scholarly journals Applying urban climate model in prediction mode—evaluation of MUKLIMO_3 model performance for Austrian cities based on the summer period of 2019

2021 ◽  
Author(s):  
Brigitta Hollósi ◽  
Maja Žuvela-Aloise ◽  
Sandro Oswald ◽  
Astrid Kainz ◽  
Wolfgang Schöner
Keyword(s):  
Urban Areas ◽  
Climate Model ◽  
Weather Prediction ◽  
Strong Dependence ◽  
Urban Climate ◽  
Summer Period ◽  
Model Simulations ◽  
Microscale Model ◽  
Prediction Mode ◽  
Climate Model Simulations

AbstractExtreme heat events are natural hazards affecting many regions of the world. This study uses an example of the six largest cities in Austria to demonstrate the potential of urban climate model simulations applied in prediction mode providing detailed information on thermal conditions. For this purpose, the urban climate model MUKLIMO_3 of the German Meteorological Service (DWD) coupled with the hydrostatic numerical weather prediction model, ALARO, is used to simulate the development of the urban heat island (UHI) in Austrian cities for the summer period of 2019 with a horizontal resolution of 100 m. In addition to the evaluation of UHI predicting skills, other relevant variables, such as humidity and wind characteristics on hourly basis, are also analysed in this paper. Model evaluation confirmed that the MUKLIMO_3 microscale model had the capacity to simulate the main thermal spatiotemporal patterns in urban areas; however, a strong dependence on the input data from the mesoscale model was found. Our results showed large benefit in prediction of maximum air temperatures in urban areas, while the relative humidity predictions of MUKLIMO_3 appear to be much less plausible and show large variety of model prediction skills. Urban climate model simulations using real atmospheric conditions can facilitate better quantification and understanding of day-to-day intra-urban variations in microclimate as well as provide a basis for evaluation of the microclimate prediction skills of mesoscale numerical models with urban extensions.

Temperature and Water Vapor Variance Scaling in Global Models: Comparisons to Satellite and Aircraft Data

2011 ◽  
Vol 68 (9) ◽  
pp. 2156-2168 ◽  
Author(s):  
B. H. Kahn ◽  
J. Teixeira ◽  
E. J. Fetzer ◽  
A. Gettelman ◽  
S. M. Hristova-Veleva ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Resolution ◽  
Climate Model ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Small Scale ◽  
Scaling Exponents ◽  
Model Simulations ◽  
Free Running ◽  
Climate Model Simulations

Abstract Observations of the scale dependence of height-resolved temperature T and water vapor q variability are valuable for improved subgrid-scale climate model parameterizations and model evaluation. Variance spectral benchmarks for T and q obtained from the Atmospheric Infrared Sounder (AIRS) are compared to those generated by state-of-the-art numerical weather prediction “analyses” and “free-running” climate model simulations with spatial resolution comparable to AIRS. The T and q spectra from both types of models are generally too steep, with small-scale variance up to several factors smaller than AIRS. However, the two model analyses more closely resemble AIRS than the two free-running model simulations. Scaling exponents obtained for AIRS column water vapor (CWV) and height-resolved layers of q are also compared to the superparameterized Community Atmospheric Model (SP-CAM), highlighting large differences in the magnitude of CWV variance and the relative flatness of height-resolved q scaling in SP-CAM. Height-resolved q spectra obtained from aircraft observations during the Variability of the American Monsoon Systems Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) demonstrate changes in scaling exponents that depend on the observations’ proximity to the base of the subsidence inversion with scale breaks that occur at approximately the dominant cloud scale (~10–30 km). This suggests that finer spatial resolution requirements must be considered for future satellite observations of T and q than those currently planned for infrared and microwave satellite sounders.

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

Investigation of the Summer Climate of the Contiguous United States and Mexico Using the Regional Atmospheric Modeling System (RAMS). Part II: Model Climate Variability

2007 ◽  
Vol 20 (15) ◽  
pp. 3866-3887 ◽  
Author(s):  
Christopher L. Castro ◽  
Roger A. Pielke ◽  
Jimmy O. Adegoke ◽  
Siegfried D. Schubert ◽  
Phillip J. Pegion
Keyword(s):  
United States ◽  
Climate Model ◽  
Weather Prediction ◽  
Regional Climate ◽  
Summer Rainfall ◽  
Atmospheric Modeling ◽  
Regional Atmospheric Modeling System ◽  
Prediction Mode ◽  
Regional Atmospheric Modeling ◽  
Seasonal Weather

Abstract Summer simulations over the contiguous United States and Mexico with the Regional Atmospheric Modeling System (RAMS) dynamically downscaling the NCEP–NCAR Reanalysis I for the period 1950–2002 (described in Part I of the study) are evaluated with respect to the three dominant modes of global SST. Two of these modes are associated with the statistically significant, naturally occurring interannual and interdecadal variability in the Pacific. The remaining mode corresponds to the recent warming of tropical sea surface temperatures. Time-evolving teleconnections associated with Pacific SSTs delay or accelerate the evolution of the North American monsoon. At the period of maximum teleconnectivity in late June and early July, there is an opposite relationship between precipitation in the core monsoon region and the central United States. Use of a regional climate model (RCM) is essential to capture this variability because of its representation of the diurnal cycle of convective rainfall. The RCM also captures the observed long-term changes in Mexican summer rainfall and suggests that these changes are due in part to the recent increase in eastern Pacific SST off the Mexican coast. To establish the physical linkage to remote SST forcing, additional RAMS seasonal weather prediction mode simulations were performed and these results are briefly discussed. In order for RCMs to be successful in a seasonal weather prediction mode for the summer season, it is required that the GCM provide a reasonable representation of the teleconnections and have a climatology that is comparable to a global atmospheric reanalysis.

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