Systematic Errors in Weather and Climate Models: Nature, Origins, and Ways Forward

Zadra, Ayrton; Williams, Keith D.; Frassoni, Ariane; Rixen, M.; Adames, Ángel F.; Berner, Judith; Bouyssel, F.; Casati, Bar Bara; Christensen, Hilary; Ek, Michael; Flato, Greg; Huang, Yi; Judt, Falko; Lin, Hai; Maloney, Eric D.; Merryfield, William J.; Niekerk, Annelize van; Rackow, Thomas; SAITO, K; Wedi, Nils; Yadav, Priyanka

doi:10.1175/bams-d-17-0287.1

Cited by 38 publications

(31 citation statements)

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“…In general, the stochastic FDDS coupling scheme introduced in this article is of relevance to other models with high ocean-to-atmosphere resolution ratio, including regular CMIP models. The scheme falls into the category of process-oriented schemes, which are currently experiencing a lot of interest in the community (Leutbecher et al, 2017;Zadra et al, 2017). The discrete approach presented here does not rely on a continuous (finite-element) representation of the ocean surface and is therefore generally applicable to other models.…”

Section: Discussionmentioning

confidence: 99%

Flow‐dependent stochastic coupling for climate models with high ocean‐to‐atmosphere resolution ratio

Rackow

Juricke

2019

Quart J Royal Meteoro Soc

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This study introduces a new flow‐dependent distribution sampling (FDDS) scheme for air–sea coupling. The FDDS scheme is implemented in a climate model and used to improve the simulated mean and variability of atmospheric and oceanic surface fields and thus air–sea fluxes. Most coupled circulation models use higher resolutions in the sea ice and ocean compared to the atmospheric model component, thereby explicitly simulating the atmospheric subgrid‐scale at the interface. However, the commonly applied averaging of surface fields and air–sea fluxes tends to smooth fine‐scale structures, such as oceanic fronts. The stochastic FDDS scheme samples the resolved spatial ocean (and sea ice) subgrid distribution that is usually not visible to a coarser‐resolution atmospheric model. Randomly drawn nodal ocean values are passed to the corresponding atmospheric boxes for the calculation of surface fluxes, aiming to enhance surface flux variability. The resulting surface field perturbations of the FDDS scheme are based on resolved dynamics, displaying pronounced seasonality with realistic magnitude. The AWI Climate Model is used to test the scheme on interannual time‐scales. Our set‐up features a high ocean‐to‐atmosphere resolution ratio in the Tropics, with grid‐point ratios of about 60:1. Compared to the default deterministic averaging, changes are largest in the Tropics leading to an improved spatial distribution of precipitation with bias reductions of up to 50%. Enhanced sea‐surface temperature variability in boreal winter further improves the seasonal phase locking of temperature anomalies associated with the El Niño–Southern Oscillation. Mean 2m temperature, sea ice thickness and concentration react with a contrasting dipole pattern between hemispheres but a joint increase of monthly and interannual variability. This first approach to implement a flow‐dependent stochastic coupling scheme shows considerable benefits for simulations of global climate, and various extensions and modifications of the scheme are possible.

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Section: Discussionmentioning

confidence: 99%

Flow‐dependent stochastic coupling for climate models with high ocean‐to‐atmosphere resolution ratio

Rackow

Juricke

2019

Quart J Royal Meteoro Soc

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“…However, R s reanalyses contain substantial biases (Slater, 2016;Wang et al, 2015;Wu et al, 2015) due to the uncertainties in clouds and aerosols in the reanalyses (Fujiwara et al, 2017). Complete knowledge of the interactions among clouds, aerosols and radiation, and the related parameterizations in the climate models or reanalyses can help to reduce the uncertainty in predicting potential future climate changes, especially at regional scales (Loew et al, 2016;Zadra et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Determining Factors of Monthly to Decadal Variability in Surface Solar Radiation in China: Evidences From Current Reanalyses

Feng

Wang

2019

JGR Atmospheres

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Clouds and aerosols play essential roles in regulating surface incident solar radiation (Rs). It has been suggested that the increased aerosol loading over China is a key factor for the decadal variability in Rs and can explain the bias in its trend from reanalyses because the reanalyses do not include the interannual variability of aerosols. In this study, we compare the Rs derived from sunshine duration at 2,400 weather stations in China and that from five reanalyses from 1980 to 2014. The determining factors for the biases in the mean values and trends of Rs from the reanalyses are examined, with the help of Rs and the cloud fraction (CF), from satellite and 2,400 weather stations. Our results show that all reanalyses overestimate the multiyear Rs by 24.10–40.00 W/m2 due to their underestimations of CF, which is more obvious in southern China. The biases in the simulated CF in the reanalyses can explain the biases in Rs by 55–41%, and the bias in clear‐sky surface solar radiation (Rc), which is primarily due to biases in aerosol loading, can explain 32–9% of the bias in Rs. The errors in the trend of the simulated CF can explain the errors in the Rs trends in the reanalyses by 73–12%, and the trend errors in the Rc can explain 43–30% of the trend error in Rs. Our study suggests that more work is needed to improve the simulation of aerosols, clouds, and aerosol‐cloud‐radiation interactions in the reanalyses.

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“…current eddy parameterizations do not seem to capture vertical eddy fluxes to full degree (Hewitt et al, 2017), local refinement to explicitly resolve regions of high eddy activity is thus a promising approach to tackle deep-ocean biases (Zadra et al, 2017).…”

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Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations with AWI-CM1.0

Rackow¹,

Sein²,

Semmler³

et al. 2018

Preprint

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Abstract. CMIP5 models show substantial biases in the deep ocean that are larger than the level of natural variability and the response to enhanced greenhouse gas concentrations. Here we analyse the influence of horizontal resolution in a hierarchy of five multi-resolution simulations with the AWI Climate Model (AWI-CM), which employs a sea ice-ocean model component formulated on unstructured meshes. The ocean grid sizes considered range from a nominal resolution of ∼1° (CMIP5-type) up to locally eddy-resolving. We show that increasing ocean resolution locally to resolve ocean eddies leads to a major reduction in deep ocean biases. A detailed diagnosis of the simulations allows to identify the origins of the biases. We find that two major sources at the surface are responsible for the deep bias in the Atlantic Ocean. Furthermore, the Southern Ocean density structure is equally improved with locally explicitly resolved eddies compared to parameterized eddies. Part of the bias reduction can be traced back towards improved surface biases over outcropping regions, which are in contact with deeper ocean layers along isopycnal surfaces. Our prototype simulations provide guidance for the optimal choice of ocean grids for AWI-CM to be used in the final runs for phase 6 of the 'Coupled Model Intercomparison Project' (CMIP6) and for the related flagship simulations in the 'High Resolution Model Intercomparison Project' (HighResMIP). Quite remarkably, retaining resolution only in areas of high eddy activity along with excellent scalability characteristics of the unstructured-mesh sea ice-ocean model enables us to perform the multi-centennial climate simulations needed in a CMIP context at (locally) eddy-resolving resolution with a throughput of 5–6 simulated years per day.

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Systematic Errors in Weather and Climate Models: Nature, Origins, and Ways Forward

Abstract: What: Hundreds of scientists involved in the development and evaluation of weather and climate models held an international workshop to discuss the nature and causes of systematic model errors across time scales.

Cited by 38 publications

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Flow‐dependent stochastic coupling for climate models with high ocean‐to‐atmosphere resolution ratio

Flow‐dependent stochastic coupling for climate models with high ocean‐to‐atmosphere resolution ratio

Determining Factors of Monthly to Decadal Variability in Surface Solar Radiation in China: Evidences From Current Reanalyses

Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations with AWI-CM1.0

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