Regional Temperature Variations Research Articles

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

Glacial interglacial variations in the natural iron fertilization during the low sea ice periods along the eastern continental margin of Antarctica

The Southern Ocean sequesters atmospheric CO2 through biological pumps, though its driving factors are debated. Modern productivity is regulated by natural iron fertilization from micronutrient influx through dust, regeneration, and Antarctic glaciers and sea ice melting (ice melt). The productivity along the eastern Antarctic continental margin was low during the last glacial period and gradually increased through the deglacial to Late Holocene, marked by distinct productivity peaks. The micronutrients also varied similarly, with reduced glacial influx and an increase in the Holocene, which may cause productivity peaks. Therefore, the coherence of enhanced productivity and micronutrient influx is considered as enhanced iron fertilization period. The productivity peaks declined within ∼1.5 kyr, mostly due to the Ekman transport and sea ice formation. Along with ice melt, the independent weathering pattern that responds with glacial- interglacial ice volume changes suggest the source of micronutrients is not terrigenous. Shelf interaction of oceanic currents can influence the water column nutrient stock significantly, while its utilization occurs during reduced ice periods (low sea ice and high glacier melting) for productivity. Therefore, enhanced natural iron fertilization periods are considered as ice low periods that occurred in a periodicity of 2 kyrs, at ∼7.5 kyr BP, ∼5.5 kyr BP, ∼4 kyr BP, ∼ 2.5 kyr BP, and ∼0.5 kyr BP, likely due to the combined effects of atmospheric and oceanographic factors driven by the high amplitude regional temperature variability during the mid to late Holocene.

A century-long China homogenized daily surface air temperature dataset (CUG-CMA CHDT)

Daily meteorological observation data of the early period (pre-1950) were critically important for investigating the long-term trends and multi-decadal scale variability of extreme climate events. The high-resolution surface air temperature (SAT) data for time period before 1950 are lacking in China. We extended the SAT observations of China back to 1840 through developing a pre-1950 daily SAT dataset. The early-period daily SAT were manually corrected for the input and clerical errors, and then according to the length or coverage of time, the main series for each of the cities was determined. The observation time system of unknown sites was determined by the minimum difference method. After these operations, the data of all sites were unified into the same format. By using the ridge regressions established based on data from modern reference stations, the missing maximum temperature (Tmax) and minimum temperature (Tmin) were interpolated. The early-period data were combined with modern data to form the long-term daily SAT dataset of 1840–2020 in China. RHtest software was used to detect and adjust the inhomogeneities in the station data series. Finally, the century-long homogenized daily SAT dataset including 45 key city stations in China was obtained. Among the stations, there are 20 stations with observation record more than one hundred years. The length of temperature observation series of 17 stations is between 80 and 100 years. The series length of the remaining 7 stations is between 68 and 80 years. Finally, the angular distance weighting (ADW) method was used to interpolate the data into grid products, and the grid size is 2.5 ° × 2.5 °. The dataset was named CUG-CMA CHDT, which is applicable in monitoring, studies and assessments of regional extreme temperature change and variability in China.

A preliminary integrated analysis of regional paleoclimate variations in China over the past ∼ 21 ka

In this study, we employ biological, geochemical and mineral, and physical proxies to quantitatively and semi-quantitatively reconstruct regional paleoclimate variations in China over the past ∼21 thousand years (ka). We have constructed state-of-the-art transfer functions between proxies and climatic variables in East Asia, revealing substantial paleoclimate variability, and demonstrate significant diversity of paleoclimate variations in regions of China over the past ∼21 ka. Compared with observational data between 1961 and 1990, averaged mean annual temperature (MAT) was ∼5 °C (3–8 °C) lower during the Last Glacial Maximum (LGM, ∼21 ± 3 ka), and ∼ 2 °C (1–3 °C) higher during Holocene Optimum (HO, ±9 ka) in China; while averaged mean annual precipitation (MAP) and averaged summer precipitation varied between 30 and 150%, with enhanced seasonality during the warmer Holocene (8–5 ka). Our quantitative and semi-quantitative paleoclimate reconstructions reveal both similarities and contrasts with previous studies of regional temperature and precipitation variability. The most remarkable paleoclimate variability is recorded in the transitional zone between the humid monsoon and arid desert regions of central and northern China. We conducted a dynamic downscaling paleoclimate simulation with a regional climate model (RegCM4), using the output from the fully-forced transient simulation of the global climate (TraCE-21 K) as the prescribed boundary conditions, to simulate regional climate changes at high spatial resolution in China over the past ∼21 ka. The results improve paleoclimate simulations at regional scales, but reveal differences in comparison with TraCE-21 K, thereby highlighting uncertainties in the simulation modeling. Moreover, we compare proxy-based paleoclimate reconstructions and the output of TraCE-21 K to identify inconsistencies in relation to regional paleoclimate variations, and confirm a generally consistent warming trend in MAT from the LGM to the HO followed by a cooling trend during the late Holocene. An overall increase in both annual and summer precipitation is observed from LGM to Holocene. This general paleoclimate pattern is likely to have been driven by northern hemisphere ice volume and atmospheric greenhouse gas concentrations (GHGs) during the last deglaciation, and boreal summer insolation during the Holocene. This study represents the first attempt to use multi sources and large datasets of proxy reconstructions and numerical simulations to evaluate regional paleoclimate variability in China since the LGM. Our research provides a comprehensive overview of climate change over the past 21 ka and underscores the critical importance of conducting high-resolution simulations and quantitative paleoclimate studies. This approach is essential for deciphering patterns of paleoclimate change that are currently poorly defined.

Recent trends and variability of temperature and atmospheric water vapor over South Asia

This study examines the recent trends and variability of temperature and atmospheric water vapor over South Asia during the summer monsoon season from 1970 to 2022. We employ gridded reanalysis and observational datasets to assess the trends and variability in surface temperature and further investigate the associated physical processes. Our results show that the surface temperature over South Asia has increased by 0.5 °C per decade. Notably, we find a statistically significant major transition in the surface temperature series in 1995, such that 1970–1994 (1995–2022) is associated with anomalous cooling (anomalous warming). Since 1995, not only have the temporal variations in regional surface temperature, atmospheric water vapor, and clear-sky downward longwave radiation been synchronous at interannual scales, but their largest spatial variations have been located mainly over the same region. A diagnosis of the atmospheric moisture budget reveals that the observed changes in water vapor convergence over South Asia are dominated by changes in mean circulation dynamics, while the change in thermodynamics is relatively small. The enhancement of the Somali low-level jet may contribute to increased water vapor in South Asia by modulating low-level circulations.

Seed priming as a promising technique for sustainable restoration of dryland

The success of plant growth and development in dryland restoration relies on seed germination, seedling development, and establishment. The lack of rainfall and extreme temperature variations in arid regions impede germination and seedling establishment, necessitating new approaches and techniques. The present review summarizes the vital role of seed priming techniques for the sustainable restoration of drylands through their effects on enhancing seed germination, seedling growth and development, plants' nutrient use efficiency, soil fertility, essential enzymes, microbial community, and stress adaptation ability, helping to alleviate the effects of abiotic stresses on plants. The importance of seed priming is discussed in both agricultural and ecological restoration contexts. Priming maximizes crop yields, reduces crop failure, and promotes eco‐friendly farming practices. In ecological restoration, seed priming enhances plant establishment and survival, contributing significantly to the restoration of native vegetation and wildlife habitats. The review categorizes seed priming techniques into six subcategories, highlighting their unique advantages for optimizing restoration efforts. The review highlights the underlying mechanisms of seed priming techniques to improve seed germination and plant development in dryland regions, particularly under salinity and drought constraints. While seed priming has proven effective, it also faces several limitations, such as cost, seed longevity, and environmental concerns. Investigating environmentally friendly priming methods and understanding the prolonged effects of priming on seed viability and the environment is crucial. Further research is needed to find cost‐effective, eco‐friendly seed priming methods, and to explore the genetic and molecular bases of stress tolerance in various plant species across diverse environments.

Thermoregulation of Feet in Cold Environments: A Study on Alpinism

Thermal comfort plays a crucial role in the performance and well-being of mountaineers, especially in extreme environments. The aim of this study was to develop a reliable protocol to assess the thermal comfort of mountaineering boots, with a specific focus on temperature variations in different regions of the foot and their correlation with physiological factors. Two different models of mountaineering boots were tested at two different environmental temperature (-15°C and -30°C). The mean skin temperature, measured according to International Standards BS EN ISO 9886:2004, was used as an indicator of overall thermal comfort. Physiological factors such as heart rate (HR), body mass index (BMI) and body surface area (BSA) were also measured to understand their relationship to thermoregulation. Kruskal-Wallis and Pearson's ProductMoment correlation tests were performed to investigate whether there was a statistically significant relationship.The results showed significant differences in foot temperature among the Testers, indicating variations in the perception of thermal comfort. The correlation analysis showed a strong positive relationship between mean skin temperature and HR, highlighting the influence of physiological factors on thermal comfort.In addition, the analysis showed that the dorsum and hallux areas had the largest temperature variations, suggesting the occurrence of vasoconstriction and potential discomfort.This study represents a preliminary approach to establishing a reliable protocol for assessing the thermal performance of cold protective footwear.

Pitfalls in diagnosing temperature extremes

Worsening temperature extremes are among the most severe impacts of human-induced climate change. These extremes are often defined as rare events that exceed a specific percentile threshold within the distribution of daily maximum temperature. The percentile-based approach is chosen to follow regional and seasonal temperature variations so that extremes can occur globally and in all seasons, and frequently uses a running seasonal window to increase the sample size for the threshold calculation. Here, we show that running seasonal windows as used in many studies in recent years introduce a time-, region-, and dataset-depended bias that can lead to a striking underestimation of the expected extreme frequency. We reveal that this bias arises from artificially mixing the mean seasonal cycle into the extreme threshold and propose a simple solution that essentially eliminates it. We then use the corrected extreme frequency as reference to show that the bias also leads to an overestimation of future heatwave changes by as much as 30% in some regions. Based on these results we stress that running seasonal windows should not be used without correction for estimating extremes and their impacts.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Physical mechanisms for the dominant summertime high-latitude atmospheric teleconnection pattern and the related Northern Eurasian climates

Summertime atmospheric teleconnection patterns over Eurasia have a significant influence on regional weather and climate. Despite extensive studies on the subtropical patterns, the high-latitude counterpart has received relatively less attention. This study proposes physical mechanisms for the formation and maintenance of the dominant high-latitude teleconnection pattern. The formation of the pattern is associated with variability in synoptic-scale eddy activity due to the meridional gradient of sea surface temperature anomalies in the vicinity of the Gulf Stream, causing a meridional shift of the central axis of storm track at the exit of Atlantic jet. The resultant convergence of transient vorticity fluxes to the west of the British Isles induces low-frequency cyclonic circulation anomalies and continued propagation of Rossby waves downstream along northern Eurasia. Once these circulation anomalies are formed, the subsequent latent heat-related diabatic anomalies over the northern Eurasian landmass act as another source of Rossby waves to maintain the teleconnection pattern. Regional temperature and precipitation variability is closely linked to the wave pattern along a route through northern Eurasia, and even precipitation over the East Asian summer monsoon region is influenced by the teleconnection pattern.

Multi-source satellite reveals the heterogeneity in water storage change over northwestern China in recent decades

The northwestern China (NWC) generally suffers from severe water shortages. To improve the management and utilization of the limited water storage in NWC, it is crucial to explore the distribution and variation of water storage. Here, we obtained the variations of terrestrial water storage (TWS), surface water area (SWA), and groundwater storage (GWS) in NWC (which refers to Xinjiang Uyghur Autonomous Region in the study) from 2002 to 2018 based on multi-source satellite data, and analyzed the factors influencing water storage changes. The results indicated that (1) SWA exhibited an increasing trend with 40.90 km2/year from 2002 to 2018 based on main lakes/reservoirs and rivers water area in NWC, due to the increase of precipitation and glacier melting. TWS and GWS showed a decreasing trend with a mean rate of −1.25 mm/year and −1.08 mm/year from 2002 to 2018 in NWC, respectively, due to the increase of arable land area and water consumption. (2) SWA in northern and southern NWC showed a decrease (-0.72 km2/year) and an increase (51.14 km2/year) from 2002 to 2018, respectively. The different rates are due to regional precipitation and temperature variations. TWS in northern and southern NWC showed a declining of −0.14 mm/year and −1.94 mm/year, respectively. GWS in northern and southern NWC showed a declining of −0.55 mm/year and −1.72 mm/year, respectively. The differences are related to the greater evapotranspiration and irrigation water consumption in southern than northern NWC. This study enriches the information on water storage in NWC and provides useful guidance for local water resource management and protection.

550‐Year Climate Periodicity in the Yunnan‐Guizhou Plateau During the Late Mid‐Holocene: Insights and Implications

AbstractSignificant multi‐centennial climate oscillations have been documented in a number of well‐dated climate records across the Holocene epoch and left various imprints in human cultural history. In this study, we developed speleothem δ13C, δ18O, trace elements, and lamina thickness records from the Yunnan‐Guizhou Plateau (YGP). Our high‐resolution and precisely dated records show a significant ∼550‐yr cycle as the dominant pattern of regional temperature and vegetation variations between ∼5,870 and ∼3,670 years ago. The phase analyses of the 550‐yr cycles among our speleothem records, other Northern Hemisphere climate records, solar activity index, and Atlantic meridional overturning circulation (AMOC) variations suggest that this climate cycle has a large spatial extent, and may be causally linked to the AMOC changes through coupled oceanic‐atmospheric processes. Additionally, the first cold phase of the ∼550‐yr cycle in our records coincides with the major cultural development on the YGP at ∼5,500–5,000 years ago, suggesting a critical relationship between climate and prehistorical cultural changes in the region.

Size and temperature drive nutrient retention potential across water bodies in China

Lentic water bodies, including lakes, reservoirs, and wetlands, retain excess nutrients in runoff from agricultural and urban activities, and protect downstream water bodies from eutrophication. To develop effective nutrient mitigation strategies, it is important to understand the controls on nutrient retention in lentic systems and what drives variability between different systems and geographical regions. Efforts to synthesize water body nutrient retention at the global scale are biased toward studies from North America and Europe. Numerous studies published in Chinese Language journals exist in the extensive China National Knowledge Infrastructure (CNKI), but are missing from global synthesis due to their absence in English language journal databases. We address this gap by synthesizing data from 417 waterbodies in China to assess hydrologic and biogeochemical drivers of nutrient retention. In this study, we found median retention of 46 and 51% for nitrogen and phosphorus, respectively, across all water bodies in our national synthesis, and on average, wetlands retain more nutrients than lakes or reservoirs. The analysis of this dataset highlights the influence of water body size on first-order nutrient removal rate constants, as well as how regional temperature variations affect nutrient retention in water bodies. The dataset was used to calibrate the HydroBio-k model, which explicitly considers the effect of residence times and temperature on nutrient retention. Application of the HydroBio-k model across China reveals patterns of nutrient removal potential, where regions with a higher density of small water bodies retain more nutrients than others, such that regions like the Yangtze River Basin with a greater proportion of smaller water bodies have greater retention rates. Our results emphasize the importance of lentic systems and their function in nutrient removal and water quality improvement, as well as the drivers and variability of these functions at the landscape scale.

Interspecific differences in thermal tolerance landscape explain aphid community abundance under climate change

A single critical thermal limit is often used to explain and infer the impact of climate change on geographic range and population abundance. However, it has limited application in describing the temporal dynamic and cumulative impacts of extreme temperatures. Here, we used a thermal tolerance landscape approach to address the impacts of extreme thermal events on the survival of co-existing aphid species (Metopolophium dirhodum, Sitobion avenae and Rhopalosiphum padi). Specifically, we built the thermal death time (TDT) models based on detailed survival datasets of three aphid species with three ages across a broad range of stressful high (34–40 °C) and low (−3∼−11 °C) temperatures to compare the interspecific and developmental stage variations in thermal tolerance. Using these TDT parameters, we performed a thermal risk assessment by calculating the potential daily thermal injury accumulation associated with the regional temperature variations in three wheat-growing sites along a latitude gradient. Results showed that M. dirhodum was the most vulnerable to heat but more tolerant to low temperatures than R. padi and S. avenae. R. padi survived better at high temperatures than Sitobion avenae and M. dirhodum but was sensitive to cold. R. padi was estimated to accumulate higher cold injury than the other two species during winter, while M. dirhodum accrued more heat injury during summer. The warmer site had higher risks of heat injury and the cooler site had higher risks of cold injury along a latitude gradient. These results support recent field observations that the proportion of R. padi increases with the increased frequency of heat waves. We also found that young nymphs generally had a lower thermal tolerance than old nymphs or adults. Our results provide a useful dataset and method for modelling and predicting the consequence of climate change on the population dynamics and community structure of small insects.

Millennial-scale climate variability over land overprinted by ocean temperature fluctuations

Variations in regional temperature have widespread implications for society, but our understanding of the amplitude and origin of long-term natural variability is insufficient for accurate regional projections. This is especially the case for terrestrial temperature variability, which is currently thought to be weak over long timescales. By performing spectral analysis on climate reconstructions, produced using sedimentary pollen records from the Northern Hemisphere over the last 8,000 years, coupled with instrumental data, we provide a comprehensive estimate of regional temperature variability from annual to millennial timescales. We show that short-term random variations are overprinted by strong ocean-driven climate variability on multi-decadal and longer timescales. This may cause substantial and potentially unpredictable regional climatic shifts in the coming century, in contrast to the relatively muted and homogeneous warming projected by climate models. Due to the marine influence, regions characterized by stable oceanic climate at sub-decadal timescales experience stronger long-term variability, and continental regions with higher sub-decadal variability show weaker long-term variability. This fundamental relationship between the timescales provides a unique insight into the emergence of a marine-driven low-frequency regime governing terrestrial climate variability and sets the basis to project the amplitude of temperature fluctuations on multi-decadal timescales and longer.

Spatial distribution and dynamics of selected mangrove forests on the east and west coasts of Sri Lanka

The climate and edaphic characteristics primarily determine the spatial distribution and dynamics of mangrove forests. The present study was initiated to determine how species composition and structural characteristics vary along with climate and substrate salinity of six mangrove forests located on the west coast, i.e., Negombo estuary, Chilaw lagoon, and Malwathu oya estuary, and east coast, i.e., Batticaloa lagoon, Uppar lagoon, and Urani lagoon. The structural parameters in terms of tree height, basal area, biomass, and density were obtained to determine the vegetation structure of mangrove forests. The current study found that although the structure of mangrove communities of the two coasts does not differ significantly (P<0.05), plant diversity in mangrove areas on the west coast is significantly higher than that of the east coast mangroves. In contrast, the biomass accumulation in west coast mangroves is relatively lower than that of east coast. Tree height was found to influence the productivity in terms of biomass increment of mangrove forests under investigation. As such, our study suggests that regional variations in salinity, temperature, and rainfall primarily serve as drivers of variation in mangrove species composition and vegetation structure of mangrove forests along the coasts of Sri Lanka.

Seismic fragility analysis of LRB-isolated bridges considering the uncertainty of regional temperatures using BP neural networks

Lead rubber bearings (LRBs) have been widely used as seismic isolators for bridges, owing to their outstanding working performance and wide applicability. However, the mechanical behavior of the main constituents of LRBs (rubber and lead) is sensitive to temperature variations. The objective of this study is to investigate the influence of regional temperature uncertainty on the seismic fragility of LRB-isolated bridges by combining joint seismic fragility and a (back propagation) BP neural network. In this study, a typical continuous beam bridge that is seismically isolated and uses LRBs is assumed to be located in three regions at different latitudes in China. Using the finite elements models of the bridge and incorporating the thermal–mechanical behavior of LRBs, concrete, and steel at different temperatures, the seismic responses of the piers and the bearings are derived by nonlinear dynamic analysis that is used to develop the statistical relationship between the regional temperature and the bridge’s seismic demand via a BP neural network. Then, the well-trained networks are applied to predict the seismic responses of the piers and the bearings at different temperatures in the three selected regions. Finally, the joint seismic fragility methodology is used to establish and compare the seismic fragility at component level and that at bridge system level in the considered regions. Analysis results show that regional temperature variations may have a notable effect on the component-level fragility as well as the bridge-level fragility.

Analysis of Extreme Temperature Variations on the Yunnan-Guizhou Plateau in Southwestern China over the Past 60 Years

Analysis of variations in 12 extreme temperature indices at 68 meteorological stations on the Yunnan-Guizhou Plateau (YGP) in southwestern China during 1960–2019 revealed widespread significant changes in all temperature indices. The temperature of the hottest days and coldest nights show significantly increasing trends, and the frequencies of the warm days and nights also present similar trends. The temperature of the coldest night has a significant and strong warming trend (0.38 °C/decade), whereas the frequency of frost days shows the fastest decrease (1.5 days/decade). Increases in the summer days are statistically significant, while a decreasing trend for the diurnal temperature range is not significant. Furthermore, there were significant differences in the changes of temperature indices between 1960–1989 and 1990–2019. Most parts of the YGP underwent significant warning, manifesting that the mountainous regions are relatively sensitive and vulnerable to climate change. The correlation coefficients between the temperature indices and various geographical factors (latitude, longitude, and height) reflect the complexity of regional temperature variability and indicate enhanced sensitivity of extreme temperatures to geographical factors on the YGP. It was also found that extreme temperatures generally had weaker correlations with the El Nino-Southern Oscillation, North Pacific Index, Southern Oscillation Index, North Atlantic Oscillation, and East Asian Summer Monsoon Index than with the South Asian summer monsoon index, Nino4 indices and Arctic Oscillation, and there were more insignificant correlations. Regional trends of the extreme temperature indices reflect the non-uniform temperature change over the YGP, which is due to the complex interaction between atmospheric circulation patterns and local topography. The results of this study have important practical significance for mitigating the adverse effects of extreme climatic changes, in particular for the YGP with its typical karst geomorphology and fragile ecological environment.

Chlorine-36 Surface Exposure Dating of Late Holocene Moraines and Glacial Mass Balance Modeling, Monte Sierra Nevada, South-Central Chilean Andes (38°S)

The development of robust chronologies of Neoglaciation from individual glaciers throughout the high-altitude Andes can provide fundamental knowledge of influences such as regional temperature and precipitation variability, and aid in predicting future changes in the Andean climate system. However, records of Late Holocene glaciation from the Central Chilean Andes are sparse, and often poorly constrained. Here, we present 36Cl surface exposure ages, dendrochronologic constraints, and glacial mass balance modeling simulations of Late Holocene glacier fluctuations in the Central-South Chilean Andes. A series of concentric moraine ridges were identified on Monte Sierra Nevada (38°S), where exposure dating of basaltic boulders was used to establish a chronology of ice recession. We infer that moraine abandonment of the most distal ridge in the valley commenced by ∼4.2 ka, and was followed by glacier margin retreat to an up-valley position. Exposure ages of the oldest Late Holocene boulders (∼2.5–0.8 ka) along the marginal extents of the moraine complex indicate fluctuations of the glacier terminus prior to ∼0.65 ka. A final expansion of the ice margin reoccupied the position of the 4.2 ka moraine, with abatement from the outermost composite moraine occurring by ∼0.70 ka, as constrained by tree-ring data from live Araucaria araucana trees. Finally, a series of nested moraines dating to ∼0.45–0.30 ka, formed from a pulsed ice recession during the latest Holocene when the lower reaches of the glacial snout was most likely debris mantled. A distributed temperature index model combined with a glacier flow model was used to quantify an envelope of possible climatic conditions of Late Holocene glaciation. The glacial modeling results suggest conditions were ∼1.5°C colder and 20% wetter during peak Neoglaciation relative to modern conditions. These records also suggest a near-coeval record of Late Holocene climate variability between the middle and high southern latitudes. Furthermore, this study presents some of the youngest 36Cl exposure ages reported for moraines in the Andes, further supporting this method as a valuable geochronologic tool for assessing Late Holocene landscape development.

Cold Temperature Limits to Biodiesel Use under Present and Future Climates in North America.

Cold weather operability is sometimes a limiting factor in the use of biodiesel blends for transportation. Regional temperature variability can therefore influence biodiesel adoption, with potential economic and environmental implications. This study assesses present and future biodiesel cold weather operability limits in North America according to temperature data from weather stations, atmospheric reanalysis, and global climate models with highest resolution over Ontario, Canada. Future temperature projections using the RCP8.5 climate change scenario show increases in the potential duration for certain seasonal fuel blends. For example, biodiesel blends whose cloud point temperature is -9 °C may expand their duration by 3-7% in North America for nonwinter seasons according to projections for 2040. Cloud point specifications among supply orbits in Ontario increase up to +6 °C during nonwinter seasons, with most increases observed in Fall and Spring. In winter, however, the modeling suggests no change in Ontario cloud point specifications because the coldest temperatures by mid-century are not significantly warmer than the past climate normal according to our climate simulations. This study provides a quantitative analysis on biodiesel usage scenarios under a changing climate, including Ontario region geographic temperature clusters that could prove useful for biodiesel blend-related decision-making.