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Atmospheric rivers (ARs) are narrow regions of strong horizontal water vapor transport that play important roles in the global water cycle, weather, and hydrology. Motivated by challenges in simulating ARs with state-of-the-art global models, this paper diagnoses model errors with a focus on relative contributions of moisture convergence, evaporation, and precipitation to AR column-integrated water vapor (IWV) budget. Using 20-year simulations by 24 global weather/climate models, budget terms are calculated for four AR sectors: postfrontal, frontal, prefrontal, and pre-AR, with biases assessed against two reanalysis products. The results indicate that each sector is unique in terms of the dominant water vapor balance, and that the terms exhibiting the largest intermodel spread are the same terms dominating the water vapor balance in each sector. Overall, simulated bulk AR characteristics (e.g., geometry, frequency, and intensity) are more sensitive to biases in IVT convergence and IWV tendency than to biases in evaporation and precipitation, although evaporation/precipitation biases do affect key AR bulk characteristics in selected sectors. The large intermodel spread (particularly for precipitation) and, in certain cases, discrepancies between the reanalysis references themselves (particularly for precipitation types) highlight the need for observational efforts that target better constraining AR processes in weather/climate models and reanalyses.

Uncertainty in calculating the spatial- and interannual variability of precipitation over India and Tibet from widely used gridded precipitation datasets is examined for the 29-year period from 1979 to 2007. Uncertainty is defined in terms of the spread among the variability calculated from multiple datasets, a useful method when multiple datasets of similar or unknown accuracy are available for analyses. The resulting uncertainty varies for regions and seasons. Geographical variations are clearly seen in the signal-to-noise ratio (SNR), with the largest uncertainty in the Jammu and Kashmir (J&K), western India-eastern Pakistan, Tibet, Hindu-Kush mountains, and Western Ghats which are characterized by either dry climate or complex terrain, or both. Seasonally, the uncertainty is larger for the December–February period (DJF) than for the June–September period (JJAS) in most of the region except J&K which is characterized by two wet seasons: winter and summer. The uncertainty in the interannual variability also varies according to regions especially in J&K where the calculated interannual variability varies by nearly a factor of two among the datasets. The uncertainty range in the calculated interannual variability is determined largely by two gauge-based data of the finest resolution, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of water resources (smallest) and India Meteorological Department (largest) in all regions. The regional and seasonal variations in the uncertainty do not appear to depend on either the spatial resolution or the length of records. This implies that analysis methodology such as the quality control of input data, spatial/temporal interpolation, and retrieval algorithms used in producing these gridded datasets plays a crucial role in determining the characteristics of precipitation climatology represented by individual datasets. Our results show that calculating precipitation characteristics must be accompanied by careful examinations of uncertainty among available datasets, especially for dry seasons and arid/mountainous regions.

The objective of this study is to estimate the limit of dynamical predictability for the tropical intraseasonal oscillation (ISO) associated with the Asian summer monsoon. Ensembles of ‘twin’ predictability experiments were carried out with the National Aeronautics and Space Administration Goddard Laboratory for the Atmospheres atmospheric general circulation model using specified annual cycle sea surface temperatures. Initial conditions were taken from a 10-year control simulation during periods of strong ISO activity identified via extended empirical orthogonal function analysis of 30–90 day band-passed tropical rainfall. From this analysis, 21 cases were chosen for each of four distinct phases of the ISO cycle, making 84 cases in total. Two different sets of small random perturbations were added to these 84 initial states. Simulations were then performed for 90 days from each of these 168 perturbed initial conditions. A measure of potential predictability was constructed based on a ratio of the signal associated with the ISO, in terms of rainfall or 200 hPa velocity potential (VP200), and the mean-square difference between sets of twin forecasts. This ratio indicates that the limit of predictability for this model's ISO extends out to about 25 days for VP200 and to about 15 days for rainfall. The predictability measure shows modest dependence on the strength of the ISO events, with stronger events having a greater limit of predictability by a few days. It also exhibits a dependence on the phase of the ISO, with greater predictability for the convective phase at short (≲5 days) lead times and for the suppressed phase at longer (≳15 days) lead times. The implications of these results as well as their associated model and analysis caveats are discussed. Copyright © 2003 Royal Meteorological Society