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1. Simple Analytical Expressions for Steady-State Vapor Growth and Collision–Coalescence Particle Size Distribution Parameter Profiles

   https://doi.org/10.1175/JAS-D-23-0052.1  

   Dunnavan, Edwin L. ; Ryzhkov, Alexander V. ( October 2023 , Journal of the Atmospheric Sciences)

   This study derives simple analytical expressions for the theoretical height profiles of particle number concentrations (N_t) and mean volume diameters (D_m) during the steady-state balance of vapor growth and collision–coalescence with sedimentation. These equations are general for both rain and snow gamma size distributions with size-dependent power-law functions that dictate particle fall speeds and masses. For collision–coalescence only,N_t(D_m) decreases (increases) as an exponential function of the radar reflectivity difference between two height layers. For vapor deposition only,D_mincreases as a generalized power law of this reflectivity difference. Simultaneous vapor deposition and collision–coalescence under steady-state conditions with conservation of number, mass, and reflectivity fluxes lead to a coupled set of first-order, nonlinear ordinary differential equations forN_tandD_m. The solutions to these coupled equations are generalized power-law functions of heightzforD_m(z) andN_t(z) whereby each variable is related to one another with an exponent that is independent of collision–coalescence efficiency. Compared to observed profiles derived from descending in situ aircraft Lagrangian spiral profiles from the CRYSTAL-FACE field campaign, these analytical solutions can on average capture the height profiles ofN_tandD_mwithin 8% and 4% of observations, respectively. Steady-state model projections of radar retrievals aloft are shown to produce the correct rapid enhancement of surface snowfall compared to the lowest-available radar retrievals from 500 m MSL. Future studies can utilize these equations alongside radar measurements to estimateN_tandD_mbelow radar tilt elevations and to estimate uncertain microphysical parameters such as collision–coalescence efficiencies.

   While complex numerical models are often used to describe weather phenomenon, sometimes simple equations can instead provide equally good or comparable results. Thus, these simple equations can be used in place of more complicated models in certain situations and this replacement can allow for computationally efficient and elegant solutions. This study derives such simple equations in terms of exponential and power-law mathematical functions that describe how the average size and total number of snow or rain particles change at different atmospheric height levels due to growth from the vapor phase and aggregation (the sticking together) of these particles balanced with their fallout from clouds. We catalog these mathematical equations for different assumptions of particle characteristics and we then test these equations using spirally descending aircraft observations and ground-based measurements. Overall, we show that these mathematical equations, despite their simplicity, are capable of accurately describing the magnitude and shape of observed height and time series profiles of particle sizes and numbers. These equations can be used by researchers and forecasters along with radar measurements to improve the understanding of precipitation and the estimation of its properties.

    

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2. Generalized Aerosol/Chemistry Interface (GIANT): A Community Effort to Advance Collaborative Science across Weather and Climate Models

   https://doi.org/10.1175/BAMS-D-23-0013.1  

   Hodzic, Alma ; Mahowald, Natalie ; Dawson, Matthew ; Johnson, Jeffrey ; Bernardet, Ligia ; Bosler, Peter A. ; Fast, Jerome D. ; Fierce, Laura ; Liu, Xiaohong ; Ma, Po-Lun ; et al ( November 2023 , Bulletin of the American Meteorological Society)

   Atmospheric aerosol and chemistry modules are key elements in Earth system models (ESMs), as they predict air pollutant concentrations and properties that can impact human health, weather, and climate. The current uncertainty in climate projections is partly due to the inaccurate representation of aerosol direct and indirect forcing. Aerosol/chemistry parameterizations used within ESMs and other atmospheric models span large structural and parameter uncertainties that are difficult to assess independently of their host models. Moreover, there is a strong need for a standardized interface between aerosol/chemistry modules and the host model to facilitate portability of aerosol/chemistry parameterizations from one model to another, allowing not only a comparison between different parameterizations within the same modeling framework, but also quantifying the impact of different model frameworks on aerosol/chemistry predictions. To address this need, we have initiated a new community effort to coordinate the construction of a Generalized Aerosol/Chemistry Interface (GIANT) for use across weather and climate models. We aim to organize a series of community workshops and hackathons to design and build GIANT, which will serve as the interface between a range of aerosol/chemistry modules and the physics and dynamics components of atmospheric host models. GIANT will leverage ongoing efforts at the U.S. modeling centers focused on building next-generation ESMs and the international AeroCom initiative to implement this common aerosol/chemistry interface. GIANT will create transformative opportunities for scientists and students to conduct innovative research to better characterize structural and parametric uncertainties in aerosol/chemistry modules, and to develop a common set of aerosol/chemistry parameterizations.

    

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3. Sinking flux of particulate organic matter in the oceans: Sensitivity to particle characteristics

   https://doi.org/10.1038/s41598-020-60424-5  

   Omand, Melissa M. ; Govindarajan, Rama ; He, Jing ; Mahadevan, Amala ( March 2020 , Scientific Reports)

   The sinking of organic particles produced in the upper sunlit layers of the ocean forms an important limb of the oceanic biological pump, which impacts the sequestration of carbon and resupply of nutrients in the mesopelagic ocean. Particles raining out from the upper ocean undergo remineralization by bacteria colonized on their surface and interior, leading to an attenuation in the sinking flux of organic matter with depth. Here, we formulate a mechanistic model for the depth-dependent, sinking, particulate mass flux constituted by a range of sinking, remineralizing particles. Like previous studies, we find that the model does not achieve the characteristic ‘Martin curve’ flux profile with a single type of particle, but instead requires a distribution of particle sizes and/or properties. We consider various functional forms of remineralization appropriate for solid/compact particles, and aggregates with an anoxic or oxic interior. We explore the sensitivity of the shape of the flux vs. depth profile to the choice of remineralization function, relative particle density, particle size distribution, and water column density stratification, and find that neither a power-law nor exponential function provides a definitively superior fit to the modeled profiles. The profiles are also sensitive to the time history of the particle source. Varying surface particle size distribution (via the slope of the particle number spectrum) over 3 days to represent a transient phytoplankton bloom results in transient subsurface maxima or pulses in the sinking mass flux. This work contributes to a growing body of mechanistic export flux models that offer scope to incorporate underlying dynamical and biological processes into global carbon cycle models.

    

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4. Carbon Export Buffering and CO 2 Drawdown by Flexible Phytoplankton C:N:P Under Glacial Conditions

   https://doi.org/10.1029/2019PA003823  

   Matsumoto, Katsumi ; Rickaby, Rosalind ; Tanioka, Tatsuro ( July 2020 , Paleoceanography and Paleoclimatology)

   Modern observations indicate that variations in marine phytoplankton stoichiometry correlate with the boundaries of major surface waters. For example, phytoplankton in the oligotrophic subtropical gyres typically have much higher C:N:P ratios (i.e., higher C:P and higher N:P ratios) than those in eutrophic upwelling regions and polar regions. Such a spatial pattern points to nutrient availability as a key environmental driver of stochiometric flexibility. Environmental dependence of phytoplankton C:N:P opens unexplored possibilities for modifying the strength of the biological pump under different climate conditions. Here we present a power law formulation of C:N:P flexibility that is driven by nutrients, temperature, and light. We embed the formulation in a global ocean carbon cycle model with multiple phytoplankton types and explore biogeochemical implications under glacial conditions. We find three key controls on export C:N:P ratio: phytoplankton physiology and community structure as well as the balance in regional production at the global level. Glacial inputs of iron and sea ice expansion are important modifiers of these three controls. We also find that global export C:N:P increases substantially under glacial conditions, and this strongly buffers global carbon export against decrease and draws down approximately 20 μatm of atmospheric CO₂. These results point to the importance of including phytoplankton C:N:P flexibility in a mix of mechanisms that drive atmospheric CO₂over glacial‐interglacial time scale. Finally, our simulations indicate decoupling of nutrients, which may provide a resolution to the longstanding disagreement regarding nutrient utilization in the glacial Southern Ocean derived from different nutrient proxies.

    

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5. Calibration and Uncertainty Quantification of a Gravity Wave Parameterization: A Case Study of the Quasi‐Biennial Oscillation in an Intermediate Complexity Climate Model

   https://doi.org/10.1029/2022MS003245  

   Mansfield, L. A. ; Sheshadri, A. ( November 2022 , Journal of Advances in Modeling Earth Systems)

   The drag due to breaking atmospheric gravity waves plays a leading order role in driving the middle atmosphere circulation, but as their horizontal wavelength range from tens to thousands of kilometers, part of their spectrum must be parameterized in climate models. Gravity wave parameterizations prescribe a source spectrum of waves in the lower atmosphere and allow these to propagate upwards until they either dissipate or break, where they deposit drag on the large‐scale flow. These parameterizations are a source of uncertainty in climate modeling which is generally not quantified. Here, we explore the uncertainty associated with a non‐orographic gravity wave parameterization given an assumed parameterization structure within a global climate model of intermediate complexity, using the Calibrate, Emulate and Sample (CES) method. We first calibrate the uncertain parameters that define the gravity wave source spectrum in the tropics, to obtain climate model settings that are consistent with properties of the primary mode of tropical stratospheric variability, the Quasi‐Biennial Oscillation (QBO). Then we use a Gaussian process emulator to sample the calibrated distribution of parameters and quantify the uncertainty of these parameter choices. We find that the resulting parametric uncertainties on the QBO period and amplitude are of a similar magnitude to the internal variability under a 2xCO₂forcing.

    

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