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• W2924884052 abstract "Abstract We seek to use ARM MJO Investigation Experiment (AMIE)-DYNAMO field campaign observations to significantly constrain height-resolved estimates of the parameterization-relevant, causal sensitivity of convective heating Q to water vapor q . In field data, Q profiles are detected via Doppler radar wind divergence D while balloon soundings give q . Univariate regressions of D on q summarize the information from a 10-layer time–pressure series from Gan Island (0°, 90°E) as a 10 × 10 matrix. Despite the right shape and units, this is not the desired causal quantity because observations reflect confounding effects of additional q -correlated casual mechanisms. We seek to use this matrix to adjudicate among candidate estimates of the desired causal quantity: Kuang’s matrix of the linear responses of a cyclic convection-permitting model (CCPM) at equilibrium. Transforming to more observation-comparable forms by accounting for observed autocorrelations, the comparisons are still poor, because (we hypothesize) larger-scale vertical velocity, forbidden by CCPM methodology, is another confounding cause that must be permitted to covary with q . By embedding and modified candidates in an idealized GCM, and treating its outputs as virtual field campaign data, we find that observations favor a factor of 2 (rather than 0 or 1) to small-domain ’s free-tropospheric causal q sensitivity of about 25% rain-rate increment over 3 subsequent hours per +1 g kg −1 q impulse in a 100-hPa layer. Doubling this sensitivity lies partway toward Kuang’s for a long domain that organizes convection into squall lines, a weak but sign-consistent hint of a detectable parameterization-relevant (causal) role for convective organization in nature. Caveats and implications for field campaign proposers are discussed." @default.
• W2924884052 created "2019-04-01" @default.
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• W2924884052 date "2019-06-01" @default.
• W2924884052 modified "2023-10-12" @default.
• W2924884052 title "Estimating Convection’s Moisture Sensitivity: An Observation–Model Synthesis Using AMIE-DYNAMO Field Data" @default.
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• W2924884052 doi "https://doi.org/10.1175/jas-d-18-0127.1" @default.
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