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Evaluation of Biases in JRA-25/JCDAS Precipitation
and Their Impact on the Global Terrestrial Carbon
Balance
Makoto Saito, Akihiko Ito, Shamil Maksyutov
To cite this version:
Makoto Saito, Akihiko Ito, Shamil Maksyutov. Evaluation of Biases in JRA-25/JCDAS
Precipita-tion and Their Impact on the Global Terrestrial Carbon Balance. Journal of Climate, American
Meteorological Society, 2011, 24 (15), pp.4109-4125. �10.1175/2011JCLI3918.1�. �hal-03203118�
Evaluation of Biases in JRA-25/JCDAS Precipitation and Their Impact
on the Global Terrestrial Carbon Balance
MAKOTOSAITO,* AKIHIKOITO,ANDSHAMILMAKSYUTOV
Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan (Manuscript received 28 June 2010, in final form 3 February 2011)
ABSTRACT
This study evaluates a modeled precipitation field and examines how its bias affects the modeling of the regional and global terrestrial carbon cycle. Spatial and temporal variations in precipitation produced by the Japanese 25-yr reanalysis (JRA-25)/Japan Meteorological Agency (JMA) Climate Data Assimilation System (JCDAS) were compared with two large-scale observation datasets. JRA-25/JCDAS captures the major distribution patterns of annual precipitation and the features of the seasonal cycle. Notable problems include over- and undersimulated areas of precipitation amount in South America, Africa, and Southeast Asia in the 308N–308S domain and a large discrepancy in the number of rainfall days. The latter problem was corrected by using a stochastic model based on the probability of the occurrence of dry and wet day series; the monthly precipitation amount was then scaled by the comparison data. Overall, the corrected precipitation performed well in reproducing the spatial distribution of and temporal variations in total precipitation. Both the cor-rected and original precipitation data were used to simulate regional and global terrestrial carbon cycles using the prognostic biosphere model Vegetation Integrative Simulator for Trace Gases (VISIT). Following bias correction, the model results showed differences in zonal mean photosynthesis uptake and respiration release ranging from 22.0 to 13.3 Pg C yr21, compared with the original data. The difference in the global terrestrial
net carbon exchange rate was 0.3 Pg C yr21, reflecting the compensation of coincident increases or decreases
in carbon sequestration and respiration loss. At the regional scale, the ecosystem carbon cycle and canopy structure, including seasonal variations in autotrophic and heterotrophic respiration and total biomass, were strongly influenced by the input precipitation data. The results highlight the need for precise precipitation data when estimating the global terrestrial carbon balance.
1. Introduction
Evaluation of the impact of industrial activity on the global climate system is a current priority in scientific research. In particular, the increase in atmospheric CO2
concentrations due to anthropogenic emissions and its potential influence on climate change have been widely discussed in terms of the global warming problem. These developments have led to increased interest in the global carbon balance of terrestrial ecosystems because of their potential functioning as carbon sinks (e.g., Nemani et al. 2003). Terrestrial ecosystems have a direct influence on
the global carbon cycle via their physiological processes, especially in the difference between the rates of photo-synthesis uptake and respiration release; consequently, the successful numerical simulation of global terrestrial CO2fluxes is a key to gaining a better understanding of
variability in atmospheric CO2 and the global carbon
budget. Yet our understanding of the behavior of the global-scale terrestrial biosphere remains incomplete, leading to uncertainties in the magnitude and spatial trends of the terrestrial carbon balance (e.g., Falkowski et al. 2000; Jones et al. 2003; Zaehle et al. 2005). To quantify the terrestrial carbon balance and reduce the uncertainty of estimates, it is necessary to further im-prove the performance of model simulations.
In simulations of global terrestrial CO2 fluxes using
prognostic biosphere models, carbon assimilation and plant and soil respiration are strongly dependent on the compartments of carbon pools in the canopy structure prognosticated by the model. The states of these com-partments are continuously modified by the assimilation
* Currentaffiliation: Laboratoire des Sciences du Climat et de l’Environnement, France.
Corresponding author address: Makoto Saito, Laboratoire des Sciences du Climat et de l’Environnement, Orme des Merisiers, F-91191 Gif sur Yvette, France.
and respiration rates of the canopy; that is, the feedback loop between canopy phenology and dynamics and CO2
fluxes. The productivity and structure of the canopy are sensitive to the changes in weather and climate, such as solar radiation, temperature, rainfall, evaporation, and CO2 concentration (Ro¨tter and van de Geijn 1999;
Roderick et al. 2001). In deciduous species, leaf phenol-ogy is mainly controlled by those environmental condi-tions (Williams et al. 1997). Accordingly, model output variables may vary with the choice of input atmospheric dataset. Consequently, when operating an offline model, it is necessary to employ comprehensive atmospheric forcing datasets with consistent temporal and spatial resolutions at the global scale and over long time scales to produce global interannual variability. The currently available forcing data that fulfill these requirements for driving a model are restricted to reanalysis or assimi-lation products. Among these, the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) and the European Cen-tre for Medium-Range Weather Forecasts (ECMWF) produce the most well-known reanalyses (Kalnay et al. 1996; Gibson et al. 1997), which have many applications, not only in biosphere modeling, and are widely used in a variety of studies (e.g., Annamalai et al. 1999; Ro¨denbeck et al. 2003; Tourigny and Jones 2009).
However, care is required when using the above reanalysis products despite their obvious benefits be-cause many investigations have identified biases in the reanalyses (e.g., Randel et al. 2000; Berg et al. 2003), resulting in poor model simulations of the surface water balance (Lenters et al. 2000), climatology (Serreze and Hurst 2000), and terrestrial ecosystem productivity (Hicke 2005). The bias in reanalysis products can arise from a number of factors, including imperfect physical param-eterization in the model, discontinuities in the operational records, and sparse coverage of additional observation datasets. Regional biases are especially likely to occur in the reanalysis precipitation field, as reanalysis precipita-tion is generated solely according to the model physics, making it dependent on the analyses and physical pa-rameterization used in the model. Indeed, Trenberth and Guillemot (1998) reported underestimations of rainfall amount and reduced interannual variability of precipita-tion in tropical areas for the NCEP–NCAR reanalysis. Betts et al. (2005) found that precipitation in the ECMWF reanalysis for the Amazon is high in the rainy season compared with observational data, whereas it is low in the dry season. Precipitation is a critical factor governing di-rect changes in vegetation productivity (e.g., Ciais et al. 2005); consequently, ecosystem modeling using reanalysis forcing with biased precipitation may produce unrealistic estimates of the terrestrial carbon balance. Therefore,
it is necessary to quantify the biases in reanalysis pre-cipitation via comparisons with independent observa-tions and to assess the uncertainty in model simulaobserva-tions associated with these biases to understand the global carbon balance.
We make an intensive assessment of the reanalysis/ assimilation dataset that was recently released by the Japan Meteorological Agency (JMA): the Japanese 25-year reanalysis (JRA-25)/JMA Climate Data As-similation System (JCDAS). One of advantages of JRA-25/JCDAS is the introduction of Special Sensor Micro-wave Imager (SSM/I) precipitable water content into the data assimilation system. For the period with the assimilation of SSM/I data, significant improvement is observed in spatial and temporal correlations of monthly mean JRA-25/JCDAS precipitation with the Climate Prediction Center (CPC) Merged Analysis of Pre-cipitation (CMAP; Xie and Arkin 1997), which is a monthly averaged precipitation dataset widely used in climate study, and the correlation coefficient is higher than those of NCEP–NCAR and ECMWF over the global area (Onogi et al. 2005; Bosilovich et al. 2008). In addition, Chinese snow data and wind speed profiles around tropical cyclones (TCs) are assimilated into the data for the first time. Incorporation of wind profiles, for example, improves detection of TC frequency espe-cially in the ocean area where observational data are sparse (Hatsushika et al. 2006). JRA-25/JCDAS, being freely available, is expected to provide operational meteorological forcing for a variety of scientific uses. In the present study, JRA-25/JCDAS precipitation is evaluated in comparison with two other observational datasets to identify the features of spatial and temporal variability. The biases in reanalysis/assimilation pre-cipitation are corrected using stochastic models. Sub-sequently, terrestrial ecosystem CO2fluxes and carbon
mass modeling at the global scale are calculated using a prognostic biosphere model with both original and corrected precipitation products, with the goal of im-proving the simulation of the terrestrial ecosystem. The overall aim of this study is to provide more robust esti-mates of the global terrestrial carbon balance.
2. Data and model
a. Large-scale precipitation datasets
The following analyses focus on JRA-25/JCDAS data, while two different long-term precipitation datasets are used as independent validations of the JRA-25/JCDAS precipitation fields (Table 1).
JMA conducted a long-term global reanalysis, JRA-25, over the period from 1979 to 2004. Subsequently, JMA started real-time operation of JCDAS after 2004.
JCDAS inherits the same system as JRA-25, and the data assimilation cycle is extended up to the present. For brevity, JRA-25/JCDAS data are referred to as JCDAS throughout this study. JCDAS products are available from 1979 to the present, and the latest datasets are being uploaded with a delay of just several days. A full description of JCDAS can be found in Onogi et al. (2007).
JCDAS has a spectral horizontal resolution of T106 (grid size of approximately 120 km) with 40 vertical layers in hybrid sigma–pressure coordinates; analyses are made every 6 h. Precipitable water retrieved from SSM/I bright-ness temperature data of the Defense Meteorological Sat-ellite Program (DMSP) are assimilated into the data after 1987. Observational data such as wind profile retrievals surrounding tropical cyclones and digitized Chinese snow depth are assimilated using a three-dimensional variational analysis method. The forecast model is a low-resolution version of the operational JMA Global Spectral Model. The land surface scheme of the Simple Biosphere model (SiB; Sellers et al. 1986) is employed with vegetation data from Dorman and Sellers (1989) to represent land surface pro-cesses, and land initial conditions are created using the scheme proposed by Tokuhiro (2002), which is a modified version of SiB. For representation of the convective pre-cipitation process in moist convectively unstable areas, a simplified Arakawa–Schubert-type scheme is used (Aonashi et al. 1997). The JCDAS precipitation rate used here is the sum of both 6-hourly convective precipitation and 6-hourly large-scale precipitation rate, which are stored in a JCDAS dataset labeled ‘‘fcst_phy2m.’’
The Climate Research Unit (CRU) at the University of East Anglia, United Kingdom, developed a 0.58 3 0.58 mean monthly terrestrial climatology, excluding Ant-arctica, for nine climate variables during the period 1961–90 (New et al. 1999). The precipitation dataset of the CRU is constructed by directly interpolating the observations over 19 000 gauge stations using a thin-plate spline technique. New et al. (2000) then extended these high-resolution climatological grid data to the pe-riod 1901–96 by combining them with monthly anomalies relative to the 1961–90 mean. Furthermore, Mitchell and Jones (2005) revised these 0.58 3 0.58 mean monthly pre-cipitation data by recalculating using an improved method. Here, we use the latest global monthly precipitation
dataset from CRU Time Series (TS) 3.1 products for the period 1979–2005.
The Global Precipitation Climatology Project (GPCP) provided a 2.58 3 2.58 global monthly precipitation dataset, combined with estimates from low-earth-orbit-satellite microwave data, geosynchronous-orbit-low-earth-orbit-satellite infrared data, and gauge observations, for the period between July 1987 and December 1995 (Huffman et al. 1997). This global monthly precipitation dataset was improved and time extended by Adler et al. (2003), as GPCP Version 2 monthly precipitation. The latest monthly GPCP dataset is labeled version 2.1 (Huffman et al. 2009). A high-resolution version, 1.08 3 1.08 global daily precipitation (GPCP 1DD), was developed by Huffman et al. (2001). GPCP 1DD consists of two esti-mates: the threshold-matched precipitation index (TMPI) over the latitude band of 408N–408S and the adjusted Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS; Smith et al. 1979) outside 408N–408S; both are scaled to sum to the GPCP version 2 monthly precipitation. The latest GPCP 1DD is labeled version 1.1 for the period from October 1996 to August 2009, and a 12-yr (1997–2008) dataset from this product is used in the present study. Details of the esti-mation algorithm are described in Huffman et al. (2001). b. Prognostic biosphere model VISIT
Here, we estimate the global terrestrial carbon cycle using a prognostic biosphere model, Vegetation In-tegrative Simulator for Trace Gases (VISIT; Ito 2010). VISIT is a process-based model of the terrestrial bio-sphere, derived from the ecosystem model of Ito and Oikawa (2002). A remarkable advance achieved in VISIT, compared with Ito and Oikawa (2002), is implementation of the schemes for trace gas emissions from soil, such as CH4and N2O. VISIT is therefore capable of simulating
changes in the structure and function of the ecosystem. Carbon dynamics in the model consists of carbon stor-age in five compartments: folistor-age, stem and branch, root, litter, and mineral soil. Global vegetation is mapped for the 15 major biomes (Table 2) developed from the Global International Geosphere-Biosphere Programme (IGBP) scheme land cover classification, and the effect of vegetation fractional coverage is considered up to the fourth dominant biome for each grid (matching 99.1%
TABLE1. Precipitation data used in this study.
Dataset Resolution Period used Data source
JCDAS JRA-25/JCDAS T106/6-hourly 1979–2009 Numerical assimilation CRU CRU TS3.1 0.58 3 0.58/monthly 1979–2005 Rain gauge
GPCP(2.1) GPCP 1DD version 1.1 1.08 3 1.08/daily 1997–2008 Satellite, rain gauge (GPCP version 2.1) (2.58 3 2.58/monthly) (1979–2008) (Satellite, rain gauge)
of the total coverage). VISIT is run on a daily 0.58 3 0.58 grid resolution using JCDAS daily climate data near the ground surface.
VISIT estimates the matter production of plants based on light extinction in the canopy, following the formu-lation provided by Monsi and Saeki (1953). The incident light intensity is attenuated progressively in the canopy, as follows:
I 5 I0e2KLAI, (1) where I and I0are the photosynthetic photon flux
den-sities (PPFD) within the canopy and at the canopy top, respectively; K is the biome-specific extinction coeffi-cient as a function of solar height; and LAI is the total leaf area index above the level at which I is estimated. In-stantaneous gross primary productivity (GPP) in the canopy is estimated by using a Michaelis-type function of PPFD:
GPP 5 ðLAI 0 P maxaI Pmax1 aI dLAI 5Pmax
K [lnfa1KI0g 2 lnfa1KI0 exp(2KLAI)g],
(2) where Pmaxis the maximum rate of CO2uptake under
light saturation, and a is the light use efficiency. Seasonal variations in these two parameters are calculated as func-tions of environmental condifunc-tions:
Pmax5P*max[Fp(Tg)][Fp(Ci)][Fp(F)], and (3) a 5 a*[Fa(Tg)][Fa(Ci)] (4) where P*maxand a* are the potential maximum Pmaxand
aunder optimum conditions, respectively; and Fpand Fa
are coefficient functions that modify Pmaxand a based
on ground surface temperature (Tg), intercellular CO2
concentration (Ci), and soil moisture (F), respectively.
Here, the value of F is estimated from the daily pre-cipitation amount by assuming the water balance for the ground surface and soil at the grid, while Tgis directly
derived from the JCDAS dataset. The sensitivities of Pmaxand a to environmental factors differ among biome
types and for C3 or C4 metabolism.
As shown in Eqs. (1) and (2), LAI is one of the key factors in VISIT when simulating carbon assimilation by vegetation photosynthesis. The value of single-sided LAI is given as a prognostic variable, as follows:
LAI 5SLAcdmMfol
2 , (5)
where SLA is the specific leaf area, cdmis the constant
coefficient of the dry matter: leaf carbon ratio, and Mfol
is the foliage carbon mass. Here, Mfoldevelops or
de-clines continuously with the costs and benefits of foliage carbon for growth, maintenance, and storage, as follows: DMfol5TPfol1EM 2 CL 2 AGRfol2Lfol, (6) where DMfolis the finite increment of Mfolduring a given
period, TPfolis the translocation of photosynthate to the
foliage, EM is the emergence of new leaf by consuming the stem and root carbon mass, CL is the construction cost of new leaves, AGRfol is the autotrophic growth
respiration of foliage, and Lfolis litterfall from foliage.
A detailed description of the ecosystem processes for allocation, dry matter production, mortality, photosynthe-sis, and respiration can be found in Ito and Oikawa (2002).
3. JCDAS precipitation anomaly
a. Comparison of spatial and temporal variability in JCDAS precipitation with observations
The three global precipitation datasets used in this study have contrasting grid resolutions. In the comparisons and analyses to follow, we use a grid resolution of 1.08 3 1.08; thus, JCDAS data were interpolated and CRU data were aggregated to achieve the desired resolution.
Zonal-averaged 9-yr annual mean JCDAS pre-cipitation over land for 1997–2005, which is the common period for which the three datasets are available, is in reasonable agreement with CRU and GPCP at a given latitude (Fig. 1). However, JCDAS precipitation over the 358–208N domain is slightly higher than that in the other datasets, by up to 0.5 mm day21, while JCDAS is locally 1.0 mm day21or more lower than CRU and GPCP in the tropics. Differences may arise because of the
TABLE2. Biome types in VISIT.
Number Biome
1 Evergreen needle-leaf forest 2 Evergreen broadleaf forest 3 Deciduous needle-leaf forest 4 Deciduous broadleaf forest 5 Mixed forest 6 Woodland 7 Wooded grassland 8 Closed shrubland 9 Open shrubland 10 Grassland 11 Cropland 12 Bare ground 13 Urban and builtup 14 Wetland
difficulty in reproducing the divergent part of the atmo-spheric circulation around the tropics (Trenberth and Smith 2008). The latitudinal profile of JCDAS yields two peaks in the tropics, at approximately 78N and 18S, whereas CRU and GPCP show a single peak at about 18S. In the case that precipitation is globally averaged over the land area for 1997–2005, the JCDAS mean annual pre-cipitation weighted by each grid area is 2.42 mm day21, similar to the figure in CRU (2.46 mm day21) and GPCP (2.46 mm day21). The interquartile range (IQR), which is an index of the amount of spread in the central part of the dataset, shows slightly less dispersion of the annual mean JCDAS precipitation (1.79 mm day21) compared with data from CRU (2.10 mm day21) and GPCP (2.05 mm day21). Figure 2 shows the spatial patterns of the three global datasets of 9-yr (1997–2005) annual mean precipitation. The annual mean map derived from JCDAS agrees with those of CRU and GPCP at a large scale. JCDAS cap-tures the broad distribution of high-precipitation areas over the active convective zones in the tropics and ex-tensive dry areas in subtropical areas. Spatial correla-tions of the annual mean maps between JCDAS and CRU, JCDAS and GPCP, and CRU and GPCP yield values of 0.85, 0.83, and 0.94, respectively. However, the regional spatial pattern of JCDAS is markedly different from the other datasets in the 308N–308S domain, es-pecially in the Amazon River basin and over the Indo-chinese peninsula. Although CRU and GPCP show areas of organized regional maximum precipitation around the equator in the Amazon basin and along coastal areas of the Indochinese peninsula, the maximum areas in JCDAS are located at several spots in these regions, showing a busy pattern.
The differences in the annual means are shown in Fig. 3 for JCDAS minus CRU and for JCDAS minus GPCP; blue and red colors indicate excess and insufficient JCDAS precipitation, respectively. The differences in mean annual precipitation amount between JCDAS and CRU and JCDAS and GPCP are within 60.5 mm day21 over 67% and 65% of the globe. Over most of the land
areas of North America, Eurasia, and Australia, the dif-ferences are less than 60.5 mm day21. Larger differ-ences are seen mainly in parts of South America, Africa, and Southeast Asia, in the 308N–308S domain. JCDAS produces relatively intense precipitation over the Andes at 108–208S, in southern Africa, and over a large area of Southeast Asia. In contrast, JCDAS precipitation is strongly suppressed over the Amazon River basin, Cen-tral Africa, and Borneo, and it is locally 4.5 mm day21less than CRU and GPCP values. This discrepancy is espe-cially pronounced over large areas of the Amazon basin. Next, to examine seasonal variations in the precipi-tation datasets at those areas for which a marked dis-agreement was obtained between JCDAS and the other datasets, time series of area mean monthly precipitation
FIG. 1. Zonal averaged 9-yr (1997–2005) annual mean pre-cipitation (mm day21) over land for JCDAS (thick solid line), CRU (dashed line), and GPCP (thin solid line).
FIG. 2. Nine-year (1997–2005) annual mean precipitation (mm day21) over land for (a) JCDAS, (b) CRU, and (c) GPCP.
for 1998–2003 are shown in Fig. 4 for six areas: the Amazon River basin (08–108S, 608–508W), central Africa (108N–08, 108–308E), and the Borneo region (58N–58S, 1108–1188E) as areas of suppressed precipitation; and the Andes (158–308S, 708–608W), southern Africa (158– 258S, 158–258E), and Southeast Asia (308–208N, 1008– 1158E) as areas of intense precipitation. CRU and GPCP show prominent wet and dry seasons in all areas, as is also generally the case for JCDAS. For areas with intense precipitation (Figs. 4b,d,f), correlation coefficients for anomalies between JCDAS and the other datasets vary from 0.72 to 0.88, except for CRU at southern Africa, and the root-mean squared error (RMSE) varies from 1.53 to 2.08 mm day21. For areas with suppressed precipitation, correlation coefficients for monthly precipitation variability are below 0.6 (0.24–0.59), and Fig. 4 shows a larger RMSE than that in areas with intense precipitation. In particular, RMSE in the Borneo region exceeds 4 mm day21(Fig. 4e). In the region of the Amazon River basin, precipitation of 10 mm day21or more is observed in the rainy season for CRU and GPCP, but JCDAS yields about half these amounts (5–6 mm day21).
Errors in CRU precipitation become larger in regions with a sparse coverage of gauge stations, such as the Amazon and the Sahara, resulting in differences in
precipitation amount when compared with the other datasets (New et al. 1999). In addition, biases in GPCP precipitation exist over the Amazon River basin (e.g., Xie et al. 2003). However, the CRU and GPCP pre-cipitations are in good agreement with each other, and their anomalies are similar to each other, in contrast to JCDAS anomalies. Accordingly, the differences shown in Figs. 3 and 4 may represent error-related biases in JCDAS precipitation. Indeed, considering the results of previous studies that provided precipitation estimates (e.g., Vose et al. 1992; Costa and Foley 1998), the JCDAS precipitation amounts for the Amazon basin and Borneo are unreasonably small, indicating that the large differ-ences described above are due mainly to biases in JCDAS precipitation.
Regarding the deficient JCDAS precipitation in the Amazon basin, Onogi et al. (2007) attributed this prob-lem to erroneous station data on surface pressure and altitude, which induces a local positive surface pressure increment and influences lower-atmospheric circulation in the JCDAS analysis, meaning that precipitation is suppressed and unrealistic drying of soil moisture occurs over the Amazon. The feedback effects from these drying surface conditions on the atmospheric model result in a further positive pressure increment and reduction in pre-cipitation. Even after some of erroneous station data were excluded from the analysis, the drying problem in the Amazon area is still present in the JCDAS products. In the Andes region, a ripple effect in water vapor is formed by the spectral conversion in the forecast model of JCDAS, resulting in excessive cloud amounts and intense pre-cipitation. Previous studies have failed to consider the causes of this precipitation problem, except in the Amazon and Andes regions.
To further explore the feature of the JCDAS pre-cipitation product, the number of rain days is examined for each grid and each month. Here, we use a daily precipitation rate of 0.1 mm day21 as a threshold in defining a rain day. Because CRU is not designed for daily variables, only GPCP is used for the comparison with JCDAS. The analysis period is extended to the 12-yr interval from 1997 to 2008; thus, 12 monthly values are used at each grid. To calculate the statistical significance of differences in the occurrences of rain days, the differences in 12-yr monthly means between JCDAS and GPCP were assessed using a two-tailed test at the 10% significance level. The test statistic is given as z 5 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p^12 ^p2 1 n11 1 n2 ^ p(1 2 ^p) s , (7)
FIG. 3. Mean annual precipitation difference (mm day21)
be-tween (a) JCDAS and CRU (JCDAS 2 CRU) and (b) JCDAS and GPCP (JCDAS 2 GPCP).
where the subscripts 1 and 2 denote JCDAS and GPCP, respectively; ^p15f1/n1; ^p25f2/n2; ^p 5 f /n; ^p1and ^p2are the probabilities of the occurrence of rain days in the datasets; ^p is the common probability of the occurrence of rain days; f1and f2are the numbers of rain days; f is
the sum of f1and f2; n1and n2the sample sizes of periods;
and n is the sum of n1and n2. If ^p1 is much greater or
smaller than ^p2, the test statistic z falls into an improb-able region of the null distribution, and the null hy-pothesis is rejected. This approach assumes that GPCP is an unbiased precipitation product for the occurrence of rain days.
Seasonal changes in the difference between datasets in terms of the occurrence of rain days over global land area are shown in Fig. 5. Here, the 10% level of signif-icant difference is equivalent to values of z being outside of 61.64, which indicates that the probability of rain day occurrence is significantly different between JCDAS and GPCP. Figure 5 shows that for the 12-yr period, the two datasets are in disagreement over large areas. The JCDAS product shows a tendency toward over-estimates (1.64 , z) in rain-day frequency rather than agreement (21.64 # z # 1.64) or underestimates (z , 21.64), especially during winter months in the Northern
Hemisphere. In contrast, the degree of agreement in-creases during summer months, although its relative frequency never exceeds 30%. The distribution of un-derestimates has the lowest frequency among the three groups. Consequently, for a large part of the global land area, the JCDAS model generally yields relatively light rainfall with a higher frequency, compared with GPCP. The above comparisons show that JCDAS precipita-tion captures the major large-scale spatial features of the annual mean and seasonal course from CRU and GPCP. However, local biases are evident in the annual mean, especially in the tropics. We identified differences in the occurrence of rain days and its seasonal variations over the globe, possibly reflecting the effects of interpolation from the larger original JCDAS grids. Considering these results, correction of JCDAS precipitation is required, for both the mean amount and for spatial and temporal patterns. b. Correction of the JCDAS precipitation anomaly
Here, biases in JCDAS precipitation are corrected for both the occurrence of monthly precipitation and the monthly mean amount. First, based on the statistical difference between the JCDAS and GPCP datasets for rain days in each grid cell per month, if the null
FIG. 4. Seasonal course of monthly precipitation (mm day21) for (a) the Amazon River basin (08–108S, 608–508W), (b) the Andes (158–308S, 708–608W), (c) central Africa (108N–08, 108–308E), (d) southern Africa (158–258S, 158–258E), (e) Borneo (58N–58S, 1108–1188E), and (f) Southeast Asia (308–208N, 1008–1158E) for JCDAS (thick solid line), CRU (dashed line), and GPCP (thin solid line). Values shown in the figure are the root-mean squared error (mm day21) for
hypothesis at the 10% significance level is rejected at a grid for a given a month, the number of monthly rain days in JCDAS is corrected using a stochastic model. The frequency and amount of rainfall are important factors for plant growth and development, but the lengths of dry and wet days are also significant factors (e.g., Frasier et al. 1987). Accordingly, we correct the occurrence of monthly precipitation using the method-ology described by Racsko and Szeidl (1991), which considers the sequence of dry and wet series of days. This procedure is currently used in the stochastic Long Ashton Research Station Weather Generator (LARS-WG) to produce the daily precipitation variable (Semenov et al. 1998; Lawless and Semenov 2005).
For day d at grid g, the probability distributions of the lengths l of dry and wet series [PD
l(d, g) and PWl (d, g),
respectively] are estimated from 12-yr precipitation time series of the GPCP product. To capture the event of long series in the same state, PD
l and PWl are estimated using
the 37-day period about day d, representing the interval [d 2 18, d 1 18]; thus, the maximum possible length of a given state is basically restricted to 1 # l # 37. The 37-day period captures 99.5% or more of whole dry/wet series in the GPCP products.
Next, both PDl(d, g) and PWl (d, g) are approximated by mixing two geometric distributions for short- and long-term events, and we estimate the parameters of the probability of success occurring in the geometric distri-bution in dry/wet conditions for both terms: lD
S(d, g),
lDL(d, g), lWS(d, g), and lWL(d, g). Here, a sequence of
events less than nine days is classified as a short-term event [1 # l # 8], and the probability of occurrence of short-term events 1 2 p(d, g) and long-term events p(g, d) under dry/wet conditions is estimated from the GPCP product. In the case that, for example, GPCP observes
both short- and long-term events under dry conditions but only short-term events under wet conditions on day d at grid g, PD
l(d, g) is approximated by mixing two
geometric distributions, whereas PW
l (d, g) is obtained
from a single geometric distribution. Although Racsko and Szeidl (1991) approximated the parameters of l(d, g) and p(d, g) under dry/wet conditions using a finite Fourier series, we directly use the estimated values of these pa-rameters in the following analyses.
In arid desert areas and wet tropical areas, there occur cases in which a dry or wet series continues uninterrupted for a 37-day period or longer, meaning that PD
l 5 0(d, g) or
PWl 5 0(d, g) (i.e., no dry or wet event occurs during the 37-day period) is observed. In these cases, PDl(d, g) or PW
l (d, g), including the event of l 5 0, is approximated
with a Poisson distribution instead of a geometric distri-bution. This is because the Poisson distribution is an ap-propriate probability for describing discrete and rare events (Wilks 2006; Zhao and Chu 2010). A single pa-rameter, mD(d, g) or mW(d, g), of the distribution is esti-mated using the corresponding GPCP product, being the same as for l(d, g).
Dry/wet time series can be generated from approxi-mations of PD
l(d, g) and PWl (d, g) with a uniform random
variable. In this estimate, the condition of dry/wet on the first day of the year y (1 January), Xd51,y,g(50 or 1), is
taken from the original JCDAS time series. If JCDAS shows a wet condition, then the length of the wet series lWis generated by a comparison of the cumulative
dis-tribution of PW
l (1, g) and with the uniform random
var-iable rubeing independently generated over the range
[0, 1]; then, the period [1, lW] is classified as the wet
condition (Xd,y,g51; d 5 1, . . . , lW). Because one
con-dition is followed by the other, the day d 5 lW11 is
determined as dry condition Xl
W11,y,g
50, and the se-quence of dry days lDis generated from the cumulative
distribution of PD
l(lW11, g), with rubeing newly
gen-erated (Xd,y,g50; d 5 lW11, . . . , lW1lD). Here, if lD
equals zero from the Poisson distribution, then Xl
W11,y,g
is converted from 0 to 1, and lW is estimated from
PD
l(lW11, g). This process is repeated until the end of
the year for the whole period of 1979–2009. This pre-cipitation correction is iterated until the null hypothesis is accepted in terms of the monthly occurrence of rain days between GPCP and Xd,y,gin the period 1997–2008.
To make effective use of the original JCDAS product, we apply newly generated dry/wet time series Xd,y,gonly
for months for which the null hypothesis was rejected; for other months, the original JCDAS time series is used. For the former months, if the condition of JCDAS precipitation on day d in year y is in agreement with Xd,y,g, the value of the original JCDAS product is
ac-cepted as the daily precipitation amount in the corrected
FIG. 5. Seasonal course of the percentage of the grid for which the test statistic of the monthly number of rainy days between JCDAS and GPCP is within 61.64 (solid line), larger than 1.64 (dashed line), and smaller than 21.64 (dotted line) for 1997–2008.
JCDAS product, Prd,y,g. For cases of disagreement,
in-volving a wet day in JCDAS and Xd,y,g50 or a dry day
and Xd,y,g51, in the former case the original JCDAS
time series is rejected and Prd,y,g50 mm day21. In the
latter case, Prd,y,g is simply simulated by the gamma
distribution (e.g., Wilks 1990; Sloughter et al. 2007). Parameters in the gamma distribution, including the shape parameter a(d, g) and the scale parameter b(d, g), are obtained for each day and each grid by fitting the gamma distribution to the JCDAS daily precipitation amount of the 37-day period around day d for 1979–2009 at grid g, using a maximum likelihood approximation. The value of Prd,y,gcorresponding to ruis then computed
inversely from the uniform cumulative probability dis-tribution of the gamma disdis-tribution, characterized by a(d, g) and b(d, g).
Next, precipitation amounts for all land areas are cor-rected for the monthly precipitation amount by scaling the monthly corrected JCDAS amount with CRU. The scaling coefficient is computed for each month and each grid for the 27-yr period from 1979 to 2005. For the period after 2005, the mean 27-yr values for each month are used as scaling coefficients; consequently, the corrected JCDAS precipitation amount after 2005 does not necessarily yield the same amount as CRU.
4. Results and discussion a. Precipitation correction
In correcting the JCDAS monthly precipitation occur-rence, the stochastic model was iterated until z statistics
between GPCP and corrected JCDAS in the 12-yr period (1997–2008) became smaller than j1.64j at each grid in each month. The corrected JCDAS precipitation was then scaled by CRU precipitation for each month. Therefore, the corrected JCDAS monthly and annual mean pre-cipitation amounts are in agreement with CRU values at each grid in the period 1979–2005; all disagreements with CRU were eliminated, along with disagreements with GPCP monthly rain day occurrence, as shown in Fig. 5.
Figures 6 and 7 show the probabilities of the occur-rence of dry/wet series on a selected day over the Am-azon River basin and the Southeast Asia regions for GPCP and for the original (PO) and corrected (PC)
JCDAS. Here, POhas a large number of no dry days and
long wet series of 37 days or more in the Amazon River region. In the Southeast Asia region, the probability of 1-day length is larger than 0.4 for dry series and is approximately 0.2 for wet series in PO. These probability
distributions are significantly different from those of GPCP. Dry/wet series in PCare greatly improved when
compared with POfor both regions; the differences
be-tween GPCP and POare not significant for both the dry
and wet series, although there are slightly more short wet series in PCthan in GPCP.
The probability distribution of daily rainfall amounts, corresponding to the period and area in Figs. 6 and 7, are shown in Figs. 8 and 9. Note that the daily amount of PC
is only scaled by the coefficient from the monthly CRU amount: it is not adjusted for the GPCP daily amount. Compared with GPCP, PC shows a lower probability
of light rainfall (less than 2 mm day21) and a higher probability of rainfall of around 10 mm day21 in the Amazon River region (Fig. 8). This discrepancy reflects
FIG. 6. Probability of the occurrence of (a) dry and (b) wet series on 31 Jan in the Amazon River basin region (08–108S, 608–508W) for 1997–2008. The solid line is JCDAS precipitation, dotted line is GPCP, and red line is corrected JCDAS.
FIG. 7. As in Fig. 6, but for the Southeast Asia region (308–208N, 1008–1158E).
the simple scaling employed in this analysis, which moves the entire PO distribution, being highly skewed to the
right. Although GPCP and PChave approximately equal
values of the cumulative probability of rainfall less than 10 mm day21(0.56 and 0.57, respectively), reduced light rainfall in PCmay be needed to improve the correction of
daily rainfall amount. On the other hand, very similar probability distributions are observed in daily precipi-tation amount between GPCP and PCin the Southeast
Asia region (Fig. 9). This is attributed to both good esti-mates in PO and good agreement with monthly
pre-cipitation variability between GPCP and CRU in this region (Fig. 4f).
Table 3 shows the correlation coefficients between daily precipitation and other meteorological parameters from the JCDAS product. Mean correlation coefficients for daily downward shortwave radiation (DSR), total cloud cover (TCC), air temperature at 2 m above the ground (Tair), and specific humidity at the surface (SH)
were calculated over the land area in 608N–608S for the period 1979–2009. Here, POshows the strongest
corre-lation with TCC and the weakest with Tair. The same
result is also seen for PC, which probably shows that the
PC field is unbiased toward a specific meteorological
variable, as a result of the correction procedure. Yet the correlation coefficients for PC are approximately half
those for PO, probably reflecting the replacement of
precipitation occurrences with corrected data. As dis-cussed by Sheffield et al. (2004), the use of independent replacements for each grid cell results in discontinuous patterns in the precipitation fields over spatial and temporal scales and results in discrepancies with other meteorological variables; for example, the production of a scattering of artificial dry grids in areas with high precipitation and of rainfall on clear days. To reduce this discrepancy and to improve the correlation, rearrange-ments of the time series of meteorological variables are
needed to obtain agreement with the corrected pre-cipitation fields. However, the focus of this study is modeling simulations of terrestrial biospheric carbon budgets. Biochemical processes and CO2exchange by
the terrestrial ecosystem are influenced by a number of external factors, including the availability of radiation, temperature, CO2, and water (Larcher 2003). Indeed,
these plural factors have a strong control on CO2fluxes
in VISIT, as shown in section 2b; consequently, the rearrangement of meteorological time series may lead to poor simulations of variability in daily CO2fluxes.
Thus, we use the original time series of JCDAS meteo-rological variables without any rearrangement, together with corrected precipitation.
Time series of difference of monthly mean POand PC
from GPCP version 2.1 monthly precipitation (GPCP2.1) over the tropical land area (308N–308S) are shown in Fig. 10a for 1979–2008. Twelve-month running means are also plotted in the figure. Thirty-year mean differences of POand PCfrom GPCP2.1 are 0.07 and 0.02 mm day21,
and their RMSEs are 0.19 and 0.13 mm day21, re-spectively. The monthly differences between PC and
GPCP2.1 occasionally reach up to 60.4 mm day21, but the performance of PCis generally better when compared
to PO. Monotonic increment trend in POis observable for
the period after 2005. This issue has been reported on the JCDAS Web site but the cause is under investigation.
The spatial correlations of monthly precipitation, cor-responding to the area and the period in Fig. 10a, are shown in Fig. 10b. Here, POshows distinct increase in
correlation in 1987, which can be related to the intro-duction of SSM/I data. JCDAS switches the satellite
FIG. 8. Probability of daily precipitation amount in the 37-day period around 31 Jan in the Amazon River basin region for 1997– 2008. The solid line is JCDAS precipitation, dotted line is GPCP, and red line is corrected JCDAS.
FIG. 9. As in Fig. 8, but for the Southeast Asia region.
TABLE 3. Mean correlation coefficients for original and cor-rected JCDAS daily precipitation compared with DSR, TCC, Tair,
and SH in 608N–608S over a 31-yr period (1979–2009).
Data DSR TCC Tair SH
Original 20.28 0.50 0.01 0.38 Corrected 20.12 0.22 0.03 0.20
sounding system from TOVS to Advanced TOVS (ATOVS) in November 1998, and the Microwave Sounding Unit (MSU) is replaced by the Advanced MSU (AMSU), which obtains temperature and humidity profiles. AMSU is significantly improved spatial resolu-tion and radiometric accuracy (Aumann et al. 2003). Correlation in POslightly increases after introducing the
ATOVS but decreases after 2002. Further study is nec-essary to evaluate this problem. As for PC, no noticeable
trend is observed for both amount and correlation, and good correlation values are shown over the entire period. b. Modeled carbon budgets from VISIT
To assess the influence of precipitation correction on simulations of the long-term terrestrial carbon balance, we estimated the global terrestrial carbon budget from 1979 to 2009 using both PCand PO, along with other
common daily JCDAS meteorological variables. The employed model has a time step of one day and a spa-tial resolution of 0.58 longitude 3 0.58 latitude. The model was initially run for a spinup period of 2100 years to ensure the equilibrium of compartments of car-bon pools. In this spinup, 31-yr meteorological variables
were repeated with variability in the atmospheric CO2
concentration from the preindustrial period. Daily var-iability in carbon fluxes and canopy structure was then simulated over a 31-yr period. The following analyses focus on the 9-yr period from 1997 to 2005.
Table 4 lists the mean annual values in four compo-nents of the terrestrial biospheric fluxes for global and latitudinal zones over a 9-yr period (1997–2005): net primary productivity (NPP), GPP, ecosystem respiration (RE), and net ecosystem productivity (NEP). For annual mean global total fluxes over land, there are minor dif-ferences in the values estimated with PCand PO. Global
GPP in PCand POis 107.4 and 106.4 Pg C yr21,
respec-tively, which is consistent with recent observation- and model-based estimates (e.g., Houghton et al. 2001; Thornton and Zimmermann 2007). Values of NPP, which denotes the sum of GPP minus autotrophic respiration (AR), are slightly less than those reported in previous studies (e.g., Schimel et al. 2001) for both PCand PO, but
they are similar to the estimate by Rayner et al. (2005) and are roughly within the range of the model compari-son by Cramer et al. (1999).
In contrast, an inconsistency in carbon fluxes between PCand POis apparent in the zonal mean. Zonal fluxes
in GPP and RE in PCare relatively low in the 608–308N
zone and are relatively high in the 08–308S zone, ranging from 22.0 to 13.3 Pg C yr21, corresponding to differ-ences of 27.4% to 19.2% compared with the zonal mean fluxes in PO. The mean annual precipitation in
each zone is (from north to south) 2.18, 1.52, and 3.57 mm day21in PC, and 2.51, 1.76, and 3.38 mm day21
in PO. Although the differences in precipitation are less
than 0.33 mm day21, which is smaller than or similar to the range of difference among global precipitation data-sets (Fekete et al. 2004), the zonal mean annual carbon fluxes in the model simulation were affected by these differences. In particular, the influence of precipitation correction on carbon fluxes was significant in the 08–308S zone, in which semiarid and arid regions are widespread and in which drought causes severe stress in terms of soil decomposition. The increase in precipitation resulting from correction leads to reduced drought stress; this
FIG. 10. Time series of (a) difference of original (black line) and corrected JCDAS (red line) monthly mean precipitation from GPCP version 2.1 over tropical land area (308N–308S) and (b) their spatial correlations.
TABLE4. Annual mean terrestrial biospheric fluxes in the period between 1997 and 2005, showing NPP, GPP, RE, and NEP. The unit is Pg C yr21in all cases.
Components (Pg C yr21)
Corrected precipitation Original precipitation
Global 608–308N 308–08N 08–308S Global 608–308N 308N–08 08–308S NPP 44.1 13.5 11.3 14.5 44.2 14.5 10.7 13.6 GPP 107.4 26.2 31.1 40.3 106.4 28.0 30.3 37.3 RE 104.4 25.4 30.2 39.1 103.8 27.4 30.0 35.8
effect is more pronounced in RE than in GPP. Conse-quently, NEP in PC, which represents the
differ-ence between GPP and RE, showed a reduction of 0.4 Pg C yr21in its zonal amount over 08–308S.
Next, we mapped the difference in 9-yr mean daily variability in GPP between PCand POfor latitudes of
808N–408S, as shown in Fig. 11a. Rapid latitudinal changes are seen in zones of enhanced and suppressed GPP, and this pattern coincides roughly with the zonal difference in precipitation amount between PCand PO
(Fig. 11b). Marked reductions in GPP were observed within the 758–658N and 208–308S zones during summer in each hemisphere. Of note, in the former zone, despite a slight reduction in precipitation (.20.5 mm day21) as a result of correction, we observed a significant decrease in zonal GPP (,21 g C m22day21) during the growing season. This occurred because water availability is a strong control on plant growth in this zone because of the high water stress that results from the small amount of annual precipitation (Figs. 1 and 2); thus, vegetation productivity is strongly affected by even a minor reduction
in precipitation. This result suggests that precise pre-cipitation data are essential for accurate predictions of seasonal variations in vegetation productivity in high-latitude parts of the Northern Hemisphere. However, it is important to remember that at high latitudes, precipi-tation datasets based on gauge observations are limited by sparse station coverage (New et al. 2000), thereby raising the possibility that the correction of precipitation using these observations could lead to increased un-certainty. This potential problem remains to be exam-ined in a future study.
A marked difference in GPP between PCand POis
observed at around the equator, mainly in the Southern Hemisphere (Fig. 11a). As mentioned above, JCDAS generally underestimates precipitation in the tropics, resulting in the formation of a band of intense posi-tive correction in precipitation throughout the year (Fig. 11b). The center of this band of overcorrection of 11.5 mm day21 is located in the Southern Hemisphere during the boreal winter, crossing the equator during the boreal summer. A zonal increment in GPP follows this seasonal fluctuation in the precipitation-correction band, exceeding 11.25 g C m22day21at its maximum. In cer-tain grids, anomalous underestimates of GPP, reflecting a precipitation bias, are similar to the observation results reported by Malhi et al. (1998), who found an annual mean GPP of 8.3 g C m22day21(30.4 t C ha21yr21) at a trop-ical rain forest, based on micrometeorologtrop-ical measure-ments. Overall, the present results suggest that bias in the original JCDAS precipitation has the potential to sub-stantially affect the spatial and temporal terrestrial carbon cycles, and thus canopy structures, in certain regions.
To assess the influence of correction for precipitation bias on terrestrial ecosystem modeling, seasonal varia-tions in various components of the ecosystem carbon cycle are shown in Fig. 12 for the Amazon River basin region (08–108S, 608–508W) in 2002. Annual amounts of PCand POwere 2110 and 891 mm, respectively, and the
seasonal course of cumulative PCwas generally twice as
large as that of PO(Fig. 12a). This difference in
pre-cipitation amount has a marked influence on the sea-sonal course of water stress Fp(F) (Fig. 12b), which is
one of the key parameters regulating specific values of Pmax, as shown in Eq. (3). Note that a smaller value of
Fp(F) results in a larger reduction in Pmax. Fp(F) in PO
gradually decreases after day of year (DOY) 120, while PClargely maintains its maximum value up to DOY 180,
and Fp(F) in PCis generally 0.1–0.3 larger than PO. Pmax
showed a strong correlation with these seasonal courses of Fp(F); Pmax in PCwas generally 1–3 mmol photon
m22s21higher than PO(Fig. 12c), reflecting the lack of
significant seasonality in temperature and CO2
concen-trations in this region, which emphasized the influence
FIG. 11. Time series of the zonal-averaged (a) daily GPP (GPP in PCminus GPP in PO) in g C m22day21and (b) daily precipitation
(PCminus PO) in mm day21over land from 808N to 408S. Daily
of Fp(F) on Pmax. Malhi et al. (1998) reported similar
results, in that water availability is the dominant factor controlling vegetation photosynthesis in the Amazon forest.
The difference in Pmaxbetween PCand POdirectly
affected the magnitude and seasonal course of GPP (Fig. 12d), yielding a difference of 647 g C m22yr21 in an-nual carbon uptake, and altered the amount of assimi-lated carbon, which increases the amount of plant dry matter. The rate of AR, which is the sum of maintenance respiration and growth respiration by plants, in PCwas
higher than that in PO(Fig. 12g), because maintenance
respiration by plants is closely related to its dry matter volume (e.g., Amthor 1984). The greater increment of
GPP than of AR, arising from precipitation correction, resulted in increases in NPP (Fig. 12e), total biomass (Fig. 12j), and LAI (Fig. 12k). These increases in bio-mass and LAI resulted in turn in increased total litterfall production (Fig. 12l) and thus an increase in the fast-soil carbon pool. This carbon-rich soil, combined with a re-duction in drought stress, resulted in a higher rate of heterotrophic respiration (HR) of PC(Fig. 12h).
Con-sequently, the increment in GPP was largely offset by the increment in RE (Fig. 12i), resulting in little differ-ence of seasonal variations in NEP (Fig. 12f). Annual NEP showed approximately neutral conditions of the ecosystem carbon balance for both PCand PO, but weak
carbon loss of 235.3 g C m22yr21 in PO became an
FIG. 12. Seasonal variations in regional mean (a) cumulative precipitation (mm), (b) water stress, and (c) Pmax(mmol CO2m22s21) for
woody plants; and (d) GPP (g C m22day21), (e) NPP (g C m22day21), (f) NEP (g C m22day21), (g) AR (g C m22day21), (h) HR (g C m22day21), (i) RE (g C m22day21), ( j) biomass (kg C m22), (k) LAI, and (l) total litterfall (g C m22day21) for the entire eco-system in the Amazon River basin region (08–108S, 608–508W) in 2002.
uptake of 125.5 g C m22yr21following correction for precipitation. To summarize, a reduction in water stress resulting from intensified precipitation associated with bias correction is generally expected to enhance pho-tosynthesis and carbon pools by plant growth, which can also be attributed to increases in AR and HR owing to enhanced litterfall production and vice versa. NEP may be substantially compensated by a coincident increase or decrease in both carbon sequestration and respiration loss, but its sign is readily changed as a result of bias correction.
Interestingly, modeled NEP in a region within the Amazon River basin showed net ecosystem carbon up-take in the dry season and release in the wet season (Fig. 12f). This seasonal variation in NEP is consistent with that reported by Saleska et al. (2003), who observed an increase in photosynthesis and a decline in total respi-ration during the dry season in Amazon forests. This
increase in photosynthesis is thought to reflect water uptake by the root system at depths greater than 5 m for the survival of a plant under high water stress (Ichii et al. 2007). VISIT predicted the general outline of the sea-sonality in carbon fluxes, although the amplitudes of modeled seasonal variations were somewhat small com-pared with observations.
In contrast to the Amazon River region, the South-east Asia region (308–208N, 1008–1158W) shows little sensitivity of seasonal variations in the ecosystem car-bon cycle to the correction of precipitation bias (Fig. 13). Annual amount of 2365 mm in PO was reduced to
1832 mm in PC. However, water stress for Pmaxwas
little affected by the correction (Fig. 13b), indicating that water supply is still sufficient after downward precipitation correction, and the water stress is not affecting canopy phenology and carbon cycle in the region.
Finally, previous studies have estimated the global terrestrial carbon balance using a variety of approaches, yielding a global terrestrial carbon uptake of 1.3 6 1.6 Pg C yr21in 1985–95 from inverse modeling (Bousquet et al. 1999), a value of 0.3–1.5 Pg C yr21for the 1980s using four process-based models (McGuire et al. 2001), and 1.26 6 0.8 Pg C yr21for the 1990s based on an O2
inventory (Keeling and Garcia 2002). The present mod-eling simulation of total NEP yielded a change of 0.3 Pg C yr21resulting from correction of the precipitation bias (Table 4). This difference is far from negligible con-sidering the range of uncertainties in individual NEP estimates reported in previous studies; consequently, uncertainty in the precipitation field is possibly a critical factor in terms of contamination of the robustness of terrestrial carbon modeling. Reanalysis data may also be problematic in the quality of other climate variables (e.g., Trenberth et al. 2001); these errors may produce further uncertainty in modeling simulations. Therefore, it is important to quantify the influence of reanalysis bias when modeling the carbon balance.
The present study has several limitations. First, it fo-cused only on the precipitation field, without considering anomalies in other climate conditions driven interactively by precipitation correction, such as the temperature and radiation fields. Second, it ignored problems regarding uncertainties in the model due to incomplete knowledge of the mechanisms of ecosystem processes (e.g., Scholze et al. 2007) and the influence of natural disturbance and land use change (e.g., Thornton et al. 2002). Given that these possible anomalies and unresolved problems have the potential to further alter ecosystem carbon cycles, they should be addressed in future studies.
5. Conclusions
JCDAS captures the main spatial features of annual precipitation over land, but poor agreement between modeled and observed data is seen in some regional features, especially in the tropics. The anomaly in JCDAS precipitation was verified in comparison with CRU and GPCP precipitation products. Mean annual JCDAS precipitation amount over the global land area showed good agreement with the other datasets. The greatest problem is the large underestimate of precipitation amount in the Amazon Basin during the wet season. In contrast, JCDAS generally shows a high frequency of rain-day oc-currence worldwide. To address these problems, the biases in JCDAS were corrected in terms of both the occurrence frequency and amount of monthly precipitation, using a stochastic model. The results reveal that the corrected data are capable of reproducing the spatial and temporal dis-tribution of the total precipitation field.
We demonstrated the simulation of global terrestrial carbon balance using a prognostic biosphere model (VISIT) with both original and corrected JCDAS pre-cipitation data. Bias correction resulted in a difference of 0.3 Pg C yr21 in the global net ecosystem carbon balance. The influence of bias correction was clearly apparent, particularly in the regional carbon cycle. The postcorrection difference in the regional precipitation fields directly affected seasonal variations in the pho-tosynthetic capacity of plants, which led to marked dif-ferences in canopy structure and carbon metabolism of the entire ecosystem in the study region. In a selected Amazon area, net carbon release changed to net uptake following correction for precipitation. Bias in the pre-cipitation field has the potential to alter the spatial and temporal patterns of the ecosystem carbon cycle; conse-quently, to obtain robust estimates of the global carbon balance, it is necessary the assess the degree of uncertainty in biospheric modeling related to bias in the input climate variables.
We conclude that JCDAS provides useful information on variability in the regional and global precipitation field, although a potential precipitation bias should be taken into account.
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