Mean Sea Surface Temperature Research Articles

A deep learning model for forecasting global monthly mean sea surface temperature anomalies

Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation (ENSO) regarded as a major source of interannual climate variability at the global scale. The ability to make long-range forecasts of SST variations and extreme marine heatwave events have potentially significant economic and societal benefits, especially in a warming climate. We have developed a deep learning time series prediction model (Unet-LSTM), based on more than 70 years (1950–2021) of ECMWF ERA5 monthly mean SST and 2-m air temperature data, to predict global 2-dimensional SSTs up to a 24-month lead. Model prediction skills are high in the equatorial and subtropical Pacific. We have assessed the ability of the model to predict SST anomalies in the Niño3.4 region, an ENSO index in the equatorial Pacific, and the Blob marine heatwave events in the northeast Pacific in detail. An assessment of the predictions of the 2019–2020 El Niño and the 2016–2017 and 2017–2018 La Niña show that the model has skill up to 18 months in advance. The prediction of the 2015–2016 extreme El Niño is less satisfactory, which suggests that subsurface ocean information may be crucial for the evolution of this event. Note that the model makes predictions of the 2-d monthly SST field and Nino 3.4 is just one region embedded in the global field. The model also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Niño, shows a short lead prediction. These results indicate the significant potential of data-driven methods to yield long-range predictions of SST anomalies.

Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation.

Hadley Cell Edge Modulates the Role of Ekman Heat Flux in a Future Climate

AbstractIn a future climate, the Hadley cell and associated trade easterlies are projected to expand poleward. This projected change in the atmospheric circulation is expected to impact the ocean through changes in the mean sea surface temperature (SST). We also expect implications for the large‐scale SST variability, because near‐surface wind is directly related to two drivers of the SST, that is, turbulent heat flux and anomalous wind‐driven Ekman heat flux. Previous studies show that in the subtropics, anomalous turbulent and Ekman heat fluxes oppose each other, acting to reduce SST variability, whereas, in the midlatitudes, they reinforce each other and enhance SST variability. Through analysis of reanalysis products and Coupled Model Intercomparison Project simulations, we find that the subtropical regions where the fluxes oppose each other are projected to expand poleward in a future climate, following the poleward expansion of the Hadley cell, with potential implications for the amplitude of subtropical SST variability.

Forced changes in El Niño–Southern Oscillation due to global warming and the associated uncertainties in ACCESS-ESM1.5 large ensembles

Understanding the forced response of El Niño–Southern Oscillation (ENSO) to future global warming (GW) is important for reliable climate projections; however, many important aspects of this response are yet to be fully understood. Here, we use two large ensembles of CMIP6 historical and SSP3-7.0 experiments (each with 40 ensemble members), performed with ACCESS-ESM1.5, to investigate the combined greenhouse gas (GHG) and aerosol forced changes in selected ENSO properties. We document the forced changes in ENSO's amplitude, power spectrum, skewness, and feedbacks and quantify the internal variability associated with these forced changes. There is a modest but statistically significant GW-induced increase in the ensemble-mean ENSO amplitude and a sizable ensemble variation (due to internal variability) with both increases (in 80% of the members) and decreases. To understand the mechanism of this variation, we examine the role of changes in the mean state and atmosphere-ocean coupling processes in the Pacific. We find that the ensemble variation of GW-induced ENSO amplitude change is most sensitive to the zonal wind forcing change. A change in the zonal gradient of mean sea surface temperatures (SSTs) also plays an important role in the ENSO amplitude change, with the changes in the atmospheric Bjerknes feedback and thermocline feedback playing a minor role. The implications and some caveats of these findings are discussed.

Spatial and temporal distributions of glycerol dialkyl glycerol tetraethers in suspended materials in the northern East China Sea: Applicability as a paleotemperature proxy

The spatial and temporal distributions of the glycerol dialkyl glycerol tetraether (GDGT) concentrations in suspended materials were investigated to determine whether the GDGT-based TEX86 can be used as a proxy for annual mean sea surface temperature in the northern East China Sea (ECS). Seawater samples were collected seasonally at three-month intervals from the surface and subsurface of the northern ECS. GDGT in the surface seawater was detected consistently across all seasons. However, the TEX86 temperatures of the surface seawater samples differed significantly from the in situ temperatures over the entire study area; in particular, TEX86 temperatures were higher (4–7 °C) than in situ temperatures in winter, and lower (9–12 °C) than in situ temperatures in summer. The occurrence of a high concentration of GDGTs near the bottom, with marked differences between TEX86 and in situ temperatures in the shallow water (~30 m) supports the possibility of sediment resuspension near the bottom in the western part and subsequent transportation eastward. GDGTs may have been resuspended owing to vertical mixing by strong winds, such as those from the monsoon (winter) and typhoons (summer), and increased turbulence near the bottom caused by tides across all seasons in the shallow waters (30–50 m). In contrast, the vertical distribution of GDGT concentration in the eastern part of the study area deeper than 100 m indicated that maximum GDGT concentrations occurred at depths of between 20 m and 50 m in all seasons. The TEX86 temperatures of the subsurface samples were similar to or warmer than in situ temperatures between 20 m and 50 m, suggesting that the main depth at which GDGT was produced in the eastern area could be the subsurface in that same depth range. A new local calibration equation was established based on the subsurface samples from the east. Our results indicate that sediment resuspension and subsurface production of GDGTs can have a significant impact on the use of GDGT-based TEX86 as a SST proxy in continental shelf area.

Climatology of sea‐effect snow in Finland

AbstractConvective sea‐effect snowfall, in the form of snowbands, is observed over the northern Baltic Sea annually. Quasi‐stationary snowbands may last up to several days over the sea and, depending on the wind direction, move towards the coast. This study provides climatology of spatial and temporal occurrence of snowbands in Finland for a 48‐year period (1973–2020). We used a set of detection criteria together with ERA5 reanalysis at off‐shore areas and FMIClimGrid gridded observational data for on‐shore areas to find the days favouring snowband formation. Only those snowband days (SBD) when snow reached the Finnish coastal mainland were considered. The total annual number of SBDs in Finland varied from 6 to 40 with an average of 16. SBDs were detected most frequently over the Gulf of Bothnia near the western coast of the country. The largest increase in snow depth (SDI) during an SBD (67 cm/day) also took place on the western coast, although the long‐term mean of SDI (3–5 cm/day) was highest over the southern coast. Throughout the country, November and December showed the highest frequency of SBDs. However, between the periods 1973–1996 and 1997–2020, the seasonal cycle of SBDs shifted 1 month forward from late autumn to mid‐winter as the decrease in the number of SBDs during December as well as the increase during January and February were statistically significant in Finland. In northern Baltic Sea, long‐term increases in monthly means of sea surface temperature (SST) and air temperature at the atmospheric level of 850 hPa (T850) were in line with the decadal changes in the occurrence of SBDs. The increasing trend in SST favours the formation of snowbands but in late autumn the probability for snowband formation decreased because even larger increases in T850 resulted in diminishing differences between SST and T850.

Biases of the Barotropic Atmospheric Circulation Variability in CMIP6 Models

Abstract Two ensembles of Atmospheric Model Intercomparison Project (AMIP) simulations, in the scope of the current Coupled Model Intercomparison Project (CMIP6), are compared with their fully coupled counterparts. The atmospheric models simulate less barotropic atmospheric circulation variability over the North Atlantic and more barotropic atmospheric circulation variability over the North Pacific when compared with reanalysis variability, at intraseasonal and interannual scales. The coupled climate simulations have smaller global barotropic variability than the corresponding AMIP simulations. The smaller variability of the coupled simulations results in no mean overestimation of the subtropical jet variability in the North Pacific, but further underestimation of the jet stream variability in the North Atlantic. The results suggest that the reduction of the biases, in the North Pacific barotropic atmospheric variability, of coupled climate simulations is achieved through compensating biases in the mean sea surface temperatures (SSTs). Moreover, the reduction of the positive biases in the North Pacific seems to be associated with a reduction of the excitation of the most unstable barotropic mode of the atmospheric circulation, which contributes also to a reduction of the barotropic atmospheric variability in the North Atlantic.

Morphologic and phylogenic characterization of two bloom-forming planktonic Prorocentrum (Dinophyceae) species and their potential distribution in the China Sea

The genus Prorocentrum includes many species that are responsible for harmful algal blooms globally, which have serious economic impacts on marine aquaculture, fisheries, and tourism, as well as major environmental and human health impacts. In this study, two bloom-forming planktonic Prorocentrum species, P. cordatum and P. sigmoides, were isolated from the coastal waters of China. Their morphology was examined in detail using light microscopy (LM) and scanning electron microscopy (SEM). The features of their periflagellar area platelet pattern, important taxonomic criteria, were revealed, and the distinctions in their morphology were compared with other closely related species. Both partial large subunit ribosomal DNA (LSU rDNA) and internal transcribed spacer region (ITS1-5.8S-ITS2) sequence-based phylogeny revealed three clades within planktonic Prorocentrum, referred to as P. micans-like (where the P. sigmoides included), P. cordatum-like, and P. triestinum-like groups. These groups showed consistencies in their thecal plate ornamentation and periflagellar area platelet pattern characteristics. Suitable habitats of P. cordatum and P. sigmoides along the coast of China were predicted with combined maximum entropy (MaxEnt) and random forest (RF) models. Highly or very highly suitable P. cordatum habitats were mainly located in the coastal waters of central to northern China. The habitat suitability range of P. sigmoides was narrower than that of P. cordatum, and concentrated near the Yangtze River Estuary. The main environmental drivers were identified by the variable importance evaluation procedure in BIOMOD 2. Mean sea surface temperature (SST) was the main distribution driver of both P. cordatum and P. sigmoides, and mean sea surface salinity (SSS), distance from coastline, and phosphorus concentrations also influenced their distribution. Ocean warming, eutrophication, and re-oligotrophication could potentially expand the distribution range of bloom-forming species. These results showed that machine learning models can play a useful role in the study of the potential distribution of harmful algal blooms.

Delineation of Eastern Beaufort Sea Sub-regions Using Self-Organizing Maps Applied to 17 Years of MODIS-Aqua Data

Satellite observations are an integral component of long-term Arctic Ocean monitoring and help identifying changes resulting from climate warming. A Self-Organizing Maps (SOM) approach was applied to four-day composite satellite images of the Eastern Beaufort Sea (EBS) acquired by the MODerate resolution Imaging Spectroradiometer over the period 2003–2019. Using sea-surface temperature (SST), suspended particulate matter concentration (SPM) and chlorophyll-a concentration (Chl-a) as input the EBS was partitioned into six biogeochemical regions. The SOM approach revealed region-specific mean conditions and seasonal cycles for all properties, particularly for SPM and Chl-a. Three of the six regions, located on the continental shelf, had the highest SST, SPM and Chl-a with earlier maxima compared to the remaining three regions which comprised the shelf edge, Canada Basin and Amundsen Gulf. While mean and maximum SST did not exhibit significant trends over the 17 years of observations, the annual maximum SST in Amundsen Gulf was reached significantly earlier in recent years compared to the start of the time series. With the exception of Amundsen Gulf, sea-ice concentration (SIC) derived from microwave satellites declined throughout the study area; monthly trends showed dramatic SIC declines in regions on the shelf during May and June, and in Canada Basin during August. Correlation analysis of properties within and between regions showed that SST and SIC were driven by large scale processes while SPM and Chl-a showed regional features. SST and Chl-a in the regions nearest the Mackenzie River showed a strong relationship during seasonal warming. The SOM approach, applied to 17 years of satellite data, revealed spatially distinct marine units with unique characteristics, emphasizing the need for regional considerations when assessing the impact of climate warming in the Arctic Ocean.

Seasonal extrema of sea surface temperature in CMIP6 models

Abstract. CMIP6 model sea surface temperature (SST) seasonal extrema averaged over 1981–2010 are assessed against the World Ocean Atlas (WOA18) observational climatology. We propose a mask to identify and exclude regions of large differences between three commonly used climatologies (WOA18, WOCE-Argo Global Hydrographic climatology (WAGHC) and the Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST)). The biases in SST seasonal extrema are largely consistent with the annual mean SST biases. However, the amplitude and spatial pattern of SST bias vary seasonally in the 20 CMIP6 models assessed. Large seasonal variations in the SST bias occur in eastern boundary upwelling regions, polar regions, the North Pacific and the eastern equatorial Atlantic. These results demonstrate the importance of evaluating model performance not simply against annual mean properties. Models with greater vertical resolution in their ocean component typically demonstrate better representation of SST extrema, particularly seasonal maximum SST. No significant relationship of SST seasonal extrema with horizontal ocean model resolution is found.

Ocean warming and increased salinity threaten Bostrychia (Rhodophyta) species from genetically divergent populations

Increased greenhouse gas concentrations in the Earth's atmosphere have resulted in global change, such as ocean warming and sea level rise. Increased salinity in estuaries is expected as a result of sea level rise and warming. Thus, we analysed the interactive effects of increased temperature and salinity on multiple physiological responses of Bostrychia montagnei and B. calliptera from two biogeographic provinces, Tropical Southwestern Atlantic (TSA) and Warm Temperate Southwestern Atlantic (WTSA). Macroalgae were cultured under three salinities (15, 25 and 35 PSU) and three temperatures: mean sea surface temperature (SST: 27 °C for TSA and 24 °C for WTSA), an RCP8.5 ocean warming scenario (SST + 5 °C), and a maximum temperature to test the algal upper thermal tolerance limits (RCP8.5 + 2 °C). Macroalgae from both localities decreased their growth under increased temperature and salinity. RCP8.5 + 2 °C was lethal for both macroalgae from TSA. RCP8.5 and RCP8.5 + 2 °C at 35 PSU were lethal for B. calliptera from WTSA, due to the interactive effects between increased temperature and salinity. Overall, increased salinity decreased the effective quantum yield and relative electron transport rate in algal photosynthesis. Our results demonstrated that the macroalgae synthesized proteins, carbohydrates (polysaccharides and low molecular weight carbohydrates), and antioxidants to tolerate detrimental temperatures and salinities. Our results also demonstrated that the macroalgae adjusted their pigment contents (phycobiliproteins, total carotenoids, and chlorophyll a) for efficient light-harvesting under thermal and saline stress. Our findings suggest that ocean warming and increased salinity in estuaries will be detrimental to B. montagnei and B. calliptera populations from both biogeographic provinces, especially to those from TSA that already live closer to their upper thermal tolerance limits.

Sea Surface Temperature Variability and Marine Heatwaves in the Black Sea

Marine heatwaves (MHWs) have recently been at the forefront of climate research due to their devastating impacts on the marine environment. In this study, we have evaluated the spatiotemporal variability and trends of sea surface temperature (SST) and MHWs in the Black Sea. Furthermore, we investigated the relationship between the El Niño–Southern Oscillation (ENSO) and MHW frequency. This is the first attempt to investigate MHWs and their characteristics in the Black Sea using high-resolution remote-sensing daily satellite SST data (0.05° × 0.05°) from 1982 to 2020. The results showed that the spatial average of the SST warming rate over the entire basin was about 0.65 ± 0.07 °C/decade. Empirical orthogonal function (EOF) analysis revealed that SST in the Black Sea exhibited inter-annual spatiotemporal coherent variability. The maximum spatial SST variability was discovered in the central Black Sea, whereas the lowest variability was in the Batumi and Caucasus anti-cyclonic eddies in the eastern Black Sea. The highest SST temporal variability was found in 1994. More than two-thirds of all MHW events were recorded in the last decade (2010–2020). The highest annual MHW durations were reported in 1994 and 2020. The highest MHW frequency was detected in 2018 (7 waves). Over the whole study period (1982–2020), a statistically significant increase in annual MHW frequency and duration was detected, with trends of 1.4 ± 0.3 waves/decade and 2.8 ± 1.3 days/decade, respectively. A high number of MHW events coincided with El Niño (e.g., 1996, 1999, 2007, 2010, 2018, and 2020). A strong correlation (R = 0.90) was observed between the annual mean SST and the annual MHW frequency, indicating that more MHWs can be expected in the Black Sea, with serious consequences for the marine ecosystem.

Marine heatwaves in the Arabian Sea

Abstract. Marine heatwaves (MHWs) are prolonged warm sea condition events that can have a destructive impact on marine ecosystems. The documentation of MHWs and assessment of their impacts is largely confined to a few regional seas or to global mean studies. The north Indian Ocean received almost no attention in this regard despite the fact that this ocean basin, particularly the Arabian Sea, has been warming at the most rapid pace among the other tropical basins in recent decades. This study shows the characteristics of MHW events for the Arabian Sea during 1982–2019. Our analysis shows that the duration (frequency) of MHWs exhibits a rapidly increasing trend of ∼20 d per decade (1.5–2 events per decade) in the northern Arabian Sea and the southeastern Arabian Sea close to the west coast of India, which is a multifold increase in MHW days (frequency) from the 80s. Notably, since the beginning of the satellite record, the years 2010 and 2016 have exhibited the maximum number of heatwave days when more than 75 % of days of the pre-monsoon and summer monsoon season experience heatwaves. The accelerated trend of the heatwave days is found to be driven by the rapid rise in the mean sea surface temperature (SST) of the Arabian Sea in the recent decade. Moreover, longer heatwave days are also associated with dominant climate modes. Among them, the Indian Ocean Basin Mode via the decaying phase of El Niño is the most influential mode contributing to more than 70 %–80 % of observed heatwave days in this basin. Further analysis of the most prolonged observed heatwave during April–June 2010 indicates that surface heat flux associated with the weaker latent heat loss and the shallow mixed layer was the primary cause of this event. Further, we note that the pre-monsoon cyclonic storms in the Arabian Sea often contribute to the waning of such heatwaves in the basin.

Comparing surface wind stress and sea surface temperature biases over the tropical and subtropical oceans in subsets of CMIP6 models categorized by frozen hydrometeors-radiation interactions

We evaluate the simulations of surface wind stress (TAU) and sea surface temperature (SST) over subtropical and tropical Pacific and Atlantic oceans in subsets of CMIP6 models that are categorized by frozen hydrometeors-radiation interactions. The CMIP6 models are divided into two subsets with combined (SON1) and separated (SON2) radiative properties of cloud ice and falling ice (snow) and compared to the set with cloud ice radiative effects only (NOS). There is evidence that these hydrometeors-radiation interaction treatments induce different atmospheric dynamic responses that influence the surface properties. Excessive westerly TAU and meridional TAU divergence away from convective zones are reduced significantly in SON1 and SON2 relative to NOS against QuikSCAT observations; while the differences between SON2 and SON1 are small. SON2 reduces cold SST biases over north oceans and equatorial zones drastically (1 to 2 K), and warm biases (up to 1 K) off the coasts of America and zonal TAU biases are reduced relative to NOS. Unlike SON2, SON1 improves SSTs mainly over south of Pacific Ocean and limited areas over the tropical belts relative to NOS although TAU is reduced drastically as in SON2, implying that other factors play a role in degrading the SST simulations in SON1 relative to SON2. SON2 outperforms NOS and SON1 in the seasonal cycles of SST mean biases and mean absolute biases averaged over the equatorial area, north ocean, and South Pacific against ERSST observations. Despite the significant improvements in TAU and SST simulations, SON2 models still exhibit non-trivial biases over south and north flanks of equatorial zones. These results suggest that there are direct linkages of TAU with SST changes resulting from the hydrometeors-radiation interactions in SON2, but not in SON1, relative to NOS, implying that a separated treatment of cloud ice and falling ice radiative properties in climate models is preferred.

Comparative phylogeography in a marine biodiversity hotspot provides novel insights into evolutionary processes across the Atlantic‐Indian Ocean transition

AbstractAimIntraspecific genetic variation is a key component of biodiversity, with higher diversity indicating greater resilience and population substructuring suggesting unique evolutionary histories. Comparative approaches, in which intraspecific genetic variation is assessed across multiple species, are powerful tools to identify evolutionary hotspots, but are still rarely applied at spatial scales relevant to conservation planning. Here, we use comparative phylogeography to understand patterns and potential drivers of genetic variation within a biodiversity and ocean warming hotspot.LocationThe South African coastline, Indian/Atlantic Oceans.MethodsA literature search was conducted to obtain mitochondrial DNA cytochrome oxidasecsubunit I and cytochromebsequence data for 17 marine fish and invertebrate species. From these data, we compared averages of haplotype and nucleotide diversity, and within‐region ΦSTbetween four biogeographic provinces in the region. Mixed linear models tested whether environmental variability, habitat preference, or geographic location significantly influence genetic variation.ResultsAverage diversity values differed between haplotype and nucleotide diversity, but both broadly displayed highest diversity levels within the South‐West bioregion, which is also a region of high levels of within‐region ΦST. Range in sea surface temperatures (SSTs) was the only significant fixed‐effect term in the haplotype diversity mixed linear models. Mean SST, stability in SSTs since the Mid‐Holocene and position within the species' geographic distribution all had no significant effect on genetic variation.Main conclusionsAlong this coastline characterized by high environmental heterogeneity, we find that variation in temperature is a prominent source of intraspecific variation. Genetic diversity differs between bioregions, but does not display higher levels within the core of each species’ range when assessed across multiple species. With elevated levels of genetic diversity, the South‐West region of the South African coast is highlighted as a conservation priority area, representing both high genetic diversity and differentiation across taxa.

Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration

Abstract. A single-column version of the CNRM-CM6-1 global climate model has been developed to ease development and validation of the boundary layer physics and air–sea coupling in a simplified environment. This framework is then used to assess the ability of the coupled model to represent the sea surface temperature (SST) diurnal cycle. To this aim, the atmospheric–ocean single-column model (AOSCM), called CNRM-CM6-1D, is implemented in a case study derived from the CINDY2011/DYNAMO campaign over the Indian Ocean, where large diurnal SST variabilities have been well documented. Comparing the AOSCM and its uncoupled components (atmospheric SCM and oceanic SCM, called OSCM) highlights the fact that the impact of coupling in the atmosphere results from both the possibility to take into account the diurnal variability of SST, which is not usually available in forcing products, and the change in mean state SST as simulated by the OSCM, with the ocean mean state not being heavily impacted by the coupling. This suggests that coupling feedbacks in the 3D model do not arise from the coupling of ocean and atmosphere vertical column physics but are more due to the large-scale dynamics resolved by the 3D model. Additionally, a sub-daily coupling frequency is needed to represent the SST diurnal variability, but the choice of the coupling time step between 15 min and 3 h does not impact the diurnal temperature range simulated much. The main drawback of a 3 h coupling is delaying the SST diurnal cycle by 5 h in asynchronous coupled models. Overall, the diurnal SST variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m. This framework is shown to be a very valuable tool to develop and validate the boundary layer physics and the coupling interface. It highlights the interest to develop other atmosphere–ocean coupling case studies.

Extreme value distributions describe interannual variability in the seasonal North Atlantic phytoplankton bloom

AbstractThe North Atlantic phytoplankton bloom depends on a confluence of environmental factors that drive transient periods of exponential phytoplankton growth and interannual variability in bloom magnitude. I analyze interannual bloom variability in the North Atlantic via extreme value theory where the generalized extreme value distribution (GEVD) is fitted spatially to annual maxima of satellite‐measured surface chlorophyll. I find excellent agreement between the observed distribution of interannual bloom maxima and those predicted from the GEVD. The spatial distribution of fitted GEVD parameters closely follows basin bathymetry where the largest extremes and heaviest distribution tails are found on the continental shelves and slopes. Trend analyses suggest weak evidence for changes in GEVD parameters, despite regional trends in mean chlorophyll levels and sea surface temperature. These results provide a framework to quantify interannual bloom variability and call for further work examining how extreme blooms propagate through food webs and contribute to carbon export.

The Trend and Interannual Variability of Marine Heatwaves over the Bay of Bengal

Marine heatwaves (MHWs) are long-lasting extreme oceanic warming events that can cause devastating effects on warm-water corals and associated ecosystems. The linear trend and interannual variability of MHWs over the Bay of Bengal (BOB) during 1982–2020 are investigated by a high-resolution daily sea surface temperature (SST) dataset. In regions where warm-water coral reefs are concentrated, annual MHW days and frequency significantly increase during 1982–2020, at rates exceeding that of the global mean. The coldest boreal winter season witnesses significant and steady increase trends in MHW days and frequency. In contrast, the trend is insignificant in the climatological warmest season (March to June) south of 15° N in the BOB, mainly due to large interannual variability. El Niño and Southern Oscillation (ENSO) dominates the interannual variability of BOB MHWs, which are highly consistent with the evolution of the mean SST. The negative phase of North Atlantic Oscillation (NAO) also modulates the occurrences of MHWs, especially over the northeastern BOB. The two climate modes synergistically explain about 50~70% of the interannual variances in the BOB’s MHWs. Correlation analysis reveals that south of 15° N in the BOB, the effect of El Niño on MHWs is evident from the boreal autumn of its developing phase to the boreal summer of its decaying phase, along with limited influence from NAO. However, in the northeast of the BOB, the effect of El Niño merely emerges from April to August of its decaying stage. In comparison, boreal winter-to-spring NAO exerts a strong control over March-to-June MHWs in the northeastern BOB. The results suggest that various climate modes may jointly or separately influence MHWs at certain seasons and locations, which is important for the seasonal prediction of MHWs. Indeed, when combining the Niño3.4 mature winter index and boreal winter-to-spring NAO index to build a regression model, it is more effective in reproducing the BOB’s MHW frequency compared to the Niño3.4 index alone.

The latitudinal temperature gradient and its climate dependence as inferred from foraminiferal δ18O over the past 95 million years

SignificanceThe temperature difference between low and high latitudes is one measure of the efficiency of the global climate system in redistributing heat and is used to test the ability of models to represent the climate system through time. Here, we show that the latitudinal temperature gradient has exhibited a consistent inverse relationship with global mean sea-surface temperature for at least the past 95 million years. Our results help reduce conflicts between climate models and empirical estimates of temperature and argue for a fundamental consistency in the dynamics of heat transport and radiative transfer across vastly different background states.

Modeling contemporary and future dimethylsulfide flux in the eastern China marginal seas

A biogeochemical model is used to simulate the sea-to-air flux of dimethylsulfide (DMS) in the Eastern China Marginal Seas (ECMS) for the period 2011 to 2015. Seasonal and inter-annual variability of sea surface temperature (SST), cloud cover (CLD), wind speed (WIND) and mixed layer depth (MLD) are analyzed. SST peaked in August and displayed a north to south gradient. CLD is around 60% and WIND is within the range 5–11 m/s. MLD ranges from 17m in summer to more than 149m in March. CHL and DMS surface concentration peaked near the Changjiang River mouth during summer.DMS field data was collected by the Ocean University of China (OUC). These data are used together with a genetic algorithm (GA) technique to calibrate both CHL and DMS related key model parameters. Simulated DMS concentrations and DMS flux reach a maximum of 6.1 nM in May and 8.8 μmol m−2d−1 in the middle of July respectively.CMIP5 experiment archive is used to provide physical forcings for the simulation under 4 × CO2 climate conditions. Compared with contemporary climate conditions, under 4 × CO2, annual mean SST increases 19%; WIND decreases 24%; MLD decreases 7.8%. DMS decreases by 25%, and DMS flux decreases by 38%. The change in DMS flux is most likely due to the significant increase of SST and decrease of wind speed. The projected decrease in MLD is likely caused by projected more precipitation in the region leading to increased fluvial discharge and a decrease in salinity under warming.