NCEP Climate Forecast System Reanalysis Research Articles

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Abstract. The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions.

Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Representation of the Convectively Coupled Kelvin Waves in Modern Reanalysis Products

Abstract This study aims to deepen our understanding of the destabilization mechanisms and the mean-state modulation of the convectively coupled Kelvin waves (CCKWs) while testing simple models for CCKWs. We examine CCKW precipitation, vertical structure, and energetics in four modern reanalyses: the fifth version of ECMWF Reanalysis (ERA5), NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the second version of the NCEP Climate Forecast System Reanalysis (CFSR), and the Japanese 55-year Reanalysis (JRA-55). The CCKW precipitation signal strength in the wavenumber–frequency domain and the geographical distribution of CCKW precipitation variability are reasonably represented in all reanalyses, although they commonly underestimate the amplitude of CCKW precipitation. Despite considerable interreanalysis differences in the vertical structure of temperature and diabatic heating anomalies, the eddy available potential energy (EAPE) generation within the CCKWs is found to be associated with the second baroclinic mode whereas the first baroclinic mode damps CCKW EAPE in three out of four reanalyses. Geographically, strong CCKW activity occurs in the areas of high mean-state sea surface temperature (SST), where the second mode EAPE generation is higher, mainly due to a stronger stratiform heating and a tighter wave–convection coupling. Our results are supportive of the simple models for CCKWs in which CCKWs are destabilized within the second baroclinic mode component.

Meteorological drought monitoring in the Upper Olifants sub-basin, South Africa

Measured climate data are often scarce and expensive to collect through a network of measurement points. There are various reanalysis products that can be used in certain regions in studies concerned with drought monitoring. This study aimed at assessment of the adequacy of using the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) rainfall data and the ERA5 rainfall data from the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring meteorological drought over the Upper Olifants sub-basin, South Africa. A comparison was made between the observed rainfall data and the two reanalysis products based on the Mean Error (ME), the Nash-Sutcliffe Efficiency (NSE) and percentage of bias (PBIAS). The data were also used in computing and comparing the results of the Standardised Precipitation Index (SPI). The CFSR were found to underestimate rainfall amounts, while the ERA5 overestimated the rainfall amounts except during winter. The ERA5 showed a better correlation which was R2 = 0.78 when compared to the one for the CFSR, R2 = 0.57. Also, the NSE, ME and PBIAS for the ERA5 products were generally better in almost all-time scales. The three products detected one of the major droughts but the CFSR were unable to detect the second major drought in the study area during the chosen study period. The study concludes that the ERA5 is a better product for meteorological drought monitoring in the study area and can be useful in predicting meteorological drought using artificial intelligence due to the spatiotemporal resolution of the product.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Study of uncertainty of satellite and reanalysis precipitation products and their impact on hydrological simulation.

Satellite and reanalysis precipitation products are potential alternatives in hydrological studies, and it is very important to evaluate their accuracy and potential use for reliable simulations. In this study, three precipitation products (Tropical Rainfall Measuring Mission 3B43 Version 7 (TRMM 3B43), spatial interpolation grid data based on 2472 national meteorological observation stations in China (GRID_0.5), and National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)) were evaluated against gauge observations in the Xiangxi River watershed of Hubei Province. The performance results indicated that the results of the three precipitation products were correlated with those of the rain gauges; however, there were differences among the three products. TRMM 3B43 tended to overestimate precipitation with the highest correlation coefficient, while NCEP-CFSR tended to underestimate precipitation with the least satisfactory performance, and the performance of GRID_0.5 ranked between them. However, the annual and monthly mean errors differed, as the errors of most of the results driven by NCEP-CFSR were lowest. The errors varied at different time scales. During years with high precipitation, the results were often underestimated, while the results are often overestimated during years with low precipitation. According to the average monthly results, the GRID_0.5 results were closest to the gauge observations for most months. During the wet season, TRMM 3B43 performed better, while NCEP-CFSR precipitation performed better during the dry season. The errors from precipitation to streamflow, NPS pollution, and water environmental capacity (WEC) driven by the three precipitation products increased gradually, ranging from 10% for precipitation to over 20% for NPS pollution and almost 100% for WEC. The error increase for NCEP-CFSR was lower than that of the other two products. Although the simulation error from precipitation to the WEC results driven by the three precipitation products gradually increased, the degree of overestimation and underestimation became smaller.

The Asian Subtropical Westerly Jet Stream in CRA-40, ERA5, and CFSR Reanalysis Data: Comparative Assessment

The Asian subtropical westerly jet (AWJ) exerts crucial influences on Eurasian continent weather and climate. This paper analyzes the advantages and limitations of CRA-40, which is China’s first generation 40-yr (1979–2018) global atmosphere and land reanalysis product, in describing the characteristics of AWJ, compared with the ECMWF Reanalysis version 5 (ERA5) and NCEP Climate Forecast System Reanalysis (CFSR). The results show a close agreement across the three reanalyses on the whole. (1) In terms of climatology, overall differences of 200-hPa zonal wind across the three reanalyses are within ± 0.5 m s−1 (i.e., ± 2%). Large differences with maxima of ± 2 m s−1 (± 5%) appear over the Iranian Plateau and south of the Tibetan Plateau in the mid-upper troposphere in winter. (2) For seasonal cycle, the position and intensity of the AWJ centers in the three reanalyses are highly consistent, with correlation coefficient over 0.98. But there are some discrepancies in the zonal shift of the western AWJ center during the transition season. (3) On the interannual timescale, intensity of all AWJ centers varies consistently among the three reanalyses, while larger differences appear in their meridional displacement, especially in the eastern AWJ center. (4) For long-term variations, the three reanalyses all present a significant northward movement of the westerly jet axis in winter, and a southward displacement over central Asia (40°–80°E) and a northward migration over East Asia (80°–110°E) in summer. Thus, this study has provided confidence that CRA-40 has comparable performance with ERA5 and CFSR in depicting the characteristics of AWJ.

Inter-comparison of long-term wave power potential in the Black Sea based on the SWAN wave model forced with two different wind fields

In this study, a long-term comparative assessment of the potential of wave power in the Black Sea was conducted using the calibrated and validated SWAN (Simulating WAves Nearshore) model forced by two well-known wind fields. The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and National Centers for Environmental Prediction/Climate Forecast System Reanalysis (NCEP/CFSR) wind fields were used, covering data from 1979 to 2018. In general, the wave power potential based on the results of the CFSR wind field was found to be slightly higher than that obtained with the ERA-Interim wind field. The greatest discrepancy between the results of the ERA-Interim and CFSR wind fields was observed in the northeastern Black Sea. The spatial distributions of the wave power were also evaluated on a seasonal scale using wave parameters obtained from the calibrated SWAN model. The wave climate obtained from both long-term and seasonal assessments indicates that the western Black Sea, especially the southwestern Black Sea, is characterized by higher wave power potential and lower variability, while the eastern Black Sea has lower wave power potential and higher variability. Stable and powerful long-term wave conditions in the southwestern Black Sea can indicate that this region is a suitable location for wave farms. In contrast, the effect of the long-term variability on wave power is greatest in the eastern Black Sea owing to the highly variable wave conditions in this region.

Comparison of NCEP-CFSR and CMADS for Hydrological Modelling Using SWAT in the Muda River Basin, Malaysia

Identification of reliable alternative climate input data for hydrological modelling is important to manage water resources and reduce water-related hazards in ungauged or poorly gauged basins. This study aims to evaluate the capability of the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) and China Meteorological Assimilation Driving Dataset for the Soil and Water Assessment Tool (SWAT) model (CMADS) for simulating streamflow in the Muda River Basin (MRB), Malaysia. The capability was evaluated in two perspectives: (1) the climate aspect—validation of precipitation, maximum and minimum temperatures from 2008 to 2014; and (2) the hydrology aspect—comparison of the accuracy of SWAT modelling by the gauge station, NCEP-CFSR and CMADS products. The results show that CMADS had a better performance than NCEP-CFSR in the climate aspect, especially for the temperature data and daily precipitation detection capability. For the hydrological aspect, the gauge station had a “very good” performance in a monthly streamflow simulation, followed by CMADS and NCEP-CFSR. In detail, CMADS showed an acceptable performance in SWAT modelling, but some improvements such as bias correction and further SWAT calibration are needed. In contrast, NCEP-CFRS had an unacceptable performance in validation as it dramatically overestimated the low flows of MRB and contains time lag in peak flows estimation.

Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.

Winter Extreme Mixed Layer Depth South of the Kuroshio Extension

AbstractChange in the extratropical wintertime-mean mixed layer has been widely studied, given its importance to both physical and biogeochemical processes. With a focus on the south of the Kuroshio Extension region where the mixed layer is deepest in March, this study shows that variation of the synoptic-scale extreme mixed layer depth (MLD) is a better precursor than the monthly mean (or nonextreme) MLD for change in the subtropical mode water formation in spring, based on the NCEP Climate Forecast System Reanalysis (1979–2010). It is found that the extreme MLD events are attributable to the accumulation of excessive surface cooling driven by the synoptic storms that characterize cold-air outbreaks. Particularly, the difference between the extreme and nonextreme MLD is primarily related to differences in the cumulative synoptic heat flux anomalies, while a change in the preconditioning upper-ocean stratification contributes almost equally to both cases. Relative contributions of oceanic and atmospheric forcing to the interannual variation of the extreme MLD are quantified using a bulk mixed layer model. Results show comparable contributions: the preconditioning stratification change accounts for ~44% of total variance of the extreme MLD, whereas the convective mixing by surface heat flux and the mechanical stirring by wind stress account for ~35% and ~13%, respectively. In addition, both the reanalysis and observational data reveal that the extreme and nonextreme MLD has been shallowed significantly during 1979–2010, which is accounted for by the strengthened stratification due to the enhanced ocean surface warming by the Kuroshio heat transport.

Long-term analysis of extreme wave characteristics based on the SWAN hindcasts over the Black Sea using two different wind fields

The long-term variations of wave characteristics in the Black Sea are evaluated by using a third-generation wave model (Simulating WAves Nearshore, SWAN), forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and National Centers for Environmental Prediction/Climate Forecast System Reanalysis (NCEP/CFSR) wind data, covering the period of 1979−2018. The model results were calibrated and validated with buoy measurements at seven stations along the Black Sea. The comparative study shows that the CFSR dataset predicted slightly greater significant wave heights than the ERA-Interim dataset. The greatest difference between two datasets in terms of wave characteristics was found in the northeastern part of the Black Sea. The long-term averages and the variations of long-term trends for wave characteristics show that southwestern part of the Black Sea was characterized by greater significant wave heights, longer mean wave periods and storm durations, and lower variability, while the northeastern part of the basin was characterized by lower significant wave heights, shorter mean wave periods and storm durations, and higher variability. The long-term trends indicate that the wave characteristics over the 40-year period are more likely to be exposed to higher variation on the eastern part of the Black Sea than the western part of the basin.

Spatiotemporal long-term trends of extreme wind characteristics over the Black Sea

In this study, long-term change of wind characteristics on the Black Sea has been investigated using two widely used data sources, i.e., European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and National Centers for Environmental Prediction/Climate Forecast System Reanalysis (NCEP/CFSR), spanning 40 years between 1979 and 2018. Spatial and seasonal variability of climatic features such as the wind speed, direction, number and duration of storms, and wind power density are discussed. Wind climate is characterized by strong, durable and stable winds in the northern and western Black Sea, and relatively weak, short-lived and highly-variable winds in the eastern Black Sea. These long-term wind patterns indicate that the eastern part of the basin is likely to be subjected to the impacts of climate change. Long-term stable and strong wind conditions in the southwest part indicate reliable, persistent and sustainable wind energy potential. Long-term and seasonal variation of wind power density (WPD) at 110 m altitude over the Black Sea is investigated. There is a significant difference in WPD values between winter and summer seasons, with around 2.8 times larger WPD in winter than that in summer. In the western Black Sea, narrow confidence intervals observed in each season indicate a low level of variation during a season and ensures stable wind power conditions.

Contributions of Convective and Orographic Gravity Waves to the Brewer–Dobson Circulation Estimated from NCEP CFSR

Abstract Contributions of convective gravity waves (CGWs) and orographic gravity waves (OGWs) to the Brewer–Dobson circulation (BDC) are examined and compared to those from resolved waves. OGW drag (OGWD) is provided by NCEP Climate Forecast System Reanalysis (CFSR), while CGW drag (CGWD) is obtained from an offline calculation of a physically based CGW parameterization with convective heating and background data provided by CFSR. CGWD contributes to the shallow branch of the BDC regardless of the season, while OGWD contributes to both the shallow and deep branches except for the summertime, when OGWs hardly propagate into the stratosphere. At 70 hPa, the annual-mean tropical upward mass fluxes from Eliassen–Palm flux divergence (EPD), OGWD, and CGWD are 68%, 7%, and 4% of the total mass flux, respectively. The tropical upward mass flux at 70 hPa shows an increasing trend during the time period from 1979 to 1998, with 28%, 18%, and 6% of the trend driven by EPD, OGWD, and CGWD, respectively. The width of the turnaround latitudes tends to narrow for the streamfunctions induced by OGWD and CGWD but tends to widen for that induced by EPD. The contributions of GWD from MERRA (MERRA-2) to the climatology and long-term trend of the BDC are 7% (7%) and 13% (4%), respectively, somewhat smaller than the contributions of CGWD plus OGWD, which are estimated from CFSR to be 12% and 20%, respectively.

A 10-Yr Global Land Surface Reanalysis Interim Dataset (CRA-Interim/Land): Implementation and Preliminary Evaluation

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.

Evaluation of Daily Precipitation Product in China from the CMA Global Atmospheric Interim Reanalysis

The China Meteorological Administration (CMA) recently produced a CMA Global Atmospheric Interim Reanalysis (CRAI) dataset for the years 2007–2016. A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation, in terms of both mean states and extreme indicators over China, is performed. Comparisons are made with other current reanalysis datasets, namely, the ECMWF interim reanalysis (ERAI), Japanese 55-yr reanalysis (JRA55), NCEP Climate Forecast System Reanalysis (CFSR), and NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), as well as NCEP Climate Prediction Center (CPC) observations. The results show that, for daily variations of rainfall during warm seasons in eastern China, CRAI and CFSR overestimate the precipitation of the main rain belt, while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2; whereas ERAI tends to underestimate the precipitation in most regions of eastern China. Two extreme metrics, the total amount of precipitation on days where daily precipitation exceeds the 95th percentile (R95pTOT) and the number of consecutive dry days (CDDs) in one month, are examined to assess the performance of reanalysis datasets. In terms of extreme events, CRAI, ERAI, and JRA55 tend to underestimate the R95pTOT in most of eastern China, whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2; and all of the reanalyses underestimate the CDDs. Among the reanalysis products, CRAI and JRA55 show better agreement with the observed R95pTOT than the other datasets, with fewer biases, higher correlation coefficients, and much more similar linear trend patterns, while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.

Evaluation of Precipitation in Multi-Generation Reanalyses with Land Observations over Zhejiang Province

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 the 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 the 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 the CFSR is better than R1 and R2. Overall, despite being a third-generation reanalysis product, the CRSR does not exhibit comprehensive superiorities over R1 and R2 in all aspects on a regional scale.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set (CCMP), NCEP climate forecast system reanalysis data set (CFSR), ERA-interim reanalysis data set (ERA-int) and Japanese 55-year reanalysis data set (JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the long-term and homogeneous observation wind speeds data recorded at ten stations. The results reveal that (1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively. The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s, respectively; (2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea; (3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988–1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

Effect of Climate Change in the Stream Flow, Crop Yields and NP Levels at White Oak Bayou Watershed Using SWAT simulation: A Case Study

Projected changes in temperature due to global climate change may have serious impacts on hydrologic processes, water resources availability, irrigation water demand, and thereby affecting the agricultural production and productivity. Therefore, understanding the impacts of climate change on crop production and water resources is of utmost importance for developing possible adaptation strategies. The White Oak Bayou, one of the several waterways that give Houston, Texas, United States its popular nickname "The Bayou City" was selected in this case study.
 SWAT model is process based and can simulate the hydrological cycle, crop yield, soil erosion and nutrient transport. It is operated with an interface in ArcView GIS using raster or vector datasets including the digital elevation model (DEM), soil properties, vegetation, LULC, and meteorological observations observed which were derived from the Consortium for Geospatial Information, National Cooperative Soil Survey, National Land Cover Database 2006, NCEP Climate Forecast System Reanalysis and USGS website in 2005-2008. The climate change scenario was based on the projected increase in temperature by the IPCC by 2100.
 This case study showed a decrease in streamflow from observed actual scenario (2005-2008) to projected increase of 4°C temperature in future climate change scenario by 2100. The evapotranspiration increased but there was a decrease in surface runoff and percolation.
 Moreover, there were greater average plant biomass and more average plant yields. Hence, the nitrogen and phosphorus uptake and removed in yield increased. Thus, the total nitrogen decreased while the total phosphorus is zero indicating loss of the Phosphorus content in the soil. Yet, this case study needs to be validated and calibrated with actual data to support the projected outcome.