Horizontally Homogeneous Rotating Radiative–Convective Equilibria at GCM Resolution

2008 ◽  
Vol 65 (6) ◽  
pp. 2003-2013 ◽  
Author(s):  
Isaac M. Held ◽  
Ming Zhao
Keyword(s):  
Surface Temperature ◽  
Atmospheric Model ◽  
Tropical Storm ◽  
Sea Surface ◽  
Tropical Storms ◽  
Average Intensity ◽  
Moist Convection ◽  
Coriolis Parameter ◽  
Global Models ◽  
Large Domain

Abstract Rotating radiative–convective equilibrium, using the column physics and resolution of GCMs, is proposed as a useful framework for studying the tropical storm–like vortices produced by global models. These equilibria are illustrated using the column physics and dynamics of a version of the GFDL Atmospheric Model 2 (AM2) at resolutions of 220, 110, and 55 km in a large 2 × 104 km square horizontally homogeneous domain with fixed sea surface temperature and uniform Coriolis parameter. The large domain allows a number of tropical storms to exist simultaneously. Once equilibrium is attained, storms often persist for hundreds of days. The number of storms decreases as sea surface temperatures increase, while the average intensity increases. As the background rotation is decreased, the number of storms also decreases. At these resolutions and with this parameterization of convection, a dense collection of tropical storms is always the end state of moist convection in the cases examined.

Optimizing COSMO_REA6 reanalysis radiation flux for a high resolution coastal ocean model

2020 ◽  
Author(s):  
Martin Vodopivec ◽  
Matjaž Ličer
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Model ◽  
Short Wave ◽  
Ocean Model ◽  
Sea Surface ◽  
Atmospheric Models ◽  
Fine Grid ◽  
Wave Flux

<p>When modelling coastal areas in high spatial resolution, it is also essential to obtain atmospheric forcing with suitably fine grid. The complex coastline and coastal orography exert strong influence on atmospheric fields, wind in particular, and the east Adriatic coast with numerous islands and coastal mountain ridges is a fine example. We decided to use a high resolution COSMO atmospheric reanalysis for our long term ROMS_AGRIF hindcasts, but in our initial experiments we found out that the atmospheric model significantly underestimates the short wave flux over the Mediterranean Sea, probably due to overestimation of high clouds formation and erroneous sea surface temperature used as a boundary condition. We explore different atmospheric models and different combinations of fluxes - direct, diffuse and clear sky solar radiation and combinations of fluxes from different atmospheric models (eg. ERA5). We compare them with solar irradiance observations at a coastal meteorological station and run year-long simulations to compare model sea surface temperature (SST) with satellite observations obtained from Coprenicus Marine Environment Monitoring Service.</p>

North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE): Statistical Modeling and Sensitivity to Sea Surface Temperature Changes

2012 ◽  
Vol 25 (2) ◽  
pp. 625-637 ◽  
Author(s):  
Gabriele Villarini ◽  
Gabriel A. Vecchi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Modeling ◽  
North Atlantic ◽  
Power Dissipation ◽  
Input Data ◽  
Tropical Storm ◽  
Sea Surface ◽  
Tropical Atlantic ◽  
Storm Activity

Abstract This study focuses on the statistical modeling of the power dissipation index (PDI) and accumulated cyclone energy (ACE) for the North Atlantic basin over the period 1949–2008, which are metrics routinely used to assess tropical storm activity, and their sensitivity to sea surface temperature (SST) changes. To describe the variability exhibited by the data, four different statistical distributions are considered (gamma, Gumbel, lognormal, and Weibull), and tropical Atlantic and tropical mean SSTs are used as predictors. Model selection, both in terms of significant covariates and their functional relation to the parameters of the statistical distribution, is performed using two penalty criteria. Two different SST datasets are considered [the Met Office’s Global Sea Ice and Sea Surface Temperature dataset (HadISSTv1) and NOAA’s extended reconstructed SST dataset (ERSSTv3b)] to examine the sensitivity of the results to the input data. The statistical models presented in this study are able to well describe the variability in the observations according to several goodness-of-fit diagnostics. Both tropical Atlantic and tropical mean SSTs are significant predictors, independently of the SST input data, penalty criterion, and tropical storm activity metric. The application of these models to centennial reconstructions and seasonal forecasting is illustrated. The sensitivity of North Atlantic tropical cyclone frequency, duration, and intensity is examined for both uniform and nonuniform SST changes. Under uniform SST warming, these results indicate that there is a modest sensitivity of intensity, and a decrease in tropical storm and hurricane frequencies. On the other hand, increases in tropical Atlantic SST relative to the tropical mean SST suggest an increase in the intensity and frequency of North Atlantic tropical storms and hurricanes.

scholarly journals Impact of Sea Surface Temperature and Soil Moisture on Seasonal Rainfall Prediction over the Sahel

2005 ◽  
Vol 18 (24) ◽  
pp. 5330-5343 ◽  
Author(s):  
Wassila M. Thiaw ◽  
Kingtse C. Mo
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Primary Source ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Large Temperature ◽  
Rainfall Prediction ◽  
The North ◽  
Southward Shift

Abstract The ensemble rainfall forecasts over the Sahel for July–September (JAS) from the NCEP Coupled Forecast System (CFS) were evaluated for the period 1981–2002. The comparison with the gauge-based precipitation analysis indicates that the predicted Sahel rainfall is light and exhibits little interannual variability. The rain belt is shifted about 4° southward. One major source of rainfall errors comes from the erroneous sea surface temperature (SST) forecasts. The systematic SST error pattern has positive errors in the North Pacific and the North Atlantic and negative errors in the tropical Pacific and the southern oceans. It resembles the decadal SST mode, which has a significant influence on rainfall over the Sahel. Because the systematic SST errors were not corrected during the forecasts, persistent errors serve as an additional forcing to the atmosphere. The second source of error is from the soil moisture feedback, which contributes to the southward shift of rainfall and dryness over West Africa. This was demonstrated by the comparison between simulations (SIMs) and the Atmospheric Model Intercomparison Project (AMIP) run. Both are forced with observed SSTs. The SIMs initialized at the end of June have realistic soil moisture and do not show the southward shift of rainfall. The AMIP, which predicts soil moisture, maintains the dryness through the summer over the Sahel. For AMIP, the decreased rainfall is contributed by the decreased evaporation (E) due to the dry soil and the shift of the large temperature gradients southward. In response, the African easterly jet (AEJ) shifts southward. Since this jet is the primary source of energy for the African waves and their associated mesoscale convective systems, these too shift southward. This negative feedback contributes to increased dryness over the Sahel.

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