Teleconnection Indices Research Articles

Wind stilling ceased in the Iberian Peninsula since the 2000s

This study analyses quality controlled and homogenized near-surface wind speed series (SWS) at 87 meteorological stations distributed across Spain and Portugal for 1961–2019. Multidecadal variability analysis of both mean and gusts SWS confirms for the first time in the region the cessation of the stilling (decline of SWS) and a possible weak reversal phenomenon (increase in SWS) in the last decades, varying its onset from 1999 to 2018, depending on the season and variable. Different atmospheric circulation drivers are evaluated to explain the stilling and reversal phenomena. Among the chosen teleconnection indices, the Western Mediterranean Oscillation index (WeMOi) is the one that presents the highest (positive) correlation with SWS, although the correlation (negative) with the North Atlantic Oscillation index and the Mediterranean Oscillation index in winter is also statistically significant (p < 0.05). Results show that the interannual variability of the WeMOi could exert a strong influence on both the stilling and reversal phenomena, as it displays statistically significant negative trends for 1961–2010 and positive ones (non-significant) for 2010–2019, in agreement with the observed SWS trends. The use of the Jenkinson and Collison weather type classification reveals the marked influence of certain weather types in modulating SWS changes (i.e., west, northwest, and anticyclonic types). We proposed the increase in atmospheric thermal stability and the northward shift of the jet stream as principal causes of the stilling phenomenon in this region. Our results improve our understanding of wind changes, and highlight the importance of regional assessments to discern their socioeconomic and environmental impacts.

Coupling analysis of surface runoff variation with atmospheric teleconnection indices in the middle reaches of the Yangtze River

Coupling analysis of surface runoff variation with atmospheric teleconnection indices in the middle reaches of the Yangtze River

The Influence of the North Atlantic Oscillation and East Atlantic Pattern on Drought in British Catchments

Drought events are influenced by a combination of both climatic and local catchment characteristics. In Great Britain the North Atlantic Oscillation (NAO) has long been recognised as the leading mode of climate variability, and studies have also noted the role of the East Atlantic Pattern (EA) as a secondary mode. This study aimed to develop an understanding of the combined influence of the NAO and EA on rainfall distribution and magnitude and the variable nature of meteorological to hydrological drought propagation. Initially, this study explores correlations between teleconnection indices and standardised precipitation and streamflow indices for 291 catchments across Great Britain, before focusing on nine case study catchments for further analysis. For each case study catchment, we use quantile regression and an analysis of drought frequency to explore the combined influence of the NAO and EA on drought conditions. Through a convergence of evidence from these analyses we make three observations. Firstly, in the winter months both the NAO and EA exert an influence on drought conditions, however there is spatial variability in the relative influence of the NAO and EA; the NAO has a stronger influence in the north-west, whilst the EA has a stronger influence in the southern and central regions. Secondly, in the summer months, less distinctive spatial differences were found, with higher probability of drought conditions under NAO+ phases, which however can be enhanced or moderated by the EA. Finally, as a result of catchment characteristics there is spatio-temporal variability in the propagation of meteorological to hydrological drought. Our findings suggest that by considering the NAO and EA in combination, we can better describe climate and drought variability. We conclude by noting the potential implications our study has on the role of monthly teleconnection forecasts in water management decision making in Great Britain, and acknowledge the current limitations associated with incorporating such understanding.

Northern Hemisphere Flow Regime Transitions, Blocking, and the Onset of Spring in the Central USA during Late Winter 2019 and 2021

Studies have shown that maxima in the time series of Northern Hemisphere (NH) integrated enstrophy (IE) can be associated with large-scale flow regime transitions and, often, the onset and decay of blocking events. During February and March 2019, and then February 2021, strong IE maxima were associated with changes in the NH flow regimes that brought very cold conditions to the central United States. The colder conditions in the central USA during late winter 2019 and 2021 were also associated with very strong Pacific or Atlantic Region blocking events. Using the NCEP re-analyses, three different teleconnection indexes, and surface weather data from nine different cities in the central USA, IE maxima, flow regime transitions, and surface weather regimes are identified. The mean temperature and precipitation characteristics for the cities named here during the different large-scale flow regime characteristics are compared. The results have demonstrated that relatively warm conditions occurred through the first part of February 2019 before a period of anomalously colder (as much as 12 °C below normal) and drier weather, with more snow, persisted into early March. This period was bookended by maxima in the NH IE time series, changes in the character of the main NH teleconnection indexes, and a strong simultaneous NH blocking episode. Following the cold period, the temperature regime returned to values that were closer to seasonal normal values, which were then discussed as a possible indicator of a transition from a winter to a spring regime.

Analysis of drought and flood alternation and its driving factors in the Yangtze River Basin under climate change

The spatial and temporal characteristics of global precipitation have changed significantly under climate change, increasing the difficulty of solving water security problems. Therefore, understanding changes in global precipitation is of great importance to comprehend and solve water security problems. This study analyzed the characteristics of drought and flood alternation in the Yangtze River Basin (YRB) based on precipitation data, and explored the driving forces of drought and flood alternation by combining relevant meteorological factors (i.e., temperature [Tem], relative humidity [Rh], wind speed [Win], and actual evaporation [ETa]) and teleconnection indices (i.e., Nino 3.4, Pacific Decadal Oscillation [PDO], Southern Oscillation Indices [SOI], North Pacific Index [NPI], and Atlantic Multi-decadal Oscillation [AMO]). The main conclusions are as follows: (1) The spatial and temporal characteristics of precipitation in the sub-basins of the YRB varied widely, with significant annual precipitation trends ranging from −5 to 9 mm/year (p < 0.05). The maximum temporal difference in the rainy season reached 1–2 months; (2) The mutations of precipitation in the period of pre-flood (PP), precipitation in the period of post-flood (PN), and long-cycle drought-flood abrupt transition index (LDFAI) in sub-basins of the YRB (e.g., Mintuojiang River Basin [MRB], Wujiang River Basin [WRB], Dongting Lake Basin [DLRB], Yichang-Hukou Reach [YC-HK], Downstream of Hukou [DHKRB], and Taihu Lake Basin [TLRB]) were prominent from 2001 to 2011; and (3) both the single factor (i.e., meteorological factors or teleconnection index) and multiple factors (i.e., meteorological factors and teleconnection index) influenced the LDFAI of each sub-basin in the YRB. Related research is important and useful for studying dry–wet changes and water security issues in the YRB.

Seasonal prediction of renewable energy generation in Europe based on four teleconnection indices

With growing amounts of wind and solar power in the electricity mix of many European countries, understanding and predicting variations of renewable energy generation at multiple timescales is crucial to ensure reliable electricity systems. At seasonal scale, the balance between supply and demand is mostly determined by the large-scale atmospheric circulation, which is uncertain due to climate change and natural variability. Here we employ four teleconnection indices, which represent a linkage between atmospheric conditions at widely separated regions, to describe the large-scale circulation at seasonal scale over Europe. For the first time, we relate each of the teleconnections to the wind and solar generation anomalies at country and regional level and we show that dynamical forecasts of the teleconnection indices allow predicting renewable generation at country level with positive skill levels. This model unveils the co-variability of wind and solar generation in European countries through its common dependence on the general circulation and the state of the teleconnections.

Analysis of South American climate and teleconnection indices

Oceanic heat anomalies affect climate in remote regions through the atmospheric cycle. South America (SA) was the first region found associated with EI Niño, which affects the fishery, agriculture, forestry, and livestock industry of SA. As approximately 60% of the total water is used for agriculture, climate changes in SA caused by ocean anomalies have led to the variability of available water, especially for irrigation water. Where the precipitation is low and/or the temperature is high, the availability and quality of water resources are under pressure. For instance, droughts associated with La Niña severely limited water supply and irrigation requirements between 25°S - 40°S in west-central Argentina and central Chile. In order to study the relationship between ocean variability and the climate of SA, 19 teleconnection indices (TI) related to Ocean abnormity are considered. The 19 indices are: the sea surface temperature (SST) and their anomaly in 4 Niño regions (SST1 + 2, SST3, SST3.4, SST4, ANOM1 + 2, ANOM3, ANOM3.4, ANOM4), Southern Oscillation Index (SOI), Oceanic Niño Index (ONI), Outgoing Longwave Radiation (OLR), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Pacific-North America (PNA), Atlantic Multi-decadal Oscillation (AMO), West and East of Indian Ocean Dipole (IODW, IODE), and the difference between IODW and IODE (IODd). High-resolution gridded climate data (1982–2016) from the Global Precipitation Climatology Centre (GPCC), the Climate Prediction Center (CPC), and the National Centers for Environmental Prediction (NCEP) are applied for correlation analyses. The results show that the 89.4% area of South American climate has a significant correlation with the SST in Niño region 1 + 2, the mean correlation coefficient is 0.55 for NCEP precipitation and 0.54 for CPC temperature. The lag duration for the remote correlation is around 2–3 months. It is the first attempt to analyze the correlation relationship based on 19 TIs, which can provide comprehensive insight into the climate of SA at a high-resolution scale. These findings are helpful for identifying the sensitive factors that affect climate in SA, for projecting the climate variables of SA, and for managing the irrigation water resources of SA.

Receiver Operating Characteristic Curve Analysis-Based Evaluation of GCMs Concerning Atmospheric Teleconnections

The evaluation of general circulation models (GCM) is a fundamental step in climate research in terms of both quality assurance/quality control and realistic representation of the dynamics of the atmospheric flows in the future projections. In this paper, a statistical method is introduced to evaluate GCMs with respect to teleconnection patterns in the winter 500 hPa geopotential height field over the Northern Hemisphere (NH). The procedure uses the combination of negative extrema method and receiver operating characteristic (ROC) curve analysis. The proposed method is demonstrated using selected general circulation models (GCMs) disseminated by the CMIP5 project. The ERA-20C reanalysis was used as a reference, supported by the NCEP/NCAR R1 reanalysis. The proposed method enables us to track changes in the geographical positions of the action centers (ACs); therefore, to detect improvement/deterioration in the GCM performance with time. It was found that the majority of the GCMs reproduce prominent teleconnections of the NH but fail to capture the eastward shift of the ACs over the Pacific Ocean in the last decades of the 20th century. The GCMs reproduce teleconnections with stronger correlations over the north-western part of the Atlantic Ocean compared to the reanalyses. The construction of mobile teleconnection indices is proposed to gain further insight into the performance of the models and to support a regional-scale analysis. The method can be easily applied to the recent CMIP6 simulations.

Temporal Variability and Predictability of Intense Cyclones in the Western and Eastern Mediterranean

Our understanding of the time variability of intense cyclones in the Mediterranean region is still lacking despite its importance for the long-term forecast of climate anomalies. This study examines the month-to-month variability and predictability of cyclones, the intensity of which exceeded the 75th percentile (intense cyclones) and the 95th percentile (extreme cyclones), over the Western and Eastern Mediterranean. The locations of cyclones were obtained by applying the method of M. Yu. Bardin on the 6-hourly 1000 hPa geopotential height data from the NCEP/NCAR reanalysis for the period 1951–2017 (67 years). It was shown that annual frequencies of cyclones were higher in the Western Mediterranean due to the contribution of spring and autumn; monthly averages were higher in the Eastern Mediterranean in December/January–March for intense/extreme cyclones. In the context of global warming, no linear trends significant at the 90% confidence level were found in the variability of intense and extreme cyclones, except for a positive trend in autumn extreme cyclones over the Eastern Mediterranean. The time series of cyclones in both parts of the Mediterranean were characterized by a pronounced interannual variability with a noticeable decadal modulation. According to spectral analysis, these interannual periods were multiples of 2–3 years corresponding to the main global teleconnection patterns. Seasonally, the most energy was concentrated in winter spectra; spring and autumn spectra had lower comparable magnitudes. The correlation analysis between the frequency of cyclones and the indices of the main atmospheric patterns showed that the main synchronous patterns for intense and extreme Mediterranean cyclones in September–April were the Mediterranean Oscillation (with the opposite signs for the Western and Eastern Mediterranean), Scandinavia pattern (positive correlation), and East Atlantic Oscillation (negative correlation). Additional important synchronous teleconnection patterns for some months were the Arctic Oscillation and East Atlantic/West Russia pattern for the Western Mediterranean, and the Polar/Eurasia pattern and Tropical Northern Hemisphere pattern for the Eastern Mediterranean. The outcome of this paper was the use of an artificial neural network model with inputs of global teleconnection indices both in the atmosphere and ocean to describe the temporal variability of the frequency of intense cyclones in the Western and Eastern Mediterranean. The predictability of intense cyclones was shown with the possibility of forecasts with a lead time of 0, 2, 4, and 6 months for the Western Mediterranean in October, January, February, April, and May, and for the Eastern Mediterranean in January, February, March, April, and May. One of the applications of this model may be in forecasting the evolution of the monthly frequency of cyclones with a lead time of 2 to 6 months.

Contributions of the climate regime shift and historical global warming to explosive cyclone activity around Japan according to large‐ensemble simulations

AbstractUsing the Database for Policy Decision‐Making for Future Climate Change (d4PDF), this study examined the impact of a tropical climate regime shift around 1998/1999 on explosive cyclone activity around Japan in boreal winter, highlighting cyclones moving along the Kuroshio Current (KC cyclones), especially northward‐migrating (N‐type, as defined in Tsukijihara et al., 2019) KC cyclones. Wave‐train patterns along the Asian jet affect the track and rapid development of N‐type cyclones, and the frequency of the top 10% of pronounced wave‐train patterns using a teleconnection index (NI10%) has increased since 1998/1999. Tropical precipitation in the region close to the Bay of Bengal increased with the climate regime shift, leading to the increased frequency of NI10%. The downstream development of wave packets tends to form a ridge with a barotropic structure east of Japan that is able to force the northward shift of the KC cyclone. In reality, there are high correlations between the inter‐annual variations in the ensemble mean frequencies of NI10%, N‐type cyclones, and tropical precipitation around the Bay of Bengal, and all three indices have increased since 1998/1999. Furthermore, the above results are nearly the same in both historical and non‐warming experiments, indicating that recent changes in the frequency of N‐type cyclones are not directly related to historical global warming.

The relationship between teleconnections, surface temperature, and cold air outbreaks

AbstractCold air outbreaks (CAOs) are extreme events that can negatively impact the agricultural industry, human health, and cause widespread power outages from increased energy consumption. Increasing the predictability of CAOs is critical to limiting the adverse impacts on society, but skilful predictions rely on well‐defined mechanisms of causation. To improve the understanding of CAO mechanisms, this study examines the relationship between atmospheric and oceanic teleconnection indices and CAOs in the Northern Hemisphere (NH) during two distinct periods, 1979–1998 and 1999–2018. Changes in the relationship between CAOs and teleconnections during these two periods are calculated and used to determine the teleconnections that currently have the strongest relationship with CAOs across the NH. Several relationships were found to no longer be significant while several other significant relationships have emerged, particularly with the Tropical Northern Hemisphere (TNH) pattern. Furthermore, upstream teleconnections, as opposed to downstream teleconnections, were generally found to have a stronger relationship with CAOs. The East Pacific Oscillation and East Pacific/North Pacific pattern were found to have the strongest relationship with CAOs in the eastern U.S. and Alaska, while the Arctic Oscillation, North Atlantic Oscillation, Greenland Blocking Index, and Scandinavian pattern have the strongest relationship with CAOs in Eurasia.

Effects of Atmospheric Circulation on Stream Chemistry in Forested Watersheds Across the Northeastern United States: Part 2. Interannual Weather Type Variability

AbstractUsing synoptic classification techniques, synoptic‐scale weather types associated with large exports of dissolved organic carbon and nitrate‐nitrogen in three forested watersheds in the northeastern United States are analyzed. In contrast to Siegert et al. (2021, https://doi.org/10.1029/2020jd033413; Part 1), which details the general synoptic conditions associated with stream chemistry variations, this study focuses on the long‐term frequency, trends, and global‐scale forcing mechanisms of individual, chemistry‐relevant, synoptic types. Nine individual types are identified as the most important to stream chemistry exports in northeastern watersheds during a 2.5‐year period of high‐frequency stream chemistry observation (2014–2016). Streamflow chemistry was the most influenced by Northwest Flow synoptic weather types in Vermont, cold front passages associated with Weak Westerly Flow types in Rhode Island, and Southwest Flow (SWF) types in Maryland. Each type provides unique atmospheric conditions that result in variations in precipitation, and thus chemical signals. From 1948 to 2017, the Arctic and North Atlantic Oscillations greatly influenced the interannual frequency of the analyzed synoptic types. When the indices were positively phased, SWF types were more frequent, while Northwest Flow and Weak Westerly Flow types were less frequent. Long‐term trends in synoptic type frequency are, inpart, due to documented changes in these teleconnection indices toward more positively phased configurations. As the climate continues to change into the 21st century, such associations may continue to drive large changes in synoptic type frequency, and thus watershed hydrology and biogeochemistry in the Northeast region.

Trends and zonal variability of extreme rainfall events over East Africa during 1960–2017

East African countries (Uganda, Kenya, Tanzania, Rwanda, and Burundi) are prone to weather extreme events. In this regard, the past occurrence of extreme rainfall events is analyzed for 25 stations following the Expert Team on Climate Change Detection and Indices (ETCCDI) regression method. Detrended fluctuation analysis (DFA) is used to show the future development of extreme rainfall events. Pearson’s correlation analysis is performed to show the relationship of extreme rainfall events between different rainfall zones and their association with El Niño-Southern Oscillation (ENSO) and Indian Ocean dipole Mode Index (DMI). The results revealed that the consecutive wet days (CWD) index experienced a decreasing trend in 72% of the stations analyzed. Moreover, the consecutive dry days (CDD) index also indicated a positive trend in 44% of the stations analyzed. Heavy rainfall days index (R10mm) showed a positive trend at 52% of the stations and was statistically significant at a few stations. In light of the extremely heavy rainfall index (R25mm), 56% of the stations revealed a decreasing trend for the index and statistically significant trend at some stations. Further, a low correlation coefficient of extreme rainfall events between the regions (r2 = − 0.27 to r2 = 0.38) and between rainfall extreme indices with the atmospheric teleconnection indices [Dipole Mode Index (DMI) and Nino 3.4] ranging from r2 = − 0.1 to r2 = 0.35 was exhibited. Most rainfall zones showed a positive correlation between the R95p index and DMI, while 5/8 of the rainfall zones experienced a negative correlation between Nino 3.4 index and the R95p Index. In light of the highly variable trends of extreme rainfall events, we recommend planning adaptation and mitigation measures that consider the occurrence of such high variability. Measures such as rainwater harvesting, storage, and use during needs, planned settlement, and improved drainage systems management supported by accurate climate and weather forecasts are highly advised.

Improving landings forecasts using environmental covariates: A case study on the Indian oil sardine (Sardinella longiceps)

AbstractCommercial landings of sardines are known to show strong year‐to‐year fluctuations. A key driver is thought to be environmental variability, to which small forage fish are especially sensitive. We examined the utility of including environmental covariates in forecasts for landings of the Indian oil sardine using a long‐term time series of quarterly catches. Potentially influential variables examined included precipitation, upwelling intensity, sea surface temperature (SST), and chlorophyll‐a concentration. All of these have been shown to be important for oil sardine growth and survival, spawning and/or movement into the nearshore fishing regions. However, improving out‐of‐sample landings forecasts using environmental covariates has often proven elusive. We tested the inclusion of environmental covariates in forecast models using generalized additive models, which allow for non‐linear responses, and dynamic linear models, which allow for time‐varying responses. Only two environmental covariates improved out‐of‐sample prediction: the 2.5‐year average regional SST and precipitation over land during June–July. The most significant improvement was with the SST covariate and post‐monsoon landings with a 19%–22% reduction in mean‐squared prediction error. Models with the second best covariate, monsoon precipitation over land, provided a 4%–8% reduction in prediction error. We also tested large‐scale ocean climate teleconnection indices. One, an index of the Atlantic Multidecadal Oscillation, also improved out‐of‐sample predictions similarly to the multiyear average regional SST. The earth's changing climate is associated with both rapid warming in the Western Indian Ocean and changes to monsoon rainfall patterns. Our work highlights these as key variables that can improve forecasting of oil sardine landings.

Impacts of large-scale teleconnection indices on chill accumulation for specialty crops in California

Although the impacts of teleconnection indices on climate metrics such as precipitation and temperature in California have been widely studied, less attention has been given to the impact on integrated climate indices such as chill accumulation. This study investigates the linkages between large-scale teleconnections and winter chill accumulation for specialty crops in California, which may enable more effective and dynamic adaptation to in-season climate variability. Three large-scale teleconnection indices were selected: Oceanic Nino Index (ONI), Pacific-North American teleconnection pattern (PNA), and Pacific Decadal Oscillation (PDO) index to assess their effects on chill accumulation. The Chill Hours Model and Dynamic Model are adopted to calculate chill accumulation in Chill Hours (CH) and Chill Portions (CP) from November to January. Three major crop-producing regions, including the Central Coast, Sacramento Valley, and San Joaquin Valley, are used as the focused regions. Our results suggest CP generally has a stronger response to teleconnection patterns than CH in California. The correlations between chill accumulation and teleconnections are generally weaker during the summer than other seasons, and significant correlation can be observed 2–10 months before the start of the chill accumulation period. Among the three teleconnection indices, ONI is most weakly correlated to chill accumulation in focused regions, while PDO shows the strongest positive correlation and explains up to 39% variability of CP. PNA presents the most widespread negative correlation with chill accumulation. When aggregated to different teleconnection modes, +3.6 above-average CP is expected during ONI positive mode; +2.3 above-average CP is expected during PDO positive mode, while +2.1 above-average CP is expected during PNA negative mode. This study provides insights on early-season chill prediction and feasible management and adaptation strategies, and the methodology presented here can be used to develop decision support tools of risk control for agricultural producers and policymakers.

The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004\u20132019

BackgroundLeptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI.MethodsWe acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected.ResultsWavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods.ConclusionsResults of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

The effects of local and regional parameters on the δ18O and δ2H values of precipitation and surface water resources in the Middle East

Located in semi-arid and arid regions in the world, the Middle East has always faced water shortage crisis. Therefore, water resources in this area should be studied by accurate methods such as the stable isotope technique. The results of the current isotopic study show the effects of the main local (temperature and precipitation) and regional parameters (teleconnection indices) on the δ18O values of precipitation and surface water resources across the Middle East. Plotting the values of δ18O vs δ2H in some of the major rivers in the Middle East on the local meteoric water lines shows that the surface water resources are dominantly recharged by local precipitation, while the deviation observed in some samples is due to evaporation. The contributions of various air masses (the precipitation events originating from the main air masses) in the recharge of the principal rivers have also been studied. The results of the mixing models demonstrate that the contributions of various air masses in the recharge of rivers vary significantly across the Middle East.

Bivariate Modelling of a Teleconnection Index and Extreme Rainfall in a Small North Atlantic Island

This paper explores practical applications of bivariate modelling via copulas of two likely dependent random variables, i.e., of the North Atlantic Oscillation (NAO) coupled with extreme rainfall on the small island of Madeira, Portugal. Madeira, due to its small size (∼740 km2), very pronounced mountain landscape, and location in the North Atlantic, experiences a wide range of rainfall regimes, or microclimates, which hamper the analyses of extreme rainfall. Previous studies showed that the influence of the North Atlantic Oscillation (NAO) on extreme rainfall is at its largest in the North Atlantic sector, with the likelihood of increased rainfall events from December through February, particularly during negative NAO phases. Thus, a copula-based approach was adopted for teleconnection, aiming at assigning return periods of daily values of an NAO index (NAOI) coupled with extreme daily rainfalls—for the period from December 1967 to February 2017—at six representative rain gauges of the island. The results show that (i) bivariate copulas describing the dependence characteristics of the underlying joint distributions may provide useful analytical expressions of the return periods of the coupled previous NAOI and extreme rainfall and (ii) that recent years show signs of increasing climate variability with more anomalous daily negative NAOI along with higher extreme rainfall events. These findings highlight the importance of multivariate modelling for teleconnections of prominent patterns of climate variability, such as the NAO, to extreme rainfall in North Atlantic regions, especially in small islands that are highly vulnerable to the effects of abrupt climate variability.

Early Growing Season Anomalies in Vegetation Activity Determine the Large‐Scale Climate‐Vegetation Coupling in Europe

AbstractThe climate‐vegetation coupling exerts a strong control on terrestrial carbon budgets and will affect the future evolution of global climate under continued anthropogenic forcing. Nonetheless, the effects of climatic conditions on such coupling at specific times in the growing season remain poorly understood. We quantify the climate‐vegetation coupling in Europe over 1982–2014 at multiple spatial and temporal scales, by decomposing sub‐seasonal anomalies of vegetation greenness using a grid‐wise definition of the growing season. We base our analysis on long‐term vegetation indices (Normalized Difference Vegetation Index and two‐band Enhanced Vegetation Index), growing conditions (including 2m temperature, downwards surface solar radiation, and root‐zone soil moisture), and multiple teleconnection indices that reflect the large‐scale climatic conditions over Europe. We find that the large‐scale climate‐vegetation coupling during the first two months of the growing season largely determines the full‐year coupling. The North Atlantic Oscillation and Scandinavian Pattern phases one‐to‐two months before the start of the growing season are the dominant and contrasting drivers of the early growing season climate‐vegetation coupling over large parts of boreal and temperate Europe. The East Atlantic Pattern several months in advance of the growing season exerts a strong control on the temperate belt and the Mediterranean region. The strong role of early growing season anomalies in vegetative activity within the growing season emphasizes the importance of a grid‐wise definition of the growing season when studying the large‐scale climate‐vegetation coupling in Europe.

Identification of atmospheric and oceanic teleconnection patterns in a 20-year global data set of the atmospheric water vapour column measured from satellites in the visible spectral range

Abstract. We used a global long-term (1995–2015) data set of total column water vapour (TCWV) derived from satellite observations to quantify to which extent the temporal patterns of various teleconnections can be identified in this data set. To our knowledge, such a comprehensive global TCWV data set was rarely used for teleconnection studies. One important property of the TCWV data set is that it is purely based on observational data. We developed a new empirical method to decide whether a teleconnection index is significantly detected in the global data set. We compared our new method to well-established hypothesis tests and found good agreement with the results of our approach. Based on our empirical method more than 40 teleconnection indices were significantly detected in the global TCWV data set derived from satellite observations. In addition to the satellite data we also applied our method to other global data sets derived from ERA-Interim. One important finding is that the spatial patterns obtained for the ERA TCWV data are very similar to the observational TCWV data set indicating a high consistency between the satellite and ERA data. Moreover, similar results are also found for two selections of ERA data (either all data or mainly clear-sky data). This finding indicates that the clear-sky bias of the satellite data set is negligible for the results of this study. However, for some indices, also systematic differences in the spatial patterns between the satellite and model data set were found probably indicating possible shortcomings in the model data. For most “traditional” teleconnection data sets (surface temperature, surface pressure, geopotential heights and meridional winds at different altitudes) a smaller number of significant teleconnection indices was found than for the TCWV data sets, while for zonal winds at different altitudes, the number of significant teleconnection indices (up to &gt; 50) was higher. The strongest teleconnection signals were found in the data sets of tropospheric geopotential heights and surface pressure. In all global data sets, no “other indices” (solar variability, stratospheric AOD or hurricane frequency) were significantly detected. Since many teleconnection indices are strongly correlated, we also applied our method to a set of orthogonalised indices, which represent the dominant independent temporal teleconnection patterns. The number of significantly detected orthogonalised indices (20) was found to be much smaller than for the original indices (42). Based on the orthogonalised indices we derived the global spatial distribution of the cumulative effect of teleconnections. The strongest effect on the TCWV is found in the tropics and high latitudes.