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G. Cesana, D. E. Waliser, D. Henderson, T. S. L’Ecuyer, X. Jiang, and J.-L. F. Li

Abstract

We assess the vertical distribution of radiative heating rates (RHRs) in climate models using a multimodel experiment and A-Train satellite observations, for the first time. As RHRs rely on the representation of cloud amount and properties, we first compare the modeled vertical distribution of clouds directly against lidar–radar combined cloud observations (i.e., without simulators). On a near-global scale (50°S–50°N), two systematic differences arise: an excess of high-level clouds around 200 hPa in the tropics, and a general lack of mid- and low-level clouds compared to the observations. Then, using RHR profiles calculated with constraints from A-Train and reanalysis data, along with their associated maximum uncertainty estimates, we show that the excess clouds and ice water content in the upper troposphere result in excess infrared heating in the vicinity of and below the clouds as well as a lack of solar heating below the clouds. In the lower troposphere, the smaller cloud amount and the underestimation of cloud-top height is coincident with a shift of the infrared cooling to lower levels, substantially reducing the greenhouse effect, which is slightly compensated by an erroneous excess absorption of solar radiation. Clear-sky RHR differences between the observations and the models mitigate cloudy RHR biases in the low levels while they enhance them in the high levels. Finally, our results indicate that a better agreement between observed and modeled cloud profiles could substantially improve the RHR profiles. However, more work is needed to precisely quantify modeled cloud errors and their subsequent effect on RHRs.

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F. M. Ralph, S. F. Iacobellis, P. J. Neiman, J. M. Cordeira, J. R. Spackman, D. E. Waliser, G. A. Wick, A. B. White, and C. Fairall

Abstract

Aircraft dropsonde observations provide the most comprehensive measurements to date of horizontal water vapor transport in atmospheric rivers (ARs). The CalWater experiment recently more than tripled the number of ARs probed with the required measurements. This study uses vertical profiles of water vapor, wind, and pressure obtained from 304 dropsondes across 21 ARs. On average, total water vapor transport (TIVT) in an AR was 4.7 × 108 ± 2 × 108 kg s−1. This magnitude is 2.6 times larger than the average discharge of liquid water from the Amazon River. The mean AR width was 890 ± 270 km. Subtropical ARs contained larger integrated water vapor (IWV) but weaker winds than midlatitude ARs, although average TIVTs were nearly the same. Mean TIVTs calculated by defining the lateral “edges” of ARs using an IVT threshold versus an IWV threshold produced results that differed by less than 10% across all cases, but did vary between the midlatitudes and subtropical regions.

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Bin Wang, Sun-Seon Lee, Duane E. Waliser, Chidong Zhang, Adam Sobel, Eric Maloney, Tim Li, Xianan Jiang, and Kyung-Ja Ha

Abstract

Realistic simulations of the Madden–Julian oscillation (MJO) by global climate models (GCMs) remain a great challenge. To evaluate GCM simulations of the MJO, the U.S. CLIVAR MJO Working Group developed a standardized set of diagnostics, providing a comprehensive assessment of statistical properties of the MJO. Here, a suite of complementary diagnostics has been developed that provides discrimination and assessment of MJO simulations based on the perception that the MJO propagation has characteristic dynamic and thermodynamic structures. The new dynamics-oriented diagnostics help to evaluate whether a model produces eastward-propagating MJOs for the right reasons. The diagnostics include 1) the horizontal structure of boundary layer moisture convergence (BLMC) that moistens the lower troposphere to the east of a convection center, 2) the preluding eastward propagation of BLMC that leads the propagation of MJO precipitation by about 5 days, 3) the horizontal structure of 850-hPa zonal wind and its equatorial asymmetry (Kelvin easterly versus Rossby westerly intensity), 4) the equatorial vertical–longitudinal structure of the equivalent potential temperature and convective instability index that reflects the premoistening and predestabilization processes, 5) the equatorial vertical–longitudinal distribution of diabatic heating that reflects the multicloud structure of the MJO, 6) the upper-level divergence that reflects the influence of stratiform cloud heating, and 7) the MJO available potential energy generation that reflects the amplification and propagation of an MJO. The models that simulate better three-dimensional dynamic and thermodynamic structures of MJOs generally reproduce better eastward propagations. This evaluation identifies a number of shortcomings in representing dynamical and heating processes relevant to the MJO simulation and reveals potential sources of the shortcomings.

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D. Kim, K. Sperber, W. Stern, D. Waliser, I.-S. Kang, E. Maloney, W. Wang, K. Weickmann, J. Benedict, M. Khairoutdinov, M.-I. Lee, R. Neale, M. Suarez, K. Thayer-Calder, and G. Zhang

Abstract

The ability of eight climate models to simulate the Madden–Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November–April) behavior. Initially, maps of the mean state and variance and equatorial space–time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space–time spectral peak in the zonal wind compared to that of precipitation. Using the phase–space representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (∼20–29 days) than observations (∼31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30–80-day band.

Several key variables associated with the model’s MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.

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I.-S. Kang, K. Jin, K.-M. Lau, J. Shukla, V. Krishnamurthy, S. D. Schubert, D. E. Waliser, W. F. Stern, V. Satyan, A. Kitoh, G. A. Meehl, M. Kanamitsu, V. Ya. Galin, Akimasa Sumi, G. Wu, Y. Liu, and J.-K. Kim

Abstract

The atmospheric anomalies for the 1997/98 El Niño–Southern Oscillation (ENSO) period have been analyzed and intercompared using the data simulated by the atmospheric general circulation models (GCMs) of 11 groups participating in the Monsoon GCM Intercomparison Project initiated by the Climate Variability and Prediction Program (CLIVAR)/Asian–Australian Monsoon Panel. Each participating GCM group performed a set of 10 ensemble simulations for 1 September 1996–31 August 1998 using the same sea surface temperature (SST) conditions but with different initial conditions. The present study presents an overview of the intercomparison project and the results of an intercomparison of the global atmospheric anomalies during the 1997/98 El Niño period. Particularly, the focus is on the tropical precipitation anomalies over the monsoon–ENSO region and the upper-tropospheric circulation anomalies in the Pacific–North American (PNA) region.

The simulated precipitation anomalies show that all of the models simulate the spatial pattern of the observed anomalies reasonably well in the tropical central Pacific, although there are large differences in the amplitudes. However, most of the models have difficulty in simulating the negative anomalies over the Maritime Continent during El Niño. The 200-hPa geopotential anomalies over the PNA region are reasonably well reproduced by most of the models. But, the models generally underestimate the amplitude of the PNA pattern. These weak amplitudes are related to the weak precipitation anomalies in the tropical Pacific. The tropical precipitation anomalies are found to be closely related to the SST anomalies not only during the El Niño seasons but also during the normal seasons that are typified by weak SST anomalies in the tropical Pacific. In particular, the pattern correlation values of the 11-model composite of the precipitation anomalies with the observed counterparts for the normal seasons are near 0.5 for the tropical region between 30°S and 30°N.

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W.-K. Tao, Y. N. Takayabu, S. Lang, S. Shige, W. Olson, A. Hou, G. Skofronick-Jackson, X. Jiang, C. Zhang, W. Lau, T. Krishnamurti, D. Waliser, M. Grecu, P. E. Ciesielski, R. H. Johnson, R. Houze, R. Kakar, K. Nakamura, S. Braun, S. Hagos, R. Oki, and A. Bhardwaj

Abstract

Yanai and coauthors utilized the meteorological data collected from a sounding network to present a pioneering work in 1973 on thermodynamic budgets, which are referred to as the apparent heat source (Q 1) and apparent moisture sink (Q 2). Latent heating (LH) is one of the most dominant terms in Q 1. Yanai’s paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q 1 data have been used to generate the look-up tables in Tropical Rainfall Measuring Mission (TRMM) LH algorithms. A set of algorithms developed for retrieving LH profiles from TRMM-based rainfall profiles is described and evaluated, including details concerning their intrinsic space–time resolutions. Included in the paper are results from a variety of validation analyses that define the uncertainty of the LH profile estimates. Also, examples of how TRMM-retrieved LH profiles have been used to understand the life cycle of the MJO and improve the predictions of global weather and climate models as well as comparisons with large-scale analyses are provided. Areas for further improvement of the TRMM products are discussed.

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F. Vitart, C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Déqué, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H.-S. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A. W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D.-J. Won, H. Xiao, R. Zaripov, and L. Zhang

Abstract

Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).

The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

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