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Interannual and climatological variations of wind stress drag coefficient (CD) are examined over the global ocean from 1998 to 2004. Here CD is calculated using high temporal resolution (3- and 6-hourly) surface atmospheric variables from two datasets: 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and 2) the Navy Operational Global Atmospheric Prediction System (NOGAPS). The stability-dependent CD algorithm applied to both datasets gives almost identical values over most of the global ocean, confirming the validity of results. Overall, major findings of this paper are as follows: 1) the CD value can change significantly (e.g., >50%) on 12-hourly time scales around the Kuroshio and Gulf Stream current systems; 2) there is strong seasonal variability in CD, but there is not much interannual change in the spatial variability for a given month; 3) a global mean CD ≈ 1.25 × 10-3 is found in all months, while CD ≥ 1.5 × 10-3 is prevalent over the North Pacific and North Atlantic Oceans and in southern high-latitude regions as well, and CD ≤ 1.0 × 10-3 is typical in the eastern equatorial Pacific cold tongue; and 4) including the effects of air–sea stability on CD generally causes an increase of >20% in comparison to the one calculated based on neutral conditions in the tropical regions. Finally, spatially and temporally varying CD fields are therefore needed for a variety of climate and air–sea interaction studies. [ABSTRACT FROM AUTHOR]

The large-scale equatorial circulation known as the Madden–Julian oscillation (MJO) has been shown to impact tropical cyclone activity in several basins around the globe. In this paper, the author utilizes an MJO index created by Wheeler and Hendon to examine its impacts on tropical genesis and intensification in the Atlantic. Large differences in frequency and intensity of tropical cyclone activity are seen, both in the tropical Atlantic as well as in the northwest Caribbean and Gulf of Mexico depending on the MJO phase. Coherent changes in upper- and lower-level winds and relative humidity are likely responsible for these differences. Since the MJO shows potential predictability out to about two weeks, the relationships discussed in this paper may be useful for short-term predictions of the probability of tropical cyclone activity in the Atlantic as a complement to the already available longer-term seasonal predictions. [ABSTRACT FROM AUTHOR]

Correlation scales have been used in the traditional scheme of three-dimensional variational data assimilation (3DVAR) to estimate the background (or first guess) error covariance matrix (the &BsansserB; matrix in brief) for the numerical forecast and reanalysis of ocean for decades. However, it is challenging to implement this scheme. On the one hand, determining the correlation scales accurately can be difficult. On the other hand, the positive definite of the &BsansserB; matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. indicated that a traditional 3DVAR only corrects certain wavelength errors, and its accuracy depends on the accuracy of the &BsansserB; matrix. Generally speaking, the shortwave error cannot be sufficiently corrected until the longwave error is corrected. An inaccurate &BsansserB; matrix may mistake longwave errors as shortwave ones, resulting in erroneous analyses. A new 3DVAR data assimilation scheme, called a multigrid data assimilation scheme, is proposed in this paper for quickly minimizing longwave and shortwave errors successively. By assimilating the sea surface temperature and temperature profile observations into a numerical model of the China Seas, this scheme is applied to a retroactive real-time forecast experiment and favorable results are obtained. Compared to the traditional scheme of 3DVAR, this new scheme has higher forecast accuracy and lower root-mean-square errors. Note that the new scheme demonstrates greatly improved numerical efficiency in the analysis procedure. [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]

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]

The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone–like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month's observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2–3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters' subjective elimination of the “overconfidence” bias in the model's forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered. [ABSTRACT FROM AUTHOR]

This paper examines how combining training-set forecasts from two separate oceanic basins affects the resulting tropical cyclone track and intensity forecasts in a particular oceanic basin. Atlantic and eastern Pacific training sets for 2002 and 2003 are combined and used to forecast 2004 eastern Pacific tropical cyclones in a real-time setting. These experiments show that the addition of Atlantic training improves the 2004 eastern Pacific forecasts. Finally, a detailed study of training-set and real-time model biases is completed in an effort to determine why cross-oceanic training may have helped in this instance. [ABSTRACT FROM AUTHOR]

This paper deals with seasonal climate forecasting using as many as 13 coupled ocean–atmosphere models. An analysis of the individual model, multimodel ensemble, and FSU synthetic superensemble (FSUSSE) climate forecasts was performed for monthly and seasonal forecasts of precipitation, sea surface temperature (SST) and surface air temperature over the Euro-Mediterranean region including the land areas of Europe, North Africa and the Near East, during the period 1989–2001. In the FSUSSE methodology, forecasts are obtained by a weighted combination of the individual coupled ocean–atmosphere model forecasts based on a training period. The set of 13 individual climate forecast models utilized in this research is comprised of the seven models in the European suite of DEMETER models, a suite of four Florida State University models, the Australian POAMA model and the NCAR CCM3 model. The FSUSSE forecasts of seasonal precipitation anomalies were found to have the lowest root mean square (RMS) errors in comparison to the models in the multimodel ensemble, and their ensemble mean. However, the anomaly correlation (AC) coefficient results for seasonal precipitation anomaly forecasts by the FSUSSE were less impressive. The equitable threat scores for the FSUSSE forecasts of seasonal precipitation were found to be better than the various models in the multimodel ensemble, but those scores for the forecasts of positive seasonal anomalies were found to be worse than most of the models in the multimodel ensemble. The FSUSSE seasonal forecasts for SST and surface air temperature for the season considered (winter) were found to be excellent for both AC coefficients and RMS errors in the forecast anomalies. [ABSTRACT FROM AUTHOR]

In this paper, we assess the skill of a seasonal forecast system to predict El Niño Southern Oscillation (ENSO) anomalies and the impact that the horizontal resolution of the atmospheric component has on the forecasts of the growing phase of strong ENSO events.The analysis has been carried out on retrospective six-month forecasts for the period 1973–2001 performed with the Istituto Nazionale di Geofisica e Vulcanologia (INGV) coupled ocean–atmosphere general circulation model (SINTEX) in the framework of the DEMETER project. Each forecast is formed by an ensemble of nine integrations that start from perturbed initial conditions. In order to consider the possible impact of the seasonal cycle on the forecasts, four different dates of the year have been chosen as initial conditions.The results indicate that, in general, the model is reasonably good in predicting the distribution of the seasonal anomalies in the tropical Pacific. However, it is also shown that the model predictions underestimate substantially the amplitude of the anomalies during the developing phase of intense El Niño episodes.The forecasts of strong ENSO events in the period here considered have been repeated, increasing the horizontal resolution of the atmospheric component. In these experiments, both the ocean model component and the oceanic initial conditions are unchanged. During the decaying phase of the oscillation, the high-resolution and low-resolution versions of the model produce similar results and the forecasts are in general good agreement with the observations. During the developing phase of ENSO, the low-resolution version of the model is unable to sustain the growth of the coupled disturbance. With the low-resolution atmosphere, the initial perturbation of the coupled system decays and the anomalies in the tropical Pacific tend to vanish very quickly. With the high-resolution atmosphere, on the other hand, the model appears to be able to sustain the growth of the disturbance, improving the quality of the forecasts both for El Niño and La Niña cases. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of four bulk-type microphysics schemes, Weather Research and Forecasting (WRF) double-moment 6-class (WDM6), WRF double-moment 7-class (WDM7), Thompson, and Morrison, focusing on hydrometeors and microphysics budgets in the WRF model version 4.1.3. Eight snowstorm cases, which can be sub-categorized as cold-low, warm-low, and air–sea interaction cases are selected, depending on the synoptic environment during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign. All simulations present a positive bias in the simulated surface precipitation for cold-low and warm-low cases. Furthermore, the simulations for the warm-low cases show a higher probability of detection score than simulations for the cold-low and air–sea interaction cases even though the simulations fail to capture the accurate transition layer for wind direction. WDM6 and WDM7 simulate abundant cloud ice for the cold-low and warm-low cases, and thus snow is mainly generated by aggregation. Meanwhile, Thompson and Morrison schemes simulate insignificant cloud ice amounts, especially over the lower atmosphere, where cloud water is simulated instead. Snow in the Thompson and Morrison schemes is mainly formed by the accretion between snow and cloud water and deposition. The melting process is analyzed as a key process to generate rain in all schemes. The discovered positive precipitation bias for the warm-low and cold-low cases can be mitigated by reducing the melting efficiency in all schemes. The contribution of melting to rain production is reduced for the air–sea interaction case with decreased solid-phase hydrometeors and increased cloud water in all simulations. [ABSTRACT FROM AUTHOR]

Over the past several decades, an increasing number of studies have focused on the global view of swell and wind sea climate. However, our understanding of wind sea and swell is still incomplete as is the lack of an integrated description for all the wave components. In this paper, the European Centre for Medium-Range Weather Forecasts (ECMWF) Era-medium wind data is used to run the WAVEWATCH III model and the global wave fields in 2010 are reproduced. Using the spectra energy partition (SEP) method, two-dimensional wave spectra were separated and detailed information for the components of wind sea and swell was obtained. We found that the highest seasonal mean energy of swell and wind sea are distributed in the respective winter hemispheres. In most seas, swell carries a large part of the wave energy with Ws being higher than 50%. Compared to swell, the global distribution of wind sea energy is highly affected by the seasons. We also established a link between inverse wave age and the ratio of swell energy to total wave energy. This study aims to improve our understanding of surface wave energy composition and thus the parameterization of global-scale wind-wave interaction and air-sea momentum flux. [ABSTRACT FROM AUTHOR]

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

Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model and a sea ice model below the atmosphere. Initialisation of different components using different methods and on different timescales can lead to inconsistencies when they are combined in the full system. Historically, the methods for initialising the ocean and the atmosphere have been typically developed separately. This paper describes an approach for combining the existing ocean and atmospheric analyses into what we categorise as a weakly coupled assimilation scheme. Here, we show the performance improvements achieved for the atmosphere by having a weakly coupled ocean–atmosphere assimilation system compared with an uncoupled system. Using numerical weather prediction diagnostics, we show that forecast errors are decreased compared with forecasts initialised from an uncoupled analysis. Further, a detailed investigation into spatial coverage of sea ice concentration in the Baltic Sea shows that a much more realistic structure is obtained by the weakly coupled analysis. By introducing the weakly coupled ocean–atmosphere analysis, the ocean analysis becomes a critical part of the numerical weather prediction system and provides a platform from which to build ever stronger forms of analysis coupling. [ABSTRACT FROM AUTHOR]

Two extreme heat events impacting the New York City (NYC), New York, metropolitan region during 7-10 June and 21-24 July 2011 are examined in detail using a combination of models and observations. The U.S. Navy's Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) produces real-time forecasts across the region on a 1-km resolution grid and employs an urban canopy parameterization to account for the influence of the city on the atmosphere. Forecasts from the National Weather Service's 12-km resolution North American Mesoscale (NAM) implementation of the Weather Research and Forecasting (WRF) model are also examined. The accuracy of the forecasts is evaluated using a land- and coastline-based observation network. Observed temperatures reached 39°C or more at central urban sites over several days and remained high overnight due to urban heat island (UHI) effects, with a typical nighttime urban-rural temperature difference of 4°-5°C. Examining model performance broadly over both heat events and 27 sites, COAMPS has temperature RMS errors averaging 1.9°C, while NAM has RMSEs of 2.5°C. COAMPS high-resolution wind and temperature predictions captured key features of the observations. For example, during the early summer June heat event, the Long Island south shore coastline experienced a more pronounced sea breeze than was observed for the July heat wave. [ABSTRACT FROM AUTHOR]

The relation between skill and fidelity of seasonal mean hindcasts of surface temperature by seven coupled atmosphere-ocean models is investigated. By definition, fidelity measures the agreement between model and observational climatological distributions, and skill measures the agreement between hindcasts and their corresponding verifications. While a relation between skill and fidelity seems intuitively plausible, it has not been checked systematically, nor is it mandated mathematically. New measures of skill and fidelity based on information theory are proposed. Specifically, fidelity is measured by the area average relative entropy between the climatological distributions of the model and observations, and skill is measured by the area averaged mutual information between forecast and verification. The fidelity measure is found to be dominated by the term measuring mean bias; that is, the discrepancy in climatological means is much larger than the discrepancy in climatological variances. Moreover, the mean bias is negatively correlated with skill at most initial months, lead times, and regions examined. Thus, models that more closely replicate the observed climatological mean tend to have better skill. [ABSTRACT FROM AUTHOR]

The development of high-resolution, fully coupled, regional Earth system model systems is important for improving our understanding of climate variability, future projections, and extreme events at regional scales. Here we introduce and present an overview of the newly developed Regional Community Earth System Model (R-CESM). Different from other existing regional climate models, R-CESM is based on the Community Earth System Model version 2 (CESM2) framework. We have incorporated the Weather Research and Forecasting (WRF) Model and Regional Ocean Modeling System (ROMS) into CESM2 as additional components. As such, R-CESM can be conveniently used as a regional dynamical downscaling tool for the global CESM solutions or/and as a standalone high-resolution regional coupled model. The user interface of R-CESM follows that of CESM, making it readily accessible to the broader community. Among countless potential applications of R-CESM, we showcase here a few preliminary studies that illustrate its novel aspects and value. These include 1) assessing the skill of R-CESM in a multiyear, high-resolution, regional coupled simulation of the Gulf of Mexico; 2) examining the impact of WRF and CESM ocean–atmosphere coupling physics on tropical cyclone simulations; and 3) a convection-permitting simulation of submesoscale ocean–atmosphere interactions. We also discuss capabilities under development such as (i) regional refinement using a high-resolution ROMS nested within global CESM and (ii) "online" coupled data assimilation. Our open-source framework (publicly available at https://github.com/ihesp/rcesm1) can be easily adapted to a broad range of applications that are of interest to the users of CESM, WRF, and ROMS. [ABSTRACT FROM AUTHOR]

Tropical rainfall is mostly convective and its subseasonal-to-seasonal (S2S) prediction remains challenging. We show that state-of-art model forecast skill 3 + 4 weeks ahead is systematically lower over land than ocean, which is matched by a similar land-ocean contrast in the spatial scales of observed biweekly rainfall anomalies. Regional differences in predictability are then interpreted using observed characteristics of daily rainfall (wet-patch size, mean intensity as well as the strength of local S2S modes of rainfall variation), and classified into six S2S predictability types. Both forecast skill and spatial scales are reduced over the continents, either because daily rainfall patches are small and poorly organized by S2S modes of variation (as over equatorial and northern tropical Africa), or where the daily mean intensity is very high (as over South and SE Asia). Forecast skill and spatial scales are largest where daily rainfall is synchronized by intraseasonal (such as the Madden-Julian Oscillation) as well as interannual ocean-atmosphere modes of variation (such as El Niño-Southern Oscillation), especially over northern Australia and parts of the Maritime Continent, and over parts of eastern, southern Africa and northeast South America. The oceans exhibit the highest skill and largest spatial scales, especially where interannual (central equatorial Pacific) or intraseasonal (central and eastern Tropical Indian Ocean and Western Pacific) variability is largest. These results provide a relevant regional typology of the potential drivers and controls on S2S predictability of tropical rainfall, informing intrinsic limits and possible improvements toward useful S2S climate prediction at regional scale. [ABSTRACT FROM AUTHOR]

In this study, we use observational and numerical model data from the Coupled Air Sea Processes and Electromagnetic Ducting Research (CASPER) field campaign to describe the mean refractive conditions offshore Duck, North Carolina. The U.S. Navy operational numerical weather prediction model known as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) performed well forecasting large-scale conditions during the experiment, with an observed warm bias in SST and cold and dry biases in temperature and humidity in the lowest 2000 m. In general, COAMPS underpredicted the number of ducts, and they were weaker and at lower height than those seen in observations. It was found that there is a noticeable diurnal evolution of the ducts, more over land than over the ocean. Ducts were found to be more frequent over land but overall were stronger and deeper over the ocean. Also, the evaporative duct height increases as one moves offshore. A case study was chosen to describe the electromagnetic properties under different synoptic conditions. In this case the continental atmospheric boundary layer dominates and interacts with the marine atmospheric boundary layer. As a result, the latter moves around 80 km offshore and then back inland after 2 h. [ABSTRACT FROM AUTHOR]

The summer northern annular mode (NAM) and the winter North Atlantic Oscillation (NAO)/winter NAM have a positive correlation from the mid-1960s to the 1980s. Namely, when the winter NAO/NAM is in a positive phase, the following summer NAM tended to be in a positive phase. During the period from the mid-1960s to the 1980s, the NAO/NAM signals extended to the stratosphere in winter. Also, the lower-tropospheric warm anomaly over northern Eurasia in winter associated with the positive phase of NAO/NAM continued into spring. In summer, the annular anomalies in the temperature and 500-hPa height fields appeared, and the high-latitude westerly wind was enhanced following the winter positive NAO/NAM. However, after circa 1990, the seasonal linkage was broken (i.e., the winter-to-summer correlation became insignificant). The stratospheric signal in the winter NAO/NAM became weak and summer signals associated with the winter NAO/NAM almost disappeared. Seasonal evolutions of atmospheric circulation and sea surface temperature (SST) anomalies associated with the winter NAO are examined for an early good-linkage period and a recent poor-linkage period. We discuss the possible causes of the linkage breakdown such as stratospheric ozone, North Atlantic SST, and Atlantic multidecadal oscillation, besides chaotic internal variability in the climate system. Simulations with the Community Earth System Model suggest that the ocean and/or sea ice with interseasonal memories possibly cause the linkage, besides large internal variability through which the linkage can take place by chance. [ABSTRACT FROM AUTHOR]

There has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden–Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean–atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed. [ABSTRACT FROM AUTHOR]

The Madden--Julian oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides a major source of tropical and extratropical predictability on a subseasonal time scale. This study conducts a quantitative evaluation of the MJO prediction skill in state-of-the-art operational models, participating in the subseasonal-to-seasonal (S2S) prediction project. The relationship of MJO prediction skill with model biases in the mean moisture fields and in the longwave cloud--radiation feedbacks is also investigated. The S2S models exhibitMJOprediction skill out to a range of 12 to 36 days. TheMJOprediction skills in the S2S models are affected by both the MJO amplitude and phase errors, with the latter becoming more important at longer forecast lead times. Consistent with previous studies, MJO events with stronger initial MJO amplitude are typically better predicted. It is found that the sensitivity to the initial MJO phase varies notably from model to model. In most models, a notable dry bias develops within a few days of forecast lead time in the deep tropics, especially across the Maritime Continent. The dry bias weakens the horizontal moisture gradient over the Indian Ocean and western Pacific, likely dampening the organization and propagation of the MJO. Most S2S models also underestimate the longwave cloud--radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelope. The models with smaller bias in the mean horizontal moisture gradient and the longwave cloud--radiation feedbacks show higher MJO prediction skills, suggesting that improving those biases would enhance MJO prediction skill of the operational models. [ABSTRACT FROM AUTHOR]

The contribution of air-sea interaction on the extended-range prediction of geopotential height at 500 hPa in the northern extratropical region has been analyzed with a coupled model form Beijing Climate Center and its atmospheric components. Under the assumption of the perfect model, the extended-range prediction skill was evaluated by anomaly correlation coefficient (ACC), root mean square error (RMSE), and signal-to-noise ratio (SNR). The coupled model has a better prediction skill than its atmospheric model, especially, the air-sea interaction in July made a greater contribution for the improvement of prediction skill than other months. The prediction skill of the extratropical region in the coupled model reaches 16-18 days in all months, while the atmospheric model reaches 10-11 days in January, April, and July and only 7-8 days in October, indicating that the air-sea interaction can extend the prediction skill of the atmospheric model by about 1 week. The errors of both the coupled model and the atmospheric model reach saturation in about 20 days, suggesting that the predictable range is less than 3 weeks. [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]

This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple time scales. To illustrate the approach, three sets of 32-yr-long simulations are analyzed for northeastern North America and for the March-May season using the Geophysical Fluid Dynamics Laboratory's Low Ocean-Atmosphere Resolution (LOAR) and Forecast-Oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach and are associated with a misrepresentation of troughs centered north of the Great Lakes and deep coastal troughs. Moreover, the intraseasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, interexperiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple time scales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics. [ABSTRACT FROM AUTHOR]

By comparison with satellite and field observations, the comprehensive performance and potential utility of near real-time forecasts using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are demonstrated by exploiting the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011) / Dynamics of the Madden-Julian Oscillation (DYNAMO) campaign. A week-long forecast was run each day using a regionally stretched version of NICAM, with the finest mesh size of 14 km over the tropical Indian Ocean (IO), throughout the intensive observation period (IOP). The simulated precipitation time series fairly represented the evolution and propagation of the observed Madden-Julian Oscillation (MJO) events, although a 30 % overprediction of precipitation over the IO domain (60 - 90°E, 10°S - 10°N) was found on average. Frequencies of strong (> 40 mm day-1) precipitation were overpredicted, while those of weak precipitation were underpredicted against satellite observations. Compared with the field observations at Gan Island, the biases in precipitation frequency were less obvious, whereas the growth of lower to middle tropospheric dry (~ 1 g kg-1) and warm (~ 1 K) biases were found. Despite these mean biases, temporal variations of the moisture and zonal wind profiles including the MJO events were reasonably simulated. Using the forecast data the moisture and energy budgets during the IOP were investigated. The diagnosis using the 7-day-mean fields captured the observed features of the MJO events. Meanwhile, significant upward transport of moisture by the grid-resolved high-frequency variability was detected throughout the IOP. The relationship between these high-frequency effects and the simulated MJO or mean biases is also discussed. [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]

The Madden-Julian oscillation (MJO), a planetary-scale eastward-propagating coherent structure with periods of 30-60 days, is a prominent manifestation of intraseasonal variability in the tropical atmosphere. It is widely presumed that small-scale moist cumulus convection is a critical part of its dynamics. However, the recent results from high-resolution modeling as well as data analysis suggest that the MJO may be understood by dry dynamics to a leading-order approximation. Simple, further theoretical considerations presented herein suggest that if it is to be understood by dry dynamics, the MJO is most likely a strongly nonlinear solitary Rossby wave. Under a global quasigeostrophic equivalent-barotropic formulation, modon theory provides such analytic solutions. Stability and the longevity of the modon solutions are investigated with a global shallow-water model. The preferred modon solutions with the greatest longevities compare well overall with the observed MJO in scale and phase velocity within the factors. [ABSTRACT FROM AUTHOR]

An ensemble of 1-month-lead seasonal retrospective forecasts generated by the Scale Interaction Experiment (SINTEX)-Frontier Research Center for Global Change (FRCGC), version 2 tuned for performance on a vector supercomputer (SINTEX-F2v), coupled global circulation model (CGCM) were downscaled using the Weather Research and Forecasting (WRF) Model to improve the forecast of the austral summer precipitation and 2-m air temperatures over Australia. A set of four experiments was carried out with the WRF Model to improve the forecasts. The first was to drive the WRF Model with the SINTEX-F2v output, and the second was to bias correct the mean component of the SINTEX-F2v forecast and drive the WRF Model with the corrected fields. The other experiments were to use the SINTEX-F2v forecasts and the mean bias-corrected SINTEX-F2v forecasts to drive the WRF Model coupled to a simple mixed layer ocean model. Evaluation of the forecasts revealed the WRF Model driven by bias-corrected SINTEX-F2v forecasts to have a better spatial and temporal representation of forecast precipitation and 2-m air temperature, compared to SINTEX-F2v forecasts. Using a regional coupled model with the bias-corrected SINTEX-F2v forecast as the driver further improved the skill of the precipitation forecasts. The improvement in the WRF Model forecasts is due to better representation of the variables in the bias-corrected SINTEX-F2v forecasts driving the WRF Model. The study brings out the importance of including air-sea interactions and correcting the global forecasts for systematic biases before downscaling them for societal applications over Australia. These results are important for potentially improving austral summer seasonal forecasts over Australia. [ABSTRACT FROM AUTHOR]

We develop a statistical model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ~45% of the variability in JJA MDA8 ozone concentrations and ~30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean-atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean-atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region. [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]

A simple air-sea coupled model, the atmospheric general circulation model (AGCM) of the National Centers for Environmental Prediction coupled to a mixed-layer slab ocean model, is employed to investigate the impact of air-sea coupling on the signals of the Atlantic Multidecadal Oscillation (AMO). A regional coupling strategy is applied, in which coupling is switched off in the extratropical North Atlantic Ocean but switched on in the open oceans elsewhere. The coupled model is forced with warm-phase AMO SST anomalies, and the modeled responses are compared with those from parallel uncoupled AGCM experiments with the same SST forcing. The results suggest that the regionally coupled responses not only resemble the AGCM simulation, but also have a stronger intensity. In comparison, the coupled responses bear greater similarity to the observational composite anomaly. Thus, air-sea coupling enhances the responses of the East Asian winter climate to the AMO. To determine the mechanism responsible for the coupling amplification, an additional set of AGCM experiments, forced with the AMO-induced tropical SST anomalies, is conducted. The SST anomalies are extracted from the simulated AMO-induced SST response in the regionally coupled model. The results suggest that the SST anomalies contribute to the coupling amplification. Thus, tropical air-sea coupling feedback tends to enhance the responses of the East Asian winter climate to the AMO. [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]

Given a biased coupled model and the atmospheric and oceanic observing system, maintaining a balanced and coherent climate estimation is of critical importance for producing accurate climate analysis and prediction initialization. However, because of limitations of the observing system (e.g., most of the oceanic measurements are only available for the upper ocean), directly evaluating climate estimation with real observations is difficult. With two coupled models that are biased with respect to each other, a biased twin experiment is designed to simulate the problem. To do that, the atmospheric and oceanic observations drawn from one model based on the modern climate observing system are assimilated into the other. The model that produces observations serves as the truth and the degree by which an assimilation recovers the truth steadily and coherently is an assessment of the impact of the data constraint scheme on climate estimation. Given the assimilation model bias of warmer atmosphere and colder ocean, where the atmospheric-only (oceanic only) data constraint produces an overcooling (overwarming) ocean through the atmosphere-ocean interaction, the constraints with both atmospheric and oceanic data create a balanced and coherent ocean estimate as the observational model. Moreover, the consistent atmosphere-ocean constraint produces the most accurate estimate for North Atlantic Deep Water (NADW), whereas NADW is too strong (weak) if the system is only constrained by atmospheric (oceanic) data. These twin experiment results provide insights that consistent data constraints of multiple components are very important when a coupled model is combined with the climate observing system for climate estimation and prediction initialization. [ABSTRACT FROM AUTHOR]

Thousands of aircraft observations of upper-ocean thermal structures have been obtained during hurricane and typhoon research field experiments in recent decades. The results from these experiments suggest a strong correlation between upper-ocean thermal variability and tropical cyclone (TC) intensity change. In response to these results, during the Office of the Federal Coordinator of Meteorology (OFCM) 2011 Interdepartmental Hurricane Conference (IHC), the Working Group for Hurricane and Winter Storms Operations and Research (WG/HWSOR) approved a 3-yr project to demonstrate the usefulness of airborne expendable bathythermographs (AXBTs) in an operational setting. The goal of this project was to initialize and validate coupled TC forecast models and was extended to improve input to statistical intensity forecast models. During the first season of the demonstration project, 109 AXBTs were deployed between 28 July and 28 August 2011. Successes included AXBT deployment from WC-130J aircraft during operational reconnaissance missions tasked by the National Hurricane Center (NHC), real-time onboard and postflight data processing, real-time data transmission to U.S. Navy and NOAA hurricane numerical prediction centers, and near-real-time assimilation of upper-ocean temperature observations into the Naval Research Laboratory Coupled Ocean-Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC) forecast model. Initial results showed 1) increased model accuracy in upper-ocean temperatures, 2) minor improvements in TC track forecasts, and 3) minor improvements in TC intensity forecasts in both coupled dynamical and statistical models [COAMPS-TC and the Statistical Hurricane Intensity Prediction Scheme (SHIPS), respectively]. [ABSTRACT FROM AUTHOR]

This study describes the development of the South China Sea (SCS) daily satellite-derived latent heat flux (SCSSLH) for the period of 1998-2011 at 0.25° × 0.25° resolution using data mainly from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI). Flux-related variables of daily TMI data smoothed with 3-day running mean were finally chosen because of the best fit with the 1727 high-quality observations from seven moored stations and 24 ship surveys. Near-surface air specific humidity was computed using the global relationship based on satellite precipitable water. Verification against 1016 high-resolution radiosonde profiles from 1998 to 2012 and the time series from the Xisha automatic weather station during 2008-10 indicate that this satellite-derived air specific humidity can reasonably capture observed mean condition and temporal variability. They are therefore used to derive SCSSLH based on the Coupled Ocean-Atmosphere Response Experiment version 3.0 (COARE 3.0) algorithm. Compared with five other latent heat flux products-the Goddard Satellite-Based Surface Turbulent Fluxes version 2 (GSSTF2), the objectively analyzed air-sea heat fluxes (OAFlux), the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data version 3 (HOAPS3), the National Centers for Environmental Prediction/Department of Energy Global Reanalysis 2 (NCEP-2), and the European Centre for Medium-Range Weather Forecasts (ECMWF)-the daily SCSSLH shows the highest spatial resolution and realistic values in the SCS, with an exception along the northern continental shelf. More importantly, the other five products seem to overestimate the latent heat flux systematically. The flux representation in this study comes not only with a better flux algorithm but also with the improved estimates of bulk variables based on in situ measurements, which further highlights the unique role of high-quality meteorological measurements and atmospheric weather stations in evaluating the air-sea interaction in the SCS. [ABSTRACT FROM AUTHOR]

A computationally efficient method of producing tropical cyclone (TC) wind analyses is developed and tested, using a hindcast methodology, for 12 Gulf of Mexico storms. The analyses are created by blending synthetic data, generated from a simple parametric model constructed using extended best-track data and climatology, with a first-guess field obtained from the NCEP-NCAR North American Regional Reanalysis (NARR). Tests are performed whereby parameters in the wind analysis and vortex model are varied in an attempt to best represent the TC wind fields. A comparison between nonlinear and climatological estimates of the TC size parameter indicates that the former yields a much improved correlation with the best-track radius of maximum wind rm. The analysis, augmented by a pseudoerror term that controls the degree of blending between the NARR and parametric winds, is tuned using buoy observations to calculate wind speed root-mean-square deviation (RMSD), scatter index (SI), and bias. The bias is minimized when the parametric winds are confined to the inner-core region. Analysis wind statistics are stratified within a storm-relative reference frame and by radial distance from storm center, storm intensity, radius of maximum wind, and storm translation speed. The analysis decreases the bias and RMSD in all quadrants for both moderate and strong storms and is most improved for storms with an rm of less than 20 n mi. The largest SI reductions occur for strong storms and storms with an rm of less than 20 n mi. The NARR impacts the analysis bias: when the bias in the former is relatively large, it remains so in the latter. [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 impact of ocean-atmosphere interactions on summer rainfall over the South Atlantic Ocean is explored through the use of coupled ocean-atmosphere models. The Brazilian Center for Weather Forecast and Climate Studies (CPTEC) coupled ocean-atmosphere general circulation model (CGCM) and its atmospheric general circulation model (AGCM) are used to gauge the role of coupled modes of variability of the climate system over the South Atlantic at seasonal time scales. Twenty-six years of summer [December-February (DJF)] simulations were done with the CGCM in ensemble mode and the AGCM forced with both observed sea surface temperature (SST) and SST generated by the CGCM forecasts to investigate the dynamics/thermodynamics of the two major convergence zones in the tropical Atlantic: the intertropical convergence zone (ITCZ) and the South Atlantic convergence zone (SACZ). The results present both numerical model and observational evidence supporting the hypothesis that the ITCZ is a thermally direct, SST-driven atmospheric circulation, while the SACZ is a thermally indirect atmospheric circulation controlling SST variability underneath-a consequence of ocean-atmosphere interactions not captured by the atmospheric model forced by prescribed ocean temperatures. Six CGCM model results of the Ensemble-based Predictions of Climate Changes and their Impacts (ENSEMBLES) project, NCEP-NCAR reanalysis data, and oceanic and atmospheric data from buoys of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) Project over the tropical Atlantic are used to validate CPTEC's coupled and uncoupled model simulations. [ABSTRACT FROM AUTHOR]

Forecast performance by coupled ocean-atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5-version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML-NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the two-tiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable. [ABSTRACT FROM AUTHOR]

Two important dynamical processes influence the extratropical zonal wind field: angular momentum transport by the thermally direct Hadley circulation (thermal-driving T) and momentum flux convergence by atmospheric waves (eddies) that develop in regions of enhanced baroclinicity (eddy-driving E). The relationship between extratropical zonal wind variability and these driving processes is investigated using 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. Indices representing the processes (iT and iE) are defined based on vertically integrated diabatic heating and meridional convergence of the meridional flux of zonal momentum by eddies, respectively. Zonal wind signatures associated with these indices are identified via composite analysis. In the Atlantic sector, zonal wind variability is mainly associated with momentum flux convergence by baroclinic eddies, supporting the established view that the Atlantic jet is primarily eddy driven. In the Pacific sector, zonal wind variability is associated with both driving processes, evidence that the Pacific jet is both thermally driven and eddy driven. The thermally driven Pacific signature reflects changes in jet strength (intensity and longitudinal extent) with some resemblance to the zonal wind anomalies of the Pacific-North America (PNA) pattern. The eddy-driven signature reflects a latitudinal shift of the jet exit region in both sectors that resembles the zonal wind anomalies of the North Atlantic Oscillation (NAO) or West Pacific (WP) patterns. [ABSTRACT FROM AUTHOR]

The height of waves at North Sea oil and gas installations is an important factor governing the degree to which operational activities may be undertaken at those facilities. A link between the North Atlantic Oscillation (NAO) and winter (defined as December-February) wave heights at North Sea oil and gas installations has been established. A tool has been developed that uses a forecast NAO index to predict the proportions of wave heights in four categories that could be used to assess the operational downtime that will be experienced in the coming winter. The wave height forecasting system is shown to have useful skill in predicting the probability of occurrence of a stormy winter, and therefore probability forecasts provide a potentially useful guide to whether more or less disruption than the 'climatological mean' might be experienced. The main limit on the skill of the wave forecasts is our very limited ability to accurately predict the NAO index on seasonal time scales. [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]

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]

Using a modified method of multiple linear regression on instrumented sea surface temperature (SST) in the two longest historical datasets [[the Extended Reconstructed SST dataset (ERSST) and the Met Office Hadley Centre Sea Ice and SST dataset (HadISST)]], it is found that the response to increased greenhouse forcing is a warm SST in the mid- to eastern Pacific Ocean in the equatorial region in the annual or seasonal mean. The warming is robustly statistically significant at the 95%% confidence level. Consistent with this, the smaller radiative heating from solar forcing produces a weak warming also in this region, and the spatial pattern of the response is neither La Niña--like nor El Niño--like. It is noted that previous reports of a cold-tongue (La Niña--like) response to increased greenhouse or to solar-cycle heating were likely caused by contaminations due to the dominant mode of natural response in the equatorial Pacific. The present result has implications on whether the Walker circulation is weakened or strengthened in a warmer climate and on coupled atmosphere--ocean climate model validation. [ABSTRACT FROM AUTHOR]

This study investigates the impact of atmosphere–ocean coupling on predicted tropical cyclone (TC) intensity change and the ocean response in the Australian region. The coupled model comprises the Australian Bureau of Meteorology’s Tropical Cyclone Limited-Area Prediction System (TC-LAPS) and a regional version of the BLUElink ocean forecasting system. A series of case study forecasts are presented and the differences between coupled and uncoupled forecasts, operational forecasts, and posterior objective analyses are compared. A coupled model ensemble is also developed that uses different first-order approximations of the effects of surface waves on surface stress in an inertial coupling method. In each of the cases, the use of reanalyzed sea surface temperatures significantly improves the prediction of TC intensity change in the intensification phase. The results show that dynamic air–sea coupling has a modest impact on intensity in cases where SST cooling is significant and is likely to be important for predicting the rate of TC intensification, peak intensity, and deintensification. Results also show that there is a definite coupled signal and suggest inherent biases in the atmospheric model that could potentially be removed. With different parameterizations of surface wave effects, results show modest sensitivity in TC intensity of up to 10 hPa in minimum surface pressure; however, in some cases there was significant sensitivity in the predicted ocean response. The results also highlight the relative increased complexity of tropical cyclone prediction in the Australian region compared to other regions. In cases where the forecast TC track was reasonably skillful, there were improvements in the predicted ocean response with respect to observations compared to an ocean reanalysis. [ABSTRACT FROM AUTHOR]

The study investigates the local climatological minimum of tropical cyclogenesis over the eastern Caribbean Sea. This area, colloquially known by forecasters as the "hurricane graveyard," is located within the deep tropical easterlies. Tropical disturbances emerging from the African continent usually pass through the Lesser Antilles and into the eastern Caribbean. Satellite imagery shows disturbances that frequently exhibit decreasing convection in an area bounded by the islands to the north and east, Venezuela to the south, and roughly 75°W longitude to the west. Quick Scatterometer (QuikSCAT)-derived surface winds during clear-sky conditions frequently show the presence of accelerating easterlies in the central Caribbean as part of the Caribbean low-level jet (CLLJ). Analysis of the National Centers for Environmental Prediction- National Center for Atmospheric Research (NCEP-NCAR) reanalysis project wind fields suggests the presence of an area of persistent low-level mass divergence over the eastern Caribbean. This implies a subsident regime that would weaken convection. Climatologically, this phenomenon reaches peak intensity in July, and then shifts toward the east and weakens during the latter half of the Atlantic hurricane season. This is reflected by the local minimum of tropical cyclogenesis points in the National Hurricane Center's best-track data in the early part of the season. The intensity of the low-level divergence in the eastern Caribbean is shown to be correlated with the Southern Oscillation index. The divergent low-level easterly flow can also enhance the vertical wind shear and help lower sea surface temperatures, further inhibiting tropical cyclogenesis. The local minimum of tropical cyclogenesis in this region has important implications to operational forecasting, because the vast majority of tropical cyclones in the Caribbean eventually affect surrounding landmasses. [ABSTRACT FROM AUTHOR]

We investigated the impact of variations in oceanic preexisting conditions on predictions of Typhoon Hai-Tang (2005) by using a coupled atmosphere-ocean model with 6-km horizontal resolution and providing the oceanic initial conditions on 12 July from 1997 to 2005 to the model. Variations in oceanic preexisting conditions caused variation in predicted central pressure of nearly 18 hPa at 72 h, whereas sea-surface cooling (SSC) induced by Hai-Tang caused a predicted central pressure difference of about 40 hPa. Warm-core oceanic eddies up to a few hundred kilometers across and a deep mixed layer climatologically distributed in the western North Pacific led to high mixed-layer heat potential, which increased latent heat flux, water vapor, and liquid water contents around Hai-Tang's center. These increases were closely associated with Hai-Tang's intensification. SSC negatively affected the eyewall, whereas variations in oceanic preexisting conditions remarkably affected spiral rainbands and the magnitude of SSC. [ABSTRACT FROM AUTHOR]

Numerous statistical and dynamical models have been developed in recent years to forecast ENSO events. However, for most of these models predictability for lead times over 10 months is limited. It has been hypothesized that the tropical Pacific thermocline structure may have critical information to permit longer lead ENSO forecasts. Models that use subsurface sea temperature information have already been known to produce better long lead forecasts. Here, a two-stage statistical ENSO forecasting model is developed and demonstrated using the spatially distributed depth of the 20°C isotherm (D20) as a proxy for the thermocline. In the first stage, a nonlinear dimension reduction method [maximum variance unfolding (MVU)] is used to decompose the D20 data into canonical modes. The leading spatial patterns as well as lagged values of Niño-3 are then used as predictors in a set of linear regression models to predict the Niño-3 index at lead times of up to 24 months. Cross-validated forecasts using this methodology are shown to have higher skill than those that use a dimension reduction of the same thermocline data using principal component analysis (PCA). The first three modes of the D20 data as revealed by MVU account for 89% of the variance of the data, as compared to only 48% of the variance if PCA is used. The spatial patterns of the MVU modes partition the data field in a different way than the PC modes, even though some similarities exist as to the main regions that are active. These patterns and their temporal structure are discussed here, with a view to understanding the possible source of the longer-range predictability of ENSO using the MVU modes. The skill of the PCA- and the MVU-based forecasts of Niño-3 varies depending on the starting month of the forecast for short lead times (5–10 months). However, for the lead times longer than 1 yr, the MVU-based forecast skill is not seasonally variable, while the PCA-based models do not provide significant skill at these lead times irrespective of the starting month of the forecast. Similar conclusions are obtained for forecast models for the Niño-3.4 and Niño-1.2 indices. The differences between the MVU- and PCA-based models are most marked for the Niño-1.2 long lead forecasts. [ABSTRACT FROM AUTHOR]

The article focuses on a research on Indian Ocean for monsoon prediction by Research Moored Array for African- Asian-Australian Monsoon Analysis and Prediction (RAMA). A new moored buoy array in the historically data-sparce Indian Ocean provides measurements to advance monsoon research and forecasting. Seasonal heating and cooling of the land in the Indian Ocean sets the stage for dramatic monsoon wind reversals and intense Seasonal rains over the Indian subcontinent, Southeast Asia, East Africa and Australia. The array complements other elements of the recently designed Indian Ocean Observing System (IndOOS) which represents an Indian Ocean contribution to RAMA.

Proxy-based paleoclimate reconstructions of tropical sea surface temperature (SST) fields may lead to better constraints of tropical climate variability in climate model projections. In this study, the authors quantify uncertainties associated with two popular SST anomaly reconstruction methods that have been applied over the last millennium. The first reconstruction method exploits the high correlation between the leading modes of variability of global precipitation and SSTs; the second method uses a multiregression model that exploits the multiple modes of covariability between precipitation and SSTs. Regardless of the proxy network density, the first method has skill only in the tropical eastern Pacific and misses some ENSO events. By contrast, the multiregression approach demonstrates high skill throughout the tropical Indo- Pacific region and predicts all ENSO events correctly. The advantage of the multiregression method lies in the second mode of covariability between SSTs and precipitation, which explains nearly 15% of the covariability between the two variables. However, when the period 1950–2000 is considered, the authors find that the nonstationarity in the second mode of covariability between SST and precipitation leads to a significant reduction of skill in the Indian Ocean and the warm pool region. This change suggests that the underlying stationarity assumption common in most climate field reconstruction methods needs to be treated more carefully, particularly in the tropics. [ABSTRACT FROM AUTHOR]