Daily Temperature Time Series Research Articles

Analyses of Observed and Anticipated Changes in Extreme Climate Events in the Northwest Himalaya

In this study, past (1970-2005) as well as future long term (2011-2099) trends in various extreme events of temperature and precipitation have been investigated over selected hydro-meteorological stations in the Sutlej river basin. The ensembles of two Coupled Model Intercomparison Project (CMIP3) models: third generation Canadian Coupled Global Climate Model and Hadley Centre Coupled Model have been used for simulation of future daily time series of temperature (maximum and minimum) and precipitation under A2 emission scenario. Large scale atmospheric variables of both models and National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis data sets have been downscaled using statistical downscaling technique at individual stations. A total number of 25 extreme indices of temperature (14) and precipitation (11) as specified by the Expert Team of the World Meteorological Organization and Climate Variability and Predictability are derived for the past and future periods. Trends in extreme indices are detected over time using the modified Mann-Kendall test method. The stations which have shown either decrease or no change in hot extreme events (i.e., maximum TMax, warm days, warm nights, maximum TMin, tropical nights, summer days and warm spell duration indicators) for 1970–2005 and increase in cold extreme events (cool days, cool nights, frost days and cold spell duration indicators) are predicted to increase and decrease respectively in the future. In addition, an increase in frequency and intensity of extreme precipitation events is also predicted.

Scaling and nonlinear behaviour of daily mean temperature time series across India

In order to ascertain the dynamics of temperature variation in India, the scaling properties of the daily mean temperature time series obtained from seven different weather stations viz. Kolkata, Chennai, New Delhi, Mumbai, Bhopal, Agartala and Ahmadabad representing different geographical zones in India has been studied. Scaling properties of the temperature profile across India has been estimated from the calculation of Hurst-Exponent parameter obtained from five different scaling methods. Hurst Exponent values confirm that all temperature time series are Fractional Brownian Motion (FBM), statistically self-affine, anti-persistent and Short Range Dependent (SRD) self similar. As SRD self similarity is a common signature of a nonlinear dynamical process, further investigation has been made to discover the presence of any nonlinear behaviour of the temperature profile of Indian climate using Delay Vector Variance (DVV) method and the present calculation confirms a deterministic nonlinear profile of the same.

Preserving Continuity of Long-Term Daily Maximum and Minimum Temperature Observations with Automation of Reference Climate Stations using Overlapping Data and Meteorological Conditions

ABSTRACTThe seasonal-by-wind bias method for aligning time series of daily maximum and minimum temperatures from past conventional staffed and new automated sites using closely collocated, overlapping observations is presented for twenty-two modernized Reference Climate Stations in Canada. The method consists of adjusting for incompatible observing times and deriving biases from the daily “manual-minus-automated” temperature differences classified into seasons and wind-speed conditions. Most of the biases vary with the season, and many show limited wind dependency. Four sets of adjusted time series are prepared based on two-year and five-year overlapping data and on seasonal bias with or without wind conditions; the adjusted data are compared with the original observations. Based on the mean of the absolute differences and examination of box plots, the results show that, for this particular set of stations, the two-year versus five-year and seasonal versus seasonal-by-wind bias adjusted time series are overall similar. The largest contribution to the improvements in the adjusted observations came from matching the times of observation. Additionally, daily temperatures are adjusted using statistical methods applied with neighbouring station data but no overlapping observations at collocated stations; it is concluded that these do not necessarily resolve the bias between staffed and automated sites.

Regional climate change trends and uncertainty analysis using extreme indices: A case study of Hamilton, Canada

This study aims to provide a deeper understanding of the level of uncertainty associated with the development of extreme weather frequency and intensity indices at the local scale. Several different global climate models, downscaling methods, and emission scenarios were used to develop extreme temperature and precipitation indices at the local scale in the Hamilton region, Ontario, Canada. Uncertainty associated with historical and future trends in extreme indices and future climate projections were also analyzed using daily precipitation and temperature time series and their extreme indices, calculated from gridded daily observed climate data along with and projections from dynamically downscaled datasets of CanRCM4 and PRECIS, and the statistically downscaled CIMP5 ensemble. A bias correction technique was applied to all raw daily temperature and precipitation time series prior to calculation of the indices.All climate models predicted increasing trends for extreme temperature indices, maximum 1-day and 5-day precipitation (RX1day and RX5day), total wet day precipitation (PRCPTOT), very heavy precipitation days (R20mm), Summer Days (SU), and Tropical Nights (TR) and decreasing trend for Forest Days (FD) and Ice Days (ID) in 2020s, 2050s, and 2080s compared to present. CanRCM4 model did consistently project values in the upper range of the CMIP5 ensemble while the PRECIS ensemble was more in-line with the CMIP5 mean values. This difference may however be a function of different emission scenarios used.

A new approach to predict soil temperature under vegetated surfaces.

In this article, the setup and the application of an empirical model, based on Newton’s law of cooling, capable to predict daily mean soil temperature (Tsoil) under vegetated surfaces, is described. The only input variable, necessary to run the model, is a time series of daily mean air temperature. The simulator employs 9 empirical parameters, which were estimated by inverse modeling. The model, which primarily addresses forested sites, incorporates the effect of snow cover and soil freezing on soil temperature. The model was applied to several temperate forest sites, managing the split between Central Europe (Austria) and the United States (Harvard Forest, Massachusetts; Hubbard Brook, New Hampshire), aiming to cover a broad range of site characteristics. Investigated stands differ fundamentally in stand composition, elevation, exposition, annual mean temperature, precipitation regime, as well as in the duration of winter snow cover. At last, to explore the limits of the formulation, the simulator was applied to non-forest sites (Illinois), where soil temperature was recorded under short cut grass. The model was parameterized, specifically to site and measurement depth. After calibration of the model, an evaluation was performed, using ~50 % of the available data. In each case, the simulator was capable to deliver a feasible prediction of soil temperature in the validation time interval. To evaluate the practical suitability of the simulator, the minimum amount of soil temperature point measurements, necessary to yield expedient model performance was determined. In the investigated case 13–20 point observations, uniformly distributed within an 11-year timeframe, have been proven sufficient to yield sound model performance (root mean square error <0.9 °C, Nash–Sutcliffe efficiency >0.97). This makes the model suitable for the application on sites, where the information on soil temperature is discontinuous or scarce.

Multifractal analysis of meteorological time series to assess climate impacts

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 65:39-52 (2015) - DOI: https://doi.org/10.3354/cr01321 Contribution to the CR Special: 'Modelling climate change impacts for food security' Multifractal analysis of meteorological time series to assess climate impacts Piotr Baranowski1,*, Jaromir Krzyszczak1, Cezary Slawinski1, Holger Hoffmann2, Jerzy Kozyra3, Anna Nieróbca3, Krzysztof Siwek4, Andrzej Gluza4 1Institute of Agrophysics, Polish Academy of Sciences, ul. Doświadczalna 4, 20-290 Lublin, Poland 2Institute of Crop Science and Resource Conservation (INRES), Katzenburgweg 5, 53115 Bonn, Germany 3Institute of Soil Science and Plant Cultivation, State Research Institute, Czartoryskich 8, 24-100 Pulawy, Poland 4Faculty of Earth Sciences and Spatial Management, UMCS, al. Kraśnicka 2cd, 20-718 Lublin, Poland *Corresponding author: pbaranow@ipan.lublin.pl ABSTRACT: Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performed with 11322 measured time series (31 yr) of daily air temperature, wind velocity, relative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. In log-log plots of the cumulative distributions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifractality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics. KEY WORDS: Multifractal analysis · Time series · Agro-meteorological parameters Full text in pdf format Supplementary material PreviousNextCite this article as: Baranowski P, Krzyszczak J, Slawinski C, Hoffmann H and others (2015) Multifractal analysis of meteorological time series to assess climate impacts. Clim Res 65:39-52. https://doi.org/10.3354/cr01321 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 65. Online publication date: September 28, 2015 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2015 Inter-Research.

A space and time scale‐dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

AbstractA geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple‐point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 and 10 km resolution for a 20 year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference data set indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple‐point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local‐scale estimates of precipitation and temperature from General Circulation Models.

Development of an effective and potentially scalable weather generator for temperature and growing degree days

The main purpose of this study is to develop an easy-to-use weather generator (WG) for the downscaling of gridded data to point measurements at regional scale. The WG is applied to daily averaged temperatures and annual growing degree days (GDD) of wheat. This particular choice of variables is motivated by future investigations on temperature impacts as the most important climate variable for wheat cultivation under irrigation in Iran. The proposed statistical downscaling relates large-scale ERA-40 reanalysis to local daily temperature and annual GDD. Long-term local observations in Iran are used at 16 synoptic stations from 1961 to 2001, which is the common period with ERA-40 data. We perform downscaling using two approaches: the first is a linear regression model that uses the ERA-40 fingerprints (FP) defined by the squared correlation with local variability, and the second employs a linear multiple regression (MR) analysis to relate the large-scale information at the neighboring grid points to the station data. Extending the usual downscaling, we implement a WG providing uncertainty information and realizations of the local temperatures and GDD by adding a Gaussian random noise. ERA-40 reanalysis well represents the local daily temperature as well as the annual GDD variability. For 2-m temperature, the FPs are more localized during the warm compared with the cold season. While MR is slightly superior for daily temperature time series, FP seems to perform best for annual GDD. We further assess the quality of the WGs applying probabilistic verification scores like the continuous ranked probability score (CRPS) and the respective skill score. They clearly demonstrate the superiority of WGs compared with a deterministic downscaling.

Integrated SWAT model and statistical downscaling for estimating streamflow response to climate change in the Lake Dianchi watershed, China

Understanding the relationships between hydrological regime and climate change is important for water resources management. In this study, the streamflow response to climate change was investigated in the Lake Dianchi watershed, which is one of the most important eutrophic lakes in China. Daily time-series of temperature and precipitation in the future periods (2020, 2050 and 2080s) were projected from HadCM3 model. Statistical downscaling model (SDSM) and the previously calibrated and validated Soil and water assessment tool (SWAT) model were used to quantify the impacts of climate change on streamflow in this watershed. The results showed that SDSM can well capture the statistical relationships between the large scale climate variables and the observed weather at regional scale. The downscaled results showed that annual average maximum and minimum temperature would rise by 4.28 (3.25) and 4.71 °C (3.33 °C) in the 2080s under A2 (B2) scenario. Annual average precipitation would decrease within the range between 20.34 and 74.12 mm under both scenarios in the future. Based on SWAT model simulation, annual average streamflow would decrease in the future by the declination of −7.12 to −21.83 % and −6.34 to −17.09 % under A2 (B2) scenarios in the outlet of this watershed. The frequency of drought and extreme rainfall events would increase in the future, which is not beneficial to protect Lake Dianchi. This study could lead to a better understanding of the streamflow response under climate change and also raised concerns about the sustainability of future water resources in Lake Dianchi watershed.

Temperature changes in the North-Western Italian Alps from 1961 to 2010

The aim of this study was to identify any possible temperature changes within the last 50 years in the North-Western Italian Alps by examining data from 16 high-altitude weather stations in the period 1961–2010. The daily temperature time series were collected, digitized and subjected to a historical research to individuate discontinuities and retrieve metadata. We also carried out the data quality control and the homogenization which allowed the climatic indices trend detection. The analysis of the temperature values showed an increase in temperature, particularly at high-altitudes sites. In fact, the stations located above 1600 m asl revealed a rise in temperatures and a decrease in the number of cold periods. For the maximum temperatures, greater increases in spring and winter have been observed, for minimum temperatures in the summer. These trends confirm that climate change is occurring in an environment particularly sensitive to temperature changes, especially during the season of snow accumulation and vegetative growth. These results may be important for policy makers to define the best adaptation strategies in order to protect one of the most sensitive environments such as mountains.

Evolution of heat wave occurrence over the Paris basin (France) in the 21st century

We analysed the temporal evolution of heat wave frequency in the Paris region in a changing future climate (1960−1989, 2020−2049, 2070−2099). Firstly, a method for extracting heat waves from observed or simulated time series of daily minimum and maximum temperatures was proposed, based on heat-impact considerations. It was evaluated over the period 1951−2009 using observations from 2 meteorological stations of the Meteo-France operational network. The past heat waves that have affected the Paris region were correctly identified in terms of both dates and durations. Eight heat waves were extracted from the data for this period of time (i.e. a mean frequency of about 1 in 7 yr). The method was then applied to a large sample of climate projections, including 1 regional climate model (RCM) following 3 emission scenarios (A2, A1B, B1) and 9 combinations of RCMs driven by different global circulation models following the A1B emission scenario only (from the European ENSEMBLES project). The historical runs generated 1 to 5 heat waves, depending on the model, with a median value of 3 events (i.e. a frequency of 1 in 10 yr). All models from the ENSEMBLES database simulated a systematic increase in the frequency of heat wave occurrences with time: 1 heat wave every 2 yr on average over 2020−2049, rising to at least 1, and up to 2, heat waves per year on average over 2070−2099. They also became much longer, with mean durations varying between 6 and 12 d over 2070−2099, and exceptional durations reaching 5 to 9 wk. A comparison of the heat waves simulated with the 3 different emission scenarios highlights the major impact of the scenario on the number of heat wave days (and the duration of heat waves) at the end of the 21st century.

Modeling the impact of climate variability on Black Sea anchovy recruitment and production

AbstractThe connection of climate variability with anchovy spawning and recruitment in the Black Sea in particular, and other ecosystems in general, was studied using a two‐way coupled lower trophic level and anchovy bioenergetics model. Climate variability was represented by a 50‐yr time series of daily temperature and vertical mixing rates with stochastic variations. Temperature was found to be the dominant factor influencing early life stages and hence population dynamics of Black Sea anchovy as marked by a high correlation of anchovy egg production and recruitment success in response to changes in temperature. Each decrease of 2°C in summer mean temperatures resulted in a delay in the timing of egg production of between 12 and 19 days. Water temperatures in the spawning season had a greater influence than the number of available spawning females on the intensity of egg production. Anchovy recruitment was similarly influenced by temperature, with decreased temperatures resulting in a significant delay in the onset of peak recruitment during the fall by 21–38 days. Also, recruitment numbers in December decreased by about 20% with decreasing temperatures. The impact of temperature on production was slightly diminished by the impact of vertical mixing. The strong linkage of climate variability with anchovy spawning and recruitment has an important prediction potential for short‐term anchovy stock estimations, which may serve fisheries management purposes.

Statistical Variability and Persistence Change in Daily Air Temperature Time Series from High Latitude Arctic Stations

In the last decades, Arctic communities have been reporting that weather conditions are becoming less predictable. Most scientific studies have not been able to consistently confirm such a trend. The question regarding the possible increase in weather variability was addressed here based on daily minimum and maximum surface air temperature time series from 15 high latitude Arctic stations from Canada, Norway, and the Russian Federation. A range of analysis methods were applied, distinguished mainly by the way in which they treat time scale. Statistical L-moments were determined for temporal windows of different lengths. While the picture provided by L-scale and L-kurtosis is not consistent with an increasing variability, L-skewness was found to change towards more positive values, reflecting an enhancement of warm spells. Haar wavelet analysis was applied both to the entire time series and to running windows. Persistence diagrams were generated based on running windows advancing through time and on local slopes of Haar analysis graphs; they offer a more nuanced view on variability by reflecting its change over time on a range of temporal scales. Local increases in variability could be identified in some cases, but no consistent change was detected in any of the stations over the studied temporal scales. The possibility for other intervals of temporal scale (e.g., days, hours, minutes) to potentially reveal a different situation cannot be ruled out. However, in the light of the results presented here, explanations for the discrepancy between variability perception and results of pattern analysis might have to be explored using an integrative approach to weather variables such as air temperature, cloud cover, precipitation, wind, etc.

Evaluation of infilling methods for time series of daily precipitation and temperature: The case of the Ecuadorian Andes

ABSTRACT Continuous time series of precipitation and temperature considerably facilitate and improve the calibration and validation of climate and hydrologic models, used inter alia for the planning and management of earth’s water resources and for the prognosis of the possible effects of climate change on the rainfall-runoff regime of basins. The goodness-of-fit of models is among other factors dependent from the completeness of the time series data. Particular in developing countries gaps in time series data are very common. Since gaps can severely compromise data utility this research with application to the Andean Paute river basin examines the performance of 17 deterministic infill methods for completing time series of daily precipitation and mean temperature. Although sophisticated approaches for infilling gaps, such as stochastic or artificial intelligence methods exist, preference in this study was given to deterministic approaches for their robustness, easiness of implementation and computational efficiency. Results reveal that for the infilling of daily precipitation time series the weighted multiple linear regression method outperforms due to considering the ratio of the Pearson correlation coefficient to the distance, giving more weight to both, highly correlated and nearby stations. For mean temperature, the climatological mean of the day was clearly the best method, most likely due to the scarcity of weather stations measuring temperature, and because the few available stations are located at different elevations in the landscape, suggesting the need to address in future studies the impact of elevation on the interpolation. Keywords: Infilling, deterministic infill methods, time series, daily rainfall, mean daily temperature, Andean Paute river basin. RESUMEN Series continuas de precipitacion y temperatura facilitan y mejoran considerablemente la calibracion y validacion de modelos hidrologicos y climaticos, utilizados entre otras cosas, para la planificacion y manejo de recursos hidricos y el pronostico de los posibles efectos del cambio climatico en el regimen lluvia-escorrentia de las cuencas hidrograficas. La bondad de ajuste de los modelos esta entre los factores que dependen de la continuidad de las series temporales. En paises en vias de desarrollo los vacios en las series temporales de variables climaticas es comun. Ya que los vacios en las series temporales pueden comprometer severamente la utilidad de los datos, este estudio aplicado en la cuenca del rio Paute en los Andes Ecuatorianos, examina el desempeno de 17 metodos deterministicos de relleno de datos diarios de las variables precipitacion y temperatura media. A pesar de la existencia de metodos de relleno mas sofisticados como metodos estocasticos o metodos de inteligencia artificial, en este estudio se dio preferencia a metodos deterministicos por su robustez, facilidad de implementacion, y eficiencia computacional. Los resultados revelan que para rellenar series temporales de precipitacion diaria, el metodo de regresion lineal multiple ponderada es el mejor, debido a la consideracion de la razon entre el coeficiente de correlacion de Pearson y la distancia con respecto a otras estaciones como factor de ponderacion, dando mayor importancia a las estaciones mas cercanas altamente correlacionadas. Para temperatura, la media climatologica del dia fue claramente el mejor metodo, posiblemente debido a la escacez de datos de estaciones cercanas localizadas tambien en elevaciones diferentes, sugiriendo la necesidad de considerar en futuros estudios el impacto de la elevacion en la interpolacion de datos. Palabras clave: Relleno de datos, metodos deterministicos de relleno, series temporales, precipitacion diaria, temperatura media del dia, cuenca del rio Paute Andean.

Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach

In this study, we present a detailed analysis of the effect of changes in cloudiness (CLD) between a future period (2071–2099) and the base period (1961–1990) on daily minimum temperature (TMIN) and maximum temperature (TMAX) in the same period for the Shikoku region, Japan. This analysis was performed using climate data obtained with the use of the Statistical DownScaling Model (SDSM). We calibrated the SDSM using the National Center for Environmental Prediction (NCEP) reanalysis dataset for the SDSM input and daily time series of temperature and CLD from 10 surface data points (SDP) in Shikoku. Subsequently, we validated the SDSM outputs, specifically, TMIN, TMAX, and CLD, obtained with the use of the NCEP reanalysis dataset and general circulation model (GCM) data against the SDP. The GCM data used in the validation procedure were those from the Hadley Centre Coupled Model, version 3 (HadCM3) for the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios and from the third generation Coupled Global Climate Model (CGCM3) for the SRES A2 and A1B scenarios. Finally, the validated SDSM was run to study the effect of future changes in CLD on TMIN and TMAX. Our analysis showed that (1) the negative linear fit between changes in TMAX and those in CLD was statistically significant in winter while the relationship between the two changes was not evident in summer, (2) the dependency of future changes in TMAX and TMIN on future changes in CLD were more evident in winter than in other seasons with the present SDSM, (3) the diurnal temperature range (DTR) decreased in the southern part of Shikoku in summer in all the SDSM projections while DTR increased in the northern part of Shikoku in the same season in these projections, (4) the dependencies of changes in DTR on changes in CLD were unclear in summer and winter. Results of the SDSM simulations performed for climate change scenarios such as those from this study contribute to local-scale agricultural and hydrological simulations and development of agricultural and hydrological models.

Application of spatial weather generator for the assessment of climate change impacts on a river runoff

In this study, the impacts of climate change on streamflow are investigated. The ensemble of outputs from three different Global Circulation Models models: GISS, CCCM, GFDL developed for the emission scenario A1B were analyzed to infer projected changes in climatological conditions for the region of the Upper and Middle Odra basin. Obtaining hydrological scenarios of future changes for the scale of subcatchment required simulating short-term and fine scaled weather patterns for this area. SWGEN model (Spatial Weather GENerator) was applied to downscale projected changes of climatological conditions to the ones required by hydrological model temporal and spatial resolution. Daily time series of solar radiation, temperature and precipitation were generated for the reference period 1981–2000 and for the time horizon 2030 and 2050. The generated data from SWGEN model were integrated in the hydrological model NAM to simulate streamflow under changed conditions with daily time step. The results show considerable changes in annual and seasonal runoff daily distributions for selected study catchment in the future time horizons of 2030 and 2050.

Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices

This study first homogenizes time series of daily maximum and minimum temperatures recorded at 825 stations in China over the period from 1951 to 2010, using both metadata and the penalized maximum t test with the first‐order autocorrelation being accounted for to detect change points and using the quantile‐matching algorithm to adjust the data time series to diminish discontinuities. Station relocation was found to be the main cause for discontinuities, followed by station automation. The effects of discontinuities on estimation of long‐term trends in the annual mean and extreme indices of temperature are illustrated. The data homogenization is shown to have improved the spatial consistency of estimated trends. Using the homogenized daily minimum and daily maximum temperature data, this study also analyzes trends in extreme temperature indices. The results show that the vast majority (85%–90%) of the 825 sites have experienced significantly more warm nights and less cold nights since 1951. There have also been more warm days and less cold days since 1951, although these trends are less extensive. About 62% of the 825 sites were found to have experienced significantly more warm days and about 50% significantly less cold days. None of the 825 sites were found to have significantly more cold nights/days or less warm nights/days. These indicate that the warming is stronger in nighttime than in daytime and stronger in winter than in summer. Thus, the diurnal temperature range was found to have significantly decreased at 49% of the 825 sites, with significant increases being identified only at 3% of these sites.

GLM 날씨 발생기를 이용한 서울지역 일일 기온 모형

확률적 날씨 발생기(Stochastic weather generator)는 일일 날씨를 생성하는데 일반적으로 사용되는 방법으로 최근에는 일반화선형모형에 기초한 확률적 날씨 발생 방법이 제안되었다. 본 논문에서는 서울지역의 일일 기온을 모형화하하기 위해서 일반화선형모형에 기초한 확률적 날씨 발생기를 고려하였다. 이 모형에서는 계절성을 나타내는 변수와 강우발생 유무가 공변수로 사용되었다. 일반적으로 확률적 날씨 발생기에서는 생성된 일일 날씨가 월별 또는 계절별 총강우량이나 평균온도에 충분한 변동을 만들어 내지 못하는 과대산포 현상이 발생하는데, 이러한 한계를 극복하기 위해 본 연구에서는 평활된 계절별 평균 온도를 일반화선형모형의 공변수로 추가하였다. 그리고 제안된 모형을 1961년부터 2011년까지 51년 동안의 서울지역 일일 평균 기온자료에 적용하였다. Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

Bias Correction of Regional Climate Model Simulations in a Region of Complex Orography

AbstractThis study presents a method to correct regional climate model (RCM) outputs using observations from automatic weather stations. The correction applies a nonlinear procedure, which recently appeared in the literature, to both precipitation and temperature on a monthly basis in a region of complex orography. To assess the temporal stability of such a correction, the correcting parameters of each variable are investigated using different time periods within the observational record. The RCM simulations used in this study to evaluate the bias-correction method are the publicly available “Reg-CM3” experiments from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project. They provide daily precipitation and temperature time series on a raster with spatial resolution of 0.22°. The analysis is performed in the Rhone catchment, located in southwestern Switzerland and characterized by highly complex orography. The results show that the nonlinear bias correction increases dramatically the accuracy not only of the RCM mean daily precipitation and temperature but also of values across the entire domain of the probability distribution. Moreover, the correction parameters seem to be reasonably independent from the sample used for their calibration, especially in the case of temperature. The good performance of the method over the considered mountainous region during the evaluation period points to the suitability of this technique for correcting RCM biases regardless of the stationarity of the climate and, therefore, also for future climate and in regions characterized by marked orography.

Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena.