Regional Climate Indices Research Articles

Climate Covariate Choice and Uncertainty in Projecting Species Range Shifts: A Case Study in the Eastern Bering Sea

ABSTRACTSpecies distribution models (SDMs) are critical to the adaptive management of fisheries under climate change. While many approaches projecting marine species range shifts have incorporated the effects of temperature on movement, there is a need to incorporate a wider suite of ecologically relevant predictors as temperature‐based SDMs can considerably under‐ or over‐estimate the rate of species responses to climate shocks. As a subarctic ecosystem at the sea ice margin, the Eastern Bering Sea (EBS) is warming faster than much of the global ocean, resulting in the rapid redistribution of key fishery and subsistence resources. To support long‐term planning and adaptation, we combine 40 years of scientific surveys with a high‐resolution oceanographic model to examine the effects of bottom temperature, oxygen, pH and a regional climate index (the extent of the EBS ‘cold pool’) on range projections through the end of the century. We use multimodel inference to partition uncertainty among earth systems models, climate scenarios and distribution model parameterizations for several ecologically and economically important EBS groundfish and crabs. Covariate choice is the primary source of uncertainty for most species, with models that account for spatial responses to the cold pool performing better and suggesting more extensive northward movements than alternative models. Models suggest declines in the probability of occurrence at low pH and oxygen concentrations for most species. We project shifts that are directionally consistent with, yet larger than those previously estimated for most species, suggesting that accounting for large‐scale climate variability in species distribution models may substantially alter range projections.

Climatic variability of wave heights in the shelf zone of the North-Eastern part of the Black Sea according field data

Wave parameters and their variability under climate change are important for forecasting in many areas, such as coastal zone management. This study analyzed the variability of key climate indices from August to October over the period 1996–2020. The relationship between these changes and changes in average monthly significant wave heights was assessed based on field measurements taken at a consistent location on the northeastern Black Sea shelf during various years within this period.The analysis revealed that negative values of NAO, AO, and EA, along with positive values (greater than 1) of EA/WR, are associated with increases in wave heights. The study identified the key periods when joint changes in these climate indices are likely to have the greatest impact on average monthly wave heights: 4 and 7 years for August, 17 and 20 years for September, and 4, 13, and 10 years for October. The discussion emphasizes that identifying the primary regional climate indices and studying their relationship with wave parameters in detail could form the foundation for zoning the Black Sea into quasi-homogeneous zones based on climate variability. This approach could also support the development of simple prognostic models for different parts of the Black Sea, taking into account the non-stationary nature of climate change.

New insights on the interannual surface mass balance variability on the South Shetland Islands glaciers, northerly Antarctic Peninsula

Few studies have assessed a comprehensive understanding of how the seasonal and interannual variability and trends of the surface mass balance (SMB), including the influence of atmospheric river (ARs), are governed by the climate on the South Shetland Islands (SSI) glaciers located in the northerly Antarctic Peninsula (AP). To address this gap, we comprehensively analyzed the correlations and regressions between seasonal and annual SMB with regional to global climate indices and a state-of-the-art AR tracking database from 1980 to 2019. The daily and monthly SMB was obtained from two physical glaciological models, which was verified against 19 years of annual and seasonal glacier-wide SMB observations available in three glaciers (Johnsons, Hurd, and Bellingshausen), showing a good ability to capture interannual and seasonal variability. Results indicate a low dependence of the SMB on main atmospheric modes of variability (e.g., El Niño-Southern Oscillation and the Southern Annular Mode), and a moderate dependence on regional climate indices based on atmospheric pressure anomalies and sea surface temperature anomalies over the Drake Passage. Furthermore, our findings reveal that ARs have different effects on the SMB depending on the season. For example, winter ARs tend to boost accumulation due to increased snowfall, while summer ARs tend to intensify surface melting due to increased sensible heat flux. Our study highlights the Drake Passage as a key region that has the potential to influence the interannual and seasonal variability of the SMB and other climate variables, such as air temperature and snowfall over the SSI. We suggest that future work should consider this region to better understand the past, present and future climate changes on the SSI and surrounding areas.

It is not all about temperature. Regionally modified climatic indices: the case of the Caribbean

ABSTRACT This letter questions the supremacy of temperature in the development of tourism climate indices, using the Caribbean as an example. A new methodology is proposed to evaluate a regional climate index based on the seasonal correlation with tourist arrivals and to investigate the weight of the different climate variables that make up the index. Results show that the new climate index fits the seasonal behaviour of tourism demand more accurately by reducing the weight of temperature in the makeup of the index and increasing that of precipitation, wind and cloud cover.

Testing models of increasing complexity to develop ecosystem‐informed fisheries advice

AbstractDespite continued calls for the application of ecosystem‐based fisheries management, tactical fisheries management continues to be heavily reliant on single‐species stock assessments. These stock assessments rarely quantitatively integrate the effects of ecosystem processes on fish stock productivity. This lack of integration is ultimately driven by the complexity of interactions between populations, ecosystems and fisheries, which produces uncertainty when defining which processes to include and how to include them. Models developed using a structured hypothesis testing framework would allow formalizing uncertainties while underscoring the importance of incorporating different population and ecosystem processes to explain non‐stationary stock productivity. Here, we develop a conceptual framework for extending and comparing population dynamics models of increasing complexity. We illustrate the utility of the framework by investigating the population and ecosystem processes that most likely affected the differential recovery of two flatfish populations (American plaice and yellowtail flounder) on the Newfoundland Grand Banks over the past three decades. We found that yellowtail flounder population dynamics were primarily driven by recruitment variability, which was negatively affected by warmer climatological conditions, as indicated by an integrated regional climate index. Meanwhile, American plaice population dynamics were affected by a combination of temporal variability in recruitment and natural mortality, where natural mortality increased during colder than average conditions. By exploring hypotheses about the effects of population and ecosystem processes on population dynamics, this modelling framework will improve understanding about the drivers of shifts in population productivity while serving as a transparent and robust approach to support ecosystem‐based fisheries management.

Signal‐to‐noise errors in free‐running atmospheric simulations and their dependence on model resolution

AbstractEnsemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal‐to‐noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal‐to‐noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal‐to‐decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi‐model ensemble of uninitialized atmosphere‐only simulations, focusing on boreal winter. To assess signal‐to‐noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi‐model ensemble‐mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher‐resolution models produce greater observation‐model correlations and greater RPC values than lower‐resolution models in all four climate indices. We find that signal‐to‐noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free‐running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal‐to‐noise errors originate in the atmosphere or in ocean–atmosphere coupling.

Typical patterns of the water temperature distribution in the upper mixed layer of the Bering Sea in winter season

Typical patterns of the water temperature distribution in the upper mixed layer of the Bering Sea in winter are determined on all available oceanographic data obtainedby research institutions of Russia, Japan, USA, and China. Previously [Luchin, 2023], the data were sorted to two sets presenting «cold» and «warm» winters. The proper set includes 2,492 oceanographic stations and the latter one – 2,130 stations. Spatial distribution of the temperature has some common patterns for both sets: the highest values (3–4 to 5–6 оC) are observed at the central and eastern passages between Aleutian Islands, primarily reflecting the Pacific waters invasion to the sea, then these waters are involved into the large-scale cyclonic gyre over the deep-water basin and transported along the continental slope that is indicated by 3оC isotherm. However, different types of the temperature distribution are well distinguished by prominent difference of its values that is amounted in 1.0–1.5о, up to 3–4о at the northwestern coast including the western part of the eastern Bering Sea slope. A wide set of potential predictors for interannual variability of thermal and dynamic conditions in the Bering Sea are examined using correlation analysis,including the global and regional climatic indices. There is concluded that the winter temperature fields are formed by several key factors, as the warm Pacific waters advection, the basin-scale cyclonic circulation, the vertical and lateral water mixing in the Aleutian sounds and at the continental slope, and fall-winter cooling of the surface layer driven by air–sea heat exchange.

A spatio-temporal analysis of the role of climatic drivers influencing extreme precipitation events in a Costa Rican basin

Regional patterns of rainfall are changing around the world, putting pressure on social, economic, and environmental systems, and changing the outlook for food, water, and health security. This is the case in the Tempisque-Bebedero River Basin (TBRB), located on the Pacific slope of northwest Costa Rica, which relies heavily on water resources for tourism, agriculture, ranching, and fish farming. In this study, we use non-stationary extreme value analysis to characterize the climatic drivers of extreme precipitation in the TBRB. Daily extreme precipitation events at stations across the TBRB are selected using a peak-above-threshold approach, and interannual climate variability is included as time-varying covariates in a non-stationary point process model, using three regional climate indices: the Caribbean Low-Level Jet (CLLJ) index, the Oceanic Niño Index (ONI), representative of the El Niño Southern Oscillation (ENSO), and the Atlantic Multidecadal Oscillation (AMO) index. We find that the magnitude and frequency of precipitation extremes in the basin are driven by the CLLJ and the AMO. The ONI is only a significant driver of extreme precipitation in the northwestern region of the basin. This is likely because ENSO controls the strength of the CLLJ, thus the dominant impact of ENSO on precipitation in the TBRB may be incorporated within the CLLJ component of our model.

Large-scale and regional climatic influences on surface temperature and precipitation in the South Shetland Islands, northern Antarctic Peninsula.

Using data from SCAR observations, ERA5 reanalysis, and regional climate model simulations (RACMO), we examined the influence of large- and regional-scale climate forcing on temperature and precipitation variations in the South Shetland Islands (SSI). Specifically, we focused on understanding how regional climate indices influence the temporal variability of temperature and precipitation on the SSI. Our findings indicate that both large- and regional-scale climate indices significantly impact the interannual and seasonal temperature variability in the SSI. For instance, the Amundsen Sea Low, characterised by low-pressure systems over the Amundsen Sea, and sea ice extent in the northwestern part of the Weddell Sea, exert a strong influence on temperature variability (r from -0.64 to -0.87; p < 0.05). In contrast, precipitation variability in this region is primarily controlled by regional climatic indices. Particularly, anomalies in atmospheric and surface pressure over the Drake Passage region strongly regulate the interannual variability of precipitation in the SSI (r from -0.46 to -0.70; p < 0.05). Large-scale climatic indices demonstrate low but statistically significant correlations, including the Southern Annular Mode and deep convection in the central tropical Pacific. Given the importance of temperature and precipitation in the glacier changes, we recommend assessing the impact of the Drake region on SSI glaciers.

Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow

Effective reservoir operation under the effects of climate change is immensely challenging. The accuracy of reservoir inflow forecasting is one of the essential factors supporting reservoir operations. This study aimed to investigate coupling models of feature selection (FS) and machine learning (ML) algorithms to predict the monthly reservoir inflow. The study was carried out using data from the Huai Nam Sai reservoir in southern Thailand. Eighteen years of monthly recorded data (i.e., reservoir inflow, reservoir storage, rainfall, and regional climate indices) with up to a 12-month time lag were utilized. Three ML techniques, i.e., multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN)were compared in their capabilities. In addition, two FS techniques, i.e., genetic algorithm (GA) and backward elimination (BE) methods, were studied with four predictable time intervals, consisting of 3, 6, 9, and 12 months in advance. Ten-fold cross-validation was used for model evaluation. Study results revealed that FS methods (i.e., GA and BE) Could improve the performance of SVR and ANN for predicting monthly reservoir inflow forecasting, but they have no effects on MLR. Different developed forecasting models were suitable for different reservoir inflow forecasting time-step-ahead. BE-ANN provided the best performance for three-time-ahead (T + 3) and nine-time-ahead (T + 9) by giving an OI of 0.9885 and 0.8818, NSE of 0.9546 and 0.9815, RMSE of 1.3155 and 1.2172 MCM/month, MAE of 0.9568 and 0.9644 MCM/month, and r of 0.9796 and 0.9804, respectively. The GA-ANN model showed the highest prediction accuracy for six-time-ahead (T + 6), with an OI of 0.8997, NSE of 0.9407, RMSE of 2.1699 MCM/month, MAE of 1.7549 MCM/month, and r of 0.9759. The ANN model showed the best prediction accuracy for twelve-time-ahead (T + 12), with an OI of 0.9515, NSE of 0.9835, RMSE of 1.1613 MCM/month, MAE of 0.9273 MCM/month, and r of 0.9835.

Long term oscillations of Mediterranean sardine and anchovy explained by the combined effect of multiple regional and global climatic indices

It is widely known that the abundance and distribution dynamics of populations of small pelagic clupeid fish, such as sardines and anchovies, are affected by large-scale climate variability, which may lead to changeovers to new regimes of small pelagics. However, long-distance climatic oscillations, such as El Niño/La Niña and the Pacific Decadal Oscillation, have been little explored in the Western Mediterranean Sea. We investigated the possible effects of the South Oscillation Index (i.e. the atmospheric oscillation coupled with the El Niño/La Niña) and Pacific Decadal Oscillation on fluctuations in catches of European anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) in the Western Mediterranean Sea, and their association with regional climate oscillations (i.e. the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, the Western Mediterranean Oscillation index, and the Arctic Oscillation). The study covered two periods: (a) landings between 1950 and 2016; and (b) abundance, biomass, and physical condition (i.e., relative condition index) between 2004 and 2016. The main large-scale climatic oscillations in the region were studied using General Additive Models to investigate the relationship between a time series of species measures of European sardine and anchovy from Geographical Subarea 06. Results show that the long-term Pacific Decadal Oscillation favours sardine landings, whereas the combined effect of the Western Mediterranean Oscillation Index and the Atlantic Multidecadal Oscillation favours anchovy. We discuss potential links between the present findings and changes in the plankton community caused by prevailing winds in the region driven by long-distance climate oscillations and their impact on the reduction in small pelagic fish populations in the study area.

Marine‐climate interactions with the blue shark (Prionace glauca) catches in the western coast of Baja California Peninsula, Mexico

AbstractFishery and size data by sex of 28,110 blue sharks (Prionace glauca) from 2162 longline sets documented by observers on board 204 fishery trips from the industrial fleet based in Ensenada, Mexico during 2006–2016, were used to conduct a spatial–temporal analysis of the catch‐per‐unit‐effort (CPUE) and its relationship with climate indices along the west coast of the Baja California Peninsula. Catch length analysis by maturity groups indicated catches were composed mainly by juvenile females (58–199 cm TL) and males (60–179 cm TL). Relationships of seasonal CPUE with sea surface temperature (SST) and Chlorophyll‐a (Chl) were analyzed determining aggregations were in areas characterized by oceanographic physical processes. From the exploratory analysis of annual correlations of climate indices with CPUE, the local climate SanDiAs Index explained most variation in CPUE. A generalized additive model (GAM) with 13 predictor variables was applied to gain insight on their relationship with the total CPUE by size and sex groups. The model explained 50.5% of the total blue shark CPUE and 65.5% for juvenile females. The GAM results revealed blue shark CPUE is influenced by five relevant factors: SST, NPGO, year, latitude with distance to coast and quarter interactions, and hooks set. There is a trend to increase or decrease of CPUE when compared with the delay of NEI and SanDiAs indices in more than 1 year. Local and regional climate indices can be successful tools for forecasting blue shark catches in the Northwestern Mexican Pacific.

Sea surface temperature variability in Indonesia and its relation to regional climate indices

Sea surface temperature (SST) is an essential indicator of ocean condition. It can reveal many physical processes interacting with it. The present study aims to investigate the spatial-temporal pattern of significant SST variability in Indonesia seas. The Empirical Orthogonal Function (EOF) and Power Spectral Density (PSD) are used to analyze monthly SST data from 1979 to 2021. These two methods are combined with correlation analysis to verify the underlying phenomena and their spatiotemporal distribution pattern using regional climate indices as the reference signal. The result shows that the most prominent feature is the annual and semi-annual oscillation due to the Asia-Australia monsoon system. The annual oscillation signature is found almost in the entire Indonesian seas, with an exception in the low-latitude area and the western Pacific region. The signature of semi-annual oscillation is also protrusive, extending across Indonesia from the Timor Sea to the South China Sea. There is also a variation of SST in correlation with Dipole Mode Index (DMI), localized on the western coast of Sumatra.

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

Seasonal and long-term sea-level variations and their forcing factors in the northern Bay of Bengal: A statistical analysis of temperature, salinity, wind stress curl, and regional climate index data

In the northern Bay of Bengal, mechanisms of seasonal sea-level variation have not previously been examined, and the understanding of longer-term inter-annual sea-level variation is also not concrete. These parameters are addressed in this study utilizing available tide gauge and satellite altimetry data. The contribution of steric sea level to seasonal and longer-term inter-annual sea-level variations is quantified, and statistical analysis is performed to determine the correlations of various atmospheric and oceanic factors with sea level. This study suggests that the trend of sea-level rise in this bay (4 ± 1.33 mm/year) is higher than the global average (3.32 ± 0.46 mm/year) for the studied period 1993 to 2018. The rate of sea-level rise is higher along the coast than in the offshore area and the highest in the central part of the coast. Sea level shows a strong seasonal variation: sea level is the lowest in the winter but the highest in autumn. The contribution from the thermosteric sea level is higher to the observed sea level from winter to early summer, whereas contributions from the halosteric sea level and wind stress curl are higher during autumn. Long-term variations in sea level show strong positive correlations with thermosteric sea level, indicating that temperature is a major local controlling factor for sea-level change. In addition to local factors, long-term sea level also varies by remote forcing (equatorial zonal wind stress), which explains approximately 36 % of the sea-level variation in this bay. Sea level is low during the combined events of positive Indian Ocean dipole (IOD) and El Niño, whereas the sea level is high during the combined events of negative IOD and La Niña. This study provides an improved understanding of seasonal and longer-term inter-annual variations of sea level and the necessary groundworks for a dedicated model study to further quantify all the components of the sea-level budget in the study areas.

Extending end‐of‐summer‐snowlines for the Southern Alps glaciers of New Zealand back to 1949

AbstractNew Zealand has a more than four‐decade long (1977–2019) continuous record of measurements of the altitude of end‐of‐summer‐snowlines (EOSS). These are obtained from oblique aerial photography during annual flights over the Southern Alps for a set of 50 ‘index glaciers’ (EOSSALPS). Average EOSSALPS is 1842 m for the 1977–2016 period. Estimates for EOSSALPS are extended from 1977 back to 1949 by using observations from New Zealand's largest glacier, the Tasman (EOSSTAS). When EOSSTAS are not available, significant regression relationships are established with air temperature, regional climate indices and hemispheric scale climate teleconnections. This analysis involves glacier year values of annual mean Hokitika temperature, the Southern Annular Mode (SAM), the El Niño/Southern Oscillation Index (ENSO) and the Interdecadal Pacific Oscillation (IPO). Results show that over the seven decades there have been significant increases in EOSSALPS, and anthropogenic temperature increases play a significant role as found for Hokitika temperature. From 1949 to 1955 EOSSALPS varied little, but then rose to 1980. Thereafter, until 2010, annual EOSSALPS stayed about the same level with small fluctuations. However, from 2010 to 2019 it rose by a further 200 m above normal. The overall increase in EOSSALPS since 1949 is most significant, averaging +42 m per decade, or a total of 300 m. The length of the two monitored fast flowing Franz Josef and Fox glaciers over the same time are mimicked well by EOSSALPS with a four‐year lag.

Stable isotope signatures in historic harbor seal bone link food web-assimilated carbon and nitrogen resources to a century of environmental change.

Anthropogenic climate change will impact nutrient cycles, primary production, and ecosystem structure in the world's oceans, although considerable uncertainty exists regarding the magnitude and spatial variability of these changes. Understanding how regional-scale ocean conditions control nutrient availability and ultimately nutrient assimilation into food webs will inform how marine resources will change in response to climate. To evaluate how ocean conditions influence the assimilation of nitrogen and carbon into coastal marine food webs, we applied a novel dimension reduction analysis to a century of newly acquired molecular isotope data derived from historic harbor seal bone specimens. By measuring bulk δ13 C and δ15 N values of source amino acids of these top predators from 1928 to 2014, we derive indices of primary production and nitrogen resources that are assimilated into food webs. We determined coastal food webs responded to climate regimes, coastal upwelling, and freshwater discharge, yet the strength of responses to individual drivers varied across the northeast Pacific. Indices of primary production and nitrogen availability in the Gulf of Alaska were dependent on regional climate indices (i.e., North Pacific Gyre Oscillation) and upwelling. In contrast, the coastal Washington and Salish Sea food webs were associated with local indices of freshwater discharge. For some regions (eastern Bering Sea, northern Gulf of Alaska) food web-assimilated production was coupled with nitrogen sources; however, other regions demonstrated no production-nitrogen coupling (Salish Sea). Temporal patterns of environmental indices and isotopic data from Washington state varied about the long-term mean with no directional trend. Data from the Gulf of Alaska, however, showed below average harbor seal δ13 C values and above average ocean conditions since 1975, indicating a change in primary production in recent decades. Altogether, these findings demonstrate stable isotope data can provide useful indices of nitrogen resources and phytoplankton dynamics specific to what is assimilated by food webs.

Influence of wave direction sequencing and regional climate drivers on sediment headland bypassing

Headland sediment bypassing is a critical component in understanding the sediment budgets of headland protected beaches, yet the bypassing is often sporadic and complex. In this study we have completed shoreline change analysis from aerial imagery spanning >60 years for all the beaches within the Noosa Headland (Queensland, Australia) region to better understand how sediment moves between compartments. Shoreline change of each compartment was evaluated in conjunction with local wave data and regional climate indices. Headland bypassing may only occur following a specific sequence of wave events, with a moderate (Hs > 2.5 m) downdrift oblique wave event being followed by a moderate shore-normal/updrift oblique wave event. Long term beach accretion of 0.56 m/year (± 0.17), primarily due to intervention, was observed at headland adjacent beaches, with negligible change for the headland embayed beaches. Volume calculations from the headland sheltered Noosa Main Beach indicate a natural sediment budget of negative 8900 m3/year over an 11-year span, with sand backpassing used to recover these losses. The change to sediment bypassing as a result in forecasted global warming induced wave direction changes is discussed, with the expectation that fewer bypassing events, with more erosive events, will occur in future. This research has implications for the management of beach compartments adjacent to large headlands.

Medicinal Plants of Arid and Semiarid Biomes of Russia

The article analyzes a set of medicinal-plant species in arid and semiarid biomes of Russia. The species diversity of medicinal plants in general, as well as those used to treat diseases of various classes, is determined. A statistical analysis of the relationships between the number of species of medicinal plants in regional biomes and climatic indices is carried out; the corresponding thematic schematic maps are constructed and analyzed. It is shown that the number of medicinal-plant species is related to the total biome species number. This, in turn, is determined by the geographic location, which determines the average annual temperature and precipitation in the regions. The closest established relationship was between the number of medicinal-plant species and the average annual air temperatures. A relatively high relationship was found between the species number and the average annual precipitation. In the analysis of the relationship between the number of medicinal-plant species used to treat certain classes of diseases and the climatic factors, the picture turned out to be similar.

On the changing relationship between North Pacific climate variability and synoptic activity over the Hawaiian Islands

AbstractThe teleconnection between tropical and extratropical climates in the North Pacific and continental regions of eastern Asia and western North America is known to vary on decadal to multidecadal time scales. In this study, the teleconnection pattern is studied with observational and reanalysis data products. The regional focus is set on the Hawaiian Islands in the central subtropical part of the North Pacific. By analysing correlations between regional climate indices and large‐scale climate modes during the years 1980 and 2014, it was found that the correlation between El Niño—Southern Oscillation (ENSO) and the synoptic weather activity over the Hawaiian Islands decreased over time. Composite analysis of the geopotential height anomalies and upper level winds suggest that the systematic shift in the North Pacific Jet (NPJ) position had an impact on the teleconnection between tropical Pacific SST and winter storm activity and precipitation variability in Hawai'i. The change in the correlations and in the NPJ structure coincides with a transition from the positive phase of the Pacific Decadal Oscillation (PDO) towards a neutral and weak negative state. This observation‐based study provides a central subtropical Pacific viewpoint in support of the growing body of research studies that have reported a major shift in the Pacific climate system during the mid‐1990s. The article further discusses the potential role of decadal‐scale changes in the North Pacific Oscillation (NPO) phase in changing the strength of the ENSO teleconnection with synoptic activity over the Hawaiian Islands. The results of this study are relevant to paleoclimate interpretation of individual proxy records as well as for regional downscaling of future rainfall for the Hawaiian Islands.