Monthly Sea Surface 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.

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

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.

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.

Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Abstract. The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO2 (pCO2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface pCO2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface pCO2 in the SCS with a reasonably high spatial resolution (0.05∘ × 0.05∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of pCO2 constrained by EOF, atmospheric pCO2, and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed pCO2 field successfully simulated the spatial and temporal patterns of sea surface pCO2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm, which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed pCO2 and in situ data ranged from −10 to 4 µatm (−2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface pCO2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface pCO2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface pCO2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO2, the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at https://doi.org/10.57760/sciencedb.02050 (Wang and Dai, 2022).

A Modified U-Net Model for Predicting the Sea Surface Salinity over the Western Pacific Ocean

The prediction of oceanic features is always an important issue in oceanography, where deep learning has been proven to be a useful tool. In this study, we applied the improved U-net model to predict the monthly sea surface salinity (SSS) over the western Pacific (WP) Ocean, and the model was designed to use the SSSs from six consecutive months to predict the SSS in the next month. The monthly satellite-based SSSs in 2015–2020 were used for model training, and the data collected after January 2021 were used to evaluate the model’s predictive abilities. The results showed that the predicted SSSs represented the general patterns of SSSs over the WP region. However, the small-scale features were smoothed out in the model, and the temporal variations were also not well captured, especially over the East China Sea and Yellow Sea (ECS&YS) region. To further evaluate the potential of the U-net model, a more specific model was conducted for the ECS&YS region (Domain 2), which successfully predicted both spatial and temporal variations in the SSSs, including the spreading and retreating of the low-salinity tongue. Based on the comparison between the two domains and sensitivity experiments, we found that the prediction biases were contributed by the spatial distributions of the SSSs, the domain size, and the filter numbers. In addition, further multi-step prediction experiments suggested that our U-net model could also be used for long-time prediction, and we have examined up to five months. Overall, this study demonstrated the great ability and potential of the U-net model for predicting SSS, even though only a few trainable data are available.

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.

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.

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.

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.

Connection between the North Atlantic sea surface temperature and the late autumn snow cover anomalies over the central Tibetan Plateau

Many studies have investigated the effects of the snow anomalies over the Tibetan Plateau (TP), yet the original causes of these anomalies are not fully understood. In this study, we explore the possible connection between the late autumn (November) central TP snow cover anomalies and the monthly sea surface temperature (SST) over the North Atlantic (NA) in autumn and find significant correlations over the mid-latitude NA during October and November. By analyzing the air-sea interaction over the NA, we show that a well-organized horseshoe-like SST anomaly pattern coupled with a surface anomalous anticyclone centered over the mid-latitude NA in October and its persistence in November may play a key role in the November central TP snow cover anomalies. Such an October surface anomalous anticyclone could effectively result in the surface turbulent heat flux anomalies via the surface wind-induced horizontal temperature advection, which is conducive to the formation of the horseshoe-like SST anomalies over the mid-latitude NA. The resultant anomalous SST pattern could further contribute to a North Atlantic Oscillation (NAO)-like pattern in November and stimulate the wave trains eastward to the TP. The anomalous anticyclone (cyclone) associated with the wave trains to the southwest (north) of the TP suppresses the precipitation over the central TP and thus reduces the local snow cover. The atmospheric circulation responses to the persistent NA-SST anomalies are well validated by the numerical experiments. The above result might help to predict the condition of the central TP snow cover anomalies in November, one month ahead.

A stochastic locally diffusive model with neural network‐based deformations for global sea surface temperature

In this work, we propose a new approach to model large, irregularly distributed spatio‐temporal global data via a locally diffusive stochastic partial differential equation (SPDE). The proposed model assumes a local deformation of the SPDE with non‐linear dependence on the covariates through a neural network. The proposed model can be fit in a computationally efficient manner using a triangulation over the sphere and sparsity of the precision matrix, as shown in an application with a large data set of simulated multi‐decadal monthly sea surface temperature.

Forecasting extreme marine heat events in key aquaculture regions around New Zealand

The Tasman Sea has been identified as a climate hotspot and has experienced several marine heatwaves (MHWs) in recent years. These events have impacted coastal regions of New Zealand (NZ), which has had a follow-on effect on local marine and aquaculture industries. Advance warning of extreme marine heat events would enable these industries to mitigate potential losses. Here we present an assessment of the forecast skill of the Australian Bureau of Meteorology’s seasonal prediction system, Australian Community Climate and Earth-System Simulator-Seasonal v1.0 (ACCESS-S1), for three key aquaculture regions around NZ: Hauraki Gulf, Western Cook Strait and Foveaux Strait. We investigate the skill of monthly sea surface temperature anomaly (SSTA) forecasts, and forecasts for SSTA exceeding the 90th percentile, which is an accepted MHW threshold. We find that the model has skill for predicting extreme heat events in all three regions at 0–2 month lead times. We then demonstrate that ACCESS-S1 was able to capture observed monthly SSTA exceeding the 90th percentile around coastal NZ during the 2019 Tasman Sea MHW at a lead time of 1 month. Finally, we discuss the relationship between SSTA in the Tasman Sea and SSTA in coastal regions of NZ, and thus the Tasman Sea as a source of model SSTA skill in the three key coastal regions. Results from this study show that skilful forecasts of ocean heat extremes in regional areas have the potential to enable marine operators in the aquaclture industry to mitigate losses due to MHWs, especially in a warming climate.

Опыт разработки методики долгосрочного прогноза сроков вскрытия ото льда рек бассейна р. Вятки

An attempt is made to develop a method for long-term forecasting of the ice breakup time for the Vyatka River basin, to identify the impact of the distribution of sea surface temperature and geopotential height in the informative regions at the levels H100 and H500 over the Northern Hemisphere on the river ice breakup. The location and boundaries of the informative regions in the fields of H100 and H500 were revealed by the discriminant analysis, the EOF expansion coefficients of the fields of anomalies of monthly mean values of H100 and H500 for January and February and the anomalies of monthly mean sea surface temperature in the North Atlantic and Northwest Pacific were used as potential predictors. The stepwise regression analysis allowed deriving good and satisfactory (S/σ = 0.45–0.73) complex prognostic equations for forecasting the ice breakup time for the Vyatka River basin. The essential influence of H100 and H500 geopotential height fields and the spatial distribution of sea surface temperature anomalies in the North Atlantic and Northwest Pacific in January and February on the river ice breakup time is revealed. It is proposed to improve the method by considering the impact of air temperature, maximum ice thickness per winter, and other indirect characteristics on the processes of river ice breakup in the Vyatka River basin. Keywords: ice regime, long-range forecast, river ice breakup, expansion coefficients, geopotential height fields, spring ice phenomena, energy-active zones of the oceans, complex prognostic equation

Опыт разработки методики долгосрочного прогноза сроков вскрытия ото льда рек бассейна р. Вятки

An attempt is made to develop a method for long-term forecasting of the ice breakup time for the Vyatka River basin, to identify the impact of the distribution of sea surface temperature and geopotential height in the informative regions at the levels H100 and H500 over the Northern Hemisphere on the river ice breakup. The location and boundaries of the informative regions in the fields of H100 and H500 were revealed by the discriminant analysis, the EOF expansion coefficients of the fields of anomalies of monthly mean values of H100 and H500 for January and February and the anomalies of monthly mean sea surface temperature in the North Atlantic and Northwest Pacific were used as potential predictors. The stepwise regression analysis allowed deriving good and satisfactory (S/σ = 0.45–0.73) complex prognostic equations for forecasting the ice breakup time for the Vyatka River basin. The essential influence of H100 and H500 geopotential height fields and the spatial distribution of sea surface temperature anomalies in the North Atlantic and Northwest Pacific in January and February on the river ice breakup time is revealed. It is proposed to improve the method by considering the impact of air temperature, maximum ice thickness per winter, and other indirect characteristics on the processes of river ice breakup in the Vyatka River basin. Keywords: ice regime, long-range forecast, river ice breakup, expansion coefficients, geopotential height fields, spring ice phenomena, energy-active zones of the oceans, complex prognostic equation

An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000\u20131849

This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.

Inter-monthly regional variation of sea surface salinity and influencing factors in the East China Sea and the adjacent Northwest Pacific Ocean

Based on 1871-2010 monthly sea surface salinity (SSS) and flow field data, and the global seabed topography data, the inter-monthly regional variation of SSS in different regions and their influencing factors are discussed after climate zoning in the East China Sea (ECS) and the adjacent Northwest Pacific Ocean (ANPO). Rresults show that: (1) the study area can be divided into the area of the ECS, the Kuroshio area of the East China Sea (KECS) and the ANPO I area, II area and III area. (2) In the continental shelf area of the ECS, the inter-monthly regional variation of SSS is significant, the inter-monthly variation range of feature points is large and the difference of SSS value at each point is great. (3) In the KECS, the inter-monthly regional variation of SSS is small. The SSS at feature points is high and the inter-monthly variation is small. From the inlet to the outlett of the KECS, the SSS value gradually increases. (4) In the ANPO, the inter-monthly regional variation of SSS is the smallest, the SSS at feature points is high, and their inter-monthly variation is small. The SSS increases with increasing longitude at the same latitude, and decreases with increasing latitude at the same longitude.

Distribution patterns of intertidal oribatid mites (Acari, Oribatida) from South African shores and their relationship to temperature

A faunistic study of the intertidal oribatid mite fauna of South Africa’s coastline revealed the presence of four species from three families, showing specific biogeographic patterns. Their occurrences show a clear east–west divide, with a small gap near East London between the Podacaridae and the other two families, the Selenoribatidae and Fortuyniidae, clearly coinciding with suggested marine biogeographic ecoregions. The podacarid Halozetes capensis is confined to the cooler warm-temperate Agulhas Ecoregion; the fortuyniid Fortuynia elamellata micromorpha and the selenoribatid Schusteria ugraseni to the warmer subtropical Natal Ecoregion; and the selenoribatid Selenoribates divergens to the tropical Delagoa Ecoregion. These distributions are an indication that the oceanic climate may be the primary factor shaping their biogeography, particularly the seawater temperatures along the coast, which are in turn affected by the Agulhas and the Benguela currents. A mean monthly sea surface temperature of approximately 22 °C apparently represents the climatic border of the distributions, with the podacarid persisting only below this temperature, and the fortuyniid and selenoribatids only above it. Global warming will certainly change these geographic ranges, and in South Africa the warm-adapted fortuyniid and selenoribatids can be expected to expand their distributions southwards, while the occurrence of the cold-adapted podacarid might be reduced to a few southwestern coastal areas in the next few decades.