Observed and Projected Changes in Temperature and Precipitation in the Core Crop Region of the Humid Pampa, Argentina

The core crop region of the Humid Pampa is one of the most productive agricultural lands around the world and depends highly on climate conditions. This study assesses climate variability, climate extremes, and observed and projected climate changes there, using 1911–2019 observations and CMIP5 model simulations. Since 1970, the annual mean temperature has risen by 1 °C and the mean annual minimum and maximum temperatures by 2 and 0.5 °C, respectively. The frequency of warm days and nights increased, and cold days and nights decreased. Heatwaves became longer and more intense, and cold waves decreased with less frost events. Annual precipitation increased by 10% from 1911, mainly in summer, and years with excess precipitation outnumbered those with a deficit. Both intense precipitation events and consecutive dry days grew, suggesting more annual precipitation falling on fewer days. Projections show a warming of 1 °C by 2035, regardless of the scenario. From then on until 2100, mean temperature will increase by 2 and 3–3.5 °C in the RCP4.5 and RCP8.5 scenarios, respectively. Annual precipitation will grow 8 and 16% from current values by 2100 in the RCP4.5 and RCP8.5 scenarios, respectively. No major precipitation changes are projected in the RCP2.6 scenario.

Changes in precipitation amounts and extremes across Xinjiang (northwest China) and their connection to climate indices

Xinjiang is a major part of China’s arid region and its water resource is extremely scarcity. The change in precipitation amounts and extremes is of significant importance for the reliable management of regional water resources in this region. Thus, this study explored the spatiotemporal changes in extreme precipitation using the Mann–Kendall (M–K) trend analysis, mutation test, and probability distribution functions, based on the observed daily precipitation data from 89 weather stations in Xinjiang, China during 1961–2018. We also examined the correlations between extreme precipitation and climate indices using the cross-wavelet analysis. The results indicated that the climate in Xinjiang is becoming wetter and the intensity and frequency of extreme precipitation has begun to strengthen, with these trends being more obvious after the 1990s. Extreme precipitation trends displayed spatial heterogeneity in Xinjiang. Extreme precipitation was mainly concentrated in mountainous areas, northern Xinjiang, and western Xinjiang. The significant increasing trend of extreme precipitation was also concentrated in the Tianshan Mountains and in northern Xinjiang. In addition, the climate indices, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Multivariate ENSO Index and Indian Ocean Dipole Index had obvious relationships with extreme precipitation in Xinjiang. The relationships between the extreme precipitation and climate indices were not clearly positive or negative, with many correlations advanced or delayed in phase. At the same time, extreme precipitation displayed periodic changes, with a frequency of approximately 1–3 or 4–7 years. These periodic changes were more obvious after the 1990s; however, the exact mechanisms involved in this require further study.

Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall

Nonstationarity is one major issue in hydrological models, especially in design rainfall analysis. Design rainfalls are typically estimated by annual maximum rainfalls (AMRs) of observations below 50 years in many parts of the world, including South Korea. However, due to the lack of data, the time-dependent nature may not be sufficiently identified by this classic approach. Here, this study aims to explore design rainfall with nonstationary condition using century-long reanalysis products that help one to go back to the early 20th century. Despite its useful representation of the past climate, the reanalysis products via observational data assimilation schemes and models have never been tested in representing the nonstationary behavior in extreme rainfall events. We used daily precipitations of two century-long reanalysis datasets as the ERA-20c by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 20th century reanalysis (20CR) by the National Oceanic and Atmospheric Administration (NOAA). The AMRs from 1900 to 2010 were derived from the grids over South Korea. The systematic errors were downgraded through quantile delta mapping (QDM), as well as conventional stationary quantile mapping (SQM). The evaluation result of the bias-corrected AMRs indicated the significant reduction of the errors. Furthermore, the AMRs present obvious increasing trends from 1900 to 2010. With the bias-corrected values, we carried out nonstationary frequency analysis based on the time-varying location parameters of generalized extreme value (GEV) distribution. Design rainfalls with certain return periods were estimated based on the expected number of exceedance (ENE) interpretation. Although there is a significant range of uncertainty, the design quantiles by the median parameters showed the significant relative difference, from −30.8% to 42.8% for QDM, compared with the quantiles by the multi-decadal observations. Even though the AMRs from the reanalysis products are challenged by various errors such as quantile mapping (QM) and systematic errors, the results from the current study imply that the proposed scheme with employing the reanalysis product might be beneficial to predict the future evolution of extreme precipitation and to estimate the design rainfall accordingly.

Impact of Climate Change on Precipitation Extremes over Ho Chi Minh City, Vietnam

In the context of climate change, the impact of hydro-meteorological extremes, such as floods and droughts, has become one of the most severe issues for the governors of mega-cities. The main purpose of this study is to assess the spatiotemporal changes in extreme precipitation indices over Ho Chi Minh City, Vietnam, between the near (2021–2050) and intermediate (2051–2080) future periods with respect to the baseline period (1980–2009). The historical extreme indices were calculated through observed daily rainfall data at 11 selected meteorological stations across the study area. The future extreme indices were projected based on a stochastic weather generator, the Long Ashton Research Station Weather Generator (LARS-WG), which incorporates climate projections from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Eight extreme precipitation indices, such as the consecutive dry days (CDDs), consecutive wet days (CWDs), number of very heavy precipitation days (R20mm), number of extremely heavy precipitation days (R25mm), maximum 1 d precipitation amount (RX1day), maximum 5 d precipitation amount (RX5day), very wet days (R95p), and simple daily intensity index (SDII) were selected to evaluate the multi-model ensemble mean changes of extreme indices in terms of intensity, duration, and frequency. The statistical significance, stability, and averaged magnitude of trends in these changes, thereby, were computed by the Mann-Kendall statistical techniques and Sen’s estimator, and applied to each extreme index. The results indicated a general increasing trend in most extreme indices for the future periods. In comparison with the near future period (2021–2050), the extreme intensity and frequency indices in the intermediate future period (2051–2080) present more statistically significant trends and higher growing rates. Furthermore, an increase in most extreme indices mainly occurs in some parts of the central and southern regions, while a decrease in those indices is often projected in the north of the study area.

Survive a Warming Climate: Insect Responses to Extreme High Temperatures

Global change includes a substantial increase in the frequency and intensity of extreme high temperatures (EHTs), which influence insects at almost all levels. The number of studies showing the ecological importance of EHTs has risen in recent years, but the knowledge is rather dispersed in the contemporary literature. In this article, we review the biological and ecological effects of EHTs actually experienced in the field, i.e., when coupled to fluctuating thermal regimes. First, we characterize EHTs in the field. Then, we summarize the impacts of EHTs on insects at various levels and the processes allowing insects to buffer EHTs. Finally, we argue that the mechanisms leading to positive or negative impacts of EHTs on insects can only be resolved from integrative approaches considering natural thermal regimes. Thermal extremes, perhaps more than the gradual increase in mean temperature, drive insect responses to climate change, with crucial impacts on pest management and biodiversity conservation.

Global response of terrestrial gross primary productivity to climate extremes

Extreme climate events undoubtedly have essential impacts on ecosystem gross primary productivity (GPP), but the global spatio-temporal patterns of GPP responses to climate extremes are unclear. In this study, we analyzed the responses of GPP to temperature and precipitation extremes during historical (1901-2016) and future (2006-2100) periods using climate extreme indices (CEIs) developed by the Expert Team on Climate Change Detection and Indices. Eight temperature-related CEIs and eight precipitation-related CEIs were used for this analysis, along with three future greenhouse gas concentration trajectory scenarios generated by the IPCC: RCP 2.6, RCP 4.5, and RCP 8.5. Our results show that under RCP 4.5 and RCP 8.5, most climate extremes are increasing from the historical period into the future, indicating a warming globe with more frequent and more intense extreme climate events. But the increasing rate is only persistently enhanced with time under scenario RCP 8.5. GPP shows a continuous negative relationship with cold CEIs and positive relationship with wet CEIs from the historical period into the future. In all zonal scales, the changed magnitude of GPP responds strongly to extreme value-related temperature extremes under different scenarios. However, the precipitation-related extremes with the strongest GPP response are various in different regions. In the future, GPP is most sensitive to temperature extremes in upper northern latitudes and in high-altitude regions (e.g., Qinghai-Tibet Plateau) and to precipitation extremes in the tropical zone. This study may provide a basis for predicting how GPP responds to climate extremes and explaining the underlying changes in the carbon cycle.

Degree-Day Climatology over Central and Southeast Europe for the Period 1961-2018 – Evaluation in High Resolution

The ongoing climate change over Central and Southeast Europe has a great potential to affect significantly the public energy demands and in particular the energy consumption in the residential heating and cooling sector. The linkage of the ambient daily extreme and mean temperatures and the energy needs for condition or heat buildings can be quantified as numerical indicators as the heating and cooling degree-days. In the present study, these indicators are calculated according the UK Met Office methodology from the daily mean and extreme temperatures, which, in turn, are computed from the output of the MESCAN-SURFEX system in the frame the FP7 UERRA project. The study, which is performed in a very high resolution, is dedicated on the analysis of the spatial patterns as well as assessment of the magnitude and statistical significance of the temporal evolution of the heating and cooling degree-days. It reveals general tendencies which are coherent with the regional climate warming, but with high spatial heterogeneities. The study confirms the essential impact of the ongoing climate change on the heating, ventilating and air-conditioning industry over Central and Southeast Europe.

Long-Term Changes and Variability of Ecologically-Based Climate Indices along an Altitudinal Gradient on the Qinghai-Tibetan Plateau

Extreme climate events are typically defined based on the statistical distributions of climatic variables; their ecological significance is often ignored. In this study, precipitation and temperature data from 78 weather stations spanning from 1960 to 2015 on the Qinghai-Tibetan Plateau were examined. Specifically, long-term and altitudinal variability in ecologically relevant climate indices and their seasonal differences was assessed. The results show that indices of daily temperatures greater than 10 °C and 25 °C show positive annual change trends during the growing season (May to September). Indices of daily rainfall greater than 2 mm, 3 mm and 5 mm positively alternate with years both in and around the growing season (May–September, April and October). In contrast, the index of daily snowfall greater than 2 mm shows opposite annual variability. Additionally, a higher altitude significantly leads to fewer days with temperature deviations above 20 °C, except for in October. The three abovementioned rainfall indices present significantly positive variability with increasing altitude during the growing season. In contrast, the snow index shows similar altitudinal changes in the months surrounding the growing season. This study allows us to better cope with the threats of climate change to vulnerable ecosystems.

Improved cyber-physical system captured post-flowering high night temperature impact on yield and quality of field grown wheat

Winter wheat (Triticum aestivum L.) is essential to maintain food security for a large proportion of the world’s population. With increased risk from abiotic stresses due to climate variability, it is imperative to understand and minimize the negative impact of these stressors, including high night temperature (HNT). Both globally and at regional scales, a differential rate of increase in day and night temperature is observed, wherein night temperatures are increasing at a higher pace and the trend is projected to continue into the future. Previous studies using controlled environment facilities and small field-based removable chambers have shown that post-anthesis HNT stress can induce a significant reduction in wheat grain yield. A prototype was previously developed by utilizing field-based tents allowing for simultaneous phenotyping of popular winter wheat varieties from US Midwest and advanced breeding lines. Hence, the objectives of the study were to (i) design and build a new field-based infrastructure and test and validate the uniformity of HNT stress application on a scaled-up version of the prototype (ii) improve and develop a more sophisticated cyber-physical system to sense and impose post-anthesis HNT stress uniformly through physiological maturity within the scaled-up tents; and (iii) determine the impact of HNT stress during grain filling on the agronomic and grain quality parameters including starch and protein concentration. The system imposed a consistent post-anthesis HNT stress of + 3.8 °C until maturity and maintained uniform distribution of stress which was confirmed by (i) 0.23 °C temperature differential between an array of sensors within the tents and (ii) statistically similar performance of a common check replicated multiple times in each tent. On average, a reduction in grain-filling duration by 3.33 days, kernel weight by 1.25% per °C, grain number by 2.36% per °C and yield by 3.58% per °C increase in night temperature was documented. HNT stress induced a significant reduction in starch concentration indicating disturbed carbon balance. The pilot field-based facility integrated with a robust cyber-physical system provides a timely breakthrough for evaluating HNT stress impact on large diversity panels to enhance HNT stress tolerance across field crops. The flexibility of the cyber-physical system and movement capabilities of the field-based infrastructure allows this methodology to be adaptable to different crops.

Long-Term Trend Analysis in Annual and Seasonal Precipitation, Maximum and Minimum Temperatures in the Southwest United States

The objective of this study is to perform trend analysis in the historic data sets of annual and crop season [May–September] precipitation and daily maximum and minimum temperatures across the southwest United States. Eighteen ground-based weather stations were considered across the southwest United States for a total period from 1902 to 2017. The non-parametric Mann–Kendall test method was used for the significance of the trend analysis and the Sen’s slope estimator was used to derive the long-term average rates of change in the parameters. The results showed a decreasing trend in annual precipitation at 44.4% of the stations with the Sen’s slopes varying from −1.35 to −0.02 mm/year while the other stations showed an increasing trend. Crop season total precipitation showed non-significant variation at most of the stations except two stations in Arizona. Seventy-five percent of the stations showed increasing trend in annual maximum temperature at the rates that varied from 0.6 to 3.1 °C per century. Air cooling varied from 0.2 to 1.0 °C per century with dominant warming phenomenon at the regional scale of the southwest United States. Average annual minimum temperature had increased at 69% of the stations at the rates that varied from 0.1 to 8 °C over the last century, while the annual temperature amplitude showed a decreasing trend at 63% of stations. Crop season maximum temperature had significant increasing trend at 68.8% of the stations at the rates varying from 0.7 to 3.5 °C per century, while the season minimum temperature had increased at 75% of the stations.

Extreme climate historical variation based on tree-ring width record in the Tianshan Mountains of northwestern China

The increasing frequency and intensity of extreme climate events have caused serious impacts on the service functions of terrestrial ecosystems and the production and life of human society in recent years. The warm nights (TN90p) variable of the 26 extreme climate indicators was the main factor controlling the tree radial growth of Schrenk spruce (Picea schrenkiana) in the Tianshan Mountains region based on the responses of tree-ring width in the 5 sample sites. Therefore, TN90p in the growth season from May to September (TN90p_5-9) during 1735-2016 was reconstructed on the basis of the time stability of the growth-climate relationships. The interpretation rate of variance of the reconstructed equation was 45.4% (R²_adj = 44.4%, F = 45.7). The reconstruction showed four relatively high TN90p_5-9 historic intervals (1747-1798, 1856-1872, 1906-1951, and 2002-2016) and four low intervals (1735-1747, 1798-1856, 1872-1900, and 1951-2002). The occurrence frequency of extreme high values was higher than that of extreme low values during the reconstruction period of 1735-2016. The extreme values of reconstruction were consistent with historical droughts and large-scale volcanic eruptions, indicating that the reconstruction series had high accuracy. Multi-window spectral periodic analysis and spatial correlation analysis revealed that TN90p_5-9 variation in the study area was affected by large-scale sea-air stress factors. In particular, the TN90p_5-9 obtained by using R/S analysis (rescaled range analysis) will continue to show an upward trend in the relative period of time in the future. This trend will lead to a further decrease in the radial growth of trees and even trigger forest death events.

Event-Based Time Distribution Patterns, Return Levels, and Their Trends of Extreme Precipitation across Indus Basin

This study presented the spatio-temporal characteristics of extreme precipitation events in the Northern Highlands of Pakistan (NHPK). Daily precipitation observations of 30 in situ meteorological stations from 1961 to 2014 were used to estimate the 11 extreme precipitation indices. Additionally, trends in time distribution patterns (TDPs) and return periods were also investigated for event based extreme precipitations (EEP). Results found that the precipitation events with an amount of 160–320 mm and with a concentration ratio of 0.8–1.0 and a duration of 4–7 consecutive days were dominant. The frequency of heavy, very heavy and extremely heavy precipitation days decreased, whereas the frequency of wet, very wet and extremely wet days increased. Most of the indices, generally, showed an increasing trend from the northeast to middle parts. The extreme precipitation events of the 20 and 50-year return period were more common in the western and central areas of NHPK. Moreover, the 20 and 50-year return levels depicted higher values (up to 420 mm) for an event duration with all daily precipitation extremes dispersed in the first half (TDP1) in the Chitral, Panjkora and Jhelum Rivers basins, whilst the maximum values (up to 700 mm) for an event duration with all daily precipitation extremes dispersed in the second half (TDP2) were observed in the eastern part of the NHPK for 20-year and eastern and south-west for 50-year, respectively.

Contribution of urbanization to the changes in extreme climate events in urban agglomerations across China

Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.

Suitability of different data sources in rainfall pattern characterization in the tropical central highlands of Kenya

Uncertainty in rainfall pattern has put rain-fed agriculture in jeopardy, even for the regions considered high rainfall potential like the Central Highlands of Kenya (CHK). The rainfall pattern in the CHK is spatially and temporally variable in terms of onset and cessation dates, frequency and occurrence of dry spells, and seasonal distribution. Appraisal of the variability is further confounded by the lack of sufficient observational data that can enable accurate characterisation of the rainfall pattern in the region. We, therefore, explored the utilisation of satellite daily rainfall estimates from the National Aeronautics and Space Administration (NASA) for rainfall pattern characterisation in the CHK. Observed daily rainfall data sourced from Kenya meteorological department were used as a reference point. The observation period was from 1997 to 2015. Rainfall in the CHK was highly variable, fairly distributed and with low intensity in all the seasons. Onset dates ranged between mid-February to mid-March and mid-August to mid-October for long rains (LR) and short rains (SR) seasons, respectively. Cessation dates ranged from late May to mid-June and mid-December to late December for the LR and SR, respectively. There was a high probability (93%) of dry spell occurrence. More research needs to be done on efficient use of the available soil moisture and on drought tolerant crop varieties to reduce the impact of drought on crop productivity. Comparison between satellite and observed rain gauge data showed close agreement at monthly scale than at daily scale, with general agreement between the two datasets. Hence, we concluded that, given the availability, accessibility, frequency of estimation and spatial resolution, satellite estimates can complement observed rain gauge data. Stakeholders in the fields of agriculture, natural resource management, environment among others, can utilise the findings of this study in planning to reduce rainfall-related risks and enhance food security.

Changes of Extreme Temperature and Its Influencing Factors in Shiyang River Basin, Northwest China

The increase in the frequency and intensity of extreme weather events around the world has led to the frequent occurrence of global disasters, which have had serious impacts on the society, economic and ecological environment, especially fragile arid areas. Based on the daily maximum temperature and daily minimum temperature data of four meteorological stations in Shiyang River Basin (SRB) from 1960 to 2015, the spatio-temporal variation characteristics of extreme temperature indices were analyzed by means of univariate linear regression analysis, Mann–Kendall test and correlation analysis. The results showed that the extreme temperatures warming indices and the minimum of daily maximum temperature (TXn) and the minimum of daily minimum temperature (TNn) of cold indices showed an increasing trend from 1960 to 2016, especially since the 1990s, where the growth rate was fast and the response to global warming was sensitive. Except TXn and TNn, other cold indices showed a decreasing trend, especially Diurnal temperature (DTR) range, which decreased rapidly, indicating that the increasing speed of daily min-temperature were greater than of daily max-temperature in SRB. In space, the change tendency rate of the warm index basically showed an obvious altitude gradient effect that decreased with the altitude, which was consistent with Frost day (FD0) and Cool nights (TN10p) in the cold index, while Ice days (ID0) and Cool days (TX10p) are opposite. The mutation of the cold indices occurred earlier than the warm indices, illustrating that the cold indices in SRB were more sensitive to global warming. The change in extreme temperatures that would have a significant impact on the vegetation and glacier permafrost in the basin was the result of the combined function of different atmospheric circulation systems, which included the Arctic polar vortex, Western Pacific subtropical high and Qinghai-tibet Plateau circulation.

Additive effects of temperature and water availability on pregnancy in a viviparous lizard

One of the greatest current threats to biodiversity is climate change. However, understanding of organismal responses to fluctuations in temperature and water availability is currently lacking, especially during fundamental life-history stages such as reproduction. To further explore how temperature and water availability impact maternal physiology and reproductive output, we used the viviparous form of the European common lizard (Zootoca vivipara) in a two-by-two factorial design manipulating both hydric and thermal conditions, for the first time. We collected blood samples and morphological measurements during early pregnancy and post-parturition to investigate how water availability, temperature and a combination of the two influence maternal phenology, morphology, physiology and reproductive output. We observed that dehydration during gestation negatively affects maternal physiological condition (lower mass gain, higher tail reserve mobilization) but has little effect on reproductive output. These effects are mainly additive to temperature regimes, with a proportional increase in maternal costs in warmer environments. Our study demonstrates the importance of considering combined effects of water and temperature when investigating organismal responses to climate changes, especially during periods crucial for species survival such as reproduction.

Changes in the Characteristics of Dry and Wet Periods in Europe (1851–2015)

This study spanning the period 1851–2015 explores the spatial and temporal characteristics of dry and wet periods in Europe as well as their variability and changes. It is based on up to 220 stations with monthly precipitation time series that have a varying data availability within the study period. The stations are classified into eight regions with similar climate characteristics. Dry and wet periods are analyzed using the decile method as well as the modified Rainfall Anomaly Index mRAI at the 3-month timescale. Spatial extent, duration, and frequency of dry and wet periods show a large multi-decadal variability resulting in comparatively small long-term trends over the entirety of Europe for the study periods 1901–2015 and 1951–2015. Nonetheless, several sub-regions show distinct changes—with opposite signals for northern and southern Europe. Spatial extent and duration of dry periods generally decreased, while wet periods show increases throughout the 20th century—particularly in Scandinavia. A simultaneous increase in the frequency of severely dry and wet years, respectively, is observed since the 1980s. This indicates that temperature increases across Europe may be connected with an increasing frequency of extremes at both sides of the probability density function of precipitation.

Climate change impacts and adaptations on health of Internally Displaced People (IDP): An exploratory study on coastal areas of Bangladesh

Every year thousands of people are being displaced in coastal areas of Bangladesh due to natural calamities associated with climate change, known as Internally Displaced Peoples (IDPs). Climate change adaptation measures play a significant role in coping with the alteration of climatic components, while various forms of barriers hinder the sustainability of adaptation. This research was conducted to understand the perception of IDPs on climate change impact on health in the coastal areas of Bangladesh, including the adaptation practices and barriers to the coping strategies. To fulfill the objective, 420 individual surveys were conducted randomly in two Sub-districts of Khulna district in Bangladesh. The findings reveal that the riverbank erosion and cyclones were the primary reasons for displacement, and the social relationships were hampered in the new places of living. Also, the temperature in summer and winter, and the rainfall intensity increased, whereas rainfall slightly decreased over the last ten years. Differences of opinion were identified about the effects of the changing climatic variables on the respondents' health between the previous and present locations. Despite practicing different adaptive strategies, the weak financial condition and a lack of access to health care information are mostly hindering the sustainability of adaptation. This research may help policymakers in taking proper initiatives to ensure sustainable adaptation practices in the coastal areas.

An extreme cold event leads to community-wide convergence in lower temperature tolerance in a lizard community

Extreme climate events are predicted to increase in frequency and severity due to contemporary climate change. Recent studies have documented the evolutionary impacts of extreme events on single species, but no studies have yet investigated whether such events can drive community-wide patterns of trait shifts. On 22 January 2020, subtropical south Florida experienced an extreme cold episode during which air temperatures dropped below the lower thermal limit of resident lizard populations. In the week immediately after the cold event, we documented decreased lower thermal limits (CTmin) of six co-occurring lizard species that vary widely in ecology, body size and thermal physiology. Although cold tolerance of these species differed significantly before the cold snap, lizards sampled immediately after had converged on the same new, lower limit of thermal tolerance. Here, we demonstrate that extreme climate events can drive substantial and synchronous community-wide trait changes and provide evidence that tropical and subtropical ectotherms-often characterized as unable to withstand rapid changes in climatic conditions-can endure climatic conditions that exceed their physiological limits. Future studies investigating the mechanisms driving these trait shifts will prove valuable in understanding the ability of ectotherm communities to mitigate climate change.

Evaluation of the Performance of CMIP6 HighResMIP on West African Precipitation

This research focuses on evaluating the High-Resolution Model Intercomparison Project (HighResMIP) simulations within the framework of the Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). We used seven of its consortiums to study how CMIP6 reproduced the West African precipitation features during the 1950–2014 historical simulation periods. The rainfall event was studied for two sub-regions of West Africa, the Sahel and the Guinea Coast. Precipitation datasets from the Climate Research Unit (CRU) TS v4.03, University of Delaware (UDEL) v5.01, and Global Precipitation Climatology Centre (GPCC) were used as observational references with the aim of accounting for uncertainty. The observed annual peak during August, which is greater than 200, 25, and 100 mm/month in the Guinea Coast, the Sahel, and West Africa as a whole, respectively, appears to be slightly underestimated by some of the models and the ensemble mean, although all the models captured the general rainfall pattern. Global climate models (GCMs) and the ensemble mean reproduced the spatial daily pattern of precipitation in the monsoon season (from June to September) over West Africa, with a high correlation coefficient exceeding 0.8 for the mean field and a relatively lower correlation coefficient for extreme events. Individual models, such as IPSL and ECMWF, tend to show high performance, but the ensemble mean appears to outperform all other models in reproducing West African precipitation features. The result from this study shows that merely improving the horizontal resolution may not remove biases from CMIP6.

Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

Analysis of Temperature Change in Uzbekistan and the Regional Atmospheric Circulation of Middle Asia during 1961–2016

Climate change and shrinking of the Aral Sea have significantly affected the region’s temperature variations. Observed interannual changes in Uzbekistan’s air temperature compared to the duration of synoptic weather types (SWT) in Middle Asia were analyzed. Nonparametric Mann–Kendall statistical test and climate trends coefficients were used to identify trend characteristics of observed temperature from 1961–2016 to the baseline period of 1961–1990. The results showed increasing temperature trends average to 1 °C in warm and cold half years over Uzbekistan. The 1991–2016 decadal temperature trend ranged from 0.25 °C/decade in the northwest to 0.52 °C/decade in the center, especially pronounced in the oasis and Aral Sea zones. There were also significant changes in the structure of regional SWT. The main difference in the structure of SWT in Middle Asia relative to the baseline period was expressed in a decrease of cold mass invasion duration from 113.4 to 76.1 days and an increase in low-gradient baric field duration from 65.8 to 134.6 days. The process of anthropogenic warming, which began in Uzbekistan in the 1960s of the twentieth century, has accelerated from the mid-1970s with a higher mean annual air temperature than the baseline period’s climate normals (1961–1990) and is associated with changes in the regional SWT over Middle Asia.

Highly selective conversion of CO2 to methanol on the CuZnO-ZrO2 solid solution with the assistance of plasma

CuZnO–ZrO₂–C was prepared by a co-precipitation method. For comparison, CuZnO–ZrO₂–PC and CuZnO–ZrO₂–CP were prepared by glow discharge plasma. The catalysts were characterized via the XRD, N₂ adsorption–desorption, TEM, SEM, EDS, XPS, CO₂-TPD and H₂-TPR techniques. The catalysts were comparatively investigated for CO₂ conversion and methanol selectivity in a fixed-bed reactor under the condition of 2 MPa, 250 °C, H₂/CO₂ = 3/1 and GHSV = 12 000 mL g⁻¹ h⁻¹. The results showed that the activities of the catalysts increased in the order of CuZnO–ZrO₂–PC > CuZnO–ZrO₂–CP > CuZnO–ZrO₂–C. Moreover, the CO₂ conversion of CuZnO–ZrO₂–C increased by 38.9% via treatment with glow discharge plasma. The results are well explained based on the CO₂-TPD and H₂-TPR characterizations of the catalysts.

CuZnO–ZrO₂–C was prepared by a co-precipitation method.

More frequent and widespread persistent compound drought and heat event observed in China

Compound drought and heat event (CDHE) causes severe impacts on agriculture, ecosystem, and human health. Based on daily maximum surface air temperature and meteorological drought composite index data in China, changing features of CDHEs in warm season from 1961 to 2018 is explored at a daily time scale based on a strict and objective definition in this study. Results reveal that CDHEs have occurred more frequently and widely in China, especially since the late 1990s. Notably, such changes are more obvious in Southwest China, eastern Northwest China, northern North China, and the coastal area of southeastern China. A prominent feature is that persistent CDHEs on a daily scale have increased significantly. To better understand climate change of compound extreme events, further studies on the physical mechanism, especially attribution analyses at a regional scale, are urgently needed.

Characterization of Spatio-Temporal Trends and Periodicity of Precipitation over Malawi during 1979–2015

Precipitation remains the key climatic parameter in sub-Saharan Africa, as it drives the economy through rain-fed agricultural production. Malawi is one of the countries most susceptible to the impacts of climate change and variability. This paper presents the characteristics of spatio-temporal trends and periodicity of precipitation in Malawi in the period from 1979 to 2015. The analysis was based on recent rain ground gauge data. In total, 31 out of 36 rainfall stations, which include some key stations from the southeast of Malawi, were selected for the study after robust homogeneity tests were applied to the datasets. Spatial distribution of annual mean precipitation showed that high amounts of rainfall are located in areas along the lake and the southeast part of Malawi. The spatial distribution of the wet season (November to April) precipitation from EOF (Empirical Orthogonal Function) analysis revealed ten wet years (1985, 1986, 1989, 1996, 1997, 1999, 2001, 2006, 2007, and 2015) and ten dry years (1981, 1983, 1987, 1990, 1992, 1994, 1995, 2005, 2011, and 2014). In general, the temporal trends analyses of seasonal (wet season) and annual precipitations both displayed slight decreasing slopes during the 37 years. The trend of precipitation per decade displayed an increase in precipitation during 1980s and 1990s, followed by a decrease in the 21st century. Furthermore, the analysis of the spatial and temporal variability and trends of rainfall showed that northern and central Malawi displayed a clearer variability than southern Malawi. Although the trends of most of the stations are not significant at 95% confidence level, the decreasing rates of rainfall in the last decade and the decreasing trends on wet season and annual scale detected by Mann–Kendall tests require closer monitoring of rainfall changes in the near future. The stations which exhibited significant trends (Naminjiwa and Dedza stations) also call for closer monitoring, since the area relies heavily on rain-fed agriculture for economic sustenance.

Effects of Urbanization on Regional Extreme-Temperature Changes in China, 1960–2016

Urbanization in China has been expanding dramatically since 1978, significantly affecting the extreme temperature changes in cities, which is a vital indicator of urban climate change. To assess urban-related effect on regional extreme-temperature changes in China, this study employed high-resolution land use data to divide meteorological stations into rural stations, suburban stations, and urban stations, and evaluated the annual and seasonal changes in extreme minimum temperature (TNN), mean temperature (Tavg) and extreme maximum temperature (TXX) at each meteorological station. The result revealed that extreme temperature indices (TNN, TXX) and Tavg increased significantly from 1960 to 2016 with varied degrees in different seasons and different regions. Extreme temperature indices in high latitudes increased more rapidly than in low latitudes; while the trends in summer are slower than in other seasons. Urbanization effects on the trends of TNN, Tavg and TXX were all statistically significant, but urbanization effects on TNN and Tavg were more significant than TXX. The urbanization effects were more significant in low altitudes, especially in North, South, Northwest and Northeast China. In North, Northwest and Northeast China, the urban-related effects on temperature increase were mainly observed in spring and winter, but in South China, the urban-related effects were more evident in summer. This study is valuable for sustainable urban planning in China.

Large-scale dynamics have greater role than thermodynamics in driving precipitation extremes over India

The changing characteristics of precipitation extremes under global warming have recently received tremendous attention, yet the mechanisms are still insufficiently understood. The present study attempts to understand these processes over India by separating the 'dynamic' and 'thermodynamic' components of precipitation extremes using a suite of observed and reanalysis datasets. The former is mainly due to changes in atmospheric motion, while the latter is driven mainly by the changes associated with atmospheric moisture content. Limited studies have attributed dynamic and thermodynamic contributions to precipitation extremes, and their primary focus has been on the horizontal atmospheric motion component of the water budget. Our study, on the other hand, implements the decomposition of vertical atmospheric motion, based on the framework proposed by Oueslati et al. (Sci Rep 9: 2859, 2019), which has often been overlooked, especially for India. With the focus on two major and recent extreme events in the Kerala and Uttarakhand regions of India, we show that the vertical atmospheric motion has a more significant contribution to the events than the horizontal atmospheric motion. Further, decomposition of the vertical atmospheric motion shows that the dynamic component overwhelms the thermodynamic component's contribution to these extreme events, which is found to be negligible. Using a threshold method to define extreme rainfall, we further extended our work to all India, and the results were consistent with those of the two considered events. Finally, we evaluate the contributions from the recently made available CMIP6 climate models, and the results are interestingly in alignment with the observations. The outcomes of this study will play a critical role in the proper prediction of rainfall extremes, whose value to climate adaptation can hardly be overemphasised.

Updated Assessment of Temperature Extremes over the Middle East–North Africa (MENA) Region from Observational and CMIP5 Data

The objective of this analysis is to provide an up-to-date observation-based assessment of the evolution of temperature extremes in the Middle East–North Africa (MENA) region and evaluate the performance of global climate model simulations of the past four decades. A list of indices of temperature extremes, based on absolute level, threshold, percentile and duration is used, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use daily near-surface air temperature (Tmax and Tmin) to derive the indices of extremes for the period 1980–2018 from: (i) re-analyses (ERA-Interim, MERRA-2) and gridded observational data (Berkeley Earth) and (ii) 18 CMIP5 model results combining historical (1950–2005) and scenario runs (2006–2018 under RCP 2.6, RCP 4.5 and RCP 8.5). The CMIP5 results show domain-wide strong, statistically significant warming, while the observation based ones are more spatially variable. The CMIP5 models capture the climatology of the hottest areas in the western parts of northern Africa and the Gulf region with the thewarmest day (TXx) > 46 °C and warmest night (TNx) > 33 °C. For these indices, the observed trends are about 0.3–0.4 °C/decade while they are 0.1–0.2 °C/decade stronger in the CMIP5 results. Overall, the modeled climate warming up to 2018, as reflected in the indices of temperature extremes is confirmed by re-analysis and observational data.

Fire as a Selection Agent for the Dissemination of Invasive Species: Case Study on the Evolution of Forest Coverage

Climate change has enhanced the occurrence of rural fires, since changes in the hydrological cycle have led to the occurrence of increasingly long and frequent periods of drought. This recurrence of rural fires in Portugal, in turn, has led to the successive elimination of vast areas traditionally occupied by native species or species of economic interest, which are being successively replaced by new species with invasive behavior. Among these, Acacia dealbata stands out for its dispersion capacity and for the area it has already occupied. In the present work, which reviews the evolution of forest cover over the last 18,000 years in the Serra da Estrela Natural Park, we intend to demonstrate that fire acts as a species selection agent and that it enhances the development of heliophile and pyrophyte species. For this purpose, an area of the municipality of Seia was selected, more specifically Casal do Rei, where the development of Acacia dealbata forests is monitored. In the end, it was concluded that, in fact, by analyzing the ages of the specimens present in these populations, fire acts as a selection agent by freeing up the space previously occupied by other species, opening the way for the growth of heliophiles and pyrophytes invasive species while enhancing their germination.

Developing a thermal stress map of Iran through modeling a combination of bioclimatic indices

Thermal stress poses significant direct and indirect risks to human health. Under climate change, both mean temperature and the frequency and intensity of extreme thermal stress events are projected to increase. Located within an arid to semi-arid region, Iran is anticipated to experience particularly intense temperature and humidity changes under climate change, potentially heightening the public health challenges associated with thermal stress. To facilitate improved adaptation to these thermal threats, accurate high spatial resolution thermal heat stress risk maps are important. This study combines various climate indices to produce such a thermal stress risk map for the reference period 1980-2010, with RCP4.5 projections for the period 2020-2049. Although the results of the various indices are statistically significantly correlated, each index returned a remarkably different spatial distribution and risk classification. Therefore, a fuzzy approach was followed through a geographical information system (GIS) to combine the results of the five bioclimatic indices and prepare a final thermal stress risk map. Based on the RCP4.5 scenario, the results indicate a notable 24.5% reduction in the areas susceptible to thermal stress at the high-risk and very high-risk levels, compared with the reference period. The lowest projected risk is for the central parts of Iran, while the southern and northern coasts of Iran were the zones of the highest risk, for which adaptation responses are most necessary.

Understanding the impact of sub-seasonal meteorological variability on corn yield in the U.S. Corn Belt

Rain-fed corn system has varied optimal environmental requirements by growth phases and regions. Understanding spatiotemporal characteristics of such requirements are important to ensure food security. To capture the stage-variant growing requirements, we develop and compare statistical models with various spatial and temporal resolutions to quantify the relationships between corn yield and meteorological factors. Multilinear regression models are trained using cross-sectional datasets pooled at three magnitudes (state, district, county) with temperature and precipitation related predictors according to three temporal resolutions (growing season, fixed month, growing phase). The models are applied to the U.S. Corn Belt for the time period of 1981-2016. The results show that average corn yield variation explained by meteorological factors can be improved to 50.2% at the agricultural district scale with growth phase resolution from ~30% at the state-level with growing season resolution. The results reveal that corn yield is most sensitive to extreme heat stress during the grain filling phase. From a spatial perspective, the northern counties in the U.S. Corn Belt are less limited by precipitation resources but are more vulnerable to extreme heat. The spatiotemporal explicit statistic modeling approach quantifies the impact and adaptation potential of changing the planting date for production. Appropriate adaptions by changing plant dates can increase the potential of corn production by 0.87 million Mg year^-1 in the Corn Belt.

Examining the impacts of future land use/land cover changes on climate in Punjab province, Pakistan: implications for environmental sustainability and economic growth

Land use and land cover changes (LULCC) significantly affect the climate at regional and global levels through different biogeophysical and biogeochemical processes. However, the effects of biogeophysical aspects of LULCC on climate have been often ignored, which may overestimate the biogeochemical effects on climate change. Thus, understanding the biogeophysical influence of land use changes on climate change in future potential scenarios is crucial. Therefore, it is necessary to identify the mechanism and land use change impacts on future climate under different scenarios through changes in underlying surface and surface energy balance. In order to fill this research gap, three simulations are performed by Weather Research Forecasting (WRF) model for the year 2010-2030 under Business-As-Usual (BAU) scenario, Rapid Economic Growth (REG) scenario, and Coordinated Environmental Sustainability (CES) scenario to evaluate the influence of future LULCC on temperature projections for the Punjab province in Pakistan. Results show that land use conversions under three scenarios induce overall climate cooling in the region. The decrease in annual average temperature in CES scenario (- 0.02 °C) is slightly greater than that in BAU and REG scenarios (- 0.01 °C). The responses of temperature to future LULCC vary in different months in all scenarios, with greater responses in warmer months, causing climate cooling. In each scenario, the response of temperature is found to be sensitive to different land transitions. The findings of the study can be a reference for policy makers, researchers, and development practitioners in their pursuit to understand the effects of land use change on climate.

Using the CHIRPS Dataset to Investigate Historical Changes in Precipitation Extremes in West Africa

This study aims to provide improved knowledge and evidence on current (1986–2015) climate variation based on six rainfall indices over five West African countries (Senegal, Niger, Burkina Faso, Ivory Coast, and Benin) using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset. On average, precipitation has increased over the central Sahel and the western Sahel. This increase is associated with increase in the number of rainy days, longer wet spells and shorter dry spells. Over the Guinea Coast, the slight increase in precipitation is associated with an increase in the intensity of rainfall with a shorter duration of wet spells. However, these mean changes in precipitation are not all statistically significant and uniform within a country. While previous studies are focused on regional and sub-regional scales, this study contributes to deliver a climate information at a country level that is more relevant for decision making and for policy makers, and to document climate-related risks within a country to feed impact studies in key sectors of the development, such as agriculture and water resources.

Increased extreme hourly precipitation over China's rice paddies from 1961 to 2012

Rice yield have been affected by the increased extreme precipitation events in recent decades. Yet, the spatio-temporal patterns of extreme precipitation by rice type and phenology remain elusive. Here, we investigate the characteristics of four extreme precipitation indices across China's rice paddy and their potential association with crop yields, by using hourly precipitation data from 1,215 stations and rice phenology observations from 45 sub-regions. The data indicate that hourly extreme precipitation have significantly increased in 1961-2012 for single rice and early rice in China but not for late rice. Rice were mainly exposed to extreme precipitation from transplantation to flowering stages. The frequency and proportion of extreme precipitation were significantly increased by 2.0-4.7% and 2.3-2.9% per decade, respectively, mainly in south China and Yangtze River Basin. The precipitation intensity and maximum hourly precipitation were increased by 0.7-1.1% and 0.9-2.8% per decade, respectively, mainly in central China and southeast coastal area. These extreme precipitation indices played a role as important as accumulated precipitation and mean temperature on the interannual variability of rice yields, regardless of rice types. Our results also highlight the urgencies to uncover the underlying mechanisms of extreme precipitation on rice growth, which in turn strengthens the predictability of crop models.

How Unusual Were June 2019 Temperatures in the Context of European Climatology?

The aims of the study were to assess the severity of temperature conditions in Europe, in June 2019, using a newly developed extremes index, as well as to evaluate circulation conditions that favored the occurrence of extremely hot days in June 2019, as seen over the long term. The main focus of this work was on two European regions particularly affected by high temperatures in June 2019, namely Central Europe and Iberia. To comprehensively characterize heat events in terms of their spatial extent and intensity, we proposed the extremity index (EI) and used it to compare hot days occurring in areas of different sizes and with different climatic conditions. The role of atmospheric circulation in the occurrence of hot days was evaluated using the Grosswetterlagen (GWL) circulation types catalog, as well as composite maps created with the bootstrap resampling technique. Our results reveal that June 2019 was unusually hot, and in terms of the magnitude of the anomaly, it has no analogue in the 70-year-long temperature record for Europe. However, the properties of heat events in the two considered regions were substantially different. The occurrence of hot days in June 2019, in Europe, was mainly associated with the GWL types forcing advection from the southern sector and co-occurrence of high-pressure systems which was significantly proven by the results of bootstrap resampling. In terms of the applicability of the new approach, the EI proved to be a useful tool for the analysis and evaluation of the severity of hot days based on their intensity and spatial range.

Climate change patterns in precipitation over Spain using CORDEX projections for 2021-2050

This work presents an analysis of the climate change scenarios in some extreme precipitation indices over Spain using simulations from the EURO-CORDEX project. Change projections of precipitation are evaluated for the near future (2021-2050) relatively to a reference past climate (1971-2000). Projections of annual precipitation show a general decrease in almost the whole region except over the central area where positive changes are detected due to a significant increase in winter. For consecutive wet days, an annual decrease is also projected over the country attributable to a significant decrease mainly observed in spring and to a lesser extent in winter. On the other hand, consecutive dry days are projected to be higher overall as a result of significant increases in spring, summer and autumn. Positive changes are also projected for the maximum daily precipitation during winter and autumn.

Identification of climate variables dominating streamflow generation and quantification of streamflow decline in the Loess Plateau, China

Precipitation-extremes-driven floods, which compose an important proportion of streamflow but cause severe adverse impacts in the Loess Plateau of China, urged the progressive implementation of ecological restoration (ER) strategies in the Loess Plateau (LP) of China. Knowledge of the linkage between climate variables (especially precipitation extremes) and streamflow generation become more essential for advanced catchment management as ER and climate variability have resulted in reduced streamflow and freshwater stress. Here, a partial least squares regression (PLSR) approach was used to investigate this issue at 16 main catchments of the LP over a reference period (1961-1979). Then, we quantified streamflow decline during the "Integrated Soil and Water Conservation" (1980-1999) and the "Grain for Grain" (2000-2015) strategies by PLSR modeling. We found that the dominant climatic variables controlling annual streamflow include heavy precipitation amount and heavy precipitation days, maximum precipitation event amount, number of consecutive wet days, annual total precipitation (daily precipitation ≥1 mm), and effective precipitation amount (daily precipitation ≥5 mm). Further, the effect of precipitation extremes on streamflow generation is stronger in drier catchments. The impacts of precipitation extremes on streamflow generation can be strengthened by agricultural cultivation and weakened by revegetation (especially reforestation). Overall, we found that climate-driven annual streamflow decreased by 7.5 mm during 1980-1999 and by 5.6 mm during 2000-2015, in comparison to 1961-1979. The dominant cause of streamflow reduction was ER, with the contribution increasing from 59% in 1980-1999 to 82% in 2000-2015. The PLSR approach enables the identification of linkages between climate variables and streamflow generation, and the prediction of climate-driven streamflow. This study yields a greater understanding of the influences of climate variability and ER on streamflow change, and is helpful to identify hydroclimatological trends and projections.

Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island

Agricultural management practices are responsible for almost two-thirds of the variations in potato tuber yield. In order to answer the research question about the remaining variability of the tuber yield, we hypothesized that climate extremes partly explain the missing component of variations of the tuber yield. Therefore, this research attempts to bridge this knowledge gap in order to generate a knowledge base for future strategies. A climate extreme dataset of the Prince Edward Island (PEI) was computed by averaging the data of five meteorological stations. In detail, changing patterns of 20 climate extreme indices were computed with ClimPACT2 software for 30 years (1989-2018) data of PEI. Statistical significance of the trends and their slope values were determined with the Mann-Kendall test and Sen’s slope estimates, respectively. Average of daily mean temperature (TMm), mean daily minimum temperature (TNm) and the occurrence of continuous dry days (CDD), significantly increased by 0.77 °C, 1.17 °C and 3.33 days., respectively, during the potato growing seasons (May-October) of the past three decades. For this period daily temperature range (DTR), frost days (FD), cold days (TX10p), cold nights (TN10p) and warmest days (TXx) showed decreasing trends of −1.01 °C, −3.75 days, −5.67 days, −11.40 nights, and −2.00 days, respectively. The principal component analysis showed that DTR, TXx, CDD, and TNm were the main factors affecting seasonal variations of tuber yield. The multiple regression model attributed ~39% of tuber yield variance to DTR, TXx, CDD, and TNm. However, these indices explained individually 21%, 19%, 16%, and 4% variation to the tuber yield, respectively. The remaining variation in the tuber yield explained by other yield affecting factors. The information generated from this study can be used for future planning about agricultural management strategies in the Island, for example, the provision of water resources for supplemental irrigation of crops during dry months.

Changes in Precipitation Extremes across Vietnam and Its Relationships with Teleconnection Patterns of the Northern Hemisphere

Understanding changes in precipitation extremes is critical for designing mitigation measures for the potential implications of a warming climate. This study assessed changes in the magnitude and frequency of precipitation extremes over Vietnam using high-quality gridded daily precipitation observations from 1980 to 2010. The annual maxima precipitation was analyzed to detect historical changes in the magnitude of precipitation extremes, while the number of heavy precipitation events, defined using the peak-over-threshold approach, was used to assess changes in the frequency of precipitation extremes. We found a strong signal of changes in the frequency of heavy precipitation, with 28.3% of Vietnam’s landmass exhibiting significant increasing trends. The magnitude of annual maxima precipitation shows a mixed pattern of changes, with less than 10% of Vietnam’s landmass exhibiting significant (both increasing and decreasing) trends. To identify possible mechanisms driving changes in precipitation, we assessed the relationship between inter-annual variations in precipitation extremes and climate variability represented by the teleconnection patterns of the Northern Hemisphere. Using five climate indices, we found that teleconnections across the Indian and Pacific Oceans have implied large control over the characteristics of precipitation extremes across Vietnam, with up to 30% of Vietnam’s landmass exhibiting a significant relationship.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

Decoupling of impact factors reveals the response of German winter wheat yields to climatic changes

Yield development of agricultural crops over time is not merely the result of genetic and agronomic factors, but also the outcome of a complex interaction between climatic and site-specific soil conditions. However, the influence of past climatic changes on yield trends remains unclear, particularly under consideration of different soil conditions. In this study, we determine the effects of single agrometeorological factors on the evolution of German winter wheat yields between 1958 and 2015 from 298 published nitrogen (N)-fertilization experiments. For this purpose, we separate climatic from genetic and agronomic yield effects using linear mixed effect models and estimate the climatic influence based on a coefficient of determination for these models. We found earlier occurrence of wheat growth stages, and shortened development phases except for the phase of stem elongation. Agrometeorological factors are defined as climate covariates related to the growth of winter wheat. Our results indicate a general and strong effect of agroclimatic changes on yield development, in particular due to increasing mean temperatures and heat stress events during the grain-filling period. Except for heat stress days with more than 31°C, yields at sites with higher yield potential were less prone to adverse weather effects than at sites with lower yield potential. Our data furthermore reveal that a potential yield levelling, as found for many West-European countries, predominantly occurred at sites with relatively low yield potential and about one decade earlier (mid-1980s) compared to averaged yield data for the whole of Germany. Interestingly, effects related to high precipitation events were less relevant than temperature-related effects and became relevant particularly during the vegetative growth phase. Overall, this study emphasizes the sensitivity of yield productivity to past climatic conditions, under consideration of regional differences, and underlines the necessity of finding adaptation strategies for food production under ongoing and expected climate change.

CMIP5-Based Spatiotemporal Changes of Extreme Temperature Events during 2021–2100 in Mainland China

The increasing number of extreme climate events is having a great impact on the terrestrial ecosystem. In this study, we applied a Taylor diagram to evaluate the 7 extreme temperature indices (ETI) of 12 models and the multi-model ensemble (MME) mean from phase 5 of the Coupled Model Intercomparison Project (CMIP5) during 1961–2005, and found that the MME has the best simulation effect. Warm indices and warm duration indices increase slowly, rapidly, and extremely under the representative concentration pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 scenarios, respectively. In contrast, the decrease in cold indices and cold duration indices are slow, rapid and extreme, respectively. The ETI from 2021–2100 under the RCP2.6 and RCP4.5 scenarios have primary periods ranging from 1–16 years. Under the RCP2.6 and RCP4.5 scenarios, the changes of warm indices are relatively largest in the basin of the central, and southeastern, while, under the RCP8.5 scenario, the changes are relatively significant, except for basin of northeast. The cold indices have the most significant decreasing trend in the Tibetan Plateau and its surrounding areas, under the three RCP scenarios. The findings from this study can provide reference for the risk management and prevention of climate disasters in the context of climate change in mainland China.

Changes in Extreme Precipitation across 30 Global River Basins

Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960–2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960–1984, wider range of the three indices over SDR and ADR were detected for 1985–2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network-Daily (GHCN-D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN-D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN-D based SWAT-simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge-based measurements can improve hydrologic model performance, especially for extreme events.

Spatio-Temporal Trends of Monthly and Annual Precipitation in Aguascalientes, Mexico

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.