[1] Using NASA's A-Train satellite measurements, we evaluate the accuracy of cloud water content (CWC) and water vapor mixing ratio (H 2 O) outputs from 19 climate models submitted to the Phase 5 of Coupled Model Intercomparison Project (CMIP5), and assess improvements relative to their counterparts for the earlier CMIP3. We find more than half of the models show improvements from CMIP3 to CMIP5 in simulating column-integrated cloud amount, while changes in water vapor simulation are insignificant. For the 19 CMIP5 models, the model spreads and their differences from the observations are larger in the upper troposphere (UT) than in the lower or middle troposphere (L/MT). The modeled mean CWCs over tropical oceans range from $3% to $15Â of the observations in the UT and 40% to 2Â of the observations in the L/MT. For modeled H 2 Os, the mean values over tropical oceans range from $1% to 2Â of the observations in the UT and within 10% of the observations in the L/MT. The spatial distributions of clouds at 215 hPa are relatively well-correlated with observations, noticeably better than those for the L/MT clouds. Although both water vapor and clouds are better simulated in the L/MT than in the UT, there is no apparent correlation between the model biases in clouds and water vapor. Numerical scores are used to compare different model performances in regards to spatial mean, variance and distribution of CWC and H 2 O over tropical oceans. Model performances at each pressure level are ranked according to the average of all the relevant scores for that level.Citation: Jiang, J. H., et al. (2012), Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA "A-Train" satellite observations,

[1] Global high-resolution (3-hourly, 0.1°Â 0.1°longitude-latitude) water vapor (6.7 mm) and window (11 mm) radiances from multiple geostationary satellites are used to document the diurnal cycle of upper tropospheric relative humidity (UTH) and its relationship to deep convection and high clouds in the whole tropics and to evaluate the ability of the new Geophysical Fluid Dynamics Laboratory (GFDL) global atmosphere and land model (AM2/LM2) to simulate these diurnal variations. Similar to the diurnal cycle of deep convection and high clouds, coherent diurnal variations in UTH are also observed over the deep convective regions, where the daily mean UTH is high. In addition, the diurnal cycle in UTH also features a land-sea contrast: stronger over land but weaker over ocean. UTH tends to peak around midnight over ocean in contrast to 0300 LST over land. Furthermore, UTH is observed to lag high cloud cover by $6 hours, and the latter further lags deep convection, implying that deep convection serves to moisten the upper troposphere through the evaporation of the cirrus anvil clouds generated by deep convection. Compared to the satellite observations, AM2/LM2 can roughly capture the diurnal phases of deep convection, high cloud cover, and UTH over land; however, the magnitudes are noticeably weaker in the model. Over the oceans the AM2/LM2 has difficulty in simulating both the diurnal phase and amplitude of these quantities. These results reveal some important deficiencies in the model's convection and cloud parameterization schemes and suggest the lack of a diurnal cycle in SST may be a shortcoming in the boundary forcing for atmospheric models.

The atmospheric moisture and temperature profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit on the NASA Aqua mission, in combination with the precipitation from the Tropical Rainfall Measuring Mission (TRMM), are employed to study the vertical moist thermodynamic structure and spatial-temporal evolution of the Madden-Julian oscillation (MJO). The AIRS data indicate that, in the Indian Ocean and western Pacific, the temperature anomaly exhibits a trimodal vertical structure: a warm (cold) anomaly in the free troposphere (800-250 hPa) and a cold (warm) anomaly near the tropopause (above 250 hPa) and in the lower troposphere (below 800 hPa) associated with enhanced (suppressed) convection. The AIRS moisture anomaly also shows markedly different vertical structures as a function of longitude and the strength of convection anomaly. Most significantly, the AIRS data demonstrate that, over the Indian Ocean and western Pacific, the enhanced (suppressed) convection is generally preceded in both time and space by a low-level warm and moist (cold and dry) anomaly and followed by a low-level cold and dry (warm and moist) anomaly.The MJO vertical moist thermodynamic structure from the AIRS data is in general agreement, particularly in the free troposphere, with previous studies based on global reanalysis and limited radiosonde data. However, major differences in the lower-troposphere moisture and temperature structure between the AIRS observations and the NCEP reanalysis are found over the Indian and Pacific Oceans, where there are very few conventional data to constrain the reanalysis. Specifically, the anomalous lower-troposphere temperature structure is much less well defined in NCEP than in AIRS for the western Pacific, and even has the opposite sign anomalies compared to AIRS relative to the wet/dry phase of the MJO in the Indian Ocean. Moreover, there are well-defined eastward-tilting variations of moisture with height in AIRS over the central and eastern Pacific that are less well defined, and in some cases absent, in NCEP. In addition, the correlation between MJO-related midtropospheric water vapor anomalies and TRMM precipitation anomalies is considerably more robust in AIRS than in NCEP, especially over the Indian Ocean. Overall, the AIRS results are quite consistent with those predicted by the frictional Kelvin-Rossby wave/conditional instability of the second kind (CISK) theory for the MJO.

The double-intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual double-ITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multi-model ensemble mean precipitation errors in the tropics. The notorious double-ITCZ bias and its big inter-model spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its inter-model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6.Plain Language Summary The double-intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models that may reduce the reliability of future climate prediction based on these models. The models have two ITCZs (i.e., zonally elongated narrow belt of high precipitation) in both hemispheres, over the equatorial central and eastern Pacific and Atlantic, instead of one ITCZ over the northern hemisphere in observations except for a short period in March and April. Here, we examine such bias in the latest models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) based on long-term annual mean precipitations in observations and models and compare the biases among the recent three generations of CMIP models (CMIP3, CMIP5, and CMIP6). We find that the double-ITCZ bias with a big inter-model spread persists in all CMIP models and still remains a serious problem in the latest CMIP6 models. However, the bias is slightly reduced in CMIP6 models from CMIP3 or CMIP5 models based on several precipitation bias indices. In addition, the annual equatorial Pacific cold tongue bias also persists in all CMIP models, but its inter-model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6.

[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] This paper documents the climatological mean features of the Atmospheric Infrared Sounder (AIRS) monthly mean tropospheric air temperature (ta, K) and specific humidity (hus, kg/kg) products as part of the Obs4MIPs project and compares them to those from NASA's Modern Era Retrospective analysis for Research and Applications (MERRA) for validation and 16 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) for CMIP5 model evaluation. MERRA is warmer than AIRS in the free troposphere but colder in the boundary layer with differences typically less than 1 K. MERRA is also drier (~10%) than AIRS in the tropical boundary layer but wetter (~30%) in the tropical free troposphere and the extratropical troposphere. In particular, the large MERRA-AIRS specific humidity differences are mainly located in the deep convective cloudy regions indicating that the low sampling of AIRS in the cloudy regions may be the main reason for these differences. In comparison to AIRS and MERRA, the sixteen CMIP5 models can generally reproduce the climatological features of tropospheric air temperature and specific humidity well, but several noticeable biases exist. The models have a tropospheric cold bias (around 2 K), especially in the extratropical upper troposphere, and a double-ITCZ problem in the troposphere from 1000 hPa to 300 hPa, especially in the tropical Pacific. The upper-tropospheric cold bias exists in the most (13 of 16) models, and the double-ITCZ bias is found in all 16 CMIP5 models. Both biases are independent of the reference dataset used (AIRS or MERRA).

Despite decades of climate research and model development, two outstanding problems still plague the latest global climate models (GCMs): the double‐Intertropical Convergence Zone (ITCZ) bias and the 2−5°C spread of equilibrium climate sensitivity (ECS). Here we show that the double‐ITCZ bias and ECS in 44 GCMs from Coupled Model Intercomparison Project Phases 3/5 are negatively correlated. The models with weak (strong) double‐ITCZ biases have high (low)‐ECS values of ~4.1(2.2)°C. This indicates that the double‐ITCZ bias is a new emergent constraint for ECS based on which ECS might be in the higher end of its range (~4.0°C) and most models might have underestimated ECS. In addition, we argue that the double‐ITCZ bias can physically affect both cloud and water vapor feedbacks (thus ECS) and is a more easily measured emergent constraint for ECS than previous ones. It can be used as a performance metric for evaluating and comparing different GCMs.

[1] We investigate the modulation of aerosols by the Madden-Julian Oscillation (MJO) using multiple, global satellite aerosol products: aerosol index (AI) from the Total Ozone Mapping Spectrometer (TOMS) on Nimbus-7, and aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites. A composite MJO analysis indicates that large variations in the TOMS AI and MODIS/AVHRR AOT are found over the equatorial Indian and western Pacific Oceans where MJO convection is active, as well as the tropical Africa and Atlantic Ocean where MJO convection is weak but the background aerosol level is high. A strong inverse linear relationship between the TOMS AI and rainfall anomalies, but a weaker, less coherent positive correlation between the MODIS/AVHRR AOT and rainfall anomalies, were found. The MODIS/AVHRR pattern is consistent with ground-based Aerosol Robotic Network data. These results indicate that the MJO and its associated cloudiness, rainfall, and circulation variability systematically influence the variability in remote sensing aerosol retrieval results. Several physical and retrieval algorithmic factors that may contribute to the observed aerosol-rainfall relationships are discussed. Preliminary analysis indicates that cloud contamination in the aerosol retrievals is likely to be a major contributor to the observed relationships, although we cannot exclude possible contributions from other physical mechanisms. Future research is needed to fully understand these complex aerosol-rainfall relationships.