Climate Time Series Research Articles

A Novel Approach for Temperature Forecasting in Climate Change Using Ensemble Decomposition of Time Series

AbstractThis paper presents FORSEER (Forecasting by Selective Ensemble Estimation and Reconstruction), a novel methodology designed to address temperature forecasting under the challenges inherent to climate change. FORSEER integrates decomposition, forecasting, and ensemble methods within a modular framework. This methodology decomposes the time series into trend, seasonal, and residual components. Subsequently, multiple optimized forecast models are applied to each component. These component models are then carefully weighted and combined through an ensemble process to generate a final robust forecast. Experimental results demonstrate that FORSEER is an efficient computational forecasting methodology for complex climate time series. Furthermore, we show that FORSEER has an equivalent forecasting performance to the M4 competition champion SMYL method for temperature series. Besides, the proposed methodology has less computational complexity than SMYL, making it a more accessible and scalable option. FORSEER's modular architecture also allows flexibility when substituting techniques depending on the context of the problem, facilitating the parallel execution of independent tasks and resulting in a strategy adaptable to multiple contexts.

Univariate and multivariate imputation methods evaluation for reconstructing climate time series data: A case study of Mosul station-Iraq

Comprehensive climate time series data is indispensable for monitoring the impacts of climate change. However, observational datasets often suffer from data gaps within their time series, necessitating imputation to ensure dataset integrity for further analysis. This study evaluated six univariate and multivariate imputation methods to infill missing values. These methods were applied to complete the subsets of time series data for precipitation, temperature, and relative humidity from Mosul station spanning 1980–2013. Artificial gaps of 5%, 10%, 20%, and 30% missing observations were introduced under scenarios of missing completely at random (MCAR) missing at random (MAR), and missing not at random (MNAR). Evaluation metrics including RMSE and Kling-Gupta Efficiency were utilized for performance evaluation. Results revealed that seasonal decomposition was the most effective univariate imputation method across all variables. For the multivariate imputation, kNN demonstrated superior performance in infilling the precipitation missing data under MCAR, while norm.predict exhibited optimal performance in the temperature missing data under all missing scenarios. Moreover, missForest was identified as the most suitable method for infilling missing relative humidity data. This study's methodology offers insights into selecting appropriate imputation methods for other climate stations, thereby enhancing the accuracy of the climate change effects analysis.

ANOVA (Benova) correction in relative homogenization: Why it is indispensable

AbstractThis paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend‐like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided.

Relative Homogenization of Climatic Time Series

Homogenization of the time series of observed climatic data aims to remove non-climatic biases caused by technical changes during the history of the climate observations. The spatial redundancy of climate information helps to recognize station-specific inhomogeneities with statistical methods, but the correct detection and removal of inhomogeneity biases is generally not easy for the combined effects of individual inhomogeneities. In a homogenization procedure, several time series of a given climatic variable observed in one climatic region are usually homogenized together via a large number of spatial comparisons between them. Such procedures are called relative homogenization. A relative homogenization procedure may include one or more homogenization cycles where a cycle includes the steps of time series comparison, inhomogeneity detection and corrections for inhomogeneities, and they may include other steps like the filtering of outlier values or spatial interpolations for infilling data gaps. Relative homogenization methods differ according to the number and content of the individual homogenization cycles, the procedure for the time series comparisons, the statistical inhomogeneity detection method, the way of the inhomogeneity bias removal, among other specifics. Efficient homogenization needs the use of tested statistical methods to be included in partly or fully automated homogenization procedures. Due to the large number and high variety of homogenization experiments fulfilled in the Spanish MULTITEST project (2015–2017), its method comparison test results are still the most informative about the efficiencies of homogenization methods in use. This study presents a brief review of the advances in relative homogenization, recalls some key results of the MULTITEST project, and analyzes some theoretical aspects of successful homogenization.

The Hawai‘i Climate Data Portal (HCDP)

Abstract The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.

Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.

Modeling deficit irrigation water demand of maize and potato in Eastern Germany using ERA5-Land reanalysis climate time series

Modeling deficit irrigation water demand of maize and potato in Eastern Germany using ERA5-Land reanalysis climate time series

Effects of major storms over a spit-tidal flat interaction system (Punta Rasa-Samborombón, Argentina)

Sandy coastal areas are very dynamic systems in which morphological changes occur over different time scales from hours to decades. However, it has been widely reported that major storms are the main responsible of the most significant changes in short to medium time scales. Major storms have been defined using a variety of environmental variables, but they are normally associated with high values of wave height, duration, return period and direction.Here, we aim to characterize types of major storms and to categorize associated morphological impacts over a complex coastal system. The study site, known as Punta Rasa, is located in the Samborombón bay in the outer part of the Río de La Plata estuary (Argentina) and corresponds to a zone of interaction between a large sandy spit and a backwash tidal flat system. Methods combine statistics of wave climate time-series, analysis of wave energy using nearshore numerical modelling (SWAN) and comparison of pre- and post-storm morphological changes by means of shoreline change detection and satellite images derived indexes (CoastSat Toolkit and NDWI index respectively).Results allowed to characterize four types of major storms impacting the study area: High-Energy Storms (HES), defined by an average storm wave below the 1 % exceedance (>2.6 m), Long-Lived Storms (LLS) represented by an exceedance of the 1 % of Du (>60 h), Storm Groups (SG) in which storm return period is <6 days and Northeastern moderate storms (NMS) defined by their eastern, onshore oriented direction. Under HES and NMS storms erosional areas are dominant over depositional, causing shoreline retreat, a growth of the end-spit and the increase on sand deposition on the back-barrier areas. Under LLS and SG storms, the morphological impact varies alongshore, with multiple erosional hotspots found along the shoreline accompanied by a general flattens of the end-spit system.

Developing spatially explicit and stochastic measures of ecological departure

Background Ecological departure is a metric applied to mapped ecological systems measuring dissimilarity between the distributions of observed and expected proportions of non-stochastic reference vegetation classes within an area. Aims We created spatially explicit measures of ecological departure incorporating stochasticity for each ecological system and all ecological systems from a central Nevada, USA, landscape. Methods Spatially explicit ecological departures were estimated from a radius from each pixel governed by a distance-decay function within a moving window. Variability was introduced by simulating replicate climate time series for each spatial reference condition and calculating departure per replicate. Key results Single-system spatial ecological departure was high and extensive, except for one area of low-elevation groundwater-dependent systems. Variance of spatial ecological departure was extensively low, except in areas of lower ecological departure, despite vegetation differences among replicates. The multiple-system ecological departure exhibited lower values. Conclusions Spatial ecological departure is warranted for efficient land management as results were concordant between non-spatial and spatial metrics; however, rapid coding languages will be required. Implications Spatially explicit ecological departure of both single and multiple systems facilitate localised vegetation and wildlife habitat management and land protection decisions.

ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data

Statistical downscaling of climate data has been widely described in the literature, with the aim of improving the reliability of local climatic parameters from coarse-resolution (often &gt;20 km) global datasets. In this article, we present ClimateDT, a dynamic downscaling web tool for monthly historical and future time series at a global scale. The core of ClimateDT is the 1 km 1981–2010 climatology from CHELSA Climate (version 2.1), where the CRU-TS layers for the period 1901-current are overlayed to generate a historic time series. ClimateDT also provides future scenarios from CMIP5 using UKCP18 projections (rcp2.6 and rcp8.5) and CMIP6 using 5 GCMs, also available on the CHELSA website. The system can downscale the grids using a dynamic approach (scale-free) by computing a local environmental lapse rate for each location as an adjustment for spatial interpolation. Local predictions of temperature and precipitation obtained by ClimateDT were compared with climate time series assembled from 12,000 meteorological stations, and the Mean Absolute Error (MAE) and the explained variance (R2) were used as indicators of performance. The average MAEs for monthly values on the whole temporal scale (1901–2022) were around 1.26 °C for the maximum monthly temperature, 0.80 °C for the average monthly temperature, and 1.32 °C for the minimum monthly temperature. Regarding monthly total precipitation, the average MAE was 19 mm. As for the proportion of variance explained, average R2 values were always greater than 0.95 for temperatures and around 0.70 for precipitation due to the different degrees of temporal autocorrelation of precipitation data across time and space, which makes the estimation more complex. The elevation adjustment resulted in very accurate estimates in mountainous regions and areas with complex topography and substantially improved the local climatic parameter estimations in the downscaling process. Since its first release in November 2022, more than 1300 submissions have been processed. It takes less than 2 min to calculate 45 locations and around 8 min for the full dataset (512 records).

A statistical method for the attribution of change‐points in segmented Integrated Water Vapor difference time series

AbstractMany segmentation or change‐point detection methods for homogenizing climate time series compare candidate station data with reference data to eliminate common climate signals and more efficiently detect spurious, non‐climatic changes. One drawback is that it is difficult to decide whether the detected change‐point is due to the candidate series or to the reference. A so‐called attribution procedure is typically applied in a post‐processing step for each detected change‐point. This article describes a new statistical method for the attribution of change‐points detected in Global Navigation Satellite System (GNSS) minus reanalysis series of integrated water vapour. It requires at least one nearby station with similar GNSS and reanalysis data. Six series of differences are formed from the four base series (BS) and are tested for a significant jump at the time of the change‐point detected in the candidate station. The six test results are analysed with a statistical predictive rule to attribute the change‐point to one, or several, of the four BS. Original aspects of our method are: (1) the significance test, which is based on a generalized linear regression approach, taking both heteroscedasticity and autocorrelation into account; (2) the predictive rule, which uses a machine learning method and is constructed from the test results obtained with the real data by using a resampling strategy. Four popular machine learning methods have been compared using cross‐validation and the best one was applied to a real data set (49 main stations with 114 change‐points). The results depend on the choice of the test significance level and the aggregation method combining the prediction results when several nearby stations are available. We find that 62% of the change‐points are attributed to GNSS, 19% to the reanalysis, and 10% are due to coincident detections.

Improved estimation of semiparametric dynamic copula models with filtered nonstationarity

This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component and study estimation of the copula model. Using the rescaled empirical distribution of the filtered data as an estimator of the marginal distribution, Chen et al. (2022) proposed a simple, yet flexible, two-step estimation procedure for the copula model. The two-step estimator works well when the tail dependence is small. However, simulations reveal that the two-step estimator may be biased in finite samples in the presence of tail dependence. To improve the performance of short-term dynamic analysis in the presence of tail dependence, we propose in this paper a pseudo sieve maximum likelihood (PSML) procedure to jointly estimate the residual copula parameter and the invariant density of the filtered residuals. We establish the root-n consistency and asymptotic distribution of the PSML estimator of any smooth functional of the residual copula parameter and invariant residual density. We further show that the PSML estimator of the residual copula parameter is asymptotically normal, with the limiting distribution independent of the filtration. Simulations reveal that in the presence of strong tail dependence, compared to the two-step estimates of Chen et al. (2022), the proposed PSML estimates have smaller biases and smaller mean squared errors even in small samples. Applications to nonstationary macro-finance and climate time series are presented.

Hazard Analysis and Vulnerability Assessment of Cultural Landscapes Exposed to Climate Change-Related Extreme Events: A Case Study of Wachau (Austria)

The present paper aims to study the Wachau Valley in Austria as a representative Cultural Landscape under threat from extreme hydrometeorological hazards linked to climate change. The primary objective is to investigate the impacts and assess the vulnerability associated with the events of heavy rain and flooding. The methodology employed consists of an investigation of recorded past events impacting the Wachau; a vulnerability ranking system; a climate time series analysis based on earth observation products; and future hazard maps at territorial level, developed with outputs from regional and global climate models. The investigation we carried out provides a vulnerability assessment of two terraced areas with a surface of about 10,000 m2 in total, characterized by the presence of dry stone walls, with different state of conservation in the Municipality of Krems (Wachau). In addition, climate projections at territorial level for the extreme climate indices R20mm, R95pTOT, and R×5day—selected for investigating the likelihood of increases/decreases in events of heavy rain and large basin flooding—are provided, with a spatial resolution of ~12 km for the near and far future (2021–2050; 2071–2100) under stabilizing (RCP 4.5) and pessimistic (RCP 8.5) scenarios. The results indicate a general increase for the three indices in the studied areas during the far future under the pessimistic scenario, suggesting a heightened risk of heavy rain and flooding. These findings aim to inform policymakers and decision-makers in their development of strategies for safeguarding cultural heritage. Furthermore, they serve to assist local stakeholders in enhancing their understanding of prioritizing interventions related to preparedness, emergency response, and recovery.

Analysis of Pros and Cons in Using the Water–Energy–Food Nexus Approach to Assess Resource Security: A Review

The water–energy–food (WEF) nexus is drawing much attention in scholarly literature as a novel alternative to address complex resources and achieve resource security. The aim of this study is to analyze and review existing nexus studies to investigate the current status of nexus research worldwide. This study used a narrative review approach to provide a comprehensive overview on the WEF nexus using a variety of databases. It is indicated that the majority of studies in Asia and Africa focused on the water–energy–food (WEF) nexus. China and Brazil had the largest nexus research. Based on the existing literature, most of attention has been paid to food production. However, food consumption patterns and dietary change are rarely evaluated, and there is a lack of study on impacts of dietary change on the WEF nexus. Moreover, there is a lack of frameworks for the evaluation of the WEF nexus under dietary change scenarios. The major challenge of the nexus approach is data availability in crop production, which can be solved by using remote sensing data. There is a lack of standard and conceptual frameworks for nexus assessment and, then, an essential need to provide a new holistic and standard approach that be applicable worldwide to increase connections between researchers and decision makers, as well as the applicability of nexus approaches. Future research must couple the development of a holistic standard approach with experimental tests in different areas, involving interdisciplinary research groups able to carry out all the experimental activities, the numerical simulations, and the statistical analyses of climatic time series (in a climate change perspective) indispensable to demonstrate the real benefits of using a WEF-derived nexus approach.

Ground deformation monitoring via PS-InSAR time series: An industrial zone in Sacco River Valley, central Italy

Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment.

Agricultural Yield Responses to Climate Variabilities in West Africa: A Food Supply and Demand Analysis

Agricultural productivity is expected to decrease under changing climate conditions that correspond to the stability of West African food systems. Although numerous studies have evaluated impacts of climate variability on crop yields, many uncertainties are still associated with climate extremes as well as the rapid population growth and corresponding dietary lifestyle. Here, we present a food supply and demand analysis based on the relationship between climate change, crop production, and population growth in three sites from southwestern Burkina Faso to southwestern Ghana. Climate and agricultural time series were analyzed by using boxplots mixed with a Mann–Kendall trend test and Sen’s slope. Food balance sheets were calculated by estimating the demand using a population growth model linked to food supply with local consumption patterns. We found almost insignificant rainfall and temperature trends for both sites in the Sudano-Guinean savannah. Conversely, the climate regime of southwestern Ghana revealed a strong significant increasing temperature over time. Crop yield trends demonstrated that maize and sorghum were significantly enhanced in both study areas of the Sudano-Guinean savannah. Southwestern Ghana depicted a different crop pattern where cassava and plantain showed a strong upward yield trend. The grouped food balance sheets across the regions illustrated a surplus for the Sudano-Guinean savannah while southwestern Ghana exhibited a deficit. Despite the growing yield of various crops, food demand is outpacing regional production.

A Comparison of the Rank-based and Slope-based Nonparametric Tests for Trend Detection in Climate Time Series

Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametric approaches. Nonparametric trend detection methods are often considered more preferable than parametric methods in certain situations due to their flexibility and robustness. Comparing various nonparametric methods of trend detection is vital in data analysis because different techniques can yield divergent results based on the same dataset. In this study, three nonparametric trend tests which were the MannKendall (MK), Sen’s Innovative Trend Analysis (ITA) and Modified Mann-Kendall by Sen’s Innovative Trend Analysis (MMK_ITA) were compared based on their power. The MK test is a rank-based test and the ITA is a slope-based test. Meanwhile, the combination of rank-based and slope-based methods is known as the MMK_ITA test. The power analysis was conducted through Monte Carlo simulation on normal, non-normal and autocorrelated time series. The simulation results indicated that test power relied on magnitude of linear trend slope, sample sizes, distribution type and variation in time series. These tests were then applied to monthly maximum temperature from 2002 until 2021 for Selangor, Malaysia. This study found that the slope-based test performed better compared to the rank-based test and their combined methods from the simulation studies and real data application based on the calculated power.

Comparison of climate time series – Part 5: Multivariate annual cycles

Abstract. This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis.

Rainfall trend analysis using the Mann-Kendall test with pyMannKendall: A case study of Jeli, Kelantan

Trend analysis was widely used as a tool to detect changes in climatic and hydrological time series data such as rainfall. Therefore, this study aims to analyse the rainfall trends in Jeli, Kelantan from 2009 to 2020 Simple linear regression was used to impute missing data in all rainfall stations. The rainfall trends in time series were tested using the Mann- Kendall test and Sens’ slope methods. This analysis was analysed using Python with pyMannKendall package. Kg. Jeli exhibited a statistically significant decreasing trend in rainfall, with a Kendall’s Tau of -0.1412 and a Sen’s slope of -0.0272 (p-value = 0.0121). However, no statistically significant rainfall trends were observed in the other three rainfall stations: Ldg. Kuala Balah, Kg. Gemang Bahru, and Kg. Ayer Lanas during the study period. Generally, this study concludes that the flood event was caused by high rainfall intensity.

Combining Seasonal and Trend Decomposition Using LOESS With a Gated Recurrent Unit for Climate Time Series Forecasting

Combining Seasonal and Trend Decomposition Using LOESS With a Gated Recurrent Unit for Climate Time Series Forecasting