General Circulation Model Grid Research Articles

A nonparametric stochastic downscaling framework for daily rainfall at multiple locations

Use of General Circulation Models (GCMs) for climate change impact assessment is often limited by their incapability at representing local features and dynamics at spatial scales finer than the in‐built GCM grid scale. This has led to the development of downscaling techniques for transfer of coarse GCM simulated weather output to finer spatial resolutions. This paper presents a nonparametric stochastic spatial downscaling framework for multisite daily rainfall occurrence and amount. At site rainfall occurrences are downscaled using a nonparametric nonhomogeneous hidden Markov model (NNHMM) that represents spatial dependence across the rainfall occurrence field using a dynamic weather state indicative of the centroid and average wetness fraction of the rainfall occurrence field. The rainfall amounts on the wet days are downscaled using a nonparametric kernel density approach that accommodates variations in the rainfall downscaling model at individual locations. Spatial dependence in the rainfall amounts is simulated by driving each of the single‐site amounts model with spatially correlated random numbers. The proposed framework is applied for downscaling of rainfall at a network of 30 rain gauge stations around Sydney in Australia, and its performance is evaluated. The analyses of the results show that the logic of providing separate treatments for rainfall occurrence and amounts at individual locations imparts considerable accuracy in the representation of characteristics of interest in hydrologic studies. These characteristics include representation of rainfall spell patterns, spatial distribution of the rainfall occurrence and amount fields, representation of low and high rainfall extremes at individual stations and across the field, as well as common indicators of water balance and variability that are of importance in a catchment scale water balance simulation.

Bias correction of daily GCM rainfall for crop simulation studies

General circulation models (GCMs), used to predict rainfall at a seasonal lead-time, tend to simulate too many rainfall events of too low intensity relative to individual stations within a GCM grid cell. Even if bias in total rainfall is corrected relative to a target location, this distortion of frequency and intensity is expected to adversely affect simulations of crop growth and yield. We present a procedure that calibrates both the frequency and the intensity distribution of daily GCM rainfall relative to a target station, and demonstrate its application to maize yield simulation at a location in semi-arid Kenya. If GCM rainfall frequency is greater than observed frequency for a given month, averaged across years, GCM rainfall frequency is corrected by discarding rainfall events below a calibrated threshold. To correct the intensity distribution, each GCM rainfall amount above the calibrated threshold is mapped from the GCM intensity distribution onto the observed distribution. We used a gamma distribution for observed rainfall intensity, and considered both gamma and empirical distributions for GCM rainfall intensity. At the study location, the proposed correction procedure corrected both the mean and variance of monthly and seasonal GCM rainfall total, frequency and mean intensity. The empirical (GCM)-gamma (observed) transformation overestimated mean intensity slightly. A simple multiplicative shift did a better job of correcting monthly and seasonal rainfall totals, but left substantial frequency and intensity bias. All of the bias correction procedures improved maize yield simulations, but resulted in substantial negative mean bias. This bias appears to be associated with a tendency for the GCM rainfall to be more strongly autocorrelated than observed rainfall, resulting in unrealistically long dry spells during the growing season. Nonlinearity of crop response to the variability of water availability across GCM realizations may also contribute. Averaging simulated yields each year across multiple GCM realizations improved yield predictions. The proposed correction procedure provides an option for using the daily output of dynamic climate prediction models for impact studies in a manner that preserves any useful predictive information about the timing of rainfall within the season. However, its practical utility for yield forecasting at a long lead-time may be limited by the ability of GCMs to simulate rainfall with a realistic time structure.

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.

The Impact of Model Resolution on Differences between Independent Column Approximation and Monte Carlo Estimates of Shortwave Surface Irradiance and Atmospheric Heating Rate

Abstract Within general circulation models (GCMs), domain average radiative fluxes are computed using plane-parallel radiative transfer algorithms that rely on cloud overlap schemes to account for clouds not resolved at the horizontal resolution of a grid cell. These parameterizations have a strong statistical approach and have difficulty being applied well to all cloudy conditions. A more physically based superparameterization has been developed that captures subgrid cloud variability using an embedded cloud system resolving model (CSRM) within each GCM grid cell. While plane-parallel radiative transfer computations are generally appropriate at the scale of a GCM grid cell, their suitability for the much higher spatially resolved CSRMs (2–4 km) is unknown because they ignore photon horizontal transport effects. The purpose of this study is to examine the relationship between model horizontal resolution and 3D radiative effects by computing the differences between independent column approximations (ICA) and 3D Monte Carlo estimates of shortwave surface irradiance and atmospheric heating rate. Shortwave radiative transfer computations are performed on a set of six 2D fields composed of stratiform and convective liquid water and ice clouds. To establish how 3D effects vary with the size of a grid cell, this process is repeated as the model resolution is progressively degraded from 200 to 20 km. For shortwave surface irradiance, the differences between the 3D and ICA results can reach 500 W m−2. At model resolutions of between 2.0 and 5.0 km the difference for almost all columns is reduced to a maximum of ±100 W m−2. For atmospheric heating rates assessed at the level of individual model cells, 3D radiative effects can approach a maximum value of ±1.2 K h−1 when the horizontal column size is 200 m. However, between model resolutions of 2.0 and 5.0 km, 3D radiative effects are reduced to well below ±0.1 K h−1 for a large majority of the cloudy cells. While this finding seems to bode well for the CSRM, the results ultimately need to be understood within the context of how 3D radiative effects impact not only heating rates but also cloud dynamics.

Evaluation of a GCM subgrid cloud‐radiation interaction parameterization using cloud‐resolving model simulations

The mosaic approach of Liang and Wang (1997) for the general circulation model (GCM) parameterization of subgrid cloud‐radiation interactions is evaluated against the validated cloud‐resolving model (CRM) simulation of the Atmospheric Radiation Measurement (ARM) intensive observation period (IOP, June 22–July 17, 1997) at the Southern Great Plains (SGP) site. The CRM‐generated cloud statistics determines the required characteristic structure differences between three primary cloud genera (convective, anvil and stratiform). It is demonstrated that the mosaic approach with the CRM cloud statistics well simulates the CRM domain‐averaged radiative quantities. The result indicates that the mosaic approach of the cloud overlap based on the cloud genera differing in formation mechanisms and of the optical inhomogeneity by cloud water path scaling can capture, respectively, the dominant effects of the cloud geometric association and optical property variability within a GCM grid.

Global consequences of interactions between clouds and radiation at scales unresolved by global climate models

Horizontal grid‐spacings in conventional atmospheric general circulation models (GCMs) are typically between 100 km and 500 km. Hence, many processes are unresolved and must be parameterized in terms of resolved variables. Development of satisfactory parameterizations of mean‐field (or domain‐average) cloud and radiative processes has been frustratingly slow. Moreover, the sensitivity of conventional GCMs to unresolved interactions between cloud and radiation is unknown. In this study, the native cloud parameterization of a GCM was replaced, in each GCM grid column, by a two‐dimensional cloud system‐resolving model (CSRM). The CSRMs used a horizontal grid‐spacing of 4 km. They were employed to assess the sensitivity of a GCM to the inclusion of interactions between cloud and radiation at unresolved scales as well as sensitivity to accurate domain‐average radiative flux profiles. For a simulation spanning one season, unresolved cloud‐radiation interactions affect the statistics of average cloud fraction and average cloud‐radiative effect, while the accuracy of domain‐average radiative flux profiles had less effect.

Preliminary Tests of Multiscale Modeling with a Two-Dimensional Framework: Sensitivity to Coupling Methods

Abstract Preliminary tests of the multiscale modeling approach, also known as the cloud-resolving convective parameterization, or superparameterization, are performed using an idealized framework. In this approach, a two-dimensional cloud-system resolving model (CSRM) is embedded within each vertical column of a general circulation model (GCM) replacing conventional cloud parameterization. The purpose of this study is to investigate the coupling between the GCM and CSRMs and suggest a revised method of coupling that abandons the cyclic lateral boundary condition for each CSRM used in the original cloud-resolving convective parameterization. In this way, the CSRM extends into neighboring GCM grid boxes while sharing approximately the same mass fluxes with the GCM at the borders of the grid boxes. With the original and revised methods of coupling, numerical simulations of the evolution of cloud systems are conducted using a two-dimensional model that couples CSRMs with a lower-resolution version of the CSRM with no physics [large-scale dynamics model (LSDM)]. The results with the revised method show that cloud systems can propagate from one LSDM grid column to the next as expected. Comparisons with a straightforward application of a single CSRM to the entire domain (CONTROL) show that the biases of the large-scale thermodynamic fields simulated by the coupled model are significantly smaller with the revised method. The results also show that the biases are near the smallest when the velocity fields of the LSDM and CSRM are nudged to each other with the time scale of a few hours and the thermodynamic field of the LSDM is instantaneously updated at each time step with the domain-averaged CSRM field.

Exploring two methods for statistical downscaling of Central European phenological time series.

In this study we set out to investigate the possibility of linking phenological phases throughout the vegetation cycle, as a local-scale biological phenomenon, directly with large-scale atmospheric variables via two different empirical downscaling techniques. In recent years a number of methods have been developed to transfer atmospheric information at coarse General Circulation Model's grid resolutions to local scales and individual points. Here multiple linear regression (MLR) and canonical correlation analysis (CCA) have been selected as downscaling methods. Different validation experiments (e.g. temporal cross-validation, split-sample tests) are used to test the performance of both approaches and compare them for time series of 17 phenological phases and air temperatures from Central Europe as microscale variables. A number of atmospheric variables over the North Atlantic and Europe are utilized as macroscale predictors. The period considered is 1951-1998. Temporal cross-validation reveals that the CCA model generally performs better than MLR, which explains 20%-50% of the phenological variances, whereas the CCA model shows a range from 40% to over 60% throughout most of the vegetation cycle. To show the validity of employing phenological observations for downscaling purposes both methods (MLR and CCA) are also applied to gridded local air temperature time series over Central Europe. In this case there is no obvious superiority of the CCA model over the MLR model. Both models show explained variances from 40% to over 70% in the temporal cross-validation experiment. The results of this study indicate that time series of phenological occurrence dates are very compatible with the needs of empirical downscaling originally developed of local-scale atmospheric variables.

Design and Analysis of Climate Model Experiments for the Efficient Estimation of Anthropogenic Signals

Abstract Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space–time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.

Effects of polar ice sheets on global sea level in high‐resolution greenhouse scenarios

Projections of future global sea level critically depend on reliable estimates of mass balance changes on the polar ice sheets. The most sophisticated tools allowing for such estimates are General Circulation Models (GCM). A major impediment until recently has been their coarse grid resolution (3°–6°) causing substantial uncertainties in the mass balance calculations on the poorly resolved ice sheets. The present study is based on a climate change experiment of highest resolution currently feasible (T106, 1.1°). The precipitation distribution significantly benefits from the more realistic orographic forcing in the high‐resolution experiment and is very accurately reproduced. A greenhouse warming experiment with doubled carbon‐dioxide concentration based on the same high‐resolution model suggests an increase in accumulation on both Greenland and Antarctic ice sheets. On the other hand, even a T106 resolution is still too coarse for the simulation of ablation on the narrow ice sheet margins, where most of the melting takes place. A simple method is presented to improve ablation diagnostics from GCMs, based on the interpolation of the reference temperature and temperature anomaly fields onto a fine mesh topography of 2 km horizontal resolution. The increase in ablation on Greenland in the greenhouse scenario is thereby smaller than the increase directly inferred from the GCM grid. As for Antarctica, it is still too cold at the time of doubled carbon‐dioxide concentration for significant ablation. The results from the greenhouse experiment with doubled carbon‐dioxide concentration thus suggest not only a mass gain in Antarctica due to the increase in accumulation, but also a mass gain in Greenland, since the enhanced ablation in the warmer climate does not fully compensate for the increased accumulation. In terms of global sea level change, these mass balance shifts correspond to a net sea level decrease of 1.2 mm y−1 at the time of doubled carbon‐dioxide. This may compensate for a substantial fraction of the melt‐induced sea level rise from smaller glaciers and ice caps, leaving thermal expansion as the dominant factor for sea level rise over the coming decades. The compensating effect, however, could fade if carbon‐dioxide concentrations in the atmosphere cannot be stabilized and continue to rise above double the present values, since the associated greenhouse warming could then become large enough to induce significant melting also on the Antarctic ice sheet.

Mapping and pseudoinverse algorithms for ocean data assimilation

Among existing ocean data assimilation methodologies, reduced-state Kalman filters are a widely studied compromise between resolution, optimality, error specification, and computational feasibility. In such reduced-state filters, the measurement update takes place on a coarser grid than that of the general circulation model (GCM); therefore, these filters require mapping operators from the GCM grid to the reduced state and vice versa. The general requirements are that the state-reduction and interpolation operators be pseudoinverses of each other, that the coarse state define a closed dynamical system, that the mapping operations be insensitive to noise, and that they be appropriate for regions with irregular coastlines and bathymetry. In this paper, we describe three efficient algorithms for computing the pseudoinverse: a fast Fourier transform algorithm that serves for illustration purposes, an exact implicit method that is recommended for most applications, and an efficient iterative algorithm that can be used for the largest problems. The mapping performance of 11 interpolation kernels is evaluated. Surprisingly, common kernels such as bilinear, exponential, Gaussian, and sinc perform only moderately well. We recommend instead three kernels, smooth, thin-plate, and optimal interpolation, which have superior properties. This study removes the computational bottleneck of mapping and pseudoinverse algorithms and makes possible the application of reduced-state filters to global problems at state-of-the-art resolutions.

On the use and validation of mosaic heterogeneity in atmospheric numerical models

The mosaic land modeling approach allows for the representation of multiple surface types in a single atmospheric general circulation model grid box. Each surface type, collectively called “tiles” correspond to different sets of surface characteristics (e.g. for grass, crop or forest). Typically, the tile space data is averaged to grid space by weighting the tiles with their fractional cover. While grid space data is routinely evaluated, little attention has been given to the tile space data. The present paper explores uses of the tile space surface data in validation with station observations. The results indicate the limitations that the mosaic heterogeneity parameterization has in reproducing variations observed between stations at the Atmospheric Radiation Measurement Southern Great Plains field site.

Downward longwave radiation in general circulation models: a case study at a semi-arid continental site

The present case study evaluates the downward longwave radiation at the surface (DLR) inseveral high-resolution (≈1°) general circulation models (GCMs) using surface observationsfrom a semiarid continental site in New South Wales, Australia (Uardry, 34.39°S, 142.30°E).This site is located on a large grassland plain uniform in both its land use and landcover type, and is therefore particularly well suited for a comparison with GCM grid mean values. Monthlyaverages of newly constructed clear-sky and all-sky DLR climatologies and the resulting cloudradiativeforcing are compared. It is shown that the GCMs exceed the observed DLR undercloud-free conditions by 10—20Wm-2 at this semiarid site on an annual basis, with a strongseasonal dependence. The calculated clear-sky fluxes are overestimated during the warmersummer season, with large absolute values of DLR, while the biases are reduced in the colderand dryer winter season with smaller fluxes. This gives direct support for recent evidence thatthe DLR model biases depend systematically on the thermal and humidity structure of thecloudless atmosphere. Fluxes from strongly emitting atmospheres tend to be overestimated, butmay be underestimated from atmospheres with smaller emission. This points to common problemsinherent in the simulation of the emission from the cloudless atmosphere in current longwaveradiation codes.The comparisons of the all-sky climatologies at Uardry show that the clear-sky biases arepartly masked in the models with an insufficient cloud-radiative forcing, thereby counterbalancingthe excessive DLR of the cloud-free atmosphere. On the other hand, when the cloudradiativeforcing is improved, the biases in the cloud-free atmosphere become fully apparent inthe all-sky fluxes.

Spatial and temporal persistence of mean monthly temperature on two GCM grid cells

AbstractThe study investigates a simple statistically based scheme for translating large scale general circulation model (GCM) information to local scale where natural and socio‐economic environments are dependent on climatic variables. Two GCM grid cells were chosen, one centred on Montpellier (France), the other on Sevilla (Spain). For each meteorological station over these cells, the relative values of mean monthly temperature compared with the GCM grid cell mean were expressed as percentages, and were called ‘relative temperatures’ (RT). Using geostatistical procedures, the spatial structure of RT was sought on both GCM grid cells, with the altitude as external drift. The months of January and July were studied during the 1979–1993 period. For each month, the characteristics of the variograms of RT are close for each year of the period. There is also a good inter‐annual persistence of spatial patterns of RT when the correlation between altitude and RT is significant. The results will be of great interest for easily estimating local values of mean monthly temperature at any place within each grid cell without further calculation. Copyright © 2001 Royal Meteorological Society

Rainfall controls on evaporation at the regional scale: An example from the Sahel

The validation of general circulation model (GCM) land surface schemes requires observations of energy and water fluxes averaged over an area of a typical GCM grid cell (100 km×100 km) for a time period long enough to include the typical climatic conditions in that area. To create such a consolidated data set, soil moisture and surface evaporation are estimated with a detailed model of the surface energy and water balance. Observations from the Hydrological Atmospheric Pilot Experiment in the Sahel (HAPEX‐Sahel) in Niger, West Africa, have shown that rainfall is a key source of spatial variability in the water and energy balance in semi arid regions. The model is therefore run for two wet seasons, forced by observations from a dense rain gauge network over an area of 10,000 km2. Analysis of the estimated surface fluxes indicates that bare soil evaporation has a strong influence on the spatial variability of evaporation during the wet season. On the other hand, leaf‐level transpiration is relatively uniform and unstressed by soil moisture for most of the period. Modest spatial variability in transpiration develops through rainfall‐induced contrasts in growth rates. The representation in GCMs of these moisture controls on evaporation can be problematic. In this case, where leaf area index is < 1, transpiration is well‐represented by areal mean surface properties. However, an interactive growth model may be a useful component of the surface scheme when considering the large interannual rainfall variability typical of the Sahel. It is also found that the effect of spatial variability due to subgrid rainfall can not be ignored when modeling interception and bare soil evaporation. Methods of calculating mean fluxes based on mean moisture variables are therefore proposed. Using such a subgrid scheme, total evaporation errors are reduced by 50%.

The use of river runoff to test CSIRO9 land surface scheme in the Amazon and Mississippi River Basins

Point validation of land surface schemes in general circulation models (GCMs) can only provide limited insight into the performance of the schemes when used over the large GCM grid cells. Streamflow, which integrates information over large areas, is a potentially useful diagnostic for assessing the land surface schemes at large spatial scales. This paper discusses the use of streamflow for assessing the performance of the CSIRO9 land surface scheme over the Amazon and the Mississippi River Basins. The paper shows that although streamflow can be a useful diagnostic tool for testing land surface schemes over large scales its utility is undermined by various problems: the atmospheric control on evapotranspiration, the possibility of compensating errors within large basins, and most importantly the lack of reliable grid-averaged atmospheric data at large spatial scales. Copyright © 2000 Royal Meteorological Society

A river flow routing scheme for general circulation models

The routing of runoff estimates from General Circulation Models (GCMs) is important to model river flow from large river basins and to estimate freshwater inflow into the oceans. Present routing approaches use arbitrary constants and empirical equations to determine travel times between the GCM grid cells. A new river flow routing scheme is developed, which uses physical catchment and river channel information and river discharge data. The scheme uses surface runoff and deep percolation data from land surface parameterization schemes, and routing is performed via linear surface and groundwater reservoirs. Geomorphological relationships between mean annual river discharge and other physical variables are used to interpolate existing information to define the channel morphology for the digital river networks at the routing model resolution. Applications of the routing scheme to the Mississippi and Amazon River Basins indicate that it performs adequately. The scheme does not require the calibration of parameters and can thus be easily used in GCMs.

Evaluation of HADCM2 and direct use of daily GCM data in impact assessment studies

This paper investigates two important aspects of methods used to explore possible effects of climatic changes on agricultural productivity on regional spatial scales. First, an evaluation of precipitation and near surface air temperature in two successive versions of the Hadley Centre General Circulation Model (GCM) has been performed to consider to what extent GCMs are capable of simulating the mean and variability of local climates. This is explored by comparing the output of an individual GCM grid box with three station observations. Several ancillary issues associated with the comparisons of observations of daily precipitation and model output that affect the statistical results are also discussed. Finally, daily data from the control and sulphate runs of the latest Hadley Centre GCM (HadCM2) have been used directly as input to the CERES-Wheat model, and the modelled yield distribution is compared to that produced with the historical data series. Our results imply that for this particular grid box covering the study region in central France, the daily raw data from HadCM2 experiment can be used directly to assess the potential impact of the greenhouse gas and sulphate aerosol radiative induced forcings and the associated climatic change on average regional winter wheat production. On the other hand, less confidence should be placed on their use regarding the estimation of future agricultural risk and variability assessment. Furthermore, a possibly more severe methodological problem that has arisen from our study is the inability of CERES-Wheat to simulate the waterlogging effects of excessive soil water on crop growth and development. Finally, we assess the potential impact of changing climate on regional winter wheat production by using the daily data from the sulphate integration up to the end of the 21st century.

Parameterizing Grid-Averaged Longwave Fluxes for Inhomogeneous Marine Boundary Layer Clouds

This paper examines the relative impacts on grid-averaged longwave flux transmittance (emittance) for marine boundary layer (MBL) cloud fields arising from horizontal variability of optical depth τ and cloud sides. First, using fields of Landsat-inferred τ and a Monte Carlo photon transport algorithm, it is demonstrated that mean all-sky transmittances for 3D variable MBL clouds can be computed accurately by the conventional method of linearly weighting clear and cloudy transmittances by their respective sky fractions. Then, the approximations of decoupling cloud and radiative properties and assuming independent columns are shown to be adequate for computation of mean flux transmittance. Since real clouds have nonzero geometric thicknesses, cloud fractions Âc presented to isotropic beams usually exceed the more familiar vertically projected cloud fractions Ac. It is shown, however, that when Ac ≲ 0.9, biases for all-sky transmittance stemming from use of Ac as opposed to Âc are roughly 2–5 times smaller than, and opposite in sign to, biases due to neglect of horizontal variability of τ. By neglecting variable τ, all-sky transmittances are underestimated often by more than 0.1 for Ac near 0.75 and this translates into relative errors that can exceed 40% (corresponding errors for all-sky emittance are about 20% for most values of Ac). Thus, priority should be given to development of general circulation model (GCM) parameterizations that account for the effects of horizontal variations in unresolved τ; effects of cloud sides are of secondary importance. On this note, an efficient stochastic model for computing grid-averaged cloudy-sky flux transmittances is furnished that assumes that distributions of τ, for regions comparable in size to GCM grid cells, can be described adequately by gamma distribution functions. While the plane-parallel, homogeneous model underestimates cloud transmittance by about an order of magnitude when 3D variable cloud transmittances are ≲ 0.2 and by ∼20% to 100% otherwise, the stochastic model reduces these biases often by more than 80%.

Multiyear present‐day and 2 × CO2 simulations of monsoon climate over eastern Asia and Japan with a regional climate model nested in a general circulation model

Two continuous 5‐year‐long simulations over eastern Asia and the Japan islands, one for present‐day climate (control) and one for climate under doubled carbon dioxide concentration (2×CO2) are completed with a regional climate model (RegCM) nested, in a one‐way mode, within a general circulation model (GCM). The GCM is run at R15 resolution (4.5×7.5° latitude × longitude), and the RegCM is run at 50‐km grid point spacing. In the control run, both the GCM and the RegCM reproduce the seasonal migration of the westerly jet but produce too strong a monsoonal circulation, which results in a significant overestimate of summer precipitation over the eastern Asian continent. The temporal evolution of the eastern Asia summer monsoon, with steady phases separated by more rapid transitions, is reproduced, but the monsoon rain belt reaches too far north, and the occurrence of tropical storms is underestimated. Regionally averaged surface air temperatures are mostly within 1–3°C of observations. Seasonal precipitation amounts over Japan are within 10–35% of observed ones. The narrow Korea and Japan land masses, which are not captured by the GCM grid, substantially affect the simulated surface precipitation climatology in the RegCM. Under 2×CO2 forcing, warming in the range of 4–11°C is simulated, greater in winter than in summer and increasing toward high latitudes. The strength of the monsoonal circulation increases in 2×CO2 conditions, leading to a general increase in precipitation over all regions by 10–30%. The simulated precipitation change shows significant regional and, within Japan, subregional structure. The Japan and Korea land masses substantially affect the summer precipitation changes, indicating the need to capture them in numerical simulations of climate change. Because of the uncertainties in the control simulations, our 2×CO2 results are intended not to provide climate change scenarios for impact assessments, but only to illustrate the model sensitivity to different forcings. Plans are under way to further test and improve the RegCM performance over the region and to use more recent GCM simulations to drive the RegCM.