Changes In Mean Annual Temperature Research Articles

Identifying temperature refuges in Utah using temperature, biota, and habitat data

AbstractUnderstanding where on landscapes to make investments, such as designating protected areas, is a critical component of biodiversity management. Locations for management actions should achieve current management objectives while also having the best chance of continued success in the future. Climate change has the potential to undermine biodiversity management, as it may lead to substantial changes in environmental conditions that are outside local managers' control. Following changes in environmental conditions, areas on the landscape may become unsuitable for the species or habitats that the initial actions were intended to benefit. The potential for local actions to be undermined by global‐scale threats makes it essential to account for and minimize exposure to temperature change. We present a series of analyses identifying priority areas for wildlife and habitat management. We conducted our analyses using a systematic landscape planning approach that identifies areas within species' ranges or current distributions of key habitats that are predicted to be less affected by future temperature change. We used the ranges of 142 animal and 149 plant species identified as species of greatest conservation need (SGCN) together with the distributions of 14 terrestrial and 19 aquatic key habitats in Utah, USA. We measured temperature change in 2 ways: as changes in mean annual temperature between 2020 and the year 2100 (temperature difference) and by quantifying how far a species range or habitat would have to shift to maintain its current temperature envelope (climate velocity). We identified the sub‐watersheds with hydrologic unit code 12 (HUC 12) that collectively encompassed the ranges of our SGCNs and key habitats while minimizing overall exposure to temperature change. These high priority HUC 12s represented areas that were not only hotspots for SGCNs and key habitats but also acted as temperature refugia, where management actions are likely to be robust to temperature change. We hope that our identification of high‐priority HUC 12s will help inform and guide future management actions to improve their long‐term outcomes.

Spatially Heterogeneous Responses of Planktonic Foraminiferal Assemblages Over 700,000 Years of Climate Change

ABSTRACTAimTo determine the degree to which assemblages of planktonic foraminifera track thermal conditions.LocationThe world's oceans.Time PeriodThe last 700,000 years of glacial–interglacial cycles.Major Taxa StudiedPlanktonic foraminifera.MethodsWe investigate assemblage dynamics in planktonic foraminifera in response to temperature changes using a global dataset of Quaternary planktonic foraminifera, together with a coupled Atmosphere–Ocean General Circulation Model (AOGCM) at 8000‐year resolution. We use ‘thermal deviance’ to assess assemblage responses to climate change, defined as the difference between the temperature at a given location and the bio‐indicated temperature (i.e., the abundance‐weighted average of estimated temperature optima for the species present).ResultsAssemblages generally tracked annual mean temperature changes through compositional turnover, but thermal deviances are evident under certain conditions. The coldest‐adapted species persisted in polar regions during warming but were not joined by additional immigrants, resulting in minimal assemblage turnover. The warmest‐adapted species persisted in equatorial regions during cooling with similarly minimal assemblage change. Assemblages at mid‐latitudes mostly tracked temperature cooling and warming.Main ConclusionsPlanktonic foraminiferal assemblages were generally able to track or endure temperature changes: as climate warmed or cooled, bio‐indicated temperature also became warmer or cooler, although to a variable degree. At polar sites under warming and at equatorial sites under cooling, the change in bio‐indicated temperature was less than, or even opposite to, what would be expected from estimated environmental change. Nevertheless, all studied species persisted across the study interval, regardless of thermal deviance—a result that highlights the resilience and inertia of planktonic foraminifera on an assemblage level to the last 700,000 years of climate change.

Multiomics analyses reveal the central role of the nucleolus and its machinery during heat stress acclimation in Pinus radiata.

Global warming is causing rapid changes in mean annual temperature and more severe drought periods. These are major contributors of forest dieback, which is becoming more frequent and widespread. In this work, we investigated how the transcriptome of Pinus radiata changed during initial heat stress response and acclimation. To this end, we generated a high-density dataset employing Illumina technology. This approach allowed us to reconstruct a needle transcriptome, defining 12 164 and 13 590 transcripts as down- and up-regulated, respectively, during a time course stress acclimation experiment. Additionally, the combination of transcriptome data with other available omics layers allowed us to determine the complex inter-related processes involved in the heat stress response from the molecular to the physiological level. Nucleolus and nucleoid activities seem to be a central core in the acclimating process, producing specific RNA isoforms and other essential elements for anterograde-retrograde stress signaling such as NAC proteins (Pra_vml_051671_1 and Pra_vml_055001_5) or helicase RVB. These mechanisms are connected by elements already known in heat stress response (redox, heat-shock proteins, or abscisic acid-related) and with others whose involvement is not so well defined such as shikimate-related, brassinosteriods, or proline proteases together with their potential regulatory elements. This work provides a first in-depth overview about molecular mechanisms underlying the heat stress response and acclimation in P. radiata.

Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0

The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.

Reconstruction of temperature and monsoon precipitation in southwestern China since the last deglaciation

Monsoon precipitation and temperature are the crucial components of the Indian monsoon climate, yet their spatial-temporal relationship remains unclear. In this study, we reconstruct the changes in monsoon precipitation and mean annual temperature simultaneously over the past 16,000 years using multiple proxies, including isoprenoid glycerol dialkyl glycerol tetraethers (isoGDGTs) and n-alkanes, from an alpine lake in southwestern China. On the one hand, the reconstructed monsoon precipitation shows a long-term increasing trend during the last deglaciation and a gradual decrease during the Holocene, with the highest-humidity period occurring in the early Holocene. Our result displays good consistency with the changes in regional mean annual precipitation, summer precipitation, and monsoon intensity during the last deglaciation and the Holocene. On the other hand, the reconstructed mean annual temperature inferred from the n-alkane proxy shows a long-term warm trend since the last deglaciation. Our result is consistent with the regional reconstruction of both mean annual and summer temperatures during the last deglaciation, while evident seasonal differences were observed for Holocene records with a cooling trend of summer temperature and a warm trend of mean annual temperature. Comprehensive comparison reveals that monsoon precipitation in the Indian monsoon region is mainly related to changes in the Intertropical Convergence Zone (ITCZ) and land-sea thermal contrast controlled by boreal summer insolation. In addition, the increase in temperature during deglaciation is primarily attributed to the escalation of greenhouse gases (GHGs), while the divergent trend of seasonal temperature during the Holocene is mainly attributed to the changes in local seasonal insolation. Our research emphasizes that monsoon precipitation is a summer signature in the Indian monsoon region, which is consistent with the changes in summer temperature, while contrasting with the changes in mean annual temperature. Therefore, it is necessary to consider the seasonal difference in temperature changes when investigating the hydrothermal combination in Indian monsoon regions.

Structure and function of microbiomes in the rhizosphere and endosphere response to temperature and precipitation variation in Inner Mongolia steppes.

Owing to challenges in the study of complex rhizosphere and endophytic microbial communities, the composition and function of such microbial communities in steppe ecosystems remain elusive. Here, we studied the microbial communities of the rhizosphere and endophytic microbes of the dominant plant species across the Inner Mongolian steppes using metagenomic sequencing and investigated their relationships with changes in mean annual temperature (MAT) and mean annual precipitation (MAP). Metagenomic sequencing based on Illumina high-throughput sequencing, using the paired end method to construct a small fragment library for sequencing. Adaptation of root systems to the environment affected the composition and function of rhizosphere and endophytic microbial communities. However, these communities exhibited distinct community assembly and environmental adaptation patterns. Both rhizosphere and endophytic microbial communities can be divided into two unrelated systems based on their ecological niches. The composition and function of the rhizosphere microbial communities were mainly influenced by MAT, while those of the endophytic microbial communities were mainly influenced by MAP. MAT affected the growth, reproduction, and lipid decomposition of rhizosphere microorganisms, whereas MAP affected reverse transcription and cell wall/membrane/envelope biogenic functions of endophytic microorganisms. Our findings reveal the composition and function of the rhizosphere and endophytic microbial communities in response to changes in MAP and MAT, which has important implications for future biogeography and climate change research.

All-cause mortality attributable to long-term changes in mean temperature and diurnal temperature variation in China: a nationwide quasi-experimental study

Previous studies have demonstrated an association between short-term exposure to ambient temperature and mortality. However, the long-term effects of elevated temperature and temperature variability on mortality have remained somewhat elusive in epidemiological studies. We conducted a comprehensive epidemiological study utilizing Chinese population census data from 2000 and 2010. Census-derived demographic and socioeconomic factors were paired with temperature data from the European Re-Analysis Land Dataset across 2823 counties. We employed a difference-in-difference approach to quantitatively examine the relationship between all-cause mortality and annual exposure to mean temperature and diurnal temperature range (DTR). Additionally, we evaluated the potential effects of socioeconomic and environmental covariate modifications on this relationship and calculated the attributable mortality. Lastly, we projected excess deaths attributable to annual temperature exposure under various shared socioeconomic pathways (SSPs, e.g. SSP126, SSP370, and SSP585). For each 1 °C rise in annual mean temperature and DTR, the mortality risk could increase by 6.12% (95% CI: 0.84%, 11.69%) and 7.72% (95% CI: 3.75%, 11.84%), respectively. Counties with high labor-force ratios and high NO2 and O3 concentrations appeared to be sensitive to the annual mean temperature and DTR. Climate warming from 2000 to 2010 may have resulted in 5.85 and 14.46 additional deaths per 10 000 people attributable to changes in annual mean temperature and DTR, respectively. The excess mortality related to changes in annual mean temperature and DTR is expected to increase in the future, with special attention warranted for long-term temperature changes in Southwest China. Our findings indicate that long-term mean temperature and DTR could significantly impact mortality rates. Given the spatial heterogeneity of increased mortality risk, the formulation of region-specific strategies to tackle climate change is crucial.

Use of CMIP6 scenarios as a reference to understand the responses of macrophyte germination and seedling growth to future warming and allelopathy co-stressors

The application of appropriate references such as CMIP6 climate scenarios for benchmarking studies of climate change on ecosystems can promote consistency among different climate change research. However, the use of CMIP6 climate scenarios is not common among experiments on the effects of climate change on freshwater ecosystems. Also, little is known about the impact of ecological factor such as allelopathy of alien species on macrophyte germination and seedling growth under different climate scenarios. In our study, by simulating three annual mean temperature changes at global warming levels of 1.5 °C (low warming scenario), 2 °C (medium warming scenario) and 4 °C (high warming scenario) corresponding to CMIP6 multi-model mean change at the corresponding global warming level, we conducted a mesocosm experiment to investigate their possible effects of different climate scenarios and allelopathy co-stressors on macrophyte germination and seedling growth. Our study showed that three warming scenarios all can facilitate macrophyte propagule germination and seedling growth, but the effect paths vary with CMIP6 warming scenarios and there are more influence pathways under high warming scenarios than under low and medium warming scenarios. Higher aqueous extract concentrations of Eichhornia crassipes can significantly stimulate macrophyte propagule germination and seedling growth. And the medium and high warming scenarios may exacerbate the impacts of allelopathic substances on macrophyte germination and seedling growth, and their effects depend on the combination of the two stressors. These results indicated that medium- and high-temperature scenarios may have greater ecological effects on macrophytes than low-temperature scenarios. Thus, our results highlighted that future climate studies need proper benchmarks such as CMIP6 warming scenarios, because it can provide relatively more accurate and realistic simulations, valid comparative results, comprehensive understanding and supportive coordination among researchers.

Holocene summer temperature record based on branched tetraethers in Northeast China

The temperature evolution during the Holocene shows conflicting patterns and yields the controversy regarding whether the Holocene followed a warming or cooling trend. Resolving this controversy is critical for understanding the underlying mechanisms of climate change and evaluating global warming on a longer timescale. Here we present a quantitative summer temperature reconstruction based on branched glycerol dialkyl glycerol tetraethers analyzed from a sedimentary sequence retrieved from the volcanic lake Kielguo crater lake in Northeast (NE) China. Our record revealed that the summer temperature in NE China followed a rough cooling trend during ~10–2.2 cal ka BP and increased after ~2.2 cal ka BP. The warmest period appeared at ~10–8.8 cal ka BP with ~15.8°C, and the coldest time occurred at ~2.3 cal ka BP with ~13.4°C. Compared with other temperature records from NE China, we conclude that the summer and winter temperature change trends in NE China were probably controlled by the summer and winter insolation respectively, and the ice volume. The mean annual temperature changes in NE China resulted from the superposition of summer cooling and winter warming during the Holocene, and the decrease in ice volume forcing the increase in mean annual temperatures before ~7–6 cal ka BP. The switch in mean annual temperature changes was probably linked with the collapse of Northern Hemisphere ice sheets.

Potential consequences for rising temperature trends in the Oti River Basin, West Africa

IntroductionOne of the ways that climate change manifest itself is through temperature changes. Though the Oti River basin has been grappling with drought incidents, there has been little or no emphasis on analyzing temperature fluctuations in the basin. This study aimed to analyze the mean annual and seasonal temperature for the observed (1981–2010) and future periods (2021–2050) over the Oti River basin.MethodsHistorical data were obtained from meteorological stations and the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER). Data quality assessment was conducted, and the NASA POWER temperature was validated against the stations' temperature. Ensemble of eight models acquired from the Coordinated Regional Climate Downscaling Experiment (CORDEX–Africa) under two Representative Concentration Pathways (RCP4.5 and RCP8.5), were used for the future projection. The mean annual and seasonal temperatures were analyzed for 1981–2010 and 2021–2050 (under RCPs 4.5 and 8.5 scenarios). The Modified Mann–Kendall test was used for trend analysis at 5% significant level.Results and discussionIn the near-future, temperature is anticipated to increase at the mean monthly scale in the ranges of +0.88°C in October to +2.65°C in January under the RCP4.5 scenario, while the RCP8.5 predicts increases between +2.71°C in July and +6.48°C in January. The annual mean temperature change for the entire basin is projected at +1.47°C (RCP4.5) and +4.2°C (RCP8.5). For the rainy season period, the RCP4.5 projects annual mean temperature changes in the ranges of −0.72°C and +1.52°C while the RCP8.5 predicts changes between +1.06°C and +4.45°C. Concerning the dry season period, the anticipated changes in the annual mean temperature under the RCP4.5 would range from −0.43°C to +2.78°C whereas that of RCP8.5 would be between +1.97°C and 7.25°C. The Modified Mann–Kendall test revealed significantly increasing trends for temperature projections in the basin under both the RCPs 4.5 and 8.5 in the basin. The study provides significant contribution to the comprehension of temperature patterns in time and space which is necessary for the sustenance of rainfed agriculture and water resources within the basin.

Future climate change effects on US forest composition may offset benefits of reduced atmospheric deposition of N and S.

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously derived growth and survival responses for 94 tree species, representing >90% of the contiguous US forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found averaged across temperature scenarios the relative abundance of 60 species were projected to decrease more than 5% and 20 species were projected to increase more than 5%; and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of US forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.

Influence of Mycorrhiza on C:N:P Stoichiometry in Senesced Leaves.

Senesced leaves play a vital role in nutrient cycles in the terrestrial ecosystem. The carbon (C), nitrogen (N) and phosphorus (P) stoichiometries in senesced leaves have been reported, which are influenced by biotic and abiotic factors, such as climate variables and plant functional groups. It is well known that mycorrhizal types are one of the most important functional characteristics of plants that affect leaf C:N:P stoichiometry. While green leaves' traits have been widely reported based on the different mycorrhiza types, the senesced leaves' C:N:P stoichiometries among mycorrhizal types are rarely investigated. Here, the patterns in senesced leaves' C:N:P stoichiometry among plants associated with arbuscular mycorrhizal (AM), ectomycorrhizal (ECM), or AM + ECM fungi were explored. Overall, the senesced leaves' C, with 446.8 mg/g in AM plants, was significantly lower than that in AM + ECM and ECM species, being 493.1 and 501.4 mg/g, respectively, which was mainly caused by boreal biomes. The 8.9 mg/g senesced leaves' N in ECM plants was significantly lower than in AM (10.4 mg/g) or AM + ECM taxa (10.9 mg/g). Meanwhile, the senesced leaves' P presented no difference in plant associations with AM, AM + ECM and ECM. The senesced leaves' C and N presented contrary trends with the changes in mean annual temperature (MAT) and mean annual precipitation (MAP) in ECM or AM + ECM plants. The differences in senesced leaves' C and N may be more easily influenced by the plant mycorrhizal types, but not P and stoichiometric ratios of C, N and P. Our results suggest that senesced leaves' C:N:P stoichiometries depend on mycorrhizal types, which supports the hypothesis that mycorrhizal type is linked to the evolution of carbon-nutrient cycle interactions in the ecosystem.

Environmental drivers of body size in North American bats

Abstract Bergmann's rule—which posits that larger animals live in colder areas—is thought to influence variation in body size within species across space and time, but evidence for this claim is mixed. We used Bayesian hierarchical models to test four competing hypotheses for spatiotemporal variation in body size within 20 bat species across North America: (1) the heat conservation hypothesis, which posits that increased body size facilitates body heat conservation (and which is the traditional explanation for the mechanism underlying Bergmann's rule); (2) the heat mortality hypothesis, which posits that increased body size increases susceptibility to acute heat stress; (3) the resource availability hypothesis, which posits that increased body size is enabled in areas with more abundant food; and (4) the starvation resistance hypothesis, which posits that increased body size reduces susceptibility to starvation during acute food shortages. Spatial variation in body mass was most consistently (and negatively) correlated with mean annual temperature, supporting the heat conservation hypothesis. Across time, variation in body mass was most consistently (and positively) correlated with net primary productivity, supporting the resource availability hypothesis. Climate change could influence body size in animals through both changes in mean annual temperature and resource availability. Rapid reductions in body size associated with increasing temperatures have occurred in short‐lived, fecund species, but such reductions will be obscured by changes in resource availability in longer‐lived, less fecund species. Read the free Plain Language Summary for this article on the Journal blog.

Modelling the impact of climate change and tillage practices on soil CO2 emissions from dry farmland in the Loess Plateau of China

Conservation tillage to agricultural fields has been widely used in the Loess Plateau of China. However, uncertainties remain regarding the long-term effects of this approach on soil carbon dioxide (CO2) emissions under future climate scenarios. We used the pre-validated DeNitrification DeComposition (DNDC) model to investigate the effects of no-tillage and straw mulch implementation on soil CO2 emissions from dry farmland in the Loess Plateau of China under different future climate scenarios and their dynamic changes. Based on the baseline period (1971–2019) and climate data (2022–2070) under RCP4.5 and RCP8.5 scenarios for two climate models, soil CO2 emissions from dry farmland were simulated under four tillage practices (CT: conventional tillage, CTS: conventional tillage with straw mulch, NT: no-tillage, NTS: no-tillage with straw mulch). The results showed that future climate change would affect soil CO2 emissions of each treatment. Compared with the baseline period, the annual mean CO2 emissions of four treatments increased under the RCP4.5 and RCP8.4 scenarios for the two climate models, with increases ranging from 5.81% to 56.73% under the RCP4.5 scenario and 24.48% to 68.75% under the RCP8.5 scenario. In addition, compared with CT and NT treatments, the CTS and NTS treatments with straw mulch had higher annual mean CO2 emissions under both climate scenarios, while the NT treatment had the lowest. Further analysis showed that the changes in soil CO2 emissions of each treatment under different future climate scenarios were related to the constant changes in annual mean temperature, annual precipitation, and atmospheric CO2 concentration. Simulation of the long-term effects on soil CO2 emissions under different tillage practices based on future climate scenarios led to the conclusion that conservation tillage practices, together with future changes in temperature, precipitation, and atmospheric CO2 concentrations, affect soil CO2 emissions, while no-tillage has potential to mitigate soil CO2 emissions from dry farmland in the Loess Plateau of China under future climate change.

Climate Change and Production of Cereal Crops in East Africa: Role of Temperature, Precipitation, Ecological and Carbon Footprint

This study was conducted to examine the impact of changes in annual mean temperature, annual mean precipitation, carbon footprint, ecological footprint, and area harvested on cereal crop production in East Africa. The study was conducted in a panel cointegration framework using annual time series from 1980 to 2018 for five East African countries i.e., Ethiopia, Kenya, Rwanda, Tanzania, and Uganda. Unit root tests were performed using LLC, IPS, ADF-Fisher, and PP-Fisher tests, while panel co-integration tests were performed using Pedroni residual, Kao residual, and Johansen Fisher panel co-integration tests. Long-run coefficients were estimated using the Pooled Mean Group/Autoregressive Distributed Lag, Panel Fully-modified OLS, and Panel Dynamic OLS models. Empirical findings from the three models revealed that increases in annual mean temperature have adverse effects on cereal crop production, while increases in annual mean precipitation, carbon footprint, ecological footprint, and area harvested have positive effects on cereal crop production in East Africa. Based on these findings, it can be suggested that prioritization of climate change adaptation strategies in the region such as the development of drought and heat-resistant crop varieties, changing in planting dates, and investment in irrigation technologies to boost cereal crops productivity could play a role in minimizing the adverse effects of changes in climate factors.

Spatio-temporal patterns of centennial-scale climate change over the Tibetan Plateau during the past two millennia and their possible mechanisms

In order to project future changes in climate and their corresponding environmental and social impacts, it is of utmost importance to understand the spatiotemporal patterns of past climate changes on the Tibetan Plateau (TP). Although numerous records of late Holocene climate changes have been developed across the TP, the spatiotemporal patterns of changes in temperature and precipitation/moisture are unclear and a comprehensive review of this topic is urgently needed. Here, we present such a synthesis using currently available temperature and precipitation/moisture records from across the TP to understand spatial and temporal variations over the past two millennia. The main conclusions are as follows: 1) Temperature variations on the TP during the past two millennia are not homogeneous and show strong seasonality. Although patterns of mean annual temperature change on the TP during the past two millennia remain contradictory, summer temperature variations are generally consistent. The summer temperature reconstructions indicate that the warmest period during the past two millennia was in the middle ages, as opposed to the recent 20th century. 2) Precipitation/moisture patterns on the TP are spatially complex. The most striking feature is that an inverse pattern of precipitation/moisture variation can be detected over most parts of the TP between 600-1400 AD and 1400-1900 AD. Specifically, the eastern TP, southwestern TP, and part of the northeastern TP were generally wetter, whereas the southeastern TP, the southern TP, the northwestern TP, and part of the northeastern TP were generally drier during 600–1400 AD as compared to during 1400–1900 AD. However, such anti-phased hydroclimatic variability is not found in either the cold period of 1–600 AD or in the current warm period of the past century. This indicates that temperature and precipitation/moisture changes appear in multiple combinations. 3) Fluctuations in the intensity and seasonal movement of the westerlies and the Indian summer monsoon (ISM) over the Tibetan Plateau, as well as the associated climate variability modes (e.g., North Atlantic Oscillation (NAO), Indian Ocean dipole (IOD), ENSO, and Intertropical Convergence Zone (ITCZ)), might be responsible for the spatially complex patterns of climate anomalies over TP during the past 2000 years. 4) The results highlight the need for more high-quality paleoclimatic and paleoenvironmental reconstructions that span the past two millennia on the TP. Such reconstructions must have unambiguous climate significance, clear seasonality, site-specific calibration, and robust dating.

A locally relevant framework for assessing the risk of sea level rise under changing temperature conditions: Application in New Caledonia, Pacific Ocean

Sea level rise is a key feature in a warmer world and its impact can be seen globally. Assessing climate change-induced sea level rise, therefore, is urgently needed particularly in small island nations, where the threats of sea level rise are immediate, but the level of preparedness is low. Here, we propose a stochastic simulator to link changes in Mean Annual Temperature (MAT) to Mean Annual Sea Level (MASEL) at the local scale. This is through what-if scenarios that are developed based on the association between local temperature and sea level. The model can provide a basis for a bottom-up impact assessment by addressing limitations of applying large-scale projections in small islands and facilitating the accessibility of the impact assessment to stakeholders. For this purpose, we decompose the MAT and MASEL signals into their linear trend and autocorrelation components as well as independent and identically distributed residual terms. We further explore the association between trend and residual terms of MAT and MASEL. If such dependencies exist, scenarios of sea level can be synthesized based on the trend and residual terms of temperature. We use linear regression to link trends of MAT and MASEL, and copulas to formulate dependencies between residuals. This allows stochastic sampling of MASEL conditioned to trend and random variability in MAT. This framework is used for retrospective and prospective simulations of MASEL in Nouméa, the capital city of New Caledonia, the Pacific. We set up six different model configurations for developing the stochastic sampler, each including various parametric options. By selecting the best setup from each configuration, we provide a multi-model stochastic projection of MASEL, assuming the persistence in current long-term trend in MAT and MASEL. We demonstrate how such simulations can be used for a risk-based impact assessments and discuss sources of uncertainty in future projections.

Precipitation and soil pH drive the soil microbial spatial patterns in the Robinia pseudoacacia forests at the regional scale

Soil microbial spatial variation across forest types would obscure the huge impact of tree species on microbial community structure and function, and the underlying mechanisms of spatial pattern in the same tree forests was poorly described. We investigated the spatial variation and drivers of microbial communities in the topsoil (0–20 cm) of Robinia pseudoacacia forests at the regional scale on the Loess Plateau. Soil bacterial and fungal communities showed a significant distance decay pattern, and fungi exhibited a more apparent spatial pattern than bacteria. Microbial diversity (Shannon-Wiener and Chao1 indexes) and the relative abundance of Actinobacteria, Gemmatimonadetes, Nitrospirae, Latescibacteria, Verrucomicrobia, Rozellomycota significantly differed among regions. Community dissimilarities of bacterial and fungal indicator species were sensitive to changes in mean annual temperature (MAT) and mean annual precipitation (MAP), respectively. However, the first axis of nonmetric multidimensional scaling (nMDS1) of soil bacterial and fungal communities were respectively associated with soil pH and MAP. The proportion of positive links of co-occurrence networks between microorganisms increased with the decrease in MAP, suggesting that the decrease in precipitation increases species coexistence. Soil bacterial and fungal community assemblies were both dominated by stochastic processes and were well predicted by the neutral community model. The effects of soil properties were larger on bacteria than fungi, and soil properties respectively explained 54.54% and 37.29% of the total variation in the bacterial and fungal community compositions. The major drivers of community dissimilarity varied between bacteria and fungi, indicating that bacterial community was mainly drove by soil pH, NH4+ and geographic distance and fungal community was mainly drove by MAP, pH and geographic distance. Our observations suggested that soil bacterial and fungal communities exhibited distance decay pattern in the Robinia pseudoacacia forests at the regional scale, and this pattern was primarily driven by soil pH and MAP.

Estimating the uncertainty of Australian area‐average temperature anomalies

AbstractAn important metric for communicating the context and impact of climate change is regionally or nationally averaged temperature anomalies. Quantifying the associated uncertainty adds confidence in the reporting of temperature records and trends. For area‐averaged temperature anomalies derived from point‐source observations we model the uncertainty by a three‐step process: (1) observation random error; (2) spatial analysis grid point uncertainty; and (3) area‐average uncertainty. Each step should reflect the method used to calculate the area average. For Australian area‐average temperature anomalies, the observation random error is modelled as the sum of the sampling uncertainty and the uncertainty in the adjustment process. Daily maximum and minimum temperature time series from the 104 non‐urban locations of the Australian Climate Observation Reference Network—Surface Air Temperature (ACORN‐SAT) version 2 dataset for 1981–2010 are used to estimate the sampling uncertainty, based on predicted location pair zero‐distance correlations having values of less than 1.0. ACORN‐SAT version 2 breakpoint information is used to estimate the uncertainty from the selection of reference stations used in the detection and homogenization processes. Monthly area‐average temperature uncertainty is estimated for the 1910–2018 period from Monte Carlo simulations using the observation uncertainty and the available ACORN‐SAT network for each month. The uncertainty in annual area‐average temperature anomalies is then obtained from the monthly uncertainties and inter‐month correlations modelled with a periodic auto‐regressive model. The ±2σ annual mean temperature area‐average uncertainty is ±0.084°C in 2018 and ± 0.176°C in 1910. The larger value in 1910 is primarily due to decreased ACORN‐SAT coverage over areas of inland Australia. The Australian annual mean temperature change and associated uncertainty from ACORN‐SAT version 2 over 1910–2018 is found to be 1.42 ± 0.28°C. This is consistent with global air temperature warming by around 1°C since 1850.

Geochemical characteristics of Holocene loess-paleosol sequences in central Chinese Loess Plateau and their implications for East Asian monsoon evolution

The geochemical composition and magnetic susceptibility (MS) of three sections (Weinan, Jingchuan and Jingbian) in the central Chinese Loess Plateau, located along a north-south climate gradient, were analyzed at high resolution in order to reconstruct changes in weathering intensity and hence Asian monsoon evolution during the Holocene. The results show distinct differences in element composition between the loess and paleosol layer, as well as spatial differences which reflect changes in mean annual temperature and annual precipitation along the climatic gradient. The measured major element compositions, combined with the results of previous research, indicate that the provenance of the Holocene loess-paleosol sequences in the central Chinese Loess Plateau was constant over time and space. The degree of chemical weathering in the study area is relatively weak and at an intermediate weathering stage. Based on a magnetic susceptibility age model, the East Asian summer monsoon changes recorded by the loess-paleosol sequences show a distinct two-stage pattern of evolution, with a gradual increase during 13.1–8.5 ka and a gradual decrease thereafter. Comparison of the results with climate records from elsewhere indicates that the East Asian monsoon evolution was mainly controlled by changes in Northern Hemisphere summer insolation and ice sheets.