Bias adjustment of the Chao estimator for the size of a population

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

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Supplementary material to "Multivariate bias adjustment of high-dimensional climate simulations: The Rank Resampling for Distributions and Dependences (R2D2) Bias Correction"

10.5194/hess-2017-747-supplement ◽

2018 ◽

Author(s):

Mathieu Vrac

Keyword(s):

Bias Correction ◽

High Dimensional ◽

Bias Adjustment ◽

Climate Simulations ◽

Supplementary Material

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Identifying robust bias adjustment methods for extreme precipitation in a pseudo-reality setting

10.5194/hess-2020-318 ◽

2020 ◽

Author(s):

Torben Schmith ◽

Peter Thejll ◽

Peter Berg ◽

Fredrik Boberg ◽

Ole Bøssing Christensen ◽

...

Keyword(s):

Time Scale ◽

Extreme Value ◽

Extreme Value Analysis ◽

Climate Factor ◽

Value Analysis ◽

Model Simulations ◽

Bias Adjustment ◽

Return Levels ◽

Hourly Data ◽

Daily Time

Abstract. Severe precipitation events occur rarely and are often localized in space and of short duration; but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of these rare events. These are usually projected using Regional Climate Model (RCM) scenario simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterizations in the RCMs, the simulated present-day climate usually has biases relative to observations. Therefore, the RCM results are often bias-adjusted to match observations. This does, however, not guarantee that bias-adjusted projected results will match future reality better, since the bias may change in a changed climate. In the present work we evaluate different bias adjustment techniques in a changing climate. This is done in an inter-model cross-validation setup, in which each model simulation in turn plays the role of pseudo-reality, against which the remaining model simulations are bias adjusted and validated. The study uses hourly data from present-day and RCP8.5 late 21st century from 19 model simulations from the EURO-CORDEX ensemble at 0.11° resolution, from which fields of selected return levels are calculated for hourly and daily time scale. The bias adjustment techniques applied to the return levels are based on extreme value analysis and include analytical quantile-matching together with the simpler climate factor approach. Generally, return levels can be improved by bias adjustment, compared to obtaining them from raw scenarios. The performance of the different methods depends of the time scale considered. On hourly time scale, the climate factor approach performs better than the quantile-matching approaches. On daily time scale, the superior approach is to simply deduce future return levels from observations and the second best choice is using the quantile-mapping approaches. These results are found in all European sub-regions considered.

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Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea

10.5194/egusphere-egu21-13557 ◽

2021 ◽

Author(s):

Andrea Lira Loarca ◽

Giovanni Besio

Keyword(s):

Bias Correction ◽

Temporal Variability ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Wave Climate ◽

Mapping Method ◽

Climate Projections ◽

Bias Adjustment ◽

Distribution Mapping

Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability.&#160;Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.&#160; While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker & Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.&#160;References:<ul><li>Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.</li> <li>Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.</li> <li>Parker, K. & Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65</li> </ul>

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