Modern-Era Retrospective Analysis For Research And Applications Research Articles

Characteristics and mechanisms of the diurnal variation of winter precipitation in Taiwan

Using gridded precipitation data remapping from rain‐gauge observations of the time period of 2001–2015 winter months (December, January and February), this study examines the spatio‐temporal characteristics of the climatological diurnal winter precipitation in Taiwan. Our results show that the timings of the maximum values of the diurnal precipitation in Taiwan exhibit clear east–west regional differences, with night‐time maxima over eastern Taiwan but early morning maxima over western Taiwan. Analyses also show that the climatological characteristics of diurnal winter precipitation documented in this study are not controlled by some unusual, heavy precipitation events and can be seen as the common features. By examining the changes in meteorological variables (including winds, relative humidity and temperature) extracted from the in situ observations and the MERRA (Modern‐Era Retrospective Analysis for Research and Applications) reanalysis, this study further suggests that (a) the radiative cooling effect and (b) the surface wind convergence induced by the interaction between the local orography, the local land–sea breeze and the large‐scale diurnal circulation changes over the East Asian–western North Pacific (EAWNP) region are two of the possible formation mechanisms responsible for the spatio‐temporal differences of the climatological diurnal winter precipitation in Taiwan. This finding sheds light on the importance of understanding the role of the large‐scale diurnal circulation changes over the EAWNP region in modulating local diurnal precipitation properties during the winter months.

Assessment of moisture budget over West Africa using MERRA-2’s aerological model and satellite data

The study assessed the performance of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) and MERRA-2 aerological (P–E*) model in reproducing the salient features of West Africa water balance including its components from 1980 to 2013. In this study we have shown that recent reanalysis efforts have generated imbalances between regional integrated precipitation (P) and surface evaporation (E), and the effect is more in the newly released MERRA-2. The atmospheric water balance of MERRA and MERRA-2 were inter-compared and thereafter compared with model forecast output of European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-I) and Japanese 55-year Reanalysis (JRA-55). Results indicated that a bias of 12–20 (5–13) mm/month in MERRA-2 (ERA-I) leads to the classification of the Sahel (14°N–20°N) as a moisture source during the West African Summer Monsoon. Comparisons between MERRA/MERRA-2 and prognostic fields from two ERA-I and JRA-55 indicated that the average P–E* in MERRA is 18.94 (52.24) mm/month which is less than ERA-I (JRA-55) over Guinea domain and 25.03 (4.53) mm/month greater than ERA-I (JRA-55) over the Sahel. In MERRA-2, average P–E* indicated 25.76 (59.06) mm/month which is less than ERA-I (JRA-55) over Guinea and 73.72 (94.22) mm/month less than ERA-I (JRA-55) over the Sahel respectively. These imbalances are due to adjustments in data assimilation methods, satellite calibration and observational data base. The change in convective P parameterization and increased re-evaporation of P in MERRA-2 is suggestive of the cause of positive biases in P and E. The little disagreements between MERRA/MERRA-2 and CRU precipitation highlights one of the major challenges associated with climate research in West Africa and major improvements in observation data and surface fluxes from reanalysis remain vital.

Surface Winds and Dust Biases in Climate Models

AbstractAn analysis of North African dust from models participating in the Fifth Climate Models Intercomparison Project (CMIP5) suggested that, when forced by observed sea surface temperatures, these models were unable to reproduce any aspects of the observed year‐to‐year variability in dust from North Africa. Consequently, there would be little reason to have confidence in the models' projections of changes in dust over the 21st century. However, no subsequent study has elucidated the root causes of the disagreement between CMIP5 and observed dust. Here I develop an idealized model of dust emission and then use this model to show that, over North Africa, such biases in CMIP5 models are due to errors in the surface wind fields and not due to the representation of dust emission processes. These results also suggest that because the surface wind field over North Africa is highly spatially autocorrelated, intermodel differences in the spatial structure of dust emission have little effect on the relative change in year‐to‐year dust emission over the continent. I use these results to show that similar biases in North African dust from the NASA Modern Era Retrospective analysis for Research and Applications (MERRA) version 2 surface wind field biases but that these wind biases were not present in the first version of MERRA.

An assessment of historical Antarctic precipitation and temperature trend using CMIP5 models and reanalysis datasets

The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts “Interim” reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979–2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year−1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution.

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model.

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

Exploring Wave‐Wave Interactions in a General Circulation Model

AbstractNonlinear interactions involving Kelvin waves with (periods, zonal wave numbers) = (3.7d, s =− 1) (UFKW1) and = (2.4d, s =− 1) (UFKW2) and s = 0 and s = 1 quasi 9 day waves (Q9DW) with diurnal tides DW1, DW2, DW3, DE2, and DE3 are explored within a National Center for Atmospheric Research (NCAR) thermosphere‐ionosphere‐mesosphere electrodynamics general circulation model (TIME‐GCM) simulation driven at its ∼30 km lower boundary by interpolated 3‐hourly output from Modern‐Era Retrospective Analysis for Research and Applications (MERRA). The existence of nonlinear wave‐wave interactions between the above primary waves is determined by the presence of secondary waves (SWs) with frequencies and zonal wave numbers that are the sums and differences of those of the primary (interacting) waves. Focus is on 10–21 April 2009, when the nontidal dynamics in the mesosphere‐lower thermosphere (MLT) region is dominated by UFKW and when identification of SW is robust. Fifteen SWs are identified in all. An interesting triad is identified involving UFKW1, DE3, and a secondary UFKW4 = (1.5d, s =− 2): The UFKW1‐DE3 interaction produces UFKW4, the UFKW4‐DE3 interaction produces UFKW1, and the UFKW1 interaction with UFKW4 produces DE3. At 120 km the dynamic range of the reconstructed latitude‐longitude zonal wind field due to all of the SW is roughly half that of the primary waves, which produced them. This suggests that nonlinear wave‐wave interactions could significantly modify the way that the lower atmosphere couples with the ionosphere.

MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN); support vector machines (SVM); and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000–2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002–2004 using a spatial resolution of 0.05°, which proved to be useful for estimating the long-term LE over North America.

Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme

Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI.

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.

Reanalysis comparisons of upper tropospheric/lower stratospheric jets and multiple tropopauses.

The representation of upper tropospheric/lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern Era Retrospective Analysis for Research and Applications), ERA-I (the ECMWF interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution: For example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterization. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude southern hemisphere winter upper tropospheric jets and multiple tropopauses, and in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry climate models.

Estimation of gross primary production in Moso bamboo forest based on light-use efficiency derived from MODIS reflectance data

ABSTRACTAssessing the contribution of Moso bamboo (Phyllostachys pubescens) forest to forest ecosystem carbon storage requires accurate estimation of gross primary production (GPP). Based on measurements of light-use efficiency (LUE), defined as the ratio of measured GPP to photosynthetically active radiation (PAR), from the eddy covariance flux tower, the linear regression model and partial least squares regression model were used for estimation of LUE using the Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data. GPP estimates were then calculated by the product of LUE estimates and PAR (named the LUE-PAR model), which was compared with GPP from the GPP algorithm designed for the MODIS sensor aboard the Aqua and Terra platforms (MOD17A2 model) and the EC-LUE model. The results revealed the PLS model performed better than the linear regression model in LUE estimation but had lager uncertainties in high and low LUE values. GPP estimates driven by a MODIS-based radiation product with high spatial resolution was more accurate than those driven by Modern-Era Retrospective Analysis for Research and Applications (MERRA) radiation product from the NASA’s Global Modelling and Assimilation Office data set. The LUE-PAR model had the highest accuracy than the other two LUE models. The GPP values derived from the EC-LUE model driven by photosynthetically active radiation (PAR) from MERRA and maximum LUE from the EC data were overestimated due to the overestimation in MERRA radiation product. The GPP values derived from the MOD17A2 model driven by PAR from the MERRA and maximum LUE from the biome properties look-up table were underestimated due to underestimation in the maximum LUE of Moso bamboo forest. This study implied that the LUE-PAR model driven by LUE estimates from the PLS model and PAR from MERRA is a superior approach in improving GPP simulations, and PAR products with high spatial resolution and accurate species-specific maximum LUE are necessary for the LUE models in estimating GPP at regional scale.

Evaluating reanalyses performance in the Baltic Sea region by using assimilated radiosonde data

ABSTRACTThe comparison between seven reanalyses – ERA‐Interim, National Centres for Environmental Predictions (NCEP)–National Center for Atmospheric Research reanalysis, NCEP Department of Energy reanalysis, NCEP–Climate Forecast System Reanalysis, Modern‐Era Retrospective Analysis for Research and Application (MERRA), MERRA‐2 and JRA‐55 – and assimilated radiosonde measurements was performed for eight radiosonde stations in the Baltic Sea region during 1980–2015 at five levels. In addition to raw radiosonde measurements, data sets corrected for temperature‐dependence (TD) and time‐lag errors in RS80‐A humidity measurements were investigated. While temperature differences between reanalyses and radiosonde stayed below 0.1 °C at the higher levels, differences up to 1.2 and 2.3 °C were revealed for the 2 m level at 00 UTC and 12 UTC, respectively. At the same time, biases in relative humidity increased with height. The new techniques assimilated in reanalyses were found to be in a disagreement and in unison with radiosonde data, depending on the level, time of the day and reanalysis investigated. The RS80‐A TD correction had a significant effect on tropospheric humidity measurements, increasing specific humidity up to 0.3 g kg−1 at the 2 m level. Based on the ranking results, JRA‐55 is the most accurate reanalysis in representing the radiosonde measurements.

A climatology of Rossby wave generation in the middle atmosphere of the Southern Hemisphere from MERRA reanalysis

AbstractA climatological characterization of Rossby wave generation events in the middle atmosphere of the Southern Hemisphere is conducted using 20 years of Modern‐Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. An automatic detection technique of wave generation events is developed and applied to MERRA reanalysis. The Rossby wave generation events with wave period of 1.25 to 5.5 days and zonal wave number from one to three dominate the Eliassen‐Palm flux divergence around the stratopause at high latitudes in the examined 20 year period. These produce an eastward forcing of the general circulation between May and mid‐August in that region. Afterward from mid‐August to the final warming date, Rossby wave generation events are still present but the Eliassen‐Palm flux divergence in the polar stratopause is dominated by low‐frequency Rossby waves that propagate from the troposphere. The Rossby wave generation events are associated with potential vorticity gradient inversion, and so they are a manifestation of the dominant barotropic/baroclinic unstable modes that grow at the cost of smearing the negative meridional gradient of potential vorticity. The most likely region of wave generation is found between 60° and 80°S and at a height of 0.7 hPa, but events were detected from 40 hPa to 0.3 hPa (which is the top of the examined region). The mean number of events per year is 24, and its mean duration is 3.35 days. The event duration follows an exponential distribution.

Representation of mid‐latitude North American coastal storm activity by six global reanalyses

ABSTRACTExtratropical cyclones often produce extreme and hazardous weather conditions, such as high winds, heavy precipitation, blizzard conditions, and flooding, all of which have detrimental environmental/physical and socio‐economic impacts. Furthermore, storm interaction with the ocean produces additional hazards, with major local impacts, including inundation and coastal erosion. The North American west coast is influenced by the North Pacific storm track and by ‘atmospheric river’ events while the east coast is particularly influenced by winter storms that track along two favoured routes: the St. Lawrence Valley and the Eastern Seaboard. Reanalysis provides an invaluable tool for studying the characteristics of storm events that are identified as causing the most severe impacts. However, reanalysis products differ substantially in spatial resolution, model physics, assimilation approach, and the data that are assimilated. This study evaluates the representation of storm activity along the mid‐latitude North American coastlines by six global reanalyses: NCEP‐1, NCEP‐2, ERA‐Interim, Modern‐Era Retrospective analysis for Research and Applications (MERRA), Climate Forecast System Reanalysis (CFSR), and Twentieth Century Reanalysis Version 2 (20CR). Storm activity representation is evaluated at annual and seasonal timescales (JFM, AMJ, JAS, OND, and ‘extended winter’ ONDFM) during the 1979–2010 time period through comparison with selected meteorological stations using single‐point surface pressure‐based proxies of extratropical storm activity. Stations are selected on the basis of record length, reporting frequency, coastal proximity, and relatively uniform spatial distribution. Comparisons are made using data extracted from the reanalysis grid box centre that is closest to each selected station. All reanalyses are found to successfully represent most aspects of mid‐latitude North American coastal strong storm activity, annually and seasonally, along both coasts. Nevertheless, ERA‐Interim, MERRA, and CFSR provide the better representations of mid‐latitude North American coastal strong storm activity, with ERA‐Interim performing best overall.

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.

Changes in the zonal mean flow, temperature, and planetary waves observed in the Northern Hemisphere mid-winter months during the last decades

Four sets of data: the UK Met Office, Modern Era Retrospective-analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis data (JRA-55), and ERA-Interim data (ERA) have been used to estimate the climatic variability of the zonal mean flow, temperature, and Stationary Planetary Waves (SPW1, SPW2) from the troposphere up to the lower mesosphere levels. The composites of the meteorological fields during mid-winter month have been averaged over the first (1995–2005) and second (2006–2016) 11 years intervals and have been compared mainly paying attention to interannual and intraseasonal variability. Results show that changes in the mean fields and SPW2 are weaker and statistical significance of these changes is lower in comparison with the changes observed in the intraseasonal variability of these characteristics. All data sets demonstrate a decrease of SPW1 amplitude at the higher-middle latitudes in the lower stratosphere and opposite effect in the upper stratosphere. However, there is an increase of the intraseasonal variability for all meteorological parameters and this rise is statistically significant. The results obtained show that UK Met Office data demonstrate stronger changes and increase of the intraseasonal variability in comparison with other data sets.

Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data

Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ETₒ. Here, we assessed the accuracy of daily ETₒ estimates derived from remote sensing (ETₒ-RS) compared with those derived from four ground-based stations (ETₒ-G) in Kurdistan (Iraq) over the period 2010–2014. Near surface air temperature, relative humidity and cloud cover fraction were derived from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS/AMSU), and wind speed at 10 m height from MERRA (Modern-Era Retrospective Analysis for Research and Application). Four methods were used to estimate ETₒ: Hargreaves–Samani (HS), Jensen–Haise (JH), McGuinness–Bordne (MB) and the FAO Penman Monteith equation (PM). ETₒ-G (PM) was adopted as the main benchmark. HS underestimated ETₒ by 2%–3% (R2 = 0.86 to 0.90; RMSE = 0.95 to 1.2 mm day−1 at different stations). JH and MB overestimated ETₒ by 8% to 40% (R2= 0.85 to 0.92; RMSE from 1.18 to 2.18 mm day−1). The annual average values of ETₒ estimated using RS data and ground-based data were similar to one another reflecting low bias in daily estimates. They ranged between 1153 and 1893 mm year−1 for ETₒ-G and between 1176 and 1859 mm year−1 for ETₒ-RS for the different stations. Our results suggest that ETₒ-RS (HS) can yield accurate and unbiased ETₒ estimates for semi-arid regions which can be usefully employed in water resources management.

An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset

Wind and solar energy resources are an increasingly large fraction of generation in global electricity systems. However, the variability of these resources necessitates new datasets and tools for understanding their economics and integration in electricity systems. To enable such analyses and more, we have developed a free web-based tool (Global Renewable Energy Atlas & Time-series, or GRETA) that produces hourly wind and solar photovoltaic (PV) generation time series for any location on the globe. To do so, this tool applies the Boland–Ridley–Laurent and Perez models to NASA’s (National Aeronautics and Space Administration) Modern-Era Retrospective Analysis for Research and Applications (MERRA) solar irradiance reanalysis dataset, and the Archer and Jacobson model to the MERRA wind reanalysis dataset to produce resource and power data, for a given technology’s power curve. This paper reviews solar and wind resource datasets and models, describes the employed algorithms, and introduces the web-based tool.

Global O3-CO Correlations in a Chemistry and Transport Model During July-August: Evaluation with TES Satellite Observations and Sensitivity to Input Meteorological Data and Emissions.

We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618hPa) O3-CO correlations determined by the measurements from Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July-August). The model is driven by three meteorological data sets (fvGCM with sea surface temperature for 1995, GEOS4-DAS for 2005, and MERRA for 2005), allowing us to examine the sensitivity of model O3-CO correlations to input meteorological data. Model simulations of radionuclide tracers (222Rn, 210Pb, and 7Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O3 values are evaluated with climatological ozone profiles from ozonesonde measurements and satellite tropospheric O3 columns. Despite the fact that three simulations show significantly different global and regional distributions of O3 and CO concentrations, all simulations show similar patterns of O3-CO correlations on a global scale. These patterns are consistent with those derived from TES observations, except in the tropical easterly biomass burning outflow regions. Discrepancies in regional O3-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O3-CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O3 and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NOx emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O3-CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O3-CO correlations than other emissions, but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O3 is consumed and CO generated during the atmospheric oxidation process of isoprene under low NOx conditions. We find that lightning NOx emissions degrade both positive correlations at mid-/high- latitudes and negative correlations in the tropics because ozone production downwind of lightning NOx emissions is not directly related to the emission and transport of CO. Our study concludes that O3-CO correlations may be used effectively to constrain the sources of regional tropospheric O3 in global 3-D models, especially for those regions where convective transport of pollution plays an important role.

The 2015/2016 El Niño Event in Context of the MERRA-2 Reanalysis: A Comparison of the Tropical Pacific with 1982/1983 and 1997/1998.

The 2015/2016 El Niño is analyzed using atmospheric/oceanic analysis produced using the Goddard Earth Observing System (GEOS) data assimilation systems. As well as describing the structure of the event, a theme of the work is to compare and contrast it with two other strong El Niños, in 1982/1983 and 1997/1998. These three El Niño events are included in the Modern-Era Retrospective analysis for Research and Applications (MERRA) and in the more recent MERRA-2 reanalyses. MERRA-2 allows a comparison of fields derived from the underlying GEOS model, facilitating a more detailed comparison of physical forcing mechanisms in the El Niño events. Various atmospheric/oceanic structures indicate that the 2015/2016 El Niño maximized in the Niño3.4 region, with the large region of warming over most of the Pacific and Indian Ocean. The eastern tropical Indian Ocean, Maritime Continent, and western tropical Pacific are found to be less dry in boreal winter, compared to the earlier two strong events. While the 2015/2016 El Niño had an earlier occurrence of the equatorial Pacific warming and was the strongest event on record in the central Pacific, the 1997/1998 event exhibited a more rapid growth due to stronger westerly wind bursts and Madden-Julian Oscillation during spring, making it the strongest El Niño in the eastern Pacific. Compared to 1982/1983 and 1997/1998, the 2015/2016 event has a shallower thermocline over the eastern Pacific with a weaker zonal contrast of sub-surface water temperatures along the equatorial Pacific. While the three major ENSO events have similarities, each are unique when looking at the atmosphere and ocean surface and sub-surface.