The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data

Weedon, Graham P.; Balsamo, Gianpaolo; Bellouin, Nicolas; Gomes, Sandra; Best, Martin; Viterbo, Pedro

doi:10.1002/2014wr015638

Cited by 977 publications

(864 citation statements)

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“…The reanalysis temperature correction methods are very similar across forcing data sets (section A3). However, for precipitation, there are a variety of reanalysis correction methods and reference gridded observations [Weedon et al, 2014], substantively leading to the larger contribution of the forcing data set to the uncertainty in projected precipitation extremes.…”

Section: /2017jd026613mentioning

confidence: 99%

“…A2.1.1. Surface Pressure The reanalysis surface pressure at the 0.5°CRU mean elevation (https://crudata.uea.ac.uk/~timm/grid/CRU_ TS_2_1.html) was calculated by incorporating the effect of elevation correction on the reanalysis temperature as in previous work [Ngo-Duc et al, 2005;Weedon et al, 2011Weedon et al, , 2014: …”

Section: /2017jd026613mentioning

confidence: 99%

“…The correction methods applied to the surface pressure and air temperature were almost the same as those used in previous work [Ngo-Duc et al, 2005;Sheffield et al, 2006;Weedon et al, 2011Weedon et al, , 2014. For temperature, a recent work reports that discontinuities in daily time series across the boundaries of calendar months could occur due to the use of largely different monthly means for adjacent months when correcting reanalysis data [Rust et al, 2015].…”

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

“…All the forcing data sets used in this study, including S14FD, would commonly have this problem. There is a difference in the correction method for wind speed because this study corrected the 12 monthly climatologies of the climatic variable, as in Iizumi et al [2014], whereas many other forcing data sets use reanalysis wind speed data without any correction [Zhao and Dirmeyer, 2003;Ngo-Duc et al, 2005;Sheffield et al, 2006;Weedon et al, 2011Weedon et al, , 2014 or with elevation correction based on the logarithm profile [Hirabayashi et al, 2008b;Hanasaki et al, 2008].…”

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

“…Although bias correction is an unavoidable procedure for using GCM outputs as the inputs of impact models, it is an additional source of uncertainty in climate risk assessments because different bias-correction methods and reference daily weather data sets often lead to different impact outcomes Hawkins et al, 2013;Seaby et al, 2015;Baker et al, 2016]. Global retrospective meteorological forcing data sets, a hybrid of reanalysis data and gridded observations [e.g., Sheffield et al, 2006;Hanasaki et al, 2008;Hirabayashi et al, 2008a;Weedon et al, 2011Weedon et al, , 2014Iizumi et al, 2014], are used as the reference climatology in impact communities when bias correction is conducted at the global scale [e.g., Hempel et al, 2013]. Earlier work examined the uncertainties in the projected climate extremes associated with Representative Concentration Pathways IIZUMI ET AL.…”

Section: Introductionmentioning

confidence: 99%

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Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

et al. 2017

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The use of different bias‐correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias‐correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias‐correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values in the near future, and RCP is the most dominant source in the distant future. Our findings encourage climate risk assessments, especially those related to precipitation extremes, to employ multiple bias‐correction methods and forcing data sets in addition to using different GCMs and RCPs.

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Section: /2017jd026613mentioning

confidence: 99%

Section: /2017jd026613mentioning

confidence: 99%

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

Section: A3 Differences Between S14fd and Other Major Forcing Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

et al. 2017

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Attribution of streamflow trends in snow and glacier melt‐dominated catchments of the Tarim River, Central Asia

Duethmann

Bolch

Farinotti

et al. 2015

Water Resources Research

161

138

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Observed streamflow of headwater catchments of the Tarim River (Central Asia) increased by about 30% over the period . This study aims at assessing to which extent these streamflow trends can be attributed to changes in air temperature or precipitation. The analysis includes a data-based approach using multiple linear regression and a simulation-based approach using a hydrological model. The hydrological model considers changes in both glacier area and surface elevation. It was calibrated using a multiobjective optimization algorithm with calibration criteria based on glacier mass balance and daily and interannual variations of discharge. The individual contributions to the overall streamflow trends from changes in glacier geometry, temperature, and precipitation were assessed using simulation experiments with a constant glacier geometry and with detrended temperature and precipitation time series. The results showed that the observed changes in streamflow were consistent with the changes in temperature and precipitation. In the Sari-Djaz catchment, increasing temperatures and related increase of glacier melt were identified as the dominant driver, while in the Kakshaal catchment, both increasing temperatures and increasing precipitation played a major role. Comparing the two approaches, an advantage of the simulation-based approach is the fact that it is based on process-based relationships implemented in the hydrological model instead of statistical links in the regression model. However, data-based approaches are less affected by model parameter and structural uncertainties and typically fast to apply. A complementary application of both approaches is recommended.

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Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

et al. 2016

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The West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO-CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high-resolution atmosphere-ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.

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The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data

Cited by 977 publications

References 35 publications

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

Attribution of streamflow trends in snow and glacier melt‐dominated catchments of the Tarim River, Central Asia

Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

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