North Atlantic Oscillation Index Research Articles

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

Dry and Wet Spells in Poland in the Period 1966–2023

The aim of this study is to present the spatial and temporal variability of the frequency of dry and wet days and dry and wet spells against the background of changes in precipitation and atmospheric circulation. The study is based on daily precipitation totals from 46 meteorological stations in Poland from 1966 to 2023. Additionally, seven circulation indices were used, namely GBI, NAO, AO, EA, EA/WR, SCAND, and AMO. Dry days are defined as days without precipitation. Wet days are days with at least 1 mm of precipitation. It was shown that dry spells are much more common than wet spells, are longer, and cover larger areas. Long-term changes in the annual characteristics of dry and wet days and spells are not statistically significant. Only the length of the most extended dry spell in the year increases. However, there are significant changes in their annual cycles. Spring is drier; in summer, precipitation decreases in the south and increases in the north; November and December, symbols of gloomy rainy weather, are increasingly drier; and rainy weather has shifted to January and February. The impact of circulation varies according to the season, with the NAO, AO, SCAND, and GBI indices having the greatest influence.

Climatic resilience: Marine heatwaves do not influence the variations of green crab (Carcinus maenas) megalopae supply patterns to a western Iberian estuary

Extreme climatic events like marine heatwaves (MHWs) are becoming more frequent, intense, and longer lasting all around the world. The consequences of these anomalously warm periods are devastating for marine ecosystems. Still, little is known about these extreme events off the western Iberia coast. Here we analyzed MHW events occurring from 1982 – 2020 on the Aveiro coast, western Iberia coast of Portugal. A total of 79 events were detected for the region, with an average duration of 15.8 days, and a mean intensity of 1.9 ºC ± 0.4 ºC above the 90th percentile of sea surface temperatures (SST) for the region. The maximum intensity of the events has increased by 0.5 ºC over the last decade. The relation between SST, and therefore, MHW events, the North Atlantic Oscillation index (NAO), and the regional Iberian Upwelling Index (UI) was identified. The intense upwelling of the region seems to mitigate the duration of warming conditions, resulting in shorter MHW events. Furthermore, the impacts of SST and MHW events on the supply patterns of Carcinus maenas megalopae were examined, utilizing daily data from 2002, 2006 - 2009, 2012, and 2013, collected at the entrance of Ria de Aveiro. Cross-correlations were employed to assess the effect of SST on megalopae supply, while ordinary least square cumulative sums were used to identify variations over time. The influence of SST on supply was noticed with a 5-to-11-day lag, but this relation changed over the years. Contrary to our hypothesis, we found no evidence supporting a diminishment in megalopae supply due to MHW events. These elusive findings, coupled with the apparent lack of influence of these extreme events, highlight the relatively weak intensity and brief duration of the MHW events in the region, coupled with the high thermal tolerance of these species.

Influence of environmental conditions on the abundance of the striped venus Chamelea gallina in the northern Alboran Sea (Western Mediterranean Sea)

Environmental conditions can easily modulate the abundance of short-lived species, including those of commercial interest. These effects can occur during key periods of their life cycle, such as the spawning and recruitment periods, which are crucial for the stability of natural populations. This study attempts to elucidate the effects of abiotic factors on an important commercial species for artisanal fisheries in the northern Alboran Sea, Chamelea gallina, using CPUE data covering a 16-year period (2004-2020). Generalised Additive Models (GAM) were used to study how the abundance of the species was influenced by a set of environmental variables (sea surface temperature, sea surface salinity, phytoplankton, dissolved oxygen, North Atlantic Oscillation index and pluviometry) at spatial and temporal scale, as well as the spatio-temporal variation considering two areas (eastern and western areas) with different oceanographic conditions. The analysis showed that the abundance of C. gallina showed fluctuations over the historical time series in this ecoregion. The largest catches are observed in the western sector of the northern Alboran Sea, where the local sediment characteristics are more suitable for the species occurrence and the upwelling effect is more pronounced. Results showed that the most influential variables in the abundances during the spawning period were sea temperature, salinity and dissolved oxygen while during the recruitment period were only salinity and dissolved oxygen. Studies on the effects of environmental variables on the life cycle of species are essential to understand complex biological processes such as the spawning and recruitment, especially in commercially exploited species and in highly dynamic environments as the Alboran Sea.

Regular widespread aggregations of the oceanic jellyfish Pelagia noctiluca in the northeast Atlantic over 11 years

The jellyfish Pelagia noctiluca form extensive aggregations in the Mediterranean Sea that can exert top-down control on prey and cause mass-mortalities in fish farms. The description of extensive aggregations of P. noctiluca in the northeast Atlantic in 2007 coupled with a subsequent mortality event of farmed salmon in Northern Ireland prompted further investigation of the species in this region. We collected standardised P. noctiluca biomass from 1,938 fisheries trawls over 11 years (2008-2018) covering ∼200,000 km2 of northeast Atlantic shelf seas. While a linear trend in P. noctiluca was not identified, the mean annual biomass exhibited significant fluctuations, with particularly elevated levels in 2009, 2013, 2014 and 2016 (0.05, 0.02, 0.01 and 0.02 kg ha-1, respectively). Mean biomass was higher in the northern (0.028 kg ha-1) and western portions (0.005 kg ha-1) of the study area compared to the southwestern (0.003 kg ha-1) and southeastern portions (0.001 kg ha-1). A binomial generalised linear mixed model of P. noctiluca biomass data indicated that higher P. noctiluca occurrence was driven by a positive Eastern Atlantic Pattern index (strongest effect), south-easterly winds, a negative North Atlantic Oscillation index and lower sea-surface temperatures (weakest effect). The fixed effects accounted for 36.81% of the variance in P. noctiluca occurrence data, while a random year effect further explained an additional 24.06% of the variance. These conditions may destabilise the European Slope Current in autumn-winter which promotes the transport of P. noctiluca from oceanic sources onto the northeast Atlantic shelf. Our findings represent a dramatic shift in our understanding of this species in the northeast Atlantic which may have significant ecological impacts, from predation on fish eggs and larvae to providing food for oceanic sunfish.

Temporal Clustering of Precipitation Driving Landslides Over the Italian Territory

AbstractThe occurrence of multiple precipitation events not‐independent in time, that is, a temporal clustering, is an example of a temporal compounding event. This type of forcing is of great relevance for the occurrence of different natural hazards, like floods and deep‐seated landslides, for which previous soil saturation plays an important role in shaping the associated hazard. Using ERA5‐Land data set and E‐OBS, we firstly investigate the spatial and temporal characteristics of temporal clustering of precipitation over the Italian territory, and we relate it with two oscillation patterns, namely North Atlantic Oscillation (NAO) and Mediterranean Oscillation Index (MOI), and with common synoptic conditions. Then, we explore the role of temporal compounding of precipitation in the generation of different movement types (complex, debris flow, fall, flow, and sliding) using the database of landslides from the Aree Vulnerate Italiane project (in Italian AVI, meaning Areas Affected by Landslides or Floods). From this study it emerges that below average values of NAO and MOI increase the probability of having clustered precipitation events. For all types of landslides, except rock falls, we observed that the majority of the events are preceded by a temporal clustering of precipitation, over longer time windows for complex events, shorter for debris flows. For rock falls, we found also a link with low minimum temperature and freeze‐thaw cycles for winter events and high maximum temperature for summer events. This work contributes to the investigation of temporal clustering of precipitation in connection with natural hazards characterized by a mechanism of saturation.

Using power system modelling outputs to identify weather-induced extreme events in highly renewable systems

In highly renewable power systems the increased weather dependence can result in new resilience challenges, such as renewable energy droughts, or a lack of sufficient renewable generation at times of high demand. The weather conditions responsible for these challenges have been well-studied in the literature. However, in reality multi-day resilience challenges are triggered by complex interactions between high demand, low renewable availability, electricity transmission constraints and storage dynamics. We show these challenges cannot be rigorously understood from an exclusively power systems, or meteorological, perspective. We propose a new method that uses electricity shadow prices—obtained by a European power system model based on 40 years of reanalysis data—to identify the most difficult periods driving system investments. Such difficult periods are driven by large-scale weather conditions such as low wind and cold temperature periods of various lengths associated with stationary high pressure over Europe. However, purely meteorological approaches fail to identify which events lead to the largest system stress over the multi-decadal study period due to the influence of subtle transmission bottlenecks and storage issues across multiple regions. These extreme events also do not relate strongly to traditional weather patterns (such as Euro-Atlantic weather regimes or the North Atlantic Oscillation index). We therefore compile a new set of weather patterns to define energy system stress events which include the impacts of electricity storage and large-scale interconnection. Without interdisciplinary studies combining state-of-the-art energy meteorology and modelling, further strive for adequate renewable power systems will be hampered.

A 7000-year record of extreme flood events reconstructed from a threshold lake in southern Norway

Recent decades have witnessed a shift in the seasonality and frequency of river floods in Norway, primarily attributed to contemporary global warming. Such changes necessitate a more comprehensive understanding of climate-flood dynamics across river systems. A significant challenge in flood risk assessment is that instrumental data records cover only the last few decades and do not capture low-frequency, high-magnitude extremes. Another challenge is the non-stationarity of the leading atmospheric pattern over Scandinavia, the North Atlantic Oscillation (NAO), which requires a spatial understanding of extreme events through time. To address this issue, we use lacustrine sediments from southernmost Norway, aiming to extend flood records beyond the scope of instrumental and historical data by exploring a threshold lake that accumulates flood deposits only when river discharge exceeds a critical level. A multi-proxy approach, which includes X-ray fluorescence (XRF), computed tomography (CT), and magnetic susceptibility (MS), was applied to fingerprint the sediments. We detected thin layers of minerogenic sediments, which we interpreted as slackwater deposits accumulated in a lake environment during extreme flood events. These fine-grained minerogenic bands were quantified using XRF and CT data to reconstruct the frequency of major floods over the past ∼7000 years. The minimum water discharge necessary for transporting sediments into the lake was assessed with the HEC-RAS hydraulic simulation software. Our record shows a high frequency of extreme floods from 7000 to 5000 cal yr BP before a significant decrease from 5000 to 2300 cal yr BP. The frequency has been high since 2300 cal yr BP, with a peak frequency after 1600 CE, coinciding with the Little Ice Age. There is a strong co-variability between a positive NAO index, winter precipitation, and major flood events over the past 120 years. A comparison between our flood record and other palaeorecords suggests that this relationship also holds on multi-millennial time scales.

The Spatio-Temporal Dynamics of Water Resources (Rainfall and Snow) in the Sierra Nevada Mountain Range (Southern Spain)

This paper describes the use of a unique spatio-temporally resolved precipitation and temperature dataset to assess the spatio-temporal dynamics of water resources over a period of almost seven decades across the Sierra Nevada mountain range, which is the most southern Alpine environment in Europe. The altitude and geographical location of this isolated alpine environment makes it a good detector of climate change. The data were generated by applying geostatistical co-kriging to significant instrumental precipitation and temperature (minimum, maximum and mean) datasets. The correlation between precipitation and altitude was not particularly high and the statistical analysis yielded some surprising results in the form of mean annual precipitation maps and yearly precipitation time series. These results confirm the importance of orographic precipitation in the Sierra Nevada mountain range and show a decrease in mean annual precipitation of 33 mm per decade. Seasonality, however, has remained constant throughout the period of the study. The results show that previous studies have overestimated the altitudinal precipitation gradient in the Sierra Nevada and reveal its complex spatial variability. In addition, the results show a clear correspondence between the mean annual precipitation and the NAO index and, to a much lesser extent, the WeMO index. With respect to temperature, there is a high correlation between minimum temperature and altitude (coefficient of correlation = −0.84) and between maximum temperature and altitude (coefficient of correlation = −0.9). Thus, our spatial temperature maps were very similar to topographic maps, but the temporal trend was complex, with negative (decreasing) and positive (increasing) trends. A dynamic model of snowfall can be obtained by using the degree-day methodology. These results should be considered when checking the local performance of climatological models.

Nonstationarity in the NAO–Gulf Stream SST Front Interaction

Abstract The interaction between the North Atlantic Oscillation (NAO) and the latitudinal shifts of the Gulf Stream sea surface temperature front (GSF) has been the subject of extensive investigations. There are indications of nonstationarity in this interaction, but differences in the methodologies used in previous studies make it difficult to draw consistent conclusions. Furthermore, there is a lack of consensus on the key mechanisms underlying the response of the GSF to the NAO. This study assesses the possible nonstationarity in the NAO–GSF interaction and the mechanisms underlying this interaction during 1950–2020, using reanalysis data. Results show that the NAO and GSF indices covary on the decadal time scale but only during 1972–2018. A secondary peak in the NAO–GSF covariability emerges on multiannual time scales but only during 2005–15. The nonstationarity in the decadal NAO–GSF covariability is also manifested in variations in their lead–lag relationship. Indeed, the NAO tends to lead the GSF shifts by 3 years during 1972–90 and by 2 years during 1990–2018. The response of the GSF to the NAO at the decadal time scale can be interpreted as the joint effect of the fast response of wind-driven oceanic circulation, the response of deep oceanic circulation, and the propagation of Rossby waves. However, there is evidence of Rossby wave propagation only during 1972–90. Here it is suggested that the nonstationarity of Rossby wave propagation caused the time lag between the NAO and the GSF shifts on the decadal time scale to differ between the two time periods.

Atmospheric teleconnections between the Arctic and the Baltic Sea region as simulated by CESM1-LE

Abstract. This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of Community Earth System Model version 1 large ensemble (CESM-LE) climate model simulations: the stationary case with pre-industrial radiative forcing and the climate change case with RCP8.5 radiative forcing. The stationary control simulation's 1800-year long time series were used for stationary teleconnection and a 40-member ensemble from the period 1920–2100 is used for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2 m level, sea-level pressure, sea ice concentration, precipitation, geopotential height, and 10 m level wind speed. The Arctic was divided into seven areas. The Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local meteorological factors are stronger. North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) climate indices can explain most teleconnections in winter and spring. During ongoing climate change, the teleconnection patterns did not show remarkable changes by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. We calculated the correlation between the parameter and its ridge regression estimation to estimate different Arctic regions' collective statistical connections with the Baltic Sea region. The seasonal coefficient of determination, R2, was highest for winter: for T2 m, R2=0.64; for sea level pressure (SLP), R2=0.44; and for precipitation (PREC), R2=0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker, with the highest R2=0.09 being for temperature in the spring. Hence, Arctic climate data forecasting capacity for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnection patterns between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century.

Modelling the Dynamics of Outbreak Species: The Case of Ditrupa arietina (O.F. Müller), Gulf of Lions, NW Mediterranean Sea

An outbreak species exhibits extreme, rapid population fluctuations that can be qualified as discrete events within a continuous dynamic. When outbreaks occur they may appear novel and disconcerting because the limiting factors of their dynamics are not readily identifiable. We present the first population hybrid dynamic model that combines continuous and discrete processes, designed to simulate marine species outbreaks. The deterministic framework was tested using the case of an unexploited benthic invertebrate species: the small, serpulid polychaete Ditrupa arietina. This species is distributed throughout the northeast Atlantic Ocean and Mediterranean Sea; it has a life cycle characterised by a pelagic dispersive larval stage, while juveniles and adults are sedentary. Sporadic reports of extremely high, variable densities (from <10 to >10,000 ind.m−2) have attracted attention from marine ecologists for a century. However, except for one decade-long field study from the Bay of Banyuls (France, Gulf of Lions, Mediterranean Sea), observations are sparse. Minimal formulations quantified the processes governing the population dynamics. Local population continuous dynamics were simulated from a size-structured model with a null immigration–emigration flux balance. The mathematical properties, based on the derived hybrid model, demonstrated the possibilities of reaching an equilibrium for the population using a single number of recruits per reproducer. Two extrapolations were made: (1) local population dynamics were simulated over 180 years using North Atlantic Oscillation indices to force recruitment variability and (2) steady-state population densities over the Gulf of Lions were calculated from a connectivity matrix in a metapopulation. The dynamics reach a macroscopic stability in both extrapolations, despite the absence of density regulating mechanisms. This ensures the persistence of D. arietina, even when strong, irregular oscillations characteristic of an outbreak species are observed. The hybrid model suggests that a macroscopic equilibrium for a population with variable recruitment conditions can only be characterised for time periods which contain several outbreak occurrences distributed over a regional scale.

Facultative migration in two thrush species (Fieldfare and Redwing): Rowanberry abundance is more important than winter weather

Facultative migration is thought to be influenced by food and weather, with birds remaining in northern areas if food is abundant and weather conditions are benign, but migrating south when food is scarce and weather is harsh. However, the relative importance of these two factors has rarely been tested with long-term data, and effects of weather are poorly documented. Here, I assess whether winter numbers of the Fieldfare (Turdus pilaris) and the Redwing (T. iliacus) in southern Norway during the period 1980–2020 varied in relation to the abundance of an important source of winter food (rowanberries, Sorbus aucuparia) and indices of winter harshness (North Atlantic Oscillation index, temperature, duration of snow cover). Two regions with contrasting winter harshness (western Norway with a maritime climate, eastern Norway with a continental climate) were compared. Winter numbers of Fieldfare in both regions, and Redwing in eastern Norway, were highest in years with high rowanberry abundance. Weather had mixed influence on thrush winter numbers, but interacted with rowanberry abundance in several cases so that small numbers of thrushes occurred with the combination of low rowanberry abundance and harsh weather. Such interactions occurred both in eastern and western Norway, and for both species, including for the Redwing in western Norway. In conclusion, facultative migration was strongly related to food availability with large numbers wintering in years with large rowanberry crops, and with additional effects of harsh weather working in concert with low rowanberry abundance to reduce wintering numbers to low levels in some years.

Prediction of seasonal sea surface temperature based on temperature and salinity of subsurface ocean using machine learning

AbstractThe sea surface temperature (SST) is not only a crucial external factor in the evolution of the atmosphere, but also a primary factor and premonition signal used in climate prediction. It is challenging to obtain a precise SST for generating accurate initial and boundary conditions in numerical models. This study employs a machine learning approach, that is, a convolutional neural network (CNN) algorithm, to predict SST on a seasonal scale. In particular, the subsurface ocean temperature (OT) and ocean salinity (OS) at depths of 5.02, 15.08, 25.16, 35.28, 45.45 and 76.55 m were used as training factors in developing a CNN prediction model. The results indicate that subsurface OT and OS can persist for 6 months or longer, with a maximum persistence of up to 12 months. Using the CNN prediction model, the SST can be reliably predicted 6 months in advance in most cases. The predicted SST has a mean bias of approximately 0–0.8 K on the globe. The bias is small (below 0.5 K) in the open ocean. The root mean square errors (RMSEs) of hindcasting for Interdecadal Pacific Oscillation, North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation indices are all less than 1.0 K. Specifically, the RMSE for El Niño prediction is less than 0.5 K. This study provides a viable method for establishing initial and boundary conditions for climate prediction.

Accumulation rates over the past 260 years archived in Elbrus ice core, Caucasus

Abstract. In this study, we present a seasonally resolved accumulation record spanning from 1750 to 2009 Common Era (CE), based on a 181.8 m ice core obtained from the Elbrus Western Plateau in the Caucasus. We implemented various methods to account for uncertainties associated with glacier flow, layer thinning, and dating. Additionally, we applied a novel approach to calculate a seasonal calendar for meteorological data, enabling comparison with ice core records. The reconstructed accumulation data were compared with available meteorological data, gridded precipitation records, and paleo-reanalysis data. Reconstructed accumulation is representative for a large region south of the Eastern European plain and Black Sea region with summer precipitation being the primary driver of precipitation variability. We identified a statistically significant relationship between changes in regional precipitation and fluctuations in the North Atlantic Oscillation (NAO) index, which is, however, not stable over the entire period covered by the ice core.

Redefining the North Atlantic Oscillation index generation using autoencoder neural network

Understanding the spatial patterns of the North Atlantic Oscillation (NAO) is vital for climate science. For this reason, empirical orthogonal function (EOF) analysis is commonly applied to sea-level pressure (SLP) anomaly data in the North Atlantic region. This study evaluated the traditional EOF-based definition of the NAO index against the autoencoder (AE) neural network-based definition, using the Hurrell NAO Index (Station-Based) as a reference. Specifically, EOF and AE were applied to monthly SLP anomaly data from ERA5 (1950–2022) to derive spatial modes of variability in the North Atlantic region. Both methods produced spatial patterns consistent with the traditional NAO definition, with dipole centers of action between the Icelandic Low and the Azores High. During boreal winter (December to March), when the NAO is most active, the AE-based method achieved a correlation of 0.96 with the reference NAO index, outperforming the EOF-based method’s correlation of 0.90. The all-season Adjusted R-squared values were 50% for the AE-based index and 34% for the EOF-based index. Notably, the AE-based index revealed several other non-linear patterns of the NAO, with more than one encoded pattern correlating at least 0.90 with the reference NAO index during boreal winter. These results not only demonstrate the AE’s superiority over traditional EOF in representing the station-based index but also uncover previously unexplored complexities in the NAO that are close to the reference temporal pattern. This suggests that AE offers a promising approach for defining climate modes of variability, potentially capturing intricacies that traditional linear methods like EOF might miss.

Warming Climate-Induced Changes in Lithuanian River Ice Phenology

Due to rising surface air temperatures, river ice is shrinking dramatically in the Northern Hemisphere. Ice cover during the cold season causes fundamental changes in river ecosystems and has important implications for nearby communities and industries. Changes caused by climate warming, therefore, affect the sustainability of key resources, livelihoods, and traditional practices. Thus far, too little attention has been paid to research into the phenomenon of river ice in the Baltic States. Since the observational data of the last sixty years are currently available, we took advantage of the unique opportunity to assess ice regime changes in the gauged rivers by comparing two climatological standard normals. By applying statistical methods (Mann–Kendall, Pettitt, SNHT, Buishand, von Neumann, and Wilcoxon rank sum tests), this study determined drastic changes in ice phenology parameters (freeze-up date, ice break-up date, and ice cover duration) of Lithuanian rivers in the last thirty-year period. The dependence of the selected parameters on local climatic factors and large-scale atmospheric circulation patterns was identified. It was established that the sum of negative air temperatures, as well as the North Atlantic Oscillation, East Atlantic, and Arctic Oscillation indices, have the greatest influence on the ice regime of Lithuanian rivers.

Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models

AbstractDynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet‐stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from empirical orthogonal function (EOF) analysis. Geopotential height data from the ERA5 reanalysis are used to generate the EOFs. Sets of predictors with known associations with these drivers are developed and used to formulate a sliding‐window NARMAX model. This model demonstrates a high degree of predictive accuracy, as indicated by its average correlation coefficients over the testing period (2006–2021): 0.78 for NAO, 0.83 for EA and 0.68 for SCA. In comparison, the SEAS5 and GloSea5 dynamical forecast models exhibit lower correlations with observed circulation changes: for NAO, the correlation coefficients are 0.51 for SEAS5 and 0.34 for GloSea5, for EA they are 0.15 and 0.09, respectively, and for SCA, they are 0.28 and 0.24, respectively. Comparison of NARMAX predictions with forecasts and hindcasts from the SEAS5 and GloSea5 models highlights areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer.

Growth Responses to Climate and Drought in Relict Cork Oak Populations as a Benchmark of the Species Tolerance

We still lack information on the long-term growth responses to climate of relict tree populations, which often persist in topoclimatic refugia. To fill that research gap, we studied three relict cork oak (Quercus suber) populations located in northern Spain using dendrochronology. The sites were subjected to humid (Zarautz), continental (Bozoó) and xeric (Sestrica) climate conditions. Cool–wet conditions during the current spring enhanced growth in Bozoó and Sestrica, whereas wet conditions in the previous October enhanced growth in Zarautz. In this site, growth also increased in response to dry conditions in the prior winter linked to high North Atlantic Oscillation indices. Correlations between the precipitation summed from the previous September to the current May peaked at the driest site (Sestrica). The strongest growth responses to drought severity were also found at this site, where growth negatively responded to 9-month early-summer droughts, followed by the continental Bozoó site, where growth was constrained by 1-month July droughts. Growth declined in response to 6-month January droughts in the wettest site (Zarautz), where cork oak was vulnerable to previous late-summer to autumn drought stress. Despite warmer and drier spring conditions that would negatively impact cork oak at the Bozoó and Sestrica sites, trees from these populations could tolerate further aridity.

Karadeniz'de 2000-2022 Yılları Arasında Yüzey Sıcaklığının Mekansal ve Zamansal Değişimi

This study presents a comprehensive assessment of the spatio-temporal variability of Sea Surface Temperature (SST) in the Black Sea at monthly to interannual scales, with a focus on understanding its connection to major large-scale atmospheric forcing during the period 2000-2022. Monthly variations of SST in the Black Sea reveal distinct seasonal patterns. The study evaluates the potential impacts of large-scale atmospheric patterns on interannual SST variations using climate indices such as the North Atlantic Oscillation (NAO), East Atlantic/West Russia (EA-WR) and El Niño-Southern Oscillation (ENSO) during the winter months. The results indicated that these large-scale atmospheric oscillations played a substantial role in influencing SST anomalies, with the NAO and EA-WR indices particularly affecting the Black Sea's SST anomalies. The NAO index exhibited negative values during warm winters and positive values during cold winters, with extreme cold and warm winters corresponding to specific years, as observed in 2003, 2006, 2012, 2017 (cold) and 2018, 2020, 2021 (warm). Notably, the relationship between NAO and SST anomalies was not as dominant during 2000-2022. This difference might be explained by the combined influence of NAO and ENSO, which is beyond the scope of this study. The EA-WR pattern was identified as another significant large-scale atmospheric dynamic affecting the Black Sea's SST. Although it explains the cold SST anomalies in certain years, it cannot account for extreme warm SST years. While the influence of ENSO remains somewhat inconclusive for the extreme warm period, the SST pattern between 2016-2022 aligns closely with El Niño events, particularly in 2018 and 2021 when positive SOI index values coincide with warm SST years in the Black Sea.