Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau
|
|
- Evangeline Manning
- 5 years ago
- Views:
Transcription
1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi: /2011jd016553, 2012 Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau Aihui Wang 1 and Xubin Zeng 2 Received 13 July 2011; revised 28 November 2011; accepted 30 December 2011; published 2 March [1] As the highest plateau in the world, the Tibetan Plateau (TP) strongly affects regional weather and climate as well as global atmospheric circulations. Here six reanalysis products (i.e., MERRA, NCEP/NCAR-1, CFSR, ERA-40, ERA-Interim, and GLDAS) are evaluated using in situ measurements at 63 weather stations over the TP from the Chinese Meteorological Administration (CMA) for and at nine stations from field campaigns (CAMP/Tibet) for The measurement variables include daily and monthly precipitation and air temperature at all CMA and CAMP/Tibet stations as well as radiation (downward and upward shortwave and longwave), wind speed, humidity, and surface pressure at CAMP stations. Four statistical quantities (correlation coefficient, ratio of standard deviations, standard deviation of differences, and bias) are computed, and a ranking approach is also utilized to quantify the relative performance of reanalyses with respect to each variable and each statistical quantity. Compared with measurements at the 63 CMA stations, ERA-Interim has the best overall performance in both daily and monthly air temperatures, while MERRA has a high correlation with observations. GLDAS has the best overall performance in both daily and monthly precipitation because it is primarily based on the merged precipitation product from surface measurements and satellite remote sensing, while ERA-40 and MERRA have the highest correlation coefficients for daily and monthly precipitation, respectively. Compared with measurements at the nine CAMP stations, CFSR shows the best overall performance, followed by GLDAS, although the best ranking scores are different for different variables. It is also found that NCEP/NCAR-1 reanalysis shows the worst overall performance compared with both CMA and CAMP data. Since no reanalysis product is superior to others in all variables at both daily and monthly time scales, various reanalysis products should be combined for the study of weather and climate over the TP. Citation: Wang, A., and X. Zeng (2012), Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau, J. Geophys. Res., 117,, doi: /2011jd Introduction [2] The Tibetan Plateau (TP) is the highest plateau in the world, and its average elevation is above 4000 m with an area of about 2.5 million square kilometers. Because of its high elevation and complex terrain, the land-atmosphere interaction over the TP directly influences the energy and water budget in the local middle troposphere, which, through large-scale atmospheric circulation, also affects weather and climate over other regions. It is generally accepted that the TP is the heat source in summer [e.g., Zhao and Chen, 2001], and it plays an important role in the Asian monsoon establishment and maintenance [e.g., Ye and Gao, 1979; 1 Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, China. 2 Department of Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA. Copyright 2012 by the American Geophysical Union /12/2011JD Yanai et al., 1992; Hsu and Liu, 2003]. The snow cover changes over the TP have a strong influence on summer rainfall in the Asian monsoon areas [Wu and Qian, 2003; Zhang et al., 2004; Zhao et al., 2007; Zuo et al., 2011]. The TP has experienced significant warming since the mid- 1950s, especially in wintertime, and the warming trend exceeded other regions at the same latitude zone [Liu and Chen, 2000]. The TP is also projected to continue warming in the future [Liu et al., 2009]. You et al. [2010] confirmed the warming trends over the TP after analyzing homogenized air temperature data, but they also argued that there is no evidence for the elevation dependency of the warming magnitude in terms of both mean temperature and climate extremes [You et al., 2008]. [3] Because of complex topography, severe weather, and harsh environmental condition over the TP, it is very difficult to obtain in situ measured meteorological variables, especially for the long term over larger areas [e.g., Tanaka et al., 2003; Y. Ma et al., 2008]. Existing surface stations are mostly 1of12
2 located at relatively low elevation areas and are usually sparse and unevenly distributed. For example, the Chinese Meteorological Administration (CMA) has established more than 750 stations over China, among which less than 80 stations are located over the TP areas, with most stations spreading over the eastern to the central part of the TP. Therefore, studies on weather and climate over the TP have to combine these in situ measurements with other data sources, such as the atmosphere reanalysis products that merge model outputs, remote sensing observations, and in situ measurements through a data assimilation system to produce retrospective estimation of meteorological variables [e.g., Kalnay et al., 1996]. [4] The most widely used reanalysis products have been developed at the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/ NCAR, referred to as NRA-1) [Kalnay et al., 1996], and at the European Center for Medium-Range Weather Forecasts (ECMWF) with both ERA-40 [Uppala et al., 2005] and ERA-interim (ERA-Int) [Simmons et al., 2006]. Recently, the NASA/GSFC Global Modeling and Assimilation Office (GMAO) developed a modern-era reanalysis for ongoing (MERRA) [Rienecker et al., 2011], and NCEP released the new product from the Climate Forecast System Reanalysis (CFSR) [Saha et al., 2010]. The Global Land Data Assimilation Systems (GLDAS) [Rodell et al., 2004] utilized common reanalysis and observation-based atmospheric data to drive multiple land surface models in order to produce high-quality land surface products, and their data are available after These reanalyses have been applied in many different ways, for example, to construct land surface forcing data [e.g., Qian et al., 2006; Sheffield et al., 2006], to detect the climate trends [e.g., Trenberth and Guillemot, 1998], and to investigate the water and energy cycles between land and atmosphere [e.g., Roads and Betts, 2000; Maurer et al., 2001]. However, reanalysis products could contain uncertainties from various sources that are inherent in the assimilation processes [Smith et al., 2001], and these biases, for instance, have significant influence on the off-line land model simulations as driven by these reanalysis data [Berg et al., 2003; Wang and Zeng, 2011]. Therefore, it is necessary to evaluate various reanalysis products with available in situ measurements before they are applied to climate studies over the TP. [5] Intercomparisons and evaluations of reanalysis products with in situ observations have been performed over individual sites, specific regions, and global land [e.g., Trenberth and Guillemot, 1998; Betts et al., 2005; Frauenfeld et al., 2005; Bosilovich et al., 2008, 2011; Wang et al., 2011; Decker et al., 2012; Rienecker et al., 2011]. ERA-Int shows a significant improvement compared with ERA-40 in the representation of global hydrological cycle and river basin hydrometeorology [Betts et al., 2009]. Wang et al. [2011] found that CFSR improved the precipitation distribution over various regions in contrast to NRA-1 and ERA-40, but overestimated downward solar radiation flux and latent heat flux over some regions. Bosilovich et al. [2008] compared annual precipitation from multireanalysis products (i.e., ERA-40, JRA-25, NRA-1, NRA-DOE) with merged satellite observations and found that the biases of the spatial pattern vary with regions in different products. Rienecker et al. [2011] found little differences between new reanalyses (i.e., ERA-Int, CFSR, and MERRA) in climate variability, although substantial differences appear in precipitation and surface fluxes. Decker et al. [2012] evaluated multireanalysis products with in situ measurements at different time scales over 33 flux tower sites across North America and found that ERA-Int generally performed best in the representation of six hourly air temperatures, and ERA-40 has the lowest bias of six hourly sensible heat flux and precipitation. Mao et al. [2010] assessed reanalysis daily extreme temperatures with homogenized observations in China and also found that the ERA-Int has the best skill scores compared with ERA-40, NRA-1, and the Japanese reanalysis (JRA-25). Over the TP, Frauenfeld et al. [2005] evaluated air temperature in ERA-40 with the station observations and found that ERA-40 underestimated the annual air temperature by about 7 C, although the correlation of both temperatures was high at the interannual scale. They also argued that a long-term climate warming trend found in the station observations was partially due to the sparse observations and land use change, but the latter was not considered in ERA-40. [6] Previous studies over the TP largely focused on air temperature and precipitation, but just a few of them considered the radiation and other meteorological variables [Frauenfeld et al., 2005; L. Ma et al., 2008, 2009; Mao et al., 2010], primarily because of the unavailable routine measurements of these variables. Besides precipitation and air temperature, other meteorological variables are also very important factors for land-atmosphere interactions. For instance, wind speed strongly affects the energy, water, and momentum transfers between land and atmosphere, and surface radiation flux is the driving force of the surface energy balance [Yang et al., 2011]. [7] The purpose of this study is to assess the near-surface meteorological data from the five atmospheric reanalysis products mentioned above and GLDAS (referred to as six reanalyses hereafter) using available in situ measurements over the TP. The near-surface meteorological variables (including air temperature, precipitation, wind, humidity, and radiation) are evaluated at daily and monthly time scales. These results will also provide a baseline for using these reanalysis data for the weather and climate study over the TP in the future. With a focus on the sparse high-elevation data over the TP region, this study also represents the third part of our series of papers on evaluating reanalysis surface variables, with the first two papers focusing on ocean surface processes [Brunke et al., 2011] and land surface processes over data-rich North America [Decker et al., 2012], respectively. 2. Data Descriptions and Analysis Methods 2.1. Data Descriptions [8] This study uses daily air temperature and precipitation data from 63 CMA weather stations over the TP and the relatively comprehensive near-surface meteorological data at nine stations during the Coordinated Enhanced Observing Period (CEOP)-Asia-Australia Monsoon Project (CAMP/ Tibet, hereafter referred to as CAMP). The general information on observations is provided in Table 1. The longterm raw station data are usually inhomogeneous for various reasons, such as the change of station locations and the 2of12
3 Table 1. Summary of in Situ Observations and Reanalysis Data Used in This Study a Data Source Variable Name Periods Temporal Resolution Spatial Resolution 63 CMA stations Prec, Ta Jan 1992 Dec 2001 Daily Station Nine CAMP stations Prec, Ta, SWdn, SWup, Jan 2002 Dec 2004 Hourly Station LWdn, LWup, Ps, Qa, Wind MERRA Same as above Same as above Hourly NRA-1 Same as above Same as above 6 hourly ERA-40 Same as above Only hourly 1.25 ERA-Int Same as above Same as above 3 hourly 0.7 GLDAS Same as above Same as above 3 hourly 1 1 CFSR Same as above Same as above 6 hourly a Prec, precipitation; Ta, air temperature, SWdn/LWdn, surface downward shortwave/longwave radiation flux; SWup/LWup, surface upward shortwave/ longwave radiation flux; Ps, surface pressure; Qa, specific humidity; and Wind, surface wind speed. Note that while an hourly CFSR output is available, only the 6 hourly data (with a coarse horizontal resolution) are used here, because we focus on daily and monthly variables. replacement of instruments [Li et al., 2009]. The daily air temperature data used in this study have been homogenized [Li and Yan, 2009]. [9] Figure 1 shows the locations of the 63 CMA stations and nine CAMP stations over the TP. The stations are very sparse and are mostly located over the eastern TP where the elevations are generally lower than over the western part. The elevations of all nine CAMP stations are higher than 4000 m, and the data are available from October 2002 to December 2004 with large data gaps over some stations. For example, over the Amdo site (32.24 N, E), the data are available only from October 2002 to September We compared reanalysis products only with available observational data. The details of observational information in CAMP/Tibet were described by Ma et al. [2005]. More recently, some new observational systems have been established or initiated over the TP [e.g., Y. Ma et al., 2008; Xu et al., 2008], which would provide more observational data and advance our understanding of surface climate over the TP, especially over the eastern part. [10] Six reanalysis products (i.e., MERRA, NRA-1, ERA- 40, ERA-Int, CFSR, and GLDAS) are also used in this study. They are different in many aspects, such as the numerical schemes and physical parameterizations in their numerical models, qualities and quantities of observational data used in the assimilation processes, and the assimilation schemes [e.g., Decker et al., 2012]. Because surface observations are assimilated in some of the reanalyses, the station observations and the reanalyses might not be totally independent. For example, surface air temperature and humidity are assimilated in ERA-40 and ERA-Int to adjust soil moisture, and CFSR assimilates hydrological quantities from a parallel land surface model forced by the merged precipitation products from surface measurements and satellite remote sensing [Wang et al., 2011]. MERRA does not assimilate precipitation over land [Rienecker et al., 2011], while NRA-1 does not assimilate any surface observations [Kalnay et al., 1996]. Although it is difficult to determine what percentages of observed precipitation (or air temperature) data at the 63 stations have actually been assimilated in CFSR (ERA-40, or ERA-Int), observations at the nine CAMP stations from field campaigns are unlikely to be assimilated in any of the reanalysis systems. The near-surface meteorological data in GLDAS are derived from the ERA-40 (from 1979 to 1993), NRA-1 (from 1994 to 1999), and the NCEP global analysis (from 2000) in combination with the merged precipitation product from surface measurements and satellite remote sensing and with observation-based downward shortwave and longwave radiation fluxes. These data are then used to drive four land models [Rodell et al., 2004]. In this study, the upward shortwave and longwave radiation fluxes are computed from the community land model [Oleson et al., 2004]. The details of each reanalysis products can be Figure 1. Topography (in meters) and locations of the 63 CMA stations (dots) and nine CAMP stations (triangles) over the Tibetan Plateau. 3of12
4 Figure 2. Surface elevation differences between reanalysis grid cells and station locations. easily found in the corresponding references and are not repeated here. [11] Considering the common period of data availability from reanalyses and in situ observations, comparisons of the CMA and CAMP station data with reanalysis products were conducted for and , respectively. Besides air temperature and precipitation, other near-surface meteorological variables (i.e., humidity, wind, surface pressure, and radiation) were also evaluated based on the CAMP data. The comparisons were focused on daily and monthly mean variables. Because the station distribution (Figure 1) is irregular, comparisons between grid cell products with station observations involve interpolations [L. Ma et al., 2008, 2009], which inevitably introduces new errors [Zhao et al., 2008]. To avoid these interpolation errors, data from each station are compared with those from a reanalysis grid cell covering this station. Figure 3. Histograms of daily precipitation statistics computed from six reanalysis products and observations over the 63 CMA stations for The correlation coefficient (r), ratio of standard derivations (s r /s obs ), standard deviation of differences (s d ) (mm/day), and mean bias (BIAS) (mm/day) are shown in the x axis, while the number of stations for each bin is shown in the y axis. 4of12
5 Figure 4. Same as Figure 3, but for monthly precipitation, with s d and BIAS in mm/mon. [12] The elevation differences between each station and the corresponding grid cell are very large because of the complex topography of the TP (e.g., the stations are mostly located over relatively flat areas or at mountain valley), which could introduce the biases of surface products in the reanalyses [e.g., Frauenfeld et al., 2005; Zhao et al., 2007]. Figure 2 shows that most of the reanalysis grid cells have higher elevations than the corresponding surface stations, and their differences are up to 1700 m over some stations. With a focus on the basic evaluations of reanalyses, this study does not consider elevation corrections of the reanalysis products. If the reanalysis products are used for other purposes such as atmospheric forcing data for land models, however, the elevation corrections are necessary [e.g., Sheffield et al., 2006] Data Analysis Methods [13] To quantify the differences between reanalysis products and observations, four statistical quantities between each reanalysis product and observations are computed at each station, including the correlation coefficients (r), ratio of standard deviations (s r /s obs ), standard deviation of the differences (s d ), and the mean bias (BIAS) (i.e., the average difference between reanalysis products and observations). Presenting all the results for the 63 CMA stations is not easy, and we use histograms and Taylor diagrams [Taylor, 2001] to facilitate comparisons in concise ways. [14] At a given station, the above statistical quantities are quite different for different reanalyses. In order to quantify and intercompare the performances of reanalysis products in terms of each statistical quantity for all stations, a ranking scheme is utilized. Brunke et al. [2003] developed a ranking scheme to score the multibulk aerodynamic algorithms in computing ocean surface turbulent fluxes. Decker et al. [2012] applied this approach to rank the bias and standard deviation of errors between reanalysis products and flux tower measurements. In this study, we extend this ranking approach to all four statistical quantities computed from each surface meteorological variable mentioned above. At each station and for each statistical quantity of a variable (e.g., precipitation), reanalysis products are ranked from 1 to 6, with 1 given to the reanalysis with the lowest value of s d or BIAS in magnitude (or the highest correlation coefficient) and 6 given to the one with the largest value of s d or BIAS in magnitude (or the lowest correlation coefficient). Note that the ranking for the CAMP period is only from 1 to 5 because ERA-40 data are unavailable during this period. For s r /s obs, 1 is given to the reanalysis with the ratio closest to 1 and 6 (for CMA stations) or 5 (for CAMP stations) is given to the reanalysis with the ratio farthest away from 1. We then average all ranking scores from all stations. The lowest and highest values represent the closest and worst relationships between reanalysis products and observations, respectively. 3. Comparison of Station Observations With Reanalyses 3.1. Precipitation [15] Figure 3 shows the distribution of statistical quantities computed from daily reanalyses and observations at the 5of12
6 Table 2a. The Average (Across 63 CMA Stations) Ranking for Each of the Four Statistical Quantities (Correlation Coefficient, Ratio of Standard Deviations, Standard Deviation of Differences, and Mean Bias) Based on (Daily and Monthly) Precipitation a r sr/sobs sd BIAS Daily Monthly Daily Monthly Daily Monthly Daily Monthly MERRA NRA ERA ERA-Int CFSR GLDAS a Both the lowest (bold) value (i.e., best performance) and highest (italicized) value (i.e., worst performance) in each column are highlighted. 63 CMA stations. The most frequent correlation is between 0.3 and 0.5 for most products. MERRA has 12 stations with correlations larger than 0.5, and ERA-40 has more than 40 stations with correlations greater than 0.4. The s r / s obs values at most stations are within (particularly in GLDAS and MERRA). The most frequent s d values are between 2 and 6 mm/day. The most frequent BIAS values are between 1 and 0 mm/day in GLDAS and between 0 and 1 mm/day in others. Based on the number of stations with relatively small s d and BIAS, GLDAS and MERRA perform better than others. For instance, in GLDAS, BIAS is within 1 to 1 mm/day at 62 stations and s d is within 0 to 4 mm/day at 45 stations. Using the ranking approach, it can be seen clearly in Table 2a that daily precipitation in GLDAS is generally closer to observations than in all other products in terms of BIAS (2.03) and s d (2.37), MERRA is best in s r / s obs (2.90), and ERA-40 is best in correlation coefficients (2.62). NRA-1 has the worst score in s d (4.52), while CFSR has the worst score in correlation (5.17). ERA-Int performs worst in both s r /s obs (3.92) and BIAS (4.29). The low BIAS of GLDAS daily precipitation is not surprising, as GLDAS combines reanalysis precipitation with surface and satellite remote sensing precipitation observations [Rodell et al., 2004]. [16] Figure 4 shows that monthly precipitation results from all reanalyses are overall in better agreement with in situ observations compared with daily precipitation results. For instance, the correlation coefficient for more than half of the stations in all six reanalyses is greater than 0.7 for monthly precipitation. Similar to the ranking scores using daily precipitation, Table 2a shows that GLDAS has the best scores in terms of BIAS (2.03) and s r /s obs (2.22) for monthly precipitation, while MERRA performs best in correlation (1.89) and s d (2.22). NRA-1 has the worst scores in both s r /s obs (4.29) and s d (4.33), while ERA-Int has the worst score in BIAS (4.27), similar to the daily precipitation. Overall, the performances of each reanalysis over the TP depend on the specific statistical quantity analyzed, and this is consistent with the findings of Bosilovich et al. [2008] that the strength and weakness of each reanalysis varies with regions. [17] Figure 5 shows the Taylor diagrams [Taylor, 2001] derived from the correlation coefficients and standard deviations of precipitation and air temperature averaged Figure 5. Taylor diagrams of (a) daily and (b) monthly precipitation (circles) and air temperatures (stars) for The correlations and ratios of standard deviations among six reanalysis products and in situ observations are first computed at each station, and then averaged across 63 stations. 6of12
7 Figure 6. Same as Figure 3 but for daily air temperature, with s d and BIAS in C. across the 63 CMA stations, providing another way to compare the reanalysis results with observations. The distance of a point to the point (1.00, 1.00) in the Taylor diagram indicates the relative skill of the reanalysis. For daily precipitation, all reanalyses have correlation coefficients of The standard deviations in most reanalyses (except MERRA) are higher than the observational values. MERRA and ERA-40 have slightly higher correlations (near 0.4) than others. For monthly precipitation, the points shown in the Taylor diagram are very scattered. Correlation coefficients are greater than 0.8 in MERRA, ERA-Int, and GLDAS and are lower in others. The standard deviations of MERRA and NRA-1 are almost identical to observations, but they are much more scattered in others Temperature [18] Figures 6 and 7 show histograms of the statistical quantities computed from daily and monthly air temperatures, respectively. For daily temperature, the correlation is greater than 0.9 at most stations and is less than 0.7 at two stations in GLDAS only (Figure 6). From the Taylor diagram (Figure 5), the standard deviations of daily air temperature from MERRA, NRA-1, ERA-Int, and GLDAS are close to unity and are below unity in the remaining two products. Figure 5 also shows that the correlation coefficients of daily air temperature are generally much larger than those from daily precipitation, while the ratio of standard deviations is closer to unity than that of daily precipitation. This indicates that daily air temperature in reanalyses is better estimated than daily precipitation in general, which will be further discussed later. All reanalyses tend to underestimate the daily air temperature, with BIAS varying from 14 C to 6 C. For example, BIAS is below 12 C at 5 stations in NRA-1. The cold bias in daily air temperature is mainly attributed to the higher surface elevation in the reanalyses (Figure 2) [also see Frauenfeld et al., 2005]. The daily air temperature of reanalyses has greater variations compared with observations, as indicated by s r /s obs greater than 1 at most stations. [19] For monthly air temperature, all reanalyses over all stations have the correlation coefficient greater than 0.8. In particular, the correlation is greater than 0.97 at nearly all stations in ERA-40, ERA-Int, and MERRA (Figure 7). The performances of GLDAS are not as good as those of the others, but the correlation is still above 0.95 at more than half of the stations. The distribution of the s r /s obs and s d (Figure 7) is similar to that obtained using the daily time series (Figure 6). NRA-1 has more stations with relatively large BIAS values (in magnitude) than other reanalyses. For instance, NRA-1 underestimates monthly air temperature by more than 9 C at 17 stations, but this occurs at 6 stations or fewer in other reanalyses (including at 2 and 4 stations only in ERA-40 and ERA-Int, respectively). The cold biases of reanalysis products (over the TP) also appear over other regions in China [L. Ma et al., 2008]. [20] The relative performances of reanalyses in daily and monthly air temperatures are provided in Table 2b. ERA-Int has the best performance in two of the four statistical quantities,based on both daily and monthly air temperature, while MERRA has the best performance in correlation. 7of12
8 Figure 7. Same as Figure 3 but for monthly air temperatures, with s d and BIAS in C. NRA-1 has the worst performance in s r /s obs and BIAS based on daily air temperature and in three of the four quantities using monthly air temperature, partly because no surface data were assimilated. As mentioned in section 2, the GLDAS air temperature data are from a combination of ERA-40 ( ), NRA-1 ( ), and NCEP global analysis ( ). Therefore, the overall performance of air temperature in GLDAS is also poor, with poorest performances in correlation and s d Other Meteorological Variables [21] As mentioned in the introduction, the severe environmental and geographical conditions restrict the number of stations available and the variables measured over the TP. Besides precipitation and air temperature, CAMP also provides observational data of surface radiation (SWdn, SWup, LWdn, and LWup), wind speed (Wind), surface pressure (Ps), and humidity (Qa) at nine stations (Figure 1 and Table 1) for the evaluation of reanalysis products. [22] Figure 8, as an example, shows the monthly time series of surface meteorological variables from both station observations and reanalysis products at the MS3478 station (31.9 N, 91.7 E, Elev m). While all reanalysis products capture the seasonal variation relatively well, the actual values differ significantly among reanalyses and between reanalyses and surface observations for most of the variables. For example, precipitation in June 2003 (ninth month in Figure 8) in ERA-Int doubles the amount in CFSR, and both are larger than the in situ observational value (Figure 8a). Among all reanalyses, NRA-1 shows the least Table 2b. The Average (Across 63 CMA Stations) Ranking for Each of the Four Statistical Quantities (Correlation Coefficient, Ratio of Standard Deviations, Standard Deviation of Differences, and Mean Bias) Based on (Daily and Monthly) Air Temperature a r s r /s obs s d BIAS Daily Monthly Daily Monthly Daily Monthly Daily Monthly MERRA NRA ERA ERA-Int CFSR GLDAS a Both the lowest (bold) value (i.e., best performance) and highest (italicized) value (i.e., worst performance) in each column are highlighted. 8of12
9 Figure 8. Comparisons of monthly meteorological variables among five reanalyses and observations at the MS3478 site (31.9 N, 91.7 E, Elev m) from October 2002 to December The variables include (a) precipitation (mm/month), (b) air temperature ( C), (c) downward shortwave radiation (W m 2 ), (d) downward longwave radiation (W m 2 ), (e) upward shortwave radiation (W m 2 ), (f) upward longwave radiation (W m 2 ), (g) surface pressure (mb), (h) specific humidity (g(kg) 1 ), and (i) wind speed (ms 1 ). consistence with the observations in almost all variables. For instance, NRA-1 significantly overestimates downward shortwave radiation (Figure 8c), partly because it did not assimilate atmospheric aerosol data. It also overestimates upward shortwave radiation (Figure 8e) because of a higher surface albedo. [23] All reanalyses capture well the variations of wind (Figure 8i), air temperature (except GLDAS and NRA-1) (Figure 8b), and humidity (except GLDAS) (Figure 8h). They largely overestimate upward longwave radiation (Figure 8f) over this site and also over other CAMP sites (figures not shown). Over this site, the surface elevation biases among five reanalyses and station locations vary from 172 m (ERA-Int) to 1443 m (NRA-1). These elevation biases largely account for the cold biases (Figure 8b) and the underestimation of surface pressure (Figure 8g) in most reanalyses. The relative performances of reanalyses are compared by computing the ranking scores, as discussed in section 3. As mentioned in section 2, CAMP stations contain large missing data. Daily statistical quantities are computed for a station with data available for at least 100 days, while monthly values are computed for a station with data available for at least 12 months during the 27 month period. Using these criteria, fewer than nine stations are available for some variables; for instance, only 6 stations meet the criteria for downward longwave radiation. [24] For the comparison of correlation coefficients, CFSR has the best scores for four of the seven variables, while 9of12
10 Table 3a. The Average (Across Nine CAMP Stations) Ranking for Daily Surface Meteorological Variables: Coefficient (r) a SWdn LWdn SWup LWup Ps Qa Wind MERRA NRA ERA-Int CFSR GLDAS a See Table 1. The ranking is based on correlation coefficient. Both the lowest (bold) value (i.e., best performance) and highest (italicized) value (i.e., worst performance) in each column are highlighted. GLDAS is the best for SWdn and LWup (Table 3a). In particular, the average score for GLDAS is 1.0 for SWdn, meaning that GLDAS has the highest correlation at every station among five reanalyses. NRA-1 is the worst for SWdn and Qa, and ERA-Int performs worst in LWup, Ps, and Wind (Table 3a). [25] For the comparison of s r /s obs, CFSR also performs best with the lowest scores for three of the seven variables and without the highest (i.e., worst) scores for any of the variables (Tables 3b and 3c). Each of the other four reanalyses has the lowest score for one of the variables but also has the highest scores for one (MERRA, ERA-Int, and GLDAS) or four variables (NRA-1). [26] For the comparison of s d, ERA-Int has the worst score for three variables, while CFSR has the best score for three variables. The average score of 1.0 for GLDAS in SWdn and MERRA in SWup indicates that they are the best for all stations among five reanalyses in these specific quantities. [27] For the comparison of BIAS, NRA-1 is the worst for six variables except Wind (Table 3d). CFSR again is the best for five of the seven variables, while GLDAS has the best scores in SWdn and LWdn (Table 3d). The relative ranking of reanalyses computed using monthly meteorological variables is very similar to the above results obtained using daily variables (table not shown). 4. Discussion and Conclusions [28] Six reanalysis products (i.e., MERRA, NRA-1, ERA- 40, ERA-Int, CFSR, and GLDAS) were evaluated with in situ measured surface meteorological variables over the Tibetan Plateau (TP). The evaluations were performed over 63 CMA stations for precipitation and temperature and nine CAMP stations for seven additional variables (upward and downward solar and longwave radiation fluxes, surface Table 3b. The Average (Across Nine CAMP Stations) Ranking for Daily Surface Meteorological Variables: Ratio of Standard Deviations (s r /s obs ) a SWdn LWdn SWup LWup Ps Qa Wind MERRA NRA ERA-Int CFSR GLDAS a See Table 1. The ranking is based on ratio of standard deviations. Both the lowest (bold) value (i.e., best performance) and highest (italicized) value (i.e., worst performance) in each column are highlighted. Table 3c. The Average (Across Nine CAMP Stations) Ranking for Daily Surface Meteorological Variables: Standard Deviation of Differences (s d ) a SWdn LWdn SWup LWup Ps Qa Wind MERRA NRA ERA-Int CFSR GLDAS a See Table 1. The ranking is based on standard deviation of differences. Both the lowest (bold) value (i.e., best performance) and highest (italicized) value (i.e., worst performance) in each column are highlighted. pressure, specific humidity, and wind speed). Stations are mostly located over the eastern TP, and daily and monthly observations at each station were compared with reanalyses at the grid cell covering the station(s). Four statistical quantities (i.e., correlation coefficient, ratio of standard deviation, standard deviation of differences, and bias) were computed and intercompared among different reanalyses. A ranking approach was also applied to quantify the relative performance of reanalysis products. [29] It is found that no single reanalysis is superior to others for all variables at both daily and monthly time scales. Reanalysis products with the best performances are different with different variables. Compared with the CMA data, ERA-Int has the best representation of air temperature, while GLDAS precipitation, which is primarily based on the merged precipitation product from surface measurements and satellite remote sensing, is closest to observations. Compared with the CAMP data, CFSR is overall superior to others. NRA-1 is the worst compared with both CMA and CAMP data, consistent with previous studies [e.g., Zhao et al., 2008; L. Ma et al., 2008, 2009; Mao et al., 2010]. Partly because of the positive elevation biases, all reanalysis products underestimate temperature and surface pressure. The overall agreement between reanalyses and surface observations is better in air temperature than in precipitation, largely because of larger errors in both modeled precipitation forecasts and in situ measurements. For example, the windinduced precipitation undercatch is sometimes as large as 20% [Yang et al., 2005], and the error in precipitation simulations is always one of the biggest issues in global modeling. Therefore, when the reanalyses are used to evaluate climate model outputs, much attention needs to be paid to the selection of the appropriate reanalysis products. [30] Because the seasonal cycle of precipitation and air temperature is much stronger than their interannual Table 3d. The Average (Across Nine CAMP Stations) Ranking for Daily Surface Meteorological Variables: Mean biases (BIAS) a SWdn LWdn SWup LWup Ps Qa Wind MERRA NRA ERA-Int CFSR GLDAS a See Table 1. The ranking is based on mean bias. Both the lowest (bold) value (i.e., best performance) and highest (italicized) value (i.e., worst performance) in each column are highlighted. 10 of 12
11 variability, the results here primarily reflect the fidelity of the reanalysis seasonal cycle. To focus on the interannual variability, observations for a longer period are needed, and statistical quantities should be computed after removing the mean seasonal cycle. [31] There are several possible reasons for the differences between reanalyses and between reanalysis products and station measurements, including the number and type of observations assimilated in each reanalysis, as discussed in section 2. Model physical parameterizations are also responsible for part of the differences. We can take the land surface model (LSM) as an example. ECMWF applied a four-layer LSM [Uppala et al., 2005; Simmons et al., 2006], while NRA-1 used a two-layer LSM. CFSR updated NRA-1 and used a four-layer LSM (Noah) [Saha et al., 2010]. MERRA coupled the Catchment-based model into its forecast model [Rienecker et al., 2011]. These LSMs are different in the partitioning of incoming precipitation into runoff, evaporation, and soil storage, as well as in the partitioning of net radiation into sensible, latent, and ground heat fluxes, which, in turn, feed back to the atmosphere. [32] The scale mismatch is another reason for the differences between reanalyses and observations. For the reanalysis products with coarse spatial resolutions (e.g., NRA-1), a grid cell may cover several stations. As an example, results in the three MERRA grid cells centered at three stations at (39 N, E), (39 N, E), and (38 N, E) would be compared with the observations at three different stations, (38.93 N, E), (38.8 N, E), and (38.23 N, E), respectively. However, observations in all three stations would be averaged for comparison with the results in the single NRA-1 grid cell centered at ( N, E). The monthly precipitation at the three MERRA grid cells differs significantly and is also quite different from that at the NRA-1 grid cell. Monthly air temperatures at the three MERRA grid cells are close to each other, but are about 5 C higher than those at the NRA-1 grid cell (figure not shown). [33] The quantitative results in this study based on the comparison of reanalysis grid cell values with point measurements may be also affected by the measurement uncertainty and representativeness. For example, the instrumental error or malfunction would introduce errors in the measurement and data collection. Tanaka et al. [2003] found that the lower-energy closure over the eastern TP is partially due to the degraded performance of the infrared hygrometer for high-frequency humidity measurements. Ma et al. [2005] also demonstrated that the energy imbalance there might be partially due to measurement errors. The data representativeness is also a serious issue over the TP because of the strong horizontal heterogeneity in elevation and land cover. In particular, topography strongly affects surface meteorological variables, such as air temperature and precipitation [e.g., Berg et al., 2003; Zhao et al., 2008]. Elevation corrections might make the comparison of point measurements with reanalysis results at different horizontal resolutions more compatible, but they would also introduce new errors because of the assumptions in the correction method. [34] Acknowledgments. This work was supported by the Department of Science and Technology of China under grant 2009CB and the National Science Foundation of China under grant (to AW) and by NASA (NNX09A021G) and NSF (AGS ) (to X. Z.). We thank NCAR for the use of the NCAR computers for obtaining ERA40, ERA-Interim, and NCEP/NCAR reanalysis data from the mass store system. The GLDAS data were acquired from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The MERRA data are hosted by the Global Modeling and Assimilation Office (GMAO), and the GES Disc is also thanked for the dissemination of the products. The CFSR data were obtained from NOAA s National Operational Mode Archive and Distribution System (NOMADS), which is maintained at NOAA s National Climatic Data Center (NCDC). The CAMP/ Tibet data were obtained from the Coordinated Enhanced Observing Period (CEOP) web site, and the authors thank all the participants in the field observations of the CAMP/Tibet. Four anonymous reviewers are thanked for their valuable and insightful comments, which also helped us to discover and correct errors in some of the figures. References Berg, A. A., J. S. Famiglietti, J. P. Walker, and P. R. Houser (2003), Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes, J. Geophys. Res., 108(D16), 4490, doi: /2002jd Betts, A., J. Ball, P. Viterbo, A. Dai, and J. Marengo (2005), Hydrometeorology of the Amazon in ERA-40, J. Hydrometeorol., 6, , doi: /jhm Betts, A., M. Köhler, and Y. Zhang (2009), Comparsion of river basin hydrometeorology in ERA-Interim and ERA-40 reanalysis with observations, J. Geophys. Res., 114, D02101, doi: /2008jd Bosilovich, M. G., J. Chen, F. R. Robertson, and R. F. Adler (2008), Evaluation of global precipitation in reanalyses, J. Appl. Meteorol. Climatol., 47, Bosilovich, M. G., F. R. Robertson, and J. Chen (2011), Global energy and water budget in MERRA, J. Clim., 24, , doi: / 2011JCLI Brunke, M., C. Fairall, X. Zeng, L. Eymard, and J. Curry (2003), Which bulk aerodynamic algorithms are least problematic in computing ocean surface turbulent fluxes?, J. Clim., 16, , doi: / (2003)016<0619:WBAAAL>2.0.CO;2. Brunke, M. A., Z. Wang, X. Zeng, M. Bosilovich, and C.-L. Shie (2011), An assessment of the uncertainties in ocean surface turbulent fluxes in 11 reanalysis, satellite-derived, and combined global data sets, J. Clim., 24, , doi: /2011jcli Decker, M., M. Brunke, Z. Wang, K. Sakaguchi, X. Zeng, and M. Bosilovich (2012), Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations, J. Clim., doi: / JCLI-D , in press. Frauenfeld, O. W., T. Zhang, and M. C. Serreze (2005), Climate change and variability using European Centre for Medium-Range Weather Forecasts reanalysis (ERA-40) temperatures on the Tibetan Plateau, J. Geophys. Res., 110, D02101, doi: /2004jd Hsu, H.-H., and X. Liu (2003), Relationship between the Tibetan Plateau heating and East Asian summer monsoon rainfall, Geophys. Res. Lett., 30(20), 2066, doi: /2003gl Kalnay, E., et al. (1996), The NCEP/NCAR 40-Year Reanalysis Project, Bull. Am. Meteorol. Soc., 77, , doi: / (1996) 077<0437:TNYRP>2.0.CO;2. Li, Q., H. Zhang, J. Chen, W. Li, X. Liu, and P. Jones (2009), A mainland China homogenized historical temperature dataset of , Bull. Am. Meteorol. Soc., 90, , doi: /2009bams Li, Z., and Z.-W. Yan (2009), Homogenized daily mean/maximum/ minimum temperature series for China from , Atmos. Oceanic Sci. Lett., 2, Liu, X., and B. Chen (2000), Climatic warming in the Tibetan Plateau during recent decades, Int. J. Climatol., 20, , doi: / ( )20:14<1729::AID-JOC556>3.0.CO;2-Y. Liu, X., Z. Cheng, L. Yan, and Z.-Y. Yin (2009), Elevation dependency of recent and future minimum surface air temperature in the Tibetan Plateau and its surroundings, Global Planet. Change, 68, , doi: /j. gloplacha Ma, L., T. Zhang, Q. Li, O. W. Frauenfeld, and D. Qin (2008), Evaluation of ERA-40, NCEP-1, and NCEP-2 reanalysis air temperatures with ground-based measurements in China, J. Geophys. Res., 113, D15115, doi: /2007jd Ma, L., T. Zhang, O. W. Frauenfeld, B. Ye, D. Yang, and D. Qin (2009), Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 reanalyses and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China, J. Geophys. Res., 114, D09105, doi: / 2008JD Ma, Y., S. Fan, H. Ishikawa, O. Tsukamoto, T. Yao, T. Koike, H. Zuo, Z. Hu, and Z. Su (2005), Diurnal and inter-monthly variation of land surface 11 of 12
12 heat fluxes over the central Tibetan Plateau area, Theor. Appl. Climatol., 80, , doi: /s Ma, Y., S. Kang, L. Zhu, B. Xu, L. Tina, and T. Yao (2008), Tibetan observation and research platform, Bull. Am. Meteorol. Soc., 89, , doi: /2008bams Mao, J., X. Shi, L. Ma, D. Kaiser, Q. Li, and P. Thornton (2010), Assessment of reanalysis daily extreme temperatures with China s homogenized historical dataset during using probability density functions, J. Clim., 23, , doi: /2010jcli Maurer, E. P., G. M. O Donnell, and D. P. Lettenmaier (2001), Evaluation of the land surface water budget in NCEP/NCAR and NCEP/ DOE reanalyses using an off-line hydrologic model, J. Geophys. Res., 106, 17,841 17,862, doi: /2000jd Oleson, K. W., et al. (2004), Technical description of the community land model (CLM), NCAR Tech. Note NCAR/TN-461+STR, 174 pp., Natl. Cent. for Atmos. Res., Boulder, Colo. Qian, T., A. Dai, K. E. Trenberth, and K. W. Oleson (2006), Simulation of global land surface conditions from 1948 to 2004: Part I: Forcing data and evaluations, J. Hydrometeorol., 7, , doi: /jhm Rienecker, M. R., et al. (2011), MERRA-NASA s Modern-Era Retrospective analysis for research and applications, J. Clim., 24, , doi: /jcli-d Roads, J., and A. K. Betts (2000), NCEP NCAR and ECMWF Reanalysis surface water and energy budgets for the Mississippi River Basin, J. Hydrometeorol., 1, 88 94, doi: / (2000) 001<0088:NNAERS>2.0.CO;2. Rodell, M., et al. (2004), The Global Land Data Assimilation System, Bull. Am. Meteorol. Soc., 85, , doi: /bams Saha, S., et al. (2010), The NCEP Climate Forecast System Reanalysis, Bull. Am. Meteorol. Soc., 91, , doi: /2010bams Sheffield, J., G. Goteti, and E. F. Wood (2006), Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling, J. Clim., 19, , doi: /jcli Simmons, A., S. Uppala, D. Dee, and S. Kobayashi (2006), ERA-Interim: New ECMWF reanalysis products from 1989 onwards, ECMWF Newslett., 110, Smith, S. R., D. M. Legler, and K. V. Verzone (2001), Quantifying uncertainties in NCEP reanalysis using high-quality research vessel observations, J. Clim., 14, , doi: / (2001) 014<4062:QUINRU>2.0.CO;2. Tanaka, K., I. Tamagawa, H. Ishikawa, Y. Ma, and Z. Hu (2003), Surface energy budget and closure of the eastern Tibetan Plateau during the GAME/Tibet IOP 1998, J. Hydrol., 283, , doi: /s (03) Taylor, K. E. (2001), Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, , doi: / 2000JD Trenberth, K. E., and C. J. Guillemot (1998), Evaluation of the atmospheric moisture and hydrological cycle in the NCEP/NCAR reanalysis, Clim. Dyn., 14, , doi: /s Uppala, S. M., et al. (2005), The ERA-40 re-analysis, Q. J. R. Meteorol. Soc., 131, , doi: /qj Wang, A., and X. Zeng (2011), Sensitivities of terrestrial water cycle simulations to the variations of precipitation and air temperature in China, J. Geophys. Res., 116, D02107, doi: /2010jd Wang, W., P. Xie, S.-H. Yoo, Y. Xue, A. Kumar, and X. Wu (2011), An assessment of the surface climate in the NCEP climate forecast system reanalysis, Clim. Dyn., 37, , doi: /s Wu, T., and Z. Qian (2003), The relation between the Tibetan winter snow and the Asian summer monsoon and rainfall: An observational investigation, J. Clim., 16, , doi: / (2003)016<2038: TRBTTW>2.0.CO;2. Xu, X., R. Zhang, X. Shi, S. Zhang, L. Bian, X. Cheng, G. Ding, T. Koike, C. Lu, and P. Li (2008), A new integrated observational system over the Tibetan Plateau (NIOST), Bull. Am. Meteorol. Soc., 89, , doi: /2008bams Yanai, M., C. Li, and Z. Song (1992), Seasonal heating of the Tibetan Plateau and its effects on the evolution of the Asian summer monsoon, J. Meteorol. Soc. Jpn., 70, Yang, D., D. Kane, Z. Zhang, D. Legates, and B. Goodison (2005), Bias corrections of long-term ( ) daily precipitation data over the northern regions, Geophys. Res. Lett., 32, L19501, doi: / 2005GL Yang, K., X.-F. Guo, J. He, J. Qin, and T. Koike (2011), On the climatology and trend of the atmospheric heat source over the Tibetan Plateau: An experiments-supported revisit, J. Clim., 24, , doi: / 2010JCLI Ye, D., and Y. X. Gao (1979), The Meteorology of the Qinghai-Xizang Plateau (in Chinese), 278 pp., Science Press, Beijing. You, Q., S. Kang, N. Pepin, and Y. Yan (2008), Relationship between trends in temperature extremes and elevation in the eastern and central Tibetan Plateau, , Geophys. Res. Lett., 35, L04704, doi: /2007gl You, Q., S. Kang, N. Pepin, W.-A. Flugel, Y. Yan, H. Behrawanm, and J. Huang (2010), Relationship between temperature trend magnitude, elevation and mean temperature in the Tibetan Plateau from homogenized surface stations and reanalysis data, Global Planet. Change, 71, , doi: /j.gloplacha Zhang, Y., T. Li, and B. Wang (2004), Decadal change of the spring snow depth over the Tibetan Plateau: The associated circulation and influence on the East Asian summer monsoon, J. Clim., 17, , doi: / (2004)017<2780:dcotss>2.0.co;2. Zhao, P., and L. X. Chen (2001), Interannual variability of atmospheric heat source/sink over the Qinghai-Xizang (Tibetan) Plateau and its relation to circulation, Adv. Atmos. Sci., 18, , doi: /s Zhao, P., Z. Zhou, and J. Liu (2007), Variability of Tibetan spring snow and its associations with the hemispheric extra-tropical circulation and East Asian summer monsoon rainfall: An observational investigation, J. Clim., 20, , doi: /jcli Zhao, T., W. Guo, and C. Fu (2008), Calibrating and evaluating reanalysis surface temperature error by topographic correction, J. Clim., 21, , doi: /2007jcli Zuo, Z. Y., R. H. Zhang, and P. Zhao (2011), The relation of vegetation over the Tibetan Plateau to rainfall in China during the boreal summer, Clim. Dyn., 36, , doi: /s A. Wang, Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, PO Box 9804, Beijing , China. (wangaihui@mail.iap.ac.cn) X. Zeng, Department of Atmospheric Sciences, University of Arizona, PO Box , Tucson, AZ , USA. 12 of 12
Uncertainties in Quantitatively Estimating the Atmospheric Heat Source over the Tibetan Plateau
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2014, VOL. 7, NO. 1, 28 33 Uncertainties in Quantitatively Estimating the Atmospheric Heat Source over the Tibetan Plateau DUAN An-Min 1, 3, WANG Mei-Rong 1, 2,
More informationMay 3, :41 AOGS - AS 9in x 6in b951-v16-ch13 LAND SURFACE ENERGY BUDGET OVER THE TIBETAN PLATEAU BASED ON SATELLITE REMOTE SENSING DATA
Advances in Geosciences Vol. 16: Atmospheric Science (2008) Eds. Jai Ho Oh et al. c World Scientific Publishing Company LAND SURFACE ENERGY BUDGET OVER THE TIBETAN PLATEAU BASED ON SATELLITE REMOTE SENSING
More informationDecrease of light rain events in summer associated with a warming environment in China during
GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L11705, doi:10.1029/2007gl029631, 2007 Decrease of light rain events in summer associated with a warming environment in China during 1961 2005 Weihong Qian, 1 Jiaolan
More informationA Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 6, 325 329 A Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model YU En-Tao 1,2,3, WANG Hui-Jun 1,2, and SUN Jian-Qi
More informationThe Interdecadal Variation of the Western Pacific Subtropical High as Measured by 500 hpa Eddy Geopotential Height
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2015, VOL. 8, NO. 6, 371 375 The Interdecadal Variation of the Western Pacific Subtropical High as Measured by 500 hpa Eddy Geopotential Height HUANG Yan-Yan and
More informationEvaluation of NCEP CFSR, NCEP NCAR, ERA-Interim, and ERA-40 Reanalysis Datasets against Independent Sounding Observations over the Tibetan Plateau
206 J O U R N A L O F C L I M A T E VOLUME 26 Evaluation of NCEP CFSR, NCEP NCAR, ERA-Interim, and ERA-40 Reanalysis Datasets against Independent Sounding Observations over the Tibetan Plateau XINGHUA
More informationWater cycle changes during the past 50 years over the Tibetan Plateau: review and synthesis
130 Cold Region Hydrology in a Changing Climate (Proceedings of symposium H02 held during IUGG2011 in Melbourne, Australia, July 2011) (IAHS Publ. 346, 2011). Water cycle changes during the past 50 years
More informationA 3DVAR Land Data Assimilation Scheme: Part 2, Test with ECMWF ERA-40
A 3DVAR Land Data Assimilation Scheme: Part 2, Test with ECMWF ERA-40 Lanjun Zou 1 * a,b,c Wei Gao a,d Tongwen Wu b Xiaofeng Xu b Bingyu Du a,and James Slusser d a Sino-US Cooperative Center for Remote
More informationAssessment of Snow Cover Vulnerability over the Qinghai-Tibetan Plateau
ADVANCES IN CLIMATE CHANGE RESEARCH 2(2): 93 100, 2011 www.climatechange.cn DOI: 10.3724/SP.J.1248.2011.00093 ARTICLE Assessment of Snow Cover Vulnerability over the Qinghai-Tibetan Plateau Lijuan Ma 1,
More informationThe Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 3, 219 224 The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times LU Ri-Yu 1, LI Chao-Fan 1,
More informationLand Data Assimilation at NCEP NLDAS Project Overview, ECMWF HEPEX 2004
Dag.Lohmann@noaa.gov, Land Data Assimilation at NCEP NLDAS Project Overview, ECMWF HEPEX 2004 Land Data Assimilation at NCEP: Strategic Lessons Learned from the North American Land Data Assimilation System
More informationSCIENCE CHINA Earth Sciences. The role of cloud height and warming in the decadal weakening of atmospheric heat source over the Tibetan Plateau
SCIENCE CHINA Earth Sciences ESEACH PAPE March 2015 Vol.58 No.3: 395 403 doi: 10.1007/s11430-014-4973-6 The role of cloud height and warming in the decadal weakening of atmospheric heat source over the
More informationADVANCES IN EARTH SCIENCE
29 2 2014 2 ADVANCES IN EARTH SCIENCE Vol. 29 No. 2 Feb. 2014. J. 2014 29 2 207-215 doi 10. 11867 /j. issn. 1001-8166. 2014. 02. 0207. Ma Yaoming Hu Zeyong Tian Lide et al. Study progresses of the Tibet
More informationThe Tibetan Plateau has the most prominent and. Climate change and related weather/environmental
ROOF of the World Climate change and related weather/environmental impacts over the Tibetan Plateau have always drawn great interest from scientists worldwide, as they directly impact the global environment
More informationNOTES AND CORRESPONDENCE. Seasonal Variation of the Diurnal Cycle of Rainfall in Southern Contiguous China
6036 J O U R N A L O F C L I M A T E VOLUME 21 NOTES AND CORRESPONDENCE Seasonal Variation of the Diurnal Cycle of Rainfall in Southern Contiguous China JIAN LI LaSW, Chinese Academy of Meteorological
More informationWater vapor sources for Yangtze River Valley rainfall: Climatology, variability, and implications for rainfall forecasting
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi:10.1029/2011jd016902, 2012 Water vapor sources for Yangtze River Valley rainfall: Climatology, variability, and implications for rainfall forecasting Jiangfeng
More informationAn Overview of Atmospheric Analyses and Reanalyses for Climate
An Overview of Atmospheric Analyses and Reanalyses for Climate Kevin E. Trenberth NCAR Boulder CO Analysis Data Assimilation merges observations & model predictions to provide a superior state estimate.
More informationObserved Trends in Wind Speed over the Southern Ocean
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051734, 2012 Observed s in over the Southern Ocean L. B. Hande, 1 S. T. Siems, 1 and M. J. Manton 1 Received 19 March 2012; revised 8 May 2012;
More informationUnderstanding land-surfaceatmosphere. observations and models
Understanding land-surfaceatmosphere coupling in observations and models Alan K. Betts Atmospheric Research akbetts@aol.com MERRA Workshop AMS Conference, Phoenix January 11, 2009 Land-surface-atmosphere
More informationResearch progress of snow cover and its influence on China climate
34 5 Vol. 34 No. 5 2011 10 Transactions of Atmospheric Sciences Oct. 2011. 2011. J. 34 5 627-636. Li Dong-liang Wang Chun-xue. 2011. Research progress of snow cover and its influence on China climate J.
More informationThe Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 1, 25 30 The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO HU Kai-Ming and HUANG Gang State Key
More informationVariations of snow cover in the source regions of the Yangtze and Yellow Rivers in China between 1960 and 1999
420 Journal of Glaciology, Vol. 53, No. 182, 2007 Variations of snow cover in the source regions of the Yangtze and Yellow Rivers in China between 1960 and 1999 YANG Jianping, DING Yongjian, LIU Shiyin,
More informationEast China Summer Rainfall during ENSO Decaying Years Simulated by a Regional Climate Model
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2011, VOL. 4, NO. 2, 91 97 East China Summer Rainfall during ENSO Decaying Years Simulated by a Regional Climate Model ZENG Xian-Feng 1, 2, LI Bo 1, 2, FENG Lei
More informationRecent weakening of northern East Asian summer monsoon: A possible response to global warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051155, 2012 Recent weakening of northern East Asian summer monsoon: A possible response to global warming Congwen Zhu, 1 Bin Wang, 2 Weihong Qian,
More informationWater Balance in the Murray-Darling Basin and the recent drought as modelled with WRF
18 th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009 http://mssanz.org.au/modsim09 Water Balance in the Murray-Darling Basin and the recent drought as modelled with WRF Evans, J.P. Climate
More informationModeling the Arctic Climate System
Modeling the Arctic Climate System General model types Single-column models: Processes in a single column Land Surface Models (LSMs): Interactions between the land surface, atmosphere and underlying surface
More informationThe altitudinal dependence of recent rapid warming over the Tibetan Plateau
Climatic Change (2009) 97:321 327 DOI 10.1007/s10584-009-9733-9 LETTER The altitudinal dependence of recent rapid warming over the Tibetan Plateau Jun Qin Kun Yang Shunlin Liang Xiaofeng Guo Received:
More informationValidation of satellite derived snow cover data records with surface networks and m ulti-dataset inter-comparisons
Validation of satellite derived snow cover data records with surface networks and m ulti-dataset inter-comparisons Chris Derksen Climate Research Division Environment Canada Thanks to our data providers:
More informationChanges in Daily Climate Extremes of Observed Temperature and Precipitation in China
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2013, VOL. 6, NO. 5, 312 319 Changes in Daily Climate Extremes of Observed Temperature and Precipitation in China WANG Ai-Hui and FU Jian-Jian Nansen-Zhu International
More informationClimate Validation of MERRA
Climate Validation of MERRA Siegfried Schubert, Michael Bosilovich, Michele Rienecker, Max Suarez, Randy Koster, Yehui Chang, Derek Van Pelt, Larry Takacs, Man-Li Wu, Myong-In Lee, Scott Weaver, Junye
More informationGLOBAL LAND DATA ASSIMILATION SYSTEM (GLDAS) PRODUCTS FROM NASA HYDROLOGY DATA AND INFORMATION SERVICES CENTER (HDISC) INTRODUCTION
GLOBAL LAND DATA ASSIMILATION SYSTEM (GLDAS) PRODUCTS FROM NASA HYDROLOGY DATA AND INFORMATION SERVICES CENTER (HDISC) Hongliang Fang, Patricia L. Hrubiak, Hiroko Kato, Matthew Rodell, William L. Teng,
More informationChanges of Terrestrial Water Storage in River Basins of China Projected by RegCM4
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2013, VOL. 6, NO. 3, 154 160 Changes of Terrestrial Water Storage in River Basins of China Projected by RegCM4 ZOU Jing 1,2, XIE Zheng-Hui 1, QIN Pei-Hua 1, MA
More informationEvaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling
Clim Dyn DOI 10.1007/s00382-016-2994-x Evaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling Ditiro B. Moalafhi 1 Jason P. Evans 2 Ashish Sharma 1 Received:
More informationComparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau
Clim Dyn DOI 10.1007/s00382-014-2310-6 Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau Qinglong You Jinzhong Min Wei Zhang Nick Pepin Shichang Kang Received:
More informationSUPPLEMENTARY INFORMATION
Intensification of Northern Hemisphere Subtropical Highs in a Warming Climate Wenhong Li, Laifang Li, Mingfang Ting, and Yimin Liu 1. Data and Methods The data used in this study consists of the atmospheric
More informationInteraction of North American Land Data Assimilation System and National Soil Moisture Network: Soil Products and Beyond
Interaction of North American Land Data Assimilation System and National Soil Moisture Network: Soil Products and Beyond Youlong Xia 1,2, Michael B. Ek 1, Yihua Wu 1,2, Christa Peters-Lidard 3, David M.
More informationCloud type climatology over the Tibetan Plateau: A comparison of ISCCP and MODIS/TERRA measurements with surface observations
GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L17716, doi: 10.1029/2006GL026890, 2006 Cloud type climatology over the Tibetan Plateau: A comparison of ISCCP and MODIS/TERRA measurements with surface observations
More informationDid we see the 2011 summer heat wave coming?
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051383, 2012 Did we see the 2011 summer heat wave coming? Lifeng Luo 1 and Yan Zhang 2 Received 16 February 2012; revised 15 March 2012; accepted
More informationWhy do dust storms decrease in northern China concurrently with the recent global warming?
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L18702, doi:10.1029/2008gl034886, 2008 Why do dust storms decrease in northern China concurrently with the recent global warming? Congwen
More informationThe increase of snowfall in Northeast China after the mid 1980s
Article Atmospheric Science doi: 10.1007/s11434-012-5508-1 The increase of snowfall in Northeast China after the mid 1980s WANG HuiJun 1,2* & HE ShengPing 1,2,3 1 Nansen-Zhu International Research Center,
More informationSUPPLEMENTARY INFORMATION
SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE1840 RECENT MULTIDECADAL STRENGTHENING OF THE WALKER CIRCULATION ACROSS THE TROPICAL PACIFIC (1) Supplementary_Figures.pdf Adobe document 1.5MB SI Guide Supplementary
More informationRe-dimensioned CFS Reanalysis data for easy SWAT initialization
Re-dimensioned CFS Reanalysis data for easy SWAT initialization Daniel R Fuka, Charlotte MacAlister, Solomon Seyoum, Allan Jones, Raghavan Srinivasan Cornell University IWMI East Africa Texas A&M Re-dimensioned
More informationA Preliminary Analysis of the Relationship between Precipitation Variation Trends and Altitude in China
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2011, VOL. 4, NO. 1, 41 46 A Preliminary Analysis of the Relationship between Precipitation Variation Trends and Altitude in China YANG Qing 1, 2, MA Zhu-Guo 1,
More informationOceanic origin of the interannual and interdecadal variability of the summertime western Pacific subtropical high
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L13701, doi:10.1029/2008gl034584, 2008 Oceanic origin of the interannual and interdecadal variability of the summertime western Pacific
More informationThe flow of Energy through the Earth s Climate System: Land and Ocean Exchanges
The flow of Energy through the Earth s Climate System: Land and Ocean Exchanges Kevin E Trenberth and John T. Fasullo NCAR, Boulder, CO, USA Correspondence: Contact trenbert@ucar.edu INTRODUCTION Weather
More informationEffect of mesoscale topography over the Tibetan Plateau on summer precipitation in China: A regional model study
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L19707, doi:10.1029/2008gl034740, 2008 Effect of mesoscale topography over the Tibetan Plateau on summer precipitation in China: A regional
More informationUC Irvine Faculty Publications
UC Irvine Faculty Publications Title Characterizing regional uncertainty in the initial soil moisture status Permalink https://escholarship.org/uc/item/027967d2 Journal Geophysical Research Letters, 30(9)
More informationArctic sea ice response to atmospheric forcings with varying levels of anthropogenic warming and climate variability
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044988, 2010 Arctic sea ice response to atmospheric forcings with varying levels of anthropogenic warming and climate variability Jinlun Zhang,
More informationThe JRA-55 Reanalysis: quality control and reprocessing of observational data
The JRA-55 Reanalysis: quality control and reprocessing of observational data Kazutoshi Onogi On behalf of JRA group Japan Meteorological Agency 29 October 2014 EASCOF 1 1. Introduction 1. Introduction
More informationEvaluation of the Twentieth Century Reanalysis Dataset in Describing East Asian Winter Monsoon Variability
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 30, NO. 6, 2013, 1645 1652 Evaluation of the Twentieth Century Reanalysis Dataset in Describing East Asian Winter Monsoon Variability ZHANG Ziyin 1,2 ( ), GUO Wenli
More informationLand Surface: Snow Emanuel Dutra
Land Surface: Snow Emanuel Dutra emanuel.dutra@ecmwf.int Slide 1 Parameterizations training course 2015, Land-surface: Snow ECMWF Outline Snow in the climate system, an overview: Observations; Modeling;
More informationA Global Water Budget Assessment
A Global Water Budget Assessment C. Adam Schlosser and Xiang Gao Joint Program on the Science and Policy of Global Change (JP Tech. Report #179, and accepted with minor revisions to J. of Hydrometeorology)
More informationWeakening relationship between East Asian winter monsoon and ENSO after mid-1970s
Article Progress of Projects Supported by NSFC Atmospheric Science doi: 10.1007/s11434-012-5285-x Weakening relationship between East Asian winter monsoon and ENSO after mid-1970s WANG HuiJun 1,2* & HE
More informationAssimilating Earth System Observations at NASA: MERRA and Beyond
Assimilating Earth System Observations at NASA: MERRA and Beyond Siegfried Schubert, Michael Bosilovich, Michele Rienecker, Max Suarez, Ron Gelaro, Randy Koster, Julio Bacmeister, Ricardo Todling, Larry
More informationConvective scheme and resolution impacts on seasonal precipitation forecasts
GEOPHYSICAL RESEARCH LETTERS, VOL. 30, NO. 20, 2078, doi:10.1029/2003gl018297, 2003 Convective scheme and resolution impacts on seasonal precipitation forecasts D. W. Shin, T. E. LaRow, and S. Cocke Center
More informationInvestigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis
Investigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis Sónia Liléo, PhD Wind resource analyst - R&D manager O2 Vind AB Stockholm, Sweden sonia.lileo@o2.se
More informationSkills of yearly prediction of the early-season rainfall over southern China by the NCEP climate forecast system
Theor Appl Climatol DOI 10.1007/s00704-014-1333-6 ORIGINAL PAPER Skills of yearly prediction of the early-season rainfall over southern China by the NCEP climate forecast system Siyu Zhao & Song Yang &
More informationEffect of anomalous warming in the central Pacific on the Australian monsoon
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L12704, doi:10.1029/2009gl038416, 2009 Effect of anomalous warming in the central Pacific on the Australian monsoon A. S. Taschetto, 1
More informationTropical stratospheric zonal winds in ECMWF ERA-40 reanalysis, rocketsonde data, and rawinsonde data
GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L09806, doi:10.1029/2004gl022328, 2005 Tropical stratospheric zonal winds in ECMWF ERA-40 reanalysis, rocketsonde data, and rawinsonde data Mark P. Baldwin Northwest
More informationEvaluation of Satellite and Reanalysis Products of Downward Surface Solar Radiation over East Asia
International Workshop on Land Use/Cover Changes and Air Pollution in Asia August 4-7th, 2015, Bogor, Indonesia Evaluation of Satellite and Reanalysis Products of Downward Surface Solar Radiation over
More informationComparison of the seasonal cycle of tropical and subtropical precipitation over East Asian monsoon area
21st International Congress on Modelling and Simulation, Gold Coast, Australia, 29 Nov to 4 Dec 2015 www.mssanz.org.au/modsim2015 Comparison of the seasonal cycle of tropical and subtropical precipitation
More informationObservational validation of an extended mosaic technique for capturing subgrid scale heterogeneity in a GCM
Printed in Singapore. All rights reserved C 2007 The Authors Journal compilation C 2007 Blackwell Munksgaard TELLUS Observational validation of an extended mosaic technique for capturing subgrid scale
More informationPolar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio
JP2.14 ON ADAPTING A NEXT-GENERATION MESOSCALE MODEL FOR THE POLAR REGIONS* Keith M. Hines 1 and David H. Bromwich 1,2 1 Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University,
More informationTransition of the annual cycle of precipitation from double-peak mode to single-peak mode in South China
Article Atmospheric Science November 2013 Vol.58 No.32: 3994 3999 doi: 10.1007/s11434-013-5905-0 Transition of the annual cycle of precipitation from double-peak mode to single-peak mode in South China
More informationContrasting impacts of spring thermal conditions over Tibetan Plateau on late-spring to early-summer precipitation in southeast China
ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 12: 309 315 (2011) Published online 6 May 2011 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/asl.343 Contrasting impacts of spring thermal conditions
More informationLight rain events change over North America, Europe, and Asia for
ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 11: 301 306 (2010) Published online 28 October 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/asl.298 Light rain events change over North
More informationCHAPTER 2 DATA AND METHODS. Errors using inadequate data are much less than those using no data at all. Charles Babbage, circa 1850
CHAPTER 2 DATA AND METHODS Errors using inadequate data are much less than those using no data at all. Charles Babbage, circa 185 2.1 Datasets 2.1.1 OLR The primary data used in this study are the outgoing
More informationRetrieving snow mass from GRACE terrestrial water storage change with a land surface model
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L15704, doi:10.1029/2007gl030413, 2007 Retrieving snow mass from GRACE terrestrial water storage change with a land surface model Guo-Yue
More informationVariations of the Asian Monsoon and Simulations and Predictions by the NCEP CFS Song Yang
Variations of the Asian Monsoon and Simulations and Predictions by the NCEP CFS Song Yang NOAA Climate Prediction Center, Camp Springs, Maryland, USA Song.Yang@noaa.gov Contents, Coauthors, and References
More informationSUPPLEMENTARY INFORMATION
SUPPLEMENTARY INFORMATION DOI: 1.138/NCLIMATE1884 SPRINGTIME ATMOSPHERIC ENERGY TRANSPORT AND THE CONTROL OF ARCTIC SUMMER SEA-ICE EXTENT Supplementary discussion In the main text it is argued that positive
More informationComparison of sensible and latent heat fluxes during the transition season over the western Tibetan Plateau from reanalysis datasets
Available online at www.sciencedirect.com Progress in Natural Science 19 (2009) 719 726 www.elsevier.com/locate/pnsc Comparison of sensible and latent heat fluxes during the transition season over the
More informationDecadal Change in the Correlation Pattern between the Tibetan Plateau Winter Snow and the East Asian Summer Precipitation during
7622 J O U R N A L O F C L I M A T E VOLUME 26 Decadal Change in the Correlation Pattern between the Tibetan Plateau Winter Snow and the East Asian Summer Precipitation during 1979 2011 DONG SI AND YIHUI
More informationThe Spring Predictability Barrier Phenomenon of ENSO Predictions Generated with the FGOALS-g Model
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 2, 87 92 The Spring Predictability Barrier Phenomenon of ENSO Predictions Generated with the FGOALS-g Model WEI Chao 1,2 and DUAN Wan-Suo 1 1
More informationSCIENCE CHINA Earth Sciences. Design and testing of a global climate prediction system based on a coupled climate model
SCIENCE CHINA Earth Sciences RESEARCH PAPER October 2014 Vol.57 No.10: 2417 2427 doi: 10.1007/s11430-014-4875-7 Design and testing of a global climate prediction system based on a coupled climate model
More informationSimulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau,
JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 5216 5230, doi:10.1002/jgrd.50457, 2013 Simulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau, 1981 2010 Donglin
More informationComparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi:10.1029/2008jd010761, 2009 Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations
More informationIAP Dynamical Seasonal Prediction System and its applications
WCRP Workshop on Seasonal Prediction 4-7 June 2007, Barcelona, Spain IAP Dynamical Seasonal Prediction System and its applications Zhaohui LIN Zhou Guangqing Chen Hong Qin Zhengkun Zeng Qingcun Institute
More informationAn evaluation of wind indices for KVT Meso, MERRA and MERRA2
KVT/TPM/2016/RO96 An evaluation of wind indices for KVT Meso, MERRA and MERRA2 Comparison for 4 met stations in Norway Tuuli Miinalainen Content 1 Summary... 3 2 Introduction... 4 3 Description of data
More informationGlobal Ocean Monitoring: A Synthesis of Atmospheric and Oceanic Analysis
Extended abstract for the 3 rd WCRP International Conference on Reanalysis held in Tokyo, Japan, on Jan. 28 Feb. 1, 2008 Global Ocean Monitoring: A Synthesis of Atmospheric and Oceanic Analysis Yan Xue,
More informationENSO and ENSO teleconnection
ENSO and ENSO teleconnection Hye-Mi Kim and Peter J. Webster School of Earth and Atmospheric Science, Georgia Institute of Technology, Atlanta, USA hyemi.kim@eas.gatech.edu Abstract: This seminar provides
More informationREQUEST FOR A SPECIAL PROJECT
REQUEST FOR A SPECIAL PROJECT 2017 2019 MEMBER STATE: Sweden.... 1 Principal InvestigatorP0F P: Wilhelm May... Affiliation: Address: Centre for Environmental and Climate Research, Lund University Sölvegatan
More informationThe East Asian winter monsoon: Re-amplification in the mid-2000s. WANG Lin* & CHEN Wen
The East Asian winter monsoon: Re-amplification in the mid-2000s WANG Lin* & CHEN Wen Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100190,
More informationImpact of Eurasian spring snow decrement on East Asian summer precipitation
Impact of Eurasian spring snow decrement on East Asian summer precipitation Renhe Zhang 1,2 Ruonan Zhang 2 Zhiyan Zuo 2 1 Institute of Atmospheric Sciences, Fudan University 2 Chinese Academy of Meteorological
More informationHigh initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044119, 2010 High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming Yuhji Kuroda 1 Received 27 May
More informationTrends of Tropospheric Ozone over China Based on Satellite Data ( )
ADVANCES IN CLIMATE CHANGE RESEARCH 2(1): 43 48, 2011 www.climatechange.cn DOI: 10.3724/SP.J.1248.2011.00043 ARTICLE Trends of Tropospheric Ozone over China Based on Satellite Data (1979 2005) Xiaobin
More informationApplication and impacts of the GlobeLand30 land cover dataset on the Beijing Climate Center Climate Model
IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS Application and impacts of the GlobeLand30 land cover dataset on the Beijing Climate Center Climate Model To cite this article:
More informationTibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals
INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. (2013) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3682 Tibetan Plateau precipitation as depicted by gauge
More informationComparison of Global Mean Temperature Series
ADVANCES IN CLIMATE CHANGE RESEARCH 2(4): 187 192, 2011 www.climatechange.cn DOI: 10.3724/SP.J.1248.2011.00187 REVIEW Comparison of Global Mean Temperature Series Xinyu Wen 1,2, Guoli Tang 3, Shaowu Wang
More informationSeasonal trends and temperature dependence of the snowfall/ precipitation day ratio in Switzerland
GEOPHYSICAL RESEARCH LETTERS, VOL. 38,, doi:10.1029/2011gl046976, 2011 Seasonal trends and temperature dependence of the snowfall/ precipitation day ratio in Switzerland Gaëlle Serquet, 1 Christoph Marty,
More informationHow much do precipitation extremes change in a warming climate?
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl052762, 2012 How much do precipitation extremes change in a warming climate? Chein-Jung Shiu, 1 Shaw Chen Liu, 1 Congbin Fu, 2,3 Aiguo Dai, 4 and
More informationP1.34 MULTISEASONALVALIDATION OF GOES-BASED INSOLATION ESTIMATES. Jason A. Otkin*, Martha C. Anderson*, and John R. Mecikalski #
P1.34 MULTISEASONALVALIDATION OF GOES-BASED INSOLATION ESTIMATES Jason A. Otkin*, Martha C. Anderson*, and John R. Mecikalski # *Cooperative Institute for Meteorological Satellite Studies, University of
More informationJOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D16, 8618, doi: /2002jd003127, 2003
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D16, 8618, doi:10.1029/2002jd003127, 2003 Intercomparison of water and energy budgets for five Mississippi subbasins between ECMWF reanalysis (ERA-40) and
More informationGlobal reanalysis: Some lessons learned and future plans
Global reanalysis: Some lessons learned and future plans Adrian Simmons and Sakari Uppala European Centre for Medium-Range Weather Forecasts With thanks to Per Kållberg and many other colleagues from ECMWF
More informationClimate Variables for Energy: WP2
Climate Variables for Energy: WP2 Phil Jones CRU, UEA, Norwich, UK Within ECEM, WP2 provides climate data for numerous variables to feed into WP3, where ESCIIs will be used to produce energy-relevant series
More informationSTUDIES ON MODEL PREDICTABILITY IN VARIOUS CLIMATIC CONDITIONS: AN EFFORT USING CEOP EOP1 DATASET
STUDIES ON MODEL PREDICTABILITY IN VARIOUS CLIMATIC CONDITIONS: AN EFFORT USING CEOP EOP DATASET KUN YANG, KATSUNORI TAMAGAWA, PETRA KOUDELOVA, TOSHIO KOIKE Department of Civil Engineering, University
More informationCOUPLING A DISTRIBUTED HYDROLOGICAL MODEL TO REGIONAL CLIMATE MODEL OUTPUT: AN EVALUATION OF EXPERIMENTS FOR THE RHINE BASIN IN EUROPE
P.1 COUPLING A DISTRIBUTED HYDROLOGICAL MODEL TO REGIONAL CLIMATE MODEL OUTPUT: AN EVALUATION OF EXPERIMENTS FOR THE RHINE BASIN IN EUROPE Jan Kleinn*, Christoph Frei, Joachim Gurtz, Pier Luigi Vidale,
More informationImplementation of Land Information System in the NCEP Operational Climate Forecast System CFSv2. Jesse Meng, Michael Ek, Rongqian Yang, Helin Wei
Implementation of Land Information System in the NCEP Operational Climate Forecast System CFSv2 Jesse Meng, Michael Ek, Rongqian Yang, Helin Wei 1 Outline NCEP CFSRR Land component CFSv1 vs CFSv2 Land
More informationAir sea satellite flux datasets and what they do (and don't) tell us about the air sea interface in the Southern Ocean
Air sea satellite flux datasets and what they do (and don't) tell us about the air sea interface in the Southern Ocean Carol Anne Clayson Woods Hole Oceanographic Institution Southern Ocean Workshop Seattle,
More informationMonsoon Activities in China Tianjun ZHOU
Monsoon Activities in China Tianjun ZHOU Email: zhoutj@lasg.iap.ac.cn CLIVAR AAMP10, Busan,, Korea 18-19 19 June 2010 Outline Variability of EASM -- Interdecadal variability -- Interannual variability
More information1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report
1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report 2. Results and Accomplishments Output from multiple land surface schemes (LSS)
More information