Mikhail Ovchinnikov, Po-Lun Ma, Colleen M. Kaul, Kyle G. Pressel, Meng Huang, Jacob Shpund, Shuaiqi Tang
{"title":"Evaluation of Autoconversion Representation in E3SMv2 Using an Ensemble of Large-Eddy Simulations of Low-Level Warm Clouds","authors":"Mikhail Ovchinnikov, Po-Lun Ma, Colleen M. Kaul, Kyle G. Pressel, Meng Huang, Jacob Shpund, Shuaiqi Tang","doi":"10.1029/2024MS004280","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <p>In numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate <span></span><math>\n <semantics>\n <mrow>\n <mo>(</mo>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <mo>)</mo>\n </mrow>\n <annotation> $(Au)$</annotation>\n </semantics></math>, which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local <span></span><math>\n <semantics>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <annotation> $Au$</annotation>\n </semantics></math> is commonly parameterized as a function of DSD moments: cloud droplet number <span></span><math>\n <semantics>\n <mrow>\n <mfenced>\n <msub>\n <mi>n</mi>\n <mi>c</mi>\n </msub>\n </mfenced>\n </mrow>\n <annotation> $\\left({n}_{c}\\right)$</annotation>\n </semantics></math> and mass <span></span><math>\n <semantics>\n <mrow>\n <mfenced>\n <msub>\n <mi>q</mi>\n <mi>c</mi>\n </msub>\n </mfenced>\n </mrow>\n <annotation> $\\left({q}_{c}\\right)$</annotation>\n </semantics></math> concentrations. When applied in a large-scale model, the grid-mean <span></span><math>\n <semantics>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <annotation> $Au$</annotation>\n </semantics></math> must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large-eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local <span></span><math>\n <semantics>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <annotation> $Au$</annotation>\n </semantics></math>, as well as its spatial variability. The analysis of an ensemble of warm low-level cloud cases shows that the E3SMv2 formulation represents the <span></span><math>\n <semantics>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <annotation> $Au$</annotation>\n </semantics></math> reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM-tuned local <span></span><math>\n <semantics>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <annotation> $Au$</annotation>\n </semantics></math> rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>n</mi>\n <mi>c</mi>\n </msub>\n </mrow>\n <annotation> ${n}_{c}$</annotation>\n </semantics></math> and its co-variability with <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>q</mi>\n <mi>c</mi>\n </msub>\n </mrow>\n <annotation> ${q}_{c}$</annotation>\n </semantics></math> in parameterizing the grid-mean <span></span><math>\n <semantics>\n <mrow>\n <mi>A</mi>\n <mi>u</mi>\n </mrow>\n <annotation> $Au$</annotation>\n </semantics></math>.</p>\n </section>\n </div>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 10","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004280","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advances in Modeling Earth Systems","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024MS004280","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
In numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate , which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local is commonly parameterized as a function of DSD moments: cloud droplet number and mass concentrations. When applied in a large-scale model, the grid-mean must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large-eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local , as well as its spatial variability. The analysis of an ensemble of warm low-level cloud cases shows that the E3SMv2 formulation represents the reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM-tuned local rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of and its co-variability with in parameterizing the grid-mean .
在将云和雨滴种群视为独立冷凝物类别的大气数值模式中,暖云中的降水起始通常用自转换率 ( A u ) $(Au)$ 表示,即通过云滴碰撞形成新雨滴的速率。作为云滴粒径分布(DSD)的函数,局部 A u $Au$ 通常被参数化为 DSD 时刻的函数:云滴数量 n c $\left({n}_{c}\right)$ 和质量 q c $\left({q}_{c}\right)$ 浓度。在大尺度模式中应用时,网格均值 A u $Au$ 还必须包括一个校正或增强因子,以考虑整个模式网格中云属性的水平变化。在本研究中,我们利用大涡流模拟(LES)评估了能源超大规模地球系统模式第 2 版(E3SMv2)气候模式中的 Au 表示,LES 明确解析了云滴光谱,因此解析了本地 A u $Au$ 及其空间变异性。对一系列暖低空云层案例的分析表明,与 LES 明确计算出的水平平均速率相比,ESMv2 公式对 A u $Au$ 的表现相当出色。然而,这种一致性是由低估的 E3SM 调整的本地 A u $Au$ 率和高估的子网格云变化增强因子共同造成的。后一种偏差可追溯到在对网格均值 A u $Au$ 进行参数化时忽略了 n c ${n}_{c}$ 的水平变化及其与 q c ${q}_{c}$ 的共变性。
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