Global Sea Surface Temperature Research Articles

JPSS VIIRS SST Reanalysis Version 3

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and ‘depth’ (proxy for in situ SST at depth of 20 cm), were produced from brightness temperatures (BTs) in the VIIRS bands centered at 8.6, 11 and 12 µm during the daytime and an additional 3.7 µm band at night, using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN3 dataset is fully archived at NASA JPL PO.DAAC and NOAA CoastWatch, and routinely supplemented in near real time (NRT) with a latency of a few hours. Delayed mode (DM) processing with a 2 months latency follows NRT, resulting in a more uniform science quality SST record. This paper documents and evaluates the performance of the VIIRS RAN3 dataset. Comparisons with in situ SSTs from drifters and tropical moorings (D+TM) as well as Argo floats (AFs) (both available from the NOAA iQuam system) show good agreement, generally within the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), in a clear-sky domain covering 18–20% of the global ocean. The nighttime SSTs compare with in situ data more closely, as expected due to the reduced diurnal thermocline. The daytime SSTs are also generally within NOAA specs but show some differences between the (D+TM) and AF validations as well as residual drift on the order of −0.1 K/decade. BT comparisons between two VIIRSs and MODIS-Aqua show good consistency in the 3.7 and 12 µm bands. The 11 µm band, while consistent between NPP and N20, shows residual drift with respect to MODIS-Aqua. Similar analyses of the 8.6 µm band are inconclusive, as the performance of the MODIS band 29 centered at 8.6 µm is degraded and unstable in time and cannot be used for comparisons.

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province.

Predicting sea surface temperatures with coupled reservoir computers

Abstract. Sea surface temperature (SST) is a key factor in understanding the greater climate of the Earth, and it is also an important variable when making weather predictions. Methods of machine learning have become ever more present and important in data-driven science and engineering, including in important areas for Earth science. Here, we propose an efficient framework that allows us to make global SST forecasts using a coupled reservoir computer method that we have specialized to this domain, allowing for template regions that accommodate irregular coastlines. Reservoir computing is an especially good method for forecasting spatiotemporally complex dynamical systems, as it is a machine learning method that, despite many randomly selected weights, is highly accurate and easy to train. Our approach provides the benefit of a simple and computationally efficient model that is able to predict SSTs across the entire Earth's oceans. The results are demonstrated to generally follow the actual dynamics of the system over a forecasting period of several weeks.

Prediction of bimodal monsoonal rainfall in the central dry zone of Myanmar using teleconnections with global sea surface temperatures

In the central dry zone of Myanmar, the mean annual rainfall is less than 1000 mm. Although rainfed agriculture is commonly practiced there, the feasibility of rainfed farming is compromised by the large fluctuations of rainfall and the frequent occurrence of dry years. The monthly distribution of rainfall follows a bimodal pattern. The intensity of the monsoonal rainfall from May to October is characterized by two peaks, an early peak (May-June) and a late peak (August–October), separated by the inter-monsoon (July). The return times of dry and wet years make management of rainfed agriculture problematic. There is very little correlation between the early and late monsoonal rainfall (r=–0.257). However, monsoonal rainfall is teleconnected to sea surface temperatures (SSTs) in certain areas of the Pacific Ocean in real time. Furthermore, at lag times of 6–9 months, there are teleconnections between the early monsoonal, inter-monsoonal, and late monsoonal rainfall and SSTs in certain areas of the Indian Ocean and Atlantic Ocean. We used an Elman artificial neural network model to predict early monsoonal, inter-monsoonal, and late monsoonal rainfall based on teleconnections with SSTs in the Indian and Atlantic oceans 6–9 months before the rainfall occurred. The correlation coefficient between the predicted and observed rainfall exceeded 0.7 in all three cases.

Tectonic degassing drove global temperature trends since 20 Ma.

The Miocene Climatic Optimum (MCO) from ~17 to 14 million years ago (Ma) represents an enigmatic reversal in Cenozoic cooling. A synthesis of marine paleotemperature records shows that the MCO was a local maximum in global sea surface temperature superimposed on a period from at least 19 Ma to 10 Ma, during which global temperatures were on the order of 10°C warmer than at present. Our high-resolution global reconstruction of ocean crustal production, a proxy for tectonic degassing of carbon, suggests that crustal production rates were ~35% higher than modern rates until ~14 Ma, when production began to decline steeply along with global temperatures. The magnitude and timing of the inferred changes in tectonic degassing can account for the majority of long-term ice sheet and global temperature evolution since 20 Ma.

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.

High-Resolution Projections of Global Sea Surface Temperatures Reveal Critical Warming in Humpback Whale Breeding Grounds

Anthropogenic greenhouse gas emissions are triggering changes in global climate and warming the ocean. This will affect many marine organisms, particularly those with high site fidelity and habitat temperature preferences, such as humpback whales on their breeding grounds. To study the impacts of a warming ocean on marine organisms, large-scale projections of climatic variables are crucial. Global models are of 0.25 - 1° (~25-100 km) resolution, and not ideal to predict localized changes. Here, we provide 0.05° resolution (~5 km) sea surface temperature (SST) projections, statistically downscaled using the delta method. We illustrate the shifting isotherms of the critical 21 and 28°C boundaries, which border the climatic envelope that humpback whales prefer for their breeding grounds, over the course of the 21st century on a decadal temporal resolution. Results show by the end of the 21st century, 35% of humpback whale breeding areas will experience SSTs above or within 1°C of current thresholds if present-day social, economic, and technological trends continue (‘middle of the road’ CMIP6 greenhouse gas trajectory SSP2-RCP4.5). This number rises to 67% under the scenario describing rapid economic growth in carbon-intensive industries (‘fossil-fueled development’ CMIP6 greenhouse gas trajectory SSP5-RCP8.5). These projections highlight the importance of reducing global greenhouse gas emissions and minimizing further SST increases to preserve ecological integrity of humpback whale breeding areas. In this context, our results emphasize the need to focus on protection of critical ocean habitat and to provide high-resolution climate data for this purpose.

Spatiotemporal neural network with attention mechanism for El Ni\xf1o forecasts

To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet makes two critical architectural improvements: it learns spatial features globally by expanding the network’s receptive field and encodes long-term sequential features with visual attention using a stateful long-short term memory network. The STANet conducts multitask learning of Nino3.4 index prediction and calendar month classification for predicted indices. In a comparison of the proposed STANet performance with the state-of-the-art model, the accuracy of the 12-month forecast lead correlation coefficient was improved by 5.8% and 13% for Nino3.4 index prediction and corresponding temporal classification, respectively. Furthermore, the spatially attentive regions for the strong El Niño events displayed spatial relationships consistent with the revealed precursor for El Niño occurrence, indicating that the proposed STANet provides good understanding of the spatiotemporal behavior of global sea surface temperature and oceanic heat content for El Niño evolution.

Plio-Pleistocene environmental variability in Africa and its implications for mammalian evolution

Understanding the climatic drivers of environmental variability (EV) during the Plio-Pleistocene and EV’s influence on mammalian macroevolution are two outstanding foci of research in African paleoclimatology and evolutionary biology. The potential effects of EV are especially relevant for testing the variability selection hypothesis, which predicts a positive relationship between EV and speciation and extinction rates in fossil mammals. Addressing these questions is stymied, however, by 1) a lack of multiple comparable EV records of sufficient temporal resolution and duration, and 2) the incompleteness of the mammalian fossil record. Here, we first compile a composite history of Pan-African EV spanning the Plio-Pleistocene, which allows us to explore which climatic variables influenced EV. We find that EV exhibits 1) a long-term trend of increasing variability since ∼3.7 Ma, coincident with rising variability in global ice volume and sea surface temperatures around Africa, and 2) a 400-ky frequency correlated with seasonal insolation variability. We then estimate speciation and extinction rates for fossil mammals from eastern Africa using a method that accounts for sampling variation. We find no statistically significant relationship between EV and estimated speciation or extinction rates across multiple spatial scales. These findings are inconsistent with the variability selection hypothesis as applied to macroevolutionary processes.

Evaluations of the sixth phase of Coupled Model Intercomparison Project model performance on precipitation over Southeast Asia based on the moisture budget

AbstractThis study evaluated the performance of the climatological precipitation over Southeast Asia using an ensemble of 48 global climate models/earth system models from the sixth phase of Coupled Model Intercomparison Project (CMIP6) suite of experiments. Multi‐source observational data sets were used to evaluate the historical simulation data of the CMIP6 ensembles towards the semi‐year mean of precipitation during November–April (NDJFMA) and May–October (MJJASO). The results indicate that the CMIP6 models were able to reproduce the large‐scale monsoonal features. Precipitation is overestimated over most eastern ocean areas in NDJFMA and over most areas in MJJASO. In terms of the ensemble bias and inter‐model uncertainty, the diagnosis of moisture budget indicates the dominant item is the dynamic convergence and the other important items have dynamic advection and evaporation. The main types of pattern bias were examined through the inter‐model empirical orthogonal function analysis, which is correlated with the global sea surface temperature and moisture flux. The climatological evaluation yielded a higher pattern correlation and a lower root‐mean square error in NDJFMA than MJJASO. Most of the models have greater standard deviations among the spatial grids than observations, especially in NDJFMA. The models with higher resolutions showed a better performance than those with lower resolutions. A model performance index is defined for the convenient comparison, the best three models are CNRM‐CM6‐1, EC‐Earth3‐Veg and NorESM2‐MM, and the worst three are INM‐CM4‐8, AWI‐ESM‐1‐1‐LR and MCM‐UA‐1‐0. This evaluation study provided useful analytics in understanding model behaviour and processes related to rainfall mechanisms which could be further examined in high‐resolution downscaling experiments in future studies.

Enhanced upward motion through the troposphere over the tropical western Pacific and its implications for the transport of trace gases from the troposphere to the stratosphere

Abstract. The tropical western Pacific (TWP) is a preferential area of air uplifting from the surface to the upper troposphere. A significantly intensified upward motion through the troposphere over the TWP in the boreal wintertime (November to March of the following year, NDJFM) has been detected using multiple reanalysis datasets. The upward motion over the TWP is intensified at rates of 8.0 ± 3.1 % per decade and 3.6 ± 3.3 % per decade in NDJFM at 150 hPa from 1958 to 2017, using JRA55 and ERA5 reanalysis datasets, while the MERRA-2 reanalysis data show a 7.5 ± 7.1 % per decade intensified upward motion for the period 1980–2017. Model simulations using the Whole Atmosphere Community Climate Model, version 4 (WACCM4), suggest that warming global sea surface temperatures (SSTs), particularly SSTs over the eastern maritime continent and tropical western Pacific, play a dominant role in the intensification of the upward motion by strengthening the Pacific Walker circulation and enhancing the deep convection over the TWP. Using CO as a tropospheric tracer, the WACCM4 simulations show that an increase in CO at a rate of 0.4 ppbv (parts per billion by volume) per decade at the layer 150–70 hPa in the tropics is mainly resulted from the global SST warming and the subsequent enhanced upward motion over the TWP in the troposphere and strengthened tropical upwelling of Brewer–Dobson (BD) circulation in the lower stratosphere. This implies that more tropospheric trace gases and aerosols from both natural maritime sources and outflow from polluted air from South Asia may enter the stratosphere through the TWP region and affect the stratospheric chemistry and climate.

A stochastic locally diffusive model with neural network‐based deformations for global sea surface temperature

In this work, we propose a new approach to model large, irregularly distributed spatio‐temporal global data via a locally diffusive stochastic partial differential equation (SPDE). The proposed model assumes a local deformation of the SPDE with non‐linear dependence on the covariates through a neural network. The proposed model can be fit in a computationally efficient manner using a triangulation over the sphere and sparsity of the precision matrix, as shown in an application with a large data set of simulated multi‐decadal monthly sea surface temperature.

High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things.

Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.

Spatial–temporal distribution and forecasting model of precipitation using dynamic-statistical information fusion

Abstract In order to explore the evolvement mechanism of hydrometeorological elements, spatial–temporal distribution of precipitation in the Huai river basin is studied by statistical drawing and empirical orthogonal function decomposition. How to make an objective combination for the predictive results of precipitation? Information fusion in data assimilation is introduced to merge the improved National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) with the multilinear regression model. Firstly, in terms of time, the annual precipitation is apt to decline at most stations within 30 years, and precipitation mainly concentrates in the flood season. The characteristics of spatial distribution are similar to topographic features. It can also be found that precipitation gradually decreases from south to north. Secondly, from statistical forecasting, the relationship between precipitation and global sea surface temperature (SST) is explored. Prediction equation is established with SST and the average precipitation. Thirdly, from dynamic model forecasting, the CFSv2 original model and the CFSv2 statistical downscaling model are used to analyze the influence of model deviation on fusion prediction. The optimum interpolation assimilation method is applied for realizing the optimal integration of statistical and dynamic model prediction. Finally, the standardized precipitation index (SPI) is calculated by the combined forecasting of annual precipitation to evaluate drought conditions. The results show that SST is an important factor affecting precipitation, which may be applied as a forecasting direction with other factors. The merged precipitation prediction skill by the CFSv2 original model and the statistical model do not have the great promotion, which is still lower than the prediction skill only by the statistical model. However, the merged precipitation prediction skill by the CFSv2 statistical downscaling model and the statistical model is better than the prediction skills by the two models mentioned above, respectively. These indicate that when the prediction difference between the models is large, the merged prediction error cannot be minimized. When the prediction skill levels are equal, there is an improvement in the merged result. So, it is necessary to revise the climate dynamic model by downscaling. What is more, the obtained drought levels match the actual disaster conditions, providing theoretical support of hydrology and meteorology for the prevention of natural disasters.

Water level change of Lake Tana, source of the Blue Nile: Prediction using teleconnections with sea surface temperatures

Water level change of Lake Tana, the source of the Blue Nile was analyzed. Correlations between the water level change and global sea surface temperature (GSST) were calculated and teleconnections were found. Prediction of water level was performed using a recurrent artificial neural network model. First, the seasonal change of water level was divided into three phases, the rising, recession 1, and recession 2 phases. The water level increased during the rising phase, decreased rapidly during the recession 1 phase, and decreased at a uniform rate during the recession 2 phase. To find teleconnections of the water level change in the rising phase with GSST, correlations between the level change and GSST were calculated. Sea regions on the Pacific Ocean indicated significant correlations with the level change at lag 0–1 month and lag 6–7 months. There was a strongly correlated sea zone over the western Pacific Ocean at time lags of 6–7 months. To predict water level change, SST time series of the correlated zone was applied to a recurrent neural network model. Predictions of changes of the rise of the water level of Lake Tana during the rainy season from teleconnections with SSTs via the neural network model simulated the observed changes well (r = 0.795). Prediction of the changes of Lake Tana’s water level with a lead time of 6–7 months can greatly facilitate management of the lake’s water resources.

Characteristics and Cause Analysis of the 1954 Yangtze Precipitation Anomalies

In 1954, the Yangtze River valley was hit by heavy precipitation anomalies, which caused large casualties and economic losses; however, systematic analyses of the causes are lacking. Adopting the latest national historical precipitation data collected by the China Meteorological Administration (CMA) and global sea surface temperature (SST) records, this retrospective study determined the spatial–temporal distribution characteristics of the precipitation in 1954 in Wuhan, a city situated in the Yangtze River valley. The results confirmed that the 1954 precipitation anomalies were characterized by a high volume and a long period of rainfall, plus numerous cloudbursts, with most of the precipitation concentrated during June and July at the mid- and low-Yangtze areas along the Yangtze. An El Niño event caused the West Pacific subtropical highs to continually move southward during the summer, creating a long-term rainband in the drainage basin. Moreover, the continued low SSTs in the Sea of Okhotsk generated an active blocking high that continuously brought high-latitude cold air into the south, boosting precipitation over the drainage basin. This study proposed a new causal model of summertime precipitation across the Yangtze River valley in 1954, whereby the unusual SST changes initially triggered atmospheric circulation anomalies, which caused the precipitation anomalies of 1954.

PacBio Isoform Sequencing and Illumina RNA Sequencing Provide Novel Insights on Responses to Acute Heat Stress in Apostichopus japonicus Coelomocytes

Significant increases in global sea surface temperatures are expected with climate change and may cause a serious challenge for marine organisms cultured in aquatic environments that are characterized by short and long-term fluctuations in water temperatures. Apostichopus japonicus, a sea cucumber with high nutritional value and pharmacological properties, is an important economic species that is widely raised in aquaculture in China. In recent years, continuous extreme high temperatures (up to 30°C) have occurred frequently in summer leading to mass mortality of sea cucumbers cultured in semi-open shallow regions seriously restricting the sustainable development of sea cucumber aquaculture. In the present study, we combined RNA-seq and PacBio single-molecular real-time (SMRT) sequencing technology to unveil the potential mechanisms of response to acute heat stress in A. japonicus coelomocytes. A total of 1,375 differentially expressed genes (DEGs) were identified in a comparison of control and 48 h heat stress (HS) groups. Pathway enrichment analysis indicated that nine important pathways induced by HS were significantly enriched (q-value < 0.05) and mostly fell into four classes: folding, sorting, and degradation, immune and infectious diseases, signal transduction, and post-transcriptional regulation. Among them, all 41 genes connected with protein processing in endoplasmic reticulum were significantly up-regulated, and 12 of these were selected and validated via qPCR. Furthermore, changes in alternative splicing (AS) were also identified in sea cucumbers following HS. A total of 1,224 and 1,251 differential alternative splicing (DAS) events were identified using splice junction counts (JC only) and reads on target and junction counts (JCEC) as the input for rMATS in CO-HS comparison. We further found that the RNA splicing-related genes were enriched in the spliceosome pathway and showed DAS in control versus heat-stressed animals. In particular, we compared and confirmed that the hsfs1 gene, the master regulator of the heat shock response, showed differentially spliced exons in response to HS. This is the first comprehensive study showing that transcriptional and post-transcriptional (AS) controls are involved in the acute heat stress response of sea cucumber coelomocytes and provides novel insights into the molecular mechanisms of echinoderm adaptation to environmental stress.

Assessing the influence of sea surface temperature and arctic sea ice cover on the uncertainty in the boreal winter future climate projections

We investigate the uncertainty (i.e., inter-model spread) in future projections of the boreal winter climate, based on the forced response of ten models from the CMIP5 following the RCP8.5 scenario. The uncertainty in the forced response of sea level pressure (SLP) is large in the North Pacific, the North Atlantic, and the Arctic. A major part of these uncertainties (31%) is marked by a pattern with a center in the northeastern Pacific and a dipole over the northeastern Atlantic that we label as the Pacific–Atlantic SLP uncertainty pattern (PA∆SLP). The PA∆SLP is associated with distinct global sea surface temperature (SST) and Arctic sea ice cover (SIC) perturbation patterns. To better understand the nature of the PA∆SLP, these SST and SIC perturbation patterns are prescribed in experiments with two atmospheric models (AGCMs): CAM4 and IFS. The AGCM responses suggest that the SST uncertainty contributes to the North Pacific SLP uncertainty in CMIP5 models, through tropical–midlatitude interactions and a forced Rossby wavetrain. The North Atlantic SLP uncertainty in CMIP5 models is better explained by the combined effect of SST and SIC uncertainties, partly related to a Rossby wavetrain from the Pacific and air-sea interaction over the North Atlantic. Major discrepancies between the CMIP5 and AGCM forced responses over northern high-latitudes and continental regions are indicative of uncertainties arising from the AGCMs. We analyze the possible dynamic mechanisms of these responses, and discuss the limitations of this work.

Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations

AbstractConsequences from a slowdown or collapse of the Atlantic meridional overturning circulation (AMOC) could include modulations to El Niño–Southern Oscillation (ENSO) and development of the Pacific meridional overturning circulation (PMOC). Despite potential ramifications to the global climate, our understanding of the influence of various AMOC and PMOC states on ENSO and global sea surface temperatures (SSTs) remains limited. Five multicentennial, fully coupled model simulations created with the Community Earth System Model were used to explore the influence of AMOC and PMOC on global SSTs and ENSO. We found that the amplitude of annual cycle SSTs across the tropical Pacific decreases and ENSO amplitude increases as a result of an AMOC shutdown, irrespective of PMOC development. However, active deep overturning circulations in both the Atlantic and Pacific basins reduce ENSO amplitude and variance of monthly SSTs globally. The underlying physical reasons for changes to global SSTs and ENSO are also discussed, with the atmospheric and oceanic mechanisms that drive changes to ENSO amplitude differing based on PMOC state. These results suggest that if climate simulations projecting AMOC weakening are realized, compounding climate impacts could occur given the far-reaching ENSO teleconnections to extreme weather and climate events. More broadly, these results provide us with insight into past geologic era climate states, when PMOC was active.

Evaluation of Sea Surface Temperatures Derived From the HY-1D Satellite

Global sea surface temperatures (SSTs) are detected by the Chinese ocean color and temperature scanner (COCTS) instruments aboard the HaiYang (HY)-1C and HY-1D satellites. In this study, the SSTs derived from the COCTS instrument on the HY-1D (COCTS/HY-1D) satellite and a nonlinear SST algorithm with corresponding coefficients were introduced. The COCTS/HY-1D SSTs recorded from April 26 to August 31, 2021, were evaluated against water temperature measurements taken at depths above 1 m from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> Quality Monitor system; root-mean-square errors (RMSEs) of 0.65 and 0.71 °C and robust standard deviations (RSDs) of 0.51 and 0.47 °C were obtained for the daytime and nighttime SSTs, respectively, using a spatiotemporal matching window of 4 h and 2.5 km. Daily gridded SSTs derived from COCTS/HY-1D were compared with those obtained from the visible infrared imaging radiometer suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in the same period, and RMSEs of 0.67 ± 0.06 and 0.81 ± 0.06 °C and RSDs of 0.49 ± 0.04 and 0.58 ± 0.05 °C were obtained for the daytime and nighttime SSTs, respectively. The COCTS/HY-1D-derived SSTs covering Gulf Stream waters were cross-validated against the VIIRS/S-NPP data as a case study, and RMSEs of 0.53 and 0.47 °C for the daytime and nighttime, respectively, were obtained.