Sea Surface Temperature Time Series Research Articles

Analysis of variability of tropical Pacific sea surface temperatures

Abstract. Sea surface temperature (SST) in the Pacific Ocean is a key component of many global climate models and the El Niño–Southern Oscillation (ENSO) phenomenon. We shall analyse SST for the period November 1981–December 2014. To study the temporal variability of the ENSO phenomenon, we have selected a subregion of the tropical Pacific Ocean, namely the Niño 3.4 region, as it is thought to be the area where SST anomalies indicate most clearly ENSO's influence on the global atmosphere. SST anomalies, obtained by subtracting the appropriate monthly averages from the data, are the focus of the majority of previous analyses of the Pacific and other oceans' SSTs. Preliminary data analysis showed that not only Niño 3.4 spatial means but also Niño 3.4 spatial variances varied with month of the year. In this article, we conduct an analysis of the raw SST data and introduce diagnostic plots (here, plots of variability vs. central tendency). These plots show strong negative dependence between the spatial standard deviation and the spatial mean. Outliers are present, so we consider robust regression to obtain intercept and slope estimates for the 12 individual months and for all-months-combined. Based on this mean–standard deviation relationship, we define a variance-stabilizing transformation. On the transformed scale, we describe the Niño 3.4 SST time series with a statistical model that is linear, heteroskedastic, and dynamical.

Spatio-Temporal Decomposition of Satellite-Derived SST–SSH Fields: Links Between Surface Data and Ocean Interior Dynamics in the Agulhas Region

In this paper, we develop a novel observation-driven methodology to explore spatio-temporal dependencies between satellite-derived sea surface height (SSH) and sea surface temperature (SST) fields. Level-set-based and registration-based criteria are defined to evaluate and detect spatial links between SSH and SST anomaly fields. The method is applied to one-year SST and SSH time series in the highly dynamical Aghulas return current region. The analysis evidences a seasonal variation of the overall correlation between SST and SSH fields. As further revealed, the coldest SST anomalies are reported to efficiently trace the lowest SSH anomalies for all seasons, while the warmest SST anomalies solely match the largest SSH anomalies during winter. The second criterion relies on the registration of SSH and SST anomaly fields. The registration energy is shown to corresponds to the seasonal influence of the mixed-layer depth revealed here by atmospheric forcing. These results show us that the SST-derived SSH reconstruction using the surface quasigeostrophic approximation should take into account stratification effects, especially during summer. As discussed, the proposed methodology can enforce the reconstruction of sea surface current from a joint analysis of satellite altimetry data and high-resolution satellite-derived SST data.

Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean

Extreme environmental events such as droughts affect millions of people all around the world. Although it is not possible to prevent this type of event, its prediction under different time horizons enables the mitigation of eventual damages caused by its occurrence. An important variable for identifying occurrences of droughts is the sea surface temperature (SST). In the tropical Atlantic Ocean, SST data are collected and provided by the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) project, which is an observation network composed of sensor buoys arranged in this region. Sensors of this type, and more generally Internet of Things (IoT) sensors, commonly lead to data losses that influence the quality of datasets collected for adjusting prediction models. In this paper, we explore the influence of temporal aggregation in predicting step-ahead SST considering different prediction horizons and different sizes for training datasets. We have conducted several experiments using data collected by PIRATA project. Our results point out scenarios for training datasets and prediction horizons indicating whether or not temporal aggregated SST time series may be beneficial for prediction.

Percolation-based precursors of transitions in extended systems.

Abrupt transitions are ubiquitous in the dynamics of complex systems. Finding precursors, i.e. early indicators of their arrival, is fundamental in many areas of science ranging from electrical engineering to climate. However, obtaining warnings of an approaching transition well in advance remains an elusive task. Here we show that a functional network, constructed from spatial correlations of the system’s time series, experiences a percolation transition way before the actual system reaches a bifurcation point due to the collective phenomena leading to the global change. Concepts from percolation theory are then used to introduce early warning precursors that anticipate the system’s tipping point. We illustrate the generality and versatility of our percolation-based framework with model systems experiencing different types of bifurcations and with Sea Surface Temperature time series associated to El Niño phenomenon.

AVHRR GAC SST Reanalysis Version 1 (RAN1)

In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.

Food for thought: Mathematical approaches for the conversion of high-resolution sclerochronological oxygen isotope records into sub-annually resolved time series

Oxygen isotope (δ18O) records of incrementally banded marine calcifiers provide high-resolution information about modern and past environmental conditions. However, given their biological origin, these records are often distorted by ontogenetic and seasonal growth pattern. To evaluate their reliability, many studies correlate skeletal δ18O records with instrumentally measured time series of environmental conditions using a variety of methods to recalculate their temporal framework. Lacking consistency and a limited traceability of many temporal alignment methods underline the need for standardized and flexible method for the temporal alignment of sclerochronological records. Here, two methods for the temporal alignment of sclerochronological data and their application on measured oxygen isotope signatures of a fish otolith and a marine bivalve shell are presented and compared. The first method is a flexible approach using a linear interpolation between carefully selected temporal reference points for recalculating ontogenetic growth effects with an optional extension accounting for seasonal growth oscillations. The second approach uses the seasonally-oscillating Von Bertalanffy growth function parameterized based on the oxygen isotope record to reconstruct the underlying growth model and reconstruct thereby its temporal framework. Using these methods, we re-align δ18O-based water temperature records of a Carlarius heudelotii otolith and a Venus crebrisulca shell with measured sea surface temperature time series and compare the resulting correlation as well as the different underlying growth models. Our results show that both methods represent useful tools for the temporal alignment of sclerochronological records. However, underlying growth models strongly differ depending on the temporal alignment method and the parameters used for the reconstruction of the temporal framework. This underlines the importance of a careful selection of a particular alignment method depending on the complexity of distortion due to seasonal and ontogenetic growth trajectories. Moreover, we show that both organisms represent reliable paleoenvironmental archives providing sub-seasonally resolved water temperature records.

Interannual variability in chlorophyll-a on the southern Queensland continental shelf and its relationship to ENSO

Coastal phytoplankton blooms can result from upwelling of colder nutrient-rich water, seasonal fluvial or anthropogenic point sources of nutrient. Here we analyze 15-year time series of monthly mean and 8-day satellite-derived chlorophyll-a (Chl-a) and sea surface temperature (SST) on the southern Queensland continental shelf (24.25–28.25°S) from March 2000 to February 2015. We examine the interannual variability in these parameters and its relationship to algal bloom dynamics. Seasonal climatological means are computed and analyzed. Empirical orthogonal function (EOF) analysis is applied to these time series. Cross-correlation and spatial correlation analyses are used to investigate the relationship between the multivariate ENSO index (MEI), Chl-a and SST. Computed eigenvectors of the time series of Chl-a and SST present a strong seasonal variability on the first EOF modes. Thus, the seasonal variability was removed by computing monthly and 8-day Chl-a and SST anomalies. The EOF analysis was then applied to the anomaly time series. Correlation analysis results show a positive correlation between MEI and the eigenvector of the first EOF of the monthly Chl-a anomaly with time lag of three to four months. We find a negative correlation between MEI and the eigenvector of the second EOF of the monthly Chl-a anomaly with time lag of three to four months. There is no correlation between MEI and eigenvectors of the monthly SST anomaly. There are significant correlations between eigenvectors of the first and second EOF modes of 8-day Chl-a and the first and second EOF modes of 8-day SST respectively. Negative correlation coefficients between 8-day anomalies of Chl-a and SST are found on the continental shelf to the east of Fraser Island and Stradbroke Island. Analysis of a particular algal bloom event indicates a negative SST anomaly and negative curl of wind stress in the waters to the southeast of Fraser Island suggesting that wind stress is possibly a secondary but significant physical driver of the upwelling system. Our results suggest that variability in phytoplankton biomass on the southern Queensland continental shelf is driven by upwelling of nutrients during the spring dry season and fluvial nutrient loads during the summer and autumn wet seasons. Seasonal change is the primary factor controlling the variability of Chl-a and SST while climate variability (ENSO) and upwelling are secondary but significant factors.

Principal nonlinear dynamical modes of climate variability.

We suggest a new nonlinear expansion of space-distributed observational time series. The expansion allows constructing principal nonlinear manifolds holding essential part of observed variability. It yields low-dimensional hidden time series interpreted as internal modes driving observed multivariate dynamics as well as their mapping to a geographic grid. Bayesian optimality is used for selecting relevant structure of nonlinear transformation, including both the number of principal modes and degree of nonlinearity. Furthermore, the optimal characteristic time scale of the reconstructed modes is also found. The technique is applied to monthly sea surface temperature (SST) time series having a duration of 33 years and covering the globe. Three dominant nonlinear modes were extracted from the time series: the first efficiently separates the annual cycle, the second is responsible for ENSO variability, and combinations of the second and the third modes explain substantial parts of Pacific and Atlantic dynamics. A relation of the obtained modes to decadal natural climate variability including current hiatus in global warming is exhibited and discussed.

Modelled and observed sea surface temperature trends for the Caribbean and Antilles

The ocean occupies 95% of the Caribbean's area and plays a leading role in the region's climate, thus making the sea surface temperature (SST) a very important regional climate index. This, in conjunction with the lack of a regionally consistent, quality-controlled surface temperature dataset increases the scientific value of using SST to characterize the regional climate and its trends. This study determines the magnitudes of the long-term SST trends in the Wider Caribbean (WC) and the Antilles. We overcome the presence of discontinuity points in the SST time series using the change point statistical technique. Annual mean SST trends combining the subperiods 1906–1969 and 1972–2005 are 1.32 ± 0.41 °C per century for the Antilles and 1.08 ± 0.32 °C per century for the WC. For the same regions during the subperiod 1972–2005, the corresponding trends are 1.41 ± 0.68 °C per century and 1.18 ± 0.49 °C per century, illustrating the warming intensification during the last four decades. A significant correlation is found between the SSTs in the Caribbean and Atlantic Multidecadal Oscillation (AMO) index, suggesting a potential link between Caribbean SSTs and the mechanisms governing the Atlantic basin-wide SSTs. Finally, the capabilities of two state-of-the-art coupled climate models, the Norwegian Earth System Model (NorESM1-M) and the Bergen Climate Model (BCM), to simulate SST in the Caribbean were tested. Both models produce an adequate simulation of the annual mean SST anomalies and SST seasonal cycle for the WC and the Antilles. The simulated annual and monthly mean SSTs are colder in the two models compared to the observations, a common feature among the majority of general circulation models participating in the Coupled Model Intercomparison Project Phase 5. However, despite these minor deficiencies both BCM and NorESM1-M are considered adequate for conducting SST simulations relevant for future climate change research in the Caribbean.

Comparison of long-term variability of Sea Surface Temperature in the Arabian Sea and Bay of Bengal

Long-term variability of Sea Surface Temperature (SST) in the Arabian Sea (AS) and Bay of Bengal (BoB) during 1880–2010 is examined using all the available data sets of monthly SST, wind speed, incoming solar radiation, cloud cover, long wave radiation, latent heat flux, net surface heat flux and surface currents. SST time series reveal an upward (increasing) trend (warming) after 1940 in both the basins. Wavelet analyses of the SST time series reveal that AS SST is controlled by semi-annual and annual forcing while the BoB SST is influenced predominantly by annual forcing. The SST responses in both the basins are in phase to the climatic events such as El Niño, La Niña and Indian Ocean Dipole, but a stronger impact is noticed in the AS SST. The climatic events affected the seasonal peaks of SST warming (April–May) and cooling (August–September) in both the basins. Time series of SST anomaly also showed in phase responses to the climatic events and an upward trend from 1960 to 2010 in both the basins. Long-term decreasing trends observed in surface wind speed, latent heat flux and advective process via the weakened surface currents, together with the long-term increasing trend in P − E (Precipitation, P minus Evaporation, E) contributed to the SST warming trend in both the basins. In association with the upward trend in P − E, the Simple Ocean Data Assimilation (SODA) Reanalysis Sea Surface Salinity time series exhibited freshening (a decreasing trend) and enhanced the salinity stratification in the BoB, thus modulated the BoB SST warming. These cumulative processes led to the warmer SSTs in the BoB compared to that in AS.

Decadal changes in South Pacific sea surface temperatures and the relationship to the Pacific decadal oscillation and upper ocean heat content

AbstractDecadal changes in Pacific sea surface temperatures (SSTs) and upper ocean heat content (OHC) remain poorly understood. We present an annual average composite coral Sr/Ca‐derived SST time series extending back to 1791 from Fiji, Tonga, and Rarotonga (FTR) in the Pacific Decadal Oscillation (PDO) sensitive region of the southwest Pacific. Decadal SST maxima between 1805 and 1830 Common Era (C.E.) indicate unexplained elevated SSTs near the end of the Little Ice Age. The mean period of decadal SST variability in this region has a period near 25 years. Decades of warmer (cooler) FTR SST co‐occur with PDO negative (positive) phases since at least ~1930 C.E. and positively correlate with South Pacific OHC (0–700 m). FTR SST is also inversely correlated with decadal changes in equatorial Pacific SST as measured by coral Sr/Ca. Collectively, these results support the fluctuating trade wind‐shallow meridional overturning cell mechanism for decadal modulation of Pacific SSTs and OHC.

Six years of OSI-SAF METOP-A AVHRR sea surface temperature

The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution global Sea Surface Temperature (SST) from the METOP-A Advanced Very High Resolution Radiometer (AVHRR) since July 2007. The SST operational processing and the validation scheme have remained unchanged for more than 6years. The global validation results against measurements are stable over time. Night-time METOP-A SSTs differ from drifting buoy SSTs by −0.05K in average with a standard deviation of 0.44K and the daytime values are respectively 0.09K and 0.56K.Seasonal statistics have been calculated on a global regular 5-degree grid for a 6-year period to review the main biases and their characteristics. There is evidence of regional and seasonal biases, indeed the multispectral regression algorithms are known (to a various degree depending upon specific implementation) to have limitations in handling the variety of atmospheric absorption conditions encountered over the global ocean. This problem has been solved for the OSI SAF geostationary SST chain by adopting a Numerical Weather Prediction (NWP) profile based correction method. The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data.An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. The METOP-A time series gives consistent results when compared to other observations or model outputs.

Is the Atlantic surface temperature a good proxy for forecasting the recruitment of European eel in the Guadalquivir estuary?

This study analysed the possibility of using the sea surface temperature (SST) of the Atlantic Ocean to predict the recruitment of European eels in one of the most important estuaries of the south of Europe. For this purpose, two different time series concerning glass eel in the Guadalquivir estuary (the first obtained from a set of fishery-independent experimental samplings in this estuary and the second from an unofficial database on commercial catches provided by one of the main local marketer-buyers) were standardised to obtain a single time series on a monthly scale. This series was correlated with a total of 368 SST time series for 368 sectors of 1.95°×1.95° of the Atlantic Ocean covering the possible migration routes of adult eels and leptocephalous larvae. The significant sectors were clustered and selected as inputs for artificial neural network models (ANNs) with the objective of obtaining a model to forecast glass eel recruitment. Globally, the best result was given by an ANN with only 12 clusters as input variables and 35 neurons in the hidden layer. For this configuration, the explained variance in the test phase was slightly higher than 79%. These results were significantly better than those obtained with classical methods. The strong correlation between predicted and observed glass eel abundance suggests that: (a) there is a marked non-linear relationship between SST and glass eel recruitment in the Guadalquivir estuary; (b) SST is a good proxy for predicting glass eel recruitment and; (c) one of the main factors responsible for the changes in abundance of this species is changes in the ocean conditions.

Air–sea interactions during strong winter extratropical storms

A high-resolution, regional coupled atmosphere–ocean model is used to investigate strong air–sea interactions during a rapidly developing extratropical cyclone (ETC) off the east coast of the USA. In this two-way coupled system, surface momentum and heat fluxes derived from the Weather Research and Forecasting model and sea surface temperature (SST) from the Regional Ocean Modeling System are exchanged via the Model Coupling Toolkit. Comparisons are made between the modeled and observed wind velocity, sea level pressure, 10 m air temperature, and sea surface temperature time series, as well as a comparison between the model and one glider transect. Vertical profiles of modeled air temperature and winds in the marine atmospheric boundary layer and temperature variations in the upper ocean during a 3-day storm period are examined at various cross-shelf transects along the eastern seaboard. It is found that the air–sea interactions near the Gulf Stream are important for generating and sustaining the ETC. In particular, locally enhanced winds over a warm sea (relative to the land temperature) induce large surface heat fluxes which cool the upper ocean by up to 2 °C, mainly during the cold air outbreak period after the storm passage. Detailed heat budget analyses show the ocean-to-atmosphere heat flux dominates the upper ocean heat content variations. Results clearly show that dynamic air–sea interactions affecting momentum and buoyancy flux exchanges in ETCs need to be resolved accurately in a coupled atmosphere–ocean modeling framework.

Warmer-get-wetter or wet-get-wetter? A criterion to classify oceanic precipitation

In this study, the temporal and spatial variations of observed global oceanic precipitation during 1979–2010 are investigated. It is found that the global trend in precipitation during this period varies at a rate of 1.5%/K of surface warming while the rate is 6.6%/K during 2006–2010. The precipitation is highly correlated with Sea Surface Temperature (SST) in both the temporal and the spatial patterns since the strong 1997–98 El Nino event. Considering the distributions of precipitation and SST, seven oceanic regions are classified and presented using the observed Global Precipitation Climatology Project (GPCP) data and Extended Reconstructed Sea Surface Temperatures, version 3 (ERSST.v3) data. Further examining the mechanisms of the classified oceanic precipitation regions is conducted using the Tropical Rainfall Measuring Mission (TRMM) satellite, GFDL-ESM-2G model precipitation and SST data and Hadley Center sea ice and SST version 1 (HadISST1) data. More than 85% of global oceanic precipitations are controlled by either one or both of the warmer-get-wetter mechanism and wet-get-wetter mechanism. It is estimated that a 0.5 SST signal-to-noise ratio, representing the trend of SST time series to the standard deviation, is a criterion to distinguish the mechanism of a region. When the SST ratio is larger than 0.5, the precipitation of this region is controlled by the warmer-get-wetter mechanism. SST, rather than the humidity, is the pivotal factor. On the other hand, when the SST ratio is less than 0.5, the precipitation is controlled by the wet-get-wetter mechanism. The SST variability is a significant factor contributing to the precipitation variation.

DAILY BLENDED MICROWAVE AND INFRARED SEA SURFACE TEMPERATURE COMPOSITION

A simple system for daily cloud free sea surface temperature (SST) composition based on thermal AVHRR and microwave TMI data is presented in this paper. Barnes’ objective analysis (Barnes, 1964) is applied as an interpolator to merge these two data sources, which have different spatial and temporal resolutions in a daily SST composition and in a regular grid product. Three comparisons were carried out as follows. First, in situ SST (daily average) measurements from eleven PIRATA´s buoys were compared. The correlation coefficients results varied from 0.89 to 0.99, and RMSE, MAE and MBE values have not exceeded 0.57 for period from 2002 to 2010. Second, comparisons between daily SST composition and average daily in situ SST collected from twenty three drifting buoys for the period from May 2008 to October 2010. The statistics results are 0.94, 0.25, 0.19 and -0.002 for correlation, RMSE, MAE and MBE, respectively. Third, SST (daily average) time series generated by OSTIA project was compared. The temporal and spatial RMSE (considering the study area) values ranged from approximately 0.21oC to 1.50oC and its average was 0.47oC for the period from January 1st to May 31st, 2006. Besides, an investigation about the influence of the data homogenization in the SST interpolation is discussed. Validation results are quite consistent (with SST composition accuracy less than 1.0oC). Thus, aiming to fulfill the numerical oceanographic model assimilation purposes and other oceanographic features studies, the developed SST product may be recommended as a candidate.

Warming of subtropical coastal waters accelerates Mnemiopsis leidyi growth and alters timing of spring ctenophore blooms

Shifts in the size, frequency, or timing of gelatinous zooplankton blooms in response to climate variability and climate change have become a concern in many coastal marine ecosys- tems worldwide. When abundant, gelatinous zooplankton can induce trophic cascades as well as alter energy flows to upper-level consumers. To quantify the role water temperature plays in reg- ulating bloom development of the ctenophore Mnemiopsis leidyi in subtropical coastal waters, we experimentally determined the effects of increases in temperature and rates of warming on growth rate (mg C d �1 ). Conditions were representative of those occurring during late winter and early spring (January to April) in the northern Gulf of Mexico. M. leidyi growth rates increased expo- nentially with temperature (9, 13, 16, and 22°C) and rates of warming (+1, +2, and +3°C wk �1 ). A biologically important temperature threshold (15.0°C, delineating negative and positive growth) as a proxy measure for bloom initiation was applied to a time-series of sea surface temperature (1975 to 2013) from Mobile Bay (Alabama, USA) and indicated climate-related warming of north- ern Gulf of Mexico has likely advanced the timing of the spring M. leidyi bloom over the past 39 yr. Results have implications for ecosystem-based approaches to fisheries management since larger ctenophore blooms, occurring earlier in the year, would be expected to intensify potential compet- itive and predatory interactions between M. leidyi, zooplankton, and planktivorous juvenile fish utilizing nearshore waters as nursery habitat during the late winter and early spring.

Qualitative and quantitative reconstructions of surface water characteristics and recent hydrographical changes in the Trondheimsfjord, central Norway

Abstract. In the present study we investigated dinocyst assemblages in the Trondheimsfjord over the last 25 to 50 yr from three well-dated multi-cores (210Pb and 137Cs) retrieved along the fjord axis. The downcore distribution of the dinocysts is discussed in view of changes in key hydrographic parameters of the surface waters (sea-surface temperatures (SSTs), sea-surface salinities (SSSs), and river discharges) monitored in the fjord. We examine the impact of the North Atlantic Oscillation pattern and of waste water supply from the local industry and agriculture on the fjord ecological state and thus dinocyst species diversity. Our results show that dinocyst production and diversity in the fjord is not evidently affected by human-induced eutrophication. Instead the assemblages appear to be mainly controlled by the NAO-related changes in nutrient availability and the physico-chemical characteristics of the surface mixed layer. Still, discharges of major rivers have been modulated since 1985 by the implementation of hydropower plants, which certainly influences the amounts of nutrients supplied to the fjord. The impact, however, is variable according to the local geographical setting, and barely differentiated from natural changes in river run off. We ultimately test the use of the modern analogue technique (MAT) for the reconstruction of winter and summer SSTs and SSSs and annual primary productivity (PP) in this particular fjord setting. The reconstructed data are compared with time series of summer and winter SSTs and SSSs measured at 10 m water depth, as well as with mean annual PPs along the Norwegian coast and in Scandinavian fjords. The reconstructions are generally in good agreement with the instrumental measurements and observations from other fjords. Major deviations can be attributed to peculiarities in the assemblages linked to the particular fjord setting and the related hydrological structure.

Multivariate Regression Analysis and Statistical Modeling for Summer Extreme Precipitation over the Yangtze River Basin, China

Extreme precipitation is likely to be one of the most severe meteorological disasters in China; however, studies on the physical factors affecting precipitation extremes and corresponding prediction models are not accurately available. From a new point of view, the sensible heat flux (SHF) and latent heat flux (LHF), which have significant impacts on summer extreme rainfall in Yangtze River basin (YRB), have been quantified and then selections of the impact factors are conducted. Firstly, a regional extreme precipitation index was applied to determine Regions of Significant Correlation (RSC) by analyzing spatial distribution of correlation coefficients between this index and SHF, LHF, and sea surface temperature (SST) on global ocean scale; then the time series of SHF, LHF, and SST in RSCs during 1967–2010 were selected. Furthermore, other factors that significantly affect variations in precipitation extremes over YRB were also selected. The methods of multiple stepwise regression and leave-one-out cross-validation (LOOCV) were utilized to analyze and test influencing factors and statistical prediction model. The correlation coefficient between observed regional extreme index and model simulation result is 0.85, with significant level at 99%. This suggested that the forecast skill was acceptable although many aspects of the prediction model should be improved.

Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty

Abstract A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.