Regional-scale Variability Research Articles

Impact of the Summer Atmospheric Heat Source over the Tibetan Plateau on Interannual Variability of Meridional Circulation on the North Side of the Tibetan Plateau

Abstract The summer atmospheric heat source (AHS) over the Tibetan Plateau (TP) induces meridional circulations in TP and its surrounding areas. Previous studies mainly focused on the monsoon circulation on the south side of TP, while the formation and maintenance mechanisms of meridional circulation on its north side remain unclear. This study compared three calculation methods of the AHS, analyzed the spatial–temporal variability of the summer AHS over the TP, and discussed its influence on the interannual variability of meridional circulation on the north side of the TP based on the two-dimensional decomposition method of atmospheric circulation and sensitivity experiments. The results indicate that in the positive AHS anomalies years, the diabatic heating of condensation latent release in southeastern TP could motivate anomalous ascending motion. Simultaneously, the increased meridional temperature gradient between the mid- and high latitudes of East Asia leads to an enhanced southward westerly jet. In this context, the region on the north side of TP, located on the north side of the westerly jet entrance, is affected by negative anomalous relative vorticity advection, prevailing anomalous descending motion, which makes the descending branch of meridional circulation significantly presented. Unlike previous studies that considered the descending branch of meridional circulation as the compensation for upward flow, the results of the linear baroclinic model (LBM) verify that the descending branch is mainly influenced by the vorticity advection related to regional scale variability of the westerly jet. This study reveals the physical mechanism of meridional circulation on the north side of TP, which offers valuable implications for seasonal forecasting in TP and Northwest China.

Unveiling Regional Climate Patterns Through Global Subsurface Ocean Temperature Data: An AI Multi-Layer Analysis Framework

AbstractRegional-scale climate variability has far-reaching implications for both local and global climate systems, impacting factors like temperature, precipitation patterns, oceanic circulation, and the occurrence of extreme weather events. However, despite these influences, there is currently no universal methodology for the automated identification of regional-scale variability modes, including those less dominant globally, and for simultaneously exploring the influence of various ocean depth layers in characterizing these modes and diagnosing regional sea level variations. The presented innovative approach addresses these critical region-specific needs by assisting in the extraction of novel regional depth-layered variability modes and establishing their correlation with regional sea level fluctuations, employing tailored machine-learning techniques. This dual-purpose is achieved through the utilization of an optimized k-means clustering method for the automatic identification of regions with shared variability patterns across all global oceans, revealing previously unexplored regional variability modes. Additionally, guided by an EOF/PC analysis, the approach facilitates an automatic exploration of depth layers that significantly contribute to explaining sea level variability, providing insights into diverse climatic regions. Furthermore, the methodology is specifically designed for a multi-scale analysis, enabling the examination of climate variability spanning from months to several years. The results obtained through this approach have the potential to support informed decision-making regarding local climate-related changes.

Characterizing the stable oxygen isotopic composition of the southeast Indian Ocean

New seawater stable oxygen isotope (δ18O) samples were collected from the southeast Indian Ocean as part of the Coring to Reconstruct Ocean Circulation and Carbon dioxide Across 2 Seas (CROCCA-2S) expedition in November – December of 2018. These data fill a gap in the δ18O sampling coverage of the southern Indian basin, providing new insights into the hydrologic and oceanographic processes influencing the δ18O distribution of the region and its relationship to salinity in the upper ocean. Our surface ocean data (<100 m)—in combination with decades of observations from the broader south Indian Ocean—show distinct δ18O – salinity characteristics on either side of ∼85°E. The balance between evaporation and precipitation yields a strong, robust δ18O – salinity relationship west of 85°E (δ18O = 0.50(±0.01) * S – 17.2(±0.22)). However, within the mesoscale eddy field initiated by the Leeuwin Current further east (∼85–120°E), our observations fall along a mixing line between the southwest Indian Ocean and data collected from the Australian coastal margin, illustrating for the first time how the unique eastern boundary system of the south Indian Ocean drives regional-scale variability in the δ18O – salinity relationship of the surface ocean. A comparison between our observations in the shallow subsurface (100–1000 m) and those from neighboring surveys reinforces this upper ocean connection across the Indo-Australian basin. Antarctic Intermediate Water from the Indian Ocean can be isotopically distinguished from the more regional Tasman Intermediate Water occupying the South Australian Bight, suggesting exchange between the two regions is most prevalent at surface and mode water depths. In deeper waters (> ∼1500 m), we observe a notable 0.87‰ spread in δ18O. This variability may represent interactions between distinct deep water masses in the region, although additional data are needed to confirm. Overall, our data provide a new look at the hydrography and isotopic chemistry of the southeast Indian Ocean, emphasizing the impact of the region's mesoscale eddy field and its interconnectivity with neighboring basins.

Utilising a multi-proxy to model comparison to constrain the season and regionally heterogeneous impacts of the Mt Samalas 1257 eruption

Abstract. The Mt Samalas eruption, thought to have occurred in summer 1257, ranks as one of the most explosive sulfur-rich eruptions of the Common Era. Despite recent convergence, several dates have been proposed for the eruption ranging between 1256–1258, with, as of yet, no single combination of evidence that has been able to robustly distinguish between and exclude the other dates proposed for the Mt Samalas eruption. Widespread surface cooling and hydroclimate perturbations following the eruption have been invoked as contributing to a host of 13th century social and economic crises, although regional-scale variability in the post-eruption climate response remains uncertain. In this study we run ensemble simulations using the UK Earth System Model (UKESM1) with a range of eruption scenarios and initial conditions in order to compare our simulations with the most complete globally resolved multi-proxy database for the Mt Samalas eruption to date, incorporating tree rings, ice cores, and historical records. This allows more precise constraints to be placed on the year and season of the Mt Samalas eruption, as well as an investigation into the regionally heterogeneous post-eruption climate response. Using a multi-proxy to model comparison, we are able to robustly distinguish between July 1257 and January 1258 eruption scenarios, where the July 1257 ensemble simulation achieves considerably better agreement with spatially averaged and regionally resolved proxy surface temperature reconstructions. These reconstructions suggest the onset of significant cooling across Asia and Europe in 1258 and thus support the plausibility of previously inferred historical connections. Model-simulated temperature anomalies also point to severe surface cooling across the Southern Hemisphere with as of yet unexplored historical implications for impacted civilisations. Model simulations of polar sulfate deposition also reveal distinct differences in the timing of ice sheet deposition between the two simulated eruption dates, although comparison of the magnitude or asymmetric deposition of sulfate aerosol remains limited by large inter-model differences and complex intra-model dependencies. Overall, the multi-proxy to model comparison employed in this study has strong potential in constraining similar uncertainties in eruption source parameters for other historical eruptions for which sufficient coincident proxy records are available, although care is needed to avoid the pitfalls of model–multi-proxy comparison.

On the regional-scale variability in flow duration curves in Peninsular India

Abstract. Peninsular India is a unique region with major mountain ranges that govern regional atmospheric circulation and precipitation variability, the monsoons, and regional geology at range of timescales and process scales. However, the landscape and climatic feature controls on streamflow variability at a regional scale using flow duration curves (FDCs) – compact descriptions of streamflow variability that offer a window into the multiple, interacting processes that contribute to streamflow variability – have received little attention. This study examines the suitability of the partitioning of (1) an annual streamflow FDC into seasonal FDCs and (2) a total streamflow FDC into fast- and slow-flow FDCs to unravel the process controls on FDCs at a regional scale, with application to low-gradient rivers flowing east from the Western Ghats in Peninsular India. The results indicate that bimodal rainfall seasonality and subsurface gradients explain the higher contribution of slow flow to total flow across the north–south gradient of the region. Shapes of fast and slow FDCs are controlled by recession parameters, revealing the role of climate seasonality and geological profiles, respectively. Systematic spatial variation across the north–south gradient is observed, highlighting the importance of the coherent functioning of landscape–hydroclimate settings in imparting a distinct signature of streamflow variability. The framework is useful to discover the role of time and process controls on streamflow variability in a region with seasonal hydro-climatology and hydro-geological gradients.

Mapping the abundance of endemic mosquito-borne diseases vectors in southern Quebec

BackgroundClimate change is increasing the dispersion of mosquitoes and the spread of viruses of which some mosquitoes are the main vectors. In Quebec, the surveillance and management of endemic mosquito-borne diseases, such as West Nile virus or Eastern equine encephalitis, could be improved by mapping the areas of risk supporting vector populations. However, there is currently no active tool tailored to Quebec that can predict mosquito population abundances, and we propose, with this work, to help fill this gap.MethodsFour species of mosquitos were studied in this project for the period from 2003 to 2016 for the southern part of the province of Quebec: Aedes vexans (VEX), Coquillettidia perturbans (CQP), Culex pipiens-restuans group (CPR) and Ochlerotatus stimulans group (SMG) species. We used a negative binomial regression approach, including a spatial component, to model the abundances of each species or species group as a function of meteorological and land-cover variables. We tested several sets of variables combination, regional and local scale variables for landcover and different lag period for the day of capture for weather variables, to finally select one best model for each species.ResultsModels selected showed the importance of the spatial component, independently of the environmental variables, at the larger spatial scale. In these models, the most important land-cover predictors that favored CQP and VEX were ‘forest’, and ‘agriculture’ (for VEX only). Land-cover ‘urban’ had negative impact on SMG and CQP. The weather conditions on the trapping day and previous weather conditions summarized over 30 or 90 days were preferred over a shorter period of seven days, suggesting current and long-term previous weather conditions effects on mosquito abundance.ConclusionsThe strength of the spatial component highlights the difficulties in modelling the abundance of mosquito species and the model selection shows the importance of selecting the right environmental predictors, especially when choosing the temporal and spatial scale of these variables. Climate and landscape variables were important for each species or species group, suggesting it is possible to consider their use in predicting long-term spatial variationsin the abundance of mosquitoes potentially harmful to public health in southern Quebec.

Spatial variability in the structure of fish assemblages associated with Laminaria hyperborea forests in the NE Atlantic

Understanding fish-habitat associations is critical for ecosystem-based approaches to management and conservation. Kelp species, which are estimated to inhabit around 25% of the world's coastline and underpin highly productive and biodiverse ecosystems, are widely recognised as important nursery and foraging habitats for coastal fish species. However, quantitative assessments of fish assemblages within kelp forests are lacking for many regions. Here, we used Baited Remote Underwater Video (BRUV) and Underwater Visual Census (UVC) to quantify fish assemblages in Laminaria hyperborea forests at eight shallow subtidal rocky reefs that spanned 9° of latitude in the United Kingdom (UK). Fish assemblages were a ubiquitous and conspicuous component of kelp forest communities at all sites. BRUV surveys recorded marked regional-scale variability, with latitudinal shifts in assemblage structure and particularly distinct assemblages recorded in southwest England. These patterns were largely due to greater abundances of Pollachius spp. (i.e. saithe and pollock) in northern regions and higher numbers of Gobiusculus flavescens (two-spotted goby) and labrid species (i.e. rock cook, goldsinny and ballan wrasses) in southern regions. Unlike BRUVs, UVC surveys did not detect significant regional-scale variation in assemblage structure but did detect significant site-level differences, highlighting differences between the two techniques. BRUV surveys also recorded clear latitudinal trends in richness, with more taxa recorded at lower latitudes. Fish assemblages in the NE Atlantic have been, and will continue to be, impacted by ocean warming and fishing activities. Greater spatiotemporal coverage of kelp forest surveys and ongoing robust monitoring is needed to better understand and manage future ecological changes.

Understanding the impact of environmental variability and fisheries on the red sea urchin population in Baja California

IntroductionThe red sea urchin fishery is one of the most important fisheries in Baja California and the only urchin fishery in México; yet little is known on understanding how local, regional, and oceanic environmental variability may affect red sea urchin populations. MethodsWe analyzed how food availability, predator abundance and environmental variability could affect red sea urchin populations developing generalized linear models under different temperature conditions: Pre-heat wave, heat wave, and post–heatwave, including sites where sea surface temperature was above, below, and on average. Models included: a) biological variables: Macrocystis pyrifera (kelp) biomass, red sea urchin (Mesocentrotus franciscanus) density, sheephead (Semicossyphus pulcher), kelp bass (Paralabrax clathratus) and spiny lobster (Panulirus interruptus) catches, and b) oceanographic variables: sea surface temperature, wave power, upwelling index, multivariate El Niño index and North Pacific Gyre Oscillation index. ResultsBetween 65 and 82% of the variability observed in red sea urchin populations was explained by different combinations of variables, depending on the thermal condition analyzed. We observed that local environmental variability, such as food availability and predator harvest are highly important factors in determining red sea urchin population changes, compared to regional and oceanic scale variables such as upwelling, El Niño, or the North Pacific Gyre Oscillation. Results show that the relative importance of these variables changed depending on the spatial and temporal scale being analyzed, meaning that under “normal or average” conditions one set of variables is important, compared to extreme environmental conditions such as El Niño or “the Blob” when a different set of variables explained the observed variability. Urchin predators’ catches were correlated with urchin density during the pre-heatwave scenario, suggesting that under “average temperature” conditions the effect of fishing on predators, and consequently on urchin density is higher than local temperature, the most important variable during warm conditions. DiscussionThis study suggests that in Baja California, red sea urchin harvest has become the most important red sea urchin population control, so efforts should be encouraged and supported by state and federal agencies to promote more resilient ecosystems in the face of environmental uncertainty. Improving management of the commercial species that inhabit kelp forest, could yield benefits for the entire ecosystem, fishers, and the red sea urchin population in Mexico.

Environmental Drivers of Nearshore Fish Community Composition and Size Structure in Glacially Influenced Gulf of Alaska Estuaries

Coastal ecosystems in Alaska are undergoing rapid change due to warming and glacier recession. We used a natural gradient of glacierized to non-glacierized watersheds (0–60% glacier coverage) in two regions along the Gulf of Alaska—Kachemak Bay and Lynn Canal—to evaluate relationships between local environmental conditions and estuarine fish communities. Multivariate analyses of fish community data collected from five sites per region in 2019 showed that region accounted for the most variation in community composition, suggesting that local effects of watershed type were masked by regional-scale variables. Seasonal shifts in community composition were driven largely by the influx of juvenile Pacific salmon (Oncorhynchus spp.) in late spring. Spatiotemporal differences among fish communities were partly explained by salinity and temperature, which accounted for 19.5% of the variation in community composition. We used a multi-year dataset from Lynn Canal (2014–2019) to examine patterns of mean length for two dominant species. Generalized additive mixed models indicated that Pacific staghorn sculpin (Leptocottus armatus) mean length varied along site-specific seasonal gradients, increasing gradually through the summer in the least glacially influenced sites and increasing rapidly to an asymptote of ~ 150 mm in the most glacially influenced sites. Starry flounder (Platichthys stellatus) mean length was more strongly related to environmental conditions, increasing with temperature and turbidity. Together, our findings suggest that community compositions of estuarine fishes show greater variation at the regional scale than the watershed scale, but species-specific variation in size distributions may indicate differences in habitat quality across watershed types within regions.

Contrasting beta diversity of spiders, carabids, and ants at local and regional scales in a black soil region, northeast China

Understanding the factors determining the formation of each community and metacommunity across a landscape is one of the most important ideas in soil animal ecology. However, the variables and parameters that shape soil arthropod communities in agroecosystems have not been resolved. These arthropods can serve as important bioindicators of field management and its sustainability. We sampled five corn plantations in each of three locations across a region spanning 600 km to come up with these determinants of the community structure of ground-dwelling spiders (Erigoninae: Araneae), carabids (Coleoptera: Carabidae), and ants (Hymenoptera: Formicidae). The analysis of the five fields within each of the three locations represent our local-scale samples, while the comparisons of the 15 sites across all three locations represent the regional scale samples. We tested the hypothesis that in the models we sampled, environmental/soil variables would drive community assembly locally (within location comparisons), but at the regional scale (between location comparisons), climatic and spatial variables would drive metacommunity assembly. The outcomes of our study showed distinct communities at each of the three locations when compared across regions but locally, fields were similar in species composition, as expected. Locally, spatial variables were important but not soil variables, regulated species richness and abundance. Turnover contributed more than nestedness to explain the biodiversity of spiders, carabids, and ants at both the local and regional scales. Neither purely climate variables, nor purely soil or spatial variables were significant enough explanations for the regional scale arthropod community composition. However, spatially structured environmental factors contributed most to explain the patterns supporting our hypothesis. We conclude that biodiversity in this agroecosystem area can be promoted by a mosaic of land uses being encouraged to increase landscape complexity at the regional scale.

Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network

Marine fish movement plays a critical role in ecosystem functioning and is increasingly studied with acoustic telemetry. Traditionally, this research has focused on single species and small spatial scales. However, integrated tracking networks, such as the Integrated Tracking of Aquatic Animals in the Gulf of Mexico (iTAG) network, are building the capacity to monitor multiple species over larger spatial scales. We conducted a synthesis of passive acoustic monitoring data for 29 species (889 transmitters), ranging from large top predators to small consumers, monitored along the west coast of Florida, USA, over 3 yr (2016-2018). Space use was highly variable, with some groups using all monitored areas and others using only the area where they were tagged. The most extensive space use was found for Atlantic tarpon Megalops atlanticus and bull sharks Carcharhinus leucas. Individual detection patterns clustered into 4 groups, ranging from occasionally detected long-distance movers to frequently detected juvenile or adult residents. Synchronized, alongshore, long-distance movements were found for Atlantic tarpon, cobia Rachycentron canadum, and several elasmobranch species. These movements were predominantly northbound in spring and southbound in fall. Detections of top predators were highest in summer, except for nearshore Tampa Bay where the most detections occurred in fall, coinciding with large red drum Sciaenops ocellatus spawning aggregations. We discuss the future of collaborative telemetry research, including current limitations and potential solutions to maximize its impact for understanding movement ecology, conducting ecosystem monitoring, and supporting fisheries management.

Regional scale trend and variability of rainfall pattern over agro-climatic zones in the mid-Mahanadi river basin of eastern India

Investigation of trends and variability in rainfall patterns is of paramount importance for the effective planning and management of rainfed cropping systems. In this study, a detailed analysis is carried out to understand the changes in rainfall patterns and characteristics at a regional scale on agro-climatic zone basis, in the mid-Mahanadi river basin. Daily rainfall data of India Meteorological Department (IMD) at 0.25° × 0.25° spatial resolution has been analyzed for the period of 1979–2013 on a monthly, seasonal, and annual timescale to investigate variability, trends, and seasonality of rainfall. Results of analysis reveal that there is large spatio-temporal variation in the river basin with a shift in seasonality of rainfall, with increased prolonged dry spells, and significant increase in extreme weather events over the agro-climatic zones (AZ). Most of the agro-climatic zones are found to have significant decreasing trends in the magnitude of rainfall during the non-monsoon season. In particular, agro-climatic zones nearer to the coastal region are more vulnerable to the high rainfall, whereas the North-Eastern-Ghat (AZ67) and Western-Central-Table (AZ68) regions are susceptible to the moisture stress. Further, analysis of groundwater level fluctuation shows a significant decline in groundwater level during the monsoon as well as non-monsoon seasons. The resulted effect of rainfall variability can be seen in terms of an increase in the prolonged dry spells as well as decline in the groundwater level. Moreover, the results of this study indicate a climate change signal at the regional scale that will help the policymakers and water managers to identify the critical agro-climatic zones in the mid-Mahanadi river basin for the formulation of efficient water resources management and planning strategies, particularly for the major crops of the rainfed agriculture regions.

Accounting for two-billion tons of stabilized soil carbon

The pedosphere is the largest terrestrial reservoir of organic carbon, yet soil-carbon variability and its representation in Earth system models is a large source of uncertainty for carbon-cycle science and climate projections. Much of this uncertainty is attributed to local and regional-scale variability, and predicting this variation can be challenging if variable selection is based solely on a priori assumptions due to the scale-dependent nature of environmental determinants. Data mining can optimize predictive modeling by allowing machine-learning algorithms to learn from and discover complex patterns in large datasets that may have otherwise gone unnoticed, thus increasing the potential for knowledge discovery. In this analysis, we identify important, regional-scale determinants for top- and subsoil-carbon stabilization in production forestland across the southeastern US. Specifically, we apply recursive feature elimination to a large suite of socio-environmental data to strategically select a parsimonious, yet highly predictive covariate set. This is achieved by recursively considering smaller and smaller covariate sets—or features—by first training the estimator on the full set to obtain feature importance. The least important features are pruned, and the procedure is recursively repeated until a desired number of covariates is identified. We show that although carbon ranges from 0.3 to 8.2 kg m−2 in the topsoil (0 to 20 cm), and from 0.4 to 17.6 kg m−2 in the subsoil (20 to 100 cm), this variability is predictably distributed with precipitation, soil moisture, nitrogen and sand content, gamma ray emissions, mean annual minimum temperature, and elevation. From our spatial predictions, we estimate that 2.6 Pg of soil carbon is currently stabilized in the upper 100 cm of production forestland, which covers 34.7 million ha in the southeastern US.

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional-scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local-scale anthropogenic variables, including wildfire history, land-use change, invasive species, and ecological restoration practices can override regional-scale variables to drive patterns of species distribution. Incorporating these human-induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human-induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field-sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: -6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: -47.9%, -41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land-use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.

New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss

Despite global recognition of the social, economic and ecological impacts of deforestation, the world is losing forests at an alarming rate. Global and regional efforts by policymakers and donors to reduce deforestation need science-driven information on where forest loss is happening, and where it may happen in the future. We used spatially-explicit globally-consistent variables and global historical tree cover and loss to analyze how global- and regional-scale variables contributed to historical tree cover loss and to model future risks of tree cover loss, based on a business-as-usual scenario. Our results show that (1) some biomes have higher risk of tree cover loss than others; (2) variables related to tree cover loss at the global scale differ from those at the regional scale; and (3) variables related to tree cover loss vary by continent. By mapping both tree cover loss risk and potential future tree cover loss, we aim to provide decision makers and donors with multiple outputs to improve targeting of forest conservation investments. By making the outputs readily accessible, we anticipate they will be used in other modeling analyses, conservation planning exercises, and prioritization activities aimed at conserving forests to meet national and global climate mitigation targets and biodiversity goals.

A statistical downscaling framework for environmental mapping

In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold—complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June–August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted components via an algorithm with fully adaptable parameters. We demonstrate how the algorithm can be extended to weather-dependent and other applications with inherent data randomness and model variability via its built-in interdisciplinary computational power that connects data sources with end-users.

Vertical distribution of aerosol optical properties in the Po Valley during the 2012 summer campaigns

Abstract. Studying the vertical distribution of aerosol particle physical and chemical properties in the troposphere is essential to understand the relative importance of local emission processes vs. long-range transport for column-integrated aerosol properties (e.g. the aerosol optical depth, AOD, affecting regional climate) as well as for the aerosol burden and its impacts on air quality at the ground. The main objective of this paper is to investigate the transport of desert dust in the middle troposphere and its intrusion into the planetary boundary layer (PBL) over the Po Valley (Italy), a region considered one of the greatest European pollution hotspots for the frequency that particulate matter (PM) limit values are exceeded. Events of mineral aerosol uplift from local (soil) sources and phenomena of hygroscopic growth at the ground are also investigated, possibly affecting the PM concentration in the region as well. During the PEGASOS 2012 field campaign, an integrated observing–modelling system was set up based on near-surface measurements (particle concentration and chemistry), vertical profiling (backscatter coefficient profiles from lidar and radiosoundings) and Lagrangian air mass transport simulations by FLEXPART model. Measurements were taken at the San Pietro Capofiume supersite (44°39′ N, 11°37′ E; 11 m a.s.l.), located in a rural area relatively close to some major urban and industrial emissive areas in the Po Valley. Mt. Cimone (44°12′ N, 10°42′ E; 2165 m a.s.l.) WMO/GAW station observations are also included in the study to characterize regional-scale variability. Results show that, in the Po Valley, aerosol is detected mainly below 2000 m a.s.l. with a prevalent occurrence of non-depolarizing particles ( &gt; 50 % throughout the campaign) and a vertical distribution modulated by the PBL daily evolution. Two intense events of mineral dust transport from northern Africa (19–21 and 29 June to 2 July) are observed, with layers advected mainly above 2000 m, but subsequently sinking and mixing in the PBL. As a consequence, a non-negligible occurrence of mineral dust is observed close to the ground ( ∼ 7 % of occurrence during a 1-month campaign). The observations unambiguously show Saharan dust layers intruding the Po Valley mixing layer and directly affecting the aerosol concentrations near the surface. Finally, lidar observations also indicate strong variability in aerosol on shorter timescales (hourly). Firstly, these highlight events of hygroscopic growth of anthropogenic aerosol, visible as shallow layers of low depolarization near the ground. Such events are identified during early morning hours at high relative humidity (RH) conditions (RH &gt; 80 %). The process is observed concurrently with high PM1 nitrate concentration (up to 15 µg cm−3) and hence mainly explicable by deliquescence of fine anthropogenic particles, and during mineral dust intrusion episodes, when water condensation on dust particles could instead represent the dominant contribution. Secondly, lidar images show frequent events (mean daily occurrence of ∼ 22 % during the whole campaign) of rapid uplift of mineral depolarizing particles in afternoon–evening hours up to 2000 m a.s.l. height. The origin of such particles cannot be directly related to long-range transport events, being instead likely linked to processes of soil particle resuspension from agricultural lands.

Local Habitat Influences on Feeding and Respiration of the Intertidal Mussels Perumytilus purpuratus Exposed to Increased pCO2 Levels

Coastal ecosystems are exposed to changes in physical-chemical properties, such as those occurring in upwelling and freshwater-influenced areas. In these areas, inorganic carbon can influence seawater properties that may affect organisms and populations inhabiting benthic habitats such as the intertidal mussel Perumytilus purpuratus. Feeding and metabolic responses were measured in adult mussels from two geographic regions (central and southern Chile) and two local habitats (river-influenced and non-river-influenced) and three pCO2 levels (380, 750, and 1200 μatm pCO2 in seawater). The feeding rates of mussels tend to increase at high pCO2 levels in seawater; however this response was variable across regions and local habitats. In contrast, there was no difference in the respiratory rate of mussels between geographic areas, but there was a significant reduction of oxygen consumption at intermediate and high levels of pCO2. The results indicate that river-influenced organisms compensate for reductions in metabolic cost at elevated pCO2 levels by having their energy demands met, in contrast with non-river-influenced organisms. The lack of regional-scale variability in the physiological performance of mussels may indicate physiological homogeneity across populations and thus potential for local adaptation. However, the local-scale influences of river- and non-river-influenced habitats may counterbalance this regional response promoting intra-population variability and phenotypic plasticity in P. purpuratus. The plasticity may be an important mechanism that allows mussels to confront the challenges of projected ocean acidification scenarios.

Field variability and vulnerability index to identify regional precision agriculture opportunity

Innovations in precision agriculture (PA) have created opportunities to achieve a greater understanding of within-field variability. However, PA adoption has been hindered by uncertainty about field-specific performance and return on investment. Uncertainty could be better addressed by using innovative analyses that provide insights into variability among fields and across a region. The objectives of this research were to: (1) generate a within-field soil clay-content variability index (VIc), (2) create a regional-scale growing-season precipitation variability index (VIp), and (3) integrate the soil and weather indices with the USDA NRCS soil erosion vulnerability index (SVI) to produce a final index that incorporated both variability and vulnerability (VVI). The interpretation of the outcomes represented by each objective supports unique decisions that land managers may consider for reducing uncertainty about implementing PA. All indices were derived using publically available information for Missouri, USA. The VIc was the ratio between the maximum and minimum clay content within fields. The VIp was calculated as the standard deviation of the total growing-season precipitation from 2006 to 2015. Significant clustering of VIc and VIp were observed along the Missouri River corridor, northeast and northwest Missouri. Fields with high VVI were mostly in the claypan soil region of northeast Missouri, and along a portion of the Missouri River dominated by loess soils on steep slopes. Southeast Missouri displayed the greatest diversity in soil and weather variability, but had low vulnerability. This research could be used as a decision-support tool to aid producers and PA service and product providers in determining where PA opportunities exist.

Regional-scale variability of cold water temperature: Implications for household water-related energy demand

Residential water use accounts for at least 80% of water-related energy (WRE) demand (primarily through water heating) in the residential urban water system. Cold water temperature (CWT) is a key determinant in predicting residential WRE but variation of CWT within water networks has not been quantified and is not accounted for in water heating energy consumption guidelines. Here, we analysed the spatiotemporal variability in CWT over the course of a year (2013) using 5760 measurements from 1255 urban water system sampling locations across the Yarra Valley Water region in Melbourne, Australia. CWT varied across the study site from 12–28°C during summer and 9–15°C during winter. Spatial clusters of higher CWT regions (hot spots) and lower CWT regions (cold spots) were also observed. The CWT variability impact on annual household WRE demand was estimated to be between −17 to +19% (−640 to +680kWh/hh.yr) change in water heating for sample households, which is dependent on the geographical location of the household within the study site. However, households located in cold spot regions will have almost twice the amount of WRE demand than average, conversely, WRE demand will be lower than average in hot spot regions.Monthly mean CWTs diverged from the Australian Standards for hot water system energy consumption guidelines value by −21 to +47%. The magnitude of CWT variability and associated energy required for water heating are comparable with the total energy used by water utilities to deliver water supply and sewage disposal services. Variation in water heating could be as large as −4.6kWh/hh.d (hot spot in March) and 3.6kWh/hh.d (cold spot in July), more than three times the total energy used to deliver water supply and sewage disposal services for this region. Accounting for CWT variability could increase accuracy of regional-scale WRE demand and hot water system performance.