Observed Sea Surface Temperature Research Articles

Exploring the drivers of tropospheric hydroxyl radical trends in the Geophysical Fluid Dynamics Laboratory AM4.1 atmospheric chemistry–climate model

Abstract. We explore the sensitivity of modeled tropospheric hydroxyl (OH) concentration trends to meteorology and near-term climate forcers (NTCFs), namely methane (CH4) nitrogen oxides (NOx=NO2+NO) carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs) and ozone-depleting substances (ODSs), using the Geophysical Fluid Dynamics Laboratory (GFDL)'s atmospheric chemistry–climate model, the Atmospheric Model version 4.1 (AM4.1), driven by emissions inventories developed for the Sixth Coupled Model Intercomparison Project (CMIP6) and forced by observed sea surface temperatures and sea ice prepared in support of the CMIP6 Atmospheric Model Intercomparison Project (AMIP) simulations. We find that the modeled tropospheric air-mass-weighted mean [OH] has increased by ∼5 % globally from 1980 to 2014. We find that NOx emissions and CH4 concentrations dominate the modeled global trend, while CO emissions and meteorology were also important in driving regional trends. Modeled tropospheric NO2 column trends are largely consistent with those retrieved from the Ozone Monitoring Instrument (OMI) satellite, but simulated CO column trends generally overestimate those retrieved from the Measurements of Pollution in The Troposphere (MOPITT) satellite, possibly reflecting biases in input anthropogenic emission inventories, especially over China and South Asia.

Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques

Abstract. The increasing demand for wind energy offshore requires more hub-height-relevant wind information, while larger wind turbine sizes require measurements at greater heights. In situ measurements are harder to acquire at higher atmospheric levels; meanwhile the emergence of machine-learning applications has led to several studies demonstrating the improvement in accuracy for vertical wind extrapolation over conventional power-law and logarithmic-profile methods. Satellite wind retrievals supply multiple daily wind observations offshore, however only at 10 m height. The goal of this study is to develop and validate novel machine-learning methods using satellite wind observations and near-surface atmospheric measurements to extrapolate wind speeds to higher heights. A machine-learning model is trained on 12 years of collocated offshore wind measurements from a meteorological mast (FINO3) and space-borne wind observations from the Advanced Scatterometer (ASCAT). The model is extended vertically to predict the FINO3 vertical wind profile. Horizontally, it is validated against the NORwegian hindcast Archive (NORA3) mesoscale model reanalysis data. In both cases the model slightly over-predicts the wind speed with differences of 0.25 and 0.40 m s−1, respectively. An important feature in the model-training process is the air–sea temperature difference; thus satellite sea surface temperature observations were included in the horizontal extension of the model, resulting in 0.20 m s−1 differences with NORA3. A limiting factor when training machine-learning models with satellite observations is the small finite number of daily samples at discrete times; this can skew the training process to higher-/lower-wind-speed predictions depending on the average wind speed at the satellite observational times. Nonetheless, results shown in this proof-of-concept study demonstrate the limited applicability of using machine-learning techniques to extrapolate long-term satellite wind observations when enough samples are available.

A Regional Air‐Sea Coupled Model Developed for the East Asia and Western North Pacific Monsoon Region

AbstractIn this study, we developed a regional coupled model for the western North Pacific (WNP) and East Asian region. The atmospheric/oceanic component of the coupled model was the Weather Research and Forecasting and the Regional Ocean Modeling System, coupled through the National Center for Atmospheric Research Coupler version 7 (CPL7). An evaluation is provided through the simulation of the climatology and interannual variability of the summer monsoon climate from 1982 to 2015. The coupled model is also compared with a solely atmospheric run, which is driven by the observed sea surface temperature (SST) as the surface boundary. The results show that the coupled model has good capability in modeling the climate in the WNP and East Asian domain, including the climatology of summer precipitation and circulation, the subseasonal (month‐to‐month) variation in the western Pacific subtropical high and associated precipitation, and the seasonal cycle of the East Asian rain belt. One merit of this model is its ability to simulate the northward and eastward shift from early to late summer. With respect to the interannual variability, the model captures the spatial pattern of the leading mode of summer precipitation, with a north–south dipole mode in the studied region. The model also reproduces the larger SST variability in the northern part of this region. The temporal correlation coefficient between the coupled simulation and observation was 0.70 for the precipitation and 0.53 for the SST leading mode. The coupled model also captures the precipitation–SST relationship, which results from reasonably considering the air–sea coupling processes in the coupled run.

Mesoscale Variability Linked to Interannual Displacement of Gulf Stream

AbstractThe impacts of interannual oscillations of the Gulf Stream (GS) on oceanic mesoscale variability are investigated using satellite observations of sea surface height (SSH) and sea surface temperature (SST) from 1993 to 2018. We show that variations in GS position, strength, and meandering status are the dominant spatiotemporal modes in regional SSH variability as they explain over 50% of the total variance. In particular, meridional shift of the GS associated with the large‐scale wind variation over the North Atlantic contributes to approximately 30% of SSH variability. We further find that this path displacement mode can drive approximately 15% of regional mesoscale variability in eddy kinetic energy and divergent eddy heat flux. This observational‐based evidence of ocean mesoscale response to GS shift infers a potentially important forcing mechanism that could drive eddy‐scale ocean variability and has far‐reaching implications for regional ocean and ecosystem dynamics in response to climate variation.

Framework for an Ocean‐Connected Supermodel of the Earth System

AbstractA supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non‐interactive multi‐model ensembles (NI), which combines model outputs a‐posteriori. Supermodels with Earth system models (ESMs) has not been developed because it is technically challenging to combine models with different state space. Here, we formulate the first supermodel framework for ESMs and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated by them on a common grid to handle discrepancies in grid and resolution. We compare the performance of two supermodel approaches to that of the NI. In the first (EW), the models are connected to the equal‐weight multi‐model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in the ocean and in the atmosphere, where the ocean drives the variability. The time variability of the supermodel multi‐model mean SST is reduced compared to observations, most where synchronisation is not achieved and is lower‐bounded by NI. The damping is larger in EW, for which variability in the individual models is also damped. Hence, under partial synchronisation, the unsynchronized variability gets damped in the multi‐model average pseudo‐observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi‐model mean in the synchronised regions. A trained supermodel remains to be tested.

Heat Flux Estimates From a Synthesis of Satellite Observations and a Hydrodynamic Model (With Application to Long Island Sound)

AbstractEstimating surface heat fluxes via direct covariance measurements or bulk formulae is observation‐intensive and costly. We present a methodology whereby we estimate net surface heat fluxes as the difference between the depth‐integrated heat tendencies and the depth‐integrated horizontal heat exchanges in a hydrodynamic model. We calibrate the model to achieve a good representation of mixing and advection and then assimilate satellite sea surface temperature (SST) observations into the model at an 8‐day scale. The SST data assimilation forces a good representation of observed temperatures and heat tendencies both at the surface and throughout the water column. We estimate the horizontal heat exchange directly from the model output and then infer the surface fluxes required to close the budget. When we apply this methodology to a model with prescribed surface heat fluxes and without data assimilation, we can recover the prescribed fluxes with an RMS error of ±10 W m−2 and an r2 of 0.998. When we compare our results to those estimated using Coupled Ocean‐Atmosphere Response Experiment bulk formulae with observations in western Long Island Sound, we find similarly good agreement.

Evaluation and attribution of historical surface air temperature changes over China simulated by coupled and uncoupled models

The increase in the average surface air temperature anomaly (SATA) over China is higher than the global average. However, the accurate simulation and attribution of regional SATA evolution remain challenging for current global climate models. This study simulates historical SATA variations over China using the coupled (FGOALS-g3) and uncoupled (atmospheric component, GAMIL3) models and examines their possible causes. Results show that both models reproduce the historical SATA variation with higher correlation coefficients (0.735 and 0.782) than many global climate models (0.25–0.56), although they overestimate or underestimate the changes of SATA to some extent in different periods. The results show that the cooling trend during 1941–1970 is well simulated with the coupled model while poorly presented with the uncoupled model; the coupled simulations particularly produce stronger long-term trends than the uncoupled ones during 1870–2014 considering full interaction among the atmosphere, ocean, and sea ice. In contrast, the uncoupled simulations reproduce better decadal and multi-decadal SATA variations owing to the constraints of the observed sea surface temperature (SST), such as the Atlantic multidecadal oscillation, and sea ice cover. Using Detection and Attribution Model Intercomparison Project (DAMIP) experiments, we found that the warming in the early 20th century and the recent 50 years is mainly driven by natural forcings and greenhouse gases (GHGs), whereas the cooling during 1941–1970 is caused by natural factors and anthropogenic aerosols. The cooling effects of anthropogenic aerosols are mainly attributed from the indirect SST-mediated responses through the atmosphere-ocean interactions in the coupled model.

Development and parameterization of a data likelihood model for geolocation of a bentho-pelagic fish in the North Pacific Ocean

State-space geolocation models feature coupled process (movement) and observation (data likelihood) models to reconstruct fish movement trajectories using electronic tag data. Development of the data likelihood model is therefore a key step in adapting a state-space geolocation model for use with different fish species, geographical regions, or types of electronic data. Here we adapt a discrete hidden Markov model for the geolocation of Pacific spiny dogfish (Squalus suckleyi, n = 154) in the North Pacific Ocean by developing a data likelihood model based on Microwave Telemetry X-tag Pop-up Satellite Archival Tag (PSAT) data. The data likelihood model consists of light-based longitude, light-based latitude, sea surface temperature (SST), temperature-depth profile (TDP), and maximum daily depth. Pacific spiny dogfish tend to occupy coastal waters where small-scale local currents and freshwater inputs make SST and TDP variables difficult to map. To address this issue, we introduce an empirical method for parameterizing SST and TDP likelihoods by calculating root mean square difference between PSAT temperature and depth values recorded at known locations (day of tag deployment and tag pop-up) and mapped values at those locations. For SST observations (n = 85), the difference between measured and mapped values did not vary seasonally or monthly and the overall root mean square error (RMSE) used to parameterize the SST likelihood was 0.9 °C. Likelihood values for SST at known locations were higher for likelihoods parameterized with the empirical value compared to variance specification methods from previous studies. For TDP, measured values differed from mapped values (n = 89) by depth, season, and month. Therefore, RMSE values used to parameterize the TDP likelihood were calculated for each depth bin (n = 27) and month. RMSE values were low (< 1 °C) for all depths during the winter but increased for depths < 100 m during the summer months. Our work provides an example of adapting state-space geolocation models for specific applications. It demonstrates the value of large numbers of tagged animals for parameterizing the data likelihood model in coastal waters as well as flexible data likelihood models with component likelihoods that can be switched on or off depending on geolocation quality.

Impact of spatially-dense in-situ observations on ocean forecasts of mixed layer and thermocline depth

ABSTRACT Ocean models, such as the Navy Coastal Ocean Model (NCOM), rely on ocean observations through data assimilation to predict the evolution of ocean phenomena accurately. The accuracy is highly dependent on the type and quantity of observations available. This work attempts to examine the impact of spatially dense in-situ profile observations on the forecasts of mixed layer depth (MLD) and thermocline depth (TD), using a three-dimensional variational data assimilation algorithm. To do this, a twin data assimilation experiment is used to simulate the impact of float deployments on the forecast of MLD and TD in a specific area of interest (AOI) of the North Atlantic. It is found that during the northern hemisphere summer the model MLD is sensitive to the sea surface temperature (SST) observations rather than the profiling floats. TD is more strongly impacted by the assimilation of float observations, and the experiment with the most floats produces the best results. During the northern hemisphere winter the profiling floats have a greater impact on the model simulation of MLD. Finally, the overall lack of substantial improvement in the winter months for the highest density of profiling floats is determined to be due to the data assimilation methodology itself.

A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

AbstractSeasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.

Sun Glint Correction Based on BRDF Model for Improving the HY-1C/COCTS Clear-Sky Coverage

The Haiyang-1C (HY-1C) satellite was launched on September 7, 2018. The Chinese Ocean Color and Temperature Scanner (COCTS) onboard HY-1C has the capacity for ocean color and sea surface temperature (SST) observations. The sun glint reflected on sea surface can interfere with the radiance reaching the field of view of satellite sensor, leading to the higher reflectance that is misclassified as cloud and restrictions of satellite data availability. Due to the lack of ability to avoid sun glint, the bidirectional reflectance distribution function (BRDF) model is implemented in the sun glint correction of HY-1C/COCTS visible images. The atmospheric direct transmittance is calculated by aerosol optical depth (AOD) and Rayleigh optical depth (ROD). The comparisons before and after sun glint correction of visible images at different locations and different time are discussed and analyzed. The spectra are compared before and after the sun glint correction, and it has an obvious decrease in both radiance and reflectance. The performance of the BRDF model in low, moderate, and severe glint contaminations is compared. The results indicate that the BRDF model is useful in the sun glint correction of visible bands and leading to more HY-1C/COCTS clear-sky coverage, as consequence can be applied as the preprocessing in the cloud detection and SST retrieval to improve the clear-sky coverage.

Characterizing global marine heatwaves under a spatio-temporal framework

Extreme warm water events known as Marine Heatwaves (MHWs) exert adverse impacts on ecosystems and economies. Currently, definitions of MHW events are all based on temperature timeseries at a single location, which limits the characterization of their spatial structures. In this study, we extend the definition of MHW events from the time domain to the spatio-temporal framework. The proposed definition involves constructing snapshots of MHWs with a spatially compact structure and tracking their evolution with time. The tracking is semi-Lagrangian as it tracks the MHW events rather than the water mass. A set of MHW metrics are introduced to measure the spatio-temporal characteristics of MHW events.Applying the new definition to the observational sea surface temperature dataset during 1982–2020 yields 8642 MHW events after excluding those occupying an equivalent area smaller than 5°×5° or lasting for less than five days. Longer-lasting MHW events tend to be larger in spatial extent and vice versa, whereas the intensity of MHW events is nearly independent from the duration and spatial extent. MHW events typically exhibit a growing, mature and decaying phase in sequence during their life cycles. The spatial extent and intensity reach their primes around the center of the life cycle. Many of the MHW events exhibit evident displacement during their life cycles with the zonal movement stronger than the meridional movement. Eastward movement is dominant for MHW events in the mid- and high-latitude regions but the opposite is true in the tropical ocean. In response to the anthropogenic warming, MHW events have occurred more frequently and become longer, larger and stronger.

Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model

Sea surface temperature (SST) observations are a critical data set for long-term climate reconstruction. However, their assimilation with an ensemble-based data assimilation method can degrade performance in the ocean interior due to spurious covariances. Assimilation in isopycnal coordinates can delay the degradation, but it remains problematic for long reanalysis. We introduce vertical localization for SST assimilation in the isopycnal coordinate. The tapering functions are formulated empirically from a large pre-industrial ensemble. We propose three schemes: 1) a step function with a small localization radius that updates layers from the surface down to the first layer for which insignificant correlation with SST is found, 2) a step function with a large localization radius that updates layers down to the last layer for which significant correlation with SST is found, and 3) a flattop smooth tapering function. These tapering functions vary spatially and with the calendar month and are applied to isopycnal temperature and salinity. The impact of vertical localization on reanalysis performance is tested in identical twin experiments within the Norwegian Climate Prediction Model (NorCPM) with SST assimilation over the period 1980–2010. The SST assimilation without vertical localization greatly enhances performance in the whole water column but introduces a weak degradation at intermediate depths (e.g., 2,000–4,000 m). Vertical localization greatly reduces the degradation and improves the overall accuracy of the reanalysis, in particular in the North Pacific and the North Atlantic. A weak degradation remains in some regions below 2,000 m in the Southern Ocean. Among the three schemes, scheme 2) outperforms schemes 1) and 3) for temperature and salinity.

Estimating Ocean Heat Uptake Using Boundary Green's Functions: A Perfect-Model Test of the Method.

Ocean heat uptake is caused by "excess heat" being added to the ocean surface by air-sea fluxes and then carried to depths by ocean transports. One way to estimate excess heat in the ocean is to propagate observed sea surface temperature (SST) anomalies downward using a Green's function (GF) representation of ocean transports. Taking a "perfect-model" approach, we test this GF method using a historical simulation, in which the true excess heat is diagnosed. We derive GFs from two approaches: (a) simulating GFs using idealized tracers, and (b) inferring GFs from simulated CFCs and climatological tracers. In the model world, we find that combining simulated GFs with SST anomalies reconstructs the Indo-Pacific excess heat with a root-mean-square error of 26% for depth-integrated changes; the corresponding number is 34% for inferred GFs. Simulated GFs are inaccurate because they are coarse grained in space and time to reduce computational cost. Inferred GFs are inaccurate because observations are insufficient constraints. Both kinds of GFs neglect the slowdown of the North Atlantic heat uptake as the ocean warms up. SST boundary conditions contain redistributive cooling in the Southern Ocean, which causes an underestimate of heat uptake there. All these errors are of comparable magnitude, and tend to compensate each other partially. Inferred excess heat is not sensitive to: (a) small changes in the shape of prior GFs, or (b) additional constraints from SF6 and bomb 14C.

Decadal variability of the interannual climate predictability associated with the Indo-Pacific oceanic channel dynamics in CCSM4

The lag correlations between the observed sea surface temperature anomalies (SSTA) in the southeastern tropical Indian Ocean in fall and those in the Pacific cold tongue at the one-year time lag are calculated in running windows of 7–11 years and are shown to have decadal variability. Similar decadal variability has also been identified in the historical simulations of the Community Climate System Model version 4 (CCSM4) that participates in the Coupled Model Intercomparison Project phase-5 (CMIP5). During the positive phases of the decadal variability, the significant lag correlations are diagnosed to be dominated by the oceanic channel dynamics, i.e., upwelling Kelvin waves associated with the Indian Ocean Dipole (IOD) force enhanced Indonesian Throughflow transport anomalies and western Pacific thermocline anomalies to propagate eastward, resulting in SSTA in the central-eastern equatorial Pacific Ocean. During the negative phases of the decadal variability, the subsurface lag correlations suggest that the western Pacific Ocean are still dominated by the oceanic channel dynamics, but do not correlate well with the SSTA in the cold tongue due to a deeper thermocline in the eastern equatorial Pacific. The IOD-ENSO teleconnection is not affected significantly by the anthropogenic forcing during either the positive or the negative decadal phases. The CCSM4 model is found to underestimate Indonesian Throughflow transport variability but overestimate the westerly wind anomalies in the western-central equatorial Pacific, which forces unrealistic anomalies in the equatorial Pacific Ocean associated with the IOD.

Upper Ocean Responses to the Tropical Cyclones Ida and Felicia (2021) in the Gulf of Mexico and the Eastern North Pacific

Tropical cyclones (TCs) are a significant component of ocean–atmosphere interactions and the climate system. These interactions determine both the development and strength of TCs, as well as various biogeochemical processes in the upper oceans, including vertical mixing and primary production. We investigated the impact of the TCs Felicia and Ida that emerged in 2021 in the eastern North Pacific and the Gulf of Mexico, respectively, using satellite observations of sea-surface temperature (SST) and surface chlorophyll a (chl-a) concentrations, and vertical profiles of temperature and salinity derived from Argo floats. Observations differed between the two study areas. Cooling of SST associated with TC Ida was observed throughout the Gulf of Mexico (&lt;0.5 °C), except for warming in a region off the Mexican coast east of Ida’s track (by about 0.5 °C). The passing of TC Felicia cooled SST in the eastern region (15°N, 115°W) and a central region (15°N, 125°W) by 0.5 °C and 0.36 °C, respectively. The passing of the TCs caused enhanced vertical mixing of the upper ocean layer in the Gulf of Mexico, with a deepening of the mixed layers from 38 m to 68 m (TC Ida). In contrast, the mixed layer in the eastern North Pacific decreased from 50 m to 20 m. For the eastern North Pacific, mixing could be related to an increase in surface chl-a and thus enhanced phytoplankton biomass was observed for 2 months after the passing of TC Felicia with a chl-a increase of 0.15 mg m−3. In the Gulf of Mexico, however, TC Ida caused the injection of a coastal phytoplankton bloom into the open Gulf, resting for more than a month after the cyclone had passed. Our findings contribute to the understanding of potential SST cooling, destratification, and enhanced primary production due to the passage of TCs in two distinct ocean regions, i.e., the open eastern North Pacific and the semi-enclosed Gulf of Mexico.

Interannual Variability of Tropospheric Moisture and Temperature and Relationships to ENSO Using COSMIC-1 GNSS-RO Retrievals

Abstract Interannual variability of tropospheric moisture and temperature are key aspects of Earth’s climate. In this study, monthly mean specific humidity (q) and temperature (T) variability is analyzed using 12 years of COSMIC-1 (C1) radio occultation retrievals between 60°N and 60°S, with a focus on the tropics. C1 retrievals are relatively independent of the a priori values for q and T within the lower/middle troposphere and upper troposphere/lower stratosphere, respectively. Tropical interannual variability is dominated by El Niño–Southern Oscillation (ENSO). Systematic increases and decreases in zonal mean q and T are observed during the 2009/10 and 2015/16 El Niño events and 2007/08 and 2010/11 La Niña events, respectively. ENSO patterns in q and T are isolated using linear regression, and anomaly magnitudes increase with altitude, reaching a maximum in the upper troposphere. Upper-tropospheric q anomalies expand from the tropics into the midlatitude lower stratosphere, and the T vertical structure is consistent with a moist adiabatic response. C1 results are compared with NCAR’s Whole Atmosphere Community Climate Model (WACCM), forced by observed sea surface temperatures, to evaluate model behavior in an idealized setting. WACCM ENSO variations in q and T generally show consistent behavior with C1 with somewhat smaller magnitudes. Case studies are conducted for major ENSO events during the study period. The spatial variability of q is closely aligned with outgoing longwave radiation (OLR) anomalies. For example, midtropospheric q increases over 100% and OLR decreases over 50 W m−2 over the central Pacific during the 2015/16 El Niño, and substantial regional q and T anomalies are observed throughout the tropics and midlatitudes for each event.

Uncertainty in Atlantic Multidecadal Oscillation derived from different observed datasets and their possible causes

As a leading mode of sea surface temperature (SST) variability over the North Atlantic in both observations and model simulations, the Atlantic Multidecadal Oscillation (AMO) can have a substantial influence on regional and global climate. By using Low-Frequency Component Analysis, we explore the uncertainties of the resulting AMO indices and the corresponding spatial patterns derived from three observational SST datasets. We found that the known coherent spatial pattern of the AMO at the basin scale over the North Atlantic appears in two out of the three datasets. Further analysis indicates that both the warming trend and the different techniques used to construct these observed gridded SSTs contribute to the AMO’s spatial coherence over the North Atlantic, especially during periods of sparse data sampling. The SST in the Extended Reconstructed SST dataset version 5 (ERSSTv5), changes from being systematically below the other datasets during the dense sampling periods on either side of the Second World War (WWII), to systematically above the other datasets during WWII, thereby introducing an artificial 10–20-year variability that affects the AMO’s spatial coherence. This coherence in the AMO’s spatial pattern is also affected by bias adjustment in ERSSTv5 at relative cool (i.e., non-summer) seasons, and by the heterogeneous North Atlantic warming pattern. The different AMO patterns can induce the different effects of wind, surface heat fluxes, and then drive ocean circulation and its heat transport convergence, especially for some seasons. For AMO indices, both the different detrending methods and different observational data result in uncertainty for the period 1935–1950. Such SST uncertainty is important to detect the relative role of the atmosphere and ocean in shaping the AMO.

Megadrought: A Series of Unfortunate La Niña Events?

AbstractMegadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving‐blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño‐Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land‐surface processes. We indeed find that megadroughts in this simulation have an amplitude‐duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

Assessing Sea Surface Temperatures Estimated from Fused Infrared and Microwave Data

Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave observations are not blocked by clouds, allowing for data collection in all weather conditions, this technological tool is characterized by low spatial resolution. Conversely, infrared observations offer high resolution but are susceptible to cloud obscuration. Accordingly, a technique that effectively fuses microwave and infrared satellite observations into a high-resolution SST field with global coverage close to the actual distribution is of practical significance. This paper describes fusing MODIS infrared remote sensing and AMSR-2 microwave remote sensing SST data with an optimal interpolation (OI) approach to produce a high-resolution SST data. The study chose the coastal Kuroshio region of China to establish an appropriate scale for examining the spatial structure of SST and attaining a more realistic picture of SST observations and impacts. The included discussion of the sources of error in the fusion process provides a reference for improving the accuracy of fused marine remote sensing data. The study also compared the fused SST results and the current international mainstream multi-temporal resolution of the three using the OI algorithm. We compared the fusion product with ARGO data with and without typhoon impact to explore and practice the OI in SST fusion when evaluating the accuracy of different data in the case of external disturbance being present. The research results have great significance for improving regional SST forecast accuracy while ensuring the applicability of various approaches to fusing SST data by incorporating the influence of typhoons in the offshore region of the East China Sea (ECS). Implications for the future development of SST fusion data are also included in the discussion.