scholarly journals Climate estimates for Eastern Amazon with OLAM model

2014 ◽  
Vol 29 (spe) ◽  
pp. 2-10
Author(s):  
Renato Ramos da Silva ◽  
Maria Isabel Vitorino ◽  
Paulo Kuhn ◽  
Daniela dos Santos Ananias
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Case Studies ◽  
Rainy Season ◽  
Initial Conditions ◽  
Regional Climate ◽  
Sea Surface ◽  
Temperature Cycle ◽  
Good Representation ◽  
Grid Scheme

The OLAM model has as its characteristics the advantage to represent simultaneously the regional and global meteorological phenomena using a refining grid scheme. During REMAM project OLAM was applied for a few case studies with the goal to evaluate its performance to estimate the regional climate for the eastern Amazon during periods of El Niño and La Niña. Case studies were performed for the rainy periods of the years 2010 and 2011 that were driven by distinct oceanic conditions. Initially, the model results were compared with local observations. The results demonstrated that OLAM was able to represent well the major precipitating regions, the diurnal temperature cycle evolution, and the wind dynamics. After that, analysis of the results demonstrated that if we provide good initial conditions and a good representation of the sea surface temperature evolution, OLAM is able to forecast with two or three months in advance if a rainy season would be wet or dry.

scholarly journals A 1/4°-Spatial-Resolution Daily Sea Surface Temperature Climatology Based on a Blended Satellite and in situ Analysis

2014 ◽  
Vol 27 (21) ◽  
pp. 8221-8228 ◽  
Author(s):  
Viva F. Banzon ◽  
Richard W. Reynolds ◽  
Diane Stokes ◽  
Yan Xue
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Spatial Resolution ◽  
Sea Surface ◽  
Temperature Cycle ◽  
Western Boundary ◽  
Good Representation ◽  
Grid Points ◽  
Environmental Prediction

Abstract A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982–2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.

scholarly journals Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones

2018 ◽  
Vol 146 (7) ◽  
pp. 2065-2088 ◽  
Author(s):  
Fei He ◽  
Derek J. Posselt ◽  
Naveen N. Narisetty ◽  
Colin M. Zarzycki ◽  
Vijayan N. Nair
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Radiative Forcing ◽  
Initial Conditions ◽  
Sea Surface ◽  
Precipitation Rate ◽  
Analysis Framework ◽  
Control Factors

Abstract This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

scholarly journals An Analysis of the Nonstationarity in the Bias of Sea Surface Temperature Forecasts for the NCEP Climate Forecast System (CFS) Version 2

2012 ◽  
Vol 140 (9) ◽  
pp. 3003-3016 ◽  
Author(s):  
A. Kumar ◽  
M. Chen ◽  
L. Zhang ◽  
W. Wang ◽  
Y. Xue ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Long Range ◽  
Initial Conditions ◽  
Sea Surface ◽  
Climate Forecast ◽  
Prediction System ◽  
Forecast System ◽  
Climate Forecast System ◽  
Forecast Bias

Abstract For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts. Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2). Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.

scholarly journals Assessment of Better Prediction of Seasonal Rainfall by Climate Predictability Tool Using Global Sea Surface Temperature in Bangladesh

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Md. Zakaria Hossain ◽  
Md. Abul Kalam Azad ◽  
Samarendra Karmakar ◽  
Md. Nazrul Islam Mondal ◽  
Mohan Das ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South Asian ◽  
Rainy Season ◽  
Regional Cooperation ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Research Centre ◽  
Positive Deviation ◽  
Climate Predictability

This study was conducted to determine better prediction result of seasonal rainfall. To evaluate the better prediction of seasonal rainfall of rainy season (15 June-15 August) by Climate Predictability Tools (CPT) in the context of using sea surface temperature (SST) of starting month of rainy season compare to using SST of one month before the rainy season. The study was carried out at the South Asian Association for Regional Cooperation Meteorological Research Centre, Dhaka; Bangladesh between January and December, 2010. A correlation between rainfall at Rangpur, Dhaka, Barisal and Sylhet and global SST of different areas of the world was studied by using the both data of 1975- 2008 years with the help of the CPT to find more positive correlated SST with observed rainfall and use as predictor for giving the prediction of the year 2009. The statistical method applied using CPT which is canonical correlation analysis. Using SST of one month before rainy season as predictor, the positive deviation of predicted rainfall from observed rainfall was 1.34 mm/day at Sylhet and 0.9 mm/day at Dhaka. The negative deviation of mean rainfall was 1.16 mm/day at Rangpur and 1.10 mm/day at Barisal. Again, using of starting one month SST of rainy season as predictor, positive deviation of predicted rainfall from observed rainfall was 4.03 mm/day at Sylhet. The positive deviation of daily mean rainfall was found 6.58 mm/day at Dhaka and 6.23 mm/day over southern Bangladesh. The study reveals that SST of one month before rainy season was better predictor than SST of starting month of rainy season.

Sign in / Sign up

Export Citation Format

Share Document