PUBLICATIONS Water Resources Research DATA AND ANALYSIS NOTE 1.12/214WR15638 Key Points: Global three hourly meteorological forcing data at half-degree spatial resolution Covers 1979 212 Improvements compared to the WATCH forcing data Correspondence to: G. P. Weedon graham.weedon@metoffice.gov.uk Citation: Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo (214), The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 5, 755 7514, doi:1.12/ 214WR15638. Received 27 MAR 214 Accepted 14 AUG 214 Accepted article online 19 AUG 214 Published online 17 SEP 214 The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data Graham P. Weedon 1, Gianpaolo Balsamo 2, Nicolas Bellouin 3, Sandra Gomes 4, Martin J. Best 5, and Pedro Viterbo 4 1 Met Office, Joint Centre for Hydrometeorological Research, Wallingford, UK, 2 European Centre for Medium-Range Weather Forecasts, Reading, UK, 3 Department of Meteorology, University of Reading, Reading, UK, 4 Instituto Dom Luiz, University of Lisbon, Lisbon, Portugal, 5 Met Office, Exeter, UK Abstract The WFDEI meteorological forcing data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA-Interim reanalysis data. We discuss the specifics of how changes in the reanalysis and processing have led to improvement over the WFD. We attribute improvements in precipitation and wind speed to the latest reanalysis basis data and improved downward shortwave fluxes to the changes in the aerosol corrections. Covering 1979 212, the WFDEI will allow more thorough comparisons of hydrological and Earth System model outputs with hydrologically and phenologically relevant satellite products than using the WFD. 1. Introduction The EU WATCH project [Harding et al., 211] included simulation of the global terrestrial water cycle in the twentieth century via a suite of hydrological models plus model intercomparison [Haddeland et al., 211]. To allow direct comparison of model outputs, the WATCH Forcing Data (WFD) were created. Covering 1958 21, the WFD was based on the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-4 reanalysis [Uppala et al., 25] interpolated to.5 3.5 resolution with sequential elevation correction of surface meteorological variables plus monthly bias correction from gridded observations. The WFD methodology allowed identical processing for every land grid point only global data sets with half-degree resolution were utilized in bias correction [Weedon et al., 21, 211]. The WFD precipitation compares favorably with TRMM satellite products and precipitation gauge data [Li et al., 213, 214]. Beyond WATCH, the WFD have proved useful for instance, in the bias correction of twenty-first century GCM outputs within ISI-MIP (Inter-Sectoral Impact Model Intercomparison Project [Hempel et al., 213; Dankers et al., 214; Prudhomme et al., 214]). WFDEI stands for: WATCH Forcing Data methodology applied to ERA-Interim data. The WFDEI uses the same methodology as the WFD, but as explained there are slight differences in the basis data, processing, and formatting. Table 1 lists the meteorological variables, variable units, file nomenclature, and outline methodology. The new data will be useful for late twentieth century and early twenty-first century forcing of global hydrological models and land surface models. The data set is especially valuable for modeling hydrological impacts in large catchments since the bias correction preserves the spatial continuity of large-scale or frontal precipitation events spanning multiple half-degree grid boxes [Weedon et al., 21, 211]. Moreover, snowfall rates are provided separately from rainfall rates, and downward shortwave radiation fluxes have been adjusted for the effects of changing atmospheric aerosol loading. Extending well beyond 21, the new data set will potentially be of more use than the WFD for comparing land hydrological and ecosystem model outputs with hydrologically and phenologically relevant satellite products, e.g., from the MODIS, GRACE, SMOS, ASCAT, and SEVERI sensors. Section 2 describes how the reanalysis data used for the WFDEI differ from those of the WFD. Section 3 covers differences in data processing especially concerning downward shortwave radiation fluxes and precipitation. Section 4 discusses validation and section 5 the differences between the WFDEI and WFD. Finally, section 6 describes formatting and access. WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 755
1.12/214WR15638 Table 1. Outline Methodology Based on Weedon et al. [21, 211] According to Variable for the WFDEI Files a Meteorological Variable WFDEI Filename Prefix Variable Units Elevation Correction After Interpolation Data Used for Monthly Bias Correction 1 m wind speed Wind_WFDEI_ m s 21 Nil Nil 2 m temperature b Tair_WFDEI_ K Via environmental lapse rate CRU TS3.1/3.21 average and CRU TS 3.1/3.21 average diurnal temperature range Surface pressure PSurf_WFDEI_ Pa Via changes in Tair Nil 2 m specific humidity Qair_WFDEI_ kg/kg Via changes in Tair and PSurf Nil Downward longwave radiation flux LWdown_WFDEI_ W m 22 Via fixed relative humidity and changes in Tair, PSurf, and Qair Nil Downward shortwave SWdown_WFDEI_ W m 22 radiation flux b Rainfall rate c Rainf_WFDEI_GPCC_ kg m 22 s 21 Final adjustment of snow/rainfall ratios (section 3.4) Rainfall rate d Rainf_WFDEI_CRU_ kg m 22 s 21 Final adjustment of snow/rainfall ratios (Section 3.4) Snowfall rate c Snowf_WFDEI_GPCC_ kg m 22 s 21 Final adjustment of snow/rainfall ratios (section 3.4) Snowfall rate d Snowf_WFDEI_CRU_ kg m 22 s 21 Final adjustment of snow/rainfall ratios (section 3.4) Nil CRU TS3.1/3.21 average cloud cover and effects of interannual changes in atmospheric aerosol loading [Weedon et al., 211] CRU TS3.1 number of wet days, GPCCv5/v6 precipitation totals, ERA-Interim ratio of rainfall/ precipitation, rainfall gauge correction CRU TS3.1 number of wet days, CRU TS3.11/ TS3.21 precipitation totals, ERA-Interim ratio of rainfall/precipitation, rainfall gauge correction CRU TS3.1 number of wet days, GPCCv5/v6 precipitation totals, ERA-Interim ratio of rainfall/ precipitation, snowfall gauge correction CRU TS3.1 number of wet days, CRU TS3.11/ TS3.21 precipitation totals, ERA-Interim ratio of rainfall/precipitation, snowfall gauge correction a Variable names in the WFDEI filenames and variable units are based on the ALMA (Assistance for Land-surface Modeling Activities) convention (http://www.lmd.jussieu.fr/ ~polcher/alma/). b 5 1979 29 using CRU TS 3.1, 21 212 using CRU TS3.21. c 5 1979 29 using GPCCv5 totals, 21 using GPCCv6 totals (NB: 211 212 GPCC-based WFDEI precipitation files are not available at date of paper completion June 214). d 5 1979 29 using CRU TS3.11 precipitation totals, 21 212 using CRU TS3.21 precipitation totals. 2. Differences Between ERA-4 and ERA-Interim ERA-4 used 3D-Var data assimilation whereas ERA-Interim involved 4D-Var [Uppala et al., 25; Dee et al., 211]. ERA-Interim uses 6 h windows to compare the GCM modeled state to observations, including allowance for their exact time stamp (not allowed for within 3D-Var), leading to stable updates that are consistent with the observations plus their errors. Compared to ERA-4, ERA-Interim used a more extensive suite of satellite, atmospheric sounding, and surface observations and provides substantial improvement in surface meteorological variables [Dee et al., 211]. Particularly relevant for hydrological variables are: improved humidity analysis; assimilation of satellite passive microwave data for total column water vapor in areas affected by cloud and rain, and assimilation of satellite-derived snow extent (in addition to observed snow depths already assimilated within ERA-4). ERA-Interim has a reduced Gaussian grid spectral model resolution of T255 (N128 or around.7 at the Equator) versus ERA-4 at T159 (N8 or around 1.13 at the Equator). This means that the original ERA- Interim data are much closer to the regular.5 3.5 spatial resolution and the elevation distribution used for the WFDEI (and WFD). 3. Data Processing 3.1. Extraction and Interpolation of Reanalysis Data The ERA-Interim reanalysis has a 12 h data assimilation cycle with analysis times generating forecast initial conditions at : and 12: UTC. Temperature, humidity, and wind speeds were obtained from the lowest atmospheric model level at 1 m. This avoids intermediate postprocessing steps (required for the ERA- Interim 2 m temperature product) and ensures consistency between these variables during spatial interpolation once adjusted to sea level. Restoration of interpolated data to the half-degree grid elevation with bias correction and sequential variable adjustment means that the final WFDEI temperature (Tair) and specific humidity values relate to the 2 m ( screen level ) levels of the observations (Table 1 [Weedon et al., 21, 211]). WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 756
1.12/214WR15638 In order to generate three hourly time series of land surface variables, an appropriate combination of data from the forecasts had to be found. The selection of forecasts balances two considerations: proximity in time to initial conditions (forecast accuracy declines over time) and avoidance of spin-up effects for clouds (affecting downward radiation) and precipitation fields [Kållberg, 211]. Temperature, specific humidity, wind speed (zonal and meridional components separately), and surface pressure were extracted from the range closest to the analysis time, i.e., the 13 to112 h forecasts. Hence, following the analysis and initial forecast condition at UTC, the data were obtained from the forecasts at 3, 6, 9, 12 UTC and following the 12 UTC analysis, the data were extracted from the forecasts at 15, 18, 21, 24 UTC. For the downward fluxes of radiation and precipitation affected by spin-up the fields are stored as accumulations starting from the analysis times and are conservative (since both energy and water have fully closed global budgets within a forecast, up to the accuracy of the numerical scheme). The three hourly surface fluxes were extracted as differences between successive forecasts from 19 to121 h. This procedure, referred to as deaccumulation, generates average fluxes for the 3 h interval preceding the time stamp. For example, after the UTC analysis time, data for 12 UTC were obtained by differencing the 19 and 112 h forecasts, data for 15 UTC used the difference of the 112 and 115 h forecasts. Similarly, data for 18 UTC used the 115 and 118 h forecasts and 21 UTC data used differences from the 118 and 121 h forecasts. After the 12 UTC analysis time, data for, 3, 6 and 9 UTC were similarly obtained from forecasts between 9 and 21 h later (i.e., extending into the next day). Following conversion from GRIB to NetCDF format, regridding to the regular half-degree latitude-longitude grid from the reduced Gaussian grid used IDL implementation of natural-neighbor interpolation by Delaunay triangulation [Lee and Schachter, 198]. 3.2. Elevation and Bias Correction The sequential elevation correction and monthly bias correction methods of Weedon et al. [21, 211] were applied to the regridded data. Initially in July 212, WFDEI files for the years 1979 29 were created using the CRU TS3.1 gridded observations [Harris et al., 213]. However, in summer 213, files for 21 212 were created using the, then just released, CRU TS3.21. For simplicity and to avoid extra computing costs and time, the existing WFDEI files were not recreated using CRU TS3.21 instead of CRU TS3.1. This is justified since on average (1979 29), the monthly Tair values are the same within 6.1 K for all grid boxes except 1 (out of 67,29). Furthermore, the average precipitation totals are the same within 64.% for all except just 672 grid boxes. The monthly gridded observations used in the bias correction of the WFDEI (Table 1) as provided by CRU and, for precipitation totals only, GPCC, involve nonhomogeneous coverage in terms of both the regional station density and the numbers of stations providing data from year to year [Harris et al., 213; Schneider et al., 213]. Average temperatures are generally well constrained by observations, but information about numbers of wet days, cloud cover, and precipitation totals have far lower station density especially at high latitudes and some tropical regions. Mountainous areas are typically poorly monitored for precipitation (gauges are often located in valleys and are thus not representative). Although corrections for orographic effects on precipitation are possible in principle [Adam et al., 26] such corrections were not feasible for the WFDEI. 3.3. Downward Shortwave Radiation Fluxes Monthly aerosol correction for downward shortwave fluxes (SWdown) was done separately for clear and cloudy sky. Consequently, corrections for aerosol loading were implemented after correction for differences between monthly modeled and observed-cloud cover (Table 1 [Weedon et al., 21, 211]). However, contrary to the understanding of Weedon et al. [211], ERA-4 and later reanalyses already included a correction for average seasonal variations in aerosol loading (but not the interannual variations [Dee et al., 211]). In areas of significant aerosol loading, particularly in the Northern Hemisphere summer [Weedon et al., 21, Figure 15] SWdown_WFD was slightly overcorrected (for the seasonal variations erroneously reducing mean monthly values). Hence, WFDEI processing of shortwave data required adjustment for interannual, but not seasonal, variations in aerosol loading. The revised aerosol distribution climatology of ERA-Interim also affected SWdown [Dee et al., 211]. WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 757
Water Resources Research 1.12/214WR15638 Figure 1. Annual average downward shortwave radiation flux (SWdown) on land outside Antarctica in 2 according to the WFDEI data, compared to WFDEI for 198 and compared to the WFD for 2 and 198. Figure 1 illustrates the annual average SWdown_WFDEI (top left) and SWdown_WFD (middle left) for 2. Particularly striking are the much larger average WFDEI values in the Sahara and southern Europe compared to the WFD. The differences (i.e., SWdown_WFDEI minus SWdown_WFD) are shown for 2 (bottom left) and for 198 (bottom right). There is a large increase ( 4 W m22) in the WFDEI data for northern Africa and central and southern Europe compared to WFD. Additionally, there is a decrease ( 3 W m22) in part of northern South America. These patterns of change and magnitude are explained by the effect of changes in aerosol distribution in ERA-Interim [Dee et al., 211, Figure 9b]. Changes in atmospheric aerosol loading have been pronounced over Europe for 1958 21 with the greatest dimming effect centered on 198 [e.g., Gedney et al., 214, Figure 1]. The increase in annual average SWdown_WFDEI due to reducing aerosol loading from 198 to 2 (Figure 1, top right) is very similar to that in SWdown_WFD (middle right). Changes in SWdown between the WFD and WFDEI are more influenced by the change in aerosol distribution climatology in ERA-Interim than by avoiding the overcorrection of annual variations in aerosol loading used in the WFD. The overcorrection of SWdown_WFD was corrected for in the detection and attribution study of Gedney et al. [214]. WEEDON ET AL. C 214. American Geophysical Union. All Rights Reserved. V 758
Water Resources Research 1.12/214WR15638 Total monthly Precipitation ERA-Interim (half-degree) Jan 2 ERA-Interim (half degree) Jul 2 9oN 6oN 3oN o 3oS 6oS 18o 6oW 12oW o 6oE 12oE 18o 18o 12oE 6oE o 6oE WFDEI-GPCC Jan 2 WFDEI-GPCC Jul 2 WFDEI-CRU Jan 2 WFDEI-CRU Jul 2 12oE 18o 9oN 6oN 3oN o 3oS 6oS 9oN 6oN 3oN o 3oS 6oS 1 2 3+ mm 1 2 3+ mm Figure 2. Total precipitation on land outside Antarctica in mm for January and July 2 according to ERA-Interim (regridded to a regular.5 3.5 latitude-longitude grid), WFDEI adjusted using GPCCv5 precipitation totals (WFDEI-GPCC), and WFDEI adjusted using CRU TS3.11 precipitation totals (WFDEI-CRU, Table 1). Note that locally the precipitation totals exceed 1 mm in January and 14 mm in July 2, but to illustrate the distribution of low-precipitation rate regions, the largest totals in the maps have been clamped at 3 mm. On average, SWdown_WFDEI has less bias compared to globally distributed observations [Iizumi et al., 214, Figure 7] and, over Europe, is likely to allow improved estimation of potential evaporation compared to using the WFD [cf. Kauffeldt et al., 213]. 3.4. Precipitation As for the WFD, the WFDEI have two sets of rainfall and snowfall files generated by using either CRU or GPCC precipitation totals (Table 1). The GPCCv6 database includes around 3 4 times as many precipitation stations as CRU (incorporating most of the latter as a subset [Schneider et al., 213]). As exemplified for January 2 and July 2 in Figure 2, the patterns of regridded ERA-Interim precipitation show a good match at a regional scale with both the CRU and GPCC observations (reflected by the WFDEI-GPCC and WFDEI-CRU monthly totals). Large local differences between the half-degree ERA-Interim totals and the observations were corrected during the bias correction of the WFDEI files (Table 1). The figure also shows the impact of the higher resolution of the GPCC product (linked to the higher station density) compared to utilizing the CRU precipitation totals. The WFD methodology provided rainfall and snowfall rates separately as diagnosed by the reanalysis rather than via simplistic use of 2 m air temperature [see Weedon et al., 211, Figure 2]. The final step of the WFD methodology for precipitation involved catch correction [Adam and Lettenmaier, 23] applied separately for rainfall and snowfall [Weedon et al., 21, 211]. However, during generation of the WFDEI WEEDON ET AL. C 214. American Geophysical Union. All Rights Reserved. V 759
1.12/214WR15638 precipitation rates, an error in the precipitation phase can arise locally where there are large elevation differences between the ERA-Interim and CRU grids (i.e., particularly in mountainous areas). For example, rainfall at low elevation on the ERA-Interim grid might be assigned to a much higher elevation on the CRU grid following interpolation where normally snow would be expected. Thus, a final new processing step was added to the WFD methodology to correct the most extreme cases of inappropriate precipitation phase. For each grid box and each calendar month over 1979 29, records of the minimum Tair during rainfall and the maximum Tair during snowfall ( phase temperature extremes ) were stored. Each phase temperature extreme is obtained from the library of eight 3 hourly steps 3 about 3 days (a month) 3 31 years, i.e., around 75 values. For each grid box and 3 h time step, the precipitation phase was switched if the combination of the phase with the elevation and bias-corrected Tair lay beyond a phase temperature extreme. Since the spatial resolution of elevation in the ERA-Interim is fairly similar to the CRU grid, in the vast majority of grid boxes and time steps, the precipitation phase remained unswitched. The lack of WFD snowfall in western Canada and Alaska was attributed by Kauffeldt et al. [213] to insufficient gauge catch correction. The catch corrections were unchanged for the WFDEI so potentially this problem could persist in these areas. However, Brun et al. [213] showed that across Northern Eurasia ERA- Interim-derived snow extent and depths show good agreement with field observations especially when scaled with GPCCv5 data (as used for the WFDEI). 4. Validation Weedon et al. [21, 211] compared three hourly WFD values to seven sites of flux tower field observations as selected given the availability of data up to 21 (the last year of WFD). We repeated this comparison for the WFDEI values using the same observations (Table 2) although there are limitations in comparing the grid box averages with flux tower data (the towers have a far smaller footprint [Weedon et al., 211]). The square of Pearson s correlation coefficient (i.e., r 2 ) is statistically significant for all variables except precipitation at Manaus. High autocorrelation in time series leads to positive bias in the calculation of Pearson s r. Hence, we also report radjusted 2 where this bias has been compensated for by prewhitening each of the paired time series prior to calculation of the correlation coefficient [Ebisuzaki, 1997]. Independently, Iizumi et al. [214] demonstrated the overall similarity in means and distributions for the WFDEI compared to near-global daily observations for: wind speed; Tair; humidity; SWdown and total precipitation. Two sites were selected, Tharandt, Germany [Gr unwald and Bernhofer, 27] and Manaus, Brazil [Araujo et al., 22], to illustrate and compare Tair, SWdown and precipitation rate observations to the WFDEI and WFD data from corresponding grid boxes (Figures 3 and 4). At Manaus, there is a substantial difference between the local time and UTC used for the WFDEI and WFD. Hence, to align the time series in Figure 4 it was necessary to shift the three hourly data to local time. Figure 3 shows that daily Tair, SWdown, and precipitation rates for the WFDEI and the WFD are in good agreement with the daily average flux tower observations at Tharandt. In particular, the reanalysis is very successful at representing the large-scale/frontal systems so that the synoptic variations in temperature, cloud cover (influencing SWdown), and precipitation rates are well reproduced. There is also remarkable agreement of subdaily temperatures. Sited under an area affected by significant aerosol loading the average SWdown_WFD at Tharandt is noticeably smaller than the average SWdown_WFDEI (section 3.3). At Manaus near the heart of Amazonia in Brazil, Figure 4 shows far lower agreement of the WFDEI and the WFD data with the observations than at Tharandt. This is indicative of the difficulty of GCM modeling of clouds that are predominantly related to local convective processes. Consequently, observed precipitation rates on subdaily and daily time scales are poorly reproduced at this site even though the data processing has adjusted the monthly precipitation to match the observations of numbers of wet days and GPCC precipitation gauge totals [Weedon et al., 21, 211]. 5. Differences Between the WFDEI and the WFD Data Sets Compared to the WFD, the WFDEI data were: derived from a different reanalysis with higher spatial resolution (section 2); adjusted to updated monthly observational data (sections 3.2 3.4), and more appropriately adjusted WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 751
1.12/214WR15638 o C mm mm mm d -1 (3h) -1 (3min) -1 W m-2 W m -2 o C 3 1 2-1 3 2 1 4 2 4 2 4 2 3 1 2-1 8 4 2 1 1 5 1 5 Flux tower (daily), WFD (daily), WFDEI (daily) Tharandt, Germany 2m Temperature Downwards shortwave radiation flux Rainfall + Snowfall rate 2 21 Year Flux tower (half hourly), WFD (3 hourly), WFDEI (3 hourly) 2 m Temperature Downwards shortwave radiation flux Rainfall + Snowfall rate February March April May 2 Figure 3. Time series of flux tower observations (black), WFD (blue), and WFDEI (red) 2 m air temperature, downward shortwave radiation flux, and rainfall plus snowfall rates at Anchor Station, Tharandt, Germany (5.96 N, 13.57 E). (top) Daily averages. (bottom) Half hourly (flux tower) and three hourly (WFD and WFDEI) data. The WFDEI and WFD precipitation data illustrated are derived from bias correction with GPCC monthly totals (rather than CRU totals). in terms of shortwave fluxes in relation to the effects of aerosol loading (section 3.3). Compared to satellite products, overall the SWdown_WFDEI is more reliable than SWdown_WFD [Iizumi et al., 214, Figure 7]. On average (monthly and longer), wind speed in the WFDEI is higher than in the WFD (averaging about 3. and 2.5 m s 21, respectively, T. Stacke, personal communication, 212). This is apparently due solely to a change in roughness lengths between ERA-Interim and ERA-4 since the methodology for generating wind speed files only involves interpolation (Table 1). On average, the WFDEI wind speed values are more reliable [Iizumi et al., 214, Figure 11]. These points of difference imply offsets between WFDEI and WFD values that will differ between location, variable, and time. Rather than blindly concatenating WFDEI and WFD data, users should first check whether offsets between WFD and WFDEI are significant for their particular application or model by utilizing the long period of data overlap (1979 21 inclusive). Across the various sites, meteorological variables and comparison metrics, there is generally an even spread of improved and degraded results in terms of agreement of the WFDEI with the field observations relative to the WFD (Table 2). However, at all sites, the WFDEI precipitation data are better than, or at least as good as, the WFD in terms of lower mean bias error, lower RMSE, and higher radjusted 2 (with the single exception of precipitation radjusted 2 at Hyyti al a). We attribute the improvements in precipitation to changes in hydrologically relevant variables within the ERA-Interim assimilation and modeling system (section 2). The different processing in terms of aerosol correction (section 3.3) lead to geographical variations in differences between SWdown_WFDEI and SWdown_WFD (Figure 1). Thus, from Table 2 at the European sites there are larger average differences (111.8, 128.1, 124.5, and 123.7 W m 22 for Hyyti al a, Tharandt, Vielsalm, and Collelongo, respectively) than elsewhere (12.3, 17.4, and 18.7 W m 22 for Harvard Forest, Bondville, and Manaus, respectively). WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 7511
1.12/214WR15638 Table 2. Comparison of Three Hourly WFDEI and WFD Data With Three Hourly FLUXNET Observations a Variable (Units) Location Flux Tower Mean WFDEI Grid Box Mean WFD Grid Box Mean WFDEI RMSE WFD RMSE 1 m wind speed (m s 21 ) Hyytiala 2.94 2.15 2.42 1.16 1.24.537.172.8 Tharandt 3.4 3.59 2.8 1.29 1.41.523.116.38 Vielsalm 2.49 4.26 2.84 2.25 1.12.653.184.161 Collelongo 1.52 2.46 2.11 1.53 1.54.287.125.15 Harvard Forest 2.38 2.47 2.36.97 1.9.367.68.84 Bondville 4.25 3.82 2.81 1.58 2.55.637.226.23 Manaus 2. 1.16 1.31 1.11 1.15.37.7.5 2 m temperature ( C) Hyytiala 4.2 4.7 4.24 2.33 2.2.949.65.552 Tharandt 8.73 8.88 8.91 2.46 2.71.917.61.31 Vielsalm 8.14 9.79 9.66 2.85 2.69.911.568.635 Collelongo b 7.34 15.2 14.91 8.41 8.46.84.5.26 Harvard Forest 8.4 8.79 8.93 4.35 3.73.86.335.354 Bondville 11.36 12.18 12.54 2.65 2.12.948.533.248 Manaus 26.3 27.26 27.3 3.18 2.97.374.34.313 Surface pressure (hpa) Hyytiala 991.5 992.6 993.2 3.1 3.5.936.588.365 Tharandt 972.2 963.4 965.1 9.5 9.7.812.599.89 Vielsalm 96.9 955.7 955.9 5.6 5.6.938.39.159 Collelongo b 84.3 93.9 896.8 66.3 57.2.8.398.285 Harvard Forest 985.2 979. 98.5 7.7 6.9.614.18.125 Bondville 99.6 991.6 993. 1.8 2.7.95.646.536 Manaus 14.2 12.9 996.1 5.5 9.8.15.33.347 2 m specific humidity (kg kg 21 ) Hyytiala.47.45.47.7.7.93.257.12 Tharandt.57.6.6.9.1.913.18.56 Vielsalm.62.66.65.12.13.816.119.98 Collelongo b.6.89.88.35.36.661.92.33 Harvard Forest.61.64.6.18.16.83.69.78 Bondville.79.71.75.18.13.99.164.25 Manaus.178.212.178.49.3.42.12.43 Downward longwave (W m 22 ) Hyytiala 294.2 285.4 287.5 34.6 31.1.675.163.188 Tharandt 315.2 36.9 314.8 37.5 27.4.412.128.49 Vielsalm 323.2 36.9 318.9 3.2 24.5.666.35.479 Collelongo 34. 297.2 292.7 31.8 45.3.575.362.92 Harvard Forest 313.5 31. 3.3 34.6 35.5.767.343.343 Bondville 319.2 312.8 315. 23.2 28.5.87.272.12 Manaus 424. 433. 422.5 17.2 17.7.431.328.269 Downward shortwave (W m 22 ) Hyytiala 1.1 1.5 88.7 98.8 61.3.674.446.752 Tharandt 12.5 129.1 11. 85.1 88.6.819.79.655 Vielsalm 11.9 126.9 12.4 77.9 74.9.847.757.731 Collelongo 145.1 171.1 147.4 131.9 95.6.716.68.747 Harvard Forest 132.2 157.8 155.5 98.6 83.3.834.758.843 Bondville 159. 181.7 174.3 88. 88.7.889.849.837 Manaus 189.9 185.3 176.6 98.4 19.3.851.821.646 Rainfall plus snowfall (mm (3 h) 21 ) Hyytiala.26.24.24.935.943.146.2.46 Tharandt.285.321.321 1.68 1.173.184.69.25 Vielsalm.314.394.394 1.48 1.97.212.97.39 Collelongo.398.337.329 2.222 2.269.63.12.8 Harvard Forest.387.427.431 3.171 3.141.1.1.1 Bondville.255.363.379 1.791 1.865.68.23.19 Manaus.956.91.643 3.874 3.888... a Weedon et al. [211] provide a location map and references to descriptions of the FLUXNET data. For each site, the countries and full years covered (inclusive) and number of points (N) are: Hyyti al a, Finland 1997 21, N 5 14,68; Tharandt, Germany 1997 21, N 5 14,68; Vielsalm, Belgium 1997 21, N 5 14.68; Collelongo, Italy 1996 21, N 5 17,536; Harvard Forest, Massachusetts, USA 1994 21, N 5 23,376; Bondville, Illinois 1997 21, N 5 14,68; Manaus, Brazil 1999 21, N 5 8768. RMSE 5 root mean square error (in variable units). r 2 5 square of Pearson s correlation coefficient, radj 2 5r2 Adjusted see section 4. r2 and radj 2 values that are significantly different from zero (P <.1) are indicated in bold (note large N), all r values are positive. Note that adjustment has been made for the difference between Greenwich Mean Time for WFDEI and WFD and the local time of the Fluxnet data. b Collelongo Flux tower elevation is about 155 m, but the half-degree grid box average elevation is 986 m causing large difference between the observed and the WFDEI (and WFD) 2 m temperature, surface pressure, and specific humidity. WFDEI r 2 WFDEI r 2 Adj WFD r 2 Adj 6. Formatting and Access Monthly WFDEI files of both three hourly and daily average data in NetCDF format use a full halfdegree grid (72 3 36 grid boxes) with the sea/large lakes flagged as missing data (NB WFD files store land points excluding Antarctica only). WFDEI values for Antarctica include elevation WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 7512
1.12/214WR15638 o C W m -2 mm d -1 W m -2 o C mm h -1 mm (3h) -1 4 3 2 1 3 2 1 1 5 1 5 1 5 4 3 2 1 5 15 1 5 1 1 Flux tower (daily), WFD (daily), WFDEI (daily) Manaus km34, Brazil 2m Temperature Downwards shortwave radiation flux Rainfall rate 2 21 Year 2m Temperature Flux tower (hourly), WFD (3 hourly), WFDEI (3 hourly) Downwards shortwave radiation flux Rainfall rate April May 21 June Figure 4. Time series of flux tower observations (black), WFD (blue), and WFDEI (red) 2 m air temperature, downward shortwave radiation flux, and rainfall rates at Manaus km34, Brazil (2.61 S, 6.21 W). (top) Daily averages. (bottom) Hourly (flux tower) and three hourly (WFD and WFDEI) data. The subdaily WFDEI and WFD data have been shifted from UTC to local time for plotting with the flux tower data. The WFDEI and WFD rainfall data illustrated are derived from bias correction with GPCC monthly totals (rather than CRU totals). correction but no bias correction due to the lack of observations. The 211 of the 67,42 CRU grid boxes outside Antarctica used in the WFD are incorrectly designated as land. These were omitted from the WFDEI files; leaving 67,29 land points outside Antarctica plus 27,533 within (the grid boxes are not of equal area). Filenames (Table 1) include the month indicated numerically and indication if daily, rather than three hourly, data. For example, WFDEI three hourly temperature data for May 21 are contained in file Tair_WF- DEI_215.nc whilst daily average rainfall data based on GPCC precipitation totals for October 21 are contained in Rainf_daily_WFDEI_GPCC_211.nc. WFDEI radiation and precipitation flux rates represent averages for the 3 h prior to the time stamps (NB: the WFD files give average fluxes for the 3 h after the time stamp). Conversion of precipitation rates from kg m 22 s 21 to accumulated millimeters of rain, or accumulated millimeters of (water equivalent) snow, requires multiplication by 1,8 (seconds in 3 h) to obtain mm (3 h) 21. For daily files, multiply by 86,4 (seconds in 24 h) to obtain mm d 21. Gzipped WFDEI files are freely available from the WATCH ftp site at IIASA, Vienna (online: ftp://rfdata:forcedata@ftp.iiasa.ac.at and click on /WATCH_Forcing_Data and then /WFDEI, or for ftp downloads: site5 ftp.iiasa.ac.at, username 5 rfdata and password 5 forcedata, then use: cwd/wfdei). The /WFDEI directory includes files listing grid box elevations and locations. An alternative source of WFDEI data is provided by the DATAGURU website for climate-related data at Lund University (V. Lehsten et al., personal communication, 214). This allows extraction of data from a user-defined grid box or region for a specified time interval (http://dataguru.nateko.lu.se/ log in, then go to Application ). WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 7513
1.12/214WR15638 Acknowledgments Rich Ellis (Centre for Ecology and Hydrology, Wallingford, UK) checked the subdaily WFDEI files using the land surface model JULES v3. [Best et al., 211]. Similarly, Tobias Stacke (MPI Hamburg, Germany) checked the daily WFDEI files using the global hydrology model MPI-HM vr44 [Stacke and Hagemann, 212]. Our thanks to David Wiberg and Pavel Kabat of IIASA, Vienna, Austria for permission to store and make freely accessible the WFDEI files on their ftp server. G.P.W., N.B., and M.B. were supported by the Joint DECC and Defra Integrated Climate Program DECC/Defra (GA111). G.P.W. was partly funded by the European Commission s 7th Framework Program, under grant agreement 282672, EMBRACE project. References Adam, J. C., and D. P. Lettenmaier (23), Adjustment of global gridded precipitation for systematic bias, J. Geophys. Res., 18(D9), 4257, doi:1.129/22jd2499. Adam, J. C., E. A. Clark, and D. P. 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Best (211), Creation of the WATCH Forcing Data and its use to assess global and regional reference crop evaporation over land during the twentieth century, J. Hydrometeorol., 12, 823 848, doi:1.1175/211jhm1369.1. WEEDON ET AL. VC 214. American Geophysical Union. All Rights Reserved. 7514