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    Average January precipitation statistics for the NCEP and corrected datasets: (a) number of precipitation days and (b) total precipitation (mm day−1) from the NCEP dataset, showing the spurious wavelike pattern in Northern Hemisphere high latitudes; (c), (d) same as in (a), (b), but as corrected by Sheffield et al. (2004) using data from the CRU TS2.0 global 1901–2000 climate dataset of MCJHN.

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    Fractional area of precipitation as a function of spatial scale for mild, midlatitude climate regions: (a) mean and (b) standard deviation for January; (c), (d) same as in (a), (b), but for July. Solid lines are the TRMM data; dashed lines are the GPCP data. The spatial scale is relative to the resolution of the precipitation datasets (TRMM = 0.25°; GPCP = 1.0°).

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    Average monthly distribution of the coefficient of variability for North America for the original daily, 1.0° GPCP dataset and three datasets that were downscaled from a 2.0° aggregated version of the GPCP data to 1.0° using various downscaling methods. The uniform method assigns precipitation values uniformly to the higher-resolution cells. The distributed approach uses a probabilistic method to determine the number of 1° grid cells within a 2° cell in which it is raining and distributes the 2° grid cell precipitation uniformly within these cells. The distributed with weighting method is the same as the distributed approach but weights the precipitation among the 1° grid cells based on the precipitation in neighboring cells. Similar results apply for the other continents.

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    Rmse over the six continents in autocorrelation for various daily lag lengths between the original daily, 1.0° GPCP dataset and three datasets that were downscaled from a 2.0° aggregated version of the GPCP data to 1.0° using various downscaling methods.

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    Difference in elevation (m) between the 2.0° and 1.0° grids. Elevation adjustments are made to air temperature, surface pressure, specific humidity, and downward longwave radiation whenever datasets are interpolated between grids.

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    Annual time series of precipitation averaged over global and continental land areas excluding Antarctica for the NCEP and CRU datasets. NCEP global mean precipitation = 2.2 mm day−1, CRU global mean precipitation = 2.0 mm day−1, and global mean bias in NCEP precipitation = 0.19 mm day−1 (70 mm yr−1).

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    Average DJF precipitation (mm day−1) for (a) NCEP, (b) NCEP scaled with the CRU dataset and adjusted for gauge biases, (c) the difference between CRU and NCEP, and (d) the difference between (b) and the CRU dataset.

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    Same as in Fig. 6, but for air temperature (°C). NCEP global mean air temperature = 7.6°C, CRU global mean air temperature = 8.1°C, and global mean bias in NCEP air temperature = −0.6°C.

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    Average seasonal difference in near-surface air temperature between the NCEP and CRU datasets (°C).

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    Average seasonal difference in downward longwave radiation (W m−2) between the NCEP and SRB datasets for 1984–94.

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    Same as in Fig. 10, but for shortwave radiation.

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    (a) Annual anomalies of global mean cloud cover for the CRU dataset (dark solid line) and cloud cover (solid line) and downward shortwave radiation (dashed line) from the NCEP dataset. (b) Annual time series of global mean downward shortwave radiation for the NCEP (solid line), SRB-QCSW (dark solid line), and NCEP (dashed line) corrected datasets. The corrected dataset has been scaled to be consistent with the SRB data and the long-term variation of the CRU cloud cover. (c) Same as in (b), but for longwave radiation for the NCEP, SRB-LW, and NCEP corrected datasets. The corrected dataset has been scaled using the probability swap method to be consistent with the mean and variability of the SRB data while retaining the year-to-year variation of the NCEP dataset. Global means are calculated over terrestrial areas excluding Antarctica.

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    Difference of monthly mean values of air temperature, precipitation, downward short- and longwave radiation, diurnal temperature range, and wet day frequency averaged over 1986–95 between the GSWP2 forcing dataset and this study. Color shading represents critical values of the Wilcoxon-signed rank test statistic at the 95% level. Red shading is where the GSWP data are greater; blue shading is where GSWP data are less. Regions where the two datasets are statistically similar are unshaded.

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Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling

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  • 1 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
  • | 2 Department of Earth System Science, University of California, Irvine, Irvine, California
  • | 3 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
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Abstract

Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0° dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation, which have been found to exhibit a spurious wavelike pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0° by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3 hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave radiation, specific humidity, surface air pressure, and wind speed) are downscaled in space while accounting for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP2). The final product provides a long-term, globally consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.

Corresponding author address: Dr. Justin Sheffield, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: justin@princeton.edu

Abstract

Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0° dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation, which have been found to exhibit a spurious wavelike pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0° by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3 hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave radiation, specific humidity, surface air pressure, and wind speed) are downscaled in space while accounting for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP2). The final product provides a long-term, globally consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.

Corresponding author address: Dr. Justin Sheffield, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: justin@princeton.edu

1. Introduction

The availability of large-scale, long-term datasets of the land surface water and energy budgets is essential for understanding the global environmental system and interactions with human activity, especially in the face of potential climatic change. However, consistent observations of components of the land surface water and energy budgets are routinely unavailable over large scales. While some terms of the surface water balance are reasonably well observed at least over some parts of the globe (precipitation and runoff in particular), other terms including evapotranspiration, soil moisture, and surface water are virtually absent of direct observations at large scales. Many of these variables are difficult to measure because of technical, monetary, and political limitations. In the case of soil moisture, which forms a key element for drought assessment and medium- and long-range prediction, global (or even regional, with only a few exceptions) in situ measurement networks are grossly inadequate for hydrologic prediction purposes, and land surface hydrology models have generally evolved without the use of direct observations of this key state variable. In terms of surface energy fluxes and evaporation, these are inherently difficult to measure and are thus essentially nonexistent over large scales. The use of remote sensing has provided great potential for the large-scale measurement of some variables (notably albedo, radiative surface temperature, and soil moisture) but is restricted to indirect quantities and, in the case of soil moisture, to low-vegetated regions and the top few centimeters.

It has been suggested that an alternative to estimating large-scale water cycle terms directly from observations is to use land surface models (LSMs), in either offline (forced with surface meteorological observations) or coupled (with an atmospheric GCM) modes (e.g., Lau et al. 1994; Liang et al. 1994; Levis et al. 1996; Werth and Avissar 2002). LSMs close the water budget by construct, so if the meteorological forcing data are accurate and model biases are small, these constructed water balance terms might be used in lieu of observations and provide a consistent picture of the water and energy budgets. Budget closure is not achievable from observations even at small scales. In fact, analyses of water and energy cycle variables estimated through observations (in situ and/or remote sensing) will not provide water cycle closure (Roads et al. 2003; Pan and Wood 2004) because of sampling and retrieval errors. However, through research activities like the North American Land Data Assimilation System (NLDAS; K. E. Mitchell et al. 2004) and Global LDAS (GLDAS; Rodell et al. 2004), the capability of land surface models to produce meaningful estimates of land surface hydrologic conditions over large areas has been demonstrated. Therefore, the contention is that observation-forced, offline simulations using state-of-the-art land surface models provide the best estimate of global water cycle variables.

Nevertheless, while estimates of water cycle variables obtained through land surface modeling are consistent, these estimates can be subject to large errors due to errors in model inputs and meteorological forcings. The importance of accurate forcings for large-scale land surface modeling efforts has been demonstrated previously (Berg et al. 2003; Fekete et al. 2004; Nijssen and Lettenmaier 2004). Results from the NLDAS project (K. E. Mitchell et al. 2004) indicated that first-order errors in the land surface simulations were due to inaccurate specification of the forcings and especially in precipitation (Robock et al. 2003; Pan et al. 2003). Other studies have shown the sensitivity of the land surface to the atmospheric forcings and especially precipitation (Berg et al. 2003; Fekete et al. 2004; Sheffield et al. 2004). The conclusion is that accurate forcings are necessary to provide accurate land surface simulations when compared to observations. The implication is that the use of sufficiently accurate forcings for land surface modeling in regions of sparse land surface observations will provide a suitable surrogate.

The availability of near-surface meteorological observations is not pervasive across all global areas and certainly not at the spatial and temporal resolutions that are required by land surface hydrologic models for most hydrologic applications. Coupled with the lack of temporal extent and consistency in the majority of observations, the development of forcing datasets using observations alone is unsatisfactory. With the increasing availability of remote sensing products, the prospect for the future is more promising, although this does not help in the development of long-term retrospective datasets that are required for extracting information about climate variability. In the global context, the use of atmospheric reanalysis products may be the only alternative for providing near-surface meteorological forcings at high temporal resolution. In contrast to the lack of terrestrial observations, the relative wealth of observations of the atmosphere and sea surface has allowed the emergence of a number of global, long-term, reanalysis datasets, such as the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR; Kalnay et al. 1996; Kistler et al. 2001), the 40- and 15-yr European Centre for Medium-Range Weather Forecasts [(ECMWF) ERA-40 and ERA-15; Gibson et al. 1997], the NCEP–Department of Energy (DOE; Kanamitsu et al. 2002), and the National Aeronautics and Space Administration Data Assimilation Office (NASA DAO; Schubert et al. 1993) reanalyses. These products are constructed using “frozen” versions of numerical weather prediction and assimilation systems that ingest a variety of atmospheric and sea surface observations to provide long-term, continuous fields in time and space of atmospheric (and land surface) variables. Although these model-derived fields may not be perfect, they are self-consistent and are used by many to force models of the land surface water and energy balances.

The power of reanalyses is their consistent and coherent framework for ingesting in situ and remote sensing data into a time- and space-discretized representation of the global land, oceans, and atmosphere, in a way that is essentially impossible to achieve directly from observations. Reanalysis has been suggested as an alternate approach to the problem of estimating the surface water balance, yet the reanalysis land surface products have many problems, including (i) the data that are assimilated are primarily atmospheric profiles of moisture, temperature, and other variables, and few (if any) land surface data are assimilated, resulting in the fact that they better represent variables, like atmospheric moisture and large-scale circulation than land surface variables like soil moisture and snow–water content; (ii) the land surface is forced by precipitation that is essentially a model output product so that errors in the model representation of precipitation (Janowiak et al. 1998; Trenberth and Guillemot 1998; Serreze and Hurst 2000), which can be quite large, are translated into errors in land surface fields like evapotranspiration, runoff, and soil moisture (e.g., Lenters et al. 2000; Maurer et al. 2001); and (iii) the effects of “nudging” the land surface to avoid drift have the effect of creating unrealistic soil moisture and of biasing (by large amounts in many cases) water budget flux terms (Betts et al. 1998, 2003a, b; Maurer et al. 2001; Roads et al. 2002a, b).

The effect that these biases have on land surface processes has to be addressed for these products to be of use as forcings in modeling studies. The results of Berg et al. (2003), who tested bias correction of the ECWMF reanalysis over North America, suggest that modelers using reanalysis products for forcing LSMs should consider a bias reduction strategy for their input forcings. Also, Sheffield et al. (2004) showed that systematic biases in reanalyses filter down into the modeled land surface fluxes and states. Ngo-Duc et al. (2005) found that precipitation biases in the NCEP–NCAR reanalysis were responsible for significant errors in modeled streamflow for continental-scale basins. Nevertheless, the results of such studies have shown that there is great potential for using hybrid datasets that combine reanalysis with observation-based datasets to remove biases. This approach retains the consistency and continuity of the reanalysis but constrains it to the best available observation datasets, which are generally available at coarser resolutions and reduced spatial and temporal extents.

This paper describes the development of a long-term, global dataset of near-surface meteorology that can be used to force models of the land surface water and energy budgets. Reanalysis products are combined with a suite of observation-based global datasets that are used to correct for biases in the monthly mean values and intramonthly statistics of the reanalysis and for downscaling in time and space to scales relevant for hydrologic applications. The dataset has global coverage over the extrapolar land surface (i.e., excluding Antarctica) at a 1.0° spatial resolution and a 3-hourly time step for 1948–2000.

Previously, a number of studies have developed large-scale, long-term datasets of a similar nature. However, these have been limited to smaller domains (e.g., Maurer et al. 2002), and/or shorter time periods [e.g., Levis et al. 1996; Nijssen et al. 2001b; International Satellite Land Surface Climatology Project (ISLSCP) I, Meeson et al. 1995; ISLSCP II, Hall et al. 2005; second Global Soil Wetness Project (GSWP2), Dirmeyer et al. 2005], or have been implemented globally, but at coarser spatial and temporal resolutions (e.g., Levis et al. 1996; Nijssen et al. 2001b; Ngo-Duc et al. 2005). This dataset represents an improvement over these products in terms of higher spatial and temporal resolution and global coverage and through the implementation of a number of enhancements in addition to correcting monthly biases and accounting for topographic effects. These enhancements include (i) adjustments to precipitation for gauge undercatch; (ii) temporal and spatial disaggregation of precipitation and downward solar radiation, accounting for observed subgrid and diurnal variability statistics; (iii) adjustment to rain day frequencies to match observed statistics; and (iv) trend correction and probability-weighted scaling for biases in downward short- and longwave (SW and LW, respectively) radiation.

2. Datasets

The forcing dataset is based on the NCEP–NCAR reanalysis, which includes near-surface meteorological variables from 1948 to the present. This time period provides the length of data necessary to infer the variability of the land surface water and energy budgets at up to multidecadal time scales. Alternative sources of reanalysis data are available (including the NCEP–DOE and ERA-40 products) that have been shown to be more accurate, in general, than the NCEP–NCAR reanalysis. However, the NCEP–NCAR reanalysis offers the benefits of a long time period and ongoing production that may offset any potential deficiencies that the bias correction methodology cannot address. Even if the ERA-40 or NCEP–DOE reanalysis has been used, the comparisons would reveal any biases that exist in these products and the correction methods could easily be applied.

The reanalysis data are combined with a suite of global, observation-based datasets of precipitation, temperature, and radiation. Table 1 summarizes the contributing datasets that are used in the development of the forcing dataset and these are described in more detail in the following sections. These observation-based datasets are generally available at coarser temporal resolutions (e.g., monthly). The reanalysis is essentially used to downscale these observation datasets to the subdaily temporal scale necessary for land surface modeling. In contrast, the observation-based datasets are generally available at higher spatial resolutions and are used to downscale the reanalysis in space. Thus, a hybrid forcing dataset is formed by using the submonthly variability in the reanalysis with bias corrections made on a monthly scale.

a. NCEP–NCAR reanalysis

The NCEP–NCAR reanalysis (hereafter referred to as the NCEP reanalysis) is a retrospective global analysis of atmospheric and surface fields extending from 1948 to the near present (Kalnay et al. 1996; Kistler et al. 2001). Available observations are assimilated into a global atmospheric spectral model implemented at a horizontal resolution of T62 (approximately 2.0°) and with 28 sigma vertical levels. The reanalysis is created using a frozen version of the data assimilation system, although assimilated observations are subject to changing observing systems. Consistent gridded output fields are generated continuously in time and are classified according to how they are determined and on their reliability. Class “A” variables are strongly influenced by assimilated observations and are therefore regarded as being the most reliable fields (e.g., upper air temperatures and geopotential height). Less reliable are class “B” variables (moisture, divergent wind, and surface parameters), which are influenced by observations and the model. Class “C” variables (surface fluxes and heating rates) are completely determined by the model and as such, are the least reliable. Precipitation is classified as a class C variable.

b. Climatic Research Unit monthly climate variables

The Climatic Research Unit (CRU) product is a 0.5° gridded dataset of monthly terrestrial surface climate variables for the period of 1901–98 (New et al. 1999, 2000) and updated to 2000 by T. D. Mitchell et al. (2004, manuscript submitted to J. Climate, hereafter MCJHN). The spatial coverage extends over all land areas including oceanic islands but excluding Antarctica. Fields of monthly climate anomalies, relative to a 1961–90 climatology, were interpolated using thin-plate splines from surface climate data. The anomaly grids were then combined with the 1961–90 climatology, resulting in grids of monthly climate over the full period. Primary variables (precipitation, mean temperature, and diurnal temperature range) are interpolated directly from station observations. The secondary variables (including rain day frequency and cloud cover) are interpolated from merged datasets comprising station observations and, in regions without station data, from synthetic data estimated using predictive relationships with the primary variables.

c. Global Precipitation Climatology Project daily precipitation

The Global Precipitation Climatology Project (GPCP) daily, 1997–present, 1.0° precipitation product (Huffman et al. 2001) is based on a combination of estimates from a merged satellite IR dataset over 40°N–40°S and a rescaling of the Susskind et al. (1997) Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) estimates at higher latitudes. Both contributing estimates are scaled to match the GPCP version 2 monthly satellite–gauge dataset totals (Huffman et al. 1997). Rain day frequencies of the IR-based estimate are adjusted to match data from the Special Sensor Microwave Imager (SSM/I) retrieval. The TOVS-based rain day frequencies are adjusted to the IR-based estimate at 40°N and 40°S separately.

d. Tropical Rainfall Measuring Mission 3-hourly precipitation

The Tropical Rainfall Measuring Mission (TRMM) is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA). The TRMM satellite was launched in November of 1997 and covers the Tropics between approximately 40°S and 40°N latitude. A number of experimental, real-time datasets based on the TRMM products and other satellite sources are currently available (Huffman et al. 2003), including the 3B42RT product, which is a merger of the 3B40RT and 3B41RT products. The 3B40RT product is a merger of all available SSM/I and TRMM Microwave Imager (TMI) precipitation estimates. The SSM/I data are calibrated to the TMI using separate global land and ocean matched histograms. The 3B41RT product consists of precipitation estimates from geostationary IR observations using spatially and temporally varying calibrations by the 3B40RT product.

e. NASA Langley monthly surface radiation budget

The NASA Langley Research Center product is available from 1983–1995 (Gupta et al. 1999) with an extension to 2001 being planned (Stackhouse et al. 2004). The primary data sources are satellite data from the International Satellite Cloud Climatology Project (ISCCP) C1 product (Rossow and Schiffer 1991) and from the Earth Radiation Budget Experiment (ERBE; Barkstrom et al. 1989). The C1 data provide cloud parameters derived from a network of geostationary satellites and from NOAA’s polar orbiters, along with temperature and humidity profiles from TOVS, on a 2.5° equal-area global grid and a 3-hourly time resolution. Monthly average clear-sky planetary albedos used for deriving surface albedos over snow/ice-free land areas were obtained from ERBE data. Two versions are available for short- and longwave radiation. First, the surface radiation budget (SRB)-SW and SRB-LW products are derived using the algorithms of Pinker and Laszlo (1992) and Fu et al. (1997), respectively. Second, the SRB-QCSW [quality check (QC)] and SRB-QCLW products are derived using the algorithms of Darnell et al. (1992) and Gupta et al. (1992), respectively. Comparison of these products with surface measurements has indicated that no one product is superior globally. For example, the SW product underestimates shortwave radiation over the higher elevations of the Tibetan Plateau and western China whereas the QCSW product does not, although comparisons undertaken for the GSWP2 over North America showed that the SW product performed better (GSWP2 forcing data available online at http://www.jamstec.go.jp/frcgc/research/p2/masuda/gswp/b1alpha.html). Given these preliminary analyses, the SRB-QCSW and SRB-LW products are used in this study.

3. Development of the forcing dataset

The development of the forcing dataset has progressed through a number of stages in terms of the spatial and temporal resolution and the sophistication of the correction methods. This has resulted in a number of intermediate products at coarser spatial and temporal resolutions. To perform calculations of the land surface water and energy cycles, land surface models in general require subdaily time series of the following near-surface atmospheric variables: precipitation, air temperature, downward short- and longwave radiation, surface pressure, specific humidity, and wind speed. Initially, the reanalysis variables were bilinearly interpolated from their native resolution of approximately 1.9° latitude × 1.875° longitude to a 2.0° regular grid with consideration for changes in elevation (see section 3b). This grid is commensurate with the observation-based datasets. Next, corrections are made to the daily precipitation statistics. All variables are then downscaled in space to 1.0° resolution (again with corrections for changes in elevation) and downscaled in time to a 3-hourly time step. Finally, biases at the monthly scale are removed. The following sections describe in detail the various stages in the development of the forcing dataset.

a. Correction of the reanalysis rain day anomaly

A high-latitude anomaly in the rain day statistics exists in the NCEP reanalysis in the winter months of the Northern Hemisphere (Cullather et al. 2000; Serreze and Hurst 2000; Sheffield et al. 2004). The anomaly results from the use of a simplified approximation for moisture divergence in the atmospheric forecast model used in the reanalysis. This results in a spurious wavelike pattern in the monthly rain day statistics that is most noticeable in the Northern Hemisphere winter at high latitudes (Fig. 1). The anomaly filters down into land surface states when the precipitation is used to force a land surface model (Sheffield et al. 2004). This study also showed the sensitivity of the land surface to the monthly rain day statistics. Using various estimates of rain day statistics but the same monthly totals to force a land surface model resulted in large differences in estimated water balance components (up to 9% error in global average evaporation and 17% in runoff, with higher values at the continental and regional scale). The conclusion is that it is vital to use the best estimates of not only monthly total precipitation but also monthly rain day statistics to achieve accurate simulations of the land surface water budget. A correction to the rain day statistics is described in detail in Sheffield et al. (2004) and a brief description is given here. The correction involves resampling the daily precipitation data to match the statistics of observation-based terrestrial daily precipitation datasets [CRU, GPCP, and a 15-yr gauge-based dataset developed by Nijssen et al. (2001b)]. To ensure consistency in the related meteorological variables, these are also resampled for the same days for which the precipitation was resampled. Figure 1 shows the correction of the NCEP precipitation using the CRU dataset. In addition to correcting the high-latitude rain day anomaly, differences that occur elsewhere are also removed. For example, in the Tropics, the high number of rain days in the NCEP dataset is reduced to the levels found in the CRU dataset. One side effect of this correction method is that spatial consistency at the daily time scale is not maintained because the correction is carried out independently on each grid cell. Sheffield et al. (2004) found that the effect of this on the large-scale terrestrial water balance was small compared to that resulting from the correction of the precipitation frequencies.

b. Spatial downscaling

1) Precipitation

Daily precipitation (corrected for monthly rain day biases) was downscaled from 2.0° to 1.0° resolution using a probabilistic approach based on relationships between precipitation intensity and grid cell fractional precipitation coverage. Precipitation varies considerably in space, especially at daily time scales, and it has been recognized that the land surface is sensitive to this variability (Johnson et al. 1993; Eltahir and Bras 1993) and that the effects on the atmosphere through enhanced feedback can be significant (Hahmann 2003). In general, low-intensity, large-area precipitation will tend to increase evaporation and infiltration compared to high-intensity, localized precipitation that will result in increased runoff production through infiltration excess.

For downscaling the precipitation data to 1.0° resolution, it is of interest to know the fractional wetted area within the 2.0° grid cell and the distribution of precipitation intensities among the 1.0° grid cells within. Fractional area is seasonally and geographically variable (Gong et al. 1994) and depends, among other factors, on storm type, grid resolution, and temporal scale (Eltahir and Bras 1993). Figure 2 shows an example of the scaling behavior of precipitation fractional area for the 0.25° TRMM and 1.0° GPCP datasets. Values for a range of spatial resolutions are shown such that the scale is relative to the dataset resolution. Fractional area of the TRMM data drops off rapidly with increasing scale but tends toward a threshold value (at larger scales) that appears to be seasonal. At the scale of the forcing dataset (1.0°), the fractional coverage of the TRMM data is on average much less than 1 (full coverage). This implies that downscaling by simply applying the 2.0° grid cell average precipitation to the four 1.0° cells may be inappropriate in terms of representing the spatial variability of wet and dry areas, with subsequent effects on the land surface hydrology. The GPCP data show similar relative scaling behavior (window sizes larger than 4.0° had a limited number of land cells and so were not included) that may indicate some form of self-similarity. Because of this and their multiyear global coverage, the GPCP data were considered suitable for downscaling the NCEP data.

The 2.0° daily data are downscaled using a probabilistic approach that relates the fractional area of precipitation with the precipitation intensity at 2.0°. From Bayesian theory, the probability of occurrence of precipitation within a 2.0° grid cell with fractional coverage (A) for a given grid cell average precipitation intensity (I) can be written as
i1520-0442-19-13-3088-e1
where p(I|A) is the conditional probability of an intensity I given a fractional area A. Probabilities for each term on the right-hand side of Eq. (1) are generated for each month and grid cell using data from the GPCP daily dataset, which has global and multiple year coverage. The NCEP daily data are then downscaled to 1.0° by sampling at random from the resultant conditional probability distribution p(A|I) to determine the spatial coverage of precipitation in terms of the number of 1.0° grid cells.

This disaggregation method was validated by reconstructing the 1.0° GPCP dataset from a 2.0° aggregated version using the probability distributions from Eq. (1). Figure 3 shows an example of the spatial statistics for the GPCP dataset and three different reconstructed versions over the North American continent. These three versions were created, respectively, by (i) distributing the 2.0° precipitation value uniformly over all 1.0° cells within; (ii) using the probabilistic approach to determine the fractional area of precipitation (number of 1.0° cells) within a 2.0° cell and distributing the 2.0° grid cell precipitation uniformly within these cells; and (iii) as for (ii), but weighting the precipitation among the wet 1.0° grid cells based on the precipitation in neighboring 2.0° cells. This final method assumes that precipitation occurrence has some spatial coherence and that the wet cells are deemed to have some simple connectivity with neighboring regions of precipitation. Figure 3 indicates that using the distributed method is little better than using a uniform approach, although the effect on land surface states may be quite different. However, weighting the distribution of the precipitation gives values of spatial variability that are consistent with the original GPCP data, although slightly higher. Similar results apply for other regions across the globe. The local autocorrelation of the original and reconstructed datasets was also calculated at various lag times to see whether the correction methods preserved the temporal characteristics of precipitation at each grid cell (Fig. 4). In general, the errors decrease with increasing lag time for all three methods. Again, the weighted method shows the least error, except for Europe and North America where all methods perform essentially the same at longer lag times.

2) Meteorological variables

The other meteorological variables (downward shortwave and longwave radiation, surface pressure, specific humidity, and wind speed) were disaggregated from 2.0° to 1.0° using bilinear interpolation but with adjustments for differences in elevation between the two grids. The effects of elevation on near-surface meteorology have been well documented and the difference in elevation between the two grids, as shown in Fig. 5, can be significant. The differences are most prominent in the foothills of mountain ranges where elevation may change by a few thousand meters within a 2.0° grid cell. Maximum differences are approximately 3000 m in the Himalayas, 1300 m in U.S. Rockies, and up to 3700 m in the Andes. To account for the differences in elevation, air temperature is first adjusted to the new grid elevation using the environmental lapse rate (6.5°C km−1). Following the methods of Cosgrove et al. (2003), which assume that the relative humidity is constant to avoid the possibility of supersaturation, the specific humidity, surface air pressure, and downward longwave radiation are also corrected for elevation changes to ensure consistency. These corrections were applied whenever a dataset (reanalysis and observational) was interpolated from one grid to another, whether for upscaling or downscaling, using the following method: first, the data were elevation adjusted to sea level (0.0-m elevation) on their native grid; then the data were interpolated to the new grid resolution and elevation adjusted to the topography of the new grid. This ensures that the interpolation procedure is free of any elevation effects on the data. For the interpolation between the 2.0° and 1.0° grids, these elevation adjustments resulted in significant changes in some regions, with a maximum change of approximately 25°C for temperature, 160 W m−2 for longwave radiation, 0.013 g g−1 for specific humidity, and 38 KPa for surface air pressure.

c. Temporal downscaling

1) Precipitation

The diurnal variation of precipitation is generally significant over land areas, especially during the summer months where diurnal amplitudes can be greater than 50% of the daily mean value (Dai 2001). High temporal resolution precipitation data (6 hourly or higher) are necessary to describe the diurnal cycle and are desirable for a multitude of hydrologic applications. Land surface hydrological processes are governed not only by the total amount of precipitation but also by the temporal structure of the precipitation, that is, the storm duration, intensity, and interstorm length. Marani et al. (1997) showed the effect of the temporal structure of precipitation on land surface hydrological processes to be considerable because of the nonlinear processes involved in partitioning precipitation. In the context of remotely sensed precipitation, which may suffer from undersampling of the diurnal cycle, similar conclusions have been reached (e.g., Soman et al. 1995; Salby and Callaghan 1997; Nijssen and Lettenmaier 2004). Yet the availability of subdaily precipitation data is intermittent in time and space, whether from gauges, radar, or remote sensing, and thus downscaling is required for large-scale applications. Direct use of the highest-resolution NCEP precipitation data (6 hourly) is unwarranted because it is acknowledged as being unreliable at less than monthly scales (Kalnay et al. 1996) and moreso given the biases in the rain day statistics as described in section 3a. The biases in storm duration and storm frequency have been partially accounted for through the correction of monthly rain day frequencies (section 3a). However, to obtain reasonable estimates of the diurnal cycle of precipitation, disaggregation of the daily values to a 3-hourly time step is necessary.

Temporal downscaling of precipitation has been attempted by many authors using a variety of techniques, including the use of probability distributions of precipitation statistics (e.g., Hershenhorn and Woolhiser 1987; Connolly et al. 1998), multifractal cascade methods (e.g., Olsson 1998; Gütner et al. 2001), and rectangular-pulses, stochastic rainfall generators (e.g., Bo et al. 1994; Cowpertwait et al. 1996). Here, a simple stochastic sampling approach is used based on 3-hourly precipitation distributions extracted from the TRMM real-time dataset. This product provides one of the few large-scale, observation-based, gridded precipitation datasets at subdaily resolution. The original TMI data suffer from undersampling of the diurnal cycle because of orbit characteristics and can only adequately describe the diurnal cycle at coarse time and space resolutions (Negri et al. 2002). However, the real-time product used here combines the TMI data with IR data to produce near-continuous coverage in time and space. Other alternative datasets could be used, including the TRMM-based Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) analysis (Hsu et al. 1997) and model-based products such as those from NASA’s Goddard Earth Observing System (GEOS), NCEP’s Global Data Assimilation System (GDAS), and the ECMWF.

The precipitation is downscaled from the daily NCEP product (with corrected rain day frequencies; section 3a) to a 3-hourly time step using a probabilistic approach based on sampling from the remote sensing–based TRMM dataset. The TRMM dataset consists of 3-hourly data covering the latitude band 50°S–50°N (see section 2). Monthly joint probability density functions (PDFs) of 3-hourly and daily precipitation amounts are derived from this dataset for each 1.0° grid cell using information from the surrounding 2.0° window. Three-hourly precipitation amounts are then sampled at random from these distributions for each NCEP daily total and then the eight 3-hourly values in each day are scaled to match this daily total. For regions outside of 50°S–50°N where TRMM precipitation data are not available, it is assumed that the PDFs are uniform across regional climate zones. Thus, joint PDFs were created for each continent and climate zone [based on the Koppen climate classification; see Critchfield (1983)] and these were used to downscale the daily NCEP data outside of 50°S–50°N, within the same climate zone and continent. This method was not feasible for regions within polar climate zones because there are no such regions within the 50°S–50°N latitude band. In this case, the probability distributions derived from cold climate zones were assumed to be representative of polar climates in the same continent.

The disaggregation method forces the statistics of the disaggregated data to match those of the TRMM dataset, while retaining the NCEP daily totals. The method was validated by recreating the TRMM product from its daily totals and indicated good performance in recreating the mean monthly diurnal cycle for different seasons and regions. The application of the disaggregation method is, however, dependent on the accuracy of the PDFs in representing actual diurnal cycles, and so is limited by the amount of data that contribute to them. The TRMM dataset used here has limited temporal coverage and may itself contain biases (Gottschalck et al. 2005). Updates from the TRMM real-time product and additional data from the retrospective version that started in 1998 will be added in the future to increase confidence in the PDFs and thus, the resulting disaggregated values. Data from gauge-based datasets (Dai 2001) that may be more reliable at regional scales could also be used.

2) Meteorological variables

The meteorological variables are simply downscaled from 6-hourly to 3-hourly resolution using linear interpolation. It is assumed that the diurnal cycle of these variables is represented adequately in the reanalysis, although the diurnal temperature range is adjusted to remove biases at the monthly scale [see section 3d(2)]. No attempt is made to adjust these variables to make them consistent with the disaggregated 3-hourly precipitation, as the relationships between precipitation and other meteorological variables are often weak. Downward solar radiation is interpolated in regard to the solar zenith angle to give a more realistic representation of the diurnal path of the sun. The type of reanalysis variable (downward shortwave and longwave radiation are time average values; air temperature, pressure, humidity, and wind speed are instantaneous values) is taken into account during the interpolation and all variables are converted into time average values.

d. Monthly bias corrections

As described in the introduction, systematic biases are inherent in the NCEP reanalysis (and other reanalysis products) at the monthly and seasonal scale. These biases are seasonally and regionally variable and will filter down into simulations of the land surface water and energy budgets. Adjustments are made to the reanalysis data (after downscaling and elevation corrections) so that the mean monthly values match those from available observation-based datasets. Adjustments are not made to the specific humidity, air pressure, and wind speed because global-scale, observation-based datasets for these variables do not exist.

1) Precipitation

The NCEP reanalysis precipitation is completely generated by the atmospheric forecast model and as such is acknowledged as being somewhat unreliable at the submonthly and local scale (Kalnay et al. 1996), although it does reveal useful information at larger space and time scales (Kalnay et al. 1996; Janowiak et al. 1998; Kistler et al. 2001). Biases in the NCEP reanalysis precipitation have been studied by many authors (e.g., Janowiak et al. 1998; Trenberth and Guillemot 1998; Serreze and Hurst 2000). Figure 6 shows the time series of global and continental average precipitation for the NCEP reanalysis and CRU datasets. The NCEP dataset is biased by 0.193 mm day−1 over global land areas excluding Antarctica, which is equivalent to about 70 mm yr−1. Errors in the NCEP reanalysis precipitation at monthly scales translate into errors in land surface fields like evapotranspiration, soil moisture, and snow cover (Sheffield et al. 2004). The effect on runoff generation has been investigated by Ngo-Duc et al. (2005), who found that biases in the NCEP reanalysis precipitation contributed the largest errors in resultant large-basin river discharge when compared to biases in air temperature and radiation. To remove the biases in the NCEP product, the daily values are scaled so that their monthly totals match those of the CRU dataset before disaggregation to a 3-hourly time step as follows:
i1520-0442-19-13-3088-e2
where the asterisk indicates a corrected value and the subscripts indicate the data source (NCEP or CRU) and the temporal resolution (3 hourly, daily, or monthly). Gauge-based precipitation measurements are often subject to losses from wind and wetting losses and due to solid precipitation (Goodison et al. 1998). Adam and Lettenmaier (2003) describe a global dataset of adjustment ratios that can be used for correcting gauge undercatch and can result in an increase in precipitation of about 12% globally. These catchment ratios can be applied to precipitation climatologies or to individual years in the reference period of the dataset (1979–98; see Adam and Lettenmaier 2003). For this study, the monthly CRU precipitation dataset is adjusted using these catchment ratios before being used to scale the NCEP daily totals.

Figure 7 shows the effect of the monthly bias corrections on the NCEP reanalysis precipitation. These adjustments result in changes in global terrestrial precipitation (excluding Antarctica) of −8.8% (−0.19 mm day−1 or −70.3 mm yr−1) after scaling to the CRU monthly values and −1.7% (−0.037 mm day−1 or 13.7 mm yr−1) after also adjusting for gauge undercatch. Although the reduction in global precipitation by scaling to the CRU values is offset by the undercatch adjustment, there are substantial regional changes. Figure 7c shows the December–February (DJF) biases in the NCEP dataset when compared to the CRU dataset. The largest biases in the reanalysis are over Greenland, the central and southeast United States (for DJF only), and in northern India during June–August (JJA; not shown). There are large positive biases in mid- and high northern latitudes during the summer (not shown), most notably in Canada and Alaska, central Europe, and throughout Eurasia to China. In the Tropics, the biases are spatially variable and seasonally dependent. For example, in Amazonia, the biases in the NCEP precipitation tend to be negative in the southwest during September–November (SON) and DJF and shift northward during the other part of the year. Conversely, large positive biases generally occur in the east in an opposite pattern. This indicates the poor representation of the seasonal cycle of the tropical moisture patterns in the NCEP dataset (Trenberth and Guillemot 1998). Of note is the correction to the spurious wavelike pattern in high northern latitudes as described in section 3a. Figure 7d shows the mean DJF map of adjustments for gauge biases, which are generally positive, with the largest increases in Greenland, the central and northeast United States, parts of northern Eurasia, and scattered regions in the Tropics.

2) Temperature

The NCEP air temperature is calculated from the modeled atmospheric variables, which are constrained by upper air observations and surface pressure, but no assimilation of screen-level observations is carried out. It is a B-class variable (Kalnay et al. 1996) in the reanalysis classification, as it is strongly influenced by the model parameterization of surface energy fluxes. Kalnay and Cai (2003) compared NCEP surface air temperature with station-based observations over the United States and found that the interannual variation was well represented, although the upward trend over time was significantly less than that observed. Similar results were found by Kistler et al. (2001) at global scales. Simmons et al. (2004) looked at continental and regional scales and again found good agreement with interannual variability and generally lower warming trends in the Northern Hemisphere but distinct and probably incorrect regions of cooling in Australia and southern South America.

Figure 8 shows the mean annual time series of 2-m air temperature for the NCEP and CRU datasets for global and continental land areas excluding Antarctica. The average annual global bias in the NCEP dataset is −0.56°C. Comparison of the seasonal average air temperatures shows much larger regional and seasonal differences (see Fig. 9). Most notably, in Siberia and western Canada, and Alaska in the Northern Hemisphere winter, biases in the NCEP reanalysis can reach in excess of 5°C. Low biases are evident in the Himalayan range and Greenland, again of the order of 5°C, with smaller biases throughout the Tropics and scattered areas in northern Africa and central Asia. Biases in air temperature can be directly linked to changes in the land surface water budget through modifications of evaporation and thus soil moisture (e.g., Qu et al. 1998). To remove these biases, the NCEP temperature data were adjusted to match the CRU monthly values by shifting the NCEP values by the difference between the NCEP and CRU monthly average values:
i1520-0442-19-13-3088-e3
In addition to scaling the 3-hourly values so that their monthly mean matched the CRU monthly values, the diurnal cycle of temperature for each day was scaled so that the monthly mean diurnal temperature range (DTR) matched the CRU monthly DTR values but the daily average value was unchanged as follows:
i1520-0442-19-13-3088-e4
Adjustments were made to the specific humidity, surface air pressure, and downward longwave radiation as outlined in section 3b(2) to make them consistent with the new temperature values.

3) Downward short- and longwave surface radiation

Incoming shortwave radiation incident at the earth’s surface is the primary energy source for the land surface and drives evapotranspiration and snowmelt. Therefore, accurate specification of these forcing fluxes is essential for land surface modeling. Snow accumulation and melt are particularly sensitive to incoming longwave radiation (e.g., Schlosser et al. 2000), although Morrill et al. (1999) found that energy and water budgets were not sensitive to the diurnal cycle of longwave radiation. Downward surface short- and longwave radiation are completely predicted by the NCEP reanalysis forecast model, and, as with precipitation and air temperature, contain systematic biases at seasonal time scales. Local-scale comparisons indicate biases in both the long- and shortwave products that may be systematic across geographic regions. Brotzge (2004) found that the NCEP dataset consistently overestimated downward surface shortwave radiation by 17%–27% over 2000–01 when compared to two Oklahoma Mesonet sites. Longwave radiation was underestimated; but to a lesser degree. Betts et al. (1996) found similar results when making a comparison with data from the First ISLSCP Field Experiment (FIFE) for 1987 and concluded that these problems are generally attributed to the NCEP model atmosphere being too transparent and to too few clouds being produced, which may be systematic of large-scale atmospheric models in general. At larger scales, comparisons with remote sensing–based data have revealed large-scale biases. For example, Berbery et al. (1999) found positive biases of 25–50 W m−2 over the United States when compared to the Geostationary Operational Environmental Satellite (GOES)-based product of Pinker and Laszlo (1992).

Several global SRB datasets have been developed in recent years including the Global Energy and Water Cycle Experiment (GEWEX)–NASA Langley Surface Radiation Budget Project 1984–95 product (Stackhouse et al. 2004) and the ISCCP global 1983–2000 product (Zhang et al. 2004). These datasets provide surface short- and longwave fluxes that have been validated against ground measurements. The latest version of the NASA Langley SRB product (release 2.0) is used here. Comparisons with ground-based measurements from the Baseline Surface Radiation Network (BSRN) indicate that errors are within measurement uncertainty. A comparison of the SRB and NCEP downward longwave data is shown in Fig. 10 as seasonal averages for 1984–95. The mean bias in the NCEP dataset is 15.8 W m−2 over global land areas excluding Antarctica. There are large regional biases of the order of 50–100 W m−2 across the Sahara, Middle East, central Asia, the Andes, and to a lesser extent, in the western United States and Australia. The biases tend to be highest in the Northern Hemisphere spring and summer. The comparison of downward shortwave radiation is summarized in Fig. 11. The mean bias in the NCEP shortwave data is −41.5 W m−2 over global land areas. The biases tend to be larger in the spring and summer of each hemisphere in mid- to high latitudes. These exceed −60 W m−2 across the northern United States and Canada, northern Europe, Siberia, and central Asia during the boreal summer and in the southern part of South America in the austral summer. In the Tropics, there is reasonable agreement throughout the year.

Analysis of station data has shown that shortwave radiation at the earth’s surface has decreased over large regions during 1960–90 (Gilgen et al. 1998), which has been attributed to increases in cloud cover. More recently, studies of station data (Wild et al. 2005) and satellite measurements (Pinker et al. 2005) indicate that these downward trends have reversed over the past decade or so, possibly due to reductions in aerosols. However, the trend in global terrestrial shortwave radiation from the reanalysis shows a spurious upward trend (Fig. 12b). Therefore, the reanalysis shortwave radiation is adjusted so that first, systematic biases are removed at the monthly scale so that it matches the mean of the SRB data for 1984–94, and second, trends over the full 50-yr period are consistent with observations.

Using the relationship between cloud cover and surface downward shortwave radiation (Thornton and Running 1999), a new time series of radiation is constructed that is consistent with observed trends. A linear regression was developed at each grid cell between the monthly anomalies of reanalysis cloud cover and shortwave radiation. This relationship was then used to predict monthly anomalies of shortwave radiation from observation-based estimates of cloud cover anomalies from the CRU dataset. The resultant time series was then converted to actual values whose monthly climatology over 1984–94 matched that of the SRB dataset. This was done by subtracting the mean monthly climatology for 1984–94 from the time series of anomalies and then adding the mean climatology of the SRB dataset. In this way, the new time series is consistent with the SRB data over the limited period of overlap but imposes the long-term trends as derived from observed cloud cover. The NCEP 3-hourly values are then scaled so that their mean values match this new monthly time series as follows:
i1520-0442-19-13-3088-e5
where the subscript SRB+CRU indicates the time series of monthly SW values derived from the CRU cloud cover and scaled to the SRB dataset.
Downward longwave radiation is bias corrected using a probability matching method that scales the reanalysis monthly values to match the mean and variability of the SRB values but retains the year-to-year variation of the NCEP data. Figure 12c shows no apparent global trend in the NCEP data, which is consistent with station-based observations of long-term trends that are within the bounds of measurement error (Wild et al. 2001). Therefore, no attempt is made to alter the long-term trends in the NCEP monthly values. The probability matching method replaces each of the NCEP monthly mean values over 1948–2000 with a monthly value from the SRB time series that has the same cumulative probability as the NCEP value. The cumulative probabilities were calculated from PDFs of the NCEP and SRB monthly time series. The new monthly time series was then used to scale the NCEP 3-hourly values as follows:
i1520-0442-19-13-3088-e6
Figure 12 shows the global mean monthly time series of downward short- and longwave radiation for the NCEP, SRB, and the scaled NCEP datasets.

4. Discussion and conclusions

The goal of this study is to provide a global dataset of forcings that has long temporal and global coverage and is consistent in time and space. In this respect, the dataset makes use of the latest available global meteorological datasets and combines them with state-of-the-art reanalysis to form a consistent, high-quality dataset. Nevertheless, an essential part of the development of any dataset is validation against independent data sources, which will quantify the errors and known biases and hopefully instill confidence in the use of the data. This is a difficult task given the general lack of large-scale observations and the fact that potential validation datasets are better utilized in the development of the forcing dataset to produce the highest-quality dataset possible.

The intended application of the forcing dataset is for long-term, large-scale modeling, where the focus of interest is on the variation of the land surface over seasonal to annual time scales and across regional and continental space scales. Here, it is more important to ensure that the statistics of the forcing data are correct rather than trying to replicate the finescale features of the historic record. For example, the forcing dataset is unlikely to recreate actual historic storm events partly because the correction of the daily precipitation frequencies may disrupt spatial coherence at the daily time scale. This is because the disaggregation and correction methodologies are designed to match the observed data only in a statistical sense while providing consistency among variables where possible, and any detrimental effects on the terrestrial water budget will be small at seasonal and regional scales.

The dataset can be evaluated by forcing a land surface model and comparing the resultant water and energy fluxes and states with observations, such as streamflow records, snow cover extent, and in situ soil moisture measurements. Several studies have shown that evaluating land surface model simulations over large areas requires a detailed examination of all aspects of the modeling process (e.g., Nijssen et al. 2001a, b; PILPS2-E Experiment, Nijssen et al. 2003; Bowling et al. 2003; the series of NLDAS papers, K. E. Mitchell et al. 2004). In addition to the errors in the forcings, there are also uncertainties in the land surface model structure, physical parameterizations, and input parameters (vegetation, soils, etc.) as well as in the observations themselves. The relative contribution of these factors to the differences from observations is difficult to discern without a detailed examination of all aspects of the modeling process and is a work in progress.

Nevertheless, it is possible to evaluate the dataset against similar bias-corrected forcing products. This is done next by comparing it to the GSWP2 forcing dataset, which uses a similar strategy to combine reanalysis with observations, although for a much shorter time period.

a. Comparison with GSWP2 forcing dataset

The goal of GSWP2 is to develop global datasets of soil moisture and other hydrologic variables from multiple land surface models and to investigate the differences and sensitivities of these models. The GSWP forcing dataset has the same temporal (3 hourly) and spatial (1.0°) resolution but for a shorter time period (1986–95), is based on the NCEP–DOE reanalysis, and is described in detail by Zhao and Dirmeyer (2003). This section compares monthly mean values of precipitation, temperature, and radiation from the two datasets as absolute differences and using the nonparametric Wilcoxon-signed rank test of differences. The monthly mean diurnal temperature range and daily precipitation frequencies are also examined, as these generally have a significant impact on the hydrologic cycle. Figure 13 shows the mean annual differences and statistical significance of differences in the monthly means for these variables.

1) Monthly mean temperature

Comparison of the monthly temperatures revealed differences that are consistent with differences in the observations used to create the two datasets. Both use long-term monthly temperature from CRU, but different versions (GSWP uses version 1.0 and this study uses the updated and extended version 2.0). Using the Wilcoxon-signed rank test, the null hypothesis that the median difference of monthly means between the two datasets is zero for each grid cell at the 95% confidence level was tested. Figure 13 shows that the null hypothesis can be rejected in the majority of regions, possibly due to changes in contributing gauges between the two versions of the CRU dataset and to the effects of using different datasets to correct for elevation effects. The GSWP uses the ISLSCP elevation product and this study uses the National Geophysical Data Center (NGDC) 2-min elevation dataset aggregated up to 1.0° resolution. The mean difference is 0.0965°C over global land areas and maximum monthly differences of up to 4°–5°C occur in parts of the Himalayas and Tibetan Plateau, Northern Greenland, and in small isolated regions scattered across the globe.

2) Monthly mean precipitation

Monthly precipitation shows widespread differences that are statistically significant, which is to be expected given the independent sources of observation data used by each dataset. The GSWP data are based on Global Precipitation Climatology Center (GPCC) monthly data, which are used to scale the NCEP–DOE submonthly precipitation amounts. Corrections are also applied for gauge undercatch. Data from the Global Precipitation Climatology Project (GPCP) are blended in for regions where the density of contributing gauges for the GPCC product is low. For this study, the CRU monthly means are used, with corrections for gauge undercatch based on the analysis of Adam and Lettenmaier (2003). Several regions stand out as being coherently biased one way or the other. The GSWP dataset is generally greater across midlatitudes in both hemispheres, with larger differences greater than 1.0 mm day−1 in Scandinavia, the Pacific Northwest, Alaska, the eastern United States, and southern South America. It is generally lower in the Tropics and high northern latitudes, most notably in Central America, the Amazon basin, northern Canada, and Greenland. The global mean annual bias in the GSWP is 0.0661 mm day−1 (24.1 mm yr−1).

3) Monthly mean downward short- and longwave radiation

For downward short- and longwave radiation, both forcing datasets use SRB products, although different versions (this study uses the SRB-QCSW and SRB-LW datasets and GSWP uses the SRB-SW and SRB-QCLW datasets). Additionally, this study uses observed cloud cover data to adjust the interannual variability of the shortwave monthly means, whereas the GSWP uses the data as is because of the direct overlap with the SRB time period. The differences between the two datasets are consistent with the differences between the SRB-SW and SRB-QCSW datasets (global mean bias for GSWP is −8.2 W m−2). The GSWP is generally smaller, with the greatest differences across a band stretching from northern Africa to Japan, and also in the western and northeast United States. For longwave, the differences are again consistent with the differences between the SRB-LW and SRB-QCLW. The SRB-QCLW (GSWP) values tend to be larger (global mean bias of 6.0 W m−2) and distributed similarly to the shortwave differences, but are lower at high latitudes.

4) Diurnal temperature range

Both datasets use CRU DTR products to correct the air temperature. As for mean temperature, the GSWP dataset uses CRU version 1.0 and version 2.0 is used here. For the GSWP dataset, the 3-hourly air temperature values are scaled by the ratio of the CRU to NCEP–DOE reanalysis DTR values but with restrictions on the size of the ratio to avoid excessive values. A similar method is used here [section 3d(2)] but with no restriction on the ratio of CRU to NCEP values. The mean global bias for GSWP is −1.4°C and maximum monthly differences can exceed 5°C. GSWP is generally larger in the Western Hemisphere and the Tropics, during the wet season, and is lower predominantly over central Asia and other dry regions worldwide.

5) Daily precipitation frequency

The distribution of precipitation within a month in terms of the number of wet and dry days plays an important role in partitioning the monthly precipitation into runoff and evaporation (Sheffield et al. 2004). The GSWP dataset makes no adjustment to daily precipitation frequencies and therefore uses the NCEP–DOE as is. For this study, the NCEP–NCAR reanalysis daily data are resampled to match observation-based datasets of precipitation frequencies. The GSWP has on average 0.48 fewer precipitation days per month globally. GSWP is generally greater in the humid Tropics (except for northwest South America) and southwest South America and is smaller in most other regions, especially in higher northern latitudes. Maximum monthly differences are generally less than 4 precipitation days but can reach 10 precipitation days in small regions in Greenland, central Siberia, and the humid Tropics.

b. Future improvements

The emphasis has been on using global-scale observation datasets to ensure consistency in space; nevertheless, better quality datasets exist in terms of spatial and temporal resolution but with smaller spatial and temporal extents. For example, for the temporal disaggregation of precipitation at high northern latitudes, it was assumed because of the lack of coverage by the TRMM dataset that the diurnal cycle in cold, midlatitude climates is representative of neighboring polar regions. Subdaily station data from Canadian surface airways products and the former Soviet Union (Razuvaev et al. 1998) are available for a significant number of high-latitude locations and can be used to derive the probability distributions used for the disaggregation. Furthermore, most monthly gridded precipitation datasets also do not allow for orographic effects. As the network of rain gauges that contributes to these datasets is not generally located in regions of complex and elevated topography, this usually results in an underestimation of precipitation, by as much as 3 times (Adam et al. 2006). The correction method of Adam et al. (2006) uses a simple catchment water balance method to calculate adjustments to precipitation. These changes may be incorporated into new versions of the dataset in the future, although concerns over consistency in time and space may make this somewhat counterproductive. In addition, as improved and extended versions of observation-based datasets used in this study become available, they will be incorporated where applicable.

c. Dataset availability

The forcing dataset will be made available over the Internet (see online at http://hydrology.princeton.edu) in the Assistance for Land-surface Modeling activities (ALMA) netcdf format (version 3), which is a standard data exchange format for land surface scheme forcing and output data. The development of the final 1.0°, 3-hourly dataset has gone through a number of intermediate stages in terms of spatial and temporal resolution and these intermediate products will also be added to the archive. The following products are available:

  • Global, 2.0°, 1948–2000, daily
  • Global, 1.0°, 1948–2000, daily
  • Global, 2.0°, 1948–2000, 3 hourly
  • Global, 1.0°, 1948–2000, 3 hourly

The variables are precipitation, air temperature, downward short- and longwave radiation, surface pressure, specific humidity, and wind speed. Global coverage indicates terrestrial regions excluding Antarctica.

d. Concluding remarks

This paper describes a long-term, high-resolution, near-surface meteorological dataset that can be used for forcing hydrologic simulations of the land surface water and energy budgets. The necessity for accurate estimates of the spatial and temporal variation in terrestrial water and energy fluxes and states is evident and is the driving force in the development of high-resolution and long-term hydroclimatological datasets. The development of the highest-quality forcing datasets is a first and vital step toward this. Through research initiatives such as the World Climate Research Program (WCRP) Climate Variability and Predictability (CLIVAR) Program and the Global Energy and Water Cycle Experiment (GEWEX), the emphasis has been on the development and enhancement of large-scale datasets, through the use of increasingly better observational datasets and new assimilation and modeling techniques. This study is intended to form a part of this process by providing a benchmark forcing dataset that combines state-of-the-art reanalysis products with the most recent observation-based datasets. The goals in the development of this dataset are to provide consistency in time and space among variables from contributing datasets while trying to achieve the highest resolution that can be supported by the data. This dataset provides a significant improvement over the original reanalysis variables and can be used for a wide variety of applications and diagnostic studies in the climatological, hydrological, and ecological sciences.

Acknowledgments

This study was carried out with funding from NASA Grants NAG5-9414 and NAG8-1517. The NCEP–NCAR reanalysis data were provided by the NOAA–CIRES Climate Diagnostics Center in Boulder, Colorado, from their Web site (see online at http://www.cdc.noaa.gov/). The GPCP dataset was downloaded from the World Data Center for Meteorology (see online at http://lwf.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html). The TRMM dataset was also downloaded (available online at http://trmm.gsfc.nasa.gov). The CRU datasets were obtained from Dr. Tim Mitchell at the Climatic Research Unit (see online at http://www.cru.uea.ac.uk) at the University of East Anglia, United Kingdom. The SRB dataset was downloaded from the NASA Langley Research Center Atmospheric Science Data Center (see online at http://eosweb.larc.nasa.gov).

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Fig. 1.
Fig. 1.

Average January precipitation statistics for the NCEP and corrected datasets: (a) number of precipitation days and (b) total precipitation (mm day−1) from the NCEP dataset, showing the spurious wavelike pattern in Northern Hemisphere high latitudes; (c), (d) same as in (a), (b), but as corrected by Sheffield et al. (2004) using data from the CRU TS2.0 global 1901–2000 climate dataset of MCJHN.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 2.
Fig. 2.

Fractional area of precipitation as a function of spatial scale for mild, midlatitude climate regions: (a) mean and (b) standard deviation for January; (c), (d) same as in (a), (b), but for July. Solid lines are the TRMM data; dashed lines are the GPCP data. The spatial scale is relative to the resolution of the precipitation datasets (TRMM = 0.25°; GPCP = 1.0°).

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 3.
Fig. 3.

Average monthly distribution of the coefficient of variability for North America for the original daily, 1.0° GPCP dataset and three datasets that were downscaled from a 2.0° aggregated version of the GPCP data to 1.0° using various downscaling methods. The uniform method assigns precipitation values uniformly to the higher-resolution cells. The distributed approach uses a probabilistic method to determine the number of 1° grid cells within a 2° cell in which it is raining and distributes the 2° grid cell precipitation uniformly within these cells. The distributed with weighting method is the same as the distributed approach but weights the precipitation among the 1° grid cells based on the precipitation in neighboring cells. Similar results apply for the other continents.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 4.
Fig. 4.

Rmse over the six continents in autocorrelation for various daily lag lengths between the original daily, 1.0° GPCP dataset and three datasets that were downscaled from a 2.0° aggregated version of the GPCP data to 1.0° using various downscaling methods.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 5.
Fig. 5.

Difference in elevation (m) between the 2.0° and 1.0° grids. Elevation adjustments are made to air temperature, surface pressure, specific humidity, and downward longwave radiation whenever datasets are interpolated between grids.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 6.
Fig. 6.

Annual time series of precipitation averaged over global and continental land areas excluding Antarctica for the NCEP and CRU datasets. NCEP global mean precipitation = 2.2 mm day−1, CRU global mean precipitation = 2.0 mm day−1, and global mean bias in NCEP precipitation = 0.19 mm day−1 (70 mm yr−1).

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 7.
Fig. 7.

Average DJF precipitation (mm day−1) for (a) NCEP, (b) NCEP scaled with the CRU dataset and adjusted for gauge biases, (c) the difference between CRU and NCEP, and (d) the difference between (b) and the CRU dataset.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 8.
Fig. 8.

Same as in Fig. 6, but for air temperature (°C). NCEP global mean air temperature = 7.6°C, CRU global mean air temperature = 8.1°C, and global mean bias in NCEP air temperature = −0.6°C.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 9.
Fig. 9.

Average seasonal difference in near-surface air temperature between the NCEP and CRU datasets (°C).

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 10.
Fig. 10.

Average seasonal difference in downward longwave radiation (W m−2) between the NCEP and SRB datasets for 1984–94.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 11.
Fig. 11.

Same as in Fig. 10, but for shortwave radiation.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 12.
Fig. 12.

(a) Annual anomalies of global mean cloud cover for the CRU dataset (dark solid line) and cloud cover (solid line) and downward shortwave radiation (dashed line) from the NCEP dataset. (b) Annual time series of global mean downward shortwave radiation for the NCEP (solid line), SRB-QCSW (dark solid line), and NCEP (dashed line) corrected datasets. The corrected dataset has been scaled to be consistent with the SRB data and the long-term variation of the CRU cloud cover. (c) Same as in (b), but for longwave radiation for the NCEP, SRB-LW, and NCEP corrected datasets. The corrected dataset has been scaled using the probability swap method to be consistent with the mean and variability of the SRB data while retaining the year-to-year variation of the NCEP dataset. Global means are calculated over terrestrial areas excluding Antarctica.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Fig. 13.
Fig. 13.

Difference of monthly mean values of air temperature, precipitation, downward short- and longwave radiation, diurnal temperature range, and wet day frequency averaged over 1986–95 between the GSWP2 forcing dataset and this study. Color shading represents critical values of the Wilcoxon-signed rank test statistic at the 95% level. Red shading is where the GSWP data are greater; blue shading is where GSWP data are less. Regions where the two datasets are statistically similar are unshaded.

Citation: Journal of Climate 19, 13; 10.1175/JCLI3790.1

Table 1.

Summary of datasets used in the construction of the forcing dataset. The temporal resolutions given here are those used in this study but original data may be available at finer temporal resolutions. Variables are precipitation (P), surface air temperature (T), downward shortwave radiation (SW), downward longwave radiation (LW), surface air pressure (Ps), specific humidity (q), wind speed (w), and cloud cover (Cld).

Table 1.
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