Abstract. Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

Abstract. The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

The model performance of a regional-scale meteorology-chemistry model (NHM-Chem) has been evaluated for the consistent predictions of the chemical, physical, and optical properties of aerosols. These properties are essentially important for the accurate assessment of air quality and health hazards, contamination of land and ocean ecosystems, and regional climate changes due to aerosol-cloud-radiation interaction processes. Currently, three optional methods are available: the five-category non-equilibrium method, the three-category non-equilibrium method, and the bulk equilibrium method. These three methods are suitable for the predictions of regional climate, air quality, and operational forecasts, respectively. In this paper, the simulated aerosol chemical, physical, and optical properties and their consistency were evaluated using various observation data in East Asia. The simulated mass, size, and deposition of SO 4 2− and NH 4 + agreed well with the observations, whereas those of NO 3 − , sea salt, and dust needed improvement. The simulated surface mass concentration (PM 10 and PM 2.5) and spherical extinction coefficient agreed well with the observations. The simulated aerosol optical thickness (AOT) and dust extinction coefficient were significantly underestimated.

Abstract. To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we developed a 5 km resolution regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM–SMAP) with an output interval of 1 h, forced by the Japanese 55-year reanalysis (JRA-55). We used in situ data to evaluate NHM–SMAP in the GrIS during the 2011–2014 mass balance years. We investigated two options for the lower boundary conditions of the atmosphere: an offline configuration using snow, firn, and ice albedo, surface temperature data from JRA-55, and an online configuration using values from SMAP. The online configuration improved model performance in simulating 2 m air temperature, suggesting that the surface analysis provided by JRA-55 is inadequate for the GrIS and that SMAP results can better simulate physical conditions of snow/firn/ice. It also reproduced the measured features of the GrIS climate, diurnal variations, and even a strong mesoscale wind event. In particular, it successfully reproduced the temporal evolution of the GrIS surface melt area extent as well as the record melt event around 12 July 2012, at which time the simulated melt area extent reached 92.4 %. Sensitivity tests showed that the choice of calculation schemes for vertical water movement in snow and firn has an effect as great as 200 Gt year−1 in the GrIS-wide accumulated SMB estimates; a scheme based on the Richards equation provided the best performance.

Abstract. The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass–dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; and coalescence, riming, and aggregation. To evaluate the model's performance, a 2-D large-eddy simulation of a cumulonimbus was conducted, and the life cycle of a cumulonimbus typically observed in nature was successfully reproduced. The mass–dimension and velocity–dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128 per cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

Abstract. A regional-scale meteorology – chemistry model (NHM-Chem v1.0) has been developed. Three options for aerosol representations are currently available: the 5-category non-equilibrium (Aitken, soot-free accumulation, accumulation internally mixed with soot, dust, and sea-salt), 3-category non-equilibrium (Aitken, accumulation, and coarse), and bulk equilibrium (submicron, dust, and sea-salt) methods. These three methods are suitable for the predictions of regional climate, air quality, and operational forecasts, respectively. The total CPU times of the 5-category and 3-category methods were 91 % and 44 % greater than that of the bulk method, respectively. The bulk equilibrium method was shown to be eligible for operational forecast purposes, namely, the surface mass concentrations of air pollutants such as O3, mineral dust, and PM2.5. The 3-category method was shown to be eligible for air quality simulations, namely, mass concentrations and depositions. However, the internal mixture assumption of soot/soot-free and dust/sea-salt particles in the 3-category method resulted in significant differences in the size distribution and hygroscopicity of the particles. Even though the 3-category method was not designed to simulate aerosol-cloud-radiation interaction processes, its performance in terms of bulk properties, such as aerosol optical thickness (AOT) and cloud condensation nuclei (CCN), was acceptable. However, some specific parameters exhibited significant differences or systematic errors. The unrealistic dust/sea-salt complete mixture of the 3-category method induced significant errors in the prediction of mineral dust containing CCN. The overestimation of soot hygroscopicity by the 3-category method induced errors in BC-containing CCN, BC deposition, and absorbing AOT (AAOT). The difference in AAOT was less pronounced because the overestimation of the absorption enhancement was compensated by the overestimation of hygroscopic growth and the consequent loss due to in-cloud scavenging.

We performed scanning tunneling microscopy and scanning tunneling spectroscopy ͑STS͒ measurements on underdoped Bi2212 crystals with doping levels p ϳ 0.11, ϳ0.13, and ϳ0.14 to examine the nature of the nondispersive 4a ϫ 4a charge order in the superconducting state at T Ӷ T c . The charge order appears conspicuously within the pairing gap, and low doping tends to favor the charge order. We point out the possibility that the 4a ϫ 4a charge order will be dynamical in itself, and pinned down over regions with effective pinning centers. The pinned 4a ϫ 4a charge order is closely related to the spatially inhomogeneous pairing gap structure, which has often been reported in STS measurements on high-T c cuprates.

We performed low-bias STM measurements on underdoped Bi2212 crystals, and confirmed that a two-dimensional (2D) superstructure with a periodicity of four lattice constants (4a) is formed within the Cu-O plane at T < Tc. This 4a×4a superstructure, oriented along the Cu-O bonding direction, is nondispersive and more intense in lightly doped samples with a zero temperature pseudogap (ZTPG) than in samples with a d-wave gap. The nondispersive 4a×4a superstructure was clearly observed within the ZTPG or d-wave gap, while it tended to fade out outside the gaps. The present results provide a useful test for various models proposed for an electronic order hidden in the underdoped region of high-Tc cuprates.KEYWORDS: STM/STS, pseudogap, electronic charge ordering, superstructure, cuprateRecently STM/STS studies on the pseudogap state of Bi 2 Sr 2 CaCu 2 O 8+δ (Bi2212) at T > T c have revealed a nondispersive two-dimensional (2D) superstructure with a periodicity of about four lattice constants (∼4a×4a superstructure) in the map of energy-resolved differential tunneling conductance dI/dV , proportional to the local density of states (LDOS).1) The nondispersive 4a×4a structure, electronic in origin, was also reported in the LDOS maps taken on the zero temperature pseudogap (ZTPG) regions of lightly doped Ca 2−x Na x CuO 2 Cl 2 (Na-CCOC) and Bi2212 samples.2,3) A similar spatial structure was first observed around the vortex cores of Bi2212 exhibiting a pseudogap-like V-shaped spectrum with no coherence peak.4) Such 2D spatial structures have attracted much attention because they can be a possible electronic order hidden in the pseudogap state.From the LDOS maps taken for the superconducting (SC) state of Bi2212, Hoffman et al. and McElroy et al. reported a strongly dispersive 2D superstructure. 5,6)This superstructure has been successfully explained in terms of a quasiparticle scattering interference. Furthermore, Howald et al. found a nondispersive ∼4a×4a superstructure with anisotropy in addition to the weakly dispersive one, and claimed that the nondispersive ∼4a×4a superstructure will be due to the so-called stripe order and coexist with superconductivity.7−10) However, the nondispersive ∼4a×4a superstructure was not confirmed in later LDOS measurements on Bi2212 at T < T c . 1,5)This inconsistency mainly originates from the difficulty in distinguishing the nondispersive ∼4a×4a superstructure from the weakly dispersive ∼4a×4a one resulting from quasiparticle scattering interference. Since the dispersive features in the LDOS are reduced by integrating the LDOS over a wide range of energy, it is desirable to investigate the nondispersive features of the LDOS by using a conventional STM technique which actually maps the LDOS integrated between E F and E F +eV s , where V s is the sample bias voltage. To clarify whether the nondispersive 4a×4a electronic superstructure persists in the SC state (T < T c ) will promote our understanding of the physics in the underdoped regionIn the present study, we performed low-bias STM me...