplankton and multiple climate-sensitive environmental drivers over the SWIP area reinforce the role of phytoplankton as a strategic element for evaluating ecosystem responses to climate variability and change.

Correlation between hydrological drought, climatic factors, reservoir operation, and vegetation cover in the Xijiang Basin, South China

The Xijiang River is known as the Golden Watercourse because of its role in the development of the Pearl River Delta Regional Economic System in China, which was made possible by its abundant water resources. At present, the hydrological regime of the Xijiang River has now become complicated, the water shortages and successive droughts pose a threat to regional economic development. However, the complexity of hydroclimatological processes with emphasizes on drought has not been comprehended. In order to effectively predict and develop the adaptation strategies to cope with the water scarcity damage caused by hydrological droughts, it is essential to thoroughly analyze the relationship between hydrological droughts and pre/post-dependent hydroclimatological factors. To accomplish this, the extreme-point symmetric mode decomposition method (ESMD) was utilized to reveal the periodic variation in hydrological droughts that is characterized by the Standardized Drought Index (SDI). In addition, the cross-wavelet transform method was applied to investigate the correlation between large-scale climate indices and drought. The results showed that hydrological drought had the most significant response to spring ENSO (El Niño-Southern Oscillation), and the response lags in sub-basins were mostly 8–9months except that in Yujiang River were mainly 5 or 8months. Signal reservoir operation in the Yujiang River reduced drought severity by 52–95.8% from January to April over the 2003–2014 time period. Similarly, the cascade reservoir alleviated winter and spring droughts in the Hongshuihe River Basin. However, autumn drought was aggravated with severity increased by 41.9% in September and by 160.9% in October, so that the land surface models without considering human intervention must be used with caution in the hydrological simulation. The response lags of the VCI (Vegetation Condition Index) to hydrological drought were different in the sub-basins. The response lag for the Hongshuihe, Yujiang, and Liujiang River Basins were mostly 0–4months, 0–1months, and 2–3months, respectively, but there was no obvious regular change pattern in the Guijiang River Basin.

Application of several data-driven techniques to predict a standardized precipitation index

Climate modeling and prediction is important in water resources management, especially in arid and semi-arid regions that frequently suffer further from water shortages. The Maharlu-Bakhtegan basin, with an area of 31 000km2 is a semi-arid and arid region located in southwestern Iran. Therefore, precipitation and water shortage in this area have many problems. This study presents a drought index modeling approach based on large-scale climate indices by using the adaptive neuro-fuzzy inference system (ANFIS), the M5P model tree and the multilayer perceptron (MLP). First, most of the climate signals were determined from 25 climate signals using factor analysis, and subsequently, the standardized precipitation index (SPI) was predicted one to 12 months in advance with ANFIS, the M5P model tree and MLP. The evaluation of the models performance by error parameters and Taylor diagrams demonstrated that performance of the MLP is better than the other models. The results also revealed that the accuracy of prediction increased considerably by using climate indices of the previous month (t – 1) (RMSE=0.802, ME=–0.002 and PBIAS=–0.47).

Multiple regression modelling approach for rainfall prediction using large-scale climate indices as potential predictors

Multiple regression modelling approach for rainfall prediction using large-scale climate indices as potential predictors

Multiple regression modelling approach for rainfall prediction using large-scale climate indices as potential predictors

Some studies established the associations with different climate indices (Southern Oscillation Index, Indian Ocean Dipole and Southern Annular Mode) and seasonal rainfalls of different parts of Australia. Nevertheless, maximum predictability of South Australian rainfall was only 20% with individual effects of potential predictor. To establish a better relationship for South Australian spring rainfall prediction, this paper presents two further investigations: 1) relationship of lagged climate indices with rainfall; 2) combined influence of these lagged climate indicators on rainfall. Multiple linear regression (MLR) modelling was used to evaluate the influence of combined predictors. Three rainfall stations were selected from South Australia as a case study. It was revealed that significantly increased rainfall predictability has been achieved through MR models using the influences of combine-lagged climate predictors. The rainfall predictability ranging from 41% to 45% has been achieved using combined lagged-indices, whereas maximum 33% predictability can be achieved using individual climate index.

Variability of ChlorophyllaConcentration and Halocline Depth in La Paz Bay (Gulf of California, Mexico) and the Relationship to Pacific Large-Scale Climatic Phenomena

ABSTRACT Guevara-Guillen, C.; Shirasago-German, B.; Perez-Lezama, E.L., and Obeso-Nieblas, M., 2018. Variability of chlorophyll a concentration and Halocline depth in La Paz Bay (Gulf of California, Mexico) and the relationship to Pacific large-scale climatic phenomena. Variability of the chlorophyll a (Chl a) concentration and halocline depth in La Paz Bay were analyzed and compared against the variability of North Pacific (NP) large-scale phenomena and local winds during 1999–2009. Chl a data were obtained from satellite images, and halocline depth was obtained from in situ data. Eight large-scale phenomenon climate indices and satellite wind data were also used in this work. Chl a was processed on three timescales—interannual, mean annual cycle, and monthly time series. The halocline depth was processed as a time series. The Chl a interannual analysis revealed a high variability in the study period, with the highest concentration in 2001 and the lowest in 2005. The Pacific Decadal Oscillation (PDO) was...

The impact of meteorological conditions on snow and ice thickness in an Arctic lake

Inter-annual variation of meteorological conditions and their effects on snow and ice thickness in an Arctic lake Unari (67.14° N, 25.73° E) were investigated for winters 1980/1981–2012/2013. The lake snow and ice thicknesses were modelled applying a thermodynamic model, and the results were compared with observations. Regression equations were derived for the relationships between meteorological parameters and modelled snow and ice properties. The composite differences of large-scale atmospheric circulation patterns between seasons of thin and thick ice were analysed. The air temperature had an increasing trend (statistical significance p<0.05) during the freezing season (1.0° C/decade), associated with an increasing trend of liquid precipitation (p<0.05) in winter. Both observed and modelled average and maximum ice thicknesses showed a decreasing trend (p<0.05). The model results were statistically more reliable (1) for lake ice than snow and (2) for seasonal means than maxima. Low temperature with less precipitation prompted the formation of columnar ice, whereas strong winds and heavy snowfall were in favour of granular ice formation. The granular (columnar) ice thickness was positively (negatively) correlated with precipitation. The seasonal mean and maximum modelled lake ice and snow thicknesses were controlled by precipitation and temperature history, with 58–86 % of the inter-annual variance explained. Using regression equations derived from data from 1980 to 2013, snow and ice thickness for the following winter seasons was statistically forecasted, yielding errors of 9–12 %. Among large-scale climate indices, the Pacific Decadal Oscillation was the only one that correlated with inter-annual variations in the seasonal average ice thickness in Lake Unari.

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

Abstract. The study presents a statistically based seasonal precipitation forecast model, which automatically identifies suitable predictors from globally gridded sea surface temperature (SST) and climate variables by means of an extensive data-mining procedure and explicitly avoids the utilization of typical large-scale climate indices. This leads to an enhanced flexibility of the model and enables its automatic calibration for any target area without any prior assumption concerning adequate predictor variables. Potential predictor variables are derived by means of a cell-wise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability-based cluster analysis. Finally, for every month and lead time, an individual random-forest-based forecast model is constructed, by means of the preliminary generated predictor variables. Monthly predictions are aggregated to running 3-month periods in order to generate a seasonal precipitation forecast. The model is applied and evaluated for selected target regions in central and south Asia. Particularly for winter and spring in westerly-dominated central Asia, correlation coefficients between forecasted and observed precipitation reach values up to 0.48, although the variability of precipitation rates is strongly underestimated. Likewise, for the monsoonal precipitation amounts in the south Asian target area, correlations of up to 0.5 were detected. The skill of the model for the dry winter season over south Asia is found to be low. A sensitivity analysis with well-known climate indices, such as the El Niño– Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East Atlantic (EA) pattern, reveals the major large-scale controlling mechanisms of the seasonal precipitation climate for each target area. For the central Asian target areas, both ENSO and NAO are identified as important controlling factors for precipitation totals during moist winter and spring seasons. Drought conditions are found to be triggered by a cold ENSO phase in combination with a positive state of NAO in northern central Asia, and by cold ENSO conditions in combination with a negative NAO phase in southern central Asia. For the monsoonal summer precipitation amounts over southern Asia, the model suggests a distinct negative response to El Niño events.