Monthly Minimum Temperatures Research Articles

Alien aquatic plants in Poland: Temporal and spatial distribution patterns and the effects of climate change

Distribution of alien aquatic plants in European freshwater ecosystems is still not fully recognized. Little is also known about the impact of climatic factors on the expansion of particular neophyte species in freshwater ecosystems. In this article, we present the first comprehensive assessment of the occurrence of non-native aquatic vascular plants in Poland (CE Europe), with a particular focus on: (1) inventory of the species, (2) temporal and spatial changes in their distribution, and (3) the climate impact on species occurrence and diversity. For this purpose, we collected both published and unpublished data on alien macrophytes occurrence in Poland covering past 70 years. In order to investigate the mechanism of alien aquatic plant expansion, climate data from 24 meteorological stations were analyzed. Redundancy analysis (RDA) was used to analyze the relationship between species composition and microclimate variables. In addition, Generalized Additive Model (GAM) was used to examine the response curves of neophyte species to temperature parameters.In total, we found records of 15 neophytes of alien aquatic plant taxa in Poland, including 14 species and collective taxon of Nymphaea cultivars. The most frequent was Elodea canadensis (over 25,000 records). Excluding this species, we counted more than 300 records of the occurrence of alien aquatic plants, of which three (E. nuttallii, Azolla filiculoides and Lemna turionifera) were the most abundant. Ten species occurred only in isolated sites. In addition, analyses showed a substantial increase in the number of the aquatic neophyte sites in the last 20 years that could be linked with the raised average monthly and yearly air temperatures in Poland.Our results showed a strong relationship between raised average temperatures in Poland and the spread of some species, e.g. Azolla filiculoides, while other species such as Elodea nuttallii and Lemna turionifera expanded irrespectively of this factor. The occurrence of some species (e.g. Vallisneria spiralis, Hygrophila polysperma) was exclusively associated to high thermally altered or thermally contaminated waters, and thus was limited to only a small part of the country. Our results confirm the major role of elevated temperatures (mean annual temperatures and minimum monthly temperatures) and thermally polluted freshwater ecosystems in the distribution of alien aquatic plants.

PREDICTIVE MODEL TO MINIMIZE THE EFFECT OF EXTREME TEMPERATURE IN SKARDU AND ASTORE, GILGIT BALTISTAN

Climate is a fundamental factor of the natural environment that has a role in both natural and human existence. Temperature is an important climatic element that influences snow melting, evaporation, and frost directly. Current study has used Mean Monthly Minimum Temperature (MMMT) of Skardu from 1972 to 2021 and of Astore from 1993 to 2021 based on the availability of data. In this work; we have used SARIMA (Seasonal Auto Regressive Integrated Moving Average Model) to forecast mean monthly minimum temperature. Skardu data is stationary at level form, which suggests SARMA model for Skardu station and Astore data is stationary at first difference so SARIMA time series is appropriate for mean monthly minimum temperature of Astore. Using Box Jenkins’s approach it is found that the most appropriate model for Skardu is SARMA(1,0)(1,0)12 and Astore is SARIMA (0,1,1)(4,1,4)12 respectively. These models have been utilized to forecast MMMT from 2022 to 2036. Yearly mean minimum temperature forecasts show that the mean minimum temperature at Skardu and Astore stations is slightly decreasing. The yearly mean minimum temperature at Skardu station is 4.0 °C in 2022 and will decrease to 2.3 °C till 2036, while at Astore station it is 4.0 °C in 2022 and will fall to 2.6 °C in 2036. Our results will be useful for decision makers and insurance companies for better future planning to minimize the effect of lowest temperature.

Recent changes in climate extremes in Guinea-Bissau

ABSTRACT Guinea-Bissau has experienced the tangible effects of climate change in recent years. An examination of the annual and monthly precipitation patterns from 1960-2020 indicates an overall decreasing trend in climatological series. In contrast, the monthly average maximum and minimum temperatures exhibit significant increasing trends during the same period. Analysing daily rainfall and maximum and minimum temperature leads us to conclude that rainfall and temperature patterns have evolved in different ways. The non-parametric Mann–Kendall test was used to assess trends in climate indices, and Sen’s slope estimator was used to detect the magnitude of trends. Despite the significant decrease in wet days, there has been an upward trend in rainfall over the 1981-2020 climatological series. Days with maximum temperatures of at least 30 and 35ºC have increased in all seasons with a very significant upward trend for 30ºC. On the other hand, absolute annual maximum temperatures have tended to decrease in Bolama and Bafatá by -0.25 and -0.17ºC per decade respectively. There has also been a decrease of 0.30ºC and 0.39ºC per decade in the minimum temperatures in Bolama and Bafata, i.e. the nights have been getting cooler, which somewhat contradicts the global trends generally reported for this same parameter.

Greater sage‐grouse chick mortality risk relative to livestock grazing management, environmental factors, and morphometric measurements

AbstractGreater sage‐grouse (Centrocercus urophasianus; sage‐grouse) populations in the western United States have declined, necessitating conservation efforts. The United States Department of Agriculture Natural Resources Conservation Service and livestock producers implemented the Sage Grouse Initiative (SGI) to improve sage‐grouse habitat using regional‐specific management actions such as rotational grazing. We assessed the effect of SGI grazing management, the influence of brood female and chick morphometric traits, and multiple environmental and anthropogenic disturbance factors on chick mortality risk in a sage‐grouse population in central Montana, USA, from 2011–2019. We used a Kaplan‐Meier survival function to evaluate chick survival, Cox proportional hazards models to evaluate chick mortality risk as a function of brood female and chick morphometric traits, and the Andersen‐Gill formulation of the Cox proportional hazards model to assess the effects of time‐dependent habitat characteristics on chick mortality risk. Survival to 45 days post‐hatch for 510 chicks varied annually from 0.26 ± 0.07 (SE) to 0.69 ± 0.07. The 45‐day survival rate for all years combined was 0.51 ± 0.03. Chick mortality risk was not affected by changes in livestock grazing management implemented through the SGI grazing program. Brood female age and body condition, sex of chicks, vegetation, and anthropogenic variables were also unassociated with chick mortality risk. There were small protective effects of chick mass adjusted for age and mean minimum monthly temperature; greater chick mass and lower monthly temperatures correlated with reduced mortality risk. Overall, our study suggests the SGI grazing program does not confer additional benefits to sage‐grouse chicks beyond existing grazing practices. Incentivizing grazing practices that adhere to fundamental principles of rangeland ecology and maintain intact rangelands may be more effective than specific prescribed grazing systems for sage‐grouse conservation in this region.

Spatiotemporal trends and covariates of Lyme borreliosis incidence in Poland, 2010–2019

Lyme borreliosis (LB) is the most commonly diagnosed tick-borne disease in the northern hemisphere. Since an efficient vaccine is not yet available, prevention of transmission is essential. This, in turn, requires a thorough comprehension of the spatiotemporal dynamics of LB transmission as well as underlying drivers. This study aims to identify spatiotemporal trends and unravel environmental and socio-economic covariates of LB incidence in Poland, using consistent monitoring data from 2010 through 2019 obtained for 320 (aggregated) districts. Using yearly LB incidence values, we identified an overall increase in LB incidence from 2010 to 2019. Additionally, we observed a large variation of LB incidences between the Polish districts, with the highest risks of LB in the eastern districts. We applied spatiotemporal Bayesian models in an all-subsets modeling framework to evaluate potential associations between LB incidence and various potentially relevant environmental and socio-economic variables, including climatic conditions as well as characteristics of the vegetation and the density of tick host species. The best-supported spatiotemporal model identified positive relationships between LB incidence and forest cover, the share of parks and green areas, minimum monthly temperature, mean monthly precipitation, and gross primary productivity. A negative relationship was found with human population density. The findings of our study indicate that LB incidence in Poland might increase as a result of ongoing climate change, notably increases in minimum monthly temperature. Our results may aid in the development of targeted prevention strategies.

The mid-domain effect in flowering phenology

The timing of flowering is an important driver of species distribution and community assembly patterns. However, we still have much to learn about the factors that shape flowering diversity (i.e., number of species flowering per period) in plant communities. One potential explanation of flowering diversity is the mid-domain effect, which states that geometric constraints on species ranges within a bounded domain (space or time) will yield a mid-domain peak in diversity regardless of ecological factors. Here, we determine whether the mid-domain effect explains peak flowering time (i.e., when most species of communities are flowering) across China. We used phenological data of 16,267 herbaceous and woody species from the provincial Flora in China and species distribution data from the Chinese Vascular Plant Distribution Database to determine relationships between the observed number of species flowering and the number of species flowering as predicted by the mid-domain effect model, as well as between three climatic variables (mean minimum monthly temperature, mean monthly precipitation, and mean monthly sunshine duration). We found that the mid-domain effect explained a significant proportion of the temporal variation in flowering diversity across all species in China. Further, the mid-domain effect explained a greater proportion of variance in flowering diversity at higher latitudes than at lower latitudes. The patterns of flowering diversity for both herbaceous and woody species were related to both the mid-domain effect and environmental variables. Our findings indicate that including geometric constraints in conjunction with abiotic and biotic predictors will improve predictions of flowering diversity patterns.

ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data

Statistical downscaling of climate data has been widely described in the literature, with the aim of improving the reliability of local climatic parameters from coarse-resolution (often >20 km) global datasets. In this article, we present ClimateDT, a dynamic downscaling web tool for monthly historical and future time series at a global scale. The core of ClimateDT is the 1 km 1981–2010 climatology from CHELSA Climate (version 2.1), where the CRU-TS layers for the period 1901-current are overlayed to generate a historic time series. ClimateDT also provides future scenarios from CMIP5 using UKCP18 projections (rcp2.6 and rcp8.5) and CMIP6 using 5 GCMs, also available on the CHELSA website. The system can downscale the grids using a dynamic approach (scale-free) by computing a local environmental lapse rate for each location as an adjustment for spatial interpolation. Local predictions of temperature and precipitation obtained by ClimateDT were compared with climate time series assembled from 12,000 meteorological stations, and the Mean Absolute Error (MAE) and the explained variance (R2) were used as indicators of performance. The average MAEs for monthly values on the whole temporal scale (1901–2022) were around 1.26 °C for the maximum monthly temperature, 0.80 °C for the average monthly temperature, and 1.32 °C for the minimum monthly temperature. Regarding monthly total precipitation, the average MAE was 19 mm. As for the proportion of variance explained, average R2 values were always greater than 0.95 for temperatures and around 0.70 for precipitation due to the different degrees of temporal autocorrelation of precipitation data across time and space, which makes the estimation more complex. The elevation adjustment resulted in very accurate estimates in mountainous regions and areas with complex topography and substantially improved the local climatic parameter estimations in the downscaling process. Since its first release in November 2022, more than 1300 submissions have been processed. It takes less than 2 min to calculate 45 locations and around 8 min for the full dataset (512 records).

The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features

In 2023 Amazonia experienced both historical drought and warm conditions. On October 26th 2023 the water levels at the port of Manaus reached its lowest record since 1902 (12.70 m). In this region, October monthly maximum and minimum temperature anomalies also surpassed previous record values registered in 2015 (+ 3 °C above the normal considering the 1981–2020 average). Here we show that this historical dry and warm situation in Amazonia is associated with two main atmospheric mechanisms: (i) the November 2022–February 2023 southern anomaly of vertical integrated moisture flux (VIMF), related to VIMF divergence and extreme rainfall deficit over southwestern Amazonia, and (ii) the June–August 2023 downward motion over northern Amazonia related to extreme rainfall deficit and warm conditions over this region. Anomalies of both atmospheric mechanisms reached record values during this event. The first mechanism is significantly correlated to negative sea surface temperature (SST) anomalies in the equatorial Pacific (November–February La Niña events). The second mechanism is significantly correlated to positive SST anomalies in the equatorial Pacific, related to the impacts of June–September El Niño on the Walker Circulation. While previous extreme droughts were linked to El Niño (warmer North Tropical Atlantic SST) during the austral summer (winter and spring), the transition from La Niña 2022–23 to El Niño 2023 appears to be a key climatic driver in this record-breaking dry and warm situation, combined to a widespread anomalous warming over the worldwide ocean.

Analyses of Climate Variability and Trends in the Oil Palm Belt of Ghana

The current production of oil palm in Africa has been severely limited by long periods of drought and perennial floods due to the increased rates of climate change. The study was aimed at assessing the climatic variability trends in 7 major Oil Palm growing areas (Benso, Mpohor, Bogoso, Twifo Praso, Kade, Juabeng and Brewaniese) located in the oil palm belt in Ghana spanning the semideciduous and the wet evergreen rainforest zones of Ghana using 30 years of historical weather data (1993-2022). The data was analysed to evaluate the extent of seasonal changes in weather over the 30-year period using the Statistical Package for Social Sciences (SPSS 20.0 version), using descriptive statistics and time series analysis. Trend analysis was conducted using the non-parametric Mann-Kendall (MK) and Sen’s slope estimator tests. A seasonal trend in rainfall was observed at all sites with maximum levels observed between May and July as well as a minor rainy season in September to Mid-November. The Mann-Kendall trend analysis showed a generally increasing trend at most stations in rainfall, temperature and solar radiation. A generally increasing trend in annual rainfall was observed from 1993-2022 with significant trends at Mpohor and Juabeng. The trend was higher in the moist evergreen rainforest zone than in the semi-deciduous rainforest zone. An average increase of 6.7 mm/yr in rainfall trend was observed for the period. An increasing trend in monthly maximum, minimum and mean temperatures were also observed. Significant increases in mean temperatures were observed at Bogoso (0.022°C/yr), Benso (0.018°C/yr), Juabeng (0.011°C/yr) and Mpohor (0.017°C/yr). The trend in mean annual solar radiation was generally not significant except in Benso where the mean solar radiation increased at the rate of 0.044 MJ/day. Higher drought levels in the first half year combined with very heavy rains later in the year are features of the ongoing climate change menace.

The correspondence between the physiological cold tolerance and the distribution border of Mediterranean sandflies in Southeast Europe.

The distribution of sandflies is influenced by several climatic factors, like winter temperature limits, summer humidity, and drought. Only a few studies were performed to search for the connection between the physiological tolerance of sandflies and the result of the Climate Envelope Models projected distribution borders. It was studied how the depth of rodent burrows could determine the occurrence of five European Phlebotomus species based on their known physiological cold tolerances. The modelled distributions based on the monthly minimum temperatures showed notable differences by soil depth and species. The observed and modelled European distribution areas of sandflies according to - 20 cm soil depth showed good coincidence with the observed occurrences of Phlebotomus neglectus , Ph. papatasi , and Ph. perfiliewi in East-Central, Southeast, and East Europe. Also, a good coincidence was found in the case of the modelled and observed occurrences of Phlebotomus perniciosus in Western Europe. It can be concluded that in their north-eastern distribution borders, sandflies can overwinter in rabbit and rodent burrows at depths of - 20 cm below the surface. The physical cold tolerance of Ph. ariasi could be underestimated. In more than - 50 cm of depth, the stable and continuously cold environment of the shelters (mean temperature less than 10°C) could inhibit the post-winter diapause activation of sandflies in the northern regions.

Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception

<p>Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change.</p>

Environmental factors associated with incidence of developmental dysplasia of the hip: a systematic review and meta-analysis

BackgroundEstablished associated factors for DDH include female sex, breech presentation, family history, congenital malformations, oligohydramnios, and maternal hyperthyroidism. However, evidence for environmental factors that may contribute to DDH is limited and inconsistent.MethodsA systematic review of medical literature was conducted to collect data on environmental factors, including latitude, longitude, average yearly precipitation, average yearly temperature, minimum monthly temperature, and maximum monthly temperature, from all institutions that published articles on DDH. Univariate linear regression analysis was used to examine the correlation between environmental factors and DDH incidence, while multiple regression analysis was conducted to identify significant associated factors for DDH incidence.ResultsData from a total of 93 unique manuscripts were analyzed, revealing a significant negative correlation between DDH incidence and temperature, including average yearly temperature (r = -0.27, p = 0.008), minimum monthly temperature (r = -0.28, p = 0.006), and maximum monthly temperature (r = -0.23, p = 0.029). Additionally, there was a significant positive correlation between DDH incidence and latitude (r = 0.27, p = 0.009), and a significant negative correlation between DDH incidence and average yearly precipitation (r = -0.29, p = 0.004). In the final multiple regression analysis, temperature, including average yearly temperature, minimum monthly temperature, and maximum monthly temperature, were identified as significant associated factors for DDH incidence.ConclusionThe findings of this study suggest an association between cold weather and DDH incidence. Further research should explore the link between cold weather and DDH incidence, offering insights into potential interventions for cold climates.

Climate Variability and Glacier Evolution at Selected Sites Across the World: Past Trends and Future Projections

AbstractThe availability of freshwater from glaciers and snowmelt is of vital importance for people and ecosystems in the context of global climate change. Here, we focus on 25 glaciers located in different climates and latitudes and investigate their recent (1958–2020) and future projected trends (2020–2050 and 2070–2100) in monthly precipitation (Pr), maximum and minimum temperatures, ice mass loss, and their relationships with cloud properties. The study sites are located in Temperate Europe (France), the Inner (Ecuador, Venezuela, and Colombia) and Outer Tropics (Bolivia and Peru), Central America (Mexico), tropical Southeast Asia (Indonesia), Equatorial Africa (Uganda), and the Southern dry and Patagonian Andes (Chile and Argentina). The climate analyses are based on TerraClimate data (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces) and 28 CORDEX (Coordinated Regional Climate Downscaling Experiment) climate simulations. Our findings reveal that, extrapolating current glacier volume change trends, almost half of the studied glaciers are likely to vanish (95%–100% volume loss) by 2050, with widespread warming and drying trends since 1958. A shift toward wetter conditions at Pico Humboldt (Venezuela) and Martial Este (Argentina) identifiable in the CORDEX simulation will very likely not have a limiting impact on glacier mass loss owing to increasing temperatures, which will raise the elevation of the rain/snow limit. Our results provide useful new information to better understand glacier‐climate relationships and future scenarios dominated by ice mass loss trends across the globe. These findings suggest serious consequences for future water availability, which exacerbate the vulnerability of local populations and ecosystems.

A novel statistical model for flood prediction in the Eel River watershed, New Brunswick, Canada

ABSTRACT A strong correlation between the effect of climate change and the increase in flooding frequency and magnitude has been reported in Canada. Consequently, there is a crucial need to examine the effects of future climate change scenarios on flooding conditions. The main objective of this research is to better understand the destructive effects of flood events under historical and future climate change conditions for a small watershed (Eel River watershed) in New Brunswick (NB), Eastern Canada. A practical model had been developed using the modified Artificial Neural Network (ANN) in MATLAB by the authors of this study. The architecture and data structure of ANN is characterized by a back propagation with the Levenberg–Marquardt method. The observed daily total precipitation, daily maximum and minimum air temperatures, daily discharge for the period 1967 to 1983, the simulated monthly maximum and minimum air temperatures, and monthly total precipitation for the period of 1996–2099 from the CanESM2, the second-generation Canadian Earth System Model (CGCM), were used as input of the model. The Representative Concentration Pathways (RCP 4.5 and 8.5), as suitable climate change scenarios, were selected based on the Intergovernmental Panel on Climate Change (IPCC) recommendations for flood studies. Daily values of temperatures, precipitations, and discharges were converted to monthly mean values for better prediction of the output results. In addition, two series of observed discharges were prepared using mean monthly (Qavg) and daily maximum discharges (Qd) as the Target of the model. For more accurate analysis, the time frames of 1996–2012 (for the historical) and 2022–2038, 2039–2055, 2056–2072, 2073–2089, and 2083–2099 (for the future) were considered with a duration of 16 years for each time frame. The output results of ANN were predicted daily maximum (Qd) and mean (Qavg) discharges under the impact of climate change scenarios. As a part of the developed model, Flood Frequency Analysis (FFA) was undertaken using the generalized extreme value (GEV) and the three-parameter lognormal (LN3) distributions based on the predicted and observed discharges. The performance of FFA and ANN were demonstrated using the Anderson–Darling (AD), the Chi-square (CS) tests and coefficient of correlation (R) and mean squared error (MSE), respectively. In conclusion, the three most critical time frames with the highest values of predicted discharges were 2022–2038, 2056–2072, and 2073–2089 for RCP4.5 and 2039–2055, 2073–2089, and 2083–2099 for RCP8.5. Also, based on the FFA, the magnitudes of flood recurrence for the future time period of 100 years will dramatically increase according to the most critical time frames of 2056–2072 and 2039–2055 for RCP 4.5 and 8.5, respectively. Findings indicated that the Eel River watershed will encounter severe floods, and about a 50% increase in mean discharge, especially for the critical time frames. Finally, flood occurrences show increasing trends due to climate change effects in the most critical time frames.

Minimum Temperature Forecasting Using Gated Recurrent Unit

Aim: To forecast the monthly average Minimum Temperature (ºC) in Coimbatore district.
 Study Design: Gated Recurrent Unit (GRU) has been employed to forecast the Minimum Temperature.
 Place and Duration of Study: Time series data for average month wise Minimum Temperature from January 1982 to September 2022 was collected from Agro Climate Research Centre, TNAU for Coimbatore District.
 Methodology: GRU which belongs to the field of deep learning has been employed to anticipate the average monthly Minimum Temperature by analyzing time series data from January 1982 to September 2022 in the district of Coimbatore. The model was trained using data from 1982 January to 2019 December and tested on data from 2020 January to 2022 September. After training and testing the algorithm was deployed to forecast Minimum Temperature for the lead time ahead.
 Results: The GRU model generated RMSE and MAE scores of 0.694ºC and 0.523ºC, respectively, for Minimum Temperature. GRU model had a Willmott’s Index of Agreement (WI) value as 0.943 that is very close to 1. This demonstrates the effectiveness of the model built to effectively predict the Minimum Temperature. The study's evaluation of the RMSE, MAE, and Willmott Index value made it readily evident that the GRU model performed quite accurately for forecasting Minimum Temperature. Gated Recurrent Unit algorithm was used to forecast the Minimum Temperature from October 2022 till December 2023 that is for the next 15 months.

Statistical Connections between Large-Scale Climate Indices and Observed Mean and Extreme Temperatures in the US from 1948 to 2018

In order to better understand the extent to which global climate variability is linked to the frequency and intensity of heat waves and overall changes in temperature throughout the United States (US), correlations between long-term monthly mean, minimum, and maximum temperatures throughout the contiguous US on the one hand and low-frequency variability of multiple climate indices (CIs) on the other hand are analyzed for the period from 1948 to 2018. The Pearson’s correlation coefficient is used to assess correlation strength, while leave-one-out cross-validation and a bootstrapping technique (p-value) are used to address potential serial and spurious correlations and assess the significance of each correlation. Three parameters defined the sliding windows over which surface temperature and CI values were averaged: window size, lag time between the temperature and CI windows, and the beginning month of the temperature window. A 60-month sliding window size and 0 lag time resulted in the highest correlations overall; beginning months were optimized on an individual site basis. High (r ≥ 0.60) and significant (p-value ≤ 0.05) correlations were identified. The Western Hemisphere Warm Pool (WHWP) and El Niño/Southern Oscillation (ENSO) exhibited the strongest links to temperatures in the western US, tropical Atlantic sea surface temperatures to temperatures in the central US, the WHWP to temperatures throughout much of the eastern US, and atmospheric patterns over the northern Atlantic to temperatures in the Northeast and Southeast. The final results were compared to results from previous studies focused on precipitation and coastal sea levels. Regional consistency was found regarding links between the northern Atlantic and overall weather and coastal sea levels in the Northeast and Southeast as well as on weather in the upper Midwest. Though the MJO and WHWP revealed dominant links with precipitation and temperature, respectively, throughout the West, ENSO revealed consistent links to sea levels and surface temperatures along the West Coast. These results help to focus future research on specific mechanisms of large-scale climate variability linked to US regional climate variability and prediction potential.

Weather parameters as a predictive tool potentially allowing for better monitoring of dairy cattle against gastrointestinal parasites hazard

In animal production, yield is critically related to animal health status. To ensure high productivity, innovative control strategies for herd and parasites monitoring are required. Gastrointestinal parasites have a strong influence on changing feed intake or nutrient use, limiting animal productivity. Serological control has been proposed, given that parasite development is largely dependent on environmental temperature and humidity. However, breeders and field veterinarians lack readily accessible climate characteristics that provide information to determine whether and when herds require laboratory examination. To help reduce the testing costs incurred by farmers, we investigated whether selected meteorological data could serve as conclusive predictors to increase the precision of herd selection for serological monitoring. Our results indicate that the selection of herds by farmers for testing can be guided by regular checking of meteorological data, especially various temperature and humidity indicators. In general, ranges of 24–28 °C, as well as − 0.5 to 7.5 °C for the monthly maximum and minimum temperature, respectively, and relative humidity (68–79%) and vapour pressure (10–15 hPa) correspond to a high antiparasitic response of the herd, expressed as the optical density ratio. It is recommended to introduce coproscopic and/or serological tests if the observed weather pattern (covering the prepatent period of parasite development) ranges within the estimated values.

Climate change effect on water temperature and evaporation in Lake Qarun

ABSTRACT Climate change can have significant effects on the water and heat budgets of lakes and reservoirs. This study examined the effect of climate change on Lake Qarun through analyzing the temporal variation in lake surface temperature and evaporation from the lake. The analysis was based on the remote-sensing dataset MOD11A1 and the ERA5 climate reanalysis dataset. Daily surface temperatures for 1-km MOD11A1 pixels were filtered to remove outliers, averaged to obtain mean lake temperature, and then aggregated to compute the monthly average lake temperature. Lake evaporation was estimated by applying an un-mixing algorithm to ERA5 evaporation fluxes for mixed land-lake pixels and land-only pixels. The resulting monthly evaporation from the lake was corrected using a regression model based on evaporation estimates available in the literature for Lake Qarun. The regression model performance was confirmed through the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and root mean square error. Over the period 1980 to 2019, findings show that annual evaporation ranged between 140 and 190 cm/year. Maximum average monthly evaporation for the study period is 21 cm in July while the minimum is 6.7 cm in February from 1980 to 2019. For the average lake surface temperature, it varied between ~31.9 and 33.9°C over the period 2000 to 2019. For the same time period, average maximum and minimum monthly temperatures were 44°C in August and 20.1°C in January. It is concluded that the average increase in evaporation and lake surface temperature is 37 mm/decade and 0.4°C/decade, respectively. The increase in both lake surface temperature and evaporation is likely due to climate change effects. However, using datasets with a larger temporal record for lake surface temperature, further studies can be applied for confirmation. Also, further confirmation can be obtained by applying the same analysis to other lakes in Egypt.

The Role of Crop Management Practices and Adaptation Options to Minimize the Impact of Climate Change on Maize (Zea mays L.) Production for Ethiopia

Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield.

Different responses of Japanese encephalitis to weather variables among eight climate subtypes in Gansu, China, 2005–2019

This study evaluated epidemic temporal aspects of Japanese encephalitis (JE) and investigated the weather threshold of JE response across eight climate subtypes between 2005 and 2019 in Gansu Province, China. Epidemiological data were collected from the China Information System for Disease Control and Prevention (CISDCP). Three epidemic temporal indices [frequency index (α), duration index (β), and intensity index (γ)] were adopted for the comparison of epidemic features among different climate subtypes. In addition, the local indicators of spatial association (LISA) technique was used to detect the hot-spot areas. The category and regression tree (CART) model was used to detect the response threshold of weather variables in hot-spot areas across climate subtypes. Among eight climate subtypes in Gansu, in most hot-spot areas (i.e., high–high clusters), α, β, and γ were detected in the climate subtypes of subtropical winter dry (Cwa), temperate oceanic continental (Cwb), and continental winter dry (Dwa and Dwb). According to the CART analysis, a minimum monthly temperature is required for Japanese encephalitis virus (JEV) transmission, with different threshold values among the climatic subtypes. In temperate climate zones (Cwa and Cwb), this threshold is 19 °C at a 1-month lag. It is lower in continental winter dry climate zones: 18 °C in Dwa (snow climate, dry winter, and hot summer) and 16 °C in Dwb (snow climate, dry winter, and warm summer). Additionally, some areas of the areas with temperate arid (BWk and BSk) had the first JE cases. Further studies to detect whether the climate change influence the JEV’s distribution in Gansu Province are needed.