. Statistical bias correction is commonly applied within climate impact modelling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. Bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean. Besides the general idea and technical details of the ISI-MIP method, we show and discuss the potential and limitations of the applied bias correction. In particular, while the trend and the long-term mean are well represented, limitations with regards to the adjustment of the variability persist which may affect, e.g. small scale features or extremes.

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A trend-preserving bias correction – The ISI-MIP approach

Regional climate modeling using convection-permitting models (CPMs; horizontal grid spacing 10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. In this study, we aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014RG000475

A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges.

Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of t...

https://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-14-00754.1

Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations

50 years of Computational Wind Engineering: Past, present and future

The ability of general circulation models (GCMs) to correctly simulate precipitation is usually assessed by comparing simulated mean precipitation with observed climatologies. However, to what extent the skill in simulating average precipitation indicates how well the models represent temporal changes is unclear. A direct assessment of the latter is hampered by the fact that freely evolving climate simulations for past periods are not set up to reproduce the specific evolution of internal atmospheric variability. Therefore, model-to-real-world comparisons of time series of daily, monthly, or annual precipitation are not meaningful. Here, for the first time, the authors quantify GCM skill in simulating precipitation variability using simulations in which the temporal evolution of the large-scale atmospheric state closely matches that of the real world. This is achieved by nudging the atmospheric states in the ECHAM5 GCM, but crucially not the precipitation field itself, toward the 40-yr European Ce...

/pdf/skill-correction-and-downscaling-of-gcm-simulated-3ucicnaaj8.pdf

Skill, Correction, and Downscaling of GCM-Simulated Precipitation

Joint modelling of obstacle induced and mesoscale changes—Current limits and challenges

Including trees in the numerical simulations of the wind flow in urban areas: Should we care?

Urban areas have well documented effects on climate, such as the urban heat island effect, reduction of wind speeds, enhanced turbulence and boundary layer heights, and changes in cloud cover and precipitation. This PhD examines the impact of the urban surface on the major agglomeration of London on local and regional climate by means of the numerical mesoscale model METRAS (Schlunzen 1988) coupled for the first time with the sophisticated urban canopy scheme BEP, developed by Martilli et al. (2002). The robustness of the new model is demonstrated through a series of simulations and sensitivity studies for an idealised urban domain. The model is then configured for the London region, and evaluated using data from a range of meteorological monitoring sites. Implementation of the urban canopy scheme results in a marked improvement in model performance. Under ideal meteorological conditions, peak urban heat island intensities of up to 2.5 K are found during night time hours, with the timing and magnitude of the peak showing good agreement with previous experimental studies for London. The new model is then used to investigate how growth of the Greater London urban area affects the urban heat island intensity. The results show that the relative fractions of urban land cover and of vegetation within the urban area have important implications for the near surface temperature, diurnal temperature range, wind speed and urban heat island intensity. The results also suggest that extensive future growth of the London urban area has the potential to increase temperatures, with significant increases for both daytime and night time. The specific forms of urban development, such as densification and spatial expansion, have an impact on these fields. These results have important implications for the design of cities and the management of urban climate.

David Grawe

Papers

Skill, Correction, and Downscaling of GCM-Simulated Precipitation

Joint modelling of obstacle induced and mesoscale changes—Current limits and challenges

Including trees in the numerical simulations of the wind flow in urban areas: Should we care?

Modelling the impact of urbanisation on regional climate in the Greater London Area

Large eddy simulation of shading effects on NO2 and O3 concentrations within an idealised street canyon