Towards Maximum Feasible Reduction in Aerosol Forcing Uncertainty (Aerosol-MFR)

几十年来，巨大的不确定性一直影响着关于大气和气候如何表现、演变和对变化的反应的模型预测，严重限制了对气候预测的信心。特别是，自前工业时代以来，对气溶胶（大气中的微小颗粒）如何影响地球能量平衡的估计，即“气溶胶辐射强迫”，是非常不确定的。政府间气候变化专门委员会（IPCC）一再将气溶胶标记为全球温度变化模拟中最大的不确定性来源。减少气候模式中气溶胶辐射强迫的不确定性是一项重大挑战。这一项目称为气溶胶-MFR，它将开发创新的统计方法，将联合收割机观测与大型气候模式集合结合起来，以确定如何最好地减少这种巨大而持久的不确定性。模型中的方程有许多不确定的输入它们包含了它们所代表的真实世界过程的许多简化，并且它们具有结构性缺陷（重要物理和化学过程的差的或缺失的表示）。由于气候模式中有许多不确定性的来源，很难确定一个最现实的模式设置，以产生与广泛的观测最一致的结果。也很难判断模型与观测值的差的一致性是否与模型中的结构缺陷或仅仅是不确定的模型输入参数的不适当设置有关。如果不知道是什么原因导致了与观测结果的不一致，我们就不知道如何改进模式并降低其不确定性。模式与观测结果的比较对于降低模拟气溶胶强迫的不确定性至关重要。然而，先前的研究表明，减少不确定性受到两个问题的强烈限制：（1）多个模型误差可以相互抵消，因此模型与观测的良好一致性可以隐藏误差，然后当模型用于进行预测时，这些误差再次变得明显（例如，气溶胶强迫）。(2)模型结构缺陷意味着，无论对参数值进行多少调整，都无法产生一个能够做出可靠预测的模型。问题是当模式与观测比较时，结构缺陷和参数值的选择的影响不容易区分，但如果我们要改进模式，这种分离是必要的。本项目将使用英国气象局的英国地球系统模式（UKESM 1）和新的统计方法来研究观测、模式输出变量和气溶胶辐射强迫之间的关系，并将识别可用于减少模型不确定性和揭示模型结构缺陷的观测组合。将调查结构缺陷的原因，并将通过模型开发活动纠正一些缺陷。这将产生一个新的模式版本，然后将对观测进行评估，以评估我们的方法导致的气溶胶强迫不确定性的减少。该项目的结果将是气候模式不确定性的减少，一个更现实的气候模式，优先考虑未来模式发展的信息，气溶胶-MFR是模型分析和改进的前沿。特别是，这将是第一次将气候模型不确定性的两个主要原因（结构模型缺陷和不确定的模型参数）一起处理。这也是第一次使用先进的统计方法来测试和优化观测结果在降低模型不确定性方面的有效性。

For several decades, large uncertainties have persistently affected model predictions of how the atmosphere and climate behaves, evolves and responds to changes, severely limiting confidence in climate projections. In particular, estimates on how aerosols (tiny particles in the atmosphere) have affected the Earth's energy balance since pre-industrial times, the 'aerosol radiative forcing', are notably uncertain. The Intergovernmental Panel on Climate Change (IPCC) has repeatedly flagged aerosols as the largest source of uncertainty in simulations of global temperature change. Reducing aerosol radiative forcing uncertainty in climate models is a significant challenge. This project, Aerosol-MFR, will develop innovative statistical approaches to combine observations and large climate model ensembles to determine how best to reduce this large and persistent uncertainty.Aerosol-climate models, like complex models from many disciplines, are highly uncertain for several reasons: the equations in the model have many uncertain inputs (parameters), they contain many simplifications of the real-world processes they represent, and they have structural deficiencies (poor or missing representations of important physical and chemical processes). Because there are so many sources of uncertainty in a climate model, it is difficult to define the most realistic set-up of a model that produces best agreement with a wide range of observations. It is also difficult to tell whether poor model agreement with observations is related to structural deficiencies in the model or just inappropriate settings of the uncertain model input parameters. Without being able to tell what is causing the poor agreement with observations, we will not know how to improve the model and reduce its uncertainty.Comparison of models with observations is vital for reducing the uncertainty in the simulated aerosol forcing. However, previous research shows that reduction in uncertainty is strongly limited by two problems: (1) Multiple model errors can cancel each other out, so good agreement of the model with observations can hide the errors, which then become apparent again when the model is used to make predictions (e.g., of the aerosol forcing). (2) Model structural deficiencies mean that no amount of adjustment of parameter values will produce a model that can make reliable projections. The problem is that the effects of structural deficiencies and choices of parameter values cannot easily be distinguished when a model is compared against observations, but this separation is necessary if we are to improve models.This project will use the Met Office's UK Earth System Model (UKESM1) and new statistical methods to investigate the relationships between observations, model output variables and aerosol radiative forcing, and will identify combinations of observations that can be used to reduce the model uncertainty and reveal model structural deficiencies. The causes of the structural deficiencies will be investigated and several will be corrected through model development activities. This will produce a new model version that will then be evaluated against observations to assess the reduction in aerosol forcing uncertainty that results from our approach.The outcomes from this project will be a reduction in climate model uncertainty, a more realistic climate model, information to prioritise future model developments, and statistical approaches that will be transferable to other models and other disciplines.Aerosol-MFR is at the frontier of model analysis and improvement. In particular, this will be the first time that the two major causes of climate model uncertainty (structural model deficiencies and uncertain model parameters) will be tackled together. It is also the first time that the effectiveness of observations to reduce model uncertainty will be tested and optimised using advanced statistical methods.