On the use of Granger causality to investigate the human influence on climate

Summary A paper recently published in “Nature” finds that there is sufficient evidence to identify the effects of human activity on global temperature. The study is based on northern and southern hemispheres’ time series temperature from 1865 to 1994 and the econometric technique applied is Granger causality analysis. In this note, we make some critical remarks on the conclusions of this study which seem to be rather inconsistent from a methodological point of view. It is shown that a more accurate application of Granger causality analysis to this problem may not allow the same strong and unambiguous conclusions. Received December 22, 2000/Revised April 23, 2001     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

基于RBF神经网络的人脸识别研究

针对人脸识别技术中存在的高维问题、小样本问题和非线性问题展开研究.围绕人脸特征提取,采用基于主成分分析和Fisher线性鉴别来克服在人脸识别中的小样本问题,同时将人脸图像从高维空间映射到低维空间从而解决了高维问题;在分类识别方面,采用具有很强的非线性映射功能的RBF神经网络进行模式分类,能够解决人脸识别中的非线性问题.在ORL人脸数据库上进行的仿真实验表明,该方法进行人脸识别具有较高的识别率.     

Detecting inhomogeneity in daily climate series using wavelet analysis

A wavelet method was applied to detect inhomogeneities in daily meteorological series, data which are being increasingly applied in studies of climate extremes. The wavelet method has been applied to a few well- established long-term daily temperature series back to the 18th century, which have been ＂homogenized＂ with conventional approaches. Various types of problems remaining in the series were revealed with the wavelet method. Their influences on analyses of change in climate extremes are discussed. The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data.     

基于残余插值的卷积神经网络去马赛克算法

在去马赛克问题中,为了精确插值倾斜边缘并提高结果图像的整体质量,提出一种基于残余插值的卷积神经网络去马赛克算法.针对Bayer格式的颜色滤波阵列,插值绿色平面时,对于红蓝通道信息不全的问题,采用同通道邻近像素值近似代替,综合考虑3个通道的梯度,运用倾斜方向的边缘检测算子,将倾斜边缘分为不同方向的边缘分别插值.在插值完成后,利用深度卷积神经网络,进一步训练插值结果.在标准的IMAX数据集上,与目前流行的算法相比,本文算法视觉上更接近原图,具有更高的峰值信噪比和更短的运行时间.     

The influence of human activity in the Arctic on climate and climate impacts

Human activities in the Arctic are often mentioned as recipients of climate-change impacts. In this paper we consider the more complicated but more likely possibility that human activities themselves can interact with climate or environmental change in ways that either mitigate or exacerbate the human impacts. Although human activities in the Arctic are generally assumed to be modest, our analysis suggests that those activities may have larger influences on the arctic system than previously thought. Moreover, human influences could increase substantially in the near future. First, we illustrate how past human activities in the Arctic have combined with climatic variations to alter biophysical systems upon which fisheries and livestock depend. Second, we describe how current and future human activities could precipitate or affect the timing of major transitions in the arctic system. Past and future analyses both point to ways in which human activities in the Arctic can substantially influence the trajectory of arctic system change.     

Direct human influence of irrigation on atmospheric water vapour and climate

Human activity increases the atmospheric water vapour content in an indirect way through climate feedbacks. We conclude here that human activity also has a direct influence on the water vapour concentration through irrigation. In idealised simulations we estimate a global mean radiative forcing in the range of 0.03 to +0.1 Wm^–2 due to the increase in water vapour from irrigation. However, because the water cycle is embodied in the climate system, irrigation has a more complex influence on climate. We also simulate a change in the temperature vertical profile and a large surface cooling of up to 0.8 K over irrigated land areas. This is of opposite sign than expected from the radiative forcing alone, and this questions the applicability of the radiative forcing concept for such a climatic perturbation. Further, this study shows stronger links than previously recognised between climate change and freshwater scarcity which are environmental issues of paramount importance for the twenty first century.     

IPCC AR6报告解读：人类活动对气候系统的影响

2021年8月9日,IPCC发布了第六次评估报告(AR6)第一工作组报告,报告第三章“人类活动对气候系统的影响”定量评估了人类活动对气候系统的影响程度以及气候模式对观测到的平均气候、气候变化和气候变率的模拟性能。报告基于气候系统的多个圈层变量的综合评估明确指出,毋庸置疑的是,自工业化以来人为影响已经使大气、海洋和陆地升温;支撑本次评估的国际耦合模式比较计划第六阶段（CMIP6）气候模式模拟的大多数大尺度气候指标的近期平均气候,相比前一次评估报告（AR5）中的CMIP5模式结果有所改进。报告在更广泛的领域和区域提供了更多证据表明气候系统中的人类活动影响,但受制于观测、模式与过程认知的不足,在大气、海洋、冰冻圈、生物圈及气候变率模态的多个指标变化中人为影响的贡献方面仍然存在不确定性甚至缺少研究。     

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998 2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.     

Detecting abrupt climate changes on different time scales

Two concepts are introduced for detecting abrupt climate changes. In the first case, the sampling frequency of climate data is high as compared to the frequency of climate events examined. The method is based on a separation of trend and noise in the data and is applicable to any dataset that satisfies some mild smoothness and statistical dependence conditions for the trend and the noise, respectively. We say that an abrupt change occurs when the first derivative of the trend function has a discontinuity and the task is to identify such points. The technique is applied to Northern Hemisphere temperature data from 1850 to 2009, Northern Hemisphere temperature data from proxy data, a.d. 200?C1995 and Holocene ??¹⁸O values going back to 11,700 years BP. Several abrupt changes are detected that are, among other things, beneficial for determining the Medieval Warm Period, Little Ice Age and Holocene Climate Optimum. In the second case, the sampling frequency is low relative to the frequency of climate events studied. A typical example includes Dansgaard?COeschger events. The methodology used here is based on a refinement of autoregressive conditional heteroscedastic models. The key element of this approach is the volatility that characterises the time-varying variance, and abrupt changes are defined by high volatilities. The technique applied to ??¹⁸O values going back to 122,950 years BP is suitable for identifying DO events. These two approaches for the two cases are closely related despite the fact that at first glance, they seem quite different.     

Spatiotemporal modeling of monthly soil temperature using artificial neural networks

Soil temperature data are critical for understanding land–atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.     

Detecting climate change signals in Saudi Arabia using mean annual surface air temperatures

Climate change signals in Saudi Arabia are investigated using the surface air temperature (SAT) data of 19 meteorological stations, well distributed across the country. Analyses are performed using cumulative sum, cumulative annual mean, and the Mann–Kendall rank statistical test for the period of 1978–2010. A notable change in SAT for the majority of stations is found around 1997. The results show a negative temperature trend (cooling) for all stations during the first period (1978–1997), followed by a positive trend (warming) in the second period (1998–2010) with reference to the entire period of analysis. The Mann–Kendall test confirms that there is no abrupt cooling at any station during the analysis period, reflecting the warming trend across the country. The warming trend is found to be 0.06 °C/year, while the cooling trend is 0.03 °C/year, which are statistically significant.     

Prediction of all India summer monsoon rainfall using error-back-propagation neural networks

Summary In this paper, multilayered feedforward neural networks trained with the error-back-propagation (EBP) algorithm have been employed for predicting the seasonal monsoon rainfall over India. Three network models that use, respectively, 2, 3 and 10 input parameters which are known to significantly influence the Indian summer monsoon rainfall (ISMR) have been constructed and optimized. The results obtained thereby are rigorously compared with those from the statistical models. The predictions of network models indicate that they can serve as a potent tool for ISMR prediction.     

中国东部夏季雨型的人工神经网络集合预测

在BP(back propagation)人工神经网络方法的基础上,考虑到历史资料的个体差异及其年代际变化会影响到样本均值,由此使得中国东部夏季雨型模拟和预测效果产生差异,故引入交叉检验及集合预报思想,以改进人工神经网络独立预报方法.在利用不同历史样本资料建立人工神经网络模型,并进行交叉检验的同时,对预测年的雨型进行预测,可获得预测年的多次预测结果.该方法在中国东部夏季四类雨型的试验预报中表现出较好效果.     

Relative importance of parameters affecting wind speed prediction using artificial neural networks

In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz.