Simulated Sea Surface Temperature Research Articles

Evaluating the impact of atmospheric forcing and air–sea coupling on near-coastal regional ocean prediction

Abstract. Atmospheric forcing applied as ocean model boundary conditions can have a critical impact on the quality of ocean forecasts. This paper assesses the sensitivity of an eddy-resolving (1.5 km resolution) regional ocean model of the north-west European Shelf (NWS) to the choice of atmospheric forcing and atmosphere–ocean coupling. The analysis is focused on a month-long simulation experiment for July 2014 and evaluation of simulated sea surface temperature (SST) in a shallow near-coastal region to the south-west of the UK (Celtic Sea and western English Channel). Observations of the ocean and atmosphere are used to evaluate model results, with a particular focus on the L4 ocean buoy from the Western Channel Observatory as a rare example of co-located data above and below the sea surface. The impacts of differences in the atmospheric forcing are illustrated by comparing results from an ocean model run in forcing mode using operational global-scale numerical weather prediction (NWP) data with an ocean model run forced by a convective-scale regional atmosphere model. The value of dynamically representing feedbacks between the atmosphere and ocean state is assessed via the use of these model components within a fully coupled ocean–wave–atmosphere system. Simulated SSTs show considerable sensitivity to atmospheric forcing and to the impact of model coupling in near-coastal areas. A warm ocean bias relative to in situ observations in the simulation forced by global-scale NWP (0.7 K in the model domain) is shown to be reduced (to 0.4 K) via the use of the 1.5 km resolution regional atmospheric forcing. When simulated in coupled mode, this bias is further reduced (by 0.2 K). Results demonstrate much greater variability of both the surface heat budget terms and the near-surface winds in the convective-scale atmosphere model data, as might be expected. Assessment of the surface heat budget and wind forcing over the ocean is challenging due to a scarcity of observations. However, it can be demonstrated that the wind speed over the ocean simulated by the convective-scale atmosphere did not agree as well with the limited number of observations as the global-scale NWP data did. Further partially coupled experiments are discussed to better understand why the degraded wind forcing does not detrimentally impact on SST results.

The UKC3 regional coupled environmental prediction system

Abstract. This paper describes an updated configuration of the regional coupled research system, termed UKC3, developed and evaluated under the UK Environmental Prediction collaboration. This represents a further step towards a vision of simulating the numerous interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land using more integrated regional coupled prediction systems at kilometre-scale resolution. The UKC3 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean surface waves (WAVEWATCH III®), coupled together using OASIS3-MCT libraries. The major update introduced since the UKC2 configuration is an explicit representation of wave–ocean feedbacks through introduction of wave-to-ocean coupling. Ocean model results demonstrate that wave coupling, in particular representing the wave-modified surface drag, has a small but positive improvement on the agreement between simulated sea surface temperatures and in situ observations, relative to simulations without wave feedbacks. Other incremental developments to the coupled modelling capability introduced since the UKC2 configuration are also detailed. Coupled regional prediction systems are of interest for applications across a range of timescales, from hours to decades ahead. The first results from four simulation experiments, each of the order of 1 month in duration, are analysed and discussed in the context of characterizing the potential benefits of coupled prediction on forecast skill. Results across atmosphere, ocean and wave components are shown to be stable over time periods of weeks. The coupled approach shows notable improvements in surface temperature, wave state (in near-coastal regions) and wind speed over the sea, whereas the prediction quality of other quantities shows no significant improvement or degradation relative to the equivalent uncoupled control simulations.

Evidence for seasonality in early Eocene high latitude sea-surface temperatures

Specific challenges still exist in our understanding of past greenhouse climate states. Whilst climate model simulations using atmospheric CO2 concentrations consistent with proxy estimates broadly align with lower latitude proxy temperature estimates, they struggle to reproduce the warming implied by proxies at higher latitudes, especially in the marine realm. This inconsistency has often led to the conclusion that climate models are insufficiently sensitive. Here, we analyse the distribution of photozoan and heterozoan carbonates, which provide important constraints for latitudinal sea surface temperature (SST) gradients, to assess data/model mismatches for the early Eocene Climatic Optimum. The carbonate facies distribution is compared against quantitative geochemical proxy temperature estimates (δ18O, Mg/Ca, clumped isotopes and TEX86) and a new HadCM3L climate simulation. Good correspondence exists between the simulated cold-month SSTs and photozoan carbonates, indicating HadCM3L is effectively reconstructing meridional temperature gradients into mid-latitudes. Whilst there is good agreement between simulated mean annual SSTs and geochemical proxy estimates in low latitudes, the δ18O, Mg/Ca and TEX86 estimates instead align with warm-month SSTs at higher latitudes. In light of the carbonate facies evidence, and consistency between our simulation and available terrestrial proxy temperature estimates, this study supports previous claims that a warm season bias exists in many middle and high latitude SST estimates. This helps resolve the discrepancy between climate simulations and marine proxies and shows that climate models and data might be more closely aligned than is appreciated. Further, we demonstrate that simple, and widely available, proxies can play a fundamental role in contextualising wider paleoclimate uncertainties.

Relationships among Intermodel Spread and Biases in Tropical Atlantic Sea Surface Temperatures

Abstract State-of-the-art general circulation models show important systematic errors in their simulation of sea surface temperatures (SST), especially in the tropical Atlantic. In this work the spread in the simulation of climatological SST in the tropical Atlantic by 24 CMIP5 models is examined, and its relationship with the mean systematic biases in the region is explored. The modes of intermodel variability are estimated by applying principal component (PC) analysis to the SSTs in the region 70°W–20°E, 20°S–20°N. The intermodel variability is approximately explained by the first three modes. The first mode is related to warmer SSTs in the basin, shows worldwide connections with same-signed loads over most of the tropics, and is connected with lower low cloud cover over the eastern parts of the subtropical oceans. The second mode is restricted to the Atlantic, where it shows negative and positive loads to the north and south of the equator, respectively, and is connected to a too weak Atlantic meridional overturning circulation (AMOC). The third mode is related to the double intertropical convergence zone bias in the Pacific and to an interhemispheric asymmetry in the net radiation at the top of the atmosphere. The structure of the second mode is closer to the mean bias than that of the others in the tropical Atlantic, suggesting that model difficulties with the AMOC contribute to the regional biases.

Comparison of Effective Radiative Forcing Calculations Using Multiple Methods, Drivers, and Models

AbstractWe compare six methods of estimating effective radiative forcing (ERF) using a set of atmosphere‐ocean general circulation models. This is the first multiforcing agent, multimodel evaluation of ERF values calculated using different methods. We demonstrate that previously reported apparent consistency between the ERF values derived from fixed sea surface temperature simulations and linear regression holds for most climate forcings, excluding black carbon (BC). When land adjustment is accounted for, however, the fixed sea surface temperature ERF values are generally 10–30% larger than ERFs derived using linear regression across all forcing agents, with a much larger (~70–100%) discrepancy for BC. Except for BC, this difference can be largely reduced by either using radiative kernel techniques or by exponential regression. Responses of clouds and their effects on shortwave radiation show the strongest variability in all experiments, limiting the application of regression‐based ERF in small forcing simulations.

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part II: Comparison of WRF/Chem and WRF/Chem-ROMS and Impacts of Air-Sea Interactions and Boundary Conditions

Air-sea interactions play an important role in atmospheric circulation and boundary layer conditions through changing convection processes and surface heat fluxes, particularly in coastal areas. These changes can affect the concentrations, distributions, and lifetimes of atmospheric pollutants. In this Part II paper, the performance of the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are intercompared for their applications over quadruple-nested domains in Australia during the three following field campaigns: The Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2) and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). The results are used to evaluate the impact of air-sea interaction representation in WRF/Chem-ROMS on model predictions. At 3, 9, and 27 km resolutions, compared to WRF/Chem, the explicit air-sea interactions in WRF/Chem-ROMS lead to substantial improvements in simulated sea-surface temperature (SST), latent heat fluxes (LHF), and sensible heat fluxes (SHF) over the ocean, in terms of statistics and spatial distributions, during all three field campaigns. The use of finer grid resolutions (3 or 9 km) effectively reduces the biases in these variables during SPS1 and SPS2 by WRF/Chem-ROMS, whereas it further increases these biases for WRF/Chem during all field campaigns. The large differences in SST, LHF, and SHF between the two models lead to different radiative, cloud, meteorological, and chemical predictions. WRF/Chem-ROMS generally performs better in terms of statistics and temporal variations for temperature and relative humidity at 2 m, wind speed and direction at 10 m, and precipitation. The percentage differences in simulated surface concentrations between the two models are mostly in the range of ±10% for CO, OH, and O3, ±25% for HCHO, ±30% for NO2, ±35% for H2O2, ±50% for SO2, ±60% for isoprene and terpenes, ±15% for PM2.5, and ±12% for PM10. WRF/Chem-ROMS at 3 km resolution slightly improves the statistical performance of many surface and column concentrations. WRF/Chem simulations with satellite-constrained boundary conditions (BCONs) improve the spatial distributions and magnitudes of column CO for all field campaigns and slightly improve those of the column NO2 for SPS1 and SPS2, column HCHO for SPS1 and MUMBA, and column O3 for SPS2 at 3 km over the Greater Sydney area. The satellite-constrained chemical BCONs reduce the model biases of surface CO, NO, and O3 predictions at 3 km for all field campaigns, surface PM2.5 predictions at 3 km for SPS1 and MUMBA, and surface PM10 predictions at all grid resolutions for all field campaigns. A more important role of chemical BCONs in the Southern Hemisphere, compared to that in the Northern Hemisphere reported in this work, indicates a crucial need in developing more realistic chemical BCONs for O3 in the relatively clean SH.

Modelling Sea Surface Temperature (SST) in the Hudson Bay Complex Using Bulk Heat Flux Parameterization: Sensitivity to Atmospheric Forcing, and Model Resolution

ABSTRACTSea surface temperature (SST) from four Nucleus for European Modelling of the Ocean (NEMO) model simulations is analyzed to study the bulk flux parameterization to compute SST over the Hudson Bay Complex (HBC) for the summer months (August and September) from 2002 to 2009. The NEMO simulation was forced with two atmospheric forcing sets with different resolutions: the Coordinated Ocean-ice Reference Experiment, version 2 (COREv2), as the lower resolution and the Canadian Meteorological Centre’s Global Deterministic Prediction System Reforecasts (CGRF) as the higher resolution. The CGRF forcing is also implemented in the third and fourth runs using different runoff data and different NEMO resolutions (1/12° versus 1/4°). Results show that all four modelled SSTs followed observed SST patterns, with regional differences in SST bias between simulations with different atmospheric forcing. The SST differences are small between simulations forced with the same atmospheric forcing but with different model resolution or runoff. This implies that the model resolution and runoff have a small effect on the simulated SST in the HBC. Moreover, to better capture the effect of near-surface temperature (Tair) on simulated SST, we conducted three analyses using the Haney flux linearization formula. Results from these assessments did not indicate any direct influence on the model-simulated SSTs by Tair. Looking at the heat flux as a signature for SST showed that both averaged spatial distribution and time series of net heat flux produced by the three CGRF forcing simulations were higher than the net heat flux generated by the CORE 2 simulation. This was generally true for all four components of the total heat flux (sensible, latent, shortwave, and longwave) individually as well. Total heat flux in summer is governed by the shortwave heat flux, with values up to 120 W m−2 in August, and the longwave heat flux is the main contributor to the total heat flux differences. These heat flux differences lead to corresponding colder model SSTs for the CGRF runs and warmer SSTs for the CORE 2 simulations.

Mean warming not variability drives marine heatwave trends

Marine heatwaves have been shown to be increasing in frequency, duration and intensity over the past several decades. Are these changes related to rising mean temperatures, changes to temperature variability, or a combination of the two? Here we investigate this question using satellite observations of sea surface temperature (SST) covering 36 years (1982–2017). A statistical climate model is used to simulate SST time series, including realistic variability based on an autoregressive model fit to observations, with specified trends in mean and variance. These simulated SST time series are then used to test whether observed trends in marine heatwave properties can be explained by changes in either mean or variability in SST, or both. We find changes in mean SST to be the dominant driver of the increasing frequency of marine heatwave days over approximately 2/3 of the ocean; while it is the dominant driver of changes in marine heatwave intensity (temperature anomaly) over approximately 1/3 of the ocean. We also find that changes in mean SST explain changes in both MHW properties over a significantly larger proportion of the world’s ocean than changes in SST variance. The implication is that given the high confidence of continued mean warming throughout the twenty-first century due to anthropogenic climate change we can expect the historical trends in marine heatwave properties to continue over the coming decades.

Integration of Advection and Mixing Using Fluid Mass-Momentum-Covering Relationships

Classical discretization of discrete fluids and parameterization of mixing bring modeling uncertainties for momentumuneven fluids by setting the grid spacing as the distance for fluids to move within one time step, using low resolutions, and introducing dozens of parameters dependent of scales and modeling processes. Low resolutions have linearized, in a rather extent, nonlinear motions and failed to forecast accelerations that can cause climate adjustments, especially during solving tide-associated dynamics. Here, I wrote Newton Law directly for discrete fluids under conservations of momentum and mass using “mass and momentum-covering relationships (MMCR)”, and combined advection and mixing terms. Mixing became a higher-order advection terms induced from speed gradients times coefficients that are functions of density, grid spacing, and the gradients of speed, density and momentum. Parameterization can be avoided and no uncertain parameters are involved in modeling. Preliminary modeling experiments showed skill for simulation of sea surface temperatures over tropical oceans where momentum was more uneven than as compared with higher latitudes.

Evaluation of Indian Summer Monsoon Rainfall Using the NCEP Global Model: An SST Impact Study

The study evaluates the Indian summer monsoon prediction skill of the Atmospheric General Circulation Model (AGCM) and the impact of sea surface temperature (SST) boundary forcing on the model performance. The National Center for Environmental Prediction's (NCEP's) T170/L42 AGCM model configured with a horizontal resolution of 75 × 75 km, with 42 vertical levels is used for the study. The SST-rainfall relationship is examined in the coupled Climate Forecast System version 2 (CFSv2) model, as CFSv2-predicted SST is used as input for the T170 model. The NCEP Global Forecast System-T170 (GFS-T170) simulations are carried out with boundary forcing of observed SST, CFSv2-predicted SST and the bias-corrected CFSv2 SST. An ensemble of seasonal runs was made using the initial conditions of May to September, and integrated up to September 30th. The significance of discontinuity in the initial conditions due to climate forecast system reanalysis (CFSR) is assessed based on the two-period approach of climatology for the two time scales of 1985–1998 and 1998–2009. CFSv2 predicted climatological summer monsoon rainfall with a significant dry bias over the three convection zones; Western Ghats, Central India and North-east India, and cold bias over the Indian ocean basin and central equatorial Pacific, with strong cold bias over a narrow region of equatorial Pacific. The model could capture 64% (16 out of 25) of the year’s rainfall anomaly signal. The skill of the model is improved in the recent period (1999–2009). The model could simulate the negative Nino 3 and excess rainfall and the La Nina event realistically for the year 1988. The model shows a large difference in Nino indices for the years 1987 and 1998, which led to the unrealistic rainfall simulation. The model has a low skill for indicating the relationship between the Indian Ocean Dipole (IOD) and Indian summer monsoon rainfall (ISMR). The CFSv2 model could not capture the strong positive correlation of the IOD and strong negative correlation of Nino 3 with the ISMR for the period 1999–2009 realistically, suggesting improvement of SST simulation in the CFSv2 model. The T170 model forced with observed SST shows wet bias in peninsular India and dry bias over North-east India, whereas that of CFSv2-predicted SST simulated a wet bias in peninsular India and widespread dry bias in North and Central India. When the model was forced with bias-corrected CFSv2 SST, the dry bias improved in North and Central India, and the intensity of wet bias increased in peninsular India. The model could capture 56, 48 and 64% of the year’s rainfall anomaly signal (positive or negative) correctly in the same sign for being forced with observed SST, CFSv2-predicted SST, and bias-corrected CFSv2 SST, respectively.

An Improved Second‐Moment Closure Model for Langmuir Turbulence Conditions: Model Derivation and Validation

AbstractIn this study, an improved second‐moment closure model, the so‐called k‐ω model, which takes into consideration the Langmuir turbulent effect in the transport equations of turbulent kinetic energy and turbulent frequency, was developed. The Langmuir turbulent effect was also included in the vertical eddy viscosity and diffusivity coefficient. Langmuir turbulence production, as well as buoyancy production, anisotropic production, and vorticity production induced by velocity shear, was included in the pressure covariance parameterization to derive the stability function. The Langmuir effect and relatively complete pressure covariance parameterization inclusion can account for the influences of Langmuir turbulence and stratification in the vertical eddy viscosity and vertical eddy diffusion coefficients. Based on the modified model, one‐dimensional numerical experiments for horizontally homogeneous flows were carried out. The results indicate that the modified model is more accurate than other models in terms of the vertical eddy viscosity, vertical turbulence velocity variance, kinetic energy, and mean velocity profile compared with the large eddy simulation results. The simulated sea surface temperature (SST) and mixed layer depth of the modified model were compared with observations at Ocean Station Papa. When considering Langmuir turbulent effects, the modified model overcomes the shortcoming that the simulated SST is higher than the observed SST. Numerical results indicate that the k‐ω model performs better than the k‐kl model, although both models consider the Langmuir turbulent effect in the simulation of upper mixing motions.

Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes

AbstractGeoengineering has been proposed as a backup approach to rapidly cool the Earth and avoid damages associated with anthropogenic climate change. In this study, we use the NCAR Community Earth System Model to conduct a series of slab‐ocean and prescribed sea surface temperature simulations to investigate the climate response to three proposed radiation management geoengineering schemes: stratospheric aerosol increase (SAI), marine cloud brightening (MCB), and cirrus cloud thinning (CCT). Our simulations show that different amounts of radiative forcing are needed for these three schemes to compensate global mean warming induced by a doubling of atmospheric CO2. With radiative forcing defined in terms of top‐of‐atmosphere energy imbalances in prescribed sea surface temperature simulations with land temperature adjustments, radiative forcing efficacy for SAI is about 15% smaller than that of CO2, and the efficacy for MCB and CCT is about 10% larger than that of CO2. In our simulations, different forcing efficacies are associated with different feedback processes for these forcing agents. Also, these geoengineering schemes produce different land‐ocean temperature change contrasts. The apparent hydrological sensitivity, that is, change in equilibrium global mean precipitation per degree of equilibrium temperature change, differs substantially between CO2, SAI, MCB, and CCT forcings, which is mainly a result of different precipitation responses during fast adjustment. After removing the component of fast adjustment, the northward movement of the Intertropical Convergence Zone in response to these forcing agents is tightly related with changes in the interhemispheric energy exchange and hemispheric temperature gradient.

Nonlinear Trends and Nonstationary Oscillations as Extracted From Annual Accumulated Precipitation at Mexico City

AbstractExtracted nonstationary oscillations and nonlinear trends of precipitation and sea surface temperature (SST) data reveal that rainfall variability in Mexico City is mainly composed by a long‐term positive trend, a multidecadal oscillation highly correlated with the Atlantic Multidecadal Oscillation (AMO), and year‐to‐year variability. The precipitation trend, lasting for more than a century, cannot be attributed to global warming or urbanization alone; rather, it can be thought of as part of a natural oscillation composed of alternating wet‐dry anomalies with a period of a couple of centuries, as past evidence indicates. To further test the dependence of the AMO‐related component, yearly SST time series were derived from a simplified model of the atmosphere‐ocean system forced by white noise. The simulated SST time series exhibits AMO‐like variability entirely consistent with the observed one, implying that North Atlantic SST multidecadal variability can be seen as the integrated response of surface ocean layers to external stochastic atmospheric forcing.

Influence of Model Bias on Simulating North Atlantic Sea Surface Temperature During the Mid-Pliocene

Climate models generally underestimate the pronounced warming in the sea surface temperature (SST) over the North Atlantic during the mid‐Pliocene that is suggested by proxy data. Here we investigate the influence of the North Atlantic cold SST bias, which is observed in many climate models, on the simulation of mid‐Pliocene surface climate in a series of simulations with the Kiel Climate Model. A surface freshwater‐flux correction is applied over the North Atlantic, which considerably improves simulation of North Atlantic Ocean circulation and SST under present‐day conditions. Using reconstructed mid‐Pliocene boundary conditions with closed Bering and Arctic Archipelago Straits, the corrected model depicts significantly reduced model‐proxy SST discrepancy in comparison to the uncorrected model. A key factor in reducing the discrepancy is the stronger and more sensitive Atlantic Meridional Overturning Circulation and poleward heat transport. We conclude that simulations of mid‐Pliocene surface climate over the North Atlantic can considerably benefit from alleviating model biases in this region.

Evaluation of the performance of IAP-AGCM4.1 in simulating the climate of West Africa

Simulated sea surface temperature (SSTs) constitutes error source in tropical climate, simulated by coupled general circulation models (GCMs). Therefore, atmospheric GCMs that use observed SSTs are expected to simulate tropical climate with reduced bias. This paper evaluates the performance of the atmosphere only GCM of the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP-AGCM4.1) in simulating the climate of West Africa. Monthly simulated climatology of precipitation and temperature in five precipitation regions are compared with observed CRU climatology. Inter-annual variability in simulated precipitation and temperature are also compared with observation. In addition, the 10th, 50th and 90th percentiles of simulated precipitation and temperature are compared with the observed precipitation and temperature using normalised deviation from mean and normalised root-mean-square difference. Finally, observed and simulated links between multivariate ENSO index (MEI) and precipitation (temperature) are compared. IAP-AGCM4.1 simulates the mean climatology well over Western part of West Africa (WWA), Central Guinea Coast (GC), and Eastern GC. At the Eastern Sahel, precipitation is over estimated at the 10th and 50th percentile. Generally, there is overestimation at the 10th percentile and underestimation at the 90th percentile. The model also simulates the observed annual variability reasonably well. As expected positive MEI leads to reduction in precipitation in substantial part of West Africa while negative MEI leads to wetness. Furthermore, temperature increases with positive MEI while it decreases with negative MEI over West Africa. However, the model reproduces the dynamics of the influence of MEI on the climate of West Africa with weaker signal.

Evaluation of Interannual Simulations and Indian Ocean Dipole Events During 2000–2014 from a Basin Scale General Circulation Model

Interannual simulations performed using an eddy-permitting ocean general circulation model (OGCM) for years 2000–2014 were validated against buoy measurements over the Indian Ocean (IO). Model-simulated fields were evaluated extensively using multiple statistical metrics to quantify the quality of model in reproducing variability in oceanic surface and subsurface features. The model-simulated sea surface temperature (SST) at different moored buoy locations exhibits high (close to + 0.9) correlation coefficient (R) with the ranges of standard deviation (SD) of simulated SST consistent with the corresponding buoy observations. The root mean square difference (RMSD) estimated between the buoy and simulated SST was found to be less than 0.6 °C at most of the buoy locations. The model-simulated subsurface temperature profile, including the thermocline, resembled good agreement with the buoy profiles. Intraseasonal and interannual variability of 20 °C isotherm (D20) was simulated reasonably well as observed at the respective buoy locations. Mean error in surface currents was low; however, the meridional component from model simulations showed a better agreement (RMSD < 0.25 m/s) with the observations as compared to zonal components (< 0.4 m/s) for the periods of buoy data availability. The Dipole Mode Index (DMI) derived from simulated SST reproduces the positive/negative Indian Ocean Dipole (IOD) events that occurred during the simulation period. Interannual variability in temperature, currents and oceanic mixed layer depth was analyzed in response to IOD events. The anomalies in equatorial currents were found to affect the strength of coastal currents along the Indian coastlines. Model simulations showed the enhanced (suppressed) coastal upwelling process along the Sumatra coast that leads to anomalous cooling (warming) off the Sumatra coast during the positive (negative) IOD events.

An investigation of CMIP5 model biases in simulating the impacts of central Pacific El Niño on the East Asian summer monsoon

The delayed impacts of the central Pacific (CP) El Nino on the East Asian summer monsoon (EASM) are evaluated by comparing historical runs from Coupled Model Intercomparison Project Phase 5 models against reanalysis data. In observations, an anomalous western North Pacific anticyclone (WNPAC), linking CP El Nino to the EASM, forms due to the transition of sea surface temperature (SST) warming into SST cooling over the CP, which generates a WNPAC through a Gill–Matsuno response. In comparison with the observational result, only one-third of the models (i.e., the type-I models) capture a weaker and smaller WNPAC, whereas the other two-thirds (i.e., the type-II models) fail to reproduce a WNPAC. The simulation biases in both of type-I models and type-II models mainly arise from an unrealistic, long-lasting CP El Nino warming, which causes a north Indian Ocean SST warming bias in models through air–sea interaction process. This north Indian Ocean SST warming generates the WNPAC through capacitor effects, which is different from the WNPAC formation mechanism in observations. This discrepancy leads to simulation biases in type-I models. In type-II models, the unrealistic CP El Nino warming persists into summer, which produces an anomalous cyclone over the central-western Pacific. The opposite effect of the CP and north Indian Ocean SST warming on the WNP atmospheric circulation leads to disappearance of the WNPAC. Hence, large simulation biases are produced in type-II models. Further analysis demonstrates the slow decay of CP El Nino is caused by the unrealistically simulated climatological SST, which creates strong warm meridional oceanic advection and results in a sustained CP El Nino warming.

Improving the Persian Gulf sea surface temperature simulation by assimilating the satellite data via the ensemble Kalman

One of the indicators representing the permissible performance of a marine model is the capability of model in providing an accurate spatiotemporal distribution of the thermal structure of domain. Data assimilation is taken into account as an expedient platform in order to achieve this goal. In this paper, the ensemble Kalman filter (EnKF) was applied as a data assimilation scheme to enhance the sea surface temperature (SST) simulation and whereupon the underwater temperature of the Persian Gulf in the finite volume community ocean model (FVCOM). The daily satellite measured SST data that obtained from advanced very high-resolution radiometer pathfinder were considered as observational assimilation data. The comparisons between the results of both proposed models including FVCOM and SST measurements were carried out to evaluate the efficiency of data assimilation. The comparisons revealed a meaningful improvement in the assimilated simulation of the spatiotemporal SST variability in the whole domain, especially in the shallow parts and near the Hormuz Strait. The root-mean-square error reduced significantly in assimilation run. The statistical comparisons of the results bias denote a positive impact of the data assimilation.

Assimilation of Different SST Datasets to a Coastal Ocean Modeling System in the Yellow and East China Sea

Kwon, K.; Choi, B.-J., and Lee, S.-H., 2018. Assimilation of Different SST Datasets to a Coastal Ocean Modeling System in the Yellow and East China Sea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1041–1045. Coconut Creek (Florida), ISSN 0749-0208.Global gridded gap-free sea surface temperature (SST) datasets were quantitatively evaluated using independent insitu datasets in the Yellow and East China Sea from September 2011 to February 2012. The assimilation effect of the four different SST datasets on a regional ocean modeling system was examined. The SST from the gridded SST datasets was compared with two ocean buoy SST data and bi-monthly routine hydrographic observation data in the Yellow Sea (YS). The root-mean-square differences (RMSDs) of the OISST, MGDSST, OSTIA, and MWIR SST datasets relative to the in-situ SST were 0.64, 0.53, 0.62, and 0.91°C, respectively. The RMSDs of SST were higher than 1°C for all datasets in the southeastern corner of the YS. Ocean circulation was simulated with a regional ocean model, and the SST data from the four SST datasets were assimilated. Assimilation of the SST to the ocean model improved the ocean current as well as temperature distribution. The daily mean simulated temperature was compared to the observed SST and vertical profiles of the temperature at the ocean buoys and hydrographic observation stations. After the assimilation of OISST, MGDSST, OSTIA and MWIR datasets, RMSDs of the simulated SST were 0.78, 0.92, 0.66 and 0.86°C, respectively, at the buoy stations. The RMSDs of the simulated subsurface temperature were 1.60, 1.67, 1.56 and 1.70°C, respectively, at the hydrographic observation stations. The simulated SSTs from the model with assimilation of the OSTIA and OISST datasets had relatively smaller RMSDs.

Central-Eastern China Persistent Heat Waves: Evaluation of the AMIP Models

Abstract Large-scale and persistent heat waves affecting central-eastern China are investigated in 40 different simulations of sea surface temperature driven global atmospheric models. The different models are compared with results from reanalysis and ground station datasets. It is found that the dynamics of heat-wave events is well reproduced by the models. However, they tend to produce too-persistent heat-wave events (lasting more than 20 days), and several hypotheses were tested to explain this bias. The daily variability of the temperatures or the seasonal signal did not explain the persistence. However, interannual variability of the temperatures in the models, and especially the sharp transition in the mid-1990s, has a large impact on the duration of heat waves. A filtering method was applied to select the models closest to the observations in terms of events persistence. The selected models do not show a significant difference from the other models for the long-term trends. Thus, the bias on the duration of the events does not impact the reliability of the model positive trends, which is mainly controlled by the changes in mean temperatures.