Long-term Climate Variability Research Articles

Diatom dynamics during the last six centuries in Lake Odensjön: a new varved sediment record from southern Sweden

AbstractVarved lake sediments offer valuable insight into past environmental conditions with high temporal resolution and precise chronological control. A combination of diatom and geochemical analyses of the recently deposited sediments of Odensjön, a small dimictic lake in southern Sweden, shows alternating light and dark laminae composed of greater amounts of biogenic silica and organic matter, respectively. As confirmed by independent radiometric dating and Pb pollution data, and supported by scanning electron microscopy of individual laminae, these features represent ongoing deposition of biogenic varves. Corresponding diatom and geochemical data obtained from a 92-cm long freeze core provide evidence of substantial lake-ecosystem dynamics during the last six centuries, related mainly to variations in light penetration and wind shear driven by human-induced changes in catchment vegetation. The diatom assemblage of Odensjön’s varved sediments is dominated by planktonic species, primarily Asterionella formosa, Fragilaria saxoplanktonica and Discostella lacuskarluki during periods of forest cover, while increased catchment openness from the mid-1500s to the late 1800s led to increased abundance of Lindavia comensis. Long-term variations in climate and land use, mediated through changing length of the ice-cover season and nutrient input, respectively, probably contributed to the observed trends, as well as to variations in the appearance and visibility of the varve record across the sampled sediment sequence. Odensjön represents the southernmost varved sediment record in Fennoscandia documented to date, offering potential to study the effects of various types of external forcing on its sensitive lacustrine ecosystem since the Late Weichselian deglaciation. In the present study, we investigated the possibility of assessing the local impacts of two major, historically documented volcanic events, Laki 1783–84 and Tambora 1815, which are known to have affected European societies. Although the mildly alkaline waters of the lake are well buffered and hence relatively resilient to volcanic acid deposition, a minor response to the Laki eruption may be recorded in the diatom stratigraphy.

Multiple serial correlations in global air temperature anomaly time series.

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

Higher-Order Convolutional Neural Networks for Essential Climate Variables Forecasting

Earth observation imaging technologies, particularly multispectral sensors, produce extensive high-dimensional data over time, thus offering a wealth of information on global dynamics. These data encapsulate crucial information in essential climate variables, such as varying levels of soil moisture and temperature. However, current cutting-edge machine learning models, including deep learning ones, often overlook the treasure trove of multidimensional data, thus analyzing each variable in isolation and losing critical interconnected information. In our study, we enhance conventional convolutional neural network models, specifically those based on the embedded temporal convolutional network framework, thus transforming them into models that inherently understand and interpret multidimensional correlations and dependencies. This transformation involves recasting the existing problem as a generalized case of N-dimensional observation analysis, which is followed by deriving essential forward and backward pass equations through tensor decompositions and compounded convolutions. Consequently, we adapt integral components of established embedded temporal convolutional network models, like encoder and decoder networks, thus enabling them to process 4D spatial time series data that encompass all essential climate variables concurrently. Through the rigorous exploration of diverse model architectures and an extensive evaluation of their forecasting prowess against top-tier methods, we utilize two new, long-term essential climate variables datasets with monthly intervals extending over four decades. Our empirical scrutiny, particularly focusing on soil temperature data, unveils that the innovative high-dimensional embedded temporal convolutional network model-centric approaches markedly excel in forecasting, thus surpassing their low-dimensional counterparts, even under the most challenging conditions characterized by a notable paucity of training data.

Widening geographic range of Rift Valley fever disease clusters associated with climate change in East Africa

BackgroundRecent epidemiology of Rift Valley fever (RVF) disease in Africa suggests growing frequency and expanding geographic range of small disease clusters in regions that previously had not reported the disease....

Climate variability impacts on runoff projection in the 21st century based on the applicability assessment of multiple GCMs: A case study of the Lushi Basin, China

In this study, we initiatively employed the improved Secure Hash Algorithm (SHA) to evaluate and compare the competencies of multiple Coupled Model Intercomparison Project Phase Six (CMIP6) General Circulation Models (GCMs). The selected GCM outputs were bias-corrected by the non-parametric quantile mapping (QM) method. The calibrated and validated data were then used to run the XAJ (Xin’anjiang) model to assess long-term climate variation impacts on runoff under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), where the results were compared with the baseline period (1976–2000). Overall, the CNRM-CM6-1-HR, FIO-ESM-2–0, INM-CM5-0 and NorESM2-MM models reconstructed better spatio-temporal evolutionary trends related to historical precipitation and evaporation, and were applied as selected GCMs for reliable runoff projection applications. The intensification amplitudes of precipitation and evaporation produced by selected GCMs gradually spread from SSP1-2.6 to SSP5-8.5 throughout the 21st century. Compared to the baseline period (257 m3/s), the extreme streamflow during the wet periods is likely to increase for SSP5-8.5 (288 ∼ 298 m3/s), SSP3-7.0 (276 ∼ 294 m3/s), and SSP2-4.5 (222 ∼ 280 m3/s), whereas SSP1-2.6 decreases to varying degrees (178 ∼ 255 m3/s). Meanwhile, during the dry periods, the extreme streamflow is projected to decrease by 28 ∼ 73 m3/s (SSP5-8.5), 32 ∼ 38 m3/s (SSP3-7.0), 8 ∼ 31 m3/s (SSP2-4.5), and 5 ∼ 33 m3/s (SSP1-2.6) from historical levels (62 m3/s). The overwhelming majority of increasing trends in the wet periods and vastly decreasing trends in the dry seasons indicate that the basin will face some floods and water shortage risks in the mid to late 21st century. The findings broaden the efficacious applications of SHA in the performance selection of GCMs and QM method in bias correction. The conclusions provide a reference for flood and drought mitigation planning and water resources policymaking in the Yellow River Basin.

Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

Atlantic Meridional Overturning Circulation (AMOC) plays a central role in long-term climate variations through its heat and freshwater transports, which can collapse under a rapid increase of greenhouse gas forcing in climate models. Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations. In this work, with a low-resolution earth system model, the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse. Through a new optimization strategy, the extra freshwater flux (FWF) parameter is determined to be the dominant one affecting the AMOC's variability. The traditional ensemble optimal interpolation (EnOI) data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO2 forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC. The results show that, under an abrupt 4×CO2 forcing in millennial simulations, the AMOC will first collapse and then re-establish by the default FWF parameter slowly. However, during the parameter adjustment process, the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC, according to their physical relationship with FWF on the interdecadal timescale.

Influence of climate change on emerging pathogens and human immunity

Global warming can be defined as the detectable increase in average global temperature in the last ten years regarding frequency and intensity. Climate change represents a long-term detectable climatic variability. The climatic system of the earth is disrupted because of the continuous production of greenhouse gases, which raises the risk of the emergence and re-emergence of human pathogens. In this review, we aimed to present the different mechanisms of climate change that increase human/pathogen exposure, introduce the recent concept of disaster microbiology, and discuss the effects of climate change on zoonoses as well as the effects of climate change on antibiotic resistance and human health.

High-Resolution Regional Ocean Climatologies with the Northwest Atlantic as an Example: A Review

The advantages of high-resolution oceanographic data analysis stems from an increased ability to capture sharp gradients in frontal zones and across mesoscale entities, especially in the coastal regions and along ocean jet-like currents, relative to coarser resolution analyses. In a sense, the finer-resolution analysis pursues the same goal as the usage of progressively reduced grid sizes in succeeding from coarse-resolution to eddy-permitting and then to eddy-resolving in numerical models of ocean circulation. Thus, the high-resolution regional climatologies are now closing the gaps existing between observations and model simulations allowing meaningful data-model comparisons in critical data-rich regions, such as the Northwestern Atlantic, Northeast Pacific Ocean, and several others. In this paper, we briefly review the locations, timing, purpose, and specificity of the regional ocean climatologies developed at the National Center for Environmental Information of the National Oceanic and Atmospheric Administration. We describe the Northwestern Atlantic Regional Climatology, version 2 in more details and demonstrate how this regional climatology contributes to a better understanding of the long-term climate state and variability

Vegetation response to climate change and human activity in southwestern China since the Last Glacial Maximum

Southwestern China, a biodiversity hotspot, underwent dramatic climate change following the Last Glacial Maximum and intensive human activity also exerted a major impact since the late Holocene. In this paper, we report a pollen record from Yilong Lake in Yunnan Province, southwestern China, to understand vegetation response to long-term climatic variation and human activities at a regional-level over the last 27,000 years. Findings show that temperate and coniferous taxa were dominant before 18 cal kyr BP reflecting the glacial climate. Between 18 and 14 cal kyr BP, thermophilic taxa increased in response to global deglaciation. A notable shift from deciduous to evergreen forests occurred at approximately 14 cal kyr BP, after which pollen concentration increased but pollen diversity decreased. Pollen assemblages indicated dense forests, primarily composed of evergreen oaks developed under warm and strong summer monsoon conditions from 13 to 8 cal kyr BP. High rates of vegetation change occurred during the middle Holocene before vegetation diversity gradually increased during the Holocene Thermal Optimum, with high variability of precipitation in the Yilong Lake region. Anthropogenic forcing progressively became the main factor affecting vegetation composition and vegetation change rates over the past three millennia.

The impact of anthropogenic climate change on pediatric viral diseases.

The adverse effects of climate change on human health are unfolding in real time. Environmental fragmentation is amplifying spillover of viruses from wildlife to humans. Increasing temperatures are expanding mosquito and tick habitats, introducing vector-borne viruses into immunologically susceptible populations. More frequent flooding is spreading water-borne viral pathogens, while prolonged droughts reduce regional capacity to prevent and respond to disease outbreaks with adequate water, sanitation, and hygiene resources. Worsening air quality and altered transmission seasons due to an increasingly volatile climate may exacerbate the impacts of respiratory viruses. Furthermore, both extreme weather events and long-term climate variation are causing the destruction of health systems and large-scale migrations, reshaping health care delivery in the face of an evolving global burden of viral disease. Because of their immunological immaturity, differences in physiology (e.g., size), dependence on caregivers, and behavioral traits, children are particularly vulnerable to climate change. This investigation into the unique pediatric viral threats posed by an increasingly inhospitable world elucidates potential avenues of targeted programming and uncovers future research questions to effect equitable, actionable change. IMPACT: A review of the effects of climate change on viral threats to pediatric health, including zoonotic, vector-borne, water-borne, and respiratory viruses, as well as distal threats related to climate-induced migration and health systems. A unique focus on viruses offers a more in-depth look at the effect of climate change on vector competence, viral particle survival, co-morbidities, and host behavior. An examination of children as a particularly vulnerable population provokes programming tailored to their unique set of vulnerabilities and encourages reflection on equitable climate adaptation frameworks.

Hydrologic changes in the Brazos River Basin and implications for Great Plains fishes

Alteration of natural flow regimes from surface water and groundwater resource development has adversely impacted aquatic habitats of resident fish throughout the Great Plains. Two endangered cyprinids, Sharpnose Shiner Notropis oxyrhynchus and Smalleye Shiner N.buccula, were historically found throughout the Brazos River Basin in Texas. However, their range has been greatly reduced by construction of impoundments and stream depletion from groundwater pumping. Successful recruitment requires flowing, unobstructed river reaches of sufficient velocity to suspend semi-buoyant fertilized fish ova. The wide, shallow stream habitats the shiners utilize are now limited to the Upper Brazos River Basin upstream of Possum Kingdom Reservoir. This study assesses the relative importance of long-term climate variability, reservoir construction, and groundwater development on Upper Brazos streamflow regimes by (1) calculating flow metrics (2) evaluating groundwater-surface water interactions and long-term groundwater inflows, and (3) using mixed-effects regression models and Poisson regression. Results show that fish habitat has been threatened through several impacted hydrologic processes: (1)groundwater development has resulted in reduced mean daily flows, peak flows, and zero-flow days, (2)upstream impoundments have increased zero-flow days and reduced mean daily flows, and (3)lower mean daily flows and peak flows now occur during droughts, suggesting water resource development—particularly after 1970—has exacerbated drought effects. We illustrate this approach using the Upper Brazos River Basin; however, the methodology applied can inform management actions to maintain spawning flows for streams in similar altered basins.

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.

Linking upwelling intensity and orbital-scale climate variability in South Africa's winter rainfall zone: Insights from a ∼70,000-year hyrax midden record

The climate of Africa's southwestern Cape is characterised by a strongly seasonal winter precipitation regime, with late Quaternary climate variability generally considered to have been driven by the position of the southern westerlies. This paper presents a unique ∼70,000 year-long palaeoclimatic record from a rock hyrax midden from South Africa's winter rainfall zone, enabling the analysis of regional climate systems since the beginning of marine isotope stage 4. The data suggest that the last glacial period was relatively humid compared to the Holocene, likely due to cooler temperatures, more extensive Antarctic sea-ice extent and an equatorward displacement of the westerly storm track. However, orbital-scale climate variability associated with the 23 kyr precessional cycle primarily correlates with changes in upwelling intensity in the Benguela system, implying an important role for the blocking of tropical easterly flow in driving long-term climatic variability. These factors combined during glacial periods to significantly amplify rainfall seasonality in the southwestern Cape, bringing more winter rainfall via mid-latitude frontal systems, while reducing the proportion of summer rainfall, particularly during the glacial periods of the late Quaternary. The results therefore highlight the need to consider a complex suite of circulation systems and dynamics when inferring drivers of long-term environmental change in the region.

Long-term trends and wave climate variability in the South Atlantic Ocean: The influence of climate indices

Linking wave climate variability and trends with climate indices is important to better understand and predict large-scale patterns of wave variability down to wave conditions at the coast. This study investigates such links in the South Atlantic Ocean using 72 years of ERA5 wave hindcast. Different wave parameters are computed, including storm wave statistics, and are further analyzed in terms of long-term trends and interannual changes. Our results indicate that, over the last decades, wave height has been significantly increasing across the entire domain, while extreme events statistics are also increasing, although with more complex spatial variability. The variations of these wave properties are primarily correlated, from low to high latitudes, with the Atlantic Multidecadal Oscillation (AMO), Tropical Southern Atlantic Index (TSA) and Southern Annular Mode (SAM), with different preferred timescales. We think that better understanding and predicting the evolution of these climate indices, including under climate change, will be critical to anticipate coastal hazards in this region.

Impact of land-use dynamics and climate change scenarios on Groundwater recharge in the case of Anger watershed, Ethiopia

An assessment of land use dynamics and climate variability impacts on hydrological processes is vital and a prerequisite for effective water resources management. This study aimed to quantify the effect of land-use changes and long-term climate variability on the Anger watershed's annual groundwater recharge, which covers a total drainage area of 7717 km2. The WetSpass (Water and Energy Transfer between Soil, Plants, and Atmosphere under quasi-Steady State) model was used to investigate the impact of land cover and climate variability on groundwater. The Mann–Kendall (MK) test was used to analyze the spatial variations and temporal trends of the climate variables in the watershed. Input data for the model, such as land use, hydro-meteorological data, soil texture, topography, and groundwater elevation parameters, were prepared in the form of gridded maps with a 30 m resolution. The model results indicate that land-use change and climate variability considerably impact distributed groundwater recharges. Groundwater recharge decreased with land use in 2000 and 2019, respectively, as compared to baseline land usage (1985). The study also demonstrates how the anticipated future combination of less precipitation and higher temperatures has a detrimental effect on the watershed's annual average groundwater recharge. Future rising temperatures and reduced precipitation are projected to result in an average annual groundwater recharge showing significant decreases in 2050, 2080, and 2110, respectively, according to scenario-based models. The result has provided valuable information on the management and response of groundwater recharge to climate and land-use changes, particularly for the Anger watershed and for the total country as well.

Assessment of Spatio-temporal variability of climate in the lower Gangetic alluvial plain.

Global climate change is a cause of concern as extreme events have intensified in recent years, with increased floods and droughts also reported in the Lower Gangetic Plain (LGP). Assessments from Regional Climatic Models (RCM) cannot capture the local climate variability necessary for devising an action plan for climate risk mitigation. The present study aims to fill this gap by assessing the long-term local-scale climate variability using Modified Mann-Kendall (MMK) and Centroidal Day (CD) shifts for the Patna district, which can be extended to other districts in the LGP. The time series is split into two halves to account for changes due to urbanization in recent three to four decades. CD analysis shows a forward shift in the monsoonal and annual rainfall in recent decades. The variability in total rainfall has become more pronounced post-1985 during monsoon, postmonsoon, and winter seasons. An increase of 64.53mm (18.9%) in surface runoff and a strong correlation between built-up area and precipitation further cement the role of urbanization in local climate change. Despite a 5.74% decrease in monsoonal rainfall, a 3.51mm/day increase in rainfall intensity is observed during the monsoon. The implications of these variabilities have posed new challenges for the agricultural production and management of water resources and the interactions between groundwater and surface runoff.

Temporal stability of soil organic carbon in grazing lands of Eastern Australia

ABSTRACT Sequestering soil organic carbon (SOC) has been identified as a critical tool to mitigate anthropogenic climate change. Therefore, the response of SOC to climate variability needs to be quantified to achieve carbon sequestration over decadal to centennial scales. This study assesses the temporal variability of SOC for a grazed catchment in Australia that has been exposed to periods of drought and flood over the last 100 years. The analysis utilises field-measured SOC data obtained 2006–2018, along with modelled SOC data derived from a linear regression model and BIOS2 model outputs (1908–2015). Catchment SOC did not vary significantly, with mean values of 3.40% (2006), 3.21% (2014) and 3.10% (2018), despite the catchment experiencing extreme drying and wetting events during the sampling period. This stability was further supported by the long-term modelling, which showed a mean SOC of 3.68% and coefficient of variance of 0.16%. SOC was believed to be buffered against short term fluctuations in climate that persist for less than the SOC turnover times (i.e., multi-decadal). This indicates that temporal changes in SOC reflects only long-term climate variability. Current SOC and sequestered SOC are therefore unlikely to become a carbon sink in the short-term due to climate variability.

The 100-Year Series of Weather-Related Fatalities in the Czech Republic: Interactions of Climate, Environment, and Society

The paper investigates weather-related fatalities over the territory of the Czech Republic in the 100-year period from 1921 to 2020. The unique database, created from documentary evidence (particularly newspapers), includes, for each deadly event, information about the weather event, the fatality itself, and related circumstances. A total of 2729 fatalities were detected during the 100-year period and were associated with various weather categories including frost (38%), convective storms (19%), floods (17%), fog (11%), snow and glaze ice (8%), windstorms (5%), and other inclement weather (2%). A detailed analysis was performed for each individual category. Fatalities occurred throughout the country, with a main maximum in winter (January) and a secondary maximum in summer (July), corresponding to the occurrence of extreme weather. Deaths were mainly interpreted as direct, caused by freezing to death/hypothermia or drowning, and occurred in the afternoon and at night in open countryside or on rivers and water bodies. Males outnumbered females, and adults outnumbered children and the elderly. Hazardous behavior was more frequent than non-hazardous behavior among victims. The information on fatalities and the structure of their characteristics strongly reflects historical milestones of the country, political and socioeconomic changes, as well as changes in lifestyle. Although important weather effects were observed on the deadliest events, the character of the data did not allow for clear evidence of the effects of long-term climate variability.

An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change

Identifying power curtailment is essential for system reliability, especially in hydro–photovoltaic (PV) hybrid energy systems (HPESs) exposed to future climate variability. However, the relatively low temporal resolution of climate projections challenges the effectiveness of current numerical simulation methods for evaluating the curtailment rate of HPESs. This study derives an analytical method based on daily hydropower and PV power rather than the commonly used hourly data in numerical methods, to reduce reliance on the temporal resolution of climate inputs. A short-term operation model is constructed to verify the effectiveness of the derived power curtailment function. A long-term operation model incorporating the analytical power curtailment function is then established to analyze variations in future curtailment rates using climate information projections. Results of a case study in the Yalong River Basin validate the accuracy of the derived PV curtailment function. Compared with the reference period, future hydropower and PV power will increase in most climate scenarios with substantial variances, whereas the PV curtailment rate exhibits an overall increase in the near future (+2.49%) and a larger increase in the far future (+5.53%). The most noticeable increases in future PV curtailment rates occur in June, July, and October (+3.15%, +5.47%, and +3.11%). The proposed analytical method provides valuable insights for the risk evaluation of HPESs by coupling the short-term power curtailment rate with mid- and long-term climate variations.

The Power Grid/Wildfire Nexus: Using GIS and Satellite Remote Sensing to Identify Vulnerabilities

The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate variables, which are not helpful when analyzing current season trends. Utility companies need data that are temporally and locally relevant. To determine the primary drivers of burn probability relative to power grid vulnerability, this study assessed potential wildfire drivers that are both readily accessible and regularly updated. Two study areas in Idaho, USA with contrasting burn probabilities were compared. Wildfire drivers were obtained and differentiated between the study areas across the 2018–2021 growing seasons. This study determined that mean wind speed, cumulative growing season precipitation, and the mean Normalized Difference Vegetation Index (NDVI) for an area of interest may be reliable indicators of burn probability on a temporally relevant scale. This assessment demonstrates a method and variables that may be utilized by municipal electric utilities, electric cooperatives, and other power utilities to determine where to harden power grid infrastructure within wildfire-prone areas.