PalaeoQUMP:using palaeodata to reduce uncertainties in climate prediction

PalaeoQUMP：利用古数据减少气候预测的不确定性

• 批准号: NE/D001544/1
• 负责人: Sandy Harrison
• 金额: $ 61.56万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FD001544%2F1
• 关键词: PalaeoQUMP; using; palaeodata; reduce; uncertainties

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.

数值模型是我们检验人类活动对气候的影响的唯一工具，例如大气中温室气体浓度的增加或土地利用的广泛变化。根据政府间气候变化专门委员会（IPCC）上一份评估报告中全球所有主要建模小组进行的模拟，对大气中二氧化碳浓度翻倍引起的全球温度变化的估计范围在1.5至4.5摄氏度之间。这种对二氧化碳翻倍的“气候敏感性”值的广泛范围使政府难以确定并获得广泛接受防止“危险”气候变化的政策。在气候模式中，许多重要的过程都是以一种简化的方式来描述的，因为否则这些模式就太复杂而无法运行。人们认为，分配给关键过程的参数值的不确定性是对二氧化碳加倍响应的模式预测中不确定性的主要来源。defra资助的项目QUMP（量化模式预测中的不确定性）通过运行一系列现代气候模拟来研究这一点，其中参数值在基于当前知识的合理范围内系统地变化。然后将这些模拟结果与最近的气候观测结果进行比较。从模拟中生成一个概率分布函数，并根据其再现观测值的现实性对单个运行进行加权。从这一比较中，QUMP估计大气CO2浓度加倍（即气候敏感性）引起全球温度变化的5-95%概率范围为2.4至5.4摄氏度。这意味着近期气候观测提供的约束条件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的预测，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，在末次盛冰期（LGM， 21000年前）海平面较低。海洋要冷得多，海冰范围更广，大气中温室气体的浓度比现在低。这些冰盖实际上在6000年前（全新世中期，MH）就消失了，但与今天相比，整个北半球的入射太阳能分布在夏季增加，冬季减少，导致季节温度对比和季风强度增加。在这些截然不同的气候条件下测试气候模型，应该对气候敏感性施加更严格的限制。我们将运行一个经过充分验证的气候模式，利用已知的太阳辐射、冰盖分布和温室气体浓度的变化来模拟LGM和MH，并运行与QUMP为现代气候所做的相同的一系列模拟（关键过程的值不同）。我们将使用LGM和MH气候的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、生长季节长度和水有效性的变化导致了LGM和MH的植被格局变化。通过强迫植被模型重现这些植被格局，我们可以得出这三个气候方面的定量估计。降水变化引起的湖泊面积变化，以古岸线和湖泊沉积物的形式有丰富的证据。同样，通过强迫湖泊模型重现这些面积变化，我们可以得出定量的降水估计。这些重建，结合基于同位素或地球化学数据的气候重建，将成为我们模拟的目标。我们的项目旨在为IPCC第五次评估报告及时提供对二氧化碳加倍的气候敏感性的更好估计。

项目成果

Precipitation scaling with temperature in warm and cold climates: An analysis of CMIP5 simulations

• DOI: 10.1002/grl.50730
• 发表时间: 2013-08
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Guangqi Li;S. Harrison;Patrick J. Bartlein;K. Izumi;I. Colin Prentice

Evaluation of seasonal climates of the Mediterranean and nothern Africa in the CMIP5 simulations

CMIP5 模拟中地中海和非洲北部季节性气候的评估

• DOI: 10.5194/cpd-9-5347-2013
• 发表时间: 2013
• 作者: Perez-Sanz A

Using paleo-climate comparisons to constrain future projections in CMIP5

• DOI: 10.5194/cpd-9-775-2013
• 发表时间: 2013
• 作者: G. Schmidt;J. Annan;Patrick J. Bartlein;B. Cook;E. Guilyardi;J. Hargreaves;S. Harrison;M. Kageyama;A. Legrande;B. Konecky;S. Lovejoy;M. Mann;V. Masson‐Delmotte;C. Risi;D. Thompson;A. Timmermann;L. Tremblay;P. Yiou

Climate model benchmarking with glacial and mid-Holocene climates

• DOI: 10.1007/s00382-013-1922-6
• 发表时间: 2014-10
• 期刊: Climate Dynamics
• 影响因子: 4.6
• 作者: S. Harrison;S. Harrison;Patrick J. Bartlein;Simon C. Brewer;I. Prentice;I. Prentice;Meighan Boyd;I. Hessler;I. Hessler;K. Holmgren;K. Izumi;K. Willis

Consistent large-scale temperature responses in warm and cold climates

在温暖和寒冷气候下一致的大范围温度响应

• DOI: 10.1002/grl.50350
• 发表时间: 2013
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Izumi K