Characterizing Convection Schemes Using Their Responses to Imposed Tendency Perturbations

Hwong, Yi-Ling; Song, Seung-Ho; Sherwood, Steven C.; Stirling, Alison; Rio, Catherine; Roehrig, Romain; Daleu, Chimene L.; Plant, Robert S.; Fuchs, David; Maher, Penelope

doi:10.1029/2021ms002461

Cited by 6 publications

(9 citation statements)

References 111 publications

(173 reference statements)

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“…There is a spread of around 20% of near‐surface RH among the SCMs (panel a), with LMDZ displaying the moistest low‐level profile (around 90%). This spread is comparable to that seen in previous SCM intercomparisons (e.g., Hwong et al., 2021; Wing et al., 2020). Hwong et al.…”

Section: Resultssupporting

confidence: 90%

“…To illustrate the main features of the various model configurations we show the RCE mean state profiles of the relative humidity (RH) of the CRM (non‐aggregated case), LMDZ and WRF SCM (for the standard convection schemes only ZM is shown as the other schemes have been presented in Hwong et al. (2021)), updraft mass flux of each plume in the WRF UW‐ org SCM, and the cold pool temperature and moisture anomalies of the LMDZ SCM, in Figure 2. There is a spread of around 20% of near‐surface RH among the SCMs (panel a), with LMDZ displaying the moistest low‐level profile (around 90%).…”

Section: Resultsmentioning

confidence: 99%

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Assessing Memory in Convection Schemes Using Idealized Tests

Hwong,

Colin,

Aglas‐Leitner

et al. 2023

J Adv Model Earth Syst

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Two assumptions commonly applied in convection schemes—the diagnostic and quasi‐equilibrium assumptions—imply that convective activity (e.g., convective precipitation) is controlled only by the large‐scale (macrostate) environment at the time. In contrast, numerical experiments indicate a “memory” or dependence of convection also on its own previous activity whereby subgrid‐scale (microstate) structures boost but are also boosted by convection. In this study we investigated this memory by comparing single‐column model behavior in two idealized tests previously executed by a cloud‐resolving model (CRM). Conventional convection schemes that employ the diagnostic assumption fail to reproduce the CRM behavior. The memory‐capable org and Laboratoire de Météorologie Dynamique Zoom cold pool schemes partially capture the behavior, but fail to fully exhibit the strong reinforcing feedbacks implied by the CRM. Analysis of this failure suggests that it is because the CRM supports a linear (or superlinear) dependence of the subgrid structure growth rate on the precipitation rate, while the org scheme assumes a sublinear dependence. Among varying versions of the org scheme, the growth rate of the org variable representing subgrid structure is strongly associated with memory strength. These results demonstrate the importance of parameterizing convective memory, and the ability of idealized tests to reveal shortcomings of convection schemes and constrain model structural assumptions.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Assessing Memory in Convection Schemes Using Idealized Tests

Hwong,

Colin,

Aglas‐Leitner

et al. 2023

J Adv Model Earth Syst

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“…Consistent with previous studies, we find that a more aggregated state is drier and frequently also warmer. This leads to a larger difference in the mean state profiles between the SCM and MCM setups and hence also their linear responses, as these responses are correlated with model mean state, as shown in Hwong et al (2021). Schemes that are relatively more disorganized in their MCM setup also display more similar adjustment responses to doubled-CO 2 forcing.…”

Section: Discussionmentioning

confidence: 95%

Can We Use 1D Models to Predict 3D Model Response to Forcing in an Idealized Framework?

Hwong

Sherwood

Fuchs

2022

J Adv Model Earth Syst

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Single‐column models (SCMs) simulations are sometimes used to evaluate model physics and aid parameterization development. However, few studies have systematically compared SCM behavior—where column boundary conditions are specified—with that of corresponding 3D models, where columns interact dynamically. Here we address this by comparing forced responses of an SCM in radiative‐convective equilibrium (RCE) with those of a multi‐column model (MCM) where the model domain is in RCE but individual columns are not, examining what factors affect the models' comparability. We find that convective organization in the MCM depends at least as much on the convection scheme as on other mechanisms known to organize convection (e.g., radiative feedback). Moreover, convective organization emerges as a robust factor affecting SCM–MCM comparability, with more aggregated states in 3D associated with larger behavior deviations from the 1D counterpart. This is found across five convection schemes and applies to simulated mean states, linear responses to small tendency perturbations, and adjustments to doubled‐CO2 forcing. Nevertheless, we find that even when convection is organized, behavior differences between pairs of schemes in the SCM are largely preserved in the MCM. This indicates that when model physics produces accurate behavior in a 1D setup, it will be more likely to do so in a 3D setup. However, our idealized RCE framework implies that these conclusions may not apply to situations with strong large‐scale forcing or encountered over land. Lastly, we demonstrate the practical value of linear responses by showing that they can accurately predict an SCM's tropospheric adjustment to doubled‐CO2 forcing.

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“…Different GCMs use different parameterization schemes and employ different assumptions and approximations (Hwong et al 2021). Therefore, the projections that they produce vary from one to another giving rise to uncertainties.…”

Section: Daily Mean and Standard Deviation Bias Correction Of Gcm Pro...mentioning

confidence: 99%

Temperature and urban heat island effect in Lublin city in Poland under changing climate

Sachindra

Ullah

Zaborski

et al. 2022

Theor Appl Climatol

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This study investigates the characteristics of the temperature regimes at an urban station (Litewski square) in Lublin city in Poland and a nearby rural station (Radawiec), and the Urban Heat Island (UHI) effect in Lublin city. In winter, spring, summer, and autumn at both urban and rural stations frequency distributions of daily minimum (Tmin), and maximum (Tmax) air temperature in 1998–2020 have shifted towards a warmer climate compared to the frequency distributions in 1974–1997. At both stations in 1974–2020, in all seasons, the annual Tmin and Tmax display increasing trends. At Litewski square and Radawiec, Tmax shows increasing trends of 0.083 and 0.088 ºC/year in summer, respectively. This is the largest increase in all four seasons. Furthermore, it is revealed that the heatwaves at both the urban and rural stations have increased in number over time. However, cold waves at both stations show a declining trend. The UHI effect in Lublin city has not increased significantly during 1974–2020. Population in Lublin city has declined over the period 1995–2020, but the population in the surrounding rural counties has increased. It is speculated that this is one of the causes of no clear increase in the UHI intensity. Apart from that, the city’s large green coverage (about 40%) is probably acting as a heating inhibitor. The annual Tmin and Tmax projected by 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs indicate that the temperature regimes at both urban and rural stations show significant increasing trends during 2015–2100 under the selected SSPs, with the highest increase under high emission scenario (SSP5-8.5) and the lowest increase under the low emission scenario (SSP1-2.6). During 2015–2100, the UHI effect in Lublin city does not show any significant increasing or decreasing trends for the majority of the GCM–SSP combinations.

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Characterizing Convection Schemes Using Their Responses to Imposed Tendency Perturbations

Cited by 6 publications

References 111 publications

Assessing Memory in Convection Schemes Using Idealized Tests

Assessing Memory in Convection Schemes Using Idealized Tests

Can We Use 1D Models to Predict 3D Model Response to Forcing in an Idealized Framework?

Temperature and urban heat island effect in Lublin city in Poland under changing climate

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