scholarly journals A Global Atmospheric Analysis Dataset Downscaled from the NCEP–DOE Reanalysis

2012 ◽  
Vol 25 (7) ◽  
pp. 2527-2534 ◽  
Author(s):  
Jung-Eun Kim ◽  
Song-You Hong
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Analysis Data ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Department Of Energy ◽  
Quality Analysis ◽  
Resolution Model ◽  
Decadal Trend ◽  
Environmental Prediction

Abstract A global atmospheric analysis dataset is constructed via a spectral nudging technique. The 6-hourly National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis from January 1979 to February 2011 is utilized to force large-scale information, whereas a higher-resolution structure is resolved by a global model with improved physics. The horizontal resolution of the downscaled data is about 100 km, twice that of the NCEP–DOE reanalysis. A comparison of the 31-yr downscaled data with reanalysis data and observations reveals that the downscaled precipitation climatology is improved by correcting inherent biases in the lower-resolution reanalysis, and large-scale patterns are preserved. In addition, it is found that global downscaling is an efficient way to generate high-quality analysis data due to the use of a higher-resolution model with improved physics. The uniqueness of the obtained data lies in the fact that an undesirable decadal trend in the analysis due to a change in the amount of observations used in reanalysis is avoided. As such, a downscaled dataset may be used to investigate changes in the hydrological cycle and related mechanisms.

scholarly journals Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

2017 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Unified Model ◽  
Seasonal Precipitation ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~ 200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

scholarly journals High Resolution Model Intercomparison Project (HighResMIP)

2016 ◽  
Author(s):  
R. J. Haarsma ◽  
M. Roberts ◽  
P. L. Vidale ◽  
C. A. Senior ◽  
A. Bellucci ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Computational Cost ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Intercomparison ◽  
Resolution Model ◽  
Intercomparison Project ◽  
High Resolution Model ◽  
The Impact

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest the possibility for significant changes in both large-scale aspects of circulation, as well as improvements in small-scale processes and extremes. However, such high resolution global simulations at climate time scales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centers and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other MIPs. Increases in High Performance Computing (HPC) resources, as well as the revised experimental design for CMIP6, now enables a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility to extend to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulation. HighResMIP thereby focuses on one of the CMIP6 broad questions: “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

scholarly journals Snow Cover Characteristics over the Main Russian River Basins as Represented by Reanalyses and Measured Data

2008 ◽  
Vol 47 (6) ◽  
pp. 1819-1833 ◽  
Author(s):  
V. Khan ◽  
L. Holko ◽  
K. Rubinstein ◽  
M. Breiling
Keyword(s):  
Interannual Variability ◽  
River Basins ◽  
Reanalysis Data ◽  
Measured Data ◽  
Department Of Energy ◽  
Weather Forecasts ◽  
Northern European ◽  
Snow Water ◽  
Russian River ◽  
Environmental Prediction

Abstract Snow water equivalents (SWE) produced by the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) and 40-yr European Centre for Medium-Range Weather Forecasts (ERA-40) reanalyses and snow depths (SD) produced by the 25-yr Japanese “JRA-25” reanalysis over the main Russian river basins for 1979–2000 were examined against measured data. The analysis included comparisons of mean basin values and correlation of anomalies, as well as seasonal and interannual variabilities and trends. ERA-40 generally provided better estimates of mean SWE values for river basins than did the NCEP–DOE reanalysis. Mean SD values from the JRA-25 reanalysis were systematically underestimated. The best correlations among the anomalies were given by ERA-40, followed by JRA-25. All reanalyses reproduced seasonal variability well, although the differences in absolute values varied substantially. The highest differences were typically connected with the snowmelt period (April and May). Interannual variability confirmed the errors of ERA-40 and JRA-25 in 1992–94 and 1979–83, respectively. Otherwise, the reproduction of the interannual variability of SWE and SD was reasonable. Strong biases in SD data from JRA-25 that decrease with time induce artificial positive trends. Significant underestimations of SWE data by ERA-40 for 1991–94 influenced the values of the trends. NCEP–DOE reasonably represented the trend found in measured data. In general, the highest discrepancies between measured and reanalysis data were found for the northern European and eastern Asian rivers (Pechora, Lena, and Amur). The assessment of the quality of SWE and SD reanalysis data can help potential users in the selection of a particular reanalysis as being appropriate to the purpose of their studies.

scholarly journals High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

2016 ◽  
Vol 9 (11) ◽  
pp. 4185-4208 ◽  
Author(s):  
Reindert J. Haarsma ◽  
Malcolm J. Roberts ◽  
Pier Luigi Vidale ◽  
Catherine A. Senior ◽  
Alessio Bellucci ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Computational Cost ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Intercomparison ◽  
Resolution Model ◽  
Intercomparison Project ◽  
High Resolution Model ◽  
The Impact

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950–2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

Effects of Localized Grid Refinement on the General Circulation and Climatology in the Community Atmosphere Model

2015 ◽  
Vol 28 (7) ◽  
pp. 2777-2803 ◽  
Author(s):  
Colin M. Zarzycki ◽  
Christiane Jablonowski ◽  
Diana R. Thatcher ◽  
Mark A. Taylor
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Precipitable Water ◽  
Atmospheric Model ◽  
Spectral Element ◽  
Horizontal Resolution ◽  
Department Of Energy ◽  
Extreme Precipitation Events ◽  
Community Atmosphere Model ◽  
Atmosphere Model

Abstract Using the spectral element (SE) dynamical core within the National Center for Atmospheric Research–Department of Energy Community Atmosphere Model (CAM), a regionally refined nest at 0.25° (~28 km) horizontal resolution located over the North Atlantic is embedded within a global 1° (~111 km) grid. A 23-yr simulation using Atmospheric Model Intercomparison Project (AMIP) protocols and default CAM, version 5, physics is compared to an identically forced run using the global 1° (~111 km) grid without refinement. The addition of a refined patch over the Atlantic basin does not noticeably affect the global circulation. In the area where the refinement is located, large-scale precipitation increases with the higher resolution. This increase is partly offset by a decrease in precipitation resulting from convective parameterizations, although total precipitation is also slightly higher at finer resolutions. Equatorial waves are not significantly impacted when traversing multiple grid spacings. Despite the grid transition region bisecting northern Africa, local zonal jets and African easterly wave activity are highly similar in both simulations. The frequency of extreme precipitation events increases with resolution, although this increase is restricted to the refined patch. Topography is better resolved in the nest as a result of finer grid spacing. The spatial patterns of variables with strong orographic forcing (such as precipitation, cloud, and precipitable water) are improved with local refinement. Additionally, dynamical features, such as wind patterns, associated with steep terrain are improved in the variable-resolution simulation when compared to the uniform coarser run.

scholarly journals Large-scale characteristics of the distribution of blowing-snow sublimation

2008 ◽  
Vol 49 ◽  
pp. 11-16 ◽  
Author(s):  
Konosuke Sugiura ◽  
Tetsuo Ohata
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Analysis Data ◽  
Global Scale ◽  
Blowing Snow ◽  
Scale Characteristics ◽  
Global Datasets ◽  
Northern And Southern Hemispheres

AbstractTo consider the large-scale characteristics of blowing-snow sublimation and its importance in the hydrological cycle in the cryosphere, we investigated the sublimation of blowing snow particles on a global scale using the global datasets of the European Centre for Medium-RangeWeather Forecasts (ECMWF) re-analysis data and the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I data for 1987. The sublimation fluxes of blowing snow particles were estimated globally with 2.5˚ resolution at 6 hour intervals. We found that the sublimation of blowing snow particles occurs more widely in the Northern Hemisphere than in the Southern Hemisphere, does not increase monotonously with latitude, and becomes more active in the polar coast regions and highlands, although the annual mean sublimation fluxes of the Northern and Southern Hemispheres are almost equal. In addition, we confirmed the characteristic seasonal changes in the area of sublimation in the Northern Hemisphere. Although we need to incorporate continuous parameters from systematic ground-based studies of the structure of blowing snow in specific fields to reduce uncertainty regarding the characteristics of blowing snow, our results point to a need to review the current understanding of the hydrological cycle.

scholarly journals Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

2018 ◽  
Vol 11 (5) ◽  
pp. 1823-1847 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Seasonal Precipitation ◽  
Physical Mechanisms ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyse the sensitivity to air–sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ∼ 200, 90 and 40 km in the zonal direction at the equator, respectively) are analysed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China but improve with finer resolution and coupling. Empirical orthogonal teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air–sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal mean time series. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

Diagnostic Comparison of Meteorological Analyses during the 2002 Antarctic Winter

2005 ◽  
Vol 133 (5) ◽  
pp. 1261-1278 ◽  
Author(s):  
Gloria L. Manney ◽  
Douglas R. Allen ◽  
Kirstin Krüger ◽  
Barbara Naujokat ◽  
Michelle L. Santee ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Large Scale ◽  
Meteorological Data ◽  
Polar Vortex ◽  
Department Of Energy ◽  
Observation System ◽  
Small Scale ◽  
Stratospheric Temperature ◽  
Transport Barriers ◽  
Environmental Prediction

Abstract Several meteorological datasets, including U.K. Met Office (MetO), European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and NASA’s Goddard Earth Observation System (GEOS-4) analyses, are being used in studies of the 2002 Southern Hemisphere (SH) stratospheric winter and Antarctic major warming. Diagnostics are compared to assess how these studies may be affected by the meteorological data used. While the overall structure and evolution of temperatures, winds, and wave diagnostics in the different analyses provide a consistent picture of the large-scale dynamics of the SH 2002 winter, several significant differences may affect detailed studies. The NCEP–NCAR reanalysis (REAN) and NCEP–Department of Energy (DOE) reanalysis-2 (REAN-2) datasets are not recommended for detailed studies, especially those related to polar processing, because of lower-stratospheric temperature biases that result in underestimates of polar processing potential, and because their winds and wave diagnostics show increasing differences from other analyses between ∼30 and 10 hPa (their top level). Southern Hemisphere polar stratospheric temperatures in the ECMWF 40-Yr Re-analysis (ERA-40) show unrealistic vertical structure, so this long-term reanalysis is also unsuited for quantitative studies. The NCEP/Climate Prediction Center (CPC) objective analyses give an inferior representation of the upper-stratospheric vortex. Polar vortex transport barriers are similar in all analyses, but there is large variation in the amount, patterns, and timing of mixing, even among the operational assimilated datasets (ECMWF, MetO, and GEOS-4). The higher-resolution GEOS-4 and ECMWF assimilations provide significantly better representation of filamentation and small-scale structure than the other analyses, even when fields gridded at reduced resolution are studied. The choice of which analysis to use is most critical for detailed transport studies (including polar process modeling) and studies involving synoptic evolution in the upper stratosphere. The operational assimilated datasets are better suited for most applications than the NCEP/CPC objective analyses and the reanalysis datasets (REAN/REAN-2 and ERA-40).

scholarly journals Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Yuanyuan Ma ◽  
Yi Yang ◽  
Xiaoping Mai ◽  
Chongjian Qiu ◽  
Xiao Long ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Spectral Nudging ◽  
Environmental Prediction ◽  
Meteorological Elements

To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and spectral nudging) are debated. Moreover, dynamical downscaling is now performed at the convection-permitting scale to reduce the parameterization uncertainty and obtain the finer resolution. To compare the performances of the two nudging techniques in this study, three sensitivity experiments (with no nudging, analysis nudging, and spectral nudging) covering a period of two months with a grid spacing of 6 km over continental China are conducted to downscale the 1-degree National Centers for Environmental Prediction (NCEP) dataset with the Weather Research and Forecasting (WRF) model. Compared with observations, the results show that both of the nudging experiments decrease the bias of conventional meteorological elements near the surface and at different heights during the process of dynamical downscaling. However, spectral nudging outperforms analysis nudging for predicting precipitation, and analysis nudging outperforms spectral nudging for the simulation of air humidity and wind speed.

scholarly journals Statistical Downscaling of Wintertime Temperatures over South Korea

2015 ◽  
Vol 32 (12) ◽  
pp. 2225-2241
Author(s):  
Seoyeon Lee ◽  
Kwang-Yul Kim
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Weather Forecast ◽  
Error Variance ◽  
Reanalysis Data ◽  
Small Scale ◽  
Atmospheric Variability ◽  
Medium Range Weather Forecast ◽  
Cseof Analysis ◽  
Environmental Prediction

AbstractReanalysis data have global coverage and faithfully render large-scale phenomena. On the other hand, regional and small-scale characteristics of atmospheric variability are poorly resolved. In an attempt to improve reanalysis data for regional use, a statistical downscaling strategy is developed based on cyclostationary empirical orthogonal function (CSEOF) analysis. The developed algorithm is applied to the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data and to the European Centre for Medium-Range Weather Forecast (ECMWF) Interim Re-Analysis (ERA-Interim) data in order to produce winter temperatures at 60 Korea Meteorological Administration (KMA) stations over the Korean Peninsula. The developed downscaling algorithm is evaluated by predicting winter daily temperatures from 17 November to 16 March for 35 years (1979–2014). For validating the downscaling algorithm the jackknife method is used, in which winter daily temperature is predicted over a 1-yr period not used for training. This procedure is repeated for the entire data period. The mean and variance of the resulting downscaled temperatures match reasonably well with those of the KMA measurements. Validation based on correlation and error variance shows that the temperatures at 60 KMA stations are faithfully reproduced based on coarse reanalysis data. The utility of this technique for downscaling model predictions based on future scenarios is also addressed.

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