Teleconnection Indices Research Articles

Long-Term Teleconnections Between Global Circulation Patterns and Interannual Variability of Surface Air Temperature over Kingdom of Saudi Arabia

Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is linked to global climate patterns, which serve as significant indicators for studying the effects of climate change on surface air temperature patterns across the KSA. The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. The first mode (EOF1) for both seasons shows positive variability across the KSA, explaining more than 45% of the variance. The second mode (EOF2) indicates negative variability in central and northern regions. The third mode (EOF3) describes positive variability but with lower variance over time. PC1 is used to describe the physical mechanism of SAT variability and correlations with global sea surface temperature (SST). The physical mechanism shows that the variability in Mediterranean troughs during the winter season and high pressure over the Indian Ocean and central Asia controls SAT variability over the KSA. The correlation coefficients (CCs) were calculated during the winter and summer season between the SAT of the KSA and six teleconnection indices, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Atlantic Meridional Mode (AMM), Pacific Warm Pool (PWP), North Atlantic Oscillation (NAO), and Tropical North Atlantic (TNA) SST for the period from 1994 to 2022. ENSO shifts from positive to negative correlations with SAT from winter to summer. IOD shows a diminished correlation with SAT due to the absence of upper air dynamics. PWP consistently enhances surface warming in both seasons through upper air convergence during both seasons. AMM and NAO have a non-significant impact on SAT; however, TNA contributes warming over central and northern parts during winter and summer seasons. The seasonal SAT variations emphasize the significant role of ENSO, PWP, and TNA across the seasons. The findings of this study can be helpful for seasonal predictability in the KSA.

Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques

After precipitation, reference evapotranspiration (ETO) plays a crucial role in the hydrological cycle as it quantifies water loss. ETO significantly impacts the water balance and holds great importance at the basin level because of the spatial distribution of managing water resources. Large scale teleconnection indices (LSTIs) play a vital role by influencing climatic variables and can be pivotal in determining ETO and its predictive variables. This study aimed to model and forecast annual ETO in Iran’s basins by utilizing LSTIs and employing various machine learning models (MLMs) such as least squares support vector machine, generalized regression neural network, multi-linear regression (MLR), and multi-layer perceptron (MLP). Initially, climate data from 122 synoptic stations covering six and 30, main and sub basins were collected, and annual ETO values were computed using the Food and Agriculture Organization 56 (PMF 56) Penman–Monteith equation. The correlations between these values and 37 LSTIs were examined within lead times ranging from 7 to 12 months. Through a stepwise approach, the most influential predictor indices (LSTIs) were selected as input datasets for the MLMs. The findings revealed the significant influence of factors such as carbon dioxide (CO2), Atlantic multidecadal oscillation, Atlantic Meridional Mode, and East Atlantic on annual ETO. Overall, all MLMs performed well in terms of the Scatter Index during both training and testing phases across all sub-basins. Furthermore, the MLP and MLR models displayed superior performance compared to other models in the training and testing evaluations based on various assessment metrics.

Elevation‐dependent warming and possible‐driving mechanisms over global highlands

AbstractElevation‐dependent warming (EDW) has been a topic of intense debate due to limited observed data in global highland areas. This study aims to fill this gap by utilizing CRU and ERA5 datasets from 1981 to 2021 to explore the trends of climate change and its elevation dependency. The anomalies of temperature indicators (Tmean, Tmax, and Tmin) in both ERA5 and CRU showed significant warming trends over global highlands. Moreover, the response of temperature indicators to elevation across global highlands is not spatially uniform. The linear regression model based on the elevation showed significant warming signals for the temperature indicators at various elevations over the global highlands. On a regional scale, Tmean and Tmax predominantly showed linear EDW over EU highlands, while Tmean in Asian highlands exhibited EDW signals at 4–5 km. Tmin showed EDW at 2.5–5.5 km with ERA5 and 3–5 km with CRU. In the Andes, EDW was prominent at 2.5–4 km. Overall, EDW signals are evident in all studied regions but vary across them. While assessing the driving factors, the results of this study indicate that total column water vapour (TCWV), snow depth (SD), snow albedo, and normalized difference vegetation index (NDVI) correlated positively with the temperature indicators. These findings emphasize the significance of elevation‐specific interactions between environmental factors and temperature in forecasting temperature changes in mountainous areas. Additionally, temperature exhibited coherence with teleconnection indices from the Atlantic and Pacific Oceans. Asian and European (EU) highlands exhibited interzonal coherence with the Pacific and Atlantic Oceans, while North American (NA) highlands showed coherence, followed by South American (SA) highlands. These findings provide a comprehensive understanding of EDW and its implications for highland regions globally, including the potential for more severe depletion of snow/ice resources in a warmer future.

Can teleconnections help to improve the seasonal prediction over the Southern African Development Community Region?

The limited skill of seasonal climate predictions in some regions of the Southern African Development Community (SADC) restricts their potential application to the development of climate services. This study explores the feasibility of improving the quality of these predictions by using the observed relationship between Essential Climate Variables (ECVs) and large-scale teleconnection indices, namely Niño3.4, the Atlantic Niño and the Indian Ocean Dipole. The underlying hypothesis is that, for certain areas, the empirical observed teleconnections could improve the predictions offered by the seasonal forecasting systems. This is achieved by implementing linear regression models between each index and ECV, for up to 12 months into the past, and for each season and grid point. After obtaining the index-derived estimates of the variables, the correlation coefficients and fair Ranked Probability Skill Scores (fRPSS) are computed and compared to those of the ECMWF SEAS5 (SEAS5) predictions for different lead times. The results show that 10–25 % of the entire domain exhibits improved correlations for the index-derived precipitation in all seasons. In the case of temperature, though, higher correlations could be observed only in six seasons (and solely for Niño3.4). Regarding fRPSS, up to 7 % of the entire area shows an improvement when using Niño3.4 to estimate temperature (in four seasons). Conversely, for precipitation there is no detected enhancement. In future work, it would be worth investigating whether a combined multi-index regression can further raise the observed increase in performance.

Long-range hydrological drought forecasting using multi-year cycles in the North Atlantic Oscillation

With global temperatures, populations and ecological stressors expected to rise, hydrological droughts are projected to have progressively severe economic and environmental impacts. As a result, hydrological drought forecasting systems have become increasingly important water resource management tools for mitigating these impacts. However, high frequency behaviours in meteorological or atmospheric conditions often limit the lead times of hydrological drought forecasts to seasonal timescales, either through poorer performance of multi-year meteorological forecasts or the lack of multi-year lags in atmosphere-hydrology systems. By contrast, low frequency behaviours in regionally important teleconnection systems (such as the North Atlantic Oscillation, NAO) offer a novel way to forecast hydrological drought at longer lead times. This paper shows that, by using a data-driven modelling approach, long-term behaviours within the NAO can be skilful predictors of hydrological drought conditions at a four-year forecasting horizon. Multi-year semi-periodic patterns in the NAO were used to forecast regional groundwater drought coverage in the UK (proportion of groundwater boreholes in drought), with the greatest forecast performance achieved for longer duration droughts, and for hydrogeological regions with longer response times. Model errors vary from 14 % (proportion of boreholes, (MAE)) in flashy hydrological regions or short droughts (<3 months), to 2 % for longer duration droughts (>8 months). Model fits of r2 up to 0.8 were produced between simulated and recorded regional drought coverage. As such our results show that teleconnection indices can be a skilful predictor of hydrological drought dynamics at multi-year timescales, opening new opportunities for long-lead groundwater drought forecasts to be integrated within existing drought management strategies in Europe and beyond.

Forecasted changes to the timing of Pacific herring Clupea pallasii spawn in a warming ocean

Pacific herring Clupea pallasii are a critical commercial and subsistence fish species and play a keystone role in the ecology and culture of the North Pacific. The annual herring spawn, in which mature herring migrate nearshore to deposit eggs along the coastline, is an important event linked to the migration of seabirds and marine mammals as well as a subsistence harvest for Alaska Natives and First Nations in British Columbia. Previous work has suggested that environmental variables and broad teleconnection indices play a role in the magnitude and phenology of spawning; however, the effects of these drivers have not been examined in the context of future climate scenarios. Here, we modeled variability in the timing of herring spawn across British Columbia and Southeast Alaska using survey data from 1951-2022. We created a model using Pacific teleconnection indices, sea surface temperature (SST), tidal height, and lagged data to predict spawn date anomalies (SDAs) across 9 spawning regions. SDAs were significantly affected by the Oceanic Niño Index, Pacific Decadal Oscillation, SST, and lagged SDAs. We then used this model to predict SDAs using projected SST from climate models and bootstrapped teleconnection data from 2025-2100. Future herring spawn timing trends earlier on average with warming SSTs, although the magnitude is relatively small, occurring 9 d earlier on average by 2100. This changing phenology, though small, varied by region and may have ecosystem-level ramifications and create timing mismatch for migratory species. However, our findings also reinforce the importance of other physical factors not measured in this study, such as photoperiod, which drive herring spawn timing.

The signature of the main modes of climatic variability as revealed by the Jenkinson‐Collison classification over Europe

AbstractThe Jenkinson‐Collison Weather Typing (JC‐WT) method uses sea‐level pressure gradients to create 27 types based on the geostrophic flow and vorticity around any extratropical target location. Typically, JC‐WTs are applied over specific locations or limited domains, thus hampering the understanding of the impact of large‐scale mechanisms on regional climate. This study explores the links between regional climate variability, as represented by the JC‐WTs, and large‐scale phenomena, to describe the synoptic‐scale variability in the North Atlantic‐European region and evaluate the JC‐WT methodology. Large‐scale circulation is here characterized by major atmospheric low‐frequency modes, namely the North Atlantic Oscillation, the East Atlantic and the Scandinavian teleconnection indices, and by atmospheric blockings. Results show that JC‐WTs coherently capture the spatial and temporal variability of the large‐scale modes and yields a characteristic response to blocking events. Overall, our results underpin the exploratory potential of this method for the analysis of the near‐surface circulation. These findings endorse the use of JC‐WTs and support the reliability and utility of the JC‐WT classification for process‐based model assessments and model selection, a crucial task for climate impact studies.

Coherency and time lag analyses between MODIS vegetation indices and climate across forests and grasslands in the European temperate zone

Abstract. Identifying the climate-induced variability in the condition of vegetation is particularly important in the context of recent climate change and plants' impact on the mitigation of climate change. In this paper, we present the coherence and time lags in the spectral response of three individual vegetation types in the European temperate zone to the influencing meteorological factors in the period 2002–2022. Vegetation condition in broadleaved forest, coniferous forest and pastures was measured with monthly anomalies of two spectral indices – normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI). As meteorological elements we used monthly anomalies of temperature (T), precipitation (P), vapour pressure deficit (VPD), evapotranspiration (ETo), and the teleconnection indices North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP). Periodicity in the time series was assessed using the wavelet transform, but no significant intra- or interannual cycles were detected in both vegetation (NDVI and EVI) and meteorological variables. In turn, coherence between NDVI and EVI and meteorological elements was described using the methods of wavelet coherence and Pearson's linear correlation with time lag. In the European temperate zone analysed in this study, NAO produces strong coherence mostly for forests in a circa 1-year band and a weaker coherence in a circa 3-year band. For pastures these interannual patterns are hardly recognisable. The strongest relationships occur between conditions of the vegetation and T and ETo – they show high coherence in both forests and pastures. There is a significant cohesion with the 8–16-month (ca. 1-year) and 20–32-month (ca. 2-year) bands. More time-lagged significant correlations between vegetation indices and T occur for forests than for pastures, suggesting a significant lag in the forests' response to the changes in T.

Long-term forecasting methods of dates of ice break-up and disappearance at the Dnipro Cascade Reservoirs by teleconnection indicators

Long-term forecasting of the ice regime at the Dnipro Cascade Reservoirs is very important, first of all, for the operation of hydroelectric power stations. In addition, such forecasts also ensure the work of other sectors of the economy, namely shipping, fisheries, municipal engineering, etc. At present, the Department for hydrological forecasting of the Ukrainian Hydrometeorological Center uses techniques developedin the middle and in thesecond half of the 20th century to compile long-term forecasts and advices about the ice regimeof water bodies of Ukraine. These techniqueswere developed only for rivers;for reservoirs,they are currently lacking.&#x0D; The main objective of our research is to develop methods for long-term forecasting of ice break-up and disappearance at the Dnipro Cascade Reservoirs by teleconnection indicators. The research was based on the recordsfor 35 water gauges located on 6 reservoirs of the Dnipro Cascade, including the destroyed Kakhovka reservoir. The dates of the ice break-up and disappearance were used for the period from the beginning of observations at each water gauge to 2020. Moreover, the teleconnection indicators were used, namely 34 atmospheric indices, sea surface temperature indices, teleconnection indices and patterns determined by the National Weather Service (NWS) of the National Oceanic and Atmospheric Administration of United States of America (NOAA USA).&#x0D; Methods of the long-term forecasting of the ice break-up and disappearance at the reservoirs of the Dnipro Cascade have been developed by searching for the best correlation or regression relationship between dates and teleconnection indicators. The probable of accepted errorsof the developed methods are in the range of 62—71 %, which corresponds to satisfactory method quality that makes it possible to recommend them for use.

Spatiotemporal Variability in Rainfall Erosivity and Its Teleconnection with Atmospheric Circulation Indices in China

Rainfall erosivity (RE) is a critical factor influencing soil erosion, and soil erosion is closely related to land ecosystem health and long-term sustainable utilization. To ensure regional stable food supply and ecological balance, it is crucial to study the spatiotemporal distribution and influencing factors of RE. This study focuses on China and its three natural regions using daily precipitation data from 611 stations from 1960 to 2020. The study analyses the spatiotemporal changes in RE. Rainfall events were classified as moderate, large, and heavy based on rainfall intensity. The RE contribution from different rainfall levels to the total RE was analyzed, and the key climatic drivers closely linked to RE were identified using random forest. The results demonstrate that (1) on a national scale, RE shows a significant increasing trend, marked by an 81.67 MJ·mm·ha−1·h−1/decade. In the subregions, the Eastern Monsoon Region (EMR) and Qinghai–Tibet Plateau Region (QTR) show a significant increasing trend, with a greater change rate in EMR of 108.54 MJ·mm·ha−1·h−1/decade, and the Northwest Arid Region (NAR) shows a nonsignificant upwards trend. (2) The average RE increases northwest–southeast nationwide, ranging from 60.15 MJ·mm·ha−1·h−1 to 31,418.52 MJ·mm·ha−1·h−1. The RE contribution generated by different rainfall levels to the total RE exhibits spatial variations. The dominant types show that EMR is influenced by heavy RE, NAR is dominated by large RE, and QTR is affected by moderate RE. (3) The REs are associated with teleconnection indices, but the impact of these indices varies in different regions. The Western Hemisphere Warm Pool has the greatest impact on the EMR, while the North Atlantic Oscillation and Atlantic Multidecadal Oscillation are the factors influencing RE in NAR and QTR, respectively. (4) On a national scale, for every 1 mm increase in annual total rainfall, the RE increased by 8.54 MJ·mm·ha−1·h−1, a sensitivity of 8.54 MJ·mm·ha−1·h−1/mm. For the three subregions, there are differences in the sensitivity of RE to changes in annual precipitation. The highest sensitivity is found in EMR, at 8.71 MJ·mm·ha−1·h−1/mm, which is greater than the sensitivity indices in NAR (6.19 MJ·mm·ha−1·h−1/mm) and QTR (3.60 MJ·mm·ha−1·h−1/mm). This study can provide theoretical references for future regional soil erosion prediction and sustainable agricultural development in China.

What Controls the Interannual Variation in Rapid Intensification Magnitude of Western North Pacific Tropical Cyclones?

AbstractThis study investigates the interannual variability of rapid intensification (RI) magnitude of western North Pacific (WNP) tropical cyclones. There is a significant correlation between basin‐averaged RI magnitude during July–November and the simultaneous Western Pacific (WP) teleconnection index from 1982 to 2021. RI magnitude is, on average, larger (smaller) in positive (negative) WP phases. During a positive WP, RI magnitude changes exhibit a southwest‐northeast dipolar pattern, with significant increases over the southwestern quadrant of the WNP and weak decreases over the northern part of the WNP. The WP teleconnection relates to RI magnitude primarily through modulation of 850–200‐hPa vertical wind shear, with less influence from 850‐hPa relative vorticity. The changes in these two dynamic conditions can be linked to WP‐induced circulation anomalies at lower and upper levels. Our results highlight that different contributors may be responsible for changes in WNP RI occurrence and RI magnitude.

Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin

The Paute river basin (southern Ecuador) suffers hydrological changes due to climate change and human activities. Hydrological changes cause extreme events and affect ecosystems, hydroelectric plants, and quality of life. It highlights the importance of understanding hydrological behavior to make appropriate decisions in extreme environments. This study seeks to predict discharges in the Paute river basin through global teleconnection indices. Multiple Linear Regression (MLR) was obtained using three different methodologies: multicollinearity analysis, Principal Component Analysis (PCA), and correlation with monthly delays. It was shown that the principal component analysis scenario obtained the best predictive fits, specifically by including 41 indices and 20 components. For the scenario using monthly delays, the best delay occurs within a single month for most seasons. Finally, with the multicollinearity analysis scenario, better results were obtained using 41 indices, although essentially the performance corresponds to the number and indices of each model. Teleconnection indices are not sufficient when used as the only input variable for download modeling and prediction, giving mostly unsatisfactory results. However, a clear trend links the behavior of flows and indices, and it is possible to improve the models based on more climatic variables or with other predictive methods.

Statistical Modeling of Monthly and Seasonal Michigan Snowfall Based on Machine Learning: A Multiscale Approach

Abstract Snow is an important component of Earth’s climate system, and snowfall intensity and variation often significantly impact society, the environment, and ecosystems. Understanding monthly and seasonal snowfall intensity and variations is challenging because of multiple controlling mechanisms at different spatial and temporal scales. Using 65 years of in situ snowfall observation, we evaluated seven machine learning algorithms for modeling monthly and seasonal snowfall in the Lower Peninsula of Michigan (LPM) based on selected environmental and climatic variables. Our results show that the Bayesian additive regression tree (BART) has the best fitting (R2 = 0.88) and out-of-sample estimation skills (R2 = 0.58) for the monthly mean snowfall followed by the random forest model. The BART also demonstrates strong estimation skills for large monthly snowfall amounts. Both BART and the random forest models suggest that topography, local/regional environmental factors, and teleconnection indices can significantly improve the estimation of monthly and seasonal snowfall amounts in the LPM. These statistical models based on machine learning algorithms can incorporate variables at multiple scales and address nonlinear responses of snowfall variations to environmental/climatic changes. It demonstrated that the multiscale machine learning techniques provide a reliable and computationally efficient approach to modeling snowfall intensity and variability.

A Case Study of Drought during Summer 2022: A Large-Scale Analyzed Comparison of Dry and Moist Summers in the Midwest USA

The summer of 2022 was very dry across Missouri and the surrounding regions including much of the Great Lakes, Midwest, and southern plains of the USA. A comparison of this summer to the dry summer of 2012 and the relatively wet summers of 2018 and 2021 was carried out using the National Centers for Environmental Prediction/National Centers for Atmospheric Research reanalysis, the Climate Prediction Center teleconnection indexes, and the blocking archive at the University of Missouri. The summer of 2022 was like that of 2012 which was characterized by a strong 500 hPa height anomaly centered over the western US and plains as well as very little blocking in the East Pacific. The summers of 2018 and 2021 were characterized by more zonal flow over the USA and more blocking in the East Pacific, similarly to the results of an earlier study. The teleconnection indexes for the prior spring and summer were largely similar for the two drier years and opposite for the wetter years. The surface conditions for the drier years were more similar while these were opposite for the wetter years. The integrated enstrophy (IE) used in earlier studies identified a change in the large-scale flow regime in early June 2022, which coincided with a decrease in the precipitation over the study region. However, one key difference was that the spring of 2022 was characterized by blocking more consistent with a wetter summer. This would have made the predictability of the drought of summer 2022 less certain.

Spatial and Temporal Variability of Extreme Precipitation Events in the Southeastern United States

Much of the Southeastern United States (SeUS) has experienced an increasing number of extreme precipitation events in recent decades. Characterizing these extreme precipitation events is critical for assessing risk from future hydroclimatic extremes and potential flash flooding. A threshold of one inch per hour (1IPH) was used to indicate an extreme precipitation event. Non-parametric tests were run to identify trends in 1IPH event frequency and locate time series change points. In the last 20 years, 1IPH events increased by 53 percent in the SeUS, and 21/61 stations recorded significant increasing trends. A change point is identified in 15/61 stations. June, July, and August are generally the peak time for 1IPH events, but Florida, Louisiana, and Mississippi recorded longer peak seasons. For the time between events, 17/61 stations recorded significant decreasing trends, implying that 1IPH events are increasing in frequency. Four teleconnection indices were positively correlated with 1IPH events. The SeUS experiences considerable tropical cyclone-induced extreme precipitation, yet only seven percent of 1IPH events overlapped with tropical cyclones. Therefore, the increasing frequency of 1IPH events is likely the result of a combination of baroclinic frontal zones or regional and mesoscale convective features. Causes for the increasing frequency of 1IPH events require further research.

Early Warning Indicators of Groundwater Drought in Mountainous Regions

AbstractAquifers in mountainous regions are susceptible to drought. However, the diverse hydroclimatology, the small and responsive aquifers, and the varied nature of interactions between groundwater and surface water lead to complex groundwater level responses that are challenging to interpret for understanding groundwater drought. In this study, generalized additive models (GAMs) are used to explore the sensitivity of summer groundwater levels to various climate and hydrological predictor variables (indicators) in each of a snowmelt‐ and rainfall‐dominated hydroclimatic regime in British Columbia, Canada. A sensitivity analysis explores individual seasonal predictor variables, station versus gridded climate data, teleconnection indices, and time series length. GAMs are then generated for different combinations of predictor variables to identify the best combination for each region. In the snowmelt‐dominated regime, maximum spring temperature, maximum snow water equivalent, and the winter Nino 3.4 index is the best combination of variables for predicting summer groundwater levels. In the rainfall‐dominated regime, maximum spring temperature, winter precipitation, and spring streamflow is the best combination. The unique combinations of predictor variables for each region can be used as early warning indicators for groundwater drought preparedness by water managers prior to the beginning of the summer. The Standardized Groundwater Level Index (SGI) is also effective at indicating which wells had pronounced responses to periods of drought in each region. However, the SGI differed among aquifers of similar type, suggesting other factors such as aquifer response mechanism and groundwater pumping may have an important influence on the SGI.

Seasonal Forecasting of Precipitation, Temperature, and Snow Mass over the Western United States by Combining Ensemble Postprocessing with Empirical Ocean–Atmosphere Teleconnections

Abstract Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer. Significance Statement Here we test the postprocessing of seasonal forecasts from two state-of-the-art seasonal prediction models for traditionally forecasted elements of precipitation and temperature as well as snowpack, which is important for water management. A two-stage procedure is utilized, including ocean and atmospheric teleconnection indices that have been shown to impact seasonal weather across the western United States. First, we adjust model output based on the average error in historic runs and then relate the remaining error to these teleconnection indices. A final step combines each adjusted model based on its historic performance. Forecasts are shown to improve upon the original models when assessed probabilistically. The snowpack forecasts perform better than temperature and precipitation forecasts with the best performance from late winter through early summer. Persistence is found to contribute strongly to the skill of snowpack and moderately to the skill of temperature.

КОЛЕБАНИЕ МАДДЕНА - ДЖУЛИАНА В КОНТЕКСТЕ ВНУТРИСЕЗОННОЙ ИЗМЕНЧИВОСТИ, ДАЛЬНИХ СВЯЗЕЙ И ПРЕДСКАЗУЕМОСТИ

The paper discusses issues related to the main features of subseasonal variability in the tropics, as well as the influence of the Madden-Julian oscillation (MJO) on atmospheric processes in the Northern Hemisphere mid-latitudes. To identify weather patterns in the tropics, the RMM (Real-time Multivariate) index proposed by M. Wheeler and H. Hendon is used. Teleconnection indices are used to quantify atmospheric processes in the Northern Hemisphere mid-latitudes. The analysis of variance leads to the conclusion that there are teleconnections between the weather patterns in the tropics and the mid-latitudes at time intervals of about 5-7 days, which are most clearly pronounced in the Pacific-North American region, as well as in Asia. The MJO effect on the circulation in the Euro-Atlantic sector is quite complex and requires consideration of additional factors, in particular, the circulation in the stratosphere and El Niño events. Using the INM-CM5 climate model (Russia), the potential of predicting the MJO over a three-week time interval is demonstrated. The results are expected to be used in the operational practice of the North Eurasia Climate Center.

Temperature and salinity trends in the northern limit of the Canary Current Upwelling System

Thermohaline time series are crucial for detecting and quantifying abiotic changes in the marine environment, and even more so in the present global change scenario. This is particularly relevant for the Ría the Vigo and its adjacent shelf, a highly productive ecosystem at the northern limit of the Canary Current Upwelling System (CanCUS). This study analyses a 34-year time series (1987–2020) of Conductivity-Temperature-Depth (CTD) casts, the longest series available to date in the region. Long-term trends, shifts, and seasonal variability of temperature and salinity were assessed and investigated in relation to regional meteorological variability and basin-scale atmospheric teleconnection indices. Generalized Additive Models (GAM) allowed us to determine that monthly thermohaline variability can be largely explained by regional meteo-climatic variability, mainly upwelling index and river discharge. Trends and shifts in some teleconnection patterns, especially the East Atlantic (EA) pattern, may also be related to both the shift in salinity in 2013 and its long-term decrease below 50 m depth. Despite the current global warming context, no statistically significant trend was observed for either the upwelling index or temperature. The spatial analysis of sea surface temperature trends suggests that our study area has been responding to climate change differently from other surrounding near-shore areas, as the Finisterre Cape or the southern Bay of Biscay. Overall, this study highlights the importance of long-term observations to elucidate the impact of climate change in the northern limit of the CanCUS and encourages caution when extrapolating conclusions from ecosystem studies on a regional scale.

Multi-year total ozone column variability at three Norwegian sites and the influence of Northern Hemisphere Climatic indices

Total ozone column (TOC) measurements are retrieved from the Ozone Monitoring Instrument (OMI) onboard the NASA Earth Observing System (EOS) Aura satellite at the three Norwegian sites: Oslo (59.9°N 10.7°E, 1 m a.s.l.), Trondheim (63.4°N 10.4°E, 3 m a.s.l.) and Andøya (69.1°N 15.7°E, 32 m a.s.l.). TOC data have been analysed from 2005 to 2021, in order to detect annual and multi-years total ozone variability. The relationship between geopotential height (GPH) at 250 hPa and total ozone column has been evaluated after showing that monthly anomalies in GPH and TOC are correlated amongst the three sites. The influence of the three Northern Hemisphere Tele Connection (TC) indices (North Atlantic Oscillation, Arctic Oscillation and Scandinavia) on TOC variability has been investigated. It is found that Scandinavia index plays a prominent role for the northernmost latitudes of Andøya and Trondheim while North Atlantic Oscillation and Arctic Oscillation indices are weakly correlated (negatively) to TOC and (positively) to GPH at Oslo. The response of TOC variability to the solar activity at the three sites is also explored and it is noticed that in the period of increasing variation of solar activity, significant TOC anomaly events are only observed in Andøya and Trondheim.