Gabriel J. Kooperman

Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5

Natural modes of variability on many timescales influence aerosol particle distributions and cloud properties such that isolating statistically significant differences in cloud radiative forcing due to anthropogenic aerosol perturbations (indirect effects) typically requires integrating over long simulations. For state-of-the-art global climate models (GCM), especially those in which embedded cloud-resolving models replace conventional statistical parameterizations (i.e., multiscale modeling framework, MMF), the required long integrations can be prohibitively expensive. Here an alternative approach is explored, which implements Newtonian relaxation (nudging) to constrain simulations with both pre-industrial and present-day aerosol emissions toward identical meteorological conditions, thus reducing differences in natural variability and dampening feedback responses in order to isolate radiative forcing. Ten-year GCM simulations with nudging provide a more stable estimate of the global-annual mean net aerosol indirect radiative forcing than do conventional free-running simulations. The estimates have mean values and 95% confidence intervals of −1.19 ± 0.02 W/m2 and −1.37 ± 0.13 W/m2for nudged and free-running simulations, respectively. Nudging also substantially increases the fraction of the worlds area in which a statistically significant aerosol indirect effect can be detected (66% and 28% of the Earths surface for nudged and free-running simulations, respectively). One-year MMF simulations with and without nudging provide global-annual mean net aerosol indirect radiative forcing estimates of −0.81 W/m2 and −0.82 W/m2, respectively. These results compare well with previous estimates from three-year free-running MMF simulations (−0.83 W/m2), which showed the aerosol-cloud relationship to be in better agreement with observations and high-resolution models than in the results obtained with conventional cloud parameterizations.

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

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.

Investigating impacts of forest fires in Alaska and western Canada on regional weather over the northeastern United States using CAM5 global simulations to constrain transport to a WRF‐Chem regional domain

An aerosol-enabled globally driven regional modeling system has been developed by coupling the National Center for Atmospheric Researchs Community Atmosphere Model version 5 (CAM5) with the Weather Research and Forecasting model with chemistry (WRF-Chem). In this modeling system, aerosol-enabled CAM5, a state-of-the-art global climate model is downscaled to provide coherent meteorological and chemical boundary conditions for regional WRF-Chem simulations. Aerosol particle emissions originating outside the WRF-Chem domain can be a potentially important nonlocal aerosol source. As a test case, the potential impacts of nonlocal forest fire aerosols on regional precipitation and radiation were investigated over the northeastern United States during the summer of 2004. During this period, forest fires in Alaska and western Canada lofted aerosol particles into the midtroposphere, which were advected across the United States. WRF-Chem simulations that included nonlocal biomass burning aerosols had domain-mean aerosol optical depths that were nearly three times higher than those without, which reduced peak downwelling domain-mean shortwave radiation at the surface by ~25 W m-2. In this classic twin experiment design, adding nonlocal fire plume led to near-surface cooling and changes in cloud vertical distribution, while variations in domain-mean cloud liquid water path were negligible. The higher aerosol concentrations in the simulation with the fire plume resulted in a ~10% reduction in domain-mean precipitation coincident with an ~8% decrease in domain-mean CAPE. A suite of simulations was also conducted to explore sensitivities of meteorological feedbacks to the ratio of black carbon to total plume aerosols, as well as to overall plume concentrations. Results from this ensemble revealed that plume-induced near-surface cooling and CAPE reduction occur in a wide range of conditions. The response of moist convection was very complex because of strong thermodynamic internal variability. Key Points Nonlocal fire emissions resulted in ~10% precipitation reduction Nonlocal fire emissions reduced peak surface shortwave radiation at by 25 W m-2 An aerosol-enabled globally driven regional modeling system is developed ©2014. American Geophysical Union. All Rights Reserved.

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present‐Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model

Abstract Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ∼25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderate rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large‐scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAMs large‐scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAMs horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large‐scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large‐scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale‐aware parameterizations, but also reveal unrecognized trade‐offs from the entanglement of precipitation frequency and total amount.

Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration

PUBLICATIONS Journal of Advances in Modeling Earth Systems RESEARCH ARTICLE 10.1002/2015MS000567 Key Points: Internal variability of U.S. MCS events dominate CRM sensitivities in superparameterized CAM Need ensembles of 1001 storms to detect sensitivities and tune superparameterized physics Correspondence to: M. S. Pritchard, mspritch@uci.edu Citation: Elliott, E. J, S. Yu, G. J. Kooperman, H. Morrison, M. Wang, and M. S. Pritchard (2016), Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration, J. Adv. Model. Earth Syst., 8, 634–649, doi:10.1002/ 2015MS000567. Received 18 OCT 2015 Accepted 7 APR 2016 Accepted article online 12 APR 2016 Published online 1 MAY 2016 Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration Elizabeth J. Elliott 1 , Sungduk Yu 1 , Gabriel J. Kooperman 1 , Hugh Morrison 2 , Minghuai Wang 3,4 , and Michael S. Pritchard 1 Department of Earth System Science, University of California Irvine, Irvine, California, USA, 2 National Center for Atmospheric Research, Boulder, Colorado, USA, 3 Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China, 4 Jiangsu Collaborative Innovation Center of Climate Change, Nanjing, China Abstract The sensitivities of simulated mesoscale convective systems (MCSs) in the central U.S. to microphysics and grid configuration are evaluated here in a global climate model (GCM) that also permits global-scale feedbacks and variability. Since conventional GCMs do not simulate MCSs, studying their sensitivities in a global framework useful for climate change simulations has not previously been possible. To date, MCS sensitivity experiments have relied on controlled cloud resolving model (CRM) studies with limited domains, which avoid internal variability and neglect feedbacks between local convection and larger-scale dynamics. However, recent work with superparameterized (SP) GCMs has shown that eastward propagating MCS-like events are captured when embedded CRMs replace convective parameterizations. This study uses a SP version of the Community Atmosphere Model version 5 (SP-CAM5) to evaluate MCS sensitivities, applying an objective empirical orthogonal function algorithm to identify MCS-like events, and harmonizing composite storms to account for seasonal and spatial heterogeneity. A five-summer control simulation is used to assess the magnitude of internal and interannual variability relative to 10 sensitivity experiments with varied CRM parameters, including ice fall speed, one-moment and two-moment microphysics, and grid spacing. MCS sensitivities were found to be subtle with respect to internal variability, and indicate that ensembles of over 100 storms may be necessary to detect robust differences in SP-GCMs. These results emphasize that the properties of MCSs can vary widely across individual events, and improving their representation in global simulations with significant internal variability may require comparison to long (multidecadal) time series of observed events rather than single season field campaigns. 1. Introduction Mesoscale convective systems (MCSs) in the central U.S. can deliver up to half of warm season accumulated rainfall in the region, and are associated with extreme flooding events. However, MCSs are governed by exotic physics of organized convection that are not captured by conventional deep convection parameter- izations [Moncrieff and Liu, 2006], and thus are missing in most global climate models (GCMs) even with a high-resolution horizontal grid size of O(25 km) [Bacmeister et al., 2014]. As a result, the climate change response of these storms and the rainfall they generate remains uncertain. 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. ELLIOTT ET AL. Superparameterized (SP) GCMs, which replace traditional convective parameterizations with embedded cloud resolving models (CRMs) in each grid column of a host GCM [Grabowski, 2001], such as the SP version of the Community Atmosphere Model (CAM) [Khairoutdinov and Randall, 2001], have been shown to simu- late an organized MCS-like storm signal. Unlike conventional versions of CAM, which tend to produce fre- quent local convection in the early afternoon, SP-CAM captures intermittent nocturnal convective systems that propagate eastward across the central U.S. [Pritchard et al., 2011]. Given the dynamic limitations of the two-dimensional CRM configuration used in SP-CAM, which can distort MCS physics relative to three-dimensional configurations in stand-alone CRM simulations [Schlesinger, 1984; Weisman et al., 1988; Nicholls and Weissbluth, 1988; Wandishin et al., 2008; Wandishin et al., 2010], it is some- what surprising that SP-CAM is able to produce these propagating systems. While it may be tempting to DETECTING SPCAM MCS SENSITIVITIES

Assessing aerosol indirect effect through ice clouds in CAM5

Ice clouds play an important role in regulating the Earth’s radiative budget and influencing the hydrological cycle. Aerosols can act as solution droplets or ice nuclei for ice crystal formation, thus affecting the physical properties of ice clouds. Because the related dynamical and microphysical processes happen at very small spatial and temporal scales, it is a great challenge to accurately represent them in global climate models. Consequently, the aerosol indirect effect through ice clouds (ice AIE) estimated by global climate models is associated with large uncertainties. In order to better understand these processes and improve ice cloud parameterization in the Community Atmospheric Model, version 5 (CAM5), we analyze in-situ measurements from various research campaigns, and use the derived statistical information to evaluate and constrain the model [1]. We also make use of new model capabilities (prescribed aerosols and nudging) to estimate the aerosol indirect effect through ice clouds, and quantif...

Assessing the Impact of Indian Irrigation on Precipitation in the Irrigation-Enabled Community Earth System Model

AbstractGlobal climate models are beginning to include explicit treatments of irrigation to investigate the coupling between human water use and the natural hydrologic cycle. However, differences i...