skip to main content


Title: A Process‐Oriented Diagnostic to Assess Precipitation‐Thermodynamic Relations and Application to CMIP6 Models
Abstract

A process‐oriented diagnostic (POD) is introduced to measure the thermodynamic sensitivity of convection in climate models. The physical basis for this POD is the observed tropical precipitation‐buoyancy relationship. Fast timescale precipitation and thermodynamic profiles over oceans are POD inputs; these are used to evaluate model precipitation sensitivities to lower‐tropospheric measures of subsaturation (SUBSATL) and undilute conditional instability. The POD is used to diagnose 24 coupled model inter‐comparison project phase six (CMIP6) models. Half the diagnosed models exhibit SUBSATLsensitivity close to observed, while six models are excessively sensitive. Parameter perturbation experiments with the Community Atmospheric Model (CAM5) support the physical basis for the POD. Increasing entrainment increases the CAM5 precipitation SUBSATLsensitivity. Switching off the convective scheme or modifying the convective trigger to be oversensitive to moisture reproduces the excessive SUBSATLsensitivity seen among CMIP6 models. Models with excessive SUBSATLsensitivities have precipitating mean states closer to grid‐scale saturation and likely support more grid‐scale convection.

 
more » « less
Award ID(s):
1936810
NSF-PAR ID:
10367634
Author(s) / Creator(s):
 ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Geophysical Research Letters
Volume:
48
Issue:
14
ISSN:
0094-8276
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature,θe, and saturation equivalent potential temperature,θes, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of theθesvertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and aθesmeasure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between largerθeslapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poorθe/θesstructure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

     
    more » « less
  2. Abstract

    Advances in high‐performance computing make it possible to run atmospheric general circulation models (AGCMs) over an increasingly wider range of grid resolutions, using either globally uniform or variable‐resolution grids. In principle, this is an exciting opportunity to resolve atmospheric process and scales in a global model and in unprecedented detail, but in practice this grid flexibility is incompatible with the non‐ or weakly converging solutions with increasing horizontal resolution that have long characterized AGCMs. In the the Community Atmosphere Model (CAM), there are robust sensitivities to horizontal resolution that have persisted since the model was first introduced over thirty years ago; the atmosphere progressively dries and becomes less cloudy with resolution, and parametrized deep convective precipitation decreases at the expense of stratiform precipitation. This study documents a convergence experiment using CAM, version 6, and argues that a unifying cause, the sensitivity of resolved dynamical modes to native grid resolution, feeds back into other model components and explains these robust sensitivities to resolution. The increasing magnitudes of resolved vertical velocities with resolution are shown to fit an analytic scaling derived for the equations of motion at hydrostatic scales. This trend in vertical velocities results in an increase in resolved upward moisture fluxes at cloud base, balanced by an increase in stratiform precipitation rates with resolution. Compensating, greater magnitude subsiding motion with resolution has previously been shown to dry out the atmosphere and reduce cloud cover. Here, it is shown that both the increase in condensational heating from stratiform cloud formation and greater subsidence drying contribute to an increase in atmospheric stability with resolution, reducing the activity of parametrized convection. The impact of changing the vertical velocity field with native grid resolution cannot be ignored in any effort to recover convergent solutions in AGCMs, and, in particular, the development of scale‐aware physical parametrizations.

     
    more » « less
  3. Abstract

    Characteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts.

     
    more » « less
  4. Simple process models and complex climate models are remarkably sensitive to the time scale of convective adjustment τ, but this parameter remains poorly constrained and understood. This study uses the linear-range slope of a semiempirical relationship between precipitation and a lower-free-tropospheric buoyancy measure BL. The BLmeasure is a function of layer-averaged moist enthalpy in the boundary layer (150-hPa-thick layer above surface), and temperature and moisture in the lower free troposphere (boundary layer top to 500 hPa). Sensitivity parameters with units of time quantify the BLresponse to its component perturbations. In moist enthalpy units, BLis more sensitive to temperature than equivalent moisture perturbations. However, column-integrated moist static energy conservation ensures that temperature and moisture are equally altered during the adjustment process. Multiple adjusted states with different temperature–moisture combinations exist; the BLsensitivity parameters govern the relationship between adjusted states, and also combine to yield a time scale of convective adjustment ~2 h. This value is comparable to τ values used in cumulus parameterization closures. Disparities in previously reported values of τ are attributed to the neglect of the temperature contribution to precipitation, and to averaging operations that include data from both precipitating and nonprecipitating regimes. A stochastic model of tropical convection demonstrates how either averaging operations or neglected environmental influences on precipitation can yield τ estimates longer than the true τ value built into the model. The analysis here culminates in construction of a precipitation closure with both moisture and temperature adjustment ( q– T closure), suitable for use in both linearized and nonlinear, intermediate-complexity models.

     
    more » « less
  5. Abstract

    With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020,https://doi.org/10.1029/2020MS002076) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

     
    more » « less