January Precipitation Research Articles

The Overlooked Role of Moist Northerlies as a Source of Summer Rainfall in the Hyperarid Atacama Desert

AbstractIn the Atacama Desert, one of the driest places on Earth, the persistent absence of water preserves the record of environmental change, making it an invaluable proxy for studying the evolution of life on Earth. Due to the scarcity of in situ measurements and difficulties in satellite remote sensing, information on precipitation characteristics is limited even for the present climate. Guided by a case study of extreme precipitation in late January 2019, we derive a conceptual framework to explain how moisture transport combined with the diurnal circulation produces rainfall. We found a synoptic‐scale weather pattern that we named “moist northerlies” (MNs) based on surface observations, reanalysis, and high‐resolution simulation. During an MN event, moisture transport from the tropical Pacific is observed in the lower free‐troposphere in the forefront of an 850 hPa low‐pressure offshore Atacama. The diurnal circulation along the western Andean slope transports the moist free tropospheric air above the marine boundary layer inland, triggering clouds and storms. A trough over the southeast Pacific and a southward displaced Bolivian High seem to drive the MNs dynamically. Long‐term observations (1960–2020) show that most of the rainy days in the hyperarid core (75%) are triggered by MNs, occurring more frequently during neutral/La Niña conditions and phases 7‐8‐1 of the Madden‐Julian oscillation (MJO). A trend analysis (1991–2020) reveals that summer water vapor along the west coast of South America has increased rapidly due to the MNs, enhancing summer rainfall in Atacama. The implications of climate change and other climate variability modes are discussed.

Potential Ecological Distribution of the Beetle Agrilus mali Matsumura (Coleoptera: Buprestidae) in China under Three Climate Change Scenarios, with Consequences for Commercial and Wild Apple Forests.

The apple jewel beetle (AJB), Agrilus mali Matsumura (Coleoptera: Buprestidae), is a dangerous pest of commercial apple orchards across China, the largest apple production country in the world, and has recently become invasive in the Xinjiang Uygur Autonomous Region (XUAR) of northwestern China, where wild apple forests also occur. This pest poses a serious threat to apple production and wild apple forests throughout the world. Global warming is expected to change the geographical distribution of A. mali in China, but the extent of this is unknown. Based on empirical data from 1951 to 2000, a MaxEnt model was used to forecast the ecological distribution of A. mali under three different climate scenarios projected in the fifth report of the Intergovernmental Panel on Climate Change. The results showed that the most important variables were the maximum temperature of November, precipitation in January, and minimum temperatures in April. Under all climate scenarios, the forecasted suitable regions for A. mali in China will expand northward in the 2050s and 2070s. The forecasted highly suitable regions will be 1.11-1.34 times larger than they are currently, and their central distributions will be 61.57-167.59 km further north. These findings suggest that the range and damage caused by A. mali in China will increase dramatically in the future. Monitoring and management measures should be implemented urgently to protect both the commercial apple industry and wild apple resources.

Predicting the potential global distribution of Ixodes pacificus under climate change.

In order to predict the global potential distribution range of Ixodes pacificus (I. pacificus) under different climate scenario models in the future, analyze the major climate factors affecting its distribution, and provide references for the transformation of passive vector surveillance into active vector surveillance, the maximum entropy model (MaxEnt) was used in this study to estimate the global potential distribution range of I. pacificus under historical climate scenarios and different future climate scenarios. The global distribution data of I. pacificus were screened by ENMtools and ArcGIS 10.8 software, and a total of 563 distribution data of I. pacificus were obtained. Maxent 3.4.1 and R 4.0.3 were used to screen climate variables according to the contribution rate of environmental variables, knife cutting method and correlation analysis of variables. R 4.0.3 was used to calculate model regulation frequency doubling and feature combination to adjust MaxEnt parameters. The model results showed that the training omission rate was in good agreement with the theoretical omission rate, and the area under ROC curve (AUC) value of the model was 0.978. Among the included environmental variables, the Tmin2 (minimum temperature in February) and Prec1 (precipitation in January) contributed the most to the model, providing more effective information for the distribution of I. pacificus. MaxEnt model revealed that the distribution range of I. pacificus was dynamically changing. The main potential suitable areas are distributed in North America, South America, Europe, Oceania and Asia. Under the future climate scenario model, the potential suitable areas show a downward trend, but the countries and regions ieeeeeeenvolved in the suitable areas do not change much. Therefore, the invasion risk of the potential suitable area of I. pacificus should be paid attention to.

Forecast models for start and peak dates of Poaceae pollen season in Tétouan (NW Morocco) using multiple regression analysis.

Poaceae pollen is one of the most widespread sources of aeroallergens in the world. The aim of this study is to build predictive models for the pollen season start day (PSsd) and peak dates of the Poaceae pollen season and thus give an overview of the climatic parameters that have the greatest influence. In Tétouan, sampling was carried out using a volumetric spore trap of the Burkard Hirst type. The relationships between the PSsd, peak dates and meteorological parameters were determined using correlation analysis. The models were constructed using multiple regression analysis with data from 2008 to 2019 and tested on data from 2022. The PSsd was especially significantly influenced by minimum temperature during winter and precipitation in the autumn of the previous year. The peak dates were significantly correlated with precipitation in January, March and April, but not with temperature. Three models were obtained for each of the season's parameters; the most accurate model for the PSsd explained a variability of 61% and includes as main predictors rainfall from the autumn of the previous year and the mean daily average temperature from 23 February to 8 March. The two most efficient peak dates models included precipitation in January and April as the main predictor variables, and explained greater variability (87 and 88%). Precipitation in autumn and the mean daily and the sum of minimum temperature in winter, showed significant decreasing tendencies. However, the PSsd trend delay was not statistically significant. This study draws the importance of the weather during preseason for grass pollen production and emphasises the usefulness of the models for allergic patients to take preventive measures and for healthcare professionals in allergy therapy.

Regional variation in branch morphological and physical traits in Abies sachalinensis associated with winter climatic conditions

ABSTRACT In boreal forests, overwintering strategies, including snow adaptation, can be expected in the branches of coniferous trees. Although it is difficult to detect resistance to snow disturbances and few studies have quantified the functional traits related to snowfall resistance, the regional variation associated with winter conditions may provide cues for identifying adaptive traits against snow damage. In common garden trials, we examined the regional variation in branch functional traits in Abies sachalinensis, one of the most important conifers in boreal forest silviculture. Morphological traits reflecting susceptibility to snow accumulation, such as branch estimated area, Young’s modulus and flexural rigidity, which represent deformability to snow loads, and base diameter and xylem density were evaluated in five-year-old branches from eight provenances. The length and estimated area of a branch showed regional variation. Significant regional variation in flexural rigidity rather than Young’s modulus and no significant correlation between xylem density and mechanical traits indicated that the physical properties of branches depend on their size rather than on their materials. A significant correlation with winter climatic conditions was observed between flexural rigidity and total monthly precipitation in January. Our results showed that the thicker the branch is, the larger the branch area and the more rigid it is. This indicates that the branch size-dependent resistance to snow loads might be associated with winter adaptation to local snowfall conditions.

Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management

The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OAtraining = 0.8398; OAvalidation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them.

Analysis of the influence of climatic conditions on the tax potential of Russian regions using cluster analysis

The tax burden of the regions is influenced by a whole range of factors, among which it is necessary to take into account the natural and climatic conditions. After all, the specialization of the regions, production capabilities, production volumes and, accordingly, the level of tax revenues to the consolidated budget of the Russian Federation depend on them. The article analyzes the tax potential of regions depending on the climatic factor. The subject of this study is a system of statistical indicators characterizing the natural and climatic conditions of the regions (temperature and precipitation in July and January for three years) and the economic results of their activities, including tax revenues to budgets. The aim of the work is to assess the influence of the climatic factor on the tax potential of the regions using the grouping method of cluster analysis. The research method is cluster analysis (k-means method), implemented using the R programming language and its packages, which allows combining regions according to the similarity in climatic conditions, while minimizing variation within groups and maximizing intergroup differences. The results of the study can be applied in the development of recommendations at the state level on optimizing the tax burden of regions for regions with similar conditions. The novelty of the study lies in the possibility of assessing and comparing the characteristics of regions with similar climatic conditions in terms of tax potential, which is necessary to develop measures for the development of regions and increase their tax potential. As a result of the study, the influence of the climatic factor on the tax potential of the regions and the need to take it into account in combination with other factors is proved.

Climatological characteristics and interannual variability of the leading mode of eastern African precipitation in January and February

AbstractThe climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA.

ВАРИАТИВНОЕ ВЛИЯНИЕ ПОГОДНО-КЛИМАТИЧЕСКИХ ФАКТОРОВ НА ОЖИДАЕМУЮ ПРОДОЛЖИТЕЛЬНОСТЬ ЖИЗНИ И ПОВОЗРАСТНУЮ СМЕРТНОСТЬ ПО ПРИЧИНЕ БОЛЕЗНЕЙ СИСТЕМЫ КРОВООБРАЩЕНИЯ НАСЕЛЕНИЯ РЕГИОНОВ РОССИЙСКОЙ ФЕДЕРАЦИИ

Different regions and population groups are disproportionately affected by current climate changes. Their effects lead to the disadaptation of physiological reactions and increasing prevalence of some weather-dependent diseases. The objective of the present study is discovering the effects of observed changes in weather and climatic factors over the period 2010-2019 on the age-specific indicators of mortality from circulatory diseases and population life expectancy, as well as model prediction of factorial effects against the background of differentiation of the sanitary-epidemiologic and socioeconomic state of Russian regions. As the initial information, the authors used the official statistical data on the regions of the Russian Federation over the period 2010-2019 for 206 parameters of the habitat and lifestyle, including eight weather and climatic factors (monthly mean air temperatures, total precipitation for January and July, and deviations from normal for these parameters). The causal relationships between the habitat factors and age-specific mortality rates were investigated using 18 artificial neural networks (R2 < 0.75). The differentiated effects of the analyzed factors on life expectancy were identified, with maximum effects on the older population. The results of the studies should be taken into account in developing regional and federal programs on improving medical and demographic conditions and providing sanitary-epidemiologic, social, and economic wellbeing.

Robust assessment of associations between weather and eastern wild turkey nest success

AbstractTemperature and precipitation have been identified as factors that potentially influence eastern wild turkey (Meleagris gallopavo silvestris) reproduction, but robust analyses testing the relationship between weather parameters and turkey nest success are lacking. Therefore, we assessed how weather influenced turkey daily nest survival using 8 years of data collected from 715 nests across the southeastern United States. We also conducted exploratory analyses investigating if weather conditions during or prior to nesting best predicted nest success. We then assessed the possible implications of climate change through 2041–2060 for future eastern wild turkey daily nest survival and nest success for variables determined significant in analyses. During incubation, positive anomalies of minimum daily temperature were associated with greater daily nest survival. Precipitation during nesting was not a good predictor of daily nest survival. Exploratory analyses unexpectedly indicated that weather conditions in January prior to incubation were more important to nest success than weather conditions during incubation. In January, negative anomalies of minimum temperature and greater average daily precipitation were associated with greater nest success. Projections of future nest success or daily nest survival based on these relationships with the predictive covariates, and informed by climate models, suggest that nest success may increase as January precipitation increases and that daily nest survival may increase as temperature during incubation increases. These positive associations could be offset by a negative association between nest success and the expected increases in January minimum average temperature. Additional research is needed to investigate causes of these relationships and assess the implications of climate change for eastern wild turkey poult survival.

Anthropogenic Influence on 2022 Extreme January–February Precipitation in Southern China

Abstract The precipitation in January–February 2022 in southern China was the second-largest amount since 1961. Anthropogenic influence reduced the likelihood of extreme events like 2022 by about 50% (55%) in HadGEM3 (CMIP6).

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted {R}^{2} of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

Minimal shift of eastern wild turkey nesting phenology associated with projected climate change

Climate change may induce mismatches between wildlife reproductive phenology and temporal occurrence of resources necessary for reproductive success. Verifying and elucidating the causal mechanisms behind potential mismatches requires large-scale, longer-duration data. We used eastern wild turkey (Meleagris gallopavo silvestris) nesting data collected across the southeastern U.S. over eight years to investigate potential climatic drivers of variation in nest initiation dates. We investigated climactic relationships with two datasets, one inclusive of successful and unsuccessful nests (full dataset) and another of just successful nests (successfully hatched dataset), to determine whether successfully hatched nests responded differently to weather changes than all nests did. In the full dataset, each 10 cm increase in January precipitation was associated with nesting occurring 0.46–0.66 days earlier, and each 10 cm increase in precipitation during the 30 days preceding nesting was associated with nesting occurring 0.17–0.21 days later. In the successfully hatched dataset, a 10 cm increase in March precipitation was associated with nesting occurring 0.67–0.74 days earlier, and an increase of one unit of variation in February maximum temperature was associated with nesting occurring 0.02 days later. We combined the results of these modeled relationships with multiple climate scenarios to understand potential implications of future climate change on wild turkey nesting phenology; results indicated that mean nest initiation date is projected to change by <0.1 day by 2040–2060. Wild turkey nesting phenology did not track changes in spring green-up timing, which could result in phenological mismatch between the timing of nesting and the availability of resources critical for successful reproduction.

Northern spotted owl nesting habitat under high potential wildfire threats along the California Coastal Redwood Forest

Large and severe wildfires, exacerbated by climate change and human behavior, are occurring more frequently in many forests across the western United States. While wildfire is a natural part of most terrestrial ecosystems, rapidly changing fire regimes have the potential to alter habitat beyond the adaptive capabilities of species. Spatial assessments of wildfire risks to species habitat may allow managers to pinpoint locations for management activities. To illustrate this, we spatially assessed wildfire risk within habitat that supports the nesting activity of the federally threatened northern spotted owl (Strix occidentalis caurina) in the California redwood coast ecoregion. To accomplish this, we built a scale-optimized ensemble nesting habitat suitability model and identified habitat with the highest wildfire hazard potential. Percent canopy cover at 100-m scale, slope at 400-m scale, and January precipitation at 800-m scale were the most influential environmental covariates for predicting northern spotted owl nesting habitat. Nearly 60% of nesting habitat was predicted to be at high or very high (>1986 index value) wildfire risks. We identified three areas in the Maple Creek Area of Humboldt County, Jackson State Demonstration Forest in Mendocino County, and Point Reyes National Seashore in Marin County, California with a high concentration of nesting habitat that are at a very high risk of experiencing high severity wildfires. We recommend these areas be targeted for future research to understand the impact of wildfire on northern spotted owl as well as management attention.

Role of internal atmospheric variability in the estimation of ionospheric response to solar and magnetospheric proton precipitation in January 2005

The purpose of this study is to investigate the role of internal atmospheric variability in the detection of ionospheric response to precipitating protons of solar and magnetospheric origin during 17–23 January 2005. For this purpose, we analyzed the results of ensemble runs of the whole atmosphere model EAGLE. We confirmed that the proton precipitation produces (directly and indirectly) significant TEC (Total Electron Content) enhancements at different latitudes. However, we also showed that internal atmospheric variability can strongly mask the forced signal, especially its after-effects, which can lead to false interpretation if only one model realization is considered. The ensemble standard deviation, or an atmospheric noise level, in TEC has a clear UT variation and spatial distribution, which defines the derivability of the signal depending on the time and the region of the forced effects. The noise itself is also affected by the increased ionization from protons, but its behavior is nonlinear and depends on the latitude, spectrum of the event and its time of occurrence. We conclude that studies of the ionospheric response to various space weather phenomena require the application of whole atmosphere models in ensemble mode to ensure statistical significance of the obtained results and a reliable interpretation of the observational data.

Why was South China extremely wet during January–February 2022 despite La Niña?

The boreal winter climate of 2022 was characterized by the occurrence of La Niña, which is one of the most predictable drivers of South China precipitation (SCP) deficit. However, surprisingly, South China (SC) received abnormal high precipitation in January–February (JF) 2022. Possible causes of the deviation of JF 2022 SCP from its historical response to La Niña are explored with observational and reanalysis data. Results suggest that the La Niña event in winter 2022 features an eastern Pacific (EP) type, which corresponds to a weaker zonal sea surface temperature (SST) gradient between the equatorial central and western Pacific than those of the historical La Niña events, leading to a weaker western North Pacific (WNP) cyclone (WNPC) anomaly. Meanwhile, the SST warming over the tropical Indian Ocean (TIO) would also weaken the La Niña-associated WNP circulation anomaly. Therefore, the flavor of La Niña and the TIO warming act in concert to dampen the La Niña-associated WNPC anomaly and the SCP deficit. But these tropical SST anomalies are still insufficient to explain the extremely high SCP. Furthermore, the investigation identifies two extra-tropical circulation patterns over Eurasia that dominate the SCP anomalies in JF 2022. One is the wave train propagating along the South Asian jet that intensifies the India–Burma trough. It enhances the SCP through exciting anomalous strong moisture transport from the Bay of Bengal and ascending motion. The other is the positive geopotential height anomaly over eastern Siberia that prompts southward cold air intrusion and convergence over the SC region. These two dynamical drivers can account for approximately 75% of the observed SCP anomaly in JF 2022. However, they may be largely attributed to the atmospheric internal dynamical processes, which implies limited seasonal predictability of this extreme wet event.

Driving forces behind the fluctuating growth of the number of successful nests in an inland population of white‐tailed eagles in Hokkaido, Japan

AbstractThis study aimed to describe the change in the number of successful nests of the white‐tailed eagle, Haliaeetus albicilla, for 25 years (1997–2021) along the Teshio River (100 km), Japan, which is a new habitat for this endangered species and identify factors driving the number of nests. The number of nests grew from two to nine. The logistic function fitted in well with the growth, and the capacity of the study area sustaining the successful nests was estimated at 6.5. The precipitation in January and April explained the deviation of the observed values from the model prediction. In particular, heavy rain in April was associated with low numbers. Forty‐six nest remains were collected from 17 nest locations. Twelve genera of birds, six genera of mammals, and four genera of fishes were identified. Fish and bird items occupied 93.6% of prey individuals. The fish proportion was similar between high‐performance years when the observed number of successful nests was higher than the model prediction and low‐performance years with a lower number than the prediction (55.2% and 51.0%). However, it was higher in the nests with two fledglings (63.0%) than those with a single fledgling (41.5%). The nearest neighbor distance (NND) of the successful nests declined with the increase in the number of nests. Based on territory size (the mean NND = 7.8 km), the study area can hold 13 nests. The process and mechanism of the dynamics of the number of nests were discussed, focusing on territoriality and weather effects.

Radial Stem Growth of the Clonal Shrub Alnus alnobetula at Treeline Is Constrained by Summer Temperature and Winter Desiccation and Differs in Carbon Allocation Strategy Compared to Co-Occurring Pinus cembra

Green alder (Alnus alnobetula) is currently the most expanding shrub species in the Alps. Because dense thickets impair tree establishment, understanding how climate affects shrub growth is essential for predictions of treeline dynamics. We evaluated ring width data from &gt;50 A. alnobetula stems sampled at treeline on Mt. Patscherkofel (Central European Alps, Austria) to identify main climatic drivers and influence of climate warming on radial stem growth (RG). We also compared RG of A. alnobetula with RG of the co-occurring treeline conifer Swiss stone pine (Pinus cembra). We addressed our questions through calculation of response functions and evaluation of climate in years showing exceptional growth deviations. Response function analyses and evaluation of growth trends during 1991–2020 revealed that RG of A. alnobetula is significantly and directly related to summer temperatures. Precipitation in January also showed a direct relationship to RG, indicating effects of frost drought on RG. Surprisingly, nitrogen fixing A. alnobetula showed strikingly lower RG compared to P. cembra, and the latter also responded more strongly to the increase in summer temperature in the course of climate warming. We explain these findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional P. cembra vs. favoring “horizontal” spread in the pioneer shrub A. alnobetula.

The influence of climatic and topographic parameters on the distribution of forest communities of the Ufa plateau

The purpose of this study was to analyse the influence of climatic and topographic parameters on the distribution of forest communities (on the alliance level) on the Ufa plateau. In order to characterize the features of the forest vegetation habitats of six alliances, topographic parameters were calculated using the digital elevation model SRTM-3, and bioclimatic variables Bioclim (CHELSA). Cluster analysis using climatic characteristics showed that the main factors in the differentiation of alliances are the amount of precipitation in January and in the winter quarter. The amount of precipitation determines the depth of soil freezing and the sufficiency of soil moisture in the initial period of the seasonal development of trees. These factors are the main ones in differentiating the habitats of the communities of the suballiance Tilio cordatae-Pinenion sylvestris from the habitats of the other forest communities. The amount of precipitation in the summer quarter is the next most important climatic parameter, differentiating the habitats of communities. Cluster analysis using topographic parameters showed that the main factors of differentiation of the habitats of the alliances are the profile and plan curvature, slope steepness and aspect. These factors determine the accumulation of soil, moisture and the temperature regime of soils.

A baseline evaluation of atmospheric and river discharge conditions in the Hudson Bay Complex during 2016–2018

In this article, we examine atmospheric and river discharge conditions within the Hudson Bay Complex for the BaySys 2016–2018 field program time frame. Investigated in particular is a subset of European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis - Interim (ERA-Interim) atmospheric forcing variables, namely 2-m surface temperature, 10-m surface winds, precipitation, and sea-level pressure, in addition to river discharge. Results from this assessment show that 2016 was characterized by unusually warm conditions (terrestrial and marine) throughout the annual cycle; 2017 by strong cyclone activity in March and high precipitation in January, October, and November; and 2018 by cold and windy conditions throughout the annual cycle. Evaluation of terrestrial conditions showed higher than normal land surface temperatures (the Hudson Bay physical watershed) for all of the 2016–2018 period (excluding a colder than normal spell August–November 2018), particularly in January (2016 and 2017), higher than normal precipitation in October (2016 and 2017), and higher than normal terrestrial discharge to the Hudson Bay Complex in March (2016 and 2017), with drier than average June through October (2016–2018).