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This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast. [ABSTRACT FROM AUTHOR]

This study presents observational findings of air–sea turbulent heat flux anomalies during the onset of the South China Sea summer monsoon (SCSSM) in 2021 and explains the mechanism for high-resolution heat flux variations. Turbulent heat flux discrepancies are not uniform throughout the basin but indicate a significant regional disparity in the South China Sea (SCS), which also experiences evident year-to-year variability. Based on buoy- and cruise-based air–sea measurements, high-temporal-resolution (less than hourly) anomalies in the latent heat flux during the SCSSM burst are unexpectedly determined by sea–air humidity differences instead of wind effects under near-neutral and mixed marine atmospheric boundary layer (MABL) stability conditions. However, latent heat anomalies are mainly induced by wind speed under changing MABL conditions. The sensible heat flux is much weaker, with its anomalies dominated by sea–air temperature differences regardless of the boundary layer condition. The observational results are used to examine the discrepancies in turbulent heat fluxes and associated air–sea variables in reanalysis products. The comparisons indicate that latent and sensible heat fluxes in the reanalysis are overestimated by approximately 55 and 3 W m−2, respectively. These overestimations are mainly induced by higher estimates of sea–air humidity/temperature differences. The relative humidity is underestimated by approximately 4.2% in the two high-resolution reanalysis products. The higher SST (near-surface specific humidity) and lower air temperature (specific air humidity) eventually lead to higher estimates of sea–air humidity/temperature differences (1.75 g kg−1/0.25°C), which are the dominant factors controlling the variations in the air–sea turbulent heat fluxes. Significance Statement: Air–sea interactions are significant in predicting the onset of East Asian monsoon systems, including the SCSSM. During the SCSSM in 2021, four buoys and cruise observations are used to investigate anomalies in the latent and sensible heat fluxes. The physical mechanism of the variations in turbulent heat fluxes under different MABL stability conditions is explored in this study. The humidity and wind speed anomalies play roles under mixed boundary conditions in determining the high-resolution variations in latent heat fluxes. Based on these observational results, the heat fluxes and associated air–sea variables from reanalysis products are compared to identify the differences in the operational systems. These comparison results can help improve the reanalysis to obtain better monsoon predictions. [ABSTRACT FROM AUTHOR]

Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (EC4) with upgraded moist physics is compared with a 9-km version (EC9) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS–Tropical Cyclones (COAMPS-TC) system (CO4) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4, resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum winds (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36-h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure–wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex for TCs with initial intensity ≥55 kt (∼28 m s−1) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid–upper-tropospheric heating. Significance Statement: Improved forecasts of tropical storms and hurricanes depend on advances in computer weather models. We tested an experimental high-resolution (4 km) version of the global ECMWF model against its 9-km counterpart to evaluate the influence of resolution on storm position and intensity. We also compared this with the 4-km U.S. Navy model, which is designed for tropical storms and hurricanes. Over a 3-month period during the active 2020 Atlantic hurricane season, we found that increasing the horizontal resolution improved intensity forecasts. The Navy model forecasts were superior for the radius of maximum winds and had lower intensity biases. Two additional experiments with the ECMWF model revealed the importance of simulating air–sea interaction in high winds and current challenges with explicitly simulating deep thunderstorm clouds in their system. [ABSTRACT FROM AUTHOR]

The Sea of Japan (SOJ) coast and adjoining orography of central Honshu, Japan, receive substantial snowfall each winter. A frequent contributor during cold-air outbreaks (CAOs) is the Japan Sea polar airmass convergence zone (JPCZ), which forms downstream of the highland areas of the Korean Peninsula (i.e., the Korean Highlands), extends southeastward to Honshu, and generates a mesoscale band of precipitation. Mesoscale polar vortices (MPVs) ranging in horizontal scale from tens (i.e., meso-β-scale cyclones) to several hundreds of kilometers (i.e., "polar lows") are also common during CAOs and often interact with the JPCZ. Here we use satellite imagery and Weather Research and Forecasting Model simulations to examine the formation, thermodynamic structure, and airflow of a JPCZ that formed in the wake of an MPV during a CAO from 2 to 7 February 2018. The MPV and its associated warm seclusion and bent-back front developed in a locally warm, convergent, and convective environment over the SOJ near the base of the Korean Peninsula. The nascent JPCZ was structurally continuous with the bent-back front and lengthened as the MPV migrated southeastward. Trajectories illustrate how air–sea interactions and flow splitting around the Korean Highlands and channeling through low passes and valleys along the Asian coast affect the formation and thermodynamic structure of the JPCZ. Contrasts in airmass origin and thermodynamic modification over the SOJ affect the cross-JPCZ temperature gradient, which reverses in sign along the JPCZ from the Asian coast to Honshu. These results provide new insights into the thermodynamic structure of the JPCZ, which is an important contributor to hazardous weather over Japan. [ABSTRACT FROM AUTHOR]

This study investigates the impact of coupled initialization on the extended-range prediction of the Madden–Julian oscillation (MJO). A set of reforecasts using combinations of the oceanic and atmospheric initial conditions produced with coupled and uncoupled data assimilation (DA) are conducted to evaluate the impact of coupling in the different domains, from the perspective of MJO forecasts. The coupled initial conditions are provided by CERA-SAT pilot coupled reanalysis for the satellite era recently produced by ECMWF. We focus on the prediction skill of the MJO using the real-time outgoing longwave radiation (OLR) MJO index in a series of reforecasts. The impact of atmospheric initial conditions produced by coupled DA shows slight benefit for the MJO prediction. However, compared with the operational ocean reanalysis, the ocean initial conditions created by CERA-SAT degrade the MJO prediction skill during the first 2–3 weeks of the reforecast by 1.5%–5.8%. A moist static energy budget analysis revealed that the underestimation of 0.2 K sea surface temperature, 1.4 W m−2 top of the atmosphere downward longwave radiation, and 3.8 W m−2 latent heat flux over the Maritime Continent leads to a small but statistically significant degradation of the MJO forecast skill. The results demonstrate that the MJO is sensitive to ocean initial conditions, and illustrate the value of the extended-range MJO prediction for evaluating the quality of coupled data assimilation, and suggest that future efforts on coupled data assimilation pay special attention to the balance of air–sea interaction processes over the warm pool area, in terms of modeling, observational needs and system design. [ABSTRACT FROM AUTHOR]

The spatial and temporal variation in multiscale structures during the rapid intensification of Hurricane Michael (2018) are explored using a coupled atmospheric–oceanic dataset obtained from NOAA WP-3D and G-IV aircraft missions. During Michael’s early life cycle, the importance of ocean structure is studied to explore how the storm intensified despite experiencing moderate vertical shear. Michael maintained a fairly symmetric precipitation distribution and resisted lateral mixing of dry environmental air into the circulation upshear. The storm also interacted with an oceanic eddy field leading to cross-storm sea surface temperature (SST) gradients of ~2.5°C. This led to the highest enthalpy fluxes occurring left of shear, favoring the sustainment of updrafts into the upshear quadrants and a quick recovery from low-entropy downdraft air. Later in the life cycle, Michael interacted with more uniform and higher SSTs that were greater than 28°C, while vertical shear imposed asymmetries in Michael’s secondary circulation and distribution of entropy. Midlevel (~4–8 km) outflow downshear, a feature characteristic of hurricanes in shear, transported high-entropy air from the eyewall region outward. This outflow created a cap that reduced entrainment across the boundary layer top, protecting it from dry midtropospheric air out to large radii (i.e., >100 km), and allowing for rapid energy increases from air–sea enthalpy fluxes. Upshear, low-level (~0.5–2 km) outflow transported high-entropy air outward, which aided boundary layer recovery from low-entropy downdraft air. This study underscores the importance of simultaneously measuring atmospheric and oceanographic parameters to understand tropical cyclone structure during rapid intensification. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of the flow-dependent forecast error covariance across the air–sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air–sea interactions. A 200-member ensemble of convection-permitting forecasts from a coupled atmosphere–ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air–sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean–atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60–80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors. [ABSTRACT FROM AUTHOR]

To produce more precise descriptions of air–sea exchanges under tropical cyclones (TCs), spaceborne synthetic aperture radar (SAR) instruments provide unique capabilities to probe the ocean surface conditions, at very high spatial resolution, and on synoptic scales. Using highly resolved (3 km) wind fields, an extensive database is constructed from RadarSat-2 and Sentinel-1 SAR acquisitions. Spanning 161 tropical cyclones, the database covers all TC intensity categories that have occurred in 5 different TC basins, and include 29 cases coincident with SFMR measurements. After locating the TC center, a specific methodology is applied to filter out areas contaminated by heavy precipitation to help extract, for each acquisition, the maximum wind speed (Vmax), its associated radius (Rmax), and corresponding outer wind radii (R34/50/64 kt). These parameters are then systematically compared with best track (BTK), and when available, SFMR airborne measurements. For collocated SFMR and SAR observations, comparisons yield root-mean-squares of 3.86 m s-1 and 3 km for ocean surface wind speeds and TC Rmax, respectively. High correlations remain for category-5 cases, with Vmax exceeding 60 m s-1. The largest discrepancies are found between BTK and SAR Rmax estimates, with Rmax fluctuations poorly captured by BTK, especially for rapidly evolving category-3, -4, and -5 TCs. In heavy precipitation (>35 mm h-1), the SAR C-band measurements may be impacted, with local ambiguities associated with rain features, as revealed by external rain measurements. Still, this large dataset demonstrates that SAR measurements have unique high-resolution capabilities, capturing the inner- and outer-core radial structure of the TC vortex, and provide independent and complementary measurements than those used for BTK estimates. [ABSTRACT FROM AUTHOR]

Most ocean data assimilation systems are tuned to process and assimilate observations to constrain features on the order of the mesoscale and larger. Typically this involves removal of observations or computing averaged observations. This procedure, while necessary, eliminates many observations from the analysis step and can reduce the overall effectiveness of a particular observing platform. Simply including these observations is not an option as doing so can produce an overdetermined, ill-conditioned problem that is more difficult to solve. An approach, presented here, aims to avoid such issues while at the same time increasing the number of observations within the assimilation. A two-step assimilation procedure with the four-dimensional variational data assimilation (4DVAR) system is adopted. The first step attempts to constrain the large-scale features by assimilating a set of super observations with appropriate background error correlation scales and error variances. The second step then attempts to correct smaller-scale features by assimilating the full observation set with shorter background error correlation scales and appropriate error variances; here the background state is taken as the analysis from the first step. Results using a real high-density observation set from underwater gliders in the region southeast of Iceland, collected during the 2017 Nordic Recognized Environmental Picture (NREP) experiment, will be shown using the Navy Coastal Ocean Model 4DVAR (NCOM-4DVAR). [ABSTRACT FROM AUTHOR]

Ensemble-based atmospheric data assimilation (DA) systems are sometimes afflicted with an underestimation of the ensemble spread near the surface caused by the use of identical boundary conditions for all ensemble members and the lack of atmosphere–ocean interaction. To overcome these problems, a new DA system has been developed by replacing an atmospheric GCM with a coupled atmosphere–ocean GCM, in which atmospheric observational data are assimilated every 6 h to update the atmospheric variables, whereas the oceanic variables are subject to no direct DA. Although SST suffers from the common biases among many coupled GCMs, two months of a retrospective analysis–forecast cycle reveals that the ensemble spreads of air temperature and specific humidity in the surface boundary layer are slightly increased and the forecast skill in the midtroposphere is rather improved by using the coupled DA system in comparison with the atmospheric DA system. In addition, surface atmospheric variables over the tropical Pacific have the basinwide horizontal correlation in ensemble space in the coupled DA system but not in the atmospheric DA system. This suggests the potential benefit of using a coupled GCM rather than an atmospheric GCM even for atmospheric reanalysis with an ensemble-based DA system. [ABSTRACT FROM AUTHOR]

The present study divides the intraseasonal variations into high-frequency intraseasonal oscillation (HF-ISO) with a 5-20-day period and low-frequency ISO (LF-ISO) with a 30-60-day period. Here, defined HF-ISO is heavily different from some previous work, because, over south China, persistent rainfall (PR) is not only related to the quasi-biweekly oscillation (10-20-day period), but also connected with the quasi-weekly oscillation (5-10-day period). Associated with a PR over south China during the first half of June 2016 (PR1606), the propagating components of the convections are largely attributed to the HF-ISOs. Moreover, both southward- and northward-propagating HF-ISOs are found in this case. Over southern China, the moisture advection dominated by anomalous flow and mean water vapor plays an important role in the southward propagation of the HF-ISO, and the cloud-radiation effect may be also, at least partially, responsible for the southward shift of the associated convective zone. Nevertheless, two other possible mechanisms are introduced to explain the cause of the northward propagation of the HF-ISO over southern China during the PR1606 period. The first is the vorticity advection, which is a dominant factor. The second mechanism is the wind-evaporation effect that plays a minor role. Over the South China Sea, the northward propagation of the HF-ISO is mainly attributed to the vertical wind shear effect and the vorticity advection effect, and the latter is relatively more important than the former in this case. The moisture advection is a supplementary effect caused by inducing a weak positive moisture tendency north of the convection center. [ABSTRACT FROM AUTHOR]

The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) intensity is strongly influenced by surface fluxes of momentum and moist enthalpy (typically parameterized in terms of "exchange coefficients" Cd and Ck, respectively). The behavior of Cd and Ck remains quite uncertain especially in high wind conditions over the ocean; moreover, moist enthalpy flux is extremely sensitive to sea surface temperature (SST). This study focuses on numerical simulations of Hurricane Katrina (2005) from an atmosphere-ocean coupled modeling system to examine the combined impacts of air-sea flux parameterizations and ocean cooling on TC evolution. Three momentum flux options-which make Cd increase, level off, or decrease at hurricane-force wind speeds-with five different Ck curves are tested. Maximum 10-m wind speed Vmax is highly sensitive to Cd, with weaker sensitivities for minimum sea level pressure Pmin and track. Atmosphere-only runs that held SST fixed yielded TCs with Pmin substantially deeper than observations. Introducing ocean coupling weakens TC intensity with much more realistic Pmin. The coupled run with the flux parameterization that decreases Cd at high wind speeds yields a simulated TC intensity most consistent with observations. This Cd parameterization produces TCs with the highest Vmax. Increasing Ck generally increases surface heat fluxes and thus TC intensity. For coupled runs using the default Ck parameterization, the simulated SST fields are similar (regardless of Cd parameterization) and agree well with satellite observations. The mesoscale oceanic eddies, which are well resolved in the ocean model, contribute to the magnitude of TC-induced SST cooling and greatly influence TC intensity. [ABSTRACT FROM AUTHOR]

Strongly coupled data assimilation emulates the real-world pairing of the atmosphere and ocean by solving the assimilation problem in terms of a single combined atmosphere-ocean state. A significant challenge in strongly coupled variational atmosphere-ocean data assimilation is a priori specification of the cross covariances between the errors in the atmosphere and ocean model forecasts. These covariances must capture the correct physical structure of interactions across the air-sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, they will allow observations in one medium to improve the analysis in the other. Here, the nature and structure of atmosphere-ocean forecast error cross correlations are investigated using an idealized strongly coupled single-column atmosphere-ocean 4D-Var assimilation system. Results are presented from a set of identical twin-type experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere-ocean error cross correlations. The results show significant variation in the strength and structure of cross correlations in the atmosphere-ocean boundary layer between summer and winter and between day and night. These differences provide a valuable insight into the nature of coupled atmosphere-ocean correlations for different seasons and points in the diurnal cycle. [ABSTRACT FROM AUTHOR]

Representation of polar lows in the new high-resolution Arctic System Reanalysis (ASR) was for the first time assessed and compared to that in the ERA-Interim. Substantial improvements were found in the 850-hPa relative vorticity and near-surface wind speed information. The latter was found to be in close agreement with satellite-derived estimates. Representation of polar lows from a widely used selective list in ERA-Interim and ASR was estimated as 48% and 89%, respectively. The proportion of polar lows represented in ASR is substantially higher than reported for other reanalyses in previous studies. Verifications were found to be sensitive to the polar low reference list used, and to the definition of a polar low. As found, when a more complete polar low list from a recent satellite-derived climatology was used, the proportion of represented events decreased to 26% and 66% for ERA-Interim and ASR, respectively. Variations in polar low representation in reanalyses were also observed in different regions, with the highest proportion resolved in the Norwegian Sea. Strong dependence between polar low sizes and their representation in ERA-Interim was found. In the case of ASR, polar low representation remains constant in the size range of 200-500 km and slightly decreases only for the smallest systems with diameters less than 200 km. Usage of the strict threshold of 43 K for the atmospheric static stability criterion was found to exclude a considerable number of otherwise well-represented polar lows. [ABSTRACT FROM AUTHOR]

Northern Chile hosts the driest place on Earth in the Atacama Desert. Nonetheless, an extreme precipitation event affected the region on 24-26 March 2015 with 1-day accumulated precipitation exceeding 40 mm in several locations and hourly mean rainfall rates higher than 10 mm h−1, producing floods and resulting in casualties and significant damage. The event is analyzed using ERA-Interim, surface station data, sounding observations, and satellite-based radar. Two main conditions favorable for precipitation were present at the time of the event: (i) a cutoff low (COL) off the coast of northern Chile and (ii) positive sea surface temperature (SST) anomalies over the eastern tropical Pacific. The circulation driven by the COL was strong but not extraordinary. Regional Climate Model, version 4 (RegCM4), is used to test the sensitivity of precipitation to SST anomalies by removing the warm SST anomaly in the eastern tropical Pacific. The cooler simulation produced very similar COL dry dynamics to that simulated in a control run (with observed SST), but suppressed the precipitation by 60%-80% over northern Chile and 100% in parts of the Atacama Desert due to the decreased availability of precipitable water. The results indicate that the warm SST anomaly over the eastern Pacific, favored by the onset of El Niño 2015/16, was instrumental to the extreme precipitation event by providing an anomalous source of water vapor transported to Atacama by the circulation ahead of the COL. [ABSTRACT FROM AUTHOR]

Atmospheric data from the Atmospheric Infrared Sounder (AIRS) were used to study an extreme warm and humid air mass transported over the Barents-Kara Seas region by an Arctic cyclone at the end of December 2015. Temperature and humidity in the region was ~10°C (>3 σ above the 2003-14 mean) warmer and ~1.4 g kg−1 (>4 σ above the 2003-14 mean) wetter than normal during the peak of this event. This anomalous air mass resulted in a large and positive flux of energy into the surface via the residual of the surface energy balance (SEB), compared to the weakly negative SEB from the surface to the atmosphere expected for that time of year. The magnitude of the downwelling longwave radiation during the event was unprecedented compared to all other events detected by AIRS in December/January since 2003. An approximate budget scaling suggests that this anomalous SEB could have resulted in up to 10 cm of ice melt. Thinning of the ice pack in the region was supported by remotely sensed and modeled estimates of ice thickness change. Understanding the impact of this anomalous air mass on a thinner, weakened sea ice state is imperative for understanding future sea ice-atmosphere interactions in a warming Arctic. [ABSTRACT FROM AUTHOR]

Based on the variability of heat content in the upper 300 m of the ocean (HC300), the feasibility of defining an index of Pacific decadal oscillation (PDO) is explored. The motivation for defining the PDO index on HC300 stems from the following considerations: (i) a need to accentuate lower-frequency variations in the monitoring of PDO and (ii) to take into account variations in the temperatures associated with the PDO that extend throughout the upper ocean (and are modulated by the seasonal cycle of mixed layer variability). It is demonstrated that an HC300-based definition is better suited to encapsulate these characteristics in the PDO variability. The variability in an HC300-based definition is also contrasted with the traditional definition of the PDO based on SSTs. [ABSTRACT FROM AUTHOR]

The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed a coupled assimilation system that ingests simultaneously ocean and atmospheric observations in a coupled ocean-atmosphere model. Employing the coupled model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate, and, conversely, assimilation of an atmospheric observation affects the ocean state. In this context, observing system experiments have been carried out withholding scatterometer surface wind data over the period September-November 2013. Impacts in the coupled assimilation system have been compared to the uncoupled approach used in ECMWF operations where atmospheric and ocean analyses are computed sequentially. The assimilation of scatterometer data has reduced the background surface wind root-mean-square error in the coupled and uncoupled assimilation systems by 3.7% and 2.5%, respectively. It has been found that the ocean temperature in the mixed layer is improved in the coupled system, while the impact is neutral in the uncoupled system. Further investigations have been conducted over a case of a tropical cyclone when strong interactions between atmospheric wind and ocean temperature occur. Cyclone Phailin in the Bay of Bengal has been selected since the conventional observing system has measured surface wind speed and ocean temperature at a high frequency. In this case study, the coupled assimilation system outperforms the uncoupled approach, being able to better use the scatterometer measurements to estimate the cold wake after the cyclone. [ABSTRACT FROM AUTHOR]

In a strongly coupled data assimilation (DA), a cross-fluid covariance is specified that allows measurements from a coupled fluid (e.g., atmosphere) to directly impact analysis increments in a target fluid (e.g., ocean). The exhaustive solution to this coupled DA problem calls for a covariance where all available measurements can influence all grid points in all fluids. Solution of such a large algebraic problem is computationally expensive, often calls for a substantial rewrite of existing fluid-specific DA systems, and, as shown in this paper, can be avoided. The proposed interface solver assumes that covariances between coupled measurements and target fluid are often close to null (e.g., between stratospheric observations and the deep ocean within a 6-h forecast cycle). In the interface solver, two separate DA solvers are run in parallel: one that produces an analysis solution in the atmosphere, and one in the ocean. Each system uses a coupled observation vector where in addition to resident measurements in the target fluid it also includes nonresident measurements in the coupled fluid that are likely to have significant influence on the analysis in the target fluid (interface measurements). An ensemble-based method is employed and a localization function for coupled ensembles is proposed. Using a coupled model for the Mediterranean Sea (in a twin setting), it is demonstrated that (i) the solution of the interface solver converges to the exhaustive solution and (ii) that in presence of poorly known error covariances, the interface solver can be configured to produce a more accurate solution than an exhaustive solver. [ABSTRACT FROM AUTHOR]

The impact of ocean-atmosphere coupling and its possible seasonal dependence upon Weather Research and Forecasting (WRF) Model simulations of seven, wintertime cyclone events was investigated. Model simulations were identical aside from the degree of ocean model coupling (static SSTs, 1D mixed layer model, full-physics 3D ocean model). Both 1D and 3D ocean model coupling simulations show that SSTs following the passage of a nor'easter did tend to cool more strongly during the early season (October-December) and were more likely to warm late in the season (February-April). Model simulations produce SST differences of up to 1.14 K, but this change did not lead to significant changes in storm track (<100 km), maximum 10-m winds (<2 m s−1), or minimum sea level pressure (≤5 hPa). Simulated precipitation showed little sensitivity to model coupling, but all simulations did tend to overpredict precipitation extent (bias > 1) and have low-to-moderate threat scores (0.31-0.59). Analysis of the storm environment and the overall simulation failed to reveal any statistically significant differences in model error attributable to ocean-atmosphere coupling. Despite this result, ocean model coupling can reduce dynamical field error at a single level by up to 20%, and this was slightly greater (1%-2%) with 3D ocean model coupling as compared to 1D ocean model coupling. Thus, while 3D ocean model coupling tended to generally produce more realistic simulations, its impact would likely be more profound for longer-term simulations. [ABSTRACT FROM AUTHOR]

Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean-atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings-namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice-ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently. [ABSTRACT FROM AUTHOR]

A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a 'weakly' coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean-sea ice and atmosphere-land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5-10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean-atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined. [ABSTRACT FROM AUTHOR]

Current methods for initializing coupled atmosphere-ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-Range Weather Forecasts coupled system, the magnitude and extent of these so-called initialization shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialized by separate oceanic and atmospheric analyses do exhibit initialization shocks in lower atmospheric temperature, when compared to forecasts initialized using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialization on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialization shocks are found to follow a change in either the atmosphere or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5 K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed. [ABSTRACT FROM AUTHOR]

Subsurface ocean observations in the equatorial tropical Pacific Ocean dramatically increased after the 1990s because of the completion of the TAO moored array and a steady increase in Argo floats. In this analysis the question explored is whether a steady increase in ocean observations can be discerned in improvements in skill of predicting sea surface temperature (SST) variability associated with El Niño-Southern Oscillation (ENSO)? The analysis is based on the time evolution of skill of sea surface temperatures in the equatorial tropical Pacific since 1982 based on a seasonal prediction system. It is found that for forecasts up to a 6-month lead time, a clear fingerprint of increases in subsurface ocean observations is not readily apparent in the time evolution of prediction skill that is dominated much more by the signal-to-noise consideration of SSTs to be predicted. Finding no clear relationship between an increase in ocean observations and prediction skill of SSTs, various possibilities for why it may be so are discussed. This discussion is to motivate further exploration on the question of the tropical Pacific observing system, its influence on the skill of ENSO prediction, and the capabilities of the current generation of coupled models and ocean data assimilation systems to take advantage of ocean observations. [ABSTRACT FROM AUTHOR]

A new low-order nonlinear stochastic model is developed to improve the predictability of the Real-time Multivariate Madden-Julian oscillation (MJO) index (RMM index), which is a combined measure of convection and circulation. A recent data-driven, physics-constrained, low-order stochastic modeling procedure is applied to the RMM index. The result is a four-dimensional nonlinear stochastic model for the two observed RMM variables and two hidden variables involving correlated multiplicative noise defined through energy-conserving nonlinear interaction. The special structure of the low-order model allows efficient data assimilation for the initialization of the hidden variables that facilitates the ensemble prediction algorithm. An information-theoretic framework is applied to the calibration of model parameters over a short training phase of 3 yr. This framework involves generalizations of the anomaly pattern correlation, the RMS error, and the information deficiency in the model forecast. The nonlinear stochastic models show skillful prediction for 30 days on average in these metrics. More importantly, the predictions succeed in capturing the amplitudes of the RMM index and the useful skill of forecasting strong MJO events is around 40 days. Furthermore, information barriers to prediction for linear models imply the necessity of the nonlinear interactions between the observed and hidden variables as well as the multiplicative noise in these low-order stochastic models. [ABSTRACT FROM AUTHOR]

During the past decade (2004/05-2013/14), the number of cold days in Hong Kong ( NCD), as a proxy of the temperature of southern China, appeared to have increased from the historical minimum, in contrast to a remarkable decline in the entire postwar period. This is related to the recent apparent changes in the large-scale circulation upstream and downstream of the East Asian winter monsoon (EAWM) region: the increase in Ural blocking (UB) that enhances cold advection from the polar region and reinforces the Siberian high and the decrease in a western Pacific (WP)-like index that corresponds to increasing meridional gradient of geopotential height over the EAWM region. Overall, UB and WP account for 26.4% of the interannual (≤8 yr) variance and 83.7% of the decadal (>8 yr) variance of NCD for the period 1948/49-2013/14, indicating that further study could lead to improvement in the prediction of NCD. [ABSTRACT FROM AUTHOR]

Previous observational analysis and modeling studies indicate that air-sea coupling plays an essential role in improving MJO simulations and extending MJO forecasting skills. However, whether the SST feedback plays an indispensable role for the existence of the MJO remains controversial, and the precise physical processes through which the SST feedback may lead to better MJO simulations and forecasts remain elusive. The DYNAMO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY) field campaign recently completed over the Indian Ocean reveals a new perspective and provides better data to improve understanding of the MJO. It is found that among the five MJO events that occurred during the DYNAMO/CINDY field campaign, only two MJO events (the November and March ones) have robust SST anomalies associated with them. For the other three MJO events (the October, December, and January ones), no coherent SST anomalies are observed. This observational scenario suggests that the roles of air-sea coupling on the MJO vary greatly from event to event. To elucidate the varying roles of air-sea coupling on different MJO events, a suite of hindcast experiments was conducted with a particular focus on the October and November MJO events. The numerical results confirm that the October MJO is largely controlled by atmospheric internal dynamics, while the November MJO is strongly coupled with underlying ocean. For the November MJO event, the positive SST anomalies significantly improve MJO forecasting by enhancing the response of a Kelvin-Rossby wave couplet, which prolongs the feedback between convection and large-scale circulations, and thus favors the development of stratiform rainfall, in turn, facilitating the production of eddy available potential energy and significantly amplifying the intensity of the model November MJO. [ABSTRACT FROM AUTHOR]

Convective initiation processes in the Madden-Julian oscillation (MJO) events that occurred during the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/Dynamics of the Madden-Julian Oscillation (DYNAMO) intensive observation period (IOP) were investigated. Two episodes that were initiated in mid-October (MJO1) and mid-November (MJO2) 2011 were analyzed using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and satellite data. Moisture budgets in the equatorial Indian Ocean (IO) domain (10°S-10°N, 60°-90°E) were analyzed in detail by separating each variable into basic-state (>80 day), intraseasonal (20-80 day), and high-frequency (<20 day) variations. The quality of the ECMWF reanalysis was also evaluated against the sounding data collected during the field campaign. In both MJO events, the increase in precipitable water started 8-9 days prior to the convective initiation. Moisture advection decomposition revealed that advection of basic moisture by intraseasonal easterly anomalies and of intraseasonal moisture anomalies by the basic zonal wind were pronounced in these two events. The nonlinear high-frequency terms in the meridional moisture advection were the same order of magnitude as the primary term in the middle troposphere, implying systematic upscale transport of moisture. As a possible mechanism of the acceleration of easterly anomalies, amplification of off-equatorial Rossby wave trains that intruded into the equatorial zone was detected during the preconditioning periods in both MJO events. [ABSTRACT FROM AUTHOR]

The atmospheric boundary layer (BL) in tropical cyclones (TCs) connects deep convection within rainbands and the eyewall to the air-sea interface. Although the importance of the BL in TCs has been widely recognized in recent studies, how physical processes affect TC structure and intensity are still not well understood. This study focuses on a particular physical mechanism through which a TC-induced upper-ocean cooling within the core circulation of the TC can affect the BL and TC structure. A coupled atmosphere-ocean model forecast of Typhoon Choi-Wan (2009) is used to better understand the physical processes of air-sea interaction in TCs. A persistent stable boundary layer (SBL) is found to form over the cold wake within the TC's right-rear quadrant, which influences TC structure by suppressing convection in rainbands downstream of the cold wake and enhancing the BL inflow into the inner core by increasing inflow angles over strong SST and pressure gradients. Tracer and trajectory analyses show that the air in the SBL stays in the BL longer and gains extra energy from surface heat and moisture fluxes. The enhanced inflow helps transport air in the SBL into the eyewall. In contrast, in the absence of a TC-induced cold wake and an SBL in an uncoupled atmosphere model forecast, the air in the right-rear quadrant within the BL tends to rise into local rainbands. The SBL formed over the cold wake in the coupled model seems to be a key feature that enhances the transport of high energy air into the TC inner core and may increase the storm efficiency. [ABSTRACT FROM AUTHOR]

Dynamics of the Madden-Julian oscillation (DYNAMO) was conducted over the equatorial Indian Ocean (IO) from October 2011 to March 2012. During mid- to late November, a strong Madden-Julian oscillation (MJO) event, denoted MJO-2, initiated in the western IO and passed through the DYNAMO observation array. Dry air intrusions associated with synoptic variability in the equatorial region played a key role in the evolution of MJO-2. First, a sharp dry air intrusion surging from the subtropics into the equatorial region suppresses convection in the ITCZ south of the equator. This diminishes subsidence on the equator associated with the ITCZ convection, which leads to an equatorward shift of convection. It is viewed as a contributing factor for the onset of equatorial convection in MJO-2. Once the MJO convection is established, a second type of dry air intrusion is related to synoptic gyres within the MJO convective envelope. The westward-propagating gyres draw drier air from the subtropics into the equatorial region on the west side of the MJO-2. This dry air intrusion contributes to a 1-2-day break in the rainfall during the active phase of MJO-2. Furthermore, the dry air intrusion suppresses convection in the westerlies of the MJO in the IO. This favors the abrupt shutdown of MJO convection during transition to the suppressed phase in DYNAMO. The two types of dry air intrusions can redistribute convection from the ITCZ to the equator and favor the eastward propagation of the MJO convection. Further study of multiple MJO events is necessary to determine the generality of these findings. [ABSTRACT FROM AUTHOR]

Forecast characteristics of Northern Hemisphere atmospheric blocking and the Madden-Julian oscillation (MJO) were diagnosed using an extensive time series (December-February 1985-2012) of daily medium-range ensemble reforecasts based on a version of the NCEP Global Ensemble Forecast System (GEFS). For blocking, (i) interannual variability of analyzed blocking frequency was quite large, (ii) the GEFS slightly underforecasted blocking frequency at longer leads in the Euro-Atlantic sector, (iii) predictive skill of actual blocking was substantially smaller than its perfect-model skill, (iv) block onset and cessation were forecast less well than overall blocking frequency, (v) there was substantial variability of blocking skill between half-decadal periods, and (vi) the reliability of probabilistic blocking forecasts degraded with increasing lead time. For the MJO, (i) forecasts of strong Indian Ocean MJOs propagated too slowly, especially the component associated with outgoing longwave radiation (OLR), that is, convection; (ii) tropical precipitation was greatly overforecast at early lead times; (iii) the ensemble predictions were biased and/or underdispersive, manifested in U-shaped rank histograms of MJO indices (magnitude forecasts were especially U shaped); (iv) MJO correlation skill was larger for its wind than for its OLR component, and was larger for the higher-amplitude MJO events; (v) there was some half-decadal variability in skill; and (vi) probabilistic skill of the MJO forecast was modest, and skill was larger when measured relative to climatology than when measured relative to a lagged persistence forecast. For longer-lead forecasts, the GEFS demonstrated little ability to replicate the changes in blocking frequency due to a strong MJO that were noted in analyzed data. [ABSTRACT FROM AUTHOR]

The climate trend in a dynamical seasonal forecasting system is examined using 33-yr multimodel ensemble (MME) forecasts from the second phase of the Canadian Historical Forecasting Project (HFP2). It is found that the warming trend of the seasonal forecast in March-May (MAM) over the Eurasian continent is in a good agreement with that in the observations. However, the seasonal forecast failed to reproduce the observed pronounced surface air temperature (SAT) trend in December-February (DJF). The possible reasons responsible for the different behaviors of the HFP2 models in MAM and DJF are investigated. Results show that the initial conditions used for the HFP2 forecast system in MAM have a warming trend over the Eurasian continent, which may come from high-frequency weather systems, whereas the initial conditions for the DJF seasonal forecast do not have such a trend. This trend in the initial condition contributes to the trend of the seasonal forecast in the first month. On the other hand, an examination of the lower boundary SST anomaly forcing shows that the SST trend in MAM has a negative SST anomaly along the central equatorial Pacific, which is favorable for a positive phase of the North Atlantic Oscillation atmospheric response and a warming over the Eurasian continent. The long-term SST trend used for the seasonal forecast in DJF, however, has a negative trend in the tropical eastern Pacific, which is associated with a Pacific-North American pattern-like atmospheric response that has little contribution to a warming in the Eurasian continent. [ABSTRACT FROM AUTHOR]

Fluxes of momentum and moist enthalpy across the air-sea interface are believed to be one of the most important factors in determining tropical cyclone intensity. Because these surface fluxes cannot be directly resolved by numerical weather prediction models, their impacts on tropical cyclones must be accounted for through subgrid-scale parameterizations. There are several air-sea surface flux parameterization schemes available in the Weather Research and Forecasting (WRF) Model; these schemes differ from one another in their formulations of the wind speed-dependent exchange coefficients of momentum, sensible heat, and moisture (latent heat). The effects of surface fluxes on the intensity and structure of tropical cyclones are examined through convection-permitting WRF simulations of Hurricane Katrina (2005). It is found that the intensity (and, to a lesser extent, structure) of the simulated storms is sensitive to the choice of surface flux parameterization scheme. In agreement with recent studies, the drag coefficient CD is found to affect the pressure-wind relationship (between minimum sea level pressure and maximum 10-m wind speed) and to change the radius of maximum near-surface winds of the tropical cyclone. Fluxes of sensible and latent heat (i.e., moist enthalpy) affect intensity but do not significantly change the pressure-wind relationship. Additionally, when low-level winds are strong, the contribution of dissipative heating to calculations of sensible heat flux is not negligible. Expanding the sensitivity tests to several dozen cases from the 2008 to 2011 Atlantic hurricane seasons demonstrates the robustness of these findings. [ABSTRACT FROM AUTHOR]

The impacts of ocean feedback on tropical cyclones (TCs) are investigated using a coupled atmosphere-ocean model under idealized TC and cold core eddy (CCE) conditions. Results reveal negative impacts of the ocean coupling on TC development. The cold wake induced by a TC not only weakens the TC intensity but also limits the expansion of the storm circulation. The presence of CCE has boosted the TC-induced sea surface temperature cooling, which conversely inhibits the TC development. The TC appears to be weakened as it encounters the CCE edge. The intensity reduction attains a maximum shortly after the TC passes over the CCE center, and simultaneously the CCE-induced asymmetry of the storm structure is most significant as well. The TC undergoes a period of recovery after departure from the CCE, lasting about 36-48 h. During this time the residual asymmetry caused by the CCE is smoothed gradually by storm axisymmetrization. The CCE has induced smaller TC size throughout the simulation even after the TC intensity has completely recovered, an indication of longer recovery time for the TC size. Notably cooler and moister eye air in the lower troposphere, just under the warm-core height, is found in the experiment with CCE. The water vapor mixing ratio budget analysis indicates that it is primarily attributed to changes in vertical advection that occurred in the eye, that is, the undermined eye subsidence associated with the suppressed eyewall convection. The horizontal patterns of vertical motion in the boundary layer are also distinctly changed by the CCE. [ABSTRACT FROM AUTHOR]

While fully coupled atmosphere-ocean models have been used to study the seasonal predictability of sea ice variations within the context of models' own variability, their capability in predicting the observed sea ice at the seasonal time scales is not well assessed. In this study, sea ice predictions from the recently developed NCEP Climate Forecast System, version 2 (CFSv2), a fully coupled atmosphere-ocean model including an interactive dynamical sea ice component, are analyzed. The focus of the analysis is the performance of CFSv2 in reproducing observed Northern Hemisphere sea ice extent (SIE). The SIE climatology, long-term trend, interannual variability, and predictability are assessed. CFSv2 contains systematic biases that are dependent more on the forecast target month than the initial month, with a positive SIE bias for the forecast for January-September and a negative SIE bias for the forecast for October-December. A large source of seasonal prediction skill is from the long-term trend, which is underestimated in the CFSv2. Prediction skill of interannual SIE anomalies is found to be primarily within the first three target months and is largest in the summer and early fall. The performance of the prediction of sea ice interannual variations varies from year to year and is found to be related to initial sea ice thickness. Potential predictability based on the forecast ensemble, its dependence on model deficiencies, and implications of the results from this study for improvements in the seasonal sea ice prediction are discussed. [ABSTRACT FROM AUTHOR]

The two-dimensional nonhydrostatic compressible dynamical core for the atmosphere has been developed by using a new nodal-type high-order conservative method, the so-called multimoment constrained finite-volume (MCV) method. Different from the conventional finite-volume method, the predicted variables (unknowns) in an MCV scheme are the values at the solution points distributed within each mesh cell. The time evolution equations to update the unknown point values are derived from a set of constraint conditions based on the multimoment concept, where the constraint on the volume-integrated average (VIA) for each mesh cell is cast into a flux form and thus guarantees rigorously the numerical conservation. Two important features make the MCV method particularly attractive as an accurate and practical numerical framework for atmospheric and oceanic modeling. 1) The predicted variables are the nodal values at the solution points that can be flexibly located within a mesh cell (equidistant solution points are used in the present model). It is computationally efficient and provides great convenience in dealing with complex geometry and source terms. 2) High-order and physically consistent formulations can be built by choosing proper constraints in view of not only numerical accuracy and efficiency but also underlying physics. In this paper the authors present a dynamical core that uses the third- and the fourth-order MCV schemes. They have verified the numerical outputs of both schemes by widely used standard benchmark tests and obtained competitive results. The present numerical core provides a promising and practical framework for further development of nonhydrostatic compressible atmospheric models. [ABSTRACT FROM AUTHOR]

A model of the marine atmospheric boundary layer is developed for ocean-only modeling in order to better represent air-sea exchanges. This model computes the evolution of the atmospheric boundary layer temperature and humidity using a prescribed wind field. These quantities react to the underlying ocean through turbulent and radiative fluxes. With two examples, the authors illustrate that this formulation is accurate for regional and global modeling purposes and that turbulent fluxes are well reproduced in test cases when compared to reanalysis products. The model builds upon and is an extension of Seager et al. [ABSTRACT FROM AUTHOR]

The performance of mesoscale meteorological models is evaluated for the coastal zone and Los Angeles area of Southern California, and for the San Joaquin Valley. Several configurations of the Weather Research and Forecasting Model (WRF) with differing grid spacing, initialization, planetary boundary layer (PBL) physics, and land surface models are compared. One configuration of the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) model is also included, providing results from an independent development and process flow. Specific phenomena of interest for air quality studies are examined. All model configurations are biased toward higher wind speeds than observed. The diurnal cycle of wind direction and speed (land-sea-breeze cycle) as modeled and observed by a wind profiler at Los Angeles International Airport is examined. Each of the models shows different flaws in the cycle. Soundings from San Nicolas Island, a case study involving the Research Vessel (R/V) Atlantis and the NOAA P3 aircraft, and satellite images are used to evaluate simulation performance for cloudy boundary layers. In a case study, the boundary layer structure over the water is poorly simulated by all of the WRF configurations except one with the total energy-mass flux boundary layer scheme and ECMWF reanalysis. The original WRF configuration had a substantial bias toward low PBL heights in the San Joaquin Valley, which are improved in the final configuration. WRF runs with 12-km grids have larger errors in wind speed and direction than those present in the 4-km grid runs. [ABSTRACT FROM AUTHOR]

Sea level pressure (SLP) acts, on the one hand, as a 'bridge parameter' to which geophysical properties at the air-sea interface (e.g., wind stress and sea surface height) are linked, and on the other hand, as an 'index parameter' by which major atmospheric oscillations, including the well-known Southern Oscillation, are defined. Using 144 yr (1854-1997) of extended reconstructed SLP data, seasonal patterns of its variability are reinvestigated in detail. New features on fundamental structure of its annual and semiannual cycles are revealed in two aspects. First, the spatiotemporal patterns of yearly and half-yearly SLPs are basically determined by a network of 'amphidromes,' which are surrounded by rotational variations. Fourteen cyclonic and anticyclonic annual SLP amphidromes (half each and often in pair) are found in the global ocean, while the numbers of the two types of semiannual amphidrome are 11 and 9, respectively. The second dominant feature in SLP variability is the pattern of oscillation or seesaw for both annual and semiannual components. At least eight oscillation zones are identified for the annual cycle, which can be categorized into a boreal winter mode and an austral winter mode. As for the semiannual cycle, the seesaw pattern is geographically divided into three regimes: the North Pacific regime, the North Atlantic regime, and the Southern Ocean regime. These findings serve as a new contribution to characterizing and understanding the seasonality of the global ocean-atmosphere system. [ABSTRACT FROM AUTHOR]

This paper presents a fast reduced filtering strategy for assimilating multiscale systems in the presence of observations of only the macroscopic (or large scale) variables. This reduced filtering strategy introduces model errors in estimating the prior forecast statistics through the (heterogeneous multiscale methods) HMM-based reduced climate model as an alternative to the standard expensive (direct numerical simulation) DNS-based fully resolved model. More importantly, this approach is not restricted to any analysis (or Bayesian updating) step from various ensemble-based filters. In a regime where there is a distinctive separation of scales, high filtering skill is obtained through applying the HMM alone with any desirable analysis step from ensemble Kalman filters. When separation of scales is not apparent as typically observed in geophysical turbulent systems, an additional procedure is proposed to reinitialize the microscopic variables to statistically reflect pseudo-observations that are constructed based on the unbiased estimates of the macroscopic variables. Specifically, these pseudo-observations are constructed offline from the conditional distributions of the microscopic forcing to the macroscopic dynamics given the macroscopic variables with the method-of-moments estimator. This HMM-based filter is comparable to the more expensive standard DNS-based filter on a stringent test bed, the two-layer Lorenz'96 model, in various regimes of scale gap, including the not so apparent one. This high filtering skill is robust in the presence of additional model errors through inconsistent pseudo-observations and even when macroscopic observations are spatially incomplete. [ABSTRACT FROM AUTHOR]

Time series of U.S. landfalling and North Atlantic hurricane counts and their ratios over the period 1878-2008 are modeled using tropical Atlantic sea surface temperature (SST), tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Two SST input datasets are employed to examine the uncertainties in the reconstructed SST data on the modeling results. Because of the likely undercount of recorded hurricanes in the earliest part of the record, both the uncorrected hurricane dataset (HURDAT) and a time series with a recently proposed undercount correction are considered. Modeling of the count data is performed using a conditional Poisson regression model, in which the rate of occurrence can depend linearly or nonlinearly on the climate indexes. Model selection is performed following a stepwise approach and using two penalty criteria. These results do not allow one to identify a single 'best' model because of the different model configurations (different SST data, corrected versus uncorrected datasets, and penalty criteria). Despite the lack of an objectively identified unique final model, the authors recommend a set of models in which the parameter of the Poisson distribution depends linearly on tropical Atlantic and tropical mean SSTs. Modeling of the fractions of North Atlantic hurricanes making U.S. landfall is performed using a binomial regression model. Similar to the count data, it is not possible to identify a single best model, but different model configurations are obtained depending on the SST data, undercount correction, and penalty criteria These results suggest that these fractions are controlled by local (related to the NAO) and remote (SOI and tropical mean SST) effects. [ABSTRACT FROM AUTHOR]

This study presents a new high-resolution satellite-derived ocean surface flux product, XSeaFlux, which is evaluated for its potential use in hurricane studies. The XSeaFlux employs new satellite datasets using improved retrieval methods, and uses a new bulk flux algorithm formulated for high wind conditions. The XSeaFlux latent heat flux (LHF) performs much better than the existing numerical weather prediction reanalysis and satellite-derived flux products in a comparison with measurements from the Coupled Boundary Layer Air-Sea Transfer (CBLAST) field experiment. Also, the XSeaFlux shows well-organized LHF structure and large LHF values in response to hurricane conditions relative to the other flux products. The XSeaFlux dataset is used to interpret details of the ocean surface LHF for selected North Atlantic hurricanes. Analysis of the XSeaFlux dataset suggests that ocean waves, sea spray, and cold wake have substantial impacts on LHF associated with the hurricanes. [ABSTRACT FROM AUTHOR]

The Madden-Julian oscillation (MJO) influences tropical cyclone formation around the globe. Convectively coupled Kelvin waves are often embedded within the MJO, but their role in tropical cyclogenesis remains uncertain. This case study identifies the influences of the MJO and a series of Kelvin waves on the formation of two tropical cyclones. Typhoons Rammasun and Chataan developed in the western North Pacific on 28 June 2002. Two weeks earlier, conditions had been unfavorable for tropical cyclogenesis because of uniform trade easterlies and a lack of organized convection. The easterlies gave way to equatorial westerlies as the convective envelope of the Madden-Julian oscillation moved into the region. A series of three Kelvin waves modulated the development of the westerlies. Cyclonic potential vorticity (PV) developed in a strip between the growing equatorial westerlies and the persistent trade easterlies farther poleward. Rammasun and Chataan emerged from the apparent breakdown of this strip. The cyclonic PV developed in association with diabatic heating from both the MJO and the Kelvin waves. The tropical cyclones also developed during the largest superposition of equatorial westerlies from the MJO and the Kelvin waves. This chain of events suggests that the MJO and the Kelvin waves each played a role in the development of Rammasun and Chataan. [ABSTRACT FROM AUTHOR]

Predictability of intraseasonal oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere--ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets [the NCEP reanalysis 1 and 2 (NCEP-R1 and -R2) and the ECMWF Re-Analysis Interim (ERA-Interim)] in describing ISO were briefly revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal-recovery method is proposed to improve ISO prediction. Although all three reanalyses underestimate the intensity of the equatorial eastward-propagating ISO, the overall quality of the ERA-Interim is better than the NCEP-R1 and -R2. When these reanalyses are used as initial conditions in the ECHAM4-University of Hawaii hybrid coupled model (UH-HCM), skillful ISO prediction reaches only about 1 week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of the divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2--4 days and U850 prediction by 5--10 days. After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength closer to the observed, whereas the rainfall spatial pattern correlation in the ERA-Interim reanalysis drops. The resultant ISO prediction skills, however, are consistently extended for all the NCEP and ERA-Interim reanalyses. Using these signal-recovered reanalyses as initial conditions, the boreal summer ISO prediction skill measured with the Wheeler--Hendon index reaches 14 days. The U850 and rainfall prediction skills, respectively, reach 23 and 18 days over Southeast Asia. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skills. Both the UH-HCM and NCEP Climate Forecast System (CFS) suffer the prediction barrier over the Maritime Continent. [ABSTRACT FROM AUTHOR]

Predictability of intraseasonal oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere--ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets [the NCEP reanalysis 1 and 2 (NCEP-R1 and -R2) and the ECMWF Re-Analysis Interim (ERA-Interim)] in describing ISO were briefly revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal-recovery method is proposed to improve ISO prediction. Although all three reanalyses underestimate the intensity of the equatorial eastward-propagating ISO, the overall quality of the ERA-Interim is better than the NCEP-R1 and -R2. When these reanalyses are used as initial conditions in the ECHAM4-University of Hawaii hybrid coupled model (UH-HCM), skillful ISO prediction reaches only about 1 week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of the divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2--4 days and U850 prediction by 5--10 days. After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength closer to the observed, whereas the rainfall spatial pattern correlation in the ERA-Interim reanalysis drops. The resultant ISO prediction skills, however, are consistently extended for all the NCEP and ERA-Interim reanalyses. Using these signal-recovered reanalyses as initial conditions, the boreal summer ISO prediction skill measured with the Wheeler--Hendon index reaches 14 days. The U850 and rainfall prediction skills, respectively, reach 23 and 18 days over Southeast Asia. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skills. Both the UH-HCM and NCEP Climate Forecast System (CFS) suffer the prediction barrier over the Maritime Continent. [ABSTRACT FROM AUTHOR]

A novel methodology is presented for the identification of the mean cycle of the Madden--Julian oscillation (MJO) along the equator. The methodology is based on a nonlinear principal component (NLPC) computed with a neural network model. The bandpass-filtered input data encompass 30 yr with zonal winds at 850 and 200 hPa plus outgoing longwave radiation (OLR). The NLPC is conditioned on a sufficiently strong MJO activity and is computed both for the pooled dataset and for the dataset stratified into seasons. The NLPC for all data depicts a circular mode formed by the first two linear principal components (LPCs) with marginal contributions by the higher-order LPCs. Hence, the mean MJO cycle throughout the year is effectively captured by the amplitude of the leading two LPCs varying in quadrature. The NLPC for individual seasons shows additional variability, which mainly arises from a subordinate oscillation of the second pair of LPCs superimposed on the annual MJO signal. In reference to the all-year solution, the difference in resolved variability approximately accounts for 9%% in solstitial seasons and 3%% in equinoctial seasons. The phasing of the third LPC is such that convective activity oscillations over the Maritime Continent as well as wind oscillations over the Indian Ocean appear enhanced (suppressed) during boreal winter (summer). Also, convective activity oscillations appear more pronounced at the date line during both winter and summer. The phasing of the fourth LPC is such that upper-level westerlies over the Atlantic region are more persistent during boreal spring than during other seasons. [ABSTRACT FROM AUTHOR]

In situ experimental data and numerical model results are presented for the Ligurian Sea in the northwestern Mediterranean. The Ligurian Sea Air--Sea Interaction Experiment (LASIE07) and LIGURE2007 experiments took place in June 2007. The LASIE07 and LIGURE2007 data are used to validate the Coupled Ocean--Atmosphere Mesoscale Prediction System (COAMPS)1 developed at the Naval Research Laboratory. This system includes an atmospheric sigma coordinate, nonhydrostatic model, coupled to a hydrostatic sigma- z-level ocean model (Navy Coastal Ocean Model), using the Earth System Modeling Framework (ESMF). A month-long simulation, which includes data assimilation in the atmosphere and full coupling, is compared against an uncoupled run where analysis SST is used for computation of the bulk fluxes. This reveals that COAMPS has reasonable skill in predicting the wind stress and surface heat fluxes at LASIE07 mooring locations in shallow and deep water. At the LASIE07 coastal site (but not at the deep site) the validation shows that the coupled model has a much smaller bias in latent heat flux, because of improvements in the SST field relative to the uncoupled model. This in turn leads to large differences in upper-ocean temperature between the coupled model and an uncoupled ocean model run. [ABSTRACT FROM AUTHOR]

Seasonal forecasting systems, and related systems for decadal prediction, are crucial in the development of adaptation strategies to climate change. However, despite important achievements in this area in the last 10 years, significant levels of skill are only generally found over regions strongly connected with the El Niñño--Southern Oscillation. With the aim of improving the skill of regional climate predictions in tropical and extratropical regions from intraseasonal to interannual time scales, a new Met Office global seasonal forecasting system (GloSea4) has been developed. This new system has been designed to be flexible and easy to upgrade so it can be fully integrated within the Met Office model development infrastructure. Overall, the analysis here shows an improvement of GloSea4 when compared to its predecessor. However, there are exceptions, such as the increased model biases that contribute to degrade the skill of Niñño-3.4 SST forecasts starting in November. Global ENSO teleconnections and Madden--Julian oscillation anomalies are well represented in GloSea4. Remote forcings of the North Atlantic Oscillation by ENSO and the quasi-biennial oscillation are captured albeit the anomalies are weaker than those found in observations. Hindcast length issues and their implications for seasonal forecasting are also discussed. [ABSTRACT FROM AUTHOR]

Composite analysis of North Atlantic midlatitudinal winter cyclones is performed using NCEP--NCAR reanalysis data for the 60-yr period from 1948 to 2007. The composites were developed using an advanced methodology involving the coordinate transform of cyclones into a nondimensional azimuthal coordinate system and the further collocation of fields. Composite analysis is performed for air--sea turbulent fluxes, heat content, precipitable water, and precipitation for 576 oceanic cyclones generated in the Gulf Stream area in winter (January--March) from 1948 to 2007. For the region of cyclone generation over the Gulf Stream, composites were analyzed for different cyclone intensities. Over the whole North Atlantic, composites were developed throughout the life cycle and for different cyclone types classified by the regions of their migration. These classifications allow the case-to-case variability to be minimized and the robustness of the composite to be boosted. In the region of cyclone generation over the Gulf Stream, characteristics of the composites strongly depend on the cyclone intensity quantified through the radial sea level pressure difference between the cyclone''s edge and its center. Stronger cyclone intensity implies larger turbulent fluxes in the rear of a cyclone and stronger precipitation in the forward part. Cyclones gradually dry with the water content and precipitation rate decreasing by about 40%% and 50%%--70%%, respectively, during the lifetime. Although composites of air--sea turbulent fluxes show locally very strong positive fluxes in the rear part of the cyclone, the total air--sea turbulent fluxes provided by cyclones are not significantly different from the averaged background fluxes. This shows that the formation of extreme air--sea fluxes by cyclones is connected to the larger-scale circulation conditions, particularly to the cyclone--anticyclone transition zones. [ABSTRACT FROM AUTHOR]