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Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

Due to their enhanced fuel economy, the market share of gasoline direct injection (GDI) vehicles has increased significantly over the past decade. However, GDI engines emit higher levels of black carbon (BC) aerosols compared to traditional port fuel injection (PFI) engines. Here, we performed coupled chemical transport and radiative transfer simulations to estimate the aerosol-induced public health and direct radiative effects of shifting the U.S. fleet from PFI to GDI technology. By comparing simulations with current emission profiles and emission profiles modified to reflect a shift from PFI to GDI, we calculated the change in aerosol (mostly BC) concentrations associated with the fleet change. Standard concentration-response calculations indicated that the total annual deaths in the U.S. attributed to particulate gasoline-vehicle emissions would increase from 855 to 1599 due to shifting from PFI to GDI. Furthermore, the increase in BC associated with the shift would lead to an annual average positive radiative effect over the U.S. of approximately +0.075 W/m

Rising atmospheric CO2 impacts on vegetation physiological processes can alter land feedbacks on precipitation and water resources, but understanding of regional differences in these changes is uncertain. We investigate the impact of rising CO2 on land water resources for different wetness levels using four Earth system models. We find an overall tendency of runoff to increase across all wetness levels. However, runoff increases in wet regions are much larger than those in dry regions, especially in wet seasons. This substantial amplification of contrasts between wet and dry regions increases at 3% per 100 ppm increase in CO2 relative to the historical period, reaching 18% for a quadrupling of CO2, quantified by a new wetting contrast index (WCI). Physiological effects suppress evapotranspiration more in wet than dry regions, which has a larger contribution than radiative forcing to the amplification of runoff contrast, reshaping the spatial distribution of future land water resources.

River flow statistics are expected to change as a result of increasing atmospheric CO2 but uncertainty in Earth system model projections is high. While this is partly driven by changing precipitation, with well-known Earth system model uncertainties, here we show that the influence of plant stomatal conductance feedbacks can cause equally large changes in regional flood extremes and even act as the main control on future low latitude streamflow. Over most tropical land masses, modern climate predictions suggest that plant physiological effects will boost streamflow, overwhelming opposing effects of soil drying driven by the effects of CO2 on atmospheric radiation, warming and rainfall redistribution. The relatively unknown uncertainties in representing eco-physiological processes must therefore be better constrained in land-surface models. To this end, we identify a distinct plant physiological fingerprint on annual peak, low and mean discharge throughout the tropics and identify river basins where physiological responses dominate radiative responses to rising CO2 in modern climate projections. Climate change is expected to impact river flows. Here, it is shown that plant physiological responses to increased CO2, rather than atmospheric changes, are the primary drivers of mean, peak and low flows throughout the tropics.

Earth system models predict a zonal dipole of precipitation change over tropical South America, with decreases over the Amazon and increases over the Andes. Much of this has been attributed to the physiological response of the rainforest to elevated CO2, which describes a basin-wide reduction in stomatal conductance and transpiration. While robust in Earth system model experiments, details of the underlying atmospheric mechanism—specifically how it evolves in the context of land-atmosphere interaction and the diurnal cycle—are unresolved. We investigate this using idealized model simulations and find that within 24 hours of a CO2 increase, changes occur over the Amazon that engender synoptic timescale feedbacks. Decreased evapotranspiration from the rainforest throttles near-surface moisture, inducing a drier, warmer, and deeper boundary layer. Above this, enhanced turbulent diffusivity increases vapor in the lower free troposphere. Together, these processes reduce convective activity and cause immediate decreases in Amazon rainfall. Over the synoptic timescale, these changes leave behind lower tropospheric moisture, which is advected westward by the background jet and increases Andean precipitation. This produces a dipole of precipitation change consistent across global and regional models as well as parameterized and resolved convection, though details are sensitive to model topography and boundary layer formulation. The mechanism reported here stresses the importance of fast timescale processes affecting stability over a period of hours that can influence longer-term vegetation-climate interactions. These results help clarify the Amazons physiological response to rising CO2 and provide insight into possible causes of historical model biases and end-of-century uncertainty in this region.

The global monsoon is characterised by transitions between pronounced dry and wet seasons, affecting food security for two-thirds of the world’s population. Rising atmospheric CO2 influences the terrestrial hydrological cycle through climate-radiative and vegetation-physiological forcings. How these two forcings affect the seasonal intensity and characteristics of monsoonal precipitation and runoff is poorly understood. Here we use four Earth System Models to show that in a CO2-enriched climate, radiative forcing changes drive annual precipitation increases for most monsoon regions. Further, vegetation feedbacks substantially affect annual precipitation in North and South America and Australia monsoon regions. In the dry season, runoff increases over most monsoon regions, due to stomatal closure-driven evapotranspiration reductions and associated atmospheric circulation change. Our results imply that flood risks may amplify in the wet season. However, the lengthening of the monsoon rainfall season and reduced evapotranspiration will shorten the water resources scarcity period for most monsoon regions.

Understanding how anthropogenic CO2 emissions will influence future precipitation is critical for sustainably managing ecosystems, particularly for drought-sensitive tropical forests. Although tropical precipitation change remains uncertain, nearly all models from the Coupled Model Intercomparison Project Phase 5 predict a strengthening zonal precipitation asymmetry by 2100, with relative increases over Asian and African tropical forests and decreases over South American forests. Here we show that the plant physiological response to increasing CO2 is a primary mechanism responsible for this pattern. Applying a simulation design in the Community Earth System Model in which CO2 increases are isolated over individual continents, we demonstrate that different circulation, moisture and stability changes arise over each continent due to declines in stomatal conductance and transpiration. The sum of local atmospheric responses over individual continents explains the pan-tropical precipitation asymmetry. Our analysis suggests that South American forests may be more vulnerable to rising CO2 than Asian or African forests.

Many conventional General Circulation Models (GCMs) in the Global Land‐Atmosphere Coupling Experiment (GLACE) tend to produce what is now recognized as overly strong land‐atmosphere (L‐A) coupling. We investigate the effects of cloud Superparameterization (SP) on L‐A coupling on timescales beyond diurnal where it has been recently shown to have a favorable muting effect hydrologically. Using the Community Atmosphere Model v3.5 (CAM3.5) and its Superparameterized counterpart SPCAM3.5, we conducted soil moisture interference experiments following the GLACE and Atmospheric Model Intercomparison Project (AMIP) protocols. The results show that, on weekly‐to‐subseasonal timescales, SP also mutes hydrologic L‐A coupling. This is detectable globally, and happens through the evapotranspiration‐precipitation segment. But on seasonal timescales, SP does not exhibit detectable effects on hydrologic L‐A coupling. Two robust regional effects of SP on thermal L‐A coupling have also been explored. Over the Arabian Peninsula, SP reduces thermal L‐A coupling through a straightforward control by mean rainfall reduction. More counterintuitively, over the Southwestern US and Northern Mexico, SP enhances the thermal L‐A coupling in a way that is independent of rainfall and soil moisture. This signal is associated with a systematic and previously unrecognized effect of SP that produces an amplified Bowen ratio, and is detectable in multiple SP model versions and experiment designs. In addition to amplifying the present‐day Bowen ratio, SP is found to amplify the climate sensitivity of Bowen ratio as well, which likely plays a role in influencing climate change predictions at the L‐A interface.

Global 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 in the formulation of irrigation schemes have produced inconsistent results, and thus the impact of irrigation on the climate system remains uncertain. To better understand the influence of irrigation on precipitation, the authors analyze simulations from the irrigation-enabled Community Land Model, version 4 (CLM4), where irrigation is applied only over a region centered on India. The addition of irrigation to the land surface has the anticipated consequence of increasing evapotranspiration locally, despite issues revealed in CLM4 of unrealistically high partitioning of irrigation water to surface runoff and unrealistically fast water drainage through the soil column. These limitations highlight a need to observationally constrain and simultaneously optimize irrigation, runoff, drainage, and evapotranspiration. Nonlocal precipitation changes as a result of Indian irrigation during the premonsoon season are examined through a hindcast framework that reveals robust hydrologic teleconnections to parts of the Arabian Sea, Bay of Bengal, and Japan on short lead times, but with strong dependence on initial synoptic conditions. On longer time scales, many of these teleconnections to Indian irrigation are easily shrouded by internal variability, but a potential geographic action center remains over the meiyu-baiu rainband indicative of a nonlocal bridge mechanism. Many of the sensitivities identified here are distinct from other global models, emphasizing the need for carefully designed irrigation-intercomparison studies.

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.

The global monsoon is characterised by transitions between pronounced dry and wet seasons, affecting food security for two-thirds of the world’s population. Rising atmospheric CO2 influences the terrestrial hydrological cycle through climate-radiative and vegetation-physiological forcings. How these two forcings affect the seasonal intensity and characteristics of monsoonal precipitation and runoff is poorly understood. Here we use four Earth System Models to show that in a CO2-enriched climate, radiative forcing changes drive annual precipitation increases for most monsoon regions. Further, vegetation feedbacks substantially affect annual precipitation in North and South America and Australia monsoon regions. In the dry season, runoff increases over most monsoon regions, due to stomatal closure-driven evapotranspiration reductions and associated atmospheric circulation change. Our results imply that flood risks may amplify in the wet season. However, the lengthening of the monsoon rainfall season and reduced evapotranspiration will shorten the water resources scarcity period for most monsoon regions., Monsoon systems have strong impacts on precipitation and food security over large areas of the world. Here, the authors show that plant responses to rising CO2 concentrations in the atmosphere play a key role in modulating seasonal rainfall and water resources over global land monsoon regions.

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.

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

Author(s): Kooperman, GJ; Fowler, MD; Hoffman, FM; Koven, CD; Lindsay, K; Pritchard, MS; Swann, ALS; Randerson, JT | Abstract: Climate change is expected to increase the frequency of flooding events and, thus, the risks of flood-related mortality and infrastructure damage. Global-scale assessments of future flooding from Earth system models based only on precipitation changes neglect important processes that occur within the land surface, particularly plant physiological responses to rising CO2. Higher CO2 can reduce stomatal conductance and transpiration, which may lead to increased soil moisture and runoff in some regions, promoting flooding even without changes in precipitation. Here we assess the relative impacts of plant physiological and radiative greenhouse effects on changes in daily runoff intensity over tropical continents using the Community Earth System Model. We find that extreme percentile rates increase significantly more than mean runoff in response to higher CO2. Plant physiological effects have a small impact on precipitation intensity but are a dominant driver of runoff intensification, contributing to one half of the 99th and one third of the 99.9th percentile runoff intensity changes.

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 CAM's large‐scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's 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., Key Points The total amount of large‐scale rain is sensitive to horizontal resolution but the intensity is notThe distribution of large‐scale rain is more realistic than the weak parameterized rain in CAMThe large‐scale rain in CAM captures the climate change intensification simulated by SPCAM

© 2014. American Geophysical Union. All Rights Reserved. Observations and regional climate modeling (RCM) studies demonstrate that global climate models (GCMs) are unreliable for predicting changes in extreme precipitation. Yet RCM climate change simulations are subject to boundary conditions provided by GCMs and do not interact with large-scale dynamical feedbacks that may be critical to the overall regional response. Limitations of both global and regional modeling approaches contribute significant uncertainty to future rainfall projections. Progress requires a modeling framework capable of capturing the observed regional-scale variability of rainfall intensity without sacrificing planetary scales. Here the United States summer rainfall response to quadrupled CO 2 climate change is investigated using conventional (CAM) and superparameterized (SPCAM) versions of the NCAR Community Atmosphere Model. The superparameterization approach, in which cloud-resolving model arrays are embedded in GCM grid columns, improves rainfall statistics and convective variability in global simulations. A set of 5 year time-slice simulations, with prescribed sea surface temperature and sea ice boundary conditions harvested from preindustrial and abrupt four times CO 2 coupled Community Earth System Model (CESM/CAM) simulations, are compared for CAM and SPCAM. The two models produce very different changes in mean precipitation patterns, which develop from differences in large-scale circulation anomalies associated with the planetary-scale response to warming. CAM shows a small decrease in overall rainfall intensity, with an increased contribution from the weaker parameterized convection and a decrease from large-scale precipitation. SPCAM has the opposite response, a significant shift in rainfall occurrence toward higher precipitation rates including more intense propagating Central United States mesoscale convective systems in a four times CO 2 climate. Key Points Large-scale dynamics are critical to regional rainfall climate change responses Superparameterization captures expected increases in rain and storm intensity Extreme rain may be decoupled from key climate change drivers in standard GCMs

An aerosol-enabled globally driven regional modeling system has been developed by coupling the National Center for Atmospheric Research's 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.

[1] Mesoscale convective systems (MCSs) can bring up to 60% of summer rainfall to the central United States but are not simulated by most global climate models. In this study, a new empirical orthogonal function based index is developed to isolate the MCS activity, similar to that developed by Wheeler and Hendon (2004) for the Madden-Julian Oscillation. The index is applied to compactly compare three conventional- and super-parameterized (SP) versions (3.0, 3.5, and 5.0) of the National Center for Atmospheric Research Community Atmosphere Model (CAM). Results show that nocturnal, eastward propagating convection is a robust effect of super-parameterization but is sensitive to its specific implementation. MCS composites based on the index show that in SP-CAM3.5, convective MCS anomalies are unrealistically large scale and concentrated, while surface precipitation is too weak. These aspects of the MCS signal are improved in the latest version (SP-CAM5.0), which uses high-order microphysics.

[1] We describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof-of-concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5-day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short- and long-time means, the 5-day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction.

Nudging as an assimilation technique has seen increased use in recent years in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5 (CAM5), due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on long-wave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations, and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. Results from both CAM5 and a second aerosol–climate model ECHAM6-HAM2 also indicate that compared to the wind-and-temperature nudging, constraining only winds leads to better agreement with the free-running model in terms of the estimated shortwave cloud forcing and the simulated convective activities. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

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...

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 world's area in which a statistically significant aerosol indirect effect can be detected (66% and 28% of the Earth's 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.