The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents ...fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Global climate models (GCMs) struggle to simulate polar clouds, especially low‐level clouds that contain supercooled liquid and closely interact with both the underlying surface and large‐scale ...atmosphere. Here we focus on GFDL's latest coupled GCM–CM4–and find that polar low‐level clouds are biased high compared to observations. The CM4 bias is largely due to moisture fluxes that occur within partially ice‐covered grid cells, which enhance low cloud formation in non‐summer seasons. In simulations where these fluxes are suppressed, it is found that open water with an areal fraction less than 5% dominates the formation of low‐level clouds and contributes to more than 50% of the total low‐level cloud response to open water within sea ice. These findings emphasize the importance of accurately modeling open water processes (e.g., sea ice lead‐atmosphere interactions) in the polar regions in GCMs.
Plain Language Summary
Extensive low‐level clouds have been observed to occur over the Arctic and Antarctic regions throughout the year. These low clouds often contain both liquid droplets and ice crystals and have spatial scales smaller than climate model grid spacing, which causes models to struggle with simulating polar cloudiness. Here we focus on a state‐of‐the‐art climate model, Geophysical Fluid Dynamics Laboratory Climate Model version 4, and investigate the modeled polar low‐level clouds with a focus on the basin‐scale spatial pattern and their seasonal variability. Compared to satellite observations, we find that excessive low‐level clouds are produced over the region covered by sea ice, especially during the winter. This overestimation of low clouds is caused by the open water within sea ice that enhances the turbulent transport of water vapor from open water to the atmosphere during non‐summer seasons, leading to the formation of low clouds. These findings indicate that the treatment of open water within sea ice regions is a highly challenging aspect of polar cloud modeling, which has a great impact on polar climate simulation and prediction.
Key Points
Polar low‐level clouds are biased high in GFDL's coupled model CM4
Polar low‐level clouds are strongly enhanced due to open water within the ice pack during non‐summer seasons
Open water with an areal fraction less than 5% makes the largest contribution to the high bias in low‐level clouds
The GFDL CM3 Coupled Climate Model Griffies, Stephen M.; Winton, Michael; Donner, Leo J. ...
Journal of climate,
07/2011, Letnik:
24, Številka:
13
Journal Article
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This paper documents time mean simulation characteristics from the ocean and sea ice components in a new coupled climate model developed at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL). The ...GFDL Climate Model version 3 (CM3) is formulated with effectively the same ocean and sea ice components as the earlier CM2.1 yet with extensive developments made to the atmosphere and land model components. Both CM2.1 and CM3 show stable mean climate indices, such as large-scale circulation and sea surface temperatures (SSTs). There are notable improvements in the CM3 climate simulation relative to CM2.1, including a modified SST bias pattern and reduced biases in the Arctic sea ice cover. The authors anticipate SST differences between CM2.1 and CM3 in lower latitudes through analysis of the atmospheric fluxes at the ocean surface in corresponding Atmospheric Model Intercomparison Project (AMIP) simulations. In contrast, SST changes in the high latitudes are dominated by ocean and sea ice effects absent in AMIP simulations. The ocean interior simulation in CM3 is generally warmer than in CM2.1, which adversely impacts the interior biases.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition ...that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major U.S. climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present day radiative imbalance vs. the implied balance in the pre-industrial as a target.
This study evaluates the tropical intraseasonal variability, especially the fidelity of Madden–Julian oscillation (MJO)simulations, in 14 coupled general circulation models (GCMs) participating in ...the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of daily precipitation from each model’s twentieth-century climate simulation are analyzed and compared with daily satellite-retrieved precipitation. Space–time spectral analysis is used to obtain the variance and phase speed of dominant convectively coupled equatorial waves, including the MJO, Kelvin, equatorial Rossby (ER), mixed Rossby–gravity (MRG), and eastward inertio–gravity (EIG) and westward inertio–gravity (WIG) waves. The variance and propagation of the MJO, defined as the eastward wavenumbers 1–6, 30–70-day mode, are examined in detail.
The results show that current state-of-the-art GCMs still have significant problems and display a wide range of skill in simulating the tropical intraseasonal variability. The total intraseasonal (2–128 day) variance of precipitation is too weak in most of the models. About half of the models have signals of convectively coupled equatorial waves, with Kelvin and MRG–EIG waves especially prominent. However, the variances are generally too weak for all wave modes except the EIG wave, and the phase speeds are generally too fast, being scaled to excessively deep equivalent depths. An interesting result isthat this scaling is consistent within a given model across modes, in that both the symmetric and antisymmetric modes scale similarly to a certain equivalent depth. Excessively deep equivalent depths suggest that these models may not have a large enough reduction in their “effective static stability” by diabatic heating.
The MJO variance approaches the observed value in only 2 of the 14 models, but is less than half of the observed value in the other 12 models. The ratio between the eastward MJO variance and the variance of its westward counterpart is too small in most of the models, which is consistent with the lack of highly coherent eastward propagation of the MJO in many models. Moreover, the MJO variance in 13 of the 14 models does not come from a pronounced spectral peak, but usually comes from part of an overreddened spectrum, which in turn is associated with too strong persistence of equatorial precipitation.The two models that arguably do best at simulating the MJO are the only ones having convective closures/triggers linked insome way to moisture convergence.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The frequency distributions of surface rain rate are evaluated in the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave/Imager (SSM/I) satellite observations and the NOAA/GFDL ...global atmosphere model version 2 (AM2). Instantaneous satellite rain-rate observations averaged over the 2.5° latitude × 2° longitude model grid are shown to be representative of the half-hour rain rate from single time steps simulated by the model. Rain-rate events exceeding 10 mm h-1are observed by satellites in most regions, with 1 mm h-1events occurring more than two orders of magnitude more frequently than 10 mm h-1events. A model simulation using the relaxed Arakawa–Schubert (RAS) formulation of cumulus convection exhibits a strong bias toward many more light rain events compared to the observations and far too few heavy rain events. A simulation using an alternative convection scheme, which includes an explicit representation of mesoscale circulations and an alternative formulation of the closure, exhibits, among other differences, an order of magnitude more tropical rain events above the 5 mm h-1rate compared to the RAS simulation. This simulation demonstrates that global atmospheric models can be made to produce heavy rain events, in some cases even exceeding the observed frequency of such events. Additional simulations reveal that the frequency distribution of the surface rain rate in the GCM is shaped by a variety of components within the convection parameterization, including the closure, convective triggers, the spectrum of convective and mesoscale clouds, and other parameters whose physical basis is currently only understood to a limited extent. Furthermore, these components interact nonlinearly such that the sensitivity of the rain-rate distribution to the formulation of one component may depend on the formulation of the others. Two simulations using different convection parameterizations are performed using perturbed sea surface temperatures as a surrogate for greenhouse gas–forced climate warming. Changes in the frequency of rain events greater than 2 mm h-1associated with changing the convection scheme in the model are greater than the changes in the frequency of heavy rain events associated with a 2-K warming using either model. Thus, uncertainty persists with respect to simulating intensity distributions for precipitation and projecting their future changes. Improving the representation of the frequency distribution of rain rates will rely on refinements in the formulation of cumulus closure and the other components of convection schemes, and greater certainty in predictions of future changes in both total rainfall and in rain-rate distributions will require additional refinements in those parameterizations that determine the cloud and water vapor feedbacks.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The recently developed GFDL Atmospheric Model version 3 (AM3), an atmospheric general circulation model (GCM), incorporates a prognostic treatment of cloud drop number to simulate the aerosol ...indirect effect. Since cloud drop activation depends on cloud-scale vertical velocities, which are not reproduced in present-day GCMs, additional assumptions on the subgrid variability are required to implement a local activation parameterization into a GCM.
This paper describes the subgrid activation assumptions in AM3 and explores sensitivities by constructing alternate configurations. These alternate model configurations exhibit only small differences in their present-day climatology. However, the total anthropogenic radiative flux perturbation (RFP) between present-day and preindustrial conditions varies by ±50% from the reference, because of a large difference in the magnitude of the aerosol indirect effect. The spread in RFP does not originate directly from the subgrid assumptions but indirectly through the cloud retuning necessary to maintain a realistic radiation balance. In particular, the paper shows a linear correlation between the choice of autoconversion threshold radius and the RFP.
Climate sensitivity changes only minimally between the reference and alternate configurations. If implemented in a fully coupled model, these alternate configurations would therefore likely produce substantially different warming from preindustrial to present day.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, ...explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction.Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations that do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models.New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.
Abstract
To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a ...function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Precipitation suppression due to an increase of aerosol number concentration in stratiform cloud is well‐known. It is not certain whether the suppression applies for deep convection. Recent studies ...have suggested increasing precipitation from deep convection with increasing aerosols under some, but not all, conditions. Increasing precipitation with increasing aerosols can result from strong interactions in deep convection between dynamics and microphysics. High cloud liquid, due to delayed autoconversion, provides more evaporation, leading to more active downdrafts, convergence fields, condensation, collection of cloud liquid by precipitable hydrometeors, and precipitation. Evaporation of cloud liquid is a primary determinant of the intensity of the interactions. It is partly controlled by wind shear modulating the entrainment of dry air into clouds and transport of cloud liquid into unsaturated areas. Downdraft‐induced convergence, crucial to the interaction, is weak for shallow clouds, generally associated with low convective available potential energy (CAPE). Aerosol effects on cloud and precipitation can vary with CAPE and wind shear. Pairs of idealized numerical experiments for high and low aerosol cases were run for five different environmental conditions to investigate the dependence of aerosol effect on stability and wind shear. In the environment of high CAPE and strong wind shear, cumulonimbus‐ and cumulus‐type clouds were dominant. Transport of cloud liquid to unsaturated areas was larger at high aerosol, leading to stronger downdrafts. Because of the large vertical extent of those clouds, strong downdrafts and convergence developed for strong interactions between dynamics and microphysics. These led to larger precipitation at high aerosol. Detrainment of cloud liquid and associated evaporation were less with lower CAPE and wind shear, where dynamically weaker clouds dominated. Transport of cloud liquid to unsaturated areas was not as active as in the environment of high CAPE and strong shear. Also, evaporatively driven differences in downdrafts at their level of initial descent were not magnified in clouds with shallow depth as much as in deep convective clouds as they accelerated to the surface over shorter distances. Hence the interaction between dynamics and microphysics was reduced, leading to precipitation suppression at high aerosol. These results demonstrate that increasing aerosol can either decrease or increase precipitation for an imposed large‐scale environment supporting cloud development. The implications for larger‐scale aspects of the hydrological cycle will require further study with larger‐domain models and cumulus parameterizations with advanced microphysics.
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