This study evaluates the performance of CMIP6 global climate models (GCMs) in simulating and projecting sea surface temperature (SST) variations across the Caribbean and the tropical Main Developing Region (MDR) that are climatically sensitive and of significant ecological and socio-economic importance. Particular focus is given to the Late Rainfall Season (LRS; August to November), which is critical for regional rainfall and hurricane activity. Using a suite of 14 CMIP6 models to create a multi-model ensemble mean, we assess SST projections under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). Model validation was conducted against the NOAA Optimum Interpolation SST (OISST) dataset using the historical period 1982-2011. Model performance for the LRS and annual was robust, with Pearson correlation coefficients (R) of 0.84, respectively, indicating that the models capture over 71% of the observed SST variability. A low root-mean-square error (RMSE) of 0.17 further confirms their fidelity in reproducing historical SSTs. Projected SST trends indicate pronounced warming in the Caribbean Sea and Gulf of Mexico, with mid-century (2036-2065) increases during the LRS and more dramatic warming by late-century (2071-2100), especially under SSP5-8.5. A spatial gradient of warming is evident, with stronger warming in the western Atlantic and a relatively cooler trend in the eastern basin. These findings underscore the strong performance of CMIP6 models in representing regional SST dynamics and highlight significant projected warming that may alter rainfall regimes, hurricane characteristics and marine ecosystems. By combining model evaluation with future projections, this study provides a reliable foundation for understanding SST-driven climate impacts and supports informed adaptation planning in the Caribbean and broader tropical Atlantic.

NOAA’s Daily Optimum Interpolation Sea Surface Temperature (DOISST) indicates that globally averaged sea surface temperature (SST) broke record in March 2023 and set new record highs in April, July, and August 2023. This has raised intense media interest and public concern about causes and connections to climate change. Our analysis indicates that the record high SSTs qualified as marine heatwaves (MHWs) and even super-MHWs as defined in this study, and are attributed to three factors: (i) a linear trend, (ii) a shift to the warm phase of the multi-decadal Pacific-Atlantic-Arctic Oscillation(PAO) pattern which is identified in this study, and (iii) the transition from the triple-dip succession of La Niña events to the 2023 El Niño event. One-Sentence Summary The extreme warm SSTs in 2023 resulted from linear warming trends, a pattern of low-frequency oscillation, and the El Niño event.

Estimation of spatial correlations should be an integral part of objective analysis of geophysical variables. However, a statistical assessment of spatial correlations has been absent from studies of objective analysis of snow depth since its debut over 2 decades ago. We show a method for computing regional spatial correlations of observed snow depth and the daily snow depth increment and fitting them to correlation functions to estimate the correlation scale parameters. Both horizontal and vertical distance correlations are computed from station observations over a well sampled part of North America. The vertical and horizontal distance correlations are fitted to exponential functions using the least square method to estimate the correlation scale parameters including the amplitude, which represents short distance correlation. Our assessment suggests a large horizontal e-folding correlation scale for both the observed snow depth and the daily increment, with implications for improving predictions in poorly monitored areas with relatively flat topography. Over mountainous terrain, vertical e-folding correlation scale for observed snow depth is much smaller than that for the daily snow depth increment and for the snow depth increment used in operational snow analyses. That means that optimal interpolation-based analysis of the increments may be more accurate than the interpolation of snow depth data.

Sea surface temperatures and vertical wind shear are essential to tropical cyclone formation. TCs need warm SSTs and low shear for genesis. Increasing SSTs and decreasing VWS influences storm development. This work analyzes SST and VWS trends for the Caribbean, surrounding region, and the Atlantic hurricane main developing region from 1982–2020. Storm intensity increases significantly during this period. Annual and seasonal trends show that regional SSTs in the MDR are warming annually at 0.0219 °C yr−1 and, per season, 0.0280 °C yr−1. Simultaneously, VWS decreases during the late rainfall season, at 0.056 m/s yr−1 in the MDR and 0.0167 m/s yr−1 in the Caribbean and surrounding area. The Atlantic Warm Pool is expanding at 0.51 km2 per decade, increasing upper atmospheric winds and driving VWS changes. Correlations of large-area averages do not show significant relationships between TC intensity, frequency, and SSTs/VWS during the LRS. The observed changes appear to be associated with regional warming SSTs impacting TC changes. Plain Language Abstract: Tropical cyclone (TC) formation requires warm ocean waters and low wind shear. Changes to sea surface anomalies and wind shear influences are essential to understanding storm development and intensification. The ability to forecast storm changes is vital to human lives and livelihoods. This work analyzes sea surface temperatures (SSTs) and vertical wind shear (VWS) trends in the Caribbean, surrounding areas, and the Atlantic main developing region (MDR). We found increasing SSTs, decreasing wind shears, an expanding Atlantic Warm Pool (AWP), and increased storm intensity during the Atlantic hurricane season.

Three-dimensional (3D) empirical orthogonal functions (EOFs) are computed and presented for two cases: (i) an atmospheric 3D EOF using the atmospheric reanalysis data from the Earth’s surface of approximately 1000 hPa atmospheric pressure level to the 1.0 hPa level at about 50 kms altitude and (ii) a coupled atmosphere-ocean 3D EOF that includes the top layer of an oceanic reanalysis. These 3D EOFs help better describe the 3D structure of the large-scale climate dynamic patterns, such as the vertical profiles of the El Niño/Southern Oscillation (ENSO). The 3D EOF tool developed in this research allows people to quantitatively describe and visually display atmosphere-ocean dynamics, particularly over the tropical Pacific. Computationally, 3D EOFs are built on a 3D covariance matrix that naturally accounts for the covariance between different pressure levels. In this way, 3D EOFs can help answer questions that may be impossible for the conventional 2D EOFs computed layer-by-layer.

Existing oceanic studies on either data reconstruction or dynamics often used 2-dimensional empirical orthogonal functions (EOF) for sea surface temperature (SST) and for deep layers. However, large-scale oceanic dynamics, such as equatorial ocean upwelling and arctic ocean ventilation, imply the existence of strong covariance among the temperatures and other parameters between different layers. These ocean dynamics are not best represented in the isolated 2-dimensional layer-by-layer calculations, while the 3-dimensional EOFs have a clear advantage. The purpose of this paper is to demonstrate 3D EOF calculations based on the NASA Jet Propulsion Laboratory (JPL) ocean general circulation model (OGCM) from surface to 5,500 meters depth, with 33 depth layers, 1-degree latitude and longitude spatial resolution, and monthly temporal resolution. We also present visualizations of the 3D EOFs and make physical interpretations of the first two EOFs. Our 3D EOF results demonstrate that (i) the 3D spatial pattern of equatorial ocean upwelling is mainly reflected in the first EOF mode and has its most variabilities within the depth layer between 100 and 400 meters, (ii) the 3D El Niño Southern Oscillation (ENSO) dynamic pattern is mainly reflected in the second EOF mode and is mostly confined from surface to the depth of 150 meters, and (iii) the lead eigenvalue from the 3D EOF calculation appears to contain some signal of oceanic warming. Additionally, our method of weighted 3D EOF computation and our 3D visualization Python code may be useful tools for both climate professionals and students.

Warming sea-surface temperatures (SSTs) have implications for the climate-sensitive Caribbean region, including potential impacts on precipitation. SSTs have been shown to influence deep convection and rainfall, thus understanding the impacts of warming SSTs is important for predicting regional hydrometeorological conditions. This study investigates the long-term annual and seasonal trends in convection using the Galvez-Davison Index (GDI) for tropical convection from 1982–2020. The GDI is used to describe the type and potential for precipitation events characterized by sub-indices that represent heat and moisture availability, cool/warm mid-levels at 500 hPa, and subsidence inversion, which drive the regional Late, Early, and Dry Rainfall Seasons, respectively. Results show that regional SSTs are warming annually and per season, while regionally averaged GDI values are decreasing annually and for the Dry Season. Spatial analyses show the GDI demonstrates higher, statistically significant correlations with precipitation across the region than with sea-surface temperatures, annually and per season. Moreover, the GDI climatology results show that regional convection exhibits a bimodal pattern resembling the characteristic bimodal precipitation pattern experienced in many parts of the Caribbean and surrounding region. However, the drivers of these conditions need further investigation as SSTs continue to rise while the region experiences a drying trend.

Arctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the water under the ice. A number of different proxy SST estimates based on SIC have been developed. In recent years more Arctic quality-control buoy SSTs have become available, allowing better validation of different estimates and the development of improved proxy estimates. Here proxy SSTs from different approaches are evaluated and an improved proxy SST method is shown. The improved proxy SSTs were tested in an SST analysis, and showed reduced bias and random errors compared to the Arctic buoy SSTs. Almost all reduction in errors is in the warm melt season. In the cold season the SIC is typically high and all estimates tend to have low errors. The improved method will be incorporated into an operational SST analysis.

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

AbstractGlobal surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of ...

This paper describes a blended sea-surface temperature (SST) data set that is part of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program product suite. Using optimum interpolation (OI), in situ and satellite observations are combined on a daily and 0.25° spatial grid to form an SST analysis, i.e., a spatially complete field. A large-scale bias adjustment of the input infrared SSTs is made using buoy and ship observations as a reference. This is particularly important for the time periods when volcanic aerosols from the El Chichón and Mt. Pinatubo eruptions are widespread globally. The main source of SSTs is the Advanced Very High Resolution Radiometer (AVHRR), available from late 1981 to the present, which is also the temporal span of this CDR. The input and processing choices made to ensure a consistent data set that meets the CDR requirements are summarized. A brief history and an explanation of the forward production schedule for the preliminary and science-quality final product are also provided. The data set is produced and archived at the newly formed National Centers for Environmental Information (NCEI) in Network Common Data Form (netCDF) at doi:10.7289/V5SQ8XB5.

AbstractThe uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantial...

AbstractThe monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° × 2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b.Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1°–0.2°C cooler north of 30°S but 0.1°–0.2°C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product...

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Described herein is the parametric and structural uncertainty quantification for the monthly Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). A Monte Carlo ensemble approach was adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant nonlinear interactions. Globally, the resulting ensemble exhibits a wider uncertainty range before 1900, as well as an uncertainty maximum around World War II. Changes at smaller spatial scales in many regions, or for important features such as Nino-3.4 variability, are found to be dominated by particular parameter choices.Substantial differences in parametric uncertainty estimates are found between ERSST.v4 and the independently derived Hadley Centre SST version 3 (HadSST3) product. The largest uncertainties are over the mid and high latitudes in ERSST.v4 but in the tropics in HadSST3. Overall, in comparison with HadSST3, ERSST.v4 has larger parametric uncertainties at smaller spatial and shorter time scales and smaller parametric uncertainties at longer time scales, which likely reflects the different sources of uncertainty quantified in the respective parametric analyses. ERSST.v4 exhibits a stronger globally averaged warming trend than HadSST3 during the period of 1910-2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap.Several additional SST datasets are used to infer the structural uncertainty inherent in SST estimates. For the global mean, the structural uncertainty, estimated as the spread between available SST products, is more often than not larger than the parametric uncertainty in ERSST.v4. Neither parametric nor structural uncertainties call into question that on the global-mean level and centennial time scale, SSTs have warmed notably.

We show a sea surface temperature (SST) warming trend for the Caribbean and surrounding region over 1982–2012. Using an optimum interpolated SST product, a 30 year climatological analysis was generated to observe annual, monthly, and seasonal trends. Results show that SSTs are increasing annually for the region. For the two Caribbean rainy seasons, the Early Rainfall Season (ERS) and the Late Rainfall Season (LRS), estimated trends at 0.0161°C yr−1 and 0.0209°C yr−1 were observed, with high statistical significance. Subregional analysis revealed that warming is greatest in the Gulf of Mexico and north of South America during the ERS and LRS. Additionally, LRS averages for 1998–2012 reflect an increase in magnitude and intensity of the Atlantic Warm Pool (AWP) since the 1983–1997 period reflected in the AWP Area Index. Extreme increases/decreases in the time series show potential correlation with El Niño and the Southern Oscillation.