Wildfires play a critical role in re-shaping boreal ecosystems and climate. It was projected that, owing to the Arctic amplification, boreal wildfires would become more frequent and severe in the coming decades. Although provoking concern, the spatiotemporal changes in boreal wildfires remain unclear, and there are substantial inconsistencies among previous findings. In this study, we performed a comprehensive analysis to determine the spatiotemporal changes in wildfires over Northern Eurasia (NEA) from 2003 to 2020 using a reconstructed Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product. We found that wildfires in NEA exhibited contrasting changes in different latitudinal zones, land cover types, and seasons from 2003 to 2020. Cropland wildfires, mainly distributed at low latitudes (50–60°N), considerably decreased by 81% during the study period. Whereas forest wildfires ignited at high latitudes (north of 60°N) have nearly tripled (increasing at rate of 11~13% per year) during the past two decades. The southwestern and northeastern NEA regions exhibited contrasting patterns of wildfire changes. The active fire counts in the southwestern NEA decreased by 90% at a rate of 0.29(±0.12) × 10⁵ per year, with cropland fires contributing to ~66% of the decrease. However, the fire counts in the northeastern NEA increased by 292% at a rate of 0.23(±0.12) × 10⁵ per year, with boreal forests contributing to ~97% of the increase. It is worth noting that the contrasting changes in wildfires during the past two decades have led to significant structural alternation in the NEA wildfire composition. Forest fires, contributing over 60% of the total fire counts in NEA nowadays, have become the predominant component of the NEA wildfires. The contrasting changes in NEA wildfires imply that more forest fires may emerge in far northern regions of the North Hemisphere as the Arctic becomes progressively warmer in the coming decades. As wildfires continue to increase, more gases and aerosols would be released to the atmosphere and cause considerable feedback to the Arctic climate. The increased wildfire-related climate feedbacks should, therefore, be seriously considered in climate models and projections.

Rapid Arctic warming is driving significant changes in boreal vegetation phenology and productivity. The potentially asynchronous response of these processes could substantially alter the relative impacts of phenological shifts on variations in gross primary productivity (GPP), but this remains poorly understood. The objective of this study is to quantify the impact of phenology extension on boreal ecosystem GPP changes across different periods from 1982 to 2018. To achieve this, we developed a statistical model that integrates vegetation phenology and physiology, and introduced a new metric, the Relative Increment Effect (RIE), to assess the contribution of phenology extension to GPP increase. Our analysis revealed that phenology extension became the dominant driver of GPP increment over time. Specifically, the overall RIE for boreal vegetation increased by 22% from the earlier period (P1: 1982–2000, 3.2) to the more recent period (P2: 2001–2018, 3.93). This increase was more pronounced for grass and shrub ecosystems. Spatial patterns showed that RIE increases were particularly concentrated at high latitudes, especially in northern Siberia. These findings suggested that phenology extension was playing an increasing role in regulating boreal ecosystem productivity, with significant implications for the boreal carbon budget under future warming scenarios.

Surface longwave downward radiation (LWDR) plays a key role in determining the Arctic surface energy budget, especially in insolation-absent boreal winter. A reliable LWDR product is essential for understanding the intrinsic physical mechanisms of the rapid changes in the Arctic climate. The Medium-Resolution Spectral Imager (MERSI-2), a major payload of the Chinese second-generation polar-orbiting meteorological satellite, FengYun-3D (FY-3D), was designed similar to the NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) in terms of the spectral bands. Although significant progress has been made in estimating clear-sky LWDR from MODIS observations using a variety of methods, few studies have focused on the retrieval of clear-sky LWDR from FY-3D MERSI-2 observations. In this study, we propose an advanced method to directly estimate the clear-sky LWDR in the Arctic from the FY-3D MERSI-2 thermal infrared (TIR) top-of-atmosphere (TOA) radiances and auxiliary information using the extremely randomized trees (ERT) machine learning algorithm. The retrieval accuracy of RMSE and bias, validated with the Baseline Surface Radiation Network (BSRN) in situ measurements, are 14.14 W/m² and 4.36 W/m², respectively, which is comparable and even better than previous studies. The scale effect in retrieval accuracy evaluation was further analyzed and showed that the validating window size could significantly influence the retrieval accuracy of the MERSI-2 clear-sky LWDR dataset. After aggregating to a spatial resolution of 9 km, the RMSE and bias of MERSI-2 retrievals can be reduced to 9.43 W/m² and −0.14 W/m², respectively. The retrieval accuracy of MERSI-2 clear-sky LWDR at the CERES SSF FOV spatial scale (approximately 20 km) can be further reduced to 8.64 W/m², which is much higher than the reported accuracy of the CERES SSF products. This study demonstrates the feasibility of producing LWDR datasets from Chinese FY-3D MERSI-2 observations using machine learning methods.

Accurate monitoring of vegetation phenology is vital for understanding climate change impacts on terrestrial ecosystems. While global vegetation greenness phenology (VGP) products are widely available, vegetation productivity phenology (VPP), which better reflects ecosystems’ carbon dynamics, remains largely inaccessible. This study introduces a novel global 500 m VPP dataset (GLASS VPP) from 2001 to 2020, derived from the GLASS gross primary productivity (GPP) product. Validation against three ground-based datasets—Fluxnet 2015, PhenoCam V2.0, and PEP725—demonstrated the dataset’s superior accuracy. Compared to the widely used MCD12Q2 VGP product, GLASS VPP reduced RMSE and bias by 35% and 63%, respectively, when validated against Fluxnet data. It also showed stronger correlations than MCD12Q2 when compared with PhenoCam (195 sites) and PEP725 (99 sites) observations, and it captured spatial and altitudinal phenology patterns more effectively. Overall, GLASS VPP exhibits a higher spatial integrity, stronger ecological interpretability, and improved consistency with ground observations, making it a valuable dataset for phenology modeling, carbon cycle research, and ecological forecasting under climate change.