Boreal peatlands store vast amounts of soil organic carbon (C) owing to the imbalance between productivity and decay rates. In the recent decades, this carbon stock has been exposed to a warming climate. During the past decade alone, the Arctic has warmed by ∼ 0.75°C which is almost twice the rate of the global average. Although, a wide range of studies have assessed peatlands' C cycling, our understanding of the factors governing source / sink dynamics of peatland C stock under a warming climate remains a critical uncertainty at site, regional, and global scales. Here our focus was on answering two key questions: (1) What drives the interannual variability of carbon dioxide (CO) fluxes at the Bonanza Creek rich fen in Alaska, and (2) What are the internal carbon allocation patterns during the study years? We addressed these knowledge-gaps using an intermediate complexity terrestrial ecosystem model calibrated by a Bayesian model-data fusion framework at a weekly timestep with publicly available eddy covariance, satellite-based earth observation, and in-situ datasets for 2014 to 2020. We found that the greening trend (a relative increase of leaf area index ∼0.12 m m by 2020) in the fen ecosystem is forced by a CO fertilisation effect which in combination resulted in increased gross primary production (GPP). Relative to 2014, GPP increased by ∼75 gC m year (by 2020; 95% confidence interval (CI): -41.35 gC m year to 213.55 gC m year) while heterotrophic respiration stayed constant. Consistent with the observed greening, our analysis indicates that the ecosystem allocated more C to foliage (∼50%) over the structural (A carbon pool consisting of branches, stems and coarse roots; ∼30%) and fine root C pools (∼20%).

Common bean ( L.) is the second most important source of dietary protein and the third most important source of calories in Africa, especially for the poor. In East Africa, drought is an important constraint to bean production. Therefore, breeding programs in East Africa have been trying to develop drought resistant varieties of common bean. To do this, breeders need information about seasonal drought stress patterns including their onset, intensity, and duration in the target area of the breeding program, so that they can mimic this pattern during field trials. Using the Decision Support for Agrotechnology Transfer (DSSAT) v4.7 model together with historical and future (Coupled Model Inter-comparison Project 6, CMIP6) climate data, this study categorized Ethiopia, Tanzania, and Uganda into different target population of environments (TPEs) based on historical and future seasonal drought stress patterns. We find that stress-free conditions generally dominate across the three countries under historical conditions (50-80% frequency). These conditions are projected to increase in frequency in Ethiopia by 2-10% but the converse is true for Tanzania (2-8% reduction) and Uganda (17-20% reduction) by 2050 depending on the Shared Socioeconomic Pathway (SSP). Accordingly, by 2050, terminal drought stresses of various intensities (moderate, severe, extreme) are prevalent in 34% of Uganda, around a quarter of Ethiopia, and 40% of the bean growing environments in Tanzania. The TPEs identified in each country serve as a basis for prioritizing breeding activities in national programs. However, to optimize resource use in international breeding programs to develop genotypes that are resilient to future projected stress patterns, we argue that common bean breeding programs should focus primarily on identifying genotypes with tolerance to severe terminal drought, with co-benefits in relation to adaptation to moderate and extreme terminal drought. Little to no emphasis on heat stress is warranted by 2050s.

Gas-phase ammonia (NH), emitted primarily from agriculture, contributes significantly to reactive nitrogen (N) deposition. Excess deposition of N to the environment causes acidification, eutrophication, and loss of biodiversity. The exchange of NH between land and atmosphere is bidirectional and can be highly heterogenous when underlying vegetation and soil characteristics differ. Direct measurements that assess the spatial heterogeneity of NH fluxes are lacking. To this end, we developed and deployed two fast-response, quantum cascade laser-based open-path NH sensors to quantify NH fluxes at a deciduous forest and an adjacent grassland separated by 700 m in North Carolina, United States from August to November, 2017. The sensors achieved 10 Hz precisions of 0.17 ppbv and 0.23 ppbv in the field, respectively. Eddy covariance calculations showed net deposition of NH (-7.3 ng NH-N m s) to the forest canopy and emission (3.2 ng NH-N m s) from the grassland. NH fluxes at both locations displayed diurnal patterns with midday peaks and smaller peaks in the afternoons. Concurrent biogeochemistry data showed over an order of magnitude higher NH emission potentials from green vegetation at the grassland compared to the forest, suggesting a possible explanation for the observed flux differences. Back trajectories originating from the site identified the upwind urban area as the main source region of NH. Our work highlights that adjacent natural ecosystems sharing the same airshed but different vegetation and biogeochemical conditions may differ remarkably in NH exchange. Such heterogeneities should be considered when upscaling point measurements, downscaling modeled fluxes, and evaluating N deposition for different natural land use types in the same landscape. Additional in-situ flux measurements accompanied by comprehensive biogeochemical and micrometeorological records over longer periods are needed to fully characterize the temporal variabilities and trends of NH fluxes and identify the underlying driving factors.

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content ( ) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating , while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables' spectral response, leading to inconsistencies in the VI- /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

Considerable amounts of starch granules can be present in the atmosphere from both natural and anthropogenic sources. The aim of this study is to investigate the variability and potential origin of starch granules in ambient air recorded at six cities situated in a region with dominantly agricultural land use. This is achieved by using a combination of laser spectroscopy bioaerosol measurements with 1 min temporal resolution, traditional volumetric Hirst type bioaerosol sampling and atmospheric modelling. The analysis of wind roses identified potential sources of airborne starch (i.e., cereal grain storage facilities) in the vicinity of all aerobiological stations analysed in this study. The analysis of the CALPUFF dispersion model confirmed that emission of dust from the location of storage towers situated about 2.5 km north of the aerobiological station in Novi Sad is a plausible source of high airborne concentrations of starch granules. This study is important for environmental health since it contributes body of knowledge about sources, emission, and dispersion of airborne starch, known to be involved in phenomena such as thunderstorm-triggered asthma. The presented approach integrates monitoring and modelling, and provides a roadmap for examining a variety of bioaerosols previously considered to be outside the scope of traditional aerobiological measurements.

Eddy covariance (EC) measurements of ecosystem-atmosphere carbon dioxide (CO) exchange provide the most direct assessment of the terrestrial carbon cycle. Measurement biases for open-path (OP) CO concentration and flux measurements have been reported for over 30 years, but their origin and appropriate correction approach remain unresolved. Here, we quantify the impacts of OP biases on carbon and radiative forcing budgets for a sub-boreal wetland. Comparison with a reference closed-path (CP) system indicates that a systematic OP flux bias (0.54 mol m s) persists for all seasons leading to a 110% overestimate of the ecosystem CO sink (cumulative error of 78 gC m). Two potential OP bias sources are considered: Sensor-path heat exchange (SPHE) and analyzer temperature sensitivity. We examined potential OP correction approaches including: i) Fast temperature measurements within the measurement path and sensor surfaces; ii) Previously published parameterizations; and iii) Optimization algorithms. The measurements revealed year-round average temperature and heat flux gradients of 2.9 °C and 16 W m between the bottom sensor surfaces and atmosphere, indicating SPHE-induced OP bias. However, measured SPHE correlated poorly with the observed differences between OP and CP CO fluxes. While previously proposed nominally universal corrections for SPHE reduced the cumulative OP bias, they led to either systematic under-correction (by 38.1 gC m) or to systematic over-correction (by 17-37 gC m). The resulting budget errors exceeded CP random uncertainty and change the sign of the overall carbon and radiative forcing budgets. Analysis of OP calibration residuals as a function of temperature revealed a sensitivity of 5 mol m K. This temperature sensitivity causes CO calibration errors proportional to sample air fluctuations that can offset the observed growing season flux bias by 50%. Consequently, we call for a new OP correction framework that characterizes SPHE- and temperature-induced CO measurement errors.

More frequent and severe disturbances increasingly open the forest canopy and initiate tree regeneration. Simultaneously, increasing weather extremes, such as drought and heat, are threatening species adapted to cool and moist climate. The magnitude of the microclimatic buffering capacity of forest canopies to mitigate hot and dry weather conditions and its disturbance-induced reduction remains poorly quantified. Also, the influence of disturbance legacies (e.g., deadwood) on forest microclimate is unresolved. In a unique manipulation experiment we investigated (i) the microclimatic buffering capacity of forest canopies in years with different climatic conditions; (ii) the impacts of spatial disturbance patterns on surface light and microclimate; and (iii) the effect of deadwood presence and type on microclimate. Treatments included two disturbance patterns (i.e., aggregated and distributed), four deadwood types (i.e., standing, downed, standing and downed, removed), and one untreated control (i.e., nine treatments in total), replicated at five sites dominated by European beech ( L.) in southeastern Germany. We measured forest floor light conditions and derived diurnal extremes and variation in temperature (T) and vapor pressure deficit (VPD) during four consecutive summer seasons (2016 - 2019). The buffering capacity of intact forest canopies was higher in warm and dry years. Surface light was significantly higher in spatially aggregated disturbance gaps compared to distributed disturbances of similar severity. An increase in surface light by 10 % relative to closed canopies elevated T and VPD by 0.42°C and 0.04 kPa, respectively. Deadwood presence and type did not affect the forest microclimate significantly. Microclimatic buffering under forest canopies can dampen the effects of climate change. However, increasing canopy disturbances result in more light penetrating the canopy, reducing the microclimatic buffering capacity of forests. We conclude that forest management should foster microclimatic buffering in forests as one element of a multi-pronged strategy to counter climate change.

Time series of stem diameter variations (SDVs) recorded by dendrometers are composed of two components: (i) irreversible radial stem growth and (ii) reversible stem shrinking and swelling caused by dynamics in water storage in elastic tissues outside the cambium. However, SDVs measured over dead outer bark (periderm) could also be affected by absorption and evaporation of water from remaining dead bark layers after smoothing the stem surface to properly mount dendrometers. Therefore, the focus of this study was to determine the influence of hygroscopicity of a thin dead outer bark layer on the reversible component of dendrometer records of Scots pine () under field conditions. To accomplish this, SDVs deduced from dendrometers mounted over dead outer bark were compared among living and dead saplings and mature trees. Results revealed that dead trees showed high synchronicity in reversible daily SDVs compared to living trees throughout several growing seasons (mean Pearson correlation coefficient () = 0.844 among saplings and = 0.902 among mature trees, respectively; <0.001). Furthermore, diurnal and long-term SDVs closely followed changes in relative air humidity (RH) in living and dead trees. A multiple linear regression analysis of environmental influence on SDVs in dead and living trees revealed that the most important predictor of daily SDVs was RH (relative importance 64 %). Hence, results indicate that dendrometers mounted over dead outer bark with a thickness of <4 mm record hygroscopic shrinking and swelling of the bark tissue, which can amplify fluctuations in whole-tree water status. To conclude, hygroscopic processes must be taken into account when extracting intra-annual radial growth, determining environmental drivers of SDVs, and evaluating changes in tree water status from SDVs recorded by dendrometers, which were mounted over even thin dead outer bark layers.

We explored the inter-individual variability in bud-burst and its potential drivers, in homogeneous mature stands of temperate deciduous trees. Phenological observations of leaves and wood formation were performed weekly from summer 2017 to summer 2018 for pedunculate oak, European beech and silver birch in Belgium. The variability of bud-burst was correlated to previous' year autumn phenology (i.e. the onset of leaf senescence and the cessation of wood formation) and tree size but with important differences among species. In fact, variability of bud-burst was primarily related to onset of leaf senescence, cessation of wood formation and tree height for oak, beech and birch, respectively. The inter-individual variability of onset of leaf senescence was not related to the tree characteristics considered and was much larger than the inter-individual variability in bud-burst. Multi-species multivariate models could explain up to 66% of the bud-burst variability. These findings represent an important advance in our fundamental understanding and modelling of phenology and tree functioning of deciduous tree species.

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat () and spring barley () was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops.

Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide.

Wetlands represent the dominant natural source of methane (CH) to the atmosphere. Thus, substantial effort has been spent examining the CH budgets of global wetlands continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH fluxes using a 3.5 year time series of simultaneous open- and closed path CH flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH fluxes were typically 6%-41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g - CH m yr). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH emissions for both EC systems were in excellent agreement (within 0.6 g - CH m yr) and averaged 18 g - CH m yr. Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7-17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Designing crop ideotype is an important step to raise genetic yield potential in a target environment. In the present study, we designed wheat ideotypes based on the state-of-the-art knowledge in crop physiology to increase genetic yield potential for the 2050-climate, as projected by the global climate model for the emission scenario, in two high-wheat-productive countries, the United Kingdom (UK) and New Zealand (NZ). Wheat ideotypes were optimized to maximize yield potential for both water-limited ( ) and potential ( ) conditions by using Sirius model and exploring the full range of cultivar parameters. On average, a 43-51% greater yield potential over the present winter wheat was achieved for in the UK and NZ, whereas a 51-62% increase was obtained for . Yield benefits due to the potential condition over water-limitation were small in the UK, but 13% in NZ. The yield potentials of wheat were 16% (2.6 t ha) and 31% (5 t ha) greater in NZ than in the UK under 2050-climate in water-limited and potential conditions respectively. Modelling predicts the possibility of substantial increase in genetic yield potential of winter wheat under climate change in high productive countries. Wheat ideotypes optimized for future climate could provide plant scientists and breeders with a road map for selection of the target traits and their optimal combinations for wheat improvement and genetic adaptation to raise the yield potential.

Automated transparent chambers have gained increasing popularity in recent years to continuously measure net CO fluxes between low-statured canopies and the atmosphere. In this study, we carried out four field campaigns with chamber measurements in a variety of mountainous grasslands. A mathematic stationary point (or critical point, a point at which the derivative of a function is zero) in the CO mixing ratio time series was found in a substantial fraction of the measurements at all the sites, which had a significant influence on the performances of the regression algorithms. The stationary point was probably due to condensed water on the inner wall of the chamber dome, which reduced the solar radiation and resulted in a reversal of the CO mixing ratio time series in the chamber (so called or CGE in this study). This effect may be the cause of the observed underestimation of daytime CO fluxes when using common linear and exponential regression models on continuous automated chamber observations. In order to avoid biased flux estimation of daytime CO fluxes, we introduced a linearly increasing term to the exponential function so as to compensate for the influence of the CGE, which gives acceptable model errors and improves the CO flux estimation by 5 % for temperate mountainous grasslands. We conclude that exponential regression models should be favoured over linear models and recommend to account for the effects of CGE by either excluding ambiguous observations from the flux computations where stationary points can be identified in the CO mixing ratio time series, or by adding a linearly increasing term to the exponential regression model.

Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat ( L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.

Disturbances alter composition, structure, and functioning of forest ecosystems, and their legacies persist for decades to centuries. We investigated how temperate forest landscapes may recover their carbon (C) after severe wind and bark beetle disturbance, while being exposed to climate change. We used the forest landscape and disturbance model iLand to quantify (i) the recovery times of the total ecosystem C, (ii) the effect of climate change on C recovery, and (iii) the differential factors contributing to C recovery. We reconstructed a recent disturbance episode (2008-2016) based on Landsat satellite imagery, which affected 39% of the forest area in the 16,000 ha study landscape. We subsequently simulated forest recovery under a continuation of business-asusual management until 2100. Our results indicated that the recovery of the pre-disturbance C stocks (C payback time) was reached 17 years after the end of the disturbance episode. The C stocks of a theoretical undisturbed development trajectory were reached 30 years after the disturbance episode (C sequestration parity). Drier and warmer climates delayed simulated C recovery. Without the fertilizing effect of CO, C payback times were delayed by 5-9 years, while C parity was not reached within the 21st century. Recovery was accelerated by an enhanced C uptake compared to undisturbed conditions (disturbance legacy sink effect) that persisted for 35 years after the disturbance episode. Future climate could have negative impacts on forest recovery and thus further amplify climate change through C loss from ecosystems, but the effect is strongly contingent on the magnitude and persistence of alleviating CO effects. Our modelling study highlights the need to consider both negative and positive effects of disturbance (i.e., C loss immediately after an event vs. enhanced C uptake of the recovering forest) in order to obtain a comprehensive understanding of disturbance effects on the forest C cycle.

Water productivity (WP) is a robust benchmark for crop production in relation to available water supply across spatial scales. Quantifying water-limited potential (WPw) and actual on-farm (WPa) WP to estimate WP gaps is an essential first step to identify the most sensitive factors influencing production capacity with limited water supply. This study combines local weather, soil, and agronomic data, and crop modeling in a spatial framework to determine WPw and WPa at local and regional levels for rainfed cropping systems in 17 (maize) and 18 (wheat) major grain-producing countries representing a wide range of cropping systems, from intensive, high-yield maize in north America and wheat in west Europe to low-input, low-yield maize systems in sub-Saharan Africa and south Asia. WP was calculated as the quotient of either water-limited yield potential or actual yield, and simulated crop evapotranspiration. Estimated WPw upper limits compared well with maximum WP reported for field-grown crops. However, there was large WPw variation across regions with different climate and soil (CV = 29% for maize and 27% for wheat), which cautions against the use of generic WPw benchmarks and highlights the need for region-specific WPw. Differences in simulated evaporative demand, crop evapotranspiration after flowering, soil evaporation, and intensity of water stress around flowering collectively explained two thirds of the variation in WPw. Average WP gaps were 13 (maize) and 10 (wheat) kg ha mm, equivalent to about half of their respective WPw. We found that non-water related factors (, management deficiencies, biotic and abiotic stresses, and their interactions) constrained yield more than water supply in half of the regions. These findings highlight the opportunity to produce more food with same amount of water, provided limiting factors other than water supply can be identified and alleviated with improved management practices. Our study provides a consistent protocol for estimating WP at local to regional scale, which can be used to understand WP gaps and their mitigation.

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO concentration that corresponds to the +1.5 °C World but overestimates the CO concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5°C and +2.0°C worlds for adaptation planning in these climate stabilization targets.

Large conifer trees in the Pacific Northwest, USA (PNW) use stored water to extend photosynthesis, both diurnally and seasonally. This is particularly important during the summer drought, which is characteristic of the region. In the PNW, climate change is predicted to result in hotter, drier summers and warmer, wetter winters with decreased snowpack by mid-century. Understanding seasonal bole water dynamics in relation to climate factors will enhance our ability to determine the vulnerability of forests to climate change. Seasonal patterns of bole water content in old-growth Douglas-fir ( (Mirb.) Franco) trees were studied in the Cascade Mountains of western Oregon, USA. Relative water content (RWC) was monitored hourly in three 400+ and three ~150 years-old trees using permanently mounted dielectric devices for 10 years. RWC increased during the late spring and early summer to maximum levels in August then decreased into fall and remained low over winter. The difference between minimum RWC in the winter and maximum in mid-summer averaged 4.5 and 2.3% for the older and younger trees, respectively, across all years. RWC closely followed growth and was positively correlated with air and soil temperature, vapor pressure deficit and photosynthetically active radiation, but lagged plant available soil water. The progressive decrease in RWC seen each year from mid-summer through fall was attributed to net daily loss of water during the summer drought. The marked increase in RWC observed from spring to mid-summer each year was hypothesized to be the period of embolism repair and water recharge in elastic tissues. We conclude that bole water content is an integral part of tree water dynamics enabling trees to extend carbon assimilation into drought periods and during periods when cold soil inhibits water uptake by roots, an adaptation that could benefit the survival of large PNW trees under climate change.

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

Increasing air temperature is expected to extend growing season length in temperate, broadleaf forests, leading to potential increases in evapotranspiration and net carbon uptake. However, other key processes affecting water and carbon cycles are also highly temperature-dependent. Warmer temperatures may result in higher ecosystem carbon loss through respiration and higher potential evapotranspiration through increased atmospheric demand for water. Thus, the net effects of a warming planet are uncertain and highly dependent on local climate and vegetation. We analyzed five years of data from the Coweeta eddy covariance tower in the southern Appalachian Mountains of western North Carolina, USA, a highly productive region that has historically been underrepresented in flux observation networks. We examined how leaf phenology and climate affect water and carbon cycling in a mature forest in one of the wettest biomes in North America. Warm temperatures in early 2012 caused leaf-out to occur two weeks earlier than in cooler years and led to higher seasonal carbon uptake. However, these warmer temperatures also drove higher winter ecosystem respiration, offsetting much of the springtime carbon gain. Interannual variability in net carbon uptake was high (147 to 364 g C m y), but unrelated to growing season length. Instead, years with warmer growing seasons had 10% higher respiration and sequestered ~40% less carbon than cooler years. In contrast, annual evapotranspiration was relatively consistent among years (coefficient of variation = 4%) despite large differences in precipitation (17%, range = 800 mm). Transpiration by the evergreen understory likely helped to compensate for phenologically-driven differences in canopy transpiration. The increasing frequency of high summer temperatures is expected to have a greater effect on respiration than growing season length, reducing forest carbon storage.