Age-related modulation of the nitrogen resorption efficiency response to growth requirements and soil nitrogen availability in a temperate pine plantation

Nitrogen (N) resorption is a key strategy for conserving N in forests, and is often affected by soil nutrient condition and N sink strength within the plant. However, our understanding of the age-related pattern of N resorption and how increasing N deposition will affect this pattern is limited. Here, we investigated N resorption along a chronosequence of stands ranging in age from 2 to 100 years old, and conducted a 4-year exogenous N input experiment in stands at age class 11, 20, and 45 in a Larix Principis-rupprechtii plantation in north China. We found a logarithmic increase in leaf N resorption efficiency (NRE) and green leaf N concentration, and a logarithmic decrease in senesced-leaf N concentration along the stand-age chronosequence. Leaf NRE was negatively correlated with plant-available N concentration. Stand-level N resorption was positively correlated with the annual N requirement for tree growth. N resorption contributed to 45, 62, and 68% of the annual N supply in the 11-, 20-, and 45-year-old stands, respectively. Our exogenous N input experiment showed that leaf NRE in the 11- and 20-year-old stands decreased 17 and 12% following a 50-kg N ha¯¹ y¯¹ input. However, leaf NRE was not affected in the 45-year-old stand. The increases in leaf NRE and the contribution of N resorption to annual N supply along stand ages suggested that, with stand development, tree growth depends more on N resorption to supply its N need. Furthermore, the leaf NRE of mature stand was not decreased under exogenous N input, suggesting that mature stands can be stronger sinks for N deposition than young stands due to their higher capacity to retain the deposited N within plants via internal cycle. Ignoring age-related N use strategies can lead to a bias in N cycle models when evaluating forest net primary production under increasing global N deposition

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m−2 y−1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y−1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation

Global assessment of spatiotemporal changes of frequency of terrestrial wind speed

Wind energy, an important component of clean energy, is highly dictated by the disposable wind speed within the working regime of wind turbines (typically between 3 and 25 m s−1 at the hub height). Following a continuous reduction ('stilling') of global annual mean surface wind speed (SWS) since the 1960s, recently, researchers have reported a 'reversal' since 2011. However, little attention has been paid to the evolution of the effective wind speed for wind turbines. Since wind speed at hub height increases with SWS through power law, we focus on the wind speed frequency variations at various ranges of SWS through hourly in-situ observations and quantify their contributions to the average SWS changes over 1981–2021. We found that during the stilling period (here 1981–2010), the strong SWS (⩾ 5.0 m s−1, the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011–2021), slight wind (0 m s−1 < SWS < 2.9 m s−1) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (⩾ 5.0 m s−1) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (−10.02 TWh yr−1, p < 0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr−1, p < 0.05) during 2011–2021.This study was supported by the National Natural Science Foundation of China (Grant No. 42071022), Guangdong Basic and Applied Basic Research Fund (2022A1515240070) and the start-up fund provided by Southern University of Science and Technology (no. 29/Y01296122). C A-M was supported by the IBER-STILLING (RTI2018-095749-A-I00, MCIU/AEI/FEDER,UE); VENTS (GVA-AICO/2021/023); the CSIC Interdisciplinary Thematic Platform (PTI) Clima (PTI-CLIMA); and the 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. RJHD was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. SJ was supported by the Ramon y Cajal program and the OPEN project (RYC2020-029993-I and TED2021-131074B-I00, MCIU/AEI/FEDER,UE)

Summer soil drying exacerbated by earlier spring greening of northern vegetation

Earlier vegetation greening under climate change raises evapotranspiration and thus lowers spring soil moisture, yet the extent and magnitude of this water deficit persistence into the following summer remain elusive. We provide observational evidence that increased foliage cover over the Northern Hemisphere, during 1982–2011, triggers an additional soil moisture deficit that is further carried over into summer. Climate model simulations independently support this and attribute the driving process to be larger increases in evapotranspiration than in precipitation. This extra soil drying is projected to amplify the frequency and intensity of summer heatwaves. Most feedbacks operate locally, except for a notable teleconnection where extra moisture transpired over Europe is transported to central Siberia. Model results illustrate that this teleconnection offsets Siberian soil moisture losses from local spring greening. Our results highlight that climate change adaptation planning must account for the extra summer water and heatwave stress inherited from warming-induced earlier greening

Elucidating hidden and enduring weaknesses in dust emission modelling

Large-scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth’s land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust-climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to two orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo-based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models over-estimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM over-estimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modelling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m−2 y−1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y−1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation