scholarly journals Daily Field Observations of Retrogressive Thaw Slump Dynamics in the Canadian High Arctic

ARCTIC ◽  
2021 ◽  
Vol 74 (3) ◽  
pp. 339-354
Author(s):  
Melissa K. Ward Jones ◽  
Wayne H. Pollard
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Polynomial Regression ◽  
Daily Precipitation ◽  
Pearson Correlation ◽  
High Arctic ◽  
Daily Basis ◽  
Field Observations ◽  
Sediment Distribution ◽  
Retrogressive Thaw Slump

With observed increases in retrogressive thaw slump (RTS) number, rates, and size in recent decades, there is a need to understand these highly dynamic landforms as they impact surrounding ecosystems and infrastructure. There is a general lack of detailed (e.g., daily) field observations of change in RTSs; we help fill this gap by monitoring three RTSs for much of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP), and cumulative thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable, and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat largely driven by sediment distribution in the RTS floor could occur within a single period of increased air temperature. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. A statistically significant correlation was found between daily mean and mean cumulative retreat with MDAT (p > 0.001) and TDD (p > 0.001 and > 0.0001) but not with total daily precipitation. Correlating mean cumulative retreat and TDD using polynomial regression generated R2 values greater than 0.99 for all three sites. Both cumulative retreat and TDD account for past and current conditions, as well as lag responses, within the monitoring period. The high R2 values for the correlation of mean cumulative retreat and TDD suggest the potential for accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however modelling is currently restricted to individual RTSs and only within short time scales. Monitoring RTSs on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales and highlights the importance of all system inputs when considering RTS retreat dynamics. 

Daily monitoring of retrogressive thaw slumps in the Fosheim Peninsula, Ellesmere Island, Canada

2020 ◽  
Author(s):  
Melissa Ward Jones ◽  
Benjamin Jones ◽  
Wayne Pollard
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Polynomial Regression ◽  
Pearson Correlation ◽  
Daily Basis ◽  
The Arctic ◽  
Regression Equations ◽  
Daily Monitoring ◽  
Sediment Redistribution ◽  
Short Time

<p>Retrogressive thaw slumps (RTS) occur from the mass wasting of ice-rich permafrost. These horseshoe-shaped features have an ablating or retreating ice-rich headwall with fluidized sediment that is transported along the RTS floor. RTS can remain active for up to decades and enlarge as the headwall retreats. With observed increases in RTS number, rates and sizes in recent decades, there is a need to understand these highly dynamic landforms, however there is a general lack of detailed field observations of RTSs. We monitored 3 RTS for over half of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP) and thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat could occur within a single period of increased air temperature. These retreat trends were observed to be largely driven by sediment redistribution in the RTS floor. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. There was a statistically significant correlation between daily mean and mean cumulative retreat with MDAT (p < 0.001) and TDD (p < 0.001 and < 0.0001) but not with TDP. Correlating mean cumulative retreat and cumulative TDD using polynomial regression (quadratic and cubic) generated R<sup>2 </sup>values greater than 0.99 for all 3 sites as these variables account for past and current conditions within the monitoring period, as well as lag responses of retreat. This suggests the potential of accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however this is currently restricted to individual RTSs and only within short time scales. We tested this idea by modelling 2 weeks of cumulative retreat in 2018 for 2 of our sites we monitored using the 2017 regression equations. Percent prediction error was 8% at one site and 16% at the other. Monitoring RTS on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales. With the widespread increased numbers of RTSs being observed around the Arctic, understanding their dynamics is critical as these landforms impact surrounding ecosystems and infrastructure which will be exacerbated with climate change.  </p>

scholarly journals Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

2021 ◽  
pp. 33-41
Author(s):  
Joyce Imara Nchom ◽  
A. S. Abubakar ◽  
F. O. Arimoro ◽  
B. Y. Mohammed
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Linear Trend ◽  
Pearson Correlation ◽  
Maximum Temperature ◽  
Data Set ◽  
International Research Institute ◽  
Wind Speed Maximum ◽  
Weather Parameters ◽  
The Relationship

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

scholarly journals Extracting relationship between air pollution and precipitation using spatio-temporal analysis in Tehran metropolis

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Shokouh Dareshiri ◽  
Mohammadreza Sahelgozin ◽  
Maryam Lotfian ◽  
Jens Ingensand
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Regression Model ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Human Life ◽  
Pearson Correlation ◽  
Influencing Factor ◽  
Polynomial Equation ◽  
Temporal Analysis

<p><strong>Abstract.</strong> Precipitation is one of the main stages of the water cycle, and it is required for the organisms to survive on the planet. In contrast, air pollution is a phenomenon that has greatly affected the human life nowadays. Population growth, development of factories and increasing number of fossil fuel vehicles are the most influencing factors on air pollution. In addition to understand nature of precipitation and air pollution, finding relationship between these two phenomena is necessary to make appropriate policies for reducing air pollution. Furthermore, studying trends of precipitation and air pollution in the past, is helpful to forecast the times and places with less precipitation and more air pollution for a better urban management. In this study, we tried to extract any probable relationship between these two parameters by investigating their monthly measured amounts in 22 municipal districts of Tehran in three epochs of time (2009, 2013 and 2017). Carbon Monoxide (CO) was considered as the indicator of air pollution. Results of the study show that the parameters have a significant relationship with each other. By using Pearson Correlation Coefficient and One-Way Variance (ANOVA) test, relationship between the data for each month and for each district of Tehran were studied separately. As the time has passed and the air pollution has increased, the correlation between the parameters in districts has decreased. In addition, during the cold months of the year, the correlations decrease since the fact that precipitation is not the only influencing factor on the air pollution due to the rise of air “Inversion”. Finally, the polynomial regression model of carbon monoxide based on precipitation was extracted for each of the three years. The model suggests a degree three polynomial equation. The obtained coefficients from the regression model show that the relationship between parameters was stronger in the years with more rainfalls. This can be due to the more significant impact of other influencing factors on air pollution, such as population density, wind direction, vehicles and factories in the areas or conditions with a less rainfall.</p>

scholarly journals The effect of air temperature on forest fire risk in the municipality of Negotin

2015 ◽  
Vol 95 (4) ◽  
pp. 67-76
Author(s):  
Stanimir Zivanovic ◽  
Milena Gocic ◽  
Radomir Ivanovic ◽  
Natasa Martic-Bursac
Keyword(s):  
Air Temperature ◽  
Forest Fires ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Fire Risk ◽  
Annual Number ◽  
Absolute Maximum ◽  
Maximum Air Temperature ◽  
Forest Fire Risk

Fires in nature are caused by moisture content in the burning material, which is dependent on the values of the climatic elements. The occurrence of these fires in Serbia is becoming more common, depending on the intensity and duration have a major impact on the state of vegetation. The aim of this study was to determine the association between changes in air temperature and the dynamics of the appearance of forest fires. To study the association of these properties were used Pearson correlation coefficients. The analysis is based on meteorological data obtained from meteorological station in Negotin for the period 1991-2010. Research has found that the annual number of fires, correlating with an average annual air temperature (p = 0.317, ? = 0.21). Also, it was found that the annual number of fires positive, medium intensity, correlate with the absolute maximum air temperature (p = 0.578, ? = 0.26), but not statistically significant (p> 0.05).

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

scholarly journals Impacts of Climatic Change on Reference Crop Evapotranspiration across Different Climatic Zones of Ningxia at Multi-Time Scales from 1957 to 2018

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Ziyang Zhao ◽  
Hongrui Wang ◽  
Cheng Wang ◽  
Wangcheng Li ◽  
Hao Chen ◽  
...  
Keyword(s):  
Water Management ◽  
Time Scales ◽  
Air Temperature ◽  
Climatic Factors ◽  
Crop Evapotranspiration ◽  
Climatic Zones ◽  
Reference Crop Evapotranspiration ◽  
Short Period ◽  
The Impact ◽  
Reference Crop

The impact of global climate change on agroecosystems is growing, affecting reference crop evapotranspiration (ET0) and subsequent agricultural water management. In this study, the climate factors temporal trends, the spatiotemporal variation, and the climate driving factors of ET0 at different time scales were evaluated across the Northern Yellow River Irrigation Area (NYR), Central Arid Zone (CAZ), and Southern Mountain Area (SMA) of Ningxia based on 20 climatic stations’ daily data from 1957 to 2018. The results showed that the Tmean (daily mean air temperature), Tmax (daily maximum air temperature), and Tmin (daily minimum air temperature) all had increased significantly over the past 62 years, whilst RH (relative humidity), U2 (wind speed at 2 m height), and SD (sunshine duration) had significantly decreasing trends across all climatic zones. At monthly scale, the ET0 was mainly concentrated from April to September. And at annual and seasonal scales, the overall increasing trends were more pronounced in NX, NYR, and SMA, while CAZ was the opposite. For the spatial distribution, ET0 presented a trend of rising first and then falling at all time scales. The abrupt change point for climatic factors and ET0 series was obtained at approximately 1990 across all climatic zones, and the ET0 had a long period of 25a and a short period of 10a at annual scale, while it was 15a and 5a at seasonal scale. RH and Tmax were the most sensitive climatic factors at the annual and seasonal scales, while the largest contribution rates were Tmax and SD. This study not only is important for the understanding of ET0 changes but also provides the preliminary and elementary reference for agriculture water management in Ningxia.

scholarly journals Aligning Pixel Values of DMSP and VIIRS Nighttime Light Images to Evaluate Urban Dynamics

2019 ◽  
Vol 11 (12) ◽  
pp. 1463 ◽  
Author(s):  
Kang Wu ◽  
Xiaonan Wang
Keyword(s):  
Robust Regression ◽  
Infrared Imaging ◽  
Polynomial Regression ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Urban Dynamics ◽  
Low Pass ◽  
Nighttime Light

The brightness of pixels in nighttime light images (NTL) has been regarded as the proxy of the urban dynamics. However, the great difference between the pixel values of NTL from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIIRS) poses obstacles to analyze economic and social development with NTL in a continuous temporal sequence. This research proposes a methodology to align the pixel values of both NTL by calibrating annual DMSP images between the years 1992–2013 with a robust regression algorithm with a quadratic polynomial regression model and simulating annual DMSP images with VIIRS images between years 2012 and 2018 with a model consisting of a power function and a Gaussian low pass filter. As a result, DMSP annual images between years 1992–2018 can be produced. Case study of Beijing and Yiwu are conducted and evaluated with local gross domestic product (GDP). Compared with the values of DMSP and VIIRS annual composites, the Pearson correlation coefficients of DMSP and simulated DMSP annual composites in 2012 and in 2013 increase significantly, while the root mean square error (RMSE) decrease evidently. In addition, the correlation of the sum of light of NTL and local GDP is enhanced with a simulation process. These results demonstrate the feasibility of the proposed method in narrowing the gap between DMSP and VIIRS NTL in pixel values.

Disentangling the confounding effects of PAR and air temperature on net ecosystem exchange at multiple time scales

2014 ◽  
Vol 19 ◽  
pp. 46-58 ◽  
Author(s):  
Zutao Ouyang ◽  
Jiquan Chen ◽  
Richard Becker ◽  
Housen Chu ◽  
Jing Xie ◽  
...  
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Net Ecosystem Exchange ◽  
Multiple Time Scales ◽  
Multiple Time

scholarly journals Increasing Daily Precipitation Intensity Associated with Warmer Air Temperatures over Northern Eurasia

2016 ◽  
Vol 29 (2) ◽  
pp. 623-636 ◽  
Author(s):  
Hengchun Ye ◽  
Eric J. Fetzer ◽  
Ali Behrangi ◽  
Sun Wong ◽  
Bjorn H. Lambrigtsen ◽  
...  
Keyword(s):  
Air Temperature ◽  
Daily Precipitation ◽  
Precipitation Intensity ◽  
The Arctic ◽  
Threshold Temperature ◽  
Northern Eurasia ◽  
Air Temperatures ◽  
Precipitation Total ◽  
Annual Precipitation Total ◽  
The Arctic Oscillation

Abstract This study uses 45 years of observational records from 517 historical surface weather stations over northern Eurasia to examine changing precipitation characteristics associated with increasing air temperatures. Results suggest that warming air temperatures over northern Eurasia have been accompanied by higher precipitation intensity but lower frequency and little change in annual precipitation total. An increase in daily precipitation intensity of around 1%–3% per each degree of air temperature increase is found for all seasons as long as a station’s seasonal mean air temperature is below about 15°–16°C. This threshold temperature may be location dependent. At temperatures above this threshold, precipitation intensity switches to decreasing with increasing air temperature, possibly related to decreasing water vapor associated with extreme high temperatures. Furthermore, the major atmospheric circulation of the Arctic Oscillation, Scandinavian pattern, east Atlantic–western Eurasian pattern, and polar–Eurasian pattern also have significant influences on precipitation intensity in winter, spring, and summer over certain areas of northern Eurasia.

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