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This catalogue includes seasonal mean precipitation fields from simulations and reanalysis used for the calculation of the Spatial Spin-Up Distance (SSUD). A total of seven simulation were conducted using the convection-permitting configuration (2.5-km grid spacing) of version 6 of the Canadian Regional Climate Model (CRCM6/GEM5; hereafter denoted as GEM2.5 for simplicity). The CRCM6/GEM5 version used here is based on version 5.0.2 of the Global Environmental Multiscale model (GEM5). GEM2.5 simulations were driven directly by the ERA5 reanalysis or by 12-km (GEM12) simulations also performed using the CRCM6/GEM5 model (which was in turn driven by the ERA5 reanalysis). The catalogue comprises seasonal mean precipitation fields from all seven GEM2.5 simulations, from the ERA5 reanalysis and from two GEM12 intermediate simulations (GEM12_SUN and GEM12_P3). Seasonal means were calculated using the common convention: December, January and February (DJF) for winter; March, April and May (MAM) for spring; June, July and August (JJA) for summer; and September, October and November (SON) for fall. Precipitations fields are given in mm/h.

Abstract The increasing atmospheric nitrous oxide (N 2 O) concentration stems from the development of agriculture. However, N 2 O emissions from global rice‐based ecosystems have not been explicitly and systematically quantified. Therefore, this study aims to estimate the spatiotemporal magnitudes of the N 2 O emissions from global rice‐based ecosystems and determine different contribution factors by improving a process‐based biogeochemical model, TRIPLEX‐GHG v2.0. Model validation suggested that the modeled N 2 O agreed well with field observations under varying management practices at daily, seasonal, and annual steps. Simulated N 2 O emissions from global rice‐based ecosystems exhibited significant increasing trends from 0.026 ± 0.0013 to 0.18 ± 0.003 TgN yr −1 from 1910 to 2020, with ∼69.5% emissions attributed to the rice‐growing seasons. Irrigated rice ecosystems accounted for a majority of global rice N 2 O emissions (∼76.9%) because of their higher N 2 O emission rates than rainfed systems. Regarding spatial analysis, Southern China, Northeast India, and Southeast Asia are hotspots for rice‐based N 2 O emissions. Experimental scenarios revealed that N fertilizer is the largest global rice‐N 2 O source, especially since the 1960s (0.047 ± 0.010 TgN yr −1 , 35.24%), while the impact of expanded irrigation plays a minor role. Overall, this study provides a better understanding of the rice‐based ecosystem in the global agricultural N 2 O budget; further, it quantitively demonstrated the central role of N fertilizer in rice‐based N 2 O emissions by including rice crop calendars, covering non‐rice growing seasons, and differentiating the effects of various water regimes and input N forms. Our findings emphasize the significance of co‐management of N fertilizer and water regimes in reducing the net climate impact of global rice cultivation. , Plain Language Summary Nitrous oxide (N 2 O) is a greenhouse gas with ∼300 times greater effect on climate warming than carbon dioxide. Global croplands represent the largest source of anthropogenic N 2 O emissions. However, the contribution of global rice‐based cropping ecosystems to the N 2 O budget remains largely uncertain because of inconsistent observed results. Inspired by the increasing availability of reliable global data sets, we improved and applied a process‐based biogeochemical model by describing the dynamics of various microbial activities to simulate N 2 O emissions from rice‐based ecosystems on a global scale. Model simulations showed that 0.18 million tons of N 2 O‐N were emitted from global rice‐based N 2 O emissions in the 2010s, which was five times larger than that in the 1910s. In the context of regional contribution, southern China, northern India, and Southeast Asia are responsible for more than 80% of the total emissions during 1910–2020. Results suggest that N fertilizer is the most important rice‐N 2 O source quantitively and that increasing irrigation exerts a buffering effect. This study confirmed the potential mitigating effect of co‐managing N fertilizer and irrigation on mitigating rice‐based N 2 O emissions globally. , Key Points N 2 O emissions from global rice‐based ecosystem increased from 0.026 to 0.18 TgN yr −1 between 1910 and 2020 Irrigated rice‐based ecosystems showed larger N 2 O fluxes than rainfed rice globally due to higher N fertilizer use and frequent aerations N fertilizer represents the largest N 2 O source, and co‐management of N fertilizer and flooding regimes is important for mitigation

Abstract The increasing atmospheric nitrous oxide (N 2 O) concentration stems from the development of agriculture. However, N 2 O emissions from global rice‐based ecosystems have not been explicitly and systematically quantified. Therefore, this study aims to estimate the spatiotemporal magnitudes of the N 2 O emissions from global rice‐based ecosystems and determine different contribution factors by improving a process‐based biogeochemical model, TRIPLEX‐GHG v2.0. Model validation suggested that the modeled N 2 O agreed well with field observations under varying management practices at daily, seasonal, and annual steps. Simulated N 2 O emissions from global rice‐based ecosystems exhibited significant increasing trends from 0.026 ± 0.0013 to 0.18 ± 0.003 TgN yr −1 from 1910 to 2020, with ∼69.5% emissions attributed to the rice‐growing seasons. Irrigated rice ecosystems accounted for a majority of global rice N 2 O emissions (∼76.9%) because of their higher N 2 O emission rates than rainfed systems. Regarding spatial analysis, Southern China, Northeast India, and Southeast Asia are hotspots for rice‐based N 2 O emissions. Experimental scenarios revealed that N fertilizer is the largest global rice‐N 2 O source, especially since the 1960s (0.047 ± 0.010 TgN yr −1 , 35.24%), while the impact of expanded irrigation plays a minor role. Overall, this study provides a better understanding of the rice‐based ecosystem in the global agricultural N 2 O budget; further, it quantitively demonstrated the central role of N fertilizer in rice‐based N 2 O emissions by including rice crop calendars, covering non‐rice growing seasons, and differentiating the effects of various water regimes and input N forms. Our findings emphasize the significance of co‐management of N fertilizer and water regimes in reducing the net climate impact of global rice cultivation. , Plain Language Summary Nitrous oxide (N 2 O) is a greenhouse gas with ∼300 times greater effect on climate warming than carbon dioxide. Global croplands represent the largest source of anthropogenic N 2 O emissions. However, the contribution of global rice‐based cropping ecosystems to the N 2 O budget remains largely uncertain because of inconsistent observed results. Inspired by the increasing availability of reliable global data sets, we improved and applied a process‐based biogeochemical model by describing the dynamics of various microbial activities to simulate N 2 O emissions from rice‐based ecosystems on a global scale. Model simulations showed that 0.18 million tons of N 2 O‐N were emitted from global rice‐based N 2 O emissions in the 2010s, which was five times larger than that in the 1910s. In the context of regional contribution, southern China, northern India, and Southeast Asia are responsible for more than 80% of the total emissions during 1910–2020. Results suggest that N fertilizer is the most important rice‐N 2 O source quantitively and that increasing irrigation exerts a buffering effect. This study confirmed the potential mitigating effect of co‐managing N fertilizer and irrigation on mitigating rice‐based N 2 O emissions globally. , Key Points N 2 O emissions from global rice‐based ecosystem increased from 0.026 to 0.18 TgN yr −1 between 1910 and 2020 Irrigated rice‐based ecosystems showed larger N 2 O fluxes than rainfed rice globally due to higher N fertilizer use and frequent aerations N fertilizer represents the largest N 2 O source, and co‐management of N fertilizer and flooding regimes is important for mitigation

Abstract Process‐based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site‐level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments. , Plain Language Summary Land surface models are useful tools to estimate and predict wetland methane (CH 4 ) flux but there is no evaluation of modeled CH 4 flux error at different time scales. Here we use a statistical approach and observations from eddy covariance sites to evaluate the performance of seven wetland models for different wetland types. The results suggest models have captured CH 4 flux variability at monthly or seasonal time scales for boreal and Arctic tundra wetlands but failed to capture the observed seasonal variability for temperate and tropical/subtropical wetlands. The analysis suggests that improving modeled flux at short time scale is important for future model development. , Key Points Significant model‐observation disagreements were found at multi‐day and weekly time scales (<15 days) Models captured variability at monthly and seasonal time (42–142 days) scales for boreal and Arctic tundra sites but not for temperate and tropical sites The model errors show that biases at multi‐day time scales may contribute to persistent systematic biases on longer time scales

Abstract In forest ecosystems, the majority of methane (CH4) research focuses on soils, whereas tree stem CH4 flux and driving factors remain poorly understood. We measured the in situ stem CH4 flux using the static chamber–gas chromatography method at different heights in two poplar (Populus spp.) forests with separate soil textures. We evaluated the relationship between stem CH4 fluxes and environmental factors with linear mixed models and estimated the tree CH4 emission rate at the stand level. Our results showed that poplar stems were a net source of atmospheric CH4. The mean stem CH4 emission rates were 97.51 ± 6.21 μg·m−2·h−1 in Sihong and 67.04 ± 5.64 μg·m−2·h−1 in Dongtai. The stem CH4 emission rate in Sihong with clay loam soils was significantly higher (P &lt; 0.001) than that in Dongtai with sandy loam soils. The stem CH4 emission rate also showed a seasonal variation, minimum in winter and maximum in summer. The stem CH4 emission rate generally decreased with increasing sampling height. Although the differences in CH4 emission rates between stem heights were significant in the annual averages, these differences were driven by differences observed in the summer. Stem CH4 emission rates were significantly and positively correlated with air temperature (P &lt; 0.001), relative humidity (P &lt; 0.001), soil water content (P &lt; 0.001) and soil CH4 flux (P &lt; 0.001). At these sites, the soil emitted CH4 to the atmosphere in summer (mainly from June to September) but absorbed CH4 from the atmosphere during the other season. At the stand level, tree CH4 emissions accounted for 2–35.4% of soil CH4 uptake. Overall, tree stem CH4 efflux could be an important component of the forest CH4 budget. Therefore, it is necessary to conduct more in situ monitoring of stem CH4 flux to accurately estimate the CH4 budget in the future.

Abstract Based on the analysis of fish otolith assemblages from surface sediments of the Lomonosov Ridge (Arctic Ocean), we demonstrate that the very low Holocene sedimentation rates and winnowing of fine sediments result in the mixing of the whole Holocene populations at the sediment surface. Specimens from the Marine Isotope Stage (MIS) 3 or older could even be recovered in the surface due to a sedimentary hiatus at some locations in the central Arctic during the last glacial maximum. Two examples illustrate that 14 C‐stratigraphies from planktic foraminifers in underlying cored sediments reflect the mixing between Holocene and MIS 3 or older populations, thus invalidating continuous age‐depth inferences based on 14 C ages. Hence, much caution is required when attempting to set paleoceanographic reconstructions based on 14 C chronologies in a low sediment accumulation rate environment such as the central Arctic Ocean. Already published paleoceanographic reconstructions from this area might thus require some revisions. , Plain Language Summary Radiocarbon ages of microfossils (fish otoliths) collected at the surface sediments of the Lomonosov Ridge, in the central Arctic Ocean, indicate that all populations that developed during the present interglacial are mixed within the approximately 1 cm‐thick surface layer. Fossil assemblages occasionally include specimens from older warm intervals. The stacking of fossil spanning thousands of years is due to the very low sediment accumulation rate of the area, the post‐depositional winnowing of fine sediments and mixing by benthic organisms. These process result in the impossibility to document the faunal evolution in the central Arctic Ocean during the last few tens of thousands of years using such fossils. , Key Points Fish otolith radiocarbon age distributions in surface sediments illustrate the mixing of Holocene and pre‐Last Glacial Maximum populations Low sedimentation rates, particle winnowing and sedimentary gaps may impact microfossil mixing and 14 C chronologies Published paleoclimate/paleoceanographic records from similar sites might thus require some reinterpretation