Sea Surface Temperature Datasets Research Articles

The Ensemble Oceanic Niño Index

AbstractWe describe a new El Niño–Southern Oscillation (ENSO) index that we call the Ensemble Oceanic Niño Index (Ensemble ONI). Ensemble ONI uses monthly sea surface temperature (SST) anomalies in the Niño3.4 region from 32 input SST datasets as the basis for the ensemble, and our new monthly ENSO index extends from the present back to the year 1850. We use the input datasets to quantify the uncertainty in the monthly index values. The uncertainty is calculated from datasets that are not completely independent from each other, but we use other estimates of uncertainty in SST, well‐documented historical ENSO events, a detailed consideration of literature‐based definitions of ENSO events, and proxy‐based determinations of past ENSO events, to show that our uncertainty estimate from the ensemble of input datasets is comparable with other studies both in terms of average magnitude and the time‐varying trend. Similar to past research, we find that ENSO events occur every 4–5 years on average, and there have been six “Super” El Niños (1877–1878, 1888–1889, 1972–1973, 1982–1983, 1997–1998, 2015–2016) that statistically rise above all other El Niños since 1850. Finally, the time span of our work shows that El Niño events were most intense at ends of both the 19th century and the 20th century, with a lull in the mid‐1900s, corroborating previous instrumental, written, and proxy records.

Weak Mesoscale Variability in the Optimum Interpolation Sea Surface Temperature (OISST)-AVHRR-Only Version 2 Data before 2007

Mesoscale sea surface temperature (SST) variability triggers mesoscale air–sea interactions and is linked to ocean subsurface mesoscale dynamics. The National Oceanic and Atmospheric Administration (NOAA) daily Optimum Interpolation SST (OISST) products, based on various satellite and in situ SST data, are widely utilized in the investigation of multi-scale SST variabilities and reconstruction of subsurface and deep-ocean fields. The quality of OISST datasets is subjected to temporal inhomogeneity due to alterations in the merged data. Yet, whether this issue can significantly affect mesoscale SST variability is unknown. The analysis of this study detects an abrupt enhancement of mesoscale SST variability after 2007 in the OISST-AVHRR-only version 2 and version 2.1 datasets (hereafter OI.v2-AVHRR-only and OI.v2.1-AVHRR-only). The contrast is most stark in the subtropical western boundary current (WBC) regions, where the average mesoscale SST variance during 2007–2018 is twofold larger than that during 1993–2006. Further comparisons with other satellite SST datasets (TMI, AMSR-E, and WindSAT) suggest that the OISST-AVHRR-only datasets have severely underestimated mesoscale SST variability before 2007. An evaluation of related documents of the OISST data indicates that this bias is mainly caused by the change of satellite AVHRR instrument in 2007. There are no corresponding changes detected in the associated fields, such as the number and activity of mesoscale eddies or the background SST gradient in these regions, confirming that the underestimation of mesoscale SST variability before 2007 is an artifact. Another OISST product, OI.v2-AVHRR-AMSR, shows a similar abrupt enhancement of mesoscale SST variability in June 2002, when the AMSR-E instrument was incorporated. This issue leaves potential influences on scientific research that utilize the OISST datasets. The composite SST anomalies of mesoscale eddies based on the OI.v2-AVHRR-only data are underestimated by up to 37% before 2007 in the subtropical WBC regions. The underestimation of mesoscale variability also affects the total (full-scale) SST variability, particularly in winter. Other SST data products based on the OISST datasets were also influenced; we identify suspicious changes in J-OFURO3 and CFSR datasets; the reconstructed three-dimensional ocean products using OISST data as input may also be inevitably affected. This study reminds caution in the usage of the OISST and relevant data products in the investigation of mesoscale processes.

A comparison of orbital-resolution, Late Pleistocene Alkenone and foraminiferal assemblage-based sea surface temperature reconstructions from the Southwest Pacific

Global and regional reconstructions of past climate conditions often incorporate sea surface temperature (SST) estimates from multiple proxies because not every paleotemperature proxy is applicable in all geographic locations. This practice of assimilating estimates from different proxies in global or regional temperature syntheses makes the implicit assumption that estimates derived from different proxies can be meaningfully intercompared. However, evidence to support the validity of this assumption is limited. Using paleotemperature data from sediments collected from ODP Site 1125 in the Southwest Pacific, we conduct a ∼1 Myr, orbital-scale SST proxy comparison of a recently published alkenone-derived SST record with a previously published foraminiferal assemblage-based SST record. These alkenone and foraminiferal assemblage SST datasets show strong structural similarity and yield remarkably similar estimates for basic climate metrics, including mean, median, standard deviation, and range. Statistical analysis indicates that the correlation between the two SST records is highly significant. In the spectral domain, the records share the same dominant 100 kyr beat, are coherent and in phase with each other at this frequency, and have the same coherence and phase relationship with benthic foraminiferal δ18O. Results from this work demonstrate that these two proxies would yield very similar estimates for the paleoclimate metrics most commonly used in empirical paleoclimate reconstructions that seek to document the evolution of climate over this interval. However, significant disparities between SST estimates derived from the two proxies exist for some time periods, particularly during glacial and interglacial extrema. This comparison suggests that treating estimates from these proxies as equivalent in studies that focus on short time windows (e.g. a few thousand to tens-of-thousands of years), particularly in investigations that seek to characterize glacial or interglacial extrema, could be potentially problematic. However, the sensitive location of Site 1125, just north of the Subtropical Front, likely accentuates the difference between temperature estimates from these proxies, which may be attenuated in other oceanographic settings. We attribute the discrepancies between the two SST records to two main causes: seasonal leakages of cold water across the Subtropical Front during glacial extrema and differential seasonality of maximum alkenone and foraminiferal production.

MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION

Variability of Sea Surface Temperature (SST) is one of the climatic features that influence global and regional climate dynamics. Missing data (gaps) in the SST dataset are worth investigating since they may statistically alter the value of the SST change. The partial least square-structural equation modeling (PLS-SEM) approach is used in this work to estimate the causality relationships between exogenous and endogenous latent variables. The findings of this study, which are significant indicators that have a loading factor value > 0.7 are as follows: i) sea surface temperature (oC) as a measure of the latent variable changes in SST, ii) wind speed (m/s) and relative humidity (%) as a measure of the latent variable of weather, and iii) air temperature (oC), long-wave solar radiation (w/m2) as a measure of climate latent variables. The size of the Rsquare value is influenced by the number of gaps. The results of the boostrapping show that the latent variables of weather and climate have a significant effect on changes in SST which are indicated by the value of tstatistics > ttabel. The structural model obtained Changes in SST (η) = -0.330 weather + 0.793 climate + ζ. The model shows that the weather has a negative coefficient, which means that the better the weather conditions, the lower the SST changes. Climate has a positive coefficient, which means that the better the climate, the SST changes will also increase. Rising sea surface temperatures caused by an increase in climate can lead to global warming, impacting El-Nino and La-Nina events.

Validation of satellite-based sea surface temperature products against in situ observations off the western coast of Sumatra

This study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1–1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

Sea Surface Temperature Prediction With Memory Graph Convolutional Networks

We develop a memory graph convolutional network (MGCN) framework for sea surface temperature (SST) prediction. The MGCN consists of two memory layers: one graph layer and one output layer. The memory layer captures SST temporal changes via temporal convolution units and gate linear units. The graph layer encodes SST spatial changes in terms of characteristics derived from graph Laplacian. The output layer encapsulates information from the previous layers and produces SST prediction results. The MGCN characterizes both the temporal and spatial changes, rendering a comprehensive SST prediction strategy. We use daily mean SST data for two areas near the Bohai Sea and the East China Sea for experimental evaluations and validate that the MGCN performs better than other traditional machine learning methods for nearshore SST prediction. In addition, we test the MGCN on weekly and monthly mean SST datasets and validate that the MGCN is robust and suitable for SST prediction.

Development of SST Validation Methodology for Upcoming SSTM/EOS-06 Sensor: A Prelude Experiment on SLSTR/Sentinel-3A

Radiometers aboard satellites provide an essential Sea Surface Temperature (SST) data archive, which is one of the key indicators of climate change. Since new radiometers are planned to fill the gap in the temporal data coverage, it is important to quantify the retrieval accuracy to utilize the data and seamlessly merge it into an existing SST data-archive. In this regard, we validate SST product from SLSTR/Sentinel-3A with in-situ measurements to create a reference frame for evaluating the new sensors products. We used NOAA <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> Quam SST dataset for validating satellite-retrievals, since it’s easily obtainable unlike skin-SST measurements. Several criteria have yielded a total of 30,353 high-quality cloud-free collocated data points between SLSTR and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> Quam SST datasets for 2017. Detailed evaluation of biases globally under different wind speeds and precipitable water vapor conditions indicates the fundamental difference between the satellite and in-situ SST, and recommendations are made for how datasets should be handled. SLSTR-retrieved skin-SST agrees with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> Quam subsurface-SST, irrespective of region and time of measurement, within -0.17±0.04K for all-day, -0.11±0.06K for daytime and -0.21±0.05K for nighttime datasets, with respective RMSE values of 0.62±0.10K, 0.58±0.09K and 0.65±0.13K. These small differences are partly due to the difference between ocean skin-temperature and subsurface-temperature, measurement uncertainties and sampling mismatch. Comprehensive validation carried out for SLSTR-retrieved SST has direct implications for future validation studies and future blended infrared, microwave and in-situ SST products.

IMPACT OF INCREASING SEA SURFACE TEMPERATURE ON POTENTIAL FISHING ZONE OF SKIPJACK TUNA Katsuwonus pelamis IN MAKASSAR STRAIT

Makassar Strait plays a vital role as one of the skipjack tuna fishing grounds in Indonesia. This study aimed to detect the skipjack tuna preference of sea surface temperature (SST) and chlorophyll-a (Chl-a) in Makassar Strait and to map out the possible shifting potential fishing zone (PFZ) of skipjack tuna by simulating sea surface temperature increase. We analyzed the skipjack catch data from July to November 2020, and we added the previous data in June, July, August, October, and December 2019 in the same study area. We analyzed together with satellite imagery data set of SST and Chl-a using GAM. We used three scenarios for the SST increase model simulation, which are 0.25, 0.5, and 1℃. The results showed that skipjack tuna is relatively high in SST ranging from 28.3 to 30.4°C and Chl-a ranging from 0.18 to 0.28 mg/m3. The PFZ based on sea surface temperature simulation showed that PFZ area form in the northern part shifting to the southern part of Makassar Strait based on the increasing sea surface temperature simulation visible in September, which shifted from 0.017-5.421⁰S to 2.923-6.802⁰S and October shifted from 0.017-6.802⁰S to 5.007-6.802⁰S. Knowing the shifting of the potential fishing zone of skipjack tuna could be an important step toward fishing operation and management for skipjack tuna resource management in Makassar Strait.

Bayesian maximum entropy interpolation of sea surface temperature data: A comparative assessment

ABSTRACT Sea surface temperature (SST) is an important oceanography attribute that has been used to study ocean climatic conditions, ocean dynamics and air-sea interactions. In the present work, the Bayesian maximum entropy (BME) method was used to interpolate a FY-3 C /VIRR satellite dataset in the region with coordinates 120°-150°W and 45°-60°S and during January 2020. A novel approach of constructing valuable soft data was developed by combining BME interpolation with highly correlated SST day and night data differences. The BME interpolation accuracy was assessed by cross-validation, and the results showed that the average RMSE (root mean squared error) was 0.700 and the average bias was 0.441°C. Furthermore, using the Argo data as a basis of comparison, the coverage and accuracy of the BME interpolation of the FY-3 C/VIRR satellite SST dataset were compared numerically with those of the Ordinary Kriging (OK) interpolation of the FY-3 C/VIRR satellite SST dataset and the Optimum Interpolation Sea Surface Temperature (OISST) of the AVHRR satellite SST dataset. It was found that BME had the best SST interpolation performance among the three methods with the lowest average bias and the largest correlation coefficient. Although OISST had a full product coverage rate overall (due to its use of more perfect treatment means), BME’s coverage rate (97.5%) considerably improved that of the FY-3 C/VIRR SST data. Also, both the BME and OK products maintained a 12hrs temporal resolution and a 0.05 decimal degrees longitude/latitude spatial resolution, which is an improvement over OISST data with a 24hrs time resolution and a 0.25 decimal degrees longitude/latitude spatial resolution. Another advantage of BME is that because of its broad theoretical support its performance in practice can be improved further as more knowledge sources become available (which can only be incorporated by BME).

Decreasing spring persistent rainfall over the Yangtze‐Huai River Valley of China during 1960–2019 and its possible causes

AbstractBased on the observations, reanalysis and sea surface temperature (SST) data sets, the spatio‐temporal distribution characteristics of the decreasing spring persistent rainfall (SPR) over the Yangtze‐Huai River Valley (YHRV) of China during 1960–2019 are revealed, and corresponding causes of the first leading two modes from the empirical orthogonal function (EOF) analysis on the SPR are also explored. Results indicate that the SPR decreases northwards and exhibits spatially consistent declining trends over the YHRV, with the maximum along the middle and lower reaches of the Yangtze River, attributing to the decrease in total spring rainfall over the YHRV. The first EOF mode of the SPR (EOF‐1) over the YHRV shows a spatial mono‐sign pattern, while the South Indian Ocean dipole plays an important role in modulating its variability. Among them, the warming SST in the southwestern Indian Ocean is more important for the spatially consistent decrease of the SPR over the YHRV. The second EOF mode of the SPR (EOF‐2) over the YHRV exhibits a spatial distribution of north–south dipole pattern, while the North Atlantic Oscillation (NAO) contributes to the variation of EOF‐2. The inter‐decadal decreases of SPR at different rates are attributed to the transition between the first leading two EOF modes of SPR associated with the inter‐decadal changes of the external forcing factors and atmospheric circulations. From mid‐1970s to 1990s when the southern Indian Ocean SST increases rapidly, a mono‐sign pattern of the SPR is observed with a rapid decrease SPR over the whole YHRV. Since the mid‐1980s, when the NAO gradually reaches a predominantly positive phase, the SPR exhibits an increasing trend over the south of the YHRV, while a decreasing trend over the north part, causing the overall slowing of the precipitation decreasing trend.

Oceanic Response to Tropical Cyclone Gonu (2007) in the Gulf of Oman and the Northern Arabian Sea: Estimating Depth of the Mixed Layer Using Satellite SST and Climatological Data

The category 5-equivalent tropical Cyclone Gonu (2007) was the strongest cyclone to enter the northern Arabian Sea and Gulf of Oman. The impact of this cyclone on the sea surface temperature (SST) cooling and deepening of the mixed layer was investigated herein using an optimally interpolated (OI) cloud-free sea surface temperature (SST) dataset, climatological profiles of water temperature, and data from Argo profilers. SST data showed a maximum cooling of 1.7–6.5 °C during 1–7 June 2007 over the study area, which is similar to that of slow- to medium-moving cyclones in previous studies. The oceanic heat budget equation with the assumptions of the dominant turbulent mixing effect was used to establish relationships between SST and mixed layer depth (MLD) for regions that were directly affected by cyclone-induced turbulent mixing. The relationships were applied to the SST maps from satellite to obtain maps of MLD for 1–7 June, when Gonu was over the study area. Comparing with the measured MLD from Argo data showed that this approach estimated the MLDs with an average error of 15%, which is an acceptable amount considering the convenience of this approach in estimating MLD and the simplifications applied in the heat budget equation. Some inconsistencies in calculating MLD were attributed to use of climatological temperature profiles that may not have appropriately represented the pre-cyclone conditions due to pre-existing cold/warm core eddies. Estimation of the diapycnal diffusion that quantified the turbulent mixing across the water column showed consistent temporal and spatial variations with the calculated MLDs.

Marine Heatwaves in Siberian Arctic Seas and Adjacent Region

We used a satellite-derived global daily sea surface temperature (SST) dataset with resolution 0.25 × 0.25∘ to analyze interannual changes in the Arctic Shelf seas from 2000 to 2020 and to reveal extreme events in SST distribution. Results show that the second decade of the 21st century for the Siberian Arctic seas turned significantly warmer than the first decade, and the increase in SST in the Arctic seas could be considered in terms of marine heatwaves. Analyzing the spatial distribution of heatwaves and their characteristics, we showed that from 2018 to 2020, the surface warming extended to the northern deep-water region of the Laptev Sea 75∘ to 81∘N. To reveal the most important forcing for the northward extension of the marine heatwaves, we used three-dimensional numerical modeling of the Arctic Ocean based on a sea-ice and ocean model forced by the NCEP/NCAR Reanalysis. The simulation of the Arctic Ocean variability from 2000 to 2020 showed marine heatwaves and their increasing intensity in the northern region of the Kara and Laptev seas, closely connected to the disappearance of ice cover. A series of numerical experiments on the sensitivity of the model showed that the main factors affecting the Arctic sea-ice loss and the formation of anomalous temperature north of the Siberian Arctic seas are equally the thermal and dynamic effects of the atmosphere. Numerical modeling allows us to examine the impact of other physical mechanisms as well. Among them were the state of the ocean and winter sea ice, the formation of fast ice polynias and riverine heat influx.

Influence of SST in Low Latitudes on the Arctic Warming and Sea Ice

Global climate models, focused on projecting anthropogenic warming, have not detected an increase in sea surface temperature (SST) at low latitudes comparable to the observed one. This appears to be one reason for the discrepancy between the model estimates of warming and reduction of the sea ice extent in the Arctic and the observed changes in the climate system. In previous studies, it was shown that short-term manifestations of the impact of low latitudes on the Arctic climate were identified in 2–3 weeks as a result of strengthening of atmospheric circulation patterns. In this paper, for the first time, a climatic relationship was established among an increase in SST, air temperature, and water vapor content at low latitudes, and a decrease in sea ice extent in the Arctic. ECMWF Re-Analysis data (ERA-Interim, ERA5), Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST), sea ice archives of the World Centers NSIDC (USA), and Arctic and Antarctic Research Institute (Russia), observations of water temperature in the Kola section (33°30’ E), calculated sea ice parameters using the Arctic and Antarctic Research Institute coupled ice-ocean circulation model (AARI–IOCM). Methods of multivariate correlation analysis, calculating spectra and coherence, and creating correlation graphs were used to obtain the results. For the first time, estimates of the effect of heat transport from low to high latitudes on climate change and sea ice extent in the Arctic over the past 40 years have been obtained, explaining a significant part of their variability. The increase in heat transport is affected by an increase in SST at low latitudes, where a significant part of the solar heat is accumulated. Due to the increase in SST, the amount of heat transported by the ocean and the atmosphere from low latitudes to the Arctic increases, leading to an increase in the air temperature, water vapor content, downward longwave radiation at high latitudes, and a decrease in the thickness and extent of winter sea ice. Potential topics include, but are not limited to: the role of heat and moisture transport in the Arctic warming, effect of SST at low latitudes on transports, linkage of warming in low latitudes and in shrinking of the Arctic sea ice.

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Niño–Southern Oscillation (ENSO) cycle.

Metop First Generation AVHRR FRAC SST Reanalysis Version 1

The first full-mission global AVHRR FRAC sea surface temperature (SST) dataset with a nominal 1.1 km resolution at nadir was produced from three Metop First Generation (FG) satellites: Metop-A (2006-on), -B (2012-on) and -C (2018-on), using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) SST enterprise system. Historical reprocessing (‘Reanalysis-1’, RAN1) starts at the beginning of each mission and continues into near-real time (NRT). ACSPO generates two SST products, one with global regression (GR; highly sensitive to skin SST), and another one with piecewise regression (PWR; proxy for depth SST) algorithms. Small residual effects of orbital and sensor instabilities on SST retrievals are mitigated by retraining the regression coefficients daily, using matchups with drifting and tropical moored buoys within moving time windows. In RAN, the training windows are centered at the processed day. In NRT, the same size windows are employed but delayed in time, ending four to ten days prior to the processed day. Delayed-mode RAN reprocessing follows the NRT with a two-month lag, resulting in a higher quality and a more consistent SST record. In addition to its completeness, the newly created Metop-FG RAN1 SST dataset shows very close agreement with in situ data (including the fully independent Argo floats), well within the NOAA specifications for accuracy (global mean bias; ±0.2 K) and precision (global standard deviation; 0.6 K) in a ~20% clear-sky domain (percent of clear-sky SST pixels to the total of ice-free ocean). All performance statistics are stable in time, and consistent across the three platforms. The Metop-FG RAN1 data set is archived at the NASA JPL PO.DAAC and NOAA NCEI. This paper documents the newly created dataset and evaluates its performance.

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.

Improving WRF‐Hydro runoff simulations of heavy floods through the sea surface temperature fields with higher spatio‐temporal resolution

AbstractThis study investigates the impact of the spatio‐temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)‐Hydro system to simulate hydrological response during two catastrophic flood events over the Eastern Black Sea (EBS) and the Mediterranean (MED) regions of Turkey. Three time‐variant and high spatial resolution external SST products (GHRSST, Medspiration and NCEP‐SST) and one coarse‐resolution and time‐invariant SST product (ERA5‐ and GFS‐SST for EBS and MED regions, respectively) already embedded in the initial and the boundary conditions datasets of WRF model are used in deriving near‐surface atmospheric variables through WRF. After the proper event‐based calibration is performed to the WRF‐Hydro system using hourly and daily streamflow data in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST‐triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher cross‐correlations (GHRSST and Medspiration) revealed significant improvement in flood hydrographs for both regions. The GHRSST dataset shows a substantial improvement in NSE (~70%), RMSE reduction up to 20%, and an increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region. The use of both GHRSST and Medspiration SST data characterized with high spatio‐temporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m3/s by reducing the overestimation seen in invariable SST (GFS) in the MED region. Improved precipitation simulation skills of the WRF model with the detailed SST representation show that the hydrographs of GHRSST and Medspiration simulations show better performance compared to the simulated hydrographs by observed precipitation.

Assessment and Intercomparison of NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1

AbstractThe NOAA Daily Optimum Interpolation Sea Surface Temperature dataset (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25° × 0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC. Our assessments indicate that biases and root-mean-square difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; also, RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data.

The severe drought of 1876–1878 in North China and possible causes

An extreme drought occurred from 1876 to 1878 in most regions of northern China, leading to a series of social impacts, including harvest failures, price inflation, and population migration. Most regions of the Northern Hemisphere concurrently experienced extreme drought. Here, we use reconstructed high-resolution hydroclimatic (Palmer Drought Severity Index/precipitation) datasets and investigate the seasonal–annual hydroclimatic spatial patterns and drought intensity in North China from 1876 to 1878. Furthermore, we select combined sea surface temperature (SST) modes with positive Indian Ocean Dipole (IOD)/Pacific Decadal Oscillation (PDO) and El Niño conditions from the 1200-year control run simulations of HadCM3 to determine the possible causes of this severe drought. The extent and intensity of the selected SST modes are similar to those in the Pacific and Indian oceans during the 1876–1878 period from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature (ERSST) dataset. The results show that the large-scale drought of 1876–1878 is mainly driven by El Niño and a positive PDO, while the effect of IOD is not significant. El Niño may trigger the circumglobal teleconnection of the Northern Hemisphere. And, the meridional disturbance of the eastward Rossby wave train at mid-latitudes may change the intensity of the troughs and ridges and further block water vapor transport from ocean to land.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies.

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).