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 1992–2001 and at nine stations from field campaigns (CAMP/Tibet) for 2002–2004. 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.
Key Points
Evaluate six reanalysis products with observations over Tibetan Plateau
The performance of each reanalysis is different with different variables
No reanalysis product is superior to others in all variables at all time scales
Soil Moisture Drought in China, 1950–2006 Wang, Aihui; Lettenmaier, Dennis P.; Sheffield, Justin
Journal of climate,
07/2011, Letnik:
24, Številka:
13
Journal Article
Recenzirano
Odprti dostop
Four physically based land surface hydrology models driven by a common observation-based 3-hourly meteorological dataset were used to simulate soil moisture over China for the period 1950–2006. ...Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated. A cluster analysis method and severity–area–duration (SAD) algorithm were applied to the soil moisture data to characterize drought spatial and temporal variability. For drought areas greater than 150 000 km² and durations longer than 3 months, a total of 76 droughts were identified during the 1950–2006 period. The duration of 50 of these droughts was less than 6 months. The five most prominent droughts, in terms of spatial extent and then duration, were identified. Of these, the drought of 1997–2003 was the most severe, accounting for the majority of the severity–area–duration envelope of events with areas smaller than 5 million km². The 1997–2003 drought was also pervasive in terms of both severity and spatial extent. It was also found that soil moisture in north central and northeastern China had significant downward trends, whereas most of Xinjiang, the Tibetan Plateau, and small areas of Yunnan province had significant upward trends. Regions with downward trends were larger than those with upward trends (37% versus 26% of the land area), implying that over the period of analysis, the country has become slightly drier in terms of soil moisture. Trends in drought severity, duration, and frequency suggest that soil moisture droughts have become more severe, prolonged, and frequent during the past 57 yr, especially for northeastern and central China, suggesting an increasing susceptibility to agricultural drought.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Lack of reliable historical basin‐scale evapotranspiration (ET) estimates is a bottleneck for water balance analyses and model evaluation on the Tibetan Plateau (TP). This study looks at four large ...basins on the TP to develop a general approach suitable for large river basins to estimate historical monthly ET. Five existing global ET products are evaluated against monthly ET estimated by the water balance method as a residual from precipitation (P), terrestrial water storage change (ΔS), and discharge (R). The five ET products exhibit similar seasonal variability, despite of the different amounts among them. A bias correction method, based on the probability distribution mapping between the reference ET and the five products during 2003–2012, effectively removes nearly all biases and significantly increases the reliability of the products. Then, the surface water balance changes for the four basins are analyzed based on the corrected ET products as well as observed P and R during 1983–2006. A trend analysis shows an upward trend for ET in the four basins for all seasons during the past three decades, along with the regional warming, as well as a dominating increasing trend in P and negative trend in R.
Key Points
We estimated the monthly evapotranspiration at four large basins in Tibetan PlateauWe evaluated five global evapotranspiration products with a water balance methodSeasonal evapotranspiration trends (1983–2006) were detected from corrected data
Potential evapotranspiration (
E
PET
) is usually calculated by empirical methods from surface meteorological variables, such as temperature, radiation and wind speed. The in-situ measured pan ...evaporation (ET
pan
) can also be used as a proxy for
E
PET
. In this study,
E
PET
values computed from ten models are compared with observed ET
pan
data in ten Chinese river basins for the period 1961–2013. The daily observed meteorological variables at 2267 stations are used as the input to those models, and a ranking scheme is applied to rank the statistical quantities (ratio of standard deviations, correlation coefficient, and ratio of trends) between ET
pan
and modeled
E
PET
in different river basins. There are large deviations between the modeled
E
PET
and the ET
pan
in both the magnitude and the annual trend at most stations. In eight of the basins (except for Southeast and Southwest China), ET
pan
shows decreasing trends with magnitudes ranging between −0.01 mm d
−1
yr
−1
and -0.03 mm d
−1
yr
−1
, while the decreasing trends in modeled
E
PET
are less than −0.01 mm d
−1
yr
−1
. Inter comparisons among different models in different river basins suggest that PET
Ham1
is the best model in the Pearl River basin, PET
Ham2
outperforms other models in the Huaihe River, Yangtze River and Yellow River basins, and PET
FAO
is the best model for the remaining basins. Sensitivity analyses reveal that wind speed and sunshine duration are two important factors for decreasing
E
PET
in most basins except in Southeast and Southwest China. The increasing
E
PET
trend in Southeast China is mainly attributed to the reduced relative humidity.
Land surface temperature (LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in ...recording procedures, and other non-climatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017. The high-quality land surface air temperature (LSAT) dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40°N due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization (MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68 × 10
3
effective breakpoints in 1.65 × 10
6
monthly records (about 20%) are detected. A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China. After the MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/− 0.5°C, and of the remaining breakpoints, only 10% are adjusted over 1.5°C. Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend 0.22°C (10 yr)
−1
. The homogenized LST dataset can be further adapted for a variety of applications (e.g., model evaluation and extreme event characterization).
The Bias Correction and Spatial Downscaling (BCSD) is a trend‐preserving statistical downscaling algorithm, which has been widely used to generate accurate and high‐resolution data set. We employ the ...BCSD technique to statistically downscale projected daily maximum temperature (DMT) over China from 13 general circulation models in Coupled Model Intercomparison Project Phase 5 (CMIP5) project to supplement the National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections data set under the Representative Concentration Pathway 2.6 (RCP2.6) scenario. We then compare the differences of DMT and four DMT‐related indices (i.e., summer days (SU), annual maximum value of DMT (TXx), intensity, and frequency of heat wave) between before and after downscaling over eight subregions of China. The results indicate that the BCSD method reduces the cool bias of the DMT over the whole China compared with original CMIP5 simulations, especially over the Qinghai‐Tibet plateau. The SU increases after downscaling for both China as a whole and most subregions except for South China. The BCSD also affects the mean value of TXx, intensity, and frequency of heat wave at subregional scales, although it shows little impact on China as a whole. Besides, the BCSD reduces the temporal variability of most indices except for the heat wave frequency. The most striking finding is that the intermodel spreads of DMT, SU, TXx, and heat wave intensity are dramatically reduced after downscaling compared with raw CMIP5 simulations. In summary, the BCSD method shows significant improvements to original CMIP5 climate projections under RCP2.6 scenario.
Key Points
The cool bias of the daily maximum temperature would reduce after statistical downscaling
The BCSD affects the mean value of extreme temperature indices at subregional scales
The BCSD method would reduce the intermodel spreads of extreme temperature indices
The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical ...hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU‐NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high‐quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi‐model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%–85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root‐mean‐square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900–2012 (1948–2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models.
Plain Language Summary
Land surface models (LSMs) have served as essential tools for simulating the response of land surface processes under changing climate. This study focuses on the performance and uncertainty of hydrological variables from historical (1900–2012) simulations in the Land Model Intercomparison Project (LMIP), which is a part of the Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP). Using various reference data sets over Chinese mainland, we evaluated precipitation, evapotranspiration, soil moisture, total runoff and snow cover fraction products from six LSMs driven by four meteorological forcing data sets. Our findings reveal that, on average, all hydrological variables are underestimated, but they exhibit a high spatial consistency of trend signs with reference data sets. Among all simulations, CLM5 stands out for its superior performance and GSWP3 forcing demonstrates the highest quality. Additionally, the Analysis of Variance (ANOVA) method is adopted to separate the simulation uncertainties into three sources from the model, the meteorological forcing data set and their interactions. It is indicated that the total uncertainty has substantially decreased in recent decades, and the model uncertainty is the dominant factor for these hydrological variables. This study may serve as some valuable references in selecting LSMs and forcing data sets in the future.
Key Points
The precipitation, evapotranspiration, soil moisture, total runoff, and snow cover fraction in LS3MIP are extensively evaluated in China
For LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall
The quality of simulated soil hydrological variables (i.e., soil moisture, evapotranspiration, and runoff) is largely dependent on the accuracy of meteorological forcing data, especially ...precipitation and air temperature. This issue is quantitatively addressed here by running the Community Land Model (CLM3.5) over China from 1993 to 2002 using the reanalysis‐based precipitation and air temperature and in situ observations in the meteorological forcing data set. Compared to the in situ measured soil moisture data, the CLM3.5 simulation can generally capture the spatial and seasonal variations of soil moisture but produces too‐wet soil in northeastern and eastern China and too‐dry soil in northwestern China. This deficiency is significantly reduced when the in situ measured precipitation data are used to drive the model. An index is also constructed to quantify the sensitivities of soil hydrological variables to variations of precipitation and air temperature. The highest sensitivity of surface hydrological variables to precipitation appears over semiarid regions, while the sensitivity to air temperature for different variables varies regionally (semiarid regions for runoff and soil moisture and humid regions for evapotranspiration (ET)). Over semiarid regions, precipitation and air temperature are equally important to the simulations of soil hydrological variables. Over humid regions, in contrast, ET is more dependent on air temperature than on precipitation, while soil moisture and runoff are less affected by air temperature.
Previous studies have demonstrated that offline land surface models (LSMs) and global hydrological models (GHMs) can reasonably reproduce streamflow in large river basins. Global reanalyses supply ...fine spatiotemporal runoff estimates, but they are not fully intercompared and evaluated in China. This study assesses the routed-runoff from five offline LSM/GHM runs (VIC-CN05.1, CLM-CFSR, CLM-ERAI, CLM-MERRA, and CLM-NCEP) and three reanalysis datasets (ERAI/Land, JRA55, and MERRA-2) against the gauged streamflow (26 stations) in major Chinese river basins during 1980–2008. The Catchment-based Macro-scale Floodplain model (CaMa-Flood) is employed to route those runoff datasets to the hydrological stations. Four statistical quantities, including the correlation coefficient (
R)
, standard deviation (STD), Nash-Sutcliffe efficiency coefficient (NSE), and relative error (RE), along with a ranking method, are used to quantify the quality of those products. The results show that the spatial patterns of both modeled and observed streamflow in summer are similar, but their magnitudes are different. Except for MERRA-2, the other products can reproduce well the interannual variability of streamflow in both the Yangtze and Yellow River basins. All products generally underestimate the magnitude and variance of monthly streamflow, while VIC-CN05.1 and JRA55 are closer to observations compared to other products. The correlation coefficients for all products are overall larger than 0.61, with the highest value (0.85) from VIC-CN05.1. In addition to CLM-MERRA, MERRA-2, and CLM-NCEP with relatively small precipitation, other products can simulate peak flow well with positive NSEs up to 0.41 (ERAI/Land). Considerable uncertainties exist among the eight products at the Yellow River outlet, which might be because the LSMs ignore frequent human activities. Based on the above statistics, performances of the eight runoff products are ranked in descending order as follows: VIC-CN05.1, ERAI/Land, JRA55, CLM-CFSR, CLM-ERAI, MERRA-2, CLM-MERRA, and CLM-NCEP, which provides a reference for flood/hydro-logical drought warning and hydroclimatic research in the future.
This study assesses the performance of temperature extremes over China in two regional climate models (RCMs), RegCM4 and WRF, driven by the ECMWF’s 20th century reanalysis. Based on the advice of the ...Expert Team on Climate Change Detection and Indices (ETCCDI), 12 extreme temperature indices (i.e., TXx, TXn, TNx, TNn, TX90p, TN90p, TX10p, TN10p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981–2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau (TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10p, and TN10p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90p, and TN90p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes, while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation, RegCM, and WRF are 0.53°C (10 yr)
−1
, 0.44°C (10 yr)
−1
, and 0.75°C (10 yr)
−1
, respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases.
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