Modelling the dominant climate signals around southern Africa

In this study, statistical techniques are employed to decompose climate signals around southern Africa into the dominant temporal frequencies, with the aim of modelling and predicting area-averaged rainfall. In the rainfall time series over the period 1900–1999, the annual cycle accounts for 83% of variance. Residual spectral energy cascades from biennial (42%) to interannual (20%) to decadal bands (3%). Regional climate signals are revealed through a multi-taper singular value decomposition analysis of sea surface temperature and sea level pressure fields over the Atlantic and Indian Oceans, in conjunction with southern Africa rainfall. Rossby wave action in the South Indian Ocean dominates the biennial scale variability. El Niño-Southern Oscillation (ENSO) and related Indian Ocean dipole patterns are important for interannual variability. Significant sea temperature and pressure fluctuations occurring 6–12 months prior to rainfall contribute biennial and interannual indices to a multi-variate model that demonstrates useful predictive skill.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

Global and hemispheric temperatures revisited

To characterize observed global and hemispheric temperatures, previous studies have proposed two types of data-generating processes, namely, random walk and trend-stationary, offering contrasting views regarding how the climate system works. Here we present an analysis of the time series properties of global and hemispheric temperatures using modern econometric techniques. Results show that: The temperature series can be better described as trend-stationary processes with a one-time permanent shock which cannot be interpreted as part of the natural variability; climate change has affected the mean of the processes but not their variability; it has manifested in two stages in global and Northern Hemisphere temperatures during the last century, while a second stage is yet possible in the Southern Hemisphere; in terms of Article 2 of the Framework Convention on Climate Change it can be argued that significant (dangerous) anthropogenic interference with the climate system has already occurred.     

Detecting climate impacts with oceanic fish and fisheries data

Anthropogenic climate change is affecting the environment of all oceans, modifying ocean circulation, temperature, chemistry and productivity. While evidence for changes in physical signals is often distinct, impacts on fishes inhabiting oceanic systems are not easily identified, and therefore, quantification of responses is less common. Correctly attributing changes associated with a changing climate from other drivers is important for the implementation of effective harvest and management strategies and for addressing associated socio-economic impacts, particularly for countries highly dependent on oceanic resources. Data supporting investigation of responses of oceanic species to climate impacts include fisheries catch, fisheries-independent surveys, and conventional and electronic tagging data. However, there are a number of challenges associated with detecting climatic responses with these data, including (i) data collection costs (ii) small sample sizes (iii) limited time series relative to temporal scales at which environmental variability occurs, (iv) changing fisher and fisheries behavior due to non-climate drivers and (v) changes in population dynamics due to natural climate variability and non-climate drivers. We highlight potential biases and suggest strategies that should be considered when using oceanic fish and fisheries data in the evaluation of climate change impacts. Consideration of these factors is important when assessing variability in exploited species and designing management responses to climate or fisheries threats.     

Mathematical models of climate evolution in Dobrudja

The understanding of processes that occur in climate change evolution and their spatial and temporal variations are of major importance in environmental sciences. Modeling these processes is the first step in the prediction of weather change. In this context, this paper presents the results of statistical investigations of monthly and annual meteorological data collected between 1961 and 2007 in Dobrudja (a region situated in the South–East of Romania between the Black Sea and the lower Danube River) and the models obtained using time series analysis and gene expression programming. Using two fundamentally different approaches, we provide a comprehensive analysis of temperature variability in Dobrudja, which may be significant in understanding the processes that govern climate changes in the region.     

Trend of climate variability in China during the past decades

Trends in precipitation and mean air temperature in China are estimated, and trend analysis on statistical moments of residuals is further used to investigate climate variability at different timescales. Trends of statistical moments for residuals (i.e. detrended series of climate records) are estimated by using least-square method and Mann?CKendall test. Results show that upward trend is detected in annual mean air temperature but no linear trend for annual precipitation in China. Weak trend is found for variability of precipitation while no trend is found for that of air temperature for China as a whole. But some regional features of climate variability are observed. It is found that the northwest of China shows a significant increasing for precipitation variability, which is consistent with previous work, especially for monthly precipitation. No change is detected in monthly mean air temperature for all stations, while small decreasing and increasing trends are detected for variability of annual mean air temperature in northeast of China and southwest of China, respectively.     

Wavelet-based combination approach for modeling sub-divisional rainfall in India

Agriculture in India is highly sensitive to climatic variations particularly to rainfall and temperature; therefore, any change in rainfall and temperature will influence crop yields. An understanding of the spatial and temporal distribution and changing patterns in climatic variables is important for planning and management of natural resources. Time series analysis of climate data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the series. In this study, the sub-divisional rainfall data of India during the period 1871 to 2016 has been investigated. One of the widely used powerful nonparametric techniques namely wavelet analysis was used to decompose and de-noise the series into time–frequency component in order to study the local as well as global variation over different scales and time epochs. On the decomposed series, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied and by means of inverse wavelet transform, the prediction of rainfall for different sub-divisions was obtained. To this end, empirical comparison was carried out toward forecast performance of the approaches namely Wavelet-ANN, Wavelet-ARIMA, and ARIMA. It is reported that Wavelet-ANN and Wavelet-ARIMA approach outperforms the usual ARIMA model for forecasting of rainfall for the data under consideration.     

交叉小波变换在区域气候分析中的应用

将交叉谱与小波变换分析方法相结合,与传统的交叉谱方法相比,交叉小波变换方法用于区域气候变化与大气环流系统之间耦合振荡行为的相关分析更具优越性,不仅可以弥补经典交叉谱分析方法存在的缺陷,而且能够发挥小波变换在时频两域都具有表征气候信号局部化特征的作用;该方法具有较强的耦合信号分辨能力,便于描述耦合信号在时频域中分布状况的优点。采用交叉小波变换分析北极涛动指数(AOI)距平与河南省月平均降水量距平、气温距平序列之间的联合统计特征及其在时频域中的相关关系,根据小波互相关系数、交叉小波凝聚谱和小波位相谱分析北极涛动对河南省气候变化的可能影响。应用结果表明:河南省降水量和气温变化与AOI之间存在着多时间尺度的显著相关振荡,年代际尺度周期上的互相关系数明显大于年际尺度周期,相关程度随耦合振荡频率的增大而减小,相关显著性取决于两者的时频域联合统计特征,时域中小波互相关系数、小波凝聚谱和小波位相谱的分布具有明显的局部化特征;说明北极涛动年际和年代际异常对河南省气候变化具有显著影响。     

Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.     

European temperature records of the past five centuries based on documentary/instrumental information compared to climate simulations

Two European temperature reconstructions for the past half-millennium, January-to-April air temperature for Stockholm (Sweden) and seasonal temperature for a Central European region, both derived from the analysis of documentary sources and long instrumental records, are compared with the output of climate simulations with the model ECHO-G. The analysis is complemented by comparisons with the long (early)-instrumental record of Central England Temperature (CET). Both approaches to study past climates (simulations and reconstructions) are burdened with uncertainties. The main objective of this comparative analysis is to identify robust features and weaknesses in each method which may help to improve models and reconstruction methods. The results indicate a general agreement between simulations obtained with temporally changing external forcings and the reconstructed Stockholm and CET records for the multi-centennial temperature trend over the recent centuries, which is not reproduced in a control simulation. This trend is likely due to the long-term change in external forcing. Additionally, the Stockholm reconstruction and the CET record also show a clear multi-decadal warm episode peaking around AD 1730, which is absent in the simulations. Neither the reconstruction uncertainties nor the model internal climate variability can easily explain this difference. Regarding the interannual variability, the Stockholm series displays, in some periods, higher amplitudes than the simulations but these differences are within the statistical uncertainty and further decrease if output from a regional model driven by the global model is used. The long-term trend of the CET series agrees less well with the simulations. The reconstructed temperature displays, for all seasons, a smaller difference between the present climate and past centuries than is seen in the simulations. Possible reasons for these differences may be related to a limitation of the traditional ‘indexing’ technique for converting documentary evidence to temperature values to capture long-term climate changes, because the documents often reflect temperatures relative to the contemporary authors’ own perception of what constituted ‘normal’ conditions. By contrast, the amplitude of the simulated and reconstructed inter-annual variability agrees rather well.     

Effects of diurnal temperature range and drought on wheat yield in Spain

This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies.

     

CLIMATE CHANGE: LONG-TERM TRENDS AND SHORT-TERM OSCILLATIONS

Identifying the Northern Hemisphere (NH) temperature reconstruction and instrumental data for the past 1000 years shows that climate change in the last millennium includes long-term trends and various oscillations. Two long-term trends and the quasi-70-year oscillation were detected in the global temperature series for the last 140 years and the NH millennium series. One important feature was emphasized that temperature decreases slowly but it increases rapidly based on the analysis of different series. Benefits can be obtained of climate change from understanding various long-term trends and oscillations. Millennial temperature proxies from the natural climate system and time series of nonlinear model system are used in understanding the natural climate change and recognizing potential benefits by using the method of wavelet transform analysis. The results from numerical modeling show that major oscillations contained in numerical solutions on the interdecadal timescale are consistent with that of natural proxies. It seems that these oscillations in the climate change are not directly linked with the solar radiation as an external forcing. This investigation may conclude that the climate variability at the interdecadal timescale strongly depends on the internal nonlinear effects in the climate system.     

SIGNAL DETECTION OF GLOBAL CLIMATE CHANGE AND EXTERNAL FORCING FACTORS

In this paper,we displayed one-dimensional climate signals,such as global temperaturevariation,Southern Oscillation Index and variation of external forcing factors,on a two-dimensional time-scale plane using compactly supported wavelet decomposition.Using the lag-correlation analysis method,and interpretative variance analysis method,and phase comparisonmethod to the wavelet analysis result,we not only gained the variation on different scales to theglobal temperature and El Nino signals,the location of the jump point and intrinsic scale of theseseries,but also indicated the magnitude,extent and time of the effect of external forcing factors onthem.We also put forward reasonable explanation to the main variation of recent 140 years.     

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.     

Inter-annual temperature and precipitation variations over the Litani Basin in response to atmospheric circulation patterns

This study examines the sensitivity of a mid-size basin’s temperature and precipitation response to different global and regional climate circulation patterns. The implication of the North Atlantic Oscillation (NAO), El Ni?o Southern Oscillation (ENSO), Indian Monsoon and ten other teleconnection patterns of the Northern Hemisphere are investigated. A methodology to generate a basin-scale, long-term monthly surface temperature and precipitation time series has been established using different statistical tests. The Litani River Basin is the focus of this study. It is located in Lebanon, east of the Mediterranean Basin, which is known to have diverse geophysical and environmental characteristics. It was selected to explore the influence of the diverse physical and topographical features on its hydroclimatological response to global and regional climate patterns. We also examine the opportunity of conducting related studies in areas with limited long-term measured climate and/or hydrological data. Litani's monthly precipitation and temperature data have been collected and statistically extrapolated using remotely sensed data products from satellites and as well as in situ gauges. Correlations between 13 different teleconnection indices and the basin’s precipitation and temperature series are investigated. The study shows that some of the annual and seasonal temperature and precipitation variance can be partially associated with many atmospheric circulation patterns. This would give the opportunity to relate the natural climate variability with the watershed’s hydroclimatology performance and thus differentiate it from other anthropogenic induced climate change outcomes.     

Local air temperature tolerance: a sensible basis for estimating climate variability

The customary representation of climate using sample moments is generally biased due to the noticeably nonstationary behaviour of many climate series. In this study, we introduce a moment-free climate representation based on a statistical model fitted to a long-term daily air temperature anomaly series. This model allows us to separate the climate and weather scale variability in the series. As a result, the climate scale can be characterized using the mean annual cycle of series and local air temperature tolerance, where the latter is computed using the fitted model. The representation of weather scale variability is specified using the frequency and the range of outliers based on the tolerance. The scheme is illustrated using five long-term air temperature records observed by different European meteorological stations.     

Downscaling daily precipitation time series using a combined circulation- and regression-based approach

The aim of this paper is to introduce a new conditional statistical model for generating daily precipitation time series. The generated daily precipitation can thus be used for climate change impact studies, e.g., crop production, rainfall–runoff, and other water-related processes. It is a stochastic model that links local rainfall events to a continuous atmospheric predictor, moisture flux, in addition to classified atmospheric circulation patterns. The coupled moisture flux is proved to be capable of capturing continuous property of climate system and providing extra information to determine rainfall probability and rainfall amount. The application was made to simultaneously downscale daily precipitation at multiple sites within the Rhine River basin. The results show that the model can well reproduce statistical properties of daily precipitation time series. Especially for extreme rainfall events, the model is thought to better reflect rainfall variability compared to the pure CP-based downscaling approach.     

Analysis of the long-term precipitation series at Athens,Greece

In this paper, the characteristics of the long-term precipitation series at Athens (1858–1985) have been statistically analyzed. This study covers both the history and the analysis of the data. The ten-year mean amounts, the monthly and annual amounts averaged over the intervals 1858–1890, 1891–1985, 1951–1980, 1858–1985, the mean number of hours of precipitation and the precipitation intensity are given. The analysis of long-term time series of climatic data (in particular precipitation) is a useful tool for the study of past climate. Different statistical techniques are used in order to depict monthly, seasonal and annual variations, as well as trends, periodicities and recurrence intervals of the amount, intensity and number of precipitation days. The analysis reveals many interesting characteristics. These characteristics of the precipitation regime are extended to a time scale from seasonal variation to a semi-secular trend. The study of such long-term series may be helpful not only in practical applications of rainfall, but also for explaining the possible physical or anthropogenic mechanisms of climatic fluctuations and tendencies. The series of precipitation at Athens is one of the longest in south-eastern Europe.     

Optimal detection and attribution of climate change: sensitivity of results to climate model differences

Fingerprint techniques for the detection of anthropogenic climate change aim to distinguish the climate response to anthropogenic forcing from responses to other external influences and from internal climate variability. All these responses and the characteristics of internal variability are typically estimated from climate model data. We evaluate the sensitivity of detection and attribution results to the use of response and variability estimates from two different coupled ocean atmosphere general circulation models (HadCM2, developed at the Hadley Centre, and ECHAM3/LSG from the MPI für Meteorologie and Deutsches Klimarechenzentrum). The models differ in their response to greenhouse gas and direct sulfate aerosol forcing and also in the structure of their internal variability. This leads to differences in the estimated amplitude and the significance level of anthropogenic signals in observed 50-year summer (June, July, August) surface temperature trends. While the detection of anthropogenic influence on climate is robust to intermodel differences, our ability to discriminate between the greenhouse gas and the sulfate aerosol signals is not. An analysis of the recent warming, and the warming that occurred in the first half of the twentieth century, suggests that simulations forced with combined changes in natural (solar and volcanic) and anthropogenic (greenhouse gas and sulfate aerosol) forcings agree best with the observations.