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1.
The present study assesses the forecast skill of the Madden–Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air–sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air–sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high-resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO–TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks’ lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting.  相似文献   

2.
Atmospheric dynamical mechanisms have been prevalently used to explain the characteristics of the summer monsoon intraseasonal oscillation (MISO), which dictates the wet and dry spells of the monsoon rainfall. Recent studies show that ocean–atmosphere coupling has a vital role in simulating the observed amplitude and relationship between precipitation and sea surface temperature (SST) at the intraseasonal scale. However it is not clear whether this role is simply ‘passive’ response to the atmospheric forcing alone, or ‘active’ in modulating the northward propagation of MISO, and also whether the extent to which it modulates is considerably noteworthy. Using coupled NCEP–Climate Forecast System (CFSv2) model and its atmospheric component the Global Forecast System (GFS), we investigate the relative role of the atmospheric dynamics and the ocean–atmosphere coupling in the initiation, maintenance, and northward propagation of MISO. Three numerical simulations are performed including (1) CFSv2 coupled with high frequency interactive SST, the GFS forced with both (2) observed monthly SST (interpolated to daily) and (3) daily SST obtained from the CFSv2 simulations. Both CFSv2 and GFS simulate MISO of slightly higher period (~60 days) than observations (~45 days) and have reasonable seasonal rainfall over India. While MISO simulated by CFSv2 has realistic northward propagation, both the GFS model experiments show standing mode of MISO over India with no northward propagation of convection from the equator. The improvement in northward propagation in CFSv2, therefore, may not be due to improvement of the model physics in the atmospheric component alone. Our analysis indicates that even with the presence of conducive vertical wind shear, the absence of meridional humidity gradient and moistening of the atmosphere column north of convection hinders the northward movement of convection in GFS. This moistening mechanism works only in the presence of an ‘active’ ocean. In CFSv2, the lead-lag relationship between the atmospheric fluxes, SST and convection are maintained, while such lead-lag is unrealistic in the uncoupled simulations. This leads to the conclusion that high frequent and interactive ocean–atmosphere coupling is a necessary and crucial condition for reproducing the realistic northward propagation of MISO in this particular model.  相似文献   

3.
This study evaluates the prediction skill of stratospheric temperature anomalies by the Climate Forecast System version 2 (CFSv2) reforecasts for the 12-year period from January 1, 1999 to December 2010. The goal is to explore if the CFSv2 forecasts for the stratosphere would remain skillful beyond the inherent tropospheric predictability time scale of at most 2 weeks. The anomaly correlation between observations and forecasts for temperature field at 50 hPa (T50) in winter seasons remains above 0.3 over the polar stratosphere out to a lead time of 28 days whereas its counterpart in the troposphere at 500 hPa drops more quickly and falls below the 0.3 level after 12 days. We further show that the CFSv2 has a high prediction skill in the stratosphere both in an absolute sense and in terms of gain over persistence except in the equatorial region where the skill would mainly come from persistence of the quasi-biennial oscillation signal. We present evidence showing that the CFSv2 forecasts can capture both timing and amplitude of wave activities in the extratropical stratosphere at a lead time longer than 30 days. Based on the mass circulation theory, we conjecture that as long as the westward tilting of planetary waves in the stratosphere and their overall amplitude can be captured, the CFSv2 forecasts is still very skillful in predicting zonal mean anomalies even though it cannot predict the exact locations of planetary waves and their spatial scales. This explains why the CFSv2 has a high skill for the first EOF mode of T50, the intraseasonal variability of the annular mode while its skill degrades rapidly for higher EOF modes associated with stationary waves. This also explains why the CFSv2’s skill closely follows the seasonality and its interannual variability of the meridional mass circulation and stratosphere polar vortex. In particular, the CFSv2 is capable of predicting mid-winter polar stratosphere warming events in the Northern Hemisphere and the timing of the final polar stratosphere warming in spring in both hemispheres 3–4 weeks in advance.  相似文献   

4.
The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.  相似文献   

5.
The seasonal footprinting mechanism (SFM) is thought to be a pre-cursor to the El Nino Southern Oscillation (ENSO). Fluctuations in the North Pacific Oscillation (NPO) impact the ocean via surface heat fluxes during winter, leaving a sea-surface temperature (SST) “footprint” in the subtropics. This footprint persists through the spring, impacting the tropical Pacific atmosphere–ocean circulation throughout the following year. The simulation of the SFM in the National Centers for Environmental Prediction (NCEP)/Climate Forecast System, version 2 (CFSv2) is likely to have an impact on operational predictions of ENSO and potentially seasonal predictions in the United States associated with ENSO teleconnection patterns. The ability of the CFSv2 to simulate the SFM and the relationship between the SFM and ENSO prediction skill in the NCEP/CFSv2 are investigated. Results indicate that the CFSv2 is able to simulate the basic characteristics of the SFM and its relationship with ENSO, including extratropical sea level pressure anomalies associated with the NPO in the winter, corresponding wind and SST anomalies that impact the tropics, and the development of ENSO-related SST anomalies the following winter. Although the model is able to predict the correct sign of ENSO associated with the SFM in a composite sense, probabilistic predictions of ENSO following a positive or negative NPO event are generally less reliable than when the NPO is not active.  相似文献   

6.
This study evaluates the potential use of the regional climate model version 3 (RegCM3) driven by (1) the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data during 1982–2001 and (2) the NCEP Climate Forecast System Version 2 (CFS2) hindcast data during 2000–2010 in forecasting Western North Pacific (WNP) tropical cyclone (TC) activity. The first experiment is conducted to investigate the ability of the model in generating a good climatology of TC activity in spatial and temporal scales, so the model could be used in the second experiment to test its ability in forecasting TC genesis and landfall. Both experiments extend through the May to October WNP-TC season. Results show that the use of RegCM3 driven by the CFSR achieves a better simulation on the temporal and spatial variation of WNP-TC genesis during 1982–2001, as compared to previous studies using the same model but driven by the ERA40 reanalysis. In addition, diagnoses on the use of RegCM3 driven by the CFS2 point out that the 2000–2010 WNP-TC genesis locations and numbers from the model are very similar to those from the observations. The skill of RegCM3 in the forecasts of landfalling TCs is higher over the Southeast Asian region than over the other sub-regions of East Asia. Potential causes for such regional differences are discussed. Most importantly, statistical analyses show that the use of RegCM3 driven by the CFS2 gives a better forecast skill than the use of CFS2 alone for the prediction of WNP-TCs making landfall in East Asia. This indicates that the use of a dynamical downscaling method for the global forecast data would likely lead to a higher forecast skill of regional TC landfalls in most of the East Asian region.  相似文献   

7.
This study examines the Indian summer monsoon hydroclimate in the National Centers for Environmental Prediction (NCEP)-Department of Energy (DOE) Reanalysis (R2), the Climate Forecast System Reanalysis (CFSR), and the Modern Era Retrospective-Analysis for Research and Applications (MERRA). The three reanalyses show significant differences in the climatology of evaporation, low-level winds, and precipitable water fields over India. For example, the continental evaporation is significantly less in CFSR compared to R2 and MERRA. Likewise the mean boreal summer 925?hPa westerly winds in the northern Indian Ocean are stronger in R2. Similarly the continental precipitable water in R2 is much less while it is higher and comparable in MERRA and CFSR. Despite these climatological differences between the reanalyses, the climatological evaporative sources for rain events over central India show some qualitative similarities. Major differences however appear when interannual variations of the Indian summer monsoon are analyzed. The anomalous oceanic sources of moisture from the adjacent Bay of Bengal and Arabian Sea play a significant role in determining the wet or dry year of the Indian monsoon in CFSR. However in R2 the local evaporative sources from the continental region play a more significant role. We also find that the interannual variability of the evaporative sources in the break spells of the intraseasonal variations of the Indian monsoon is stronger than in the wet spells. We therefore claim that instead of rainfall, evaporative sources may be a more appropriate metric to observe the relationship between the seasonal monsoon strength and intraseasonal activity. These findings are consistent across the reanalyses and provide a basis to improve the predictability of intraseasonal variability of the Indian monsoon. This study also has a bearing on improving weather prediction for tropical cyclones in that we suggest targeting enhanced observations in the Bay of Bengal (where it is drawing the most moisture from) for improved analysis during active spells of the intraseasonal variability of the Indian monsoon. The analysis suggests that the land–atmosphere interactions contribute significant uncertainty to the Indian monsoon in the reanalyses, which is consistent with the fact that most of the global reanalyses do not assimilate any land-surface data because the data are not available. Therefore, the land–atmosphere interaction in the reanalyses is highly dependent on the land-surface model and it’s coupling with the atmospheric model.  相似文献   

8.
This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream.  相似文献   

9.
Daily output from the hindcasts by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) is analyzed to understand the skill of forecasting atmospheric variability on quasi-biweekly (QBW) time scale. Eight dominant quasi-biweekly oscillation (QBWO) modes identified by the extended empirical orthogonal function analysis are focused. In the CFSv2, QBW variability exhibits a significant weakening tendency with lead time for all seasons. For most QBWO modes, the variance drops to only 50 % of the initial value at lead time of 11–15 days. QBW variability has better prediction skill in the winter hemisphere than in the summer hemisphere. Skillful forecast can reach about 10–15 days for most modes but those in the winter hemisphere have better forecast skills. Among the eight QBWO modes, the North Pacific mode and the South Pacific (SP) mode have the highest forecast skills while the Asia–Pacific mode and the Central American mode have the lowest skills. For the Asia–Pacific and Central American modes, the forecasted QBWO phase shows an obvious eastward shift with increase in lead time compared to observations, indicating a smaller propagating speed. However, the predicted feature for the SP mode is more realistic. Air–sea coupling on the QBW time scale is perhaps responsible for the different prediction skills for different QBWO modes. In addition, most QBWO modes have better forecasting skills in El Niño years than in La Niña years. Different dynamical mechanisms for various QBWO modes may be partially responsible for the differences in prediction skill among different QBWO modes.  相似文献   

10.
利用最新的CFSR(Climate Forecast System Reanalysis)再分析及观测的降水和地表气温资料驱动陆面水文耦合模式CLHMS(Coupled Land surface and Hydrologic Model System),对淮河流域1980~2003年共24年的水文水循环过程进行了模拟,系统评估了CLHMS对淮河流域水文过程的模拟能力及其不确定性。分析结果表明,CLHMS模式对淮河流域水文过程具有良好的模拟能力,模式尤其对湿润年份流域的水量平衡以及河道流量的季节、年际变化具有很强的模拟能力,而对降水偏少的干旱年份,模式模拟的河道流量通常会高于观测实况,与实况间存在着一定的偏差,而这也是导致CLHMS对流域水文过程模拟能力存在显著年代际差异的主要原因。基于三组不同降水强迫的流域水文过程模拟结果比较表明,降水驱动资料准确与否是陆面水文模拟最主要的不确定性来源之一,正是由于CFSR再分析降水与观测降水之间存在较大的差异,从而导致CFSR降水驱动下模式模拟的淮河流域河道流量与观测存在较大的偏差,其模拟性能相对较差。进一步分析还表明,可以保持较强降水日变化的时间解集方法,也是保证合理模拟流域水文过程的重要因素。  相似文献   

11.
The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.  相似文献   

12.
Reanalyses, based on numerical weather prediction methods assimilating past observations, provide continuous precipitation datasets and represent interesting options for assessing the climatology of regions with sparse station networks (e.g., northern Canada). However, reanalysis series cannot be used directly because of possible biases and mismatch between their spatial and temporal resolutions with that needed for local applications. To address these issues, a Stochastic Model Output Statistics (SMOS) approach was selected to post-process precipitation series simulated by the Climate Forecast System Reanalysis (CFSR) across Canada. This approach uses CFSR precipitation as a covariate and is based on two regression models: the first one is a logistic regression that deals with precipitation occurrence, and the second is a vector generalized linear model for precipitation intensity. At-site post-processed daily precipitation series are randomly generated using the SMOS approach, and selected climate indicators from the Expert Team on Climate Change Detection and Indices, which is jointly sponsored by the Commission for Climatology of the World Meteorological Organization's (WMO) World Climate Data and Monitoring Programme, the Climate Variability and Predictability Programme of the World Climate Research Programme, and the Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology (CCI/CLIVAR/JCOMM) are estimated and compared with corresponding observed and CFSR values. The two models in the SMOS approach, in addition to adequately correcting systematic biases, produced better predictions than the climatology of the wet and dry and intensity sequences. Additionally, the SMOS generally yields consistent climate indices when compared with those from CFSR without post-processing, though there is still room for improvement for specific indices (e.g., annual maximum of cumulative wet days).  相似文献   

13.
MJO prediction in the NCEP Climate Forecast System version 2   总被引：3，自引：0，他引：3  
The Madden–Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999–2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S–15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction.  相似文献   

14.
In this study, we examine the characteristics of the boreal summer monsoon intraseasonal oscillation (BSISO) using the second version of the Climate Forecast System (CFSv2) and revisit the role of air–sea coupling in BSISO simulations. In particular, simulations of the BSISO in two carefully designed model experiments are compared: a fully coupled run and an uncoupled atmospheric general circulation model (AGCM) run with prescribed sea surface temperatures (SSTs). In these experiments an identical AGCM is used, and the daily mean SSTs from the coupled run are prescribed as a boundary condition in the AGCM run. Comparisons indicate that air–sea coupling plays an important role in realistically simulating the BSISO in CFSv2. Compared with the AGCM run, the coupled run not only simulates the spatial distributions of intraseasonal rainfall variations better but also shows more realistic spectral peaks and northward and eastward propagation features of the BSISO over India and the western Pacific. This study indicates that including an air–sea feedback mechanism may have the potential to improve the realism of the mean flow and intraseasonal variability in the Indian and western Pacific monsoon region.  相似文献   

15.
The boreal spring Antarctic Oscillation(AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP's Climate Forecast System, version 2(CFSv2), in predicting the boreal spring AAO for the period 1983–2015. The results indicate that CFSv2 has poor skill in predicting the spring AAO, failing to predict the zonally symmetric spatial pattern of the AAO, with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI). Considering the interannual increment approach can amplify the prediction signals, we firstly establish a dynamical–statistical model to improve the interannual increment of the AAOI(DY AAOI), with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice. This dynamical–statistical model demonstrates good capability in predicting DY AAOI, with a significant correlation coefficient of 0.58 between the observation and prediction during 1983–2015 in the two-year-out cross-validation. Then, we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI. The improved AAOI shows a significant correlation coefficient of 0.45 with the observed AAOI during 1983–2015. Moreover, the unrealistic atmospheric response to March–April–May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO. This study gives new clues regarding AAO prediction and short-term climate prediction.  相似文献   

16.
The predictable patterns and predictive skills of monsoon precipitation in the Northern Hemisphere summer (June–July–August) are examined using reforecasts (1983–2010) from the National Center for Environmental Prediction Climate Forecast System version 2 (CFSv2). The possible connections of these predictable patterns with global sea surface temperature (SST) are investigated. The empirical orthogonal function analysis with maximized signal-to-noise ratio is used to isolate the predictable patterns of the precipitation for three regional monsoons: the Asian and Indo-Pacific monsoon (AIPM), the Africa monsoon (AFM), and the North America monsoon (NAM). Overall, the CFSv2 well predicts the monsoon precipitation patterns associated with El Niño-South Oscillation (ENSO) due to its good prediction skill for ENSO. For AIPM, two identified predictable patterns are an equatorial dipole pattern characterized by opposite variations between the equatorial western Pacific and eastern Indian Ocean, and a tropical western Pacific pattern characterized by opposite variations over the tropical northwestern Pacific and the Philippines and over the regions to its west, north, and southeast. For NAM, the predictable patterns are a tropical eastern Pacific pattern with opposite variations in the tropical eastern Pacific and in Mexico, the Guyana Plateau and the equatorial Atlantic, and a Central American pattern with opposite variations in the eastern Pacific and the North Atlantic and in the Amazon Plains. The CFSv2 can predict these patterns at least 5 months in advance. However, compared with the good skill in predicting AIPM and NAM precipitation patterns, the CFSv2 exhibits little predictive skill for AFM precipitation, probably because the variability of the tropical Atlantic SST plays a more important than ENSO in the AFM precipitation variation and the prediction skill is lower for the tropical Atlantic SST than the tropical Pacific SST.  相似文献   

17.
Using a continuous multi-decadal simulations over the period 1981–2010, subseasonal to seasonal simulations of the Climate Forecast System version 2 (CFSv2) over Iran against the Climatic Research Unit (CRU) dataset are evaluated. CFSv2 shows cold biases over northern hillsides of the Alborz Mountains with the Mediterranean climate and warm biases over northern regions of the Persian Gulf and the Oman Sea with a dry climate. Magnitude of the model bias for 2-m temperature over different regions of Iran varies by season, with the least bias in temperate seasons of spring and autumn, and the largest bias in summer. The model bias decreases as temporal averaging period increases from seasonal to annual. The forecast generally produces dry and wet biases over dry and wet regions of Iran, respectively. In general, 2-m temperature over Iran is better captured than precipitation, but the prediction skill of precipitation is generally high over western Iran. Averaged over Iran, observations indicated that 2-m temperature has been gradually increasing during the studied period, with a rate of approximately 0.5 °C per decade, and the upward trend is well simulated by CFSv2. Averaged over Iran, both observations and simulation results indicated that precipitation has been decreasing in spring, with averaged decreasing trends of 0.8 mm (observed) and 1.7 mm (simulated) per season each year during the period 1981–2010. Observations indicated that the maximum increasing trend of 2-m temperature has occurred over western Iran (nearly 0.7 °C per decade), while the maximum decreasing trend of annual precipitation has occurred over western and parts of southern Iran (nearly 45 to 50 mm per decade).  相似文献   

18.
A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models, and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions. In this paper, we report the development of a 10-yr China Meteorological Administration (CMA) global Land surface ReAnalysis Interim dataset (CRA-Interim/Land; 2007–2016, 6-h intervals, approximately 34-km horizontal resolution). The dataset was produced and evaluated by using the Global Land Data Assimilation System (GLDAS) and NCEP Climate Forecast System Reanalysis (CFSR) global land surface reanalysis datasets, as well as in situ observations in China. The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land, GLDAS, and CFSR climatology are highly consistent, while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets. Compared with ground observations in China, CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0-10-cm soil layer and has higher correlations and slightly lower root mean square errors (RMSE) for the 10-40-cm soil layer. However, CRA-Interim/Land shows negative biases in 10-40-cm soil moisture in Northeast China and north of central China. For ground temperature and the soil temperature in different layers, CRA-Interim/Land behaves better than the CFSR, especially in East and central China. CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters. Therefore, this dataset is potentially a critical supplement to the CRA-Interim. Further evaluation of the CRA-Interim/Land, assimilation of near-surface atmospheric forcing variables, and extension of the current dataset to 40 yr (1979–2018) are in progress.  相似文献   

19.
This study examines the prediction skill of the contiguous United States (CONUS) precipitation in summer, as well as its potential sources using a set of ensemble hindcasts conducted with the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 and initialized from four independent ocean analyses. The multiple ocean ensemble mean (MOCN_ESMEAN) hindcasts start from each April for 26 summers (1982–2007), with each oceanic state paired with four atmosphere-land states. A subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) project for the same period, from the same initial month and with the same total ensemble size, is also analyzed. Compared with CFSRR, MOCN_ESMEAN is distinguished by its oceanic ensemble spread that introduces potentially larger perturbations and better spatial representation of the oceanic uncertainty. The prediction skill of the CONUS precipitation in summer shows a similar spatial pattern in both MOCN_ESMEAN and CFSRR, but the results suggested that initialization from multiple ocean analyses may bring more robust signals and additional skills to the seasonal prediction for both sea surface temperature and precipitation. Among the predictable areas for precipitation, the northwestern CONUS (NWUS) is the most robust. A further analysis shows that the enhanced summer precipitation prediction skill in NWUS is mainly associated with the El Niño/Southern Oscillation, with possible influence also from the Pacific Decadal Oscillation. Through this work, we argue that a large ensemble is necessary for precipitation forecast in mid-latitudes, such as the CONUS, and taking into account of the oceanic initial state uncertainty is an efficient way to build such an ensemble.  相似文献   

20.
Based on various statistical indices, the abilities of multi-generation reanalyses, namely the NCEP/NCAR Reanalysis 1 (R1), the NCEP-DOE Reanalysis 2 (R2) and the NCEP Climate Forecast System Reanalysis (CFSR), to reproduce the spatiotemporal characteristics of precipitation over Zhejiang Province are comprehensively compared. The mean absolute bias percentages for three reanalyses are 20% (R1), 10% (R2) and 37% (CFSR). R2 (R1) gives the best (worst) general depiction of the spatial characteristics of the observed precipitation climatology, whereas a significant wet bias is noticed in CFSR. All reanalyses reasonably reproduce the interannual variability with the correlation coefficients of 0.72 (R1), 0.72 (R2) and 0.84 (CFSR). All reanalyses well represent the first two modes of the observed precipitation through Empirical Orthogonal Function analysis, with CFSR giving the best capture of the principal components. The root-mean-square error (RMSE) is the largest (smallest) in CFSR (R2). The large RMSE of CFSR in summer (especially in June) contributes mostly to its systematic wet bias. After 2001, the wet-bias of CFSR substantially weakens, probably attributed to increasing observations assimilated in the CFSR. On a monthly basis, the percentage of neutral bias cases are similar for all reanalyses, while the ratio of positive (negative) bias cases for CFSR is distinctly larger (smaller) than that of R1 and R2. The proportions of negative bias cases for R1 and R2 begin to increase after 2001 while keeping stable for CFSR. On a daily basis, all reanalyses give good performances of reproducing light rain; however, the reflection of moderate rain and heavier rain by CFSR is better than R1 and R2. Overall, despite being a third-generation reanalysis product, CFSR does not exhibit comprehensive superiorities over R1 and R2 in all aspects on a regional scale.  相似文献   

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