Diurnal Cycle Of Sea Surface Temperature Research Articles

Diurnal Variability of the Upper Ocean Simulated by a Climate Model

AbstractWe use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed.

Predicting the Tropical Sea Surface Temperature Diurnal Cycle Amplitude Using an Improved XGBoost Algorithm

As a critical physical parameter in the sea–air interface, sea surface temperature (SST) plays a crucial role in the sea–air interaction process. The SST diurnal cycle is one of the most critical changes that occur in the various time scales of SST. Currently, accurate simulation and prediction of SST diurnal cycle amplitude remain challenging. The application of machine learning in marine environment research, simulation, and prediction has received increasing attention. In this study, a regression prediction model for SST diurnal cycle amplitude was constructed based on TOGA/COARE buoy-observed data and an extreme gradient boosting algorithm (XGBoost). The XGBoost algorithm was also optimized using label distribution smoothing (LDS) to respond to the problem of uneven cycle amplitude size distribution. The results showed that the LDS-XGB model outperformed various empirical models and other machine learning models in terms of prediction error and prediction accuracy while effectively improving the data imbalance problem without losing model accuracy and achieving accurate and efficient predictions of the SST diurnal cycle amplitude. This work is a good demonstration of the integration of marine science and machine learning, which indicates that machine learning plays an important role in the model parametrizations and understanding the mechanisms.

Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration

Abstract. A single-column version of the CNRM-CM6-1 global climate model has been developed to ease development and validation of the boundary layer physics and air–sea coupling in a simplified environment. This framework is then used to assess the ability of the coupled model to represent the sea surface temperature (SST) diurnal cycle. To this aim, the atmospheric–ocean single-column model (AOSCM), called CNRM-CM6-1D, is implemented in a case study derived from the CINDY2011/DYNAMO campaign over the Indian Ocean, where large diurnal SST variabilities have been well documented. Comparing the AOSCM and its uncoupled components (atmospheric SCM and oceanic SCM, called OSCM) highlights the fact that the impact of coupling in the atmosphere results from both the possibility to take into account the diurnal variability of SST, which is not usually available in forcing products, and the change in mean state SST as simulated by the OSCM, with the ocean mean state not being heavily impacted by the coupling. This suggests that coupling feedbacks in the 3D model do not arise from the coupling of ocean and atmosphere vertical column physics but are more due to the large-scale dynamics resolved by the 3D model. Additionally, a sub-daily coupling frequency is needed to represent the SST diurnal variability, but the choice of the coupling time step between 15 min and 3 h does not impact the diurnal temperature range simulated much. The main drawback of a 3 h coupling is delaying the SST diurnal cycle by 5 h in asynchronous coupled models. Overall, the diurnal SST variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m. This framework is shown to be a very valuable tool to develop and validate the boundary layer physics and the coupling interface. It highlights the interest to develop other atmosphere–ocean coupling case studies.

Modulation of observed sea surface temperature variation by the quasi‐biweekly oscillation in the tropical western Pacific during boreal summer

AbstractUsing 17‐year in situ buoy observations, this study investigates how sea surface temperature (SST) over the tropical western Pacific Ocean is modulated by the quasi‐biweekly oscillation (QBWO) during boreal summer. The 10‐buoy mean phase‐by‐phase composites of SST indicate that SST evolution lags behind the convection cycle by approximately one quarter, with the maximum SST occurring in Phase −90° (29.68°C) and the minimum SST occurring in Phase 90° (29.37°C). The diurnal cycle of SST (dSST) is in phase with the convection cycle, with the minimum dSST occurring during the convectively active phase (0.34°C) and the maximum dSST occurring during the convectively suppressed phase (0.61°C). The fluctuation in the dSST reaches 0.27°C, which is similar to that in the SST (0.31°C). This kind of oscillation can be well explained by the evolution of the sea surface forcing parameters, especially the shortwave radiation and wind speeds, observed from the buoys. In addition, the composite range of the dSST suggests an interesting asymmetry between Phase −90° and 90°. The interaction of the background summer mean state and the 10–30‐day intraseasonal anomalies causes the asymmetry in wind speed between Phase −90° and 90°, which in turn results in the asymmetry in the dSST between these two phases. Since QBWOs are more likely to occur during El Niño developing summers than during El Niño decaying summers, the 10–30‐day intraseasonal SST and dSST variances during El Niño developing summers are stronger than those during El Niño decaying summers.

A case‐study of the coupled ocean–atmosphere response to an oceanic diurnal warm layer

AbstractA modelling case‐study based on observations from the Dynamics of the Madden–Julian Oscillation field campaign is presented and discussed. It aims at investigating the ocean–atmosphere coupling and the marine atmospheric boundary‐layer structure over an oceanic diurnal warm layer. This case corresponds to the development of a diurnal warm layer characterized by a sea surface temperature diurnal cycle of ∼2°C. A 1D oceanic model with high vertical resolution is used to investigate the mechanisms responsible for the establishment and decay of the diurnal warm layer highlighting competing impact of the absorption of the solar radiation, the turbulent transport and the surface heat loss. An atmospheric large‐eddy simulation coupled to the 1D oceanic model is then presented and extensively evaluated against the numerous observations available for this case. The simulation is able to reproduce the surface fluxes and the main boundary‐layer structures. This study thus provides a case to investigate the ability of parametrizations to handle the ocean–atmosphere coupling and its impact on the atmospheric boundary layer.

Rectification of the Intraseasonal SST Variability by the Diurnal Cycle of SST Revealed by the Global Tropical Moored Buoy Array

AbstractIt has been suggested that the intraseasonal sea surface temperature (SST) variability in the tropical oceans can be amplified by the diurnal cycle of SST (dSST). Here, by analyzing the global tropical moored buoy array for the first time, we find that the intraseasonal SST variability is indeed amplified by the dSST in most of the tropical oceans, especially in the Indo‐Pacific warm pool, but weakened in the equatorial cold tongues of the Pacific and Atlantic Oceans. Such a divergent response is associated with the difference in atmosphere‐ocean interaction processes over these two regions. In the warm pool region, SST responds to the intraseasonal atmospheric variability, resulting in in‐phase intraseasonal fluctuations between SST and dSST that amplify the intraseasonal SST variability. However, in the cold tongue region, SST drives the atmospheric changes, which leads to out‐of‐phase intraseasonal fluctuations between SST and dSST and thus the inhibition of the intraseasonal SST variability.

Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators

Abstract. Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not described or are insufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost. One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by atmospheric conditions, and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1 m. The CCA OO method is used to parameterise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from the GOTM and improve upon existing parameterisations, showing the potential of this method for use in data assimilation systems.

Role of SST feedback in the prediction of the boreal summer monsoon intraseasonal oscillation

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO.

A 31-year Global Diurnal Sea Surface Temperature Dataset Created by an Ocean Mixed-Layer Model

A dataset of hourly sea surface temperature (SST) from the period 1 January 1982 to 31 December 2012, and covering the global ocean at a resolution of 0.3° × 0.3°, was created using a validated ocean mixed-layer model (MLSST). The model inputs were heat flux and surface wind speed obtained from the Coupled Forecast System Reanalysis dataset. Comparisons with in-situ data from the Tropical Atmosphere Ocean array and the National Data Buoy Center showed that the MLSST fitted very well with observations, with a mean bias of 0.07°C, and a root-mean-square error (RMSE) and correlation coefficient of 0.37°C and 0.98, respectively. Also, the MLSST fields successfully reproduced the diurnal cycle of SST in the in-situ data, with a mean bias of −0.005°C and RMSE of 0.26°C. The 31-year climatology revealed that the diurnal range was small across most regions, with higher values in the eastern and western equatorial Pacific, northern Indian Ocean, western Central America, northwestern Australia, and several coastal regions. Significant seasonal variation of diurnal SST existed in all basins. In the Atlantic and Pacific basins, this seasonal pattern was oriented north–south, following the variation in solar insolation, whereas in the Indian basin it was dominated by monsoonal variability. At the interannual scale, the results highlighted the relationship between diurnal and interannual variations of SST, and revealed that the diurnal warming in the central equatorial Pacific could be a potential climatic indicator for ENSO prediction.

Diurnal Cycle of Precipitation and Cloud Clusters in the MJO and ITCZ Over the Indian Ocean

AbstractSatellite observations of the diurnal cycle of precipitation and cloud clusters show a dominant nighttime maximum over the tropical Indian Ocean, similar to other basins. The nighttime maximum is associated with the relatively long lifetime of large convective systems that initiated during the afternoon when the sea surface temperature (SST) reached its diurnal maximum as first showed in Chen and Houze (1997, https://doi.org/10.1002/qj.49712353806). Shipborne and island‐based radar data from Dynamics of the Madden‐Julian Oscillation show distinct characteristics of the diurnal cycle in the equatorial region and Intertropical Convergence Zone (ITCZ). A secondary afternoon precipitation maximum occurs under light surface winds (<5 m/s) when the diurnal cycle of SST is large (0.5–1.7 °C) in the equatorial region during the suppressed phase of the Madden‐Julian Oscillation (MJO). The afternoon maximum was from short‐lived convective systems with rain rates >10 mm/hr. In contrast, during the active MJO and in the ITCZ, the secondary afternoon maximum is mostly absent as the surface winds were generally >5 m/s and reduced afternoon SST warming to less than 0.5 °C. The Tropical Rainfall Measurement Mission Multiplatform Precipitation Analysis does not resolve the secondary afternoon maximum in heavier rain rates from short‐lived small systems, but it suggests a more pronounced night‐morning maximum in the ITCZ than in the MJO. Infrared 208‐K cloud cluster analysis show that this difference in the morning maximum was due to the greater number of long‐lasting, large convective systems (>~200 km in diameter) persisting into the morning in the ITCZ.

Estimating Sea Surface Temperature Measurement Methods Using Characteristic Differences in the Diurnal Cycle

AbstractLack of reliable observational metadata represents a key barrier to understanding sea surface temperature (SST) measurement biases, a large contributor to uncertainty in the global surface record. We present a method to identify SST measurement practice by comparing the observed SST diurnal cycle from individual ships with a reference from drifting buoys under similar conditions of wind and solar radiation. Compared to existing estimates, we found a larger number of engine room‐intake (ERI) reports post–World War II and in the period 1960–1980. Differences in the inferred mixture of observations lead to a systematic warmer shift of the bias adjusted SST anomalies from 1980 compared to previous estimates, while reducing the ensemble spread. Changes in mean field differences between bucket and ERI SST anomalies in the Northern Hemisphere over the period 1955–1995 could be as large as 0.5°C and are not well reproduced by current bias adjustment models.

Evaluation of three temperature profiles of a sublayer scheme to simulate SST diurnal cycle in a global ocean general circulation model

AbstractThe diurnal cycle of sea surface temperature (SST) plays an important role in the upper ocean and climate system. One way to represents the diurnal variation of SST in Ocean General Circulation Models (OGCMs) with coarse vertical resolution is to parameterize the diurnal cycle of SST without considering detailed dynamic processes within the mixed layer. In this study, a diagnostic sublayer parameterization scheme following Schiller and Godfrey (2005) was incorporated into a global OGCM (NEMO), for the first time, to simulate the diurnal cycle of SST. Moreover, three different sublayer temperature profiles (constant, linear, and exponential) were evaluated. Comparison with satellite SST and mooring temperature data indicated that the parameterization scheme with an exponential temperature profile showed the most reasonable diurnal warming simulation results, significantly improving the probability of (from 1.7% with constant temperature profile to 13.4%) diurnal SST amplitude larger than 1.0°C. Furthermore, the mean biases in each season were all reduced to less than 0.16°C, in good agreement with observations.

The diurnal cycle of sea-surface temperature and estimation of the heat budget of the Mediterranean Sea

The diurnal cycle in sea-surface temperature (SST) is reconstructed for the year 2013 by combining numerical model analyses and satellite measurements using Optimal Interpolation (OI). The method is applied to derive hourly Mediterranean SST fields using Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and Mediterranean Forecasting System analyses (Copernicus Marine Environment Monitoring Service - Analysis and Forecast product). The evaluation of the Diurnal OI SST (DOISST) values against drifter measurements results in a mean bias of −0.1°C and a RMS of 0.4°C. The DOISST fields reproduce well the diurnal cycle in SST including extreme Diurnal Warming events as measured by drifting buoys. We evaluate the impact of resolving the SST diurnal cycle, including extreme events, on estimates of the heat budget of the Mediterranean Sea over an entire annual cycle. It results in the mean annual difference in the heat loss derived using SSTs with and without diurnal variations of 4 Wm−2 with a peak of 9 Wm−2 in July. This value is comparable to several other sources of uncertainty in the calculation of the heat and water budgets of the Mediterranean Sea. The results are an important step toward reducing uncertainties in the “Mediterranean Sea Heat Budget Closure Problem”.

Climatological diurnal variability in sea surface temperature characterized from drifting buoy data

AbstractDrifting buoy sea‐surface temperature (SST) records have been used to characterize the diurnal variability of ocean temperature at a depth of order 20 cm. We use measurements covering the period 1986–2012 from the International Comprehensive Ocean‐Atmosphere Data Set (ICOADS) version 2.5, which is a collection of marine surface observations that includes individual SST records from drifting buoys. Appropriately transformed, this dataset is well suited for estimation of the diurnal cycle, since many drifting buoys have high temporal coverage (many reports per day), and are globally distributed. For each drifter for each day, we compute the local‐time daily SST variation relative to the local‐time daily mean SST. Climatological estimates of subdaily SST variability are found by averaging across various strata of the data: in 10° latitudinal bands as well as globally; and stratified with respect to season, wind speed and cloud cover. A parameterization of the diurnal variability is fitted as a function of the variables used to stratify the data, and the coefficients for this fit are also provided with the data. Results are consistent with expectations based on the previous work: the diurnal temperature cycle peaks in early afternoon (circa 2 pm local time); there is an increase in amplitude and a decrease in seasonality towards the equator. Generally, the ocean at this depth cools on windy days and warms on calm days, so that a component of subdaily variability is the SST tendency on slower timescales. By not ‘closing’ the diurnal cycle when stratified by environmental conditions, this dataset differs from previously published diurnal‐cycle parameterizations. This thorough characterization of the SST diurnal cycle will assist in interpreting SST observations made at different local times of day for climatological purposes, and in testing and constraining models of the diurnal‐cycle and air‐sea interaction at high temporal resolution.

A multilevel ocean mixed layer model resolving the diurnal cycle: Development and validation

AbstractThe representation of transient air‐sea interactions is critical to the prediction of the sea surface temperature diurnal cycle and daily variability. This study develops a multilevel upper ocean model to more realistically resolve these interactions. The model is based on the one‐dimensional turbulence kinetic energy closure developed by Noh et al. [2011], and incorporates new numerical techniques and improved schemes for model physics. The primary improvements include: (1) a surface momentum flux penetration scheme to better depict velocity shear in the diurnal mixed layer; (2) a solar penetration scheme to improve the penetration of visible and near‐infrared bands of solar radiation into the mixed layer ocean; (3) a scheme to resolve the cool‐skin and warm‐layer effects on sea skin temperature; (4) a vertical grid stretch scheme to achieve higher near‐surface resolution with fewer vertical levels; (5) a trapezoidal time integration scheme for flexible time steps; (6) a relaxation term of the previous daily mean difference between observed and modeled sea surface temperature. According to the numerical experiments based on the TOGA‐COARE IMET mooring buoy data and the validation by observations from the National Data Buoy Center, NOAA, the results indicate that the new upper ocean mixed layer model improves the simulation of the diurnal cycle of SST and sea skin temperature, especially in amplitude.

Characterisation and quantification of regional diurnal SST cycles from SEVIRI

Abstract. Hourly SST (sea surface temperature) fields from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) offer a unique opportunity for the characterisation and quantification of the diurnal cycle of SST in the Atlantic Ocean, the Mediterranean Sea and the northern European shelf seas. Six years of SST fields from SEVIRI are validated against the Advanced Along-Track Scanning Radiometer (AATSR) Reprocessed for Climate (ARC) data set. The overall SEVIRI–AATSR bias is −0.07 K, and the standard deviation is 0.51 K, based on more than 53 × 106 match-ups. Identification of the diurnal signal requires an SST foundation temperature field representative of well-mixed conditions which typically occur at night-time or under moderate and strong winds. Such fields are generated from the SEVIRI archive and are validated against pre-dawn SEVIRI SSTs and night-time SSTs from drifting buoys. The different methodologies tested for the foundation temperature fields reveal variability introduced by averaging night-time SSTs over many days compared to single-day, pre-dawn values. Diurnal warming is most pronounced in the Mediterranean and Baltic seas while weaker diurnal signals are found in the tropics. Longer diurnal warming duration is identified in the high latitudes compared to the tropics. The maximum monthly mean diurnal signal can be up to 0.5 K in specific regions.

Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea

This work focuses on the reconstruction of Sea Surface Temperature (SST) diurnal cycle through combination of numerical model analyses and geostationary satellite measurements. The approach takes advantage of geostationary satellite observations as the diurnal signal source to produce gap-free optimally interpolated (OI) hourly SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing one to interpolate SST anomalies using satellite data acquired at different times of the day.The method is applied to reconstruct the hourly Mediterranean SST field during summer 2011 using SEVIRI data and Mediterranean Forecasting System analyses. A synthetic cloud reconstruction experiment demonstrated that the OI SST method is able to reconstruct an unbiased SST field with a RMS=0.16°C with respect to SEVIRI observations. The OI interpolation estimate, the model first guess and the SEVIRI data are evaluated using drifter and mooring measurements. Special attention is devoted to the analysis of diurnal warming (DW) events that are particularly frequent in the Mediterranean Sea. The model reproduces quite well the Mediterranean SST diurnal cycle, except for the DW events. Due to the thickness of the model surface layer, the amplitude of the model diurnal cycle is often less intense than the corresponding SEVIRI and drifter observations. The Diurnal OI SST (DOISST) field, resulting from the blending of model and SEVIRI data via optimal interpolation, reproduces well the diurnal cycle including extreme DW events. The evaluation of DOISST products against drifter measurements results in a mean bias of −0.07°C and a RMS of 0.56°C over interpolated pixels. These values are very close to the corresponding statistical parameters estimated from SEVIRI data (bias=−0.16°C, RMS=0.47°C). Results also confirm that part of the mean bias between temperature measured by moorings at 1m depth and the satellite observations can be ascribed to the different nature of the measurements (bulk versus skin).

Effects of the diurnal cycle in solar radiation on the tropical Indian Ocean mixed layer variability during wintertime Madden-Julian Oscillations

[1] The effects of solar radiation diurnal cycle on intraseasonal mixed layer variability in the tropical Indian Ocean during boreal wintertime Madden-Julian Oscillation (MJO) events are examined using the HYbrid Coordinate Ocean Model. Two parallel experiments, the main run and the experimental run, are performed for the period of 2005–2011 with daily atmospheric forcing except that an idealized hourly shortwave radiation diurnal cycle is included in the main run. The results show that the diurnal cycle of solar radiation generally warms the Indian Ocean sea surface temperature (SST) north of 10 � S, particularly during the calm phase of the MJO when sea surface wind is weak, mixed layer is thin, and the SST diurnal cycle amplitude (dSST) is large. The diurnal cycle enhances the MJO-forced intraseasonal SST variability by about 20% in key regions like the Seychelles-Chagos Thermocline Ridge (SCTR; 55 � –70 � E, 12 � –4 � S) and the central equatorial Indian Ocean (CEIO; 65 � –95 � E, 3 � S–3 � N) primarily through nonlinear rectification. The model also well reproduced the upper-ocean variations monitored by the CINDY/DYNAMO field campaign between September-November 2011. During this period, dSST reaches 0.7 � C in the CEIO region, and intraseasonal SST variability is significantly amplified. In the SCTR region where mean easterly winds are strong during this period, diurnal SST variation and its impact on intraseasonal ocean variability are much weaker. In both regions, the diurnal cycle also has a large impact on the upward surface turbulent heat flux QT and induces diurnal variation of QT with a peak-to-peak difference of O(10 W m � 2 ).

Westerly wind events, diurnal cycle and central Pacific El Niño warming

In the past three decades, the strongest central Pacific (CP) El Niño event was observed in 2009–2010 by satellites. When intensity of this CP El Niño reached its maximum, large diurnal variations of sea surface temperature (SST) were also observed from tropical atmosphere ocean moorings in the central equatorial Pacific. Solar radiation in the equatorial central Pacific is larger than 140W/m2, which leads to the amplitude of diurnal cycle of SST primarily determined by large-scale wind patterns. Intraseasonal westerly wind events (WWEs) can lead to an eastward displacement of the warm pool and also can weaken the trade winds in central Pacific. When the occurrence of equatorial WWEs is more than 20 days in a month, monthly mean wind speed in central equatorial Pacific has high possibility of wind speed less than 3m/s, thus has pronounced diurnal cycle of SST. The diurnal cycle of SST will rectify daily mean SST. Reduced mixing at the base of the mixed layer and suppression of entrainment due to the accumulated effect of diurnal cycle may lead to warmer SST in the following month. This study suggests the occurrence of more diurnal SST events may contribute to the increasing intensity of the CP El Niño events.

Impact of intra-daily SST variability on ENSO characteristics in a coupled model

This paper explores the impact of intra-daily Sea Surface Temperature (SST) variability on the tropical large-scale climate variability and differentiates it from the response of the system to the forcing of the solar diurnal cycle. Our methodology is based on a set of numerical experiments based on a fully global coupled ocean–atmosphere general circulation in which we alter (1) the frequency at which the atmosphere sees the SST variations and (2) the amplitude of the SST diurnal cycle. Our results highlight the complexity of the scale interactions existing between the intra-daily and inter-annual variability of the tropical climate system. Neglecting the SST intra-daily variability results, in our CGCM, to a systematic decrease of 15% of El Niño—Southern Oscillation (ENSO) amplitude. Furthermore, ENSO frequency and skewness are also significantly modified and are in better agreement with observations when SST intra-daily variability is directly taken into account in the coupling interface of our CGCM. These significant modifications of the SST interannual variability are not associated with any remarkable changes in the mean state or the seasonal variability. They can therefore not be explained by a rectification of the mean state as usually advocated in recent studies focusing on the diurnal cycle and its impact. Furthermore, we demonstrate that SST high frequency coupling is systematically associated with a strengthening of the air-sea feedbacks involved in ENSO physics: SST/sea level pressure (or Bjerknes) feedback, zonal wind/heat content (or Wyrtki) feedback, but also negative surface heat flux feedbacks. In our model, nearly all these results (excepted for SST skewness) are independent of the amplitude of the SST diurnal cycle suggesting that the systematic deterioration of the air-sea coupling by a daily exchange of SST information is cascading toward the major mode of tropical variability, i.e. ENSO.