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Abstract Adjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed for a particular catching-type gauge. Based on field experiments or numerical simulation, collection efficiency curves as a function of wind speed also involve further explanatory variables such as surface air temperature and/or precipitation type. However, while the wind speed or wind speed and temperature approach is generally effective at reducing the measurement bias, it does not significantly reduce the root-mean-square error (RMSE) of the residuals, implying that part of the variance is still unexplained. In this study, we show that using precipitation intensity as the explanatory variable significantly reduces the scatter of the residuals. This is achieved by optimized curve fitting of field measurements from the Marshall Field Site (Colorado, United States), using a nongradient optimization algorithm to ensure optimal binning of experimental data. The analysis of a recent quality-controlled dataset from the Solid Precipitation Intercomparison Experiment (SPICE) campaign of the World Meteorological Organization confirms the scatter reduction, showing that this approach is suitable to a variety of locations and catching-type gauges. Using computational fluid dynamics simulations, we demonstrate that the physical basis of the reduction in RMSE is the correlation of precipitation intensity with the particle size distribution. Overall, these findings could be relevant in operational conditions since the proposed adjustment of precipitation measurements only requires wind sensor and precipitation gauge data.

Abstract The effects of nitrogen (N) deposition on soil organic carbon (C) and greenhouse gas (GHG) emissions in terrestrial ecosystems are the main drivers affecting GHG budgets under global climate change. Although many studies have been conducted on this topic, we still have little understanding of how N deposition affects soil C pools and GHG budgets at the global scale. We synthesized a comprehensive dataset of 275 sites from multiple terrestrial ecosystems around the world and quantified the responses of the global soil C pool and GHG fluxes induced by N enrichment. The results showed that the soil organic C concentration and the soil CO 2 , CH 4 and N 2 O emissions increased by an average of 3.7%, 0.3%, 24.3% and 91.3% under N enrichment, respectively, and that the soil CH 4 uptake decreased by 6.0%. Furthermore, the percentage increase in N 2 O emissions (91.3%) was two times lower than that (215%) reported by Liu and Greaver ( Ecology Letters , 2009, 12:1103–1117). There was also greater stimulation of soil C pools (15.70 kg C ha −1  year −1 per kg N ha −1  year −1 ) than previously reported under N deposition globally. The global N deposition results showed that croplands were the largest GHG sources (calculated as CO 2 equivalents), followed by wetlands. However, forests and grasslands were two important GHG sinks. Globally, N deposition increased the terrestrial soil C sink by 6.34 Pg CO 2 /year. It also increased net soil GHG emissions by 10.20 Pg CO 2 ‐Geq (CO 2 equivalents)/year. Therefore, N deposition not only increased the size of the soil C pool but also increased global GHG emissions, as calculated by the global warming potential approach.

Abstract Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon–nitrogen interactions tend to be more realistic. Using observation‐based estimates of global photosynthesis, we quantify the global BP of non‐cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model‐estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Abstract To evaluate the present sea ice changes in a longer‐term perspective, the knowledge of sea ice variability on preindustrial and geological time scales is essential. For the interpretation of proxy reconstructions it is necessary to understand the recent signals of different sea ice proxies from various regions. We present 260 new sediment surface samples collected in the (sub‐)Arctic Oceans that were analyzed for specific sea ice (IP 25 ) and open‐water phytoplankton biomarkers (brassicasterol, dinosterol, and highly branched isoprenoid [HBI] III). This new biomarker data set was combined with 615 previously published biomarker surface samples into a pan‐Arctic database. The resulting pan‐Arctic biomarker and sea ice index (PIP 25 ) database shows a spatial distribution correlating well with the diverse modern sea ice concentrations. We find correlations of P B IP 25 , P D IP 25 , and P III IP 25 with spring and autumn sea ice concentrations. Similar correlations with modern sea ice concentrations are observed in Baffin Bay. However, the correlations of the PIP 25 indices with modern sea ice concentrations differ in Fram Strait from those of the (sub‐)Arctic data set, which is likely caused by region‐specific differences in sea ice variability, nutrient availability, and other environmental conditions. The extended (sea ice) biomarker database strengthens the validity of biomarker sea ice reconstructions in different Arctic regions and shows how different sea ice proxies combined may resolve specific seasonal sea ice conditions. , Key Points IP 25 provides information about modern sea ice cover on a (sub‐)Arctic‐wide scale All PIP 25 indices correlate well with spring and autumn sea ice concentrations on a (sub‐)Arctic‐wide scale The combination of biomarker data and dinoflagellate cysts may yield an approach to reconstruct sea ice conditions during different seasons

Abstract Spruce budworm (SBW) outbreaks are a major natural disturbance in boreal forests of eastern North America. During large‐scale infestations, aerial spraying of bacterial insecticides is used to suppress local high‐density SBW populations. While the primary goal of spraying is the protection of wood volume for later harvest, it should also maintain carbon stored in trees. This study provides the first quantitative analysis of the efficacy of aerial spraying against SBW on carbon dynamics in balsam fir, spruce, and mixed fir–spruce forests. In this study, we used the TRIPLEX‐Insect model to simulate carbon dynamics with and without spray applications in 14 sites of the boreal forest located in various regions of Québec. We found that the efficacy of aerial spraying on reducing annual defoliation was greater in the early stage (<5 yr since the outbreak began) of the outbreak than in later (5–10 yr since the outbreak began) stage. Our results showed that more net ecosystem productivity is maintained in balsam fir (the most vulnerable species) than in either spruce or mixed fir–spruce forests following spraying. Also, average losses in aboveground biomass due to the SBW following spraying occurred more slowly than without spraying in balsam fir forests. Our findings suggest that aerial spraying could be used to maintain carbon in conifer forests during SBW disturbances, but that the efficacy of spray programs is affected by host species and stage of the SBW outbreak.