Sea surface temperature evaluation of the Coastal Ocean Forecast System

2002 ◽  
Author(s):  
J.R. Schultz ◽  
F. Aikman
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Ocean ◽  
Sea Surface ◽  
Forecast System

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 Evaluating the Potential to Extract High-Resolution Paleoclimate Information from the Near-Shore New Zealand Molluscan Species Austrovenus stutchburyi

2021 ◽  
Author(s):  
◽  
Jessica J. Orsman
Keyword(s):  
Growth Rate ◽  
New Zealand ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Trace Element ◽  
Coastal Ocean ◽  
Redox Conditions ◽  
Sea Surface ◽  
Austrovenus Stutchburyi ◽  
Molluscan Species

<p>Li, B, Mg, Al, Mn, Cu, Zn, As, Sr, Ba and U/Ca ratios were measured by laser ablation inductively coupled plasma mass spectrometry for 11 modern Austrovenus stutchburyi clams to assess the potential of this molluscan species as a proxy for paleo-ocean temperature and environmental change. A. stutchburyi is an intertidal, infaunal, bivalve, widespread in New Zealand coastal regions and throughout the Quaternary-Pliocene sedimentary rock record. Five individuals from Ligar Bay and Estuary (South Island, New Zealand) were analysed to evaluate the variability between individuals calcifying in similar environmental conditions. A further six individuals were sampled from a range of latitudes (38˚ to 40˚) in the North Island, New Zealand to evaluate variability between individuals from different environments. A strong positive correlation between growth rate and Mg, Al, Mn, Sr, Ba and U/Ca ratios was observed, and a marked negative correlation was found between the same trace element/Ca ratios and ontogenetic age as growth rates slow during the molluscs' life. Thus, biological effects are the primary influence on trace element incorporation in A. stutchburyi. No clear seasonal variations were observed in the Mg and Sr/Ca ratio profiles through A. stutchburyi shells representing time periods of several years. Furthermore, for two shells for which chronologies could be reliably constructed, there were no significant correlations between Mg and Sr/Ca ratios and sea surface temperature. When Mg/Ca ratios were normalised to Sr/Ca ratios in order to eliminate the growth rate effect on trace element incorporation into the mollusc shells, some of the remaining variations appeared to visually correlate positively with sea surface temperature in several sections of a shell. However, a quantitative correlation did not confirm this (r² = 0.012). It is likely that neither Mg nor Sr incorporation into A. stutchburyi shell are primarily thermodynamically controlled. Several coincident Ba/Ca peaks in two of the Ligar Bay shells are most likely caused by environmental processes such as short periods of phytoplankton blooms or elevated seawater Ba/Ca from river flooding. Mn/Ca and U/Ca variations in A. stutchburyi from different coastal sites with different sediment characteristics appeared to be linked to the redox conditions prevailing at an open ocean sand-dominated environment (Ligar Bay) versus tidal mud flat environments (e.g. Miranda). Thus, while A. stutchburyi is unlikely to be a useful archive for past coastal ocean temperatures, it holds considerable promise for tracking past changes in coastal ocean productivity and river run-off, as well as sediment redox conditions.</p>

Assimilation of sea surface temperature in the POL Coastal Ocean Modelling System

2007 ◽  
Vol 65 (1-4) ◽  
pp. 27-40 ◽  
Author(s):  
I. Andreu-Burillo ◽  
J. Holt ◽  
R. Proctor ◽  
J.D. Annan ◽  
I.D. James ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Ocean ◽  
Sea Surface ◽  
Ocean Modelling ◽  
Modelling System

scholarly journals An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

2020 ◽  
Vol 12 (7) ◽  
pp. 1140
Author(s):  
Dimitrios N. Androulakis ◽  
Andrew Clive Banks ◽  
Costas Dounas ◽  
Dionissios P. Margaris
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Close Correlation ◽  
Coastal Ocean ◽  
Sea Surface ◽  
Human Society ◽  
In Situ Data ◽  
Satellite Sst ◽  
Near Shore

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

scholarly journals Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

2021 ◽  
Vol 3 ◽  
Author(s):  
Atul K. Sahai ◽  
Manpreet Kaur ◽  
Susmitha Joseph ◽  
Avijit Dey ◽  
R. Phani ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Real Time ◽  
Initial Conditions ◽  
Sea Surface ◽  
Prediction System ◽  
Forecast System ◽  
Time Prediction ◽  
Extended Range ◽  
Physics Ensemble

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

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