Process Based Earth System Model Evaluation

气候变化和空气污染是当今人类面临的两个最大挑战。臭氧和颗粒物是对人类健康特别有害的污染物。最近的研究表明，仅在英国，它们就导致50,000人额外死亡，每年造成80-220亿英镑的经济负担。臭氧和颗粒物在气候变化中也起着重要作用。臭氧吸收红外线辐射，导致气候变暖。粒子散射和吸收入射的太阳辐射，并改变云的性质。这导致了与地球气候的复杂相互作用，一些类型的气溶胶污染使气候变暖，而另一些类型的气溶胶污染使气候变冷。未来的空气质量既取决于污染物排放的变化，也取决于气候变化。此外，气候变暖可能导致空气污染恶化，进而可能导致额外的变暖，这意味着空气污染和气候之间可能存在复杂的反馈。为了帮助理解这些复杂的相互作用和反馈，科学家们开发了地球系统模型，其中包括对重要物理和生物地球化学过程的描述。政策制定者越来越多地使用这些模型来预测未来的空气质量和气候，并指导政策决策。因此，重要的是这些模型要经过严格的测试。这项测试涉及使用过去几十年在世界各地进行的对大气成分的详细观测。到目前为止，大多数模型评估都涉及到测试模型是否模拟了当前大气污染物的平均浓度。虽然这是模型评估中有用和必要的第一步，但它并不测试模型是否准确地模拟了污染物在不断变化的排放或气候变化下的浓度变化。例如，该模型是在给定特定排放变化的情况下还是在未来气候变化情景下捕捉到污染物浓度的真实变化？这一点尤其重要，因为这些预测支撑着减少空气质量的政策建议。在这个项目中，我们将综合对臭氧和颗粒物及其大气前体的长期(数十年)观测。我们将利用这些观察来探索在过去几十年中观察到的趋势和变异性。然后，我们将开发一个模型观测框架，该框架可用于评估模型模拟观察到的变异性和趋势的效果。我们将使用模型比较练习的现有模型输出来测试最先进的地球系统模型。最后，我们将探索驱动模拟可变性和趋势的模型过程。我们的结果将向科学界通报地球系统模型的保真度。这个项目将有助于改进我们的模型，并让我们对预测更有信心。

Climate change and air pollution are two of the biggest challenges facing humanity today. Ozone and particulate matter are pollutants that are particularly harmful to human health. Recent studies have suggested that in the UK alone they cause 50,000 extra deaths and result in a financial burden of £8-22 billion per year. Both ozone and particulate matter also play an important role in climate change. Ozone absorbs infra-red radiation resulting in a warming of the climate. Particles scatter and absorb incoming solar radiation and alter the properties of clouds. This results in complex interactions with the Earth's climate, with some types of aerosol pollution warming climate whereas others cool climate. Future air quality depends both on changes to emissions of pollutants and to changes in climate. Furthermore, a warming climate can result in worsened air pollution, which in turn can drive additional warming, meaning that complex feedbacks are possible between air pollution and climate.To help understand these complex interactions and feedbacks scientists have developed Earth System Models that include a description of the important physical and biogeochemical processes. These models are increasingly being used by policy makers to make predictions about future air quality and climate and to guide policy decisions. It is therefore important that the models are rigorously tested. This testing involves using detailed observations of atmospheric composition that have been made over the past few decades at locations around the world. Most model evaluation to date has involved testing whether the models simulate current average concentrations of atmospheric pollutants. Whilst this is a useful and necessary first step in model evaluation it does not test whether the model accurately simulates the change in concentration of a pollutant under changing emissions or changing climate. For example, does the model capture the real-world change in concentrations of a pollutant given a particular change in emission or under a future climate change scenario? This is particularly important as these predictions under-pin policy recommendations for air quality abatement. In this project we will synthesis long-term (multi-decadal) observations of ozone and particulate matter and their atmospheric precursors. We will use these observations to explore trends and variability that have been observed over the past few decades. We will then develop a model-observation framework that can be used to evaluate how well models simulate observed variability and trends. We will test state-of-the-art Earth System Models using existing model output from model intercomparison exercises. Finally, we will explore the model processes that are driving simulated variability and trends.Our results will inform the scientific community as to the fidelity of Earth System Models. This project will help improve our models and give us more confidence in our predictions.