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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.

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.

Yang, Y, Russell LM, Xu L, Lou SJ, Lamjiri MA, Somerville RCJ, Miller AJ, Cayan DR, DeFlorio MJ, Ghan SJ, Liu Y, Singh B, Wang HL, Yoon JH, Rasch PJ.  2016.  Impacts of ENSO events on cloud radiative effects in preindustrial conditions: Changes in cloud fraction and their dependence on interactive aerosol emissions and concentrations. Journal of Geophysical Research-Atmospheres. 121:6321-6335.   10.1002/2015jd024503   AbstractWebsite

We use three 150 year preindustrial simulations of the Community Earth System Model to quantify the impacts of El Nino-Southern Oscillation (ENSO) events on shortwave and longwave cloud radiative effects (CRESW and CRELW). Compared to recent observations from the Clouds and the Earth's Radiant Energy System data set, the model simulation successfully reproduces larger variations of CRESW and CRELW over the tropics. The ENSO cycle is found to dominate interannual variations of cloud radiative effects. Simulated cooling (warming) effects from CRESW (CRELW) are strongest over the tropical western and central Pacific Ocean during warm ENSO events, with the largest difference between 20 and 60 W m(-2), with weaker effects of 10-40 W m(-2) over Indonesian regions and the subtropical Pacific Ocean. Sensitivity tests show that variations of cloud radiative effects are mainly driven by ENSO-related changes in cloud fraction. The variations in midlevel and high cloud fractions each account for approximately 20-50% of the interannual variations of CRESW over the tropics and almost all of the variations of CRELW between 60 degrees S and 60 degrees N. The variation of low cloud fraction contributes to most of the variations of CRESW over the midlatitude oceans. Variations in natural aerosol concentrations explained 10-30% of the variations of both CRESW and CRELW over the tropical Pacific, Indonesian regions, and the tropical Indian Ocean. Changes in natural aerosol emissions and concentrations enhance 3-5% and 1-3% of the variations of cloud radiative effects averaged over the tropics.

Xu, L, Pierce DW, Russell LM, Miller AJ, Somerville RCJ, Twohy CH, Ghan SJ, Singh B, Yoon J-H, Rasch PJ.  2015.  Interannual to decadal climate variability of sea salt aerosols in the coupled climate model CESM1.0. Journal of Geophysical Research: Atmospheres. :2014JD022888.   10.1002/2014JD022888   AbstractWebsite

This study examines multi-year climate variability associated with sea salt aerosols and their contribution to the variability of shortwave cloud forcing (SWCF) using a 150-year simulation for pre-industrial conditions of the Community Earth System Model version 1.0 (CESM1). The results suggest that changes in sea salt and related cloud and radiative properties on interannual timescales are dominated by the ENSO cycle. Sea salt variability on longer (interdecadal) timescales is associated with low-frequency variability in the Pacific Ocean similar to the interdecadal Pacific Oscillation (IPO), but does not show a statistically significant spectral peak. A multivariate regression suggests that sea salt aerosol variability may contribute to SWCF variability in the tropical Pacific, explaining up to 20-30% of the variance in that region. Elsewhere, there is only a small sea salt aerosol influence on SWCF through modifying cloud droplet number and liquid water path that contributes to the change of cloud effective radius and cloud optical depth (and hence cloud albedo), producing a multi-year aerosol-cloud-wind interaction.

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.

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.

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.

Bowman, TE, Maibach E, Mann ME, Somerville RCJ, Seltser BJ, Fischhoff B, Gardiner SM, Gould RJ, Leiserowitz A, Yohe G.  2010.  Time to Take Action on Climate Communication. Science. 330:1044-1044.   10.1126/science.330.6007.1044   AbstractWebsite