Global Sea Surface Temperature Data Research Articles

Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature.

Climate change poses a critical threat to the Pantanal, the largest wetland in the world. Models indicate an increase in the frequency of extreme precipitation events and extended periods of drought. These changes can amplify consequences for Pantanal’s ecological functioning, which has already experienced intensive human modification of its hydrological system and environmental health. The present study analyzed the spatial and temporal dynamics of rainfall and resulting extremes in the Brazilian area of the Upper Paraguay River Basin (UPRB) along with a co-evaluation of the global Sea Surface Temperature data (SST). The predicted results indicate that wet extreme precipitation events will become more frequent in the highlands, while severe and prolonged droughts triggered by warming SSTs in the Northern Hemisphere (North Atlantic and North Pacific oceans) will affect the Pantanal. The linear relations between precipitation with SST of very specific oceanic regions and even from specific oceanic indexes obtained in the present study significantly improve the forecasting capacity, mainly from a resulting reduction to two months of the lead-time between SST warming to concomitant precipitation impacts, and by explaining 80% of Pantanal´s precipitation variation from major oceanic indexes (e.g., ENSO, PDO, NAO, ATL3). Current SST trends will result in inter- and intra-annual flooding dynamic alterations, drastically affecting the Pantanal ecosystem functioning, with consequences for wildlife diversity and distribution. Regarding the foreseeable global climate and land use change scenarios, the results from the present study provide solid evidence that can be used at different decision-making levels (from local to global) for identifying the most appropriate management practices and effectively achieving sustainability of the anthropic activity occurring in the Pantanal.

Variation of Anomalous Convergence Around Kalimantan Island in Lower Troposphere and Its Role in Connecting the East Asian Summer Monsoon and Australian Winter Monsoon

AbstractUsing the data from ERA‐interim, the Hadley Centre Global Sea Surface Temperature data set, and the Climate Prediction Center Merged Analysis of Precipitation, after removing the simultaneous El Niño and Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) signals, we have investigated variations of anomalous convergence around Kalimantan Island in lower troposphere in associations with the East Asian summer and Australian winter monsoons. Results indicate that when negative sea surface temperature anomalies appear in the western Pacific on both sides of the equator, the atmospheric cooling over the northwestern Pacific and from the South Pacific Convergence Zone (SPCZ) to Australia is favorable for the maintenance of anticyclonic anomalies over the areas from the western Pacific to the South China Sea and from the south of the equator to the northeast of Australia. These anomalous anticyclonic circulations can induce equatorial easterly anomalies, which subsequently facilitate the maintenance of anomalous convergence around Kalimantan Island. In turn, the anomalous convergence in Kalimantan Island is favorable for the maintenance of anomalous anticyclonic over the northwestern Pacific and SPCZ and thereby intensifies both the western Pacific subtropical high and the Australian High, inducing concurrent intensifications of the East Asian summer monsoon and Australian winter monsoon. Related to the anomalous convergence over Kalimantan Island, precipitation and temperature decrease from the western Pacific to SPCZ but increase from the Indian Ocean to western Maritime Continent and Australia, while temperature is lower than normal in Kalimantan. The above results have remarkable implications for in‐depth understanding of the mechanism behind anomalies of the East Asian summer monsoon and Australian winter monsoon.

Network-based identification and characterization of teleconnections on different scales

Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.

Optimized Sampling for Multiscale Dynamics

The characterization of intermittent, multiscale and transient dynamics using data-driven analysis remains an open challenge. We demonstrate an application of the Dynamic Mode Decomposition (DMD) with sparse sampling for the diagnostic analysis of multiscale physics. The DMD method is an ideal spatiotemporal matrix decomposition that correlates spatial features of computational or experimental data to periodic temporal behavior. DMD can be modified into a multiresolution analysis to separate complex dynamics into a hierarchy of multiresolution timescale components, where each level of the hierarchy divides dynamics into distinct background (slow) and foreground (fast) timescales. The multiresolution DMD is capable of characterizing nonlinear dynamical systems in an equation-free manner by recursively decomposing the state of the system into low-rank spatial modes and their temporal Fourier dynamics. Moreover, these multiresolution DMD modes can be used to determined sparse sampling locations which are nearly optimal for dynamic regime classification and full state reconstruction. Specifically, optimized sensors are efficiently chosen using QR column pivots of the DMD library, thus avoiding an NP-hard selection process. We demonstrate the efficacy of the method on several examples, including global sea-surface temperature data, and show that only a small number of sensors are needed for accurate global reconstructions and classification of El Ni\~no events.

Is the global sea surface temperature rise accelerating?

This is an exploratory investigation to search for the presence of an acceleration in global sea surface temperature rise, which is essential to identify anthropogenic contributions to the climate change during the 20th century. A weighted statistical model with an acceleration parameter was built progressively to reconstruct the variations in the global sea surface temperature data considering statistically significant confounders and autoregressive disturbances in the process. From the preliminary residual analysis of a weighted regression model, emerged a parsimonious model with first order autoregressive disturbances with a deterministic trend, acceleration and periodicity of 69 yr and its 138 yr subharmonic. The final model solution, selected from 29 alternative combinations of the model parameters using Mallows's Cp metric, revealed a statistically significant deterministic trend, 0.40 ± 0.03 °C/c (p < 0.01), and acceleration, 0.67 ± 0.11 °C/c2 (p < 0.01) explaining 33% of the global sea surface temperature variations. The combined yearly trend and acceleration in global sea surface temperature as predicted by the model, exhibit a strong correlation with the yearly increase in the global CO2 concentrations observed during the 20th century.

Reply to comment by Karnauskas et al. on “Equatorial Pacific coral geochemical records show recent weakening of the Walker circulation”

In our paper describing a new coral record from Butaritari, we hypothesized that comparing the temporal trends in our records to coral records from farther east in the equatorial Pacific may support the evidence for a weakening of a Walker circulation, documented elsewhere in the literature [Power and Smith, 2007; Tokinaga et al., 2012]. Weakening of the Walker circulation is expected under global warming due to an imbalance in the rate of change in different aspects of the hydrological cycle [Vecchi and Soden, 2007]. We thank Karnauskas et al. [2015] for recognizing the value of our Butaritari coral climate reconstruction, and we appreciate their critique of our study. The Karnauskas et al. [2015] analyses strengthen our argument regarding the utility of interisland coral-proxy derived sea surface temperature (SST) gradients as a Walker circulation metric, but we disagree with their interpretation of decadal variability in our records. Here we provide additional analyses, which confirm that our reconstruction [Carilli et al., 2014] shows a long-term weakening of the Walker circulation over 1972-1998. We also document that significant decadal variations in Walker circulation strength, and for particular choices of start and end years over which trends are calculated, are able to show slight Walker strengthening. Overall, we conclude that Walker circulation variations are more nuanced than either our original publication [Carilli et al., 2014] or the subsequent Karnauskas et al. [2015] comment would suggest. Karnauskas et al. [2015] also provide a detailed analysis of Equatorial Undercurrent (EUC) activity near the Gilbert Islands and argue that the EUC does not strongly affect Butaritari. Our original publication did not claim to find significant EUC/Butaritari linkages, and we appreciate the diligence of Karnauskas et al. [2015] for ruling this out as a possibility.

An Assessment of GCM Skill in Simulating Persistence across Multiple Time Scales

Abstract Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and amplitude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for individual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4); when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables.

An evaluation of the effect of recent temperature variability on the prediction of coral bleaching events

Over the past 30 years, warm thermal disturbances have become commonplace on coral reefs worldwide. These periods of anomalous sea surface temperature (SST) can lead to coral bleaching, a breakdown of the symbiosis between the host coral and symbiotic dinoflagellates which reside in coral tissue. The onset of bleaching is typically predicted to occur when the SST exceeds a local climatological maximum by 1 degrees C for a month or more. However, recent evidence suggests that the threshold at which bleaching occurs may depend on thermal history. This study uses global SST data sets (HadISST and NOAA AVHRR) and mass coral bleaching reports (from Reefbase) to examine the effect of historical SST variability on the accuracy of bleaching prediction. Two variability-based bleaching prediction methods are developed from global analysis of seasonal and interannual SST variability. The first method employs a local bleaching threshold derived from the historical variability in maximum annual SST to account for spatial variability in past thermal disturbance frequency. The second method uses a different formula to estimate the local climatological maximum to account for the low seasonality of SST in the tropics. The new prediction methods are tested against the common globally fixed threshold method using the observed bleaching reports. The results find that estimating the bleaching threshold from local historical SST variability delivers the highest predictive power, but also a higher rate of Type I errors. The second method has the lowest predictive power globally, though regional analysis suggests that it may be applicable in equatorial regions. The historical data analysis suggests that the bleaching threshold may have appeared to be constant globally because the magnitude of interannual variability in maximum SST is similar for many of the world's coral reef ecosystems. For example, the results show that a SST anomaly of 1 degrees C is equivalent to 1.73-2.94 standard deviations of the maximum monthly SST for two-thirds of the world's coral reefs. Coral reefs in the few regions that experience anomalously high interannual SST variability like the equatorial Pacific could prove critical to understanding how coral communities acclimate or adapt to frequent and/or severe thermal disturbances.

Modeling of Tropical Forcing of Persistent Droughts and Pluvials over Western North America: 1856–2000*

Abstract The causes of persistent droughts and wet periods, or pluvials, over western North America are examined in model simulations of the period from 1856 to 2000. The simulations used either (i) global sea surface temperature data as a lower boundary condition or (ii) observed data in just the tropical Pacific and computed the surface ocean temperature elsewhere with a simple ocean model. With both arrangements, the model was able to simulate many aspects of the low-frequency (periods greater than 6 yr) variations of precipitation over the Great Plains and in the American Southwest including much of the nineteenth-century variability, the droughts of the 1930s (the “Dust Bowl”) and 1950s, and the very wet period in the 1990s. Results indicate that the persistent droughts and pluvials were ultimately forced by persistent variations of tropical Pacific surface ocean temperatures. It is argued that ocean temperature variations outside of the tropical Pacific, but forced from the tropical Pacific, act to strengthen the droughts and pluvials. The persistent precipitation variations are part of a pattern of global variations that have a strong hemispherically and zonally symmetric component, which is akin to interannual variability, and that can be explained in terms of interactions between tropical ocean temperature variations, the subtropical jets, transient eddies, and the eddy-driven mean meridional circulation. Rossby wave propagation poleward and eastward from the tropical Pacific heating anomalies disrupts the zonal symmetry, intensifying droughts and pluvials over North America. Both mechanisms of tropical driving of extratropical precipitation variations work in summer as well as winter and can explain the year-round nature of the precipitation variations. In addition, land–atmosphere interactions over North America appear important by (i) translating winter precipitation variations into summer evaporation and, hence, precipitation anomalies and (ii) shifting the northward flow of moisture around the North Atlantic subtropical anticyclone eastward from the Plains and Southwest to the eastern seaboard and western Atlantic Ocean.

Low‐frequency variations in global mean sea level: 1950–2000

Low‐frequency variability in global mean sea level (GMSL) is studied for the period 1950–2000 by interpolating sparse tide gauge data to a global grid using empirical orthogonal functions (EOFs) of sea level variability determined from TOPEX/Poseidon (T/P) altimeter data. Results are based on data with long‐term trends removed. The fact that the results do not have secular trends is an artifact of the analysis and should not be interpreted as an indication that sea level is not rising. The EOF reconstruction technique is discussed, and the resulting GMSL time series is compared to GMSL time series from Geosat and T/P altimetry and proxy GMSL time series estimated from global sea surface temperature data. The error assessment suggests the accuracy of the GMSL time series reconstructed from the tide gauge data is 2–4 mm RMS for a 1 year running mean smoothing and about 1 mm for a 5 year running mean smoothing. Several El Niño/La Niña events are evident in the GMSL, as well as significant low‐frequency variability at a 10–12 year period. GMSL appears to have been generally lower than normal in the late 1960s, throughout the 1970s, and in the 1980s. GMSL appears to have been generally higher than normal in the late 1950s and early 1960s and in the early 1980s and has been rising throughout the 1990s, when T/P is observing. The implication of the low‐frequency signals on the determination the secular rate of GMSL from satellite altimetry is discussed.

ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal‐to‐noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

A non-regression-coefficients method of sea surface temperature retrieval from space

Abstract For several years now NOAA/NESDIS have derived an operational global sea surface temperature (SST) product from the AVHRR instrument on the NOAA satellites. This is done using the MCSST and CPSST algorithms which contain coefficients that are determined from a regression analysis of satellite data against in situ surface data. The current algorithms are used to provide global SST data without taking into account the latitude, climate or location of the satellite data, although the CPSST coefficients do have a weak dependence on the satellite brightness temperatures. Because of this global application the current SST algorithms have inherent errors due to local climate influences. In this paper a new SST algorithm is developed that does not rely on regression analysis to derive its coefficients. By using the spatial variation of the brightness temperatures in a small area (50 km by 50 km) it is possible to derive the appropriate coefficients to use in the algorithm. The SST field can thus be deriv...

The Simulated Indian Monsoon: A GCM Sensitivity Study

A series of sensitivity experiments are conducted in an attempt to understand and correct deficiencies in the simulation of the seasonal mean Indian monsoon with a global atmospheric general circulation model. The seasonal mean precipitation is less than half that observed. This poor simulation in seasonal integrations is independent of the choice of initial conditions and global sea surface temperature data used. Experiments are performed to test the sensitivity of the Indian monsoon simulation to changes in orography, vegetation, soil wetness, and cloudiness. The authors find that the deficiency of the model precipitation simulation may be attributed to the use of an enhanced orography in the integrations. Replacement of this orography with a mean orography results in a much more realistic simulation of Indian monsoon circulation and rainfall. Experiments with a linear primitive equation model on the sphere suggest that this striking improvement is due to modulations of the orographically forced waves in the lower troposphere. This improvement in the monsoon simulation is due to the kinematic and dynamical effects of changing the topography, rather than the thermal effects, which were minimal. The magnitude of the impact on the Indian monsoon of the other sensitivity experiments varied considerably, but was consistently less than the impact of using the mean orography. However, results from the soil moisture sensitivity experiments suggest a possibly important role for soil moisture in simulating tropical precipitation, including that associated with the Indian monsoon.

Modelling the influence of global sea surface temperatures on the variability and predictability of seasonal Sahel rainfall

An atmospheric general circulation model has been forced by observed global sea surface temperature data from ten individual years. The results support the idea that oceanic effects tend to dominate the forcing of both interannual and interdecadal variability of Sahel rainfall, with different local atmospheric mechanisms dominating in different years. Further experiments indicate that skillful predictions of July–September Sahel rainfall are possible using the persistence of June SST anomalies.