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Microbes affect the global carbon cycle that influences climate change and are in turn influenced by environmental change. Here, we use data from a long‐term whole‐ecosystem warming experiment at a boreal peatland to answer how temperature and CO2 jointly influence communities of abundant, diverse, yet poorly understood, non‐fungi microbial Eukaryotes (protists). These microbes influence ecosystem function directly through photosynthesis and respiration, and indirectly, through predation on decomposers (bacteria and fungi). Using a combination of high‐throughput fluid imaging and 18S amplicon sequencing, we report large climate‐induced, community‐wide shifts in the community functional composition of these microbes (size, shape, and metabolism) that could alter overall function in peatlands. Importantly, we demonstrate a taxonomic convergence but a functional divergence in response to warming and elevated CO2 with most environmental responses being contingent on organismal size: warming effects on functional composition are reversed by elevated CO2 and amplified in larger microbes but not smaller ones. These findings show how the interactive effects of warming and rising CO2 levels could alter the structure and function of peatland microbial food webs—a fragile ecosystem that stores upwards of 25% of all terrestrial carbon and is increasingly threatened by human exploitation. [ABSTRACT FROM AUTHOR]

Intensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions. Plain Language Summary: Irrigation is the most important water use sector accounting for about 70% of the global freshwater withdrawals and contributing 75% of China's total grain production. Application of irrigation water significantly alters agroecosystem water cycle, energy cycle and carbon cycle. The magnitudes of irrigation impact are also influenced by climate variability. In this study, we simulated irrigation benefits for maize gross primary productivity (GPP) using land surface models and satellite observations. The effects of climate interactions on the irrigation benefits were untangled using an explainable machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). We found that alterations in water cycle (water supply) and energy cycle (surface cooling) accounted for 54% ± 19% and 23% ± 20% of the irrigation benefits variability respectively, the rest being due to temperature‐dominated climate interactions. With the climate change driven by persist warming, the irrigation benefits will be substantially reduced for those regions under ongoing heat stress, such as North China Plain. Future maize productivity will be greatly threatened by changing climate, considering the prevailing negative trends for the irrigation benefits throughout the nation. Reinforcing anthropogenic activities such as irrigation water management and planting structure adjustment are strongly required to improve crop resilience to climate variability. Key Points: Irrigation contribution to the maize gross primary productivity across 15 agricultural regions is quantified in ChinaThe irrigation benefits explained by water supply and cooling are identified, and the rest being due to climate interactionsChanging climate both increases and decreases irrigation benefits for maize over different regions, contingent on heat conditions [ABSTRACT FROM AUTHOR]

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Abstract Intensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions.

In the context of the ongoing United Nations Framework Convention on Climate Change (UNFCCC) process, it seems important to focus attention not only on global mean surface air temperature (GSMT) but also on the climate of specific regions in order to gain insights into the dynamics of the changes, the timescales of the periodic components, the local trends and the relationships between climatic variables in the region of interest. This is important for scientists as well as for policymakers. This paper provides an analysis of the changes in local air temperature and precipitation depth in exceptionally long observational records and examines the relationships between these two variables. The focus is on monthly values. Temperature maximum, minimum, range, and cumulative precipitation depth are considered. The wavelet analysis shows that the scale of variation is different for temperature and precipitation and that the behavior of the temperature range values diverges from the behavior of the minimum and maximum values. The timescale of important changes in the long-term trend is, however, similar. Results also suggest that the main mode of variability is persistent through time in the series of temperature maximum, minimum, and range but not in precipitation depth. This is a clear evidence of climate change. All series show variances that change over time and are, as expected, nonstationary. The analysis of the wavelet coherence shows that the relationship between precipitation and temperature evolves through time, and its intensity varies considering different time scales. The association between these climatic variables is particularly strong in the last decade. Is it noteworthy that the analysis of the coherence suggests that temperature is leading to rain and not the other way around. This highlights the impact of global warming on the hydrologic cycle and on related human activities. [ABSTRACT FROM AUTHOR]

This article investigates the economic impacts of changes in climatic conditions on Vietnamese agriculture. We apply the two‐step Hsiao method to a 10‐year panel of household data which focuses on the production of 20 crops across seven regions in Vietnam. This study allows for variable market feedbacks across regions that grow different selections of crops. In this way, our article differs from most panel Ricardian analyses which assume uniform market shocks on households. Our analysis also includes climate interactions to allow the effects of temperatures to be dependent on the levels of rainfall. Panel evidence from the Ricardian model suggests heterogeneous climate impacts across seasons and regions. Rising seasonal temperatures are associated with losses to most regions, with spring temperatures being the exception. Increases in summer precipitation are valuable to mitigate the negative effects of rising temperatures. Changes in climate normal should not be the focus of policymakers since the simulation indicates marginal losses to agricultural productivity, both in the short term and the long term. Regions with cool climates are likely to be most affected by the projected climate change. [ABSTRACT FROM AUTHOR]

In this study, a numerical model was developed to predict perfluorooctane sulfonate (PFOS) migration in a contaminated site due to precipitation and evapotranspiration over 100 years. The soil physicochemical properties including sorption isotherm characteristics were used to model PFOS transport (advection and dispersion), and attenuation (adsorption and decay) processes using the GeoStudio software. The model was calibrated using the results of a leachate test on a reconstituted contaminated soil specimen. The simulation of the contaminated site showed that in the current situation where the site is covered by a concrete slab, PFOS remains at the site beneath the concrete slab for a long time with decay being the dominant factor contributing to its reduction over time. It took several decades for PFOS to contaminate groundwater. The PFOS level in groundwater reached the threshold value for drinking water in 25 years and continued to increase in the long term, extending horizontally to an area up to five times the size of the contaminated site. This issue needs to be considered when digging boreholes for drinking water close to the contaminated site. [ABSTRACT FROM AUTHOR]

Coffea canephora (robusta coffee) is the most heat‐tolerant and 'robust' coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 =.51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers. [ABSTRACT FROM AUTHOR]

Abstract: Litterfall dynamics in tropical forests are a good indicator of overall tropical forest function, indicative of carbon invested in both photosynthesising tissues and reproductive organs such as flowers and fruits. These dynamics are sensitive to changes in climate, such as drought, but little is known about the long‐term responses of tropical forest litterfall dynamics to extended drought stress. We present a 15‐year dataset of litterfall (leaf, flower and fruit, and twigs) from the world's only long‐running drought experiment in tropical forest. This dataset comprises one of the longest published litterfall time series in natural forest, which allows the long‐term effects of drought on forest reproduction and canopy investment to be explored. Over the first 4 years of the experiment, the experimental soil moisture deficit created only a small decline in total litterfall and leaf fall (12% and 13%, respectively), but a very strong initial decline in reproductive litterfall (flowers and fruits) of 54%. This loss of flowering and fruiting was accompanied by a de‐coupling of all litterfall patterns from seasonal climate variables. However, following >10 years of the experimental drought, flower and fruiting re‐stabilised at levels greater than in the control plot, despite high tree mortality in the drought plot. Litterfall relationships with atmospheric drivers were re‐established alongside a strong new apparent trade‐off between litterfall and tree growth. Synthesis. We demonstrate that this tropical forest went through an initial shock response during the first 4 years of intense drought, where reproductive effort was arrested and seasonal litterfall patterns were lost. However, following >10 years of experimental drought, this system appears to be re‐stabilising at a new functional state where reproduction is substantially elevated on a per tree basis; and there is a new strong trade‐off between investment in canopy production and wood production. [ABSTRACT FROM AUTHOR]

Future droughts are expected to become more severe and frequent under future climate change scenarios, likely causing widespread tree mortality in the western USA. Coping with an uncertain future requires an understanding of long-term ecosystem responses in areas where prolonged drought is projected to increase. Tree-ring records are ideally suited for this task. We developed 24 tree-ring chronologies from 20 U.S. Forest Service Forest Inventory and Analysis (FIA) plots in the southwestern USA. Climate variables were derived from the PRISM climate dataset (800-m grid cells) to capture the bimodal precipitation regime of winter snow and summer monsoonal rainfall, as well as warm-season vapor-pressure deficit (VPD) and winter minimum temperature. Based on mixed linear models, radial growth from 1948 to 2013 for four conifer species ( Pinus edulis, Juniperus osteosperma, Pinus ponderosa , and Picea engelmannii ) responded negatively to warm-season VPD and positively to cold-season precipitation. Pinus spp. benefited from warm-season precipitation linked to the North American monsoon, and Pinus spp. and J. osteosperma radial growth increased with warmer cold-season minimum temperature. However, warmer cold-season minimum temperatures countered the beneficial influence of cold-season precipitation for radial growth in Pinus spp. and J. osteosperma , while P. engelmannii was unaffected. Also, enhanced drying effects of warm-season VPD associated with decreased cold-season precipitation negatively affected radial growth of Pinus spp. and P. engelmannii . Of the four conifer species studied, Pinus spp. are most affected by droughts since 1948, while P. engelmannii and J. osteosperma appear to be more resilient. Investigating seasonal climate responses and interaction effects on radial growth in areas impacted by severe drought helps identify species that may be particularly at risk from climate change impacts in the Anthropocene. [ABSTRACT FROM AUTHOR]

The changes mainly occurred in the hydrological cycle are associated with anthropogenic changes. In Brazil are already seen possible changes in the southwestern region that has suffered from water scarcity in their reservoirs. Understanding the Bowen ratio in the Amazon region becomes important to try to understand this ecosystem, since such information connect regional climate change. The latent heat fluxes were analyzed (ÁE) and sensible heat (H) in REBIO Jaru to determine the reason for Bowen and seek possible climate interactions in humid, wet-dry, dry, dry-wet periods in 2009. The results observed in these periods in the Bowen ratio were 0:21; 0:24; 0:30; 0:21, and show that the forest little changes with seasonality during the year unlike the transition of biomes (Amazon - cerrado) and pasture area. [ABSTRACT FROM AUTHOR]

Fire regimes often vary at fine spatial scales in response to factors such as topography or fuels while climate usually synchronizes fires across broader scales. We investigated the relative influence of top-down and bottom-up controls on fire occurrence in ponderosa pine ( Pinus ponderosa) forests in a highly fragmented landscape at Mount Dellenbaugh, in northwestern Arizona. Our study area of 4,000 ha was characterized by patches of ponderosa pine forest in drainages that were separated by a matrix of pinyon-juniper woodlands, sagebrush shrublands, and perennial grasslands. We reconstructed fire histories from 135 fire-scarred trees in sixteen 25-ha sample sites placed in patches of mature ponderosa forest. We found that, among patches of ponderosa forest, fires were similar in terms of frequency but highly asynchronous in terms of individual years. Climate synchronized fire but only across broader spatial scales. Fires occurring at broader scales were associated with dry years that were preceded by several wet years. The remarkable level of asynchrony at finer scales suggests that bottom-up factors, such as site productivity and fuel continuity, were important in regulating fire at Mount Dellenbaugh. Understanding where bottom-up controls were historically influential is important for prioritizing areas that may best respond to fuel treatment under a warming climate. [ABSTRACT FROM AUTHOR]