Sea Surface Temperature Index Research Articles

Sea surface temperature variability in the Arctic Ocean and its marginal seas in a changing climate: Patterns and mechanisms

Abstract Understanding temporal variability of sea surface temperature (SST) patterns plays a crucial role in providing insights into the mechanisms causing extreme weather and climate events as well as oceanic and atmospheric teleconnections. This study presents an in-depth analysis of the SST patterns of the Arctic Ocean and its marginal seas on interannual and seasonal timescales from 1982 to 2018. The results reveal potential relationships between SST and climatic variables in order to improve our understanding of underlying physical mechanisms influencing the SST variations in a changing climate. Our findings disclose that the Arctic Ocean shows an overall warming trend, and the Nordic Seas have the highest SST compared to its neighboring seas. The Barents Sea shows spatially varying seasonal trends due to ice cover changes and warm water circulation within the Nordic Seas. Correlation analysis was also performed to facilitate further understanding of climate-induced SST changes. It reveals that climate variables interact differently with the Arctic Ocean SST on a regional scale and vary with different degrees of influence. Notable relationships between SST and climate variables improve understanding of differing trends on spatial and temporal scales. In addition, the wavelet coherence speculates that a significant in-phase relationship exists between SST and Greenland Blocking Index (GBI), which facilitates further studies exploring the complex mechanisms causing teleconnection patterns related to the Arctic Ocean.

Seasonal precipitation in South Central Chile: trends in extreme events since 1900

We study a regional precipitation time series, built upon seven meteorological records from South Central Chile (SCC; 37° - 42°S), which together cover the period 1900 - 2019. As a first objective, we investigated changes in the return period (RP) of dry ( P80) seasonal extreme events of precipitation (SEE), for each season. We observed a reduction in the RP of wet SEE during 1900 - 1950 in all seasons. Contrarily, the dry SEE RP shows a reduction from 1950 to the present in all seasons. This phenomenon is noteworthy since 1900 for summer and winter, and since 1930 for autumn. Spring registers a constant RP value from 1990 onwards. As a second objective, we study possible relationships between seasonal precipitation variability and climate modes, such as the Southern Annular Mode (SAM) and the Tripolar Index (TPI) of sea surface temperature (SST) over the Pacific Ocean. Summer and autumn precipitation register a significant negative correlation with SAM activity at interannual and decadal scales, while winter and spring precipitation show a significant positive correlation with SST variability over multiple regions of the Pacific Ocean (including the tropics and New Zealand) and the Southern Ocean (Amundsen-Bellingshausen Sea). Finally, we confirm that SAM strongly modulates precipitation in SCC, especially in autumn, and that SEE variability in SCC is considerably characterized by climate modes of tropical and extra-tropical origin.

Surface Ocean Hydrographic Changes in the Western Pacific Marginal Seas Since the Early Holocene

Climatic changes in the western Pacific marginal seas are influenced by global forcing and regional processes, including monsoons and ocean circulation. To better understand the process of hydrographic and temperature changes, we applied the UK’37 as our index of Sea Surface Temperature (SST) and TEX86 as the index of Subsurface Water Temperature (SWT) for the last 8400 years using the sediment core MZ01 from the continental shelf of the East China Sea (ECS). To focus on centennial and millennial variabilities, the original SST and SWT are filtered with the Ensemble Empirical Mode Decomposition (EEMD) of the Hilbert-Huang Transform (HHT), with the confidence defined by a new method, the Continuity Superposition Error Calculation Method (CSECM). The SST and SWT both have a quasi-period of 1000-2000 years, exhibiting some teleconnection with the north Atlantic climatic changes. The SWT decreased during approximately 6-4 ka and then increased by ~4°C to the late Holocene, almost anti-phase with the SST. The stronger Asian winter monsoon and China Coastal Current (CCC), are very likely responsible for the decreased SST in the late Holocene. In contrast, the increased SWT may imply that the stronger CCC has brought more Changjiang Diluted Water (CDW) southward and formed a thicker barrier layer in the ECS, which dampened bottom water heat loss that was transported from the Taiwan Warm Current (TWC) and Western Kuroshio Branch Current (WKBC). This process is tested by the hosing experiment that supports stronger stratification when the north Atlantic cooled. The combined results by UK’37 and TEX86 provide a new insight into the interaction mechanism among the winter monsoon, precipitation and the Kuroshio Current, and also raises caution to take more regional factors into account in the application of TEX86.

Relationship between Rainfall Pattern in North Central and Southern Nigeria and Some Tropical Climate Systems

Monthly rainfall data from meteorological stations in Nigeria are analyzed from 1951 to 1992, to establish their relationship with some Tropical climate systems. The climate systems include Tropical South Atlantic (TSA) sea surface temperature index, North Atlantic Ocean (NAO) atmospheric index, Tropical North Atlantic (TNA) sea surface temperature index, Central India Precipitation (CIP) and and Outgoing Longwave Radiation Anomaly (OLRA)). The rainfall pattern was given in terms of the August break (quantified in terms of the monsoon break intensity (MBI) and annual rainfall anomaly index (RAI). Partial correlation analysis was carried out to determine the effect of the tropical climate systems on the linear association between them and the stations in Nigeria. Our results show that the rainfall anomalies although sometimes intense do not have predictable patterns. The tele connection between CIP and total rainfall in Nigeria suggests that the rainfall patterns in Nigeria is likely to be modulated by the Tropical Easterly Jet (TEJ) connecting rainfall pattern in Central India to that in Nigeria. The August break is observed to be highly variable and does not show a clear discernable pattern of variability. Its variability may be connected with multiple forcings from ocean and mesoscale circulations.

Quantifying the drivers and predictability of seasonal changes in African fire

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches

Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R2 value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region.

Interannual variability of rainfall in the Guinean Coast region and its links with sea surface temperature changes over the twentieth century for the different seasons

The summer Guinean Coast (GC) rainfall (GCR) displays a strong variability on different timescales that are driven by Sea Surface Temperature (SST) variations and amplified by land–atmosphere processes. However, the relationships between the GCR and SST modes of variability in the pre-monsoon (March–May, MAM), post-monsoon (October–November, ON) and Harmattan (December–February, DJF) seasons are not well known nor understood. Using observational dataset covering the twentieth century, we extend the conclusion obtained in previous studies that mainly analyzed the summer period (June–September, JJAS) by considering changes in SST-rainfall linkages throughout the year. We show that, in boreal winter, SST interannual variability in the tropical basins are anticorrelated with the GCR. The South Atlantic Ocean Dipole (SAOD) and the Atlantic Nino (ATL3) appear, however, as major drivers of the pre-monsoon and monsoon GCR. In MAM, both modes are in opposite phases with the GCR. Below normal SST in the tropical South Atlantic in MAM leads to a surface divergence south of the equator, and the resulting southerlies bring moist air into coastal Guinea, increasing rainfall. During JJAS, ATL3 and SAOD are in phase with the GCR. During ON, the eastern Mediterranean Sea anomalous warming strengthens the Saharan Heat Low, whose extension in the tropical North Atlantic enhances the low-level westerly Jet. This jet transports moisture into GC. The stationarity of the correlations between the GCR and SST indices has also been assessed, and the strongest and most stationary links are obtained during the monsoon season.

Using large-scale climate drivers to forecast meteorological drought condition in growing season across the Australian wheatbelt

Recurring drought has caused large crop yield losses in Australia during past decades. Long-term drought forecasting is of great importance for the development of risk management strategies. Recently, large-scale climate drivers (e.g. El Niño-Southern Oscillation) have been demonstrated as useful in the application of drought forecasting. Machine learning-based models that use climate drivers as input are commonly adopted to provide drought forecasts as these models are easy to develop and require less information compared to physical-based models. However, few machine learning-based models have been developed to forecast drought conditions during growing season across all Australian cropping areas. In this study, we developed a growing season (Apr.-Nov.) meteorological drought forecasting model for each climate gauging location across the Australian wheatbelt based on multiple lagged (past) large-scale climate indices and the Random Forest (RF) algorithm. The Standardized Precipitation Index (SPI) was used as the response variable to measure the degree of meteorological drought. Results showed that the RF model could provide satisfactory drought forecasts in the eastern areas of the wheatbelt with Pearson's correlation coefficient r > 0.5 and normalized Root Mean Square Error (nRMSE) < 23%. Forecasted drought maps matched well with observed drought maps for three representative periods. We identified NINO3.4 sea surface temperature and Multivariate ENSO Index as the most influential indices dominating growing season drought conditions across the wheatbelt. In addition, lagged impacts of large-scale climate drivers on growing season drought conditions were long-lasting and the indices in previous year could also potentially affect drought conditions during current year. As large-scale climate indices are readily available and can be rapidly used to feed data driven models, we believe the proposed meteorological drought forecasting models can be easily extended to other regions to provide drought outlooks which can help mitigate adverse drought impacts.

Time‐varying relationships between ocean conditions and sockeye salmon productivity

AbstractEnvironmental change is occurring at unprecedented rates in many marine ecosystems. Yet, environmental effects on fish populations are commonly assumed to be constant across time. In this study, I tested whether relationships between ocean conditions and productivity of North American sockeye salmon (Oncorhynchus nerka) stocks have changed over the past six decades. Specifically, I evaluated the evidence for non‐stationary relationships between three widely used ocean indices and productivity of 45 sockeye salmon stocks using hierarchical Bayesian models. The ocean indices investigated were the Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), and sea surface temperature (SST). I found partial support for time‐varying salmon–ocean relationships. Non‐stationary relationships were strongest for the NPGO and weaker for the SST and PDO indices. Productivity–NPGO correlations tended to shift gradually over time with opposite trends for stocks in British Columbia (B.C.) and western Alaska; for B.C. stocks, the NPGO correlations shifted from significantly negative prior to 1980 to significantly positive after 1990, whereas for western Alaska stocks, the correlations shifted from positive to negative. Productivity–SST correlations showed declining trends for B.C. and Gulf of Alaska stocks, that is, correlations became more negative (B.C.) or less positive (Gulf of Alaska) over time. For the PDO, correlations weakened during the 1980s for western Alaska and B.C. stocks. Overall, these results provide evidence for time‐varying relationships between salmon productivity and environmental conditions over six decades, highlighting the need to recognize that historical responses of salmon populations to environmental change may not be indicative of future responses.

Spatial synchrony in the response of a long range migratory species (Salmo salar) to climate change in the North Atlantic Ocean.

A major challenge in understanding the response of populations to climate change is to separate the effects of local drivers acting independently on specific populations, from the effects of global drivers that impact multiple populations simultaneously and thereby synchronize their dynamics. We investigated the environmental drivers and the demographic mechanisms of the widespread decline in marine survival rates of Atlantic salmon (Salmo salar) over the last four decades. We developed a hierarchical Bayesian life cycle model to quantify the spatial synchrony in the marine survival of 13 large groups of populations (called stock units, SU) from two continental stock groups (CSG) in North America (NA) and Southern Europe (SE) over the period 1971-2014. We found strong coherence in the temporal variation in postsmolt marine survival among the 13 SU of NA and SE. A common North Atlantic trend explains 37% of the temporal variability of the survivals for the 13 SU and declines by a factor of 1.8 over the 1971-2014 time series. Synchrony in survival trends is stronger between SU within each CSG. The common trends at the scale of NA and SE capture 60% and 42% of the total variance of temporal variations, respectively. Temporal variations of the postsmolt survival are best explained by the temporal variations of sea surface temperature (SST, negative correlation) and net primary production indices (PP, positive correlation) encountered by salmon in common domains during their marine migration. Specifically, in the Labrador Sea/Grand Banks for populations from NA, 26% and 24% of variance is captured by SST and PP, respectively and in the Norwegian Sea for populations from SE, 21% and 12% of variance is captured by SST and PP, respectively. The findings support the hypothesis of a response of salmon populations to large climate-induced changes in the North Atlantic simultaneously impacting populations from distant continental habitats.

A Bayesian Structural Time Series Approach for Predicting Red Sea Temperatures

Sea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long-term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment management of the Red Sea ecosystem. In this work, we build a data-driven Bayesian structural time series model and show its effectiveness in predicting future SST seasons with a high accuracy, and identifying the main predictive factors of future SST variability among a large number of factors, including regional SST and large-scale climate indices. The modeling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for five months ahead.

Shifts in trait-based and taxonomic macrofauna community structure along a 27-year time-series in the south-eastern North Sea.

Current research revealed distinct changes in ecosystem functions, and thus in ecosystem stability and resilience, caused by changes in community structure and diversity loss. Benthic species play an important role in benthic-pelagic coupling, such as through the remineralization of deposited organic material, and changes to benthic community structure and diversity have associated with changes in ecosystem functioning, ecosystem stability and resilience. However, the long-term variability of traits and functions in benthic communities is largely unknown. By using abundance and bioturbation potential of macrofauna samples, taken along a transect from the German Bight towards the Dogger Bank in May 1990 and annually from 1995 to 2017, we analysed the taxonomic and trait-based macrofauna long-term community variability and diversity. Taxonomic and trait-based diversity remained stable over time, while three different regimes were found, characterised by changes in taxonomic and trait-based community structure. Min/max autocorrelation factor analysis revealed the climatic variables sea surface temperature (SST) and North Atlantic Oscillation Index (NAOI), nitrite, and epibenthic abundance as most important environmental drivers for taxonomic and trait-based community changes.

Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition

Streamflow is the result of complex climatic and hydrological interactions that are driven by atmosphere–ocean circulation. Teleconnection analysis of streamflow is significant for identifying the atmosphere- and climate-related indices of hydrology series. To mine the physical information of streamflow series more effectively, ensemble empirical mode decomposition (EEMD) was employed to decompose streamflow series into inherent stationary components with different periodic oscillations and a trend. In this study, important climate indices were identified based on the correlation coefficients, which were calculated using cross-correlation. According to the selected indicators, the decomposed and streamflow series were regressed using an artificial neural network (ANN) and support vector regression (SVR). In total, 130 climate phenomenon indices, pre-runoff, and 1–12-month time lags were considered as teleconnection variables. The results show that EEMD can be used to extract the period and trend of streamflow, and that the decomposed hydrology components have much stronger correlations than the original runoff with the climate phenomenon indices. The original streamflow was the most closely correlated with the series from the previous month, which is an autocorrelation. However, more physical information was obtained through the teleconnection of the sub-streamflow series. The periodic oscillations was explained by relatively diverse atmospheric circulation and sea surface temperature indices with different time lags. The low-frequency periodic series were better represented than the high-frequency series by the climate phenomenon indices. In addition, Nino 3.4, Nino 4, the Warm-pool ENSO Index, and the ENSO Modoki Index were representative of the high-frequency component; the mid-frequency signals were sensitive to the Solar Flux Index, Total Sunspot Number Index, Pacific Decadal Oscillation Index, and Southern Oscillation Index; the low-frequency series were influenced by the Atlantic Multi-decadal Oscillation Index, North African Subtropical High Area Index, North Atlantic Triple Index, etc.; and the Indian Ocean Basin-Wide Index, Atlantic Multi-decadal Oscillation Index, Western Pacific Warm Pool Strength Index, and East Pacific 850 mb Trade Wind Index expressed well the long-term trend of monthly streamflow. Meanwhile, the regression results of the decomposed series obtained by the ANN and SVR exhibited better statistical performance than those of the original series, especially for the EEMD-SVR.

Indian Ocean wind speed variability and global teleconnection patterns

Summary The influence of the local sea surface temperature (SST) and remote ENSO (El Nino-Southern Oscillation) indices on the wind speed (WS) data were explored for the Indian Ocean region. Relationships among the parameters were studied using spatial correlation plots and significant correlation ranges. Two months (July and January) representing opposite monsoon phases were selected for analysis for the period 1950–2016. There was a significant negative correlation between WS and SST over the Bay of Bengal (BOB) during July. Although different ENSO indices correlated differently in different areas of the Indian Ocean, the region off the coast of Sri Lanka was most significantly teleconnected. The southwest monsoon locally impacted the WS and SST relationship and the WS parameter was remotely teleconnected in both the monsoon seasons. Further empirical orthogonal function (EOF) analysis was applied on the 67 years WS data of the BOB region to extract the dominant mode representing maximum variability of the total variance. The temporal pattern of the first principal component (PC1) of WS data was linked to the North Atlantic Oscillations in January and the Atlantic Multidecadal Oscillation in July respectively. The continuous wavelet power spectra of the PC1 of WS showed significant regions in the 2–4-year band resembling the ENSO variability. Wavelet coherence applied between PC1 of WS and the ENSO indices showed greatest values for January in the 8–16-year band and for July in the 0–4-year band. A close relationship was established between the WS variability in BOB and the ENSO indices.

Paleoceanographic and paleoclimatic reconstruction based on marine palynomorphs in the period 1811–1907 CE in the Magdalena Basin, Baja California Sur, Mexico

Biennial variations of marine palynomorph concentrations mirrored oceanographic conditions in the Magdalena Basin, Baja California Sur (Mexico), in the period from 1811 to 1907 (common era, CE), for which the sea surface temperature index (SSTI) showed predominantly negative anomalies. We analyzed 49 samples of laminated sediments taken from a core extracted at a water depth of 520 m. Changes in dinoflagellate assemblages and copepod egg abundances were compared to SSTI records and to El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) reconstructions. Copepods were the most abundant and showed a significant correlation with SSTI. Among the dinoflagellates, heterotrophs were the most abundant and did not correlate with SSTI. The lowest concentration of autotrophs showed a significant and inverse correlation with SSTI. The canonical correlation analysis also indicated that copepods and autotrophs were modulated by variations in SSTI. Dinoflagellate richness and concentrations and copepod egg concentrations were low, indicating low productivity, and these values coincided with the negative SSTI anomalies. In contrast, the increase in concentrations from 1881 to 1907 CE coincided with positive SSTI anomalies. Also, even when marine palynomorphs showed no significant correlation with either ENSO or PDO, their concentrations increased during age intervals with combined negative conditions (La Niña, negative PDO) and decreased during combined warm conditions (El Niño, positive PDO).

Influence of northwest Indian Ocean sea surface temperature and El Niño–Southern Oscillation on the winter precipitation in Iran

Abstract This paper studies the impact of northwest Indian Ocean (NIO) sea surface temperature (SST) anomalies between the regions 0–29°N and 45–80°E and El Niño–Southern Oscillation (ENSO) on the winter precipitation in Iran. The data include monthly precipitation from 45 weather stations in Iran, monthly NIO SST with a 2° horizontal resolution and Southern Oscillation Index (SOI) for a 40-year period (1975–2014). This study utilizes canonical correlation analysis and partial correlation to investigate the simultaneous relationship between SST, SOI, and winter precipitation variability. A running correlation method is also used to test the stationarity of these relationships. Analyses of the results reveal that both NIO SST and ENSO index are significantly correlated with the winter precipitation in Iran but with opposite sign. Moreover, a clear variability exists in the correlations revealing a distinct non-stationarity in the relationship over time. The results of partial correlation analysis suggest that the ENSO index has a stronger effect on the winter precipitation in Iran than NIO SST.

Seasonal and inter-annual variability of net primary production in the NW Iberian margin (1998–2016) in relation to wind stress and sea surface temperature

Seasonal and inter-annual variability of satellite-derived net primary production (NPP) in the NW Iberian margin and its relationship with the offshore Ekman transport (-Qx) and a variety of sea surface temperature (SST) indices have been examined over the period 1998–2016. Seasonality explained about 55% of NPP variability over the shelf and the adjacent ocean. Maximum NPP rates occurred by May in the adjacent ocean, and were delayed until July over the shelf. The excess production (ΔNPP) over the shelf compared to the ocean domain represented 0.45 ± 0.02 g C m−2 d−1 or 37 ± 2% of the total NPP and peaked in August, in between the maximum -Qx and the maximum SST difference between the shelf and the adjacent ocean (ΔSST). During the upwelling season, the inter-annual variability of NPP in the adjacent ocean correlated positively with the spring stratification (SSTstr) and the average -Qx over the upwelling season and ΔNPP correlated inversely with ΔSST. Conversely, during the downwelling season, ΔNPP correlated with the average stratification (SSTdown) over the downwelling season. The rates of change of NPP with the climate-related variables (-Qx, ΔSST, SSTstr, SSTdown), obtained from these regressions, may be used to test the sensitivity of the NW Iberian upwelling productivity to climate change.

Climate and disturbance influence self-sustaining stand dynamics of aspen (Populus tremuloides) near its range margin.

Species that are primarily seral may form stable (self-sustaining) communities under certain disturbance regimes or environmental conditions, yet such populations may also be particularly vulnerable to ecological change. Aspen (Populus spp.) are generally considered seral throughout the Northern Hemisphere, including P.tremuloides, the most widely distributed tree species in North America. Recent declines in aspen populations have occurred, especially along drought-sensitive margins of its range and where fire exclusion and herbivory have promoted community transition. However, aspen also forms stable stands, and examination of the mechanisms that influence persistence can offer conservation insights, especially where populations are vulnerable to changing climate or altered disturbance dynamics. We sampled tree age and stand characteristics of isolated aspen forests in the arid Great Basin (USA) to determine if (1) aspen communities are more fire-dependent and seral or fire-independent and stable; (2) ungulate browsing inhibits aspen stability; and (3) temporal patterns of vegetative reproduction (i.e., ramet establishment or "suckering") are correlated with climate. Aspen size and age class densities strongly fit negative exponential distributions, whether grouped geographically or by functional type, suggesting landscape-scale persistence. Continuous age distributions and high proportions of recruitment-sized to overstory trees suggest stability at stand scales, with exceptions including stands with higher browsing pressure. Few stands had evidence of fire, and relationships between dead tree size and variability in live tree size suggest a lack of fire dependency. Several 5-yr averaged climate variables and one sea surface temperature index were correlated with aspen ramet establishment densities over time, with strongest relationships occurring ~5yr prior to establishment year, often followed by inverse relationships ~1yr after. Indeed, aspen establishment density for a recent 41-yr period was reliably reconstructed using antecedent climate conditions derived from a single drought index. Temporally synchronized aspen ramet establishment across the study region may be due to climate-driven storage of nonstructural carbohydrate reserves in clonal root systems later used for regeneration. Complex regeneration dynamics of these self-sustaining aspen stands, especially sensitivity to climate variability, suggest they may serve as harbingers of ecological change in the arid Great Basin and in other aspen populations near their range margin.

Estimation of Monsoon Seasonal Precipitation Teleconnection with El Niño-Southern Oscillation Sea Surface Temperature Indices over the Western Ghats of Karnataka

The Western Ghats (WG) of India are basically north-to-south oriented mountains with three distinct meteorological divisions. These mountains exhibit the characteristic features of precipitation and distribution during the summer monsoon season and possess latitudinal variations. It is a well-known fact that sea surface temperature (SST) combined with the El Nino-Southern Oscillation (ENSO) enacts a predominant role in the precipitation over the entire Western Ghats during the summer monsoon season. Whereas the Nino regions affect the variability of the Western Ghats’ precipitation in an asymmetric relationship. Nevertheless, the simulation of precipitation has been evidenced to be difficult. The current study attempts to predict the seasonal precipitation over the coastal region and the Western Ghats of Karnataka. The relationship between summer monsoon precipitation (SMP) and SST is examined up to eight seasons by conducting the correlation analysis with three seasons that lag before the onset of the monsoon season. The significant and positively correlated lagged Nino indices with the SMP index are identified as the predictors. The selected predictors are used for predicting the SMP by using statistical models, the multiple linear regression model and the artificial neural network (ANN) model. The statistical models are based on the combined lagged indices and the principle component as the predictor. The results of the statistical models on comparison suggest that neural network models have a better predictive skill than the linear regression models. Neural network models with combined lagged indices being used as predictors are slightly better, but a few more climatic parameters must be verified and the usage of this method on other meteorological divisions of the West Coast of India needs to be further investigated.

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

ABSTRACT Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow.