Comparing the skill of different reanalyses and their ensembles as predictors for daily air temperature on a glaciated mountain (Peru).
TL;DR: The most important findings are: ERA-int, CFSR, and MERRA show considerably higher skill than NCEP-R and JCDAS; differences in skill appear especially during dry and intermediate seasons in the Cordillera Blanca.
Abstract: It is well known from previous research that significant differences exist amongst reanalysis products from different institutions. Here, we compare the skill of NCEP-R (reanalyses by the National Centers for Environmental Prediction, NCEP), ERA-int (the European Centre of Medium-range Weather Forecasts Interim), JCDAS (the Japanese Meteorological Agency Climate Data Assimilation System reanalyses), MERRA (the Modern Era Retrospective-Analysis for Research and Applications by the National Aeronautics and Space Administration), CFSR (the Climate Forecast System Reanalysis by the NCEP), and ensembles thereof as predictors for daily air temperature on a high-altitude glaciated mountain site in Peru. We employ a skill estimation method especially suited for short-term, high-resolution time series. First, the predictors are preprocessed using simple linear regression models calibrated individually for each calendar month. Then, cross-validation under consideration of persistence in the time series is performed. This way, the skill of the reanalyses with focus on intra-seasonal and inter-annual variability is quantified. The most important findings are: (1) ERA-int, CFSR, and MERRA show considerably higher skill than NCEP-R and JCDAS; (2) differences in skill appear especially during dry and intermediate seasons in the Cordillera Blanca; (3) the optimum horizontal scales largely vary between the different reanalyses, and horizontal grid resolutions of the reanalyses are poor indicators of this optimum scale; and (4) using reanalysis ensembles efficiently improves the performance of individual reanalyses.
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TL;DR: In this paper, the ability of seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 to reproduce present climate conditions in Europe and Africa was evaluated from a downscaling perspective, taking into account the requirements of both statistical and dynamical approaches.
Abstract: The present study assesses the ability of seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 to reproduce present climate conditions in Europe and Africa. This is done from a downscaling perspective, taking into account the requirements of both statistical and dynamical approaches. ECMWF’s ERA-Interim reanalysis is used as reference for an evaluation of circulation, temperature and humidity variables on daily timescale, which is based on distributional similarity scores. To additionally obtain an estimate of reanalysis uncertainty, ERA-Interim’s deviation from the Japanese Meteorological Agency JRA-25 reanalysis is calculated. Areas with considerable differences between both reanalyses do not allow for a proper assessment, since ESM performance is sensitive to the choice of reanalysis. For use in statistical downscaling studies, ESM performance is computed on the grid-box scale and mapped over a large spatial domain covering Europe and Africa, additionally highlighting those regions where significant distributional differences remain even for the centered/zero-mean time series. For use in dynamical downscaling studies, performance is specifically assessed along the lateral boundaries of the three CORDEX domains defined for Europe, the Mediterranean Basin and Africa.
165 citations
Cites background or methods from "Comparing the skill of different re..."
...Albeit seldom assessed in downscaling studies (Koukidis and Berg 2009; Hofer et al. 2012), reanalysis uncertainty is relevant for (1) the evaluation of ESM performance and (2) the applicability of the downscaling methods themselves....
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...With respect to (2), calibrating SD-methods and coupling RCMs require the large-scale predictor/boundary data to reflect ‘real’ atmospheric processes (Fernández et al. 2007; Koukidis and Berg 2009; Hofer et al. 2012)....
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TL;DR: In this article, the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing and distributional properties (i.e., correlation tests) and test results from seven downscaled methods were used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia.
Abstract: . Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.
125 citations
Book•
07 Apr 2010TL;DR: In this article, a conexion entre el rapido aumento de la temperatura de la atmosfera and the accion humana is discussed.
Abstract: La moderna ciencia ambiental ha logrado ejercer un profundo impacto en la cultura popular contemporanea, mediante la difusion de algunas ideas de indole catastrofista que cuestionan la tradicional nocion ilustrada del progreso humano ad-infinitum Una de las mas afortunadas es sin lugar a dudas la teoria del “calentamiento global”, la cual sugiere la existencia de una conexion entre el rapido aumento de la temperatura de la atmosfera y la accion humana El exito del concepto se ha traducido en una gigantesca produccion cientifica relacionada al tema y financiada por multitud de instituciones academicas, estatales y privadas
95 citations
TL;DR: In this paper, a 4-year long time series of distributed surface energy and mass balance (SEB/SMB) calculated using a process-based model driven by observations at Shallap Glacier (Cordillera Blanca, Peru) is used to calibrate a regression-based downscaling model that links the local SEB/sMB fluxes to atmospheric reanalysis variables on a monthly basis.
Abstract: . The El Nino/Southern Oscillation (ENSO) is a major driver of climate variability in the tropical Andes, where recent Nino and Nina events left an observable footprint on glacier mass balance. The nature and strength of the relationship between ENSO and glacier mass balance, however, varies between regions and time periods, leaving several unanswered questions about its exact mechanisms. The starting point of this study is a 4-year long time series of distributed surface energy and mass balance (SEB/SMB) calculated using a process-based model driven by observations at Shallap Glacier (Cordillera Blanca, Peru). These data are used to calibrate a regression-based downscaling model that links the local SEB/SMB fluxes to atmospheric reanalysis variables on a monthly basis, allowing an unprecedented quantification of the ENSO influence on the SEB/SMB at climatological time scales (1980–2013, ERA-Interim period). We find a stronger and steadier anti-correlation between Pacific sea-surface temperature (SST) and glacier mass balance than previously reported. This relationship is most pronounced during the wet season (December–May) and at low altitudes where Nino (Nina) events are accompanied with a snowfall deficit (excess) and a higher (lower) radiation energy input. We detect a weaker but significant ENSO anti-correlation with total precipitation (Nino dry signal) and positive correlation with the sensible heat flux, but find no ENSO influence on sublimation. Sensitivity analyses comparing several downscaling methods and reanalysis data sets resulted in stable mass balance correlations with Pacific SST but also revealed large uncertainties in computing the mass balance trend of the last decades. The newly introduced open-source downscaling tool can be applied easily to other glaciers in the tropics, opening new research possibilities on even longer time scales.
43 citations
TL;DR: In this paper, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines and the results show that the attained performance is sensitive to the reanalysis considered if climate change signal-bearing variables (temperature and/or specific humidity) are included in the predictor field.
Abstract: This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981–2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal–bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in o...
42 citations
Cites result from "Comparing the skill of different re..."
...…time series over the period 1981–2000 using a cross-validation scheme, results are found to be sensitive to the reanalysis dataset selected for calibration, which is in agreement with the few previous studies addressing this issue (Koukidis and Berg 2009; Hofer et al. 2012; Park et al. 2013)....
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References
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TL;DR: The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible, except that the horizontal resolution is T62 (about 210 km) as discussed by the authors.
Abstract: The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...
28,145 citations
"Comparing the skill of different re..." refers background or methods or result in this paper
...Environmental Prediction, NCEP (Kalnay et al. 1996; Kanamitsu et al. 2002; Saha et al. 2010); the European Centre for Medium-Range Weather Forecasts, ECMWF (Uppala et al....
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...Global reanalysis data are generated at four institutions worldwide (in cooperation with partner institutions not M. Hofer (&) Innrain 52f, Institute of Meteorology and Geophysics, University of Innsbruck, 6020 Innsbruck, Austria e-mail: Marlis.Hofer@uibk.ac.at B. Marzeion Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria T. Mölg Chair of Climatology, Technische Universität Berlin, Berlin, Germany mentioned here for brevity): the National Centers for Environmental Prediction, NCEP (Kalnay et al. 1996; Kanamitsu et al. 2002; Saha et al. 2010); the European Centre for Medium-Range Weather Forecasts, ECMWF (Uppala et al. 2005; Dee et al. 2011); the Japan Meteorological Agency, JMA (Onogi et al. 2007); and the National Aeronautics and Space Administration NASA (Rienecker et al. 2011)....
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...Even though reanalysis, by using the methods of numerical weather prediction, is the most accurate way to interpolate atmospheric data in time and space, its usefulness to document climatic trends and variability is debated (e.g., Kalnay et al. 1996; Bengtsson et al. 2004)....
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...Reanalysis data documentations and many other studies report about these limitations (e.g., Kalnay et al. 1996; Trenberth et al. 2001; Uppala et al. 2005; Rood and Bosilovich 2009; Chelliah et al. 2011; Dee et al. 2011)....
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...Here, we compare the skill of NCEP-R (reanalyses by the National Centers for Environmental Prediction, NCEP), ERA-int (the European Centre of Medium-range Weather Forecasts Interim), JCDAS (the Japanese Meteorological Agency Climate Data Assimilation System reanalyses), MERRA (the Modern Era Retrospective-Analysis for Research and Applications by the National Aeronautics and Space Administration), CFSR (the Climate Forecast System Reanalysis by the NCEP), and ensembles thereof as predictors for daily air temperature on a high-altitude glaciated mountain site in Peru....
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TL;DR: ERA-Interim as discussed by the authors is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which will extend back to the early part of the twentieth century.
Abstract: ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society
22,055 citations
European Centre for Medium-Range Weather Forecasts1, Lawrence Livermore National Laboratory2, Chinese Academy of Sciences3, Japan Meteorological Agency4, University of Reading5, Met Office6, Max Planck Society7, Royal Netherlands Meteorological Institute8, National Center for Atmospheric Research9, National Oceanic and Atmospheric Administration10
TL;DR: ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions as mentioned in this paper.
Abstract: ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re-analysis period, with assimilable data provided by a succession of satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean-buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA-40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA-40. This benefited from many of the changes introduced into operational forecasting since the mid-1990s, when the systems used for the 15-year ECMWF re-analysis (ERA-15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized.
A number of results indicative of the overall performance of the data assimilation system, and implicitly of the observing system, are presented and discussed. The comparison of background (short-range) forecasts and analyses with observations, the consistency of the global mass budget, the magnitude of differences between analysis and background fields and the accuracy of medium-range forecasts run from the ERA-40 analyses are illustrated. Several results demonstrate the marked improvement that was made to the observing system for the southern hemisphere in the 1970s, particularly towards the end of the decade. In contrast, the synoptic quality of the analysis for the northern hemisphere is sufficient to provide forecasts that remain skilful well into the medium range for all years. Two particular problems are also examined: excessive precipitation over tropical oceans and a too strong Brewer-Dobson circulation, both of which are pronounced in later years. Several other aspects of the quality of the re-analyses revealed by monitoring and validation studies are summarized. Expectations that the ‘second-generation’ ERA-40 re-analysis would provide products that are better than those from the firstgeneration ERA-15 and NCEP/NCAR re-analyses are found to have been met in most cases. © Royal Meteorological Society, 2005. The contributions of N. A. Rayner and R. W. Saunders are Crown copyright.
7,110 citations
TL;DR: In this article, statistical methods in the Atmospheric Sciences are used to estimate the probability of a given event to be a hurricane or tropical cyclone, and the probability is determined by statistical methods.
Abstract: (2007). Statistical Methods in the Atmospheric Sciences. Journal of the American Statistical Association: Vol. 102, No. 477, pp. 380-380.
7,052 citations
"Comparing the skill of different re..." refers background or methods in this paper
...The skill score, SSclim, can be calculated (Wilks 2006)...
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...The skill score, SSclim, can be calculated (Wilks 2006) SSclim ¼ 1 mse mseclim ð4Þ based on mse, the mean squared error mse ¼ 1 ncv X 2cv cv ¼ ys;v ŷs;vðxs;vÞ ð5Þ and mseclim, the mean squared error of the reference forecast, here a cross-validation-based estimate of the sample variance, as…...
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...SSclim is a measure of the covariance between modelled and observed time series (similar to the squared correlation coefficient, r(2)), but accounts further for errors in estimating the variance (reliability of the forecast), and for model biases (see Murphy 1988; Wilks 2006)....
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...SSclim is a measure of the covariance between modelled and observed time series (similar to the squared correlation coefficient, r2), but accounts further for errors in estimating the variance (reliability of the forecast), and for model biases (see Murphy 1988; Wilks 2006)....
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Book•
03 Jun 2011
TL;DR: The second edition of "Statistical Methods in the Atmospheric Sciences, Second Edition" as mentioned in this paper presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting.
Abstract: Praise for the First Edition: 'I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences' - "BAMS" ("Bulletin of the American Meteorological Society"). Fundamentally, statistics is concerned with managing data and making inferences and forecasts in the face of uncertainty. It should not be surprising, therefore, that statistical methods have a key role to play in the atmospheric sciences. It is the uncertainty in atmospheric behavior that continues to move research forward and drive innovations in atmospheric modeling and prediction. This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. "Statistical Methods in the Atmospheric Sciences, Second Edition" will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines. This book presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting. Chapters feature numerous worked examples and exercises. Model Output Statistic (MOS) includes an introduction to the Kalman filter, an approach that tolerates frequent model changes. It includes a detailed section on forecast verification, including statistical inference, diagrams, and other methods. It provides an expanded treatment of resampling tests within nonparametric tests. It offers an updated treatment of ensemble forecasting. It provides expanded coverage of key analysis techniques, such as principle component analysis, canonical correlation analysis, discriminant analysis, and cluster analysis. It includes careful updates and edits throughout, based on users' feedback.
6,768 citations