scholarly journals A novel bias correction methodology for climate impact simulations

2015 ◽  
Vol 6 (2) ◽  
pp. 1999-2042 ◽  
Author(s):  
S. Sippel ◽  
F. E. L. Otto ◽  
M. Forkel ◽  
M. R. Allen ◽  
B. P. Guillod ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Terrestrial Ecosystem ◽  
Climate Impact ◽  
Correlation Structure ◽  
Terrestrial Biosphere ◽  
Terrestrial Ecosystem Model ◽  
Multivariate Correlation ◽  
Impact Simulations

Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes.

scholarly journals A novel bias correction methodology for climate impact simulations

2016 ◽  
Vol 7 (1) ◽  
pp. 71-88 ◽  
Author(s):  
S. Sippel ◽  
F. E. L. Otto ◽  
M. Forkel ◽  
M. R. Allen ◽  
B. P. Guillod ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Terrestrial Ecosystem ◽  
Climate Impact ◽  
Correlation Structure ◽  
Terrestrial Biosphere ◽  
Terrestrial Ecosystem Model ◽  
Multivariate Correlation ◽  
Impact Simulations

Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes.

scholarly journals Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin

2009 ◽  
Vol 6 (4) ◽  
pp. 5377-5413 ◽  
Author(s):  
W. Terink ◽  
R. T. W. L. Hurkmans ◽  
P. J. J. F. Torfs ◽  
R. Uijlenhoet
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Correction Method ◽  
Climate Impact ◽  
Reanalysis Data ◽  
Infiltration Capacity ◽  
Rhine Basin ◽  
The Mean ◽  
Precipitation And Temperature

Abstract. In many climate impact studies hydrological models are forced with meteorological forcing data without an attempt to assess the quality of these forcing data. The objective of this study is to compare downscaled ERA15 (ECMWF-reanalysis data) precipitation and temperature with observed precipitation and temperature and apply a bias correction to these forcing variables. The bias-corrected precipitation and temperature data will be used in another study as input for the Variable Infiltration Capacity (VIC) model. Observations were available for 134 sub-basins throughout the Rhine basin at a temporal resolution of one day from the International Commission for the Hydrology of the Rhine basin (CHR). Precipitation is corrected by fitting the mean and coefficient of variation (CV) of the observations. Temperature is corrected by fitting the mean and standard deviation of the observations. It seems that the uncorrected ERA15 is too warm and too wet for most of the Rhine basin. The bias correction leads to satisfactory results, precipitation and temperature differences decreased significantly. Corrections were largest during summer for both precipitation and temperature, and for September and October for precipitation only. Besides the statistics the correction method was intended to correct for, it is also found to improve the correlations for the fraction of wet days and lag-1 autocorrelations between ERA15 and the observations.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

2018 ◽  
Vol 22 (6) ◽  
pp. 3175-3196 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Grid Cell ◽  
Dependence Structure ◽  
Historical Period ◽  
Temporal Properties ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Site Dependence

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell  ×  number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

scholarly journals A new terrestrial biosphere model with coupled carbon, nitrogen, and phosphorus cycles (QUINCY v1.0; revision 1772)

2019 ◽  
Author(s):  
Tea Thum ◽  
Silvia Caldararu ◽  
Jan Engel ◽  
Melanie Kern ◽  
Marleen Pallandt ◽  
...  
Keyword(s):  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Ecosystem Dynamics ◽  
Soil Conditions ◽  
Monitoring Networks ◽  
Nitrogen And Phosphorus ◽  
Element Cycling ◽  
Terrestrial Biosphere ◽  
Ecosystem Monitoring ◽  
Terrestrial Ecosystem Model

Abstract. The dynamics of terrestrial ecosystems are shaped by the coupled cycles of carbon, nitrogen and phosphorus, and strongly depend on the availability of water and energy. These interactions shape future terrestrial biosphere responses to global change. Many process-based models of the terrestrial biosphere have been gradually extended from considering carbon-water interactions to also including nitrogen, and later, phosphorus dynamics. This evolutionary model development has hindered full integration of these biogeochemical cycles and the feedbacks amongst them. Here we present a new terrestrial ecosystem model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which is formulated around a consistent representation of element cycling in terrestrial ecosystems. This new model includes i) a representation of plant growth which separates source (e.g. photosynthesis) and sink (growth rate of individual tissues, constrained by nutrients, temperature, and water availability) processes; ii) the acclimation of many ecophysiological processes to meteorological conditions and/or nutrient availabilities; iii) an explicit representation of vertical soil processes to separate litter and soil organic matter dynamics; iv) a range of new diagnostics (leaf chlorophyll content; 13C, 14C, and 15N isotope tracers) to allow for a more in-depth model evaluation. We present the model structure and provide an assessment of its performance against a range of observations from global-scale ecosystem monitoring networks. We demonstrate that the framework is capable of consistently simulating ecosystem dynamics across a large gradient in climate and soil conditions, as well as across different plant functional types. To aid this understanding we provide an assessment of the model's sensitivity to its parameterisation and the associated uncertainty.

scholarly journals Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

2018 ◽  
Vol 22 (4) ◽  
pp. 2163-2185 ◽  
Author(s):  
Stefan Liersch ◽  
Julia Tecklenburg ◽  
Henning Rust ◽  
Andreas Dobler ◽  
Madlen Fischer ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Model Performance ◽  
Climate Impact ◽  
Hydrological Models ◽  
Blue Nile ◽  
Projected Change ◽  
Model Ensembles

Abstract. Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970–1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q20 and Q80. On the other hand, bias-corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and characteristics and the effect of bias correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.

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