scholarly journals Changes of Variability in Response to Increasing Greenhouse Gases. Part I: Temperature

2007 ◽  
Vol 20 (21) ◽  
pp. 5455-5467 ◽  
Author(s):  
R. J. Stouffer ◽  
R. T. Wetherald
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Annual Data ◽  
Tropospheric Temperature ◽  
High Latitudes ◽  
Near Surface ◽  
Temperature Variance ◽  
General Reduction

Abstract This study documents the temperature variance change in two different versions of a coupled ocean–atmosphere general circulation model forced with estimates of future increases of greenhouse gas (GHG) and aerosol concentrations. The variance changes are examined using an ensemble of 8 transient integrations for the older model version and 10 transient integrations for the newer one. Monthly and annual data are used to compute the mean and variance changes. Emphasis is placed upon computing and analyzing the variance changes for the middle of the twenty-first century and compared with those found in a control integration. The large-scale variance of lower-tropospheric temperature (including surface air temperature) generally decreases in high latitudes particularly during fall due to a delayed onset of sea ice as the climate warms. Sea ice acts to insolate the atmosphere from the much larger heat capacity of the ocean. Therefore, the near-surface temperature variance tends to be larger over the sea ice–covered regions, than the nearby ice-free regions. The near-surface temperature variance also decreases during the winter and spring due to a general reduction in the extent of sea ice during winter and spring. Changes in storminess were also examined and were found to have relatively little effect upon the reduction of temperature variance. Generally small changes of surface air temperature variance occurred in low and midlatitudes over both land and oceanic areas year-round. An exception to this was a general reduction of variance in the equatorial Pacific Ocean for the newer model. Small increases in the surface air temperature variance occur in mid- to high latitudes during the summer months, suggesting the possibility of more frequent and longer-lasting heat waves in response to increasing GHGs.

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 Projected Changes in Daily Variability and Seasonal Cycle of Near-Surface Air Temperature over the Globe during the Twenty-First Century

2019 ◽  
Vol 32 (24) ◽  
pp. 8537-8561 ◽  
Author(s):  
Jiao Chen ◽  
Aiguo Dai ◽  
Yaocun Zhang
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Seasonal Cycle ◽  
Surface Air Temperature ◽  
First Century ◽  
The Arctic ◽  
Boreal Summer ◽  
High Latitudes ◽  
Low Latitudes ◽  
Twenty First Century

Abstract Increases in atmospheric greenhouse gases will not only raise Earth’s temperature but may also change its variability and seasonal cycle. Here CMIP5 model data are analyzed to quantify these changes in surface air temperature (Tas) and investigate the underlying processes. The models capture well the mean Tas seasonal cycle and variability and their changes in reanalysis, which shows decreasing Tas seasonal amplitudes and variability over the Arctic and Southern Ocean from 1979 to 2017. Daily Tas variability and seasonal amplitude are projected to decrease in the twenty-first century at high latitudes (except for boreal summer when Tas variability increases) but increase at low latitudes. The day of the maximum or minimum Tas shows large delays over high-latitude oceans, while it changes little at low latitudes. These Tas changes at high latitudes are linked to the polar amplification of warming and sea ice loss, which cause larger warming in winter than summer due to extra heating from the ocean during the cold season. Reduced sea ice cover also decreases its ability to cause Tas variations, contributing to the decreased Tas variability at high latitudes. Over low–midlatitude oceans, larger increases in surface evaporation in winter than summer (due to strong winter winds, strengthened winter winds in the Southern Hemisphere, and increased winter surface humidity gradients over the Northern Hemisphere low latitudes), coupled with strong ocean mixing in winter, lead to smaller surface warming in winter than summer and thus increased seasonal amplitudes there. These changes result in narrower (wider) Tas distributions over the high (low) latitudes, which may have important implications for other related fields.

Recent Trends in Land Surface Temperature on the Tibetan Plateau

2006 ◽  
Vol 19 (12) ◽  
pp. 2995-3003 ◽  
Author(s):  
Yuichiro Oku ◽  
Hirohiko Ishikawa ◽  
Shigenori Haginoya ◽  
Yaoming Ma
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Precipitation Amount ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Diurnal Range

Abstract The diurnal, seasonal, and interannual variations in land surface temperature (LST) on the Tibetan Plateau from 1996 to 2002 are analyzed using the hourly LST dataset obtained by Japanese Geostationary Meteorological Satellite 5 (GMS-5) observations. Comparing LST retrieved from GMS-5 with independent precipitation amount data demonstrates the consistent and complementary relationship between them. The results indicate an increase in the LST over this period. The daily minimum has risen faster than the daily maximum, resulting in a narrowing of the diurnal range of LST. This is in agreement with the observed trends in both global and plateau near-surface air temperature. Since the near-surface air temperature is mainly controlled by LST, this result ensures a warming trend in near-surface air temperature.

Study on Urban Thermal Environment based on Diurnal Temperature Range

2020 ◽  
Author(s):  
Zheng Guo ◽  
Miaomiao Cheng
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Environment ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Near Surface ◽  
Diurnal Range ◽  
Urban Thermal Environment

<p>Diurnal temperature range (includes land surface temperature diurnal range and near surface air temperature diurnal range) is an important meteorological parameter, which is a very important factor in the field of the urban thermal environmental. Nowadays, the research of urban thermal environment mainly focused on surface heat island and canopy heat island.</p><p>Based on analysis of the current status of city thermal environment. Firstly, a method was proposed to obtain near surface air temperature diurnal range in this study, difference of land surface temperature between day and night were introduced into the improved temperature vegetation index feature space based on remote sensing data. Secondly, compared with the district administrative division, we analyzed the spatial and temporal distribution characteristics of the diurnal range of land surface temperature and near surface air temperature.</p><p>The conclusions of this study are as follows:</p><p>1 During 2003-2012s, the land surface temperature and near surface air temperature diurnal range of Beijing were fluctuating upward. The rising trend of the near surface air temperature diurnal range was more significant than land surface temperature diurnal range. In addition, the rise and decline of land surface temperature and near surface air temperature diurnal range in different districts were different. In the six city districts, the land surface temperature and near surface air temperature diurnal range in the six areas of the city were mainly downward. The decline trend of near surface air temperature diurnal range was more significant than land surface temperature diurnal range.</p><p>2 During 2003-2012s, the land surface temperature and near surface air temperature diurnal range of Beijing with similar characteristics in spatial distribution, with higher distribution land surface temperature and near surface air temperature diurnal range in urban area and with lower distribution of land surface temperature and near surface air temperature diurnal range in the Northwest Mountainous area and the area of Miyun reservoir.</p>

Temperate-zone reforestation as a tool for local climate adaptation? 

2021 ◽  
Author(s):  
Kimberly Novick
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Climate Adaptation ◽  
Heat Waves ◽  
Surface Air Temperature ◽  
Climate Mitigation ◽  
Surface Cooling ◽  
Local Cooling ◽  
Near Surface ◽  
Local Climate

<p>In addition to dramatic reductions to anthropogenic greenhouse gas emissions, most pathways for limiting global warming to less than 2 degrees C rely on managed alterations to the land surface designed to increase land carbon uptake and storage (so-called “natural climate solutions”, or NCS). Reforestation is the NCS with the largest estimated climate mitigation potential, and at least in energy-limited temperate climates, evidence is mounting that transitions from short-stature ecosystems (croplands, grasslands) to forests substantially reduce surface temperature. In this way, reforestation, at least in some places, may also represent a useful tool for local climate adaptation. However, existing work on the topic has tended to focus on how reforestation affects mean annual and seasonal surface temperature, with comparatively less attention paid to the biophysical impacts of reforestation when local cooling would be most beneficial (i.e. at mid-day, and especially droughts and heat waves). Moreover, while surface temperature is a critical driver of ecosystem processes, arguably the near-surface air temperature is the more relevant target for climate adaptation. The duality between reforestation impacts on surface and air temperature has historically been challenging to deconvolve, and thus we do not yet understand the extent to which forest surface cooling extends to the air. In this talk, new strategies are discussed for blending flux tower data and remote sensing observations to uncover the links between reforestation, surface energy balance, and near-surface air temperature dynamics, with a particular emphasis on how plant water use strategies mediate these relationships during summer days and periods of hydrologic stress.  </p>

scholarly journals Validation of reanalysis Southern Ocean atmosphere trends using sea ice data

2020 ◽  
Vol 20 (23) ◽  
pp. 14757-14768
Author(s):  
William R. Hobbs ◽  
Andrew R. Klekociuk ◽  
Yuhang Pan
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Surface Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Surface Energy Budget ◽  
Close Coupling ◽  
In Situ Data ◽  
Temperature Trends

Abstract. Reanalysis products are an invaluable tool for representing variability and long-term trends in regions with limited in situ data, and especially the Antarctic. A comparison of eight different reanalysis products shows large differences in sea level pressure and surface air temperature trends over the high-latitude Southern Ocean, with implications for studies of the atmosphere's role in driving ocean–sea ice changes. In this study, we use the established close coupling between sea ice cover and surface temperature to evaluate these reanalysis trends using the independent, 30-year sea ice record from 1980 to 2010. We demonstrate that sea ice trends are a reliable validation tool for most months of the year, although the sea ice–surface temperature coupling is weakest in summer when the surface energy budget is dominated by atmosphere-to-ocean heat fluxes. Based on our analysis, we find that surface air temperature trends in JRA55 are most consistent with satellite-observed sea ice trends over the polar waters of the Southern Ocean.

scholarly journals An energy budget approach to understand the Arctic warming during the Last Interglacial

2021 ◽  
Author(s):  
Marie Sicard ◽  
Masa Kageyama ◽  
Sylvie Charbit ◽  
Pascale Braconnot ◽  
Jean-Baptiste Madeleine
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Air Temperature ◽  
Energy Budget ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Last Interglacial ◽  
Near Surface ◽  
Arctic Warming ◽  
The Arctic Ocean

Abstract. The Last Interglacial period (129–116 ka BP) is characterized by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the northern hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and pi-Control simulations performed with the IPSL-CM6A-LR model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a near-surface air temperature rise of 3.2 °C over the Arctic region. This warming is primarily due to a strong positive surface downwelling shortwave radiation anomaly over continental surfaces, followed by large heat transfers from the continents back to the atmosphere. The surface layers of the Arctic Ocean also receives more energy, but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchanges from the continental surfaces towards the atmosphere are strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation, but occurs in autumn with a temperature increase of 4.0 °C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiations over the Arctic ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (−1.9 × 106 and −3.4 × 106 km2 respectively), which exposes the warm oceanic surface and allows heat stored by the ocean in summer and water vapour to be released. This study highlights the crucial role of the sea ice cover variations, the Arctic ocean, as well as changes in polar clouds optical properties on the Last Interglacial Arctic warming.

scholarly journals A New Model to Downscale Urban and Rural Surface and Air Temperatures Evaluated in Shanghai, China

2018 ◽  
Vol 57 (10) ◽  
pp. 2267-2283 ◽  
Author(s):  
Dongwei Liu ◽  
C. S. B. Grimmond ◽  
Jianguo Tan ◽  
Xiangyu Ao ◽  
Jie Peng ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Wave Radiation ◽  
Climate Services ◽  
Near Surface ◽  
Surface Temperatures ◽  
Air Temperatures

AbstractA simple model, the Surface Temperature and Near-Surface Air Temperature (at 2 m) Model (TsT2m), is developed to downscale numerical model output (such as from ECMWF) to obtain higher-temporal- and higher-spatial-resolution surface and near-surface air temperature. It is evaluated in Shanghai, China. Surface temperature (Ts) and near-surface air temperature (Ta) submodels account for variations in land cover and their different thermal properties, resulting in spatial variations of surface and air temperature. The net all-wave radiation parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature submodel, the objective hysteresis model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near-surface air temperature submodel considers the horizontal and vertical energy changes for a column of well-mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land-cover types, values are more similar. Downscaled, higher-temporal- and higher-spatial-resolution air temperatures are compared to observations at 110 automatic weather stations across Shanghai. After downscaling with TsT2m, the average forecast accuracy of near-surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stress.

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