Melissa A. Burt

Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model

PUBLICATIONS Journal of Advances in Modeling Earth Systems RESEARCH ARTICLE 10.1002/2015MS000574 Key Points: Superparameterization improves the rainfall amount mode and extreme rates relative to TRMM 3B42 Mean rainfall and dry day frequency biases do not improve much with superparameterization Conventional and superparameterized rainfall intensity statistics are similar poleward of 508 Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model Gabriel J. Kooperman 1 , Michael S. Pritchard 1 , Melissa A. Burt 2 , Mark D. Branson 2 , and David A. Randall 2 Department of Earth System Science, University of California, Irvine, California, USA, 2 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA Abstract This study evaluates several important statistics of daily rainfall based on frequency and Supporting Information: Supporting Information S1 Correspondence to: G. J. Kooperman, gkooperm@uci.edu Citation: Kooperman, G. J., M. S. Pritchard, M. A. Burt, M. D. Branson, and D. A. Randall (2016), Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model, J. Adv. Model. Earth Syst., 8, 140–165, doi:10.1002/2015MS000574. Received 28 OCT 2015 Accepted 29 DEC 2015 Accepted article online 2 JAN 2016 Published online 1 FEB 2016 amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization—Super-Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual-mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double-ITCZ bias. However, unfolding intensity distributions reveal several improvements in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden-Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, with- out sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM’s low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 508, where more rainfall is produced by resolved-scale processes in CAM, few differences discriminate the rainfall properties of the two models. These results are discussed in light of their implication for future rainfall changes in response to climate forcing. 1. Introduction C 2016. The Authors. V This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. KOOPERMAN ET AL. Rainfall is an intrinsic characteristic of a region’s climate, by definition determining whether the region is a desert or rainforest [Peel et al., 2007]. As the Earth warms, global mean precipitation is expected to increase by 1–3% 8C 21 due to radiative constraints [Allen and Ingram, 2002; Pendergrass and Hartmann, 2014a; Ste- phens and Ellis, 2008], but regional changes are much less robust [Dai, 2006; Mahlstein et al., 2012; Stocker et al., 2013]. Regional rainfall is driven over time by changes in circulation, moisture transport, and local evaporation [Trenberth et al., 2003]. These changes can depend on complex interactions between rainfall, large-scale dynamics, and surface sensible and latent heat fluxes, especially over land where soil moisture coupling plays an important role [Seneviratne et al., 2010]. Interactions linked to the second-order statistics of rainfall (e.g., frequency and intensity) can determine whether rain is intercepted by the canopy, infiltrates the soil, or runs off the surface, thus influencing the soil moisture [Lawrence et al., 2011; Ramirex and Senar- ath, 2000]. In turn, the soil moisture effects local evaporation and sensible heat fluxes, which project onto large-scale dynamics and downstream moisture transport [Dirmeyer et al., 2009; Koster et al., 2004; Senevir- atne et al., 2010]. These second-order rainfall characteristics are expected to change even more than the mean, up to 7% 8C 21 on global scales, and even larger on regional scales [O’Gorman, 2015; Trenberth et al., 2003]. For these reasons, and because rainfall frequency and intensity control the prevalence of devas- tating drought or flood conditions, it is critical they be realistically simulated in global climate models (GCMs). RAINFALL INTENSITY STATISTICS IN SPCAM

Effects of explicit atmospheric convection at high CO2

Significance The representation of clouds and convection has an enormous impact on simulation of the climate system. This study addresses concerns that conventional parameterizations may bias the response of climate models to increased greenhouse gases. The broadly similar response of two models with parameterized and nonparameterized convection and clouds suggests that state-of-the-art predictions, based on parameterized climate models, may not necessarily be strongly biased in either direction (too strong or too weak warming). At the same time, large differences in simulated tropical variability and Arctic sea ice area suggest that improvement in convection and cloud representations remains essential. The effect of clouds on climate remains the largest uncertainty in climate change predictions, due to the inability of global climate models (GCMs) to resolve essential small-scale cloud and convection processes. We compare preindustrial and quadrupled CO2 simulations between a conventional GCM in which convection is parameterized and a “superparameterized” model in which convection is explicitly simulated with a cloud-permitting model in each grid cell. We find that the global responses of the two models to increased CO2 are broadly similar: both simulate ice-free Arctic summers, wintertime Arctic convection, and enhanced Madden–Julian oscillation (MJO) activity. Superparameterization produces significant differences at both CO2 levels, including greater Arctic cloud cover, further reduced sea ice area at high CO2, and a stronger increase with CO2 of the MJO.

Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model

Changes in the character of rainfall are assessed using a holistic set of statistics based on rainfall frequency and amount distributions in climate change experiments with three conventional and superparameterized versions of the Community Atmosphere Model (CAM and SPCAM). Previous work has shown that high-order statistics of present-day rainfall intensity are significantly improved with superparameterization, especially in regions of tropical convection. Globally the two modeling approaches project a similar future increase in mean rainfall, especially across the Inter-Tropical Convergence Zone (ITCZ) and at high latitudes, but over land SPCAM predicts a smaller mean change than CAM. Changes in high-order statistics are similar at high-latitudes in the two models, but diverge at lower latitudes. In the tropics SPCAM projects a large intensification of moderate and extreme rain rates in regions of organized convection associated with the Madden Julian Oscillation, ITCZ, monsoons, and tropical waves. In contrast, this signal is missing in all versions of CAM, which are found to be prone to predicting increases in the amount but not intensity of moderate rates. Predictions from SPCAM exhibit a scale-insensitive behavior with little dependence on horizontal resolution for extreme rates, while lower resolution (∼2˚) versions of CAM are not able to capture the response simulated with higher resolution (∼1˚). Moderate rain rates analyzed by the “amount mode” and “amount median” are found to be especially telling as a diagnostic for evaluating climate model performance and tracing future changes in rainfall statistics to tropical wave modes in SPCAM. This article is protected by copyright. All rights reserved.

Promoting professional identity, motivation, and persistence: Benefits of an informal mentoring program for female undergraduate students

Women are underrepresented in a number of science, technology, engineering, and mathematics (STEM) disciplines. Limited diversity in the development of the STEM workforce has negative implications for scientific innovation, creativity, and social relevance. The current study reports the first-year results of the PROmoting Geoscience Research, Education, and SuccesS (PROGRESS) program, a novel theory-driven informal mentoring program aimed at supporting first- and second-year female STEM majors. Using a prospective, longitudinal, multi-site (i.e., 7 universities in Colorado/Wyoming Front Range & Carolinas), propensity score matched design, we compare mentoring and persistence outcomes for women in and out of PROGRESS (N = 116). Women in PROGRESS attended an off-site weekend workshop and gained access to a network of volunteer female scientific mentors from on- and off-campus (i.e., university faculty, graduate students, and outside scientific professionals). The results indicate that women in PROGRESS had larger networks of developmental mentoring relationships and were more likely to be mentored by faculty members and peers than matched controls. Mentoring support from a faculty member benefited early-undergraduate women by strengthening their scientific identity and their interest in earth and environmental science career pathways. Further, support from a faculty mentor had a positive indirect impact on women’s scientific persistence intentions, through strengthened scientific identity development. These results imply that first- and second- year undergraduate women’s mentoring support networks can be enhanced through provision of protégé training and access to more senior women in the sciences willing to provide mentoring support.