Climate

While ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when changes in the hydrological cycle are concerned. In this paper, we present hydro-meteorological trends based on observations from a hydrological research basin in Eastern Austria between 1979 and 2019. The analyzed variables include air temperature, precipitation, and catchment runoff. Additionally, the number of wet days, trends for catchment evapotranspiration, and computed potential evapotranspiration were derived. Long-term trends were computed using a non-parametric Mann–Kendall test. The analysis shows that while mean annual temperatures were decreasing and annual temperature minima remained constant, annual maxima were rising. Long-term trends indicate a shift of precipitation to the summer, with minor variations observed for the remaining seasons and at an annual scale. Observed precipitation intensities mainly increased in spring and summer between 1979 and 2019. Catchment actual evapotranspiration, computed based on catchment precipitation and outflow, showed no significant trend for the observed time period, while potential evapotranspiration rates based on remote sensing data increased between 1981 and 2019. Full article

The Indian Himalayan Region is home to nearly 50 million people, more than 50% of whom are dependent on springs for their sustenance. Sustainable management of the nearly 3 million springs in the region requires a framework to identify the springs most vulnerable to change agents which can be biophysical or socio-economic, internal or external. In this study, we conceptualize vulnerability in the Indian Himalayan springs. By way of a systematic review of the published literature and synthesis of research findings, a scheme of identifying and quantifying these change agents (stressors) is presented. The stressors are then causally linked to the characteristics of the springs using indicators, and the resulting impact and responses are discussed. These components, viz., stressors, state, impact, and response, and the linkages are used in the conceptual framework to assess the vulnerability of springs. A case study adopting the proposed conceptual model is discussed for Mathamali spring in the Western Himalayas. The conceptual model encourages quantification of stressors and promotes a convergence to an evidence-based decision support system for the management of springs and the dependent ecosystems from the threat due to human development and climate change. Full article

Prediction of precipitation changes caused by global climate change is a practical and scientific problem of high complexity. To advance, we look at the record of all available rain gauges in Colombia and at the CHIRPS database to estimate trends in essential variables describing precipitation, including HY-INT, an index of the hydrologic cycle’s intensity. Most of the gauges and cells do not show significant trends. Moreover, the signs of the statistically significant trends are opposite between the two datasets. Satisfactory explanation for the discrepancy remains open. Among the CHIRPS database’s statistically significant trends, the western regions (Pacific and Andes) tend to a more intense hydrologic cycle, increasing both intensity and mean dry spell length, whereas for the northern and eastern regions (Caribbean, Orinoco, and Amazon), the tendencies are opposite. This dipole in trends suggests different mechanisms: ENSO affects western Colombia more directly, whereas rainfall in the eastern regions depends more on the Atlantic Ocean, Caribbean Sea, and Amazon basin dynamics. Nevertheless, there is countrywide accord among gauges and cells with significant increasing trends for annual precipitation. Overall, these observations constitute essential evidence of the need for developing a more satisfactory theory of climate change effects on tropical precipitation. Full article

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops. Full article

The El Niño Southern Oscillation (ENSO) is a well-known cause of year-to-year climatic variations on Earth. Floods, droughts, and other natural disasters have been linked to the ENSO in various parts of the world. Hence, modeling the ENSO’s effects and the anomaly of the ENSO phenomenon has become a main research interest. Statistical methods, including linear and nonlinear models, have intensively been used in modeling the ENSO index. However, these models are unable to capture sufficient information on ENSO index variability, particularly on its temporal aspects. Hence, this study adopted functional data analysis theory by representing a multivariate ENSO index (MEI) as functional data in climate applications. This study included the functional principal component, which is purposefully designed to find new functions that reveal the most important type of variation in the MEI curve. Simultaneously, graphical methods were also used to visualize functional data and capture outliers that may not have been apparent from the original data plot. The findings suggest that the outliers obtained from the functional plot are then related to the El Niño and La Niña phenomena. In conclusion, the functional framework was found to be more flexible in representing the climate phenomenon as a whole. Full article

This study identified factors affecting livestock farmers’ agricultural drought resilience to food insecurity in Northern Cape Province, South Africa. Data of 217 smallholder livestock farmers were used in a principal component analysis to estimate the agricultural drought resilience index. The structural equation approach was then applied to assess smallholder livestock farmers’ resilience to food insecurity. The study found that most smallholder livestock farmers (81%) were not resilient to agricultural drought. Assets (β = 0.150), social safety nets (β = 0.001), and adaptive capacity (β = 0.171) indicators positively impacted households’ resilience to food insecurity with 5% significance. Climate change indicators negatively impacted households’ resilience to food insecurity. Two variables were included under climate change, focusing on drought, namely drought occurrence (β = −0.118) and drought intensity (β = −0.021), which had a negative impact on household resilience to food insecurity with 10% significance. The study suggests that smallholder livestock farmers need assistance from the government and various stakeholders to minimize vulnerability and boost their resilience to food insecurity. Full article

This study analyses the interactions and impacts between multiple factors i.e., urban greening, building layout, and meteorological conditions that characterise the urban microclimate and thermal comfort in the urban environment. The focus was on two neighbourhoods of Lecce city (southern Italy) characterised through field campaigns and modelling simulations on a typical hot summer day. Field campaigns were performed to collect greening, building geometry, and microclimate data, which were employed in numerical simulations of several greening scenarios using the Computational Fluid Dynamics-based and microclimate model ENVI-met. Results show that, on a typical summer day, trees may lead to an average daily decrease of air temperature by up to 1.00 °C and an improvement of thermal comfort in terms of Mean Radiant Temperature (MRT) by up to 5.53 °C and Predicted Mean Vote (PMV) by up to 0.53. This decrease is more evident when the urban greening (in terms of green surfaces and trees) is increased by 1266 m² in the first neighbourhood and 1988 m² in the second one, with respect to the current scenario, proving that shading effect mainly contributes to improving the urban microclimate during daytime. On the contrary, the trapping effect of heat, stored by the surfaces during the day and released during the evening, induces an increase of the spatially averaged MRT by up to 2 °C during the evenings and a slight deterioration of thermal comfort, but only locally where the concentration of high LAD trees is higher. This study contributes to a better understanding of the ecosystem services provided by greening with regard to microclimate and thermal comfort within an urban environment for several hours of the day. It adds knowledge about the role of green areas in a Mediterranean city, an important hot spot of climate change, and thus it can be a guide for important urban regeneration plans. Full article

As part of the European Green Deal, the EU aims to become climate-neutral and reach net-zero greenhouse gas emissions by 2050. Ιn this context, EU member states are required to develop a national strategy to achieve the required emissions reductions under the Paris Agreement and EU climate goals. Western Macedonia is a region in North-western Greece with its economy largely dominated by lignite mining, lignite-fired power plants and district heating systems. In 2019, the Greek Government set the goal of withdrawing all lignite plants by 2028, with most units being withdrawn already by 2023. This decision has had an immense socio-economic impact on the region of Western Macedonia. This research work reflects the current situation at the socio-economic and socio-political level in Western Macedonia and discusses the policies implemented in the context of the lignite phase-out process to ensure a just transition for households and businesses of the region. Although there is not a ‘one-size-fits-all’ blueprint for successful low-carbon transitions of high-carbon intensive regional economies, the main target of our paper is understanding the impacts, challenges and opportunities of decarbonizing Western Macedonia. Full article

Recent climatic trends of two nearby stations in Sydney were examined in terms of hourly ambient air temperature and wind direction for the time period 1999–2019. A reference was set for the monthly number of cooling (CDH) and heating (HDH) degree hours and the number of monthly hours that temperatures exceeded 24 °C (T24) or were below 14 °C (T14), parameters affecting not only the energy demands but also the quality of life. The degree hours were linked to the dominant synoptic conditions and the local phenomena: sea breeze and inland winds. The results indicated that both areas had higher mean monthly number of HDH (980–1421) than CDH (397–748), thus higher heating demands. The results also showed a higher mean monthly number of T14 (34–471) than T24 (40–320). A complete spatiotemporal profile of the climatic variations was given through the analysis of their dynamic progress and correlation. In order to estimate the daily values of CDH and HDH, T24 and T14 empirical models were calculated per month based on the maximum and minimum daily air temperatures. The use of forecasted weather conditions and the created empirical models may later be used in the energy planning scenarios. Full article

Consistent time series rainfall datasets are important in performing climate trend analyses and agro-hydrological modeling. However, temporally consistent ground-based and long-term observed rainfall data are usually lacking for such analyses, especially in mountainous and developing countries. In the absence of such data, satellite-derived rainfall products, such as the Climate Hazard Infrared Precipitations with Stations (CHIRPS) and Global Precipitation Measurement Integrated Multi-SatellitE Retrieval (GPM-IMERG) can be used. However, as their performance varies from region to region, it is of interest to evaluate the accuracy of satellite-derived rainfall products at the basin scale using ground-based observations. In this study, we evaluated and demonstrated the performance of the three-run GPM-IMERG (early, late, and final) and CHIRPS rainfall datasets against the ground-based observations over the Ziway Lake Basin in Ethiopia. We performed the analysis at monthly and seasonal time scales from 2000 to 2014, using multiple statistical evaluation criteria and graphical methods. While both GPM-IMERG and CHIRPS showed good agreement with ground-observed rainfall data at monthly and seasonal time scales, the CHIRPS products slightly outperformed the GPM-IMERG products. The study thus concluded that CHIRPS or GPM-IMERG rainfall data can be used as a surrogate in the absence of ground-based observed rainfall data for monthly or seasonal agro-hydrological studies. Full article

Global climate change is predicted to increase temperatures and change the distribution of precipitation. However, there is high uncertainty regarding the regional occurrence and intensity of climate change. Therefore, this work examines the effects of climate parameters on the long-term yields of winter barley and assesses the parameters affecting plant development throughout the year and in specific growth phases. The investigation was carried out in an area with Pleistocene loess, a highly fertile site in Germany. The effect of climate on crop yields was modeled with monthly weather parameters and additional indices such as different drought parameters, heat-related stress, late spring frost, early autumn frost, and precipitation-free periods. Residuals and yield values were treated as dependent variables. The residuals were determined from long-term yield trends using the autoregressive integrated moving average (ARIMA) method. The results indicated that temperature and precipitation are significant in all calculations in all variants, but to a lesser degree when considered as sums or mean values, compared with specific indices (e.g., frost-alternating days, the temperature threshold, the precipitation intensity, rain-free days, the early/late frost index, and the de Martonne–Reichel dryness index). The inter-annual variations in crop yields were mainly determined by the prevailing climatic conditions in winter as well as the transition periods from the warmer season to winter and vice versa. The main winter indices were the temperature threshold, frost-alternating days, and precipitation intensity. During the main growth periods, only the precipitation intensity was significant. These findings can be attributed to the high available field water capacity of this site, which overcomes the need for summer precipitation if the soil water storage is replenished during winter. Full article

The change in future climate will have a prominent impact on crop production and water requirement. Crop production is directly related to climatic variables. Temperature, solar radiation, wind, precipitation, CO₂ concentration and other climatic variables dictate crop yield. This study, based on long-term historical data, investigates the patterns and changes in climatic variables (precipitation, temperature, and solar radiation) that would most significantly affect the future crop production in many parts of the world, and especially in India, where most farmers depend on rainfall for rice production. Statistical analyses—box and whisker plot, mean absolute error, Taylor diagram, double mass curve, Mann–Kendall trend test, and projected climate change—were used to assess the significance of the climatic factors for the purpose of agricultural modeling. Large variability in precipitation may cause the flash floods and affect the farming, and at the same time, increase in temperature from baseline period will lead to high water requirement by crops, and may cause drought if rainfall does not occur. Decrease in solar radiation will affect crop growth and development, and thus, would hamper the crop production. The results of this study would be useful in identifying the negative issues arising from climate change in future agricultural practices in Bihar, India. Furthermore, the results can also help in developing management strategies to combat the climate change impact on crop production. Full article

This study presents an analysis of extreme temperature events over southeastern USA from 1978 to 2017. This region is part of the so-called ‘warming hole’ where long-term surface temperature trends are negative or non-significant, in contrast with the remainder of the country. This study examines whether this distinctive characteristic reflects on the region’s trends in temperature extremes. Daily maximum and minimum temperatures from the US Historical Climatology Network were used to compute extreme indices recommended by the Expert Team on Climate Change Detection and Indices. Temperature extreme indices computed for all stations using the RClimDex package were gridded onto a regular latitude–longitude grid, and a spatiotemporal analysis of associated trends was performed. The results point to a tendency toward warming due to increasing trends in the annual occurrence of the hottest day, the warmest night, warm days, warm nights, summer days, tropical nights, and warm spells, as well as decreases in cool nights, cool days, and frost days. Statistically significant trend changes over large portions of the Southeast were dominated by increases in the frequency of the coldest night, summer days, and warm nights, and decreases in cool nights and frost days. Comparison of our results with other global and regional studies indicate that most of the extreme temperature changes over the Southeast are consistent with findings from other parts of the United States (US) and the world. Overall, this study shows that being part of the ‘warming hole’ does not preclude southeastern US from an intensification of temperature extremes, whether it is an increase in warm extremes or a decrease in cold ones. Further, the results suggest that, should the current trends continue in the long term, the Southeast will not be considered as being part of a warming hole anymore. Full article

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r² = 0.73 whereas the NDVI showed satisfactory performance with r² = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r² = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI. Full article

A predictive understanding of interactions between vegetation and climate has been a grand challenge in terrestrial ecology for over 200 years. Developed in recent decades, continental-scale monitoring of climate and forest dynamics enables quantitative examination of vegetation–climate relationships through a data-driven paradigm. Here, we apply a data-intensive approach to investigate forest–climate interactions across the conterminous USA. We apply multivariate statistical methods (stepwise regression, principal component analysis) including machine learning to infer significant climatic drivers of standing forest basal area. We focus our analysis on the ecoregional scale. For most ecoregions analyzed, both stepwise regression and random forests indicate that factors related to precipitation are the most significant predictors of forest basal area. In almost half of US ecoregions, precipitation of the coldest quarter is the single most important driver of basal area. The demonstrated data-driven approach may be used to inform forest-climate envelope modeling and the forecasting of large-scale forest dynamics under climate change scenarios. These results have important implications for climate, biodiversity, industrial forestry, and indigenous communities in a changing world. Full article

This study analyzed the variability of rainfall and temperature in southwest coastal Bangladesh and assessed the impact of such variability on local livelihood in the last two decades. The variability analysis involved the use of coefficient of variation (CV), standardized precipitation anomaly (Z), and precipitation concentration index (PCI). Linear regression analysis was conducted to assess the trends, and a Mann–Kendall test was performed to detect the significance of the trends. The impact of climate variability was assessed by using a livelihood vulnerability index (LVI), which consisted of six livelihood components with several sub-components under each component. Primary data to construct the LVIs were collected through a semi-structed questionnaire survey of 132 households in a coastal polder. The survey data were triangulated and supplemented with qualitative data from focused group discussions and key informant interviews. The results showed significant rises in temperature in southwest coastal Bangladesh. Though there were no discernable trends in annual and seasonal rainfalls, the anomalies increased in the dry season. The annual PCI and Z were found to capture the climate variability better than the currently used mean monthly standard deviation. The comparison of the LVIs of the present decade with the past indicated that the livelihood vulnerability, particularly in the water component, had increased in the coastal polder due to the increases in natural hazards and climate variability. The index-based vulnerability analysis conducted in this study can be adapted for livelihood vulnerability assessment in deltaic coastal areas of Asia and Africa. Full article

We compared the effect on autumn (October, November, December) precipitation over Iran during two types of El Niño–Southern Oscillation (ENSO) phase transitions from the perspective of anomalies in wave activity flux and sea level pressure along the Atlantic–Mediterranean storm track, as well as precipitation. We used Oceanic Niño Index (ONI) to identify the transition phases of ENSO (El Niño to La Niña and also La Niña to El Niño, referred to as type 1 and type 2, respectively). Climate data during the period of 1950 to 2019 used in this study is derived from NCEP-NCAR reanalysis. In order to investigate the intensity and direction of Rossby wave trains in different ENSO transitions, we used the wave activity flux parameter, and to evaluate the statistical significance of values, we calculated Student’s t-test. The impact of the Atlantic storm track on the Mediterranean storm track was shown to be greater in type 2 transitions. Further, the existence of a stronger wave source region in the Mediterranean region during type 2 transitions was established. Results also showed the weakening of the Iceland low and Azores high pressure in type 1 transitions and the reinforcement of both in type 2, with the differences being significant at up to a 99% confidence level. Pressure values over Iran were at or below normal in type 1 years and below normal in type 2. Finally, the composite analysis of precipitation anomaly revealed that during ENSO type 1 transitions, most regions of Iran experienced low precipitation, while in type 2, the precipitation was more than average, statistically significant at 75% confidence level or higher over the northern half of the country. Full article

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods. Full article