Seasonal Mean Sea Surface Temperature Research Articles

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

Indian summer monsoon variability forecasts in the North American multimodel ensemble

The representation of the seasonal mean and interannual variability of the Indian summer monsoon rainfall (ISMR) in nine global ocean-atmosphere coupled models that participated in the North American Multimodal Ensemble (NMME) phase 1 (NMME:1), and in nine global ocean-atmosphere coupled models participating in the NMME phase 2 (NMME:2) from 1982–2009, is evaluated over the Indo-Pacific domain with May initial conditions. The multi-model ensemble (MME) represents the Indian monsoon rainfall with modest skill and systematic biases. There is no significant improvement in the seasonal forecast skill or interannual variability of ISMR in NMME:2 as compared to NMME:1. The NMME skillfully predicts seasonal mean sea surface temperature (SST) and some of the teleconnections with seasonal mean rainfall. However, the SST-rainfall teleconnections are stronger in the NMME than observed. The NMME is not able to capture the extremes of seasonal mean rainfall and the simulated Indian Ocean-monsoon teleconnections are opposite to what are observed.

A Systematic Comparison of Particle Filter and EnKF in Assimilating Time‐Averaged Observations

AbstractThe particle filter (PF) and the ensemble Kalman filter (EnKF) are two promising and popularly adopted types of ensemble‐based data assimilation methods for paleoclimate reconstruction. However, no systematic comparison between them has been attempted. We compare these two uncertainty based methods in pseudoproxy experiments where synthetic seasonal mean sea surface temperature observations are assimilated. Their skills are evaluated with regards to local, hemispherically averaged and globally averaged analysis error, and their ability to capture large‐scale modes of variability. It is found that the EAKF (Ensemble Adjustment Kalman filter, a variant of EnKF) performs better than the PF with only one third of the ensemble size, despite PF's theoretical superiority in allowing for non‐Gaussian statistics and nonlinear dynamics. The success of the EAKF is attributed to the facts that (1) Gaussian assumption is somewhat appropriate for this application; (2) The EAKF is less sensitive to sampling errors than the PF due to the different methodological natures. Sixteen members are enough to estimate accurate covariance for the EAKF, but 48 (even 96) members still underrepresent the state space of high‐dimensional system for the PF. Our study highlights the importance of a large localization radius in the application of the EnKF to paleoclimate reconstruction due to the sparse proxy network and suggests that additional techniques, such as localization or clustered particle filter, are needed to improve the PF for paleoclimate reconstruction, in addition to the simple importance resampling currently adopted by most research.

Feedback of 10–20-day intraseasonal oscillations on seasonal mean SST in the tropical Western North Pacific during boreal spring through fall

The present study documents the factors for year-to-year changes in the intensity of 10–20-day intraseasonal oscillations (ISOs) and investigates the feedback of the 10–20-day ISO intensity on seasonal mean sea surface temperature (SST) change in the tropical western North Pacific during boreal spring through fall. An analysis of local correlation reveals a significant negative correlation of the 10–20-day ISO intensity and the seasonal mean SST tendency in the tropical western North Pacific during spring, summer, and fall, suggesting a plausible feedback of the ISO intensity on seasonal mean SST anomaly. The 10–20-day ISO intensity change over the tropical western North Pacific is influenced by El Nino-Southern Oscillation (ENSO) through modulation of vertical shear of zonal winds, lower-level moisture, and upward motion. Due to the phase dependence of location of these ENSO-induced background field changes, the ISO intensity is subject to ENSO influence in different regions during the three seasons. The feedback of the 10–20-day ISO intensity on local seasonal mean SST change in the tropical western North Pacific is demonstrated by separating latent heat flux anomalies into components on different time scales. The ISO-induced latent heat flux anomalies may accumulate in a season and overcome interannual anomalies due to seasonal mean changes. Thus, the ISO-induced surface heat flux change may play an important role in the seasonal mean SST anomaly in the tropical western North Pacific.

Decadal change in the boreal summer intraseasonal oscillation

A decadal change in activity of the boreal summer intraseasonal oscillation (BSISO) was identified at a broad scale. The change was more prominent during August–October in the boreal summer. The BSISO activity during 1999–2008 (P2) was significantly greater than that during 1984–1998 (P1). Compared to P1, convection in the BSISO was enhanced and the phase speed of northward-propagating convection was reduced in P2. Under background conditions, warm sea surface temperature (SST) anomalies in P2 were apparent over the tropical Indian Ocean and the western tropical Pacific. The former supplied favorable conditions for the active convection of the BSISO, whereas the latter led to a strengthened Walker circulation through enhanced convection. This induced descending anomalies over the tropical Indian Ocean. Thermal convection tends to be suppressed by descending anomalies, whereas once an active BSISO signal enters the Indian Ocean, convection is enhanced through convective instability by positive SST anomalies. After P2, the BSISO activity was weakened during 2009–2014 (P3). Compared to P2, convective activity in the BSISO tended to be inactive over the southern tropical Indian Ocean in P3. The phase speed of the northward-propagating convection was accelerated. Under background conditions during P3, warmer SST anomalies over the maritime continent enhance convection, which strengthened the local Hadley circulation between the western tropical Pacific and the southern tropical Indian Ocean. Hence, the convection in the BSISO over the southern tropical Indian Ocean was suppressed. The decadal change in BSISO activity correlates with the variability in seasonal mean SST over the tropical Asian monsoon region, which suggests that it is possible to predict the decadal change.

A study of the predictability of sea surface temperature over the tropics

In this study, reasons for spatial variability in prediction skill of seasonal mean sea surface temperature (SST) are investigated. The analysis explores the connection between the spatial variation of SST prediction skill in tropical latitudes and local simultaneous correlation between observed SST and precipitation (SST-P). The results show that high (low) SST prediction skills and slow (fast) decay in skill with lead time are generally collocated with large positive (weakly positive or negative) SST-P correlations. The reasons for spatial variation in SST-P correlation can be explained by whether the primary forcing is from the ocean to the atmosphere or vice versa. Over regions where the ocean is generally known to force the atmosphere, it is found that SST-P correlation has large positive values. Over regions where the atmosphere forces ocean, SST-P correlation is weak due to intrinsically unpredictable nature of atmospheric variability. The physical explanation for spatial variation in SST-P correlation, and apparent link between the spatial variations in SST-P correlation and spatial variations in skill of SST predictions also establishes a physical basis for the latter. As a corollary, low SST prediction skill in coupled forecast models over certain geographical regions may be due to the inherent limits on predictability in addition to the contribution from model biases or initialization errors.

ENSO phase-locking to the boreal winter in CMIP3 and CMIP5 models

In this study, the El Nino-Southern Oscillation (ENSO) phase-locking to the boreal winter in CMIP3 and CMIP5 models is examined. It is found that the models that are poor at simulating the winter ENSO peak tend to simulate colder seasonal-mean sea-surface temperature (SST) during the boreal summer and associated shallower thermocline depth over the eastern Pacific. These models tend to amplify zonal advection and thermocline depth feedback during boreal summer. In addition, the colder eastern Pacific SST in the model can reduce the summertime mean local convective activity, which tends to weaken the atmospheric response to the ENSO SST forcing. It is also revealed that these models have more serious climatological biases over the tropical Pacific, implying that a realistic simulation of the climatological fields may help to simulate winter ENSO peak better. The models that are poor at simulating ENSO peak in winter also show excessive anomalous SST warming over the western Pacific during boreal winter of the El Nino events, which leads to strong local convective anomalies. This prevents the southward shift of El Nino-related westerly during boreal winter season. Therefore, equatorial westerly is prevailed over the western Pacific to further development of ENSO-related SST during boreal winter. This bias in the SST anomaly is partly due to the climatological dry biases over the central Pacific, which confines ENSO-related precipitation and westerly responses over the western Pacific.

Satellite-derived variability of the Aegean Sea ecohydrodynamics

Eight years of AVHRR-derived sea surface temperature (SST) and SeaWiFS-derived surface chlorophyll (Chl) data (1998–2005) are used to investigate key processes affecting the spatial and temporal variability of the two parameters in the Aegean Sea. Seasonal mean SST and Chl maps are constructed using daily data to study seasonal dynamics whereas empirical orthogonal function (EOF) and correlational analysis is applied to the 8-day composite SST and Chl anomaly time-series in order to study the variability and co-variability of the two parameters from subseasonal to interannual time-scales. The seasonal mean fields show that Black Sea cold and chlorophyll-rich waters enter through the Dardanelles Strait and they are accumulated in the north-eastern part of the Aegean Sea, steered by the Samothraki anticyclone. Large chlorophyll concentrations are encountered in the hydrological front off the Dardanelles Strait as well as in coastal areas affected by large riverine/anthropogenic nutrient loads. The SST seasonal mean patterns reveal strong cooling that is associated with upwelling along the eastern boundary of the basin during summer due to strong northerly winds, a process which is not present in the surface chlorophyll climatology. The Chl dataset presents much stronger sub-seasonal variability than SST, with large variations in the phase and strength of the phytoplankton seasonal cycles. EOF analysis of the anomaly time-series shows that SST non-seasonal variability is controlled by synoptic weather variations and anomalies in the north–south wind-stress component regulating the summer coastal upwelling regime. Mean SST and Chl patterns, and their associated variations, are not closely linked implying that Black Sea and riverine inputs mainly control the intra-annual and interannual variability of the surface chlorophyll in the Aegean Sea rather than mixing and/or upwelling processes.

Meteorological forcing of long-term temperature variations of the Dutch coastal waters

Time series of observations of the sea surface temperature (SST) at 12 stations in the Dutch coastal zone are analyzed to establish whether an earlier published nearly 150 year long SST time series from the Marsdiep tidal inlet is representative for the whole Dutch coastal zone. The annual cycles (SST range and phase) as well as the long-term SST trends at decadal scales from other estuaries agree with the Marsdiep time series. An increasing SST trend since 1982 is a phenomenon of the whole Dutch coastal zone. In order to increase the understanding of the causes of the observed SST variability, a multiple linear regression model is constructed, which links locally determined seasonal meteorological and oceanographic forcing factors to the seasonal mean SST. The oceanographic forcing factor is the SST value from the preceding season, representing persistence due to thermal inertia of the sea. Season to season changes of the atmospheric circulation, connected with SST variability, are represented by seasonal mean wind components as forcing factors, e.g. the western winds in winter which bring relatively warm air masses to Western Europe. For the seasons where shortwave solar radiation is the dominant term in the local heat budget (spring and summer), the number of bright sun hours is used as forcing factor, roughly representing the effects of changing cloudiness. The annual mean SST, derived from the regression models for the four seasons, applied to 4 locations along the Dutch coast, correlates quite well, not only for the year to year variability ( R = 0.88) but also for the longer-term SST trends ( R = 0.95). An explicit local greenhouse effect is not required as separate forcing factor to explain the recent warming trend of Dutch coastal waters starting in the early 1980s; coincident variations in wind statistics and cloudiness are a sufficient explanation.

Global net community production estimated from the annual cycle of surface water total dissolved inorganic carbon

Global net community production is determined, for the first time, from the decrease in salinity (S)ߚnormalized total dissolved inorganic carbon (NCT = CT × 35/S) inventory in the surface mixed layer corrected for changes due to net airߚsea CO2 exchange and diffusive carbon flux from the upper thermocline. Changes in the mixed layer NCT inventory are estimated using a derived annual cycle of NCT and global records of the mixed layer depth. The annual NCT cycle is deduced from regional algorithms relating NCT to sea surface temperature (SST) and nitrate (NO3−), along with global records of seasonal mean SST and NO3−, and from the monthly mean surface partial pressure of CO2 and total alkalinity fields using thermodynamic models. The two methods show similar regional trends and yield global net community production estimates of 6.7 and 8.0 Gt C (1 Gt C = 1 × 1012 kg carbon), respectively. The two global estimates are not significantly different and represent an 8–month period of 1990 (warming period) during which the mixed layer NCT concentration decreases. However, the estimates do not account for net community production during a 4–month cooling period. Ratios of net community production during the warming and cooling periods are estimated from multiyear sediment trap data at the Hawaii Ocean Timeߚseries (228N, 1588W) and Ocean Weather Station P (50°N, 145°W) sites. Global extrapolation of these ratios yields annual rates of net community production of 9.1 ± 2.7 and 10.8 ± 2.7 Gt C yr−1.

The Relationships between Tropical Pacific and Atlantic SST and Northeast Brazil Monthly Precipitation

The monthly patterns of northeast Brazil (NEB) precipitation are analyzed in relation to sea surface temperature (SST) in the tropical Pacific and Atlantic Oceans, using singular value decomposition. It is found that the relationships between precipitation and SST in both basins vary considerably throughout the rainy season (February–May). In January, equatorial Pacific SST is weakly correlated with precipitation in small areas of southern NEB, but Atlantic SST shows no significant correlation with regional precipitation. In February, Pacific SST is not well related to precipitation, but south equatorial Atlantic SST is positively correlated with precipitation over the northern Nordeste, the latter most likely reflecting an anomalously early (or late) southward migration of the ITCZ precipitation zone. During March, equatorial Pacific SST is negatively correlated with Nordeste precipitation, but no consistent relationship between precipitation and Atlantic SST is found. Atlantic SST–precipitation correlations for April and May are the strongest found among all months or either ocean. Precipitation in the Nordeste is positively correlated with SST in the south tropical Atlantic and negatively correlated with SST in the north tropical Atlantic. These relationships are strong enough to determine the structure of the seasonal mean SST–precipitation correlations, even though the corresponding patterns for the earlier months of the season are quite different. Pacific SST–precipitation correlations for April and May are similar to those for March. Extreme wet (dry) years for the Nordeste occur when both Pacific and Atlantic SST patterns for April and May occur simultaneously. A separate analysis reinforces previous findings in showing that SST in the tropical Pacific and the northern tropical Atlantic are positively correlated and that tropical Pacific–south Atlantic correlations are negligible. Time-lagged analyses show the potential for forecasting either seasonal mean or monthly precipitation patterns with some degree of skill. In some instances, individual monthly mean SST versus seasonal mean (February–May) precipitation relationships differ considerably from the corresponding monthly SST versus monthly precipitation relationships. It is argued that the seasonal mean relationships result from the relatively strong monthly relationships toward the end of the season, combined with the considerable persistence of SST in both oceans.

Singular Value Decomposition of Wintertime Sea Surface Temperature and 500-mb Height Anomalies

Abstract Single field principal component analysis (PCA), direct singular value decomposition (SVD), canonical correlation analysis (CCA), and combined principal component analysis (CPCA) of two fields are applied to a 39-winter dataset consisting of normalized seasonal mean sea surface temperature anomalies over the North Pacific and concurrent 500-mb height anomalies over the same region. The CCA solutions are obtained by linear transformations of the SVD solutions. Spatial patterns and various measures of the variances and covariances explained by the modes derived from the different types of expansions are compared, with emphasis on the relative merits of SVD versus CCA. Results for two different analysis domains (i.e., the Pacific sector versus a full hemispheric domain for the 500-mb height field) are also compared in order to assess the domain dependence of the two techniques. The SVD solution is also compared with the results of 28 Monte Carlo simulations in which the temporal order of the SST gri...