Historical Climate Variability Research Articles

Climate-affected multi-decadal variations of biogenic volatile organic compounds in Pinus tabuliformis growth rings

Long-term dynamics of biogenic volatile organic compounds (BVOCs) in trees are rarely reported, despite environmental factors (such as climate change) influencing their growth and the subsequent chemical accumulation. For this, tree growth rings provide a promising biological proxy for the long-time variation and correlation with environmental changes. Therefore, tree rings from Pinus tabuliformis (two stem disks and 40 tree cores) were collected in the Taihang Mountain Macaque National Nature Reserve of China. These samples were divided into seven 5-year resolutions over the 34-year period 1985 to 2018. This enabled analysis of multi-decadal variations of compounds and their correlation with climate variability. A total of 292 BVOCs were detected; however, only 18 compounds were found together across all the 7 growth-periods. Temporal analyses showed decreasing trends for monoterpenes (0.026%/yr) and diterpenes (0.120%/yr), whereas alcohols and oxygenated monoterpenes showed increasing trends at 0.031%/yr and 0.042%/yr, respectively. Correlation analyses showed no obvious link to yearly precipitation, while seasonal temperature had a negative effect on monoterpenes and diterpenes but positive effects on alcohols and oxygenated monoterpenes (all P < 0.05). The present study showed that dendrochronology is a suitable method for re-establishing the biological effects from historical climate variability on key tree species.

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.

Constraining long-term model predictions for woody growth using tropical tree rings.

The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO2 (Ca ). When forced with historical Ca , ED2.2-hydro reproduced the magnitude of increases in intercellular CO2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing Ca based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to Ca and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing Ca . More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by Ca rise predicted by biosphere models.

Caribbean salinity anomalies contributed to variable North Atlantic circulation and climate during the Common Era.

Paleoceanographic reconstructions show that the strength of North Atlantic currents decreased during the Little Ice Age. In contrast, the role of ocean circulation in climate regulation during earlier historical epochs of the Common Era (C.E.) remains unclear. Here, we reconstruct sea surface temperature (SST) and salinity in the Caribbean Basin for the past 1700 years using the isotopic and elemental composition of planktic foraminifera tests. Centennial-scale SST and salinity variations in the Caribbean co-occur with (hydro)climate changes in the Northern Hemisphere and are linked to a North Atlantic SST forcing. Cold phases around 600, 800, and 1400 to 1600 C.E. are characterized by Caribbean salinification and Gulf of Mexico freshening that implies reductions in the strength of North Atlantic surface circulation. We suggest that the associated changes in the meridional salt advection contributed to the historical climate variability of the C.E.

Quantifying Trends of Historical Climatic Variables in the Jessore Region of Bangladesh

The aim of the study was to detect trends in salient key climate variables in the Jessore region of Bangladesh for the years 1985–2014, and the data was collected from the Bangladesh Meteorological Department (BMD). Annual rainfall, annual maximum temperature, and annual cloud coverage were analyzed using the non-parametric Mann-Kendall test, linear regression, and LOWESS curve to detect trends in the series. The rainfall and cloud coverage showed a decline trend at a rate of (4.50 mm/year; 45.02 mm/decade) and (0.045 octas/year; 0.45829 oktas/decade), respectively, whereas temperature manifested an increment trend (0.0285°C/year; 0.2854°C/decade), where cloud coverage was the only significant variable. The structural breakdown point was found using the graphical method and the Chow test, which showed in 2004 that both the series containing breakdown points and cloud coverage exhibit statistical significance.

Exploring the Centennial-Scale Climate History of Southern Brazil with Ocotea porosa (Nees & Mart.) Barroso Tree-Rings

This article explores the dendrochronological potential of Ocotea porosa (Nees & Mart) Barroso (Imbuia) for reconstructing past climate conditions in the General Carneiro region, Southern Brazil, utilizing well-established dendroclimatic techniques. A total of 41 samples of Imbuia were subjected to dendroclimatic analysis to reconstruct precipitation and temperature patterns over the period from 1446 to 2011. Notably, we achieved the longest reconstructions of spring precipitation and temperature for the Brazilian southern region, spanning an impressive 566-year timeframe, by employing a mean chronology approach. To achieve our objectives, we conducted a Pearson’s correlation analysis between the mean chronology and the climatic time series, with a monthly temporal resolution employed for model calibration. Impressively, our findings reveal significant correlations with coefficients as high as |rx,P| = 0.32 for precipitation and |rx,T| = 0.45 for temperature during the spring season. Importantly, our climate reconstructions may elucidate a direct influence of the El Niño—South Oscillation phenomenon on precipitation and temperature patterns, which, in turn, are intricately linked to the natural growth patterns of the Imbuia trees. These results shed valuable light on the historical climate variability in the Southern Brazil region and provide insights into the climatic drivers affecting the growth dynamics of Ocotea porosa (Nees & Mart) Barroso.

Using long-term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models.

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea-Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best-performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.

Evidence of fruit syndromes in the recently diverged wild tomato clade opens new possibilities for the study of fleshy fruit evolution

Societal Impact StatementFleshy fruits provide humans with many flavorful and nutritious crops. Understanding the diversity of these plants is fundamental to managing agriculture and food security in a changing world. This study surveyed fruit trait variation across species of tomato wild relatives and explored associations among color, size, shape, sugars, and acids. These wild tomato species native to South America can be interbred with the economically important cultivated tomato. Beyond its application to tomatoes, deepening our knowledge of how fruit traits evolve together is valuable to crop improvement efforts aimed at breeding more nutritious and appealing varieties of fruits.Summary Fleshy fruits display a striking diversity of traits, many of which are important for agriculture. The evolutionary drivers of this variation are not well understood, and most studies have relied on variation found in the wild. Few studies have explored this question on a fine‐grained scale with a group of recently diverged species while controlling for environmental effects. We developed the tomato clade as a novel system for fruit trait evolution research by presenting the first common garden‐based systematic survey of variation and phylogenetic signal in color, nutrition, and morphology traits across all 13 species of tomato wild relatives (Solanum sect. Lycopersicon). We laid the groundwork for further testing of potential evolutionary drivers by assessing patterns of clustering and correlation among disperser‐relevant fruit traits as well as historical climate variables. We found evidence of two distinct clusters of associated fruit traits defined by color, sugar type, and malic acid concentration. We also observed correlations between a fruit's external appearance and internal nutrient content that could function as honest signals to dispersers. Analyses of historical climate and soil variables revealed an association between red/orange/yellow fruits and high annual average temperature. Our results establish the tomato clade as a promising system for testing hypotheses on the drivers of divergence behind early‐stage fleshy fruit evolution, particularly selective pressure from frugivores.

Long-term annual climate trends around the Breton Plots area, Alberta: is there any evidence of local climate change?

The objective of this research was to investigate the long-term trends in historical climate variables using the data collected near the classical Breton Plots (Alberta, Canada) and to determine if the data show any evidence of local climate change. The climate data used for the study were obtained from the Alberta Climate Information Service for the years 1901–2020. Various parametric statistical analyses were conducted to determine if monotonic trends occurred in the climate variables over time, and the analyses were conducted on the annual data as well as the 30 year climate normals. Large fluctuations in annual climate variables occurred, but a positive linear trend was observed in the average annual and growing season minimum air temperatures over time. Between 1901 and 2020 the annual minimum air temperature average increased at a rate of 0.3 °C for every 10 years. During the winter periods (December, January, and February, inclusive) from 1901 to 2020, the minimum air temperature average increased at an even higher rate of 0.5 °C every 10 years. Overall, the 30 year climate normals for minimum and maximum air temperatures increased for most seasons. The rates of increase were largest over the winter period, at 0.5 °C every 10 years for minimum air temperature and at 0.3 °C every 10 years for maximum air temperature. Strong linear increases occurred over time for growing degree days, number of frost-free days, total annual precipitation, growing season precipitation, and off-season precipitation.

Impact of historical climate variability on rice production in Mainland Southeast Asia across multiple scales

Climate change poses significant pressure on global food production. Although previous work has explored impacts of climate, management, and genetics on food production, additional research is needed to examine the effects of large-scale climate modes at local and regional scales. This study explores the impact of climate variability on rice yield in Mainland Southeast Asia from 1961-2017 at three different spatial scales: the whole Mainland Southeast Asia region, country-level (Cambodia, Laos, Myanmar, Thailand, and Vietnam), and province-level for Vietnam. Annual rice yields over this period have nearly tripled with Vietnam experiencing the largest increases. Correlations between annual rice yield anomalies at the regional and country levels and climate data reveal clear influences of tropical climate variability associated with the El Niño-Southern Oscillation and the Pacific Meridional Mode. At the provincial level in Vietnam, many provinces show similar correlation patterns for the spring-summer season of rice (e.g., a co-occurring La Niña and positive phase of the Pacific Meridional Mode in the preceding boreal winter and spring are associated with increased yields in spring-summer rice). However, the late summer-fall season rice yield anomalies show much weaker correlations with tropical climate patterns. Variations across provinces were also noted, particularly between the Red River and Mekong River Deltas. The history of this 56-year period, which included the Vietnam-American War and changes in land management policies, makes it challenging to disentangle the effects of climate variability and social factors on rice yields in these areas. However, these results highlight the importance of using a multidisciplinary and multiscale approach to help inform local and regional decision-making. • We examined climate and rice yields in Mainland Southeast Asia at 3 scales. • Correlations revealed clear influences of tropical climate variability. • Provinces in Vietnam showed varying results by rice season and location. • Future climate change may shift planting timing and threaten national exports. • A multidisciplinary and multiscale approach can help disentangle effects on rice.

Variability in the minimum temperature over two centuries in the overlap region between the fringe of the Asian westerly region and the temperate continental-monsoon climate transition zone.

The overlap region between the eastern fringe of the Asian westerly region and the temperate continental-monsoon climate transition zone is sensitive to climate changes and is characterized by fragile ecosystems. Uncovering the long-term historical climate variability patterns in this region is necessary. A standardized tree-ring width chronology was constructed based on the tree-ring samples collected from four representative tree species in four typical areas in the overlap region, and the 203- to 343-year annual mean minimum temperature series in the overlap region were reconstructed. The reconstructed series overlapped well with extreme climate events and low-temperature periods recorded in historical data. Therefore, the reconstructed model is stable and reliable. As suggested by the reconstructed series, the annual average minimum temperature in the overlap region changes sharply from east to west, and the periodicity change in the overlap region shows a trend of gradually weakening from the east and west ends to the middle. In the nineteenth century, the high-latitude area was in the high-temperature period, and the entire overlap region experienced significant low-temperature periods lasting 20-45years until the 1950s. The western part had an earlier low-temperature period start time, a longer cooling duration, and a slower cooling rate than the central part. The overlap region experienced a significant warming period in approximately the last half-century, with temperatures increasing faster in the western and eastern parts than in the central part. The temperature variability in the overlap region was more intense in the last two centuries, with shorter periodicities and a larger proportion of cold periods. The central and western parts of the Asian westerly region, the mid- to high-latitude regions of the transition zone, and the overlap region experienced significant low-temperature periods or drastic cooling trends (the Little Ice Age) in the first half of the nineteenth century and significant warming trends afterwards due to global warming. The influences of these changes may have been exacerbated by the westerly circulation. The results of this study provide new insights into the use of dendroclimatology to extract temperature series in the Asian westerly region and the transition zone and a reference for research on global climate change.

Tree-ring-based seasonal temperature reconstructions and ecological implications of recent warming on oak forest health in the Zagros Mountains, Iran

Abrupt changes in temperature have especially strong impacts on fragile ecosystems located in semi-arid regions. In this study, we analyzed tree-ring widths (TRW) of Mediterranean cypress (Cupressus sempervirens var. horizontalis) in the Zagros Mountains, Iran. Furthermore, we separately measured earlywood width (EWW) and latewood width (LWW) of Persian oak (Quercus brantii Lindl.) to examine if intra-annual resolution of tree-ring parameters of Q. brantii tree rings can be used as high-resolution paleoclimate proxies. Climate-growth relationships revealed that mean monthly maximum temperatures (Tmax) are a dominant factor determining radial tree growth and negatively affect both oak and cypress in the Zagros Mountains. Accordingly, we reconstructed two different seasonal windows of past Tmax variability, namely, January–March and June–August over the periods 1860–2015 and 1560–2015, respectively. Regime shift detection identified twelve warm and nine cold significant regime shifts in our summer Tmax reconstructions. The longest hot summer period occurred in the twentieth century, and two warm regime shifts occurred in 1999 and 2008. The highest values of the warm summer regime shift index occurred in 2008, which coincided with fungal pathogen attacks and insect outbreak of the oak leaf roller moth (Tortrix viridana L.) in the Zagros oak woodlands. Interestingly, we found common warm and cold periods in historic climate variability between the summer and winter Tmax reconstructions. Warm and cold regime shifts occurred simultaneously from 1955 to 2015, and significant regional warm summer and winter regime shifts have occurred between 2008 and 2015. The winter and summer Tmax reconstructions show high spatial correlations with large areas in West Asia, North Africa, and the eastern Mediterranean region. Our results strengthen initial studies on past climate variability in Iran and contribute to an enhanced understanding of past temperature variability in West Asia.

Global forests are influenced by the legacies of past inter-annual temperature variability

Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.

Hourly historical and near-future weather and climate variables for energy system modelling

Abstract. Energy systems are becoming increasingly exposed to the impacts of weather and climate due to the uptake of renewable generation and the electrification of the heat and transport sectors. The need for high-quality meteorological data to manage present and near-future risks is urgent. This paper provides a comprehensive set of multi-decadal, time series of hourly meteorological variables and weather-dependent power system components for use in the energy systems modelling community. Despite the growing interest in the impacts of climate variability and climate change on energy systems over the last decade, it remains rare for multi-decadal simulations of meteorological data to be used within detailed simulations. This is partly due to computational constraints, but also due to technical barriers limiting the use of meteorological data by non-specialists. This paper presents a new European-level dataset which can be used to investigate the impacts of climate variability and climate change on multiple aspects of near-future energy systems. The datasets correspond to a suite of well-documented, easy-to-use, self-consistent, hourly- and nationally aggregated, and sub-national time series for 2 m temperature, 10 m wind speed, 100 m wind speed, surface solar irradiance, wind power capacity factor, solar power factor, and degree days spanning over 30 European countries. This dataset is available for the historical period 1950–2020 and is accessible from https://doi.org/10.17864/1947.000321 (Bloomfield and Brayshaw, 2021a). As well as this a companion dataset is created where the ERA5 reanalysis is adjusted to represent the impacts of near-term climate change (centred on the year 2035) based on five high-resolution climate model simulations. These data are available for a 70-year period for central and northern Europe. The data are accessible from https://doi.org/10.17864/1947.000331 (Bloomfield and Brayshaw, 2021b). To the authors’ knowledge, this is the first time a comprehensive set of high-quality hourly time series relating to future climate projections has been published, which is specifically designed to support the energy sector. The purpose of this paper is to detail the methods required for processing the climate model data and illustrate the importance of accounting for climate variability and climate change within energy system modelling from the sub-national to European scale. While this study is therefore not intended to be an exhaustive analysis of climate impacts, it is hoped that publishing these data will promote greater use of climate data within energy system modelling.

NorCPM1 and its contribution to CMIP6 DCPP

Abstract. The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF). We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products. The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

High historical variability weakens the effects of current climate differentiation on microbial community dissimilarity and assembly.

Understanding the influences of global climate change on soil microbial communities is essential in evaluating the terrestrial biosphere's feedback to this alarming anthropogenic disturbance. However, little is known about how intra-site historical climate variability can mediate the influences of current climate differences on community dissimilarity and assembly. To fill this gap, we examined and disentangled the interactive effects of historical climate variability and current climate differences on the soil bacterial community dissimilarity and stochasticity of community assembly among 143sites from 28 forests across eastern China. We hypothesize that the relative importance of stochasticity and community dissimilarity are related to historical climate variability and that an increasing sum of intra-site historical variability enhances stochasticity while reduces dissimilarity between two communities. To test our hypothesis, we statistically controlled for covariates between sites including differences in soil chemistry, plant diversity, spatial distance, and seasonal climate variations at annual timescales. We observed that an increase in inter-site current climate differences led to a reduced impact of stochasticity in community assembly and a pronounced divergence between communities. In stark contrast, when communities were subjected to a high level of intra-site historical climate fluctuation, the observed impact incurred from current climate differences was substantially weakened. Moreover, the influence of increased historical variability was consistent along the gradient of current temperature differences between sites. However, effects induced by historical fluctuation in precipitation were disproportional and only evident when small inter-site differences were observed. Consequently, if the prior climate variability is ignored, especially regarding environmental factors like temperature, we assert that the influence current climate differentiation has on regulating community dissimilarity and assembly stochasticity will be underestimated. Together, our findings highlight the importance and need of explicitly controlling the mean and the historical variability of climate factors for the next "generation" of climate change experiments to come.

Uncertainty of Intensity-Duration-Frequency Curves Due to Adoption or Otherwise of the Temperature Climate Variable in Rainfall Disaggregation

Most areas around the world lack fine rainfall records which are needed to derive Intensity-Duration-Frequency (IDF) curves, and those that are available are in the form of daily data. Thus, the disaggregation of rainfall data from coarse to fine temporal resolution may offer a solution to that problem. Most of the previous studies have adopted only historical rainfall data as the predictor to disaggregate daily rainfall data to hourly resolution, while only a few studies have adopted other historical climate variables besides rainfall for such a purpose. Therefore, this study adopts and assesses the performance of two methods of rainfall disaggregation one uses for historical temperature and rainfall variables while the other uses only historical rainfall data for disaggregation. The two methods are applied to disaggregate the current observed and projected modeled daily rainfall data to an hourly scale for a small urban area in the United Kingdom. Then, the IDF curves for the current and future climates are derived for each case of disaggregation and compared. After which, the uncertainties associated with the difference between the two cases are assessed. The constructed IDF curves (for the two cases of disaggregation) agree in the sense that they both show that there is a big difference between the current and future climates for all durations and frequencies. However, the uncertainty related to the difference between the results of the constructed IDF curves (for the two cases of disaggregation) for each climate is considerable, especially for short durations and long return periods. In addition, the projected and current rainfall values based on disaggregation case which adopts historical temperature and rainfall variables were higher than the corresponding projections and current values based on only rainfall data for the disaggregation.

Climate Variability and Change Affect Crops Yield under Rainfed Conditions: A Case Study in Gedaref State, Sudan

It is projected that, on average, annual temperature will increase between 2 °C to 6 °C under high emission scenarios by the end of the 21st century, with serious consequences in food and nutrition security, especially within semi-arid regions of sub-Saharan Africa. This study aimed to investigate the impact of historical long-term climate (temperature and rainfall) variables on the yield of five major crops viz., sorghum, sesame, cotton, sunflower, and millet in Gedaref state, Sudan over the last 35 years. Mann–Kendall trend analysis was used to determine the existing positive or negative trends in temperature and rainfall, while simple linear regression was used to assess trends in crop yield over time. The first difference approach was used to remove the effect of non-climatic factors on crop yield. On the other hand, the standardized anomaly index was calculated to assess the variability in both rainfall and temperature over the study period (i.e., 35 years). Correlation and multiple linear regression (MLR) analyses were employed to determine the relationships between climatic variables and crops yield. Similarly, a simple linear regression was used to determine the relationship between the length of the rainy season and crop yield. The results showed that the annual maximum temperature (Tmax) increased by 0.03 °C per year between the years 1984 and 2018, while the minimum temperature (Tmin) increased by 0.05 °C per year, leading to a narrow range in diurnal temperature (DTR). In contrast, annual rainfall fluctuated with no evidence of a significant (p > 0.05) increasing or decreasing trend. The yields for all selected crops were negatively correlated with Tmin, Tmax (r ranged between −0.09 and −0.76), and DTR (r ranged between −0.10 and −0.70). However, the annual rainfall had a strong positive correlation with yield of sorghum (r = 0.64), sesame (r = 0.58), and sunflower (r = 0.75). Furthermore, the results showed that a longer rainy season had significant (p < 0.05) direct relationships with the yield of most crops, while Tmax, Tmin, DTR, and amount of rainfall explained more than 50% of the variability in the yield of sorghum (R2 = 0.70), sunflower (R2 = 0.61), and millet (R2 = 0.54). Our results call for increased awareness among different stakeholders and policymakers on the impact of climate change on crop yield, and the need to upscale adaptation measures to mitigate the negative impacts of climate variability and change.

Daylight Saving Time policy and energy consumption

Daylight Saving Time is argued to be effective in saving energy. Turkey is one of the few countries that annulled the clock changes and remained in the summertime zone in 2016. Therefore, the country provides a unique natural experiment to test and confirm this policy change. This paper studies the impact of clock changes on electric energy consumption and load shift. We use historical electrical energy consumption, electricity prices, and relevant atmospheric essential climate variables data in Turkey between 2012–2020. We adopt Multiple Linear Regression, Difference in Differences and Interrupted Time Series methodologies to analyse the historical data. This paper shows that the Daylight Saving Time policy does not lead to a measurable amount of electrical energy savings. Furthermore, it does not cause a noticeable continuous daily load shift throughout the year. We also claim that our findings should apply to countries such as the United States, India, Japan, Australia or China, and continents of Africa and South America, whose latitudes are between 42.0° north and south of the equator.

A Robust Drought Index Accounting Changing Precipitation Characteristics

AbstractA realistic evaluation of drought onset is inevitable for the effective implementation of mitigation strategies. Meteorological droughts indicating the onset of drought propagation are usually quantified through unbiased indices that consider the month‐wise magnitude variations in historical climatic variables, while completely ignoring their intramonthly distributions. However, the applicability of such indices under the present scenario of changing climate, where intense short wet spells are reportedly increasing, is questionable. Such changes in the wet spells could eventually lead to prolonged intramonthly dry spells, which in turn will pose many agricultural and socioeconomic risks. To monitor the changed scenario realistically, we propose a new drought index—the Standardized Net‐Precipitation Distribution Index (SNEPI), which incorporates the distribution characteristics of the daily net‐precipitation variable. The applicability of SNEPI is critically evaluated using synthetically generated and observed precipitation series over six diverse climatic locations of India, at 1‐, 3‐, 6‐, and 12‐month time scales. The utility of SNEPI is more profound for longer time scales, understandably due to the enhanced emphasis imparted on the distribution of rainfall spells by the longer periods. Standardized Net‐Precipitation Distribution Index proves to be efficient in capturing the present scenario of increasing dryness (wetness) in wet (dry) regions when compared with the traditional Standardized Precipitation Evapotranspiration Index. Hence, the proposed index, which incorporates the changing precipitation characteristics, may aid in better quantification and monitoring of meteorological drought under the scenario of changing climate.