Multiproxy summer and winter surface air temperature field reconstructions for southern South America covering the past centuries

We statistically reconstruct austral summer (winter) surface air temperature fields back to ad 900 (1706) using 22 (20) annually resolved predictors from natural and human archives from southern South America (SSA). This represents the first regional-scale climate field reconstruction for parts of the Southern Hemisphere at this high temporal resolution. We apply three different reconstruction techniques: multivariate principal component regression, composite plus scaling, and regularized expectation maximization. There is generally good agreement between the results of the three methods on interannual and decadal timescales. The field reconstructions allow us to describe differences and similarities in the temperature evolution of different sub-regions of SSA. The reconstructed SSA mean summer temperatures between 900 and 1350 are mostly above the 1901?C1995 climatology. After 1350, we reconstruct a sharp transition to colder conditions, which last until approximately 1700. The summers in the eighteenth century are relatively warm with a subsequent cold relapse peaking around 1850. In the twentieth century, summer temperatures reach conditions similar to earlier warm periods. The winter temperatures in the eighteenth and nineteenth centuries were mostly below the twentieth century average. The uncertainties of our reconstructions are generally largest in the eastern lowlands of SSA, where the coverage with proxy data is poorest. Verifications with independent summer temperature proxies and instrumental measurements suggest that the interannual and multi-decadal variations of SSA temperatures are well captured by our reconstructions. This new dataset can be used for data/model comparison and data assimilation as well as for detection and attribution studies at sub-continental scales.     

基于贝叶斯模型的器测、古气候重建与气候模拟数据的融合试验

气象部门馆藏的西部最早的器测气象资料始于20世纪30年代,不能满足建立20世纪以来中国气候变化序列的需求,而古气候重建或气候模拟资料则可以延伸到器测时代以前。为了探讨长序列多源气候资料序列融合方法,采用贝叶斯方法对中国北疆地区8条树轮气温重建资料、器测资料与国际耦合模式比较计划第5阶段（CMIP5）模式模拟资料进行了融合试验。首先利用器测资料对气温代用资料进行校验与网格重建,并以此作为贝叶斯模型的先验分布,然后,用泰勒图选出了该区域气候模拟效果最佳的几个模式;最后将网格重建和气候模拟序列用贝叶斯模型进行了融合试验。结果表明,贝叶斯融合模型能有效提取各种数据来源的有用信息进行融合,融合结果的长期变化（线性）趋势更接近器测气候序列,并在一定程度上提高了序列的精度,减小了结果的不确定性;并且,融合结果能够纠正先验分布及气候模拟数据的明显偏差,为长年代气候序列重建提供了一个可行的思路。      

Pairwise comparisons to reconstruct mean temperature in the Arctic Atlantic Region over the last 2,000 years

Existing multi-proxy climate reconstruction methods assume the suitably transformed proxy time series are linearly related to the target climate variable, which is likely a simplifying assumption for many proxy records. Furthermore, with a single exception, these methods face problems with varying temporal resolutions of the proxy data. Here we introduce a new reconstruction method that uses the ordering of all pairs of proxy observations within each record to arrive at a consensus time series that best agrees with all proxy records. The resulting unitless composite time series is subsequently calibrated to the instrumental record to provide an estimate of past climate. By considering only pairwise comparisons, this method, which we call PaiCo, facilitates the inclusion of records with differing temporal resolutions, and relaxes the assumption of linearity to the more general assumption of a monotonically increasing relationship between each proxy series and the target climate variable. We apply PaiCo to a newly assembled collection of high-quality proxy data to reconstruct the mean temperature of the Northernmost Atlantic region, which we call Arctic Atlantic, over the last 2,000 years. The Arctic Atlantic is a dynamically important region known to feature substantial temperature variability over recent millennia, and PaiCo allows for a more thorough investigation of the Arctic Atlantic regional climate as we include a diverse array of terrestrial and marine proxies with annual to multidecadal temporal resolutions. Comparisons of the PaiCo reconstruction to recent reconstructions covering larger areas indicate greater climatic variability in the Arctic Atlantic than for the Arctic as a whole. The Arctic Atlantic reconstruction features temperatures during the Roman Warm Period and Medieval Climate Anomaly that are comparable or even warmer than those of the twentieth century, and coldest temperatures in the middle of the nineteenth century, just prior to the onset of the recent warming trend.     

200?years of European temperature variability: insights from and tests of the proxy surrogate reconstruction analog method

Spatially resolved climate reconstructions are commonly derived from long instrumental series and proxy data via linear regression based approaches that use the main modes of the climate system. Such reconstructions have been shown to underestimate climate variability and are based upon the assumption that the main modes of climate variability are stationary back in time. Climate models simulate physically consistent climate fields but cannot be taken to represent the real past climate trajectory because of their necessarily simplified scope and chaotic internal variability. Here, we present sensitivity tests of, and a 200-year temperature reconstruction from, the PSR (Proxy Surrogate Reconstruction) method. This method simultaneously capitalizes on the individual strengths of instrumental/proxy data based reconstructions and model simulations by selecting the model states (analogs) that are most similar with proxy/instrumental data available at specific places and specific moments of time. Sensitivity experiments reveal an optimal PSR configuration and indicate that 6,500 simulation years of existing climate models provide a sufficient pool of possible analogs to skillfully reconstruct monthly European temperature fields during the past 200?years. Reconstruction verification based upon only seven instrumental stations indicates potential for extensions back in time using sparse proxy data. Additionally the PSR method allows evaluation of single time series, in this case the homogeneity of instrumental series, by identifying inconsistencies with the reconstructed climate field. We present an updated European temperature reconstruction including newly homogenized instrumental records performed with the computationally efficient PSR method that proves to capture the total variance of the target.     

Comparison of climate field reconstruction techniques: application to Europe

This paper presents a comparison of principal component (PC) regression and regularized expectation maximization (RegEM) to reconstruct European summer and winter surface air temperature over the past millennium. Reconstruction is performed within a surrogate climate using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) 1.4 and the climate model ECHO-G 4, assuming different white and red noise scenarios to define the distortion of pseudoproxy series. We show how sensitivity tests lead to valuable “a priori” information that provides a basis for improving real world proxy reconstructions. Our results emphasize the need to carefully test and evaluate reconstruction techniques with respect to the temporal resolution and the spatial scale they are applied to. Furthermore, we demonstrate that uncertainties inherent to the predictand and predictor data have to be more rigorously taken into account. The comparison of the two statistical techniques, in the specific experimental setting presented here, indicates that more skilful results are achieved with RegEM as low frequency variability is better preserved. We further detect seasonal differences in reconstruction skill for the continental scale, as e.g. the target temperature average is more adequately reconstructed for summer than for winter. For the specific predictor network given in this paper, both techniques underestimate the target temperature variations to an increasing extent as more noise is added to the signal, albeit RegEM less than with PC regression. We conclude that climate field reconstruction techniques can be improved and need to be further optimized in future applications.     

Past millennial solar forcing magnitude

A set of global climate model simulations for the last thousand years developed by the Max Planck Institute is compared with paleoclimate proxy data and instrumental data, focusing on surface temperatures for land areas between 30° and 75°N. The proxy data are obtained from six previously published Northern Hemispheric-scale temperature reconstructions, here re-calibrated for consistency, which are compared with the simulations utilizing a newly developed statistical framework for ranking several competing simulations by means of their statistical distance against past climate variations. The climate model simulations are driven by either “low” or “high” solar forcing amplitudes (0.1 and 0.25 % smaller total solar irradiance in the Maunder Minimum period compared to the present) in addition to several other known climate forcings of importance. Our results indicate that the high solar forcing amplitude results in a poorer match with the hemispheric-scale temperature reconstructions and lends stronger statistical support for the low-amplitude solar forcing. However, results are likely conditional upon the sensitivity of the climate model used and strongly dependent on the choice of temperature reconstruction, hence a greater consensus is needed regarding the reconstruction of past temperatures as this currently provides a great source of uncertainty.     

Solar signals in global climatic change

Based on the physical background that varying solar activity should lead to variations of the ‘solar constant’ and that the climate system may respond sensitively even to small solar variations, a correlation analysis is performed where hemispheric and global averages of the annual mean surface air temperature are compared with the variations of a variety of solar forcing parameters: sunspots, related hypotheses including variations of the quasi-eleven-year solar cycle length, solar diameter variations and gravitational effects. This analysis is based on the 1881–1988 period, for the northern hemisphere including proxy data 1671–1988. Cross correlations and correlations moving in time reveal some instability effects which are hard to interpret. The temperature variance components which may be hypothetically explained by solar forcing are small. Similarly, a seasonal and regional signal and signal-to-noise analysis based on a gridded temperature time series 1890–1985 reveals small signals which do not exceed roughly 1.5 K in the arctic winter (maximum) or 0.2-0.3 K on a global average.     

ISOTOPE RECORDS FROM MONGOLIAN AND ALPINE ICE CORES AS CLIMATE INDICATORS

The link between long term changes in the isotopic composition of precipitation and surface air temperature at a given location is of exceptional importance for paleoclimatic studies, as ahs been demonstrated by many recent publications based on the isotope records from polar ice cores. By means of direct comparison with instrumental data, this paper evaluates the potential of the deuterium and oxygen-18 records from two continental glaciers for monitoring climatic trends. The isotopic data presented characterize climatically contrasted enviroments. The records from the Swiss glacier show distinct seasonal variations. Oxygen-18 is fairly well correlated with the instrumental record of atmospheric temperature; the seasonal differences in deuterium excess reflect nearness to the oceanic moisture source. By contrast, the isotope data from the Mongolian site show poor correlation with atmospheric temperature. The seasonal variations in deuterium excess, with higher values during summer time, indicate that precipitation largely originates from re-evaporated continental moisture sources. In both cases however, the correlation with temperature is significantly improved by the elimination of values derived from years where major changes in seasonal distribution and/or snow loss obviously have occurred, thereby distoring the isotopic ratios for that particular year. Depending on the site selected for study, the stable isotope composition of ice cores should therefore be viewed not only as a proxy for atmospheric temperature, but also as an additional hydrometeorological parameter and source indicator for atmospheric moisture.     

RECONSTRUCTING MIDDLE-TIBET CLIMATE DURING THE LAST 600 YEARS BY DENDROCLIMATOLOGICAL METHOD*

Based on four tree-ring chronologies which was analyscd with appropriate collection and accurate dating in the middle Tibetan Plateau,an essential procedure on reconstruction of past climate has been pointed out in this paper.First,the response function of each dendrochronology has been built and used to estimate how ring-width growth responds to variations in monthly climatic change.Second,the climate factors which could be produced with different tree-ring series have been selected.Then,the transfer function equation,including a new set of orthogonal variables,can be used to reconstruct local past temperature or precipitation.It should be emphasized that prior growth has been considered in the relationship between climate factor and tree-ring chronology,Besides,some different periods for calibration and verification have been divided.And some statistics and other kinds of proxy data have been adopted as test approaches.As a result,the variations of air temperature during the last 600 years and precipitation during the last 340 years were reconstructed by combining the same types of tree-ring series in the middle Tibet.     

土壤湿度影响中国夏季气候的数值试验

利用"全球土壤湿度计划第2阶段"提供的土壤湿度资料强迫区域气候模式RegCM3,通过数值试验讨论了土壤湿度对东亚夏季气候模拟效果的影响。结果表明,合理考虑土壤湿度的作用,能够提高区域气候模式对中国夏季降水和2 m气温的空间分布型及逐日变化的模拟效果;模拟结果与观测的相关分析显示,降水和2 m气温的年际变化都得到了有效改进,这种改进在气温上尤为明显。不过上述改进具有区域依赖性。数值试验结果表明,气温对土壤湿度的敏感性强于降水,这也从一个侧面说明提高降水模拟效果的难度。总体而言,合理的土壤湿度能够提高区域气候模式对中国夏季气候的模拟能力。因此,合理描述土壤湿度的变化,是提高中国夏季气候预报技巧的潜在途径之一。     

Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM

Urban-rural difference of land cover is the key determinant of urban heat island (UHI). In order to evaluate the impact of land cover data on the simulation of UHI, a comparative study between up-to-date CORINE land cover (CLC) and Urban Atlas (UA) with fine resolution (100 and 10 m) and old US Geological Survey (USGS) data with coarse resolution (30 s) was conducted using the Weather Research and Forecasting model (WRF) coupled with bulk approach of Noah-LSM for Berlin. The comparison between old data and new data partly reveals the effect of urbanization on UHI and the historical evolution of UHI, while the comparison between different resolution data reveals the impact of resolution of land cover on the simulation of UHI. Given the high heterogeneity of urban surface and the fine-resolution land cover data, the mosaic approach was implemented in this study to calculate the sub-grid variability in land cover compositions. Results showed that the simulations using UA and CLC data perform better than that using USGS data for both air and land surface temperatures. USGS-based simulation underestimates the temperature, especially in rural areas. The longitudinal variations of both temperature and land surface temperature show good agreement with urban fraction for all the three simulations. To better study the comprehensive characteristic of UHI over Berlin, the UHI curves (UHIC) are developed for all the three simulations based on the relationship between temperature and urban fraction. CLC- and UA-based simulations show smoother UHICs than USGS-based simulation. The simulation with old USGS data obviously underestimates the extent of UHI, while the up-to-date CLC and UA data better reflect the real urbanization and simulate the spatial distribution of UHI more accurately. However, the intensity of UHI simulated by CLC and UA data is not higher than that simulated by USGS data. The simulated air temperature is not dominated by the land cover as much as the land surface temperature, as air temperature is also affected by air advection.     

Variations in momentum,mass and heat fluxes due to changes in the sea surface temperature of Hudson Bay

Abstract

The atmospheric model of Danard et al. (1983) is used to investigate the changes in heat, mass and momentum fluxes at the air‐sea interface in Hudson Bay when the seasonal sea surface temperature is varied. Comparisons of model predictions with data from a meteorological buoy located 400 km offshore showed that the model predicted the variations in wind speed and air temperature fairly well but underestimated their magnitudes. In addition it provided offshore heat and mass fluxes for which no direct observations were available.

The most important parameter determining air‐sea fluxes is the temperature difference between air and water. This determines the stability and the degree of vertical convection of the air. In the spring the colder water stabilizes the air, which depresses vertical convection. This reduces wind stress and evaporation while increasing the heat flux into the water. During the fall, the opposite occurs. The sea surface temperature is thus buffered against man‐made changes. When the temperature is decreased, for example, as the result of hydroelectric development in surrounding watersheds, the heat flux into the water increases while the wind stress decreases. Both effects increase the sea surface temperature, opposing the initial decrease. A one‐degree depression of sea surface temperature in summer is slowly offset by increased heating and no noticeable change in temperature remains at the end of the fall.     

Acoustic Travel Time Tomography – A Method for Remote Sensingof the Atmospheric Surface Layer

Summary Acoustic travel time tomography is demonstrated as a technique for remote monitoring of near surface air temperature and wind fields. An experimental procedure is presented which provides line-averaged values of the effective sound speed changing with air temperature and wind vector. In this study the travel times of sound signals at defined propagation paths between different acoustic sources and receivers were measured. The travel time data were inverted into effective sound speed values by using a special tomographic algorithm (Simultaneous Iterative Reconstruction Technique) to obtain area-averaged meteorological quantities (air temperature, wind speed). The method of acoustic travel time tomography will be applied to the atmosphere in order to directly provide spatially averaged data which are needed for evaluation of large-eddy simulation and microscale meteorological models as well as to complete conventional point measurements. Received November 4, 1998 Revised April 7, 1999     

Evidence for recent warming from perturbed geothermal gradients: examples from eastern Canada

Recent variations of the surface temperature of the Earth can be inferred from borehole temperature measurements. Generalized inversion is used to extract the information from the data; the potential of the method is evaluated. Tests were performed with synthetic data to demonstrate the effectiveness of the inversion to recover the gross features of the surface temperature history even when the data are affected by noise and errors. The tests show that it is possible to reconstruct the long term changes in ground temperature during the past 300 years; the resolution decreases with time, in particular if noise and errors must be filtered. Temperature logs, obtained in eastern Canada, and not suspected of being affected by non-climatic factors, have been inverted. The analysis confirms that eastern Canada has experienced warming by 1 to 2°C over the past 100–200 years. The relationship between air and ground temperatures has been examined. In eastern Canada ground temperature follows air temperature closely in summer but stays well above air temperature in winter. The number of days with snow on the ground correlates with the difference between annual mean ground and air temperature.Contribution to Clima Locarno — Past and Present Climate Dynamics; Conference September 1990, Swiss Academy of Sciences — National Climate-Program     

Reconstruction of Quaternary temperature fields by dynamically consistent smoothing

Conventional methods of palaeoclimate reconstruction provide estimates of climatic parameters using proxy data which have originated from individual sites. These reconstructions yield information on the local environment but only limited information on spatial scales that are required for model-data intercomparisons. We present here a new approach that connects these different scales by an upscaling of the local palaeoinformation together with a dynamically consistent spatial smoothing. A probabilistic data-based method for local reconstructions is combined with a dynamic constraint on the reconstructed climate parameter which stabilises the reconstruction on the target scale. The variational analysis leads to climatological fields being optimised with respect to the proxy data and to the prescribed dynamics in a statistically consistent way. This method allows a probabilistic approach of quality control of the palaeodata in terms of their spatial consistency and homogeneity and for an estimation of reconstruction errors. The method was applied to palaeobotanical data to reconstruct near-surface temperature fields constrained by simple linear dynamics. An approximate approach was used to estimate the magnitude of reconstruction errors in terms of standard deviations. Reconstructed January and July mean temperature of the early Eemian (∼ 125,000 years bp) have errors with a median value of about 1.8°C in January and about 1.1°C in July. Reconstructions across Europe show positive temperature anomalies for Scandinavia and near the East coast of the Baltic Sea. In contrast, early Eemian temperatures were apparently quite similar to those found today in Central Europe, as no drastic differences were reconstructed between the Eemian and modern (1961–1990) climate. This implies somewhat stronger temperature gradients in the Eemian than are observed today. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

一种改进的时间序列重建方法及其在地下水埋深估计中的应用

带噪声传递函数(TFN)模型可通过相关性分析在输入输出序列时间上同步的情况下估计输出序列。基于TFN模型、Kalman滤波和复合型混合演化(SCE-UA)算法,发展一种新的时间序列重建方法,并将其用于地下水埋深估计。该方法将高阶TFN模型表述成状态空间,并用Kalman滤波进行状态估计,基于SCE-UA方法优化TFN模型参数,能够在输入输出序列异步的情况下率定TFN模型并用于时间序列重建。最后,利用已有降水和地下水观测资料验证该方法,并重建了中国东北部分地区40年地下水埋深序列,结果表明该方法有较好精度且能反映埋深变化对降水的响应,在各类时间序列重建中具有一定推广性。     

Dynamic normals of surface air temperature

Cited are the latest data on the contemporary climate changes in surface air temperature (to the year 2011 inclusive). Substantiated is the necessity of extending the concept on the normals and anomalies of surface air temperature under conditions of the changing climate. Formulated is a concept of “dynamic normals” and “dynamic anomalies” of surface air temperature taking account of current trends of climate change and interseasonal variations. It is proposed to adopt this concept both for the problems of forthcoming climate change assessments and for the long-range forecasting out of the “limit of predictability.” An adaptive regression method is proposed to compute such dynamic normals as a function of the indicator of global climate changes. Presented are the skill scores of dynamic normals as of “intermediate” forecasts (an analogue of the climate forecast under a changing climate).     

利用慢特征分析法提取二维非平稳系统中的外强迫特征

慢特征分析法（Slow Feature Analysis,SFA）是一个从快变的信号中提取慢变特征的有效方法,它的提出丰富了人们对非平稳系统外强迫特征的重建手段。本文以Henon映射为基础,构造二维非平稳系统模型,尝试SFA方法在二维复杂非平稳系统中重建外强迫特征的能力。试验表明,SFA方法能够较好地从单时变参数Henon映射中提取出外强迫信号;通过结合小波变换技术,可以还原双时变参数Henon映射中的外强迫信号。另外,本文利用SFA方法重建了北京市气温的外强迫信号,分析其外强迫信号的尺度特征及其可能的物理机制。这些工作将为气候系统驱动力的研究提供新的思路。     

多层土壤温度模拟及其检验

对陆面过程模式(BATS)土壤温度模拟进行了改进,提出了一个利用气象站资料模拟土壤温度的模式。研究结果表明：采用热扩散方程模拟多层土壤温度,可与观测站的资料直接比较;模式能很好地模拟各层土壤温度的年变化、季变化、日变化。冬季下层土壤温度高于上层土壤温度,夏季上层土壤温度高于下层土壤温度,上下层温度的转换时间大约在3月份和10月份,这与实测土壤温度的年变化非常一致;模式较准确地模拟了各层土壤温度垂直方向变化的时滞效应。     

Winter-to-spring temperature dynamics in Turkey derived from tree rings since AD 1125

In the eastern Mediterranean in general and in Turkey in particular, temperature reconstructions based on tree rings have not been achieved so far. Furthermore, centennial-long chronologies of stable isotopes are generally also missing. Recent studies have identified the tree species Juniperus excelsa as one of the most promising tree species in Turkey for developing long climate sensitive stable carbon isotope chronologies because this species is long-living and thus has the ability to capture low-frequency climate signals. We were able to develop a statistically robust, precisely dated and annually resolved chronology back to AD 1125. We proved that variability of δ¹³C in tree rings of J. excelsa is mainly dependent on winter-to-spring temperatures (January–May). Low-frequency trends, which were associated with the medieval warm period and the little ice age, were identified in the winter-to-spring temperature reconstruction, however, the twentieth century warming trend found elsewhere could not be identified in our proxy record, nor was it found in the corresponding meteorological data used for our study. Comparisons with other northern-hemispherical proxy data showed that similar low-frequency signals are present until the beginning of the twentieth century when the other proxies derived from further north indicate a significant warming while the winter-to-spring temperature proxy from SW-Turkey does not. Correlation analyses including our temperature reconstruction and seven well-known climate indices suggest that various atmospheric oscillation patterns are capable of influencing the temperature variations in SW-Turkey.