Daily Temperature Time Series Research Articles

A Resampling Approach for Correcting Systematic Spatiotemporal Biases for Multiple Variables in a Changing Climate

AbstractSystematic biases in climate model simulations are commonly addressed using univariate bias correction algorithms that involve matching of mean, variance, and quantiles. These approaches work well for a single variable and location and effectively mimic the observed temporal structure in the corrected series. The intervariable, interspace, and high‐ or low‐frequency temporal dependencies that characterize observed hydrological records are often left untouched and lead to substantial biases in applications such as catchment modeling where their correct representation is critical. In the approach presented here, changes in the dependence attributes are ascertained by resampling of the historical ranks into what these might resemble in the future. The proposed approach is not limited in terms of the number of variables, grid points in space, and the time scale considered. Most importantly, it maintains the shift in dependence and other attributes between the current and the future climate as ascertained by a climate model. The approach is illustrated using daily time series of temperature, precipitation, relative humidity, and wind speed simulated by a regional climate model at 8,910 grid points over Australia. Spatial, temporal, and cross‐variable dependence attributes of the corrected simulations at daily and aggregated time scales are compared against quantile mapping and substantial improvements in performance identified. Resampling of corrected ranks offers a very simple, flexible, and effective general purpose multivariate, multitime, and multilocation bias correction alternative for current and future climate. As the approach works in three dimensions, space, time, and variables, it is denoted as 3DBC, or three‐dimensional bias correction.

A periodic mixed linear state‐space model to monthly long‐term temperature data

AbstractIn recent decades, the world has been confronted with the consequences of global warming; however, this phenomenon is not reflected equally in every part of the globe. Thus, the warming phenomenon must be monitored in a more regional or local scale. This paper analyzes monthly long‐term time series of air temperatures in three Portuguese cities: Lisbon, Oporto, and Coimbra. We propose a periodic state‐space framework, associated with a suitable version of the Kalman filter; which allows for the estimation of monthly warming rates taking into account the seasonal behavior and serial correlation. Results about the monthly mean of the daily midrange temperature time series show that there are different monthly warming rates. The greatest annual mean rise was found in Oporto with 2.17°C, whereas in Lisbon and Coimbra, it was respectively 0.62°C and 0.55°C per century.

A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278. https://doi.org/10.1007/s00382-016-3443-6 , 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32. https://doi.org/10.1002/joc.3402 , 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.

Exploring the financial risk of a temperature index: a fractional integrated approach

This paper introduces a new temperature index, which can suitably represent the underlying of a weather derivative. Such an index is defined as the weighted mean of daily average temperatures measured in different locations. It may be used to hedge volumetric risk, that is the effect of unexpected fluctuations in the demand/supply for some specific commodities—of agricultural or energy type, for example—due to unfavorable temperature conditions. We aim at exploring the long term memory property of the volatility of such an index, in order to assess whether there exist some long-run paths and regularities in its riskiness. The theoretical part of the paper proceeds in a stepwise form: first, the daily average temperatures are modeled through autoregressive dynamics with seasonality in mean and volatility; second, the assessment of the distributional hypotheses on the parameters of the model is carried out for analyzing the long term memory property of the volatility of the index. The theoretical results suggest that the single terms of the index drive the long memory of the overall aggregation; moreover, interestingly, the proper selection of the parameters of the model might lead both to cases of persistence and antipersistence. The applied part of the paper provides some insights on the behaviour of the volatility of the proposed index, which is built starting from single daily average temperature time series.

Monitoring observed changes in warm-days extremes over Turkey

This study aimed to analyze warm-days changes extracted from daily maximum temperature time-series of 71 stations in Turkey during 1961–2016. First, the trend analysis of warm-days events indicated that the annual count of warm-days occurrences has been significance rising by a rate of 1.4 days per decade over more than 90 percent of the studied stations. Thus, there are consistent patterns in daily warming throughout the study area. The spatial maps showed that the lowest frequency in the minimum annual number of warm-days occurred in western areas by a total number of 21 days and its highest occurred in the eastern area by a total number of 35 days. Moreover, the highest frequency in the mean and minimum annual count of warm-days observed in the northwestern lowlands and their frequency has decreased toward the eastern mountainous. Also, the highest range of differences between the annual count of maximum and minimum values has happened in the eastern regions which are characterized by high elevation and the lower existence of atmospheric humidity. Therefore, a higher moisture content of the atmosphere in lowland coastal stations favors severe warm-days and the increased risk of human health.

Quantifying excess deaths related to heatwaves under climate change scenarios: A multicountry time series modelling study.

BackgroundHeatwaves are a critical public health problem. There will be an increase in the frequency and severity of heatwaves under changing climate. However, evidence about the impacts of climate change on heatwave-related mortality at a global scale is limited.Methods and findingsWe collected historical daily time series of mean temperature and mortality for all causes or nonexternal causes, in periods ranging from January 1, 1984, to December 31, 2015, in 412 communities within 20 countries/regions. We estimated heatwave–mortality associations through a two-stage time series design. Current and future daily mean temperature series were projected under four scenarios of greenhouse gas emissions from 1971–2099, with five general circulation models. We projected excess mortality in relation to heatwaves in the future under each scenario of greenhouse gas emissions, with two assumptions for adaptation (no adaptation and hypothetical adaptation) and three scenarios of population change (high variant, median variant, and low variant). Results show that, if there is no adaptation, heatwave-related excess mortality is expected to increase the most in tropical and subtropical countries/regions (close to the equator), while European countries and the United States will have smaller percent increases in heatwave-related excess mortality. The higher the population variant and the greenhouse gas emissions, the higher the increase of heatwave-related excess mortality in the future. The changes in 2031–2080 compared with 1971–2020 range from approximately 2,000% in Colombia to 150% in Moldova under the highest emission scenario and high-variant population scenario, without any adaptation. If we considered hypothetical adaptation to future climate, under high-variant population scenario and all scenarios of greenhouse gas emissions, the heatwave-related excess mortality is expected to still increase across all the countries/regions except Moldova and Japan. However, the increase would be much smaller than the no adaptation scenario. The simple assumptions with respect to adaptation as follows: no adaptation and hypothetical adaptation results in some uncertainties of projections.ConclusionsThis study provides a comprehensive characterisation of future heatwave-related excess mortality across various regions and under alternative scenarios of greenhouse gas emissions, different assumptions of adaptation, and different scenarios of population change. The projections can help decision makers in planning adaptation and mitigation strategies for climate change.

Estimation of Future Probable Maximum Precipitation in Korea Using Multiple Regional Climate Models

In this study, future probable maximum precipitations (PMPs) based on future meteorological variables produced from three regional climate models (RCMs) of 50-km spatial resolution provided by Coordinated Regional Climate Downscaling Experiment (CORDEX) are projected. In order to estimate future PMPs, the hydro-meteorological method is applied. The key future meteorological variable used to analyze the rate of change of future PMPs is the dew-point temperature. Future 12-h persistence 100-year return period extreme dew-point temperatures obtained from future daily dew-point temperature time series by using the scale-invariance method are applied to estimate future PMPs. As a result of estimating future PMPs using several RCMs and representative concentration pathways (RCPs) scenarios, the spatial distribution of future PMPs is expected to be similar to that of the present, but PMPs tend to increase in the future. In addition, it can be seen that the difference in PMPs estimated from various RCMs and RCP scenarios is getting bigger in the future. Especially after 2070, the difference has increased even more. In the short term, it is proposed to establish climate change adaptation policies with an 18% increase in PMPs, which is the ensemble average in the future year 2050.

Estimation of the Source Apportionment of Phosphorus and Its Responses to Future Climate Changes Using Multi-Model Applications

The eutrophication issue in the Yangtze Basin was considered, and the phosphorus loads from its tributary, the Modaoxi River, were estimated. The phosphorus flux and source apportionment of the Modaoxi River watershed were modeled and quantified, and their changes with respect to future projected climate scenarios were simulated with multiple model applications. The Regional Nutrient Management (ReNuMa) model based on Generalized Watershed Loading Functions (GWLF) was employed as a tool to model the hydrochemical processes of the watershed and thereby estimate the monthly streamflow and the phosphorus flux as well as its source apportionment. The Long Ashton Research Station Weather Generator (LARS-WG) was used to predict future daily weather data through the statistical downscaling of the general circulation model (GCM) outputs based on projected climate scenarios. The synthetic time series of daily precipitation and temperatures generated by LARS-WG were further used as input data for ReNuMa to estimate the responses of the watershed hydrochemical processes to future changed climate conditions. The results showed that both models could be successfully applied and that the future wetter and warmer climate trends would have generally positive impacts on the watershed phosphorus yields, with greater contributions coming from runoff. These results could provide valuable support for local water environmental management.

Investigating dynamical features in the long-term daily maximum temperature time series recorded at Adrián Jara, Paraguay

In this study, we analyse the daily series of maximum temperature measured at Adrian Jara, Paraguay, to disclose features possibly linked with climatological indices that are thought to regulate, in general, the climatic variability of South America. This station is well suited to perform this investigation because it is located in a rural not urbanized area. Our findings reveal that the analyzed series is modulated by several cycles: the annual and half-annual cycle; the 8-year cycle that could be driven by 8-year cycle of the baroclinic waves at the Southern Hemisphere; the 2–3-year periodicity, which seems linked with the Pacific Decadal Oscillation; the 15–16 years periodicity that seems to be induced by the low-frequency fluctuations of SST in the North Atlantic. It is also found an apparent shift in the maximum temperature in the late 1989 that seems to be in agreement with similar findings by other studies that documented such potential jump in the climate system.

Evaluation of multisite synthetic data generated by spatial weather generator and long climate data series

In this paper a new validation test for the spatial weather generator SWGEN producing the multisite daily time series of solar radiation, temperature and precipitation is presented. The method was tested by comparing statistics of 1000 years of generated data with extra long series of 35 years of observed weather parameters and 24 sites of meteorological stations for south-west Poland. The method evaluation showed that the means (sums) and variances of generated data were comparable with observed climatic data aggregated for months, seasons and years.

Modeling Future Projections of Temperature-Related Excess Morbidity Due to Infectious Gastroenteritis Under Climate Change Conditions in Japan

Background: Climate change has marked implications for the burden of infectious diseases. We examined temperature-infectious gastroenteritis associations throughout Japan and projected temperature-related morbidity concomitant with climate change for the 2090s. Methods: Daily time series of average temperature and morbidity for infectious gastroenteritis cases in 2005-2015 were collated from the 47 Japanese prefectures. A two-stage time series analysis was adopted to estimate temperature-infectious gastroenteritis relationships. Time series of present and future daily average temperature fluctuations were projected for the four climate change scenarios of representative concentration pathways (RCPs) according to five general circulation models. Excess morbidity for high and low temperatures and the net change in 1990-2099 were projected for each climate change scenario by assuming the absence of adaptation and population alterations. Findings: In 2005-2015, 11,529,833 infectious gastroenteritis cases were reported. There were net reductions in temperature-induced excess morbidity under higher emission scenarios. The net change in 2090-2099 compared to 2010-2019 was -0.3% (95% empirical confidence interval [eCI]: -1.5-0.7) for RCP2.6, -2.4% (95% eCI: -4.7--0.5) for RCP4.5, -3.5% (95% eCI: -6.7-0.1) for RCP6.0, and -6.7% (95% eCI: -11.5--2.9) for RCP8.5. Spatial heterogeneity in the temperature-morbidity relationship was observed among prefectures (Cochran Q test, p<0.001; I2=96.1%). Interpretation: Japan may experience a net reduction in temperature-related excess morbidity due to infectious gastroenteritis in higher emission scenarios. Further projections of specific pathogen-induced infectious gastroenteritis due to climate change are warranted. Funding Statement: This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 15K08714, 16H05247, and 18K11666; and the Medical Research Council UK (Grant ID: MR/M022625/1). Declaration of Interests: The authors have declared that no competing interests exist. Ethics Approval Statement: The contents of this study were approved by the Ethics Committee of the Kyushu University Graduate School of Medical Sciences. Given that this was a retrospective observational study that used national surveillance data and the enrolled subjects were deidentified, there was no need to acquire informed consent.

A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature

Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs), to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD) of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels) between April and August (2015–2016). We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%), but a portion of them showed one or more shifts among states (17%). We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature.

Detecting soil temperature trends in Northeast Iran from 1993 to 2016

Soil temperature is a fundamental agrometeorological variable which has a measureable influence on the development of plants. A comprehensive analysis of daily soil temperature trends at predetermined depths can provide a quantitative understanding for the sustainable management of agricultural systems, such as mitigating the adverse effects of anticipated temperature stress, supporting confidence levels of ideal seed sowing dates, as well as irrigation treatment durations and intervals. In this paper, daily soil temperature trends were analyzed (using the Mann-Kendall test) at depths of 5, 10, 20, 30, 50 and 100cm at seven discrete meteorological stations located throughout northeastern Iran during a consecutive period from 1993 to 2016. The results of this research indicate that in most of the study stations there exists a non-significant negative trend in soil temperature in the first half of January and also in the second half of December; these trends become significant as the soil gets deeper. For the warmer months of the year, especially from April to the end of August, soil temperature trends are positive and significant (or near significant) and these positive trends are of greater significance with a corresponding increase in soil depth. Agri-environmental agencies and practitioners may benefit from the approach used in this research (i.e. using the Mann-Kendall test to analyze trends in daily soil temperature time series) since it is an easily transferable approach that can provide a comprehensive geospatial and temporal analysis of crop and plant patterns in different regions and environmental conditions. Additionally, farmers may use daily soil temperature trends as a means to encourage the protection and mitigation of a variety of projected environmental stresses, such as water scarcity, irrigation demands, seasonal temperature fluctuations, and redundant crop rotation.

Extreme maximum temperature events and their relationships with large-scale modes: potential hazard on the Iberian Peninsula

The aim of this paper is to analyze spatiotemporal distribution of maximum temperatures in the Iberian Peninsula (IP) by using various extreme maximum temperature indices. Thresholds for determining temperature extreme event (TEE) severity are defined using 99th percentiles of daily temperature time series for the period 1948 to 2009. The synoptic-scale fields of such events were analyzed in order to better understand the related atmospheric processes. The results indicate that the regions with a higher risk of maximum temperatures are located in the river valleys of southwest and northeast of the IP, while the Cantabrian coast and mountain ranges are characterized by lower risk. The TEEs were classified, by means of several synoptic fields (sea level pressure, temperature, and geopotential height at 850 and 500 hPa), in four clusters that largely explain their spatiotemporal distribution on the IP. The results of this study show that TEEs mainly occur associated with a ridge elongated from Subtropical areas. The relationships of TEEs with teleconnection patterns, such as the North Atlantic Oscillation (NAO), Western Mediterranean Oscillation (WeMO), and Mediterranean Oscillation (MO), showed that the interannual variability of extreme maximum temperatures is largely controlled by the dominant phase of WeMO in all seasons except wintertime where NAO is prevailing. Results related to MO pattern show less relevance in the maximum temperatures variability. The correct identification of synoptic patterns linked with the most extreme temperature event associated with each cluster will assist the prediction of events that can pose a natural hazard, thereby providing useful information for decision making and warning systems.

Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models

Future climate forcing data at the temporal and spatial scales needed to drive hydrologic models are not readily available. Simple methods to derive these data from historical data or General Circulation Model (GCM) results may not adequately capture future hydrological variability. This study assessed streamflow response to daily future climate forcing data produced by a new method using subsets of multi-model GCM ensembles for the mid-21st century period in northeast Kansas. Daily timeseries of precipitation and temperature were developed for six future climate scenarios: stationary, uniform 10% changes in precipitation; shifts based on a 15-GCM ensemble-mean; and shifts based on three seasonally-consistent subsets of GCMs representing Spring–Summer combinations that were wetter or drier than the historical period. The analysis of daily streamflow and hydrologic index statistics were conducted. Stationary 10% precipitation shifts generally bounded the monthly mean streamflow projections of the other scenarios, and the 15-GCM ensemble-mean captured non-stationary effects of annual and seasonal hydrological response, but did not identify important intra-annual shifts in drought and flood characteristics. The seasonally-consistent subset ensembles produced a range of distinct monthly streamflow trends, particularly for extreme low-flow and high-flow events. Meaningful water management and planning for the future will require hydrological impact simulations that reflect the range of possible future climates. Use of GCM ensemble-mean climate forcing data without consideration of the range of seasonal patterns among models was demonstrated to remove important seasonal hydrologic patterns that were retained in the subset ensemble-mean approach.

Analysis of long-term variation of the annual number of warmer and colder days using Mahalanobis distance metrics — A case study for Athens

According to contemporary observations and analyses an increase in the number of warm days is evident, associated to global warming and the air temperature increase. At the same time, many questions regarding the variation of the yearly number of warm (and cold) day anomalies remain unanswered.In the present work, we used a 99-year long time series of daily maximum air temperatures from the historical, climatic station in Athens, Greece, to produce data sets of sequences of the yearly number of days with anomalies significantly larger (warmer)/lower (colder), with respect to the long-term mean anomalies for the same days. The main goal was to introduce and test the appropriateness of our approach, in assessing changes of the local climate system during last century. This approach is based on the Mahalanobis distance metrics of the variability of the annual number of warmer and colder days derived from the long period data sets of the daily max air temperatures.We show that in Athens the features of the local climate system, assessed by the variability of warmer and colder days, have significantly changed during the observation period.In particular, essential changes are revealed in the temporal correlations of the variability of the annual number of warmer and colder days, during different sub-periods in the past century. Greater changes are found in the last two to three decades, though similar or even stronger changes have also occurred in the past.

Multifractal scaling properties of daily air temperature time series

The aim of the current research study is to examine the scaling properties of the mean daily, maximum and minimum air temperature time series of a single coastal site, located at the island of Crete in Greece. The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the time series long-term correlation and the singularity spectrum to estimate the multifractality degree. The analysis reveals that the daily temperature time series exhibit a multifractal behavior, are positive long-term correlated and that their multifractal structure is insensitive to local fluctuations with large magnitudes.

Warming trends of perialpine lakes from homogenised time series of historical satellite and in-situ data

The availability of more than thirty years of historical satellite data is a valuable source which could be used as an alternative to the sparse in-situ data. We developed a new homogenised time series of daily day time Lake Surface Water Temperature (LSWT) over the last thirty years (1986–2015) at a spatial resolution of 1km from thirteen polar orbiting satellites. The new homogenisation procedure implemented in this study corrects for the different acquisition times of the satellites standardizing the derived LSWT to 12:00 UTC. In this study, we developed new time series of LSWT for five large lakes in Italy and evaluated the product with in-situ data from the respective lakes. Furthermore, we estimated the long-term annual and summer trends, the temporal coherence of mean LSWT between the lakes, and studied the intra-annual variations and long-term trends from the newly developed LSWT time series. We found a regional warming trend at a rate of 0.017°Cyr−1 annually and 0.032°Cyr−1 during summer. Mean annual and summer LSWT temporal patterns in these lakes were found to be highly coherent. Amidst the reported rapid warming of lakes globally, it is important to understand the long-term variations of surface temperature at a regional scale. This study contributes a new method to derive long-term accurate LSWT for lakes with sparse in-situ data thereby facilitating understanding of regional level changes in lake's surface temperature.

Climate projections in the Hornsund area, Southern Spitsbergen

Abstract The aim of this study was to provide an estimation of climate variability in the Hornsund area in Southern Spitsbergen in the period 1976-2100. The climatic variables were obtained from the Polar-CORDEX initiative in the form of time series of daily air temperature and precipitation derived from four global circulation models (GCMs) following representative concentration pathways (RCP) RCP 4.5 and RCP 8.5 emission scenarios. In the first stage of the analysis, simulations for the reference period from 1979 to 2005 were compared with observations at the Polish Polar Station Hornsund from the same period of time. In the second step, climatic projections were derived and monthly and annual means/sums were analysed as climatic indices. Following the standard methods of trend analysis, the changes of these indices over three time periods - the reference period 1976-2005, the near-future period 2021-2050, and far-future period 2071-2100 - were examined. The projections of air temperature were consistent. All analysed climate models simulated an increase of air temperature with time. Analyses of changes at a monthly scale indicated that the largest increases were estimated for winter months (more than 11°C for the far future using the RCP 8.5 scenario). The analyses of monthly and annual sums of precipitation also indicated increasing tendencies for changes with time, with the differences between mean monthly sums of precipitation for the near future and the reference period similar for each months. In the case of changes between far future and reference periods, the highest increases were projected for the winter months.

Regional impacts of global change: seasonal trends in extreme rainfall, run-off and temperature in two contrasting regions of Morocco

Abstract. In Morocco, socio-economic activities are highly vulnerable to extreme weather events. This study investigates trends in mean and extreme rainfall, run-off and temperature, as well as their relationship with large-scale atmospheric circulation. It focuses on two Moroccan watersheds: the subhumid climate region of Bouregreg in the north and the semi-arid region of Tensift in the south, using data from 1977 to 2003. The study is based on a set of daily temperature, precipitation and run-off time series retrieved from weather stations in the two regions. Results do not show a homogeneous behaviour in the two catchments; the influence of the large-scale atmospheric circulation is different and a clear spatial dependence of the trend analysis linked to the distance from the coast and the mountains can be observed. Overall, temperature trends are mostly positive in the studied area, while weak statistically significant trends can be identified in seasonal rainfall, extreme rainfall events, average run-off and extreme run-off events.