Jackson Tan

Increases in tropical rainfall driven by changes in frequency of organized deep convection

Increasing global precipitation has been associated with a warming climate resulting from a strengthening of the hydrological cycle. This increase, however, is not spatially uniform. Observations and models have found that changes in rainfall show patterns characterized as ‘wet-gets-wetter’ and ‘warmer-gets-wetter’. These changes in precipitation are largely located in the tropics and hence are probably associated with convection. However, the underlying physical processes for the observed changes are not entirely clear. Here we show from observations that most of the regional increase in tropical precipitation is associated with changes in the frequency of organized deep convection. By assessing the contributions of various convective regimes to precipitation, we find that the spatial patterns of change in the frequency of organized deep convection are strongly correlated with observed change in rainfall, both positive and negative (correlation of 0.69), and can explain most of the patterns of increase in rainfall. In contrast, changes in less organized forms of deep convection or changes in precipitation within organized deep convection contribute less to changes in precipitation. Our results identify organized deep convection as the link between changes in rainfall and in the dynamics of the tropical atmosphere, thus providing a framework for obtaining a better understanding of changes in rainfall. Given the lack of a distinction between the different degrees of organization of convection in climate models, our results highlight an area of priority for future climate model development in order to achieve accurate rainfall projections in a warming climate.

On the Identification of the Large-Scale Properties of Tropical Convection Using Cloud Regimes

AbstractThe use of cloud regimes in identifying tropical convection and the associated large-scale atmospheric properties is investigated. The regimes are derived by applying cluster analysis to satellite retrievals of daytime-averaged frequency distributions of cloud-top pressure and optical thickness within grids of 280 km by 280 km resolution from the International Satellite Cloud Climatology Project between 1983 and 2008. An investigation of atmospheric state variables as a function of cloud regime reveals that the regimes are useful indicators of the archetypal states of the tropical atmosphere ranging from a strongly convecting regime with large stratiform cloudiness to strongly suppressed conditions showing a large coverage with stratocumulus clouds. The convectively active regimes are shown to be moist and unstable with large-scale ascending motion, while convectively suppressed regimes are dry and stable with large-scale descending winds. Importantly, the cloud regimes also represent several tran...

A Novel Approach to Identify Sources of Errors in IMERG for GPM Ground Validation

AbstractThe comparison of satellite and high-quality, ground-based estimates of precipitation is an important means to assess the confidence in satellite-based algorithms and to provide a benchmark for their continued development and future improvement. To these ends, it is beneficial to identify sources of estimation uncertainty, thereby facilitating a precise understanding of the origins of the problem. This is especially true for new datasets such as the Integrated Multisatellite Retrievals for GPM (IMERG) product, which provides global precipitation gridded at a high resolution using measurements from different sources and techniques. Here, IMERG is evaluated against a dense network of gauges in the mid-Atlantic region of the United States. A novel approach is presented, leveraging ancillary variables in IMERG to attribute the errors to the individual instruments or techniques within the algorithm. As a whole, IMERG exhibits some misses and false alarms for rain detection, while its rain-rate estimate...

Performance of IMERG as a Function of Spatiotemporal Scale

The Integrated Multi-satellitE Retrievals for GPM (IMERG), a global high-resolution gridded precipitation data set, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scale and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final run of MERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5 ) and accumulation periods (from 0.5 h to 24 h) over a region in the southeastern US. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multi satellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

The consequences of a local approach in statistical models of convection on its large‐scale coherence

Organized tropical convection is a crucial mechanism in the climate system, but its representation in climate models through parametrization schemes has numerous shortcomings. One of these shortcomings is that they are deterministic despite the statistical nature of the relationship they are representing. Several attempts at devising a stochastic parametrization scheme have been made, many of which assume a local approach, that is, one in which the convection in a grid box is determined without consideration of the previous time steps and the surrounding boxes. This study seeks to explore the effect of this assumption on the coherence of convection using cloud regimes, which represent various modes of tropical convection. First, we analyze the coherence of observed convection beyond the typical size of a model grid box and time step. Then, we evaluate the consequences of the local assumption on this coherence in simple statistical models. Cloud regimes in the real world show high degrees of coherence, manifesting in their lifetimes, areas, and inter-regime relationships. However, in a local statistical model, they are too small, too short-lived, and have incorrect relationships between each other. This can be improved by incorporating time memory and spatial dependence in the modeling. Our results imply that a local approach to a statistical representation of convection is not viable, and a statistical model must account for nonlocal influence in order to have large-scale convective coherence that more closely resembles the real world.

Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

AbstractPrecipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in ...

Evaluating rainfall errors in global climate models through cloud regimes

Global climate models suffer from a persistent shortcoming in their simulation of rainfall by producing too much drizzle and too little intense rain. This erroneous distribution of rainfall is a result of deficiencies in the representation of underlying processes of rainfall formation. In the real world, clouds are precursors to rainfall and the distribution of clouds is intimately linked to the rainfall over the area. This study examines the model representation of tropical rainfall using the cloud regime concept. In observations, these cloud regimes are derived from cluster analysis of joint-histograms of cloud properties retrieved from passive satellite measurements. With the implementation of satellite simulators, comparable cloud regimes can be defined in models. This enables us to contrast the rainfall distributions of cloud regimes in 11 CMIP5 models to observations and decompose the rainfall errors by cloud regimes. Many models underestimate the rainfall from the organized convective cloud regime, which in observation provides half of the total rain in the tropics. Furthermore, these rainfall errors are relatively independent of the model’s accuracy in representing this cloud regime. Error decomposition reveals that the biases are compensated in some models by a more frequent occurrence of the cloud regime and most models exhibit substantial cancellation of rainfall errors from different regimes and regions. Therefore, underlying relatively accurate total rainfall in models are significant cancellation of rainfall errors from different cloud types and regions. The fact that a good representation of clouds does not lead to appreciable improvement in rainfall suggests a certain disconnect in the cloud-precipitation processes of global climate models.

Evaluating TMPA rainfall over the sparsely gauged East African Rift

AbstractAccurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are p...

Contrasting the Co-variability of Daytime Cloud and Precipitation over Tropical Land and Ocean

The co-variability of cloud and precipitation in the extended tropics (35°N−35°S) is investigated using contemporaneous datasets for a 13-year period. The goal is to quantify potential relationships between cloud type amounts and precipitation events of particular strength. Particular attention is paid to whether the relationships exhibit different 10 characteristics over tropical land and ocean. A primary analysis metric is the correlation coefficient between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in one-degree grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) dataset aggregated to the same grid. 15 It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs over ocean for cumulonimbus clouds and the heaviest rainfall. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found that both over ocean and land the anti-correlation of strong precipitation with “weak” (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between 20 weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud-precipitation anti-correlations. Weak correlations between weaker rainfall and clouds indicate poor predictability for precipitation when cloud types are known, and this is even more true over land than over ocean.