scholarly journals To what extent does the detection of climate change in Hungary depend on the choice of statistical methods?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Beatrix Izsák ◽  
Tamás Szentimrey
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Linear Trend ◽  
Meteorological Data ◽  
Warming Trend ◽  
Data Series ◽  
Data Sets ◽  
Raw Data ◽  
Temperature Trends ◽  
Trend Data

AbstractThe trend analysis of meteorological time series has gained prominence in recent decades, the most common method being the so-called ‘linear analytical trend analysis’. Until the mid-1990s, trend analysis was commonly performed on non-homogenized data sets, which frequently led to erroneous conclusions. Nowadays, only homogenized data sets are examined, so it really is possible to detect climate change in long meteorological data sets. In this paper, the methodology of linear trend analysis is summarized, the way in which the model can be validated is demonstrated, and there is a discussion of the results obtained if unjustified discontinuities caused by changing measurement conditions, such as the relocation of stations, changes in measurement time, or instrument change occur. On the basis of an examination of records for the preceding 118 years, it is possible to state that both annual and seasonal mean temperature trends display a significant warming trend. In the case of homogenized data series, the change is significant over the entire territory of Hungary; in the case of raw data series, however, the change is not significant everywhere. The validity of the linear model is tested using the F-test, a task as yet carried out on the entire Hungarian data series, series comprising records for over 100 years. Furthermore, neither has a comparison been made of the trend data for raw data series and the homogenized data series with the help of information on station history to explore the causes of inhomogeneity.

scholarly journals Long-term changes of the upper stratosphere as seen by Japanese rocketsondes at Ryori (39°N, 141°E)

1999 ◽  
Vol 17 (9) ◽  
pp. 1210-1217 ◽  
Author(s):  
P. Keckhut ◽  
K. Kodera
Keyword(s):  
Middle Atmosphere ◽  
Linear Trend ◽  
Warming Trend ◽  
Least Square ◽  
Atmospheric Composition ◽  
Structure Evolution ◽  
Data Series ◽  
Data Sets ◽  
Volcanic Aerosols ◽  
Small Change

Abstract. Wind and temperature profiles measured routinely by rockets at Ryori (Japan) since 1970 are analysed to quantify interannual changes that occur in the upper stratosphere. The analysis involved using a least square fitting of the data with a multiparametric adaptative model composed of a linear combination of some functions that represent the main expected climate forcing responses of the stratosphere. These functions are seasonal cycles, solar activity changes, stratospheric optical depth induced by volcanic aerosols, equatorial wind oscillations and a possible linear trend. Step functions are also included in the analyses to take into account instrumental changes. Results reveal a small change for wind data series above 45 km when new corrections were introduced to take into account instrumental changes. However, no significant change of the mean is noted for temperature even after sondes were improved. While wind series reveal no significant trends, a significant cooling of 2.0 to 2.5 K/decade is observed in the mid upper stratosphere using this analysis method. This cooling is more than double the cooling predicted by models by a factor of more than two. In winter, it may be noted that the amplitude of the atmospheric response is enhanced. This is probably caused by the larger ozone depletion and/or by some dynamical feedback effects. In winter, cooling tends to be smaller around 40-45 km (in fact a warming trend is observed in December) as already observed in other data sets and simulated by models. Although the winter response to volcanic aerosols is in good agreement with numerical simulations, the solar signature is of the opposite sign to that expected. This is not understood, but it has already been observed with other data sets.Key words. Atmospheric composition and structure (evolution of one atmosphere; pressure · density · and temperature) · Meteorology and atmospheric dynamics (middle atmosphere dynamics)

scholarly journals A data portal for regional climatic trend analysis in a Peruvian High Andes region

2011 ◽  
Vol 6 (1) ◽  
pp. 219-226 ◽  
Author(s):  
M. Schwarb ◽  
D. Acuña ◽  
Th. Konzelmann ◽  
M. Rohrer ◽  
N. Salzmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Climate Change Adaptation ◽  
Air Temperature ◽  
Data Series ◽  
Adaptation Measures ◽  
Climatic Trend ◽  
Temperature And Precipitation ◽  
Temperature Trends ◽  
Data Portal

Abstract. In the frame of a Swiss-Peruvian climate change adaptation initiative (PACC), operational and historical data series of more than 100 stations of the Peruvian Meteorological and Hydrological Service (SENAMHI) are now accessible in a dedicated data portal. The data portal allows for example the comparison of data series or the interpolation of spatial fields as well as download of data in various data formats. It is thus a valuable tool supporting the process of data homogenisation and generation of a regional baseline climatology for a sound development of adequate climate change adaptation measures. The procedure to homogenize air-temperature and precipitation data series near Cusco city is outlined and followed by an exemplary trend analysis. Local air temperature trends are found to be in line with global mean trends.

Changes in the Amplitude of the Temperature Annual Cycle in China and Their Implication for Climate Change Research

2011 ◽  
Vol 24 (20) ◽  
pp. 5292-5302 ◽  
Author(s):  
Cheng Qian ◽  
Congbin Fu ◽  
Zhaohua Wu
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Linear Trend ◽  
Surface Air Temperature ◽  
Mean Amplitude ◽  
Warming Trend ◽  
Ensemble Empirical Mode Decomposition ◽  
Surface Solar Radiation ◽  
Climate Change Research ◽  
Annual Cycles

Abstract Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.

scholarly journals Temperature trends at the Mauna Loa Observatory, Hawaii

2010 ◽  
Vol 6 (5) ◽  
pp. 1685-1699
Author(s):  
B. D. Malamud ◽  
D. L. Turcotte ◽  
C. S. B. Grimmond
Keyword(s):  
Linear Trend ◽  
Warming Trend ◽  
Time Of Day ◽  
Time Data ◽  
Night Time ◽  
Mauna Loa ◽  
Temperature Trends ◽  
Hourly Temperature ◽  
Local Standard ◽  
Temperature Records

Abstract. Observations at the Mauna Loa Observatory, Hawaii, established the systematic increase of anthropogenic CO2 in the atmosphere. For the same reasons that this site provides excellent globally averaged CO2 data, it may provide temperature data with global significance. Here, we examine hourly temperature records, averaged annually for 1977–2006, to determine linear trends as a function of time of day. For night-time data (22:00 to 06:00, LST (local standard time)) there is a near-uniform warming of 0.040 °C y−1. During the day, the linear trend shows a slight cooling of −0.013 °C y−1 at 12:00 (noon, LST). Overall, at Mauna Loa Observatory, there is a mean warming trend of 0.021 °C y−1. The dominance of night-time warming results in a relatively large annual decrease in the diurnal temperature range (DTR) of −0.050 °C y−1. These trends are consistent with the observed increases in the concentrations of CO2 and its role as a greenhouse gas, and indicate the possible relevance of the Mauna Loa temperature measurements to global warming.

scholarly journals Examining Yam Production in Response to Climate Change in Nigeria: A Co-Integration Model Approach

2020 ◽  
Vol 9 (4) ◽  
pp. 42
Author(s):  
Cynthia W. Angba ◽  
Richard N. Baines ◽  
Allan J. Butler
Keyword(s):  
Climate Change ◽  
Error Correction ◽  
Trend Analysis ◽  
Linear Trend ◽  
Error Correction Model ◽  
Series Data ◽  
Integration Model ◽  
Correction Model ◽  
Linear Trend Analysis ◽  
Model Approach

This study addressed yam production in response to climate change in Cross River State using a co-integration model approach. The specific objectives of this paper are to analyze the trend in yam production, annual precipitation, and annual temperature, and to analyze the impact of climate variables on yam production. Time-series data from 1996 to 2017 was used. Based on the analysis, which constituted a linear trend analysis, co-integration test, and error correction model, the study came up with robust findings. The linear trend analysis for yam production revealed a steady increase in output between the years 2005 and 2016. The result of the rainfall trend analysis showed the presence of rainfall variability and irregularity. The trend line for temperature showed an overall downward trend for the period under study. However, the Error Correction Model result showed that temperature was statistically significant and negatively impacted yam production. The study recommends that policymakers should take appropriate steps to encourage the development of pest- and disease-tolerant yam varieties because an increase in temperature leads to the proliferation of insects, pests, and diseases.

scholarly journals Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method

2020 ◽  
Author(s):  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Meteorological Parameters ◽  
Global Climate ◽  
Data Series ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Analysis Method ◽  
Distinctive Features ◽  
Monthly Precipitation Data

Abstract The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis. The Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, the IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins. Data from ten precipitation observation stations in Susurluk Basin were used. Data were provided by the General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

scholarly journals Trends In Climate Variables (Temperature And Rainfall) And Local Perceptions Of Climate Change In Lamu, Kenya

2020 ◽  
Vol 13 (3) ◽  
pp. 102-109
Author(s):  
Maingey Yvonne ◽  
Gilbert Ouma ◽  
Daniel Olago ◽  
Maggie Opondo
Keyword(s):  
Climate Change ◽  
Local Community ◽  
Linear Trend ◽  
Meteorological Data ◽  
Household Survey ◽  
Weather Data ◽  
Climate Variables ◽  
Local Perceptions ◽  
Maximum Temperatures ◽  
Negative Impacts

Community  adaptation to the negative impacts of climate change benefits from an analysis of both the trends in climate variables and people’s perception of climate change. This paper contends that members of the local community have observed changes in temperature  and rainfall patterns and that these perceptions can be positively correlated with meteorological records. This is particularly useful for remote regions like Lamu whereby access to weather data is spatially and temporally challenged. Linear trend analysis is employed to describe the change in temperature and rainfall in Lamu using monthly data obtained from the Kenya Meteorological Department (KMD) for the period 1974–2014. To determine local perceptions and understanding of the trends, results from a household survey are presented. Significant warming trends have been observed in the study area over the period 1974–2014. This warming is attributed to a rise in maximum temperatures. In contrast to temperature, a clear picture of the rainfall trend has not emerged. Perceptions of the local community closely match the findings on temperature, with majority of the community identifying a rise in temperature over the same period. The  findings suggest that the process of validating community perceptions of trends with historical meteorological data analysis can promote adaptation planning that is inclusive and responsive to local experiences.

scholarly journals Is it feasible to estimate radiosonde biases from interlaced measurements?

2018 ◽  
Vol 11 (5) ◽  
pp. 3021-3029
Author(s):  
Stefanie Kremser ◽  
Jordis S. Tradowsky ◽  
Henning W. Rust ◽  
Greg E. Bodeker
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Synthetic Data ◽  
Internal Variability ◽  
Data Series ◽  
Data Sets ◽  
Climate Variables ◽  
Climate Data ◽  
Reference Quality ◽  
New Instruments

Abstract. Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and as such are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure, and humidity, together with well-characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive at measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.

scholarly journals Assessing the impacts of climate change on the high altitude snow- and glacier-fed hydrological regimes of Astore and Hunza, the sub-catchments of Upper Indus Basin

2018 ◽  
Vol 11 (2) ◽  
pp. 479-490 ◽  
Author(s):  
Suhaib Bin Farhan ◽  
Yinsheng Zhang ◽  
Adnan Aziz ◽  
Haifeng Gao ◽  
Yingzhao Ma ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
High Altitude ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Winter Precipitation ◽  
Indus Basin ◽  
Temperature Trends ◽  
Low Altitude ◽  
Times Series Analysis

Abstract Evaluation of the impacts of prevailing climate change on rivers and water resources is significantly important in order to successfully manage water resources, particularly in snow-fed and glacier-fed catchments. The basic aim of this research was to assess the impacts of climatic variability on Astore and Hunza river-flows by employing long-term in-situ hydro-meteorological data. Times-series analysis of high- and low-altitude station data revealed consistent summer cooling, and warming in winter and spring seasons in both Karakoram and western Himalayan basins of Hunza and Astore, respectively. The intensity of these changes was not found to be identical in both basins, i.e. Hunza depicts slightly higher summer cooling rates and slightly lower annual, winter and spring warming rates as compared to Astore. Subsequently, the significant increase in annual precipitation of Hunza was also not found to be identical with Astore precipitation, which shows only a slight increase of precipitation. Notwithstanding, comparable temperature trends were observed at both high- and low-altitude stations; however, on the contrary, precipitation shows a different pattern of behavior, i.e. significantly increased winter precipitation at high-altitude Astore stations was in contrast to the precipitation recorded by low-altitude stations. The study suggested that climate change is significantly influencing the characteristics and hydrological resources of this region.

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