Ma, Hsi-Yen, A Cheska Siongco, Stephen A Klein, Shaocheng Xie, Alicia R Karspeck, Kevin Raeder, Jeffrey L Anderson, Jiwoo Lee, Ben P Kirtman, William J Merryfield, Hiroyuki Murakami, and Joseph J Tribbia, January 2021: On the correspondence between seasonal forecast biases and long-term climate biases in sea surface temperature. Journal of Climate, 34(1), DOI:10.1175/JCLI-D-20-0338.1. Abstract
The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.
Eyring, Veronika, Peter Cox, G M Flato, Peter J Gleckler, G Abramowitz, P Caldwell, William D Collins, B K Gier, A Hall, F Hoffman, George C Hurtt, Alexandra Jahn, C Jones, Stephen A Klein, and John P Krasting, et al., February 2019: Taking climate model evaluation to the next level. Nature Climate Change, 9(2), DOI:10.1038/s41558-018-0355-y. Abstract
Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.
Randall, David A., A Del Genio, Leo J Donner, William D Collins, and Stephen A Klein, July 2016: The Impact of ARM on Climate Modeling In The Atmospheric Radiation Measurement (ARM) Program: The First 20 Years, 57, DOI:10.1175/AMSMONOGRAPHS-D-15-0050.1.
A set of GFDL AM2 sensitivity simulations by varying an entrainment threshold rate to control deep convection occurrence are used to investigate how cumulus parameterization impacts tropical cloud and precipitation characteristics. In the Tropics, model convective precipitation (CP) is frequent and light, while large-scale precipitation (LSP) is intermittent and strong. With deep convection inhibited, CP decreases significantly over land and LSP increases prominently over ocean. This results in an overall redistribution of precipitation from land to ocean. A composite analysis reveals that cloud fraction (low and middle) and cloud condensate associated with LSP is substantially larger than those associated with CP. With about the same total precipitation and precipitation frequency distribution over the Tropics, simulations having greater LSP fraction tend to have larger cloud condensate and low and middle cloud fraction.
Simulations having greater LSP fraction tend to be drier and colder in the upper-troposphere. The induced unstable stratification supports strong transient wind perturbations and LSP. Greater LSP also contributes to greater intraseasonal (20-100 day) precipitation variability. Model LSP has a close connection to the low level convergence via the resolved grid-scale dynamics and thus a close coupling with the surface heat flux. Such wind-evaporation feedback is essential to the development and maintenance of LSP and enhances model precipitation variability. LSP has stronger dependence and sensitivity on column moisture than CP. The moisture-convection feedback, critical to tropical intraseasonal variability, is enhanced in simulations with large LSP. Strong precipitation variability accompanied by the worse mean state implies that an optimal precipitation partitioning is critical to model tropical climate simulation.
Lin, Yanluan, Leo J Donner, Stephen A Klein, and Ming Zhao, et al., May 2012: TWP-ICE global atmospheric model intercomparison: convection responsiveness and resolution impact. Journal of Geophysical Research: Atmospheres, 117, D09111, DOI:10.1029/2011JD017018. Abstract
Results are presented from an intercomparison of atmospheric general circulation model
(AGCM) simulations of tropical convection during the Tropical Warm Pool-International Cloud
Experiment (TWP-ICE). The distinct cloud properties, precipitation, radiation, and vertical diabatic
heating profiles associated with three different monsoon regimes (wet, dry, and break) from available
observations are used to evaluate 9 AGCM forecasts initialized daily from realistic global analyses. All
models captured well the evolution of large-scale circulation and thermodynamic fields, but cloud
properties differed substantially among models. Compared with the relatively well simulated top-heavy
heating structures during the wet and break period, most models had difficulty in depicting the bottomheavy
heating profiles associated with cumulus congestus during the dry period. The best performing
models during this period were the ones whose convection scheme was most responsive to the free
tropospheric humidity.
Compared with the large impact of cloud and convective parameterizations on model cloud and
precipitation characteristics, resolution has relatively minor impact on simulated cloud properties.
However, one feature that was influenced by resolution in several models was the diurnal cycle of
precipitation. Peaking at a different time from convective precipitation, large-scale precipitation
generally increases in high resolution forecasts and modulates the total precipitation diurnal cycle.
Overall, the study emphasizes the need for convection parameterizations that are more responsive to
environmental conditions as well as the substantial diversity among large-scale cloud and precipitation
schemes in current AGCMs. This experiment has demonstrated itself to be a very useful testbed for
those developing cloud and convection schemes for AGCMs.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical-system component of earth-system models and models for decadal prediction in the near-term future, for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model.
Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud-droplet activation by aerosols, sub-grid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with eco-system dynamics and hydrology.
Most basic circulation features in AM3 are simulated as realistically, or more so, than in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks and the intensity distributions of precipitation remain problematic, as in AM2.
The last two decades of the 20th century warm in CM3 by .32°C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of .56°C and .52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol cloud interactions, and its warming by late 20th century is somewhat less realistic than in CM2.1, which warmed .66°C but did not include aerosol cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming in a way that is consistent with observed global temperature changes.
Klein, Stephen A., and Jean-Christophe Golaz, et al., May 2009: Intercomparisons of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud. Quarterly Journal of the Royal Meteorological Society, 135(641), DOI:10.1002/qj.416. Abstract
Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) programme's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud-top temperature of - 15 °C. The average liquid water path of around 160 g m-2 was about two-thirds of the adiabatic value and far greater than the average mass of ice which when integrated from the surface to cloud top was around 15 g m-2.
Simulations of 17 single-column models (SCMs) and 9 cloud-resolving models (CRMs) are compared. While the simulated ice water path is generally consistent with observed values, the median SCM and CRM liquid water path is a factor-of-three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path.
Despite this underestimate, the simulated liquid and ice water paths of several models are consistent with observed values. Furthermore, models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter exists. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics.
Teixeira, João, Bjorn Stevens, Christopher S Bretherton, R T Cederwall, James D Doyle, Jean-Christophe Golaz, A A M Holtslag, Stephen A Klein, J K Lundquist, David A Randall, A P Siebesma, and P M M Soares, April 2008: Parameterization of the atmospheric boundary layer: A view from just above the inversion. Bulletin of the American Meteorological Society, 89(4), DOI:10.1175/BAMS-89-4-453.
Ming, Yi, V Ramaswamy, Leo J Donner, Vaughan T J Phillips, Stephen A Klein, Paul Ginoux, and Larry W Horowitz, February 2007: Modeling the interactions between aerosols and liquid water clouds with a self-consistent cloud scheme in a general circulation model. Journal of the Atmospheric Sciences, 64(4), DOI:10.1175/JAS3874.1. Abstract
To model aerosol-cloud interactions in general circulation
models (GCMs), a prognostic cloud scheme of cloud liquid water and amount is expanded to include droplet number concentration (Nd) in a way that allows them to be calculated using the same large-scale and convective updraft velocity field. In the scheme, the evolution of droplets fully interacts with the model meteorology. An explicit treatment of cloud condensation nuclei (CCN) activation enables the scheme to take into account the contributions to Nd of multiple aerosol species (i.e., sulfate, organic, and sea-salt aerosols) and to consider kinetic limitations of the activation process. An implementation of the prognostic scheme in the Geophysical Fluid Dynamics Laboratory (GFDL) AM2 GCM yields a vertical distribution of Nd with a characteristic maximum in the lower troposphere; this feature differs from the profile that would be obtained if Ndis diagnosed from the sulfate mass concentration based on an often-used empirical relationship. Prognosticated Nd exhibits large variations with respect to the sulfate mass concentration. The mean values are generally consistent with the empirical relationship over ocean, but show negative biases over the Northern Hemisphere midlatitude land, perhaps owing to the neglect of subgrid variations of large-scale ascents and inadequate convective sources. The prognostic scheme leads to a substantial improvement in the agreement of model-predicted present-day liquid water path (LWP) and cloud forcing with satellite measurements compared to using the empirical relationship.
The simulations with preindustrial and present-day aerosols show that the
combined first and second indirect effects of anthropogenic sulfate and organic aerosols give rise to a steady-state global annual mean flux change of -1.8 W m-2, consisting of -2.0 W m-2 in shortwave and 0.2 W m-2 in longwave. The ratios of the flux changes in the Northern Hemisphere (NH) to that in Southern Hemisphere (SH) and of the flux changes over ocean to that over land are 2.9 and 0.73, respectively. These estimates are consistent with the averages of values from previous studies stated in a recent review. The model response to higher Nd alters the cloud field; LWP and total cloud amount increase by 19% and 0.6%, respectively. Largely owing to high sulfate concentrations from fossil fuel burning, the NH midlatitude land and oceans experience strong radiative cooling. So does the tropical land, which is dominated by biomass burning-derived organic aerosol. The computed annual, zonal-mean flux changes are determined to be statistically significant, exceeding the model's natural variations in the NH low and midlatitudes and in the SH low latitudes. This study reaffirms the major role of sulfate in providing CCN for cloud formation.
The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.
Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.
The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.
Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).
Manuscript received 8 December 2004, in final form 18 March 2005
Jiang, Xianan, Ngar-Cheung Lau, and Stephen A Klein, October 2006: Role of eastward propagating convection systems in the diurnal cycle and seasonal mean of summertime rainfall over the U.S. Great Plains. Geophysical Research Letters, 33, L19809, DOI:10.1029/2006GL027022. Abstract
By diagnosing the 3-hourly North American Regional Reanalysis rainfall data set for the 1979–2003 period, it is illustrated that the eastward propagation of convection systems from the Rockies to the Great Plains plays an essential role for the warm season climate over the central U.S. This eastward propagating mode could be the deciding factor for the observed nocturnal rainfall peak over the Great Plains. The results also suggest that nearly half of the total summer mean rainfall over this region is associated with these propagating convection systems. For instance, the extreme wet condition of the 1993 summer may be attributed to the frequent occurrence of propagating convection events and enhanced diurnal rainfall amplitude over the Great Plains. Thus, proper representation of this important propagating component in GCMs is essential for simulating the diurnal and seasonal mean characteristics of summertime rainfall over the central US.
Klein, Stephen A., Xianan Jiang, J S Boyle, Sergey Malyshev, and Shang-Ping Xie, 2006: Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach. Geophysical Research Letters, 33, L18805, DOI:10.1029/2006GL027567. Abstract
Weather forecasts started from realistic initial conditions are used to diagnose the large warm and dry bias over the United States Southern Great Plains simulated by the GFDL climate model. The forecasts exhibit biases in surface air temperature and precipitation within 3 days which appear to be similar to the climate bias. With the model simulating realistic evaporation but underestimated precipitation, a deficit in soil moisture results which amplifies the initial temperature bias through feedbacks with the land surface. The underestimate of precipitation may be associated with an inability of the model to simulate the eastward propagation of convection from the front-range of the Rocky Mountains and is insensitive to an increase of horizontal resolution from 2° to 0.5° latitude.
Pincus, Robert, Richard S Hemler, and Stephen A Klein, 2006: Using Stochastically Generated Subcolumns to Represent Cloud Structure in a Large-Scale Model. Monthly Weather Review, 134(12), DOI:10.1175/MWR3257.1. Abstract
A new method for representing subgrid-scale cloud structure in which each model column is decomposed into a set of subcolumns has been introduced into the Geophysical Fluid Dynamics Laboratory’s global atmospheric model AM2. Each subcolumn in the decomposition is homogeneous, but the ensemble reproduces the initial profiles of cloud properties including cloud fraction, internal variability (if any) in cloud condensate, and arbitrary overlap assumptions that describe vertical correlations. These subcolumns are used in radiation and diagnostic calculations and have allowed the introduction of more realistic overlap assumptions. This paper describes the impact of these new methods for representing cloud structure in instantaneous calculations and long-term integrations. Shortwave radiation computed using subcolumns and the random overlap assumption differs in the global annual average by more than 4 W m−2 from the operational radiation scheme in instantaneous calculations; much of this difference is counteracted by a change in the overlap assumption to one in which overlap varies continuously with the separation distance between layers. Internal variability in cloud condensate, diagnosed from the mean condensate amount and cloud fraction, has about the same effect on radiative fluxes as does the ad hoc tuning accounting for this effect in the operational radiation scheme. Long simulations with the new model configuration show little difference from the operational model configuration, while statistical tests indicate that the model does not respond systematically to the sampling noise introduced by the approximate radiative transfer techniques introduced to work with the subcolumns
Sun, D-Z, T Zhang, C Covey, Stephen A Klein, William D Collins, J J Hack, J T Kiehl, Gerald A Meehl, Isaac M Held, and M J Suarez, 2006: Radiative and Dynamical Feedbacks over the Equatorial Cold Tongue: Results from Nine Atmospheric GCMs. Journal of Climate, 19(16), DOI:10.1175/JCLI3835.1. Abstract
The equatorial Pacific is a region with strong negative feedbacks. Yet coupled general circulation models (GCMs) have exhibited a propensity to develop a significant SST bias in that region, suggesting an unrealistic sensitivity in the coupled models to small energy flux errors that inevitably occur in the individual model components. Could this “hypersensitivity” exhibited in a coupled model be due to an underestimate of the strength of the negative feedbacks in this region? With this suspicion, the feedbacks in the equatorial Pacific in nine atmospheric GCMs (AGCMs) have been quantified using the interannual variations in that region and compared with the corresponding calculations from the observations. The nine AGCMs are the NCAR Community Climate Model version 1 (CAM1), the NCAR Community Climate Model version 2 (CAM2), the NCAR Community Climate Model version 3 (CAM3), the NCAR CAM3 at T85 resolution, the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Atmospheric Model, the Hadley Centre Atmospheric Model (HadAM3), the Institut Pierre Simon Laplace (IPSL) model (LMDZ4), the Geophysical Fluid Dynamics Laboratory (GFDL) AM2p10, and the GFDL AM2p12. All the corresponding coupled runs of these nine AGCMs have an excessive cold tongue in the equatorial Pacific.
The net atmospheric feedback over the equatorial Pacific in the two GFDL models is found to be comparable to the observed value. All other models are found to have a weaker negative net feedback from the atmosphere—a weaker regulating effect on the underlying SST than the real atmosphere. Except for the French (IPSL) model, a weaker negative feedback from the cloud albedo and a weaker negative feedback from the atmospheric transport are the two leading contributors to the weaker regulating effect from the atmosphere. The underestimate of the strength of the negative feedbacks by the models is apparently linked to an underestimate of the equatorial precipitation response. All models have a stronger water vapor feedback than that indicated in Earth Radiation Budget Experiment (ERBE) observations. These results confirm the suspicion that an underestimate of the regulatory effect from the atmosphere over the equatorial Pacific region is a prevalent problem. The results also suggest, however, that a weaker regulatory effect from the atmosphere is unlikely solely responsible for the hypersensitivity in all models. The need to validate the feedbacks from the ocean transport is therefore highlighted.
Wyant, M C., Christopher S Bretherton, Julio T Bacmeister, J T Kiehl, Isaac M Held, Ming Zhao, Stephen A Klein, and Brian J Soden, 2006: A comparison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity. Climate Dynamics, 27(2-3), DOI:10.1007/s00382-006-0138-4. Abstract
Low-latitude cloud distributions and cloud responses to climate perturbations are compared in near-current versions of three leading U.S. AGCMs, the NCAR CAM 3.0, the GFDL AM2.12b, and the NASA GMAO NSIPP-2 model. The analysis technique of Bony et al. (Clim Dyn 22:71–86, 2004) is used to sort cloud variables by dynamical regime using the monthly mean pressure velocity ω at 500 hPa from 30S to 30N. All models simulate the climatological monthly mean top-of-atmosphere longwave and shortwave cloud radiative forcing (CRF) adequately in all ω-regimes. However, they disagree with each other and with ISCCP satellite observations in regime-sorted cloud fraction, condensate amount, and cloud-top height. All models have too little cloud with tops in the middle troposphere and too much thin cirrus in ascent regimes. In subsidence regimes one model simulates cloud condensate to be too near the surface, while another generates condensate over an excessively deep layer of the lower troposphere. Standardized climate perturbation experiments of the three models are also compared, including uniform SST increase, patterned SST increase, and doubled CO2 over a mixed layer ocean. The regime-sorted cloud and CRF perturbations are very different between models, and show lesser, but still significant, differences between the same model simulating different types of imposed climate perturbation. There is a negative correlation across all general circulation models (GCMs) and climate perturbations between changes in tropical low cloud cover and changes in net CRF, suggesting a dominant role for boundary layer cloud in these changes. For some of the cases presented, upper-level clouds in deep convection regimes are also important, and changes in such regimes can either reinforce or partially cancel the net CRF response from the boundary layer cloud in subsidence regimes. This study highlights the continuing uncertainty in both low and high cloud feedbacks simulated by GCMs.
Gordon, N D., J R Norris, C Weaver, and Stephen A Klein, 2005: Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model. Journal of Geophysical Research, 110, D15S17, DOI:10.1029/2004JD005027. Abstract
Global climate models typically do not correctly simulate cloudiness associated with midlatitude synoptic systems because coarse grid spacing prevents them from resolving dynamics occurring at smaller scales and there exist no adequate parameterizations for the effects of these subgrid-scale dynamics. Comparison of modeled and observed cloud properties averaged over similar regimes (e.g., compositing) aids the diagnosis of simulation errors and identification of meteorological forcing responsible for producing particular cloud conditions. This study uses a k-means clustering algorithm to objectively classify satellite cloud scenes into distinct regimes based on grid box mean cloud fraction, cloud reflectivity, and cloud top pressure. The spatial domain is the densely instrumented southern Great Plains site of the Atmospheric Radiation Measurement Program, and the time period is the cool season months (November–March) of 1999–2001. As a complement to the satellite retrievals of cloud properties, lidar and cloud radar data are analyzed to examine the vertical structure of the cloud layers. Meteorological data from the constraint variational analysis is averaged for each cluster to provide insight on the large-scale dynamics and advective tendencies coincident with specific cloud types. Meteorological conditions associated with high and low subgrid spatial variability are also investigated for each cluster. Cloud outputs from a single-column model version of the GFDL AM2 atmospheric model forced with meteorological boundary conditions derived from observations and a numerical weather prediction model were compared to observations for each cluster in order to determine the accuracy with which the model reproduces attributes of specific cloud regimes.
Kim, B-G, Stephen A Klein, and J R Norris, August 2005: Continental liquid water cloud variability and its parameterization using Atmospheric Radiation Measurement data. Journal of Geophysical Research, 110, D15S08, DOI:10.1029/2004JD005122. Abstract
Liquid water path (LWP) variability at scales ranging from roughly 200 m to 20 km in continental boundary layer clouds is investigated using ground-based remote sensing at the Oklahoma site of the Atmospheric Radiation Measurement (ARM) program. Twelve episodes from the years of 1999 to 2001 are selected corresponding to conditions of overcast, liquid water single-layered cloud. In contrast to previous studies of marine boundary layer clouds, variability in cloud-top height in these clouds is comparable to that of cloud base, and most continental clouds appear to be subadiabatic. In agreement with previous studies of marine boundary layer clouds, variations in LWP are well related to the variations in cloud thickness. LWP variability exhibits significantly negative correlation with the static stability of the inversion near cloud top; larger cloud variability is associated with less stable inversions. A previously developed parameterization of LWP variability is extended to account for the differing conditions of continental clouds. The relationship between fluctuations in LWP and cloud thickness suggests that cloud parameterizations treating variations in LWP at these scales should include the effects of subgrid-scale fluctuations in cloud thickness. One such treatment is proposed within the context of a statistical cloud scheme.
Klein, Stephen A., Robert Pincus, Cecile Hannay, and K-M Xu, 2005: How might a statistical cloud scheme be coupled to a mass-flux convection scheme?Journal of Geophysical Research, 110, D15S06, DOI:10.1029/2004JD005017. Abstract
The coupling of statistical cloud schemes with mass-flux convection schemes is addressed. Source terms representing the impact of convection are derived within the framework of prognostic equations for the width and asymmetry of the probability distribution function of total water mixing ratio. The accuracy of these source terms is quantified by examining output from a cloud-resolving model simulation of deep convection. Practical suggestions for the inclusion of these source terms in large-scale models are offered.
Pincus, Robert, Cecile Hannay, Stephen A Klein, K-M Xu, and Richard S Hemler, 2005: Overlap assumptions for assumed probability distribution function cloud schemes in large-scale models. Journal of Geophysical Research, 110, D15S09, DOI:10.1029/2004JD005100. Abstract
Cloud vertical structure influences the fluxes of precipitation and radiation throughout the atmosphere. This structure is not predicted in large-scale models but is instead applied in the form of overlap assumptions. In their current guise, overlap assumptions apply to the presence or absence of clouds, and new data sets have led to the development of empirical formulations described by exponential decay from maximum to random overlap over a characteristic length scale. At the same time, cloud parameterizations in many large-scale models have been moving toward assumed PDF schemes that predict the distribution of total water within each grid cell, which will require overlap assumptions that may be applied to cells with specified internal variability. This paper uses a month-long cloud-resolving model simulation of continental convection to develop overlap assumptions for use with assumed PDF cloud schemes in large-scale models. An observing system simulation experiment shows that overlap assumptions derived from millimeter-wavelength cloud radar observations can be strongly affected by the presence of precipitation and convective clouds and, to a lesser degree, by limited sampling and reliance on the frozen turbulence assumption. Current representations of overlap can be extended with good accuracy to treat the rank correlation of total water in each grid cell, which provides a natural way to treat vertical structure in assumed PDF cloud schemes. The scale length that describes an exponential fit to the rank correlation of total water depends on the state of the atmosphere: convection is associated with greater vertical coherence (longer scale lengths), while wind shear decreases vertical coherence (shorter scale lengths). The new overlap assumptions are evaluated using cloud physical properties, microphysical process rates, and top-of-atmosphere radiative fluxes. These quantities can be reproduced very well when the exact cloud structure is replaced with its statistical equivalent and somewhat less well when the time mean vertical structure is imposed. Overlap formulations that treat total water can also be used to determine the variability in clear-air relative humidity, which might be used by convection and aerosol parameterizations.
Santer, B D., T M L Wigley, C Mears, F J Wentz, Stephen A Klein, D J Seidel, K E Taylor, P W Thorne, Michael F Wehner, Peter J Gleckler, J S Boyle, William D Collins, Keith W Dixon, C Doutriaux, M Free, Qiang Fu, J E Hansen, G S Jones, R Ruedy, T R Karl, John R Lanzante, Gerald A Meehl, V Ramaswamy, G Russell, and Gavin A Schmidt, 2005: Amplification of surface temperature trends and variability in the tropical atmosphere. Science, 309(5740), DOI:10.1126/science.1114867. Abstract
The month-to-month variability of tropical temperatures is larger in the troposphere than at Earth's surface. This amplification behavior is similar in a range of observations and climate model simulations and is consistent with basic theory. On multidecadal time scales, tropospheric amplification of surface warming is a robust feature of model simulations, but it occurs in only one observational data set. Other observations show weak, or even negative, amplification. These results suggest either that different physical mechanisms control amplification processes on monthly and decadal time scales, and models fail to capture such behavior; or (more plausibly) that residual errors in several observational data sets used here affect their representation of long-term trends.
Xu, K-M, M Zhang, Z A Eitzen, S Ghan, Stephen A Klein, X Wu, Shang-Ping Xie, Mark Branson, A Del Genio, S F Iacobellis, Marat Khairoutdinov, W Lin, Ü Lohmann, David A Randall, R C J Somerville, Y C Sud, G K Walker, A Wolf, J J Yio, and J Zhang, 2005: Modeling springtime shallow frontal clouds with cloud-resolving and single-column models. Journal of Geophysical Research, 110, D15S04, DOI:10.1029/2004JD005153. Abstract
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.
Zhang, M, W Lin, Stephen A Klein, Julio T Bacmeister, Sandrine Bony, R T Cederwall, A Del Genio, J J Hack, Norman G Loeb, Ü Lohmann, P Minnis, I Musat, Robert Pincus, P Stier, M J Suarez, M J Webb, J B Wu, Shang-Ping Xie, M-S Yao, and J Zhang, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. Journal of Geophysical Research, 110, D15S02, DOI:10.1029/2004JD005021. Abstract
To assess the current status of climate models in simulating clouds, basic cloud climatologies from ten atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are also, however, large uncertainties in satellite high thin clouds to effectively constrain the models. The majority of models only simulated 30–40% of middle-top clouds in the ISCCP and CERES data sets. Half of the models underestimated low clouds, while none overestimated them at a statistically significant level. When stratified in the optical thickness ranges, the majority of the models simulated optically thick clouds more than twice the satellite observations. Most models, however, underestimated optically intermediate and thin clouds. Compensations of these clouds biases are used to explain the simulated longwave and shortwave cloud radiative forcing at the top of the atmosphere. Seasonal sensitivities of clouds are also analyzed to compare with observations. Models are shown to simulate seasonal variations better for high clouds than for low clouds. Latitudinal distribution of the seasonal variations correlate with satellite measurements at >0.9, 0.6–0.9, and −0.2–0.7 levels for high, middle, and low clouds, respectively. The seasonal sensitivities of cloud types are found to strongly depend on the basic cloud climatology in the models. Models that systematically underestimate middle clouds also underestimate seasonal variations, while those that overestimate optically thick clouds also overestimate their seasonal sensitivities. Possible causes of the systematic cloud biases in the models are discussed.
for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2–LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to E1 Niño– Southern Oscillation (ENSO).
One AM2–LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2–LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2– LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden–Julian oscillation.
An ensemble of 10 AM2–LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2–LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.
Seidel, D J., J K Angell, M Free, J R Christy, R Spencer, Stephen A Klein, John R Lanzante, C Mears, M Schabel, F J Wentz, D E Parker, P W Thorne, and A Sterin, 2004: Uncertainty in signals of large-scale climate variations in radiosonde and satellite upper-air temperature datasets. Journal of Climate, 17(11), 2225-2240. Abstract PDF
There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset.
The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upper- air temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.
Lanzante, John R., Stephen A Klein, and D J Seidel, 2003: Temporal homogenization of monthly radiosonde temperature data. Part I: Methodology. Journal of Climate, 16(2), 224-240. Abstract PDF
Historical changes in instrumentation and recording practices have severely compromised the temporal homogeneity of radiosonde data, a crucial issue for the determination of long-term trends. Methods developed to deal with these homogeneity problems have been applied to a near–globally distributed network of 87 stations using monthly temperature data at mandatory pressure levels, covering the period 1948–97. The homogenization process begins with the identification of artificial discontinuities through visual examination of graphical and textual materials, including temperature time series, transformations of the temperature data, and independent indicators of climate variability, as well as ancillary information such as station history metadata. To ameliorate each problem encountered, a modification was applied in the form of data adjustment or data deletion. A companion paper (Part II) reports on various analyses, particularly trend related, based on the modified data resulting from the method presented here.
Application of the procedures to the 87-station network revealed a number of systematic problems. The effects of the 1957 global 3-h shift of standard observation times (from 0300/1500 to 0000/1200 UTC) are seen at many stations, especially near the surface and in the stratosphere. Temperatures from Australian and former Soviet stations have been plagued by numerous serious problems throughout their history. Some stations, especially Soviet ones up until 1970, show a tendency for episodic drops in temperature that produce spurious downward trends. Stations from Africa and neighboring regions are found to be the most problematic; in some cases even the character of the interannual variability is unreliable. It is also found that temporal variations in observation time can lead to inhomogeneities as serious as the worst instrument-related problems.
Lanzante, John R., Stephen A Klein, and D J Seidel, 2003: Temporal homogenization of monthly radiosonde temperature data. Part II: Trends, Sensitivities, and MSU comparison. Journal of Climate, 16(2), 241-262. Abstract PDF
Trends in radiosonde-based temperatures and lower-tropospheric lapse rates are presented for the time periods 1959–97 and 1979–97, including their vertical, horizontal, and seasonal variations. A novel aspect is that estimates are made globally of the effects of artificial (instrumental or procedural) changes on the derived trends using data homogenization procedures introduced in a companion paper (Part I). Credibility of the data homogenization scheme is established by comparison with independent satellite temperature measurements derived from the microwave sounding unit (MSU) instruments for 1979–97. The various analyses are performed using monthly mean temperatures from a near–globally distributed network of 87 radiosonde stations.
The severity of instrument-related problems, which varies markedly by geographic region, was found, in general, to increase from the lower troposphere to the lower stratosphere, although surface data were found to be as problematic as data from the stratosphere. Except for the surface, there is a tendency for changes in instruments to artificially lower temperature readings with time, so that adjusting the data to account for this results in increased tropospheric warming and decreased stratospheric cooling. Furthermore, the adjustments tend to enhance warming in the upper troposphere more than in the lower troposphere; such sensitivity may have implications for “fingerprint” assessments of climate change. However, the most sensitive part of the vertical profile with regard to its shape was near the surface, particularly at regional scales. In particular, the lower-tropospheric lapse rate was found to be especially sensitive to adjustment as well as spatial sampling. In the lower stratosphere, instrument-related biases were found to artificially inflate latitudinal differences, leading to statistically significantly more cooling in the Tropics than elsewhere. After adjustment there were no significant differences between the latitude zones.
Shine, K P., M S Bourqui, Piers M Forster, S H E Hare, U Langematz, P Braesicke, V Grewe, M Ponater, C Schnadt, C A Smith, J D Haigh, John Austin, Neal Butchart, Drew Shindell, W J Randel, T Nagashima, R W Portmann, S Solomon, D J Seidel, John R Lanzante, Stephen A Klein, V Ramaswamy, and M Daniel Schwarzkopf, 2003: A comparison of model-simulated trends in stratospheric temperatures. Quarterly Journal of the Royal Meteorological Society, 129(590), DOI:10.1256/qj.02.186. Abstract
Estimates of annual-mean stratospheric temperature trends over the past twenty years, from a wide variety of models, are compared both with each other and with the observed cooling seen in trend analyses using radiosonde and satellite observations. The modelled temperature trends are driven by changes in ozone (either imposed from observations or calculated by the model), carbon dioxide and other relatively well-mixed greenhouse gases, and stratospheric water vapour.
The comparison shows that whilst models generally simulate similar patterns in the vertical profile of annual-and global-mean temperature trends, there is a significant divergence in the size of the modelled trends, even when similar trace gas perturbations are imposed. Coupled-chemistry models are in as good agreement as models using imposed observed ozone trends, despite the extra degree of freedom that the coupled models possess.
The modelled annual- and global-mean cooling of the upper stratosphere (near 1 hPa) is dominated by ozone and carbon dioxide changes, and is in reasonable agreement with observations. At about 5 hPa, the mean cooling from the models is systematically greater than that seen in the satellite data; however, for some models, depending on the size of the temperature trend due to stratospheric water vapour changes, the uncertainty estimates of the model and observations just overlap. Near 10 hPa there is good agreement with observations. In the lower stratosphere (20-70 hPa), ozone appears to be the dominant contributor to the observed cooling, although it does not, on its own, seem to explain the entire cooling.
Annual- and zonal-mean temperature trends at 100 hPa and 50 hPa are also examined. At 100 hPa, the modelled cooling due to ozone depletion alone is in reasonable agreement with the observed cooling at all latitudes. At 50 hPa, however, the observed cooling at midlatitudes of the northern hemisphere significantly exceeds the modelled cooling due to ozone depletion alone. There is an indication of a similar effect in high northern latitudes, but the greater variability in both models and observations precludes a firm conclusion.
The discrepancies between modelled and observed temperature trends in the lower stratosphere are reduced if the cooling effects of increased stratospheric water vapour concentration are included, and could be largely removed if certain assumptions were made regarding the size and distribution of the water vapour increase. However, given the uncertainties in the geographical extent of water vapour changes in the lower stratosphere, and the time period over which such changes have been sustained, other reasons for the discrepancy between modelled and observed temperature trends cannot be ruled out.
Free, M, I Durre, John R Lanzante, Stephen A Klein, and Brian J Soden, et al., 2002: Creating Climate Reference Datasets: CARDS Workshop on Adjusting Radiosonde Temperature Data for Climate Monitoring. Bulletin of the American Meteorological Society, 83(6), 891-899. Abstract PDF
Homogeneous upper-air temperature time series are necessary for climate change detection and attribution. About 20 participants met at the National Climatic Data Center in Asheville, North Carolina on 11-12 October 2000 to discuss methods of adjusting radiosonde data for inhomogeneities arising from instrument and other changes. Representatives of several research groups described their methods for identifying change points and adjusting temperature time series and compared the results of applying these methods to data from 12 radiosonde stations. The limited agreement among these results and the potential impact of these adjustments on upperair trends estimates indicate a need for further work in this area and for greater attention to homogeneity issues in planning future changes in radiosonde observations.
Xu, S, K-M Xu, R T Cederwall, P Bechtold, A Del Genio, Stephen A Klein, D G Cripe, S Ghan, D Gregory, S F Iacobellis, and S K Krueger, et al., 2002: Intercomparison and evaluation of cumulus parametrizations under summertime midlatitude continental conditions. Quarterly Journal of the Royal Meteorological Society, 128(582), 1095-1135. Abstract
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 in 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.
Ghan, S, David A Randall, K-M Xu, R T Cederwall, D G Cripe, J J Hack, S F Iacobellis, Stephen A Klein, S K Krueger, Ü Lohmann, J Pedretti, and A Robock, et al., 2000: A comparison of single column model simulations of summertime midlatitude continental convection. Journal of Geophysical Research, 105(D2), 2091-2124. Abstract PDF
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 or 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.
Jakob, Christian, and Stephen A Klein, 2000: A parameterization of the effects of cloud and precipitation overlap for use in general-circulation models. Quarterly Journal of the Royal Meteorological Society, 126 Part C(568), 2525-2544. Abstract PDF
The necessity for treating the effects of vertically varying cloud fraction when parameterizing microphysical processes in general-circulation models (GCMs) was recently highlighted by Jakob and Klein. In this study a parameterization to include such effects in a GCM is developed, and the new scheme is applied in the ECMWF global model. The basic idea of the new scheme is to separate the model's rain and snow fluxes into a cloudy and a clear-sky part. The scheme is tested using the subgrid-scale precipitation model of Jakob and Klein as a benchmark. The impact of the new scheme on the model climate is also investigated. # It is shown that the new parameterization leads to a better representation of the effects of cloud and precipitation overlap, and that it alleviates most of the problems connected with their treatment in the current scheme. Due to the better treatment of cloud and precipitation overlap the new parameterization leads to a reduction in precipitation evaporation and an increase in accretion rates. When tested in seasonal model integrations the new scheme produces a drier tropical mid-troposphere with consequences for the hydrological cycle.
Klein, Stephen A., Christian Jakob, and J-J Morcrette, 2000: An examination of frontal clouds simulated by the ECMWF model In Workshop on Cloud Processes and Cloud Feedbacks in Large-Scale Models, WCRP-110, WMO/TD No. 993, Geneva, Switzerland, World Climate Research Programme, 76-83.
Norris, J R., and Stephen A Klein, 2000: Low cloud type over the ocean from surface observations. Part III: Relationship to vertical motion and the regional surface synoptic environment. Journal of Climate, 13(1), 245-256. Abstract PDF
Composite large-scale dynamical fields contemporaneous with low cloud types observed at midlatitude Ocean Weather Station(OWS) C and eastern subtropical OWS N are used to establish representative relationships between low cloud type and the synoptic environment. The composites are constructed by averaging meteorological observations of surface wind and sea level pressure from volunteering observing ships (VOS) and analyses of sea level pressure, 1000-mb wind, and 700-mb pressure vertical velocity from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis project on those dates and times of day when a particular low cloud type was reported at the OWS.
VOS and NCEP results for OWS C during summer show that bad-weather stratus occurs with strong convergence and ascent slightly ahead of a surface low center and trough. Cumulus-under-stratocumulus and moderate and large cumulus occur with divergence and subsidence in the cold sector of an extratropical cyclone. Both sky-obscuring fog and no-low-cloud typically occur with southwesterly flow from regions of warmer sea surface temperature and differ primarily according to slight surface convergence and stronger warm advection in the case of sky-obscuring fog or surface divergence and weaker warm advection in the case of no-low-cloud. Fair-weather stratus and ordinary stratocumulus are associated with a mixture of meteorological conditions, but differ with respect to vertical motion in the environment. Fair-weather stratus occurs most commonly in the presence of slight convergence and ascent, while stratocumulus often occurs in the presence of divergence and subsidence.
Surface divergence and estimated subsidence at the top of the boundary layer are calculated from VOS observations. At both OWS C and OWS N during summer and winter these values are large for ordinary stratocumulus, less for cumulus-under-stratocumulus, and least (and sometimes slightly negative) for moderate and large cumulus. Subsidence interpolated from NCEP analyses to the top of the boundary layer does not exhibit such variation, but the discrepancy may be due to deficiencies in the analysis procedure or the boundary layer parameterization of the NCEP model. The VOS results suggest that decreasing divergence and subsidence in addition to increasing sea surface temperature may promote the transition from stratocumulus to trade cumulus observed over low-latitude oceans.
Pincus, Robert, and Stephen A Klein, 2000: Unresolved spatial variability and microphysical process rates in large-scale models. Journal of Geophysical Research, 105(D22), 27,059-27,066. Abstract PDF
Prognostic cloud schemes in large scale models are typically formulated in terms of grid-cell average values of cloud condensate concentration q, although variability in q at spatial scales smaller than the grid cell is known to exist. Because the source and sink processes modifying q are nonlinear, the process rates computed using the mean value of q are biased relative to process rates which account for sub-grid scale variability. A preliminary assessment shows that these biases can modify instantaneous process rates by as much as a factor of two. Observations of q at a continental site suggest that the bias is avoided in current practice through the arbitrary tuning of model parameters. Models might be improved if sub-grid scale variability in q were explicitly considered; several approaches to this goal are suggested.
Jakob, Christian, and Stephen A Klein, 1999: The role of vertically varying cloud fraction in the parametrization of microphysical processes in the ECMWF model. Quarterly Journal of the Royal Meteorological Society, 125(555), 941-965. Abstract PDF
General-circulation models (GCMs) have generally treated solely the radiative impacts of vertically varying cloud fraction by using a cloud-overlap assumption. In this study, the microphysical impacts of vertically varying cloud fraction are addressed by developing a subgrid-scale precipitation model which resolves the vertical variation of cloud fraction. This subgrid model subdivides the grid boxes into homogeneous columns which are either clear or completely cloudy. By comparing the column-averaged microphysical quantities from the subgrid-scale precipitation model with the parametrization in the European Centre for Medium-Range Forecasts (ECMWF) model, the ability of the ECMWF model to account for the subgrid nature of cloud and precipitation microphysics is assessed. It is found that the ECMWF model overestimates the evaporation of precipitation in the tropical mid-troposophere. This results from (a) an incorrect parametrization of the area of the grid box covered by precipitation, and (b) the inadequacy of assuming a single value for the precipitation rate in the grid box. #In addition to assessing the ability of the ECMWF model to parametrize the subgrid nature of cloud micro-physics, the subgrid precipitation model is used to show that the cloud-overlap assumption has a large impact on the evaporation of precipitation. In light of the current trend towards more sophisticated cloud and precipitation parametrizations in GCMs, more attention should be paid to the impact of vertical cloud-fraction variations on the parametrized microphysics.
Klein, Stephen A., and Christian Jakob, 1999: Validation and sensitivities of frontal clouds simulated by the ECMWF model. Monthly Weather Review, 127(10), 2514-2531. Abstract PDF
Clouds simulated by the European Centre for Medium-Range Weather Forecasts (ECMWF) model are composited to derive the typical organization of clouds surrounding a midlatitude baroclinic system. Comparison of this composite of about 200 cyclones with that based on satellite data reveals that the ECMWF model quite accurately simulates the general positioning of clouds relative to a low pressure center. However, the optical depths of the model's high/low clouds are too small/large relative to the satellite observations, and the model lacks the midlevel topped clouds observed to the west of the surface cold front.
Sensitivity studies with the ECMWF model reveal that the error in high-cloud optical depths is more sensitive to the assumptions applied to the ice microphysics than to the inclusion of cloud advection or a change of horizontal resolution from 0.5625º to 1.69º lat. This reflects the fact that in the ECMWF model gravitational settling is the most rapid process controlling the abundance of ice in the high clouds of midlatitude cyclones. These results underscore the need for careful evaluation of the parameterizations of microphysics and radiative properties applied to ice in large-scale models.
Klein, Stephen A., Brian J Soden, and Ngar-Cheung Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. Journal of Climate, 12(4), 917-932. Abstract PDF
In an El Niño event, positive SST anomalies usually appear in remote ocean basins such as the South China Sea, the Indian Ocean, and the tropical North Atlantic approximately 3 to 6 months after SST anomalies peak in the tropical Pacific. Ship data from 1952 to 1992 and satellite data from the 1980s both demonstrate that changes in atmospheric circulation accompanying El Niño induce changes in cloud cover and evaporation which, in turn, increase the net heat flux entering these remote oceans. It is postulated that this increased heat flux is responsible for the surface warming of these oceans. Specifically, over the eastern Indian Ocean and South China Sea, enhanced subsidence during El Niño reduces cloud cover and increases the solar radiation absorbed by the ocean, thereby leading to enhanced SSTs. In the tropical North Atlantic, a weakening of the trade winds during El Niño reduces surface evaporation and increases SSTs. These relationships fit the concept of an "atmospheric bridge" that connects SST anomalies in the central equatorial Pacific to those in remote tropical oceans.
Larson, K M., D L Hartmann, and Stephen A Klein, 1999: The role of clouds, water vapor, circulation, and boundary layer structure in the sensitivity of the tropical climate. Journal of Climate, 12(8), 2359-2374. Abstract PDF
The physical mechanisms that affect the tropical sea surface temperature (SST) are investigated using a two-box equilibrium model of the Tropics. One box represents the convecting, warm SST, high humidity region of the Tropics, and the other box represents the subsidence region with low humidity, boundary layer clouds, and cooler SST. The two regions communicate by energy and moisture fluxes that are proportional to the strength of the overturning circulation that couples the two regions. The boundary layer properties in the subsiding region are predicted with a mixing line model. Humidity above the inversion in the subsiding region is predicted from moisture conservation.
The humidity above the inversion in the subsiding region increases rapidly with temperature, but this has less effect on the sensitivity than expected because the inversion lowers as the humidity above the inversion is increased. Some of the increased greenhouse effect of the free troposphere can be offset by decreased greenhouse effect of the boundary layer. Increasing the area of the warm, convective region increases the SSTs, because of the greenhouse effect of the greater upper-tropospheric water vapor in the convective region. The circulation strength is constrained by radiative cooling in the cold pool. The strength of the circulation decreases with increasing convective area, because the increase in dry static stability overwhelms the increase in cooling rate. Although they have strong individual effects on longwave and shortwave radiation, high clouds in the convective region do not affect the tropical SSTs strongly because their net radiative forcing at the top of the atmosphere is small. Low clouds in the subsidence region have a strong cooling effect on the tropical SST because they strongly reduce net radiative heating at the top of the atmosphere. A negative feedback is produced if the low clouds are predicted from the observed relationship between stratus cloud amount and lower-tropospheric stability.
Pincus, Robert, S A McFarlane, and Stephen A Klein, 1999: Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites. Journal of Geophysical Research, 104(D6), 6183-6191. Abstract PDF
Cloud optical properties vary dramatically at spatial scales smaller than typical grid cells in large-scale models, which can cause a significant overestimate of cloud albedo by the model. This plane parallel homogeneous (PPH) albedo bias exist may be reduced if the mean cloud optical thickness and the amount of variability are available, but little is known about how much variability exists in nature and to what factors it is sensitive. The authors combine 1331 observations made by volunteer surface observers with satellite imagery to assess the relationships between cloud fraction, cloud optical properties, and cloud type in marine boundary layer clouds off the coast of California during summer. Estimates of cloud fraction from the two datasets are in best agreement when a reflectance threshold between 0.09 and 0.10 is used. Satellite-derived cloud fraction increases slowly with sensor resolution at spatial scales from 1 to 32 km. Cloud fraction in scenes dominated by cumulus is much more sensitive to the reflectance threshold used for cloud detection than are scenes containing stratiform clouds. The mean magnitude of the PPH bias found here, 0.025, is considerably smaller than those found in other recent studies. When fit to the observed distributions of optical thickness both log-normal and gamma distributions substantially reduce the PPH bias. The mean and dispersion of log optical thickness are related to cloud type: optical thickness increases as cloud type changes from cumuliform to stratiform, while the relative amount of variability decreases. The authors suggest a basis for the parameterization of unresolved variability in large scale models.
Klein, Stephen A., 1997: Comments on "Moist Convective Velocity and Buoyancy Scales". Journal of the Atmospheric Sciences, 54(23), 2775-2777. PDF
Klein, Stephen A., 1997: Synoptic variability of low-cloud properties and meteorological parameters in the subtropical trade wind boundary layer. Journal of Climate, 10(8), 2018-2039. Abstract PDF
Synoptic variability of low-cloud properties, temperature advection, and thermodynamic soundings of the trade wind boundary layer are analyzed, using the long data record from ocean weather station November (30° N, 140° W). The variations in low-cloud amount at this subtropical site are most strongly correlated with variations in temperature advection, the stability of the lower troposphere, and the relative humidity of the cloud layer. No single predictor is capable of explaining more than 13% of the variance in low-cloud amount. However, the amount of variance explained increases considerably when the data are averaged over several days. Four parameterizations for the amount of stratiform cloud under a subsidence inversion are tested against the observed amount of low clouds. The four parameterizations are based upon relative humidity, the inversion strength, a mixing line slope, and the amount of condensed water. All parameterizations are positively correlated with the observed cloud amounts, although the variance explained is less than 16%.
