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• Precipitation was repackaged to simulate 4-level of precipitation variability. • Extra-extreme precipitation variability scenario potentially reduced N 2 O emissions. • Effects of precipitation variability on N 2 O fluxes had high interannual variations. • Soil, microbes, functional genes, and plants explained 61% variation in N 2 O fluxes. High temporal precipitation variability, characterized by less frequent but larger-magnitude precipitation events, is increasing. However, how precipitation variability affects N 2 O emissions and the underlying mechanisms remain unclear, especially at multiple-year scales. We conducted a 3-year manipulative experiment in which the same long-term mean growing season precipitation total was repackaged into events of inversely varying magnitude and frequency to simulate four levels of precipitation variability (extra-extreme, extreme, medium, and normal) in a semiarid grassland. Cumulative N 2 O emissions was the smallest under the extra-extreme precipitation variability scenario (6 very large rainfall events), 26% less than emission under the other three treatments (10, 16, and 24 rainfall events) over the three growing seasons. However, this difference was almost entirely due to three sampling events in the third year. Plant community (biomass and biodiversity), soil abiotic properties (water, dissolved organic carbon and pH), soil microbial biomass (carbon, nitrogen and the ratio), and soil functional genes (archaeal and bacterial amoA, nirS, nirK, narG , and nosZ) explained 61% of the variation in N 2 O emissions in response to the precipitation variability. Structural equation modelling indicated that the precipitation variability had direct positive effects, and indirect negative effects via soil abiotic properties, on soil functional genes that ultimately had positive effects on N 2 O emissions. The reduction in N 2 O emission in the extra-extreme precipitation variability scenario in the third year was due to low levels of soil water content, soil pH, aboveground biomass, and microbial biomass carbon simultaneously. Our results suggest that at the multi-year timescale, semiarid grasslands may have negative feedbacks to future precipitation regimes with higher variability. [Display omitted] [ABSTRACT FROM AUTHOR]

• N 2 O flux is sensitive and insensitive to drought and heat wave, respectively. • Soil water content is a first-order predictor of seasonal variation of N 2 O flux. • Inter-annual variations of N 2 O flux are impacted by precipitation distribution. • Archaeal amoA and nirK mainly regulates climate extremes effects on N 2 O flux. Nitrous oxide (N 2 O), the third most important greenhouse gas, contributes to the increasing frequency and severity of climate extremes. Disentangling feedbacks of climate extremes on terrestrial N 2 O emission is important for forecasting future climate changes. Here, we experimentally imposed extreme drought and heat wave events during three years in a semiarid grassland to investigate the responses of N 2 O flux. We identified that N 2 O flux suppression during droughts was mediated by soil water content (SWC), microbial biomass carbon (MBC), soil inorganic nitrogen (SIN) and dissolved organic carbon (DOC) contents, and the abundance of archaeal amoA, nirK, and narG. However, bacterial amoA, nirS, and nosZ remained stable. Upon rewetting following droughts, the SWC, SIN, DOC, archaeal amoA, nirK, narG, and resultant N 2 O fluxes recovered to the magnitude of the ambient control. In contrast, heat waves alone or in combination with drought did not impact N 2 O fluxes or the underlying physical, chemical and microbial states. Stepwise multiple linear regression suggested that SWC, DOC, and MBC were the key factors regulating immediate responses of N 2 O flux to climate extremes while the major factors regulating seasonal mean N 2 O flux in response to climate extremes were archaeal amoA abundance, nirK abundance, and MBC. Our results suggest that N 2 O fluxes were sensitive to droughts but insensitive to heat waves. Soil moisture induced changes in substrate availability, and the community size of total and functional microorganisms in soil jointly regulated N 2 O responses to climate extremes. The relative importance of regulating factors shifted at different timescales. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we conducted eddy covariance (EC) measurements of water vapor exchange over a typical steppe in a semi-arid area of the Inner Mongolia Plateau, China. Measurement sites were located within a 25-year-old enclosure with a relatively low leaf area index (∼1.5m2 m−2) and dominated by Leymus chinensis. Energy balance closure was (H +LE)=17.09+0.69×(Rn− G) (W/m2; r 2 =0.95, n =6596). Precipitation during the two growing seasons of the study period was similar to the long-term average. The peak evapotranspiration in 2004 was 4mmd−1, and 3.5mmd−1 in 2003. The maximum latent heat flux was higher than the sensible heat flux, and the sensible heat flux dominated the energy budget at midday during the entire growing season in 2003; latent heat flux was the main consumption component for net radiation during the 2004 growing season. During periods of frozen soil in 2003 and 2004, the sensible heat flux was the primary consumption component for net radiation. The soil heat flux component was similar in 2003 and 2004. The decoupling coefficient (between 0.5 and 0.1) indicates that evapotranspiration was strongly controlled by saturation water vapor pressure deficit (VPD) in this grassland. The results of this research suggest that energy exchange and evapotranspiration were controlled by the phenology of the vegetation and soil water content. In addition, the amount and frequency of rainfall significantly affect energy exchange and evapotranspiration upon the Inner Mongolia plateau. [Copyright &y& Elsevier]

• Temperate and alpine grasslands were weak carbon sinks. • Elevated water consistently increased carbon fluxes in temperate and alpine grasslands. • Increased temperature reduced carbon fluxes in temperate grasslands and increased carbon fluxes in alpine grasslands. Temperate grassland (TG) and alpine grassland (AG) are two distinct grassland types with different climatic backgrounds and adaptation patterns. Little is known about the universal and divergent responses of TGs and AGs to climate change, particularly with regard to the carbon (C) fluxes. The large-scale responses of the C fluxes in different grasslands to climate change remain unclear, with only a few comparative studies of distinct responses of the C fluxes in TGs and AGs to climate change hinders our understanding of this subject. In this study, we conducted a large-scale transect study across the Mongolian, Loess, and Tibetan Plateaus in China to reveal the similarities and differences in the responses of the C fluxes in TGs and AGs to climate change. We used C flux data measured by eddy covariance from ChinaFLUX and published literature, covering the period from 2003 to 2020. The results showed that TGs and AGs in China were weak C sinks. Net ecosystem productivity in TGs and AGs exhibited opposite trends with increasing latitude, longitude, and altitude. Elevated water (mean annual precipitation and soil water content) consistently increased the C fluxes. Conversely, increased temperature (mean annual temperature and mean annual soil temperature) reduced the C fluxes in TGs and increased the C fluxes in AGs. The water factors promoted the C fluxes in both TGs and AGs by improving the leaf area indices. Conversely, the temperature factors had a strong direct, negative effect on the C fluxes in TGs and a weak direct, positive effect on the C fluxes in AGs, emphasizing the divergent patterns of the C fluxes in TGs and AGs and their responses to climate change. Overall, this study enhances our knowledge on C sinks and various grassland hydrothermal sensitivities. [ABSTRACT FROM AUTHOR]

The Community Land Model (CLM) is an advanced process-based land surface model that simulates carbon, nitrogen, water vapor and energy exchanges between terrestrial ecosystems and the atmosphere at various spatial and temporal scales. We use observed carbon and water fluxes from five representative Chinese Terrestrial Ecosystem Flux Research Network (ChinaFLUX) eddy covariance tower sites to systematically evaluate the new version CLM4.5 and old version CLM4.0, and to generate insights that may inform future model developments. CLM4.5 underestimates the annual carbon sink at three forest sites and one alpine grassland site but overestimates the carbon sink of a semi-arid grassland site. The annual carbon sink underestimation for the deciduous-dominated forest site results from underestimated daytime carbon sequestration during summer and overestimated nighttime carbon emission during spring and autumn. Compared to CLM4.0, the bias of annual gross primary production (GPP) is reduced by 24% and 28% in CLM4.5 at two subtropical forest sites. However, CLM4.5 still presents a large positive bias in annual GPP. The improvement in net ecosystem exchange (NEE) is limited, although soil respiration bias decreases by 16%–43% at three forest sites. CLM4.5 simulates lower soil water content in the dry season than CLM4.0 at two grassland sites. Drier soils produce a significant drop in the leaf area index and in GPP and an increase in respiration for CLM4.5. The new fire parameterization approach in CLM4.5 causes excessive burning at the Changbaishan forest site, resulting in an unexpected underestimation of NEE, vegetation carbon, and soil organic carbon by 46%, 95%, and 87%, respectively. Overall, our study reveals significant improvements achieved by CLM4.5 compared to CLM4.0, and suggests further developments on the parameterization of seasonal GPP and respiration, which will require a more effective representation of seasonal water conditions and the partitioning of net radiation between sensible and heat fluxes. [ABSTRACT FROM AUTHOR]

Abstract: Gross primary productivity (GPP) is a key component of land–atmospheric carbon exchange. Reliable calculation of regional/global GPP is crucial for understanding the response of terrestrial ecosystems to climate change and human activity. In recent years, many light use efficiency (LUE) models driven by remote sensing data have been developed for calculating GPP at various spatial and temporal scales. However, some studies show that GPP calculated by LUE models was biased by different degrees depending on sky clearness conditions. In this study, a two-leaf light use efficiency (TL-LUE) model is developed based on the MOD17 algorithm to improve the calculation of GPP. This TL-LUE model separates the canopy into sunlit and shaded leaf groups and calculates GPP separately for them with different maximum light use efficiencies. Different algorithms are developed to calculate the absorbed photosynthetically active radiation for these two groups. GPP measured at 6 typical ecosystems in China was used to calibrate and validate the model. The results show that with the calibration using tower measurements of GPP, the MOD17 algorithm was able to capture the variations of measured GPP in different seasons and sites. But it tends to understate and overestimate GPP under the conditions of low and high sky clearness, respectively. The new TL-LUE model outperforms the MOD17 algorithm in reproducing measured GPP at daily and 8-day scales, especially at forest sites. The calibrated LUE of shaded leaves is 2.5–3.8 times larger than that of sunlit leaves. The newly developed TL-LUE model shows lower sensitivity to sky conditions than the MOD17 algorithm. This study demonstrates the potential of the TL-LUE model in improving GPP calculation due to proper description of differences in the LUE of sunlit and shaded leaves and in the transfer of direct and diffuse light beams within the canopy. [Copyright &y& Elsevier]