Monthly Sea Surface Temperature Research Articles

Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.

SST trends in certain areas of the Barents Sea in the winter season and mechanisms of their formation

The climate changes observed over the past few decades are most clearly manifested in the Arctic Ocean. Sea surface temperature (SST) is one of the most reliable indicators of climate change. In this paper we analyze the changes of winter SST for the western, northeastern and southeastern regions of the Barents Sea and examine the relationship of the emerging STS trends with the influence of various external factors. The working data set is represented by average monthly SST values taken from the ERA-5 reanalysis for the period 1949–2023 with a spatial resolution of 0.25×0.25° and average water temperature values on the Kola Meridian section in the 0–50 m layer. Additionally, the Arctic Oscillation (AO), Arctic Dipole (AD) and Atlantic Multidecadal Oscillation (AMO) indices were used as external factors that may affect SST variability. The time series analyzed was divided into three periods: 1949–1969, 1970–1990, 1991–2023, where the variability of the analyzed parameters was different. Thus, in the first period the trend in SST changes was negative, for the second period it was slightly negative or neutral, and for the third period it was positive. It is shown that SST in all the regions of the Barents Sea has undergone significant changes, which were most noticeable in the “warm” period of 1991–2023, when the rate of SST increasing was up to 10·10-2 °C/year in areas under the warm Atlantic water influence. The analysis of SST variability in the Barents Sea shows that the positive anomalies observed in the recent years are most likely associated with the changes in the atmospheric circulation. The Wavelet coherence analysis showed the closest agreement between the changes in the sea surface temperature and the AD index in the winter season, and with the AMO index.

Future warming of the Gulf of Oman: SST trends under contrasting climate scenarios by 2050

This study investigated potential changes in sea surface temperature (SST) within the Gulf of Oman over the next three decades (2020–2050) under contrasting climate scenarios. Monthly SST data from three Coupled Model Intercomparison Project Phase 6 (CMIP6) models were obtained from the Earth System Grid Federation (ESGF) database for a defined spatial grid (0.5° x 0.5°) encompassing the Gulf of Oman. Future changes (2020–2050) in SST were analyzed under two contrasting Shared socioeconomic Pathways (SSPs): SSP245 (optimistic) and SSP585 (pessimistic).A time series analysis employing a weighted average approach across all three models was conducted. This analysis revealed a projected increase in SST for most seasons and months, except for summer. Further trend analysis was performed using the non-parametric Mann-Kendall test and Sen's slope estimator. This analysis indicated a moderate warming trend in the Gulf of Oman's SST over the next 30 years, with an average increase of approximately 1°C. Spatial analysis of SST was also conducted, identifying minimum and maximum temperature values in shallow regions, the Strait of Hormuz, and its connection to the Persian Gulf. The findings of this study suggest a potential for significant warming in the Gulf of Oman over the coming decades, with implications for regional marine ecosystems and weather patterns. Understanding these projected changes is crucial for informing adaptation strategies and mitigating the potential negative impacts of climate change in the region.

Optimal Proxy Indices for Annual Marine Heatwave Characteristics Using Monthly Sea Surface Temperature

Optimal Proxy Indices for Annual Marine Heatwave Characteristics Using Monthly Sea Surface Temperature

Future Changes in the Intensity and Duration of Marine Heat and Cold Waves: Insights from Coupled Model Initial-Condition Large Ensembles

Abstract The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (FM) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted FM to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with FM outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both FM and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models.

Long term of sea surface temperature prediction for Indonesia seas using multi time-series satellite data for upwelling dynamics projection

Global warming, which impacts global temperatures, has led to an increase in sea surface temperature (SST). This rise significantly affects marine ecological systems, especially in Indonesia. As a result, long-term forecasting of SST dynamics is essential for shaping various policies. However, most studies on SST forecasting have focused on short-term predictions in local or medium-sized areas and often overlook the dynamic upwelling that influences SST. In this paper, we propose a five-year SST prediction for the Indonesian seas, incorporating multi-time-series satellite data to project upwelling dynamics. We utilized two deep learning time series models to construct a predictive model that estimates monthly SST values. This model employs extensive multiple satellite data, encompassing 14,934 points (including SST with a resolution of 0.090, wind, ENSO, heat flux, and solar radiation) from 2003 to 2021. To enrich the training data and mitigate overfitting, we applied data augmentation for time series. Experimental results reveal that all satellite datasets correlate with SST over five years. The 1D-Convolution Neural Network outperformed the Long-Short Term Memory model, exhibiting the lowest mean absolute error of 0.39 °C compared to 0.45 °C. Our model detected a consistent upwelling dynamic over a five-year pattern in the Indonesian seas. These findings suggest that our proposed model offers accurate and efficient long-term monthly SST predictions, crucial for upwelling projections.

Impact of Climate Change on Coral Reefs Degradation at West Lombok, Indonesia

Coral reefs are one of the ecosystems that provide economic and environmental benefits to coastal communities in Indonesia. However, coral reef ecosystems are also one of the ecosystems threatened by climate change at the local scale. The waters of North Sekotong, West Lombok, Indonesia, are a tropical coastal system with beautiful coral reefs and marine ecosystems. Coral reef damage has been widespread in this area due to increased water temperatures. Increased water temperature results in coral reef degradation. Field surveys were conducted on May 23-28, 2016, in collaboration with the Marine and Coastal Resources Research and Development Center, Ministry of Marine Affairs and Fisheries, and coral reef mapping using Landsat 7 and Landsat 8 during 2002 - 2016 as well as processing monthly sea surface temperature (SST) data from the AquaModis and Oi SST V2 satellites and daily SST data from the NOAA Coral Reef Watch satellite. Changes in coral cover area were compared with temperature changes due to climate change. The increase in temperature creates a hotspot phenomenon in the coral reef ecosystem, resulting in coral reef degradation. The results showed that coral reefs in this area have degraded by 17.55% or 78.21 Ha from 455.68 Ha (2002) to 367.46 Ha (2016), with a degradation rate of 2.8 Ha/year in 2002 - 2014; 8.1 Ha/year (2014 - 2014) and 36 Ha/year (2015 - 2016) caused by an increase in SST which caused a hotspot phenomenon with a high enough intensity that there was an increase in temperature in 2016 which reached 9.77oC.

Increased captures of the critically endangered leatherback turtle (Dermochelys coriacea) around New Zealand: the contribution of warming seas and fisher behavior

Five species of sea turtles are known to occur in New Zealand waters, with the leatherback turtle (Dermochelys coriacea) being the most frequently reported. In New Zealand all sea turtles are protected, but there are currently no fisheries bycatch mitigation measures. We describe fishery captures of leatherbacks from Ministry observer and fisher self-reported data. A generalized additive model (GAM) was then used to evaluate which factors might explain the observed year trend in captures. Between fishing years 2007–08 and 2020–21 (years starting 1 October), there were 217 captures of leatherback turtles, an annual average of 15.5. Reported captures increased substantially to 50 in 2020–21. Nearly all (97.7%) captures were reported from surface longline fisheries. Because of sparse observer coverage most captures (85.3%) were self-reported by fishers. Within the main fishery, just 9.4% of the vessels reported 94.5% of the leatherback captures, and one vessel reported 40.4% of all captures. Some non-reporting of captures seems likely. The GAM estimated a higher probability of capture with increasing SST from a constant-over-time spatial pattern of monthly sea surface temperature, and predicted the increase in captures in 2020–21. Much of the increase in bycatch could therefore be explained by the fleet moving into warmer areas where the probability of leatherback capture was higher. Capture mitigation measures could include restricting fishing in turtle ‘hotspot’ times and areas. New Zealand waters should be recognized as an important seasonal foraging ground for leatherback turtles where capture mitigation measures are necessary.

Role of environmental conditions in structuring the stock trajectory of Thunnus albacares, Th. alalunga and Th. obesus in the South Pacific Region

The lifestyle and culture of South Pacific Island countries have been long intertwined with oceanic resources. These countries are heavily dependent on tuna resources for their economies and socioeconomic livelihoods. Despite their importance, the mechanisms behind tuna stock trajectory patterns need to be better understood. With changing climatic and environmental conditions, it has become vital to understand the impact of these changes on tuna resources and if possible include them in long-term tuna harvest and management plans. A significant portion of the stock dynamics of yellowfin tuna (Thunnus albacares), albacore tuna (Th. alalunga) and bigeye tuna (Th. obesus) in the South Pacific Region may possibly be explained only by the environmental factors of sea surface temperature (SST) and Atlantic Multidecadal Oscillation AMO. The relationship of monthly SST and AMO was investigated with time series stock patterns of Th. albacares, Th. alalunga and Th. obesus in the Eastern and Western Pacific Ocean for the years 1972 to 2019. Monthly variables that exhibited significant correlation with CPUE variables were used in the Generalised Linear Model and Generalized Additive Model to reproduce the CPUE trajectory of the three tuna species from 1972 to 2019. Results showed that a significant portion of stock dynamics of Th. albacares, Th. alalunga and Th. obesus can be explained well by two environmental conditions of SST and AMO. This shows that a large portion of tuna variation in the Eastern and Southern Pacific is related to environmental conditions. Models with single variables are evidence of the significant individual effect of SST and AMO on stock time series of each tuna species. Models with two variables had a better fit in comparison to models with a single variable for all tuna stocks. Possibilities of two significantly different patterns in the trajectory of the three tuna species and environmental conditions used in the models were also observed. The trajectory patterns seemed to change around the 1990s and had significantly different means, indicating possible regime shifts. Environmental conditions play a highly significant role in structuring tuna stock trajectory in the South Pacific and need to be included in tuna management / harvest plans to ensure sustainability of this important resource. The importance of regime shifts should be recognised and further investigated for possible inclusion in tuna sustainability plans due to their influence on long-term tuna trajectory patterns.

Rectifying low-frequency variability in future climate sea surface temperature simulations: are corrections for extreme change scenarios realistic?

Most procedures for redressing systematic bias in climate modeling are calibrated using current climate observations, and perform well. However, their performance in the future climate remains uncertain as no observations exist to compare against. In this context, we use the current and future climate outputs of an ultra-high resolution of Community Earth System Model (UHR-CESM) as the representative truth and bias correct monthly sea surface temperature (SST) simulations of eight Coupled Model Intercomparison Project 6 models over the Niño 3.4 region. A time-frequency bias correction approach is used to correct for bias in distributional, trend, and spectral attributes present in the models. This results in a near perfect power spectrum of the bias corrected current climate model simulations. Considering all correction procedures remain unchanged into the future, the overall representation of the corrected SST simulations shows improvement with consistency across models for the doubled CO2 scenario, but higher variability and lower consistency in the quadrupled CO2 concentration scenario.

Monitoring and Quantification of Carbon Dioxide Emissions and Impact of Sea Surface Temperature on Marine Ecosystems as Climate Change Indicators in the Niger Delta Using Geospatial Technology

The Niger Delta marine environment has experienced a series of environmental disasters since the inception of oil and gas exploration, which can be attributed to climate change. Carbon dioxide (CO2) emissions and sea surface temperature (T) ties associated with burning fossil fuels, such as gas flaring, vehicular traffic, and marine vessel movement along the sea, are increasing. Using data extracted from the NASA Giovanni satellite’s Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), this study mapped the carbon footprint and T along the coastline into the deep sea from 2003 to 2011, using ArcGIS software. The spatial distribution of CO2 and T concentrations determined by the inverse distance weighting (IDW) method reveals variations in the study area. The results show an increase in the quantity of the mean tropospheric CO2 from July 2003 to December 2011, from 374.5129 ppm to 390.7831 ppm annual CO2 emissions, which also reflects a continuous increase. The average Monthly sea surface temperature had a general increasing trend from 25.79 °C in July 2003 to 27.8 °C in December, with the Pearson correlation coefficient between CO2 and T indicating 50% strongly positive, 20% strongly negative, 20% weakly positive,and 10% weakly negative. CO2 levels, like temperature, follow a seasonal cycle, with a decrease during the wet season due to precipitation dissolving and plant uptake during the growing season, and then a rise during the dry season. Carbon capture and storage technologies must be implemented to benefit the marine ecosystem and human well-being.

Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region

ABSTRACT The Coral Triangle (CT) and the South China Sea (SCS) are the world’s great tropical seas, located in the Indo-Pacific (IP) region. It is home to the richest marine ecosystem on Earth, with a total of 76% reef-building coral species as well as 37% coral reef fish species. Unfortunately, this sensitive area is now vulnerable to Sea Surface Temperature (SST) warming. This research explored the possible consequences of SST warming on the rich ecosystems of the IP region, specifically on bleaching of its coral reefs. Reefbase provided coral bleaching records together with the daily NOAA AVHRR Optimum Interpolation (OI) SST V2 dataset (OISSTv2) were used to explore the relationship between coral bleaching and SST in the IP region. Three different categories of monthly mean SST were tested as potential covariates: minimum SST, mean SST and maximum SST, obtained from the OISSTv2. The fitted logistic regression (LR) model revealed a significant and large correlation between coral bleaching and annual maximum monthly mean SST in the study area using the bleaching data from an online database and the time-series of AVHRR images. Predicted maps of coral bleaching based on the LR model were highly consistent with NOAA Coral Reef Watch (CRW) Degree heating Weeks (DHW) maps. However, some important discrepancies resulted from the more specific local fitting used in the LR model. The maximum SST was forecasted from 2020 to 2100 based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset under the Representative Concentration Pathways (RCP2.6) scenario. The fitted logistic regression model was employed to transform the forecasted maximum SST values into maps of the probability of coral bleaching from 2020 to 2100. The results provide considerable cause for concern, including the likelihood of widespread coral bleaching in many places in the IP region over the next 30 years.

Abrupt Increase in ENSO Variability at 700 CE Triggered by Solar Activity

AbstractThe El Niño–Southern Oscillation (ENSO) is the dominant interannual variability affecting global weather and climate. However, limited observational records hinder our understanding of the evolution of natural ENSO variability and its driving mechanisms. In this study, ENSO variations from 665 to 749 CE (Common Era) were investigated using monthly sea surface temperature (SST) records of the northern South China Sea derived from coral strontium to calcium ratios of sub‐fossil Porites obtained at the Xisha Islands. The results suggested the existence of cold conditions in this period relative to the twentieth century. No change in SST seasonality for the CE was found that is in agreement with the orbitally controlled insolation seasonality at that time. ENSO variability during 665–749 CE was enhanced by 39% than that during 1980–2014 CE and exhibited fluctuations. A persistent dampened ENSO variability with frequent El Niño events from 665 to 700 CE was followed by a rapid increase in variability to a level double that of the present. The frequency of La Niña events increased during this time. The abrupt transformation of ENSO activity at 700 CE was attributed to the increased natural radiative forcing induced by intense solar irradiance and weak volcanism. This paper demonstrates that ENSO variability can be influenced by external forcings, such as changes in solar and volcanic activity, and not only by the internal dynamics of the climate system.

Küresel iklim değişikliğiyle mücadelede deniz yüzeyi sıcaklığının ileri beslemeli yapay sinir ağları ile tahminlenmesi: Rize örneği, Türkiye

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.

Clustering tropical cyclone genesis on ENSO timescales in the Southwest Pacific

Tropical cyclones (TCs) as a natural hazard pose a major threat and risk to the human population globally. This threat is expected to increase in a warming climate as the frequency of severe TCs is expected to increase. In this study, the influence of different monthly sea surface temperature (SST) patterns on the locations and frequency of tropical cyclone genesis (TCG) in the Southwest Pacific (SWP) region is investigated. Using principal component analysis and k-means clustering of monthly SST between 1970 and 2019, nine statistically different SST patterns are identified. Our findings show that the more prominent ENSO patterns such as the Modoki El Niño (i.e., Modoki I and Modoki II) and Eastern Pacific (EP) El Niño impact the frequency and location of TCG significantly. Our results enhance the overall understanding of the TCG variability and the relationship between TCG and SST configurations in the SWP region. The results of this study may support early warning system in SWP by improving seasonal outlooks and quantification of the level of TC-related risks for the vulnerable Pacific Island communities.

A deep learning model for forecasting global monthly mean sea surface temperature anomalies

Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation (ENSO) regarded as a major source of interannual climate variability at the global scale. The ability to make long-range forecasts of SST variations and extreme marine heatwave events have potentially significant economic and societal benefits, especially in a warming climate. We have developed a deep learning time series prediction model (Unet-LSTM), based on more than 70 years (1950–2021) of ECMWF ERA5 monthly mean SST and 2-m air temperature data, to predict global 2-dimensional SSTs up to a 24-month lead. Model prediction skills are high in the equatorial and subtropical Pacific. We have assessed the ability of the model to predict SST anomalies in the Niño3.4 region, an ENSO index in the equatorial Pacific, and the Blob marine heatwave events in the northeast Pacific in detail. An assessment of the predictions of the 2019–2020 El Niño and the 2016–2017 and 2017–2018 La Niña show that the model has skill up to 18 months in advance. The prediction of the 2015–2016 extreme El Niño is less satisfactory, which suggests that subsurface ocean information may be crucial for the evolution of this event. Note that the model makes predictions of the 2-d monthly SST field and Nino 3.4 is just one region embedded in the global field. The model also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Niño, shows a short lead prediction. These results indicate the significant potential of data-driven methods to yield long-range predictions of SST anomalies.

Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation.

Frequency of different types of El Niño events under global warming

AbstractThe El Niño–Southern Oscillation (ENSO) is the dominant natural climate pattern that influences the global climate on subdecadal timescales. Thus, a better understanding of the impacts of global warming on the characteristics of ENSO events offers large socioeconomic benefits. Changes in the frequency of the eastern Pacific (EP), central Pacific (CP) and total El Niño events are analysed in the future period (2051–2100) under the two shared socioeconomic pathways (SSPs) scenarios (i.e., SSP2‐4.5 and SSP5‐8.5) compared to the historical period (1951–2000) using outputs of eight models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Types of El Niño events are diagnosed based on pattern correlation coefficients (PCCs) between monthly sea surface temperature (SST) anomalies and the first two leading empirical orthogonal function (EOF) modes of SST anomalies in the tropical Pacific. Based on the ensemble of models, the number of total El Niño events decreases by 26 and 16% under the SSP2‐4.5 and SSP5‐8.5 scenarios, respectively, in the future compared to the historical period. A smaller decrease in the number of total El Niño events under the faster warming rate of the SSP5‐8.5 scenario suggests that the response of ENSO dynamics to global warming is not linear. Analysis of the Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5) dataset during the period 1951–2020 indicates that the ratio of the number of CP El Niño to EP El Niño has substantially increased over the past two decades. Nevertheless, under both the SSP2‐4.5 and SSP5‐8.5 scenarios in the future period, the ratio of the number of CP El Niño to EP El Niño does not change considerably compared to that in the historical period. This suggests that the recent increase in the frequency of CP El Niño could be a result of multidecadal variations rather than anthropogenic global warming.

Variability and trends of temperature and rainfall over three agro-ecological zones in North Shewa, Central Ethiopia

This study was conducted to analyze rainfall and temperature variability and trends over three Agro-Ecological Zones (AEZs) in Central Ethiopia. Global weather data for Soil and Water Assessment Tool (SWAT) and global mean monthly sea surface temperature (SST) data series were used for analysis. Mann–Kendall (MK) test was utilized to analyze rainfall and temperature trends. Sen’s slope estimator was employed to find out the rate of change. The study detected an annual increase of 0.07 °C (p < 0.001) in mean maximum temperature at Kolla AEZ. It also showed a 0.06 °C rise per annum (p < 0.001) for both Dega and Woina Dega AEZs. The average annual minimum temperature has increased by 0.03 °C per year at Kolla (p < 0.001), Woina Dega (p < 0.05), and Dega (p < 0.01), which means an increase of 1.05 °C between 1979 and 2013. Results from precipitation concentration index (PCI) revealed the highest percentage (97.1%) of irregular distributions in annual rainfall pattern at Kolla AEZ, followed by Woina Dega (82.9%). Standardized rainfall anomalies (SRAs) calculated in the study also showed higher percentage (28.6%) of drought in Kolla AEZ, which experienced drought once in every 3 or 4 years. The study revealed negative annual rainfall anomalies for 18 years in Kolla and 16 years in both Dega and Woina Dega AEZs. Rising temperatures and irregular rainfall patterns may induce some adverse impacts on the livelihoods of rural populations in general, their subsistence agricultural production processes and food availability in particular. Policymakers and stakeholders should give priority in designing and introducing pro-poor plus geographically differentiated adaptive strategies.