Surface Temperature Analysis Research Articles

The Role of Buoy and Argo Observations in Two SST Analyses in the Global and Tropical Pacific Oceans

Abstract The relative roles of buoy and Argo observations in two sea surface temperature (SST) analyses are studied in the global ocean and tropical Pacific Ocean over 2000–16 using monthly Extended Reconstructed SST version 5 (ERSSTv5) and Daily Optimum Interpolation SST version 2 (DOISST). Experiments show an overall higher impact by buoys than Argo floats over the global oceans and an increasing impact by Argo floats. The impact by Argo floats is generally larger in the Southern Hemisphere than in the Northern Hemisphere. The impact on trends and anomalies of globally averaged SST by either one is small when the other is used. The warming trend over 2000–16 remains significant by including either buoys or Argo floats or both. In the tropical Pacific, the impact by buoys was large over 2000–05 when the number of Argo floats was low, and became smaller over 2010–16 when the number and area coverage of Argo floats increased. The magnitude of El Niño and La Niña events decreases when the observations from buoys, Argo floats, or both are excluded. The impact by the Tropical Atmosphere Ocean (TAO) and Triangle Trans-Ocean Buoy Network (TRITON) is small in normal years and during El Niño events. The impact by TAO/TRITON buoys on La Niña events is small when Argo floats are included in the analysis systems, and large when Argo floats are not included. The reason for the different impact on El Niño and La Niña events is that the drifting buoys are more dispersed from the equatorial Pacific region by stronger trade winds during La Niña events.

Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

The Importance of Unresolved Biases in Twentieth-Century Sea Surface Temperature Observations

Abstract A new analysis of sea surface temperature (SST) observations indicates notable uncertainty in observed decadal climate variability in the second half of the twentieth century, particularly during the decades following World War II. The uncertainties are revealed by exploring SST data binned separately for the two predominant measurement types: “engine-room intake” (ERI) and “bucket” measurements. ERI measurements indicate large decreases in global-mean SSTs from 1950 to 1975, whereas bucket measurements indicate increases in SST over this period before bias adjustments are applied but decreases after they are applied. The trends in the bias adjustments applied to the bucket data are larger than the global-mean trends during the period 1950–75, and thus the global-mean trends during this period derive largely from the adjustments themselves. This is critical, since the adjustments are based on incomplete information about the underlying measurement methods and are thus subject to considerable uncertainty. The uncertainty in decadal-scale variability is particularly pronounced over the North Pacific, where the sign of low-frequency variability through the 1950s to 1970s is different for each measurement type. The uncertainty highlighted here has important—but in our view widely overlooked—implications for the interpretation of observed decadal climate variability over both the Pacific and Atlantic basins during the mid-to-late twentieth century.

Diagnosis of abnormal temperature rise observed on a 275 kV oil-filled cable surface — A case study

This paper presents a case study on a 275 kV oil-filled cable. The condition assessment and diagnosis are based on analysis of cable surface temperature in relation to its current load and insulation dielectric loss. The work was initiated by a local abnormal temperature rise of 5.2 °C in cable surface temperature, which was observed during a routine inspection. The temperature rise occurred at bend area with a length of approximately one metre in the Blue phase. No PD activity was identified using on-line PD measurement. The relation between cable surface temperature, cable core temperature and cable insulation condition was then simulated based on the thermal model of power cables. According to simulation analysis, poor condition of cable insulation or oil from an oil duct penetrating a region under the cable surface were identified as possible reasons for the problem observed. An in service X-ray scanning technique was employed for further investigation and to aid diagnosis. The X-ray images revealed a slight distortion of the PVC sheath and the presence of multiple voids between cable insulation paper and the lead sheath. It was concluded that an oil leakage from the oil duct to the voids under the cable lead sheath was responsible for the local cable surface temperature rise. The result removed the concern of incipient cable breakdown, and a potential unplanned outage.

Recent global warming as confirmed by AIRS

This paper presents Atmospheric Infra-Red Sounder (AIRS) surface skin temperature anomalies for the period 2003 through 2017, and compares them to station-based analyses of surface air temperature anomalies (principally the Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP)). The AIRS instrument flies on EOS Aqua, which was launched in 2002 and became stable in September 2002. AIRS surface temperatures are completely satellite-based and are totally independent of any surface-based measurements. We show in this paper that satellite-based surface temperatures can serve as an important validation of surface-based estimates and help to improve surface-based data sets in a way that can be extended back many decades to further scientific research. AIRS surface temperatures have better spatial coverage than those of GISTEMP, though at the global annual scale the two data sets are highly coherent. As in the surface-based analyses, 2016 was the warmest year yet.

Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.

Uniform consistency rate of kNN regression estimation for functional time series data

ABSTRACTIn this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.

Defining the Northeast Monsoon of India

AbstractThis study introduces an objective definition for onset and demise of the northeast Indian monsoon (NEM). The definition is based on the land surface temperature analysis over the Indian subcontinent. It is diagnosed from the inflection points in the daily anomaly cumulative curve of the area-averaged surface temperature over the provinces of Andhra Pradesh, Rayalseema, and Tamil Nadu located in the southeastern part of India. Per this definition, the climatological onset and demise dates of the NEM season are 6 November and 13 March, respectively. The composite evolution of the seasonal cycle of 850-hPa winds, surface wind stress, surface ocean currents, and upper-ocean heat content suggest a seasonal shift around the time of the diagnosed onset and demise dates of the NEM season. The interannual variations indicate onset date variations have a larger impact than demise date variations on the seasonal length, seasonal anomalies of rainfall, and surface temperature of the NEM. Furthermore, it is shown that warm El Niño–Southern Oscillation (ENSO) episodes are associated with excess seasonal rainfall, warm seasonal land surface temperature anomalies, and reduced lengths of the NEM season. Likewise, cold ENSO episodes are likely to be related to seasonal deficit rainfall anomalies, cold land surface temperature anomalies, and increased lengths of the NEM season.

Infrared thermography as a non-invasive method for the evaluation of heat stress in pigs kept in pens free of cages in the maternity

Infrared thermography is a non-invasive method of body surface temperature analysis in pigs. This study aimed to evaluate the body surface temperature of sows and piglets kept in individual pens free of cages with or without cooling system with the use of infrared thermography. The study was conducted during the summer and autumn in Pirassununga, Brazil. Twenty-six sows during lactating phase and 281 piglets until 21 days-old were evaluated in two treatments: cooled pens with fans and water sprinklers on the roof and non-cooled pens. Both areas had ceramic roof tiles. Thermographic images were collected at intervals of seven days in the morning and afternoon in sows and randomly in five piglets per litter, and salivary cortisol were collected only on sows. The thermographic images were analyzed using the software IRSoft Version 3.6 Testo. Every three days respiratory rate and rectal temperatures were collected from sows and five piglets per litter. It was included fixed effect of treatment, period of the day, seasons and their interactions, besides the correlations of Pearson. In summer and autumn, the hottest surface area of sows was the mammary gland and the coldest the vaginal. For the piglets, the hottest area in the summer was the head and the coldest the snout. Summer presented the highest surface temperatures of the sugarcane bagasse bed, concrete floor and roof. No correlations were found between air temperature of and facilities. The respiratory rate presented moderate correlation with the back and with the snout of the sows. Rectal temperatures were higher in summer and in the afternoon, but it was similar between treatments. Although the cooling system had reduced the air temperature of the pens free of cage, it was not enough to reduce body superficial temperatures of the sows and piglets during lactating phase. Nevertheless, the use of infrared thermography allows to identify the hottest and coldest surface body areas of pigs and can be a tool to assess pig facilities and animal welfare.

Critical analysis of rural-urban transitions and transformations in Lucknow city, India

An unplanned urban sprawl is emerging as a major threat for urban development, especially in developing countries. In recent decades, ever increasing population and urbanization has led to the urban sprawl. The dynamic nature of urbanization creates ambiguities in measurements of urbanization growth and its extent. Temporal analysis of land-use/land-cover (LULC) and identification of transition points are required for sustainable development of the city. Therefore, the sustainable model is required for critical analysis of urbanization and its impact. This paper analyzes the spatiotemporal dynamics of the urbanization process at an urban, suburban, sub-rural and rural level that has occurred in Lucknow, the capital city of the state Uttar Pradesh of India. The micro-level study has been performed to measure the extent and growth of urbanization. The model is processed using remotely sensed satellite data of Landsat sensor of the year 1995, 2005 and 2016. The workflow is initiated using LULC classification map of two decades 1995–2016. The classification result depicts an increased built-up area i.e., 77.15 sq. km to 143.70 sq. km from 1995 to 2005 and increased to 199.92 sq. km from 2005 to 2016. The accuracy assessment of LULC is found to be 90%, 92% and 94% for 1995, 2005 and 2016 respectively. The micro- level analysis is performed using urban landscape quantification in which urbanness analysis of each pixel is estimated and then the pixel is allocated according to built-up densities of spatial area. Further these densities are classified in seven classes those are (i) Urban built-up, (ii) Suburban built-up, (iii) Rural built-up, (iv) Urbanized open land (UOL), (v) Captured open land (COL), (vi) Rural open land (ROL) and, (vii) Water. Finally, the urban footprint maps are generated using the results of urban landscape quantification. The critical and detailed analysis of the peri-urban interface (PUI) is performed by demarcating the villages where significant changes are observed and integrated with the driving factors responsible for the changes. The expansion analysis is performed for quantifying the sprawl taking place in the city. Mainly four types of expansions are identified and mapped that are infill, extension, leapfrog and ribbon expansions. Land surface temperature (LST) analysis is performed over the study area for analyzing the adverse effect of urbanization over the transition points (Villages). Land surface temperature variation has been monitored over these transition points and their unfavorable effects on the environment.

Time Series Analysis of Land Surface Temperatures in 20 Earthquake Cases Worldwide

Earthquakes are reported to be preceded by anomalous increases in satellite-recorded thermal emissions, but published results are often contradicting and/or limited to short periods and areas around the earthquake. We apply a methodology that allows to detect subtle, localized spatio-temporal fluctuations in hyper-temporal, geostationary-based land surface temperature (LST) data. We study 10 areas worldwide, covering 20 large (Mw > 5.5) and shallow (<35 km) land-based earthquakes. We compare years and locations with and without earthquake, and we statistically evaluate our findings with respect to distance from epicentra and temporal coincidence with earthquakes. We detect anomalies throughout the duration of all datasets, at various distances from the earthquake, and in years with and without earthquake alike. We find no distinct repeated patterns in the case of earthquakes that happen in the same region in different years. We conclude that earthquakes do not have a significant effect on detected LST anomalies.

Intercomparison of long-term sea surface temperature analyses using the GHRSST Multi-Product Ensemble (GMPE) system

Six global, gridded, gap-free, daily sea surface temperature (SST) analyses covering a period of at least 20 years have been intercompared: ESA SST CCI analysis long-term product v1.0, MyOcean OSTIA reanalysis v1.0, CMC 0.2 degree, AVHRR_ONLY Daily 1/4 degree OISST v2.0, HadISST2.1.0.0 and MGDSST. A seventh SST product of the ensemble median of all six has also been produced using the GMPE (Group for High Resolution SST Multi-Product Ensemble) system. Validation against independent near-surface Argo data, a long timeseries of moored buoy data from the tropics and anomalies to the GMPE median have been used to examine the temporal and spatial homogeneity of the analyses. A comparison of the feature resolution of the analyses has also been undertaken. A summary of relative strengths and weaknesses of the SST datasets is presented, intended to help users to make an informed choice of which analysis is most suitable for their proposed application.

Numerical Experimental Study for the Effect of a Binary Typhoon System on the Extreme Rainfall of Typhoon Morakot (0908)

ABSTRACT A high-resolution numerical simulation of the extreme rainfall caused by typhoon Morakot (0908) over Taiwan Province, China, was made using the WRF-ARW/NCAR model (Version 3.2), ERA Interim reanalysis data (resolution: 1.5°×1.5°) from the European Centre for Medium-Range Weather Forecasts (ECMWF), and global real-time sea surface temperature analysis data (RTGSST, 0.5°×0.5°) from NCEP/NOAA. The numerical simulation results showed that the extreme rainfall caused by Morakot over Taiwan was closely related to water vapor transport of the southwesterly flow. However, the effect of a binary typhoon system between Morakot and Severe Tropical Storm Goni (0907) was also an important factor. Goni strengthened the intensity of the southwesterly flow and the water vapor transport to Morakot and resulted in the heavy rainfall increasing over central and southern mountainous areas of Taiwan. Furthermore, the effect of the binary typhoon system increasing the northward component of the track of Morakot, and the typhoon's slow translation to Taiwan, caused the longtime and persistent severe rainfall over southern Taiwan. After removing Goni's circulation in the model initial field, the cumulative precipitation was greatly reduced by 35.78%, 33.03% and 31.5% in the 18-, 6- and 2-km resolution model results, respectively.

Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies

Land surface temperature (LST) is one of the key parameter used for analyzing the heat energy balance and thermal flux of land surfaces. It is also useful for making urban heat transfer models, water resource management, climate change modeling, and environmental studies. This study is to find the surface temperature of Jubail Industrial City, which is one of the biggest industrial areas in the world. The study also aims to analyze the spatial and temporal variations of LST of the city. Landsat 8 Thermal Infrared Remote Sensor (TIRS) data has been used for this study and the surface temperature has been estimated by using single-channel (SC) method. The study reveals that the surface temperature is relatively low and ranging from 20 to 30 °C in January. However, the industrial area and some parts of the residential area have more temperature than the rest of the city. During the month of March, the temperature increases gradually and reaches high in June. During the summer, the surface temperature in the residential area of the city is around 40–50 °C. The temperature in the sub urban areas is moderate; however, high temperature (50–55 °C) has been recorded in the industrial area of the city. Significant heat islands of temperature more than 60 °C have also been noted near the iron and steel factories of the industrial area. In the month of September, the land surface temperature in most part of the city is lower than that of peak summer.

Satellite-Driven Land Surface Temperature (LST) Using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) Data and Its Association with Built-Up and Green Cover Over Urban Delhi, India

One of the most important impacts of urbanisation in Indian cities is the conversion of green belts and agriculture land into the built-up area in the periphery (Chadchan and Shankar, Int J Sustainable Built Environ 1:36–49, 2012; Pandey and Seto, J Environ Manag 148:53–66, 2015). With these physical changes, i.e. decrease in green cover and increase in built-up, the land surface temperature (LST) is bound to increase. The green area is a basic need of any city because it is a must for a healthy life and also maintains the aesthetic and ecological beauty in the urban areas (Low et al. 2007). The present study aims to analyse the association between built-up, green cover and land surface temperature for which district-level analysis of the normalised differential built-up index (NDBI), normalised differential vegetation index (NDVI) and land surface temperature (LST) has been done over the urban area of Delhi. In this study, Landsat 7 (ETM+ SLC) for 2003, Landsat 5 (TM) for 2010, and Landsat 8 (OLI/TIRS) for 2017 have been used together with Survey of India (SOI) toposheet of Delhi at 1:25,000. Indices like NDBI, NDVI and LST are calculated for 2003, 2010 and 2017 using the spectral radiance model (SRM), the mono-window algorithm (MWA) and the split window algorithm (SWA). Thereafter, district-wise NDBI, NDVI and LST are extracted by using clip tools in ArcGIS 10.5 software. To analyse the relationship between built-up and green cover with LST, correlation is done in SPSS software and a scatter diagram is made to assess the correlation amongst the variables. The further surface temperature profile is created to know which part of the Delhi has the highest and lowest temperatures on a particular surface. The study shows that NDVI and LST are negatively correlated with each other as vegetation has a cooling effect on the land surface temperature whereas NDBI and LST are positively correlated with each other. The studies show a change in the distribution of vegetation cover and gradually increase in the built-up land which results in the increase in land surface temperature to about 3.31 °C in the last 14 years. The result shows that MWA give the most accurate result in this study since RMSE of MWA is the lowest (0.71 °C) amongst the three algorithms used in the study. Temporal analysis of land surface temperature by all the three algorithms shows the increase in land surface temperature of Delhi between 2003 and 2017.

The Influences of Orography and Ocean on Track of Typhoon Megi (2016) Past Taiwan as Identified by HWRF

AbstractTyphoon Megi (2016) headed northwestward toward Taiwan with southward deflection near landfall. In this study, Hurricane Weather Research and Forecast system (HWRF) was used to investigate the mechanism of the track changes over free ocean and track deflection near landfall. HWRF simulations using more realistic Hybrid Coordinate Ocean Model sea surface temperature (SST) analysis improve the northward biased track compared with that using Global Forecast System SST, due to the initial cooler SST below the storm path. The initial warmer Global Forecast System SST leads to a northward track shifting with an overintensified typhoon. As the Princeton Ocean Model is coupled, the SST over South China Sea becomes warmer leading to a northward track shifting compared to a southward shifting induced by the upper ocean cooling due to the typhoon‐ocean interactions in the vicinity of the typhoon. Regardless of track shifting, southward deflection near landfall is mainly controlled by orographic effects of the Central Mountain Range (CMR). Cyclonic northerly is enhanced to the west of the typhoon center ahead and over the CMR that results in southward deflection. Diagnostics of potential vorticity (PV) tendency budget indicates that southward deflection can be explained by the southeastward tendency of latent heating effects near landfall. The combined effects of latent heating and cyclonic rotation of positive wave numer‐1 (WN‐1) potential vorticity vertical advection dominate the southward deflection when the typhoon is closer to Taiwan. Furthermore, the typhoon movement near landfall is slowed down mainly due to WN‐1 negative vertical differential latent heating over the northern CMR.

A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

Impact of freshwater plumes on intraseasonal upper ocean variability in the Bay of Bengal

The Monsoon Intraseasonal Oscillations (MISO) is known to be strongly coupled to the upper ocean variability in the Bay of Bengal (BoB). Here, we analyze high-resolution moored observations from the northern BoB (18 °N, in comparison with an array of moorings to the south along 90 °E (8, 12, and 15 °N) to examine the observed temperature and salinity variability during the 2015 summer monsoon season. The heat budget analysis indicates that the mixed-layer (ML) temperature tendency at 18 °N in late summer (August and September) deviated greatly from the tendency that is expected based on surface heat flux alone. Examination of moored ML salinity and satellite sea surface salinity (SSS) fields suggests that southward extension of riverine freshwater plumes to the mooring site in late summer plays an important role in modulating the heat balance. The associated shoaling of the ML results in enhanced penetrative heat loss and warming beneath the surface layer. Price-Weller-Pinkel (PWP) 1-D model simulations, which account for vertical mixing, suggest that the entrainment of this subsurface warm water under monsoon wind improves closure of the temperature balance. Long-term analysis of satellite-based sea surface temperature (SST) and rainfall data indicates that late summer ML shoaling is accompanied by amplified intraseasonal (10–60 day) SST variability and reduced MISO precipitation variability. Overall, these results demonstrate the critical importance of freshwater plumes to improved understanding of the upper-ocean heat budget and air-sea interaction in the BoB.

Sea Surface Temperature (SST) Variability of the Eastern Coastal Zone of the Gulf of California

The coastal zones are areas with a high flow of energy and materials where diverse ecosystems are developed. The study of coastal oceanography is important to understand the variability of these ecosystems and determine their role in biogeochemical cycles and climate change. Sea surface temperature (SST) analysis is indispensable for the characterization of physical and biological processes, and it is affected by processes at diverse timescales. The purpose of this work is to analyze the oceanographic variability of the Eastern Coastal Zone of the Gulf of California through the study of the SST from time series analysis of monthly data obtained from remote sensors (AVHRR-Pathfinder Version 5.1 and Version 5 resolution of 4 km, MODIS-Aqua, resolution of 4 km) for the period 1981 to 2016. The descriptive analysis of SST series showed that the values decrease from south to north, as well as the amplitude of the warm period decrease from south to north (cold period increase from south to north). The minimum values occurred during January and February, and ranged between 18 and 20 °C; and maximum values, of about 32 °C, arose in August and September. Cluster analysis allowed to group the data in four regions (south, center, midriff islands and north), the spectral analysis in each region showed frequencies of variation in scales: Annual (the main), seasonal, semiannual, and interannual. The latter is associated with the El Niño and La Niña climatological phenomena.

Intrinsic Pink-Noise Multidecadal Global Climate Dynamics Mode.

Understanding multidecadal variability is an essential goal of climate dynamics. For example, the recent phenomenon referred to as the "global warming hiatus" may reflect a coupling to an intrinsic, preindustrial, multidecadal variability process. Here, using a multifractal time-series method, we demonstrate that 42 data sets of 79 proxies with global coverage exhibit pink-noise characteristics on multidecadal timescales. To quantify the persistence of this behavior, we examine high-resolution ice core and speleothem data to find pink noise in both pre- and postindustrial periods. We examine the spatial structure with an empirical orthogonal function analysis of the monthly averaged surface temperature from 1901 to 2012. The first mode clearly shows the distribution of ocean heat flux sinks located in the eastern Pacific and the Southern Ocean and has pink-noise characteristics on a multidecadal timescale. We hypothesize that this pink-noise multidecadal spatial mode may resonate with externally driven greenhouse gas forcing, driving large-scale climate processes.