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Abstract Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.

Urban focused semantically segmented datasets (e.g. ADE20k or CoCo) have been crucial in boosting research and applications in urban areas by providing rich sources of delineated objects in Street View Images (SVI). However, there is a lack of similar datasets for agricultural and rural landscapes. By focusing on such underrepresented landscapes, we created a dataset containing images with visually segmented objects that were labeled following a thematically relevant set of classes. The dataset contains 1784 north-looking landscape images with their corresponding annotated masks from across Europe. Images were sourced from the Land Use and Coverage Area frame Survey (LUCAS), following a strict sampling and acquisition protocol. Objects were fully delineated on the street (eye) level or so-called landscape images for a set of 35 relevant classes (e.g. cropped fields, dense woody features, field margins, stone walls). This modest dataset, due to the cost of segmentation, might provide limitations for some applications (due to class imbalances). However, initial segmentation labels open the potential for the rapid (semi-supervised) growth of a larger dataset using LUCAS or other street level imagery. Although uncertainties remain, this annotated dataset is a first step toward integrating LUCAS image data within a landscape segmentation context. This can support land-use and land-cover change assessments, comparison and integration with Earth Observation based products, improved structural characterization of vegetation, as well as biodiversity and landscape heterogeneity monitoring. The data are structured in two folders which store the images and the masks. Also included is a csv file with the label and codes corresponding to the masks and another csv file data with geolocation information and ancillary data derived from the Harmonized LUCAS in-situ land-cover and land-use database for each image.

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

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

Abstract Tropical forest phenology directly affects regional carbon cycles, but the relation between species‐specific and whole‐canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large‐scale impacts of human‐induced climate change. Ground‐based long‐term (1937–1956) phenological observations of 140 tropical tree species are recovered, species‐specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand‐level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species‐specific decoupling in time along a bi‐modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy‐wide scaling and the interpretation of remotely sensed phenology signals.

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

Many plant phenological events are sensitive to temperature, leading to changes in the seasonal cycle of ecosystem function as the climate warms. To evaluate the current and future implications of temperature changes for plant phenology, researchers commonly use a metric of temperature sensitivity, which quantifies the change in phenology per degree change in temperature. Here, we examine the temperature sensitivity of phenology, and highlight conditions under which the widely used days-per-degree sensitivity approach is subject to methodological issues that can generate misleading results. We identify several factors, in particular the length of the period over which temperature is integrated, and changes in the statistical characteristics of the integrated temperature, that can affect the estimated apparent sensitivity to temperature. We show how the resulting artifacts can lead to spurious differences in apparent temperature sensitivity and artificial spatial gradients. Such issues are rarely considered in analyses of the temperature sensitivity of phenology. Given the issues identified, we advocate for process-oriented modelling approaches, informed by observations and with fully characterised uncertainties, as a more robust alternative to the simple days-per-degree temperature sensitivity metric. We also suggest approaches to minimise and assess spurious influences in the days-per-degree metric.

Aim: To quantify the intra‐community variability of leaf‐out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers and deduce its ecological consequences with regard to radiation received and exposure to late frost. Location: Eastern North America (ENA) and Europe (EUR). Time Period: 2009–2022. Major Taxa Studied: Temperate deciduous forest trees. Methods: We developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra‐community variability of the amount of radiation received and of exposure to late frost. Results: Leaf‐out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf‐out to temperature was identical in both regions (−3.4 days per °C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species‐rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf‐out resulted in higher ICVLo. ICVLo resulted in ca. 8% difference of radiation received from leaf‐out to September among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts. Main Conclusions: We conducted the first intercontinental analysis of the variability of leaf‐out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo (difference in terms of radiation received and exposure to late frost), which should be explored in the context of ongoing climate change, which affects trees differently according to their phenological niche. [ABSTRACT FROM AUTHOR]

Abstract Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Research infrastructures play a key role in launching a new generation of integrated long-Term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-Access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Grassland productivity is regulated by both temperature and the amount and timing of precipitation. Future climate change is therefore expected to influence grassland phenology and growth, with consequences for ecosystems and economies. However, the interacting effects of major shifts in temperature and precipitation on grasslands remain poorly understood and existing modelling approaches, although typically complex, do not extrapolate or generalize well and tend to disagree under future scenarios. Here we explore the potential responses of North American grasslands to climate change using a new, data-informed vegetation-hydrological model, a network of high-frequency ground observations across a wide range of grassland ecosystems and CMIP5 climate projections. Our results suggest widespread and consistent increases in vegetation fractional cover for the current range of grassland ecosystems throughout most of North America, despite the increase in aridity projected across most of our study area. Our analysis indicates a likely future shift of vegetation growth towards both earlier spring emergence and delayed autumn senescence, which would compensate for drought-induced reductions in summer fractional cover and productivity. However, because our model does not include the effects of rising atmospheric CO 2 on photosynthesis and water use efficiency, climate change impacts on grassland productivity may be even larger than our results suggest. Increases in the productivity of North American grasslands over this coming century have implications for agriculture, carbon cycling and vegetation feedbacks to the atmosphere.

The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.

Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests. Plain Language Summary: Evergreen broadleaved forests in central Africa near the equator have a unique behavior where their leaf growth and ability to take in carbon peak twice a year. However, the reason underlying this behavior is not well understood, and the current process‐oriented terrestrial biosphere models can not represent this double peak. In this study, we integrated a new module, which uses sunlight as the main factor for when leaves fall and new ones grow in the forest, into a popular process‐oriented terrestrial biosphere model called ORCHIDEE, to simulate this unique behavior in central African forests (hereafter ORCHIDEE‐AFP). We tested our model using real‐world data from the forests acquired at the site level and satellite images. The results show that our new model can successfully simulate when leaves change and how much carbon the forests take in. The new model demonstrates better performance than the standard model. Our improved model will be useful for predicting the future of these forests more accurately under climate change. Key Points: Solar radiation is an adequate climate factor to drive the bimodal leaf phenology of central tropical African rainforestsWe developed a new phenology scheme with solar radiation‐triggered leaf shedding and flushing for the ORCHIDEE land surface model (LSM)The ORCHIDEE LSM with the new phenology scheme captures central tropical African rainforests' bimodal leaf phenology and photosynthesis [ABSTRACT FROM AUTHOR]

Tropical forest phenology directly affects regional carbon cycles, but the relation between species‐specific and whole‐canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large‐scale impacts of human‐induced climate change. Ground‐based long‐term (1937–1956) phenological observations of 140 tropical tree species are recovered, species‐specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand‐level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species‐specific decoupling in time along a bi‐modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy‐wide scaling and the interpretation of remotely sensed phenology signals. [ABSTRACT FROM AUTHOR]

Historical sources report manifold on hazardous past climate and weather events that had considerable impacts on society. Studying changes in the occurrence or mechanisms behind such events is, however, hampered by a lack of spatially and temporally complete weather data. In particular, the spring season has received less attention in comparison to summer and winter but is nevertheless relevant, since weather conditions in spring can delay vegetation and create substantial damage due to late-frost events. For Switzerland, we created a daily high-resolution (1 × 1 km 2) reconstruction of temperature and precipitation fields from 1763 to 1960 that forms, together with present-day meteorological fields, a 258-year-long gridded data set. With this data set, we study changes in long-term climate and historical weather events based on climate and phenological indices focusing on the spring season. Climate and phenological indices show few changes in the mean during the first 200 years compared to the most recent period from 1991 to 2020, where climate change signals clearly emerged in many indices. We evaluate the climate and phenological indices for three cases of extreme spring weather conditions: an unusually warm spring, two late-frost events, and three cold springs. Warm springs are much more frequent in the 21st century, but a very warm and early spring also occurred in 1862. Spring temperatures, however, do not agree on how anomalously warm the spring was when comparing the Swiss temperature reconstruction with reanalyses that extend back to 1868. The three springs of 1785, 1837, and 1853 were particularly cold, with historical sources reporting, for example, prolonged lake freezing and abundant snowfall. Whereas the springs of 1837 and 1853 were characterized by cold and wet conditions, in the spring of 1785 wet days were below average, and frost days reached an all-time maximum, in particular in the Swiss Plateau, indicating inversion conditions. Such conditions are in line with a high occurrence of northeasterly and high-pressure weather types and historical sources describing Bise conditions, a regional wind in the Alpine area related to inversions. Studying such historical events is valuable, since similar atmospheric conditions can lead to cold springs affecting vegetation growth and agricultural production. [ABSTRACT FROM AUTHOR]

Abstract Monodominant patches of forest dominated by Gilbertiodendron dewevrei are commonly found in central African tropical forests, alongside forests with high species diversity. Although these forests are generally found sparsely distributed along rivers, their occurrence is not thought to be (clearly) driven by edaphic conditions but rather by trait combinations of G. dewevrei that aid in achieving monodominance. Functional community structure between these monodominant and mixed forests has, however, not yet been compared. Additionally, little is known about nondominant species in the monodominant forest community. These two topics are addressed in this study. We investigate the functional community structure of 10 one‐hectare plots of monodominant and mixed forests in a central region of the Congo basin, in DR Congo. Thirteen leaf and wood traits are measured, covering 95% (basal area weighted) of all species present in the plots, including leaf nutrient contents, leaf isotopic compositions, specific leaf area, wood density, and vessel anatomy. The trait‐based assessment of G. dewevrei shows an ensemble of traits related to water use and transport that could be favorable for its location near forest rivers. Moreover, indications have been found for N and P limitations in the monodominant forest, possibly related to ectomycorrhizal associations formed with G. dewevrei. Reduced leaf N and P contents are found at the community level for the monodominant forest and for different nondominant groups, as compared to those in the mixed forest. In summary, this work shows that environmental filtering does prevail in the monodominant G. dewevrei forest, leading to lower functional diversity in this forest type, with the dominant species showing beneficial traits related to its common riverine locations and with reduced soil N and P availability found in this environment, both coregulating the tree community assembly.