scholarly journals Study on the Relationship between the Oceanic Nino Index and Surface Air Temperature and Precipitation Rate over the Kingdom of Saudi Arabia

2016 ◽  
Vol 04 (05) ◽  
pp. 146-162 ◽  
Author(s):  
Yehia Hafez
2012 ◽  
Vol 02 (03) ◽  
pp. 307-321 ◽  
Author(s):  
Hosny Mohamed Hasanean ◽  
Abdel Rahman Khalaf AL-Khalaf

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yehia Hafez ◽  
Abdulhaleem Labban

This paper presents a recent study of the relationship between precipitation rate (PR) over Saudi Arabia (SA) within the months of the fall season and climatic indices. The fall monthly PR data spanning the study period between 1948 and 2018 is considered. In addition, the monthly climatic index records (arctic oscillation (AO), global surface air temperature (GSAT), multivariate ENSO index (MEI), North Atlantic Oscillation (NAO) index, Nino 3.4 index, and Southern Oscillation Index (SOI)) for the fall months were also considered. The statistical trend, anomaly, and correlation analyses are applied in this study. The results reveal that the sweeping changes in PR show generally positive trends throughout the fall seasons of the past decades. Moreover, the climatic indices have an effect on the PR over SA within the fall months and season. During the study period, the most substantial relationship recorded, with an inverse correlation of −0.7, is between the PR over SA and the climatic index of GSAT for September and October. Moreover, there is a clear correlation of +0.5 between the PR over SA and the ENSO and Nino 3.4 index for October and November.


2005 ◽  
Vol 18 (16) ◽  
pp. 3217-3228 ◽  
Author(s):  
D. W. Shin ◽  
S. Cocke ◽  
T. E. LaRow ◽  
James J. O’Brien

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.


Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1543
Author(s):  
Reinhardt Pinzón ◽  
Noriko N. Ishizaki ◽  
Hidetaka Sasaki ◽  
Tosiyuki Nakaegawa

To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.


2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.


Author(s):  
Abdullah AL Shehry ◽  
Simon Rogerson ◽  
N. Ben Fairweather ◽  
Mary Prior

The e-government paradigm refers to utilizing the potential of Information and Communication Technology (ICT) in the whole government body to meet citizens’ expectations via multiple channels. It is, therefore, a radical change within the public sector and in the relationship between a government and its stakeholders. In the light of that, the Kingdom of Saudi Arabia has a keen interest in this issue and thus it has developed a national project to implement e-government systems. However, many technological, managerial, and organisational issues must be considered and treated carefully before and after going online. Based on an empirical study, this article highlights the key organisational issues that affect e-government adoption in the Kingdom of Saudi Arabia at both national and agency levels.


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