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Author(s): Rutz, JJ; Shields, CA; Lora, JM; Payne, AE; Guan, B; Ullrich, P; O’Brien, T; Leung, LR; Ralph, FM; Wehner, M; Brands, S; Collow, A; Goldenson, N; Gorodetskaya, I; Griffith, H; Kashinath, K; Kawzenuk, B; Krishnan, H; Kurlin, V; Lavers, D; Magnusdottir, G; Mahoney, K; McClenny, E; Muszynski, G; Nguyen, PD; Prabhat, M; Qian, Y; Ramos, AM; Sarangi, C; Sellars, S; Shulgina, T; Tome, R; Waliser, D; Walton, D; Wick, G; Wilson, AM; Viale, M | Abstract: Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12, and 24 km over the Great Plains of the United States. We also examined the sensitivity of the simulated precipitation to different spectral nudging approaches and the cumulus parameterizations. The rainfall characteristics in the observations and simulations were defined as a hourly diurnal cycle of precipitation and a joint probability distribution function (JPDF) between duration and peak intensity of precipitation events over the Great Plains in summer. We calculated a JPDF for each dataset and the overlapping area between observed and simulated JPDFs to measure the similarity between the two JPDFs. Comparison of the diurnal precipitation cycles between observations and simulations does not reveal the added value of high-resolution simulations. However, the performance of NU-WRF simulations measured by the JPDF metric strongly depends on horizontal resolution. The simulation with the highest resolution of 4 km shows the best agreement with the observations in simulating duration and intensity of wet spells. Spectral nudging does not affect the JPDF significantly. The effect of cumulus parameterizations on the JPDFs is considerable but smaller than that of horizontal resolution. The simulations with lower resolutions of 12 and 24 km show reasonable agreement but only with the high-resolution observational data that are aggregated into coarse resolution and spatially averaged.

Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and meteorological/hydrological extremes. Increasing evidence shows ARs have signatures and impacts in many regions across different continents. However, global-scale characterizations of AR representations in weather and climate models have been very limited. Using a recently developed AR detection algorithm oriented for global applications, the representation of AR activities in multi-decade weather/climate simulations is evaluated. The algorithm is applied to 6-hourly (daily) integrated water vapor transport from 22 (2) global weather/climate models that participated in the GASS-YoTC Multi-model Experiment, including four models with ocean-atmosphere coupling and two models with super-parameterization. Multiple reanalysis products are used as references to help quantify model errors in the context of reanalysis uncertainty. Model performance is examined for key aspects of ARs (frequency, intensity, geometry, seasonality), with the focus on identifying and understanding systematic errors in simulated ARs. The results highlight the range of model performances relative to reanalysis uncertainty in representing the most basic features of ARs. Among the 17 metrics considered, AR frequency, the zonal component of the integrated water vapor transport (IVT), fractional zonal circumference, fractional total meridional IVT, and three seasonality metrics have consistently large errors across all models. Possible connections between AR simulation qualities and aspects of model configurations are discussed. Despite the lack of a monotonic relationship, the importance of model horizontal resolution to the overall quality of AR simulation is suggested by the evaluation results.

The coupled global climate model (GCM) fidelity in representing upper ocean salinity including near sea surface bulk salinity (SSS) is evaluated in this study, with a focus on the Pacific Ocean. The systematic biases in ocean surface evaporation (E) minus precipitation (P) and SSS are found to be fairly similar in the twentieth century simulations of the Coupled Model Intercomparison Phase 3 (CMIP3) and Phase 5 (CMIP5) relative to the observations. One of the potential causes of the CMIP model biases is the missing representation of the radiative effects of precipitating hydrometeors (i.e., snow) in most CMIP models. To examine the radiative effect of cloud snow on SSS, sensitivity experiments with and without such effect are conducted by the National Center for Atmospheric Research-coupled Community Earth System Model (CESM). This study investigates the difference in SSS between sensitivity experiments and its relationship with atmospheric circulation, E - P and air-sea heat fluxes. It is found that the exclusion of the cloud snow radiative effect in CESM produces weaker Pacific trade winds, resulting in enhanced precipitation, reduced evaporation, and a reduction of the upper ocean salinity in the tropical and subtropical Pacific. The latter results in an improved comparison with climatological upper ocean bulk salinity. The introduction of cloud snow also altered the budget terms that maintain the time-mean salinity in the mixed layer.

Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and regional weather/hydrology. A technique is developed for objective detection of ARs on the global domain based on characteristics of the integrated water vapor transport (IVT). AR detection involves thresholding 6-hourly fields of ERA-Interim IVT based on the 85th percentile specific to each season and grid cell and a fixed lower limit of 100 kg m−1 s−1 and checking for the geometry requirements of length >2000 km, length/width ratio >2, and other considerations indicative of AR conditions. Output of the detection includes the AR shape, axis, landfall location, and basic statistics of each detected AR. The performance of the technique is evaluated by comparison to AR detection in the western North America, Britain, and East Antarctica with three independently conducted studies using different techniques, with over ~90% agreement in AR dates. Among the parameters tested, AR detection shows the largest sensitivity to the length criterion in terms of changes in the resulting statistical distribution of AR intensity and geometry. Global distributions of key AR characteristics are examined, and the results highlight the global footprints of ARs and their potential importance on global and regional scales. Also examined are seasonal dependence of AR frequency and precipitation and their modulation by four prominent modes of large-scale climate variability. The results are in broad consistency with previous studies that focused on landfalling ARs in the west coasts of North America and Europe.

We take advantage of climate simulations from two multimodel experiments to characterize and evaluate the cloud phase partitioning in 16 general circulation models (GCMs), specifically the vertical structure of the transition between liquid and ice in clouds. We base our analysis on the ratio of ice condensates to the total condensates (phase ratio, PR). Its transition at 90% (PR90) and its links with other relevant variables are evaluated using the GCM-Oriented Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Cloud Product climatology, reanalysis data, and other satellite observations. In 13 of 16 models, the PR90 transition height occurs too low (6 km to 8.4 km) and at temperatures too warm (−13.9°C to −32.5°C) compared to observations (8.6 km, −33.7°C); features consistent with a lack of supercooled liquid with respect to ice above 6.5 km. However, this bias would be slightly reduced by using the lidar simulator. In convective regimes (more humid air and precipitation), the observed cloud phase transition occurs at a warmer temperature than for subsidence regimes (less humid air and precipitation). Only few models manage to roughly replicate the observed correlations with humidity (5/16), vertical velocity (5/16), and precipitation (4/16); 3/16 perform well for all these parameters (MPI-ESM, NCAR-CAM5, and NCHU). Using an observation-based Clausius-Clapeyron phase diagram, we illustrate that the Bergeron-Findeisen process is a necessary condition for models to represent the observed features. Finally, the best models are those that include more complex microphysics.

Author(s): Ryoo, JM; Waliser, DE; Waugh, DW; Wong, S; Fetzer, EJ; Fung, I | Abstract: We investigate transport pathways of water vapor associated with landfalling atmospheric river (AR) events that result in precipitation along the West Coast of the U.S. for winters of 1997-2010. The water vapor transport pathways are determined by computing back trajectories with a trajectory model using the Modern Era Retrospective analysis for Research and Applications reanalysis data set. The majority of AR events (86%) over the West Coast of the U.S. are grouped into three trajectory types, and two of them are closely associated with the AR events. We designate the first type as Ascending near landfall and of Tropical Origin (AT), the second type as Ascending near landfall and of Extratropical Origin (AE), and the third type as Descending or parallel near landfall and of Extratropical Origin (DE), which is accompanied but not directly associated with the AR events. The magnitude and spatial distribution of precipitation of a given AR event are found to be strongly determined by the type of trajectories. In general, AR events composed of both AT and AE trajectories have more frequent precipitation over a broad region of the western U.S. and AR events composed of both AT and DE trajectories have intense precipitation over the southwestern U.S. due to AT trajectories. AR events of AT-only trajectories have intense precipitation, especially over the northwestern U.S., but are less frequent compared to those of AT + AE trajectories. In addition, different patterns of trajectory types among AR events are closely linked to upper level potential vorticity (PV) anomalies; 66% of AR events are associated with anticyclonic Rossby wave breaking events.

Global weather and climate models often exclude the effects of precipitating hydrometeors and convective core mass on radiative fluxes. In particular, many models split the ice phase into separate “cloud ice” and “snow” categories representing the smaller and larger ice particles, respectively; a separation that is generally not well defined in observations. A version of the European Centre for Medium-Range Weather Forecasts (ECMWF) global numerical weather prediction model which includes the radiative effects of cloud liquid, cloud ice, and precipitating snow is used to investigate the impact of including and excluding the radiative effects of the precipitating snow category. The results show that exclusion of precipitating snow in the radiation calculations leads to differences in the shortwave and longwave radiative fluxes of 5–15 W m−2 in strongly precipitating and convective areas. These differences are of the same order of magnitude as the systematic errors in the model compared to satellite observations. Corresponding biases in the radiative heating profiles are on the order of 0.15 K d−1. The results imply that precipitating snow should be included in the radiative calculations in all weather and climate models in the context of improving model fidelity and reducing compensating errors.

Conventional global climate models (GCMs) often consider radiation interactions only with small-particle/suspended cloud mass, ignoring large-particle/falling and convective core cloud mass. We characterize the radiation and atmospheric circulation impacts of frozen precipitating hydrometeors (i.e., snow), using the National Center for Atmospheric Research coupled GCM, by conducting sensitivity experiments that turn off the radiation interaction with snow. The changes associated with the exclusion of precipitating hydrometeors exhibit a number differences consistent with biases in CMIP3 and CMIP5 (Coupled Model Intercomparison Project Phase 3 and Phase 5), including more outgoing longwave flux at the top of atmosphere and downward shortwave flux at the surface in the heavily precipitating regions. Neglecting the radiation interaction of snow increases the net radiative cooling near the cloud top with the resulting increased instability triggering more convection in the heavily precipitating regions of the tropics. In addition, the increased differential vertical heating leads to a weakening of the low-level mean flow and an apparent low-level eastward advection from the warm pool resulting in moisture convergence south of the Intertropical Convergence Zone and north of the South Pacific Convergence Zone (SPCZ). This westerly bias, with effective warm and moist air transport, might be a contributing factor in the model's northeastward overextension of the SPCZ and the concomitant changes in sea surface temperatures, upward motion, and precipitation. Broader dynamical impacts include a stronger local meridional overturning circulation over the middle and east Pacific and commensurate changes in low and upper level winds, large-scale ascending motion, with a notable similarity to the systematic bias in this region in CMIP5 upper level zonal winds.

[1] We evaluate the annual mean radiative shortwave flux downward at the surface (RSDS) and reflected shortwave (RSUT) and radiative longwave flux upward at top of atmosphere (RLUT) from the twentieth century Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 3 (CMIP3) simulations as well as from the NASA GEOS5 model and Modern-Era Retrospective Analysis for Research and Applications analysis. The results show that a majority of the models have significant regional biases in the annual means of RSDS, RLUT, and RSUT, with biases from −30 to 30 W m−2. While the global average CMIP5 ensemble mean biases of RSDS, RLUT, and RSUT are reduced compared to CMIP3 by about 32% (e.g., −6.9 to 2.5 W m−2), 43%, and 56%, respectively. This reduction arises from a more complete cancellation of the pervasive negative biases over ocean and newly larger positive biases over land. In fact, based on these biases in the annual mean, Taylor diagram metrics, and RMSE, there is virtually no progress in the simulation fidelity of RSDS, RLUT, and RSUT fluxes from CMIP3 to CMIP5. A persistent systematic bias in CMIP3 and CMIP5 is the underestimation of RSUT and overestimation of RSDS and RLUT in the convectively active regions of the tropics. The amount of total ice and liquid atmospheric water content in these areas is also underestimated. We hypothesize that at least a part of these persistent biases stem from the common global climate model practice of ignoring the effects of precipitating and/or convective core ice and liquid in their radiation calculations.

In this study, MODIS fine mode fraction and MISR non-spherical fraction are 2used to derive dust and smoke AOT components (tau(sub dust) and tau(sub smoke)) over the tropical Atlantic, and their variabilities related to the Madden-Julian Oscillation (MJO) are then investigated. Both MODIS and MISR show a very similar dust and smoke winter climatology. tau(sub dust) is found to be the dominant aerosol component over the tropical Atlantic while tau(sub smoke) is significantly smaller than tau(sub dust). The daily MODIS and MISR tau(sub dust) are overall highly correlated, with the correlation coefficients typically about 0.7 over the North Atlantic. The consistency between the MODIS and MISR dust and smoke aerosol climatology and daily variations give us confidence to use these two data sets to investigate their relative contributions to the total AOT variation associated with the MJO. However, unlike the MISR dust discrimination, which is based on particle shape retrievals, the smoke discrimination is less certain, based on assumed partitioning of maritime aerosol for both MISR and MODIS. The temporal evolution and spatial patterns of the tau(sub dust) anomalies associated with the MJO are consistent between MODIS and MISR. The tau(sub dust) anomalies are very similar to those of tau anomalies, and are of comparable magnitude. In contrast, the MJO-related tau(sub smoke) anomalies are rather small, and the tau(sub mar) anomalies are negligible. The consistency between the MODIS and MISR results suggests that dust aerosol is the dominant component on the intra-seasonal time scale over the tropical Atlantic Ocean.

[1] The effect of orbital sampling is one of the chief uncertainties in satellite–climate model comparisons. In the context of an ongoing activity to make satellite data more accessible for model evaluation (i.e., obs4MIPs), six variables (temperature, specific humidity, ozone, cloud water, cloud cover, and ocean surface wind) associated with six satellite instruments are evaluated for the orbital sampling effect. Comparisons are made between reanalysis and simulated satellite-sampled data in terms of bias and pattern similarity. It is found that the bias introduced by orbital sampling for long-term annual means, monthly climatologies, and monthly means is largely negligible, which is within ~3% of the standard deviation of the three quantities for most fields. The bias for 2-hPa temperature and specific humidity, while relatively large (9–10%), is within the estimated observational uncertainty. In terms of pattern similarity, cloud water and upper level specific humidity are the most sensitive to orbital sampling among the variables considered, with the magnitude of the sampling effect dependent on the spatial resolution—insignificant at 1.25° × 1.25° resolution for both. For all variables considered, orbital sampling effects are not an important consideration for model evaluation at 1.25° × 1.25° resolution. At 0.5° × 0.5°, orbital sampling is potentially important for cloud water and upper level specific humidity when evaluating model long-term annual means and monthly climatologies, and for cloud water when evaluating monthly means, all in terms of pattern similarities. Orbital sampling is not an important factor for evaluating zonal means in call cases considered.

We investigate the connection between the equatorial Madden‐Julian Oscillation (MJO) and different types of the Northern Hemisphere mid‐winter major stratospheric sudden warmings (SSWs), i.e., vortex‐displacement and vortex‐split SSWs. The MJO‐SSW relationship for vortex‐split SSWs is stronger than that for vortex‐displacement SSWs, as a result of the stronger and more coherent eastward propagating MJOs before vortex‐split SSWs than those before vortex‐displacement SSWs. Composite analysis indicates that both the intensity and propagation features of MJO may influence the MJO‐related circulation pattern at high latitudes and the type of SSWs. A pronounced Quasi‐Biennial Oscillation (QBO) dependence is found for vortex‐displacement and vortex‐split SSWs, with vortex‐displacement (‐split) SSWs occurring preferentially in easterly (westerly) QBO phases. The lagged composites suggest that the MJO‐related anomalies in the Arctic are very likely initiated when the MJO‐related convection is active over the equatorial Indian Ocean (around the MJO phase 3). Further analysis suggests that the QBO may modulate the MJO‐related wave disturbances via its influence on the upper tropospheric subtropical jet. As a result, the MJO‐related circulation pattern in the Arctic tends to be wave number‐one/wave number‐two ~25–30 days following phase 3 (i.e., approximately phases 7–8, when the MJO‐related convection is active over the western Pacific) during easterly/westerly QBO phases, which resembles the circulation pattern associated with vortex‐displacement/vortex‐split SSWs.

This study examines the simulation quality of the surface heat flux fields produced during a climate simulation of the Navy Operational Global Atmospheric Prediction System, version 3.4, with a reduced spectral truncation of T63 and 18 levels (herineafter referred to as NOGAPS-CL). Comparisons are made between a 17-year NOGAPS-CL simulation using monthly sea surface temperatures as surface boundary conditions and a number of validating data sets consisting of ship, satellite, and/or reanalysis-based surface heat fluxes, precipitation, top of the atmosphere radiation budget, water vapor, cloud frequency, surface wind stress, and tropospheric winds. In this extended, long-range integration, NOGAPS-CL underpredicts the net surface shortwave flux in much of the subtropical oceans and overpredicts the net shortwave flux in the western Pacific warm pool and the midlatitude oceans, when compared to several satellite-derived climatological data sets. In addition, NOGAPS-CL over predicts the latent heat flux in much of the subtropics and under predicts the latent heat flux over the northern ocean western boundary currents and under the storm track regions that extend eastward from them. These shortwave and evaporation biases combine to produce errors in the surface net heat flux, with too little heat entering the subtropical/tropical oceans and too much heat loss in the midlatitudes oceans. Examination of related quantities indicates that the tropical climate biases are coupled to shortcomings in the convective cloud and/or boundary layer parameterizations which leads to the premature release of moist instability from the boundary layer in regions just outside the deep convective zones. This leads to enhanced climatological cloudiness, rainfall, and surface evaporation, as well as to a reduction in the surface shortwave flux and outgoing longwave radiation (OLR), in the subtropical regions. Furthermore, because of this early release of the moist static energy, there is a reduction in clouds, rainfall and water vapor content, as well as enhanced surface shortwave flux and outgoing longwave radiation, in the deep convective zones. The reduction in rainfall and enhanced OLR reduces the strength of the tropical large-scale circulation, which in turn reduces the strength of the subsidence in the subtropical regions which normally acts to suppress the convection processes in these regions. The implications of these results are discussed in terms of the relationship among the forecast model climatological surface fluxes, convection, clouds, and the dynamical processes, as well as their similarities to other climate models and their possible impact on the simulation of transient systems.

Two years of surface shortwave flux data, from five buoys in the subtropical North Atlantic Ocean during the Subduction Experiment, were used to examine shortwave absorption in the atmosphere, and its partitioning between the clear and cloudy sky. Robust methods were used to isolate the clear-sky shortwave observations so that they could be directly compared to values derived using a single-column version of the National Center for Atmospheric Research Community Climate Model radiation code. The model-derived values agreed with the observations to within 0.5% mean relative error. Additional analysis showed that the model-data clear-sky surface shortwave differences showed no systematic relationship with respect to column water vapor amount. These results indicate that clear-sky absorption of shortwave radiation appears to be well modeled by current theory. Model-derived clear-sky surface shortwave values were combined with the observed (all-sky) values to determine the surface shortwave cloud forcing. The mean of these series were combined with 5-year mean Earth Radiation Budget Experiment derived top of the atmosphere (TOA) cloud forcing values to estimate the surface to TOA cloud forcing ratio. The resulting values range between 1.25 and 1.59. These values, along with the agreement between modeled and observed clear-sky surface shortwave, support the suggestion that our current theoretical radiative transfer models do not properly account for the amount of shortwave energy absorbed by the cloudy atmosphere. Mean values from the 2-year shortwave flux time series were compared to mean values from two climatologies derived from bulk parameterizations that utilize ship-based cloud reports. These comparisons show that the Oberhuber climatology underestimates the surface shortwave flux by ∼20% (∼40 W m -2 ), while the Esbensen and Kushnir climatology underestimates the flux by ∼4% (∼8 W m -2 ). The observed mean values were also compared to five satellite-derived climatologies. These comparisons showed much better and more consistent agreement, with relative bias errors ranging from about -1 to 6%. Comparisons to contemporaneous, daily-average satellite derived values show relatively good agreement as well, with relative biases of the order of 2% (∼3-9 W m -2 ) and root-mean-square differences of ∼10% (25-30 W m -2 ). Aspects of the role aerosols play in the above results are discussed along with the implications of the above results on the integrity of open-ocean buoy measurements of surface shortwave flux and the possibility of using the techniques developed in this study to remotely monitor the operating condition of buoy-based shortwave radiometers.

[1] The height of the planetary boundary layer (PBL) is an important parameter that relates to the various processes associated with the PBL. In this paper, we use Global Positioning System radio occultation (GPSRO) measurements to derive a global climatology of PBL heights. Utilizing the strength of GPSRO in capturing fine vertical structures, the top of the PBL is defined to be the height at which the vertical gradient of the refractivity or water vapor partial pressure is minimum, corresponding to the height where the refractivity or water vapor pressure changes most rapidly. A “sharpness parameter” is defined that quantifies the applicability of these definitions. The sharpness parameter is largest over the subtropical regions characterized by strong subsidence. When the sharpness parameter is large, the refractivity- and moisture-based heights are shown to converge. We derived global PBL height climatology using three years (Dec. 2006–Nov. 2009) of COSMIC/FORMOSAT-3 measurements and compared with values calculated from ECMWF Reanalysis Interim (ERA-Int). We found that the mean PBL heights from GPSRO shared similar spatial and seasonal variations with ERA-Int; however, GPSRO heights were higher by 500 m. The standard deviation was also higher from GPSRO, especially in the tropics. We present detailed comparisons between GPSRO and ERA-Int over the Pacific Ocean and the Sahara desert and examine the PBL height distributions as well as its annual and diurnal variabilities. These results suggest that the underlying causes of the bias between GPSRO and ERA-Int likely vary from region to region.

[1] We perform an observationally based evaluation of the cloud ice water content (CIWC) and path (CIWP) of present-day GCMs, notably 20th century CMIP5 simulations, and compare these results to CMIP3 and two recent reanalyses. We use three different CloudSat + CALIPSO ice water products and two methods 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 CIWP, there are factors of 2–10 in the differences between observations and models for a majority of the GCMs and for a number of regions. However, there are a number of CMIP5 models, including CNRM-CM5, MRI, CCSM4 and CanESM2, as well as the UCLA CGCM, that perform well compared to our past evaluations. Systematic biases in CIWC vertical structure occur below the mid-troposphere where the models overestimate CIWC, with this bias arising mostly from the extratropics. The tropics are marked by model differences in the level of maximum CIWC (∼250–550 hPa). Based on a number of metrics, the ensemble behavior of CMIP5 has improved considerably relative to CMIP3, although neither the CMIP5 ensemble mean nor any individual model performs particularly well, and there are still a number of models that exhibit very large biases despite the availability of relevant observations. The implications of these results on model representations of the Earth radiation balance are discussed, along with caveats and uncertainties associated with the observational estimates, model and observation representations of the precipitating and cloudy ice components, relevant physical processes and parameterizations.

[1] In this study, we examine the detailed spatiotemporal patterns and vertical structure of the intraseasonal temperature variability in the upper troposphere and lower stratosphere (UTLS) associated with the Madden-Julian Oscillation (MJO) using the temperature profiles from the recent Global Positioning System radio occultation (GPS RO) measurements including the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. The MJO-related temperature anomalies in the UTLS are smaller near the equator ( 1.2 K). Near the equator, the temperature anomalies exhibit an eastward tilt with height from the upper troposphere (UT) to the lower stratosphere (LS) and their magnitudes and signs are determined by the strength of convective anomalies and vertical pressure level. The subtropical temperature anomalies have similar magnitudes and patterns at a given location between the UT (250 hPa to 150 hPa) and the LS (150 hPa to 50 hPa) except for opposite signs that change around 150 hPa. The subtropical warm (cold) anomalies in the UT and cold (warm) anomalies in the LS are typically collocated with the subtropical positive (negative) tropopause height anomalies/cyclones (anticyclones) and flank or lie to the west of equatorial enhanced (suppressed) convection. We also compare the intraseasonal temperature variability in the UTLS related to the MJO between the GPS RO and Atmospheric Infrared Sounder (AIRS) measurements to highlight the new features of the GPS RO temperature anomalies and to evaluate the quality of the AIRS temperature in the UTLS considering the GPS RO temperature in the UTLS as the benchmark. Both AIRS and GPS RO have a very consistent vertical structure in the subtropical UTLS with a high correlation coefficient 0.92 and similar magnitudes. Both AIRS and GPS RO also show a generally consistent vertical structure of the intraseasonal temperature anomalies in the equatorial UTLS. However, GPS RO reveals many detailed fine-scale vertical structures of the equatorial temperature anomalies between 150 and 50 hPa that are not well captured by AIRS. Furthermore, the equatorial temperature anomalies are about 40% underestimated in AIRS in comparison to GPS RO, over the equatorial Indian and western Pacific Oceans for 250 hPa and over all longitudes for 100 hPa. The low sampling within the optically thick clouds and low vertical resolution near the tropopause may both contribute to these deficiencies of AIRS.

[1] This study validates the cloud ice water content (IWC, non-precipitating ice/non-snow) produced by a unique prognostic cloud ice parameterization when used in the UCLA atmospheric general circulation model against CloudSat observations, and also compares it with the ERA-Interim reanalysis. A distinctive aspect of this parameterization is the novel treatment of the conversion of cloud ice to precipitating snow. The ice-to-snow autoconversion time scale is a function of differential infrared radiative heating and environmental static stability. The simulated IWC is in agreement with CloudSat observations in terms of its magnitude and three-dimensional structure. The annual and seasonal means of the zonal-mean IWC profiles from the simulations both show a local maximum in the upper troposphere in the tropics associated with deep convection, and other local maxima in the mid-troposphere in midlatitudes in both hemispheres associated with storm tracks. In contrast to the CloudSat values, the reanalysis shows much smaller IWC values in the tropics and much larger values in the lower troposphere in midlatitudes. The different vertical structures and magnitudes of IWC between the simulations and the reanalysis are likely due to differences in the parameterization of various processes in addition to the ice-to-snow autoconversion, including ice sedimentation, temperature thresholds for ice deposition and cumulus detrainment of cloud ice. However, a series of sensitivity experiments supports the conclusion that the model with a constant autoconversion time scale cannot reproduce the correct IWC distribution in both the tropics and midlatitudes, which strongly suggests the importance of physically based effects on the autoconversion timescale.

The relative success of the Community Atmosphere Model with superparameterized convection (SP-CAM) in simulating the space-time characteristics of the Madden Julian Oscillation encourages us to examine its simulation of the Indian summer monsoon and monsoon intraseasonal oscillations (MISOs). While the model simulates the onset and withdrawal of the Indian monsoon realistically, it has a significant wet bias in boreal summer precipitation over the Asian monsoon region. The space-time characteristics of the MISOs simulated by the SP-CAM are examined in detail and compared with those of the observed MISO to gain insight into the model's bias in simulating the seasonal mean. During northern summer, the model simulates a 20 day mode and a 60 day mode in place of the observed 15 and 45 day modes, respectively. The simulated 20 day mode appears to have no observed analog with a baroclinic vertical structure and strong northward propagation over Indian longitudes. The simulated 60 day mode seems to be a lower-frequency version of the observed 45 day mode with relatively slower northward propagation. The model's underestimation of light rain events and overestimation of heavy rain events are shown to be responsible for the wet bias of the model. More frequent occurrence of heavy rain events in the model is, in turn, related to the vertical structure of the higher-frequency modes. Northward propagation of the simulated 20 day mode is associated with a strong cyclonic vorticity at low levels north of the heating maximum associated with a smaller meridional scale of the simulated mode. The simulated vertical structure of heating indicates a strong maximum in the upper troposphere between 200 and 300 hPa. Such a heating profile seems to generate a higher-order baroclinic mode response with smaller meridional structure, stronger low-level cyclonic vorticity, enhanced low-level moisture convergence, and higher precipitation. Therefore, the vertical structure of heating simulated by the cloud-resolving model within SP-CAM may hold the key for improving the precipitation bias in the model.

Significant systematic biases in the moisture fields within the tropical Pacific trade wind regions are found in the Coupled Model Intercomparison Project (CMIP3/CMIP5) against profile and total column water vapor (TotWV) estimates from the Atmospheric Infrared Sounder and TotWV from the Special Sensor Microwave/Imager. Positive moisture biases occur in conjunction with significant biases of eastward low-level moisture convergence north of the South Pacific Convergence Zone and south of the Intertropical Convergence Zone—the V-shaped regions. The excessive moisture there is associated with overestimates of reflected upward shortwave (RSUT), underestimates of outgoing longwave radiation (RLUT) at the top of atmosphere (TOA), and underestimates of downward shortwave flux at the surface (RSDS) compared to Clouds and the Earth's Energy System, Energy Balance and Filled data. We characterize the impacts of falling snow and its radiation interaction, which are not included in most CMIP5 models, on the moisture fields using the National Center for Atmospheric Research-coupled global climate model (GCM). A number of differences in the model simulation without snow-radiation interactions are consistent with biases in the CMIP5 simulations. These include effective low-level eastward/southeastward wind and surface wind stress anomalies, and an increase in TotWV, vertical profile of moisture, and cloud amounts in the V-shaped region. The anomalous water vapor and cloud amount might be associated with the model increase of RSUT and decrease of RLUT at TOA and decreased RSDS in clear and all sky in these regions. These findings hint at the importance of water vapor-radiation interactions in the CMIPS/CMIP5 model simulations that exclude the radiative effect of snow.