Makar Paul A, Cheung Philip, Hogrefe Christian, Akingunola Ayodeji, Alyuz Ummugulsum, Bash Jesse O, Bell Michael D, Bellasio Roberto, Bianconi Roberto, Butler Tim, Cathcart Hazel, Clifton Olivia E, Hodzic Alma, Kioutsioukis Ioannis, Kranenburg Richard, Lupascu Aurelia, Lynch Jason A, Momoh Kester, Perez-Camanyo Juan L, Pleim Jonathan, Ryu Young-Hee, San Jose Roberto, Schwede Donna, Scheuschner Thomas, Shephard Mark W, Sokhi Ranjeet S, Galmarini Stefano
Environment and Climate Change Canada, Toronto, Canada.
Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, USA.
Atmos Chem Phys. 2025 Mar 14;25(5):3049-3107. doi: 10.5194/acp-25-3049-2025.
Exceedances of critical loads for deposition of sulfur (S) and nitrogen (N) in different ecosystems were estimated using European and North American ensembles of air quality models, under the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4), to identify where the risk of ecosystem harm is expected to occur based on model deposition estimates. The ensembles were driven by common emissions and lateral boundary condition inputs. Model output was regridded to common North American and European 0.125° resolution domains, which were then used to calculate critical load exceedances. Targeted deposition diagnostics implemented in AQMEII4 allowed for an unprecedented level of post-simulation analysis to be carried out and facilitated the identification of specific causes of model-to-model variability in critical load exceedance estimates. Datasets for North American critical loads for acidity for forest soil water and aquatic ecosystems were created for this analysis. These were combined with the ensemble deposition predictions to show a substantial decrease in the area and number of locations in exceedance between 2010 and 2016 (forest soils: 13.2% to 6.1 %; aquatic ecosystems: 21.2% to 11.4 %). All models agreed regarding the direction of the ensemble exceedance change between 2010 and 2016. The North American ensemble also predicted a decrease in both the severity and total area in exceedance between the years 2010 and 2016 for eutrophication-impacted ecosystems in the USA (sensitive epiphytic lichen: 81.5% to 75.8 %). The exceedances for herbaceous-community richness also decreased between 2010 and 2016, from 13.9% to 3.9 %. The uncertainty associated with the North American eutrophication results is high; there were sharp differences between the models in predictions of both total N deposition and the change in N deposition and hence in the predicted eutrophication exceedances between the 2 years. The European ensemble was used to predict relatively static exceedances of critical loads with respect to acidification (4.48% to 4.32% from 2009 to 2010), while eutrophication exceedance increased slightly (60.2% to 62.2 %). While most models showed the same changes in critical load exceedances as the ensemble between the 2 years, the spatial extent and magnitude of exceedances varied significantly between the models. The reasons for this variation were examined in detail by first ranking the relative contribution of different sources of sulfur and nitrogen deposition in terms of deposited mass and model-to-model variability in that deposited mass, followed by their analysis using AQMEII4 diagnostics, along with evaluation of the most recent literature. All models in both the North American and European ensembles had net annual negative biases with respect to the observed wet deposition of sulfate, nitrate, and ammonium. Diagnostics and recent literature suggest that this bias may stem from insufficient cloud scavenging of aerosols and gases and may be improved through the incorporation of multiphase hydrometeor scavenging within the modelling frameworks. The inability of North American models to predict the timing of the seasonal peak in wet ammonium ion deposition (observed maximum was in April, while all models predicted a June maximum) may also relate to the need for multiphase hydrometeor scavenging (absence of snow scavenging in all models employed here). High variability in the relative importance of particulate sulfate, nitrate, and ammonium deposition fluxes between models was linked to the use of updated particle dry-deposition parameterizations in some models. However, recent literature and the further development of some of the models within the ensemble suggest these particulate biases may also be ameliorated via the incorporation of multiphase hydrometeor scavenging. Annual sulfur and nitrogen deposition prediction variability was linked to SO and HNO dry-deposition parameterizations, and diagnostic analysis showed that the cuticle and soil deposition pathways dominate the deposition mass flux of these species. Further work improving parameterizations for these deposition pathways should reduce variability in model acidifying-gas deposition estimates. The absence of base cation chemistry in some models was shown to be a major factor in positive biases in fine-mode particulate ammonium and particle nitrate concentrations. Models employing ammonia bidirectional fluxes had both the largest- and the smallest-magnitude biases, depending on the model and bidirectional flux algorithm employed. A careful analysis of bidirectional flux models suggests that those with poor NH performance may underestimate the extent of NH emission fluxes from forested areas. Model-measurement fusion in the form of a simple bias correction was applied to the 2016 critical loads. This generally reduced variability between models. However, the bias correction exercise illustrated the need for observations which close the sulfur and nitrogen budgets in carrying out model-measurement fusion. Chemical transformations between different forms of sulfur and nitrogen in the atmosphere sometimes result in compensating biases in the resulting total sulfur and nitrogen deposition flux fields. If model-measurement fusion is only applied to some but not all of the fields contributing to the total deposition of sulfur or nitrogen, the corrections may result in greater variability between models or less accurate results for an ensemble of models, for those cases where an unobserved or unused observed component contributes significantly to predicted total deposition. Based on these results, an increased process-research focus is therefore recommended for the following model processes and for observations which may assist in model evaluation and improvement: multiphase hydrometeor scavenging combined with updated particle dry-deposition, cuticle, and soil deposition pathway algorithms for acidifying gases, base cation chemistry and emissions, and NH bidirectional fluxes. Comparisons with satellite observations suggest that oceanic NH emission sources should be included in regional chemical transport models. The choice of a land use database employed within any given model was shown to significantly influence deposition totals in several instances, and employing a common land use database across chemical transport models and critical load calculations is recommended for future work.
在空气质量模型评估国际倡议第4阶段(AQMEII4)下,利用欧洲和北美的空气质量模型集合,估算了不同生态系统中硫(S)和氮(N)沉降的临界负荷超标情况,以便根据模型沉降估算确定预计会出现生态系统损害风险的区域。这些模型集合由共同的排放和侧向边界条件输入驱动。模型输出被重新网格化到北美和欧洲通用的0.125°分辨率区域,然后用于计算临界负荷超标情况。AQMEII4中实施的目标沉降诊断使得能够进行前所未有的模拟后分析,并有助于识别临界负荷超标估算中模型间差异的具体原因。为此分析创建了北美森林土壤水和水生生态系统酸度临界负荷的数据集。将这些数据集与模型集合的沉降预测相结合,结果表明,2010年至2016年期间,超标区域和地点数量大幅减少(森林土壤:从13.2%降至6.1%;水生生态系统:从21.2%降至11.4%)。所有模型在2010年至2016年期间集合超标变化方向上达成一致。北美模型集合还预测,2010年至2016年期间,美国受富营养化影响的生态系统超标情况的严重程度和总面积均有所下降(敏感附生地衣:从81.5%降至75.8%)。2010年至2016年期间,草本群落丰富度的超标情况也有所下降,从13.9%降至3.9%。与北美富营养化结果相关的不确定性很高;模型在总氮沉降预测以及氮沉降变化预测方面存在显著差异,因此在这两年的预测富营养化超标情况方面也存在差异。欧洲模型集合用于预测酸化临界负荷超标情况相对稳定(2009年至2010年从4.48%降至4.32%),而富营养化超标情况略有增加(从60.2%增至62.2%)。虽然大多数模型在这两年间显示出与模型集合相同的临界负荷超标变化,但模型间超标情况的空间范围和幅度差异显著。通过首先根据沉积质量以及该沉积质量的模型间差异对硫和氮沉积不同来源的相对贡献进行排名,然后使用AQMEII4诊断方法对其进行分析,并结合对最新文献的评估,详细研究了这种差异的原因。北美和欧洲模型集合中的所有模型在硫酸盐、硝酸盐和铵的观测湿沉降方面均存在年度净负偏差。诊断和最新文献表明,这种偏差可能源于气溶胶和气体的云清除不足,通过在建模框架中纳入多相水凝物清除可能会有所改善。北美模型无法预测湿铵离子沉积季节性峰值的时间(观测到的最大值出现在4月,而所有模型预测的最大值出现在6月),这也可能与需要多相水凝物清除有关(此处使用的所有模型均未考虑雪清除)。模型间颗粒硫酸盐、硝酸盐和铵沉积通量相对重要性的高度变异性与一些模型中更新的颗粒干沉积参数化的使用有关。然而,最新文献以及模型集合中一些模型的进一步发展表明,通过纳入多相水凝物清除,这些颗粒偏差也可能得到改善。年度硫和氮沉积预测的变异性与SO和HNO干沉积参数化有关,诊断分析表明,角质层和土壤沉积途径主导了这些物种的沉积质量通量。进一步改进这些沉积途径的参数化工作应能减少模型酸化气体沉积估算的变异性。一些模型中缺乏碱金属阳离子化学被证明是细模式颗粒铵和颗粒硝酸盐浓度正偏差的主要因素。采用氨双向通量的模型偏差幅度最大和最小,这取决于所采用的模型和双向通量算法。对双向通量模型的仔细分析表明,那些NH性能较差的模型可能低估了森林地区NH排放通量的程度。以简单偏差校正形式进行的模型 - 测量融合应用于2016年的临界负荷。这通常减少了模型间的变异性。然而,偏差校正工作表明,在进行模型 - 测量融合时,需要能够闭合硫和氮收支的观测数据。大气中不同形式的硫和氮之间的化学转化有时会导致最终总硫和氮沉积通量场出现补偿性偏差。如果仅对部分而非所有对硫或氮总沉积有贡献的场应用模型 - 测量融合,对于那些未观测到或未使用的观测分量对预测总沉积有显著贡献的情况,校正可能会导致模型间更大的变异性或模型集合的结果准确性降低。基于这些结果,因此建议对以下模型过程以及可能有助于模型评估和改进的观测给予更多的过程研究关注:多相水凝物清除结合更新的颗粒干沉积、角质层和土壤沉积途径算法用于酸化气体、碱金属阳离子化学和排放以及NH双向通量。与卫星观测的比较表明,区域化学传输模型应纳入海洋NH排放源。在多个实例中,任何给定模型中使用的土地利用数据库的选择被证明会显著影响沉积总量,建议未来工作在化学传输模型和临界负荷计算中采用通用的土地利用数据库。