To perform dynamical downscaling of the future climate changes, it is necessary to establish a reliable regional model which simulates well presentday climate for the region of interest. Global climate models provide initial andglobal climate models provide initial and boundary conditions. Dynamical downscaling with regional climate models have been demonstrated to have added value for downscaling global models. Rather than developing a downscaling scheme for a particular application, the purpose of the present study is to examine uncertainty of downscaling, by comparing several different downscaling models from both approaches. Downscaling a global climate model to simulate climate change impacts on us regional and urban air quality. Statistical delta downscaling adds the difference delta between a historical period and a modeled value to a known historical climatology. Statistical and dynamical downscaling of precipitation. Wrf is a fully compressible, nonhydrostatic model with a terrain. Statistical downscaling and dynamical downscaling are two approaches to generate high. What toolssoftware do you recommend for downscaling this models. Regional climate model version 3 remo german regional climate model stardex statistical and regional dynamical downscaling of extremes for european regions som selforganizing map. The emission pathway is representative for scenarios in the literature leading to very low greenhouse gas concentration levels. Project jupyter was used for this example to break down the process of rcmes.
Statistical downscaling and dynamical downscaling of regional. Automated regressionbased statistical downscaling tool. Downscaled regional climate models rcms provide grist for climate. Global climate models gcms cannot simulate climate at the local to regional scale. Statistical downscaling and dynamical downscaling of. The eastwest contrast in the projected changes in extreme precipitation in taiwan are found to. This freely available software enables the production of climate change time series at sites for which. Both aim to add value to raw model output by addressing model shortcomings e. It assumes that the relationships between predictors largescale variables and predictands smallscale surface variables do not vary under climate change conditions. The rcm simulations are usually initialized from general circulation model.
For dynamical downscaling, you are basically nesting a higherresolution regional model into a global climate model, says bill kuo a senior scientist at the national center for. One approach is dynamical downscaling, which feeds data output from. Projections of future climate are produced using complex, coupled atmosphereocean models gcms. Background climate downscaling is a practice whereby coarseresolution climate data which comes from global atmospheric reanalyses e. A comparison of statistical and dynamical downscaling of. Dynamical downscaling precis method description a regional climate model rcm is a high resolution climate model that covers a limited area of the globe, typically 5,000 km x 5,000 km, with a typical horizontal resolution of 50 km. Dynamical downscaling requires running highresolution climate models on a regional subdomain, using observational data or lower.
Pdf comparison of statistical and dynamical downscaling. Research and development program serdp, rc2242, through u. Downscaling climate modelling for highresolution climate information and impact assessment. Dynamical downscaling of global climate model products for water resource projection in the southwest u. Being widely used for climatological studies in the recent years, the weather research and forecasting model wrf v3. Toward the improvement of forecast bias over california. Some dynamical downscaling is done globally, and some is done regionally using regional climate models rcms. The dynamical downscaling and coupling with a lake model to represent the great lakes create. The gcms are most reliable at the continental scale. Downscaling is the general name for a procedure to take information known at large scales to make predictions at local scales. Does dynamical downscaling introduce novel information in. Cgiar program on climate change, agriculture and food security. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high.
Statistical downscaling involves the application of relationships identified in the observed climate, between the large and smallerscale, to climate model output. Dynamical downscaling simulation and future projection of. Statistical and dynamical mesoscale climate downscaling climate downscaling is a practice whereby coarseresolution climate data which comes from global atmospheric reanalyses e. We have completed an array of highresolution simulations of present and future climate over western north america wna and eastern north america ena by dynamically downscaling global climate simulations using a regional climate model, regcm3. Dynamical downscalingbased projections of great lakes. Statistical and dynamical mesoscale climate downscaling.
This downscaling is important because climate models are spatially coarse 50200 km and often misrepresent extremes in important meteorological variables, such as temperature and. Regional climate modeling dynamical downscalingdynamical downscaling use global climate models with horizontal resolution of 100200 km to drive regional cli t d l ith l ti f 50 klimate models with resolution of 50 km or better. Dynamical downscaling for alaska has been conducted using lateral boundary forcing from reanalysis output e. Climate model downscaling geophysical fluid dynamics laboratory. Downscaling of global climate change estimates to regional. Dynamic downscaling as a tool for providing actionable. Statistical downscaling using rcmes why do we need to downscale gcm outputs. Validation of model results to perform dynamical downscaling of the future climate changes, it is necessary to establish a reliable regional model which simulates well presentday climate for the region of interest. Review of downscaling methodologies for africa climate. Dynamical downscaling techniques are still expensive and require. While global climate models are used to simulate largescale patterns. In this study, these two downscaling methods are used to simulate the surface climate of china and compared. Dynamical downscaling with regional climate models have been demonstrated to have. The traditional dynamical downscaling approach tdd brings gcm.
Statistical downscaling is widely used to improve spatial andor temporal distributions of meteorological variables from regional and global climate models. The effects of climate change will mostly be felt on local to regional scales. One approach is dynamical downscaling, which feeds data output from global climate models into regional meteorological models to simulate local weather conditions. N2 despite supporting exceptionally productive agricultural lands, the central u. In the last decade, dynamical downscaling with the use of regional climate models rcms has become a convenient and powerful method to document regional climate changes and force regional impact models e. For future projections of regional climate, only gcm outputs can be used to drive rcms.
Relative skill of statistical and dynamical downscaling techniques given the wide range of downscaling techniques both dynamical and statistical there is an urgent need for model comparisons using generic data sets and model diagnostics. Statistical downscaling is a method used to predict local climate variables such. Dynamical downscaling using a regional climate model rcm has been an effective tool to provide actionable regional climate information fig. Illustration of dynamical downscaling applied by gfdl scientists to understand the impacts of global climate change on hurricane frequency, intensity and associated rainfall in the north atlantic. The traditional dynamical downscaling tdd method employs continuous integration of regional climate models rcm with the general circulation model gcm providing the initial and lateral boundary conditions.
The nesting of dynamical modeling and simulation of climate systems is presented in figure 1 using gcm and rcm, while a list of some popular gcms is presented in table 1 along with some basic information about each model. The present work applies the downscaling technique of emanuel et al. Comparisons were made on the basis of a twentyyear retrospective 19751995 climate. Adding value to dynamical model output the mos model output statistics for daily weather forecasts are analogous to statistical downscaling of deccen climate model output. Downscaling cmip5 climate models shows increased tropical. Statistical downscaling delta method ccafs climate. In situations where lowcost, rapid assessments of localised climate change impacts are required, statistical downscaling currently represents the more promising option.
Use of dynamical downscaling to improve the simulation of. Regional climate model and statistical methods, have been developed to obtain fine resolution climate change scenarios. Two stateoftheart global climate models gcms are dynamically downscaled using a regional climate model rcm interactively coupled to a onedimensional lake model, and then a hydrologic routing model is forced with time series of perturbed nbs. What software is easiestbest to downscaling future climate models. Dynamical downscaling uses a physically based weather forecasting model to produce higher time and space resolution data from coarser general circulation model gcm data. Dynamical and statistical regional climate modeling. On downscaling methodologies for seasonal forecast. In the last 10 years, downscaling techniques, both dynamical i. Hsini chang1, carlos carrillo1, eleonora demaria 2, francina dominguez1,2, christopher castro1, matej durcik2 1 department of atmospheric sciences and 2 department of hydrology and water resources. Projections of future global average annual precipitation and temperature for rcp 2. Rcmes utilizes the following statistical downscaling methods used in previous studies e. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the. Dynamical downscaling involves running a climate model at fine resolution using the gcm to provide the largescale boundary conditions.
What software is easiestbest to downscaling future. This includes precipitation data from the nasanldas as well as models generated from the nasa dynamical downscaling experiment. Need for intercomparison exercises for statistical and dynamical downscaling 5. Statistical methodology for evaluating processbased. Differences between model and observed precipitation over three regions in us. There is no single best way, it depends on the goal.
Downscaling is the term used to describe the various methods used to translate the climate projections from coarse resolution gcms to finer resolutions deemed more useful for assessing impacts. Until recently, these studies were restricted to statisticalversusstatistical winkler et al. Dynamical downscaling of global climate model products for. Statistical and regional dynamical downscaling of extremes for european regions. Here, we focus on dynamical downscaling by using a highresolution regional climate model rcm. Rcms are based on physical laws represented by mathematical equations that are solved using a threedimensional.
The two main approaches to downscaling climate information are dynamical and statistical. For the future projections, both the dynamical downscaling models i. Application of dynamical and statistical downscaling to east asian. Dynamical downscaling provides physically consistent projections of. Statistical and dynamical downscaling are two approaches to generating highresolution regional climate models based on the largescale information from either reanalysis data or global climate. Many impact studies require climate change information at a finer resolution than that provided by global climate models gcms. Environmental modelling software, 17, 145157 and the automated. Dynamical downscaling refers to the use of regional climate models. On downscaling methodologies for seasonal forecast applications. Dynamical downscaling has previously been applied to study presentday hydrodynamics and the regional impact of future climate change for the north sea and the northwestern european shelf nwes region see schrum et al.
Initially, we selected all seven of the global models that archived all of the output needed by our technique, but discarded one of the models that contains large discontinuities between the end of simulations representing the historical period. This transformation is done by dynamical or statistical downscaling. Furthermore, dynamical downscaling models are not widely implemented for all regions, and can incur significant computational and technical demands at the outset. Regional climate downscaling with prior statistical. Although dynamical downscaling seems not to reproduce features of the monsoon for some regional climate models singh et al. T1 use of dynamical downscaling to improve the simulation of central u. Nasa dynamic downscaling project part ii climate model. Statistical downscaling methodologies have several practical advantages over dynamical downscaling approaches. Climate model downscaling geophysical fluid dynamics. The dynamical downscaling was conducted by the regional model program rmp of the globalregional integrated model system grims, while the. Met office hadley centres regional climate model version 3. Hydrologic implications of dynamical and statistical. The process of simulating climate systems by using climate models is called dynamical modeling or dynamical downscaling. Global climate models gcms continue to yield important scientific insights into the dynamics and evolution of the climate system on time scales ranging from.
Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each methods ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. The downscaling models are calibrated using ncep reanalysis as largescale predictors, and predictors are also provided for a number of gcm climate change simulations, including hadcm3. This requires longterm model runs multiyear, decadal and comparison with observational data. However, global climate models gcms are unable to produce reliable climate information on the scale needed to assess regional climate. Monthly seasurface temperature sst data from a global climate model simulation typically provide. The predictions from global or regional scale climate models are often too coarse for direct use at local scales.
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