High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

Haarsma, Reindert J.; Roberts, Malcolm; Vidale, Pier Luigi; Senior, C. A.; Bellucci, Alessio; Bao, Qing; Chang, Ping; Corti, Susanna; Fučkar, Neven S.; Guémas, Virginie; Hardenberg, Jost von; Hazeleger, Wilco; Kodama, Chihiro; Koenigk, Torben; Leung, L. Ruby; Lu, Jian; Luo, Jie; Mao, Jiafu; Mizielinski, Matthew S.; Mizuta, Ryo; Nobre, Paulo; Satoh, Masaki; Scoccimarro, Enrico; Semmler, Tido; Small, R. Justin; Storch, Jin Song von

doi:10.5194/gmd-9-4185-2016

Cited by 775 publications

(799 citation statements)

References 132 publications

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“…Our study shows that the role of resolution in different GCMs is not necessarily the same and it is therefore interesting and important to explore the role of resolution systematically in multi-model studies. The simulations currently carried out within CMIP6-HighResMIP (Haarsma et al, 2016), e.g. in the PRIMAVERA 1 project, will allow for such studies based on a well-designed ensemble of high-resolution coupled GCMs.…”

Section: Discussionmentioning

confidence: 99%

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Schiemann

Vidale

Shaffrey

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Limited spatial resolution is one of the factors that may hamper applications of global climate models (GCMs), in particular over Europe with its complex coastline and orography. In this study, the representation of European mean and extreme precipitation is evaluated in simulations with an atmospheric GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the mid-latitudes. The continent-wide root-mean-square error in mean precipitation in the 25 km model is about 25 % smaller than in the 135 km model in winter. Clear improvements are also seen in autumn and spring, whereas the model's sensitivity to resolution is very small in summer. Extreme precipitation is evaluated by estimating generalised extreme value distributions (GEVs) of daily precipitation aggregated over river basins whose surface area is greater than 50 000 km 2 . GEV location and scale parameters are measures of the typical magnitude and of the interannual variability of extremes, respectively. Median model biases in both these parameters are around 10 % in summer and around 20 % in the other seasons. For some river basins, however, these biases can be much larger and take values between 50 % and 100 %. Extreme precipitation is better simulated in the 25 km model, especially during autumn when the median GEV parameter biases are more than halved, and in the North European Plains, from the Loire in the west to the Vistula in the east. A sensitivity experiment is conducted showing that these resolution sensitivities in both mean and extreme precipitation are in many areas primarily due to the increase in resolution of the model orography. The findings of this study illustrate the improved capability of a global high-resolution model in simulating European mean and extreme precipitation.

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Section: Discussionmentioning

confidence: 99%

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Schiemann

Vidale

Shaffrey

et al. 2018

Hydrol. Earth Syst. Sci.

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“…Variable-resolution GCMs (VR-GCMs) can overcome these weaknesses of either coarse-resolution GCMs or RCMs and serve as a better tool to quantify the impacts of LAAs in snow. Although GCMs with globally uniform high resolutions (10-30 km) may be an ideal tool to simulate the snowpack and snow-aerosol-radiation interactions, they are not widely applied due to the constraints of computational resources (e.g., Haarsma et al, 2016). Instead, using VRGCMs is a more economic approach and has gained increasing utility in recent years (e.g., Zarzycki et al, 2014a, b;Sakaguchi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

Liu

Lin

et al. 2018

Atmos. Chem. Phys.

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Abstract. The deposition of light-absorbing aerosols (LAAs), such as black carbon (BC) and dust, onto snow cover has been suggested to reduce the snow albedo and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) with a regionally refined highresolution (0.125 • ) grid to quantify the impacts of LAAs in snow in the Rocky Mountain region during the period 1981-2005. We first evaluate the model simulation of LAA concentrations both near the surface and in snow and then investigate the snowpack and runoff changes induced by LAAs in snow. The model simulates similar magnitudes of near-surface atmospheric dust concentrations as observations in the Rocky Mountain region. Although the model underestimates near-surface atmospheric BC concentrations, the model overestimates BC-in-snow concentrations by 35 % on average. The regional mean surface radiative effect (SRE) due to LAAs in snow reaches up to 0.6-1.7 W m −2 in spring, and dust contributes to about 21-42 % of total SRE. Due to positive snow albedo feedbacks induced by the LAA SRE, snow water equivalent is reduced by 2-50 mm and snow cover fraction by 5-20 % in the two regions around the mountains (eastern Snake River Plain and southwestern Wyoming), corresponding to an increase in surface air temperature by 0.9-1.1 • C. During the snow melting period, LAAs accelerate the hydrologic cycle with monthly runoff increases of 0.15-1.00 mm day −1 in April-May and reductions of 0.04-0.18 mm day −1 in June-July in the mountainous regions. Of all the mountainous regions, the Southern Rockies experience the largest reduction of total runoff by 15 % during the later stage of snowmelt (i.e., June and July). Compared to previous studies based on field observations, our estimation of dust-induced SRE is generally 1 order of magnitude smaller in the Southern Rockies, which is ascribed to the omission of larger dust particles (with the diameter > 10 µm) in the model. This calls for the inclusion of larger dust particles in the model to reduce the discrepancies. Overall these results highlight the potentially important role of LAA interactions with snowpack and the subsequent impacts on the hydrologic cycles across the Rocky Mountains.

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“…It is used in Haarsma et al (2013) and within the framework of HighResMIP (Haarsma et al, 2016). It is based on the assumption that SIC is a function of SST.…”

Section: Look-up Table Methodsmentioning

confidence: 99%

Assessing bias-corrections of oceanic surface conditions for atmospheric models

Beaumet¹,

Krinner²,

Déqué³

et al. 2017

Preprint

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Abstract. Future sea-surface temperature and sea-ice concentration from coupled ocean-atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcing for the downscaling of future climate experiment.Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly and a quantile-quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new 5 analog method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiment and some real-case applications using observations. With respect to other previously existing methods for SIC, the analog method is a substantial improvement for the bias correction of future sea-ice concentrations.

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High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

Cited by 775 publications

References 132 publications

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

Assessing bias-corrections of oceanic surface conditions for atmospheric models

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