Surface Temperature Variability Research Articles

Seasonal Sea Surface Temperature Variations at the Northern Limit of the Kuroshio Current in Marine Isotope Stage 20–18

AbstractThis study presents a high time‐resolution alkenone unsaturation ratios (UK'37) in the Chiba composite section, a marine sedimentary sequence formed in the Kazusa forearc basin in the northwestern Pacific under the influence of the Kuroshio and Oyashio currents. The UK'37 reflects summer sea surface temperature (SST) and records glacial‐interglacial to sub‐millennial‐scale variations spanning the marine isotope stage (MIS) 19, an analog of our Holocene interglacial. At the glacial‐interglacial scale, summer UK'37‐SST and previously published winter SST fluctuations are in tandem with East Asian Summer and Winter monsoon variabilities, respectively. Millennial‐scale UK'37‐SST variations correspond to meltwater events in the North Atlantic. Summer temperature amplitudes maybe half those in winter, suggesting the stable dominance of the Kuroshio Current during the summer and significant winter cooling caused by the Oyashio Current intrusion. Our study reveals the unique seasonally different atmosphere‐ocean coupling system during MIS 19 at the subarctic front in the northwestern Pacific.

Impact of Oceanographic Variability on Chlorophyll-a Concentration and Sea Surface Temperature in North Maluku Waters and its Influence on Fish Abundance

The waters of North Maluku are rich in fisheries resources, which are important for the local economy and marine biodiversity. The variability of oceanographic conditions, such as chlorophyll-a and sea surface temperature, plays a significant role in determining fish abundance in this region. This study analyzes the seasonal variations of chlorophyll-a and sea surface temperature during 2021, as well as their impact on the productivity of the waters. Satellite imagery data of chlorophyll-a and sea surface temperature were analyzed using spatial interpolation. The results show seasonal fluctuations in chlorophyll-a concentration and sea surface temperature, where higher chlorophyll-a concentrations support primary productivity and fish abundance during mid-year. The highest chlorophyll-a concentration occurred in June (0.212 mg/m³) and the lowest in February (0.103 mg/m³). The negative phase of the Indian Ocean Dipole (IOD) reduces upwelling intensity, decreases nutrient availability, and impacts the decline in primary productivity. The highest sea surface temperature was recorded in November (30.99°C) and the lowest in August (29.87°C). These oceanographic variations affect the availability of phytoplankton, which is crucial in the marine food chain, and have implications for sustainable fisheries management in North Maluku. The results of this study can also serve as a reference for fish distribution or abundance modeling, such as with the maximum entropy method, to better understand the factors influencing fish distribution patterns in the region.

Impact of Victoria Mode on Late‐Season Tropical Cyclone Genesis Over the Western North Pacific

AbstractThe Victoria Mode (VM) plays a vital role in shaping Pacific Sea Surface Temperature (SST) variability. However, its impact on late‐season tropical cyclone genesis (TCG) over the western North Pacific (WNP) remains underexplored. This study investigated the influence of VM on WNP TCG from 1965 to 2020. Significant relationships emerge in interannual (6 year) and decadal (11 year) timescales. On the interannual timescale, VM correlates positively with TCG west of 145°E and negatively to the east. On the decadal timescale, the negative relationship spans the entire WNP. Victoria Mode directly modulates vertical motion and induces low‐latitude anticyclonic circulation across the low‐latitude North Pacific by altering surface wind convergence and diabatic heating. This, in turn, shapes environmental conditions, affecting TCG. Dynamic factors are more important in WNP TCG changes than thermodynamic factors. Critical environmental conditions in VM's impact on TCG include four dynamic factors: relative vorticity, vertical velocity, meridional gradient of zonal wind, and vertical shear of zonal winds. Vertical shear of zonal winds is particularly influential. Distinct correlation patterns and mechanisms are diagnosed between different timescales, attributed to anomalous SST observed over the WNP on the interannual timescale but not on the decadal timescale. Furthermore, this study illustrates the complexity of region‐ and time‐dependent relationships between environmental conditions and TCG. Such dependencies pose challenges for conventional genesis potential index models in accurately capturing the spatial characteristics of the VM‐TCG relationship. Overall, our findings uncover VM's substantial influence on WNP TCG, enhancing understanding of key environmental conditions and mechanisms driving interannual and decadal TCG variations.

A sub-fossil coral Sr∕Ca record documents northward shifts of the Tropical Convergence Zone in the eastern Indian Ocean

Abstract. Sea surface temperature (SST) variability in the south-eastern tropical Indian Ocean is crucial for rainfall variability in Indian Ocean rim countries. A large body of literature has focused on zonal variability associated with the Indian Ocean Dipole (IOD) which peaks in austral spring. In today's climate, northward shifts of the Tropical Convergence Zone (TCZ) co-vary with the IOD, and it is unclear whether these shifts may also occur independently. We have developed a new monthly resolved Sr/Ca record from a sub-fossil coral cored at Enggano Island (Sumatra, Indonesia). Core sections containing diagenetic phases are omitted from the SST reconstruction. U/Th dating shows that the Sr/Ca-based SST record extends from 1869–1918 and from 1824–1862 with a relative age uncertainty of ±3 years (2σ). At Enggano Island, coastal upwelling and cooling in austral spring impact SST seasonality and are coupled to the latitudinal position of the TCZ. The sub-fossil coral indicates an increase in SST seasonality between 1856 and 1918 relative to the 1930–2008 period. We attribute this to enhanced cooling due to stronger south-easterly (SE) winds driven by a northward shift in the TCZ in austral spring. A nearby sediment core indicates colder SSTs and a shallower thermocline prior to ∼1930. These results are consistent with an increase in the north–south SST gradient in the eastern Indian Ocean, calculated from historical temperature data, that is not seen in the zonal SST gradient. We conclude that the relationship between meridional and zonal variability in the eastern Indian Ocean is non-stationary and modulated by the long-term evolution of temperature gradients.

Spatial, environmental and trophic niche partitioning by seabirds in a climate change hotspot.

For similar species to co-occur in places where resources are limited, they need to adopt strategies that partition resources to reduce competition. Our understanding of the mechanisms behind resource partitioning among sympatric marine predators is evolving, but we lack a clear understanding of how environmental change is impacting these dynamics. We investigated spatial and trophic resource partitioning among three sympatric seabirds with contrasting biological characteristics: greater crested terns Thalasseus bergii (efficient flyer, limited diver, and preference for high quality forage fish), little penguins Eudyptula minor (flightless, efficient diver, and preference for high quality forage fish) and silver gulls Chroicocephalus novaehollandiae (efficient flyer, limited diver and generalist diet). We investigated interannual variability in resource partitioning in relation to environmental variability in a climate change hotspot influenced by the warm and intensifying East Australian Current (EAC). Sampling was conducted from 2012 to 2014 during the austral summer breeding season of seabirds at Montague Island, Australia. Daily seabird movements were monitored using GPS trackers and feather tissues were collected and processed for stable isotope analysis (δ15N and δ13C). Generalised Linear Mixed Models were used to assess how changes in oceanographic conditions influenced space use for each species. Schoener's D and Bayesian mixing models were used to respectively investigate the levels of yearly inter-specific environmental and trophic niche overlaps. Crested terns and little penguins were less likely to be observed in warm, saline EAC waters and crested terns and silver gulls had smaller foraging areas on days when more than 30% of available habitat was classified as EAC origin. All species preferred areas with low variability in sea surface temperature (<0.5°C). Terns and penguins occupied similar marine trophic levels, with penguins having larger isotopic niche spaces in 2014 when the EAC was more dominant in the study area. Gulls occupied the lowest trophic level, with the widest niche and lowest interannual variability in niche area. As the EAC intensifies along the southeast coast of Australia under climate change, interspecific competition for resources may increase, with the greatest impacts on species like little penguins that have relatively restricted foraging ranges. This study suggests that species-specific biological traits and behavioural plasticity should be accounted for when predicting the effects of climate change on marine species.

Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest Algorithm

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, Machine Learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest Machine Learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters.

An Interpretable Deep Inference Model With Dynamic Constraints for Forecasting the Evolution of Sea Surface Variables in the South China Sea

AbstractAn interpretable deep inference forecasting model is designed to improve the forecasting capability of sea surface variables. By incorporating the air‐sea coupling mechanism as a dynamic constraint, the interpretability and forecasting performance of the model are improved. More specifically, our findings underscore the critical role of air‐sea interactions in forecasting sea surface variables, especially sea surface temperature (SST) variations induced by tropical cyclones (TCs). Additionally, Liang‐Kleeman information flow (IF), a causal inference method, is introduced to optimize the selection of predictors. Using satellite remote sensing data, our study demonstrates the model's capability in realizing sea surface multivariate forecasts in the South China Sea (SCS) within 10 days. More importantly, the experimental results prove the applicability of the model in both normal and extreme weather conditions, highlighting its effectiveness in enhancing sea surface variables forecasting.

Spatiotemporal Impact Analysis of Land Surface Temperature Variations: A Case Study of the Northeastern Part of Baghdad Province, Iraq (2000-2024)

Spatiotemporal Impact Analysis of Land Surface Temperature Variations: A Case Study of the Northeastern Part of Baghdad Province, Iraq (2000-2024)

Influence of winter Saharan dust on equatorial Atlantic variability

While Saharan dust’s influence on sea surface temperature variability in the tropical North Atlantic is well-documented, its effects on the equatorial region remain underexplored. This relationship is particularly important due to the strong influence of equatorial Atlantic variability on both local and remote climates. Here, we use observational and reanalysis data to investigate Saharan dust’s role in boreal winter, a period when dust transport is typically near the equator. A unique footprint of Saharan dust forcing is revealed, as well as a complex, non-monotonic response. Specifically, in contrast to the expected cooling due to shortwave blocking by Saharan dust, lower tropospheric warming, and stabilization lead to a strong sea surface warming off the coast of northwestern Africa and to the development of an off-equatorial warm front. The front drives cross-equatorial winds that induce a northward shift of the Atlantic rain belt, equatorial cooling, and equatorial wave activity leading to delayed equatorial warming. Winter Saharan dust is therefore an important contributor to equatorial Atlantic variability, with cross-regional implications.

Extreme droughts in the Amazon Basin during cyclic ENSO events coupled with Indian Ocean Dipole modes and Tropical North Atlantic warming.

The teleconnections between El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and Tropical North Atlantic warming (+TNA) play a critical role in characterizing extreme drought events in the Amazon Basin (AB). This study examines the seven most recent drought extreme events up to 2023, using seasonal composites of the sea surface temperature and atmospheric variables over a five-quarter period starting at the austral spring(-1) of the year preceding that when the lowest water level at Manaus port was recorded. Two distinct patterns emerge, driven by consecutive ENSO events with opposite phases, referred to as cyclic La Niña-El Niño and cyclic El Niño-La Niña drought events. For these events, IOD and ENSO modes are coupled in the same phase during the springs, with ENSO triggering and enhancing IOD, and the IOD, in turn, enhancing and sustaining ENSO through Walker circulation. This interaction can amplify extratropical Rossby waves in both hemispheres, originating from the equatorial Indian and Pacific Oceans. Notably, strong positive ENSO and IOD phases trigger or sustain the +TNA by weakening northeasterlies through a Pacific-North America wave train. The IOD, ENSO and +TNA, individually or combined, influence atmospheric circulation patterns over South America through Rossby waves and anomalous Walker and Hadley circulation patterns, causing dry periods in the AB marked by negative precipitation anomalies across specific regions or the entire AB, consequently modulating the water levels at Manaus. The strong coupling of these three tropical modes is crucial to leading multiyear drought events in the AB. This study underscores the potential for a robust climate forecasting system by monitoring the oceanic indices.

Widening of Wind Stress Anomalies Amplifies ENSO in a Warming Climate

Abstract Climate change simulations generally indicate the strengthening of El Niño–Southern Oscillation (ENSO) sea surface temperature (SST) variability through the twenty-first century, yet a robust physical mechanism explaining this change across different models is still lacking, and the projections for ENSO amplitude exhibit a large spread. Most commonly, changes in the background state of the tropical Pacific are invoked to explain these changes of ENSO. Here, we show that changes in the structure of wind stress anomalies associated with ENSO are potentially as important as these background state changes. Specifically, changes in the magnitude, meridional width, and zonal structure of wind stress anomalies can explain approximately 53% of the intermodel variance in the projected change of ENSO magnitude through the twenty-first century as well as 43% in ENSO periodicity changes. Among these changes in the wind structure, the meridional widening of wind anomalies plays the most important role. To demonstrate that these changes are indeed critical, we develop a hybrid model of ENSO based on the Community Earth System Model, version 2, which incorporates a dynamical ocean coupled to a simplified statistical atmosphere within the tropical Pacific. In the absence of external forcing and corresponding mean-state changes, the imposed changes in wind stress anomalies in this hybrid model result in an increase of ENSO amplitudes of nearly 10% along the equator. Our results are also theoretically supported by a recharge-oscillator model that incorporates the meridional wind structure. Thus, changes in the structure of wind stress anomalies, together with changes in the mean state, likely play a critical role in the projected strengthening of ENSO.

Climate response to stratospheric aerosol injection during the Harmattan season in West Africa

Abstract Stratospheric Aerosol Injection (SAI), a proposed climate intervention, aims to reduce the amount of solar radiation reaching the Earth's surface by increasing the reflectivity of the atmosphere, thereby offsetting the warming effect of greenhouse gases. During the Harmattan season (December-February) in West Africa, a natural meteorological phenomenon injects dust and sand particles into the atmosphere, leading to a cooling effect. In this study, we investigate the influence of SAI on West African surface temperature, dust, and other meteorological variables using the Whole Atmosphere Community Climate Model (WACCM) under the Shared Socioeconomic Pathway 2-4.5 (SSP2-4.5) scenario and the Assessing Responses and Impacts of Solar Climate Intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) dataset. Our findings indicate that SAI intervention significantly impacts the projected surface temperatures, specific humidity, and wind speed changes during the Harmattan season. Compared to a future without SAI, the intervention shows a significant net cooling effect over most parts of West Africa during the mid-future period (2050-2069). Also, SAI intervention significantly decreases moisture content over southern and northern West Africa in the near-future (2035-2054), mainly due to the net cooling effects over West Africa, when compared to a future without SAI. This feature is enhanced in the mid-future period. The cooling effects of SAI are likely to reduce the air's capacity to hold moisture, leading to lower specific humidity levels relative to a future without SAI. It could also have negative implications, such as increased aridity compared to a future without SAI in the northern and central regions of West Africa. These findings also highlight the potential for SAI to improve air quality in certain areas but also underscore the need for careful consideration of implementation strategies and possible trade-offs. The changes from SAI observed are specific to the ARISE simulation and may differ from other SAI simulations.

Thermal environment and erosion of comet 67P/Churyumov-Gerasimenko

This paper focuses on how insolation affects the nucleus of comet 67P/Churyumov-Gerasimenko over its current orbit. We aim to better understand the thermal environment of the nucleus, in particular its surface temperature variations, erosion, relationship with topography, and how insolation affects the interior temperature for the location of volatile species (H_2O and CO_2). We have developed two thermal models to calculate the surface and subsurface temperatures of 67P over its 6.45-year orbit. The first model, with high resolution (300 000 facets), calculates surface temperatures, taking shadows and self-heating into account but ignoring thermal conductivity. The second model, with lower resolution (10 000 facets), includes thermal conductivity to estimate temperatures down to ∼3 m below the surface. The thermal environment of 67P is strongly influenced by its large obliquity (52^∘), which causes significant seasonal effects and polar nights. The northern hemisphere is the coldest region, with temperatures of 210--300 K. H_2O is found in the first few centimetres, while CO_2 is found deeper (∼2 m) except during polar night around perihelion, when CO_2 accumulates near the surface. Cliffs erode 3--5 times faster than plains, forming terraces. The equatorial region receives maximum solar energy (8.5times10^9 J m^-2 per orbit), with maximum surface temperatures of 300--350 K. On the plains, H_2O is found in the first few centimetres, while CO_2 is found deeper (∼2 m) and never accumulates near the surface. In the southern hemisphere, a brief intense perihelion heating raises temperatures to 350--400 K, which is followed by a 5-year polar night when surface temperatures drop to 55 K. Here H_2O remains in the first few centimetres, while CO_2 accumulates shallowly during polar night, enriching the region. Erosion is maximal in the southern hemisphere and concentrated on the plains, which explains the observed overall flatness of this hemisphere compared to the northern one. Over one orbit, the total energy from self-heating is 17% of the total energy budget, and 34% for thermal conduction. Our study contributes to a better understanding of the surface changes observed on 67P.

A method for long-term year-round water temperature monitoring in salmonid spawning habitats in remote dynamic streams

Warming temperatures have added urgency to characterizing the thermalscapes and thermal tolerances of cold-adapted fishes to effectively manage and conserve such species. This is especially relevant at high latitude and high elevation streams, which are experiencing rapid environmental changes, yet are data-poor, remote, and difficult to access. Here, we describe a method to assess temporal and spatial variation in surface and hyporheic water temperatures that can be effectively deployed to remain year-round in remote dynamic streams. We then demonstrate the utility of this method by assessing Dolly Varden Salvelinus malma spawning sites in a remote river. Characterizing and quantifying the amount of viable thermal habitat for cold-adapted species improves predictions of how warming may affect high latitude and high elevation stream ecosystems. Together with species-specific thermal tolerances, this information can then be used to identify the thermal refugia that are essential for conservation of endemic species, and assess risks associated with range expansions of potentially colonizing species.

How Do Model Biases Affect Large-Scale Teleconnections That Control Southwest U.S. Precipitation? Part II: Seasonal Models

Abstract We explore the skill in predicting southwest United States (SWUS) October–March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest–southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño–like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic-flow-inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests reexamining high-latitude drivers of SWUS precipitation.

Modelling the association between the El Niño Southern Oscillation and levels of legacy pollutants in Alaskan seabird eggs from 1999 to 2010

BackgroundThe circumpolar north has long been a sink for legacy persistent organic pollutants (POPs). Under a warmer climate, these contaminants are predicted to remobilize within environmental media where they are subject to climate processes, underscoring the need to examine the effects of large-scale climate oscillations on the fate of legacy POPs. MethodsWe apply piecewise log-linear spline regression models with non-parametric bootstrapping to measure the association between the El Niño Southern Oscillation (ENSO), a measure of Pacific sea surface temperature variability, and levels of polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), and polybrominated diphenyl ethers (PBDEs) measured in common murre (Uria aalge) and thick-billed murre (Uria lomvia) eggs collected from the Gulf of Alaska and Bering Sea between 1999 and 2010. ResultsIn thick-billed murres, average PCB concentrations increased 143 % per unit increase in ONI during the warm phase of the ENSO, with a meta-regression pooled estimate of 0.887 (95 % CI: 0.674, 1.099). Similarly, average PBDE concentrations increased 562 % per unit increase in ONI during the warm phase, with a pooled estimate of 1.890 (95 % CI: 0.647, 3.133). During the cool phase, average PCB concentrations decreased 59 % per unit increase in ONI, with a pooled estimate of −0.892 (95 % CI: −1.400, −0.384). In common murres, average PCB concentrations decreased 51.4 % per unit increase in ONI during the warm phase, with a meta-regression pooled estimate of −0.722 (95 % CI: −0.894, −0.549), and increased 39.5 % per unit increase in ONI during the cool phase, with a pooled estimate of 0.333 (95 % CI: 0.185, 0.481). Average PBDE concentrations decreased 79.2 % per unit increase in ONI during the warm phase, with a pooled estimate of −1.572 (95 % CI: −2.357, −0.787). ConclusionOur results indicate associations between the ENSO and PCB and PBDE loads in Alaskan murre eggs, suggesting the ENSO may be an important factor in the fate of legacy POPs within Alaskan marine food webs. To our knowledge, this is the first paper to link the ENSO and levels of legacy POPs in Alaska's environment and may be relevant to health and food security in this region.

Analyzing trend and heatwaves of 15 Years of Sea Surface Temperature Variations along the Italian Adriatic Coast.

Water temperature is a vital parameter impactingthe growth and survival of aquatic life.Using satellite-derived infrared data, this studyanalysed the trend of sea surface temperature (SST)from 2008 to 2022of the Adriatic coastal watersofItalian regions. The "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis" product collected from theCopernicus Marine Serviceof European Copernicus programme was used, as a good compromise among spatial accuracy, temporal frequency and coverage. SST were derived in 176 locations, placed in the Adriatic Sea from the southern limit of the lagoon of Venice (Veneto) to Santa Maria di Leuca (LE), at a distance from the coast between 500 m and 5000 m (0.3 - 2.7 nautical miles). Time series analysis was applied to average value of daily SST calculated from the selected spatial locationsto identify the additive model components: trend, seasonality and random effects. The trend component was isolated and assessed using a linear regression modelto determine its significance and magnitude.A0.010 °C/year increase in SST was observed. Additionally, marine heatwaves and cold spells were consistently registered throughout the entire observation period, with a north-south gradient in intensity.

Comparison of ARIMA and Machine Learning Methods for Predicting Urban Land Surface Temperature in Jakarta

Climate change is a global challenge that requires serious attention from various parties, including the government. The existence of surface temperature and various other parameters is certainly closely related to climate change. In this context, this study was conducted to identify the best model in predicting urban land surface temperature in the Jakarta area, as one of the steps to understand and deal with the impacts of climate change. The research data used comes from MERRA-2, NASA, which provides datasets for various climate analyses. A comparison of ARIMA, SVR, LSTM, and ANN methods was conducted to evaluate the performance of each model in forecasting land surface temperature. The results show that the Long-Short Term Memory (LSTM) model provides the best performance with MAPE and values of 0.8381 and 0.8628. This model has an advantage over other models because it can remember various information that has been stored for a long period of time and can delete irrelevant information. This shows that LSTM is effective in capturing the pattern and variability of the Earth's surface temperature in the Jakarta area. Based on these findings, the government is expected to take concrete steps to address the impacts of climate change, especially issues related to increasing urban land temperature in Jakarta, such as reducing the use of private vehicles and switching to public transportation, expanding green open space, and relocating residents to reduce density.

Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia

Climate predictions spanning 10-year periods, known as Decadal Climate Predictions (DCPs), have become an important aspect of the latest Coupled Model Intercomparison Project (CMIP6). These DCPs have the capability to capture the El Niño-Southern Oscillation (ENSO) phenomena, which affects heatwave frequency in Southeast Asia over years to decades. This research assesses the ability of six General Circulation Model (GCM) DCPs to predict surface temperature over the Southeast Asian region, using the dcpp-A hindcast as the main product. The metrics of Anomaly Correlation Coefficient (ACC) and Mean Error (ME) are employed to assess the model outputs, with 51 hindcast datasets spanning initial years from 1960 to 2010 and ERA5 reanalysis data serving as the reference. The evaluation reveals that DCP model skill varies across lead times and subregions, with no single model consistently outperforming the others. The highest correlation values are observed during the September-October-November (SON) season, and the ENSEMBLE model demonstrates the ability to increase correlation values compared to the individual DCP models. However, the ENSEMBLE approach is unable to effectively reduce ME values due to the contrasting errors among individual models. PBIAS metric aligns with the ME, consistently identifying similar areas of underestimation (mainland) and overestimation (maritime continent) across the models. Despite these challenges, the evaluation results highlight the potential of DCPs in predicting surface temperature variability for the Southeast Asian region over decadal periods, particularly in capturing ENSO-related signals. Further improvements in model initializations, internal variability representation, and bias reduction are necessary to enhance the utility of CMIP6 decadal predictions for heatwave preparedness and mitigation strategies in this vulnerable region.

Urban heat island relation with land use land cover change in Hetauda sub-metropolitan city of Nepal

Urban areas are expanding globally at the expense of natural productive land which affects the quality of life of urban residents. Hetauda sub-metropolitan city of Nepal has been undergoing rapid urban growth for the last few decades causing local climatic effects such as land surface temperature (LST) variation. Thus, exploring spatio-temporal changes in land use, land cover (LULC), and urban heat island (UHI) analysis could be an effective means of exposing local environmental issues caused by anthropogenic activities. Development in thermal Remote Sensing and Geographic Information System (GIS) has enabled the monitoring of spatial LST, UHI, and its correlation to LULC. We used Landsat 8 OLI/TIRS satellite data and a supervised classification algorithm for land use land classification for the years 1995, 2008, and 2018 in Arc map software. The spatial pattern of LST was obtained through mathematical calculation of the thermal band of Landsat images. Correlation analysis was applied to explore the relationship between LST, LULC types, and LUCL indices. The LST was higher for urban/built-up and cultivated land use types. There was approximately 4°C mean LST variation for all three years of study. The regression analysis showed a positive correlation of urban/built-up with the Normal Difference Built-Up Index (NDBI) however a negative correlation with the Normal Difference Vegetation Index (NDVI) which implies that green structure weakens the UHI effects while urban/built-up areas strengthen the UHI. Overall, the study can be useful for urban planners in sustainable urban planning and management as well as to raise public awareness of climate change and the warming effect.