Model Grid Box Research Articles

Impact of parametric uncertainties on the present-day climate and on the anthropogenic aerosol effect

Abstract. Clouds constitute a large uncertainty in global climate modeling and climate change projections as many clouds are smaller than the size of a model grid box. Some processes, such as the rates of rain and snow formation that have a large impact on climate, cannot be observed. The uncertain parameters in the representation of these processes are therefore adjusted in order to achieve radiation balance. Here we systematically investigate the impact of key tunable parameters within the convective and stratiform cloud schemes and of the ice cloud optical properties on the present-day climate in terms of clouds, radiation and precipitation. The total anthropogenic aerosol effect between pre-industrial and present-day times amounts to −1.00 W m−2 obtained as an average over all simulations as compared to −1.02 W m−2 from those simulations where the global annual mean top-of-the atmosphere radiation balance is within ±1 W m−2. Thus tuning of the present-day climate does not seem to have an influence on the total anthropogenic aerosol effect. The parametric uncertainty regarding the above mentioned cloud parameters has an uncertainty range of 25% between the minimum and maximum value when taking all simulations into account. It is reduced to 11% when only the simulations with a balanced top-of-the atmosphere radiation are considered.

A flow‐dependent estimate for the sampling error

A pragmatic method is presented for obtaining a flow‐dependent estimate of the sampling error which arises when comparing line measurements (whose horizontal extent is quasi one‐dimensional (1‐D)) with model grid box averages (whose horizontal extent is 2‐D). The errors are computed from synthetic data which share important statistical properties with the observed field and which can be generated from the information obtained from the 1‐D measurements. Flow dependency is obtained through a statistical measure (or “score”) which is linked to the sampling error through a quasi‐empirical relationship that is obtained from the synthetic data. The success of the method particularly depends on how well the employed score differentiates situations with different error magnitudes. A score is proposed which is based on local estimates of the structure function maximum (i.e., the maximum over neighborhoods of different spatial extent) and therefore exploits information from different space scales. Since the proposed method is heuristic (in the sense that the adequacy of the synthetic data cannot be inferred from first principles), a large part of this paper is devoted to demonstrating the robustness of this method in the context of data from scanning satellite instruments for which 1‐D estimates can be compared directly with the corresponding 2‐D averages.

Mid-21st century climate and weather extremes in Cyprus as projected by six regional climate models

We present climate change projections and apply indices of weather extremes for the Mediterranean island Cyprus using data from regional climate model (RCM) simulations driven by the IPCC A1B scenario within the ENSEMBLES project. Daily time-series of temperature and precipitation were used from six RCMs for a reference period 1976–2000 and for 2026–2050 (‘future‘) for representative locations, applying a performance selection among neighboring model grid-boxes. The annual average temperatures of the model ensemble have a ±1.5°C bias from the observations (negative for maximum and positive for minimum temperature), and the models underestimate annual precipitation totals by 4–17%. The climatological annual cycles for the observations fall within the 1σ range of the 6-model average, highlighting the strength of using multi-model output. We obtain reasonable agreement between models and observations for the temperature-related indices of extremes for the recent past, while the comparison is less good for the precipitation-related extremes. For the future, the RCM ensemble shows significant warming of 1°C in winter to 2°C in the summer for both maximum and minimum temperatures. Rainfall is projected to decrease by 2–8%, although this is not statistically significant. Our results indicate the shift of the mean climate to a warmer state, with a relatively strong increase in the warm extremes. The precipitation frequency is projected to decrease at the inland Nicosia and at the coastal Limassol, while the mountainous Saittas could experience more frequent 5–15 mm/day rainfall. In future, very hot days are expected to increase by more than 2 weeks/year and tropical nights by 1 month/year. The annual number of consecutive dry days shows a statistically significant increase (of 9 days) in Limassol. These projected changes of the Cyprus climate may adversely affect ecosystems and the economy of the island and emphasize the need for adaptation strategies.

Postprocessing of simulated precipitation for impact research in West Africa. Part II: A weather generator for daily data

Data from global and regional climate models refer to grid cells and, hence, are basically different from station data. This particularly holds for variables with enhanced spatio-temporal variability like precipitation. On the other hand, many applications like for instance hydrological models require atmospheric data with the statistical characteristics of station data. Here, we present a dynamical-statistical tool to construct virtual station data based on regional climate model output for tropical West Africa. This weather generator (WEGE) incorporates daily gridded rainfall from the model, an orographic term and a stochastic term, accounting for the chaotic spatial distribution of local rain events within a model grid box. In addition, the simulated probability density function of daily precipitation is adjusted to available station data in Benin. It is also assured that the generated data are still consistent with other model parameters like cloudiness and atmospheric circulation. The resulting virtual station data are in excellent agreement with various observed characteristics which are not explicitly addressed by the WEGE algorithm. This holds for the mean daily rainfall intensity and variability, the relative number of rainless days and the scaling of precipitation in time. The data set has already been used successfully for various climate impact studies in Benin.

Carbon source/sink information provided by column CO<sub>2</sub> measurements from the Orbiting Carbon Observatory

Abstract. We quantify how well column-integrated CO2 measurements from the Orbiting Carbon Observatory (OCO) should be able to constrain surface CO2 fluxes, given the presence of various error sources. We use variational data assimilation to optimize weekly fluxes at a 2°×5° resolution (lat/lon) using simulated data averaged across each model grid box overflight (typically every ~33 s). Grid-scale simulations of this sort have been carried out before for OCO using simplified assumptions for the measurement error. Here, we more accurately describe the OCO measurements in two ways. First, we use new estimates of the single-sounding retrieval uncertainty and averaging kernel, both computed as a function of surface type, solar zenith angle, aerosol optical depth, and pointing mode (nadir vs. glint). Second, we collapse the information content of all valid retrievals from each grid box crossing into an equivalent multi-sounding measurement uncertainty, factoring in both time/space error correlations and data rejection due to clouds and thick aerosols. Finally, we examine the impact of three types of systematic errors: measurement biases due to aerosols, transport errors, and mistuning errors caused by assuming incorrect statistics. When only random measurement errors are considered, both nadir- and glint-mode data give error reductions over the land of ~45% for the weekly fluxes, and ~65% for seasonal fluxes. Systematic errors reduce both the magnitude and spatial extent of these improvements by about a factor of two, however. Improvements nearly as large are achieved over the ocean using glint-mode data, but are degraded even more by the systematic errors. Our ability to identify and remove systematic errors in both the column retrievals and atmospheric assimilations will thus be critical for maximizing the usefulness of the OCO data.

Representing glaciers in a regional climate model

A glacier parameterization scheme has been developed and implemented into the regional climate model REMO. The new scheme interactively simulates the mass balance as well as changes of the areal extent of glaciers on a subgrid scale. The temporal evolution and the general magnitude of the simulated glacier mass balance in the European Alps are in good accordance with observations for the period 1958–1980, but the strong mass loss towards the end of the twentieth century is systematically underestimated. The simulated decrease of glacier area in the Alps between 1958 and 2003 ranges from −17.1 to −23.6%. The results indicate that observed glacier mass balances can be approximately reproduced within a regional climate model based on simplified concepts of glacier-climate interaction. However, realistic results can only be achieved by explicitly accounting for the subgrid variability of atmospheric parameters within a climate model grid box.

Applying climate model precipitation scenarios for urban hydrological assessment: A case study in Kalmar City, Sweden

There is growing interest in the impact of climate change on urban hydrological processes. Such assessment may be based on the precipitation output from climate models. To date, the model resolution in both time and space has been too low for proper assessment, but at least in time the resolution of available model output is approaching urban scales. In this paper, 30-min precipitation from a model grid box covering Kalmar City, Sweden, is compared with high-resolution (tipping-bucket) observations from a gauge in Kalmar. The model is found to overestimate the frequency of low rainfall intensities, and therefore the total volume, but reasonably well reproduce the highest intensities. Adapting climate model data to urban drainage applications can be done in several ways but a popular way is the so-called Delta Change (DC) method. In this method, relative changes in rainfall characteristics estimated from climate model output are transferred to an observed rainfall time series, generally by multiplicative factors. In this paper, a version of the method is proposed in which these DC factors (DCFs) are related to the rainfall intensity level. This is achieved by calculating changes in the probability distribution of rainfall intensities and modelling the DCFs as a function of percentile. Applying this method in Kalmar indicated that in summer and autumn, high intensities will increase by 20–60% by year 2100, whereas low intensities remain stable or decrease. In winter and spring, generally all intensity levels increase similarly. The results were transferred to the observed time series by varying the volume of the tipping bucket to reflect the estimated intensity changes on a 30-min time scale. In an evaluation of the transformed data at a higher 5-min resolution, effects on the intensity distribution as well as single precipitation events were demonstrated. In particular, qualitatively different changes in peak intensity and total volume are attainable, which is required in light of expected future changes of the precipitation process and a step forward as compared with simpler DC approaches. Using the DC transformed data as input in urban drainage simulations for a catchment in Kalmar indicated an increase of the number of surface floods by 20–45% during this century.

Tripleclouds: An Efficient Method for Representing Horizontal Cloud Inhomogeneity in 1D Radiation Schemes by Using Three Regions at Each Height

Abstract Radiation schemes in general circulation models currently make a number of simplifications when accounting for clouds, one of the most important being the removal of horizontal inhomogeneity. A new scheme is presented that attempts to account for the neglected inhomogeneity by using two regions of cloud in each vertical level of the model as opposed to one. One of these regions is used to represent the optically thinner cloud in the level, and the other represents the optically thicker cloud. So, along with the clear-sky region, the scheme has three regions in each model level and is referred to as “Tripleclouds.” In addition, the scheme has the capability to represent arbitrary vertical overlap between the three regions in pairs of adjacent levels. This scheme is implemented in the Edwards–Slingo radiation code and tested on 250 h of data from 12 different days. The data are derived from cloud retrievals using radar, lidar, and a microwave radiometer at Chilbolton, southern United Kingdom. When the data are grouped into periods equivalent in size to general circulation model grid boxes, the shortwave plane-parallel albedo bias is found to be 8%, while the corresponding bias is found to be less than 1% using Tripleclouds. Similar results are found for the longwave biases. Tripleclouds is then compared to a more conventional method of accounting for inhomogeneity that multiplies optical depths by a constant scaling factor, and Tripleclouds is seen to improve on this method both in terms of top-of-atmosphere radiative flux biases and internal heating rates.

A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics

AbstractA stochastic parameterization scheme for deep convection is described, suitable for use in both climate and NWP models. Theoretical arguments and the results of cloud-resolving models are discussed in order to motivate the form of the scheme. In the deterministic limit, it tends to a spectrum of entraining/detraining plumes and is similar to other current parameterizations. The stochastic variability describes the local fluctuations about a large-scale equilibrium state. Plumes are drawn at random from a probability distribution function (PDF) that defines the chance of finding a plume of given cloud-base mass flux within each model grid box. The normalization of the PDF is given by the ensemble-mean mass flux, and this is computed with a CAPE closure method. The characteristics of each plume produced are determined using an adaptation of the plume model from the Kain–Fritsch parameterization. Initial tests in the single-column version of the Unified Model verify that the scheme is effective in producing the desired distributions of convective variability without adversely affecting the mean state.

A Climatology of Heat Waves from a Multimillennial Simulation

Abstract A 10 000-yr unforced simulation with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 coupled global climatic model has been used to investigate the occurrence of heat waves over the globe. Results are presented for both seasonal (summer mean) and daily heat waves. Geographical distributions of the occurrence rates of these heat waves are shown for various magnitudes of surface temperature anomalies. The heat waves have specific geographical preferences with regions where relatively frequent, intense, and long-lasting heat waves occur. Time series over all 10 000 yr of the heat waves for the selected model grid boxes illustrate the differing temporal variabilities at these locations, as well as identifying the occurrences of extreme heat waves. To this end, the observed European heat wave of 2003 was simulated remarkably well in its overall characteristics; it occurs once in this simulation. Heat waves for various continental locations are shown to occur as isolated spatial and temporal events, and not as part of larger-scale systems over continental-size domains, suggesting stochastic forcing as a contributor to the initiation of the heat waves. Regional plots of selected heat waves at monthly intervals illustrated the considerable spatial variability, progression, and variation in the intensity of the heat waves. Comparison of year-long daily surface temperature anomalies for heat-wave years for simulated and observed conditions at individual model grid boxes indicated substantial agreement, while spatial plots permitted the progress of a short-term heat wave over the United States to be followed. Multidecadal time series plots of intense heat waves also showed basic similarities between the simulation and observations, despite the brevity of the latter. The simulated time series suggest that more extreme heat waves than currently are observed, owing to the brevity of the observations, may be a possibility as a consequence solely of natural variability. An examination of the physical processes associated with a heat wave showed mutually consistent climatic relationships, such that a heat wave was associated with reduced rainfall and consequently reduced soil moisture content, evaporation, and cloud cover, and increased insolation at the surface. These combined changes created the surface temperature increase intrinsic to the heat wave. All heat waves examined for different regions experienced negative rainfall anomalies prior to a heat wave. The cause of these rainfall anomalies was not readily apparent. While an ENSO influence on heat waves is shown to exist in the simulation, not all ENSO events produce heat waves, suggesting that stochastic influences may determine when a major heat wave occurs in conjunction with these events. The limitations of the adequacy of the model ENSO may, however, have had an influence in this regard.

Assessment of water exchange between a discharge region and the open sea – A comparison of different methodological concepts

Two different methods of estimating the water exchange through the Baltic coastal region of Laxemar have been used, consisting of particle trajectories and passive tracers. Water is traced from and to a small discharge region near the coast. The discharge material in this region is treated as zero-dimensional particles or tracers with neutral buoyancy. The real discharge material could be a leakage of radio-nuclides through the sea floor from an underground repository of nuclear waste. Water exchange rates between the discharge region and the model domain are estimated using both forward and backward trajectories as well as passive tracers. The Lagrangian trajectories can account for the time evolution of the water exchange while the tracers give one average age per model grid box. Water exchange times such as residence time, age and transient times have been calculated with trajectories but only the average age (AvA) for tracers. The trajectory calculations provide a more detailed time evolution than the tracers. On the other hand the tracers are integrated “on-line” simultaneously in the sea circulation model with the same time step while the Lagrangian trajectories are integrated “off-line” from the stored model velocities with its inherent temporal resolution, presently 1 h. The sub-grid turbulence is parameterised as the Laplacian diffusion for the passive tracers and with an extra stochastic velocity for trajectories. The importance of the parameterised sub-grid turbulence for the trajectories is estimated to give an extra diffusion of the same order as the Laplacian diffusion by comparing the Lagrangian dispersions with and without parameterisation. The results of the different methods are similar but depend on the chosen diffusivity coefficient with a slightly higher correlation between trajectories and tracers when integrated with a lower diffusivity coefficient.

Cloudnet

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

Three‐dimensional volume‐averaged soil moisture transport model with a scalable parameterization of subgrid topographic variability

Subgrid variability of subsurface moisture flux transport is strongly influenced by the local variation of topographic attributes, such as elevation, slope, and curvature. A three‐dimensional volume‐averaged soil moisture transport (VAST) model is developed to incorporate these effects using the volume‐averaged Richards equation. The small‐perturbation approach is used to decompose the equation into mean and fluctuation, which are then averaged over the model grid box. This formulation explicitly incorporates the variability of moisture flux due to subgrid variation of topographic attributes. The model is independent of scale, but the parameters need to be estimated at the model scale. It is demonstrated that the flux contribution from the subgrid variability can be comparable to that of mean flux, particularly under drier moisture conditions. This formulation can be substituted for subsurface moisture transport schemes in most existing land surface models.

Statistical Parameterization of Heterogeneous Oceanic Convection

Abstract A statistical convective adjustment scheme is proposed that attempts to account for the effects of mesoscale and submesoscale variability of temperature and salinity typically observed in the oceanic convective regions. Temperature and salinity in each model grid box are defined in terms of their mean, variance, and mutual correlations. Subgrid-scale instabilities lead to partial mixing between different layers in the water column. This allows for a smooth transition between the only two states (convection on and convection off) allowed in standard convective adjustment schemes. The advantage of the statistical parameterization is that possible instabilities associated with the sharp transition between the two states, which are known to occasionally affect the large-scale model solution, are eliminated. The procedure also predicts the generation of correlations between temperature and salinity and the presence of convectively induced upgradient fluxes that have been obtained in numerical simulations of heterogeneous convection and that cannot be represented by standard convective adjustment schemes.

Predicting cloud droplet number concentration in Community Atmosphere Model (CAM)‐Oslo

A new framework for calculating cloud droplet number, including a continuity equation for cloud droplet number concentration, has been developed and implemented in an extended version of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 2.0.1 (CAM‐2.0.1). The new continuity equation for cloud droplet number concentration consists of a nucleation term and several microphysical sink terms. The nucleation term is calculated on the basis of a parameterization of activation of cloud condensation nuclei (CCN). A subgrid distribution of vertical velocity is used to calculate the range of supersaturations determining the activation within each model grid box. The aerosol types considered in this study are sea salt, sulfate, black carbon, organic carbon, and mineral dust. The horizontal and vertical distributions of sulfate and carbonaceous aerosols are calculated on the basis of AEROCOM (http://nansen.ipsl.jussieu.fr/AEROCOM) sources. Microphysical sink terms for cloud droplets are obtained from a prognostic cloud water scheme, assuming a direct proportionality between loss of cloud water and loss of cloud droplets. On the basis of the framework described above, the cloud droplet number concentration and cloud droplet effective radius are determined. Cloud microphysical and radiative properties compare reasonably well with satellite observations, giving an indication of the soundness of our approach. Our method of fitting the aerosol size distribution with lognormal modes has been evaluated and was found not to introduce systematic errors in our approach. The aerosol indirect effect estimated in the new framework ranges from −0.13 W/m2 to −0.72 W/m2, which is significantly smaller than in most other comparable studies. This is largely due to the introduction of microphysical sinks for cloud droplets and a cloud droplet activation scheme which accounts for the so‐called competition effect among CCN. As we are not allowing aerosol effects on cloud microphysics and radiation to feed back on the model meteorology, our estimates of the aerosol indirect effect do not include changes in relative humidity and cloud cover.

Using dynamical downscaling to close the gap between global change scenarios and local permafrost dynamics

Even though we can estimate the zonation of present-day permafrost from deep-soil temperatures obtained from global coupled atmosphere-ocean general circulation models (GCMs) by accounting for heat conduction in the frozen soil, it is impossible to explicitly resolve soil properties, vegetation cover and ice contents in great details. On the local scale, descriptions of the heterogeneous soil structure in the Arctic exist only for limited areas. Semi-empirical approaches, e.g. based on the Stefan [Stefan, J., 1891. Über die Theorie der Eisbildung, insbesondere über Eisbildung im Polarmeere. Ann. Phys. 42, 269–286] formula, give a more realistic depiction of permafrost temperatures and active layer thicknesses while at the same time avoiding problems inevitably associated with the explicit treatment of soil freezing and thawing. The coarse resolution of contemporary GCMs models that prevents a realistic description of soil characteristics, vegetation, and topography within a model grid box is the major limitation for use in permafrost modelling. We propose to narrow the gap between typical GCMs on one hand and local permafrost models on the other by introducing as an intermediate step a high resolution regional climate model (RCM) to downscale surface climate characteristics to a scale comparable to that of a detailed permafrost model. Forcing the permafrost model with RCM output results in a more realistic depiction of present-day mean annual ground temperature and active layer depth, in particular in mountainous regions. By using global climate change scenarios as driving fields, one can obtain permafrost dynamics in high temporal resolution on the order of years. For the 21st century under the IPCC SRES scenarios A2 and B2, we find an increase of mean annual ground temperature by up to 6 K and of active layer depth by up to 2 m within the East Siberian transect. According to these simulations, a significant part of the transect will suffer from permafrost degradation by the end of the century.

Parameterization of the thermal impacts of sub-grid orography on numerical modeling of the surface energy budget over East Asia

A parameterization scheme for the thermal effects of subgrid scale orography is incorporated into a regional climate model (developed at Nanjing University) and its impact on modeling of the surface energy budget over East Asia is evaluated. This scheme includes the effect of terrain slope and orientation on the computation of solar and infrared radiation fluxes at the surface, as well as the surface sensible and latent heat fluxes. Calculations show that subgrid terrain parameters alter the diurnal cycle and horizontal distributions of surface energy budget components. This effect becomes more significant with increased terrain slope, especially in winter. Due to the inclusion of the subgrid topography, the surface area of a model grid box changes over complex terrain areas. Numerical experiments, with and without the subgrid scale topography scheme, show that the parameterization scheme of subgrid scale topography modifies the distribution of the surface energy budget and surface temperature around the Tibetan Plateau. Comparisons with observations indicate that the subgrid topography scheme, implemented in the climate model, reproduces the observed detailed spatial temperature structures at the eastern edge of the Tibetan Plateau and reduces the tendency to overestimate precipitation along the southern coastal areas of China in summer.

Defining dry/wet spells for point observations, observed area averages, and regional climate model gridboxes in Europe

A new method for optimising threshold values of dry/wet spells is evaluated. A set of indices is used to find the best threshold giving good correspondence between the frequency of dry/wet spells in Hadley Centre regional model (HadRM3) output, reference observations with predetermined thresholds, and area-averaged observations. The analyses focus on selected model gridboxes in 3 different European climate regimes (Sweden, UK, Italy), where station data are available from several locations. In addition, a pan-European analysis using the European Climate Assessment (ECA) dataset is carried out. Generally, there is good agreement between point observations and the corresponding area average using the common thresholds of 0.1 or 1.0 mm with observational data. Applying the optimal thresholds on the model output is important, as it typically results in substantially better agreement between the simulated and observed series of dry/wet days. The fitted optimal pan-European dry/wet threshold is (1) 0.47 or 0.15 mm, depending on model version, for the observed point data threshold of 0.1 mm, and (2) 1.2 or 0.56 mm, depending on model version, for the threshold of 1.0 mm.

Some Characteristics of Model-Predicted Precipitation during the Summer Monsoon over India

AbstractFor the summer monsoon seasons of 1995, 1996, and 1997 the day-1 to day-4 forecasts of precipitation from both the National Centre for Medium Range Weather Forecasting (NCMRWF) and the European Centre for Medium-Range Forecasts (ECMWF) models reproduce the main features of the observed precipitation pattern when averaged over the whole season. On average, less than 30% of all rain gauge stations in India report rain on a given day during the monsoon season. The number of observed rainy days increases to 41% after spatial averaging over ECMWF model grid boxes and to 50% after spatial averaging over NCMRWF model grid boxes. The NCMRWF model forecasts have 10%–15% more rainy days, mostly in the light or moderate precipitation categories, when compared with the spatial average of observed values. Seasonal accumulated values of all of India’s average precipitation show a slight increase with the forecast lead time for the NCMRWF model and a small decrease for the ECMWF model. The weekly accumulated values of forecast precipitation from both models, averaged over the whole of India, are in good phase relationship (∼0.9 in most cases) with the observed value for forecasts with a lead time up to day 4. Values of statistical parameters, based on the frequency of occurrence in various classes, indicate that the NCMRWF model has some skill in predicting precipitation over India during the summer monsoon. The NCMRWF model forecasts have higher trend correlation with the observed precipitation over India than do the ECMWF model forecasts. The mean error in precipitation is, however, much less in the ECMWF model forecasts, and the spatial distribution of seasonal average medium-range forecasts of ECMWF is closer to that observed along the west coast mountain ridgeline.

Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations

In this study it is shown that the availability of a very high resolution dataset of land surface characteristics leads to the improvement of a surface runoff parameterization scheme. The improved parameterization scheme was developed for application in global and regional climate models and is a further development of the Arno scheme that is widely used in climate models. Here, surface runoff is computed as infiltration excess from a "bucket" type reservoir which takes the subgrid variability of soil saturation within a model gridbox into account. Instead of prescribing a distribution of subgrid scale soil water capacities as in the original Arno scheme, the array of high resolution soil water capacities taken from a global 1 km dataset of land surface parameters is used to obtain individual fractional saturation curves for each model gridbox. From each saturation curve, the three parameters (a shape parameter describing the shape of the subgrid distribution of soil water capacities, subgrid minimum and maximum soil water capacity) required in the modified formulation of the scheme are derived via optimization. As in the original Arno scheme applied in the ECHAM general circulation model and the REMO regional climate model, topography variations will influence the distribution of saturated subgrid areas within a model gridbox. At most gridboxes the net effect of these changes is such that more runoff is produced for high soil water contents and less runoff for low soil water contents. A validation of simulated discharge computed with a simplified land surface scheme applied to reanalysis data of the European Centre for Medium-Range Weather Forecasts and a hydrological discharge model has shown that these changes lead to a more realistic simulation of the annual cycle of discharge for several catchments. In particular this could be shown for the Yangtze Kiang and Amur catchments where adequate input data are available.