Arctic ecosystems play a crucial role in the global carbon cycle and are highly sensitive to warming, which is occurring at a rate more than twice as fast as the global average. This warming has led to earlier spring greenup and snowmelt, with significant variations depending on local conditions and vegetation. While much research focuses on temperature's impact on earlier greenup, recent studies highlight the role of delayed snowmelt, where persistent snow cover delays greenup, counteracting warming's effects. Unlike temperature-driven greenup, snowmelt timing is influenced by both temperature and precipitation. Projections indicate increased winter precipitation in the Arctic, potentially leading to greater variability in snowmelt timing compared to greenup. Understanding the interplay between greenup and snowmelt timing and their impact on vegetation growth and the tundra carbon cycle is therefore crucial. Depending on the relative changes in snowmelt and greenup, ecosystems will experience more frequent early or delayed snowmelt events. For example, ecosystems with occasional delayed snowmelt in high-snowfall years may be increasingly impacted by early snowmelt if snowmelt advances faster than greenup. Conversely, delayed snowmelt's impact on vegetation growth (and thus the carbon cycle) will be amplified in ecosystems where greenup advances more rapidly than snowmelt. Failing to separate the effects of snowmelt timing from warming-driven early greenup could lead to underestimation of the temperature sensitivity of vegetation and misrepresentation of ecosystem responses to future climate change. Previous studies show conflicting results on the relationship between earlier snowmelt and carbon uptake, ranging from increased carbon uptake associated with higher spring temperatures to weak or negative correlations. Similarly, research on the effect of delayed snowmelt on vegetation shows conflicting results, with some studies reporting increased vegetation greenness with higher snow accumulation, and others showing decreased reproductive success due to shortened growing seasons. These studies, however, often fail to adequately separate the confounding effects of covarying factors. This paper uses a combination of data and model simulations to improve our understanding of the Arctic tundra's carbon response to changing snowmelt and greenup timing under varied hydrometeorological conditions.

Existing literature presents conflicting findings regarding the impact of altered snowmelt timing on Arctic tundra carbon dynamics. Some studies suggest enhanced carbon uptake due to earlier snowmelt, often linked to warmer spring temperatures. However, other studies report weak or even negative correlations, citing factors like frost-drought damage. Similarly, the influence of delayed snowmelt has been investigated with varying outcomes; some studies indicate increased vegetation greenness with higher snow accumulation, while others report reductions in reproductive success because of shorter growing seasons. This lack of consensus necessitates further investigation into the complex relationship between snowmelt timing, vegetation growth, and carbon cycling in Arctic tundra ecosystems. Many studies rely solely on data analysis, which cannot fully account for the covarying effects of temperature, precipitation, and snowmelt on vegetation growth and carbon dynamics. Process-based models offer a potential solution, but comprehensive investigations incorporating varied hydrometeorological conditions are still needed.

This study investigated the effects of early greenup and delayed snowmelt on Arctic tundra vegetation growth and carbon cycling using a process-based model (Ecosystem Demography model version 2, ED2) and observational data (flux tower measurements and remote sensing products) from seven sites in Alaska (2001–2018). The study sites spanned a latitudinal gradient, allowing analysis of varying climatic limits (temperature and water availability). Long-term trends in greenup and snowmelt timings were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) data. A case study on three sites (with flux data exceeding five years) evaluated changes in flux due to delayed snowmelt, examining correlations between flux timings and meteorological timings with and without delayed snowmelt years. The ED2 model, enhanced with a snowmelt-growing season index (SGSI), was used to investigate the net and lagged effects of changes in snowmelt timing on vegetation growth and carbon uptake across the seven sites. Climatic limits were quantified using the NARR data, examining the limitations imposed by temperature, water, and radiation on vegetation growing days and carbon fluxes. The latitudinal gradients in climatic variables (temperature, precipitation, radiation), snowmelt and greenup timings were analyzed using statistical methods including Spearman's rho and Mann-Kendall tests. A forward stepwise multiple regression analysis explored the carbon flux response to climatic variations at each site. The ED2 model was calibrated and validated against observational data from the US-Atq site. The effects of early and delayed snowmelt were evaluated using least-squares linear regression on years with similar meteorological conditions, and the net effects of early greenup and delayed snowmelt were compared, considering the climatic limitations of each site and season. The spatial representativeness of the landscape at the MODIS spatial scale was assessed using PhenoCam sites, NCDC stations, and ground data to account for landscape heterogeneity at high latitudes.

Analysis revealed strong latitudinal gradients in climatic conditions, greenup and snowmelt timings, and their changes. Snowmelt and greenup occurred earlier at lower latitudes. While both snowmelt and greenup timings advanced over the study period, the rate of greenup advance was generally faster, implying a higher likelihood of delayed snowmelt at higher latitudes. The case study using flux data showed that delayed snowmelt significantly impacted the carbon cycle, delaying vegetation uptake and reducing early growing season net ecosystem productivity (NEP). A 10-day delay in snowmelt reduced early-GS NEP by 36.7–59.0 g C m−2. The ED2 model simulations showed minimal effects of early snowmelt on vegetation growth and carbon fluxes when meteorological conditions and greenup timing were similar, with a slight increase in leaf area index (LAI) during the early growing season at most sites. However, soil temperatures significantly decreased at higher latitudes due to early snowmelt exposure to cold air. Delayed snowmelt significantly reduced LAI during the early growing season across all sites, leading to lower gross primary productivity (GPP) and autotrophic respiration (Ra). Interestingly, during the peak and late growing seasons, GPP and Ra increased even with LAI reduction at some sites, suggesting strong meteorological control over vegetation physiological activity at higher latitudes. Soil moisture increased with delayed snowmelt, particularly at the weakly water-limited site, alleviating water stress. Soil temperatures decreased during the early GS but increased during the peak GS at most sites. The response of heterotrophic respiration (Rh) to soil temperature changes varied depending on site conditions, being more sensitive to soil temperature in temperature-limited sites and soil moisture in less temperature-constrained sites. The net effects of early greenup and delayed snowmelt differed depending on climatic limits and season. During the early growing season, NEP increases due to early greenup were amplified at strongly co-limited (temperature and water) sites, while NEP decreases due to delayed snowmelt were less pronounced at these sites due to water stress relief. At the GS scale, the positive NEP response to early greenup was considerably greater at strongly co-limited sites compared to other sites, while the negative NEP response to delayed snowmelt was much smaller at these sites.

This study demonstrates the significant influence of both early greenup and delayed snowmelt on Arctic tundra carbon dynamics. The findings emphasize the importance of considering the combined effects of these factors, especially the increasingly frequent delayed snowmelt events at higher latitudes, to accurately predict future carbon cycling in this region. The amplification of carbon uptake with early greenup at strongly co-limited sites highlights the sensitivity of these ecosystems to warming temperatures, yet the alleviation of water stress with delayed snowmelt at these same sites shows a complex interaction. The discrepancy between the flux data-based and model-based estimates of NEP reduction due to delayed snowmelt likely arises from the different approaches; the case study focuses on extreme events, while the model incorporates varying degrees of delayed snowmelt and minimizes the influence of interannual meteorological variability. The results indicate that omitting the impact of delayed snowmelt leads to overestimation of carbon uptake at high latitudes and underestimation at lower latitudes. These findings have significant implications for refining carbon cycle models and improving climate change predictions, particularly in the Arctic.

This study demonstrates that the response of Arctic tundra ecosystems to climate change, particularly warming-driven changes in greenup and snowmelt timing, is significantly impacted by local climatic conditions (temperature and water limitations). The interplay of early greenup and delayed snowmelt, both in magnitude and timing, results in complex carbon responses that vary across the latitudinal gradient. Accurate predictions of future carbon cycling in Arctic tundra ecosystems require comprehensive models that account for the varying effects of these factors. Future research should focus on improving process-based models to account for high latitude landscape heterogeneity and incorporating more detailed vegetation phenology models, facilitated by improved ground-based phenology data. This deeper understanding will significantly enhance our capacity to predict the effects of future climate change on these critical ecosystems.

The study relies on remote sensing data for assessing greenup and snowmelt timings, which can be subject to uncertainties associated with data resolution, methodological differences in phenological transition detection, and limitations in data quality at high latitudes due to factors like low sun angle and cloud cover. While spatial representativeness was assessed, the heterogeneity of the Arctic landscape could still influence the results. The ED2 model, while comprehensive, represents a simplification of complex ecosystem processes and may not fully capture all interactions. The case study analysis is limited by the relatively small number of years with delayed snowmelt events, which might affect the robustness of conclusions. The model's ability to predict the interaction of various factors under different environmental conditions requires future validation using more comprehensive and extensive field data.