scholarly journals Attribution and Impacts of Upper-Ocean Biases in CCSM3

2006 ◽  
Vol 19 (11) ◽  
pp. 2325-2346 ◽  
Author(s):  
W. G. Large ◽  
G. Danabasoglu

Abstract The largest and potentially most important ocean near-surface biases are examined in the Community Climate System Model coupled simulation of present-day conditions. They are attributed to problems in the component models of the ocean or atmosphere, or both. Tropical biases in sea surface salinity (SSS) are associated with precipitation errors, with the most striking being a band of excess rainfall across the South Pacific at about 8°S. Cooler-than-observed equatorial Pacific sea surface temperature (SST) is necessary to control a potentially catastrophic positive feedback, involving precipitation along the equator. The strength of the wind-driven gyres and interbasin exchange is in reasonable agreement with observations, despite the generally too strong near-surface winds. However, the winds drive far too much transport through Drake Passage [>190 Sv (1 Sv ≡ 106 m3 s−1)], but with little effect on SST and SSS. Problems with the width, separation, and location of western boundary currents and their extensions create large correlated SST and SSS biases in midlatitudes. Ocean model deficiencies are suspected because similar signals are seen in uncoupled ocean solutions, but there is no evidence of serious remote impacts. The seasonal cycles of SST and winds in the equatorial Pacific are not well represented, and numerical experiments suggest that these problems are initiated by the coupling of either or both wind components. The largest mean SST biases develop along the eastern boundaries of subtropical gyres, and the overall coupled model response is found to be linear. In the South Atlantic, surface currents advect these biases across much of the tropical basin. Significant precipitation responses are found both in the northwest Indian Ocean, and locally where the net result is the loss of an identifiable Atlantic intertropical convergence zone, which can be regained by controlling the coastal temperatures and salinities. Biases off South America and Baja California are shown to significantly degrade precipitation across the Pacific, subsurface ocean properties on both sides of the equator, and the seasonal cycle of equatorial SST in the eastern Pacific. These signals extend beyond the reach of surface currents, so connections via the atmosphere and subsurface ocean are implicated. Other experimental results indicate that the local atmospheric forcing is only part of the problem along eastern boundaries, with the representation of ocean upwelling another likely contributor.

2021 ◽  
pp. 1
Author(s):  
Yaru Guo ◽  
Yuanlong Li ◽  
Fan Wang ◽  
Yuntao Wei

AbstractNingaloo Niño – the interannually occurring warming episode in the southeast Indian Ocean (SEIO) – has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced fresh-water transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward fresh-water advection near the eastern boundary, which is critical in causing the strong freshening (> 0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with the El Niño-Southern Oscillation (0.57, 0.77, and 0.70 with Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (-0.27, -0.42, and -0.35) during 1993-2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.


2019 ◽  
Vol 11 (8) ◽  
pp. 919 ◽  
Author(s):  
Ziyao Mu ◽  
Weimin Zhang ◽  
Pinqiang Wang ◽  
Huizan Wang ◽  
Xiaofeng Yang

Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation.


2020 ◽  
Author(s):  
Samir Pokhrel ◽  
Hasibur Rahaman ◽  
Hemantkumar Chaudhari ◽  
Subodh Kumar Saha ◽  
Anupam Hazra

<p>IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.</p>


2016 ◽  
Vol 33 (3) ◽  
pp. 339-351 ◽  
Author(s):  
Hai Zhi ◽  
Rong-Hua Zhang ◽  
Fei Zheng ◽  
Pengfei Lin ◽  
Lanning Wang ◽  
...  

2015 ◽  
Vol 28 (19) ◽  
pp. 7717-7740 ◽  
Author(s):  
Maud Comboul ◽  
Julien Emile-Geay ◽  
Gregory J. Hakim ◽  
Michael N. Evans

Abstract This study formulates the design of optimal observing networks for past surface climate conditions as the solution to a data assimilation problem, given a realistic proxy system model (PSM), paleoclimate observational uncertainties, and a network of current and proposed observing sites. The method is illustrated with the design of optimal networks of coral δ18O records, chosen among candidate sites, and used to jointly infer sea surface temperature (SST) and sea surface salinity (SSS) fields from the Community Climate System Model, version 4, last millennium simulation over the 1850–2005 period. It is shown that an existing paleo-observing network accounts for approximately 20% of the SST variance, and that adding 25 to 100 optimal pseudocoral sites would boost this fraction to 35%–52%. Characterizing the SST variance alone, or jointly with the SSS, leads to similar optimal networks, which justifies using coral δ18O records for SST reconstructions. In contrast, the network design for reconstructing SSS alone is fundamentally different, emphasizing the hydroclimatic centers of action of El Niño–Southern Oscillation. In all cases, network design depends strongly on the amplitude of the observational error, so replicates may be more beneficial than the exploration of new sites; these replicates tend to be chosen where proxies are already informative of the large-scale climate field(s). Finally, extensions to other types of paleoclimatic observations are discussed, and a path to operationalization is outlined.


2013 ◽  
Vol 118 (10) ◽  
pp. 5109-5116 ◽  
Author(s):  
Audrey Hasson ◽  
Thierry Delcroix ◽  
Jacqueline Boutin

Sign in / Sign up

Export Citation Format

Share Document