摘要 :
Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at ...
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Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at local scales. Statistical downscaling techniques are widely used to correct biases of GCM projections. The objective of this study was to evaluate the ability of nine statistical downscaling methods from three available statistical downscaling categories to simulate daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations representing arid to humid climate regions. The three downscaling categories included perfect prognosis (PP), model output statistics (MOS), and stochastic weather generator (SWG). To minimize the effect of GCM projection error on downscaling quality, the National Centers for Environmental Prediction (NCEP) Reanalysis 1 data at a 2.5° grid spacing (treated as observed grid data) were downscaled to the four weather stations (representing arid, semi-arid, sub humid, and humid regions) using the nine downscaling methods. The station observations were divided into calibration and validation periods in a way that maximized the differences in annual precipitation means between the two periods for assessing the ability of each method in downscaling non-stationary climate changes. All methods were ranked with three metrics (Euclidean distance, sum of absolute relative error, and absolute error) for their ability in simulating precipitation amounts at daily, monthly, yearly, and annual maximum scales. After eliminating the poorest two performers in simulating precipitation mean, distribution, frequency, and temporal sequence, the top four remaining methods in ascending order were Distribution-based Bias Correction (DBC), Generator for Point Climate Change (GPCC), SYNthetic weather generaTOR (SYNTOR), andLO-Cal Intensity scaling (LOCI). DBC and LOCI are bias-correction methods, and GPCC and SYNTOR are generator-basedmethods. The differences in performances among the downscaling methods were smaller within each downscaling category than between the categories. The performance of each method varied with the climate conditions of each station. Overall results indicated that the SWG methods had certain advantages in simulating daily precipitation distribution, frequency, and temporal sequence for non-stationary climate changes.
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Large-scale wave reanalysis databases (0.1°-1° spatial resolution) provide valuable information for wave climate research and ocean applications which require long-term time series (＞ 20 years) of hourly sea state parameters. H...
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Large-scale wave reanalysis databases (0.1°-1° spatial resolution) provide valuable information for wave climate research and ocean applications which require long-term time series (＞ 20 years) of hourly sea state parameters. However, coastal studies need a more detailed spatial resolution (50-500 m) including wave transformation processes in shallow waters. This specific problem, called downscaling, is usually solved applying a dynamical approach by means of numerical wave propagation models requiring a high computational time effort. Besides, the use of atmospheric reanalysis and wave generation and propagation numerical models introduce some uncertainties and errors that must be dealt with. In this work, we present a global framework to downscale wave reanalysis to coastal areas, taking into account the correction of open sea significant wave height (directional calibration) and drastically reducing the CPU time effort (about 1000×) by using a hybrid methodology which combines numerical models (dynamical downscaling) and mathematical tools (statistical downscaling). The spatial wave variability along the boundaries of the propagation domain and the simultaneous wind fields are taking into account in the numerical propagations to performance similarly to the dynamical downscaling approach. The principal component analysis is applied to the model forcings to reduce the data dimension simplifying the selection of a subset of numerical simulations and the definition of the wave transfer function which incorporates the dependency of the wave spatial variability and the non-uniform wind forcings. The methodology has been tested in a case study on the northern coast of Spain and validated using shallow water buoys, confirming a good reproduction of the hourly time series structure and the different statistical parameters.
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To assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban-rural gradients, complex topography, a...
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To assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban-rural gradients, complex topography, and sub-synoptic atmospheric phenomena. We present here an approach to dynamical downscaling using analysis nudging, where the entire domain is constrained to coarser-resolution parent data. Here meteorology from the North American Regional Reanalysis and the North American Regional Climate Change Assessment Program data archive are used as parent data and downscaled with the Advanced Research version of the Weather Research and Forecasting model to a 12 km × 12 km horizontal resolution over the Eastern U.S. Our results show when analysis nudging is applied to all variables at all levels, mean fractional errors relative to parent data are less than 2% for maximum 2 m temperatures, less than 15% for minimum 2 m temperatures, and less than 18% for10 m wind speeds. However, the skill of representing fields that are not nudged, such as boundary layer height and precipitation, is less clear. Our results indicate that though nudging can be a useful tool for consistent, comparable studies of downscaling climate for regional and local impacts, which variables are nudged and at what levels should be carefully considered based on the climate impact(s) of study.
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摘要 :
Statistical downscaling methods describe a statistical relationship between large-scale atmospheric variables such as temperature, humidity, precipitation, etc., and local-scale meteorological variables like precipitation. This st...
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Statistical downscaling methods describe a statistical relationship between large-scale atmospheric variables such as temperature, humidity, precipitation, etc., and local-scale meteorological variables like precipitation. This study examines the potential predictor variables selected from the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set for downscaling monthly precipitation in Tahtali watershed in Turkey. An approach based on the assessment of all possible regression types was used to select the predictors among the NCEP reanalysis data set, and artificial neural network (ANN)-based downscaling models were designed separately for each station in the basin. The results of the study showed that precipitation, surface and sea level pressures, air temperatures at surface, 850-, 500-, and 200-hPa pressure levels, and geopotential heights at 850- and 200-hPa pressure levels are the most explanatory NCEP/NCAR parameters for the study area. It was concluded that ANN-based downscaling models can be implemented to downscale coarse-scale atmospheric parameters to monthly precipitation at station scale by using the above parameters as inputs in the study area.
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Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniq...
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Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.
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Rice is an important commodity in the Philippines. In the Cagayan Valley (CV), rice production provides employment to more than half of the region's population and any climate variability and change can cause negative impacts on c...
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Rice is an important commodity in the Philippines. In the Cagayan Valley (CV), rice production provides employment to more than half of the region's population and any climate variability and change can cause negative impacts on crop production and people's livelihoods. This paper attempts to understand projected climate changes in seasonal rainfall and mean temperature (2011-2040) to inform climate change adaptation planning in CV. The climate change projections were provided to crop and water resource modeling, agricultural market modeling, food insecurity vulnerability analysis, community-based climate change adaptation planning, and policy simulation.
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We develop a dynamical-statistical downscaling approach by coupling the PRECIS regional modelling system and a statistical methodSCADSto construct very high resolution climate projections for studying climate change impacts at loc...
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We develop a dynamical-statistical downscaling approach by coupling the PRECIS regional modelling system and a statistical methodSCADSto construct very high resolution climate projections for studying climate change impacts at local scales. The coupled approach performs very well in hindcasting the mean temperature of present-day climate, while the performance for precipitation is relatively poor due to its high spatial variability and nonlinear nature but its spatial patterns are well captured. We then apply the coupled approach for projecting the future climate over the province of Ontario, Canada at a fine resolution of 10 km. The results show that there would be a significant warming trend throughout this century for the entire province while less precipitation is projected for most of the selected weather stations. The projections also demonstrate apparent spatial variability in the amount of precipitation but no noticeable changes are found in the spatial patterns.
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We review coordinated efforts for producing regional climate projections through dynamical and statistical downscaling tools driven by global climate model output. Such projections are affected by multiple sources of uncertainty b...
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We review coordinated efforts for producing regional climate projections through dynamical and statistical downscaling tools driven by global climate model output. Such projections are affected by multiple sources of uncertainty both at the global model and at the downscaling levels. The characterization of these uncertainties and the production of robust regional to local projections for use in impact studies require the completion of properly designed large ensembles of experiments. Toward this purpose, several regional coordinated efforts have been conducted in the past, particularly involving regional climate models, but because of the lack of a common experiment protocol, the transfer of know-how across them has been difficult. This problem isbeing addressed in the Coordinated Regional Downscaling Experiment (CORDEX), a framework designed to produce the next generation of worldwide high-resolution regional climate projections through a fully coordinated experiment protocol.
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The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer local- or catchment-scale spatial resolution. This paper prov...
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The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer local- or catchment-scale spatial resolution. This paper provides an assessment of two downscaling approaches for simulation of daily rainfall over Sydney, Australia. The two downscaling alternatives compared include a multivariate multisite statistical downscaling model based on semi-parametric conditional simulation and a dynamical downscaling approach that uses the National Center for Atmospheric Research (NCAR) weather research and forecasting (WRF) model. The two approaches are evaluated for their ability to reproduce important at-site rainfall statistics at a network of 45 raingauge stations and regional statistics over the catchment area of the Warragamba Dam (9,050 km~2). The results indicate that the simulations from these approaches capture many regionally observed climate features, including the simulated seasonal and annual means and daily extreme rainfall values. Further analyses suggest that the statistical downscaling approach provides improved simulations of attributes related to point rainfall, spell lengths and amounts, whereas the dynamical approach is well-suited for applications where regionally averaged rainfall is of primary concern.
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Stationarity in the relationship between causal variables and target variables is the fundamental assumption of statistical downscaling models. However, we hypothesize that this assumption may not be valid in a c
Stationarity in the relationship between causal variables and target variables is the fundamental assumption of statistical downscaling models. However, we hypothesize that this assumption may not be valid in a changing climate. This study develops a downscaling technique in which the relationship between causal and target variables is considered to be time‐varying rather than static. The proposed time‐varying downscaling model (TVDM) is utilized to downscale monthly precipitation over India to 0.25 × 0.25° gridded scale using the large‐scale outputs from multiple general circulation models (GCMs), namely the Hadley Centre Coupled Model version 3 (HadCM3), coupled Hadley Centre Global Environmental Model version 2‐Earth System model (HadGEM2‐ES) and Canadian Earth System Model version 2 (CanESM2). Observed precipitation data are obtained from the India Meteorological Department (IMD), Pune. For future projection, the temporal evolution of each of the TVDM parameters is investigated using its deterministic (trend and periodicity) and stochastic components. TVDM is found to outperform the most commonly used statistical downscaling model (SDSM) and regional climate model (RCM) output at all the locations. The Regional Climate Model version 4 (RegCM4) precipitation data (RCM outputs) are obtained from the Coordinated Regional Climate Downscaling Experiment (CORDEX) data portal supplied by Indian Institute of Tropical Meteorology (IITM), Pune. The proposed model (TVDM) differs from the existing stationarity assumption‐based approaches in updating the relationship between causal and target variables over time. It is understood that parameter uncertainty is the major issue in consideration of non‐stationarity. Still, the TVDM is found to be very useful in the context of climate change due to its time‐varying comp
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