Sensitivity of seasonal migration to climatic variability in central India

To adapt forest ecosystems and forest management to climate change, it is essential to know which forest regions and which tree species are resilient to climate variability and which ones are possibly affected most by past and anticipated future changes. In this contribution, for the main forest regions of Türkiye and six tree species, recent climate variability and trends were quantified and statistically correlated to record tree defoliation and vitality. Climate variables considered are maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and total precipitation (Prcp), which are compared to forest health responses recorded as part of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) on 277 plots across forests along the Black Sea and Mediterranean regions. In addition, long-term data on satellite measurements of the normalized difference vegetation index (NDVI) were extracted for the same 277 plots for the period 2008–2020. Firstly, 30 years (1991–2020) of reanalysis of climate variables from ECMWF were extracted for all plots; secondly, individual correlations and cross-correlations of climate variables and tree health and vitality were computed for the period 2008–2020 (significance level of 95%) for the four most dominant species from the Black Sea forests (F. orientalis, Q. cerris, P. sylvestris, P. orientalis) and two species from Mediterranean forests (P. brutia and C. libani). Temperature showed a stronger effect on most species than precipitation. Finally, time-lagged correlations were analyzed for seven-time lags (significance level of 95%) to evaluate legacy effect. The analysis revealed that different tree species from the two regions show different responses to climate variables. Species in the Mediterranean region are more resistant to droughts and climatic variations. Legacy effects of defoliation and NDVI have lasted for at least 2 years.

The Humboldt current system (HCS) sustains the highest global fishing catch for individual species. It is susceptible to interannual and decadal climate variability, which cause species-, community-, and ecosystem-level changes. Therefore, systematically exploring changes in the fishery ecosystem structure driven by climate variability is beneficial for fishery management in the region. In this study, a combination of large-scale climate, regional environmental, and functional groups catch data was used to detect regime shifts in the fishery ecosystem structure within the HCS and to investigate the possible impact mechanisms of climate variability. The results indicated that obvious decadal changes in the fishery ecosystem structure within the HCS align with inferred regime shifts in the early to mid-1970s, mid-1980s, and late 1990s. These shifts corresponded well to climate and regional environment regime shifts during these periods. Among the climate and environmental variables studied, the first and third principal components of climate index and the first principal component of regional environmental variables showed higher ecological importance for fishery ecosystem structure variations within the HCS. This suggest that fluctuations in the Aleutian Low and El Niño–Southern Oscillation significantly affected the regional environment, characterized by heat and wind speed, and consequently induced alterations in the fishery ecosystem structure. This study contributes to holistic understanding of climate-driven changes in the fishery ecosystem structure within the HCS, providing a robust foundation for ecosystem-based fisheries management.

Successful management of the water resources directly depends on our understanding of the heterogeneity of changing climate and consequent response of annual and seasonal streamflows in different climatic regions. This study was undertaken to quantify the spatial and temporal variability of different climatic variables and their subsequent impacts on streamflows of river Jhelum and its tributaries during last more than three decades. Mann–Kendall trend statistic and Sen’s slope estimator test were used for assessing the trends and variability in climatic and streamflow variables. Data obtained from 11 hydrometric and 6 meteorological stations for different time intervals were used for analysis. The results pointed toward various significant trends in both hydrological and meteorological stations. Kashmir Himalayas witnessed rise in mean maximum temperature (+ 0.05 °C/year) and mean minimum temperature (+ 0.01 °C/year) with substantial reduction in precipitation (− 4.2 mm/year) from 1980 to 2015. Analysis of the streamflow trends revealed significant decreasing trends in all the hydrometric stations with the highest decrease in spring and summer seasons. Furthermore, the correlation analysis revealed a significant negative correlation between increase in temperature and streamflow, while strong positive correlation was realized with precipitation at both annual and seasonal scales.

Climate variability is one of the major factors affecting the supply of ecosystem services and the well-being of people who rely on them. Despite the substantial effects of climate variability on ecosystem goods and services, empirical researches on these effects are generally lacking. Thus, this study examines the spatiotemporal impacts of climate variability on selected ecosystem services in Maze National Park and its surroundings, in southwestern Ethiopia. We conducted climate trend and variability analysis by using the Mann-Kendall (MK) trend test, Sen's slope estimator, and innovative trend analysis (ITA). Relationships among ecosystem services and climate variables were evaluated using Pearson's correlation coefficient (r), while partial correlation was used to evaluate the relationship among key ecosystem services and potential evapotranspiration (PET). The MK tests show a decreasing trend for both mean annual and main rainy season rainfall, with Sen's slope (β) = -0.721 and β = -0.1.23, respectively. Whereas, the ITA method depicted a significant increase in the second rainy season rainfall (Slope(s) = 1.487), and the mean annual (s = 0.042), maximum (s = 0.024), and minimum (s = 0.060) temperature. Spatial correlations revealed significant positive relationships between ecosystem services and the mean annual rainfall and Normalized Difference Vegetation Index (NDVI), while negative correlations with the mean annual temperature. Additionally, temporal correlations highlighted positive relationships among key ecosystem services and the main rainy season rainfall. The maximum and minimum temperatures and ecosystem services were negatively correlated; whereas, there was strong negative correlations between annual (r = -0.929), main rainy season (r = -0.990), and second rainy season (r = -0.814) PET and food production. Thus, understanding the spatiotemporal variability of climate and the resulting impacts on ecosystem services helps decision-makers design ecosystem conservation and restoration strategies to increase the potential of the ecosystems to adapt to and mitigate the impacts of climate variability.

There is a significant lack of historical climate data in the Southern Hemisphere compared to the northern latitudes. To address this data scarcity and to improve understanding of regional climate variability, historical instrumental observations were recovered for southeastern Australian (SEA) for the 1788–1859 period. Instrumental observations of temperature, atmospheric pressure, rainfall and raindays were rescued from 39 archival sources, and examined to identify observer biases and inhomogeneities. The rescued data provide continuous information on SEA climate variability from 1826 to 1859, with short periods of observations identified between 1788–1791, 1803–1805 and 1821–1824. Quality control and homogenization of each data source indicates that the historical observations successfully capture regional interannual climate variability. The historical records exhibit high correlations between neighbouring observations and related climate variables. The instrumental observations also display very good agreement with documentary climate reconstructions, further verifying their quality. As an example of how this new historical dataset may be used, regional averages of the observations were calculated to estimate interannual climate variability across SEA from 1826 to 1859. Prolonged dry conditions were identified in various parts of the region during 1837–1843 and 1845–1852, while wet conditions were noted from 1836 to 1838, primarily in southern SEA. Anomalously cold periods were also identified in 1835–1836 and 1848–1849, in general agreement with temperature reconstructions from other regions of the Southern Hemisphere. This new dataset provides a valuable source of subdaily to monthly information on SEA climate variability for future climate analysis, palaeoclimate reconstruction verification and historical studies.

The study assessed the changes in climatic variables in Hadeja Nguru wetland. Parameters evaluated includes; variation in climatic variables over a 40-year window (1979-2019) Data on climatic variables on daily basis were obtained for a period 40years and Satellite imageries of the study area were officially downloaded from the United State Geological Survey website. Paired sample T-test and chi-square test of association were used in assessing the variation in climatic factors. Result showed that indicated sparse distribution as a major effect of climate change. Decreased production in crops, land shade, damaged of harvested crops, increase in insect population, shortening the time of germinating seeds were thought to be the consequences of reduction in rainfall, excessive rainfall, irregular rain pattern and increase in temperatures respectively, Food scarcity, reduction in income due to depletion of crops were also pinpointed as impacts of climate change. Information on climate change and its impact were obtained majorly from radio, while major mitigating strategy for changing climatic variables. Maximum and minimum temperatures, precipitation, wind speed, relative humidity and solar intensity between 1979 and 2019 were 40.07℃ and 36.46℃, 1180.36mm and 58.18mm, 2.97m/s and 2.29m/s, 0.33g/kg and 0.22g/kg and 23.68w/m2 and 20.70w/m2 respectively. There was also a significant Chi-square value (33.481a) between respondents’ effect of climate change and increase in farm sizes. It is therefore concluded that there has been fluctuations in climatic variables. There is the need to put in place right policies to protect and preserve wetlands to enhance their sustainability and resilience to climatic changes and variability.

The study assessed the changes in climatic variables in Hadeja Nguru wetland. Parameters evaluated includes; variations in climatic variables over a 40-year window (1979-2019) Data on climatic variables on daily basis were obtained for a period 40years and Satellite imageries of the study area were officially downloaded from the United State Geological Survey website. Paired sample T-test and chi-square test of association were used in assessing the variation in climatic factors. Result showed that indicated sparse distribution as a major effect of climate change. Decreased in crops production, land shade, destruction of harvested crops, increase in insect population and shortened seed germination period were thought to be the consequences of reduction in rainfall, excessive rainfall, irregular rainfall pattern and increase in temperatures, Food scarcity, reduction in income due to depletion of crops were also pinpointed as impacts of climate change.. Maximum and minimum temperatures, precipitation, wind speed, relative humidity and solar intensity between 1979 and 2019 were 40.07 and 36.46, 1180.36mm and 58.18mm, 2.97m/s and 2.29m/s, 0.33g/kg and 0.22g/kg and 23.68w/m2 and 20.70w/m2 respectively. There was also a significant Chi-square value (33.481a) between respondents’ effect of climate change and increase in farm sizes. The paper concluded that there were fluctuations in climatic variables. There is the need to put in place right policies to protect and preserve wetlands to enhance their sustainability and resilience to climatic changes and variability.

<p> <span><span>Tropical Africa has been experiencing a long term drying trend for the last two decades. Climate change and variability has an influence on rain-fed agriculture under the Tropics. Many studies have investigated on the role of climate change and variability on crop yields, but with a limited number of predictors. We use detailed gridded crop statistics time series data to examine how recent climate inter-annual variability led to variations in maize yields. The added-value of this study is, that it integrates for the first time different sets of variables on different spatial scales, 107 in total: local, regional and global. A cross-validated model output statistics (MOS) approach is applied to choose physically motivated predictors. Both climate variables and maize yields were de-trended. The results revealed that inter-annual climate variability accounts for globally more than 35 percent of the observed maize variability in Tropical Africa. Our study uniquely illustrates spatial patterns in the relationship between climate variability and maize yield variability, highlighting where variations in different group of predictors interact and explain maize yield variability. Overall, temperature and precipitation principal component variables are preferably selected by the model. The next step of the study will consist of using the MOS equation to forecast future maize yield changes based on climate model output. The implication of the study is that, it will generate policy interventions towards buffering future crop production from climate variability.</span></span></p>

The role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial, and the effect of climatic variability, in particular, has largely been ignored. For instance, it was recently revealed that the proposed link between climate change and widespread amphibian declines, putatively caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), was tenuous because it was based on a temporally confounded correlation. Here we provide temporally unconfounded evidence that global El Niño climatic events drive widespread amphibian losses in genus Atelopus via increased regional temperature variability, which can reduce amphibian defenses against pathogens. Of 26 climate variables tested, only factors associated with temperature variability could account for the spatiotemporal patterns of declines thought to be associated with Bd. Climatic predictors of declines became significant only after controlling for a pattern consistent with epidemic spread (by temporally detrending the data). This presumed spread accounted for 59% of the temporal variation in amphibian losses, whereas El Niño accounted for 59% of the remaining variation. Hence, we could account for 83% of the variation in declines with these two variables alone. Given that global climate change seems to increase temperature variability, extreme climatic events, and the strength of Central Pacific El Niño episodes, climate change might exacerbate worldwide enigmatic declines of amphibians, presumably by increasing susceptibility to disease. These results suggest that changes to temperature variability associated with climate change might be as significant to biodiversity losses and disease emergence as changes to mean temperature.

The persistent rise in food prices amid worsening climate variability presents a critical development challenge in Nigeria and across Sub-Saharan Africa. Despite increasing concern, few studies have provided long-term, integrated assessments of how climatic changes, particularly shifts in temperature and rainfall, affect staple food prices. Existing literature often focuses on short-term correlations or excludes trend-based growth modelling, leaving a gap in understanding the structural co-evolution of climate and food systems. This study fills that gap by empirically analysing the trends and growth patterns in climate variables and food prices in Nigeria from 1991 to 2024. Using secondary data, the study employs descriptive statistics, exponential growth models, and quadratic time trend functions to evaluate the behaviour of key climate variables and selected staple food prices. The findings reveal a significant and accelerating increase in both minimum and maximum temperatures across the study period, while rainfall exhibits a mild negative trend with increasing interannual variability. Food prices show consistent upward trends, with all four staples experiencing statistically significant growth rates and positive acceleration, especially after 2015. These results suggest a co-evolving dynamic between climate stress and food market volatility, highlighting the vulnerability of Nigeria’s rain-fed agriculture and fragmented food distribution systems. The study concludes that climate variability contributes to rising food prices and market instability, with far-reaching implications for food security and rural livelihoods. It recommends targeted investments in climate-resilient agriculture, market infrastructure, and early warning systems to mitigate the compound risks of environmental and economic shocks.

Climate variability and change in arid regions are important factors in controlling emission, frequency and movement of dust storms. This study provides robust statistical methods including univariate Mann‐Kendall block bootstrapping method and three bivariate trend assessment methods, Covariance Inversion Test, Covariance Sum Test and The Covariance Eigenvalue to detect trends in dust storm frequency across arid regions of Iran in relation to climate variability and trend in recent decades. In this regard, the annual number of dust storms from 25 stations in central arid and semi‐arid regions of Iran were selected. In addition, five major climatic variables including annual rainfall, annual maximum and average wind speed, annual maximum and average temperature were also collected. The univariate trend test indicates both increasing and decreasing trend in dust storm frequency and climate variables. The bivariate trend test shows a strong and statistically significant relationship between trend of climate variables and dust storm frequency for most of the stations across the region. Among climate variables, rainfall change has an inverse impact on dust storm frequency while wind speed and temperature have direct covariance structure with dust storm frequency. The wind speed also seems to be the most effective climate driver on dust storm frequency in arid regions of Iran, followed by temperature. The results also shows that local conditions that are not considered in this study may also play a significant role in dust storm emission in some parts of the region.

We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heat-waves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.

PurposeThe purpose of this study is to analyze the seasonal spatiotemporal climate variability in the Borena zone of Ethiopia and its effects on agriculture and livestock production. By examining these climate variables in relation to global sea surface temperatures (SST) and atmospheric pressure systems, the study seeks to understand the underlying mechanisms driving local climate variability. Furthermore, it assesses how these climate variations impact crop yields, particularly wheat and livestock production, providing valuable insights for developing effective adaptation strategies and policies to enhance food security and economic stability in the region.Design/methodology/approachThe design and methodology of this study involve a multifaceted approach to analyzing seasonal spatiotemporal climate variability in the Borena zone of Ethiopia. The research uses advanced statistical techniques, including rotated empirical orthogonal function (EOF) and rotated principal component analysis (RPCA), to identify and quantify significant patterns in seasonal rainfall, temperature and drought indices over the period from 1981 to 2022. These methods are used to reveal the spatiotemporal variations and trends in climate variables. To understand the causal mechanisms behind these variations, the study correlates seasonal rainfall data with global SST and examines atmospheric pressure systems and wind vectors. In addition, the impact of climate variability on agricultural and livestock production is assessed by linking observed climate patterns with changes in crop yields, particularly wheat and livestock productivity. This comprehensive approach integrates statistical analysis with environmental and agricultural data to provide a detailed understanding of climate dynamics and their practical implications.FindingsThe findings of this study reveal significant seasonal spatiotemporal climate variability in the Borena zone of Ethiopia, characterized by notable patterns and trends in rainfall, temperature and drought indices from 1981 to 2022. The analysis identified that over 84% of the annual rainfall occurs during the March to May (MAM) and September to November (SON) seasons, with MAM contributing approximately 53% and SON over 31%, highlighting these as the primary rainfall periods. Significant spatiotemporal variations were observed, with northwestern (35.4%), southern (34.9%) and northeastern (19.3%) are dominant variability parts of the zone during MAM season, similarly southeastern (48.7%), and northcentral (37.8%) are dominant variability parts of the zone during SON season. Trends indicating that certain subregions experience more pronounced changes in climate variables in both seasons. Correlation with global SST and an examination of atmospheric pressure systems elucidated the mechanisms driving these variations, with significant correlation with the southern and central part of Indian Ocean. This study also found that fluctuations in climate variables significantly impact crop production, particularly wheat and livestock productivity in the region, underscoring the need for adaptive strategies to mitigate adverse effects on agriculture and food security.Research limitations/implicationsThe implications of this study highlight the need for robust adaptation strategies to mitigate the effects of climate variability. Detailed research on seasonal climate patterns and the specific behaviors of livestock and crops is essential. Gaining a thorough understanding of these dynamics is critical for developing resilient adaptation strategies tailored to the unique ecological and economic context of the Borana zone. Future research should focus on seasonal climate variations and their implications to guide sustainable development and livelihood adjustments in the region.Originality/valueThis study offers significant originality and value by providing a detailed analysis of seasonal spatiotemporal climate variability in the Borena zone of Ethiopia, using advanced statistical techniques such as rotated EOF and RPCA. By integrating these methods with global SST data and atmospheric pressure systems, the research delivers a nuanced understanding of how global climatic factors influence local weather patterns. The study’s novel approach not only identifies key trends and patterns in climate variables over an extensive historical period but also links these findings to practical outcomes in crop and livestock production. This connection is crucial for developing targeted adaptation strategies and policies, thereby offering actionable insights for enhancing agricultural practices and food security in the region. The originality of this work lies in its comprehensive analysis and practical relevance, making it a valuable contribution to both climate science and regional agricultural planning.

Using non-systematic surveys to investigate effects of regional climate variability on Australasian gannets in the Hauraki Gulf, New Zealand

In-depth knowledge of smallholder farmers’ perception of changing climate variables such as recurrent and protracted droughts, late onset of rainfall, early cessation of rainfall and their coping adaptation strategies are very significant in designing climate resilient agriculture among smallholder food crop farmers in sub-Saharan Africa (SSA). This paper examines smallholder farmers’ perceptions of climate variability vis-á-vis meteorological and satellite remote sensing data and their implications for climate smart agriculture technologies. Integration of meteorological, satellite remote sensing and farm-level data were used. Multistage sampling procedure was used to select four towns, eight communities and 398 smallholder food crop farmers. Spearmans’ rank correlation coefficient and Standardized Precipitation Index were used to assess the distribution of climate variables. In addition, three vegetation drought characteristic indices, Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Water Supply Vegetation Index (WSVI) were used to examine drought conditions within the basin. The results indicated that smallholder farmers in the Offin river basin perceived recurrent and prolonged droughts, rising temperatures, late onset of rainfall, early cessation of rainfall, increasing dry spells, reduction in the length of rainfall season and shorten cropping season as a main indicators of climate variability. The findings further revealed that farmers’ perceptions on climate variability strongly agrees with meteorological and satellite remote sensing data which not only demonstrated rising temperature and frequent and prolonged droughts but also late onset and early cessation of rainfall and reduction in growing season rainfall. Smallholder food crop farmers in the Offin river basin have a high awareness of variation in climate condition and have taken coping strategies to reduce the effects of climate change and climate variability. Smallholder food crop farmers in the basin have also adopted climate smart agriculture technologies such as crop management techniques, integrated soil and nutrient management practices, tillage and residue management, small scale irrigation systems, inland valleys cropping and renewable energy systems to increase agricultural productivity and build resilience to climate variability. The policy implication is that, smallholder food crop farmers’ knowledge on climate variability should be considered as a practical input in designing and planning climate variability coping adaptation and mitigation strategies.