scholarly journals Influence of vegetation cover change on the decadal trend of summer seasonal air temperature in the Pannonian Basin

2021 ◽  
Author(s):  
Albert Ruman ◽  
Anna Ruman
Keyword(s):  
Air Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Anthropogenic Impact ◽  
Pannonian Basin ◽  
Great Influence ◽  
Surface Air Temperature ◽  
Climate System ◽  
Earth’S Climate ◽  
The Impact

Abstract The influence of land surface vegetation on the atmosphere processes in the planetary boundary layer is of great importance for the study of weather and climatic conditions in the Earth’s climate system. Vegetation, as an integral part of the Earth’s climate system, has a great influence on the exchange of energy between land and the atmosphere and, consequently, a significant role in defining weather and climate patterns at the global, regional and local scales. However, due to the constant anthropogenic impact, this vegetation system is continuously changing mostly due to deforestation, afforestation, and forest fires which make it difficult to present them during the research of the Earth’s climate system. The aim of this study is to examine the impact of the regional vegetation change on the seasonal surface air temperature and was performed using the Max-Planck-Institute Earth System Model. The region of our research is located in the Pannonian Basin and is one of many regions in which the anthropogenic impact on geophysical changes in the environment is considerable. The study was carried out over a ten-year period, from 2002 to 2011, during which we showed that the change in the presence percentage between certain types of vegetation leads to warming up as well as cooling down of air during the summer season. We have also shown to what extent this change in vegetation has an impact on the surface air temperature trend as well as on the change in the albedo and flux of sensible heat.

Late 20th century increase in northern Svalbard glacier-derived runoff tracked by encrusting coralline algae

2020 ◽  
Author(s):  
Steffen Hetzinger ◽  
Jochen Halfar ◽  
Zoltan Zajacz ◽  
Marco Möller ◽  
Max Wisshak
Keyword(s):  
Global Warming ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
High Arctic ◽  
Climate System ◽  
Coralline Algae ◽  
The Arctic ◽  
Surface Ocean ◽  
The Impact ◽  
Arctic Surface

<p>The Arctic cryosphere is changing at a rapid pace due to global warming and the large-scale changes observed in the Arctic during the past decades exert a strong influence throughout the global climate system. The warming of Arctic surface air temperatures is more than twice as large as the global average over the last two decades and recent events indicate new extremes in the Arctic climate system, e.g. for the last five years Arctic annual surface air temperature exceeded that of any year since 1900 AD. Northern Spitsbergen, Svalbard, located in the High Arctic at 80°N, is a warming hotspot with an observed temperature rise of ~6°C over the last three decades indicating major global warming impacts. However, even the longest available datasets on Svalbard climatic conditions do not extend beyond the 1950s, inhibiting the study of long-term natural variability before anthropogenic influence. Ongoing climate trends strongly affect the state of both glaciers and seasonal snow in Svalbard. Modeled data suggest a marked increase in glacier runoff during recent decades in northern Svalbard. However, observational data are sparse and short and the potential effects on the surface ocean are unclear.<br>This study focuses on the ultra-high-resolution analysis of calcified coralline algal buildups growing attached to the shallow seafloor along Arctic coastlines. Analysis of these new annually-layered climate archives is based on the long-lived encrusting coralline algae <em>Clathromorphum compactum</em>, providing a historic perspective on recently observed changes. Here, we present a 200-year record of past surface ocean variability from Mosselbukta, Spitsbergen, northern Svalbard. By using algal Ba/Ca ratios as a proxy for past glacier-derived meltwater input, we investigate past multi-decadal-scale fluctuations in land-based freshwater contributions to the ocean surface layer. Our records, based on multiple coralline algal specimens, show a strong and statistically significant increasing trend in algal Ba/Ca ratios from the 1990s onwards, suggesting a drastic increase in land-based runoff at Mosselbukta. The drastic rate of increase is unprecedented during the last two centuries, directly capturing the impact of amplified surface air temperature warming on coastal high Arctic surface ocean environments.</p><p> </p>

scholarly journals Seasonally changing contribution of sea ice and snow cover to uncertainty in multi-decadal Eurasian surface air temperature trends based on CESM simulations

2021 ◽  
Author(s):  
Zhaomin Ding ◽  
Renguang Wu
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Western Siberia ◽  
Russian Far East ◽  
Far East ◽  
Surface Air Temperature ◽  
The Caspian Sea ◽  
Sea Ice Concentration ◽  
Northern Siberia ◽  
The Impact

AbstractThis study investigates the impact of sea ice and snow changes on surface air temperature (SAT) trends on the multidecadal time scale over the mid- and high-latitudes of Eurasia during boreal autumn, winter and spring based on a 30-member ensemble simulations of the Community Earth System Model (CESM). A dynamical adjustment method is used to remove the internal component of circulation-induced SAT trends. The leading mode of dynamically adjusted SAT trends is featured by same-sign anomalies extending from northern Europe to central Siberia and to the Russian Far East, respectively, during boreal spring and autumn, and confined to western Siberia during winter. The internally generated component of sea ice concentration trends over the Barents-Kara Seas contributes to the differences in the thermodynamic component of internal SAT trends across the ensemble over adjacent northern Siberia during all the three seasons. The sea ice effect is largest in autumn and smallest in winter. Eurasian snow changes contribute to the spread in dynamically adjusted SAT trends as well around the periphery of snow covered region by modulating surface heat flux changes. The snow effect is identified over northeast Europe-western Siberia in autumn, north of the Caspian Sea in winter, and over eastern Europe-northern Siberia in spring. The effects of sea ice and snow on the SAT trends are realized mainly by modulating upward shortwave and longwave radiation fluxes.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Nature-based cooling potential: a multi-type green infrastructure evaluation in Toronto, Ontario, Canada

2021 ◽  
Author(s):  
Vidya Anderson ◽  
William A. Gough
Keyword(s):  
Urban Agriculture ◽  
Air Temperature ◽  
Green Infrastructure ◽  
Urban Climate ◽  
Surface Air Temperature ◽  
Green Roofs ◽  
Near Surface ◽  
Green Walls ◽  
The Impact ◽  
Agriculture Systems

AbstractThe application of green infrastructure presents an opportunity to mitigate rising temperatures using a multi-faceted ecosystems-based approach. A controlled field study in Toronto, Ontario, Canada, evaluates the impact of nature-based solutions on near surface air temperature regulation focusing on different applications of green infrastructure. A field campaign was undertaken over the course of two summers to measure the impact of green roofs, green walls, urban vegetation and forestry systems, and urban agriculture systems on near surface air temperature. This study demonstrates that multiple types of green infrastructure applications are beneficial in regulating near surface air temperature and are not limited to specific treatments. Widespread usage of green infrastructure could be a viable strategy to cool cities and improve urban climate.

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 Seasonal Surface Air Temperature and Precipitation in the FSU Climate Model Coupled to the CLM2

2005 ◽  
Vol 18 (16) ◽  
pp. 3217-3228 ◽  
Author(s):  
D. W. Shin ◽  
S. Cocke ◽  
T. E. LaRow ◽  
James J. O’Brien
Keyword(s):  
Heat Flux ◽  
Air Temperature ◽  
Climate Model ◽  
Initial Conditions ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Community Land Model ◽  
Temperature And Precipitation ◽  
The Impact ◽  
Convective Scheme

Abstract The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations.

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.

Sensitivity of Latent Heat Flux from PILPS Land-Surface Schemes to Perturbations of Surface Air Temperature

1998 ◽  
Vol 55 (11) ◽  
pp. 1909-1927 ◽  
Author(s):  
Weiqing Qu ◽  
A. Henderson-Sellers ◽  
A. J. Pitman ◽  
T. H. Chen ◽  
F. Abramopoulos ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature

scholarly journals An Evaluation of Model Output Statistics for Subseasonal Streamflow Forecasting in European Catchments

2019 ◽  
Vol 20 (7) ◽  
pp. 1399-1416
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Linear Models ◽  
Surface Air Temperature ◽  
Grid Cell ◽  
Model Output ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Total Runoff ◽  
Model Output Statistics

AbstractSubseasonal and seasonal forecasts of the atmosphere, oceans, sea ice, or land surfaces often rely on Earth system model (ESM) simulations. While the most recent generation of ESMs simulates runoff per land surface grid cell operationally, it does not typically simulate river streamflow directly. Here, we apply the model output statistics (MOS) method to the hindcast archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). Linear models are tested that regress observed river streamflow on surface runoff, subsurface runoff, total runoff, precipitation, and surface air temperature simulated by ECMWF’s forecast systems S4 and SEAS5. In addition, the pool of candidate predictors contains observed precipitation and surface air temperature preceding the date of prediction. The experiment is conducted for 16 European catchments in the period 1981–2006 and focuses on monthly average streamflow at lead times of 0 and 20 days. The results show that skill against the streamflow climatology is frequently absent and varies considerably between predictor combinations, catchments, and seasons. Using streamflow persistence as a benchmark model further deteriorates skill. This is most pronounced for a catchment that features lakes, which extend to about 14% of the catchment area. On average, however, the predictor combinations using the ESM runoff simulations tend to perform best.

Sign in / Sign up

Export Citation Format

Share Document