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Phenology shifts influence regional climate by altering energy, and water fluxes through biophysical processes. However, a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to regional climate remains elusive. Using long-term remote sensing observations and Weather Research and Forecasting (WRF) model simulations, we investigated vegetation phenology changes from 2003 to 2020 and quantified their biophysical controls on the regional climate in Northeast China. Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests, while advanced green-up and delayed dormancy extended the growing season in croplands. This prolonged presence and increased maximum green cover intensifiedmore » climate-vegetation interactions, resulting in more significant surface cooling in croplands compared to forests. Surface cooling from forest phenology changes was prominent during May’s green-up (-0.53 ± 0.07 °C), while crop phenology changes induced cooling throughout the growing season, particularly in June (-0.47 ± 0.15 °C), July (-0.48 ± 0.11 °C), and September (-0.28 ± 0.09 °C). Furthermore, we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model, with aerodynamic resistance emerging as the dominant factor. Crucially, our findings underscored that the land surface temperature (LST) sensitivity, exhibited substantially higher values in croplands rather than temperate forests. These strong sensitivities, coupled with the projected continuation of phenology shifts, portend further growing season cooling in croplands. These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature, emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.« less

Understanding the interactions between temperature regimes and vegetation cover is one of the key scientific topics in global environmental change and land surface process research. Recently, the prominent global greening trend has inspired considerable interest in examining climate feedbacks to vegetation changes. However, the impacts of recent widespread vegetation cover changes on soil temperature have been less documented and have received insufficient attention. Here, a high-resolution regional climate model was used to examine the potential impact of vegetation cover changes over the past 37 years, obtained as satellite observational data, on regional air and soil temperatures across the Heilong-Amur Rivermore » Basin. Our sensitivity experiments indicated that the 1.85% increase in the regional fractional vegetation cover (FVC) from 1982 to 2018 cooled the air temperature by 0.045°C and the soil temperature by 0.19°C and that this cooling effect would continue in the near future, from 2016-2018 to 2051-2053. We found that soil temperatures were more sensitive to vegetation cover changes than air temperatures, particularly in mid-high latitude forest ecosystems; this finding helps to explain the nonsignificant decrease in soil temperatures in forest ecosystems over the past 40 years detected in the ERA5-Land reanalysis. Further, our results also suggest that vegetation restoration in forest ecosystems could mitigate the effects of climate change on permafrost environments by increasing the number of soil freezing days.« less

Mid-high latitude Northeast China witnessed significant crop greening from 2001 to 2020, as evidenced by satellite records and field observations. The land surface temperature of croplands during the growing season showed a decreasing trend, suggesting negative surface temperature feedback to crop greening of agricultural ecosystems in mid-high latitude Northeast China. Here, using time-series remote sensing products and long-term scenario simulations, the present study highlights that crop greening can slow climate warming. Our study noted a stronger surface cooling effect induced by crop greening during the growing season in the day than at the night, which contributed to asymmetric diurnal temperaturemore » cycle changes in Northeast China. In addition, our biophysical mechanism analysis revealed aerodynamic and surface resistances as the major driving factors for the daytime land surface temperature (LST) cooling effect induced by crop greening, while the ground heat flux and ambient temperature feedback as the major attributes of the nighttime LST cooling impact due to crop greening.« less

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters includingmore » growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.« less