Key Climate Variables Research Articles

Past variations and future projection of runoff in typical basins in 10 water zones, China

Understanding the historical and future changing characteristics of key climatic variables and runoff in 10 major river zones in China is essential for water resources evaluation and management. To this end, the historical and future changing trends of key hydrometeorological variables, including precipitation, potential evapotranspiration, and runoff were analyzed in detail for each water zone across China. The climate elasticity method was also established to quantify the impacts of climate change and human activities on historical runoff variations. The results indicate that the characteristics and causes of runoff variations in China were generally spatially heterogeneous. The runoff in water-scarce river basins of northern China decreased significantly during the period of 1961-2018, variations of which were more sensitive to human activities. For southern water zones in China, the runoff showed no significant trend and climate change was the main influencing factor. On basis of 9 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble simulations under three different shared socioeconomic pathways (ssp126, ssp245 and ssp585), the future runoff in 10 typical basins of the water zones were projected and the results suggested an increasing trend of runoff over China, thanks to increasing precipitation in the rest 21 century. While under ssp585, the rising air temperature tends to evaporate more water and offset the effect of precipitation increase to some extent, resulting in that the increments of runoff under ssp585 are not necessarily greater than those under ssp245 and ssp126. Overall, our study could be used as a basis to support climate adaptation strategies and policies to cope with future water resources conditions.

Prediction of the potential distribution of the predatory mite Neoseiulus californicus McGregor in China using MaxEnt

Neoseiulus californicus McGregor is a specialized predator of Tetranychidae mites. Recently, commercially produced N. californicus have been shown to be effective in the integrated management of pest mites in scientific trials, and this has attracted the attention of both entomologists and the agricultural industry. In this study, we collected geographic location information on N. californicus, used MaxEnt to simulate its potential suitable distribution in China, and discussed the dominant environmental variables restricting its geographical distribution. The highly suitable areas were mainly located in Hunan, Sichuan, Zhejiang, Fujian, Guangxi, Jiangsu, Anhui, Guangdong, Guizhou, Chongqing, Yunnan and Jiangxi, and the total area was 106.82 × 104 km2. Moderately and lowly suitable areas were 91.44 × 104 km2 and 19.04 × 104 km2, respectively. The key climatic variables affecting the distribution of N. californicus were the maximum temperature in November (11.31–25.7 °C), maximum temperature in the warmest month (25.02–33.91 °C), precipitation in the coldest quarter (125.17–586.87 mm), precipitation in April (48.67–472.71 mm), precipitation in October (60.8–378.18 mm) and temperature seasonality (97.79–836.85).

Impact of vegetation greening on carbon and water cycle in the African Sahel-Sudano-Guinean region

The African Sahel-Sudano-Guinean region is one of the largest water limited environments in the world, thus making it highly vulnerable to climate change. Recent studies have shown vegetation greening in the region, but few have investigated the impact of this greening on carbon and water cycles. We used a combination of earth observation (EO) data and a diagnostic model to evaluate the extent of the vegetation greening and its impacts on carbon sequestration potential (i.e., Gross Primary Productivity-GPP) and the water cycle (i.e., Evapotranspiration-ET and Water Use Efficiency-WUE) from 1982 to 2015. Additionally, we evaluated the influence of key climatic variables (i.e., precipitation, temperature, and solar radiation) on vegetation greening, carbon sequestration potential and the water cycle. Our results showed widespread vegetation greening during the first half of the study period (1982–2000), driven mainly by increase in precipitation. However, the rate of greening reduced or became stagnant during the latter half of the study (2000–2015), but did not revert to pre-greening levels of 1980s, implying a persistent ecosystem change. The vegetation greening and increased precipitation resulted in a ~ 17.95% increase in GPP (from ~3.9 PgC/year in 1982 to ~4.6 PgC/year in 2000) and a ~ 21.28% increase in ET (from ~47 mm/year in 1982 to ~57 mm/year in 2015). The WUE showed an overall reduction, mainly attributed to large increases in ET not matched by similar magnitude of increases in GPP. Currently, there is lack of consensus on the magnitude of the contribution of drylands to the global carbon and water cycle. This study shows that drylands undergoing ecosystem change, coupled with climate change, may in future become important contributors to the global carbon and water cycle. Therefore, they could play a key role in future global warming and climate change mitigation strategies.

Modulation of plant functional traits under essential plant nutrients during seasonal regime in natural forests of Garhwal Himalayas

AimWe estimated how seasonality in conjunction with key climatic variables affects morphological and physiological plant functional traits (PFTs) and soil essential (micro and macro) nutrients in oak, pine, and mixed forests.MethodThe different PFTs were tested using several laboratory methods and using the portable photosynthesis system Li-COR 6400 XT, Lincoln NE, USA. Likewise, some chemical traits and soil nutrients were analyzed by using CHNS analyzer.ResultsIn this study, physiological traits such as CO2 assimilation rate, stomatal conductance and transpiration rate were significantly higher in the rainy season followed by summer and winter seasons. Among the different forest land uses, physiological traits and resource use efficiency have been significantly higher in oak forest (OF) compared to the mixed forest (MF) and pine forest (PF). Likewise, the concentration of macro–micro nutrients was also recorded higher during the rainy season. The concentration of macro–micro nutrients was higher in OF than in MF and PF in different forest land uses.ConclusionChanges in PFTs ultimately affect the ecosystem services imparted by the different forests. Such changes lead to the local adaptation of these forests through the interaction between PFTs and soil nutrients.

Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm

Complete hydrological time series are crucial for water and energy resources management and modelling in a changing climate. The reliability of the non-parametric stochastic machine learning algorithm MissForest was assessed for gap-filling of daily streamflow time series in a data-scarce region with strong climatic variability. A total of 1,586 reconstructions of streamflows for 1970–2016 were analyzed. Overall, MissForest performed satisfactorily to well, allowing a precise and reliable simulation of the missing data quickly and automatically. MissForest performance increased with the number of predictor records and record length, achieving satisfactory results with 20 or more records having 15 or more years in length. Reconstructed daily streamflow time series of rivers with natural flow regimes were simulated with good performance, which slightly decreased for discharge magnitude alterations by runoff inputs from urbanized areas and water diversion for irrigation. In cases of severe alterations of the flow regime, such as by hydropeaking, MissForest failed at filling daily streamflow series gaps. Reconstructed hydrographs allow analysis of streamflow change and variability and their interactions with key climatic variables.

The Influence of Key Climate Variables on Mass Balance of Naimona'nyi Glacier on a North‐Facing Slope in the Western Himalayas

AbstractGlacier mass changes in the Himalayas impact on sustainable water resources and hazards in downstream regions. However, mass balance variation and its drivers on north‐facing slopes in the Himalayas are not well understood. This study presents the meteorological conditions, energy and mass balance characteristics for Naimona'nyi Glacier on a north‐facing slope in the western Himalayas, based on energy‐mass balance model and high‐altitude measurements from October 2010 to September 2018. The average annual mass balance measured for Naimona'nyi Glacier was −0.39 m water equivalent a−1 and point mass balances below 6,070 m above sea level were always negative. Simulations showed that melt during the ablation season dominated the annual mass balance and the interannual variability in the mass balance, and that variability in melt energy was controlled by albedo during the ablation season. The annual mass balance was found to be significantly correlated with annual precipitation during the 2011–2018 period, which is accounted for by the change in ablation‐season albedo and melt energy. The average mass balance for glaciers in the western Himalayas were higher during the 2010–2019 period than during the 2000–2009 period, partly because of higher annual precipitation during the 2010–2019 period. Over the last 20 years, the increased precipitation in the cold season was driven by a cyclonic circulation anomaly over the northwestern Indian Peninsula, which may be linked to the strengthened North Atlantic Oscillation, and the increased precipitation in the ablation season was driven by an anomaly in the cyclonic circulation over Central India, which may be related to enhanced deep convection over the Indian subcontinent and the enhanced Atlantic multidecadal oscillation.

Climate-induced rice yield anomalies linked to large-scale atmospheric circulation in Bangladesh using multi-statistical modeling

Connecting climate-induced yield anomalies to the key climatic variables (KCVs) and large-scale atmospheric circulation index (LACI) is crucial to developing a strategic policy for food security in developing countries including Bangladesh. However, the effects of the KCVs and LACI on rice yield are still less explored in Bangladesh. This study aims to investigate daily climatic datasets from 18 sites and five LACIs during 1980–2017 to explore climate-induced yield anomalies to the KCVs and LACIs in different sub-zones of Bangladesh using the decoupling model and ensemble empirical mode decomposition (EEMD). We demarcated four sub-zones (northern, southwestern, south-central, and western) with different climate-induced yield index (CIYI) of winter Boro rice oscillations by employing principal component analysis. The CIYI time series in the northern zone was dominated by a 2–4-year oscillation, whereas the CIYI in the western zone demonstrated a prominent 6.5-year oscillation. Among the four sub-zones, south-central and northern zones had the most notable CIYI-KCV and CIYI-LACI associations, while the potential evapotranspiration (PET) in March and multivariate ENSO indices (MEI) in January were identified as the best yield prediction indicator. Wavelet coherence analysis indicated significant in-phase and out-phase coherences between KCVs and CIYI fluctuations at different time-frequency bands in these sub-zones. The random forest model also confirmed the MEI as the key driver influencing the rice yield fluctuation in Bangladesh. The isotopic signature of rainwater also demonstrated that the temperature variation is the main driver for event-based precipitation change in the south-central rice-growing zone. These outcomes can provide a scientific basis to take adaptive measures to mitigate the reduction in rice yield in western and northern Bangladesh for associated decision-makers and practitioners.

Rain-Fed Rice Yield Fluctuation to Climatic Anomalies in Bangladesh

To examine the rain-fed Aman rice yield fluctuation due to climatic anomalies overtimes in Bangladesh, we used climate-induced yield index (CIYI), ensemble empirical mode decomposition (EEMD), step-wise multiple regression, isotopic signature, wavelet transform coherence (WTC) and random forest (RF) model. In this work, daily multiple source climatic data which were collected between 1980 and 2017, from 18 weather stations and five atmospheric circulation indices were used for this purpose. The key findings were as follows; by employing principal component analysis (PCA), six temporal variability modes were identified as six corresponding sub-regions with various Aman rice CIYI fluctuations. The Aman rice CIYI in different sub-regions represented a noteworthy 3–4-year quasi-oscillation using the EEMD. The key climate variables (KCVs) including the potential evapotranspiration and cloud cover in September, the minimum temperature in August, and precipitation in July, August, and October were the best rice yield prediction signals in these sub-regions. The results suggest that Aman rice yield could likely decline by 33.59%, and 3.37% in the southwestern and southeastern regions, respectively, if KCV increased by 1 °C or 1%. The RF model suggests that the Indian Ocean Dipole (IOD) significantly influenced the rice yield. Isotopic signatures were employed to confirm the fluctuation and anti-amount effect during the Aman rice-growing period in Bangladesh. The results obtained in this study could be used as a guideline for sustainable mitigation and adaptation measures in managing agro-meteorological hazards in Bangladesh.

Assessing the effects of climate variable and timescale selection on uncertainties in dryness/wetness trends in conterminous China

AbstractAnalysing dryness/wetness trends, which is typically achieved by examining drought indices, is an essential approach for assessing extreme climate events. However, since there are differences among studies in terms of selected climate variables and timescales used to calculate drought indices, there is uncertainty regarding the magnitude and direction of the dryness/wetness, trend even for the same region. We used 40 years weather station data (1970–2010) and studied the uncertainties of dryness/wetness trends in conterminous China. The effects of selected climate variables, timescales and their interaction on the dryness/wetness trends were quantified by comparing the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index (SPEI) with the Thornthwaite equation and SPEI with the Penman–Monteith equation at 3‐, 12‐ and 24‐month timescales. The results indicated that (a) higher uncertainties occurred in relatively arid and humid regions; (b) timescale tended to exaggerate the effect of climate variables, even though its effect was substantially lower than climate variables; (c) relative humidity and precipitation were the primary climate variables affecting observed dryness/wetness trend uncertainties. Since the key climate variables changed depending on the selected timescale, the role of timescale should be further examined the Inner Mongolia and Northeast regions. In general, the study results suggest that the trend analysis of dryness/wetness should include the interaction of both climate variable and timescale selection, particularly when a single drought index is used to estimate the dryness/wetness trend in diverse regions.

Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP) Simulations with the Coupled Model CAS FGOALS-f3-L

The datasets for the tier-1 Scenario Model Intercomparison Project (ScenarioMIP) experiments from the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System model, finite-volume version 3 (CAS FGOALS-f3-L) are described in this study. ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Considering future CO2, CH4, N2O and other gases’ concentrations, as well as land use, the design of ScenarioMIP involves eight pathways, including two tiers (tier-1 and tier-2) of priority. Tier-1 includes four combined Shared Socioeconomic Pathways (SSPs) with radiative forcing, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6, 4.5, 7.0 and 8.5 W m−2, respectively. This study provides an introduction to the ScenarioMIP datasets of this model, such as their storage location, sizes, variables, etc. Preliminary analysis indicates that surface air temperatures will increase by about 1.89°C, 3.07°C, 4.06°C and 5.17°C by around 2100 under these four scenarios, respectively. Meanwhile, some other key climate variables, such as sea-ice extension, precipitation, heat content, and sea level rise, also show significant long-term trends associated with the radiative forcing increases. These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.

Challenges in the selection of atmospheric circulation patterns for the wind energy sector

AbstractAtmospheric circulation patterns that prevail for several consecutive days over a specific region can have consequences for the wind energy sector as they may lead to a reduction of the wind power generation, impacting market prices or repayments of investments. The main goal of this study is to develop a user‐oriented classification of atmospheric circulation patterns in the Euro‐Atlantic region that helps to mitigate the impact of the atmospheric variability on the wind industry at seasonal timescales. Particularly, the seasonal forecasts of these frequencies of occurrence can be also beneficial to reduce the risk of the climate variability in wind energy activities. K‐means clustering has been applied on the sea level pressure from the ERA5 reanalysis to produce a classification with three, four, five and six clusters per season. The spatial similarity between the different ERA5 classifications has revealed that four clusters are a good option for all the seasons except for summer when the atmospheric circulation can be described with only three clusters. However, the use of these classifications to reconstruct wind speed and temperature, key climate variables for the wind energy sector, has shown that four clusters per season are a good choice. The skill of five seasonal forecast systems in simulating the year‐to‐year variations in the frequency of occurrence of the atmospheric patterns is more dependent on the inherent skill of the sea level pressure than on the number of clusters employed. This result suggests that more work is needed to improve the performance of the seasonal forecast systems in the Euro‐Atlantic domain to extract skilful forecast information from the circulation classification. Finally, this analysis illustrates that from a user perspective it is essential to consider the application when selecting a classification and to take into account different forecast systems.

Trutinor: A Conceptual Study for a Next-Generation Earth Radiant Energy Instrument

Uninterrupted and overlapping satellite instrument measurements of Earth’s radiation budget from space are required to sufficiently monitor the planet’s changing climate, detect trends in key climate variables, constrain climate models, and quantify climate feedbacks. The Clouds and Earth’s Radiant Energy System (CERES) instruments are currently making these vital measurements for the scientific community and society, but with modern technologies, there are more efficient and cost-effective alternatives to the CERES implementation. We present a compact radiometer concept, Trutinor (meaning “balance” in Latin), with two broadband channels, shortwave (0.2–3 μm) and longwave (5–50 μm), capable of continuing the CERES record by flying in formation with an existing imager on another satellite platform. The instrument uses a three-mirror off-axis anastigmat telescope as the front optics to image these broadband radiances onto a microbolometer array coated with gold black, providing the required performance across the full spectral range. Each pixel of the sensor has a field of view of 0.6°, which was chosen so the shortwave band can be efficiently calibrated using the Moon as an on-orbit light source with the same angular extent, thereby reducing mass and improving measurement accuracy, towards the goal of a gap-tolerant observing system. The longwave band will utilize compact blackbodies with phase-change cells for an absolute calibration reference, establishing a clear path for SI-traceability. Trutinor’s instrument breadboard has been designed and is currently being built and tested.

A statistical analysis of lossily compressed climate model data

The data storage burden resulting from large climate model simulations continues to grow. While lossy data compression methods can alleviate this burden, they introduce the possibility that key climate variables could be altered to the point of affecting scientific conclusions. Therefore, developing a detailed understanding of how compressed model output differs from the original is important. Here, we evaluate the effects of two leading compression algorithms, sz and zfp, on daily surface temperature and precipitation rate data from a widely used climate model. While both algorithms show promising fidelity with the original output, detectable artifacts are introduced even at relatively tight error tolerances. This study highlights the need for evaluation methods that are sensitive to errors at different spatiotemporal scales and specific to the particular climate variable of interest.

Half-century urban drying in Shijiazhuang City**Submitted to Environmental Research Letters for possible publication.

Climatic and environmental change in urban areas attracts more and more attention with rapid urbanization worldwide. Change in key surface climatic variables in urban areas is a core component of urban climatic change. Here we report an analysis of relative humidity (RH) change in Shijiazhuang City, China, over the period 1963–2012, based on the RH data of a typical urban station and the surrounding rural stations. We found that the annual mean RH at the urban station underwent a more significant downward trend (−0.74%/10 yr) than those at the four rural stations (−0.16%/10 yr on average), and the most significant decrease occurred after 1990s; urbanization effect on the decrease of annual mean RH at the urban station reached −0.59%/10 yr, which was highly significant, with the urbanization contribution to the overall RH decrease being 78.7%, indicating that the trend of urban drying was caused dominantly by the urbanization; urbanization effects on seasonal mean RH decrease at the urban station were −0.60%/10 yr, −0.61%/10 yr, −0.67%/10 yr and −0.46%/10 yr respectively for spring, summer, autumn and winter, which were all significant statistically, with a 100% urbanization contribution for winter; rising temperature, dropping evaporation and decreasing precipitation days relative to rural areas were found to be the main factors affecting the urban RH decrease. The conclusions are of practical significance to further understand the characteristics and causes of RH change at a typical urban meteorological station in North China, and to detect the large-scale climatic and environmental change for assessing the impact of climate change on agricultural production and water cycle.

Sensitivity of simulated climate over the MENA region related to different land surface schemes in the WRF model

The effects of different land surface scheme (LSS) implementations on the simulated climate of the Middle East and North Africa (MENA) have been investigated with the Weather Research and Forecasting (WRF) regional model. Six simulations were carried out using four different LSSs (Noah, NoahMP, CLM, RUC) for the period 2000–2010, driven by ERA-Interim meteorological reanalyses at a horizontal resolution of 50 km. Deviations of key surface climate variables, radiation, and turbulent fluxes from the different LSS runs are presented relative to the default Noah scheme. The simulated annual mean climate variables in the MENA (indicating uncertainty) range from 0.7 to 2.4 ∘C for air temperature, 2.0 to 3.4 ∘C for land temperature, and 5 to 25 mm/month (54–65%) for precipitation. The Noah scheme deviates less than − 1 W/m2 from the domain-wide surface energy balance and the NoahMP less than − 2 W/m2, while for CLM and RUC the deviation is 3–4 W/m2. Considering the differences among the surface energy balance from the various LSSs compared to the reference Noah, a surface climate response is calculated, and average LSS-induced climate sensitivity is derived for the air (and land) temperature of 0.1 ∘C per W/m2 and − 6 mm per W/m2 for precipitation. The LSS-induced range in the modelled climate is of similar magnitude to the climate change projection estimates for the region, which highlights the importance of carefully selecting a land surface scheme in the regional climate simulations.

Responses of Yield Fluctuation of Winter Oilseed Rape to Climate Anomalies in South China at Provincial Scale

The regional differences for responses of winter oilseed rape yield to climate changes was not reported in China, and the statistical climate–yield relationships at the provincial scale in south China should be a valuable attempt. With using provincial data of climate variables and oilseed rape yields during 1978–2016, the main findings were as follows: (1) based on 14 provincial climate-induced yield index (CIYI) series for oilseed rape yield, south China could be divided into five sub-regions with different temporal variations in CIYI by using principal component analysis, and they were Yangtze River delta (Region I), central south China (Region II), southwest China (Region III), southernmost China (Region IV) and southeastern coast (Region V), respectively; (2) the correlation and regression analysis between CIYI and monthly climate variables indicated that the precipitation of April, average potential evapotranspiration of November, average potential evapotranspiration of October, average mean temperature of May, and average potential evapotranspiration of September were the key climate variables (KCV) affecting yield in Region I, II, III, IV and V, respectively; (4) among the five sub-regions, the impacts of KCV changes on yields in southwest China were more notable, and the increasing trend of average potential evapotranspiration of November caused the decrease of yield by 2.08% in 1990s and 4.24% after 2000, respectively. These results could be employed as references for the assessment and mitigation of agro-meteorological disasters in south China.

Durability and Climate Change—Implications for Service Life Prediction and the Maintainability of Buildings

Sustainable building practices are rooted in the need for reliable information on the long-term performance of building materials; specifically, the expected service-life of building materials, components, and assemblies. This need is ever more evident given the anticipated effects of climate change on the built environment and the many governmental initiatives world-wide focused on ensuring that structures are not only resilient at their inception but also, can maintain their resilience over the long-term. The Government of Canada has funded an initiative now being completed at the National Research Council of Canada’s (NRC) Construction Research Centre on “Climate Resilience of Buildings and Core Public infrastructure”. The outcomes from this work will help permit integrating climate resilience of buildings into guides and codes for practitioners of building and infrastructure design. In this paper, the impacts of climate change on buildings are discussed and a review of studies on the durability of building envelope materials and elements is provided in consideration of the expected effects of climate change on the longevity and resilience of such products over time. Projected changes in key climate variables affecting the durability of building materials is presented such that specifications for the selection of products given climate change effects can be offered. Implications in regard to the maintainability of buildings when considering the potential effects of climate change on the durability of buildings and its components is also discussed.

Climate adaptation pathways for agriculture: Insights from a participatory process

Climate change presents significant risks and opportunities for agriculture. Agricultural producers are likely to be adversely affected by changes in higher mean temperatures, more frequent extreme climatic events, and an increase in inter-annual weather variability, with implications for established management practices. While probabilities of future change in key climatic variables become more refined, significant uncertainties remain, complicating efforts at adaptation action on the ground. Adaptation pathways planning allows stakeholders to consider a range of possible futures, identify and evaluate adaptation options, and sequence them over time. The aim is to have a robust plan that is flexible enough to deliver desired outcomes regardless of how the future unfolds. We developed and applied a pathways approach to support regional adaptation planning in Hawke’s Bay, New Zealand, a premier food- and wine-producing region, where changing land use, competition for freshwater, and climate change, are presenting challenges to agricultural producers and rural communities. Working with a range of stakeholders from local government, community and the region’s diverse agricultural sectors, comparative case study analysis in two catchments is used to identify values relating to productive landscapes, likely impacts of climate change and potential adaptation options at the local level. Actions for key areas of decision making were evaluated and sequenced over time, providing the basis for a regional adaptation pathway. The results highlight the complex interaction between climatic and non-climatic drivers of change at the local and regional scale, and the need to closely consider trade-offs and synergies in any adaptations. With adaptation rapidly emerging as a priority for policy-making and practice, the results can help inform and empower stakeholders to implement actions towards climate adapted futures, and demonstrate the utility of pathways approaches for local- and regional planning.

Impact of rainfall seasonality on intraspecific trait variation in a shrub from a Mediterranean climate

Abstract Selection pressures along climate gradients give rise to predictable variation in plant functional traits of individual species suggestive of local adaptation. Species whose ranges include winter rainfall, Mediterranean climates, or other strongly seasonal climates, may be exposed to divergent selection pressures at different ends of seasonality gradients. Here, we evaluate how rainfall seasonality in conjunction with other key climatic variables impacts patterns of trait variation in Pelargonium scabrum, a woody shrub from the Greater Cape Floristic Region of South Africa. This biodiversity hotspot encompasses a Mediterranean climate (wet winters and hot, dry summers) and displays steep gradients in temperature and water availability. We used Bayesian regression models to evaluate leaf trait–trait and trait–climate relationships among 26 populations. Models included rainfall seasonality and its interaction with other climate variables (mean annual temperature, mean annual precipitation and potential evapotranspiration) as predictors to test for the impact of climate variation on three leaf traits: size, dissection and leaf mass per area (LMA). We evaluated model explanatory power by calculating Bayesian R2 values, and predictive power via leave‐one‐out cross‐validation. Trait–trait associations were modulated by rainfall seasonality, including a reversal in the relationship between leaf size and dissection depending on the proportion of rain received in winter. Trait–climate models were improved by including rainfall seasonality as a predictor for both explanatory and predictive power. For leaf dissection and LMA, we detected significant interactions between rainfall seasonality and other environmental variables, leading to reversals in the relationships between these traits and the three environmental variables depending on the proportion of winter rainfall. Differences in the timing of rainfall, coupled with strong differences in the covariation of climate variables, impose divergent selection pressures on P. scabrum populations resulting in divergence of trait values, trait integration and responses to climate gradients. These patterns are consistent with local adaptation of P. scabrum populations mediated by the interactions between temperature and the amount and timing of rainfall. Species arrayed along broad climate gradients represent an excellent opportunity for investigating patterns of trait variation and abundances and distributions of species in relation to future changes in climate. A free Plain Language Summary can be found within the Supporting Information of this article.

Determining the impact of key climatic factors on geographic distribution of wild Akebia trifoliate

Abstract Based on the first-hand data of field investigation, existing literature, books and plant specimens database resources, the complete and available spatial database of wild Akebia trifoliate in China was established firstly. After analyzing the geographical distribution of Akebia trifoliate and its ecological factors, such as climate, soil and topographic characteristics, based on the existing literature, we selected 16 climatic indicators and used statistical analysis to study key climatic variables affecting its growth from a macro perspective. The results show that: (1) According to the characteristic roots and contribution rate obtained by principal component method, three principal factors are determined. Their corresponding value of three principal factors were 6.0538 and 37.84%, 4.7487 and 29.68%, 3.3822 and 21.14% respectively. All the characteristic roots indicate that the three principal factors are playing a dominant role in climate variables of influencing the distribution of Akebia trifoliate. (2) The first principal factor is called “heat factor”, the most important role determining geographical distribution of Akebia trifoliate, including the variables with larger factor load such as active accumulated temperature (≥10℃), warm index, biological temperature and annual average temperature. It shows that Akebia trifoliate tends to grow in areas with higher heat conditions. The second principal factor is called “humidity factor”, the second role for Akebia trifoliate, including the variables with larger factor load such as evapotranspiration rate, humidity index, annual precipitation, growing season precipitation and relative humidity. So it shows that Akebia trifoliate tends to be in areas with sufficient precipitation and high humidity. The third principal factor is called “illumination factor”, including the variables with larger load such as annual sunshine hours, growing season sunshine hours and extreme low temperature. So Akebia trifoliate likes the areas with relatively short sunshine hours. (3) Heat factor and humidity factor are the main limiting factors for its growth. The significant climatic variables offer a basis for the selection of ecological factors and the determination of weight in the suitability analysis of Akebia trifoliate. These results could provide a theoretical basis for the artificial cultivation of Akebia trifoliate. The ideas and methods for wild Akebia trifoliate in this paper could give a reference for the study of other valuable wild plants.