Climatological Sea Surface Temperature Research Articles

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

Tropical Atlantic variability in EC-EARTH: impact of the radiative forcing

AbstractUnderstanding the impact of radiative forcing on climate variability and change in the Tropical Atlantic is crucial for different socio-economic sectors, given their substantial impacts in both local and remote regions. To properly evaluate the effect of a changing climate on the variability, the use of standard transient historical and scenario simulations requires very large ensembles. A computationally cheaper alternative implemented in this study consists of performing two 250-year-long atmosphere-ocean coupled simulations with EC-EARTH 3.3 (CMIP6 version) with fixed radiative forcing at the years 2000 and 2050, representative of present and future climate conditions, respectively. The changes in the leading modes of Tropical Atlantic variability (TAV), including the Atlantic Niño/Niña and the Subtropical North Atlantic pattern, have been assessed in three target seasons: spring (MAM), summer (JJ) and early winter (ND). While the change in sea surface temperature (SST) climatology shows homogeneous warming, the difference between future and present SST variability exhibits a distinct behaviour consistent along the seasonal cycle, with a decrease in the equatorial region and an increase at subtropical latitudes. This study explores the processes associated with the suppressed/enhanced TAV, with a particular focus on the less-explored early winter season. In agreement with previous studies, the Atlantic Meridional Overturning Circulation (AMOC) shows a weakening in strength, but the results also show an increase in variability. The AMOC-related deepening of the equatorial thermocline and the flattening linked to weakened trade winds are consistent with the suppressed SST variability of the Atlantic Niño/Niña. On the other hand, the enhanced SST variability at subtropical latitudes is mainly associated with an increase in turbulent heat flux variability, with a minor contribution of the mixed layer depth variability. Variability in turbulent heat flux is influenced primarily by latent heat flux, connected to changes in precipitation variability.

Historical Marine Cold Spells in the South China Sea: Characteristics and Trends

Marine cold spells (MCSs) are extreme ocean temperature events impacting marine organisms, yet their characteristics and trends in the South China Sea (SCS) historical period remain unclear. This study systematically analyzes sea surface temperature (SST) and MCSs in the SCS using satellite observation data (OISSTv2.1) from 1982 to 2022. The climatological mean SST ranges from 22 °C near the Taiwan Strait to 29 °C near the Nansha Islands, showing notable variations. Annual SST anomalies demonstrate a heterogeneous spatial trend of approximately 0.21 ± 0.16 °C/decade (p < 0.01) across the SCS, indicating an increase in SST over time. MCS analysis uncovers spatial non-uniformity in frequency, with higher values near the Beibu Gulf and Hainan Island, and longer durations in the northeastern coastal areas. Statistical analysis indicates normal distributions for frequency and duration trends but skewness for intensity and cumulative intensity, reflecting extreme values. Winter months exhibit larger MCS occurrence areas and higher mean intensities, illustrating seasonal variability. Anticipated changes will significantly impact the ecological structure and functioning of the SCS.

Interhemispheric propagation of seasonal sea surface temperature fluctuations over the western Pacific in CMIP6

AbstractInterannual sea surface temperature (SST) variability in the western Pacific is a critical research topic in climate change, as it can significantly impact regional and global weather patterns. Previous observational studies have shown that SST in the southwestern Pacific (SWP) can effectively serve as a precursor signal for SST in the western tropical Pacific (WTP). In this study, we evaluate and compare the modelling capabilities of 32 CMIP6 models to simulate the relationship between SWP and WTP SST. The results indicate that while some CMIP6 models can simulate the spatial connection and physical processes between the SWP and WTP, there are significant intermodel differences. Specifically, the models that best simulate the cross‐equatorial propagation of SWP SST anomalies are those that accurately simulate the cross‐equatorial wind anomalies induced by SWP SST anomalies, highlighting the key role of wind‐evaporation‐SST feedback in SST propagation. Further analysis reveals that the intermodel spread in cross‐equatorial SST propagation is attributable to model biases in simulating climatological SST and wind speed. Models with warm climatological equatorial SST and strong wind speeds provide favourable conditions for the cross‐equatorial propagation of SWP‐related SST anomalies towards the north. Warm SST and strong inter‐season wind speeds near the equator facilitate the northward propagation of the SWP warm signal, leading to increased warming in the WTP.

Understanding Biases in Indian Ocean Seasonal SST in CMIP6 Models

AbstractThe latest generation of climate models continue to exhibit biases in their representation of climatological sea surface temperature (SST), affecting their ability to simulate climate variability including the Indian Ocean Dipole which is typically too strong. Here, we analyze the surface layer heat budget of the Indian Ocean to diagnose the processes leading to biases in climatological SST biases in 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, in comparison to a suite of observational and reanalysis products. In the western tropical Indian Ocean, we find that weaker than observed winds reduce the strength of surface currents leading to warm SST bias. In the south‐eastern tropical Indian Ocean, overly strong southeasterly winds are associated with overestimated coastal upwelling that increases cooling across CMIP6 models. In the Arabian Sea, overly strong surface winds increase latent heat loss and leads to cool SST biases. We also analyze other regions like the Bay of Bengal, where a persistent cool bias cannot be explained by seasonal heat budgets, and southern Indian Ocean, where overly strong surface winds are responsible for cool SST biases. These biases in surface processes are supported by intermodel relationships between relevant variables, thus explaining differences in simulating the climatological SSTs across the model ensemble. Our analysis suggests that biases in atmospheric processes in particular surface winds are a primary cause of biases in Indian Ocean SST. Reducing these biases would improve the simulation of climate variability in the Indian Ocean toward more reliable climate projections and predictions.

Retaining Short‐Term Variability Reduces Mean State Biases in Wind Stress Overriding Simulations

AbstractPositive feedbacks in climate processes can make it difficult to identify the primary drivers of climate phenomena. Some recent global climate model (GCM) studies address this issue by controlling the wind stress felt by the surface ocean such that the atmosphere and ocean become mechanically decoupled. Most mechanical decoupling studies have chosen to override wind stress with an annual climatology. In this study we introduce an alternative method of interannually varying overriding which maintains higher frequency momentum forcing of the surface ocean. Using a GCM (NCAR CESM1), we then assess the size of the biases associated with these two methods of overriding by comparing with a freely evolving control integration. We find that overriding with a climatology creates sea surface temperature (SST) biases throughout the global oceans on the order of ±1°C. This is substantially larger than the biases introduced by interannually varying overriding, especially in the tropical Pacific. We attribute the climatological overriding SST biases to a lack of synoptic and subseasonal variability, which causes the mixed layer to be too shallow throughout the global surface ocean. This shoaling of the mixed layer reduces the effective heat capacity of the surface ocean such that SST biases excite atmospheric feedbacks. These results have implications for the reinterpretation of past climatological wind stress overriding studies: past climate signals attributed to momentum coupling may in fact be spurious responses to SST biases.

Regime change in northwest Atlantic sea surface temperatures revealed using a quantile approach

Trends in sea surface temperatures (SST) of the US Northeast Continental Shelf and the adjacent Slope Sea were analyzed using a quantile approach. SST through the period 1982–2021 were analyzed for change in annual mean and range. In addition, the mean of each 25% quantile of the data by year was calculated. Using the full time series, the climatological SSTs at each quantile was determined, which in turn were used with the annual data to determine the number of days temperature fell within the limits of each climatological quantile. The time series of the data parameters were summarized by grid location and for a series of representative index areas. The index areas were analyzed for evidence of change points or regime shifts using the STARS (Sequential T-test Analysis of Regime Shifts) algorithm. The widely reported SST warming in this region was reinforced with our analysis; our findings also revealed new aspects of the change of thermal conditions in important ways. Mean annual temperature trended at approximately 0.36 °C per decade; however, warm quantile SSTs increased by more than 1.25 °C per decade in the Slope Sea, representing a five degree increase over the study period. The change point analysis suggested the change in thermal regime began as early as 2005 or 2007 with the study region fully enveloped by regime change by 2012. A previously identified change point in 1999 appears to be related to the duration of warming as opposed to SST levels. Mean SST alone does not capture the nature of high temperature exposure for living marine species in the study area; these regions may be experiencing a month or more of stressfully higher SST. The thermal effects derived using the quantile approach suggest different types of thermal exposures compared to heat wave analyses, especially related to cumulative exposure.

Causes of Diverse Impacts of ENSO on the Southeast Asian Summer Monsoon among CMIP6 Models

Abstract This study examines the fidelity of 47 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the influence of El Niño–Southern Oscillation (ENSO) on the Southeast Asian summer monsoon (SEASM) during the ENSO decaying summer. The response of the SEASM to ENSO shows a large model spread among the models, some of which even simulate opposite signs of SEASM anomalies compared to the observed values. The bad-performance models (BPMs) are therefore selected to be compared with both the good-performance models (GPMs) and observations to explore the possible causes of the deficiency. Results show that in the BPMs, the ENSO-related warm sea surface temperature (SST) anomalies extend too far westward in the western equatorial Pacific (WEP) and they do not dissipate in the El Niño decaying summer in comparison with those in the GPMs and observations, interfering with the effect of ENSO on the SEASM. The slow decay of WEP SST anomalies from the El Niño mature winter to the decaying summer in the BPMs is mainly caused by a weak negative shortwave radiation feedback due to a low sensitivity of convection to local SST anomalies, which is related to the cold bias in climatological SST over this region. On the other hand, from the mature winter to the decaying summer of El Niño, the El Niño–related anomalous eastward current does not reverse to a westward current in the BPMs, which also contributes to the slow decay of WEP SST anomalies via inducing excessively persistent warm zonal advection. Significance Statement We investigate the possible causes of the diverse impacts of El Niño–Southern Oscillation (ENSO) on the Southeast Asian summer monsoon (SEASM) among 47 CMIP6 models. We find that a plausible reason for the deficiency of some models in simulating the influence of ENSO on the monsoon is that the sea surface temperature (SST) anomalies associated with ENSO are unrealistic in the western equatorial Pacific (WEP) in these models. Further diagnoses indicate that the unrealistic WEP SST anomalies are related to the cold bias of the climatological SST, which could lead to a weak negative shortwave radiation feedback and excessively persistent warm zonal advection. The information provided in this study is useful for improving the skill of the climate models in representing the ENSO–SEASM relationship.

Impacts of Nudged Sea Surface Temperature on Tropical Precipitation, Moisture, and Vertical Velocity in an Earth System Model

Abstract Understanding the effect of the nudged sea surface temperature (SST) on precipitation simulation is important in Earth system models. In this study, we analyzed the role of nudged SST and air–sea interaction in precipitation simulation by using the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM2). CAS-ESM2 fully coupled runs with and without nudged tropical SST to observations, and the atmosphere-only runs (AMIP) are comprehensively evaluated against observations and reanalysis over the tropical ocean from 1982 to 2015. The precipitation is affected by the nudged SST and air–sea interaction by means of changes in both thermodynamic and dynamic processes. Simulation with the nudged SST dramatically improves the climatological precipitation fields over the tropics and four regional areas. As the simulated SSTs are nudged toward the observations, CAS-ESM2 well reproduces the mean annual values in column water vapor (CWV), vertical structure of specific humidity, and large-scale circulation. The nudged SST run also captures a wide range of the complex interactions between SST, precipitation, moisture, and large-scale circulations, as measured by the monthly relationships and bivariate probability distribution function. The biases in heavy precipitation associated with the CWV induced under moist conditions are decreased by applying the nudging technique. In particular, the nudging run performs better in reproducing the SST–precipitation covariability than the control and AMIP runs. However, CAS-ESM2 with nudged tropical SST simulates the degradation of strong ascending motions, which is similar to the AMIP runs and reflects the deviation of the atmospheric model. Our results confirm that the fully coupled run using the nudged SST is a method for improving the precipitation simulation across the tropical oceans. Significance Statement Understanding the sources of precipitation errors in Earth system models is challenging because of the complex error compensation and air–sea interaction. When the simulated SST is nudged toward observations, few studies have focused on the changes in both thermodynamic and dynamic processes via atmospheric large-scale circulation, moisture, and changes in the SST–precipitation relationship at the same time. Here, we carry out two CMIP6 historical experiments (a fully coupled control run and an SST-nudged run) and the atmosphere-only runs by using CAS-ESM2 from 1982 to 2015. Then, we validate and analyze the climatology of SST, precipitation, moisture, large-scale circulation, and their complex interactions over the tropical oceans and different regional areas. The fully coupled nudged-SST run outperforms the fully free coupled run in reproducing the climatology and complex interactions between SST, precipitation, moisture, and large-scale circulations across the tropics. Moreover, the biases in the SST–precipitation covariability are significantly alleviated in the nudging experiments. We confirmed that the SST nudging method is a potential way to improve precipitation simulation in CAS-ESM2.

California margin temperatures modulate regional circulation and extreme summer precipitation in the desert Southwest

In August 2022, Death Valley, the driest place in North America, experienced record flooding from summertime rainfall associated with the North American monsoon (NAM). Given the socioeconomic cost of these type of events, there is a dire need to understand their drivers and future statistics. Existing theory predicts that increases in the intensity of precipitation is a robust response to anthropogenic warming. Paleoclimatic evidence suggests that northeast Pacific (NEP) sea surface temperature (SST) variability could further intensify summertime NAM rainfall over the desert southwest. Drawing on this paleoclimatic evidence, we use historical observations and reanalyzes to test the hypothesis that warm SSTs on the southern California margin are linked to more frequent extreme precipitation events in the NAM domain. We find that summers with above-average coastal SSTs are more favorable to moist convection in the northern edge of the NAM domain (southern California, Arizona, New Mexico, and the southern Great Basin). This is because warmer SSTs drive circulation changes that increase moisture flux into the desert southwest, driving more frequent precipitation extremes and increases in seasonal rainfall totals. These results, which are robust across observational products, establish a linkage between marine and terrestrial extremes, since summers with anomalously warm SSTs on the California margin have been linked to seasonal or multi-year NEP marine heatwaves. However, current generation earth system models (ESMs) struggle to reproduce the observed relationship between coastal SSTs and NAM precipitation. Across models, there is a strong negative relationship between the magnitude of an ESM’s warm SST bias on the California margin and its skill at reproducing the correlation with desert southwest rainfall. Given persistent NEP SST biases in ESMs, our results suggest that efforts to improve representation of climatological SSTs are crucial for accurately predicting future changes in hydroclimate extremes in the desert southwest.

Simulated and projected SST of Asian marginal seas based on CMIP6 models

Sea surface temperature (SST) is an important element in studying the global ocean-atmospheric system, as well as its simulation and projection in climate models. In this study, we evaluate the simulation skill of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the climatological SST in the Asian Marginal Seas (AMS), known as the most rapidly warming region over the global ocean. The results show that the spatial patterns and seasonal variability of Asian Marginal Seas (AMS) climatological SST simulated by the CMIP6 models are generally in good agreement with the observations, but there are simulation biases in the values. In boreal winter, the simulated climatological SST tends to be overestimated in the Japan/East Sea and the East China Seas (ECSs) by up to 2°C, while being underestimated in the Sea of Okhotsk by up to 2°C. In boreal summer, the simulated climatological SSTs are overestimated in the Indonesian seas and western Arabian Sea, while being underestimated in the Sea of Okhotsk and the northern ECSs by 1.2–1.5 and 2°C, respectively. Furthermore, we calculate the projected sea surface warming trends in the AMS under different future scenarios in the CMIP6 models. The results show warming trends of 0.8–1.8, 1.7–3.4, and 3.8–6.5°C/century for the Shared Socio-Economic Pathway (SSP) of low- (global radiative forcing of 2.6 W/m² by the year 2100), medium- (global radiative forcing of 4.5 W/m² by 2100) and high-end (8.5 W/m² by 2100) pathways, respectively. In addition, the middle and high latitudes of the AMS are found to have faster warming trends than the low latitudes, with the most rapidly warming occurring in the Sea of Okhotsk, which is around 2 times larger than the global mean SST warming trend. The SST warming trends are relatively slow in the South China Sea and the Indonesian seas, roughly equal to the global mean SST warming trend.

Evaluating ENSO's Spatiotemporal Complexity in the CWB CFS 1‐Tiered Model Hindcasts

AbstractUsing hindcasts produced by a coupled climate model, this study evaluates whether the model can forecast the observed spatiotemporal complexity in the El Niño−Southern Oscillation (ENSO) during the period 1982−2011: the eastern Pacific (EP), central Pacific‐I (CP‐I) and ‐II (CP‐II) types of El Niño, and the multi‐year evolution events of El Niño occurred in 1986–1988 (i.e., 1986/87/88 El Niño) and La Niña occurred in 1998–2000 (i.e., 1998/99/00 La Niña). With regard to the spatial complexity, it is found that the CP‐I type of El Niño is the easiest to hindcast, the CP‐II is second, and the EP is most difficult to hindcast as its amplitude is significantly underestimated in the model used here. The model deficiency in hindcasting the EP El Niño is related to a warm bias in climatological sea surface temperatures (SSTs) in the tropical eastern Pacific. This warm bias is related to model biases in the strengths of the Pacific Walker circulation and South Pacific high, both of which are notably weaker than observed. As for the temporal complexity, the model successfully hindcasts the multi‐year evolution of the 1998/99/00 La Niña but fails to accurately hindcast the 1986/87/88 El Niño. This contrasting model performance in hindcasting multi‐year events is found to be related to a cold bias in climatological SSTs in the tropical central Pacific. This cold bias result enables the model La Niña, but not El Niño, to activate intrabasin tropical‒subtropical interactions associated with the Pacific Meridional Mode that produce the multi‐year evolution pattern.

Internal Variability of the Climate System Mirrored in Decadal‐Scale Trends of Surface Solar Radiation

AbstractDecadal‐scale unforced climate variability associated with low‐frequency ocean (or coupled) modes can be identified within the time‐evolving sea surface temperature (SST) pattern. We seek to find a relationship between 12 well‐known climate modes of variability (El Niño–Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, etc.), which are reflected in the SST pattern, and decadal trends in downwelling shortwave radiation at Earth's surface (surface solar radiation, surface shortwave radiation [SSR]). The analysis is performed using the pre‐industrial control runs (piControl) of 57 models from the Coupled Model Intercomparison Project—Phase 6. We find that regional SSR trends occur during periods when a climate mode is transitioning and show a mirroring pattern (opposite sign SSR trends) when the mode transitions in the opposite direction. Unforced trends in clear‐sky SSR are mostly driven by SST‐induced water vapor changes, while all‐sky SSR trends show a complex spatial structure with trends of different signs in different regions. Unforced SSR trends are generally weaker in simulations with climatological SSTs. The role of natural aerosols as another potentially relevant factor for SSR variability is briefly addressed and we find that constantly higher aerosol loads result in increased variability for clear‐sky conditions, but not for all‐sky conditions. The results from this study suggest that all‐sky dimming and brightening in different parts of the world are enhanced rather than suppressed by internal variability related to an SST‐pattern with the exception of India and China, where both effects are present. A practical application can be the planning of photovoltaic energy facilities given areas where internal variability is generally smaller or affects SSR in opposite directions.

Understanding Models' Global Sea Surface Temperature Bias in Mean State: From CMIP5 to CMIP6

AbstractThis paper evaluates sea surface temperature (SST) biases of coupled models participating in Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Overall, CMIP6 models perform better than CMIP5 ones in reproducing SST climatology, with lower multi‐model ensemble mean (MME) globally averaged absolute bias (1.17 vs. 1.31 K). MME bias in global mean annual SST shifts from cooling (−0.09 ± 0.52 K) to warming (0.23 ± 0.60 K). Regionally, in CMIP6 cooling biases over the Northwest Pacific and North Atlantic are reduced by 20% and 18%, while warming biases over the Northeast Pacific, Southeast Atlantic and Southern Ocean are increased by 25%, 16% and 107% respectively. These changes are mainly attributed to the combined effects from aggravated positive (or alleviated negative) bias in clear‐sky surface downward longwave radiation, and alleviated negative bias in cloud radiative effect, partially reduced by enhanced cooling bias in clear‐sky surface downward shortwave radiation.

Disentangling the Driving Mechanisms of the Tripole Mode of Summer Rainfall over Eastern China

Abstract The spatial distribution of summer rainfall anomalies over eastern China often shows a tripole pattern with rainfall anomalies over the Yangtze River basin varies in opposite phase with North China and South China. It is not clear whether this tripole pattern is an intrinsic atmospheric mode or it is remotely forced. Using two sets of model outputs from 20 models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5), this paper investigates the driving mechanisms of this leading rainfall mode and its major influencing factors. One set (piControl) is fully coupled atmosphere–ocean simulations under constant preindustrial forcing and the other (sstClim) is atmosphere-alone models forced by prescribed climatological sea surface temperatures (SSTs). By comparing results from these two different sets of simulations, it is found that the tripole pattern is the leading mode of summer precipitation variability over eastern China with or without oceanic forcing. It can be regarded as an intrinsic atmospheric mode although air–sea interaction can modify its temporal variability. The cyclonic–anticyclonic atmospheric circulation anomaly over the northern North Pacific is identified as a key factor in both experiments. As atmospheric internal variability, it is related to a circumglobal zonal wave train propagating along the westerly jet stream. When air–sea interactions involved, modulation from SST anomalies is exerted through the meridional Pacific–Japan/East Asia–Pacific wave train propagating along the East Asian coast. Our results suggest that the North Pacific could be another key region providing potential predictability to the East Asian monsoon in addition to the Indo-Pacific.

A Pliocene Precipitation Isotope Proxy‐Model Comparison Assessing the Hydrological Fingerprints of Sea Surface Temperature Gradients

The Pliocene offers insights into future climate, with near-modern atmospheric pCO2 and global mean surface temperature estimated to be 3-4°C above pre-industrial. However, the hydrological response differs between future global warming and early Pliocene climate model simulations. This discrepancy results from the use of reduced meridional and zonal sea surface temperature (SST) gradients, based on foraminiferal Mg/Ca and Alkenone proxy evidence, to force the early Pliocene simulation. Subsequent, SST reconstructions based on the organic proxy TEX86, have found warmer temperatures in the warm pool, bringing the magnitude of the gradient reductions into dispute. We design an independent test of Pliocene SST scenarios and their hydrological cycle "fingerprints." We use an isotope-enabled General Circulation Model, iCAM5, to model the distribution of water isotopes in precipitation in response to four climatological SST and sea-ice fields representing modern, abrupt 4×CO2, late Pliocene and early Pliocene climates. We conduct a proxy-model comparison with all the available precipitation isotope proxy data, and we identify target regions that carry precipitation isotopic fingerprints of SST gradients as priorities for additional proxy reconstructions. We identify two regions with distinct precipitation isotope (D/H) fingerprints resulting from reduced SST gradients: the Maritime Continent (D-enriched due to reduced convective rainfall) and the Sahel (wetter, more deep convection, D-depleted). The proxy-model comparison using available plant wax reconstructions, mostly from Africa, is promising but inconclusive. Additional proxy reconstructions are needed in both target regions and in much of the world for significant tests of SST scenarios and dynamical linkages to the hydrological cycle.

Mitigating Climate Biases in the Midlatitude North Atlantic by Increasing Model Resolution: SST Gradients and Their Relation to Blocking and the Jet

Abstract Starting to resolve the oceanic mesoscale in climate models is a step change in model fidelity. This study examines how certain obstinate biases in the midlatitude North Atlantic respond to increasing resolution (from 1° to 0.25° in the ocean) and how such biases in sea surface temperature (SST) affect the atmosphere. Using a multimodel ensemble of historical climate simulations run at different horizontal resolutions, it is shown that a severe cold SST bias in the central North Atlantic, common to many ocean models, is significantly reduced with increasing resolution. The associated bias in the time-mean meridional SST gradient is shown to relate to a positive bias in low-level baroclinicity, while the cold SST bias causes biases also in static stability and diabatic heating in the interior of the atmosphere. The changes in baroclinicity and diabatic heating brought by increasing resolution lead to improvements in European blocking and eddy-driven jet variability. Across the multimodel ensemble a clear relationship is found between the climatological meridional SST gradients in the broader Gulf Stream Extension area and two aspects of the atmospheric circulation: the frequency of high-latitude blocking and the southern-jet regime. This relationship is thought to reflect the two-way interaction (with a positive feedback) between the respective oceanic and atmospheric anomalies. These North Atlantic SST anomalies are shown to be important in forcing significant responses in the midlatitude atmospheric circulation, including jet variability and the storm track. Further increases in oceanic and atmospheric resolution are expected to lead to additional improvements in the representation of Euro-Atlantic climate.

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

Generation and Assessment of ARGO Sea Surface Temperature Climatology for the Indian Ocean Region

ARGO program was conceived with an aim to generate near real-time ocean observations as the primary in-situ sources for use in operational oceanography studies. Two decades-long ARGO near-surface temperature data set was used for generating monthly gridded ARGO sea surface temperature (ASST) product on a climatological scale. Data interpolating variational analysis (DIVA) method was employed for generating the product with a spatial resolution of 0.25° x 0.25° for the Tropical Indian Ocean. This monthly ASST product was evaluated using five different climatological SST products derived from in-situ and satellite measurements. Various statistics such as BIAS, RMSE, coefficient of correlation, and skill scores were generated to evaluate the reliability of the ASST product. Further, the ASST product was validated with climatological in-situ SST obtained from RAMA and OMNI moorings deployed in the Indian Ocean. Statistical comparisons showed low BIAS and RMSE, and high correlation and skill scores with most of the buoys locations and the gridded SST products. Results concluded that the near-surface temperature data from ARGO can be used along with other SST data sets in the generation of high-resolution blended SST products.

Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations

AbstractBretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine‐resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi‐year ML‐corrected simulations. Here four fine‐resolution (∼25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by −4, 0, +4, and +8 K. A data set of state‐dependent corrective tendencies is then derived through nudging the ∼200 km model to the coarsened state of the fine‐resolution simulations in each climate. Along with the surface radiative fluxes, the corrective tendencies of temperature and specific humidity are machine‐learned as functions of the column state. ML predictions for the fluxes and corrective tendencies are applied in 5.25 years ∼200 km resolution simulations in each climate, and improve the spatial pattern errors of land precipitation by 8%–28% and land surface temperature by 19%–25% across the four climates. The ML has a neutral impact on the pattern error of oceanic precipitation.