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Abstract Like many western boundary currents, the East Australian Current (EAC) extension is projected to get stronger and warmer in the future. The CMIP5 multimodel mean (MMM) projection suggests up to 5°C of warming under an RCP85 scenario by 2100. Previous studies employed Sverdrup balance to associate a trend in basin wide zonally integrated wind stress curl (resulting from the multidecadal poleward intensification in the westerly winds over the Southern Ocean) with enhanced transport in the EAC extension. Possible regional drivers are yet to be considered. Here we introduce the NEMO‐OASIS‐WRF coupled regional climate model as a framework to improve our understanding of CMIP5 projections. We analyze a hierarchy of simulations in which the regional atmosphere and ocean circulations are allowed to freely evolve subject to boundary conditions that represent present‐day and CMIP5 RCP8.5 climate change anomalies. Evaluation of the historical simulation shows an EAC extension that is stronger than similar ocean‐only models and observations. This bias is not explained by a linear response to differences in wind stress. The climate change simulations show that regional atmospheric CMIP5 MMM anomalies drive 73% of the projected 12 Sv increase in EAC extension transport whereas the remote ocean boundary conditions and regional radiative forcing (greenhouse gases within the domain) play a smaller role. The importance of regional changes in wind stress curl in driving the enhanced EAC extension is consistent with linear theory where the NEMO‐OASIS‐WRF response is closer to linear transport estimates compared to the CMIP5 MMM. , Plain Language Summary In recent decades, enhanced warming, severe marine heatwaves, and increased transport by the East Australian Current have led to the invasion of nonnative species and the destruction of kelp forests east of Tasmania. The East Australian Current extension is projected to get stronger and warmer in the future. We seek to better understand coupled climate model projections for the Tasman Sea. This is difficult because there is large model diversity and considerable uncertainty as to how and where future changes will occur. In addition, little is known about the possible importance of regional versus large‐scale changes in surface time‐mean winds in driving future circulation changes. Here we use a single limited‐domain ocean‐atmosphere coupled model that takes the average model projections as its inputs and finds that changes in the regional wind stress are most important for the enhanced projected East Australian Current extension. We also find that these projected changes are consistent with simple linear theory and the simulated regional changes in wind stress. , Key Points NEMO‐OASIS‐WRF coupled regional climate model is evaluated and introduced as a new tool for analyzing Tasman Sea climate projections NEMO‐OASIS‐WRF projections suggest that local atmospheric changes drive 73% of the projected 12 Sv increase in EAC extension transport The importance of regional changes in wind stress curl driving the enhanced EAC extension is consistent with linear theory

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