scholarly journals On the Ocean Mixed Layer influence on the genesis of Mediterranean Tropical-Like cyclones

2021 ◽  
Author(s):  
Antonio Ricchi ◽  
Giovanni Liguori ◽  
Leone Cavicchia ◽  
Mario Marcello Miglietta ◽  
Davide Bonaldo ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Case Studies ◽  
Mixed Layer ◽  
Heat Content ◽  
Heat Fluxes ◽  
Ocean Heat Content ◽  
Sea Surface ◽  
Ocean Mixed Layer ◽  
Vast Number

<p>The Mediterranean basin is the formation site of a vast number and type of cyclones. Among these, we can occasionally identify intense vortices showing tropical characteristics, called Tropical-Like Cyclones (TLC). Their development has been studied in several case studies, showing the influence of synoptic scale upper level forcings and mesoscale features, such as the sea surface temperature and the characteristics of the air masses on the formation area. The importance of Sea Surface Temperature (SST) consists in modulating the intense latent and sensible heat fluxes, which control the development of the TLC. For tropical cyclones, one of the most studied factors in recent years is the ocean heat content in the formation basin of these storms. We plan here to extend this analysis to TLC. Besides innovative studies with coupled atmosphere-waves-ocean numerical models, a simpler approach for investigating the sole effect of the ocean heat content consists of adopting a simplified ocean description by varying the local characteristics of the Ocean Mixed Layer (OML). In this work we use the WRF (Weather Research and Forecasting system) model, in standalone (atmospheric) mode, with 3 km grid spacing, forced with GFS-GDAL (0.25°x0.25° horizontal resolution) and SST initialization provided by the MFS-CMEMs Copernicus dataset. Two case studies of TLC are examined here, namely ROLF (06-09/11/2011) and IANOS (14-19/09/2020). The ocean is simulated with an OML approach, with SST updated at each iteration as a function of the atmospheric heat fluxes and with an average mixed layer deph (MDL) provided by the MFS-CMEMS dataset. For each TLC studied, the MDL is modified by increasing and decreasing its depth by 10 mt, 30 mt, 50 mt . The preliminary results show how the structure of the MDL influences  the intensity of the cyclone but also the structure and precipitation both in terms of quantity and location. </p>

On the Ocean Mixed Layer influence on the genesis of Mediterranean Tropical-Like cyclones

2020 ◽  
Author(s):  
Antonio Ricchi ◽  
Davide Bonaldo ◽  
Mario Marcello Miglietta ◽  
Sandro Carniel
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Case Studies ◽  
Mixed Layer ◽  
Heat Content ◽  
Heat Fluxes ◽  
Ocean Heat Content ◽  
Sea Surface ◽  
Lapse Rate ◽  
Ocean Mixed Layer

<p>The Mediterranean basin is the formation site of a vast number and type of cyclones. Among these, we can occasionally identify intense vortices showing tropical characteristics, called Tropical-Like Cyclones (TLC) or MEDIcanes (Mediterranean Hurricane). Their development has been studied in several case studies, showing the influence of synoptic scale upper level forcings and mesoscale features, such as the sea surface temperature and the characteristics of the air masses on the formation area. The importance of Sea Surface Temperature (SST) consists in modulating the intense latent and sensible heat fluxes, which control the development of the TLC. For tropical cyclones, one of the most studied factors in recent years is the ocean heat content in the formation basin of these storms. We plan here to extend this analysis to TLC. Besides innovative studies with coupled atmosphere-waves-ocean numerical models, a simpler approach for investigating the sole effect of the ocean heat content consists of adopting a simplified ocean (1-Dimensional) description by varying the local characteristics of the Ocean Mixed Layer (OML). In this work we use the WRF (Weather Research and Forecasting system) model, in standalone (atmospheric) mode, with 3 km grid spacing, forced with GFS-GDAL (0.25°x0.25° horizontal resolution) and SST initialization provided by the MFS-CMEMs Copernicus dataset. Three case studies of TLC are examined here, namely ROLF (06-09/11/2011), ILONA (19-21/01/2014) and NUMA (11-20/11/2017). The ocean is simulated with an OML approach, with SST updated at each iteration as a function of the atmospheric heat fluxes and with an average mixed layer deph (MDL) provided by the MFS-CMEMS dataset. For each TLC studied, the MDL is modified by increasing and decreasing its depth by 50% and increasing and decreasing its lapse rate by 50%. The results show how the structure of the MDL influences not only the intensity of the cyclone but also the structure and precipitation both in terms of quantity and location. These outcomes suggest that, as for hurricanes, also for MEDICANES the heat content of the mass of seawater plays a fundamental role in their intensification, suggesting further studies also in a climate change perspective.</p>

scholarly journals Insights into Decadal North Atlantic Sea Surface Temperature and Ocean Heat Content Variability from an Eddy-Permitting Coupled Climate Model

2019 ◽  
Vol 32 (18) ◽  
pp. 6137-6161 ◽  
Author(s):  
B. I. Moat ◽  
B. Sinha ◽  
S. A. Josey ◽  
J. Robson ◽  
P. Ortega ◽  
...  
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
Mixed Layer ◽  
North Atlantic ◽  
Climate Model ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Sea Surface

Abstract An ocean mixed layer heat budget methodology is used to investigate the physical processes determining subpolar North Atlantic (SPNA) sea surface temperature (SST) and ocean heat content (OHC) variability on decadal to multidecadal time scales using the state-of-the-art climate model HadGEM3-GC2. New elements include development of an equation for evolution of anomalous SST for interannual and longer time scales in a form analogous to that for OHC, parameterization of the diffusive heat flux at the base of the mixed layer, and analysis of a composite Atlantic meridional overturning circulation (AMOC) event. Contributions to OHC and SST variability from two sources are evaluated: 1) net ocean–atmosphere heat flux and 2) all other processes, including advection, diffusion, and entrainment for SST. Anomalies in OHC tendency propagate anticlockwise around the SPNA on multidecadal time scales with a clear relationship to the phase of the AMOC. AMOC anomalies lead SST tendencies, which in turn lead OHC tendencies in both the eastern and western SPNA. OHC and SST variations in the SPNA on decadal time scales are dominated by AMOC variability because it controls variability of advection, which is shown to be the dominant term in the OHC budget. Lags between OHC and SST are traced to differences between the advection term for OHC and the advection–entrainment term for SST. The new results have implications for interpretation of variations in Atlantic heat uptake in the CMIP6 climate model assessment.

scholarly journals Sea Surface Temperature and Ocean Heat Content during Tropical Cyclones Pam (2015) and Winston (2016) in the Southwest Pacific Region

2020 ◽  
Author(s):  
ASHNEEL CHANDRA ◽  
SUSHIL KUMAR
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Heat Content ◽  
Heat Fluxes ◽  
Ocean Heat Content ◽  
Sea Surface ◽  
Pacific Region ◽  
Sst Cooling ◽  
Argo Profiles

AbstractThe sea surface temperature (SST) and upper ocean heat content (OHC) have been explored along the track of two tropical cyclones (TCs), TC Pam (2015) and TC Winston (2016). These TCs severely affected the islands of Vanuatu and Fiji, in the South Pacific Region (8°–30°S, 140°E– 170°W). The SST decreased by as much as 5.4°C along the tracks of the TCs with most cooling occurring to the left of the TCs tracks relative to TCs motion. SST cooling of 1-5°C has generally been observed during both the forced and relaxation stages of TC passage. The Argo profiles near the TC revealed observable mixed layer deepening. Subsurface warming was also observed post-TC passage from the temperature profile of one of the floats after the passage of both TCs. The OHC and heat fluxes are seen to play an important part in TC intensification as both these TCs intensified after passing over the regions of high OHC and enhanced heat fluxes. Apart from the traditionally used OHC obtained up to the depth of the 26°C isotherm (QH), the OHC was also determined up to the depth of the 20°C isotherm (QH,20). The QH and QH,20 values decreased in the majority of cases post TC passage while QH,20 increased in one instance post-TC passage for both the TCs. QH,20 has also been used to identify heat energy changes at deeper levels and correlated well with the traditionally used OHC during the weaker stages of the TCs.

scholarly journals A New Method to Produce Sea Surface Temperature Using Satellite Data Assimilation into an Atmosphere–Ocean Mixed Layer Coupled Model

2013 ◽  
Vol 30 (12) ◽  
pp. 2926-2943 ◽  
Author(s):  
Eunjeong Lee ◽  
Yign Noh ◽  
Naoki Hirose
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Satellite Data ◽  
Weather Prediction ◽  
Coupled Model ◽  
Sea Surface ◽  
New Method ◽  
Ocean Mixed Layer

Abstract A new method of producing sea surface temperature (SST) data for numerical weather prediction is suggested, which is obtained from the assimilation of satellite-derived SST into an atmosphere–ocean mixed layer coupled model. The Weather Research and Forecasting (WRF) Model and the Noh mixed layer model are used for the atmosphere and ocean mixed layer models, respectively. Data assimilation (DA) is carried out in two steps, based on the estimation from the covariance matching method that the daily mean SST of satellite data is more accurate than the model data, if the number of data in a grid per day is sufficiently large—that is, the daily mean SST bias correction in the first DA and the sequential SST anomaly correction in the second DA. For the second DA, the model restarts from the initial condition corrected by the first DA, and DA is applied every 30 min using the nudging method. The daily mean and the diurnal variation of satellite SST are assimilated to the bulk and skin SST, respectively. The modeled results with the new data assimilation scheme are validated by statistical comparison with independent satellite and buoy data such as correlation coefficient, root-mean-square difference, and bias. Furthermore, the sensitivity and seasonal variation of the weighting factor in the second DA are examined. The new approach illustrates the possibility of applying the atmosphere–ocean mixed layer coupled model for the production of SST data combined with the assimilation of satellite data.

scholarly journals Upper-Ocean Mixed Layer and Wintertime Sea Surface Temperature Anomalies in the North Pacific

2006 ◽  
Vol 19 (2) ◽  
pp. 300-307 ◽  
Author(s):  
Tomohiko Tomita ◽  
Masami Nonaka
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
North Pacific ◽  
Sea Surface ◽  
Upper Ocean ◽  
Ocean Mixed Layer ◽  
The North ◽  
The Impact ◽  
The North Pacific

Abstract In the North Pacific, the wintertime sea surface temperature anomaly (SSTA), which is represented by March (SSTAMar), when the upper-ocean mixed layer depth (hMar) reaches its maximum, is formed by the anomalous surface forcing from fall to winter (S′). As a parameter of volume, hMar has a potential to modify the impact of S′ on SSTAMar. Introducing an upper-ocean heat budget equation, the present study identifies the physical relationship among the spatial distributions of hMar, S′, and SSTAMar. The long-term mean of hMar adjusts the spatial distribution of SSTAMar. Without the adjustment, the impact of S′ on SSTAMar is overestimated where the hMar mean is deep. Since hMar is partially due to seawater temperature, it leads to nonlinearity between the S′ and the SSTAMar. When the SSTAMar is negative (positive), the sensitivity to S′ is impervious (responsive) with the deepening (shoaling) of the hMar compared to the linear sensitivity. The thermal impacts from the ocean to the atmosphere might be underestimated under the assumption of the linear relationship.

Exploratory Analysis of Upper-Ocean Heat Content and Sea Surface Temperature Underlying Tropical Cyclone Rapid Intensification in the Western North Pacific

2020 ◽  
Vol 33 (3) ◽  
pp. 1031-1050 ◽  
Author(s):  
Cheng-Hsiang Chih ◽  
Chun-Chieh Wu
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Pacific ◽  
Western North Pacific ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Sea Surface ◽  
Upper Ocean ◽  
Rapid Intensification ◽  
Upper Ocean Heat Content

AbstractThe statistical relationships between tropical cyclones (TCs) with rapid intensification (RI) and upper-ocean heat content (UOHC) and sea surface temperature (SST) from 1998 to 2016 in the western North Pacific are examined. RI is computed based on four best track datasets in the International Best Track Archive for Climate Stewardship (IBTrACS). The statistical analysis shows that the UOHC and SST are higher in the RI duration than in non-RI duration. However, TCs with high UOHC/SST do not necessarily experience RI. In addition, the UOHC and SST are lower in the storm inner-core region due to storm-induced ocean cooling, and the UOHC reduces more significantly than the SST along the passages of TCs in the lower-latitude regions. Moreover, most of the RI (non-RI) duration is associated with the higher (lower) UOHC, but this is not the case for the SST pattern. Meanwhile, the TC intensification rate during the RI period does not appear to be sensitive to the SST, but shows statistically significant differences in the UOHC. In addition, there is a statistically significant increasing trend in the UOHC underlying TCs from 1998 to 2016. It is also noted that the percentages of the TCs with RI show different polynomial and linear trends based on different calculations of the RI events and RI durations. Finally, it is shown that there is no statistically significant difference in the UOHC, SST, and the percentage of RI among the five categories of ENSO events (i.e., strong El Niño, weak El Niño, neutral, weak La Niña, and strong La Niña).

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