Plant phenology influences both the terrestrial carbon cycle and land-atmosphere interactions and, therefore, can potentially modify large-scale circulations in the atmosphere. However, considerable discrepancies are present among models and between model simulations and observations of plant phenology, adding large uncertainties to future climate projections. Here, we modified plant phenology in the Northern Hemisphere in the Community Earth System Model and conducted simulations to characterize how differences in plant phenology influence land-atmosphere coupling. Plant phenology changes the land surface and land-atmosphere interactions by directly modulating absorbed solar radiation and evapotranspiration and indirectly modifying cloud feedback and snow-albedo feedback. Over the Northern Hemisphere, the largest effects occur from March to June when seasonal deciduous phenology is modified from satellite-derived values to model simulations, which results in a >3 K increase in surface temperature that propagates to 500 hPa (similar to 5-km height). Phenology-induced changes in canopy evapotranspiration and surface temperature depend on soil moisture availability during the growing season. Surface temperature decreases significantly due to increasing latent heat flux and cloud reflection where soil moisture is abundant, while soil moisture control over evapotranspiration increases, and surface temperature remains little changed or even increases in more arid regions. Characterizing the influence of phenology on biogeophysical processes is critical, as significant impacts are present both at the land surface and in the atmospheric layers above. SIGNIFICANCE STATEMENT: Plant phenology influences both biogeochemical and biogeophysical processes in climate models. However, considerable discrepancies exist between satellite-derived phenology and state-of-the-art climate model simulations, and their influences on land-atmosphere coupling remain unclear. Here, we prescribe phenology discrepancies in different phenology plant function types (PFTs) to assess their influences on Earth's system and find significant PFT-dependent impacts. The largest impacts are in high-latitude regions dominated by seasonal deciduous PFTs in late winter to spring when increasing plant activity, cloud feedback, and snow-albedo feedback all cause increased surface temperature. Growing-season temperature decreases where soil moisture is abundant due to increasing evapotranspiration and cloud feedback. Understanding the potential influences of phenology discrepancies is critical for disentangling phenology-induced changes and uncertainties in future climate projections.

Abstract Emerging responses of northern tree cover and composition under climate change have consequences for ecosystem functioning. Satellite-based global tree and land cover datasets have improved our understanding of northern tree cover dynamics. Yet, definitions of forests, design of retrieval algorithms, different spatial resolutions and satellite sensor quality can introduce uncertainties in these data. Here, our objective was to identify consistent patterns of recent change directions of tree cover and tree cover composition over northern lands using multiple datasets. We found large differences in area of tree cover changes in these datasets, ranging from 9% to 82% for increasing trends over North America and Europe. Consequently, we generated a synthesis map that captures consistent trends in tree cover in the four global datasets and found that the synthesis map showed higher agreement with visually interpreted tree cover changes from very high-resolution imagery . This result reflects improved consistency in areas of multi-dataset agreement, rather than improved absolute accuracy. The new synthesis map revealed a substantially larger area of increasing tree cover trends as compared to decreases. This pattern of net increases in tree cover was also relatively consistent among the major northern biomes, whereby large areas of increasing tree cover were particularly evident in boreal regions. Drier boreal and temperate biomes showed more areas with decreasing tree cover in comparison to wetter biomes. Our comparison of changes in the cover of evergreen and deciduous trees based on two land cover products uncovered large disagreements, making it nearly impossible at this point to attribute the identified tree cover changes in regards to forest type. These results may serve as a reference for model simulations of vegetation dynamics. Our findings also call for concerted efforts to produce more consistent tree cover and land cover datasets especially tree cover composition.

Changes in Arctic tundra vegetation, driven by climate change, may be inducing major shifts in ecosystem services and the Arctic carbon budget, and altering high latitude feedbacks to the climate system. Field-based studies have documented warming-induced shrub expansion, and remote sensing has revealed heterogeneous, but primarily positive, trends in peak summer greenness across the Arctic. However, efforts to move beyond remotely sensed measures of spectral greening to quantify the spatial extent and rate of shrub expansion have been constrained by spectral similarities among tundra vegetation types, limited ground truth data, low revisit frequency of satellite observations, and sub-pixel heterogeneity of land cover at medium spatial resolution (30 m). To address these challenges, we developed a methodology that integrates high spatial resolution (2 m) commercial satellite imagery with Harmonized Landsat and Sentinel-2 observations in a machine learning framework, and used it to produce annual maps for 2016 to 2023 of sub-pixel land cover fractions at 30-m spatial resolution across three Arctic tundra ecoregions spanning 3.35 × 105 km2 between the Seward and Tuktoyaktuk Peninsulas. Uncertainty was quantified at each pixel via Monte Carlo resampling. Independent accuracy assessments yielded good accuracies (mean squared errors of 15.98% and 11.89% for low-stature vegetation and erect shrub cover, respectively), that were comparable to or exceeded previous mapping efforts. Further, repeat commercial satellite image pairs enabled the first assessment of mapped fractional cover change in Arctic tundra (R2 of 0.46 and 0.55, change direction accuracies of 77% and 78% for low-stature vegetation and erect shrub cover, respectively). This novel, scalable, multi-sensor approach to fractional land cover mapping produced the first annual maps of land cover fractions in the Arctic tundra, which support more accurate representation of vegetation dynamics and their linkages to climate change and disturbance processes.

Arctic and boreal regions (ABRs) are experiencing rapid warming and increasingly severe wildfires, threatening their roles as global carbon sinks. High quality time series maps of aboveground biomass (AGB) are key for characterizing and attributing spatiotemporal dynamics of carbon stocks in these regions. However, existing maps at regional to global scales often lack the spatial resolution or temporal coverage needed to capture the heterogeneous and dynamic nature of Arctic-boreal AGB change. To address these limitations, we developed annual (1984-2022) 30-m resolution AGB density maps for Alaska and Canada (11.2 x 10(6) km(2)). The maps were produced by using extensive training datasets, including 45,002 unique ground plots and 100,000 km(2) of airborne lidar data, and time-series spectral features derived from the Continuous Change Detection and Classification (CCDC) algorithm fitted on Landsat Collection 2 Surface Reflectance. Using the eXtreme Gradient Boosting model, we generated annual wall-to-wall maps of AGB along with associated uncertainties. Our maps suggest a similar to 41 Pg stock of AGB at 2022, representing a 12% increase from 1984. Our maps achieve high accuracy and low bias on holdout testing data (R-2 = 0.72, Bias= -5.03%, RMSE% = 62.7%), representing an average of 16.0 percentage points increase in R-2 values and 22.7 percentage points decrease in relative bias compared to six existing AGB products. We show using repeat ground measurements that these maps effectively capture AGB loss and recovery due to fire and harvest, gradual AGB changes, and both live and dead tree AGB components in boreal regions. By integrating extensive calibration data with multi-decadal satellite observations and advanced machine learning techniques, these map products provide a robust tool for advancing the understanding of carbon dynamics under global change in Arctic-boreal North America.

Salt marshes are ecologically important and geographically extensive ecosystems situated at the land-ocean interface that are threatened by coastal erosion and flooding from sea-level rise. The Virginia Coast Reserve (VCR) is a large and widely studied coastal system located in the mid-Atlantic region of the United States with extensive salt marshes. While previous studies have mapped salt marsh extent in the VCR using satellite data, no previous research has specifically attempted to monitor changes in low marsh, high marsh, and other coastal land cover types located in the reserve. In this paper, we used time series of satellite imagery from Landsat to map land cover and salt marsh at 30 m spatial resolution between 2005 and 2021. We used these maps to generate a land cover and salt marsh change product for the VCR during the study period. As part of this analysis, we estimated changes in the area of low marsh versus high marsh. Between 2005 and 2021, the area of low marsh in the VCR decreased by 9.25 +/- 5.31 km2, while the area of high marsh decreased by 8.56 +/- 3.88 km2. Marsh expansion seaward and marsh encroachment landward were significantly smaller than marsh loss, resulting in a substantial net loss in both low and high marsh. The unbiased estimate of the net loss rate of salt marshes in the VCR (0.91 +/- 0.41 km2/yr) is substantially higher than previous estimates derived by mapping changes between independent land-cover maps compiled for different years. Barrier island migration is the primary driver of low and high marsh loss. Salt marshes in the bays and near the mainland are more stable than back-barrier marshes, with upland marsh migration being the dominant change in these areas.

Land cover maps provide essential characterizations of Earth's land surface ecological and biophysical properties, supporting diverse scientific, governmental, and public applications. Recent global land cover products derived from Sentinel-2 data, including ESA WorldCover, ESRI's land use and land cover product, and Google's Dynamic World, offer high-quality information but are temporally limited to approximately the last decade. This study presents the Global Land Cover mapping and Estimation (GLanCE) dataset, which provides long-term annual land cover, land cover change, and Enhanced Vegetation Index 2 (EVI2) metrics spanning multiple decades. The first global release encompasses the Americas, Europe, Africa, Asia, and Oceania, distributed via Google Earth Engine and as tiled products through the Land Processes Distributed Active Archive Center (LP-DAAC) using continental projected coordinate systems. The dataset was produced by applying the Continuous Change Detection and Classification (CCDC) algorithm to the complete Landsat archive, followed by iterative sub-continental machine learning classification using CCDC-derived features and multi-source global training data. Seven Level 1 classes are mapped: trees, herbaceous cover, shrubs, water, developed, barren, and ice/snow. Overall, continental accuracies for land cover maps ranged from 75% to 94% across years, depending on how the primary and secondary reference labels are included. User's and producer's accuracies mostly exceed 80% for the tree, water, and herbaceous classes and are above 60% for developed, while shrub and barren show variable performance due to environmental variability and differential post-processing. Accuracy improves when incorporating high-confidence secondary reference labels. This paper reports on an ongoing project, focusing on the nature of the products, the evolution of the methods, initial analysis of the accuracy of the results and the status of the project as well as lessons learned.

Principal component analyses are often applied to spatial data towards inference on latent modes of spatial variation. These analyses are widespread across domains including spatial transcriptomics and environmental sciences, where the modes of spatial variation are represented by corresponding factors of gene expression or remotely sensed time series measurements. Many methods have been proposed for incorporating spatial information into a probabilistic PCA framework; however, there are three main drawbacks to currently available approaches. First, the loadings matrices are not orthogonal, and subsequent orthogonalization of those loadings corrupts the original prior spatial information. Furthermore, currently proposed methods assume stationarity in their spatial prior. Finally, current methods typically do not achieve linear-time computational complexity with respect to the number of spatial locations. To resolve these problems, we first parameterize the model directly with orthogonal loadings. For the prior distribution, we derive the sampling distribution of an SVD transformation with k unique and m-k repeated singular values. We then show under this model that the maximum a posteriori estimator for the orthogonal loadings is the eigendecomposition of S + 1/nΣ, where S is the empirical covariance matrix and Σ is the prior spatial covariance. We develop a minorization-maximization-within-EM algorithm that is linear in computational complexity with respect to the number of spatial locations. We further extend our MM-EM algorithm to handle held-out locations and develop a validation strategy for optimizing the nonstationary prior covariance. Our methodology is used to infer the spatial distribution of direction-specific length scales in a human brain spatial transcriptomics case study, as well as a continental-scale phenology case study in sub-Saharan Africa.

While there are many global geospatial datasets representing the extent of agriculture, they predominantly represent croplands. Only a couple of global data products represent the full global agricultural footprint, including both cropland and pastures. Our own research team's most recent complete publicly available agricultural land cover dataset, including both croplands, and pastures, represents circa 2000. These data, distributed on a graticule of 5 arcmin (∼ 10 km2 at the Equator), have been integrated into a considerable number and diversity of research studies, modelling, data science, and media applications. Further, users of these data have been interested in them for studying a variety of issues such as land use, food security, climate change, and biodiversity loss. Here we present an updated dataset on the global distribution of agricultural lands (cropland and pasture) circa 2015 (15 years on since the initial study). Past studies that have constructed such datasets have been one-off exercises that have been infrequently repeated due to the amount of effort required. Therefore, in this work, we developed a transparent and reproducible approach to update our data product while also enabling easier reproduction of future datasets. We distribute our 2015 product at the same resolution and formats as the prior product, and accompany it with a full set of replicable code and data for reconstruction. In this article we explain how the data was constructed, with links to the permanent DOIs where the data can be readily downloaded by the user community (Mehrabi et al., 2024; https://doi.org/10.5281/zenodo.11540553).

Dryland ecosystems are expected to expand globally as a result of rising atmospheric water demand and vapor pressure deficit. However, the nature and magnitude of how water-limited ecosystems are adapting to increases in aridity is unclear. Here we examine changes in ecosystem water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET), in global water-limited regions over the past two decades. Our analysis uses remotely sensed data, process-based models, and reanalysis datasets to quantify changes in WUE and examine the role that changes in atmospheric CO2, atmospheric water demand, and soil moisture exert on WUE dynamics in water-limited ecosystems. Our results show that on average WUE increased by 17% in water-limited regions worldwide. Asia, North America, and Africa showed the largest increases in WUE (24%, 17%, and 17%, respectively), followed by Europe, South America, and Oceania (15%, 10%, and 9%, respectively). Ecosystems with low mean annual WUE showed the largest increases of WUE. CO2 fertilization from increasing atmospheric CO2 concentrations was the dominant driver behind observed changes in WUE, especially in the Northern Hemisphere. Our findings indicate that vegetation in water-limited ecosystems is adapting to climate change by optimizing water use efficiency but also suggest that the ability of many ecosystems to adapt may decrease as they become drier.

Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (-6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO2 gradient, and O2 trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis.

Land surface phenology (LSP) metrics derived from remote sensing are widely used to monitor vegetation phenology over large areas and to characterize how the growing seasons of terrestrial ecosystems are responding to climate change. Until recently, however, most LSP studies relied on coarse spatial resolution sensors, which makes assigning direct linkages between LSP metrics and ecological processes and properties challenging due to scale mismatches and because substantial variation in phenology and ecological properties are often present at sub-pixel scale in coarse resolution LSP metrics. In this study, we leverage publicly available LSP data products with three orders of magnitude difference in spatial resolution derived from Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), Landsat and Sentinel-2 (HLS, 30 m), and PlanetScope (3 m) imagery to examine and characterize the nature, magnitude, and sources of the agreement and disagreement in LSP metrics across spatial scales. Our results provide three key conclusions: (1) LSP metrics from three sensors showed consistently high cross-scalar agreement across sites (r(2) = 0.70-0.97), suggesting that they all effectively capture geographic variation in LSP; (2) within-site cross-scalar agreement between LSP metrics was systematically lower relative to agreement across sites, but mean absolute differences were consistent across and within sites (generally <14 days for day of year-based metrics, with a few exceptions); and (3) local-scale composition and heterogeneity in land cover is a key factor that controls cross-scalar agreement in LSP metrics. In particular, we found that site-level heterogeneity in land cover (measured via entropy) and the proportion of evergreen versus deciduous land cover types explain up to half of site-to-site variance in local-scale cross-scalar agreement in LSP metrics. Results from this study support the internal consistency and quality of the three LSP data products examined, and more generally, provide guidance regarding the choice of spatial resolution for different applications and land cover conditions, and yield new insights related to how LSP observations scale across different sensors and spatial resolutions.

There is a lack of methodological results for continuous time change detection due to the challenges of noninformative prior specification and efficient posterior inference in this setting. Most methodologies to date assume data are collected according to uniformly spaced time intervals. This assumption incurs bias in the continuous time setting where, a priori, two consecutive observations measured closely in time are less likely to change than two consecutive observations that are far apart in time. Models proposed in this setting have required MCMC sampling which is not ideal. To address these issues, we derive the heterogeneous continuous time Markov chain that models change point transition probabilities noninformatively. By construction, change points under this model can be inferred efficiently using the forward backward algorithm and do not require MCMC sampling. We then develop a novel loss function for the continuous time setting, derive its Bayes estimator, and demonstrate its performance on synthetic data. A case study using time series of remotely sensed observations is then carried out on three change detection applications. To reduce falsely detected changes in this setting, we develop a semiparametric mean function that captures interannual variability due to weather in addition to trend and seasonal components.

Meteorological droughts are increasing in intensity, frequency, and duration due to climate change. These events may have substantial impacts on vegetation productivity that influence the global carbon balance. Effects vary considerably, however, with the intensity of the drought as well as local abiotic and biotic conditions such as vegetation type, soil type, and the timing of the drought. Productivity is primarily reduced because droughts decrease the efficiency with which plants can convert atmospheric CO2 into carbohydrates, largely because of stomatal closure when energy is not limiting. However, another aspect by which droughts can reduce productivity is by shortening the growing season length (GSL). GSL reduction may be particularly pronounced in vegetation communities already sensitive to precipitation variability, in particular, short-rooted grassland and croplands ecosystems. Here, we use evidence from satellite observations of ecosystem activity, meteorological measurements, and data from eddy-covariance flux towers to reveal the impact of several large-scale meteorological droughts on vegetation productivity on natural and managed ecosystems. In particular, we show that the timing of the drought is important, with late droughts being particularly diminishing to productivity. We also demonstrate that while plant physiological responses to drought dominate the reduction in productivity, the diminishment of GSL plays an underappreciated role. These results have wide implications for the future carbon balance under a changing climate, and suggests that ecosystem models could better explain productivity by incorporating the effects of droughts on GSL.

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

This paper provides a review and summary status of the research underway by the NASA Terra Aqua Suomi-NPP Land Discipline Team to provide continuity of global land data products from the NASA Moderate resolution Imaging Spectroradiometer (MODIS) to the Visible Infrared Imaging Radiometer Suite (VIIRS). The two MODIS instruments on the NASA Earth Observing System Terra (morning overpass) and Aqua (afternoon overpass) platforms have provided more than twenty years of data. The peer-reviewed land products generated from MODIS are now being transitioned to production using VIIRS inputs, with the intention of providing dynamic continuity for the Aqua observations. As part of that process, the products from the two instruments are undergoing intercomparison and evaluation. These results are provided where available and show promising levels of agreement and accuracy in all cases. The paper also offers options for establishing continuity of Terra MODIS data products.

Accurate simulation of plant phenology is important in Earth system models as phenology modulates land-atmosphere coupling and the carbon cycle. Evaluations based on grid-cell average leaf area index (LAI) can be misleading because multiple plant functional types (PFT) may be present in one model grid cell and PFTs with different phenology schemes have different LAI seasonal cycles. Here we examined PFT-specific LAI amplitudes and seasonal cycles in the Community Land Model versions 5.0 and 4.5 (CLM5.0 and CLM4.5) and their relationship with the onset of growing season triggers in the Northern Hemisphere. LAI seasonal cycle and spring onset in CLM show the best agreement with MODIS for temperature-dominated deciduous PFTs. Although the agreement in LAI amplitude between CLM5.0 and MODIS is better than CLM4.5, the agreement in seasonal cycles is worse in CLM5.0. CLM5.0 also simulates higher soil moisture and shows lower influences of soil moisture on LAI amplitudes and seasonal cycles. While productivity depends on the environmental factors to which the plant is exposed during any given growing season, differences in phenology sensitivity to its environment necessitate a decoupling between the seasonality of LAI and GPP, which in turn could lead to biases in the carbon cycle as well as surface energy balance and hence land-atmosphere interactions. Because the discrepancy not only depends on parameterizing phenology but phenology-environment relationship, future improvements to other model components (e.g., soil moisture) could better align the seasonal cycle of LAI and GPP.

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Regenerative agricultural practice adoption on conventionally managed fields has gained momentum as a climate mitigation strategy, given the ability of these practices to sequester carbon or reduce greenhouse gas emissions. However, the geospatial and temporal variability of the impact of specific practices, such as cover cropping or no-till, pose challenges for scalable quantification of emissions reduction and deploying incentives to drive increased adoption. To quantify impact while accounting for variability and uncertainty at scale, Indigo Ag created a monitoring, reporting, and verification (MRV) pipeline to produce agricultural soil carbon credits produced at large scales (hundreds of thousands of hectares). The pipeline ingests field data from enrolled farmers, checks data quality, uses hybrid soil sampling and biogeochemical modeling to produce estimates of emissions reduction and uncertainty, and then applies deductions based on calculated uncertainty and leakage to quantify total project-wide carbon credits and monitor for durability of carbon. The implementation of a carbon project (CAR1459) from 2018 to 2022 on 553,743 ha of U.S. cropland utilizing the pipeline is estimated to have reduced emissions by 398,408.5 tCO2e, amounting to 296,662 tCO2e of soil carbon credits after uncertainty deductions. This paper explores the effect sizes associated with specific regenerative practice changes across the project domain. Cover cropping consistently resulted in a net positive climate impact and reduced emissions by 1.29 tCO2e per hectare per year, on average. Introduction of no-till was more common in the project, but it had a lower average emissions reduction of 0.38 tCO2e per hectare per year. Effect sizes for no-till vary spatiotemporally and are typically low in the first several years after adoption but increase in subsequent years. Agricultural carbon programs that capture and incentivize the nuance of outcomes of practices rather than the implementation of practices, can promote adoption of the right management practice to be deployed on the right field for maximum environmental benefit.

BackgroundClimate-change-induced shifts in the timing of leaf emergence during spring have been widely documented and have important ecological consequences. However, mechanistic knowledge regarding what controls the timing of spring leaf emergence is incomplete. Field-based studies under natural conditions suggest that climate-warming-induced decreases in cold temperature accumulation (chilling) have expanded the dormancy duration or reduced the sensitivity of plants to warming temperatures (thermal forcing) during spring, thereby slowing the rate at which the timing of leaf emergence is shifting earlier in response to ongoing climate change. However, recent studies have argued that the apparent reductions in temperature sensitivity may arise from artefacts in the way that temperature sensitivity is calculated, while other studies based on statistical and mechanistic models specifically designed to quantify the role of chilling have shown conflicting results.MethodsWe analysed four commonly used combinations of phenology and temperature datasets obtained from remote sensing and ground observations to elucidate whether current model-based approaches robustly quantify how chilling, in concert with thermal forcing, controls the timing of leaf emergence during spring under current climate conditions.ResultsWe show that widely used modeling approaches that are calibrated using field-based observations misspecify the role of chilling under current climate conditions as a result of statistical artefacts inherent to the way that chilling is parameterised. Our results highlight the limitations of existing modelling approaches and observational data in quantifying how chilling affects the timing of spring leaf emergence and suggest that decreasing chilling arising from climate warming may not constrain near-future shifts towards earlier leaf emergence in extra-tropical ecosystems worldwide.