Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models
01 Aug 2011-Journal of Hydrometeorology (American Meteorological Society)-Vol. 12, Iss: 4, pp 556-578
TL;DR: In this article, a methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations.
Abstract: Future climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Me...
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Potsdam Institute for Climate Impact Research1, International Livestock Research Institute2, Norwegian Water Resources and Energy Directorate3, University of Reading4, Met Office5, University of Kassel6, City College of New York7, International Centre for Theoretical Physics8, University of Nottingham9, University of Tokyo10, Chinese Academy of Sciences11, National Institute for Environmental Studies12, American Museum of Natural History13, Goethe University Frankfurt14, Max Planck Society15, Utrecht University16, University of Bonn17
TL;DR: It is shown that climate change is likely to exacerbate regional and global water scarcity considerably and GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.
Abstract: Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.
1,295 citations
Cites methods from "Impact of a Statistical Bias Correc..."
...All required climate variables have been bias-corrected (55) toward an observation-based dataset (56) using a newly developed method (21) that builds on earlier approaches (57) but was specifically designed to preserve the long-term trends in temperature and precipitation projections to facilitate climate change studies....
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TL;DR: The bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project (IMIMIP), was presented in this paper.
Abstract: . Statistical bias correction is commonly applied within climate impact modelling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. Bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean. Besides the general idea and technical details of the ISI-MIP method, we show and discuss the potential and limitations of the applied bias correction. In particular, while the trend and the long-term mean are well represented, limitations with regards to the adjustment of the variability persist which may affect, e.g. small scale features or extremes.
961 citations
Cites background or methods from "Impact of a Statistical Bias Correc..."
...In any case bias correction is tantamount to introducing a new level of uncertainty comparable in magnitude to the spread of the climate projections across the climate models or with regards to the emission pathways (Hagemann et al., 2011)....
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...…correction of simulation data is broadly applicable to the climate impacts research (Robock et al., 1993; Berg et al., 2003; Ines and Hansen, 2006; Hagemann et al., 2011; Dosio and Paruolo, 2011), since it offers crucial advantages for impact modelling applications compared to using raw climate…...
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...In any case bias correction is tantamount to introducing a new level of uncertainty comparable in magnitude to the spread of the climate projections across the climate models or with regards to the emission pathways ( Hagemann et al. , 2011). The choice of an appropriate methodology depends strongly on the context. A review of state-of-the-art bias correction methods is given by Maraun et al.(2010). Statistical bias correction of simulation data is broadly applicable to the climate impacts research ( Robock et al....
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...…ERA-40 and CRU is included in the WFD. Additionally, the monthly mean for precipitation is corrected with the Global Precipitation Climatology Centre full dataset version 4 (GPCC) to account for the systematic underestimation of precipitation measurements in the WFD (cf.Hagemann et al., 2011)....
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...As in previous bias correction applications (e.g. in Water-MIP), we assume that the observational and simulated datasets are well approximated by a gamma distribution (excluding the days with zero precipitation)....
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Norwegian Water Resources and Energy Directorate1, International Livestock Research Institute2, Potsdam Institute for Climate Impact Research3, Wageningen University and Research Centre4, University of Kassel5, National Institute for Environmental Studies6, Max Planck Society7, City College of New York8, Utrecht University9, University of Bonn10
TL;DR: The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K).
Abstract: Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future.
953 citations
Cites background or methods from "Impact of a Statistical Bias Correc..."
...Bias correction can impact present-day simulated runoff numbers strongly, but the impact on projected relativewaterflux changes, which is the focus in this paper, are much smaller (23, 26)....
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...Note also that bias correction has been applied to the GCM data (23, 24)....
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...CMIP3 data were prepared for the hydrological model simulations within the WATCH project (3, 23), and the CMIP5 data were prepared for ISI-MIP (24)....
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University of Chicago1, University of East Anglia2, Potsdam Institute for Climate Impact Research3, University of Kassel4, Utrecht University5, City College of New York6, Argonne National Laboratory7, University of Nottingham8, Norwegian Water Resources and Energy Directorate9, International Institute for Applied Systems Analysis10, Wageningen University and Research Centre11, National Institute for Environmental Studies12, Lund University13, Columbia University14, Goddard Institute for Space Studies15, University of Tokyo16, University of Natural Resources and Life Sciences, Vienna17, Max Planck Society18, Chinese Academy of Sciences19, University of Bonn20
TL;DR: This work compares ensembles of water supply and demand projections driven by ensemble output from five global climate models and suggests surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.
Abstract: We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.
827 citations
TL;DR: In this article, the first international validation of reanalysis for wind energy, testing NASA's MERRA and MERRA-2 in 23 European countries, was reported, showing significant spatial bias, overestimating wind output by 50% in northwest Europe and underestimating by 30% in the Mediterranean.
802 citations
Cites methods from "Impact of a Statistical Bias Correc..."
...Several methods of bias correction are employed, ranging in complexity from additive and linear scale factors to quantile mapping [39,40]....
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References
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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
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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.
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TL;DR: In this paper, a database of monthly climate observations from meteorological stations is constructed and checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomalities with opposite signs in different seasons.
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4,011 citations
Additional excerpts
...1 (CRU; Mitchell and Jones 2005)....
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01 Jul 2000
TL;DR: Nakicenovic, N., Alcamo, J., Davis, G., Vries, B. van; Victor, N.; Zhou, D. de; Fenhann, J.; Gaffin, S.; Gregory, K.; Grubler, A.; Jung, T. La; Michaelis, L.; Mori, S; Morita, T.; Pepper, W.; Pitcher, H.; Price, L., Riahi, K; Rogner, H-H.; Sankovski, A; Schlesinger, M.; Shuk
Abstract: Author(s): Nakicenovic, N.; Alcamo, J.; Davis, G.; Vries, B. de; Fenhann, J.; Gaffin, S.; Gregory, K.; Grubler, A.; Jung, T.Y.; Kram, T.; Rovere, E.L. La; Michaelis, L.; Mori, S.; Morita, T.; Pepper, W.; Pitcher, H.; Price, L.; Riahi, K.; Roehrl, A.; Rogner, H-H.; Sankovski, A.; Schlesinger, M.; Shukla, P.; Smith, S.; Swart, R.; Rooijen, S. van; Victor, N.; Zhou, D.
3,431 citations
"Impact of a Statistical Bias Correc..." refers methods in this paper
...…simulations followed specific assumptions for the evolution of greenhouse gases and aerosols, which have been defined by the Intergovernmental Panel on Climate Change (IPCC; Houghton et al. 2001) and are described in the IPCC Special Report on Emission Scenarios (SRES; Nakićenović et al. 2000)....
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