Abstract

The authors quantify systematic differences between modern observation- and reanalysis-based estimates of atmospheric heating rates and identify dominant variability modes over tropical oceans. Convergence of heat fluxes between the top of the atmosphere and the surface are calculated over the oceans using satellite-based radiative and sensible heat fluxes and latent heating from precipitation estimates. The convergence is then compared with column-integrated atmospheric heating based on Tropical Rainfall Measuring Mission data as well as the heating calculated using temperatures from the Atmospheric Infrared Sounder and wind fields from the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Corresponding calculations using MERRA and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis heating rates and heat fluxes are also performed. The geographical patterns of atmospheric heating rates show heating regimes over the intertropical convergence zone and summertime monsoons and cooling regimes over subsidence areas in the subtropical oceans. Compared to observation-based datasets, the reanalyses have larger atmospheric heating rates in heating regimes and smaller cooling rates in cooling regimes. For the averaged heating rates over the oceans in 40°S–40°N, the observation-based datasets have net atmospheric cooling rates (from −15 to −22 W m⁻²) compared to the reanalyses net warming rates (5.0–5.2 W m⁻²). This discrepancy implies different pictures of atmospheric heat transport. Wavelet spectra of atmospheric heating rates show distinct maxima of variability in annual, semiannual, and/or intraseasonal time scales. In regimes where deep convection frequently occurs, variability is mainly driven by latent heating. In the subtropical subsidence areas, variability in radiative heating is comparable to that in latent heating.

Abstract

In this study, satellite passive microwave sensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q ₁ − Q_R ) where Q ₁ is the apparent heat source and Q_R is the radiative heating rate in regions of precipitation. The TMI heating algorithm (herein called TRAIN) is calibrated or “trained” using relatively accurate estimates of heating based on spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based on a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically integrated condensation and surface precipitation.

Estimates of Q ₁ − Q_R from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q ₁ produced by combining TMI Q ₁ − Q_R with independently derived estimates of Q_R show reasonable agreement with rawinsonde-based analyses of Q ₁ from two field campaigns, although the satellite estimates exhibit heating profile structures with sharper and more intense heating peaks than the rawinsonde estimates.

Abstract

A global analysis that optimally combines observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. In this study it is demonstrated that assimilating precipitation and total precipitable water (TPW) derived from the Tropical Rainfall Measuring Mission Microwave Imager (TMI) can significantly improve the quality of global analysis. It is shown that assimilating the 6-h averaged TMI rainfall and TPW retrievals improves not only the hydrological cycle, but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). Notably, assimilating TMI rain rates improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the Tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Assimilating these data also improves the instantaneous wind and temperature fields in the analysis, leading to better short-range forecasts in the Tropics. Ensemble forecasts initialized with analyses incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day, better 500-hPa geopotential height forecasts up to 5 days, and better 200-hPa divergent winds up to 2 days. These results demonstrate the potential of using high quality spaceborne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications.

Abstract

Owing to its profound influences on global energy balance, accurate representation of low cloud variability in climate models is an urgent need for future climate projection. In the present study, marine low cloud variability on intraseasonal time scales is characterized, with a particular focus over the Pacific basin during boreal summer and its association with the dominant mode of tropical intraseasonal variability (TISV) over the eastern Pacific (EPAC) intertropical convergence zone (ITCZ). Analyses indicate that, when anomalous TISV convection is enhanced over the elongated EPAC ITCZ, reduction of low cloud fraction (LCF) is evident over a vast area of the central North Pacific. Subsequently, when the enhanced TISV convection migrates to the northern part of the EPAC warm pool, a “comma shaped” pattern of reduced LCF prevails over the subtropical North Pacific, along with a pronounced reduction of LCF present over the southeast Pacific (SEPAC). Further analyses indicate that surface latent heat fluxes and boundary heights induced by anomalous low-level circulation through temperature advection and changes of total wind speed, as well as midlevel vertical velocity associated with the EPAC TISV, could be the most prominent factors in regulating the intraseasonal variability of LCF over the North Pacific. For the SEPAC, temperature anomalies at the top of the boundary inversion layer between 850 and 800 hPa play a critical role in the local LCF intraseasonal variations. Results presented in this study provide not only improved understanding of variability of marine low clouds and the underlying physics, but also a prominent benchmark in constraining and evaluating the representation of low clouds in climate models.

Abstract

Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

Abstract

The Madden–Julian oscillation (MJO) is a fundamental mode of the tropical atmosphere variability that exerts significant influence on global climate and weather systems. Current global circulation models, unfortunately, are incapable of robustly representing this form of variability. Meanwhile, a well-accepted and comprehensive theory for the MJO is still elusive. To help address this challenge, recent emphasis has been placed on characterizing the vertical structures of the MJO. In this study, the authors analyze vertical heating structures by utilizing recently updated heating estimates based on the Tropical Rainfall Measuring Mission (TRMM) from two different latent heating estimates and one radiative heating estimate. Heating structures from two different versions of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses/forecasts are also examined. Because of the limited period of available datasets at the time of this study, the authors focus on the winter season from October 1998 to March 1999.

The results suggest that diabatic heating associated with the MJO convection in the ECMWF outputs exhibits much stronger amplitude and deeper structures than that in the TRMM estimates over the equatorial eastern Indian Ocean and western Pacific. Further analysis illustrates that this difference might be due to stronger convective and weaker stratiform components in the ECMWF estimates relative to the TRMM estimates, with the latter suggesting a comparable contribution by the stratiform and convective counterparts in contributing to the total rain rate. Based on the TRMM estimates, it is also illustrated that the stratiform fraction of total rain rate varies with the evolution of the MJO. Stratiform rain ratio over the Indian Ocean is found to be 5% above (below) average for the disturbed (suppressed) phase of the MJO. The results are discussed with respect to whether these heating estimates provide enough convergent information to have implications on theories of the MJO and whether they can help validate global weather and climate models.