PalaeoQUMP:using palaeodata to reduce uncertainties in climate prediction

数值模型是我们研究人类活动后果的唯一工具，例如大气中温室气体浓度的增加或土地利用的广泛变化对气候的影响。根据全球所有主要建模小组为政府间气候变化专门委员会(IPCC)上一份评估报告运行的模拟，对大气二氧化碳浓度加倍所造成的全球气温变化的估计从1.5摄氏度到4.5摄氏度不等。这一大范围的气候敏感性值到二氧化碳倍增，使得各国政府很难定义和获得广泛接受，以防止“危险的”气候变化的政策。在气候模型中，许多重要的过程都是以简化的方式描述的，因为否则模型将过于复杂，无法运行。人们认为，分配给关键过程的参数值的不确定性是二氧化碳倍增反应模型预测中的一个主要不确定性来源。DEFRA资助的项目QUMP(模型预测中的量化不确定性)通过对现代气候进行一系列模拟来研究这一问题，在这些模拟中，参数值在现有知识的合理范围内被系统地改变。然后将这些模拟与对最近气候的观测进行比较。从模拟中生成概率分布函数，其中各个运行根据它们在再现观测时的真实性进行加权。从这一比较中，QUMP估计，大气二氧化碳浓度加倍(即气候敏感度)导致全球气温变化的5%-95%的概率范围为2.4至5.4摄氏度。这意味着最近的气候观测提供的约束不足以减少预测中的不确定性。改进我们对气候敏感性的估计的一种方法是使用其他信息来评估模型预测，例如来自地质过去的关于气候与今天非常不同的时间的信息。冰盖覆盖了北半球的大部分地区，海平面在最后一次冰川盛期(LGM，21,000年前)时较低。海洋变得更冷，海冰更广，大气中温室气体的浓度比现在低得多。到6000年前(全新世中期，MH)，这些冰盖实际上已经消失，但与今天相比，进入北半球的太阳能在夏季的分布增加，在冬季的分布减少，导致季节温差和季风强度增加。在这些非常不同的气候下测试气候模型，应该会对气候敏感性施加更大的限制。我们将运行一个经过良好测试的气候模型，使用LGM和MH的太阳辐射、冰盖分布和温室气体浓度的已知变化，并像QUMP对现代气候所做的那样，运行相同的一系列模拟(关键过程具有不同的值)。我们将使用LGM和MH气候重建来评估这些模拟。湖泊和泥炭沼泽沉积物中的花粉有大量证据表明，冬季温度、生长季的长度以及LGM和MH的水可获得性的变化导致了植被格局的变化。通过强迫植被模型再现这些植被模式，我们可以得出对气候的这三个方面的定量估计。降水变化引起的湖泊面积变化，有大量的老海岸线和湖泊沉积的证据。同样，通过强迫湖泊模型再现这些面积变化，我们可以得出定量降雨量估计。这些重建，与基于同位素或地球化学数据的气候重建相结合，将成为我们模拟的目标。我们的项目旨在及时为IPCC第五次评估报告提供对二氧化碳加倍的气候敏感性的更好估计。

Numerical models are the only tool we have to examine the consequences of human activities, such as increasing greenhouse gas concentrations in the atmosphere or widespread changes in land use, on climate. Estimates of the change in global temperature caused by doubling the atmospheric CO2 concentration, based on simulations run by all the major modeling groups worldwide for the last Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), ranged from 1.5 to 4.5 degrees C. This wide range of values for the 'climate sensitivity' to a doubling of CO2 makes it difficult for governments to define and gain wide acceptance for policies to prevent 'dangerous' climate change. Many important processes are described in a simplified way in climate models, because the models would otherwise be too complex to run. It is thought that uncertainties about parameter values assigned to key processes are a major source of uncertainty in model predictions of the response to doubling CO2. The DEFRA-funded project QUMP (Quantifying Uncertainties in Model Prediction) has investigated this by running a series of simulations of the modern climate in which parameter values are systematically altered within plausible ranges based on current knowledge. These simulations are then compared with observations of the recent climate. A probability distribution function is generated from the simulations, with individual runs weighted according to their realism in reproducing the observations. From this comparison, QUMP estimated that the 5-95% probability range for the global temperature change caused by doubling the atmospheric CO2 concentration (i.e. the climate sensitivity) is 2.4 to 5.4 degrees C. This means the constraints supplied by recent observations of climate are insufficient to reduce uncertainties in prediction. A way of improving our estimate of climate sensitivity is to use other information to evaluate the model predictions, e.g. information from the geological past about times when climate was very different from today. Ice sheets covered much of the northern hemisphere and sea level was lower at the last glacial maximum (LGM, 21,000 years ago). The oceans were much colder, sea ice was more extensive and the atmospheric concentration of greenhouse gases was lower than present. These ice sheets had virtually disappeared by 6000 years ago (mid-Holocene, MH), but the distribution of incoming solar energy across the northern hemisphere was increased in summer and decreased in winter compared to today, resulting in increases in seasonal temperature contrast and the strength of the monsoons. Testing climate models under these very different climates should put stronger limits on climate sensitivity. We will run a well-tested climate model, using known changes in solar radiation, ice-sheet distribution, and greenhouse gas concentrations for the LGM and MH, and run the same series of simulations (with different values for key processes) as QUMP has done for modern climate. We will evaluate these simulations using reconstructions of LGM and MH climate. There is abundant evidence from pollen in lake and peat-bog sediments for changes in vegetation patterns caused by changes in winter temperature, the length of the growing season and water availability at the LGM and MH. By forcing a vegetation model to reproduce these vegetation patterns we can derive quantitative estimates of these three aspects of climate. There is abundant evidence in the form of old shorelines and lake sediments for changes in lake area caused by changes in precipitation. Again, by forcing a lake model to reproduce these changes in area we can derive quantitative precipitation estimates. These reconstructions, combined with climate reconstructions based on isotopic or geochemical data, will form targets for our simulations. Our project is intended to provide a better estimate of the climate sensitivity to doubling CO2 in time for the IPCC Fifth Assessment Report.