Arctic Clouds and Precipitation in the Community Earth System Model Version 2.

The Arctic climate is changing rapidly, warming at about twice the rate of the planet. Global climate models (GCMs) are invaluable tools both for understanding the drivers of these changes and predicting future Arctic climate evolution. While GCMs are continually improving, there remain difficulties in representing cloud processes which occur on scales smaller than GCM resolution. Since clouds influence the Arctic energy and water cycles, their accurate representation in models is critical for robust future projections. In this work, we examine the representation of Arctic clouds and precipitation in the Community Earth System Model (CESM) with the Community Atmosphere Model (CAM), comparing the newly released version (CESM2 with CAM6) with its predecessor (CESM1 with CAM5). To isolate changes in the Arctic mean state, we compare preindustrial control runs. Arctic cloud ice has decreased slightly, while cloud water has increased dramatically in CESM2. Annual mean liquid-containing cloud (LCC) frequency has increased from 19% in CESM1 to 51% in CESM2. Since LCCs strongly modulate downwelling radiation at the surface, their increase has led to an increase in mean downwelling longwave (+22 W m-2) and corresponding decrease in downwelling shortwave (-23 W m-2) radiation. The mean Arctic surface temperature increased from 257 K in CESM1 to 260 K in CESM2, with the largest seasonal difference in winter (+6 K). Annual average snowfall has decreased slightly (-1 mm month-1), while rainfall has increased (+5 mm month-1).

Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes.

Global climate models (GCMs) disagree with other lines of evidence on the rapid adjustments of cloud cover and liquid water path to anthropogenic aerosols. Attempts to use observations to constrain the parameterizations of cloud processes in GCMs have failed to reduce the disagreement. We propose using observations sensitive to the relevant cloud processes rather than only to the atmospheric state and focusing on process realism in the absence of aerosol perturbations in addition to the process susceptibility to aerosols. We show that process-sensitive observations of precipitation can reduce the uncertainty on GCM estimates of rapid cloud adjustments to aerosols. The feasibility of an observational constraint depends on understanding the precipitation intensity spectrum in both observations and models and also on improving methods to compare the two.

Observing Convective Aggregation.

Convective self-aggregation, the spontaneous organization of initially scattered convection into isolated convective clusters despite spatially homogeneous boundary conditions and forcing, was first recognized and studied in idealized numerical simulations. While there is a rich history of observational work on convective clustering and organization, there have been only a few studies that have analyzed observations to look specifically for processes related to self-aggregation in models. Here we review observational work in both of these categories and motivate the need for more of this work. We acknowledge that self-aggregation may appear to be far-removed from observed convective organization in terms of time scales, initial conditions, initiation processes, and mean state extremes, but we argue that these differences vary greatly across the diverse range of model simulations in the literature and that these comparisons are already offering important insights into real tropical phenomena. Some preliminary new findings are presented, including results showing that a self-aggregation simulation with square geometry has too broad distribution of humidity and is too dry in the driest regions when compared with radiosonde records from Nauru, while an elongated channel simulation has realistic representations of atmospheric humidity and its variability. We discuss recent work increasing our understanding of how organized convection and climate change may interact, and how model discrepancies related to this question are prompting interest in observational comparisons. We also propose possible future directions for observational work related to convective aggregation, including novel satellite approaches and a ground-based observational network.

An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles.

Data from several coincident satellite sensors are analyzed to determine the dependence of cloud and precipitation characteristics of tropical regions on the variance in the water vapor field. Increased vapor variance is associated with decreased high cloud fraction and an enhancement of low-level radiative cooling in dry regions of the domain. The result is found across a range of sea surface temperatures and rain rates. This suggests the possibility of an enhanced low-level circulation feeding the moist convecting areas when vapor variance is large. These findings are consistent with idealized models of self-aggregation, in which the aggregation of convection is maintained by a combination of low-level radiative cooling in dry regions and mid-to-upper-level radiative warming in cloudy regions.

Responses of Tropical Ocean Clouds and Precipitation to the Large-Scale Circulation:Atmospheric-Water-Budget-Related Phase Space and Dynamical Regimes.

An atmospheric-water-budget-related phase space is constructed with the tendency terms related to dynamical convergence (QCON ≡ -Q∇ · V) and moisture advection (QADV ≡ -V · ∇Q) in the water budget equation. Over the tropical oceans, QCON accounts for large-scale dynamical conditions related to conditional instability, and QADV accounts for conditions related to lower-tropospheric moisture gradient. Two reanalysis products [MERRA and ERA-Interim (ERAi)] are used to calculate QCON and QADV. Using the phase space as a reference frame, the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure (CTP) and cloud optical depth (COD) are used to evaluate simulated clouds in the GISS-E2 general circulation model. In regimes of divergence over the tropical oceans, moist advection yields frequent high- to midlevel medium-thickness to thick clouds associated with moderate stratiform precipitation, while dry advection yields low-level thin clouds associated with shallow convection with lowered cloud tops. In regimes with convergence, moist and dry advection modulate the relative abundance of high-level thick clouds and low-level thin to medium-thickness clouds. GISS-E2 qualitatively reproduces the cloud property dependence on moisture budget tendencies in regimes of convergence but with larger COD compared to MODIS. Low-level thick clouds in GISS-E2 are the most frequent in regimes of near-zero convergence and moist advection instead of those of large-scale divergence. Compared to the Global Precipitation Climatology Project product, MERRA, ERAi, and GISS-E2 have more rain in regimes with deep convection and less rain in regimes with shallow convection.