Effects of sea surface temperature and large-scale dynamics on the thermodynamic equilibrium state and convection over the tropical western Pacific

1999 ◽  
Vol 104 (D6) ◽  
pp. 6093-6100 ◽  
Author(s):  
Xiaoqing Wu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Equilibrium State ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Thermodynamic Equilibrium ◽  
Large Scale ◽  
Western Pacific ◽  
Sea Surface ◽  
Tropical Western Pacific ◽  
Thermodynamic Equilibrium State

scholarly journals Effects of Diurnal Variations on Tropical Equilibrium States: A Two-Dimensional Cloud-Resolving Modeling Study

2007 ◽  
Vol 64 (2) ◽  
pp. 656-664 ◽  
Author(s):  
Shouting Gao ◽  
Yushu Zhou ◽  
Xiaofan Li
Keyword(s):  
Equilibrium State ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Diurnal Variations ◽  
Equilibrium States ◽  
Sea Surface ◽  
Two Dimensional ◽  
Time Invariant

Abstract Effects of diurnal variations on tropical heat and water vapor equilibrium states are investigated based on hourly data from two-dimensional cloud-resolving simulations. The model is integrated for 40 days and the simulations reach equilibrium states in all experiments. The simulation with a time-invariant solar zenith angle produces a colder and drier equilibrium state than does the simulation with a diurnally varied solar zenith angle. The simulation with a diurnally varied sea surface temperature generates a colder equilibrium state than does the simulation with a time-invariant sea surface temperature. Mass-weighted mean temperature and precipitable water budgets are analyzed to explain the thermodynamic differences. The simulation with the time-invariant solar zenith angle produces less solar heating, more condensation, and consumes more moisture than the simulation with the diurnally varied solar zenith angle. The simulation with the diurnally varied sea surface temperature produces a colder temperature through less latent heating and more IR cooling than the simulation with the time-invariant sea surface temperature.

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

Sign in / Sign up

Export Citation Format

Share Document