scholarly journals A Spatio-Temporal Statistical Downscaling Approach to Deriving Extreme Rainfall IDF Relations at Ungauged Sites in the Context of Climate Change

10.29007/nfk8 ◽  
2018 ◽  
Author(s):  
Truong-Huy Nguyen ◽  
Van-Thanh-Van Nguyen ◽  
Hoang-Lam Nguyen
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Extreme Rainfall ◽  
Climate Simulation ◽  
Ungauged Sites ◽  
Spatio Temporal ◽  
Statistical Criteria

This paper proposes an efficient spatio-temporal statistical downscaling approach for estimating IDF relations at an ungauged site using daily rainfalls downscaled from global climate model (GCM) outputs. More specifically, the proposed approach involves two steps: (1) a spatial downscaling using scaling factors to transfer the daily downscaled GCM extreme rainfall projections at a regional scale to a given ungauged site and (2) a temporal downscaling using the scale-invariance GEV model to derive the distribution of sub-daily extreme rainfalls from downscaled daily rainfalls at the same location. The feasibility and accuracy of the proposed approach were evaluated based on the climate simulation outputs from 21 GCMs that have been downscaled by NASA to a regional 25-km scale for two different RCP 4.5 and 8.5 scenarios and the observed extreme rainfall data available from a network of 15 raingauges located in Ontario, Canada. The jackknife technique was used to represent the ungauged site conditions. Results based on different statistical criteria have indicated the feasibility and accuracy of the proposed approach.

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.

AN EQUATION OF STATE FOR LAND SURFACE CLIMATES

2011 ◽  
Vol 21 (12) ◽  
pp. 3577-3587 ◽  
Author(s):  
KLAUS FRAEDRICH ◽  
FRANK SIELMANN
Keyword(s):  
Equation Of State ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
State Parameter ◽  
Lake Area ◽  
Rainfall Runoff ◽  
Change Analysis

A biased coinflip Ansatz provides a stochastic regional scale surface climate model of minimum complexity, which represents physical and stochastic properties of the rainfall–runoff chain. The solution yields the Schreiber–Budyko relation as an equation of state describing land surface vegetation, river runoff and lake areas in terms of physical flux ratios, which are associated with three thresholds. Validation of consistency and predictability within a Global Climate Model (GCM) environment demonstrates the stochastic rainfall–runoff chain to be a viable surrogate model for regional climate state averages and variabilites. A terminal (closed) lake area ratio is introduced as a new climate state parameter, which quantifies lake overflow as a threshold in separating water from energy limited climate regimes. A climate change analysis based on the IPCC A1B scenario is included for completeness.

scholarly journals A comprehensive history of climate and habitat stability of the last 800&thinsp,000 years

2019 ◽  
Author(s):  
Mario Krapp ◽  
Robert Beyer ◽  
Stephen L. Edmundson ◽  
Paul J. Valdes ◽  
Andrea Manica
Keyword(s):  
Climate Model ◽  
Temporal Dynamics ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Geological Time ◽  
Comprehensive Overview ◽  
Long Term Stability ◽  
History Of

Abstract. A detailed and accurate reconstruction of the past climate is essential in understanding the interactions between ecosystems and their environment through time. We know that climatic drivers have shaped the distribution and evolution of species, including our own, and their habitats. Yet, spatially-detailed climate reconstructions that continuously cover the Quaternary do not exist. This is mainly because no paleoclimate model can reconstruct regional-scale dynamics over geological time scales. Here we develop a statistical emulator, the Global Climate Model Emulator (GCMET), which reconstructs the climate of the last 800 000 years with unprecedented spatial detail. GCMET captures the temporal dynamics of glacial-interglacial climates as an Earth System Model of Intermediate Complexity would whilst resolving the local dynamics with the accuracy of a Global Climate Model. It provides a new, unique resource to explore the climate of the Quaternary, which we use to investigate the long-term stability of major habitat types. We identify a number of stable pockets of habitat that have remained unchanged over the last 800 thousand years, acting as potential long-term evolutionary refugia. Thus, the highly detailed, comprehensive overview of climatic changes through time delivered by GCMET provides the needed resolution to quantify the role of long term habitat change and fragmentation in an ecological and anthropological context.

scholarly journals Chaotic Statistical Downscaling (CSD): Application and Comparison in the Bogotá River Basin

10.29007/wkcx ◽  
2018 ◽  
Author(s):  
Freddy Duarte ◽  
Gerald Corzo ◽  
Germán Santos ◽  
Oscar Hernández
Keyword(s):  
Time Delay ◽  
Phase Space ◽  
Predictive Model ◽  
River Basin ◽  
Statistical Downscaling ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Deterministic Chaos ◽  
Climatic Information

This study presents a new statistical downscaling method called Chaotic Statistical Downscaling (CSD). The method is based on three main steps: Phase space reconstruction for different time steps, identification of deterministic chaos and a general synchronization predictive model. The Bogotá river basin was used to test the methodology. Two sources of climatic information are downscaled: the first corresponds to 47 rainfall gauges stations (1970-2016, daily) and the second is derived from the information of the global climate model MPI-ESM-MR with a resolution of 1,875° x 1,875° daily resolution. These time series were used to reconstruct the phase space using the Method of Time-Delay. The Time-Delay method allows us to find the appropriate values of the time delay and the embedding dimension to capture the dynamics of the attractor. This information was used to calculate the exponents of Lyapunov, which shows the existence of deterministic chaos. Subsequently, a predictive model is created based on the general synchronization of two dynamical systems. Finally, the results obtained are compared with other statistical downscaling models for the Bogota River basin using different measures of error which show that the proposed method is able to reproduce reliable rainfall values (RMSE=73.37).

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.

Statistical Downscaling for Climate Science

2019 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Climatic Changes ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

scholarly journals Mid-Pliocene global climate simulation with MRI-CGCM2.3: set-up and initial results of PlioMIP Experiments 1 and 2

2012 ◽  
Vol 5 (3) ◽  
pp. 793-808 ◽  
Author(s):  
Y. Kamae ◽  
H. Ueda
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Climate Simulation ◽  
Polar Regions ◽  
The North ◽  
Set Up

Abstract. The mid-Pliocene (3.3 to 3.0 million yr ago), a globally warm period before the Quaternary, is recently attracting attention as a new target for paleoclimate modelling and data-model synthesis. This paper reports set-ups and results of experiments proposed in Pliocene Model Intercomparison Project (PlioMIP) using a global climate model, MRI-CGCM2.3. We conducted pre-industrial and mid-Pliocene runs by using the coupled atmosphere-ocean general circulation model (AOGCM) and its atmospheric component (AGCM) for the PlioMIP Experiments 2 and 1, respectively. In addition, we conducted two types of integrations in AOGCM simulation, with and without flux adjustments on sea surface. General characteristics of differences in the simulated mid-Pliocene climate relative to the pre-industrial in the three integrations are compared. In addition, patterns of predicted mid-Pliocene biomes resulting from the three climate simulations are compared in this study. Generally, difference of simulated surface climate between AGCM and AOGCM is larger than that between the two AOGCM runs, with and without flux adjustments. The simulated climate shows different pattern between AGCM and AOGCM particularly over low latitude oceans, subtropical land regions and high latitude oceans. The AOGCM simulations do not reproduce wetter environment in the subtropics relative to the present-day, which is suggested by terrestrial proxy data. The differences between the two types of AOGCM runs are small over the land, but evident over the ocean particularly in the North Atlantic and polar regions.

scholarly journals Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0

2018 ◽  
Vol 11 (4) ◽  
pp. 1665-1681 ◽  
Author(s):  
Oliver Fuhrer ◽  
Tarun Chadha ◽  
Torsten Hoefler ◽  
Grzegorz Kwasniewski ◽  
Xavier Lapillonne ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Atmospheric Model ◽  
Climate Simulation ◽  
Performance Portability ◽  
Domain Specific ◽  
Hardware Architectures ◽  
Graphics Processing

Abstract. The best hope for reducing long-standing global climate model biases is by increasing resolution to the kilometer scale. Here we present results from an ultrahigh-resolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs (graphics processing units). The dynamical core of the model has been completely rewritten using a domain-specific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators on this scale. At a grid spacing of 930 m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596 MWh per simulated year. Furthermore, we propose a new memory usage efficiency (MUE) metric that considers how efficiently the memory bandwidth – the dominant bottleneck of climate codes – is being used.

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