Surface Temperature Patterns Research Articles

Unravelling spatiotemporal patterns in global sea surface temperatures with dynamic mode decomposition

AbstractExtracting global sea surface temperature (SST) patterns is essential for understanding climate variability in both regional and global perspective. Traditional methods, limited by assumptions of orthogonality, hinder our ability to fully capture the dynamics of global SST, particularly in the complex Pacific basin. This study introduces dynamic mode decomposition (DMD), a powerful method from fluid dynamics, to extract global SST patterns since 1900. Our analysis shows that DMD successfully captures well‐known patterns like global warming and the Atlantic Multidecadal Oscillation (AMO). More importantly, it reveals three Pacific SST modes that are spatially non‐orthogonal but exhibit distinct characteristics. Spectral analysis shows that these Pacific modes have distinct periodicities: Pacific Multi‐decadal Mode (PMDM, ~50 years), Quasi‐decadal Mode (PQDM, 8–16 years) and Interannual Mode (PIAM, 2–8 years). This highlights DMD's strength in not only capturing known patterns but also separating modes with similar spatial features but unique dynamics. Further analysis reveals significant differences in their impacts on global air temperature. During positive phases, all three modes induce tropical warming, with PIAM exerting the strongest influence and PMDM the weakest. In the Northern Hemisphere, both PQDM and PIAM drive mid‐latitude cooling and high‐latitude warming, with a more pronounced effect from PQDM. PMDM, in contrast, triggers widespread cooling across the Northern extratropics. In the Southern Hemisphere, PMDM's influence on temperature exhibits a distinct latitudinal banding pattern. PIAM and PQDM, on the other hand, exhibit a regionalized impact, primarily concentrated in the Amundsen and Ross Seas, with PQDM's influence extending to the Antarctic continent.

What Determines the East Asian Winter Temperature during El Niño?—Role of the Early Onset El Niño and Tropical Indian Ocean Warming

Abstract Atmospheric teleconnections from the Pacific El Niño are key to determining the East Asian winter climate. Using the database for policy decision-making for future climate change (d4PDF) large-ensemble simulations, the present study investigates a mechanism for the warm and cold East Asian winters during El Niño with a focus on atmospheric teleconnections triggered by anomalous sea surface temperature (SST) patterns in the tropical Indo-Pacific. Our results show that the western Pacific (WP) teleconnection pattern plays a primary role in the warm winters in East Asia. The WP pattern tends to appear in years when both an early El Niño and the positive phase of the Indian Ocean dipole (IOD) mode develop in boreal autumn. In those years, the tropical Indian Ocean (TIO) strongly warms in the following winter, forming a distinct zonal contrast in precipitation anomalies over the tropical Indo-Pacific through a reduced Walker circulation. The Rossby wave source anomalies indicate that the WP pattern is associated with the weakened Indo-Pacific Walker circulation. By contrast, the WP pattern does not dominate in the cold winters due to the absence of strong TIO warming. The present study proposes a mechanism that promotes the excitation of the WP pattern through the upper-troposphere divergence in East Asia associated with the Walker circulation modulated by the tropical Indo-Pacific interbasin interaction. Significance Statement The East Asian winter temperature variability is controlled not only by the strong atmospheric internal variability in the midlatitudes and high latitudes but also by remote forcing from the tropical ocean. Our study investigates how El Niño exerts diverse impacts on the East Asian winter temperature, depending on where atmospheric convection intensifies in the tropical Pacific Ocean and the Indian Ocean. Our results show that an intense warming of the tropical Indian Ocean and the early development of El Niño are the major factors for warm winters in East Asia. Given that a precursor of the intense Indian Ocean warming appears in boreal autumn, our findings should contribute to the improvement of seasonal prediction for the East Asian winter climate.

Land surface temperature and NDVI patterns in the petroleum and non-petroleum regions in Southern Iraq (Al-Basra)

Land surface temperature and NDVI patterns in the petroleum and non-petroleum regions in Southern Iraq (Al-Basra)

Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations

Projections of near-term climate change in the next few decades are subject to substantial uncertainty from internal climate variability. Approaches to reduce this uncertainty by constraining the phasing of climate variability based on large ensembles of climate simulations have recently been developed. These approaches select those ensemble members that are in closer agreement with sea surface temperature patterns from either observations or initialized decadal predictions. Previous studies demonstrated the benefits of these constraints for projections up to 20 years into the future, but these studies applied the constraints to different ensembles of climate simulations, which prevents a consistent comparison of methods or identification of specific advantages of one method over another. Here we apply several methods to constrain internal variability phases, using either observations or decadal predictions as constraining reference, to an identical multi-model ensemble consisting of 311 simulations from 37 models from the Coupled Model Intercomparison Project phase 6 (CMIP6), and compare their forecast qualities. We show that constraining based on both observations and decadal predictions significantly enhances the skill of 10 and 20-year projections for near-surface temperatures in some regions, and that constraining based on decadal predictions leads to the largest added value in terms of probabilistic skill. We further explore the sensitivity to different implementations of the constraint that focus on the patterns of either internal variability alone or a combination of internal variability and long-term changes in response to forcing. Looking into the near-term future, all variations of the constraints suggest an accelerated warming of large parts of the Northern Hemisphere for the period 2020–2039, in comparison to the unconstrained CMIP6 ensemble.

Hot rings on Io observed by Juno/JIRAM

It has been thought that Io’s many paterae may contain lava lakes, but observations by NASA’s Galileo spacecraft at sufficiently high resolution were limited to a few locations, such as Loki Patera. Data acquired by NASA’s Juno mission in May 2023 reveal a common set of thermal characteristics for at least ten paterae on Io, with bright (hot) “thermal rings” around the perimeter of their floors. Loki, Surt, Fuchi, Amaterasu, Mulungu, Chors, and Dazhbog Patera, and four unnamed paterae, all show the same pattern of surface temperature. This new Juno/JIRAM data suggests that hot rings around paterae are a common phenomenon, and that they are indicative of active lava lakes. All the investigated paterae lack recent lava flows on their flanks, suggesting that at the time of observations, the level of the lake was not high enough to overflow the rim. These observations provide insight about the characteristics of paterae’s activity, which may involve either central upwelling of magma, or up-and-down “piston-like” vertical motion of the lake surface. Tidal forces, which are extreme at Io, could play a role as well. Future observations from Juno, particularly during the closest flybys, may indicate which mechanism is more plausible.

Rainy Season Migration across the Northeast Coast of Brazil Related to Sea Surface Temperature Patterns

Rainy Season Migration across the Northeast Coast of Brazil Related to Sea Surface Temperature Patterns

Possible shift in controls of the tropical Pacific surface warming pattern.

Changes in the sea surface temperature (SST) pattern in the tropical Pacific modulate radiative feedbacks to greenhouse gas forcing, the pace of global warming and regional climate impacts. Therefore, elucidating the drivers of the pattern is critically important for reducing uncertainties in future projections. However, the causes of observed changes over recent decades, an enhancement of the zonal SST contrast coupled with a strengthening of the Walker circulation, are still debated. Here we focus on the role of external forcing and review existing mechanisms of the forced response categorized as either an energy perspective that adopts global and hemispheric energy budget constraints or a dynamical perspective that examines the atmosphere-ocean coupled processes. We then discuss their collective andrelative contributions to the past and future SST pattern changes and propose a narrative that reconciles them. Although definitive evidence is not yet available, our assessment suggests that the zonal SST contrast has been dominated by strengthening mechanisms in the past, but will shift towards being dominated byweakening mechanisms in the future. Finally, we present opportunities to resolve the model-observations discrepancy regarding the recent trends.

The Coupled Impacts of Atmospheric Composition and Obliquity on the Climate Dynamics of TRAPPIST-1e

Planets in multiplanet systems are expected to migrate inward as near-resonant chains, thus allowing them to undergo gravitational planet–planet interactions and possibly maintain a nonzero obliquity. The TRAPPIST-1 system is in such a near-resonant configuration, making it plausible that TRAPPIST-1e has a nonzero obliquity. In this work, we use the ExoCAM general circulation model to study the possible climates of TRAPPIST-1e at varying obliquities and atmospheric compositions. We vary obliquity from 0° to 90° and the partial pressure of carbon dioxide from 0.0004 bar (modern Earth-like) to 1 bar. We find that models with a higher obliquity are hotter overall and have a smaller day–night temperature contrast than the lower-obliquity models, which is consistent with previous studies. Most significantly, the superrotating high-altitude jet becomes subrotating at high obliquity, thus impacting cloud and surface temperature patterns. As the amount of carbon dioxide increases, the climate of TRAPPIST-1e becomes hotter, cloudier, and less variable. From modeled thermal phase curves, we find that the impact of obliquity could potentially have observable consequences due to the effect of cloud coverage on the outgoing longwave radiation.

The South American precipitation trends under (or not) El Niño‐Southern Oscillation influences and relationship with large‐scale circulation

AbstractThe precipitation trend patterns in South America (SA) are determined using trend empirical orthogonal function analysis for the 1951–2016 period. The associated large‐scale tropical and extratropical anomalous circulation patterns are also examined. The words “total” and “residual” refer to the monthly anomalies and monthly anomalies without the El Niño–Southern Oscillation (ENSO) effects, respectively. The total precipitation features a positive trend in southeastern SA (SESA, southern Brazil, Uruguay, most of eastern Argentina) and northern Chile, and a negative trend over central‐eastern Brazil and central Amazonia. The residual precipitation shows an increased positive trend over most of the coastal extension of northern SA and Colombia; a weak positive trend over southern Brazil, northeastern Argentina, and northern Chile; and a negative trend over central‐eastern SA and western Amazonia. The differences between the total and residual precipitation trend patterns in tropical SA is explained as responses to total and residual zonally asymmetric anomalous sea surface temperature (SST) patterns, respectively. The total SST pattern along the equatorial Pacific configures the Pacific Decadal Oscillation, which impacts ENSO variability and as response intensifies the Walker circulation. Without the ENSO, the Walker cell is mainly driven by the tropical Indian and Atlantic Oceans, which configure residual asymmetric anomalous warming. Furthermore, the warming in the equatorial Indian and eastern Pacific Oceans, in the presence of ENSO, induces a Rossby wave train‐type anomalous pattern that extends across the South Pacific into SA and modulates the atmospheric anomalous circulation over SESA. In this region, an anomalous anticyclonic accompanied by an intensified South American Low‐Level Jet induces a moisture transport to SESA. This anticyclone is also observed in the absence of ENSO but is weaker. The results suggest the importance of ocean warming in the western Pacific‐Indian in the modulation of extratropical teleconnections to SESA in the tropical ocean warming scenario.

Combining Spatial Downscaling Techniques and Diurnal Temperature Cycle Modelling to Estimate Diurnal Patterns of Land Surface Temperature at Field Scale

Combining Spatial Downscaling Techniques and Diurnal Temperature Cycle Modelling to Estimate Diurnal Patterns of Land Surface Temperature at Field Scale

Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward.

The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning.

Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents' basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that has been imposed by the neighboring country Myanmar which has forced the government to shelter almost a million Rohingya refugees in less than 3years (2017-2020). The government had no other option but to acquire almost 24.1 km2 of forest areas only to construct refugee camps for the Rohingyas which has led to catastrophic environmental outcomes. This study has analyzed the land use and land surface temperature pattern change of the Rohingya camp area for the course of 1997 to 2022 with a 5-year interval rate. Future prediction of the land use and temperature of Teknaf and Ukhiya was also done in this process using a machine learning algorithm for the years 2028 and 2034. The analysis says that in the camp area, from 1997 to 2017, percentage of settlements increased from 5.28 to 11.91% but in 2022, it reached 70.09%. The same drastically changing trend has also been observed in the land surface temperature analysis. In the month of January, the average temperature increased from 18.86 to 21.31°C between 1997 and 2017. But in 2022. it was found that the average temperature had increased up to 25.94°C in only a blink of an eye. The future prediction of land use also does not have anything pleasing in store.

Quantifying Downstream Climate Impacts of Sea Surface Temperature Patterns in the Eastern Tropical Pacific Using Clustering

El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures, limiting the ability to link individual ENSO flavors with downstream phenomena. Hierarchical and k-means clustering methods are used to construct Eastern Pacific patterns from the ERSST dataset spanning 1950 to 2021. Cluster analysis allows for the direct linkage of individual SST years/seasons to ENSO phase, providing insight into ENSO flavors and associated downstream impacts. In this study, four clusters are revealed, each depicting unique SST patterns influenced by ENSO and Pacific Meridional Mode (PMM) phases. A case study demonstrating the utility of the clusters was also carried out using accumulated cyclone energy (ACE) in the Atlantic and Eastern Pacific basins. Results showed that Eastern Pacific (EP) El Niño suppresses Atlantic tropical cyclone (TC) activity, while Central Pacific (CP) La Niña enhances it. Further, EP El Niño, coupled with positive PMM, amplifies ACE. Ultimately, the methods used herein offer a cleaner analysis tool for identifying dominant SSTA patterns and employing those patterns to diagnose downstream climatic effects.

Patterns of surface temperature and link to environmental variables in Weddell seals from the western Antarctic Peninsula

Patterns of surface temperature and link to environmental variables in Weddell seals from the western Antarctic Peninsula

Polar Vortex Disruptions by High Latitude Ocean Warming

AbstractMid‐latitude extreme cold outbreaks are associated with disruptions of the polar vortex, which often happen abruptly in connection to a sudden stratospheric warming. Understanding global warming (particularly Arctic amplification) impacts on forecasting such events is challenging for the scientific community. Here we apply clustering analysis on the Northern Annular Mode to identify surface precursors and the governing mechanisms causing polar vortex disruption events. Two clusters of vortex breakdown emerge; 65% of the events, mainly displacements, are associated with high‐latitude Ocean warming in the North Pacific and in Barents‐Kara Sea. Such warming may cause large scale modifications of the tropospheric flow that favors a slowdown of the stratospheric vortex. The persistence of Ocean surface temperature patterns favors polar vortex disruptions, potentially improving prediction skills at the sub‐seasonal to seasonal time scales.

Last Glacial Maximum pattern effects reduce climate sensitivity estimates.

Here, we show that the Last Glacial Maximum (LGM) provides a stronger constraint on equilibrium climate sensitivity (ECS), the global warming from increasing greenhouse gases, after accounting for temperature patterns. Feedbacks governing ECS depend on spatial patterns of surface temperature ("pattern effects"); hence, using the LGM to constrain future warming requires quantifying how temperature patterns produce different feedbacks during LGM cooling versus modern-day warming. Combining data assimilation reconstructions with atmospheric models, we show that the climate is more sensitive to LGM forcing because ice sheets amplify extratropical cooling where feedbacks are destabilizing. Accounting for LGM pattern effects yields a median modern-day ECS of 2.4°C, 66% range 1.7° to 3.5°C (1.4° to 5.0°C, 5 to 95%), from LGM evidence alone. Combining the LGM with other lines of evidence, the best estimate becomes 2.9°C, 66% range 2.4° to 3.5°C (2.1° to 4.1°C, 5 to 95%), substantially narrowing uncertainty compared to recent assessments.

Weakened western Indian Ocean dominance on Antarctic sea ice variability in a changing climate.

Patterns of sea surface temperature (SST) anomalies of the Indian Ocean Dipole (IOD) exhibit strong diversity, ranging from being dominated by the western tropical Indian Ocean (WTIO) to the eastern tropical Indian Ocean (ETIO). Whether and how the different types of IOD variability patterns affect the variability of Antarctic sea ice is not known, nor is how the impact may change in a warming climate. Here, we find that the leading mode of austral spring Antarctic sea ice variability is dominated by WTIO SST variability rather than ETIO SST or El Niño-Southern Oscillation. WTIO warm SST anomalies excite a poleward-propagating Rossby wave, inducing a tri-polar anomaly pattern characterized by a decrease in sea ice near the Amundsen Sea but an increase in regions on both sides. Such impact has been weakening in the two decades post-2000, accompanied by weakened WTIO SST variability. Under greenhouse warming, climate models project a decrease in WTIO SST variability, suggesting that the reduced impact on Antarctic sea ice from the IOD will likely to continue, facilitating a fast decline of Antarctic sea ice.

Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach

Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering the urban heat island (UHI). This study aims to elucidate the spatiotemporal variations and alterations of LST in urban areas compared to LULC changes. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development. Using Landsat images from 2000 to 2021, the analysis employed an exploratory time-series analysis of LST. The study revealed a noticeable variability in LST (20 to 69 °C), which was predominantly influenced by seasonal variability and LULC changes. The study also provided insights into how LST varies within different LULC at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes. This study accounts for changing land surfaces’ complex physical energy interaction over time. The methodology offers a replicable model for other similarly structured, rapidly urbanised regions utilising novel semi-automatic processing of LST from Landsat images, potentially inspiring future research in various urban planning and monitoring contexts.

Long-term variations in terrestrial carbon cycles and atmospheric CO2 levels: Exploring impacts on global ecosystem and climate in the aftermath of end-Cretaceous mass extinction

The short-term climate and environmental consequences (<∼1.0–10 Kyr) immediately following the end-Cretaceous extinction have been attributed to transient effects originating from Deccan volcanism and the Chicxulub impact. These two events have received significant attention for their role in precipitating extreme climate conditions. In contrast, the long-term (> ∼100 Kyr) climate and environmental changes of the post-extinction have been extensively studied in the marine systems, yet their ramifications within terrestrial ecosystems are less well-characterized and require further elucidation. This study presents a carbon isotope analysis of pedogenic carbonates from matured calcisols in the Nanxiong Basin, South China, to reconstruct carbon cycles and atmospheric CO2 concentrations (pCO2) spanning from 76.0 Ma to 62.0 Ma, aiming to decipher the long-term terrestrial environmental and climatic conditions. Results show that δ13C values range from −12.4‰ to −5.0‰ (mean − 9.0‰) and pCO2 varies between ∼250 ppmV and ∼ 2200 ppmV (mean 920 ppmV). When combined with δ13C data from Songliao Basin, NE China, the carbon cycle variation exhibits a 13C collapse and smooth towards subsequent rebound, indicating a process of ∼600 Kyr deterioration, ∼600 Kyr stabilization and ∼ 400 Kyr recovery in the terrestrial ecosystem. The fluctuation of estimated pCO2 aligns with the change pattern of sea surface temperatures, attesting to the strong perturbation of climate and environment at the same pace as the carbon cycle variations. It is proposed that ecosystems and environments in both terrestrial and surface ocean experienced a more unstable, difficult and erratic recovery process and were much more sensitive to climatic changes than in deep ocean for ∼1.5 million years in the aftermath of the end-Cretaceous mass extinction. In addition, the divergence of proxy variations from expected effects implies the Deccan eruption and Chicxulub impact could not have played a long-term role in governing the climatic and environmental perturbations following the Cretaceous-Paleogene Boundary.

Spatial and temporal patterns of land surface temperature in Greenland from 2000-2019

Temperature dynamics on the island of Greenland are an important factor in shaping ecological events. Investigating the land surface temperature (LST) patterns is critical for understanding ecological dynamics across different regions. Further melting of the Greenland ice sheet could deva state marine and terrestrial ecosystems. This study used data from Moderate Resolution Imaging Spectroradiometer satellites to understand the seasonal patterns and patterns of LST over the entire island. Focusing on the period between 2000 and 2019, this study used a natural cubic spline model to identify seasonal patterns for all sub-regions. The data were seasonally adjusted and filtered with a second-order autocorrelation component. The spline was fitted again to identify the LST pattern, and a multivariate regression model was then used to adjust for spatial correlation. We illustrate that most of the land surface of Greenland hasstable temperature trends. These observed patterns in LST in Greenland during the study period suggest that the observed ice-sheet melting in Greenland within the last two decades could be due to other factors, not necessarily LST patterns.