Clouds, Aerosol, & Climate: Projects

CP-MIP: Cold Pool Model Intercomparison Project

satellite image — Trade cumulus cold pools underneath a "Flower" type cloud, Barbados. Image: NASA Worldview

CP-MIP studies the fidelity of model representation of convective cold pools. The goals of CP-MIP are the identification, characterization, and quantification of model biases through comparison with observed cold pool statistics, the improved representation of cold pools in simulations, and convergence of models towards a robust basis for the study of cold pools in the atmosphere. CP-MIP develops concepts, analytical approaches, and software tools, and coordinates community efforts to conduct and analyze simulations and observations. The first stage of CP-MIP focuses on shallow convective cold pools over the tropical oceans, which are primarily associated with trade cumulus clouds.

Point of Contact: Jan Kazil, University of Colorado CIRES at NOAA and Raphaela Vogel, University of Hamburg

Objectives

satellite imagery — Cold pools are created by cool downdrafts associated with precipitation. Once a downdraft reaches the surface, the cool air spreads outwards, creating a cloud-free region delineated by an arc of convection in the convergence zone. Illustration: Jan Kazil, NOAA / CIRES; Images: NASA Worldview

Characterization and quantification of cold pool properties as simulated by different models:

within individual simulation frameworks, such as large eddy simulations (LES),
across simulation frameworks, such as LES, regional models, and storm-resolving models.

Characterization of simulated cold pools properties and their variability in the context of

the state of the atmosphere, ocean, aerosol, and clouds,
model framework, model assumptions, process representations, simulation setup

Evaluation of simulated cold pool properties with observations,
Identification of causes of biases in simulated cold pools and quantification of their sensitivity to simulation setup and process representations.

Phase 1: Collaboration with EUREC⁴A-MIP and Lagrangian LES intercomparison

In collaboration with the CP-MIP partners,

EUREC⁴A-MIP
EUREC⁴A Lagrangian LES intercomparison

the project will

Formulate model outputs and diagnostics within ongoing modeling efforts for CP-MIP.
Develop and distribute software to implement the diagnostics.
Formulate a cold pool analysis protocol, develop and distribute software tools for its implementation.
Collect model output from the participating projects and conduct analysis following the cold pool analysis protocol.
Synthesize the analysis into technical and scientific insights, to be published.

Model output and diagnostics requested for CP-MIP

Time series

Cloud water path
Rain water path
Surface precipitation
Surface latent heat flux
Surface sensible heat flux
Inversion height
Top-of-atmosphere incoming solar flux

Time-height curtains

Pressure
Temperature
Water vapor mixing ratio (specific humidity)
Cloud water mixing ratio
Rain water mixing ratio
Horizontal wind speed U and V
Vertical wind speed variance W2
Vertical wind speed skewness W3
TKE
TKE production due to buoyancy, resolved

3D fields

Every 30 minutes:

Pressure
Temperature
Wind speed U, V, W
Water vapor mixing ratio (specific humidity)
Aerosol number concentration
Cloud microphysical quantities (cloud water, cloud drop number, rain water, rain drop number mixing ratios)

2D fields

High temporal resolution (5 minutes or faster) 2D fields are requested to track the change in cold pool properties and their statistics in the different models. Fields at 10 m are requested as ship and buoy data are adjusted from the instrument altitude to a height of 10 m above the surface using the COARE algorithm (e.g. Quinn et al., 2021).

10 m temperature
10 m specific humidity
10 m U and V wind
Mixed layer height following Touzé-Peiffer et al. (2022) [see Software]
Cloud water path
Rain water path
Surface precipitation
CAPE (convective available potential energy)
Vertical velocity at 400 m altitude

Profiles and time series at select locations

For models that can flexibly write high-frequency single grid-point output, CP-MIP requests vertical profiles and time series at the following locations for comparison with observations

Barbados Cloud Observatory (BCO) (13.16° N, 59.43° W)
EUREC4A (HALO) circle:

Center (13.3° N, 57.7° W)
North (14.3° N, 57.7° W)
South (12.3° N, 57.7° W)
East (13.3° N, 56.7° W)
West (13.3° N, 58.7° W)

Northwest Tropical Atlantic Station (NTAS) buoy (15° N, 51° W)

The following quantities are requested:

Time series of

10 m temperature
10 m specific humidity
10 m U and V wind
Surface sensible and latent heat flux

Vertical profiles of

T, qv, ql, U, V, W

Preliminary cold pool analysis protocol

Cold pool area fraction and number
Probability density distributions in cold pools of

Temperature
Specific humidity
Wind speed
Mixed layer height

Cold pool size distribution
Correlation with cloud and rain water path

Software

Mixed layer height for cold pool detection

CP-MIP provides code to calculate the mixed layer height using the algorithm developed by Touzé-Peiffer et al. (2022). CP-MIP requests that participants diagnose the mixed layer height in their simulations using this algorithm. This will allow comparison of the mixed layer height across models, as well as its comparison with radiosonde cold pool detections, for which the algorithm was developed.

The algorithm defines the mixed layer height H_mix as the lowest altitude Z above Z_min = 20 m where the virtual potential temperature Θ_ν is higher than its mass-weighted average from Z_min to Z by a fixed threshold ε = 0.2 K. The original algorithm of Touzé-Peiffer et al. (2022) uses Z_min = 100 m.

Θ_υ is calculated assuming that the air of the lowest layers is not saturated, so that the mixing ratio of liquid water in the air can be neglected. It is hence approximated as Θ_υ = Θ(1 + 0.61r), r being the mixing ratio of water vapor. The calculation of H_mix thus requires only the vertical profiles of pressure, temperature and humidity.

The code provided contains a calculation of the mixed layer height

as the discrete variable, with model (mass) levels as values
as a continuous variable, using linear interpolation

Download

Fortran code, algorithm only (3.9 KiB) (v1.31, starting height for cold pool detection = 20 m, calculates mixed layer height and the mean ("dry") Θυ in the mixed layer)

Water vapor mesoscale anomaly initialization