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D. E. Waliser, J. A. Ridout, S. Xie, and M. Zhang

Abstract

The objective of this study is to examine the effectiveness of the variational objective analysis (VOA) for producing realistic diagnoses of atmospheric field program data. Simulations from the Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) were sampled in a manner consistent with a typical field program using idealized sounding arrays and, surface and top of the atmosphere flux information. These data were then subject to a conventional form of analysis in which only a mass constraint was applied, hereafter referred to as the reference analysis, as well as to the complete VOA procedure. The diagnosed results from both analyses were then compared to time- and domain-averaged quantities from the model.

The results showed that for diagnosed vertical velocity and vertical advective tendencies, the VOA values typically exhibited considerably smaller errors compared to the values from the reference analyses, with the level of improvement and overall accuracy being dependent on synoptic and sampling conditions. The improvements tend to be greatest during disturbed conditions, with the errors typically being smaller and comparable between the two analyses during undisturbed conditions. The errors for both analyses increase as the spatial domain decreases and for the most part decrease with more frequent temporal sampling. However, the improvement achieved by having more frequent sampling is rather modest for the VOA since it already incorporates time-mean surface and TOA fluxes as constraints and thus indirectly incorporates some aspects of the variability between soundings. Highly relevant is the finding that overall the errors in vertical velocity and vertical advective tendencies from the reference analyses have a magnitude similar to, or greater than, the variability of the field being diagnosed, whereas the errors in these quantities from the VOA are typically less than the variability of the field. The analysis also showed no obvious systematic level-by-level improvement gained by the VOA analysis over the reference analysis in diagnosing the horizontal moisture flux convergence, mass divergence, or horizontal advective tendencies, notwithstanding the VOA's application of column-integrated constraints of mass, moisture, heat, and momentum conservation.

Additional soundings were found to be more beneficial to the reference analyses than the VOA analyses and in some cases allowed the error characteristics of the reference analysis to become similar to those of the VOA analysis. Noteworthy is the finding that the results from the VOA analyses using five soundings were often as good or better than the results from the reference analyses using nine soundings. The impact that hydrometeor measurements would have in providing additional constraints on the VOA was also investigated. The impact was found to be mostly negligible when averaging over relatively large space scales or timescales. On the other hand, for frequent sampling (e.g., 1–3 h) and small spatial scales (i.e., <∼100 km), there is a definite favorable impact on the VOA results for highly disturbed periods. The implications that the above results have on conducting atmospheric field programs and analyzing their results are discussed.

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Y. Zheng, D. E. Waliser, W. F. Stern, and C. Jones

Abstract

This study compares the tropical intraseasonal oscillation (TISO) variability in the Geophysical Fluid Dynamics Laboratory (GFDL) coupled general circulation model (CGCM) and the stand-alone atmospheric general circulation model (AGCM). For the AGCM simulation, the sea surface temperatures (SSTs) were specified using those from the CGCM simulation. This was done so that any differences in the TISO that emerged from the two simulations could be attributed to the coupling process and not to a difference in the mean background state. The comparison focused on analysis of the rainfall, 200-mb velocity potential, and 850-mb zonal wind data from the two simulations, for both summer and winter periods, and included comparisons to analogous diagnostics using NCEP–NCAR reanalysis and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) rainfall data.

The results of the analysis showed three principal differences in the TISO variability between the coupled and uncoupled simulations. The first was that the CGCM showed an improvement in the spatial variability associated with the TISO mode, particularly for boreal summer. Specifically, the AGCM exhibited almost no TISO variability in the Indian Ocean during boreal summer—a common shortcoming among AGCMs. The CGCM, on the other hand, did show a considerable enhancement in TISO variability in this region for this season. The second was that the wavenumber–frequency spectra of the AGCM exhibited an unrealistic peak in variability at low wavenumbers (1–3, depending on the variable) and about 3 cycles yr−1 (cpy). This unrealistic peak of variability was absent in the CGCM, which otherwise tended to show good agreement with the observations. The third difference was that the AGCM showed a less realistic phase lag between the TISO-related convection and SST anomalies. In particular, the CGCM exhibited a near-quadrature relation between precipitation and SST anomalies, which is consistent with observations, while the phase lag was reduced in the AGCM by about 1.5 pentads (∼1 week). The implications of the above results, including those for the notions of “perfect SST” and “two tier” experiments, are discussed, as are the caveats associated with the study's modeling framework and analysis.

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D. E. Waliser, K. M. Lau, W. Stern, and C. Jones

The objective of this study is to estimate the limit of dynamical predictability of the Madden–Julian oscillation (MJO). Ensembles of “twin” predictability experiments were carried out with the NASA Goddard Laboratory for the Atmospheres (GLA) atmospheric general circulation model (AGCM) using specified annual cycle SSTs. Initial conditions were taken from a 10-yr control simulation during periods of strong MJO activity identified via extended empirical orthogonal function (EOF) analysis of 30–90-day bandpassed tropical rainfall. From this analysis, 15 cases were chosen when the MJO convective center was located over the Indian Ocean, Maritime Continent, western Pacific Ocean, and central Pacific Ocean, respectively, making 60 MJO cases in total. In addition, 15 cases were selected that exhibited very little to no MJO activity. Two different sets of small random perturbations were added to these 75 initial states. Simulations were then performed for 90 days from each of these 150 perturbed initial conditions. A measure of potential predictability was constructed based on a ratio of the signal associated with the MJO, in terms of rainfall or 200-hPa velocity potential (VP200), and the mean-square error between sets of twin forecasts. This ratio indicates that useful predictability for this model's MJO extends out to about 25–30 days for VP200 and to about 10–15 days for rainfall. This is in contrast to the timescales of useful predictability associated with persistence forecasts or forecasts associated with daily “weather” variations, which in either case extend out only to about 10–15 days for VP200 and 8–10 days for rainfall. The predictability measure shows modest dependence on the phase of the MJO, with greater predictability for the convective phase at short (< ~5 days) lead times and for the suppressed phase at longer (> ~15 days) lead times. In addition, the predictability of intraseasonal variability during periods of weak MJO activity is significantly diminished compared to periods of strong MJO activity. The implications of these results as well as their associated model and analysis caveats are discussed.

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Stefan Liess, Duane E. Waliser, and Siegfried D. Schubert

Abstract

Our ability to predict active and break periods of the Asian summer monsoon is intimately tied to our ability to predict the intraseasonal oscillation (ISO). The present study analyzes the upper limit of potential predictability of the northern summer ISO, as it is simulated by the ECHAM5 atmospheric general circulation model forced with climatological SSTs. The leading extended empirical orthogonal functions of precipitation, computed from a 10-yr control simulation, are used to define four different phases of the ISO. Fourteen-member ensembles of 90-day hindcasts are run for each phase of the three strongest ISO events identified in the 10-yr control run. Initial conditions for each ensemble are created from the control simulation using a breeding method.

The signal-to-noise ratio is analyzed over a region that covers the core of the Asian summer monsoon activity. Over Southeast Asia, the upper limit for predictability of precipitation and 200-hPa zonal wind is about 27 and 33 days, respectively. Over India, values of more than 15 days occur for both variables. A spatial analysis of the different phases of the ISO reveals that the predictability follows the eastward- and northward-propagating ISO during the active and break phases of the monsoon. Precipitation reveals increased predictability at the end of the convective phase. Analogous, 200-hPa zonal wind shows strongest predictability during low and easterly anomalies. This potential predictability is considerably higher than for numerical forecasts of typical weather variations, particularly for the Tropics, indicating that useful forecasts of monsoon active and break events may be possible with lead times of more than two weeks for precipitation and the dynamics. A closer look at the breeding method used here to initialize the hindcasts shows the importance of appropriate ensemble experiment designs.

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L. E. Lucas, D. E. Waliser, P. Xie, J. E. Janowiak, and B. Liebmann

Abstract

Due to its long record length (approximately 25 years), the outgoing longwave radiation (OLR) dataset has been used in a multitude of climatological studies including studies on tropical circulation and convection, the El Niño–Southern Oscillation (ENSO) phenomenon, and the earth's radiation budget. Although many of the climatological studies using OLR have proven invaluable, proper interpretation of the low-frequency components of the data could be limited by the presence of biases introduced by changes in the satellite equatorial crossing time (ECT). Since long-term global changes could be masked or contaminated by this instrumental bias, it is necessary to take steps to ensure that the daily, global OLR dataset is free from such biases and is as accurate as possible.

The goal of this study is to derive a method for estimating the ECT biases in the daily, global OLR dataset. Our analysis utilizes a Procrustes targeted empirical orthogonal function rotation (REOF) on an interpolated OLR dataset to try to isolate and remove the two major ECT biases—afternoon satellite orbital drift and the abrupt transitions from a morning satellite to an afternoon satellite—from the dataset. Two targeted REOF analyses are performed to separate and distinguish between these two artificial satellite bias modes. A “common ECT” of approximately 0245 LST is established for the dataset by removing an estimate of these two ECT biases.

Results from the analysis indicate that changes in ECTs can cause large regional biases over both ocean and tropical landmasses. The afternoon satellite ECT drift-bias accounts for 0.4% of the pentad anomaly variance. During a single satellite series (e.g., NOAA-11), the afternoon drift-bias can introduce a difference as large as 10.5 W m−2 in the OLR values collected over most tropical landmasses. The morning to afternoon satellite transition bias accounts for 0.9% of the pentad anomaly variance, and is shown to cause a bias of 12 W m−2 in the OLR values over most tropical landmasses during the NOAA-SR satellite series. The data are corrected by removing a statistically derived synthetic eigenvector that is associated with each of the ECT bias modes. This synthetic eigenvector is used instead of the exact values of the satellite bias eigenvector to ensure that only the artificial variability is removed from the dataset.

The two REOF modes produced in this study are nearly orthogonal to each other having a correlation of only 0.17. This near orthogonality suggests that the use of the two-mode method presented in this study can more adequately describe the individual nature of each of the two ECT biases than a single REOF mode examined in previous studies. However, due to the presence of other forms of variability, it is likely that this study's estimate of the ECT bias includes ECT-related bias as well as some aspects of variability that may be associated with sensor changes, intersatellite calibration and/or natural climate variability. The strengths and limitations of the above technique are discussed, as are suggestions for future efforts.

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E. C. Massoud, H. Lee, P. B. Gibson, P. Loikith, and D. E. Waliser

Abstract

This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

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J.-L. F. Li, D. E. Waliser, G. Stephens, and Seungwon Lee

Abstract

The authors present an observationally based evaluation of the vertically resolved cloud ice water content (CIWC) and vertically integrated cloud ice water path (CIWP) as well as radiative shortwave flux downward at the surface (RSDS), reflected shortwave (RSUT), and radiative longwave flux upward at top of atmosphere (RLUT) of present-day global climate models (GCMs), notably twentieth-century simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), and compare these results to those of the third phase of the Coupled Model Intercomparison Project (CMIP3) and two recent reanalyses. Three different CloudSat and/or Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined ice water products and two methods are used to remove the contribution from the convective core ice mass and/or precipitating cloud hydrometeors with variable sizes and falling speeds so that a robust observational estimate can be obtained for model evaluations.

The results show that, for annual mean CIWC and CIWP, there are factors of 2–10 (either over- or underestimate) in the differences between observations and models for a majority of the GCMs and for a number of regions. Most of the GCMs in CMIP3 and CMIP5 significantly underestimate the total ice water mass because models only consider suspended cloud mass, ignoring falling and convective core cloud mass. For the annual means of RSDS, RLUT, and RSUT, a majority of the models have significant regional biases ranging from −30 to 30 W m−2. Based on these biases in the annual means, there is virtually no progress in the simulation fidelity of RSDS, RLUT, and RSUT fluxes from CMIP3 to CMIP5, even though there is about a 50% bias reduction improvement of global annual mean CIWP from CMIP3 to CMIP5. It is concluded that at least a part of these persistent biases stem from the common GCM practice of ignoring the effects of precipitating and/or convective core ice and liquid in their radiation calculations.

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Man-Li C. Wu, Siegfried D. Schubert, Max J. Suarez, Philip J. Pegion, and Duane E. Waliser

Abstract

The Madden–Julian oscillation (MJO) is known to have a substantial impact on the variability of the Asian–Australian summer monsoons. An important, but not well understood, aspect of the MJO–monsoon connection is the meridional propagation of bands of enhanced or reduced precipitation that are especially pronounced during the northern summer. In this study, the nature of the seasonality of the MJO is examined, with a focus on the meridional propagation, using both observations and simulations with an atmospheric general circulation model (AGCM).

A key result is that the AGCM, when forced with idealized eastward propagating equatorial dipole heating anomalies, reproduces the salient features of the observed seasonality in the precipitation and wind fields associated with the MJO, including meridional propagation into the Indian and Australian summer monsoon regions. An analysis of the simulations and observations shows that the off-equatorial precipitation anomalies are initiated by surface frictional convergence/divergence associated with the Rossby wave response to the leading pole of the equatorial heating dipole. The off-equatorial precipitation anomalies develop further by interacting with the trailing pole of the equatorial dipole heating to produce a northwest–southeast (or southwest–northeast) oriented line of surface convergence/divergence that propagates to the east. Since the prescribed heating does not vary by season, the seasonal asymmetry in the response must be the result of the seasonal changes in the background state. In particular, the results suggest that seasonal changes in both the vertical wind shear and static stability play a role.

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Xianan Jiang, Duane E. Waliser, Matthew C. Wheeler, Charles Jones, Myong-In Lee, and Siegfried D. Schubert

Abstract

Motivated by an attempt to augment dynamical models in predicting the Madden–Julian oscillation (MJO), and to provide a realistic benchmark to those models, the predictive skill of a multivariate lag-regression statistical model has been comprehensively explored in the present study. The predictors of the benchmark model are the projection time series of the leading pair of EOFs of the combined fields of equatorially averaged outgoing longwave radiation (OLR) and zonal winds at 850 and 200 hPa, derived using the approach of Wheeler and Hendon. These multivariate EOFs serve as an effective filter for the MJO without the need for bandpass filtering, making the statistical forecast scheme feasible for the real-time use. Another advantage of this empirical approach lies in the consideration of the seasonal dependence of the regression parameters, making it applicable for forecasts all year-round. The forecast model exhibits useful extended-range skill for a real-time MJO forecast. Predictions with a correlation skill of greater than 0.3 (0.5) between predicted and observed unfiltered (EOF filtered) fields still can be detected over some regions at a lead time of 15 days, especially for boreal winter forecasts. This predictive skill is increased significantly when there are strong MJO signals at the initial forecast time. The analysis also shows that predictive skill for the upper-tropospheric winds is relatively higher than for the low-level winds and convection signals. Finally, the capability of this empirical model in predicting the MJO is further demonstrated by a case study of a real-time “hindcast” during the 2003/04 winter. Predictive skill demonstrated in this study provides an estimate of the predictability of the MJO and a benchmark for the dynamical extended-range models.

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Xianan Jiang, Eric D. Maloney, Jui-Lin F. Li, and Duane E. Waliser

Abstract

As a key component of tropical atmospheric variability, intraseasonal variability (ISV) over the eastern North Pacific Ocean (ENP) exerts pronounced influences on regional weather and climate. Since general circulation models (GCMs) are essential tools for prediction and projection of future climate, current model deficiencies in representing this important variability leave us greatly disadvantaged in studies and prediction of climate change. In this study, the authors have assessed model fidelity in representing ENP ISV by analyzing 16 GCMs participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Among the 16 CMIP5 GCMs examined in this study, only seven GCMs capture the spatial pattern of the leading ENP ISV mode relatively well, although even these GCMs exhibit biases in simulating ISV amplitude. Analyses indicate that model fidelity in representing ENP ISV is closely associated with the ability to simulate a realistic summer mean state. The presence of westerly or weak mean easterly winds over the ENP warm pool region could be conducive to more realistic simulations of the ISV. One hypothesis to explain this relationship is that a realistic mean state could produce the correct sign of surface flux anomalies relative to the ISV convection, which helps to destabilize local intraseasonal disturbances. The projected changes in characteristics of ENP ISV under the representative concentration pathway 8.5 (RCP8.5) projection scenario are also explored based on simulations from three CMIP5 GCMs. Results suggest that, in a future climate, the amplitude of ISV could be enhanced over the southern part of the ENP while reduced over the northern ENP off the coast of Mexico/Central America and the Caribbean.

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