Temperature trends with reduced impact of ocean air temperature

2018 ◽  
Vol 29 (4) ◽  
pp. 613-632
Author(s):  
Frank Lansner ◽  
Jens Olaf Pepke Pedersen
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Temperature Data ◽  
Ocean Temperature ◽  
Moderating Effects ◽  
Climatic Effects ◽  
Greenhouse Gasses ◽  
Temperature Trends ◽  
The World ◽  
Ocean Winds

Temperature data 1900–2010 from meteorological stations across the world have been analyzed and it has been found that all land areas generally have two different valid temperature trends. Coastal stations and hill stations facing ocean winds are normally more warm-trended than the valley stations that are sheltered from dominant oceans winds. Thus, we found that in any area with variation in the topography, we can divide the stations into the more warm trended ocean air-affected stations, and the more cold-trended ocean air-sheltered stations. We find that the distinction between ocean air-affected and ocean air-sheltered stations can be used to identify the influence of the oceans on land surface. We can then use this knowledge as a tool to better study climate variability on the land surface without the moderating effects of the ocean. We find a lack of warming in the ocean air sheltered temperature data – with less impact of ocean temperature trends – after 1950. The lack of warming in the ocean air sheltered temperature trends after 1950 should be considered when evaluating the climatic effects of changes in the Earth’s atmospheric trace amounts of greenhouse gasses as well as variations in solar conditions.

scholarly journals Is the World Ocean Warming? Upper-Ocean Temperature Trends: 1950–2000

2007 ◽  
Vol 37 (2) ◽  
pp. 174-187 ◽  
Author(s):  
D. E. Harrison ◽  
Mark Carson
Keyword(s):  
Vertical Structure ◽  
World Ocean ◽  
Time Variability ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Similar Magnitude ◽  
Subsurface Temperature ◽  
Temperature Trends ◽  
Uncertainty Estimates ◽  
The World

Abstract Subsurface temperature trends in the better-sampled parts of the World Ocean are reported. Where there are sufficient observations for this analysis, there is large spatial variability of 51-yr trends in the upper ocean, with some regions showing cooling in excess of 3°C, and others warming of similar magnitude. Some 95% of the ocean area analyzed has both cooled and warmed over 20-yr subsets of this period. There is much space and time variability of 20-yr running trend estimates, indicating that trends over a decade or two may not be representative of longer-term trends. Results are based on sorting individual observations in World Ocean Database 2001 into 1° × 1° and 2° × 2° bins. Only bins with at least five observations per decade for four of the five decades since 1950 are used. Much of the World Ocean cannot be examined from this perspective. The 51-yr trends significant at the 90% level are given particular attention. Results are presented for depths of 100, 300, and 500 m. The patterns of the 90% significant trends are spatially coherent on scales resolved by the bin size. The vertical structure of the trends is coherent in some regions, but changes sign between the analysis depths in a number of others. It is suggested that additional attention should be given to uncertainty estimates for basin average and World Ocean average thermal trends.

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

Correlation of air temperature and land surface temperature of Landsat-8 in cities of South Korea

2020 ◽  
Author(s):  
Sungwon Choi ◽  
Donghyun Jin ◽  
Noh-hun Seong ◽  
Daeseong Jung ◽  
Kyung-soo Han
Keyword(s):  
Correlation Analysis ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Data ◽  
Landsat 8 ◽  
In Situ Data ◽  
Urban Greenery ◽  
Important Position

<p>Recently, there are many problems in urban area such as urban thermal island phenomenon, changes in urban green area, changes in urban weather and various urban types. And surface temperature data have been utilized in many areas to identify these phenomena. This means that surface temperatures is an important position in urban greenery and weather. High temporal and spatial resolution satellite data are needed to continuously observe the phenomenon in urban areas. In addition, the surface temperature varies from type of indicator, topography, and various factors, so there is a limit to the in-situ data for observing changes throughout the city. Therefore, various organizations around the world are currently conducting surface temperature measurements using satellites. However, the use of data in clear pixel is essential for accurate surface temperature calculations using satellites, but the accuracy of results will be reduced if the data from in the pixel which conclude clouds.</p><p>Therefore, we tried to solve these problems by analyzing the correlation between the air temperature data and the Landsat-8 LST data. The variables used in the correlation analysis are air temperature, Landsat-8 LST, NDVI and NDWI, and the study period is 2014 to 2016 and the study area is South Korea's five cities (Seoul, Busan, Daejeon, Daegu, Gwangju). For correlation analysis, the air temperature data points provided by the Korea Meteorological Administration and the Landsat-8 pixels were matched, and the correlation coefficient calculated by the correlation analysis was applied to the Landsat-8 satellite to calculate the LST. We validated by direct comparison the re-produced Landsat-8 LST with observed Landsat-8 LST. And the result of validation showed a high correlation of 0.9. It shows that compensation for the satellite's shortcomings from clouds by using the correlation between temperature and LST.</p>

scholarly journals The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth

2014 ◽  
Vol 6 (1) ◽  
pp. 61-68 ◽  
Author(s):  
T. J. Osborn ◽  
P. D. Jones
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Research Unit ◽  
Google Earth ◽  
Temperature Data ◽  
Climatic Research Unit ◽  
Annual Data ◽  
Data Set ◽  
Precise Version

Abstract. The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

scholarly journals Improving the Performance of Temperature Index Snowmelt Model of SWAT by Using MODIS Land Surface Temperature Data

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Yan Yang ◽  
Takeo Onishi ◽  
Ken Hiramatsu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Data ◽  
Temperature Index ◽  
Simulation Performance ◽  
Modis Land Surface Temperature ◽  
Snowmelt Model ◽  
Surface Temperature Data

Simulation results of the widely used temperature index snowmelt model are greatly influenced by input air temperature data. Spatially sparse air temperature data remain the main factor inducing uncertainties and errors in that model, which limits its applications. Thus, to solve this problem, we created new air temperature data using linear regression relationships that can be formulated based on MODIS land surface temperature data. The Soil Water Assessment Tool model, which includes an improved temperature index snowmelt module, was chosen to test the newly created data. By evaluating simulation performance for daily snowmelt in three test basins of the Amur River, performance of the newly created data was assessed. The coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) were used for evaluation. The results indicate that MODIS land surface temperature data can be used as a new source for air temperature data creation. This will improve snow simulation using the temperature index model in an area with sparse air temperature observations.

scholarly journals Multiparameter Spatiotemporal Anomalies with Some of the Strong Earthquakes (M > 6:0) in 2018 around the World

2019 ◽  
Author(s):  
Azad Rasul
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Earthquake Forecasting ◽  
Specific Humidity ◽  
Negative Anomaly ◽  
Positive Anomaly ◽  
The World ◽  
Before And After

One of the most destructive natural disasters is the earthquake which brings enormous risks to humankind. The objective of the current study was to determine the Earthquake’s remote sensing multiparameter (i.e. land surface temperature (LST), air temperature, specific humidity, precipitation and wind speed) spatiotemporal anomaly of many earthquake samples occurred during 2018 around the world. In this research 11 earthquake (M > 6:0) studied (4 samples selected in a land with transparent sky situations, 3 samples in land within cloudy situations and 4 samples in marine earthquakes). The interquartile range (IQR) and mean ± 2σ methods utilized to improve the efficiency of anomalous differences. As a result, based on the IQR method, negative anomaly before the event detected during the daytime in Mexico and during the nighttime in Afghanistan. In addition, a negative outlier of brightness temperature (BT) detected in Alaska before, after and during the event. In contrast, based on IQR and mean ± 2σ positive anomaly detected in precipitation before and after the event in all investigated examples. According to mean ± 2σ, negative anomaly LST, specific humidity, sea surface temperature (SST_100) and wind detected in most examined earthquake samples. In contrast, positive SST_0 anomaly observed in Fiji and Honduras after the earthquake. Our results suggested in marine earthquakes, for earthquake forecasting we can merge a prior negative anomaly in the wind speed and SST_100. Regarding the in land cloudy sky earthquakes, merging anomaly parameters could be the negative prior anomaly in BT, skin temperature, in contrast, a positive anomaly in precipitation. In land transparent sky earthquake, usually negative prior anomalies in air temperature, specific humidity and LST.

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