Climate Variability Change Research Articles

Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data

Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model’s performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model’s performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate.

AN ECONOMIC ANALYSIS OF WHEAT PRODUCTION IN THE LIGHT OF CLIMATE CHANGE IN INDIA

India is the second-largest producer of wheat in the world. Uttar Pradesh, Punjab, Haryana, Madhya Pradesh, Rajasthan, Bihar, and Gujarat are the major wheat-producing states. Out of all of these states, Uttar Pradesh produces the larger quantity of wheat. Wheat is exported by India to Bangladesh, Sri Lanka, the United Arab Emirates, Yemen, the Philippines, and Indonesia. This study aims to analyze the effects of climate change and other non-climatic factors on wheat production in India between 1991 and 2021. The researcher used climate and non-climate change variables: mean, maximum, minimum temperature, rainfall, production, net MSP, and cultivation area. This work tested long-run cointegration using an Auto-Regressive Distributed Lag (ARDL) bound test cointegration technique to evaluate short-run relationships among modeled variables. CUSUM and CUSUMQ tests assure model reliability and validity. The study’s findings demonstrated that the cultivated area has a major role in enhancing wheat output in the short and long term, while precipitation favors wheat production. In India, a 1% increase in cultivation area can increase 1.3% in wheat production in the long run. In the short run, a 1% increase in cultivation area increased 0.58% wheat production in India. Furthermore, although the temperature has little immediate effect, it is crucial for boosting wheat output over the long term.

Bioclimatic influence on water chemistry and dissolved organic matter in shallow temperate lakes of Andean Patagonia: A gradient approach

Abstract We addressed the influence of bioclimatic variables (precipitation, temperature and vegetation) on physicochemical properties, carbon (C) and nutrients dynamics in shallow temperate lakes in the northern Patagonian Andes. Four shallow lakes (mean depth < 15 m) located along the Andean Patagonian bioclimatic gradient, and characterised by a west‐to‐east decrease in precipitation, increase in temperature and changes in vegetation type, were studied during wet and dry periods. Physicochemical variables, total nitrogen and phosphorus (TN and TP) and particulate and dissolved organic matter (POM and DOM) were analysed. Multivariate analyses were performed to evaluate the influence of environmental variables on lake water chemistry and to identify the main factors associated with spatial and temporal differences in lake DOM pools. Spatial and seasonal decreases in precipitation and warming were reflected in reduced hydrological connectivity of the lakes. The reduced connectivity resulted in increased conductivity, pH, alkalinity, dissolved organic carbon (DOC) and TN concentrations. Such conditions favoured the loss of lake DOM aromaticity, humic content and molecular weight/size, and increased the degradation signals and the biologically produced DOM fraction. Lakes responded uniformly to changes in climate variables along the Andean Patagonian gradient. Differences among lakes in water chemistry and DOM composition, were associated at the gradient scale with variation in precipitation, temperature and catchment vegetation, which may provide insights into C and water chemistry trajectories under forecasted climate trends. Our results suggest that drought and warming trends could result in less hydrological connectivity, shrinking terrestrial DOM fluxes to lakes while concomitantly increasing internal degradation processes.

Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning

The parameters of the GR4J-CemaNeige coupling model (GR4neige) are typically treated as constants. However, the maximum capacity of the production store (parX1) exhibits time-varying characteristics due to climate variability and vegetation coverage change. This study employed differentiable parameter learning (dPL) to identify the time-varying parX1 in the GR4neige across 671 catchments within the United States. We built two types of dPL, including static and dynamic parameter networks, to assess the advantages of the time-varying parameter. In the dynamic parameter network, we evaluated the impact of potential evapotranspiration (PET), precipitation (P), temperature (T), soil moisture (SM), and normalized difference vegetation index (NDVI) datasets on the performance of dPL. We then compared dPL with the empirical functional method (fm). The results demonstrated that the dynamic parameter network outperformed the static parameter network in streamflow estimation. There were differences in streamflow estimation among the dynamic parameter network driven by various input features. In humid catchments, simultaneously incorporating all five factors, including PET, P, T, SM, and the NDVI, achieved optimal streamflow simulation accuracy. In arid catchments, it was preferable to introduce PET, T, and the NDVI separately for improved performance. dPL significantly outperformed the empirical fm in estimating streamflow and uncalibrated intermediate variables, like evapotranspiration (ET). Both the derived parX1 from dPL and the empirical fm exhibited significant spatiotemporal variation across 671 catchments. Notably, compared to parX1 obtained through the empirical fm, parX1 derived from dPL exhibited a distinct spatial clustering pattern. This study highlights the potential of dPL in enhancing model accuracy and contributes to understanding the spatiotemporal variation characteristics of parX1 under the influence of climate factors, soil conditions, and vegetation change.

Hotter drought and trade‐off between fast and slow growth strategies as major drivers of tree‐ring growth variability of global conifers

Abstract Temporal growth variability is an important indicator of ecosystem function under climate change. However, we still lack a unified understanding of how climate conditions, climate change (trends and variability), nitrogen (N) deposition, functional traits and stand factors together affect radial growth variability. Using global conifer tree‐ring records (123 species from 1780 sites) during 1970–2010 to calculate growth variability, we assessed how abiotic and stand factors affect growth variability directly and indirectly via functional traits with boosted regression tree and structural equation models, and examined the differences among continents (North America, Asia and Europe). We found: (a) growth variability was mainly affected by warm‐induced drought and increased at lower latitudes. Climate warming in winter could decrease growth variability, but this effect is by far not enough to offset the threat of hotter drought; (b) there existed a trade‐off between fast‐ and slow‐growing (drought tolerance) strategies for global conifer species, and abiotic and stand factors affected growth variability via functional traits. Contrary to common conjecture, species with higher drought tolerance revealed higher growth variability due to their occupation of more xeric sites, and may also because higher investment in drought tolerance leads to less investment remaining for growth; (c) older trees revealed higher growth variability due to their more conservative growth strategy, while at large scales, taller trees showed lower growth variability due to occupying more productive sites; and (d) moderate N deposition could reduce growth variability by leading conifers to adopt a more fast‐growing strategy (e.g. in Asia), but long‐term and excessive N deposition led to increased growth variability (e.g. in North America and Europe). Synthesis. Our results suggest that coniferous forests in water‐limited regions should be more vulnerable to hotter drought, and the ‘fast–slow’ growth strategies may be key in regulating the effects of various abiotic and stand factors on ecosystem stability. Moreover, future hotter drought and N deposition will severely threaten conifer growth, especially for old trees and conifers at lower latitudes.

Possible role of anthropogenic climate change in the record-breaking 2020 Lake Victoria levels and floods

Abstract. Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides throughout the region. These rains drove the levels of Lake Victoria to a record-breaking maximum in the second half of May 2020. The combination of high lake levels, consequent shoreline flooding, and flooding of tributary rivers caused hundreds of casualties and damage to housing, agriculture, and infrastructure in the riparian countries of Uganda, Kenya, and Tanzania. Media and government reports linked the heavy precipitation and floods to anthropogenic climate change, but a formal scientific attribution study has not been carried out so far. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and then investigate to what extent human-induced climate change influenced the probability and magnitude of the record-breaking lake levels and associated flooding by applying a multi-model extreme event attribution methodology. Using remote-sensing-based flood mapping tools, we find that more than 29 000 people living within a 50 km radius of the lake shorelines were affected by floods between April and July 2020. Precipitation in the basin was the highest recorded in at least 3 decades, causing lake levels to rise by 1.21 m between late 2019 and mid 2020. The flood, defined as a 6-month rise in lake levels as extreme as that observed in the lead-up to May 2020, is estimated to be a 63-year event in the current climate. Based on observations and climate model simulations, the best estimate is that the event has become more likely by a factor of 1.8 in the current climate compared to a pre-industrial climate and that in the absence of anthropogenic climate change an event with the same return period would have led lake levels to rise by 7 cm less than observed. Nonetheless, uncertainties in the attribution statement are relatively large due to large natural variability and include the possibility of no observed attributable change in the probability of the event (probability ratio, 95 % confidence interval 0.8–15.8) or in the magnitude of lake level rise during an event with the same return period (magnitude change, 95 % confidence interval 0–14 cm). In addition to anthropogenic climate change, other possible drivers of the floods and their impacts include human land and water management, the exposure and vulnerability of settlements and economic activities located in flood-prone areas, and modes of climate variability that modulate seasonal precipitation. The attribution statement could be strengthened by using a larger number of climate model simulations, as well as by quantitatively accounting for non-meteorological drivers of the flood and potential unforced modes of climate variability. By disentangling the role of anthropogenic climate change and natural variability in the high-impact 2020 floods in the Lake Victoria basin, this paper contributes to a better understanding of changing hydrometeorological extremes in eastern Africa and the African Great Lakes region.

Carious lesions as evidence for different adaptation strategies during the middle-late Holocene in the Gansu region, northwest China

The natural environment of the Gansu-Qinghai region in northwest China exhibits spatial variation, resulting in distinct adaptive strategies among populations in different geographical areas. In this study, we analyzed the diachronic trend and regional variations in caries prevalence among 10 different middle and late-Holocene groups by examining dental caries data to explore the correlation between different adaptation strategies and caries frequency. Frequency data was used to compare dental caries between populations, and the Chi-square test was employed to detect statistical differences. A multidisciplinary approach was employed to investigate the relationship between these changes and the adaptive strategies adopted by the populations in this region. The result shows that there was a gradual increase in caries prevalence over time in eastern Gansu, which corresponded with development of millet farming and social hierarchy. In the Hexi Corridor, caries prevalence exhibited fluctuations attributable to climate variability, human migration, and regime change. The research proposes that changes in adaptive strategies due to various social and environmental factors are reflected in human teeth, while also presenting a novel endeavor of aggregating a large, multisite bioarchaeological dataset in order to investigate the interactions between Holocene populations and palaeoenvironments in northwest China.

Enhancing building energy efficiency through a climate adaptive design: Considering upcoming extreme climate conditions

Known as one of the significant sources of impacting building energy efficiency, climate change can drastically alter the long-term energy consumption patterns of buildings. It is widely acknowledged in the literature that designs based on conventional pathways relying solely on historical weather data disregard the effects of the changing climate and lose their intended energy performance over time. Several endeavors have been made to generate weather files capable of capturing the essence of the changing climate and carrying its variability. In line with these efforts, the synthesized weather files have emerged as a promising alternative to the commonly used morphing method for incorporating climate change variability into building energy designs. However, a comprehensive framework with a potential to accommodate this variability in this context is yet to be investigated. Addressing this gap, the present study aims to propose a novel framework that can facilitate dynamic design upgrades in response to the changing climate including the extreme climate conditions. This framework has been applied to a case study building in Australia and around 31% annual thermal load reduction, on average, has been achieved. Furthermore, the results showed that conventional design pathways are likely to leave buildings inadequately prepared against the upcoming climate conditions. Therefore, the proposed framework is capable of generating a dynamic design that not only maintains the expected energy performance by incorporating the changing climate into its structure but also draws a more realistic picture of the building energy demand pattern over time.

Anatomically modern human dispersals into Europe during MIS 3: Climate stability, paleogeography and habitat suitability

The initial large-scale dispersal of Anatomically Modern Humans (AMHs) into Europe, associated with the Aurignacian technocomplex, occurred during Marine Isotope Stage 3 (MIS 3), a critically unstable climatic period. The impact of climate change (millennial-scale Dansgaard-Oeschger events) and climate variability (annual and seasonal variation) on the mobility and initial dispersal of AMHs on the continent is not fully demonstrated. Here we show that both climate change and variability affected the spatial behavior of Aurignacian groups and structured their arrival on the continent. Using Random Forest, a machine learning algorithm, we produced habitat suitability (HS) models for AMHs under stadial (GS) and interstadial (GI) climate conditions. These models demonstrate that climate variability was a key factor governing the spatial behavior of human groups across MIS 3. They also illustrate that the structure and distribution of suitable habitat in Europe were affected by climatic conditions, with implications in terms of our species’ adaptability and behavioral plasticity. Finally, our results support the suggestion that initial dispersals followed a Mediterranean coastal route, likely under interstadial conditions.

A framework for estimating the anthropogenic part of Antarctica’s sea level contribution in a synthetic setting

The relative contributions of anthropogenic climate change and internal variability in sea level rise from the West Antarctic Ice Sheet are yet to be determined. Even the way to address this question is not yet clear, since these two are linked through ice-ocean feedbacks and probed using ice sheet models with substantial uncertainty. Here we demonstrate how their relative contributions can be assessed by simulating the retreat of a synthetic ice sheet setup using an ice sheet model. Using a Bayesian approach, we construct distributions of sea level rise associated with this retreat. We demonstrate that it is necessary to account for both uncertainties arising from both a poorly-constrained model parameter and stochastic variations in climatic forcing, and our distributions of sea level rise include these two. These sources of uncertainty have only previously been considered in isolation. We identify characteristic effects of climate change on sea level rise distributions in this setup, most notably that climate change increases both the median and the weight in tails of distributions. From these findings, we construct metrics quantifying the role of climate change on both past and future sea level rise, suggesting that its attribution is possible even for unstable marine ice sheets.

Simple hydrological models for assessing the impact of forest cover change on streamflow for large catchments with a dry tropical climate

ABSTRACT Hydrological models are widely used to assess the impact of climate variability and land use change on streamflow. However, it is uncertain whether these models are capable of simulating the impact given parameter interactions and inter-annual climate variability for large dry catchments. Two such catchments with extensive land clearing were selected to test three models – AWBM, Sacramento and SimHyd – in terms of their capacity to simulate streamflow during periods of vastly different levels of forest cover. AWBM outperformed others, and thus was further simplified as AWBM_C a with minimum performance deterioration to have only one transferable parameter: the average storage capacity, C a. A reduction of 21–28% in the C a value for the post-clearing period indicates a decrease in the amount of water stored for transpiration and consequently an increase in runoff-generation capacity. AWBM_C a can be used to predict the impact of forest cover change on streamflow with different C a values.

Strategies of Communities Faced with Climate Change in the Sahelian Zone of Burkina Faso: The Case of the Namissiguima Municipality in the Province of Yat Enga (Northern Region)

Climate change is a concern of our time, both at global level and at community level, where its impacts vary according to context. According to the Intergovernmental Panel on Climate Change (IPCC), the causes of this change are anthropogenic and require urgent action on the part of humanity in terms of mitigation but also in terms of adaptation to strengthen the resilience of populations. The municipality of Namissiguima (13° 36′ 00″ north and 2° 13′01″ west), in the province of Yat Enga in Burkina Faso, is no exception. Located in the Sahelian agro-climatic zone of Burkina Faso and receiving less than 600mm of water per year, it has a population of almost 59,000, 51.7% of whom are women and 48.3% men, whose main occupations are farming and livestock rearing (94.6%). Through a participatory analysis using the “Trousse a outils planification et suivi-evaluation des capacites d’adaptation au changement climatique (TOP-SECAC)”, this paper aims to analyze local communities’ perceptions of climate variability and change and their perceived causes, to assess the impacts of climate variability and climate change on communities’ livelihoods, and to discuss the adaptation strategies used by local people to mitigate these impacts.

Seasonal Variations in the Toughness of Leaves: A Case Study Using Griselinia littoralis.

Potential effects of climate change include greater extremes of temperature and increased severity of storms. Many plants have evolved to resist the challenges of winter (freezing, dehydration, and wind) in a process known as cold hardening. Sensing reducing temperatures, they make structural changes at the cellular level to increase their mechanical resistance and prevent damage. Previous work on this topic, though extensive, has been conducted under laboratory conditions rather than in the field, and while many workers have observed changes to cell wall thickness and composition, which imply increased mechanical strength, few have actually measured strength or any other parameter describing structural integrity. This paper describes experiments on a model system designed to measure the structural integrity of leaf laminae from plants growing naturally in the field over extended periods, allowing seasonal variations to be captured. Standard engineering properties-tensile strength and fracture toughness-were measured for leaves of Griselinia littoralis on 19 separate occasions over a 12-month period. Toughness (rather than strength) was found to be the controlling mechanical property. Toughness values were found to change significantly during the year, by more than a factor of 2. Toughness correlated strongly with average daily soil temperature, but with a lag of about 1-2 weeks, suggesting that this is the time needed for structural adjustments to take place. Highest toughness values occurred in winter, confirming cold hardening. Increasing temperature in the spring was associated with decreasing toughness, but in the summer, when highest temperatures occurred, toughness increased again. This apparent "hot hardening" may be a response to dehydration. Results imply that a given leaf is able to both increase and decrease its toughness in response to temperature changes, demonstrating excellent plasticity of response. This case study of a single species establishes a method of reliably measuring changes in a plant's structural integrity due to cold hardening and other seasonal variations, which may be used to investigate the effects of climate change and other variables.

Consequences of Climate Change at World Level: a Theoretical Review

IPCC defines climate change as any “change in the state of the climate that can be identified (e.g., using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. It refers to any change in climate over time, whether due to natural variability or because of human activity” (IPCC, 2007a, p. 30). Climate change phenomena is very threating which impacts the lifeforms and our livelihoods on this earth. The world has been experiencing frequent extreme environmental conditions. This phenomenon is not a frequent/instant event, it is a consolidated outcome of so many years due to anthropogenic and natural causes. The present paper reported the various causes and consequences of climate change at the world level. In fact, many organisation/committees/conferences were established to overcome the adverse effects of climate change and for the survival of the living organisms, respectively. Secondary data is used to identify variability of climatic features and the same information from various reports, research articles, and other sources. This review is an assessment of climate change phenomena at the worldwide level. It is found that the government participation is necessary for the development processes. There should be some rules and regulations for developed and developing countries to overcome the variability of climate change. To sum up, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global pledge to report its horrible consequences to safeguard global sustenance.

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

AbstractAlthough considerable effort has been deployed to understand the impact of climate variability and vegetation change on runoff in major basins across Africa, such studies are scarce in the Gulf of Guinea Basin (GGB). This study combines the Budyko framework and elasticity concept along with geospatial data to fill this research gap in 44 nested sub‐basins in the GGB. Annual rainfall from 1982 to 2021 show significant decreasing and increasing trends in the northern and southern parts of the GGB, respectively. Annual potential evapotranspiration (PET) also shows significant increasing trends with higher magnitudes observed in the northern parts of the GGB. Changing trends in climate variables corroborates with shift to arid and wetter conditions in the north and south, respectively. From 2000 to 2020 vegetation cover estimated using enhanced vegetation index (EVI) shows significant increasing trends in all sub‐basins including those experiencing a decline in annual rainfall. Vegetation composition measured using vegetation continuous fields (VCFs) from 2000 to 2020 show an increase in tree canopy cover (TC), a decline in short vegetation cover and marginal changes in bare ground cover (BG). Elasticity coefficients show that a 10% increase in annual rainfall and PET may lead to a 33% increase and 24% decline in runoff, respectively. On the other hand, a 10% increase in EVI may lead to a 4% decline in runoff while a 10% increase in TC, SV and BG may reduce runoff by 4% and increase runoff by 3% and 2%, respectively. Even though changes are marginal, decomposing vegetation into different parameters using EVI and VCFs may lead to different hydrological effects on runoff which is one of the novelties of this study that may be used for implementing nature‐based solutions. The study also demonstrates that freely available geospatial data together with analytical methods are a promising approach for understanding the impact of climate variability and vegetation change on hydrology in data‐scarce regions.

Validation of global precipitation time series products against tree ring records and remotely sensed vegetation greenness.

Global interpolated climate products are widely used in ecological research to investigate biosphere-climate interactions and to track ecological response to climate variability and climate change. In turn, biological data could also be used for an independent validation of one aspect of climate data quality. All else being equal, more variance explained in biological data identifies the better climate data product. Here, we compare seven global precipitation time series products, including gauge-based datasets (CRU-TS, UDEL-TS, GPCC), re-analysis products (ERA5, CHELSA), a satellite-based dataset (PERSIANN) and a multi-source product that draws on gauge, re-analysis, and satellite data (MSWEP). We focus on precipitation variables, because they are more difficult to interpolate than temperature, and show larger divergence among gridded data products. Our validation is based on 20 years of remotely sensed vegetation greenness (MODIS-EVI) and 120 years of tree ring records from the International Tree Ring Data Bank (ITRDB). The results for the 20-year EVI based validation shows that all gauge and re-analysis data products performed similarly, but were outperformed by the multi-source MSWEP product, especially in regions with low weather station coverage, such as Africa. For analyzing long 120-year time-series, UDEL-TS showed superior performance prior to the 1940s, with especially large margins for northern Asia and the Himalayas region. For other regions, CRU-TS and GPCC could be recommended. We provide maps that can guide the best regional choice of climate product for research involving time series of biological response to historic climate variability and climate change.

Climate change impacts on coffee production in Indonesia: A review

Indonesia is ranked in the top 5 coffee production countries in the world with the largest production being Arabica and Robusta coffee beans. Approximately ninety percent of coffee produced in Indonesia is being exported and as the trend in local consumption increases, the demand for coffee is escalating. However, future coffee production is challenged by climate change. Background: This paper outlines the impacts of climate change variables including environmental stressors on coffee production quality and quantity of Arabica and Robusta species, discusses the influences on suitable areas for coffee cultivation and the coffee pests related to coffee productivity, as well as suggests mitigation and adaptation approaches in Indonesia. Methods: The literature review approach was utilized to complete this paper which involves collecting, analyzing, and correlating the findings of prior studies. The paper only considered literature with the following terms in the title; climate change impact, coffee production, mitigation, adaptation, and Indonesia. Finding: Increasing temperature degrades coffee production both in Arabica and Robusta coffee beans. The overall suitable area for cultivating coffee is shifting towards higher altitudes However, Indonesia is projected to lose more than half of the suitable area for cultivating Arabica by the year 2050. Climate change improves coffee pests' thermal tolerance and enables them to grow in warm temperatures. Conclusion: Common mitigation approaches include shade plantation and soil moisturization control which both improve coffee productivity. Another strategy is the cultivation calendar modified the coffee life cycle with climate prediction.

Extreme weather events and Spatio-temporal characterization of climate change variables in Bangladesh during 1975–2019

Bangladesh is susceptible to climate change, thus a detailed study, including the analyses of trends, sub-trends, extreme events and indices was conducted to obtain a complete picture of the climate change pattern in Bangladesh utilizing daily rainfall, maximum, minimum and average temperature data of 26 stations from 1975 to 2019 using R 4.0.2 software. For the trend analysis Mann Kendal (MK), modified Mann Kendall (mMK), Innovative Trend Analysis (ITA) and Sen’s slope methods were used. Sub-trend analysis was conducted using ITA. Standard Anomaly Index (SAI) has been used to identify the frequency and severity of extreme events. ClimPACT2 software was used to check the homogeneity and calculate the extremes of temperature and rainfall data. Our analysis showed that during the last four decades, climate variables changed their patterns and trend heterogeneously over Bangladesh. Most stations showed decremental rainfall trend when central part of the country showed a substantial decrease. The northern and central parts of the country showed significant growth of trend for annual average temperature. The temperature in the monsoon season increased, whereas those in dry season decreased. The rainfall and maximum temperature were inversely related during monsoon whereas during dry season both of them decrease. The pre-monsoon and post-monsoonal rainfall also showed decreasing trends, indicating prevailing drought conditions especially in northern and central parts of the country. The SAI analysis showed alternating drought and wet years in almost all the stations. In the past 20 years, the country’s western region experienced more drought years than before whereas the coastal region experienced more wet years. The analysis of climate extreme indices suggests that, Consecutive Dry Days (CDD), Hot Days (TX90P) and Hot Nights (TN90P) show significant increasing trend throughout the country. The agricultural productivity, water resource management and food security are anticipated to benefit from this study.

A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate

Abstract El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate. Significance Statement The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

Intensifying human-driven heatwaves characteristics and heat related mortality over Africa

Heatwaves in Africa are expected to increase in frequency, number, magnitude, and duration. This is significant because the health burden is only expected to worsen as heatwaves intensify. Inadequate knowledge of the climate’s impact on health in developing nations such as Africa makes safeguarding the health of vulnerable groups at risk challenging. In this study, we quantify possible roles of human activity in heatwave intensification during the historical period, and project the future risk of heat-related mortality in Africa under two Representative Concentration Pathways (RCP26) and (RCP60). Heatwaves are measured using the Excess Heat Factor (EHF); the daily minimum (Tn ) and maximum (Tx ) are used to compute the EHF index; by averaging Tx and Tn . Two heat factors, significance and acclimatization are combined in the EHF to quantify the total excess heat. Our results confirm the intensification of heatwaves across Africa in recent years is due anthropogenic activity (increase in greenhouse gas concentration and changes in land use). The Return event highlights the potential future escalation of heatwave conditions brought on by climate change and socioeconomic variables. RCP26 projects a substantial rise in heat-related mortality, with an increase from about 9000 mortality per year in the historical period to approximately 23 000 mortality per year at the end of the 21st century. Similarly, RCP60 showed an even more significant increase, with heat-related mortality increasing to about 43 000 annually. This study highlights the potentially growing risk of intensifying heatwaves in Africa under different emission scenarios. It projects a significant increase in heatwave magnitude, number, duration, frequency, and heat-related mortality. Africa’s low adaptive capacity will amplify the impact, emphasizing the need for emissions reduction and effective adaptation measures.