Retrieving latent heating vertical structure from cloud and precipitation Profiles—Part I: Warm rain processes
Highlights
► An exploratory study on physics-based warm rain latent heat retrieval algorithm. ► Utilize the full information of the vertical structures of cloud and rainfall. ► Directly link water mass measurements to latent heat at instantaneous pixel level. ► Applicable at various stages of cloud system life cycle.
Introduction
Latent heating (LH) released from the formation of precipitation plays an important role in the hydrologic and energy cycle in the Earth climate system. It is the main energy source for driving atmospheric circulations [1], [2]. Changes in the spatial and temporal distribution of LH can cause significant atmospheric responses at multiple layers [3], [4]. LH also receives feedback from the atmospheric circulation [5]. Global warming and the associated changes in global circulation may cause significant changes in LH. For example, in the 1997/1998El Niño event, the sea surface temperatures (SST) in the equatorial eastern Pacific were up to 3 °C warmer than the climatology, and the large-scale vertical circulation changed from subsidence to ascent [6]. Li et al. [7] found that the precipitation growth rate in the middle layer of the atmosphere became faster than that in Non-El Niño conditions, indicating more LH released at higher altitudes.
Our understanding of the vertical structure of LH has been evolving with the progress in observations and modeling studies during the past decades. With the “hot tower” concept proposed by Riehl and Malkus [8], it was generally believed that LH is positive throughout the whole column (a convective heating pattern). With the identification and understanding of the importance of mesoscale convective systems (MCSs) in the tropical water and energy cycle [9], [10], [11], it has been broadly accepted that the vertical LH profile in tropics is dominated by the MCSs, in which the deep convective rain produce heating through the whole column with a maximum at the midlevel; and the stratiform rain produces heating in the upper layer but cooling in the lower layer. The overall LH profile related to the MCSs is determined by the convective/stratiform rain fractions, with a higher maximum level of LH than that of a conventional convective LH pattern [11], [12], [13]. Modeling studies also demonstrated that this LH pattern (MCS-like) indeed produces a better simulation of Walker circulation than that produced by the conventional convective LH pattern [14], and LH associated with a proper setting of convective/stratiform rain fraction in models can result in an elevated circulation center and produce better simulations in both global and regional scale circulations [3], [4].
Recently, Li et al. [7] studied the changes of rainfall vertical structure induced by El Niño in the tropical eastern Pacific and found that the combined effect of larger vertical extent and greater growth rate of rainfall in the middle layer under El Niño conditions further shifted latent heating upward in addition to the impact of horizontal changes in the convective/stratiform rain fractions. Such additional latent heating shift further elevates circulation centers and strengthens the upper layer circulation.
In contrast to the MCS-like LH profiles, LH released from warm rain (rain top is lower than the freezing level) exhibits a shallow heating pattern. Shallow heating is an important mechanism for Madden–Julian Oscillation (MJO) (e.g., [15], [16], [17], [18]); using shallow LH profile in the model can simulate the propagation speeds of MJO more accurately [19], [20]. It is clear that quantifying both horizontal and vertical distributions of LH associated with different types of rain processes and rainfall profiles is crucial for understanding atmospheric circulations and cloud and precipitation feedbacks to climate change. It provides useful climate diagnostic data and ultimately, validation data for model-based analyses of large-scale heating distributions.
Unfortunately the spatial and temporal distribution of latent heating cannot be measured directly. Although the horizontal distribution of column integrated LH generally can be determined by the surface precipitation ([21], [22], [23]), estimating the vertical distribution of LH is still a very challenging task. Our current knowledge of global LH vertical distribution, either estimated from satellite measurements or diagnosed from sounding or reanalysis datasets, has huge uncertainties. In particular, there are substantial discrepancies in the vertical distributions among various LH estimations, and the differences are relatively larger in the lower atmosphere than in the upper atmosphere ([2], [24], [25], [5], [18]). This suggests that the existing estimations of condensation and evaporation heating from the warm rain process are relatively more problematic. It is urgent to develop a new approach for estimating LH vertical distribution, with maximizing information from current and future measurements capability that directly links to the cloud and rainfall vertical structures. We conduct an exploratory study of developing physical based method to estimate LH with the assumption that both rain rate and cloud water content profiles can be simultaneously measured. Although such assumption may not be valid for current atmospheric remote sensing technology, this non-operational method proposes a physical way to link rain and cloud water mass measurements to the LH directly. This paper only focuses on the warm rain and another companion paper will focus on the deep convective and stratiform rains.
Section snippets
Current retrieval schemes and cloud-resolving model
Existing satellite retrievals of LH can be categorized into two groups, depending on how the LH profile is derived. The first group is CRM-based schemes, including the Convective-Stratiform Heating (CSH, [26], [27]); the Goddard Profiling Heating (GPROF, [28], [29]); the Spectral Latent Heating (SLH, [30], [31], [32], [14]); and the TRAIN (TRMM precipitation radar trained radiometer heating algorithm, [33]). A common feature among them is that the retrieved LH profile is seleced from a prior
The warm rain LH profile retrieval algorithm
Without ice formation mechanisms, as shown in Fig. 2, warm rain formation processes can be separated into the cloud droplet formation process and the rain hydrometeor formation process. LH released from the cloud formation process is due to the condensation of water vapor to cloud droplets. As warm raindrops are formed and grown mainly from cloud droplets through the auto-conversion and collection processes, there is no phase change in these processes. Although a few rain droplets may grow
Evaluation of LH retrieval algorithm
LH retrievals and their applications are being conducted over a large range of space and time scales, from pixel (a few km) to region (∼600 km) spatially, and from instantaneous to monthly temporally. Extensive evaluation and validation at various scales are critical for the success of all LH retrieval algorithms. In particular, a more independent approach for validating LH estimates is desirable, such as the radiosonde-based diagnostic heat budget analysis. However, due to lack of independent
Conclusion
Accurate knowledge of latent heating (LH) profile is crucial for understanding the atmospheric dynamics, including the MJO, Monsoon, and ENSO. LH is released or consumed within the atmosphere as a result of multiple processes related to the phase changes of water. These processes are not directly measurable with current remote sensing or even with in-situ instruments, resulting in most satellite LH products heavily relying on cloud-resolving models. Utilizing the limited information of observed
Acknowledgment
This work was supported by US DOE's Atmospheric System Research program (Office of Science, OBER) under contract DE-FG02-03ER63531, by the NSF under contract AGS-1138495, and by the NOAA Educational Partnership Program with Minority Serving Institutions (EPP/MSI) under cooperative agreements NA17AE1625 and NA17AE1623, and by the Strategic Priority Research Program—Climate Change (Carbon Budget and Relevant Issues of the Chinese Academy of Sciences, Grant No. XDA05100303), and NSFC (41230419,
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