Cloud Parameterization Schemes Research Articles

Comparison of Coupled Model Intercomparison Project Phases 5 and 6 in Simulating Diurnal Cloud Cycle

Cloud dynamics and their response to future climate change continue to present a significant source of uncertainty in climate predictions. Besides the average cloud properties, the diurnal cloud cycle (DCC) exerts a substantial influence on Earth’s energy balance by reflecting solar radiation during the daytime and continuously absorbing and reemitting longwave radiation throughout the whole day. Previous studies have demonstrated that climate models exhibit certain discrepancies in simulating the DCC; however, less research attention has been paid to the patterns of these DCC biases and their impacts on modeling the Earth’s energy balance. Here, we employ satellite data to compare DCC patterns in Coupled Model Intercomparison Project Phase 5 (CMIP5) and their latest versions in CMIP6 at both regional and global scales. We found that some of the latest climate models tend to have larger DCC biases when using satellite observations as the references, and the radiative effects due to DCC changes account for nearly 50% of the changes in total cloud radiative effects (CREs), suggesting that the DCC biases play a significant role in modelingthe global energy budget. We therefore call for improving cloud parameterization schemes with particular attention to their diurnal cycle to reduce their impacts on future climate projections.

ENSO phase locking, asymmetry and predictability in the INMCM Earth system model

Abstract Advanced numerical climate models are known to exhibit biases in simulating some features of El Niño–Southern Oscillation (ENSO), which is a key mode of interannual climate variability. In this study we analyze how two fundamental features of observed ENSO – asymmetry between hot and cold states and phase-locking to the annual cycle – are reflected in two different versions of the INMCM Earth system model (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project). We identify the above ENSO features using the conventional empirical orthogonal functions (EOF) analysis, which is applied to both observed and simulated upper ocean heat content (OHC) data in the tropical Pacific. We obtain that the observed tropical Pacific OHC variability is described well by two leading EOF-modes, which roughly reflect the fundamental recharge-discharge mechanism of ENSO. These modes exhibit strong seasonal cycles associated with ENSO phase locking while the revealed nonlinear dependencies between amplitudes of these cycles reflect ENSO asymmetry.We also assess and compare the predictability of observed and simulated ENSO based on linear inverse modelling. We find that the improved INMCM6 model has significant benefits in simulating described features of observed ENSO as compared with the previous INMCM5 model. The improvements of the INMCM6 model providing such benefits are discussed. We argue that proper cloud parameterization scheme is crucial for accurate simulation of ENSO dynamics with numerical climate models.

ПАРАМЕТРИЗАЦИЯ ПРОТИВОИЗЛУЧЕНИЯ АТМОСФЕРЫ В ГЛЯЦИОЛОГИЧЕСКИХ ПРИЛОЖЕНИЯХ

Downward longwave radiation (atmospheric counter-radiation) plays a significant role in the formation of the net radiation regime of a mountain glacier. For this reason, its role should not be underestimated in glaciological calculations, especially in the warm period of the year, during the melting season. Glaciological models often do not take into account individual components of the longwave radiation flux, for example, radiation reflected from the mountain slopes surrounding the glacier. The choice of the algorithm for the parameterization of counter-radiation and cloudiness affects the accuracy of model calculations. The well-known parameterization algorithms for atmospheric counter-radiation are analyzed from the point of view of their applicability in glaciological modeling in a specific geographical region: the Inner Tien Shan. The calculation results are compared with the observational data for the Karabatkak glacier (the northern slope of the Terskey Ala-Too range). The choice of the optimum algorithms of a simple cloud parameterization scheme is substantiated taking into account the influence of surrounding relief.

Impact of meteorological parameterization schemes on CTM model simulations

Meteorology plays a key role in regional aerosol concentration and distribution. Microphysics and cloud formation processes in the atmosphere is interlinked with aerosol and their removal processes. To characterize their role in modulating the aerosols/pollutants concentration simulation by a chemical transport model (CTM, here we have taken CHIMERE model); we have performed this study with different microphysical (MP) schemes (Kessler Scheme/Lin Scheme/WRF single Moment 3-class (WSM3) scheme) and cumulus cloud parameterization (CU) schemes (Kain-Fritsch Scheme/Betts-Miller-Janjic Scheme/Grell 3D) of Weather Research and Forecasting Model (WRF). We have used the WRF model over a domain (3°S-41.8°N; 59.5°-102.5°E) with the resolution of (0.25° × 0.25°) while CHIMERE model simulations are performed over the domain (6°-37.5°N; 67°-95.5°E) with the similar resolution for a selected period of monsoon. In total nine combinations are framed with MP and CU schemes to observe the sensitivity of CTM to these schemes. Simulated results are compared with the satellite (TRMM/MODIS) and reanalysis data (MERRA-2) to appraise the model's performance with various parameterization scheme combinations. Results indicate that despite the same initial and boundary conditions and model configuration, notable differences occurred in the simulated meteorological parameters with different scheme combinations. Results suggested that CTM performed in a reliable range with cumulus scheme Betts-Miller-Janjic Scheme (BMJ) and Grell 3D scheme with microphysical parameterization scheme Purdue Lin Scheme over Indian continent. The study also suggests that a single set may not produce good results for all the parameters/pollutants; thus, we have to choose the parameterization schemes which give optimal results for all the parameters/pollutants. These results also infer that ensemble modeling could also lead to a better option than using single simulations.

An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models

To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m2) and CERES-EBAF observation (6.1 W/m2). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models’ cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.

Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds (rhminl) and the auto-conversion-size-threshold-for-ice-to-snow left( {dcs} right). The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on dcs and rhminl. The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in dcs. Overall, we find that rhminl, dcs, ai, and as are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate.

Diurnal variation of summer precipitation modulated by air pollution: observational evidences in the beijing metropolitan area

Studying precipitation diurnal variation characteristics is crucial to better understand their formation mechanism. Moreover, it is fundamental in assessing regional climate variability and to validate the effectiveness of cloud and precipitation parameterization schemes in weather and climate models. Based on observational data from 2008 to 2017, this manuscript objective is to assess the aerosol effects on summer precipitation on an hourly-scale diurnal variation basis in the Beijing metropolitan area. The results put in evidence that the precipitation frequency and duration on polluted days were 25.0% and 14.8% lower with respect to clean days. No significant differences were observed in total daily accumulated rainfall between clear and polluted days. While heavy precipitation mean intensity on polluted days increased by 13.5%, which is potentially linked with aerosol microphysical effects. Note that precipitation occurs earlier on polluted days, with peak time of 1 ∼ 2-hour in advance compared with clean days over the urban areas of Beijing, which may be primarily ascribed to the influence of advanced turning local circulation of mountain-valley breezes and urban heat island circulation due to aerosol-radiation effects.

Evaluation of the Diurnal Variation of Upper Tropospheric Humidity in Reanalysis Using Homogenized Observed Radiances from International Geostationary Weather Satellites

A near global dataset of homogenized clear-sky 6.5-μm brightness temperatures (BTs) from international geostationary (GEO) weather satellites has recently been generated and validated. In this study, these radiance measurements are used to construct the diurnal variation of upper tropospheric humidity (UTH) and to evaluate these diurnal variations simulated by five reanalysis datasets over the 45° N–45° S region. The features of the diurnal variation described by the new dataset are comparable with previous observational studies that a land–sea contrast in the diurnal variation of UTH is exhibited. Distinct diurnal variations are observed over the deep convective regions where high UTH exists. The evaluation of reanalysis datasets indicates that reanalysis systems still have considerable difficulties in capturing the observed features of the diurnal variation of UTH. All five reanalysis datasets present the largest wet biases in the afternoon when the observed UTH experiences a diurnal minimum. Reanalysis can roughly reproduce the day–night contrast of UTH but with much weaker amplitudes and later peak time over both land and ocean. Comparison of the geographical distribution of the diurnal variation shows that both ERA5 and MERRA-2 could capture the larger diurnal variations over convective regions. However, the diurnal amplitudes are widely underestimated, especially over convective land regions, while the phase biases are relatively larger over open oceans. These results suggest that some deficiencies may exist in convection and cloud parameterization schemes in reanalysis models.

The Impact of Cloud Representation on the Sub-Seasonal Forecasts of Atmospheric Teleconnections and Preferred Circulation Regimes in the Northern Hemisphere

The impact of cloud representation on the simulation of mid-latitude recurrent large-scale flows and forecast skill of mid-latitude atmospheric teleconnections is evaluated using the Community Climate System Model, version 4 (CCSM4), and the super-parameterized CCSM4 (SP-CCSM4). Patterns of low-level atmospheric circulation anomalies and convection associated with the Madden–Julian oscillation (MJO) are affected by the method used for the representation of cloud processes. The configuration of the model using super-parameterization for the representation of cloud processes produces MJO-related patterns that agree better with observations than the configuration of the model using a conventional cloud parameterization scheme. The recurrent circulation regimes of the mid-latitudes are also sensitive to the representation of cloud processes. In the North Atlantic sector, the inability of CCSM4 to simulate the Scandinavian blocking regime is corrected in the super-parameterized version of the model. In the North Pacific sector, the strength of the clustering (measured by a variance ratio) is too large in CCSM4 compared with observations and SP-CCSM4. The SP-CCSM4 model has better forecast skill for the MJO amplitude and phase than the model with conventional representation of moist convective processes. In turn, the improved forecast skill of the super-parameterized model results in better forecast skill for mid-latitude teleconnections in 500 hPa geopotential height anomalies forced by the MJO convection.

Parameter Modulation of Madden-Julian Oscillation Behaviors in BCC_CSM1.2: The Key Role of Moisture-Shallow Convection Feedback

To reveal key parameter-related physical mechanisms in simulating Madden-Julian Oscillation (MJO), seven physical parameters in the convection and cloud parameterization schemes of Beijing Climate Center Climate System Model (BCC_CSM1.2) are perturbed with Latin hypercube sampling method. A new strategy is proposed to select runs with good and poor MJO simulations among 85 generated ones. Outputs and parameter values from good and poor simulations are composited separately for comparison. Among the seven chosen parameters, a decreased value of precipitation efficiency for shallow convection, higher values of relative humidity threshold for low stable clouds and evaporation efficiency for deep convective precipitation are crucial to simulate a better MJO. Changes of the three parameters act together to suppress heavy precipitation and increase the frequency of light rainfall over the Indo-Pacific region, supplying more moisture in low and middle troposphere. As a result of a wetter lower troposphere ahead of the MJO main convection, the low-level moisture preconditioning along with the leading shallow convection tends to be enhanced, favorable for MJO’s further development and eastward propagation. The MJO’s further propagation across the Maritime Continent (MC) in good simulations is accompanied with more land precipitation dominated by shallow convection. Therefore, the above-mentioned three parameters are found to be crucial parameters out of the seven ones for MJO simulation, providing an inspiration for better MJO simulation and prediction with this model. This work is valuable as it highlights the key role of moisture-shallow convection feedback in the MJO dynamics.

The Fast Response of the Tropical Circulation to CO2 Forcing

Atmosphere-only CMIP5 idealized climate experiments with quadrupling of atmospheric CO2 are analyzed to understand the fast response of the tropical overturning circulation to this forcing and the main mechanism of this response. A new metric for the circulation, based on pressure velocity in the subsidence regions, is defined, taking advantage of the dynamical stability of these regions and their reduced sensitivity to the GCM’s cloud and precipitation parameterization schemes. This definition permits us to decompose the circulation change into a sum of relative changes in subsidence area, static stability, and heating rate. A comparative analysis of aqua- and Earth-like planet experiments reveals the effect of the land–sea contrast on the total change in circulation. On average, under the influence of CO2 increase without surface warming, the atmosphere radiatively cools less, and this drives the 3%–4% slowdown of the tropical circulation. Even in an Earth-like planet setup, the circulation weakening is dominated by the radiatively driven changes in the subsidence regions over the oceans. However, the land–sea differential heating contributes to the vertical pattern of the circulation weakening by driving the vertical expansion of the tropics. It is further found that the surface warming would, independently of the CO2 effect, lead to up to a 12% slowdown in circulation, dominated by the enhancement of the static stability in the upper troposphere. The two mechanisms identified above combine in the coupled experiment with abrupt quadrupling, causing a circulation slowdown (focused in the upper troposphere) of up to 18%. Here, the independent effect of CO2 has a considerable impact only at time scales less than one year, being overtaken quickly by the impact of surface warming.

Effects of Explicit Convection on Land Surface Air Temperature and Land‐Atmosphere Coupling in the Thermal Feedback Pathway

AbstractSimulating and understanding continental temperature extremes is a critical issue in Earth System Modeling. Conventional general circulation models are impaired by imperfect cloud and boundary layer parameterization schemes with implications for potentially unrealistic distributions of atmospheric variables and land‐atmosphere coupling signals. In this study we examine a modern version of Superparameterization (SP) in the Community Atmosphere Model v.5 to examine impacts of SP on the characteristics of land surface air temperature, thermal land‐atmosphere coupling, and the relationship between them. The results show that SP in the Community Atmosphere Model (SPCAM) improves mean land surface air temperature at daily time scales regionally compared to Community Atmosphere Model 5 and better simulates thermal land‐atmosphere coupling (soil moisture‐surface air temperature coupling) in several well‐known hot spots (e.g., the Great Plains, Sahel, and India). Detailed analysis of regional probability distribution functions reveals how the intrinsic relationships of atmosphere variables, land‐atmosphere coupling, and temperature extremes are modified by SP. Regression‐type metrics are also used to examine global land‐atmosphere coupling, with some expected limitations where multiple coupling regimes coexist temporally. Stratifying results by soil moisture percentile proves illuminating in this regard and reveals that SP frequently amplifies land‐atmosphere thermal coupling at the dry end of regional coupling regimes.

On the Importance of a Consistent Treatment of Prognostic Moisture Variables between Convective and Microphysical Parameterizations

Abstract Analysis of WRF Model output from experiments using two double-moment microphysics schemes is carried out to demonstrate that there can be an inconsistency between the predicted mass and number concentrations when a single-moment convective parameterization is used together with a double-moment microphysics scheme. This inconsistency may arise because the grid-scale and subgrid-scale cloud schemes generally apply different levels of complexity to the parameterized microphysical processes. In particular, when a multimoment formulation is used in the microphysics scheme and other physical parameterizations modify only the mass-related moment while the values of the second (or higher) moment for individual hydrometeors remain unchanged, an unintended modification of the particle size distribution occurs. Simulated radar reflectivity is shown to be a valuable tool in diagnosing this inconsistency. In addition, potential ways to minimize the problem are explored by including number concentration calculations in the cumulus parameterization that are consistent with the assumptions of hydrometeor sizes in the microphysics parameterization. The results of this study indicate that it is physically preferable to unify microphysical assumptions between the grid-resolved and subgrid cloud parameterization schemes in weather and climate simulation models.

Sensitivity of AGCM‐simulated regional JJAS precipitation to different convective parameterization schemes

ABSTRACTThis study examines the effects of convective parameterization schemes (CPSs) on the simulated June–September (JJAS) precipitation climatology, variability and predictability over southwestern Arabian Peninsula and northeast Africa during 1981–2014 within an atmospheric global climate model framework. The three CPSs used are: the simplified Arakawa–Schubert (SAS) scheme; the SAS scheme with Tokioka modification (STOK) and the Emanuel (EMAN) scheme coupled with a probability distribution function‐based cloud parameterization scheme. SAS and STOK overestimate JJAS total precipitation over the selected domain compared to observations, while EMAN underestimates. EMAN provides the most realistic distribution of precipitation and spatial distribution of convective precipitation despite the tendency for underestimating the total precipitation. The principal patterns of precipitation variability (as measured by the leading empirical orthogonal functions) in EMAN and STOK are clearly related to the observed pattern, while the maximum variability in SAS occurs in a completely different location. The El Niño Southern Oscillation‐related JJAS precipitation teleconnection simulated by the SAS scheme is very weak as compared to observations and other two CPSs. The signal‐to‐noise ratio estimated by SAS and STOK is very low as compared to EMAN. Correlation analysis shows that EMAN performs better than the other two CPSs. Low (high) outgoing longwave radiation (OLR) values are indicative of enhanced (suppressed) convection and hence more (less) cloud coverage. The root mean square error estimated for OLR in EMAN is lower than that of the other two CPSs. The vertical structure of specific humidity and temperature shows less error in EMAN than that of the other two CPSs, which could be a reason for better predictability over the region of interest.

Impact of Different Cumulus Parameterization Schemes in SAUDI-KAU AGCM

In global climate models (GCMs), the convection is parameterized, since the typical scale of this process is smaller than the model resolution. This study examines the impact of two different cumulus parameterization schemes on the simulated climate using single-column model (SCM) as well as in an atmospheric global climate model (AGCM). The two schemes used are: the Simplified Arakawa–Schubert (SAS) scheme; and the Emanuel scheme coupled with a probability distribution function-based cloud parameterization scheme (EMAN). The humidity, temperature, cloud fraction, and precipitation simulations are improved in EMAN as compared to that of SAS in SCM. Climatological simulations (1981-2014) conducted using an AGCM at a moderate resolution (T106L44: 1.125° × 1.125°) indicated that the use of the EMAN improved the results. The precipitation over the tropical belt also showed improvements in terms of the distributions, biases, and association with observation. These improvements are attributable to a better vertical structure of temperature, especially in the tropics, due to the more realistic estimation of the temperature and moisture fields by the EMAN. The error estimated in outgoing long-wave radiation for EMAN is lower than that of the SAS. The vertical structure of specific humidity and temperature shows less error in EMAN as compared to SAS. Results using the SCM and AGCM reveal the benefits of using the EMAN in comparison to the SAS which includes better simulation of the relative humidity, temperature, and precipitation fields.

Cloud radiative forcing induced by layered clouds and associated impact on the atmospheric heating rate

A quantitative analysis of cloud fraction, cloud radiative forcing, and cloud radiative heating rate (CRH) of the single-layered cloud (SLC) and the multi-layered cloud (MLC), and their differences is presented, based on the 2B-CLDCLASS-LIDAR and 2B-FLXHR-LIDAR products on the global scale. The CRH at a given atmospheric level is defined as the cloudy minus clear-sky radiative heating rate. The statistical results show that the globally averaged cloud fraction of the MLC (24.9%), which is primarily prevalent in equatorial regions, is smaller than that of the SLC (46.6%). The globally averaged net radiative forcings (NET CRFs) induced by the SLC (MLC) at the top and bottom of the atmosphere (TOA and BOA) and in the atmosphere (ATM) are–60.8 (–40.9),–67.5 (–49.6), and 6.6 (8.7) W m-2, respectively, where the MLC contributes approximately 40.2%, 42.4%, and 57% to the NET CRF at the TOA, BOA, and in the ATM, respectively. The MLC exhibits distinct differences to the SLC in terms of CRH. The shortwave CRH of the SLC (MLC) reaches a heating peak at 9.75 (7.5) km, with a value of 0.35 (0.60) K day-1, and the differences between SLC and MLC transform from positive to negative with increasing altitude. However, the longwave CRH of the SLC (MLC) reaches a cooling peak at 2 (8) km, with a value of–0.45 (–0.42) K day-1, and the differences transform from negative to positive with increasing altitude. In general, the NET CRH differences between SLC and MLC are negative below 7.5 km. These results provide an observational basis for the assessment and improvement of the cloud parameterization schemes in global models.

Impact of revised cloud microphysical scheme in CFSv2 on the simulation of the Indian summer monsoon

ABSTRACTRole of the cloud parameterization scheme and critical relative humidity (RHcrit) for large‐scale precipitation is examined for simulating Indian summer monsoon (ISM) by the National Centers for Environmental Prediction (NCEP) climate forecast system version 2 (CFSv2). The major biases of the model simulations namely dry bias over the major continents, cold tropospheric temperature (TT) bias and cold sea surface temperature (SST) bias are related to biases in distribution of clouds. This study evaluates the role of variable RHcrit to get better simulation of high level clouds and reduce TT bias and cloud microphysical parameterization to improve the meridional gradient of TT towards achieving better simulation of south Asian monsoon precipitation. Sensitivity experiments of CFSv2 with the modified RHcrit and cloud microphysical scheme compared to the control simulation show that while the RHcrit leads to some development of the cloud distribution and contributes to some progress of the dry bias over India, the cloud microphysics changes lead to a significant improvement of the cloud simulations. Particularly, revised cloud microphysics scheme coupled with modified RHcrit results in a much improved global distribution of cloud fraction with zonal mean cloud fraction being close to observation. This leads to significant improvement in the meridional gradient of TT leading to rainfall over south Asian monsoon region. The dry bias is not only reduced over the Indian subcontinent but also over other regions of global tropics such as the central Africa and the northern South America. The annual cycle of all India rainfall is in good agreement with observation not only in amount but also in the onset and withdrawal phases. Thus, modifications in the cloud microphysical parameterization scheme in CFSv2 have played a vital role in simulation of the ISM in particular. The sensitivity experiments demonstrate the betterment of the mean monsoon and may lead to help improve monsoon forecasts.

The Characterization of Ice Hydrometeor Gamma Size Distributions as Volumes in N0–λ–μ Phase Space: Implications for Microphysical Process Modeling

Abstract Gamma distributions represent particle size distributions (SDs) in mesoscale and cloud-resolving models that predict one, two, or three moments of hydrometeor species. They are characterized by intercept (N0), slope (λ), and shape (μ) parameters prognosed by such schemes or diagnosed based on fits to SDs measured in situ in clouds. Here, ice crystal SDs acquired in arctic cirrus during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and in hurricanes during the National Aeronautic and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) are fit to gamma distributions using multiple algorithms. It is shown that N0, λ, and μ are not independent parameters but rather exhibit mutual dependence. Although N0, λ, and μ are not highly dependent on choice of fitting routine, they are sensitive to the tolerance permitted by fitting algorithms, meaning a three-dimensional volume in N0–λ–μ phase space is required to represent a single SD. Depending on the uncertainty in the measured SD and on how well a gamma distribution matches the SD, parameters within this volume of equally realizable solutions can vary substantially, with N0, in particular, spanning several orders of magnitude. A method to characterize a family of SDs as an ellipsoid in N0–λ–μ phase space is described, with the associated scatter in N0–λ–μ for such families comparable to scatter in N0, λ, and μ observed in prior field campaigns conducted in different conditions. Ramifications for the development of cloud parameterization schemes and associated calculations of microphysical process rates are discussed.

Evaluation of cloud and precipitation parameterization using a single-column model: A TWP-ICE case study

Cloud and precipitation parameterization schemes are evaluated, and their sensitivity to the method and/or parameters used to determine cloud physical processes is examined using a singlecolumn version of the Unified Model (SCUM). In the experiment for TWP-ICE, cloud fraction is overestimated (underestimated) in the upper (lower) troposphere due to the wet (dry) bias. The precipitation rate is well simulated during the active monsoon period, but overestimated during the suppressed monsoon and clear skies periods. In the moist convection scheme, trigger condition and entrainment process affect the lower tropospheric humidity through the impact on convective occurrence frequency and intensity, respectively. Strengthening the trigger condition and using the adaptive entrainment method alleviate the low-level dry bias. In the microphysics scheme, more large-scale precipitation is produced with prognostic rain, due to rain sedimentation considering vertical velocity of rain drop, than with diagnostic rain. Less ice/snow deposition with the prognostic two-ice category results in lower ice water content and upper-level cloud fraction than with the diagnostic splitting method for the twoice category. In the cloud macrophysics scheme, the prognostic cloud fraction and cloud/ice water content scheme produces a larger cloud fraction and more cloud/ice water content than the diagnostic scheme, mainly due to detrainment from moist convection (cloud source) that surpasses the effect of convective heating and drying (cloud sink). This affects temperature by influencing the radiative, convective, and microphysical processes. The experiment with combined modifications in cloud and precipitation schemes shows that interaction between modified moist convection and cloud macrophysics schemes results in more alleviation of the cold bias not only at the lower levels but also at the upper levels.

Comparison of NCEP/NCAR and ERA-40 total cloud cover with surface observations over the Tibetan Plateau

The annual and seasonal total cloud cover (TCC) variations in the eastern and central Tibetan Plateau (TP) during 1961–2005 are analysed using 71 surface observational stations. The mean TCC decreases from the southeastern to the northwestern TP, consistent with the patterns of atmospheric moisture in the region. The annual mean TCC shows a significant decreasing trend of −0.09 percent decade−1, mainly contributed by winter. About 65% of the stations show significant downward trends on the annual basis with large trend magnitudes occurring in the central TP. The seasonal patterns confirm the annual patterns in most cases. Compared with the surface observations, both National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP/NCAR) (1961–2005) and ERA-40 (1961–2001) can reproduce the decreasing TCC trends. The shift of TCC before and after the mid-1980s is obvious in observations and both reanalyses, reflecting the changes of large-scale atmospheric circulation. However, NCEP/NCAR underestimates and ERA-40 overestimates observations on the annual and seasonal basis, presumably caused by the different cloud parameterization schemes. A Taylor diagram diagnose summarizes the discrepancies between observations and reanalyses.