Constraining the response of the hydrological cycle, land surface and regional weather to global change (HYDRA)

IPCC的CMIP-3和CMIP-5模式的相互比较侧重于大气-海洋耦合系统对给定排放或浓度情景的大尺度温度响应的不确定性，将降尺度、水文循环和生物圈中的不确定性视为额外的误差源。这种完全耦合的方法只允许相对粗分辨率的全球模型和有限的集合大小，具有大的系统误差和水文变量的低信噪比，这些问题在初始年代际预测中持续存在。因此，降水观测在约束降水预报方面的作用很小，导致极端降雨发生频率等高影响变量的不确定性存在较大且可能非物理的范围。因此，cip风格的模拟已经被发现对我们的关键利益相关者之一保险风险建模的使用有限。这种不确定性可能是不必要的，因为我们知道海洋表面温度和冰盖（sstic）在过去几十年中是如何演变的，以及水文循环是如何响应的，所以我们应该直接使用这些信息来约束大气和陆地表面参数。此外，所有研究都表明，未来几十年大规模外部驱动的SSTIC变化的自由度范围有限。因此，对CMIP“排放情景驱动”模式的一个强有力的补充方法是“温度情景驱动”方法，在这种方法下，一系列大规模的SSTIC变化被用来驱动更高分辨率的模型，直接或通过海洋混合层的简单模型中的弛豫。这使得在最近的观测期内可以得到更大的集合和更小的偏差，从而对大气、水文和陆地表面响应的不确定性进行更系统的探索。与同一年的观测资料进行详细比较，包括由卫星获得的大气顶通量，应该能够对大气和陆地表面参数施加比不使用耦合模式和仅限于大尺度气候学和最近趋势进行比较时更为严格的限制。将模拟降水趋势与观测数据进行定量比较的一个关键挑战是降水特征（如辐合带）位置的系统性偏差。我们将使用为神经成像开发的图像扭曲技术来解决这个问题，该技术已经在中试规模上得到证明，可以纠正气候模型中的特征位置偏差。这也将提供一个强大的工具来检测外部驱动的特征位置变化，比如哈德利环流的扩张。我们将运行由过去60年观测到的stic驱动的全球大气/陆地表面模式的大型集合，以及从广泛来源（包括cip -3、cip -5、UKCP09和climateprediction.net摄动物理集合）获得的到2040年的预估变化。将利用对过去60年的重复模拟，消除估计的人为影响特征，以解决最近观测到的降水、陆地表面变量和径流的变化在多大程度上可归因于人类影响。数千个成员的集合将允许详细绘制水文和陆地表面变量的分布，在很大程度上消除对其潜在统计时刻的预测中的随机不确定性。这些模拟的一个代表性子集将用于驱动欧洲的嵌套区域模型，其输出将用于驱动径流模型，以评估其在洪水和干旱风险建模中的效用。建模框架将提供给国际合作伙伴，以解决其他区域的问题。所有的模拟都将使用climateprediction.net公共资源分布式计算来执行，从而最大限度地降低成本和对环境的影响。

The IPCC's CMIP-3 and CMIP-5 model inter-comparisons focus on uncertainty in the large-scale temperature response of the coupled atmosphere-ocean system to a given emissions or concentration scenario, treating uncertainty in downscaling, in the hydrological cycle and in the biosphere as additional sources of error. This fully coupled approach only permits relatively coarse-resolution global models and limited ensemble sizes, with large systematic errors and low signal-to-noise in hydrological variables, problems which persist in initialized decadal forecasts. As a result, precipitation observations play only a minor role in constraining precipitation forecasts, resulting in large and potentially unphysical ranges of uncertainty on high-impact variables such as the frequency of occurrence of extreme rainfall. Hence CMIP-style simulations have been found to be of limited use by what should be one of our key stakeholders, insurance risk modeling. Much of this uncertainty may be unnecessary since we know how sea surface temperatures and ice cover (SSTICs) have evolved over the past few decades and how the hydrological cycle has responded, so we should be using this information directly to constrain atmospheric and land-surface parameters. Moreover, all studies suggest a limited range of degrees of freedom in the large-scale externally-driven SSTIC change over the next few decades. Hence a powerful complementary approach to the CMIP 'emissions scenario driven' paradigm is the 'temperature scenario driven' approach under which a range of large-scale SSTIC changes are used to drive higher-resolution models, either directly or by relaxation in a simple model of the ocean mixed layer. This allows much larger ensembles and reduced bias over the recent observational period, providing a more systematic exploration of uncertainty in the atmospheric, hydrological and land-surface response. Detailed comparison with observations for the same years, including satellite-derived top-of-atmosphere fluxes, should allow much tighter constraints to be placed on atmospheric and land-surface parameters than is possible when coupled models are run free and comparisons are restricted to large-scale climatology and recent trends. A key challenge in quantitative comparison of simulated precipitation trends with observations is systematic biases in the location of precipitation features such as convergence zones. We will address this using image-warping techniques developed for neuro-imaging which have been demonstrated on a pilot scale to correct feature-location biases in climate models. These will also provide a powerful tool to detect externally-driven shifts in feature location, such as an expansion of the Hadley circulation. We will run large ensembles of global atmospheric/land-surface models driven with observed SSTICs over the past 60 years together with projected changes to 2040 derived from a broad range of sources, including CMIP-3, CMIP-5 and the UKCP09 and climateprediction.net perturbed physics ensembles. Repeat simulations of the past 60 years with the estimated signature of anthropogenic influence removed will be used to address how far recent observed changes in precipitation, land-surface variables and run-off can be attributed to human influence. Multi-thousand-member ensembles will allow the distribution of hydrological and land-surface variables to be mapped in detail, largely eliminating stochastic uncertainty from predictions of their underlying statistical moments. A representative subset of these simulations will be used to drive nested regional models over Europe, and the output used to drive run-off models to evaluate their utility for flood and drought risk modeling. The modeling framework will be made available to international partners to address other regions. All simulations will be performed using climateprediction.net public resource distributed computing, minimizing both their cost and environmental impact.