Inference of past climate from borehole temperature data using Bayesian Reversible Jump Markov chain Monte Carlo

Estimates of past climate derived from borehole temperatures are assuming a greater importance in context of the millennial temperature variation debate. However, recovery of these signals is usually performed with regularization which can potentially lead to underestimation of past variation when noise is present. In this work Bayesian inference is applied to this problem with no explicit regularization. To achieve this Reversible Jump Markov chain Monte Carlo is employed, and this allows models of varying complexity (i.e. variable dimensions) to be sampled so that it is possible to infer the level of ground surface temperature (GST) history resolution appropriate to the data. Using synthetic examples, we show that the inference of the GST signal back to more than 500 yr is robust given boreholes of 500 m depth and moderate noise levels and discuss the associated uncertainties. We compare the prior information we have used with the inferred posterior distribution to show which parts of the GST reconstructions are independent of this prior information. We demonstrate the application of the method to real data using five boreholes from southern England. These are modelled both individually and jointly, and appear to indicate a spatial trend of warming over 500 yr across the south of the country.     

Comparisons of borehole temperature–depth profiles and surface air temperatures in the northern plains of the USA

Temperature–depth profiles measured in boreholes contain a record of temperature changes at the Earth's surface. The degree to which these profiles and surface air temperature records track each other is quantitatively assessed for the northern plains of the USA. Surface air temperature records are used as a forcing function to generate synthetic transient temperature profiles which are compared with transient temperatures derived from borehole temperature–depth data. These comparisons indicate that surface air and ground temperatures are correlated. Furthermore, these comparisons yield a long-term mean temperature tied to the meteorological record which provides a context for interpreting contemporary warming trends. Our results indicate that warming recorded in surface air temperature time series represents a positive departure above baseline temperature estimates.     

Reconstructing air temperature at eleven remote alpine and arctic lakes in Europe from 1781 to 1997 AD

Pristine and sensitive environments, such as remote alpine and arctic lakes, are particularly susceptible to the effects of climate change. However, these remote environments do not have sufficiently long instrumental climate records to support studies on contemporary climate change. The issue of the scarcity of instrumental climate data at remote regions is addressed by reconstructing monthly mean air temperatures from 1781 to 1997 AD at eleven remote alpine and arctic lakes in Europe, as part of the MOuntain LAke Research (MOLAR) project. Stepwise multiple regression is applied to establish linear transfer functions of temperatures between each of eleven upland records and twenty homogenised long lowland records. Twelve monthly transfer functions are obtained for each lake. The skill of these transfer functions is found to range typically between 60 and 99%. The lower skill values generally correspond to winter months. The temperature reconstructions obtained using the transfer functions need to be corrected with vertical temperature gradients. Air-temperature lapse rates were obtained for each lake region by spatial interpolation of radiosonde air-temperature data (1990–1997). The resulting reconstructions at each lake were checked using air-temperature data (1996–1997) from automatic weather stations installed at the lakes during the MOLAR project. We estimate the typical reconstruction errors to be about 1.3 °C for low-sun months and about 0.98 °C for high-sun months. Trend analyses on the reconstructed annual mean air temperatures at the lakes show two distinct types of trends for the 19th and 20th centuries. During the period 1801–1900, the western European lakes show no significant trend whereas annual mean air temperatures at the eastern European lakes decrease significantly. The period 1901–1997 presents a warming trend at all but the Fennoscandian lakes. Our results are in good agreement with previous studies on the spatial distribution and magnitude of temperature change in Europe. Principal component analysis performed on the reconstructed annual mean air temperature reveals two different regimes of trends for the past two centuries. It also allows a regional clustering of the inter-annual variability of air temperature at the lakes to be identified.     

The impact of tree cover loss on land surface temperature: A case study of central Massachusetts using Landsat Thematic Mapper thermal data

Land surface temperature (LST) variability results from diversity in urban surface materials over space and time such that locations with impervious urban cover experience higher temperature and larger temperature variation compared to non-impervious cover. There is growing awareness that warmer temperatures in urban locations negatively impact city residents by increasing heat related death and energy usage during heat waves. However, little previous work investigates the linkages between urban tree cover loss events related to invasive species eradication and urban heat trends. This paper examines the variation in LST using Landsat-5 Thematic Mapper (TM) thermal imagery in Worcester County, Massachusetts where over 30,000 trees were removed since 2008 to eradicate the invasive Asian Longhorned Beetle (ALB), most of which existed in urban residential areas. Throughout the study area a 10% loss in tree canopy cover caused a 0.7 °C increase in LST whereas a 10% increase in sub-canopy impervious surface area exposed due to tree loss caused 1.66 °C increase in LST. The Burncoat and Greendale neighborhoods in northern Worcester experienced a combined 48% tree cover loss from 2008 to 2010 due to ALB eradication and an average relative LST increase of 2.4 °C (range 0.6–4.1 °C). Given that areas with an increase in exposed impervious surface produce greater temperature increases than areas of tree loss, future tree replanting efforts may focus on locations that reduce exposed impervious surfaces.     

中国自然地理学中的气候变化研究前沿进展

气候学是自然地理学的重要组成部分;过去气候变化和当代气候增暖的影响与适应研究是中国自然地理学在气候变化领域的2个主要研究方向。本文综述了这2个研究方向的主要进展,包括：分别简述研究的意义、发展历程与主要科学问题;梳理总结中国过去气候变化重建与时空特征分析,历史时期气候变化的影响,全球增暖背景下中国气候区划变动特征,全球增暖对中国自然生态系统、水资源、农业等方面影响的区域差异,气候变化的综合风险区划等方面的前沿进展,特别是在各方面所取得的主要科学认识;以期为未来的深入研究,特别是编制相关领域和学科发展战略提供参考。     

Terrestrial heat flow and lithospheric thermal structure in the Chagan Depression of the Yingen-Ejinaqi Basin,north central China

The Chagan Depression in the Yingen-Ejinaqi Basin, located at the intersection of the Paleo-Asian Ocean and the Tethys Ocean domains is an important region to gain insights on terrestrial heat flow, lithospheric thermal structure and deep geodynamic processes. Here, we compute terrestrial heat flow values in the Chagan Depression using a large set of system steady-state temperature data from four representative wells and rock thermal conductivity. We also estimate the “thermal” lithospheric thickness, mantle heat flow, ratio of mantle heat flow to surface heat flow and Moho temperature to evaluate the regional tectonic framework and deep dynamics. The results show that the heat flow in the Chagan Depression ranges from 66.5 to 69.8 mW/m², with an average value of 68.3 ± 1.2 mW/m². The Chagan Depression is characterized by a thin “thermal” lithosphere, high mantle heat flow, and high Moho temperature, corresponding to the lithospheric thermal structure of “cold mantle and hot crust” type. We correlate the formation of the Yingen-Ejinaqi Basin to the Early Cretaceous and Cenozoic subduction of the western Pacific Plate and the Cenozoic multiple extrusions. Our results provide new insights into the thermal structure and dynamics of the lithospheric evolution in central China.     

拉萨近45年浅层地温的变化特征

利用1961-2005年拉萨0～40cm各层逐月平均地温,采用气候倾向率、累积距平、信噪比等气候统计方法,研究了近45年拉萨浅层平均地温的变化趋势、气候突变和异常年份等。结果表明:浅层各季节平均地温均呈现为极显著的升高趋势,升温率为0.43～0.60℃/10a,春季最大、夏季最小。各层年平均地温以0.45～0.66℃/10a的升温率显著上升,40cm深度的升温率最大,与同时期平均气温的升温率比较,地温比气温对气候变暖的响应更强。20世纪60年代至90年代浅层年、季平均地温呈明显的逐年代升高趋势,以冬、春季最为明显。60年代到80年代中期为偏冷阶段,80年代后期至90年代地温为偏暖阶段。各浅层平均地温在1986年秋季均发生了突变,冬季突变时间都出现在1984年。年平均地温除在40cm处1999年异常偏高外,其它各层为异常偏低年份,且发生在20世纪60年代。     

The use of high-resolution gridded climate data in the development of chironomid-based inference models from remote areas

The development of chironomid-based air temperature inference models in high latitude regions often relies on limited spatial coverage of meteorological data and/or on punctual measurements of water temperature at the time of sampling. The use of simple linear regression to relate air temperature and latitude was until recently the best method to characterize the air temperature gradient along a latitudinal gradient. However, recent studies have used high-resolution gridded climate data to develop new chironomid-based air temperature inference models. This innovative approach has, however, never been further analyzed to test its reliability. This study presents a method using ArcGIS^® to extract air temperatures from a high-resolution global gridded climate data set (New et al. 2002) and to incorporate these new data in a variety of chironomid-based air temperature inference models to test their performance. Results suggest that this method is reliable and produces better estimates of air temperature and will be helpful in the development of further quantitative air temperature inference models in remote areas.     

Diatom transfer-functions for quantifying past air temperature, pH and total organic carbon concentration from lakes in northern Sweden

The relationships between diatoms (Bacillariophyceae) in surface sediments of lakes and summer air temperature, pH and total organic carbon concentration (TOC) were explored along a steep climatic gradient in northern Sweden to provide a tool to infer past climate conditions from sediment cores. The study sites are in an area with low human impact and range from boreal forest to alpine tundra. Canonical correspondence analysis (CCA) constrained to mean July air temperature and pH clearly showed that diatom community composition was different between lakes situated in conifer-, mountain birch- and alpine-vegetation zones. As a consequence, diatoms and multivariate ordination methods can be used to infer past changes in treeline position and dominant forest type. Quantitative inference models were developed to estimate mean July air temperature, pH and TOC from sedimentary diatom assemblages using weighted averaging (WA) and weighted averaging partial least squares (WA-PLS) regression. Relationships between diatoms and mean July air temperature were independent of lake-water pH, TOC, alkalinity and maximum depth. The results demonstrated that diatoms in lake sediments can provide useful and independent quantitative information for estimating past changes in mean July air temperature (R² _jack = 0.62, RMSEP = 0.86 °C; R² and root mean squared error of prediction (RMSEP) based on jack-knifing), pH (R² _jack = 0.61, RMSEP = 0.30) and TOC (R² _jack = 0.49, RMSEP = 1.33 mg l^-1). The paper focuses mainly on the relationship between diatom community composition and mean July air temperature, but the relationships to pH and TOC are also discussed.     

海冰和海温对西北地区中部6月降水异常的协同影响

利用1961年以来美国国家环境预报中心(NCEP)月平均再分析风场、高度场、NOAA重构海表温度以及西北地区中部54个气象站6月逐日降水资料、1979年以来北极10个区域的海冰面积,通过分析2019年6月西北地区中部降水异常的成因,揭示出对该区域6月降水具有显著影响的关键海区海冰面积及时段、北大西洋三极子(NAT)关键...