The seasonal greening of Northern Hemisphere (NH) ecosystems, due to extended growing periods and enhanced photosynthetic activity, could modify near-surface warming by perturbing land-atmosphere energy exchanges, yet this biophysical control on warming seasonality is underexplored. By performing experiments with a coupled land-atmosphere model, here we show that summer greening effectively dampens NH warming by -0.15 ± 0.03 °C for 1982-2014 due to enhanced evapotranspiration. However, greening generates weak temperature changes in spring (+0.02 ± 0.06 °C) and autumn (-0.05 ± 0.05 °C), because the evaporative cooling is counterbalanced by radiative warming from albedo and water vapor feedbacks. The dwindling evaporative cooling towards cool seasons is also supported by state-of-the-art Earth system models. Moreover, greening-triggered energy imbalance is propagated forward by atmospheric circulation to subsequent seasons and causes sizable time-lagged climate effects. Overall, greening makes winter warmer and summer cooler, attenuating the seasonal amplitude of NH temperature. These findings demonstrate complex tradeoffs and linkages of vegetation-climate feedbacks among seasons.

Abstract Wildfire is one of the dominant disturbances in the Siberian boreal forest, affecting the capacity of forests to uptake carbon. Impacts of wildfires on forest carbon uptake are substantially varied by tree compositions, however, the loss of carbon uptake capability across different forest types remains uncertain. Here, we tracked post-fire changes in gross primary production (GPP) across five forest types in Siberia, namely, evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), deciduous broadleaf forests (DBF), mixed forests (MF), and woody savannas (WSVN), using satellite-based observation data from 2001 to 2023. Results revealed substantial reductions in GPP after wildfire followed by gradual recovery except DBF. The GPP changes one year after wildfire (ΔGPP 1yr ) and recovery rates of GPP notably differed by forest types. In ENF, ΔGPP 1yr reached −63.4 g C m −2 yr −1 , which is larger than that in MF, DNF, and WSVN by −20.6, −31.3, and −42.7 g C m −2 yr −1 , respectively. Consistently, the GPP recovery rate is the lowest in ENF, at 63.7%. Furthermore, significant decreasing trends in 5-year mean of ΔGPP 1 yr in DNF, MF, and WSVN exceed −1.0 g C m −2 yr −1 yr −1 , indicating intensified impacts of increased wildfire intensity and/or extent on forest carbon. Our findings highlight the urgent need for establishing fire management and forest recovery strategies adapted to different forest types to enhance ecosystem resilience.

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate carbon emission estimates are essential for guiding climate action toward net zero emissions by 2050. The Bayesian inverse method, combined with atmospheric CO₂ measurements and the transport model, can serve as an independent verification approach to improve accuracy. In this study, we developed a Bayesian inverse modelling framework using ground- and space-based measurements and applied it to Seoul to test the framework and constrain its CO₂ emissions. By leveraging the high temporal resolution of ground-based in situ observations and the broad spatial coverage of satellite data, we improved the accuracy of emission estimates. Our results indicate a 4.43 % increase in posterior emissions compared to prior estimates, suggesting that the prior emissions were slightly underestimated. The spatiotemporal variability of posterior emissions increased significantly, enabling us to track CO₂ fluctuations and assess the impact of carbon reduction policies over time and space. Additionally, the mean absolute error was reduced, improving the agreement between simulated and observed CO₂ enhancements. We thoroughly investigated the performance of the inverse model through a sensitivity analysis that considered different observational network configurations. The most substantial reductions in uncertainties (19.2 %) were observed when all available observations were used. The extensive coverage of satellite observations enabled further corrections in areas not covered by ground observations. Overall, this study highlights the importance of combining multiple observational sources to better constrain urban CO₂ emissions. The framework also shows strong potential for application in other cities and can support the development of effective climate mitigation policies.