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Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)-Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.

Large-scale conditions over subtropical marine stratocumulus areas are extracted from global climate models (GCMs) participating in phase 3 of the Coupled Model Intercomparison Project (CMIP3) and used to drive an atmospheric mixed-layer model (MLM) for current and future climate scenarios. Cloud fraction is computed as the fraction of days where GCM forcings produce a cloudy equilibrium MLM state. This model is a good predictor of cloud fraction and its temporal variations on time scales longer than 1 week but overpredicts liquid water path and entrainment. GCM cloud fraction compares poorly with observations of mean state, variability, and correlation with estimated inversion strength (EIS). MLM cloud fraction driven by these same GCMs, however, agrees well with observations, suggesting that poor GCM low cloud fraction is due to deficiencies in cloud parameterizations rather than large-scale conditions. However, replacing the various GCM cloud parameterizations with a single physics package (the MLM) does not reduce intermodel spread in low-cloud feedback because the MLM is more sensitive than the GCMs to existent intermodel variations in large-scale forcing. This suggests that improving GCM low cloud physics will not by itself reduce intermodel spread in predicted stratocumulus cloud feedback. Differences in EIS and EIS change between GCMs are found to be a good predictor of current-climate MLM cloud amount and future cloud change. CMIP3 GCMs predict a robust increase of 0.5–1 K in EIS over the next century, resulting in a 2.3%–4.5% increase in MLM cloudiness. If EIS increases are real, subtropical stratocumulus may damp global warming in a way not captured by the GCMs studied.

The possibility of anthropogenic ocean warming has led to a range of concerns, from impacts on fisheries and ocean acidification to rising sea level and changes in tropical cyclone frequency and intensity. This study substantially strengthens the attribution of the recently observed global ocean warming to human activity.

In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge–satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted.

The Weather Research and Forecasting (WRF) model version 3.0.1 is used in both short-range (days) and long-range (years) simulations to explore the California wintertime model wet bias. California is divided into four regions (the coast, central valley, mountains, and Southern California) for validation. Three sets of gridded surface observations are used to evaluate the impact of measurement uncertainty on the model wet bias. Short-range simulations are driven by the North American Regional Reanalysis (NARR) data and designed to test the sensitivity of model physics and grid resolution to the wet bias using eight winter storms chosen from four major types of large-scale conditions: the Pineapple Express, El Niño, La Niña, and synoptic cyclones. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to assess the robustness of microphysics and cumulus parameterizations to resolution changes. Additionally, long-range simulations driven by both NARR and general circulation model (GCM) data are performed at low resolution to gauge the impact of the GCM forcing on the model wet bias.These short- and long-range simulations show that low-resolution runs tend to underpredict precipitation in the coast region and overpredict it elsewhere in California. The sensitivity test of WRF physics in short-range simulations indicates that model precipitation depends most strongly on the microphysics scheme, though convective parameterization is also important, particularly near the coast. In contrast, high-resolution (2 km) simulation increases model precipitation in all regions. As a result, it improves the forecast bias in the coast region while it downgrades the model performance in the other regions. It is also found that the choice of validation dataset has a significant impact on the model wet bias of both short- and long-range simulations. However, this impact in long-range simulations appears to be a secondary contribution as compared to its counterpart from the GCM forcing.

This paper describes a series of 6-day large eddy simulations of a deep, sometimes drizzling stratocumulus-topped boundary layer based on forcings from the East Pacific Investigation of Climate (EPIC) 2001 field campaign. The base simulation was found to reproduce the observed mean boundary layer properties quite well. The diurnal cycle of liquid water path was also well captured, although good agreement appears to result partially from compensating errors in the diurnal cycles of cloud base and cloud top due to overentrainment around midday. At all times of the day, entrainment is found to be proportional to the vertically integrated buoyancy flux. Model stratification matches observations well; turbulence profiles suggest that the boundary layer is always at least somewhat decoupled. Model drizzle appears to be too sensitive to liquid water path and subcloud evaporation appears to be too weak. Removing the diurnal cycle of subsidence had little effect on simulated liquid water path. Simulations with changed droplet concentration and drizzle susceptibility showed large liquid water path differences at night, but differences were quite small at midday. Droplet concentration also had a significant impact on entrainment, primarily through droplet sedimentation feedback rather than through drizzle processes.

In this paper, an idealized framework based on a cloud-topped mixed layer model is developed for investigating feedbacks between subtropical stratocumulus (Sc) and global warming. The two principal control parameters are Sc-region sea surface temperature (SST) and intertropical convergence zone (ITCZ) SST (which controls the temperature and mean subsidence profiles above the Sc). The direct effect of CO2 doubling (leaving all other parameters fixed) is tested and found to somewhat reduce liquid water path; discussion of this effect on the SST-change simulations is included. The presence of a cold boundary layer is found to significantly affect the temperature and subsidence rate just above cloud top by enhancing lower-tropospheric diabatic cooling in this region. A simple representation of this effect (easily generalizable to a more realistic boundary layer model) is developed. Steady-state solutions are analyzed as a function of local and ITCZ SST. Two climate change scenarios are considered. The first scenario is an equal increase of local and ITCZ SSTs. In this case, predicted boundary layer depth and cloud thickness increase. This is found in a simplified context to result from subsidence and entrainment decreases due to increased static stability in a warmer climate. In the second case, local SST change is diagnosed from a surface energy balance under the assumption that ocean heat transport remains unchanged. In this case, predicted boundary layer depth decreases. Cloud continues to thicken with rising ITCZ SST, but at a rate much reduced in comparison to the equal-warming scenario. This cloud shading feedback keeps SST in the Sc region nearly constant as the ITCZ SST increases. Model sensitivity to aerosol indirect effects is also considered by varying the assumed droplet concentration. The resulting change in liquid water path is small, suggesting a weaker dependence on second indirect effect than found in previous studies.

© The Authors. Published by Elsevier B.V. We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades. These topics have been presented within a workshop titled, "Numerical and Computational Developments to Advance Multiscale Earth System Models (MSESM'15)," as part of the International Conference on Computational Sciences, Reykjavik, Iceland, June 1-3, 2015.

Mixed-layer budgets of boundary layer mass, moisture, and liquid water static energy are estimated from 6 days of data collected at 20°S, 85°W (a region of persistent stratocumulus) during the East Pacific Investigation of Climate (EPIC) stratocumulus cruise in 2001. These budgets are used to estimate a mean diurnal cycle of entrainment and, by diagnosing the fluxes of humidity and liquid water static energy necessary to maintain a mixed-layer structure, of buoyancy flux. Although the entrainment rates suggested by each of the budgets have significant uncertainty, the various methods are consistent in predicting a 6-day mean entrainment rate of 4 ± 1 mm s−1, with higher values at night and very little entrainment around local noon. The diurnal cycle of buoyancy flux suggests that drizzle, while only a small term in the boundary layer moisture budget, significantly reduces subcloud buoyancy flux and may induce weak decoupling of surface and cloud-layer turbulence during the early morning hours, a structure that is maintained throughout the day by shortwave warming. Finally, the diurnal cycle of entrainment diagnosed from three recently proposed entrainment closures is found to be consistent with the observationally derived values.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° × 1.25° finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.

The diverse set of Earth system models used to conduct the CMIP5 ensemble can partly sample the uncertainties in future climate projections. However, combining those projections is complicated by the fact that models developed by different groups share ideas and code and therefore biases. The authors propose a method for combining model results into single or multivariate distributions that are more robust to the inclusion of models with a large degree of interdependency. This study uses a multivariate metric of present-day climatology to assess both model performance and similarity in two recent model intercomparisons, CMIP3 and CMIP5. Model characteristics can be interpolated and then resampled in a space defined by independent climate properties. A form of weighting can be applied by sampling more densely in the region of the space close to the projected observations, thus taking into account both model performance and interdependence. The choice of the sampling distribution’s parameters is a subjective decision that should reflect the researcher’s prior assumptions as to the acceptability of different model errors.

The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.