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Allison, I, Bindoff NL, Bindschadler RA, Cox PM, de Noblet N, England MH, Francis JE, Gruber N, Haywood AM, Karoly DJ, Kaser G, Quéré LC, Lenton TM, Mann ME, McNeil BI, Pitman AJ, Rahmstorf S, Rignot E, Schellnhuber HJ, Schneider SH, Sherwood SC, Somerville RCJ, Steffen K, Steig EJ, Visbeck M, Weaver. AJ.  2011.  The Copenhagen Diagnosis: Updating the world on the latest climate science. :xiv,98p.., Burlington, MA: Elsevier Abstract
Book Chapter
Walsh, J, Wuebbles D, Hayhoe K, Kossin JP, Kunkel K, Stephens GL, Thorne PD, Vose RS, Wehner B, Willis J, Anderson D, Kharin V, Knutson T, Landerer FW, Lenton TM, Kennedy JJ, Somerville R.  2014.  Appendix 3: Climate Science Supplement. Climate Change Impacts in the United States: The Third National Climate Assessment. ( Mellilo JM, Richmond T(TC), Yohe GW, Eds.).:735-789.: U.S. Global Change Research Program   10.7930/J0KS6PHH   Abstract

This appendix provides further information and discussion on climate science beyond that presented in Ch. 2: Our Changing Climate. Like the chapter, the appendix focuses on the observations, model simulations, and other analyses that explain what is happening to climate at the national and global scales, why these changes are occurring, and how climate is projected to change throughout this century. In the appendix, however, more information is provided on attribution, spatial and temporal detail, and physical mechanisms than could be covered within the length constraints of the main chapter.

Walsh, J, Wuebbles D, Hayhoe K, Kossin JP, Kunkel K, Stephens GL, Thorne PD, Vose RS, Wehner B, Willis J, Anderson D, Kharin V, Knutson T, Landerer FW, Lenton TM, Kennedy JJ, Somerville R.  2014.  Appendix 4: Frequently Asked Questions (Question E). Climate Change Impacts in the United States: The Third National Climate Assessment. ( Mellilo JM, Richmond T(TC), Yohe GW, Eds.).:790-820.: U.S. Global Change Research Program   10.7930/J0G15XS3   Abstract

E. Is it getting warmer at the same rate everywhere? Will the warming continue?Temperatures are not increasing at the same rate everywhere, because temperature changes in a given location depend on many factors. However, average global temperatures are projected to continue increasing throughout the remainder of this century due to heat-trapping gas emissions from human activities.

Walsh, J, Wuebbles D, Hayhoe K, Kossin JP, Kunkel K, Stephens GL, Thorne PD, Vose RS, Wehner B, Willis J, Anderson D, Doney S, Feeley R, Hennon PA, Kharin V, Knutson T, Landerer FW, Lenton TM, Kennedy JJ, Somerville R.  2014.  Ch. 2: Our Changing Climate. Climate Change Impacts in the United States: The Third National Climate Assessment. ( Mellilo JM, Richmond T(TC), Yohe GW, Eds.).:19-67.: U.S. Global Change Research Program   10.7930/J0KW5CXT   Abstract

This chapter summarizes how climate is changing, why it is changing, and what is projected for the future. While the focus is on changes in the United States, the need to provide context sometimes requires a broader geographical perspective. Additional geographic detail is presented in the regional chapters of this report. Further details on the topics covered by this chapter are provided in the Climate Science Supplement and Frequently Asked Questions Appendices.

Solomon, S, Qin D, Manning M, Alley RB, Berntsen TK, Bindoff N, Chen Z, Chidthaisong A, Gregory JM, Hegeri GC, Heimann M, Hewitson B, Hoskins BJ, Joos F, Jouzel J, Kattsov V, Lohmann U, Matsuno T, Molina M, Nicholls N, Overpeck JT, Raga G, Ramaswamy V, Ren J, Rusticucci M, Somerville RCJ, Stocker TF, Whetton P, Wood RA, Wratt D.  2007.  Technical Summary. Climate change 2007 : the physical science basis : contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. ( Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H, Eds.)., Cambridge; New York: Cambridge University Press Abstract
Journal Article
Ghan, S, Randall D, Xu KM, Cederwall R, Cripe D, Hack J, Iacobellis S, Klein S, Krueger S, Lohmann U, Pedretti J, Robock A, Rotstayn L, Somerville R, Stenchikov G, Sud Y, Walker G, Xie SC, Yio J, Zhang MH.  2000.  A comparison of single column model simulations of summertime midlatitude continental convection. Journal of Geophysical Research-Atmospheres. 105:2091-2124.   Doi 10.1029/1999jd900971   AbstractWebsite

Eleven different single-column models (SCMs) and one cloud ensemble model (CEM) are driven by boundary conditions observed at the Atmospheric Radiation Measurement (ARM) program southern Great Plains site for a 17 day period during the summer of 1995. Comparison of the model simulations reveals common signatures identifiable as products of errors in the boundary conditions. Intermodel differences in the simulated temperature, humidity, cloud, precipitation, and radiative fluxes reflect differences in model resolution or physical parameterizations, although sensitive dependence on initial conditions can also contribute to intermodel differences. All models perform well at times but poorly at others. Although none of the SCM simulations stands out as superior to the others, the simulation by the CEM is in several respects in better agreement with the observations than the simulations by the SCMs. Nudging of the simulated temperature and humidity toward observations generally improves the simulated cloud and radiation fields as well as the simulated temperature and humidity but degrades the precipitation simulation for models with large temperature and humidity biases without nudging. Although some of the intermodel differences have not been explained, others have been identified as model problems that can be or have been corrected as a result of the comparison.

Kooperman, GJ, Pritchard MS, Ghan SJ, Wang MH, Somerville RCJ, Russell LM.  2012.  Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5. Journal of Geophysical Research-Atmospheres. 117   10.1029/2012jd018588   AbstractWebsite

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/m(2) and -1.37 +/- 0.13 W/m(2) for 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/m(2) and -0.82 W/m(2), respectively. These results compare well with previous estimates from three-year free-running MMF simulations (-0.83 W/m(2)), 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. Citation: Kooperman, G. J., M. S. Pritchard, S. J. Ghan, M. Wang, R. C. J. Somerville, and L. M. Russell (2012), Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5, J. Geophys. Res., 117, D23204, doi:10.1029/2012JD018588.

Baker, WE, Kung EC, Somerville RCJ.  1978.  An Energetics Analysis of Forecast Experiments with NCAR General Circulation Model. Monthly Weather Review. 106:311-323.   10.1175/1520-0493(1978)106<0311:aeaofe>;2   AbstractWebsite

The energetics in numerical weather forecast experiments with the NCAR general circulation model have been analyzed. The 6-layer, 5-degree, second-generation global model was used to make two 10-day forecasts with the same initial conditions. The two experiments differed primarily in the methods of convective parameterization.Hemispheric integrals of the model energies and energy transformations are presented in the context of their approach to a quasi-equilibrium climatology. Spectral and spatial analyses of the eddy energies and transformations provide further insight into the model response to the initial conditions. After the initial adjustment, the eddy kinetic energy appears to lag the conversion from eddy available potential energy to eddy kinetic energy by at least 48 h in the long waves (wavenumbers 1–4) and by approximately 24 h in the baroclinic waves (wavenumbers 5–7), whereas little or no time lag is apparent in the short waves (wavenumbers 8–12).The sensitivity of the forecast energetics to two different convective parameterizations is also examined. There is little appreciable difference between the two experiments in the eddy kinetic energy integrals during the first 36 h of the forecast, but temporal patterns of the eddy transformations are distinctly different after 12 h.

Baker, WE, Kung EC, Somerville RCJ.  1977.  Energetics Diagnosis of the NCAR General Circulation Model. Monthly Weather Review. 105:1384-1401.   10.1175/1520-0493(1977)105<1384:edotng>;2   AbstractWebsite

A comprehensive energetics analysis has been performed on the NCAR general circulation model. The analysis involves January and July simulation experiments with the 6-layer, 5-degree, second-generation model with two different convective schemes. Spectral analysis of the energy transformations in the wave-number domain was performed separately on a global and hemispheric basis as well as for the tropics and mid-latitudes. Latitudinal distributions of energy variables were also examined.A qualitative agreement with observational estimates is generally recognized in the transformations of eddy energies. Quantitatively, however, the eddy energies, conversions and energy transfer between wavenumbers are weaker than observational estimates. It is noteworthy that substantial differences exist in the energetics of the two versions of the model with different convective schemes.

Xie, SC, Xu KM, Cederwall RT, Bechtold P, Delgenio AD, Klein SA, Cripe DG, Ghan SJ, Gregory D, Iacobellis SF, Krueger SK, Lohmann U, Petch JC, Randall DA, Rotstayn LD, Somerville RCJ, Sud YC, Von Salzen K, Walker GK, Wolf A, Yio JJ, Zhang GJ, Zhang MG.  2002.  Intercomparison and evaluation of cumulus parametrizations under summertime midlatitude continental conditions. Quarterly Journal of the Royal Meteorological Society. 128:1095-1135.   10.1256/003590002320373229   AbstractWebsite

This study reports the Single-Column Model (SCM) part of the Atmospheric Radiation Measurement (ARM)/the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) joint SCM and Cloud-Resolving Model (CRM) Case 3 intercomparison study, with a focus on evaluation Of Cumulus parametrizations used in SCMs. Fifteen SCMs are evaluated under summertime midlatitude continental conditions using data collected at the ARM Southern Great Plains site during the summer 1997 Intensive Observing Period. Results from ten CRMs are also used to diagnose problems in the SCMs. It is shown that most SCMs can generally capture well the convective events that were well-developed within the SCM domain, while most of them have difficulties in simulating the occurrence of those convective events that only occurred within a small part of the domain. All models significantly underestimate the surface stratiform precipitation. A third of them produce large errors in surface precipitation and thermodynamic structures. Deficiencies in convective triggering mechanisms are thought to be one of the major reasons. Using a triggering mechanism that is based on the vertical integral of parcel buoyant energy without additional appropriate constraints results in overactive convection, which in turn leads to large systematic warm/dry biases in the troposphere. It is also shown that a non-penetrative convection scheme can underestimate the depth of instability for midlatitude convection, which leads to large systematic cold/moist biases in the troposphere. SCMs agree well quantitatively with CRMs in the updraught mass fluxes, while most models significantly underestimate the downdraught mass fluxes. Neglect of mesoscale updraught and downdraught mass fluxes in the SCMs contributes considerably to the discrepancies between the SCMs and the CRMs. In addition, uncertainties in the diagnosed mass fluxes in the CRMs and deficiencies with cumulus parametrizations are not negligible. Similar results are obtained in the sensitivity tests when different forcing approaches are used. Finally. sensitivity tests from an SCM indicate that its simulations can be greatly improved when its triggering mechanism and closure assumption are improved.

Zhao, Z, Kooperman GJ, Pritchard MS, Russell LM, Somerville RCJ.  2014.  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. Journal of Geophysical Research: Atmospheres. 119:2013JD020973.   10.1002/2013JD020973   AbstractWebsite

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.

Xu, KM, Zhang MH, Eitzen MA, Ghan SJ, Klein SA, Wu XQ, Xie SC, Branson M, Delgenio AD, Iacobellis SF, Khairoutdinov M, Lin WY, Lohmann U, Randall DA, Somerville RCJ, Sud YC, Walker GK, Wolf A, Yio JJ, Zhang JH.  2005.  Modeling springtime shallow frontal clouds with cloud-resolving and single-column models. Journal of Geophysical Research-Atmospheres. 110   10.1029/2004jd005153   AbstractWebsite

This modeling study compares the performance of eight single-column models (SCMs) and four cloud-resolving models (CRMs) in simulating shallow frontal cloud systems observed during a short period of the March 2000 Atmospheric Radiation Measurement (ARM) intensive operational period. Except for the passage of a cold front at the beginning of this period, frontal cloud systems are under the influence of an upper tropospheric ridge and are driven by a persistent frontogenesis over the Southern Great Plains and moisture transport from the northwestern part of the Gulf of Mexico. This study emphasizes quantitative comparisons among the model simulations and with the ARM data, focusing on a 27-hour period when only shallow frontal clouds were observed. All CRMs and SCMs simulate clouds in the observed shallow cloud layer. Most SCMs also produce clouds in the middle and upper troposphere, while none of the CRMs produce any clouds there. One possible cause for this is the decoupling between cloud condensate and cloud fraction in nearly all SCM parameterizations. Another possible cause is the weak upper tropospheric subsidence that has been averaged over both descending and ascending regions. Significantly different cloud amounts and cloud microphysical properties are found in the model simulations. All CRMs and most SCMs underestimate shallow clouds in the lowest 125 hPa near the surface, but most SCMs overestimate the cloud amount above this layer. These results are related to the detailed formulations of cloud microphysical processes and fractional cloud parameterizations in the SCMs, and possibly to the dynamical framework and two-dimensional configuration of the CRMs. Although two of the CRMs with anelastic dynamical frameworks simulate the shallow frontal clouds much better than the SCMs, the CRMs do not necessarily perform much better than the SCMs for the entire period when deep and shallow frontal clouds are present.

Shen, SSP, Velado M, Somerville RCJ, Kooperman GJ.  2013.  Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method. Journal of Geophysical Research: Atmospheres.   10.1002/jgrd.50408   AbstractWebsite

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.

Rahmstorf, S, Cazenave A, Church JA, Hansen JE, Keeling RF, Parker DE, Somerville RCJ.  2007.  Recent climate observations compared to projections. Science. 316:709-709.   10.1126/science.1136843   AbstractWebsite

We present recent observed climate trends for carbon dioxide concentration, global mean air temperature, and global sea level, and we compare these trends to previous model projections as summarized in the 2001 assessment report of the Intergovernmental Panel on Climate Change (IPCC). The IPCC scenarios and projections start in the year 1990, which is also the base year of the Kyoto protocol, in which almost all industrialized nations accepted a binding commitment to reduce their greenhouse gas emissions. The data available for the period since 1990 raise concerns that the climate system, in particular sea level, may be responding more quickly to climate change than our current generation of models indicates.

Kooperman, GJ, Pritchard MS, Somerville RCJ.  2014.  The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR community atmosphere model. Journal of Advances in Modeling Earth Systems.   10.1002/2014MS000306   Abstract

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 CO2 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 CO2 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 CO2 climate.

Kooperman, GJ, Pritchard MS, Somerville RCJ.  2013.  Robustness and sensitivities of central US summer convection in the super-parameterized CAM: Multi-model intercomparison with a new regional EOF index. Geophysical Research Letters. 40:3287-3291.   10.1002/grl.50597   AbstractWebsite

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.

Xie, SC, Zhang MH, Branson M, Cederwall RT, Delgenio AD, Eitzen ZA, Ghan SJ, Iacobellis SF, Johnson KL, Khairoutdinov M, Klein SA, Krueger SK, Lin WY, Lohmann U, Miller MA, Randall DA, Somerville RCJ, Sud YC, Walker GK, Wolf A, Wu XQ, Xu KM, Yio JJ, Zhang G, Zhang JH.  2005.  Simulations of midlatitude frontal clouds by single-column and cloud-resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period. Journal of Geophysical Research-Atmospheres. 110   10.1029/2004jd005119   AbstractWebsite

[1] This study quantitatively evaluates the overall performance of nine single-column models (SCMs) and four cloud-resolving models (CRMs) in simulating a strong midlatitude frontal cloud system taken from the spring 2000 Cloud Intensive Observational Period at the Atmospheric Radiation Measurement ( ARM) Southern Great Plains site. The evaluation data are an analysis product of constrained variational analysis of the ARM observations and the cloud data collected from the ARM ground active remote sensors (i.e., cloud radar, lidar, and laser ceilometers) and satellite retrievals. Both the selected SCMs and CRMs can typically capture the bulk characteristics of the frontal system and the frontal precipitation. However, there are significant differences in detailed structures of the frontal clouds. Both CRMs and SCMs overestimate high thin cirrus clouds before the main frontal passage. During the passage of a front with strong upward motion, CRMs underestimate middle and low clouds while SCMs overestimate clouds at the levels above 765 hPa. All CRMs and some SCMs also underestimated the middle clouds after the frontal passage. There are also large differences in the model simulations of cloud condensates owing to differences in parameterizations; however, the differences among intercompared models are smaller in the CRMs than the SCMs. In general, the CRM-simulated cloud water and ice are comparable with observations, while most SCMs underestimated cloud water. SCMs show huge biases varying from large overestimates to equally large underestimates of cloud ice. Many of these model biases could be traced to the lack of subgrid-scale dynamical structure in the applied forcing fields and the lack of organized mesoscale hydrometeor advections. Other potential reasons for these model errors are also discussed in the paper.

Allison, I, Bindoff NL, Bindschadler RA, Cox PM, de Noblet N, England MH, Francis JE, Gruber N, Haywood AM, Karoly DJ, Kaser G, Quéré LC, Lenton TM, Mann ME, McNeil BI, Pitman AJ, Rahmstorf S, Rignot E, Schellnhuber HJ, Schneider SH, Sherwood SC, Somerville RCJ, K.Steffen, Steig EJ, Visbeck M, Weaver AJ.  2009.  The Copenhagen Diagnosis, 2009: Updating the world on the Latest Climate Science. :60. Abstract