scholarly journals Predictable Patterns and Their Corresponding Signal Sources of Midsummer Surface Air Temperature Over Eastern China in the ECMWF S2S Forecasts

2021 ◽  
Author(s):  
Zikang Jia ◽  
Zhihai Zheng ◽  
Yufan Zhu ◽  
Naihui Zang ◽  
Guolin Feng ◽  
...  
Keyword(s):  
High Pressure ◽  
Surface Temperature ◽  
Air Temperature ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Subtropical High ◽  
The South ◽  
The North ◽  
Predictable Pattern ◽  
Ecmwf Model

Abstract The maximum signal-to-noise empirical orthogonal function (MSN EOF) method is used to evaluate the midsummer 2-m air temperature (T2m) over Eastern China of subseasonal to seasonal scale forecast data in ECMWF model, and investigate the underlying mechanisms between temperature modes and predictable sources. The first predictable pattern mainly presents the dipole mode of positive value in the south and negative value in the north. The model captures the signal of the transition from preceding El Niño to La Niña and accompanying tropical Indian Ocean warm surface temperature. In the summer of transforming years, the West Pacific Subtropical High is stronger and westward, meanwhile the southwest monsoon strengthens, which are the main direct influence factors of the high pressure in the south and the more precipitation in the north. Compared with observations, although the model captures the relationship between the temperature mode and the previous sea surface temperature signal, it obscures the mediating role of the Western Pacific Subtropical High. The second predictable pattern is the warmer characteristic of the Yangtze River valley (YRV), and North Atlantic Oscillation which the atmospheric internal variability is the main signal. The wave train propagating from northwestern Russia to Northeast Asia is the main cause of the abnormal high pressure over YRV. The third mode is mainly the temperature trend item, and the spatial characteristics of observation and model are quite different. ECMWF model shows high forecasting skills in the three modes, and presents high (low) surface pressure in areas with high (lower) temperatures, reduced (increased) precipitation and increased (reduced) solar radiation, which proving the model simulates the potential mechanism of circulation anomalies affecting surface air temperature commendably.

scholarly journals A Study of the Surface Air Temperature Variations in Nigeria

2017 ◽  
Vol 11 (1) ◽  
pp. 54-70 ◽  
Author(s):  
Najib Yusuf ◽  
Daniel Okoh ◽  
Ibrahim Musa ◽  
Samson Adedoja ◽  
Rabia Said
Keyword(s):  
Air Temperature ◽  
Cloud Cover ◽  
Coastal Region ◽  
Surface Air Temperature ◽  
The South ◽  
Climate Type ◽  
Temperature Variations ◽  
North Central ◽  
North East ◽  
The North

Background: Simultaneous measurements of air temperature were carried out using automatic weather stations at 14 tropical locations in Nigeria. Diurnal variations were derived from the 5-minute update cycle initial data for the years ranging between 2007 and 2013. The temperature trends in Nigeria revealed a continuous variability that is seasonally dependent within any particular year considered. Method: The analysis was carried out using available data from the network and the results are presented with a focus to characterize the temperature variations at different locations in the country using the mean, maximum and minimum temperatures from the north which is arid in nature to the south, which is a tropical monsoon climate type and a coastal region. Result: In overall, temperature variations in Nigeria were observed to have higher values in the far north, attributed to the influence of Sahara Desert, which has less cloud cover and therefore is more transparent to solar irradiance and lowers values in the south, where there are more cloud cover and abundant vegetation. Conclusion: Measured maximum and minimum temperatures in Nigeria are respectively 43.1°C at Yola (north-east part of Nigeria) and 10.2°C for Jos (north-central part of Nigeria). The least temperature variations were recorded for stations in the southern part of the country, while the largest variations were recorded in the north-central region of the country.

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

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

scholarly journals Temperature variability in Serbia in the second half of the 20th century

2004 ◽  
Vol 84 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Milan Radovanovic ◽  
Vladan Ducic
Keyword(s):  
20Th Century ◽  
Surface Air Temperature ◽  
The South ◽  
Intergovernmental Panel ◽  
Dominant Type ◽  
Climatic Regions ◽  
Middle Latitudes ◽  
The North ◽  
Winter Temperatures ◽  
Global Surface

According to data of IPCC (Intergovernmental Panel for Climatic Change), the global surface air temperature increased to 0,6 ? 0,2 ?C in the 20th century. Weber G. R., (1995) quotes that there is a trend of cold in the last 60 years in the middle latitudes including Europe, too. Starting from already mentioned perplexities we have tried to perceive the problem of climate variability in Serbia in the second half of the 20th century, when it came to very important increasing of concentration of CO2. With that aim we observed the decade values of average annual temperatures in the network of 20 climatic stations. In the period 1951 - 1990 a decrease of temperature was registered in 13 stations while in other stations an increase was less than 0,1 ?C. Explorers from Bulgaria (Alexandrov V., 2000) and Hungary (Domonkos P., Zoboki J., 2000) came to similar results, too. However, if we take in account the last decade 20th century the number of stations with positive changes is enlarged on 15. Stations that have small changes and those with decrease of temperature were localized in the south and south eastern part of the country, and they are mainly coincided with before separated climatic regions with maritime pluviometric regime (Radovanovic M., 2001). Using Dzerdzevskis B. L., (1975) division on three main types of circulation in the north hemisphere, we found that the increase of temperatures in the last decade 20th century is above all caused by change of dominant type of circulation from the south meridian to zonal. An analysis of seasonal changes showed that in the last five decades 20th century it came to decrease of winter temperatures in almost half of the stations in contrast with results of paleoclimatics models of possible greenhouse effect.

scholarly journals Influence of ENSO on North American subseasonal surface air temperature variability

2021 ◽  
Vol 2 (2) ◽  
pp. 395-412
Author(s):  
Patrick Martineau ◽  
Hisashi Nakamura ◽  
Yu Kosaka
Keyword(s):  
North America ◽  
Air Temperature ◽  
North American ◽  
Surface Air Temperature ◽  
Tropical Pacific ◽  
La Niña ◽  
Western North America ◽  
The North ◽  
Subseasonal Variability ◽  
La Nina

Abstract. The wintertime influence of tropical Pacific sea surface temperature (SST) variability on subseasonal variability is revisited by identifying the dominant mode of covariability between 10–60 d band-pass-filtered surface air temperature (SAT) variability over the North American continent and winter-mean SST over the tropical Pacific. We find that the El Niño–Southern Oscillation (ENSO) explains a dominant fraction of the year-to-year changes in subseasonal SAT variability that are covarying with SST and thus likely more predictable. In agreement with previous studies, we find a tendency for La Niña conditions to enhance the subseasonal SAT variability over western North America. This modulation of subseasonal variability is achieved through interactions between subseasonal eddies and La Niña-related changes in the winter-mean circulation. Specifically, eastward-propagating quasi-stationary eddies over the North Pacific are more efficient in extracting energy from the mean flow through the baroclinic conversion during La Niña. Structural changes of these eddies are crucial to enhance the efficiency of the energy conversion via amplified downgradient heat fluxes that energize subseasonal eddy thermal anomalies. The enhanced likelihood of cold extremes over western North America is associated with both an increased subseasonal SAT variability and the cold winter-mean response to La Niña.

Surface air–soil temperature relationship and shallow soil thermal regime: a case study using a soil temperature observational dataset for Spain.

2021 ◽  
Author(s):  
Camilo Melo Aguilar ◽  
Fidel González Rouco ◽  
Norman Steinert ◽  
Elena García Bustamante ◽  
Felix García Pereira ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
Thermal Regime ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Environmental Variable ◽  
Chemical Processes ◽  
Strong Connection ◽  
Shallow Subsurface

<p>The land-atmosphere interactions via the energy and water exchanges at the ground surface generally translate into a strong connection between the surface air temperature (SAT) and the ground surface temperature (GST). In turn, the surface temperature affects the amount of heat flowing into the soil, thus controlling the subsurface temperature profile. As soil temperature (ST) is a key environmental variable that controls various physical, biological and chemical processes, understanding the relationship between SAT and GST and STs is important.</p><p>In situ ST measurements represent the most adequate source of information to evaluate the distribution of temperature in soils and to address its influence on soil biological and chemical processes as well as on climate feedbacks. However, ST observations are scarce both in space and time. Therefore, the development of ST observational datasets is of great interest to promote analyses regarding the soil thermodynamics and the response to atmospheric warming.</p><p>We have developed a quality-controlled dataset of Soil Temperature Observations for Spain (SoTOS). The ST data are obtained from the Spanish meteorological agency (AEMET), including ST at different layers down to a depth of 1 m (i.e., 0.05, 0.1, 0.2, 0.5 and 1 m depth) for 39 observatories for the 1985–2018 period. Likewise, 2m air temperature has also been included for the same 39 sites.</p><p>SoTOS is employed to evaluate the shallow subsurface thermal regime and the SAT–GST relationship on interannual to multidecadal timescales. The results show that thermal conduction is the main heat transfer mechanism that controls the distribution of soil temperatures in the shallow subsurface. Regarding the SAT-GST relationship, there is a strong connection between SAT and GST. However, the SAT–GST coupling may be disrupted on seasonal to multidecadal timescales due to variations in the surface energy balance in response to decreasing soil moisture conditions over the last decade at some SoTOS sites. This results in larger GST warming relative to SAT. Such a response may have implications for climate studies that assume a strong connection between SAT and GST such as air temperature estimations from remote sensing products or even for palaeoclimatic analyses.</p>

Winter surface air temperature prediction over Japan using artificial neural networks

2021 ◽  
Author(s):  
J. V. Ratnam ◽  
Masami Nonaka ◽  
Swadhin K. Behera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Ann Model ◽  
Anomaly Correlation ◽  
The North ◽  
Skill Scores ◽  
Artificial Neural

AbstractThe machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

scholarly journals Contribution of SST change to multidecadal global and continental surface air temperature trends between 1910 and 2013

2019 ◽  
Vol 54 (3-4) ◽  
pp. 1295-1313
Author(s):  
Yidan Xu ◽  
Jianping Li ◽  
Cheng Sun ◽  
Xiaopei Lin ◽  
Hailong Liu ◽  
...  
Keyword(s):  
Air Temperature ◽  
North Atlantic ◽  
Surface Air Temperature ◽  
Western Pacific ◽  
Multidecadal Variability ◽  
The North ◽  
Temperature Trends ◽  
Community Atmosphere Model ◽  
Community Earth System Model ◽  
Goddard Institute

AbstractThe global mean surface air temperature (GMST) shows multidecadal variability over the period of 1910–2013, with an increasing trend. This study quantifies the contribution of hemispheric surface air temperature (SAT) variations and individual ocean sea surface temperature (SST) changes to the GMST multidecadal variability for 1910–2013. At the hemispheric scale, both the Goddard Institute for Space Studies (GISS) observations and the Community Earth System Model (CESM) Community Atmosphere Model 5.3 (CAM5.3) simulation indicate that the Northern Hemisphere (NH) favors the GMST multidecadal trend during periods of accelerated warming (1910–1945, 1975–1998) and cooling (1940–1975, 2001–2013), whereas the Southern Hemisphere (SH) slows the intensity of both warming and cooling processes. The contribution of the NH SAT variation to the GMST multidecadal trend is higher than that of the SH. We conduct six experiments with different ocean SST forcing, and find that all the oceans make positive contributions to the GMST multidecadal trend during rapid warming periods. However, only the Indian, North Atlantic, and western Pacific oceans make positive contributions to the GMST multidecadal trend between 1940 and 1975, whereas only the tropical Pacific and the North Pacific SSTs contribute to the GMST multidecadal trend between 2001 and 2013. The North Atlantic and western Pacific oceans have important impacts on modulating the GMST multidecadal trend across the entire 20th century. Each ocean makes different contributions to the SAT multidecadal trend of different continents during different periods.

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