Homogeneous Time Series Research Articles

Improved Method to Estimating Parameters of a Poisson Hidden Markov Model Using Bayesian Approach

Expectation-Maximization (EM) is a popular method for estimating parameters of the Poisson-Hidden Markov Model (P-HMM). However, over-dispersion in comparison to the Poisson distribution remains a concern. This study developed a Bayesian method to Poisson count models. The study compares the Mean Square Errors and sufficiency of the EM to the Gibbs sampler technique using Akaike Information Criterion, Bayesian Information Criterion, and Deviance Information Criterion as model selectors. The Maximum Likelihood Estimates and Maximum a \textit{posteriori} were calculated using both simulated and real data in this case. The study's findings indicate that using any of the two approaches depends on the data type and the sample size. Whereas the Poisson Hidden Markov model, which uses the EM algorithm is preferred when using Zero-inflated data with a sample size  n ≤ 20 , the Bayesian Poisson-Hidden Markov Model, which uses a Gibbs sampler is better used for Heavy and Mixture data types irrespective of the sample size. The model predicted parameters of simulated data with remarkable accuracy and produced some unique statistical property results. This method is applicable to Poisson-hidden Markov models with homogeneous time series.

Using the Arima Method with Minitab Applications for Forecasting Work Order Projects of Casting Construction (Case Study: PT. Bumi Sarana Beton)

The ARIMA method is a non-stationary homogeneous time series model that uses the procedure for applying the Autoregressive model or scheme and the Moving Average in preparing its forecasts. The purpose of this study was to determine the application of the Autoregressive Integrated Moving Average (ARIMA) method and minitab application at PT. Bumi Sarana Beton in planning the estimated number of work orders. It can be concluded that for K-225 the best model is the ARIMA model (2,0,0) because it has the lowest MSE value and for K-400 the best model to use is the ARIMA model (2,0,2 ) because it has the smallest value. From the results of the research that has been done, it can be concluded that the production of K-225 in 2022 is 3573.50 m3, while using the ARIMA method in 2023 the total production is 3920.61 m3 and in 2024 the total production is 3573.50 m3. to 3824.36 m3. Production of K-400 in 2022 was 2015.00 m3, while using the ARIMA method in 2023 a total production of 1857.07 m3 was obtained and in 2024 a total production of 2045.89 m3 was obtained.

Air Surface Temperature Variability and Trends from Satellite Homogenized Time Series Data Over Tunis 2003–2021

Abstract Monthly Air Surface Temperature (AST) data for Tunis were acquired by the Atmospheric InfraRed Sounder (AIRS) dataset (2003–2021), the enrolments of 77 grids, each located in Spatial resolution 1°x1°, were converted into monthly and annual data and analysed. The time series of AST in Tunis were investigated for temporal and spatial trends during study period over six climate stations each having sufficient data available were utilized for this purpose. The monthly time series showed similar variations and fluctuated of AST, minimum (decreases, January) and maximum (increases, July) in trend, with standard deviation (294.15 + 14.02 K°). Most stations, appeared positive trends in their annual AST series, only at Sfax negative, with higher trends for central stations than those closer to the desert and coast. The trends were lowest over coastal area. The Comparisons among selected six stations (Tunis, Tabarka, Thala, Sfax, Medenine, and EL-Borma) between the AST observed by AIRS and in-situ data showed close agreement range from 0.38 to 3.6 K°, and approximately the same in the north-to-south transect throughout the year. The validation results plainly evident a high correlation coefficient (R, 0.995, 0.997, 0.994, 0.94, 0.974 and 0.95), and the asset values of (R2) was 0.991, 0.995, 0.987, 0.93, 0.993, and 0.977 for Tunis, Sfax, Tabarka, Thala, Medenine, and EL-Borma respectively. The satellite observation (AIRS) is able to investigate the variations of the atmosphere AST over different zones.

EI Eridani: A star under the influence

Context. Homogeneous photometric time series spanning decades provide a unique opportunity to study the long-term cyclic behavior of active spotted stars such as our target EI Eridani. In addition, with ultraprecise space photometry data, it is possible to investigate the accompanying flare activity in detail. However, the rotation period of ≈2 days for EI Eri makes it impossible to achieve time-resolved surface images from a single ground-based observing site. Therefore, for this purpose, spectroscopic data from a multi-site observing campaign are needed. Aims. We use our photometric time series of more than 40 yr to analyze the long-term behavior of EI Eri. We investigate flare activity using photometric data obtained with the Transiting Exoplanet Survey Satellite (TESS). The MUlti-SIte Continuous Spectroscopy (MUSICOS) campaign in 1998 was designed to achieve high-resolution, multi-wavelength spectroscopic observations from many sites around the globe, which meant that uninterrupted phase coverage of EI Eri became available. We use these data to reconstruct successive surface-temperature maps of the star in order to study the changes of starspots on a very short timescale. Methods. We used long-term seasonal period analysis of our photometric time series to study changes in the rotational period. We also applied short-term Fourier-transform to look for activity cycle-like changes. We also studied the phase and frequency distribution of hand-selected flares. We applied our multi-line Doppler imaging code to reconstruct four consecutive Doppler images. These images were also used to measure surface differential rotation with our cross-correlation technique. In addition, we carried out tests to demonstrate how Doppler imaging is affected by the fact that the data came from several different instruments with different spectral resolutions. Results. Seasonal period analysis of the light curve reveals a smooth, significant change in period, possibly indicating the evolution of active latitudes. Temperature curves from B − V and V − I show slight differences, indicating the activity of EI Eri is spot dominated. Short-term Fourier transform reveals smoothly changing cycles of between 4.5 and 5.5 yr and of between 8.9 and 11.6 yr. The time-resolved spotted surface of EI Eri from Doppler imaging enabled us to follow the evolution of the different surface features. Cross-correlating the consecutive Doppler maps reveals surface shear of α = 0.036 ± 0.007. Our tests validate our approach and show that the surface-temperature distribution is adequately reconstructed by our method. The tests also indicate how accurately the cross-correlation method can reproduce the surface shear as a function of the spectral resolution.

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

AbstractIn this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

A Methodology for Air Temperature Extrema Characterization Pertinent to Improving the Accuracy of Climatological Analyses

The suggested methodology for the characterization of temperature extrema presents a multistep preprocessing procedure intended to derive extrema time series of correctly identified and thermally defined daily air temperature extrema pairs. The underlying conceptual framework for this approach was developed in response to the existing gaps in the current state of daily extrema identification and the development of extrema-based synthetic air temperature time series. A code consisting of a series of algorithms was developed to establish four-parameter criteria for a more accurate representation of daily variability that allows easy replication of temperature distribution based on the correct characterization of daily temperature patterns. The first preprocessing step consists of subjecting the high-frequency temperature time series to a theoretical diurnal observing window that imposes latitudinally and seasonally crafted limits for the individual identification of daily minima and maxima. The following pre-processing step involves the supplementation of air temperature extrema with the information on the occurrence of extrema timing deemed as vital information for the reconstruction of the temperature time series. The subsequent step involves the application of an innovative temperature pattern recognition algorithm that identifies physically homogeneous air temperature populations based on the information obtained in previous steps. The last step involves the use of a metric for the assessment of extrema temperature and timing parameters’ susceptibility to climate change. The application of the presented procedure to high-frequency temperature data yields two strains of physically homogeneous extrema time series with the preserved characteristics of the overall temperature variability. In the present form, individual elements of this methodology are applicable for correcting historical sampling and air temperature averaging biases, improving the reproducibility of daily air temperature variation, and enhancing the performance of temperature index formulae based on daily temperature extrema. The objective of this analysis is the eventual implementation of the presented methodology into the practice of systematic temperature extrema identification and preprocessing of temperature time series for the configuration of physically homogeneous air temperature subpopulations.

A Modification to Phase Estimation for Distributed Scatterers in InSAR Data Stacks

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

ВИКОРИСТАННЯ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ HOMER ДЛЯ КОНТРОЛЮ ЯКОСТІ ТА ГОМОГЕНІЗАЦІЇ РЯДІВ ЩОМІСЯЧНИХ СУМ ОПАДІВ

Palamarchuk L.V., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivOsadchyi V.I., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivSkrynyk O.A., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv, National University of Bioresources and Nature Management, Kyiv Kyreieva Z.M., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv, Taras Shevchenko National University of Kyiv Sidenko V.P., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivOshurok D.O., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivSkrynyk O.Y., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv In our work, we present a digital dataset of monthly atmospheric precipitation sums collected at 177 meteorological stations and 47 precipitation posts in Ukraine during the period of 1946-2020. Quality control check and homogenization of the time series were performed by means of the HOMER software. The quality control procedure revealed 1316 anomaly values (outliers), which constitute 0.7% of the total amount of considered precipitation measurements. A significant part of the detected outliers (465) was identified as rough errors, which were corrected after analysis of original paper sources. Simultaneous use of the observation data from meteorological stations and precipitation posts allowed to improve accuracy/quality of the latter (by comparing them with corresponding measurements from the meteorological stations) and make precipitation fields more consistent. The homogenization procedure detected 265 breaks. Such breaks are usually considered as moments of time when abrupt shifts in time series evolution are happened. The main reasons for breaks are station/post relocations, replacement of measurement devices etc., namely any non-climatic factors. According to the WMO recommendations, the obtained homogenized time series can be applied to study regional climate including its variability and persistent change.

MUPHOTEN: A MUlti-band PHOtometry Tool for TElescope Network

The early and complete temporal characterization of optical, fast, transient sources requires continuous and multiband observations over different timescales (hours to months). For time-domain astronomy, using several telescopes to analyze single objects is the usual method, allowing the acquisition of highly sampled light curves. Taking a series of images each night helps to construct an uninterrupted chain of observations with a high cadence and low duty cycle. Speed is paramount, especially at early times, in order to capture early features in the light curve that help determine the nature of the observed transients and assess their astrophysical properties. However, the problem of rapidly extracting source properties (temporal and color evolution) with a heterogeneous data set remains. Consequently, we present Muphoten, a general and fast-computation photometric pipeline able to address these constraints. It is suitable for extracting transient brightness over multitelescope and multiband networks to create a single homogeneous photometric time series. We show the performance of Muphoten with observations of the optical transient SN 2018cow (from 2018 June to 2018 July), monitored by the GRANDMA network and with the publicly available data of the Liverpool Telescope.

CEEMDAN hybridized with LSTM model for forecasting monthly rainfall

The risks of severe weather events due to climate changes, including droughts and floods require accurate and timely forecasting of rainfall. But, the rainfall time series contains nonlinear and non-stationary data which lowers the model performance. This paper attempts to solve the nonlinear and non-stationary challenges imposed by the rainfall forecasting models by building a hybrid model based on complete ensemble empirical mode decomposition with Adaptive Noise(CEEMDAN) combined with long short-term memory (LSTM) for forecasting All India monthly rainfall. For monthly rainfall forecasting, homogeneous Indian monthly rainfall time series dataset (1871–2016) is used. Complete ensemble empirical mode decomposition decomposes the rainfall time series data into Intrinsic Mode Functions (IMF) and residual element. Each IMF and residual is forecasted using the LSTM after determining the significant lags. The forecasted intrinsic mode functions and the residual elements are reconstructed to obtain the forecasted rainfall value. The proposed model performance has been verified against existing models. Compared with single LSTM model, the forecasted values prove that the model achieves good performance in predicting monthly rainfall time series.

Homogenized century-long surface incident solar radiation over Japan

Abstract. Surface incident solar radiation (Rs) plays a key role in climate change on Earth. Rs can be directly measured, and it shows substantial variability on decadal scales, i.e. global dimming and brightening. Rs can also be derived from the observed sunshine duration (SunDu) with reliable accuracy. The SunDu-derived Rs has been used as a reference to detect and adjust inhomogeneity in the observed Rs. However, both the observed Rs and SunDu-derived Rs may have inhomogeneity. In Japan, SunDu has been measured since 1890, and Rs has been measured since 1961 at ∼100 stations. In this study, the observed Rs and SunDu-derived Rs were first checked for inhomogeneity independently using the statistical software RHtests. If confirmed by the metadata of these observations, the detected inhomogeneity was adjusted based on the RHtests quantile-matching method. Second, the two homogenized time series were compared to detect further possible inhomogeneity. If confirmed by the independent ground-based manual observations of cloud cover fraction, the detected inhomogeneity was adjusted based on the reference dataset. As a result, a sharp decrease of more than 20 W m−2 in the observed Rs from 1961 to 1975 caused by instrument displacement was detected and adjusted. Similarly, a decline of about 20 W m−2 in SunDu-derived Rs due to steady instrument replacement from 1985 to 1990 was detected and adjusted too. After homogenization, the two estimates of Rs agree well. The homogenized SunDu-derived Rs show an increased at a rate of 0.9 W m−2 per decade (p<0.01) from 1961 to 2014, which was caused by a positive aerosol-related radiative effect (2.2 W m−2 per decade) and a negative cloud cover radiative effect (−1.4 W m−2 per decade). The brightening over Japan was the strongest in spring, likely due to a significant decline in aerosol transported from Asian dust storms. The observed raw Rs data and their homogenized time series used in this study are available at https://doi.org/10.11888/Meteoro.tpdc.271524 (Ma et al., 2021).

The New Radiosounding HARMonization (RHARM) Data Set of Homogenized Radiosounding Temperature, Humidity, and Wind Profiles With Uncertainties

AbstractObservational records are more often than not influenced by residual non‐climatic factors which must be detected and adjusted for prior to their usage. In this work, we present a novel approach, named Radiosounding HARMonization (RHARM), providing a homogenized data set of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for 697 radiosounding stations globally. The RHARM method has been used to adjust twice daily (0000 and 1200 UTC) radiosonde data holdings at 16 pressure levels in the range 1,000–10 hPa, from 1978 to present, provided by the Integrated Global Radiosonde Archive. Relative humidity (RH) data are limited to 250 hPa. The applied adjustments are interpolated to all reported levels. RHARM is the first data set to provide homogenized time series with an estimation of the observational uncertainty at each sounding pressure level. By construction, RHARM adjusted fields are not affected by cross‐contamination of biases across stations and are fully independent of reanalysis data. Analysis of trends for temperature, RH and winds highlights increased geographical coherency of trends over 1978–2000 globally, but especially in the Northern Hemisphere and South America. RHARM shows warming trends of 0.39 K/decade at 300 hPa in the Northern Hemisphere and of 0.25 K/decade in the tropics. The RHARM adjustments also reduce differences with the European Centre for Medium‐Range Weather Forecast ERA5 reanalysis, with the strongest effect in the Northern Hemisphere for temperature and relative humidity. For wind speed, the comparison indicates a good agreement with ERA5 in the troposphere.

A tutorial on reproducing a predefined autocovariance function through AR models: application to stationary homogeneous isotropic turbulence

Sequential methods for synthetic realisation of random processes have a number of advantages compared with spectral methods. In this article, the determination of optimal autoregressive (AR) models for reproducing a predefined target autocovariance function of a random process is addressed. To this end, a novel formulation of the problem is developed. This formulation is linear and generalises the well-known Yule-Walker (Y-W) equations and a recent approach based on restricted AR models (Krenk-Møller approach, K-M). Two main features characterise the introduced formulation: (i) flexibility in the choice for the autocovariance equations employed in the model determination, and (ii) flexibility in the definition of the AR model scheme. Both features were exploited by a genetic algorithm to obtain optimal AR models for the particular case of synthetic generation of homogeneous stationary isotropic turbulence time series. The obtained models improved those obtained with the Y-W and K-M approaches for the same model parsimony in terms of the global fitting of the target autocovariance function. Implications for the reproduced spectra are also discussed. The formulation for the multivariate case is also presented, highlighting the causes behind some computational bottlenecks.

Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany

Estimation of abundance with wide spatiotemporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a hierarchical Bayesian modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon (Salmo salar) populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates, and fishing effort. Additional data of total or partial abundance collected in four closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied.

Assessment of precipitation trends and its implications in the semi-arid region of Southern India

Rainfall is a complex meteorological phenomenon with high spatio-temporal variability. These variations in hydrologic time series can be better analyzed using homogeneity and trend tests. In this paper, three homogeneity tests were applied to the annual rainfall series of 1980–2013 from 30 raingauge stations over the study area. The rainfall stations with homogeneous time series were subjected to cluster analysis (Agglomerative Hierarchical Clustering technique) and thereafter traditional trend tests (Mann-Kendall test and Spearman Rank-order Correlation test) and one emerging trend test (called ‘Innovative Trend test’) were applied to individual clusters. Based on the homogeneity test results, 27 rainfall stations with homogeneous time series were classified under six clusters. The results of the trend analysis revealed that a distinct pattern of increasing (decreasing) rainfall trend exists in the northern (southern) part of the study area. Although the traditional tests detected insignificant trends, the Innovative Trend (IT) test indicated the trends to be significant in all the clusters. However, the magnitude of trends detected by the traditional trend tests and non-traditional trend tests is similar for all the clusters. It is concluded that the emerging IT test is highly sensitive to the changes in the rainfall time series. The findings of this study suggest that the orography, population density, industrialization and agricultural intensity lead to a precipitation gradient in the study area.

Trends in probabilities of temperature records in the non‐stationary climate of South Africa

AbstractThe temporal and spatial distribution of the frequency of breaking highest and lowest daily maximum and minimum temperature records over the period of 1951–2019 has been investigated for South Africa. Temperature records are station specific and defined as either larger or smaller than any previous values in a time series of specific year‐days. Daily maximum and minimum temperatures from homogenized time series of 25 weather stations in South Africa were analysed. Aspects considered to influence the frequency of record‐breaking events were the warming trend and variance. The study found that the record‐breaking frequencies of the highest daily maximum (high Tmax) and some high daily minimum temperature (high Tmin) records were higher than the theoretically expected number in a stationary climate. This was particularly apparent near the end of the analysis period. The ratio of highest maximum to lowest minimum temperature records was almost an equal 1:1 ratio near the start of the analysis period and increased to a about 4:1 in the last decade of the period. Focusing on the last decade, that is, 2010–2019 the study found that there is a different spatial pattern between the occurrence of high Tmax and high Tmin records. For high Tmax records the highest number were mostly recorded by stations over the central parts of the country (e.g., Kimberley, Glen College and Bloemfontein). In contrast, the highest number of high Tmin records were less confined spatially. Even when considering the general warming due to climate change, many more high temperature records are broken than expected in certain regions and on average. We deduce that the higher than expected numbers of high Tmax and high Tmin records in the latter part of the analysis period were mainly due to the variability in the warming trend with acceleration in the last decade.

A New Adaptive Absolute Method for Homogenizing GNSS‐Derived Precipitable Water Vapor Time Series

AbstractThe inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization.

Impacts of the FLEGT Action Plan and the EU Timber Regulation on EU Trade in Timber Product

The EU Timber Regulation (EUTR) is a key element in the efforts of the European Union to curb the trade in illegal timber products. This study helps to remedy the lack of systematic, statistical analysis of the EUTR’s potential impacts on international trade in timber products. Using cointegration intervention—or shock—models we quantify potential shifts in import prices and quantities of tropical hardwood lumber and oak lumber after the entry into effect of the EUTR. We further estimate import demand models to assess the relation between temperate and tropical hardwood products and whether there was a structural change in demand elasticities after the entry into force of the EUTR. The shock model analysis indicates, for most of the bilateral trade flows where we observe cointegration and a significant shock variable, increasing import prices and decreasing import quantities of tropical hardwood lumber following the EUTR start date, consistent with a contraction of the supply of tropical timber. The results of the import demand models do not give a clear indication as to whether oak lumber is a complementary or substitute product for tropical hardwood lumber, and there are no clear signs of structural changes in demand elasticities. Aside from the analysis, an important contribution of the paper is the procedure for building a long and homogeneous time series of tropical hardwood lumber.

Very rare heat extremes: quantifying and understanding using ensemble re-initialization

AbstractHeat waves such as the one in Europe 2003 have severe consequences for the economy, society, and ecosystems. It is unclear whether temperatures could have exceeded these anomalies even without further climate change. Developing storylines and quantifying highest possible temperature levels is challenging given the lack of long homogeneous time series and methodological framework to assess them. Here, we address this challenge by analysing summer temperatures in a nearly 5000-year pre-industrial climate model simulation, performed with the Community Earth System Model CESM1. To assess how anomalous temperatures could get, we compare storylines, generated by three different methods: (1) a return-level estimate, deduced from a generalized extreme value distribution, (2) a regression model, based on dynamic and thermodynamic heat wave drivers, and (3) a novel ensemble boosting method, generating large samples of re-initialized extreme heat waves in the long climate simulation.All methods provide consistent temperature estimates, suggesting that historical exceptional heat waves as in Chicago 1995, Europe 2003 and Russia 2010 could have been substantially exceeded even in the absence of further global warming. These estimated unseen heat waves are caused by the same drivers as moderate observed events, but with more anomalous patterns. Moreover, altered contributions of circulation and soil moisture to temperature anomalies include amplified feedbacks in the surface energy budget. The methodological framework of combining different storyline approaches of heat waves with magnitudes beyond the observational record may ultimately contribute to adaptation and to the stress testing of ecosystems or socio-economic systems to increase resilience to extreme climate stressors.

Consistency of total column ozone measurements between the Brewer and Dobson spectroradiometers of the LKO Arosa and PMOD/WRC Davos

Abstract. Total column ozone measured by Brewer and Dobson spectroradiometers at Arosa and Davos, Switzerland, have systematic seasonal variations of around 1.5 % using the standard operational data processing. Most of this variability can be attributed to the temperature sensitivity of approx. +0.1 %/K of the ozone absorption coefficient of the Dobson spectroradiometer (in this study D101). While the currently used Bass and Paur ozone absorption cross-sections produce inconsistent results for Dobson and Brewer, the use of the ozone absorption cross-sections from Serdyuchenko et al. (2014) in conjunction with an effective ozone temperature dataset produces excellent agreement between the four Brewers investigated (of which two are double Brewers) and Dobson D101. Even though other ozone absorption cross-sections available in the literature are able to reduce the seasonal variability as well, all of those investigated produce systematic biases in total column ozone between Brewer and Dobson of +2.1 % to −3.2 %. The highest consistency in total column ozone from Brewers and Dobson D101 at Arosa and Davos is obtained by applying the Rayleigh scattering cross-sections from Bodhaine et al. (1999), the ozone absorption cross-sections from Serdyuchenko et al. (2014), the effective ozone temperature from either ozone-sondes or the European Centre for Medium-Range Weather Forecasts (ECMWF), and the measured line spread functions of Brewer and Dobson. The variability of 0.9 % between Brewer and Dobson for single measurements can be reduced to less than 0.1 % for monthly means. As shown here, the applied methodology produces consistent total column ozone datasets between Brewer and Dobson spectroradiometers, with average differences of 0.0 % and a remaining seasonal variability of 0.11 %. For collocated Brewer and Dobson spectroradiometers, as is the case for the Arosa and Davos total column ozone times series, this allows for the merging of these two distinct datasets to produce a homogeneous time series of total column ozone measurements. Furthermore, it guarantees the long-term future of this longest total column ozone time series, by proposing a methodology for how to eventually replace the ageing Dobson spectroradiometer with the state-of-the art Brewer spectroradiometer.