Estimating Convection’s Moisture Sensitivity: An Observation–Model Synthesis Using AMIE-DYNAMO Field Data

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.

Using Megha‐Tropiques satellite data to constrain humidity in regional convective simulations: A northern Australian test case

“…Several studies in the past have applied the perturbation method to understand the source and growth of errors in models during initialization (Jankov et al ., 2007; Schwartz et al ., 2010; Wapler et al ., 2010; Blázquez et al ., 2013; Zeng et al ., 2014; Clain et al ., 2015), but this may not work for longer simulations which become insensitive to the initialization. Here, other techniques may prove handy to investigate moisture sensitivity to convection which have been explored in the past associated with humidity field relaxations (Derbyshire et al ., 2004), transient impulses (Tulich and Mapes, 2010) and linear response functions (Mapes et al ., 2019).…”

Continuous Structural Parameterization: A method for representing different model parameterizations within one structure demonstrated for atmospheric convection

“…A straightforward improvement to our CSP would be to introduce higher order and cross terms into the regression calculations using discrete orthogonal polynomials. We could also introduce more variables into the analysis, although we note that past work has found θ and q to be satisfactory for analyzing both model output and observed effects of convection (Johnson et al, 2016;Mapes et al, 2019;Yanai et al, 1973). Another framework entirely is evolutionary genetic programming, which uses Darwinian evolution to produce models from combinations of simple functions (e.g., Makkeasorn et al, 2008).…”

Toward Form-Function Relationships for Mesoscale Structure in Convection

“…A system has sensitivities and impacts, which together comprise its responses (these are micro-function measures, such as budget impacts and their conditionality). A system's response characteristics are not easy to estimate from observations if it is intimately coupled to other systems, either directly or via mutual coupling to some overarching main supersystem (for more discussion see Mapes et al 2019). One role a subsystem can play is adjustment, if it is deterministically responsive to larger-system variables with a timescale much shorter than the cared-about evolution timescale of that larger system.…”

“…In our familiar fluid dynamics vector calculus, these are rarely specified, except when an unusual coordinate metric is used in the vertical, but they are encountered more in thermodynamics and are crucial in abstract spaces (Nolte 2010) where orthogonality becomes meaningless. For illustration, Mapes et al (2017) elaborated the radiative kernels of Soden et al (2008) and several other "importance" metrics of water vapor as highly-annotated partial derivatives, and Mapes et al (2019) used such notation to express why simple regressions of field data are not estimates of the quantities needed for convection parameterization, despite having the same physical units and even comparable magnitudes.…”