Model Grid Box Research Articles

Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment

Abstract. A significant share of aviation's climate impact is due to persistent contrails. Thus, avoiding the creation of contrails that exert a warming impact is a crucial step in approaching the goal of sustainable air transportation. For this purpose, a reliable forecast of when and where persistent contrails are expected to form is needed (i.e. a reliable prediction of ice supersaturation). With such a forecast at hand, it would be possible to plan aircraft routes on which the formation of persistent contrails can be avoided. One problem on the way to these forecasts is the current systematic underestimation of the frequency and degree of ice supersaturation at cruise altitudes in numerical weather prediction due to the practice of “saturation adjustment”. In this common parameterisation, the air inside cirrus clouds is assumed to be exactly at ice saturation, while measurement studies have found cirrus clouds to be quite often out of equilibrium. In this study, we propose a new ice-cloud scheme that overcomes saturation adjustment by explicitly modelling the decay of the in-cloud humidity after nucleation, thereby allowing for both in-cloud super- and subsaturation. To achieve this, we introduce the in-cloud humidity as a new prognostic variable and derive the humidity distribution in newly generated cloud parts from a stochastic box model that divides a model grid box into a large number of air parcels and treats them individually. The new scheme is then tested against a parameterisation that uses saturation adjustment, where the stochastic box model serves as a benchmark. It is shown that saturation adjustment underestimates humidity, both shortly after nucleation, when the actual cloud is still highly supersaturated, and also in aged cirrus if the temperature keeps decreasing, as the actual cloud remains in a slightly supersaturated state in this case. The new parameterisation, on the other hand, closely follows the behaviour of the stochastic box model in any considered case. The improvement in comparison with saturation adjustment is largest if slow updraughts occur in relatively clean air in models with a high spatial and temporal resolution. We conclude that our parameterisation is promising but needs further testing in more realistic frameworks.

Numerical simulation and evaluation of global ultrafine particle concentrations at the Earth's surface

Abstract. A new global dataset of annually averaged ultrafine particle (UFP) concentrations at the Earth's surface for the years 2015–2017 has been developed through numerical simulations using the ECHAM/MESSy Atmospheric Chemistry model (EMAC). We present total and size-resolved concentrations along with their interannual variability. Size distributions of emitted particles from the contributing source sectors have been derived based on literature reports. The model results of UFP concentrations are evaluated using particle size distribution and particle number concentration measurements from available datasets and the literature. While we obtain reasonable agreement between the model results and observations (logarithmic-scale correlation of r=0.76 for non-remote, polluted regions), the highest values of observed, street-level UFP concentrations are systematically underestimated, whereas in rural environments close to urban areas the model generally overestimates observed UFP concentrations. As the relatively coarse global model does not resolve concentration gradients in urban centres and industrial UFP hotspots, high-resolution data of anthropogenic emissions are used to account for such differences in each model grid box, obtaining UFP concentrations with unprecedented 0.1∘×0.1∘ horizontal resolution at the Earth's surface. This observation-guided downscaling further improves the agreement with observations, leading to an increase in the logarithmic-scale correlation between observed and simulated UFP concentrations to r=0.84 in polluted environments (and 0.95 in all regions), a decrease in the root mean squared logarithmic error (from 0.57 to 0.43), and removal of discrepancies associated with air quality and population density gradients within the model grid boxes. The model results are made publicly available for studies on public health and other impacts of atmospheric UFPs, as well as for intercomparison with other regional and global models and datasets.

Representing the Subgrid Surface Heterogeneity of Precipitation in a General Circulation Model

AbstractPrecipitation is highly variable on spatial scales smaller than a typical general circulation model (GCM) grid box. Neglecting this subgrid‐scale variability can impact the simulation of the underlying land surface and its coupling with the atmosphere. In this study we present a scheme to stochastically disaggregate precipitation from an atmospheric grid to an underlying mosaic of surface tiles, using an assumed probability distribution of subgrid precipitation derived from observations. Unlike previous GCM‐based schemes, our approach includes a treatment of memory to allow persistence of subgrid features. In single column model experiments, we demonstrate the ability of the scheme to reproduce observed precipitation statistics at the spatial scale of the surface tiles. The root mean square error of hourly spatial standard deviation of precipitation is reduced from 1.90 to 0.96 mm hr−1 when disaggregation is applied. The scheme increases the spatial standard deviations of surface heat fluxes, soil moisture and temperature, and we show that incorporating memory amplifies these increases by a factor of 2–4. We also document increases in mean precipitation runoff and the Bowen ratio.

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

Accounting for the three‐dimensional nature of mountain waves: Parametrising partial critical‐level filtering

Gravity waves generated by mountains are multiscale and three‐dimensional. Current orographic gravity wave drag parametrisation schemes assume that the waves are two‐dimensional, varying only in the vertical and along one horizontal direction. These schemes, therefore, do not represent the process of partial critical‐level filtering, whereby a portion of the wave spectrum is saturated where the winds parallel to the wavevectors become small. This results in an unrealistic vertical distribution of the momentum flux and forcing of the waves on the mean flow. In this paper, a method of accounting for partial critical‐level filtering in an orographic gravity wave drag parametrisation using the full spectrum of realistic topography is presented. This is achieved through binning of the expression for linear hydrostatic surface stresses, computed using Fourier transforms of the subgrid orographic heights within model grid boxes, into wavevector directions. The parametrisation is compared with idealised nonlinear simulations of flow over complex topography and is shown to perform well as the number of wavevector direction bins is increased. Implementation of the scheme into the Met Office Unified Model is tested using short‐range 5‐day forecasts. As is found from idealised simulations, the binned scheme leads to less forcing in the troposphere and increased forcing in the stratosphere within the model. The binned scheme is shown to alleviate biases in the upper stratosphere, between 45 and 65 km, as well as having significant local effects in the troposphere.

Physical characteristics of frozen hydrometeors inferred with parameter estimation

Abstract. Frozen hydrometeors are found in a huge range of shapes and sizes, with variability on much smaller scales than those of typical model grid boxes or satellite fields of view. Neither models nor in situ measurements can fully describe this variability, so assumptions have to be made in applications including atmospheric modelling and radiative transfer. In this work, parameter estimation has been used to optimise six different assumptions relevant to frozen hydrometeors in passive microwave radiative transfer. This covers cloud overlap, convective water content and particle size distribution (PSD), the shapes of large-scale snow and convective snow, and an initial exploration of the ice cloud representation (particle shape and PSD combined). These parameters were simultaneously adjusted to find the best fit between simulations from the European Centre for Medium-range Weather Forecasts (ECMWF) assimilation system and near-global microwave observations covering the frequency range 19 to 190 GHz. The choices for the cloud overlap and the convective particle shape were particularly well constrained (or identifiable), and there was even constraint on the cloud ice PSD. The practical output is a set of improved assumptions to be used in version 13.0 of the Radiative Transfer for TOVS microwave scattering package (RTTOV-SCATT), taking into account newly available particle shapes such as aggregates and hail, as well as additional PSD options. The parameter estimation explored the full parameter space using an efficient assumption of linearly additive perturbations. This helped illustrate issues such as multiple minima in the cost function, and non-Gaussian errors, that would make it hard to implement the same approach in a standard data assimilation system for weather forecasting. Nevertheless, as modelling systems grow more complex, parameter estimation is likely to be a necessary part of the development process.

Balance conditions in variational data assimilation for a high‐resolution forecast model

AbstractThis paper explores the role of balance relationships for background‐error covariance modelling as the model's grid box decreases to convective scales. Data assimilation (DA) analyses are examined from a simplified convective‐scale model and DA system (called ABC‐DA) with a grid box size of 1.5 km in a 2D 540 km (longitude), 15 km (height) domain. The DA experiments are performed with background‐error covariance matrices () modelled and calibrated by switching on/off linear balance (LB) and hydrostatic balance (HB), and by observing a subset of the ABC variables, namely (meridional wind), (scaled density, a pressure‐like variable), and (buoyancy, a temperature‐like variable). Calibration data are sourced from two methods of generating proxies of forecast errors. One uses forecasts from different latitude slices of a 3D parent model (here called the latitude slice method), and the other uses sets of differences between forecasts of different lengths but valid at the same time (the National Meteorological Center method). Root‐mean‐squared errors computed over the domain from identical twin DA experiments suggest that there is no combination of LB/HB switches that give the best analysis for all model quantities. However it is frequently found that the ‐matrices modelled with both LB and HB do perform the best. A clearer picture emerges when the errors are examined at different spatial scales. In particular it is shown that switching on HB in mostly has a neutral/positive effect on the DA accuracy at ‘large’ scales, and switching off the HB has a neutral/positive effect at ‘small’ scales. The division between ‘large’ and ‘small’ scales is between 10 and 100 km. Furthermore, one hour forecast‐error correlations computed between control parameters find that correlations are small at large scales when balances are enforced, and at small scales when balances are not enforced (ideal control parameters have zero cross‐correlations). This points the way to modelling with scale‐dependent balances.

The impact of inhomogeneous emissions and topography on ozone photochemistry in the vicinity of Hong Kong Island

Abstract. Global and regional chemical transport models of the atmosphere are based on the assumption that chemical species are completely mixed within each model grid box. However, in reality, these species are often segregated due to localized sources and the influence of topography. In order to investigate the degree to which the rates of chemical reactions between two reactive species are reduced due to the possible segregation of species within the convective boundary layer, we perform large-eddy simulations (LESs) in the mountainous region of Hong Kong Island. We adopt a simple chemical scheme with 15 primary and secondary chemical species, including ozone and its precursors. We calculate the segregation intensity due to inhomogeneity in the surface emissions of primary pollutants and due to turbulent motions related to topography. We show that the inhomogeneity in the emissions increases the segregation intensity by a factor of 2–5 relative to a case in which the emissions are assumed to be uniformly distributed. Topography has an important effect on the segregation locally, but this influence is relatively limited when considering the spatial domain as a whole. In the particular setting of our model, segregation reduces the ozone formation by 8 %–12 % compared to the case with complete mixing, implying that the coarse-resolution models may overestimate the surface ozone when ignoring the segregation effect.

A Bimodal Diagnostic Cloud Fraction Parameterization. Part I: Motivating Analysis and Scheme Description

AbstractCloud fraction parameterizations are beneficial to regional, convection-permitting numerical weather prediction. For its operational regional midlatitude forecasts, the Met Office uses a diagnostic cloud fraction scheme that relies on a unimodal, symmetric subgrid saturation-departure distribution. This scheme has been shown before to underestimate cloud cover and hence an empirically based bias correction is used operationally to improve performance. This first of a series of two papers proposes a new diagnostic cloud scheme as a more physically based alternative to the operational bias correction. The new cloud scheme identifies entrainment zones associated with strong temperature inversions. For model grid boxes located in this entrainment zone, collocated moist and dry Gaussian modes are used to represent the subgrid conditions. The mean and width of the Gaussian modes, inferred from the turbulent characteristics, are then used to diagnose cloud water content and cloud fraction. It is shown that the new scheme diagnoses enhanced cloud cover for a given gridbox mean humidity, similar to the current operational approach. It does so, however, in a physically meaningful way. Using observed aircraft data and ground-based retrievals over the southern Great Plains in the United States, it is shown that the new scheme improves the relation between cloud fraction, relative humidity, and liquid water content. An emergent property of the scheme is its ability to infer skewed and bimodal distributions from the large-scale state that qualitatively compare well against observations. A detailed evaluation and resolution sensitivity study will follow in Part II.

Evaluating Empirical Lightning Parameterizations in Global Atmospheric Models

AbstractLightning fundamentally influences atmospheric chemistry as it is the largest natural source of NOx in the upper troposphere. To include lightning, global models rely on experimental parameterizations. We use global atmospheric model diagnostics (2.0 × 2.5° horizontal resolution, 38°S–38°N, for the years 2012–2013) to evaluate three lightning parameterizations that are based on: (1) cloud top height, (2) vertical ice‐mass flux, and (3) meteorological variables including aerosols. We apply conversions between convective/cloud area and model grid box area in these calculations to compare each parameterization's spatial, temporal, and spectral characteristics with an observed lightning climatology from the Tropical Rainfall Measuring Mission Lightning Imaging Sensor. Domain‐wide median values of parameterized lightning differ from observations by a factor of 0.6–3.1. The three parameterizations depict spatial (Pearson correlation (r = 0.80–0.84) and temporal patterns (r = 0.79–0.95) that match observations well. The parameterized median land‐ocean lightning contrast ranges from a factor of 2.82 (using environmental factors with aerosols) to 596.0 (using cloud top height), compared to 22.9 in observations. One‐to‐one comparisons suggest that lightning parameterizations can explain approximately 55%–64% of the observed monthly lightning variance, depending on the parameterization. The highest correlation and minimum error bias statistics (e.g., Logarithmic Mean Bias, LMB) are found for the lightning parameterization that employs environmental factors with aerosols (r = 0.80, LMB = +0.002), whereas comparable correlation and generally higher error bias are found using vertical ice fluxes (r = 0.80, LMB = +0.27) and cloud top height (r = 0.74, LMB = −0.08). We show how the biases in lightning estimates are sensitive to uncertainties in the convective area and suggest ways to minimize bias overall.

The decomposition of cloud–aerosol forcing in the UK Earth System Model (UKESM1)

Abstract. Climate variability in the North Atlantic influences processes such as hurricane activity and droughts. Global model simulations have identified aerosol–cloud interactions (ACIs) as an important driver of sea surface temperature variability via surface aerosol forcing. However, ACIs are a major cause of uncertainty in climate forcing; therefore, caution is needed in interpreting the results from coarse-resolution, highly parameterized global models. Here, we separate and quantify the components of the surface shortwave effective radiative forcing (ERF) due to aerosol in the atmosphere-only version of the UK Earth System Model (UKESM1) and evaluate the cloud properties and their radiative effects against observations. We focus on a northern region of the North Atlantic (NA) where stratocumulus clouds dominate (denoted the northern NA region) and a southern region where trade cumulus and broken stratocumulus dominate (southern NA region). Aerosol forcing was diagnosed using a pair of simulations in which the meteorology is approximately fixed via nudging to analysis; one simulation has pre-industrial (PI) and one has present-day (PD) aerosol emissions. This model does not include aerosol effects within the convective parameterization (but aerosol does affect the clouds associated with detrainment) and so it should be noted that the representation of aerosol forcing for convection is incomplete. Contributions to the surface ERF from changes in cloud fraction (fc), in-cloud liquid water path (LWPic) and droplet number concentration (Nd) were quantified. Over the northern NA region, increases in Nd and LWPic dominate the forcing. This is likely because the already-high fc there reduces the chances of further large increases in fc and allows cloud brightening to act over a larger region. Over the southern NA region, increases in fc dominate due to the suppression of rain by the additional aerosols. Aerosol-driven increases in macrophysical cloud properties (LWPic and fc) will rely on the response of the boundary layer parameterization, along with input from the cloud microphysics scheme, which are highly uncertain processes. Model grid boxes with low-altitude clouds present in both the PI and PD dominate the forcing in both regions. In the northern NA, the brightening of completely overcast low cloud scenes (100 % cloud cover, likely stratocumulus) contributes the most, whereas in the southern NA the creation of clouds with fc of around 20 % from clear skies in the PI was the largest single contributor, suggesting that trade cumulus clouds are created in response to increases in aerosol. The creation of near-overcast clouds was also important there. The correct spatial pattern, coverage and properties of clouds are important for determining the magnitude of aerosol forcing, so we also assess the realism of the modelled PD clouds against satellite observations. We find that the model reproduces the spatial pattern of all the observed cloud variables well but that there are biases. The shortwave top-of-the-atmosphere (SWTOA) flux is overestimated by 5.8 % in the northern NA region and 1.7 % in the southern NA, which we attribute mainly to positive biases in low-altitude fc. Nd is too low by −20.6 % in the northern NA and too high by 21.5 % in the southern NA but does not contribute greatly to the main SWTOA biases. Cloudy-sky liquid water path mainly shows biases north of Scandinavia that reach between 50 % and 100 % and dominate the SWTOA bias in that region. The large contribution to aerosol forcing in the UKESM1 model from highly uncertain macrophysical adjustments suggests that further targeted observations are needed to assess rain formation processes, how they depend on aerosols and the model response to precipitation in order to reduce uncertainty in climate projections.

Observed and Simulated Precipitation over Northeastern North America: How Do Daily and Subdaily Extremes Scale in Space and Time?

AbstractThe characterization of extreme precipitation at fine spatiotemporal scale represents a paramount challenge in hydroclimate sciences due to large uncertainties affecting the precipitation estimation from existing datasets. Comparing the spatiotemporal structure of precipitation extremes estimated from different datasets thus represents an essential step for climate model evaluation, as it provides insight into a model’s ability to simulate atmospheric processes occurring at different scales. This study compares the probability distributions and the annual and diurnal cycles of occurrence of daily and subdaily precipitation annual maxima (AM) estimated over northeastern North America from five observed and simulated datasets: meteorological station series, the bias-corrected (CRT) satellite CMORPH, version 1.0, and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), version 2, gridded datasets, various Canadian RCM, version 5 (CRCM5), simulations, and a 13-yr convection-permitting WRF, version 3.4.1, simulation. ERA-Interim-driven CRCM5 and WRF simulations well reproduced subdaily extreme quantiles and the AM annual and diurnal cycles observed at stations, while CMORPH and MSWEP displayed good performance only for daily and longer extreme statistics. The spatiotemporal statistical structure of precipitation extremes is then assessed considering the variation of AM quantiles across various spatial scales and durations. The results suggest that a two-parameter analytical relationship well describes the AM spatiotemporal structure at the regional scale, allowing us to approximate some crucial properties of point precipitation extremes from gridded datasets. Averaging the estimates from various members of the initial-condition CRCM5 Large Ensemble (CRCM5-LE) also made it possible to reduce the sampling errors and robustly estimate the AM spatiotemporal structure at the local scale of each model grid box.

Projected Changes in the Probability Distributions, Seasonality, and Spatiotemporal Scaling of Daily and Subdaily Extreme Precipitation Simulated by a 50‐Member Ensemble Over Northeastern North America

AbstractGlobal warming is expected to produce modifications in the intensity, as well as in the seasonality and spatiotemporal structure of extreme precipitation. In the present study, the temporal evolution of simulated daily and subdaily precipitation extremes was analyzed to assess how they respond to climate warming over different time horizons. Pooling series from the recent 50‐member Canadian Regional Climate Model v5 Large Ensemble, the probability distributions, date and time of occurrences, and spatiotemporal structure of simulated Annual Maxima (AM) precipitation were analyzed at various spatial scales and for durations between 1 hr and 3 days. In agreement with previous studies, the results underline the large increases in AM precipitation quantiles, especially for the shortest durations and for the more extreme events (i.e., longest return periods), and modifications in their spatiotemporal scaling properties and annual and diurnal cycles. For instance, subdaily AM extremes are expected to occur later in the evening, while, no matter the duration, the extremes are expected to occur over a wider period of the year in future climate. Finally, the analysis of projected AM probability distributions showed that heavy‐tail Generalized Extreme Value (GEV) distributions will most likely be observed in the future climate, with some model grid boxes experiencing a significant increase of GEV shape parameters. These results may have major consequences in terms of the occurrence and possible impacts of the most extreme precipitation events.

Verification of land-atmosphere coupling in forecast models, reanalyses and land surface models using flux site observations.

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring.

Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF‐RTFDDA

AbstractIn this study, a lightning data assimilation (LDA) scheme was developed and implemented in the National Center for Atmospheric Research Weather Research and Forecasting‐Real‐Time Four‐Dimensional Data Assimilation system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid boxes, column‐integrated graupel mass is first calculated using an observation‐based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical qg vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved qg fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short‐term (0–6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short‐term lightning and precipitation forecasts by improving the model simulation of the qg fields, updrafts, cold pool, and front locations. The improvements were most notable in the first 2 h, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0–2 h) applications.

Regime dependence of ice cloud heterogeneity – a convective life‐cycle effect?

Cloud condensate varies on scales smaller than those typically resolved by global weather and climate models. In order to accurately predict the radiative and microphysical process rates representative of the entire model grid box, the effect of the subgrid‐scale heterogeneity of cloud must be taken into account. In this study, observed ice water content retrieved from A‐Train satellite observations is used to explore how spatial ice condensate variability, characterized by the fractional standard deviation (FSD, the standard deviation divided by the mean), varies with cloud regime. FSD is generally lower for overcast cloud scenes, but an additional predictor based on convective activity is needed to capture the high FSD associated with more turbulent clouds and reproduce the observed latitudinal and height variations of FSD. Convective clouds that extend only a few kilometres above the freezing level are likely to be smaller at an earlier stage in their life cycle, and actively growing with a higher FSD. In contrast, more mature clouds reaching the tropopause are larger and generally have a lower variability and smaller FSD. To capture this life‐cycle effect, a new parametrization is tested which uses the ratio of a model's convectively detrained condensate to the existing cloud condensate mass as a proxy for the cloud's convective life stage to highlight areas with enhanced condensate variability. The parametrization is scale‐adaptive, situation‐dependent and captures seasonally varying global patterns and the zonal mean vertical structure of observed ice condensate variability well. Ground‐based observations obtained from five Atmospheric Radiation Measurement sites provide independent confirmation that the parametrization satisfactorily captures condensate variability in high‐altitude ice clouds.

An efficient model approach for very high resolution orographic precipitation

One major challenge in climate‐related issues is the lack of accurate high‐resolution precipitation data on the part of observations and climate model simulations, imposing profound constraints on various impact studies, for instance in the fields of hydrology, glaciology, agronomy and hazard assessment. Here, we present a physical model approach for orographic precipitation based on linear theory and combined with a stochastic weather generator which takes an intermediate position between computationally expensive high‐resolution regional climate models and purely statistical downscaling techniques. The model is applied to a mountainous region in central Germany and run over a decade in a 0.005° resolution (≈500 m). The resulting precipitation fields are spatially and temporally compared with station data from a very dense meteorological network and with a 6 km regional reanalysis.The spatial correlation between local station data and corresponding model grid boxes is between 0.72 and 0.80. The presented linear model also agrees well with the observed spatial pattern of day‐to‐day variability, mean intensity, weak and extreme precipitation events. It is close to the performance of the high‐resolution regional reanalysis COSMO‐REA6, yet computationally far more efficient. Combined with the weather generator, the linear model matches the statistical properties of the station data almost perfectly and offers a consistent precipitation dataset at 0.005° resolution. The model can be applied to any larger‐scale model or observational dataset, including climate change projections.

Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (Ni) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and Ni are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (Dcs), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to Dcs. Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

Robust Identification of Local Biogeophysical Effects of Land-Cover Change in a Global Climate Model

Abstract Land-cover change (LCC) happens locally. However, in almost all simulation studies assessing biogeophysical climate effects of LCC, local effects (due to alterations in a model grid box) are mingled with nonlocal effects (due to changes in wide-ranging climate circulation). This study presents a method to robustly identify local effects by changing land surface properties in selected “LCC boxes” (where local plus nonlocal effects are present), while leaving others unchanged (where only nonlocal effects are present). While this study focuses on the climate effects of LCC, the method presented here is applicable to any land surface process that is acting locally but is capable of influencing wide-ranging climate when applied on a larger scale. Concerning LCC, the method is more widely applicable than methods used in earlier studies. The study illustrates the possibility of validating simulated local effects by comparison to observations on a global scale and contrasts the underlying mechanisms of local and nonlocal effects. In the MPI-ESM, the change in background climate induced by extensive deforestation is not strong enough to influence the local effects substantially, at least as long as sea surface temperatures are not affected. Accordingly, the local effects within a grid box are largely independent of the number of LCC boxes in the isolation approach.

Estimates of Global Surface Hydrology and Heat Fluxes from the Community Land Model (CLM4.5) with Four Atmospheric Forcing Datasets

Abstract Global land surface hydrology and heat fluxes can be estimated by running a land surface model (LSM) driven by the atmospheric forcing dataset. Previous multimodel studies focused on the impact of different LSMs on model results. Here the sensitivity of the Community Land Model, version 4.5 (CLM4.5), results to the atmospheric forcing dataset is documented. Together with the model default global forcing dataset (CRU–NCEP, hereafter CRUNCEP), three newly developed, reanalysis-based, near-surface meteorological datasets (i.e., MERRA, CFSR, and ERA-Interim) with the precipitation adjusted by the Global Precipitation Climatology Project monthly product were used to drive CLM4.5. All four simulations were run at 0.5° × 0.5° grids from 1979 to 2009 with the identical initialization. The simulated monthly surface hydrology variables, fluxes, and the forcing datasets were then evaluated against various observation-based datasets (soil moisture, runoff, snow depth and water equivalent, and flux tower measurements). To partially avoid the mismatch between model gridbox values and point measurements, three approaches were taken. The model simulations based on three newly constructed forcing datasets are overall better than the simulation from CRUNCEP, in particular for soil moisture and snow quantities. The ensemble mean from the CLM4.5 simulations using the four forcing datasets is generally superior to individual simulations, and the ensemble mean latent and sensible heat fluxes over global land (60°S–90°N) are 42.8 and 40.3 W m−2, respectively. The differences in both precipitation and other atmospheric forcing variables (e.g., air temperature and downward solar radiation) contribute to the differences in simulated results. The datasets are available from the authors for further evaluation and for various applications.