Impact of improved Ocean initial condition in Climate Forecast System (CFSv2) Hindcast run

2020 ◽  
Author(s):  
Samir Pokhrel ◽  
Hasibur Rahaman ◽  
Hemantkumar Chaudhari ◽  
Subodh Kumar Saha ◽  
Anupam Hazra
Keyword(s):  
Monsoon Rainfall ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Climate Forecast ◽  
Surface Currents ◽  
Forecast System ◽  
Surface Salinity ◽  
Climate Forecast System ◽  
The Indian Ocean

<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>

scholarly journals Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

2021 ◽  
Vol 13 (15) ◽  
pp. 2995
Author(s):  
Frederick M. Bingham ◽  
Severine Fournier ◽  
Susannah Brodnitz ◽  
Karly Ulfsax ◽  
Hong Zhang
Keyword(s):  
High Resolution ◽  
Time Window ◽  
Single Point ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats ◽  
Statistical Measures ◽  
Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

Ocean Salinity Aspects of the Ningaloo Niño

2021 ◽  
pp. 1
Author(s):  
Yaru Guo ◽  
Yuanlong Li ◽  
Fan Wang ◽  
Yuntao Wei
Keyword(s):  
Fresh Water ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Ocean Salinity

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.

Assessing Sea Surface Salinity Derived by Aquarius in the Indian Ocean

2014 ◽  
Vol 11 (4) ◽  
pp. 719-722 ◽  
Author(s):  
Smitha Ratheesh ◽  
Rashmi Sharma ◽  
Rajesh Sikhakolli ◽  
Raj Kumar ◽  
Sujit Basu
Keyword(s):  
Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The Indian Ocean

Influence of the Madden-Julian Oscillation on sea surface salinity in the Indian Ocean

2011 ◽  
Vol 38 (17) ◽  
pp. n/a-n/a ◽  
Author(s):  
Gary Grunseich ◽  
Bulusu Subrahmanyam ◽  
Anthony Arguez
Keyword(s):  
Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Madden Julian Oscillation ◽  
Surface Salinity ◽  
The Indian Ocean

Meridional Distribution of Surface CO2 along 67°E of the Indian Ocean

2020 ◽  
Author(s):  
Dong-Jin Kang ◽  
Sang-Hwa Choi ◽  
Daeyeon Kim ◽  
Gyeong-Mok Lee
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Seawater ◽  
Surface Salinity ◽  
The North ◽  
The Indian Ocean ◽  
North And South

<p>Surface seawater carbon dioxide was observed from 3 °S to 27 °S along 67 °E of the Indian Ocean in April 2018 and 2019. Partial pressure of CO<sub>2</sub>(pCO<sub>2</sub>) in the surface seawater and the atmosphere were observed every two minutes using an underway CO2 measurement system (General Oceanics Model 8050) installed on R/V Isabu. Surface water temperature and salinity were measured as well. The pCO<sub>2</sub> was measured using Li-7000 NDIR. Standard gases were measured every 8 hours in five classes with concentrations of 0 µatm, 202 µatm, 350 µatm, 447 µatm, and 359.87 µatm. The fCO<sub>2</sub> of atmosphere remained nearly constant at 387 ± 2 µatm, but the surface seawater fCO<sub>2</sub> peaked at about 3 °S and tended to decrease toward the north and south. The distribution of fCO<sub>2</sub> in surface seawater according to latitude tends to be very similar to that of sea surface temperature. In order to investigate the factors that control the distribution of fCO<sub>2</sub> in surface seawater, we analyzed the sea surface temperature, sea surface salinity, and other factors. The effects of salinity are insignificant, and the surface fCO<sub>2</sub> distribution is mainly controlled by sea surface temperature and other factors that can be represented mainly by biological activity and mixing.</p>

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 Representation of MJO Variability in the NCEP Climate Forecast System

2011 ◽  
Vol 24 (17) ◽  
pp. 4676-4694 ◽  
Author(s):  
Scott J. Weaver ◽  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
El Nino ◽  
La Niña ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
La Nina ◽  
The Indian Ocean ◽  
Ncep Climate Forecast System

The Madden–Julian oscillation (MJO) is arguably the most important intraseasonal mode of climate variability, given its significant modulation of global climate variations and attendant societal impacts. Advancing the current understanding and simulation of the MJO using state-of-the-art climate data and modeling systems is thus a necessary goal for improving MJO prediction capability. MJO variability is assessed in NOAA/NCEP reanalyses and two versions of the Climate Forecast System (CFS), CFS version 1 (CFSv1) and its update version 2 (CFSv2). The analysis leans on a variety of diagnostic procedures and includes MJO sensitivity to varying El Niño–Southern Oscillation (ENSO) phases. It is found that significant improvements have been realized in the representation of MJO variations in the new NCEP Climate Forecast System reanalysis (CFSR) as evidenced by outgoing longwave radiation (OLR) power spectral analysis and more coherent propagation characteristics of precipitation and 850-hPa zonal winds over the Eastern Hemisphere in CFSR-only depictions. Conversely, while modest improvements are realized in the CFSv2 as compared to CFSv1, in general the simulation of the MJO continues to be a challenge. Both versions produce strong eastward propagating variance of convection and wind fields in the intraseasonal frequency band. However, the simulated MJO propagates slower than the observed with difficulties traversing the Maritime Continent into the western Pacific, as noted in many previous modeling studies. The CFS shows robust intraseasonal simulations over the west Pacific during El Niño years with diminished simulation capability over the Indian Ocean during La Niña years. This is likely a manifestation of the preference for La Niña MJO activity to occur over the Indian Ocean and the simulation challenges over that domain.

Prediction Skill and Bias of Tropical Pacific Sea Surface Temperatures in the NCEP Climate Forecast System Version 2

2013 ◽  
Vol 26 (15) ◽  
pp. 5358-5378 ◽  
Author(s):  
Yan Xue ◽  
Mingyue Chen ◽  
Arun Kumar ◽  
Zeng-Zhen Hu ◽  
Wanqiu Wang
Keyword(s):  
Tropical Pacific ◽  
Sea Surface ◽  
Eastern Pacific ◽  
Prediction Skill ◽  
Cold Bias ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
La Nina ◽  
Onset Phase

Abstract The prediction skill and bias of tropical Pacific sea surface temperature (SST) in the retrospective forecasts of the Climate Forecast System, version 2 (CFSv2), of the National Centers for Environmental Prediction were examined. The CFSv2 was initialized from the Climate Forecast System Reanalysis (CFSR) over 1982–2010. There was a systematic cold bias in the central–eastern equatorial Pacific during summer/fall. The cold bias in the Niño-3.4 index was about −2.5°C in summer/fall before 1999 but suddenly changed to −1°C around 1999, related to a sudden shift in the trade winds and equatorial subsurface temperature in the CFSR. The SST anomaly (SSTA) was computed by removing model climatology for the periods 1982–98 and 1999–2010 separately. The standard deviation (STD) of forecast SSTA agreed well with that of observations in 1982–98, but in 1999–2010 it was about 200% too strong in the eastern Pacific and 50% too weak near the date line during winter/spring. The shift in STD bias was partially related to change of ENSO characteristics: central Pacific (CP) El Niños were more frequent than eastern Pacific (EP) El Niños after 2000. The composites analysis shows that the CFSv2 had a tendency to delay the onset phase of the EP El Niños in the 1980s and 1990s but predicted their decay phases well. In contrast, the CFSv2 predicted the onset phase of the CP El Niños well but prolonged their decay phase. The hit rate for both El Niño and La Niña was lower in the later period than in the early period, and the false alarm for La Niña increased appreciably from the early to the later period.

scholarly journals Salinity changes in the Agulhas leakage area recorded by stable hydrogen isotopes of C<sub>37</sub> alkenones during Termination I and II

2014 ◽  
Vol 10 (1) ◽  
pp. 251-260 ◽  
Author(s):  
S. Kasper ◽  
M. T. J. van der Meer ◽  
A. Mets ◽  
R. Zahn ◽  
J. S. Sinninghe Damsté ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Saline Water ◽  
Planktonic Foraminifera ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Agulhas Current ◽  
Salinity Changes ◽  
Leakage Area ◽  
The Indian Ocean

Abstract. At the southern tip of Africa, the Agulhas Current reflects back into the Indian Ocean causing so-called "Agulhas rings" to spin off and release relatively warm and saline water into the South Atlantic Ocean. Previous reconstructions of the dynamics of the Agulhas Current, based on paleo-sea surface temperature and sea surface salinity proxies, inferred that Agulhas leakage from the Indian Ocean to the South Atlantic was reduced during glacial stages as a consequence of shifted wind fields and a northwards migration of the subtropical front. Subsequently, this might have led to a buildup of warm saline water in the southern Indian Ocean. To investigate this latter hypothesis, we reconstructed sea surface salinity changes using alkenone δD, and paleo-sea surface temperature using TEXH86 and UK'37, from two sediment cores (MD02-2594, MD96-2080) located in the Agulhas leakage area during Termination I and II. Both UK'37 and TEXH86 temperature reconstructions indicate an abrupt warming during the glacial terminations, while a shift to more negative δDalkenone values of approximately 14‰ during glacial Termination I and II is also observed. Approximately half of the isotopic shift can be attributed to the change in global ice volume, while the residual isotopic shift is attributed to changes in salinity, suggesting relatively high salinities at the core sites during glacials, with subsequent freshening during glacial terminations. Approximate estimations suggest that δDalkenone represents a salinity change of ca. 1.7–1.9 during Termination I and Termination II. These estimations are in good agreement with the proposed changes in salinity derived from previously reported combined planktonic Foraminifera δ18O values and Mg/Ca-based temperature reconstructions. Our results confirm that the δD of alkenones is a potentially suitable tool to reconstruct salinity changes independent of planktonic Foraminifera δ18O.

scholarly journals Salinity changes in the Agulhas leakage area recorded by stable hydrogen isotopes of C<sub>37</sub> alkenones during Termination I and II

2013 ◽  
Vol 9 (3) ◽  
pp. 3209-3238 ◽  
Author(s):  
S. Kasper ◽  
M. T. J. van der Meer ◽  
A. Mets ◽  
R. Zahn ◽  
J. S. Sinninghe Damsté ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Saline Water ◽  
Planktonic Foraminifera ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Agulhas Current ◽  
Salinity Changes ◽  
Temperature Reconstructions ◽  
The Indian Ocean

Abstract. At the southern tip of the African shelf, the Agulhas Current reflects back into the Indian Ocean causing so called "Agulhas rings" to spin off and release relatively warm and saline water into the South Atlantic Ocean. Previous reconstructions of the dynamics of the Agulhas current, based on paleo sea surface temperature and sea surface salinity proxies, inferred that Agulhas leakage from the Indian Ocean to the South Atlantic is reduced as a consequence of changes in wind fields related to a northwards migration of ice masses and the subtropical front during glacial stages. Subsequently, this might have led to a build-up of warm saline water in the southern Indian Ocean. To investigate this latter hypothesis, we reconstructed sea surface salinity changes using alkenone δ D, and paleo sea surface temperature using TEXH86 and UK'37, from two sediment cores (MD02-2594, MD96-2080) located in the Agulhas leakage area during Termination I and II. Both UK'37 and TEXH86 temperature reconstructions infer an abrupt warming during the glacial terminations, which is different from the gradual warming trend previously reconstructed based on Mg/Ca ratios of Globigerina bulloides. These differences in temperature reconstructions might be related to differences in the growth season or depth habitat between organisms. A shift to more negative δ Dalkenone values of approximately 14‰ during glacial Termination I and approximately 13‰ during Termination II is also observed. Approximately half of these shifts can be attributed to the change in global ice volume, while the residual isotopic shift is attributed to changes in salinity, suggesting relatively high salinities at the core sites during glacials, with subsequent freshening during glacial terminations. Approximate estimations suggest that δ Dalkenone represents a salinity change of ca. 1.7–2 during Termination I and ca. 1.5–1.7 during Termination II. These estimations are in good agreement with the proposed changes in salinity derived from previously reported combined planktonic foraminifera δ18O values and Mg/Ca-based temperature reconstructions. Our results show that the δ D of alkenones is a potentially suitable tool to reconstruct salinity changes independent of planktonic foraminifera δ18O.

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