scholarly journals Sea Surface Temperature Influence on Terrestrial Gross Primary Production along the Southern California Current

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0125177 ◽  
Author(s):  
Janet J. Reimer ◽  
Rodrigo Vargas ◽  
David Rivas ◽  
Gilberto Gaxiola-Castro ◽  
J. Martin Hernandez-Ayon ◽  
...  
Keyword(s):  
Primary Production ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern California ◽  
Gross Primary Production ◽  
California Current ◽  
Sea Surface ◽  
Temperature Influence

scholarly journals Improving Remote Sensing-based Estimation of Mangrove Forest Gross Primary Production by Quantifying Environmental Stressors: Sea Surface Temperature, Salinity, and Photosynthetic Active Radiation

2021 ◽  
Author(s):  
Yuhan Zheng ◽  
Wataru Takeuchi
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gross Primary Production ◽  
Sea Surface ◽  
Photosynthetic Active Radiation ◽  
Mangrove Forests ◽  
Active Radiation ◽  
Mangrove Ecosystems

Abstract Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

scholarly journals Can we use sea surface temperature and productivity proxy records to reconstruct Ekman upwelling?

2019 ◽  
Vol 15 (6) ◽  
pp. 1985-1998
Author(s):  
Anson Cheung ◽  
Baylor Fox-Kemper ◽  
Timothy Herbert
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Current System ◽  
California Current ◽  
Sea Surface ◽  
California Current System ◽  
Proxy Records ◽  
Multiple Variables ◽  
Ekman Upwelling

Abstract. Marine sediments have greatly improved our understanding of the climate system, but their interpretation often assumes that certain climate mechanisms operate consistently over all timescales of interest and that variability at one or a few sample sites is representative of an oceanographic province. In this study, we test these assumptions using modern observations in an idealized manner mimicking paleo-reconstruction to investigate whether sea surface temperature and productivity proxy records in the Southern California Current System can be used to reconstruct Ekman upwelling. The method uses extended empirical orthogonal function (EEOF) analysis of the covariation of alongshore wind stress, chlorophyll, and sea surface temperature as measured by satellites from 2002 to 2009. We find that EEOF1 does not reflect an Ekman upwelling pattern but instead much broader California Current processes. EEOF2 and 3 reflect upwelling patterns, but these patterns are timescale dependent and regional. Thus, the skill of using one site to reconstruct the large-scale dominant patterns is spatially dependent. Lastly, we show that using multiple sites and/or multiple variables generally improves field reconstruction. These results together suggest that caution is needed when attempting to extrapolate mechanisms that may be important on seasonal timescales (e.g., Ekman upwelling) to deeper time but also the advantage of having multiple proxy records.

scholarly journals Analisis Spasial dan Temporal Sebaran Suhu Permukaan Laut di Perairan Sumatera Barat

2017 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Alfajri Alfajri ◽  
Mubarak Mubarak ◽  
Aras Mulyadi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Fluctuation ◽  
Water Mass ◽  
El Nino ◽  
Sea Surface ◽  
Temperature Influence ◽  
Daily Data ◽  
West Sumatra ◽  
Sunlight Intensity

This study was conducted on March-April 2016 in West Sumatra Waters. This study aimed to know distribution and sea surface temperature fluctuation daily and monthly in West Sumatra Waters and to know the factor that influences distribution and fluctuation of sea surface temperature in West Sumatra Waters. Sea surface temperature has taken from 3 stations which: Pariaman Waters, Padang-Pariaman Regency Waters and Bungus Waters, Padang. The result of daily data sea surface temperature by Aqua-Modis from 15 February, 20 February, 25 February, 2 March, 7 March and 12 March 2016 On West Sumatra Waters showed that the highest sea surface temperature was 34,54°C occured on 15 February and the lowest was 27,41°C on 12 March 2016. Average of monthly sea surface temperature on April 2015-March 2016 was about 27,07-34,98°C. The highest sea surface temperature occured on February and March 2016 and the lowest occurred on April and October 2015. Based on observation of monthly sea surface temperature knowed that sea surface temperature on western season increased and sea surface temperature and eastern season decreased. Observation showed that sea surface temperature influence by water mass moved because muson wind. Water mass moved impact to distribution of sea surface temperature on waters. The high or low of sea surface temperature in waters estimated because of sunlight intensity and rain on waters. As high the sunlight intensity to the waters so sea surface temperature on waters will increased and as high the rain so the sea surface temperature will decreased. El Nino phenomenon that occurred on February and March 2016 because sea surface temperature on that month was increased.

scholarly journals Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

2019 ◽  
Vol 11 (17) ◽  
pp. 1964 ◽  
Author(s):  
Jorge Vazquez-Cuervo ◽  
Jose Gomez-Valdes ◽  
Marouan Bouali ◽  
Luis Miranda ◽  
Tom Van der Stocken ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Current System ◽  
California Current ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
California Current System ◽  
The Impact ◽  
Buoy Measurements

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.

Assessment of sea-surface temperature at 42°N in the California Current over the last 30,000 years

1995 ◽  
Vol 10 (4) ◽  
pp. 763-773 ◽  
Author(s):  
Fredrick G. Prahl ◽  
Nicklas Pisias ◽  
Margaret A. Sparrow ◽  
Anne Sabin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
California Current ◽  
Sea Surface
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