Geographical differences in the tropical precipitation‐moisture relationship and rain intensity onset

Ahmed, Farooq; Schumacher, Courtney

doi:10.1002/2016gl071980

Cited by 33 publications

(48 citation statements)

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“…To calculate the occurrence frequency, first the number of occurrences in each precipitation rate bin is divided by the total number of days for each forecast lead time and grid point. Then the average across the first 4 weeks over the Indo‐Pacific Ocean (60–180°E, 15°S to 15°N) is computed from this; land area is masked out since convection is typically forced by the diurnal cycle more than moisture‐convection coupling (Ahmed & Schumacher, ). Only control simulations are used from the reforecasts in these calculations.…”

Section: Mjo Processes and Mean Statementioning

confidence: 99%

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MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts

Kim

Janiga²,

Pegion

2019

JGR Atmospheres

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The Madden‐Julian oscillation (MJO) is the leading source of global subseasonal predictability; however, many dynamical forecasting systems struggle to predict MJO propagation through the Maritime Continent. Better understanding the biases in simulated physical processes associated with MJO propagation is the key to improve MJO prediction. In this study, MJO prediction skill, propagation processes, and mean state biases are evaluated in reforecasts from models participating in the Subseasonal Experiment (SubX) and Subseasonal to Seasonal (S2S) prediction projects. SubX and S2S reforecasts show MJO prediction skill out to 4.5 weeks based on the Real‐time Multivariate MJO index consistent with previous studies. However, a closer examination of these models' representation of MJO propagation through the Maritime Continent reveals that they fail to predict the MJO convection, associated circulations, and moisture advection processes beyond 10 days with most of models underestimating MJO amplitude. The biases in the MJO propagation can be partly associated with the following mean biases across the Indo‐Pacific: a drier low troposphere, excess surface precipitation, more frequent occurrence of light precipitation rates, and a transition to stronger precipitation rates at lower humidity than in observations. This indicates that deep convection occurs too frequently in models and is not sufficiently inhibited when tropospheric moisture is low, which is likely due to the representation of entrainment.

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Section: Mjo Processes and Mean Statementioning

confidence: 99%

“…Journal of Geophysical Research: Atmospheres from this; land area is masked out since convection is typically forced by the diurnal cycle more than moisture-convection coupling (Ahmed & Schumacher, 2017). Only control simulations are used from the reforecasts in these calculations.…”

Section: 1029/2019jd031139mentioning

confidence: 99%

MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts

Kim

Janiga²,

Pegion

2019

JGR Atmospheres

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“…This exponential behavior in the moisture-precipitation relationship has repeatedly been found in the studies that used various types of observations of water vapor and precipitation (Gilmore, 2015;Holloway & Neelin, 2009;Neelin et al, 2008;Raymond et al, 2007Raymond et al, , 2011. It is worthwhile to note that recent studies have found that the moisture-convection coupling is the strongest over tropical oceans, and it becomes weaker in coastal areas (Bergemann & Jakob, 2016) and over land (Ahmed & Schumacher, 2017) where the convection is typically forced by sea breeze or daytime surface heating. the coefficients a d and P r obtained by Bretherton et al (2004) (e.g., Adames & Kim, 2016Liu & Wang, 2016;Sobel & Maloney, 2012).…”

Section: Introductionmentioning

confidence: 99%

Reexamining the Nonlinear Moisture‐Precipitation Relationship Over the Tropical Oceans

Rushley

Kim

Bretherton

et al. 2018

Geophysical Research Letters

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Bretherton et al. (2004) used the Special Sensor Microwave Imager (SSM/I) version 5 product to derive an exponential curve that describes the relationship between precipitation and column relative humidity (CRH) over the tropical oceans. The curve, which features a precipitation pickup at a CRH of about 0.75 and a rapid increase of precipitation with CRH after the pickup, has been widely used in the studies of the tropical atmosphere. This study reexamines the moisture‐precipitation relationship by using the version 7 SSM/I data, in which several biases in the previous version are corrected, and evaluates the relationship in the Coupled Model Intercomparison Project phase 5 (CMIP5) models. In the revised exponential curve derived using the updated satellite data, the precipitation pickup occurs at a higher CRH (~0.8), and precipitation increases more slowly with CRH than in the previous curve. In most CMIP5 models, the precipitation pickup is too early due to the common model bias of overestimated (underestimated) precipitation in the dry (wet) regime.

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“…Both observational (33)(34)(35)(36) and modeling (37) studies have suggested that the transition to deep convection is highly sensitive to the availability of moisture in the lower free troposphere, thus explaining the observed precipitation-column-water-vapor relation (33,36,(38)(39)(40)(41)(42)(43). Previous efforts have indicated that the observed sharp increase in precipitation as a function of column water vapor is associated with effects of air entrained in the lower troposphere (33,36).…”

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confidence: 99%

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

Schiro

Ahmed

Giangrande

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014-2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep-inflow mixing yields a strong precipitation-buoyancy relation across the tropics. Deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.

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Geographical differences in the tropical precipitation‐moisture relationship and rain intensity onset

Cited by 33 publications

References 50 publications

MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts

MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts

Reexamining the Nonlinear Moisture‐Precipitation Relationship Over the Tropical Oceans

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

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