Sea Surface Temperature Analysis Research Articles

Signals of intense primary production in response to Ulva prolifera bloom in the Yellow Sea during summer 2021

The variability in changing climate has been predicted to lead to significant changes in the world's oceans primary productivity. Recent years have experienced more intense algal blooms during the spring-summer months in many coastal waters. Here we present results from the analysis of satellite ocean color products from MODIS-Terra datasets during June 2 to July 20, 2021, corresponding to the most intense period of the Enteromorpha (Ulva) prolifera algae (U. prolifera) bloom in the Yellow Sea. Because the studies on sea surface temperature (SST) variability over the U. prolifera bloom have not yet been fully explored by satellite measurements in the offshore waters of Qingdao, China, we analyzed the MODIS SST and their relationship with the U. prolifera blooms in the 2021 summer months. Analysis of SST showed a variation in the range of 18–24 °C in near-shore waters in response to the bloom of green algae. We find that a significant thermal front with a gradient magnitude of 0.03 °C/km was developed over the shelf and offshore regions during the algal bloom period. The observed changes in thermal fronts are driven by high-wind speed and Ekman pumping-velocity processes. We concluded that the changes in SSTs with a range of 20–23 °C, high-magnitude thermal fronts and wind speed, and Ekman pumping velocity resulted in the intensification of the largest bloom of U. prolifera in the Yellow Sea during the study period.

Clustering tropical cyclone genesis on ENSO timescales in the Southwest Pacific

Tropical cyclones (TCs) as a natural hazard pose a major threat and risk to the human population globally. This threat is expected to increase in a warming climate as the frequency of severe TCs is expected to increase. In this study, the influence of different monthly sea surface temperature (SST) patterns on the locations and frequency of tropical cyclone genesis (TCG) in the Southwest Pacific (SWP) region is investigated. Using principal component analysis and k-means clustering of monthly SST between 1970 and 2019, nine statistically different SST patterns are identified. Our findings show that the more prominent ENSO patterns such as the Modoki El Niño (i.e., Modoki I and Modoki II) and Eastern Pacific (EP) El Niño impact the frequency and location of TCG significantly. Our results enhance the overall understanding of the TCG variability and the relationship between TCG and SST configurations in the SWP region. The results of this study may support early warning system in SWP by improving seasonal outlooks and quantification of the level of TC-related risks for the vulnerable Pacific Island communities.

Citizen scientists’ dive computers resolve seasonal and interannual temperature variations in the Red Sea

Dive computers have the potential to provide depth resolved temperature data that is often lacking especially in close to shore, but spatiotemporal assessment of the robustness of this citizen science approach has not been done. In this study, we provide this assessment for the Red Sea, one of the most dived areas in the world. A comparison was conducted between 17 years of minimum water temperatures collected from SCUBA dive computers in the northern Red Sea (23–30° N, 32–39.4° E), satellite-derived sea surface temperatures from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) optimal interpolation product, and depth-banded monthly mean in-situ temperature from the TEMPERSEA dataset, which incorporates data originating from several in-situ recording platforms (including Argo floats, ships and gliders). We show that dive computer temperature data clearly resolve seasonal patterns, which are in good agreement in both phase and amplitude with OSTIA and TEMPERSEA. On average, dive computer temperatures had an overall negative bias of (–0.5 ± 1.1) °C compared with OSTIA and (–0.2 ± 1.4) °C compared with TEMPERSEA. As may be expected, increased depth-related biases were found to be associated with stratified periods and shallower mixed layer depths, i.e., stronger vertical temperature gradients. A south-north temperature gradient consistent with values reported in the literature was also identifiable. Bias remains consistent even when subsampling just 1% of the total 9310 dive computer datapoints. We conclude that dive computers offer potential as an alternative source of depth-resolved temperatures to complement existing in situ and satellite SST data sources.

Observation-based Sea surface temperature trends in Atlantic large marine ecosystems

Variations in Sea Surface Temperature (SST) are an important driver of marine species abundance in Large Marine Ecosystems (LMEs). Studies concerned with climate change induced SST trends within these LMEs have so far been relying on satellite data and reanalysis products, with the disadvantages of only having short time-periods available and having to rely on the ability of the models to correctly simulate SST-dynamics, respectively. Here, we provide for the first time a long-term trend analysis of SST for 17 LMEs of the Atlantic Ocean over two different time-periods (1957–2020 and 1980–2020) based on in-situ data gathered from three data collections. We sort our results according to warming categories that were established in an earlier study, i.e., “cooling” (below 0 °C/dec), “slow” (0–0.07 °C/dec), “moderate” (0.07–0.14 °C/dec), “fast” (0.14–0.21 °C/dec) and “superfast” (above 0.21 °C/dec). Our results show a persistent “slow” to “superfast” warming in all considered LMEs. However, the sparse data coverage induces large uncertainties, so that many LMEs cannot uniquely be assigned to one warming category only. We detect no systematic changes in the seasonal SST amplitude of the considered LMEs. We find that the LMEs of the North Atlantic warm faster than those of the South Atlantic and that this difference is increasing with time. Out of the North Atlantic LMEs, the Norwegian Sea, North Sea, Celtic-Biscay Shelf, Gulf of Mexico and the Northeast U.S. Continental Shelf belong exclusively to the superfast warming category for the period 1980–2020.

A new operational Mediterranean diurnal optimally interpolated sea surface temperature product within the Copernicus Marine Service

Abstract. Within the Copernicus Marine Service, a new operational MEDiterranean diurnal optimally interpolated sea surface temperature (MED DOISST) product has been developed. This product provides hourly mean maps (level 4) of subskin SST at 1/16∘ horizontal resolution over the Mediterranean Sea from January 2019 to the present. Subskin is the temperature at ∼1 mm depth of the ocean surface and then potentially subjected to a large diurnal cycle. The product is built by combining hourly SST data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation and model analyses from the Mediterranean Forecasting System (MedFS) through optimal interpolation. SEVIRI and MedFS (first layer) SST data are respectively used as the observation source and first guess. The choice of using a model output as first guess represents an innovative alternative to the commonly adopted climatologies or previous day analyses, providing physically consistent estimates of hourly SSTs. The accuracy of the MED DOISST product is assessed here by comparison against surface drifting buoy measurements covering the years 2019 and 2020. The diurnal cycle reconstructed from DOISST is in good agreement with the one observed by independent drifter data, with a mean bias of 0.041±0.001 K and root mean square difference (RMSD) of 0.412±0.001 K. The new SST product is more accurate than the input MedFS SST during the central warming hours, when the model, on average, underestimates drifter SST by 1/10 of a degree. The capability of DOISST to reconstruct diurnal warming events, which may reach intense amplitudes larger than 5 K in the Mediterranean Sea, is also analyzed. Specifically, a comparison with the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) diurnal skin SST product, SEVIRI, MedFS, and drifter data shows that the DOISST product is able to reproduce more accurately diurnal warming events larger than 1 K. This product can contribute to improving the prediction capability of numerical models that assimilate or correct the heat fluxes starting from level 4 SST data and the monitoring of surface heat budget estimates and temperature extremes which can have significant impacts on the marine ecosystem. The full MED DOISST product (released on 4 May 2021) is available upon free registration at https://doi.org/10.48670/moi-00170 (CNR, 2021). The reduced subset used here for validation and review purposes is openly available at https://doi.org/10.5281/zenodo.5807729 (Pisano, 2021).

Inland lake temperature initialization via coupled cycling with atmospheric data assimilation

Abstract. Application of lake models coupled within earth-system prediction models, especially for predictions from days to weeks, requires accurate initialization of lake temperatures. Commonly used methods to initialize lake temperatures include interpolation of global sea-surface temperature (SST) analyses to inland lakes, daily satellite-based observations, or model-based reanalyses. However, each of these methods have limitations in capturing the temporal characteristics of lake temperatures (e.g., effects of anomalously warm or cold weather) for all lakes within a geographic region and/or during extended cloudy periods. An alternative lake-initialization method was developed which uses two-way-coupled cycling of a small-lake model within an hourly data assimilation system of a weather prediction model. The lake model simulated lake temperatures were compared with other estimates from satellite and in situ observations and interpolated-SST data for a multi-month period in 2021. The lake cycling initialization, now applied to two operational US NOAA weather models, was found to decrease errors in lake surface temperature from as much as 5–10 K vs. interpolated-SST data to about 1–2 K compared to available in situ and satellite observations.

A Warm and A Cold Spot in Cape Cod Waters Amid the Recent New England Shelf Warming

Despite the widely recognized warming of the New England Continental Shelf (NES), climate patterns of the shelf’s economically and ecologically important coastal environments remain less examined. Here we use a satellite sea-surface temperature (SST) analysis gridded on 0.05°C spatial resolution to show, for the first time, the existence of a warm and a cold spot in the environs of Cape Cod, Massachusetts amid the NES warming of the past 15 years. The warm spot refers to an increasing warming trend in shallow waters of Nantucket Sound sheltered by the islands of Martha’s Vineyard and Nantucket. The summer SST maxima have increased by 3.1±1.0°C (p<0.1), about three times faster than the warming elsewhere on the NES, and the summer season has lengthened by 20 ± 7 days (p<0.1). The cold spot refers to an increasing cooling trend over Nantucket Shoals, an area of shallow sandy shelf that extends south and southeast from Nantucket Island and also known for strong tidal mixing. The strong cooling trend during June–August reduced the SST maxima by -2.5±1.2°C (p<0.1) and shortened the warm season by -32 ± 11 days (p<0.1). Away from the Cape Cod waters, the broad warming on the shelf is attributable to a forward shifted annual cycle. The shift is most significant in August–November, during which the summer temperatures lingered longer into the fall, producing a pronounced warming and delaying the onset of the fall season by 13 ± 6 days (p<0.1). The three different patterns of SST phenology trends displayed by the respective warm spot, the cold spot, and the broad shelf highlight the highly dynamically diverse responses of coastal waters under climate warming. Finally, the study showed that spatial resolution of SST datasets affects the characterization of the spatial heterogeneity in the nearshore SSTs. The widely used Optimum Interpolation SST (OISST) on 0.25°C resolution was examined. Although the two SST datasets agree well with the measurements from the moored buoys at four locations, OISST does not have the cold spot and shows a higher rate of warming on the shelf.

The diurnal cycle of the lightning potential index over the Mexican tropical continental region during tropical cyclone Bud

AbstractAtmospheric processes over the Mexican continental territory can be influenced by the occurrence of tropical cyclones (TCs) over the adjacent oceans. Furthermore, the Mexican territory is characterized by the presence of diurnal cycles of lightning. The Lightning Potential Index (LPI), that is a measure of the potential for charge generation and separation that leads to lightning production in convective storms, was assessed for the diurnal variability of lightning that exhibited a strong diurnal cycle over the Mexican continental territory when TC Bud was over the adjacent eastern Pacific Ocean. The assessment, from 0000 UTC 10 June to 2000 UTC 15 June 2018, used the Weather Research and Forecasting (WRF) model with a new hybrid terrain‐following sigma‐pressure vertical coordinate. Two ensembles with various cumulus and microphysical parameterizations were performed with a grid spacing of 2 km. In one ensemble, sea surface temperature (SST) was prescribed from the Real‐time global (RTG) SST analysis product and allowed to evolve interactively with the modeled atmosphere. Then, all the ensemble members were compared against available observations from the World Wide Lightning Location Network (WWLLN) to evaluate which model configurations perform best. It is not known if the LPI is capable of reproducing diurnal cycles of lightning over tropical regions; and the results allow gaining an understanding of the LPI when it reproduces the observed diurnal variability of lightning over land. The ensemble members that had better performances were those that included the prescribed SST.

The Effect of Dust Storm on Sea Surface Temperature in the Western Basin of Persian Gulf

A dust storm is one of the costliest and most destructive events in many desert regions. This research investigates the effect of dust storm on sea surface temperature (SST) in the western zone of the Persian Gulf, especially Bushehr Province and its beaches in the years 2008 and 2009. Therefore, some climate and sea parameters such as SST, salinity, air temperature, wind velocity and direction, evaporation, horizontal visibility, sunshine hours and radiation, simultaneously measured in a specific period of time, were analyzed by comparing each of them with satellite data. Sea surface temperature analysis in summer shows that the maximum SST in Persian Gulf along neighbor waters to Bushehr County and central regions in northern section of Persian Gulf is about 34–36 °C. The SST amplitude variation in these places in summer ranges from 28 to 34 °C and when there are dust phenomena, it is from 29.5 to 31 °C. The outcome of this study shows that the SST increases during dusting phenomena and this causes an increase in vapor and as a result a decrease in temperature occurs. On the other hand, vapor increase leads to a growth in the amount and layer of earth’s cloud cover and finally it causes an effective decrease in short-wave sunshine and the temperature and the vapor on surface decrease. As a result, the decrease in sea surface temperature terminates.

Distribution of Chlorophyll-a and Sea Surface Temperature for Skipjack in Nias Water

The fishing area for skipjack is always in a state of change following changes in environmental conditions, which naturally causes fish to choose suitable habitats. Sea surface temperature and chlorophyll-a are factors that affect skipjack fishing areas. The purpose of this study was to look at the potential distribution of sea surface temperature and chlorophyll-a for skipjack fishing areas in the waters of Nias. The data was taken from the Aqua MODIS (Moderate-Resolution Imaging Spectroradiometer) satellite from 2015-2018. Data processing is divided into three seasons, namely transitional season I, east season, and transitional season II. The results showed that the waters of Nias have an average sea surface temperature of 29.40°C – 31.05°C with an average chlorophyll-a ranging from 0.108 mg/m3 – 0.146 mg/m3. Based on the analysis of sea surface temperature and chlorophyll-a, the potential fishing area is assumed to be in transition II with an average seasonal catch of 2497.8 kg with a potential temperature range of 29.73°C and a range of chlorophyll-a the potential is 0.14 mg/m3.

Estimation of Fluctuations and Global Tele-connections in the Monsoonal Rainfall and Associated Floods for Barmer District

Abstract: This paper is about the fluctuations and global teleconnections in the monsoonal rainfall and associated floods of Barmer district, Rajasthan, India. For investigation of precipitation and related floods, the precipitation information for period of 1901-2002 is collected from India Meteorological Department (IMD) for Barmer district. Inter-monsoon rainfall variability analysis, percent departure from mean, Normalized Accumulated Departure from Mean (NADM) and conditional probability technique for El Nino and Southern Oscillation (ENSO) have been used for the analyses of monsoonal rainfall data and its effect on floods. The outcomes demonstrate year to year varieties in the precipitation with huge takeoff from mean precipitation. It is seen that greater part of the floods are related with positive takeoff from mean precipitation. The NADM shows epochal conduct of high and low monsoon precipitation of the basin. For analysis of ENSO Sea Surface Temperature (SST) data is obtained from National Oceanic and Atmospheric Administration (NOAA) for years 1950-2019. It is observed that, during La Nina years the rainfall is higher than average annual rainfall. The analysis of ENSO reveals that the probability of the occurrence of the floods is high during cold (La Nina) events and it is very low during warm (El Nino) events. Keywords: Monsoon rainfall, Global Tele-connection, SST, ENSO effect, floods

Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland

As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the upper ocean and, consequently, the effect of their changes on species populations. In this study, nineteen years (1998–2016) of monthly data of essential climate variables (ECVs), chlorophyll (Chl-a), and the diffuse attenuation coefficient (K490) were used, together with previous analyses of sea surface temperature (SST), to investigate the temporal and spatial variability of surface waters around Ireland. The study area was restricted to specific geographically delineated divisions, as defined by the International Council of the Exploration of the Seas (ICES). The results showed that SST and Chl-a were positively and significantly correlated in ICES divisions corresponding to oceanic waters, while in coastal divisions, SST and Chl-a showed a significant negative correlation. Chl-a and K490 were positively correlated in all cases, suggesting an important role of phytoplankton in light attenuation. Chl-a and K490 had significant trends in most of the divisions, reaching maximum values of 1.45% and 0.08% per year, respectively. The strongest seasonal Chl-a trends were observed in divisions VIId and VIIe (the English Channel), primarily in the summer months, followed by northern divisions VIa (west of Scotland) and VIb (Rockall) in the winter months.

Impact of Cloud Reduction on MODIS Thermal Infrared Sea Surface Temperature Retrieval

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of −33.45 °C, −6.35 °C, and −4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study.

Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales.

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

A New Globally Reconstructed Sea Surface Temperature Analysis Dataset since 1900

A New Globally Reconstructed Sea Surface Temperature Analysis Dataset since 1900

Influence of climate on multiple tree-ring parameters of Pinus kesiya from Manipur, Northeast India

Tree-rings of Pinus kesiya from southern region of Manipur, Northeast India were used to develop chronologies of multiple tree-ring parameters that are: total-ring width (TRW), earlywood width (EW), latewood width (LW) and adjusted latewood (LWadj). The time span of these chronologies is 39 years (1980–2018 C.E.) and we compared their growth responses with monthly and daily climatic records. The comparison revealed a broadly consistent pattern of climate sensitivity with daily climate exhibiting higher correlation. The climate signals during pre-monsoon (March–May) were recorded in TRW and EW, whereas late-monsoon to post-monsoon climate signals were recorded in LW and LWadj. The spatial correlation analysis of tree-ring parameters and global sea surface temperature (SST) showed a positive relationship between tree growth with tropical Pacific Ocean and Indian Ocean during winter (December–February) and pre-monsoon (March–May) seasons. The LW and LWadj were also correlated with peak summer monsoon (July–August) SST over the tropical Pacific Ocean. IADFs observed in EW (E-IADF) were caused by dry and warm conditions during March–April. IADFs in LW (L-IADF) occurred due to a combination of enhanced rainfall and temperature during post-monsoon (October–November) season. Evidence of stand-specific micro-climatic conditions in the formation IADFs in this species was also found. This study showed that multiple parameters of P. kesiya provides a lucid understanding of climate response on its growth and can be considered as a proxy for studying sub-seasonal changes in past environmental conditions in longer records.

Evaluation of AIRS and CrIS SST Measurements Relative to Three Globally Gridded SST Products Between 2013 and 2019

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work.

Satellite products performance based on in-situ data to evaluate SST variability in a subtropical inner shelf in Brazil

This study compares in-situ, satellite (MODIS Terra and Aqua daytime and nighttime), and reanalysis (Operational Sea Surface Temperature and Sea Ice Analysis – OSTIA) sea surface temperature (SST) data of the inner shelf of Paraná – Brazil at daily scales. There are match-up pairs for all products covering the whole range of in-situ SST (19°C–30°C) and every month, consisting of an appropriate method to validate our satellite datasets in the study area. Although there is a good statistical agreement between MODIS retrievals and in-situ SST, cloud coverage over the study area results in a large number of gaps along with the time series. Thus, dominant features of the SST variability patterns were characterized based on 35 years (1985–2018) of OSTIA SST. There is a strong influence of the Brazil Coastal Current during late winter months contrasting with warmer waters from the Brazil Current during summer. Local high-frequency forcing dominates SST variability, mostly high-energy meteorological events, which act like pulses that intensify mixing processes, resulting in a stair-like variability pattern. At adequate temporal and spatial scales, the relatively cost-effective, spatially explicit, frequent, and continuous remote sensing data can provide the necessary information to support the reliance of our civilization on the ocean. As the SST regime in coastal waters differs from the open waters, the results presented here are unique. They can provide references to understanding the dominant scales of complex coastal dynamics, a key to predict impacts, draw scenarios and mitigate risks, supporting the sustainability of marine and coastal ecosystem services.

Long-Term Trends and Impact of SARS-CoV-2 COVID-19 Lockdown on the Primary Productivity of the North Indian Ocean

COrona VIrus Disease (COVID) 2019 pandemic forced most countries to go into complete lockdown and India went on complete lockdown from 24th March 2020 to 8th June 2020. To understand the possible implications of lockdown, we analyze the long-term distribution of Net Primary Productivity (NPP) in the North Indian Ocean (NIO) and the factors that influence NPP directly and indirectly, for the period 2003–2019 and 2020 separately. There exists a seasonal cycle in the relationship between Aerosol Optical Depth (AOD), Chlorophyll-a (Chl-a) and NPP in agreement with the seasonal transport of aerosols and dust into these oceanic regions. In Arabian Sea (AS), the highest Chl-a (0.58 mg/m3), NPP (696.57 mg/C/m2/day) and AOD (0.39) are observed in June, July, August, and September (JJAS). Similarly, maximum Chl-a (0.48 mg/m3) and NPP (486.39 mg/C/m2/day) are found in JJAS and AOD (0.27) in March, April, and May (MAM) in Bay of Bengal. The interannual variability of Chl-a and NPP with wind speed and Sea Surface Temperature (SST) is also examined, where the former has a positive and the latter has a negative feedback to NPP. The interannual variability of NPP reveals a decreasing trend in NPP, which is interlinked with the increasing trend in SST and AOD. The analysis of wind, SST, Chl-a, and AOD for the pre-lockdown, lockdown, and post lockdown periods of 2020 is employed to understand the impact of COVID-19 lockdown on NPP. The assessment shows the reduction in AOD, decreased wind speeds, increased SST and reduced NPP during the lockdown period as compared to the pre-lockdown, post-lockdown and climatology. This analysis is expected to help to understand the impact of aerosols on the ocean biogeochemistry, nutrient cycles in the ocean biogeochemical models, and to study the effects of climate change on ocean ecosystems.

Sensitivity of WRF-derived hydrometeorological extremes to sea surface temperatures in regions with complex topography and diverse climate

This study investigates the impact of sea surface temperature (SST) describing the lower boundary of Weather Research and Forecasting (WRF) model on the extreme weather events that occurred on the Mediterranean (MED) and the Eastern Black Sea (EBS) regions of Turkey. For each region, one extreme event case is selected and characterized as a summer convective system in EBS and as a winter synoptic system in MED region. The SST impact on the WRF model forecasts of these events is accomplished in two ways. First, the SST analysis is conducted by making a 10-days simulation for each event with and without activating the SST options (update and skin options) available in the model configuration. In these runs, the selected initial and boundary condition datasets, Global Forecasting System (GFS) for the MED region and the ERA5 for the EBS regions, provide corresponding internal SST data. Second, the prediction performance of the model is evaluated among the simulations of non-external, time-varying (GFS and ERA5 SST) and external, time–varying, high-resolution SST products (The Group for High Resolution Sea Surface Temperature; GHRSST, Medspiration, and NCEP) for each event. The results show that the WRF model simulations, even for short-term event predictions, are highly sensitive to time-variant SST options. The overestimation and high spreading feature of maximum and total precipitation are realistically reduced by time-variant SST products. The Medspiration and the NCEP sources for the MED region and the GHRSST and the Medspiration sources for the EBS region yielded warmer SSTs among the simulations. By establishing the amplified air-sea interactions, these sources provide more appropriate peak precipitation distributions in both regions. Furthermore, based on this study, the effect of using high-resolution SST (GHRSST and Medspiration) is more prominent in predicting convective event developed over complex topography of the EBS while the resolution effect is not much critical for synoptic system simulation even under the complex topography in MED. Lastly, the highest mean correlation of conditional hourly rain is calculated as 0.65 with the Medspiration product on MED and 0.48 with the GHRSST product on the EBS region.