Global Climate Oscillations Research Articles

Exploring climate shifts in the Ganga-Brahmaputra basin based on rainfall and temperature variability.

Climate change has a significant impact on the Ganga-Brahmaputra (GB) basin, the major food belt of India, which frequently experiences flooding and varied incidences of drought. The current study examines the changing trend of rainfall and temperature in the GB basin over a period of 30years to identify areas at risk with an emphasis on the Paris Agreement's mandate to keep increasing temperatures below 2°C. The maximum temperature anomaly in the middle Ganga plains recorded an increase of more than 1.5°Cyear-1 in 1999, 2005, and 2009. Some extreme events were observed in the Brahmaputra basin during 1999, 2009, and 2010, where a prominent temperature increase of 1.5°Cyear-1 was observed. The minimum temperature revealed an increasing trend for the G-B basin with an anomalous increase of 0.04 to 0.06°Cyear-1. The rainfall variability across the Ganga basin shows a rising tendency over the lower Ganga region while the Brahmaputra basin showed a downward trend. To identify the statistical relation between the Global climatic oscillations and regional climate, Standardized Precipitation Index (SPI) and Niño 3.4 were used. The wet and dry period estimation shows a rise in flood conditions in the Ganga basin whereas, in the Brahmaputra basin, an increase in drought frequency was observed. The correlation based on Niño 3.4 and SPI3 presents a negative relation for the monsoon season in the G-B basin revealing a situation of drought occurrence (SPI3 below 0) with increased Nino 3.4 values (El Niño above + 0.4C).

Historical catastrophic floods at the southern edge of the Atacama Desert: A multi-archive reconstruction of the Copiapó river extreme events

The last hydrometeorological extreme event that caused large floods in the southern Atacama Desert in March 2015 raised concern about how little was known about the fluvial dynamic of these arid basins. Understanding the response of intermittent and ephemeral rivers in drylands to the present context of global change is critical to preserve the ecological and human systems they support, to sustainably manage their scarce water resources and to develop flood risk management plans. We have studied the instrumental and historical record and explored the potential of the Copiapó River geological record in the comprehension of how extraordinary the 2015 flood was and how its fluvial dynamic relates with global climate oscillations. We have identified 36 flood events that have occurred in the last 400 years: 22 of them have been classified as ordinary rises of the river flow (discharges <30 m3/s), 11 as extraordinary floods in which the damage is confined to areas adjacent to the river (discharges 30–180 m3/s), and only 3 as catastrophic floods (discharges >180 m3/s), including the 2015 flood event. The incorporation of the historical and palaeohydrological data into the flood frequency analysis results in an increase of the magnitude of the flood quantiles in which large flood events occur with an average recurrence interval of 120 years. Most of the flood events were caused by heavy rains that are largely linked to the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation with a superimposed effect of the ENSO. Discharges >30 m3/s, i.e., extraordinary and catastrophic floods, occur with positive phases of the PDO and the ENSO. Further exploration of the fluvial geological record of the Copiapó River will help lengthening to thousands of years the flood record what will help improving communities' resilience by anticipating flood hazards in the current global change context, in which stronger rainfall events modulated by ENSO and ENSO-like conditions are expected.

Weakened casual feedback loops following intensive restoration efforts and climate changes in a large shallow freshwater lake

Understanding how phytoplankton interacts with local and regional drivers as well as their feedbacks is a great challenge, and quantitative analyses of the regulating role of human activities and climate changes on these feedback loops are also limited. By using monthly monitoring dataset (2000–2017) from Lake Taihu and empirical dynamic modelling to construct causal networks, we quantified the strengths of causal feedbacks among phytoplankton, local environments, zooplankton, meteorology as well as global climate oscillation. Prevalent bidirectional causal linkages between phytoplankton biomass (chlorophyll a) and the tested drivers were found, providing holistic and quantitative evidence of the ubiquitous feedback loops. Phytoplankton biomass exhibited the highest feedbacks with total inorganic nitrogen and ammonia and the lowest with nitrate. The feedbacks between phytoplankton biomass and environmental factors from 2000 to 2017 could be classified into two groups: the local environments (e.g., nutrients, pH, transparency, zooplankton biomass)-driven enhancement loops promoting the response of the phytoplankton biomass, and the climate (e.g., wind speed)-driven regulatory loops suppressing it. The two counterbalanced groups modified the emergent macroecological patterns. Our findings revealed that the causal feedback networks loosened significantly after 2007 following nutrient loading reduction and unsuccessful biomanipulation restoration attempts by stocking carp. The strength of enhancement loops underwent marked decreases leading to reduced phytoplankton responses to the tested drivers, while the climate (decreasing wind speed, warming winter)-driven regulatory loops increased– like a tug-of-war. To counteract the self-amplifying feedback loops, the present eutrophication mitigation efforts, especially nutrient reduction, should be continued, and introduction of alternative measures to indirectly regulate the critical components (e.g., pH, Secchi depth, zooplankton biomass) of the loops would be beneficial.

Unraveling the complex phylogeographic history of freshwater fishes in Lower Central America: A study of the electric fish Brachyhypopomus occidentalis

Lower Central America (LCA) has a complex biogeographic history shaped by the rise of the Isthmus of Panama and the global climatic oscillations of the Pleistocene. These events have been crucial in structuring biodiversity in LCA, but their consequences for the distribution and partitions of genetic diversity across the region remain to be elucidated. We combined complete mitochondrial genomes and nuclear ultraconserved elements (UCEs) to study the phylogeographic history and population genetic structure of the electric fish Brachyhypopomus occidentalis in LCA. Our results are consistent with the known phylogeographic history of B. occidentalis in LCA, but we update this history in several important ways that help illuminate the phylogeographic history of freshwater fishes in the region. We provide: i) support for three waves of colonization, two of which occurred prior to the final closure of the Panama Isthmus; ii) a more precise understanding of each colonization event, with evidence for a larger footprint of the first event, as well as genetic exchange across the continental divide in subsequent events; and iii) evidence for high levels of previously unrecognized population genetic structure across LCA. This updated model of colonization and diversification of B. occidentalis consists of three waves of dispersal and colonization, which triggered the evolution of geographic breaks in both nuclear and mitochondrial genomes across LCA. These processes are tightly linked to the dynamic uplift of the Isthmus, recent volcanic activity in the region, and the sea-level oscillations of the Pleistocene. These results improve previous phylogeographic inferences regarding the distribution and diversification of freshwater fishes in LCA, and generate testable hypotheses to guide future research exploring the factors shaping biodiversity in the region.

Noctiluca blooms intensify when northwesterly winds complement northeasterlies in the northern Arabian Sea: Possible implications

Wind systems are known as nutrient sources playing significant roles in the oceanic realm and global climate oscillations. This study explores, for the first time, the effect of winds on the winter blooms of the mixotrophic dinoflagellate, the green variant of Noctiluca scintillans (NSG) in the northern Arabian Sea. When the NSG abundance was lower (i.e., <∼10000 cells l−1), it was coupled to silicic acid (H4SiO4), on which diatoms (phytoplankton) in turn depended. At higher abundance (i.e., NSG>∼10000 cells l−1), H4SiO4 and H4SiO4:DIN (dissolved inorganic nitrogen) ratio fell. The NSG was then intensely green and chlorophyll-a richer, attributed to a change in the mode of NSG's nutrition from heterotrophy to autotrophy-dominance. The back-trajectory model revealed that the winds were mostly northeasterly (NE) initially (during February) and were north-westerly (NW) towards the end of winter (March). Separately for the NE and NW winds, the NSG abundance was 10655±18628 and 28896±46225 cells l−1, respectively. The H4SiO4:DIN ratio correspondingly reached <0.2 and ≥0.4. The NSG was modelled with high significance (p<0.001, N=33) versus the NE and NW wind speeds. Thus, while the NE winds deepened the mixed layer and caused nutrient enrichment and phytoplankton production, the NW winds facilitated the recovery of the H4SiO4:DIN ratio and economical use of H4SiO4 for phytoplankton production. It is hypothesized that this process is helped by iron input from NW desert winds during the latter part of winter when the NSG blooms intensify.

Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation

Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we investigated teleconnections between global climate oscillations and the peak precipitation season from February to June in the Tian-Shan and Pamir Mountains of Central Asia. Using hierarchical climate regionalization, we identified seven subregions with distinct precipitation patterns, and assessed correlations with selected climate oscillations at different time lags. We then simulated the seasonal precipitation in each subregion from 1979 to 2020 using the most prevalent teleconnections as predictors with support vector regression (SVR). Our findings indicate that the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Eastern Atlantic/West Russia pattern are among the major determinants of the seasonal precipitation. The dominant lead-lag times of these oscillations make them reliable predictors ahead of the season. We detected notable teleconnections with the North Atlantic Oscillation and Scandinavian Pattern, with their strongest associations emerging after onset of the season. While the SVR-based models exhibit robust prediction skills, they tend to underestimate precipitation in extremely wet seasons. Overall, our study highlights the value of appropriate spatial and temporal aggregations for exploring the impacts of climate teleconnections on precipitation in complex terrains.

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimates &#x2013; such as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI time-series measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.

Hydrological drought variability and its teleconnections with climate indices

In this study, the spatial–temporal variability of hydrological drought in Meriç Basin, Turkey, has been examined, and its relationship with global climate oscillations has been explored. Prior to drought analysis, streamflow data gaps were filled, and series homogeneity was examined. The standardized streamflow index (SSFI) was used to detect drought periods from monthly streamflow data of nine stations well spread over the study area. The hydrological drought intensity, magnitude, and duration were investigated using the SSFI time series. The drought variability was assessed by performing trend analysis and wavelet transforms. Trend analysis showed an increase in drought events in the Northern region and a decrease in the Southern region of the basin. Wavelet analysis reveals that the 4–10 year scales explicate the drought series pattern. The identification of climate patterns that are most associated with drought variability is performed via correlation analysis. The investigation showed that distinct climate patterns simultaneously influence drought events. Significantly, the influence of El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO) is notable. The analysis demonstrated a remarkable correlation between ENSO and SSFI series at the interannual scale (2–8 years). The obtained phase difference suggests that the maximum dry (wet) conditions coincide with La Nina (El Nino) events. In addition, positive NAO phases and negative AMO phases lead to drought occurrence.

Linking human activities and global climatic oscillation to phytoplankton dynamics in a subtropical lake

Human activities and climate change are two major stressors affecting lake ecosystems as well as phytoplankton communities worldwide. However, how the temporal dynamics of phytoplankton are directly or indirectly linked to anthropogenic activities and climatic oscillation remains unclear. We assessed the annual trends (1988–2018) in phytoplankton abundance (PA) in Lake Dongting, China and related it to five groups of variables characterizing human activities, global climate oscillation, water nutrients, hydrology, and meteorology. We found a significant increase in PA, urbanization (Upop), total nitrogen (TN), fertilizer application (FA), number of summer days (SU), and the warm speed duration index (WSDI) and a significant decrease in the water discharge of three inlets (TIWD) and the sediment discharge of three inlets (TISD) and four tributaries (FTSD) and the net sediment deposition (NSD). However, no significant annual trends were observed for the number of rainstorm days (R50mm), the simple precipitation intensity index (SDII) and yearly anomalies of El Niño-Southern oscillation events (ENSOi). Cross-correlation Function analyses demonstrated that the operation of the Three George Dam (TGD) strengthened the effects of hydrology, rainfall patterns and ENSOi on phytoplankton, but strongly weakened the association between water nutrients, human activities and phytoplankton abundance. Path analysis revealed that TP, TN, FA, R50 mm as well as WSDI had a direct positive effect on PA, while a direct negative effect was found for ENSOi, NSD and TISD. Human activities (Upop and FA), warming (WSDI and SU), and rainfall patterns (SDII and R50 mm) exerted indirect controls on phytoplankton through changes in water nutrients and hydrology. Climate change (ENSOi) had a direct effect on PA, but also showed twelve indirect pathways via changes in hydrology and meteorology (both positive and negative effects were found). Overall, meteorology contributed most markedly to the variations of PA (29.3%), followed by hydrology (25.3%), human activities (24%), water nutrients (10.5%), and ENSOi (1.9%). Our results highlight a strongly causal connection between human activities as well as global climate change and phytoplankton and the benefits of considering multiple environmental drivers in determining the temporal dynamics of lake biotic communities.

Bulk and compound-specific \u03b413C and n-alkane indices in a palustrine intermontane record for assessing environmental changes over the past 320\xa0ka: the Padul Basin (Southwestern Mediterranean realm)

Here we provide valuable information about the palaeoenvironmental evolution of Southwestern Mediterranean region during the last ca. 320 ka through a biomarker-based study of the longest continuous continental Quaternary record in the Iberian Peninsula. The n-alkane content and δ13C values of these lipids were measured in 300 samples taken from the uppermost 55 m of the Padul Basin (PB) record. The δ13C signal of long-chain n-alkanes was a reliable proxy for C4/C3 terrestrial vegetation composition in the basin, as emergent macrophytes made a minor contribution to these homologues. In contrast, the δ13C values of C23 and C25 alkanes reflected mainly phases of increasing water level of the lacustrine/palustrine water body since aquatic macrophytes contain a large proportion of these compounds. Low δ13C values were attributed to a marked contribution of plants using the C3 photosynthetic pathway. Intervals with the lowest δ13C values were attributed to an important input of angiosperms, although they could also be explained by changing environmental conditions or environmental stress, as large shifts in δ13C occurred in long-chain homologues typically abundant in terrestrial plants. Shifts in δ13C of medium-chain homologues reflected limited CO2 availability induced by water temperature, salinity, pH, enhanced productivity, low atmospheric pCO2, or stagnant barriers, rather than the abundance of aquatic macrophytes. Our results also suggest enhanced isotopic fractionation during lipid synthesis by aquatic macrophytes within MIS 7 and the Holocene, leading to increased δ13C values of bulk OM and of long-chain n-alkanes. Hence, the δ13C logs were ideal for studying the contribution of aquatic macrophytes to the lipid and isotopic composition of sediments and for the reconstruction of palaeoenvironmental conditions. These results confirmed that C4 plants had a low presence in the PB. Comparison with biomarker analysis and pollen data of the PB and other records of the Southwestern Mediterranean revealed that δ13C values of bulk OM and of long-chain n-alkanes reflected global climatic oscillations during MIS 7 and the episodes Heinrich Events 3, 2, 1 and Younger Dryas.

Climate teleconnections, interannual variability, and evolution of the rainfall regime in a tropical Caribbean island: case study of Barbados

A limited number of studies have focused on the hydroclimate dynamics of tropical Caribbean islands. The present study aims to analyze the rainfall regime in Barbados. CHIRPS gridded dataset, at a resolution of 0.05°×0.05°, providing daily rainfall data from January 1981 until 2018 was used. The variables analyzed were the annual and seasonal maximum rainfall, the total annual and seasonal rainfall, and the number of rainy days per year and per season. Potential change points in rainfall time-series were detected with a Bayesian multiple change point detection procedure. Time series were then analyzed for detection of trends using the modified Mann-Kendall test. The true temporal slopes of the rainfall time series were obtained with the Theil-Sen’s statistic. The links between rainfall and various global climate oscillation indices were also investigated. Results indicate that no change points or significant trends were observed in the annual rainfall time series. However, it was found that some climate indices have a strong correlation with precipitation on the island, especially for the total rainfall and the number of rainy days. A stationary and non-stationary frequency analysis is carried out on the rainfall annual variables using climate oscillation indices as covariates, and uncertainties on quantile estimates are identified. It is shown that non-stationary models lead to a better fit to rainfall data. Empirical mode decomposition (EMD) is used for the long-term prediction of hydro-climatic time series. Rainfall annual time series were extended with this method for a period of 20 years. Results indicate that, within that period, annual maximum rainfall will increase by about 12 mm (or 0.6 mm/year), total annual rainfall will increase by about 200 mm (or 10 mm/year) and the number of rainy days per year will see a slight decrease by about 3 days (or 0.15 day/year).

Palaeoenvironmental changes in Eocene Tibetan lake systems traced by geochemistry, sedimentology and palynofacies

• Late Eocene continental climate in Central Asia remains poorly understood. • Tibetan lake deposits preserve records of Eocene palaeoenvironmental changes. • Saline lakes in east-central Tibet existed in a cool and semi-arid desert-steppe. • Late Eocene aridification caused a palaeoenvironmental shift in Central Asia. • Tibetan palaeoclimate was primarily influenced by regional to global drivers. Ancient lake deposits preserve detailed records of Cenozoic environmental changes, providing information on past climate, vegetation, precipitation and lake chemistry. This study focuses on palaeoenvironmental changes recorded in Eocene limnic environments across what is today the modern Tibetan Plateau. We describe a section dated as late Eocene (~38–37 Ma) and integrate these findings within a regional context of similarly-aged Tibetan lake deposits across the plateau. These sedimentary archives of environmental change indicate a period of late Eocene aridification and cooling in the lead-up to the greenhouse-icehouse transition, which remains poorly understood in Central Asia. We show, based on geochemical, sedimentological, and palynofacies analyses, that a large saline lake existed within a semi-arid to arid steppe environment in the Nangqian Basin, east-central Tibet. The saline lake experienced cyclic drying intervals with shifts to a playa lake / mudflat system. Evidence of increased aridity is recorded in the upper part of the section, including a thinning of gypsum beds, decrease in palynomorph abundance, and concurrent increase in wood debris and amorphous organic matter. This is consistent with late Eocene aridity in Asia, drying of the playa lake, and an impoverished desert-steppe vegetation. Grain size data and geochemistry indicate a stable provenance of sedimentary material, suggesting that tectonic activity did not dominate sedimentation in east-central Tibet during deposition of these successions. Rather, palaeoenvironmental changes across the Tibetan region were most probably controlled by global climate oscillations and retreat of the proto-Paratethys Sea during the late Eocene: knowledge that is relevant for ecological interpretations through the Cenozoic, Quaternary and to the present.

The impacts of climate variability on coffee yield in five indonesian coffee production centers

Coffee is an annual crop sensitive to climate variability. Most Indonesian coffee is cultivated on marginal lands that are vulnerable to environmental changes, including climate. Indonesia's climate variability is influenced by several factors, including the monsoon, local aspects, and global climate oscillations such as the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). It is crucial to identify the impacts of climate variability on both the production and the economy to develop adaptative measures. This study aims to determine the effects of global climate variability, namely ENSO and IOD, on coffee production in several Indonesian production centers. It uses annual coffee production data in the five major production centers in Indonesia. The ENSO indicators used in this study were the Oceanic Nino Index (ONI) in the Nino 3.4 region and the IOD indicator in the Dipole Mode Index (DMI). The production anomaly analysis approach in neutral years and the extreme ENSO and IOD phases were applied in this study. The results showed that the effects of ENSO and IOD were different in each region. The highest decline in production occurred in the La-Nina year in almost all production centers. The decline in output in the La-Nina year ranged from 6 to 22%. Meanwhile, the IOD that had a decreasing effect on production was positive IOD with a decrease ranging from 1 to 15%. Key words: ENSO; IOD; Economy; Climate change.

Long-term forecasting of wind speed in the UAE using nonlinear canonical correlation analysis (NLCCA)

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. Several linear and nonlinear multivariate techniques have been used to predict the stochastic character of wind speed. Wind speed forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective power grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study, nonlinear canonical correlation analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed of UAE. The wind dataset was obtained from six ground stations spread within the country. The first mode of NLCCA captured the nonlinear mode of the teleconnection indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the relative root mean square error (rRMSE) and relative mean absolute error (MAER). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behavior of the dataset of variables than linear canonical correlation analysis (CCA) model in terms of the correlation and root mean square error.

Quantifying precipitation extremes and their relationships with large‐scale climate oscillations in a tropical country, Singapore: 1980–2018

Extreme precipitation indices (EPIs) were defined to quantify the precipitation extremes in Singapore, a typical tropical country situated near the equator. The paper investigated the spatial and temporal variability of precipitation extremes based on seventeen EPIs using non‐parametric Mann‐Kendall test and Sen’s slope, and further explored the linear and nonlinear relationships between precipitation extremes and four large‐scale global climate oscillations using correlation and wavelet analysis, during the period of 1980–2018 in Singapore. The results indicated that the trends of precipitation extremes varied for different EPIs, regions and stations. Increasing trends dominated thirteen out of seventeen EPIs. The trends of EPIs were scattered and irregularly distributed. The cross‐correlation analysis between different EPIs demonstrated that annual total precipitation on wet days (PRCPTOT) was strongly correlated with other EPIs. The result of composite analysis indicated that El Niño Southern Oscillation (ENSO) exerted stronger impacts on southwest monsoon season (SMS) precipitation than PRCPTOT and northeast monsoon season (NMS) precipitation. The SMS precipitation composite suggested that ENSO created more influence on dry spells than wet spells. The linear and nonlinear relationships revealed that all climate oscillations were negatively correlated with precipitation. The wavelet coherence and phase differences were consistent with the results of correlation analysis, indicating possible prediction of precipitation extremes using climate oscillations as potential predictors.

El Niño and the Nomads: Global Climate, Local Environment, and the Crisis of Pastoralism in Late Ottoman Kurdistan

Abstract This article explores the impacts of environmental crises on pastoral nomads in Ottoman Kurdistan/Armenia in the late nineteenth-century. It demonstrates that the climatic fluctuations characterizing these environmental crises were synchronized with global climatic oscillations, specifically the El Niño Southern Oscillation. Recurrent episodes of severe drought and cold dramatically affected these groups, who were unable to withstand extreme changes in temperature and precipitation. Back-to-back drought episodes created a shortage of water, dried up pastures and damaged forage, while severe cold resulted in high rates of premature death among herd animals. These climatic events thus had devastating economic and social consequences.

Effects of Global Climate Oscillations on Intermonthly to Interannual Variability of Sea levels along the English Channel Coasts (NW France)

Summary This work examines the multiscale variability in sea level along the English Channel coasts (NW France) using a wavelet multiresolution decomposition of water level values and climate oscillations in order to gain insights in the connection between the global atmospheric circulation and the local-scale variability of the monthly extreme surges. Changes in surges have exhibited different oscillatory components from the intermonthly (∼3–6-months) to the interannual scales (∼1.5-years, ∼2–4-years, ∼5–8-years) with mean explained variances of ∼40% and ∼25% of the total variability respectively. The correlation between the multiresolution components of surges and 28 exceptional stormy events with different intensities has revealed that energetic events are manifested at all timescales while moderate events are limited to short scales. By considering the two hypotheses of (1) the physical mechanisms of the atmospheric circulation change according to the timescales and (2) their connection with the local variability improves the prediction of the extremes, the multiscale components of the monthly extreme surges have been investigated using four different climate oscillations (Sea Surface Temperature (SST), Sea-Level Pressure (SLP), Zonal Wind (ZW), and North Atlantic Oscillation (NAO)); results show statistically significant correlations with ∼3–6-months, ∼1.5-years, ∼2–4-years, and ∼5–8-years, respectively. Such physical links, from global to local scales, have been considered to model the multiscale monthly extreme surges using a time-dependent Generalized Extreme Value (GEV) distribution. The incorporation of the climate information in the GEV parameters has considerably improved the fitting of the different timescales of surges with an explained variance higher than 30%. This improvement exhibits their nonlinear relationship with the large-scale atmospheric circulation.

Synoptic Meteorology Explains Temperate Forest Carbon Uptake

AbstractWhile substantial attention has been paid to the effects of both global climate oscillations and local meteorological conditions on the interannual variability of ecosystem carbon exchange, the relationship between the interannual variability of synoptic meteorology and ecosystem carbon exchange has not been well studied. Here we use a clustering algorithm to identify a summertime cyclonic precipitation system northwest of the Great Lakes to determine (a) the association at a daily scale between the occurrence of this system and the local meteorology and net ecosystem exchange at three Great Lakes region forested eddy covariance sites and (b) the association between the seasonal prevalence of this system and the summertime net ecosystem exchange of these sites. We find that temperature, in addition to precipitation and cloud cover, is an important explanatory factor for the suppression of net ecosystem productivity that occurs during these cyclonic events in this region. In addition, the prevalence of this cyclonic system can explain a significant proportion of the interannual variability in summertime forest ecosystem exchange in this region. This explanatory power is not due to a simple accumulation of low‐productivity days that cooccur with this meteorological event, but rather a broader association between the frequency of these events and several aspects of prevailing seasonal conditions. This work demonstrates the usefulness of conceptualizing meteorology in terms of synoptic systems for explaining the interannual variability of regional carbon fluxes.

Data-driven control of water reservoirs using an emulator of the climate system

This study presents a novel approach to combine a data-driven control strategy with an emulator model of the climate system in order to make the optimal control of water systems more flexible and adaptive to the increasing frequency and intensity of extreme events. These latter are often associated with global climate anomalies, which are difficult to model and incorporate into optimal control algorithms. In this paper, we compare a traditional control policy conditioned only on the reservoir storage with an informed controller that enlarges the state space to include the emulated dynamics of global Sea Surface Temperature anomalies. The multi-purpose operations of Lake Como in Italy, accounting for flood control and water supply, is used as a case study. Numerical results show that the proposed approach provides a 59% improvement in system performance with respect to traditional solutions. This gain further increases during extreme drought episodes, which are influenced by global climate oscillations.

Detecting the State of the Climate System via Artificial Intelligence to Improve Seasonal Forecasts and Inform Reservoir Operations

AbstractIncreasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected, and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance. We apply the framework to the Lake Como basin, a regulated lake in northern Italy mainly operated for flood control and irrigation supply. Numerical results show the existence of notable teleconnection patterns dependent on both ENSO and the North Atlantic Oscillation over the Alpine region, which contribute in generating skilful seasonal precipitation and hydrologic forecasts. The use of this information for conditioning the lake operations produces an average 44% improvement in system performance with respect to a baseline solution not informed by any forecast, with this gain that further increases during extreme drought episodes. Our results also suggest that observed preseason sea surface temperature anomalies appear more valuable than hydrologic‐based seasonal forecasts, producing an average 59% improvement in system performance.