Modern-Era Retrospective Analysis For Research And Applications Data Research Articles

Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands

Reducing the uncertainties in carbon balance assessment is essential for better pastureland management in arid areas. Climate forcing data are some of the major uncertainty sources. In this study, a modified Biome-BGC grazing model was driven by an ensemble of reanalysis data of the Climate Forecast System Reanalysis data (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), to study the effect of climate change and grazing on the net ecosystem exchange (NEE) of the pasturelands in Central Asia. Afterwards, we evaluated the performance of corresponding climate datasets over four major pastureland types, and quantified the modeling uncertainty induced by climate forcing data. Our results suggest that (1) a significant positive trend in temperature and a negative trend in precipitation were obtained from the three climate datasets. The average precipitation is apparently higher in the CFSR and MERRA data, showing the highest temperature value among the data sets; (2) pasturelands in Central Asia released 2.10 ± 1.60 Pg C in the past 36 years. The highest values were obtained with the CFSR (−1.53 Pg C) and the lowest with the MERRA (−2.35 Pg C) data set; (3) without grazing effects, pasturelands in Central Asia assimilated 0.13 ± 0.06 Pg C from 1981–2014. Grazing activities dominated carbon release (100%), whereas climate changes dominated carbon assimilation (offset 6.22% of all the carbon release). This study offered possible implications to the policy makers and local herdsmen of sustainable management of pastureland and the adaptation of climate change in Central Asia.

Insight into the precipitation behavior of gridded precipitation data in the Sina basin.

This paper examines the performance of three gridded precipitation data sets, namely, Global Precipitation Climatology Centre (GPCC), Tropical Precipitation Measuring Mission (TRMM), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), for the duration of 25years using 9 rain gauge data sets of the Sina basin, India. Statistical measures were employed to measure the performance in reproducing the rainfall and to assess its ability to detect the rainfall/no rainfall events, its structure, pattern, and spatio-temporal variations in the monthly and annual time scales. Compromise programming (CP) is used to rank the statistical performances of selected gridded precipitation data sets and found that TRMM attained first rank for the 8 stations followed by MERRA. The precipitation concentration index (PCI) checks the pattern and distribution of rainfall and found that observed data shows a uniform distribution in the basin; however, all the three gridded data sets failed to demonstrate uniform distribution. Categorical metrics like Probability of Detection (POD) and False Alarm Ratio (FAR) revealed that TRMM followed by MERRA and GPCC have good capabilities to detect rainfall/no rainfall events at different thresholds. All the trends drawn between observed data set and gridded precipitation data sets revealed that the MERRA data tend to underestimate and the TRMM and GPCC data tend to overestimate the values and intensities of rainfall data sets at most of the stations for both monthly and annual time scales. The data analysis of extreme rainfall points at monthly and annual time scales exhibits better performance of TRMM data sets. Overall, the TRMM data set is capable in replicating different characteristics of the observed data in the study area and could be used for hydro-meteorological and climatic studies when continuous observed data set is not available.

Spatiotemporal Investigations of Aerosol Optical Properties Over Bangladesh for the Period 2002–2016

This study investigates the aerosol optical properties over Bangladesh using Terra MODIS-based collection 06 (DT and DB) aerosol optical depth (AOD), ozone monitoring instrument (OMI)-based aerosol absorption optical depth (AAOD), emission database for global atmospheric research (EDGAR) measured black carbon (BC) and organic carbon (OC), and modern-era retrospective analysis for research and applications (MERRA) retrieved dust. In addition, ground-based aerosol robotic network (AERONET) retrieved optical properties such as aerosol volume size distribution, single scattering albedo (SSA), and asymmetry parameter (ASY) are studied in understanding the behavior of aerosol properties. Both the satellite-based MODIS DB and DT algorithms detect the high AOD (> 0.5) all over Bangladesh except for a small portion in the eastern side. High AOD is also observed in all seasons except for SON. AOD significantly (at 95% level) increased over the period 2002–2016. The correlation coefficient between MODIS and AERONET AOD at Dhaka University site is 0.78 (0.76) for DT (DB). The Expected Error envelope is found 75.70% (54.38%) with small (large) RMSE for DT (DB) product. OMI-based AAOD indicates the presence of absorbing aerosols over the study area which is confirmed with AEROENT-based SSA and ASY. Three different types of absorbing aerosols such as BC, OC, and dust are identified from the EDGAR and MERRA data. In Bangladesh, the BC, OC, and dust are significantly (at 95% level) increasing. Further work is suggested to simulate and assess aerosols against the observations, which will help projecting aerosols in the future climate.

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

In this study we evaluated the wind resources of wind farms in the Changhua offshore area of Taiwan. The offshore wind farm in Zone of Potential (ZoP) 26 was optimized through an economic evaluation. The annual energy production (AEP) of the offshore wind farm in ZoP 26 was predicted for 10 and 25 years with probabilities of 50%, 75%, and 90% by using measured mast data, measure-correlate-predict (MCP) data derived from Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Central Weather Bureau (CWB) data. When the distance between the turbines in a wind farm was decreased from 12D to 6D, the turbine number increased from 53 to 132, while the capacity factor decreased slightly from 48.6% to 47.6%. MCP data derived from the inland CWB station with similar levels of wind resources can be used to accurately predict the power generation of the target offshore wind farm. The use of MCP with mast data as target data, together with CWB and MERRA data as reference data, proved to be a feasible method for predicting offshore wind power generation in places where a mast is available in a neighboring area.

Sensitivity of Historical Simulation of the Permafrost to Different Atmospheric Forcing Data Sets from 1979 to 2009

AbstractNumerical simulation is of great importance to the investigation of changes in frozen ground on large spatial and long temporal scales. Previous studies have focused on the impacts of improvements in the model for the simulation of frozen ground. Here the sensitivities of permafrost simulation to different atmospheric forcing data sets are examined using the Community Land Model, version 4.5 (CLM4.5), in combination with three sets of newly developed and reanalysis‐based atmospheric forcing data sets (NOAA Climate Forecast System Reanalysis (CFSR), European Centre for Medium‐Range Weather Forecasts Re‐Analysis Interim (ERA‐I), and NASA Modern Era Retrospective‐Analysis for Research and Applications (MERRA)). All three simulations were run from 1979 to 2009 at a resolution of 0.5° × 0.5° and validated with what is considered to be the best available permafrost observations (soil temperature, active layer thickness, and permafrost extent). Results show that the use of reanalysis‐based atmospheric forcing data set reproduces the variations in soil temperature and active layer thickness but produces evident biases in their climatologies. Overall, the simulations based on the CFSR and ERA‐I data sets give more reasonable results than the simulation based on the MERRA data set, particularly for the present‐day permafrost extent and the change in active layer thickness. The three simulations produce ranges for the present‐day climatology (permafrost area: 11.31–13.57 × 106 km2; active layer thickness: 1.10–1.26 m) and for recent changes (permafrost area: −5.8% to −9.0%; active layer thickness: 9.9%–20.2%). The differences in air temperature increase, snow depth, and permafrost thermal conditions in these simulations contribute to the differences in simulated results.

An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer

Abstract. The global-scale patterns and covariances of subtropical marine boundary layer (MBL) cloud fraction and spatial variability with atmospheric thermodynamic and dynamic fields remain poorly understood. We describe an approach that leverages coincident NASA A-train and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data to quantify the relationships in the subtropical MBL derived at the native pixel and grid resolution. A new method for observing four subtropical oceanic regions that capture transitions from stratocumulus to trade cumulus is demonstrated, where stratocumulus and cumulus regimes are determined from infrared-based thermodynamic phase. Visible radiances are normally distributed within stratocumulus and are increasingly skewed away from the coast, where trade cumulus dominates. Increases in MBL depth, wind speed, and effective radius (re), and reductions in 700–1000 hPa moist static energy differences and 700 and 850 hPa vertical velocity correspond with increases in visible radiance skewness. We posit that a more robust representation of the cloudy MBL is obtained using visible radiance rather than retrievals of optical thickness that are limited to a smaller subset of cumulus. The method using the combined A-train and MERRA data set has demonstrated that an increase in re within shallow cumulus is strongly related to higher MBL wind speeds that further correspond to increased precipitation occurrence according to CloudSat, previously demonstrated with surface observations. Hence, the combined data sets have the potential of adding global context to process-level understanding of the MBL.

Wind over complex terrain – Microscale modelling with two types of mesoscale winds at Nygårdsfjell

Nygårdsfjell, a complex terrain near Norwegian-Swedish border, is characterized by its significant wind resources. The feasibility of using mesoscale winds as input to microscale model is studied in this work. The main objective is to take into account the actual terrain effects on wind flow over complex terrain. First set of mesoscale winds are modelled with Weather Research and Forecasting (WRF) numerical tool whereas second set of mesoscale winds are taken from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data system. WindSim, a computational fluid dynamics based numerical solver is used as microscale modelling tool. The results suggest that the performance of microscale model is largely dependent upon the quality of mesoscale winds as input. The proposed coupled models achieve improvements in wind speed modelling, especially during cold weather. WRF-WindSim coupling showed better results than MERRA-WindSim coupling in all three test cases, as root mean square error (RMSE) decreased by 70.9% for the February case, 61.5% for October and 14.4% for June case respectively. Raw mesoscale winds from the WRF model were also more correct than the mesoscale winds from MERRA data set when extracted directly at the wind turbine by decreasing the RMSE by 62.6% for the February case, 62.7% for October and 23.7% for June case respectively. The difference of RMSE values between the mesoscale winds directly at wind turbine versus the coupled meso-microscale model outputs are not conclusive enough to indicate any specific trend.

A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data

A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50 m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function yn=((1.96+ye)−1.4)/0.302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last 3 h m so a third order AR model was used to generate hourly 50 m wind speed residuals. The MERRA daily average 50 m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50 m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data.

Evaluation of reanalysis, spatially interpolated and satellite remotely sensed precipitation data sets in central Asia

AbstractThe accuracy of any gridded climatic data sets is as important as their availability for regional climate and ecological studies. In this study, the accuracy of estimated precipitation in central Asia from three recently developed reanalysis data sets, Modern‐Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re‐Analysis (ERA‐Interim), and Climate Forecast System Reanalysis (CFSR), is evaluated through comparisons with observations from 399 stations during 1979–2010. An interpolated precipitation data set from station observations and a satellite remotely sensed data set, Tropical Rainfall Measuring Mission (TRMM) 3B42, are included in the evaluation. Major results show that MERRA data have higher accuracy than ERA‐Interim and CFSR, although they all overestimate the observed precipitation especially in late spring and early summer months, suggesting errors in their ways of representing convective precipitation in that region. In comparison, the interpolated and satellite‐sensed data, which provide no upper air information/data, have higher accuracy. While all these data sets have difficulty in describing stations' precipitation in mountainous areas, the reanalysis data sets have particularly large discrepancies. In examining the discrepancy in the reanalysis data, a Precipitation‐Topography Partial Least Squares method is proposed to incorporate certain terrain/geographic effects on precipitation in mapping the gridded data to the station locations for comparison with the observation. The outcome suggests that the estimated station precipitation by this new method is closer to the observed than the method without considering those factors. The improvement by this method and by possible other methods taking into account different details/aspects of the influences indicates that it is only meaningful to compare the accuracy or relevance of gridded data sets to station observations in a relative sense among various data sets.

Snow process estimation over the extratropical Andes using a data assimilation framework integrating MERRA data and Landsat imagery

AbstractA data assimilation framework was implemented with the objective of obtaining high‐resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985–2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in‐situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49–0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (−0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA‐conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel‐wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near‐peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA‐forced modeled SWE estimates, further increasing the utility of the MERRA data for high‐resolution snow modeling applications.

Validating Atmospheric Reanalysis Data using Tropical Cyclones as Thermometers

Abstract Temperatures in the upper troposphere of the atmosphere, near the tropopause, play a key role in the evolution of tropical cyclones (TC) by controlling their potential intensity (PI), which describes the thermodynamically based maximum TC intensity that the environment will support. Accurately identifying past trends in PI is critical for understanding the causes of observed changes in TC intensity, but calculations of PI trends using different atmospheric reanalysis products can give very different results, largely due to differences in their representation of upper-tropospheric temperatures. Without a means to verify the fidelity of the upper-tropospheric temperatures, PI trends calculated from these products are very uncertain. Here, a method is introduced to validate the upper-tropospheric temperatures in the reanalysis products by using the TCs themselves as thermometers. Using a 30-yr global dataset of TC cloud-top temperatures and three widely utilized atmospheric reanalysis products—Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF interim reanalysis (ERA-Interim), and NCEP–NCAR Global Reanalysis 1—it is shown that storm-local upper-level temperatures in the MERRA and ERA-Interim data vary similarly to the TC cloud-top temperatures on both interannual and decadal time scales, but the NCEP–NCAR data have substantial biases that introduce an increasing trend in storm-local PI not found in the other two products. The lack of global storm-local PI trends is due to a balance between temporal increases in the mean state and the poleward migration of TCs into lower climatological PI, and it has significant implications for the detection and attribution of mean TC intensity trends.

On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

Abstract. The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Reanalysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets that are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Intertropical Convergence Zone (ITCZ) by 15–20% compared to both COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5–10%. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5–25° is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10% in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9% between winter and summer.

Intensity of climate variability derived from the satellite and MERRA reanalysis temperatures: AO, ENSO, and QBO

Satellite measurements (Atmospheric InfraRed Sounder/Advanced Microwave Sounding Unit-A, MODerate resolution Imaging Spectroradiometer) and the Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis have been utilized to analyze the relative influence of the climate variability (AO: Arctic Oscillation, ENSO: El Niño-Southern Oscillation, QBO: Quasi-Biennial Oscillation) on the zonal-mean temperature and wind variations over the globe from September 2002 to August 2011. We also extended the usage of MERRA data for the period of 1979–2011; furthermore, three climate indices of AO, NINO3.4, and QBO were used as the corresponding climate indicators. The correlations between the temperature anomalies and the climate indices indicate that the tropospheric temperature variability in the mid-latitude (30–60N) linked to both AO and ENSO has been more pronounced over ocean than over land. However, the low stratospheric temperature variability in the mid-latitude is mainly associated with ENSO and QBO. The north–south symmetric patterns over the globe are seen in the wind anomaly distributions for ENSO and QBO, but not for AO. The ENSO events are globally vigorous but also localized during the recent 9 years compared with those based on the period of 1979–2011. The tropospheric warming and stratospheric cooling phenomena during this period are more remarkable in the recent 9 years, although according to IPCC (2012). their linkage to the ENSO variability is still uncertain. The ENSO is found to have more significant impact on the tropospheric and low stratosphere temperature variability over the tropics in the recent period, consistent with more active zonal wind meridional circulations. The discrepancies between satellite observations and MERRA are also discussed. The estimated relative impact of the three major concurrent large-scale climate phenomena on regional temperature variability can be of great use in its long-term predictability.

Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF Using Flux Tower Observations

Abstract Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.

Utilizing Reanalysis and Synthesis Datasets in Wind Resource Characterization for Large-Scale Wind Integration

As wind plants become a more substantial portion of the generation resource, the ability of and manner in which this new fleet of generation supports meeting the power system load in a given area must be quantified in order to ensure security of supply. This paper describes the manner in which a reanalysis dataset—the National Aeronautics and Space Administration (NASA) Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset—was utilized in conjunction with the National Renewable Energy Laboratory (NREL) Eastern Wind Integration Dataset in order to perform an estimation of the interannual variability in wind power production as related to the capacity value of the investigated potential wind plants. Also described in the paper is a comparison of the MERRA data with publicly available wind data collected by the University of Massachusetts Wind Energy Center (UMass WEC).