scholarly journals Is time a variable like the others in multivariate statistical downscaling and bias correction?

2021 ◽  
Vol 12 (4) ◽  
pp. 1253-1273
Author(s):  
Yoann Robin ◽  
Mathieu Vrac
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Synthetic Data ◽  
The Other ◽  
Strong Increase ◽  
Temporal Properties

Abstract. Bias correction and statistical downscaling are now regularly applied to climate simulations to make then more usable for impact models and studies. Over the last few years, various methods were developed to account for multivariate – inter-site or inter-variable – properties in addition to more usual univariate ones. Among such methods, temporal properties are either neglected or specifically accounted for, i.e. differently from the other properties. In this study, we propose a new multivariate approach called “time-shifted multivariate bias correction” (TSMBC), which aims to correct the temporal dependency in addition to the other marginal and multivariate aspects. TSMBC relies on considering the initial variables at various times (i.e. lags) as additional variables to be corrected. Hence, temporal dependencies (e.g. auto-correlations) to be corrected are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a vector auto-regressive (VAR) process. In a second evaluation, we work in a “perfect model” context where a regional climate model (RCM) plays the role of the (pseudo-)observations, and where its forcing global climate model (GCM) is the model to be downscaled or bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing the number of lags too much does not necessarily improve the temporal properties, and an overly strong increase in the number of dimensions of the dataset to be corrected can even imply some potential instability in the adjusted and/or downscaled results, calling for a reasoned use of this approach for large datasets.

scholarly journals Is time a variable like the others in multivariate statistical downscaling and bias correction?

2021 ◽  
Author(s):  
Yoann Robin ◽  
Mathieu Vrac
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Synthetic Data ◽  
The Other ◽  
Strong Increase ◽  
Temporal Properties

Abstract. Bias correction and statistical downscaling are now regularly applied to climate simulations to make then more usable for impact models and studies. Over the last few years, various methods were developed to account for multivariate – inter-site or inter-variable – properties in addition to more usual univariate ones. Among such methods, temporal properties are either neglected or specifically accounted for, i.e., differently from the other properties. In this study, we propose a new multivariate approach called “Time Shifted Multivariate Bias Correction” (TSMBC), which targets to correct the temporal dependency in addition to the other marginal and multivariate aspects. TSMBC relies on considering the initial variables at various times (i.e., lags) as additional variables to correct. Hence, temporal dependencies (e.g., auto-correlations) to correct are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a Vector Auto Regressive (VAR) process. In a second evaluation, we work in a “perfect model” context where a Regional Climate Model (RCM) plays the role of the (pseudo-) observations, and where its forcing Global Climate Model (GCM) is the model to be downscaled/bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing too much the number of lags to consider does not necessarily improve the temporal properties and a too strong increase in the number of dimensions of the dataset to correct can even imply some potential instability in the adjusted/downscaled results, calling for a reasoned use of this approach for large datasets.

Estimates of Twenty-First-Century Flood Risk in the Pacific Northwest Based on Regional Climate Model Simulations

2014 ◽  
Vol 15 (5) ◽  
pp. 1881-1899 ◽  
Author(s):  
Eric P. Salathé ◽  
Alan F. Hamlet ◽  
Clifford F. Mass ◽  
Se-Yeun Lee ◽  
Matt Stumbaugh ◽  
...  
Keyword(s):  
Pacific Northwest ◽  
Statistical Downscaling ◽  
Flood Risk ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Hydrologic Model ◽  
The Pacific ◽  
Physically Based

Abstract Results from a regional climate model simulation show substantial increases in future flood risk (2040–69) in many Pacific Northwest river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics. While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric rivers. This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF) Model implemented at 12-km resolution for the period 1970–2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted from each water year and flood statistics for various return intervals are estimated.

scholarly journals A new bias-correction method for precipitation over complex terrain suitable for different climate states

2019 ◽  
Author(s):  
Patricio Velasquez ◽  
Martina Messmer ◽  
Christoph C. Raible
Keyword(s):  
Bias Correction ◽  
Complex Terrain ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Correction Method ◽  
Climate Simulation ◽  
Climate Modelling ◽  
Bias Correction Method

Abstract. This work presents a new bias-correction method for precipitation that considers orographic characteristics, which makes it flexible to be used under highly different climate conditions, e.g., glacial conditions. The new bias-correction and its performance are presented for Switzerland using a regional climate simulation under perpetual 1990 conditions at 2-km resolution driven by a simulation performed with a global climate model. Comparing the regional simulations with observations, we find a strong seasonal and height dependence of the bias in precipitation commonly observed in regional climate modelling over complex terrain. Thus, we suggest a 3-step correction method consisting of (i) a separation into different orographic characteristics, (ii) correction of low intensity precipitation, and finally (iii) the application of empirical quantile mapping, which is applied to each month separately. Testing different orographic characteristics shows that separating in 400-m height-intervals provides the overall most reasonable correction of the biases in precipitation and additionally at the lowest computational costs. The seasonal precipitation bias induced by the global climate model is fully corrected, whereas some regional biases remain, in particular positive biases in winter over mountains and negative biases in winter and summer in deep valleys and Ticino. The biases over mountains are difficult to judge, as observations over complex terrain are afflicted with uncertainties, which may be more than 30 % above 1500 m a.s.l. A rigorous cross validation, which trains the correction method with independent observations from Germany, Austria and France, exhibits a similar performance compared to just using Switzerland as training and verification region. This illustrates the robustness of the new method. Thus, the new bias-correction provides a flexible tool which is suitable in studies where orography strongly changes, e.g., during glacial times.

scholarly journals A new bias-correction method for precipitation over complex terrain suitable for different climate states: a case study using WRF (version 3.8.1)

2020 ◽  
Vol 13 (10) ◽  
pp. 5007-5027
Author(s):  
Patricio Velasquez ◽  
Martina Messmer ◽  
Christoph C. Raible
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Complex Terrain ◽  
Climate Model ◽  
Model Simulation ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Correction Method ◽  
Bias Correction Method

Abstract. This work presents a new bias-correction method for precipitation over complex terrain that explicitly considers orographic characteristics. This consideration offers a good alternative to the standard empirical quantile mapping (EQM) method during colder climate states in which the orography strongly deviates from the present-day state, e.g. during glacial conditions such as the Last Glacial Maximum (LGM). Such a method is needed in the event that absolute precipitation fields are used, e.g. as input for glacier modelling or to assess potential human occupation and according migration routes in past climate states. The new bias correction and its performance are presented for Switzerland using regional climate model simulations at 2 km resolution driven by global climate model outputs obtained under perpetual 1990 and LGM conditions. Comparing the present-day regional climate model simulation with observations, we find a strong seasonality and, especially during colder months, a height dependence of the bias in precipitation. Thus, we suggest a three-step correction method consisting of (i) a separation into different orographic characteristics, (ii) correction of very low intensity precipitation, and (iii) the application of an EQM, which is applied to each month separately. We find that separating the orography into 400 m height intervals provides the overall most reasonable correction of the biases in precipitation. The new method is able to fully correct the seasonal precipitation bias induced by the global climate model. At the same time, some regional biases remain, in particular positive biases over high elevated areas in winter and negative biases in deep valleys and Ticino in winter and summer. A rigorous temporal and spatial cross-validation with independent data exhibits robust results. The new bias-correction method certainly leaves some drawbacks under present-day conditions. However, the application to the LGM demonstrates that it is a more appropriate correction compared to the standard EQM under highly different climate conditions as the latter imprints present-day orographic features into the LGM climate.

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 Downscaling Atmosphere-Ocean Global Climate Model Precipitation Simulations over Africa Using Bias-Corrected Lateral and Lower Boundary Conditions

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 493 ◽  
Author(s):  
Leonard Druyan ◽  
Matthew Fulakeza
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Lower Boundary ◽  
Climate Projections ◽  
African Easterly Jet ◽  
Modèle Simulation ◽  
Goddard Institute ◽  
Bias Corrections

A prequel study showed that dynamic downscaling using a regional climate model (RCM) over Africa improved the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM: ModelE) simulation of June–September rainfall patterns over Africa. The current study applies bias corrections to the lateral and lower boundary data from the AOGCM driving the RCM, based on the comparison of a 30-year simulation to the actual climate. The analysis examines the horizontal pattern of June–September total accumulated precipitation, the time versus latitude evolution of zonal mean West Africa (WA) precipitation (showing monsoon onset timing), and the latitude versus altitude cross-section of zonal winds over WA (showing the African Easterly Jet and the Tropical Easterly Jet). The study shows that correcting for excessively warm AOGCM Atlantic sea-surface temperatures (SSTs) improves the simulation of key features, whereas applying 30-year mean bias corrections to atmospheric variables driving the RCM at the lateral boundaries does not improve the RCM simulations. We suggest that AOGCM climate projections for Africa should benefit from downscaling by nesting an RCM that has demonstrated skill in simulating African climate, driven with bias-corrected SST.

scholarly journals Uncovering the shortcomings of a weather typing method

2020 ◽  
Vol 24 (5) ◽  
pp. 2671-2686 ◽  
Author(s):  
Els Van Uytven ◽  
Jan De Niel ◽  
Patrick Willems
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Precipitation Amount ◽  
Winter Season ◽  
Added Value ◽  
Typing Method ◽  
Weather Typing ◽  
Precipitation Changes

Abstract. In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.

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