scholarly journals Addressing the assumption of stationarityin statistical bias correction of temperature

2016 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Time Series ◽  
Bias Correction ◽  
Statistical Characteristics ◽  
Statistical Bias ◽  
Climate Variable ◽  
Processing Procedure ◽  
Change Impact ◽  
The Mean ◽  
Temperature Time

Abstract. Bias correction of climate variables has become a standard practice in Climate Change Impact (CCI) studies. While various methodologies have been developed, their majority assumes that the statistical characteristics of the biases between the modeled data and the observations remain unchanged in time. However, it is well known that this assumption of stationarity cannot stand in the context of a climate. Here, a method to overcome the assumption of stationarity and its drawbacks is presented. The method is presented as a pre-post processing procedure that can potentially be applied with different bias correction methods. The methodology separates the stationary and the non-stationary components of a time series, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method prevents the distortion and allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation, but also the higher and lower percentiles of the climate variable. Daily temperature time series obtained from five Euro CORDEX RCM models are used to illustrate the improvements of this method.

scholarly journals A method to preserve trends in quantile mapping bias correction of climate modeled temperature

2017 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Mapping Bias ◽  
Temperature Signal ◽  
Change Impact ◽  
The Mean ◽  
Model Temperature ◽  
Daily Time

Abstract. Bias correction of climate variables is a standard practice in Climate Change Impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long term statistics due to the time dependency that the temperature bias. Here, a method to overcome this issue without compromising the day to day correction statistics is presented. The methodology separates the model temperature signal into a normalized and a residual component relatively to the molded reference period climatology, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. The methodology is tested on daily time series obtained from five Euro CORDEX RCM models, to illustrate the improvements of this method.

scholarly journals Performance assessment of six bias correction methods using observed and RCM data at upper Awash basin, Oromia, Ethiopia

2021 ◽  
Author(s):  
Bekan Chelkeba Tumsa
Keyword(s):  
Time Series ◽  
Bias Correction ◽  
Swat Model ◽  
Correction Method ◽  
Linear Scaling ◽  
Power Transformation ◽  
Highly Qualified ◽  
Scaling Methods ◽  
Change Impact ◽  
Akaki River

Abstract Selecting a suitable bias correction method is important to provide reliable inputs for evaluation of climate change impact. Their influence was studied by comparing three discharge outputs from the SWAT model. The result after calibration with original RCM indicate that the raw RCM are heavily biased, and lead to streamflow simulation with large biases (NSE = 0.1, R2 = 0.53, MAE = 5.91 mm/°C, and PBIAS = 0.51). Power transformation and linear scaling methods performed best in correcting the frequency-based indices, while the LS method performed best in terms of the time series-based indices (NSE = 0.87, R2 = 0.78, MAE = 3.14 mm/°C, PBIAS = 0.24) during calibration. Meanwhile, daily translation was underestimating simulated streamflow compared with observed and considered as the least performing method. Precipitation correction method has higher visual influence than temperature, and its performance in streamflow simulations was consistent and significantly considerable. Power transformation and variance scaling showed highly qualified performance compared to others with indicated time series value (NSE = 0.92, R2 = 0.88, MAE = 1.58 mm/°C and PBIAS = 0.12) during calibration and validation of streamflow. Hence, PT and VARI methods were the dominant methods which remove biasness from RCM models at Akaki River basin.

scholarly journals A method to preserve trends in quantile mapping bias correction of climate modeled temperature

2017 ◽  
Vol 8 (3) ◽  
pp. 889-900 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Mapping Bias ◽  
Temperature Signal ◽  
Change Impact

Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Detection of groundwater flux changes in response to two large earthquakes using long-term bedrock temperature time series

2020 ◽  
Vol 590 ◽  
pp. 125245
Author(s):  
Qiongying Liu ◽  
Shunyun Chen ◽  
Lichun Chen ◽  
Peixun Liu ◽  
Zhuzhuan Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Groundwater Flux ◽  
Temperature Time ◽  
Large Earthquakes

Pronounced long-term juvenation in the spawning stock of Arcto-Norwegian cod (Gadus morhua) and possible consequences for recruitment

2008 ◽  
Vol 65 (3) ◽  
pp. 523-534 ◽  
Author(s):  
Geir Ottersen
Keyword(s):  
Time Series ◽  
Gadus Morhua ◽  
Experimental Studies ◽  
Reproductive Capacity ◽  
Fish Stocks ◽  
Stock Biomass ◽  
Clear Link ◽  
Spawning Stock ◽  
The Mean

The oldest and largest individuals are disappearing from many fish stocks worldwide as a result of overexploitation. This has been suggested to impair recruitment through decreasing the reproductive capacity of the spawners and increasing the mortality rate of the offspring. By using a time series on spawners biomass by age class for Arcto-Norwegian cod (Gadus morhua) from 1913–2004, I have documented pronounced changes in the spawning stock, including a trend towards younger fish, a less diverse distribution across ages, and a declining proportion of repeat spawners. Despite the total spawning stock biomass (SSB) being at similar levels now as in 1933, the mean age in the SSB has declined from 10–12.5 years to 7–8 years during the study period, and the percentage of fish of age 10 or above in the SSB has decreased from ~97% to ~10%. Contrary to earlier theoretical and experimental studies, no clear link between age structure and recruitment was found here. Recruitment to the Arcto-Norwegian cod stock may thus be more robust towards spawner juvenation than expected, possibly because of strong recruitment compensation.

scholarly journals Generation of Daily Synthetic Series of Inflow Volume to the Las Cruces Dam, Nay., Mexico, Using the Svanidze Method

10.29007/2pj3 ◽  
2018 ◽  
Author(s):  
Itzel Velazquez ◽  
Maritza Arganis ◽  
Ramón DomÍnguez Mora ◽  
Rosalva Mendoza Ramírez ◽  
Eliseo Carrizosa Elizondo
Keyword(s):  
Time Series ◽  
Normal Distribution ◽  
Correlation Coefficient ◽  
Hydroelectric Project ◽  
Historical Series ◽  
Periodic Time Series ◽  
The Mean ◽  
First Moment ◽  
Distribution Assumption

The generation of synthetic series is important for simulations of the behavior in the long term of a reservoir or systems of them. The Svanidze method is easy to use to generate periodic time series for a selected period of time (monthly, fortnightly, weekly). Compared with other methods (eg PAR, PARMA) this method does not require a normal distribution assumption for the series. In this work the Svanidze method was applied to obtain synthetic series of the daily inflow volume to the Las Cruces hydroelectric project, located in the state of Nayarit, Mexico; with this method we achieve the objective of reproducing the behavior of the historical series at least in its first moment (the mean). In addition, similar correlation coefficient are observed from one day to the next with respect to what happened historically.

scholarly journals Analysis of dynamics for anomalies of the ice cover in the Okhotsk Sea in the period from 1882 to 2015

2016 ◽  
Vol 185 (2) ◽  
pp. 228-239
Author(s):  
Vladimir M. Pishchalnik ◽  
Valery A. Romanyuk ◽  
Igor G. Minervin ◽  
Alevtina S. Batuhtina
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Regime Shift ◽  
Ice Cover ◽  
Satellite Observations ◽  
Okhotsk Sea ◽  
Ice Conditions ◽  
The Mean ◽  
Analysis Of Dynamics

The time-series for the ice cover dynamics in the Okhotsk Sea in the period from 1882 to 2015 are reconstructed on the base of shipboard, airborne, and satellite observations and measurements of the air temperature at the coastal meteorological stations. Abnormality of the ice conditions is estimated relative to the “climate norm” determined as the mean seasonal variation for the 1961-1990. Long-term variability of the ice cover is analyzed. Its regime shift with change of trend is revealed in the late 1970s - early 1980s that corresponds to the regime shift of the air temperature variability in the northern hemisphere.

scholarly journals Spatial-temporal analysis of temperature in Lake Shira based on long-term observations

2021 ◽  
Author(s):  
O.S. Volodko ◽  
L.A. Kompaniets ◽  
L.V. Gavrilova
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Temporal Analysis ◽  
Temperature Time Series ◽  
Lake Shira ◽  
Temperature Time ◽  
Spatial Temporal Analysis

Long-term in-situ measurements of temperature were conducted in lake Shira during 2013-2015. The principal component analysis of temperature time series allowed to identify period of generation and propagation of internal waves. The spectral analysis revealed the dominance of the oscillations with periods of 21.3, 10.6 and 5.3 h.

scholarly journals Spatial and Temporal Distribution of Air Temperature in the Northern Hemisphere

2013 ◽  
Vol 6 (3) ◽  
pp. 177-182
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temporal Distribution ◽  
Surface Air Temperature ◽  
Temperature Time Series ◽  
Mean Values ◽  
The Mean ◽  
Temperature Time

In the present study, the spatial and temporal surface air temperature variability for the Northern Hemisphere has been examined, for the period 1900-1996. Factor Analysis has been applied to 5o Latitude x 10o Longitude grid box data covering the area from almost the equator to 70o N. These data are anomalies of the mean annual air temperature from the respective mean values of the period 1961- 1990. The analysis showed that, mainly 20 regions were determined in the Northern Hemisphere with significantly covariant air temperature time series. The comparison of the trends of the mean annual surface air temperature time series of these regions, revealed such common characteristics as the minimum of the first decade of the 20th century and the recent years warming. The results of this study are also compared to the respective results of a former study in which data for the last half of the century (1948-1996) have been analyzed. The findings extracted indicate the stability of climate distribution in Northern Hemisphere during the 20th century.

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