Showing papers on "Meteorological reanalysis published in 2016"

ERA-20C: An Atmospheric Reanalysis of the Twentieth Century

Abstract: The ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climat...

The JRA-55 Reanalysis: Representation of Atmospheric Circulation and Climate Variability

Abstract: This study investigates the quality of the Japanese 55-year Reanalysis (JRA-55), which is the second global reanalysis constructed by the Japan Meteorological Agency (JMA), by comparing it with other reanalyses and observational datasets. Improvements were found in the representation of atmospheric circulation on an isentropic surface and in the consistency of momentum budget based on the mass-weighted isentropic zonal mean method. The representation of climate variability in several regions was also examined. In the tropics, the frequencies of high spatial correlations with precipitation, which were estimated using the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis, are clearly higher in JRA-55 than in JRA-25. The results indicate that JRA-55 generally improved the representations of phenomena on a wide range of space–time scales, such as equatorial waves, and transient eddies in the storm track regions, compared with JRA-25 during the satellite era. Moreover, JRA-55 improved the temporal consistency compared with the older reanalyses throughout the reanalysis period. In the stratosphere, we found larger discrepancies between reanalyses for the extra-tropical stratosphere during the Southern Hemisphere (SH) winter. Comparisons with radiosonde temperature revealed that JRA-55 has a smaller bias in temperature than the other reanalyses in the extra-tropical SH winter before 1979. Some issues in JRA-55 were also identified. The amplitude of equatorial waves and Madden–Julian oscillation in JRA-55 are weaker than in the other reanalyses. JRA-55 shows unrealistic strong cooling in South America and Australia, although the spatial distribution of the long-term temperature trends in JRA-55 is the closest to an observational dataset of global historical surface temperature.

A coupled data assimilation system for climate reanalysis

Abstract: A coupled data assimilation system has been developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), which is intended to be used for the production of global reanalyses of the recent climate. The system assimilates a wide variety of ocean and atmospheric observations and produces ocean–atmosphere analyses with a coupled model. Employing the coupled-model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate and, conversely, assimilation of an atmospheric observation affects the ocean state. This covariance between atmosphere and ocean induced by the analysis method is illustrated with simple numerical experiments. Realistic data assimilation experiments based on the global observing system are then used to assess the quality of the assimilation method. Comparison with an uncoupled system shows a mostly neutral impact overall, with slightly improved temperature estimates in the upper ocean and lower atmosphere. These preliminary results are considered of interest for the ongoing community efforts focusing on coupled data assimilations.

Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013

Abstract: . Long dynamical atmospheric reanalyses are widely used for climate studies, but data-assimilative reanalyses of ocean and sea ice in the Arctic are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. A 23-year reanalysis has been completed for the period 1991–2013 and is the multi-year physical product in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center (ARC MFC). This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region, in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near-surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system. In addition, statistics of the reduced centered random variables (RCRVs) confirm the reliability of the ensemble for most of the assimilated variables. Occasional discontinuities of these statistics are caused by the changes of the input data sets or the data assimilation settings, but the statistics remain otherwise stable throughout the reanalysis, regardless of the density of observations. Furthermore, no data type is severely less dispersed than the others, even though the lack of consistently reprocessed observation time series at the beginning of the reanalysis has proven challenging.

Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica

Abstract: The glaciers within the Amundsen Sea Embayment (ASE), West Antarctica, are amongst the most rapidly retreating in Antarctica. Meteorological reanalysis products are widely used to help understand and simulate the processes causing this retreat. Here we provide an evaluation against observations of four of the latest global reanalysis products within the ASE region—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), Japanese 55-year Reanalysis (JRA-55), Climate Forecast System Reanalysis (CFSR), and Modern Era Retrospective-Analysis for Research and Applications (MERRA). The observations comprise data from four automatic weather stations (AWSs), three research vessel cruises, and a new set of 38 radiosondes all within the period 2009–2014. All four reanalyses produce 2 m temperature fields that are colder than AWS observations, with the biases varying from approximately −1.8°C (ERA-I) to −6.8°C (MERRA). Over the Amundsen Sea, spatially averaged summertime biases are between −0.4°C (JRA-55) and −2.1°C (MERRA) with notably larger cold biases close to the continent (up to −6°C) in all reanalyses. All four reanalyses underestimate near-surface wind speed at high wind speeds (>15 m s−1) and exhibit dry biases and relatively large root-mean-square errors (RMSE) in specific humidity. A comparison to the radiosonde soundings shows that the cold, dry bias at the surface extends into the lower troposphere; here ERA-I and CFSR reanalyses provide the most accurate profiles. The reanalyses generally contain larger temperature and humidity biases, (and RMSE) when a temperature inversion is observed, and contain larger wind speed biases (~2 to 3 m s−1), when a low-level jet is observed.

Assessment of summer monsoon precipitation derived from five reanalysis datasets over East Asia

Abstract: Precipitation, which is the predominant component of the East Asian summer monsoon (EASM), may have large uncertainties among reanalysis datasets. We comprehensively evaluate the performance of five reanalysis datasets in reproducing the EASM precipitation. These datasets are NCEP/NCAR Reanalysis 1 project (NCEP1), NCEP/US Department of Energy Atmospheric Model Intercomparison Project II reanalysis (NCEP2), Japanese 25-year Reanalysis project (JRA-25), Interim ECMWF Reanalysis (ERA-Interim), and Modern Era Retrospective-analysis for Research and Applications (MERRA). Results show that the five reanalysis datasets can generally reproduce the climatology and interannual variability of EASM precipitation. Especially, MERRA and ERA-Interim have the highest skills. Considering different-class precipitation, large uncertainties exist in the category of non-rainfall and heavy rainfall. The five reanalysis datasets overestimate the non-rainfall frequency, and JRA-25 and NCEP2 overestimate the heavy rainfall frequency. The well-known interdecadal variation around the mid-1990s can also be better depicted by ERA-Interim and MERRA. For the linear trend of precipitation, only MERRA can reasonably reproduce the increasing tendency over southern China and the western Pacific and the decreasing tendency over the Indo-China Peninsula. Based on EOF analysis, the spatial–temporal structure of EASM precipitation has been examined. MERRA, NCEP1 and ERA-Interim can better capture both the spatial patterns and principle components of the first two EOF modes. Based on our evaluation, the preferential reanalysis datasets for investigating the EASM precipitation are ERA-Interim and MERRA, which also permit the more precise investigation of interannual to decadal variability.

Evaluation of the CMCC eddy‐permitting global ocean physical reanalysis system (C‐GLORS, 1982–2012) and its assimilation components

Abstract: Ocean reanalyses are data assimilative simulations, aimed at estimating the four-dimensional state of the ocean over long periods, in a way as consistent over time as possible. They are designed for a wide range of climate applications, such as climate monitoring and low-frequency variability studies, along with several downstream applications (e.g. biogeochemical and fishery modelling, initial conditions for long-range coupled predictions, regional model nesting). An upgraded version of the Euro-Mediterranean Center for Climate Change (CMCC) eddy-permitting global ocean reanalysis, named CMCC Global Ocean Reanalysis System (C-GLORS) version 4, was recently released. The reanalysis covers the meteorological satellite era (1982–2012). This article details the configuration of the reanalysis system and provides an extensive validation, focusing on the evaluation of main indexes related to climate monitoring. Cumulative denial experiments are also conducted, in order to understand the relative impact of assimilation components included in C-GLORS (i.e. altimetric data, variational assimilation, bias correction and surface nudging). Results indicate that C-GLORS proves reliable in simulating long-term means, heat and freshwater trends, sea-level variability, mean surface circulation and transports, eddy variability and meridional overturning circulation and its associated heat transport, except for a few specific issues (overestimation of volume transports in the Southern Ocean and slight underestimation of the Atlantic ocean meridional overturning circulation and associated heat transport, the latter mostly linked to underestimation of western boundary northward transports). The results also demonstrate the complementarity of the assimilation components, all improving verification skill scores, for example the importance of the variational assimilation for the simulation of the reanalysis small-scale variability, the importance of the bias-correction scheme for correcting subsurface salinity errors or the role of surface nudging in driving the North Atlantic ocean circulation. The analyses presented here offer ideas for improving C-GLORS further and for the requirements of next-generation ocean reanalysis systems.

A high‐resolution regional reanalysis for Europe. Part 1: Three‐dimensional reanalysis with the regional HIgh‐Resolution Limited‐Area Model (HIRLAM)

Abstract: A high-resolution regional reanalysis for Europe. Part 1 : Three-dimensional reanalysis with the regional HIgh-Resolution Limited-Area Model (HIRLAM)

Depiction of drought over sub-Saharan Africa using reanalyses precipitation data sets

Abstract: Reanalysis precipitation is routinely used as a surrogate for observations due to its high spatial and temporal resolution and global coverage, and it has been widely used in hydrologic and agricultural applications. The resultant product and analysis are largely dependent on the accuracy of the reanalysis precipitation data set. In this study, we analyze the impact of reanalysis precipitation uncertainties on drought depiction. Five reanalyses precipitation data sets (Climate Forecast System Reanalysis, R1: National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research reanalysis, R2: NCEP/Department of Energy reanalysis II, 20CR: The Twentieth Century Reanalysis version 2 and Modern-Era Retrospective Analysis for Research and Applications) are evaluated from 1979 to 2012 over sub-Saharan Africa against an observational-based reference: The Princeton Global Meteorological Forcing data set. Results show that all the reanalyses precipitation data sets provide a relatively good representation of the long-term statistics of spatiotemporal drought characteristics in sub-Saharan Africa. However, deficiencies are found in the estimation of drought severity, the spatial pattern, and temporal persistency of droughts. Drought depiction over central Africa appears more problematic due to a lack of observational data. A comparison of drought depiction based on the Standardized Precipitation Index reveals higher monthly to seasonal precipitation variability that further increases after ~1999 due to changes in the observations included in the reanalyses assimilation systems.

A high-resolution regional reanalysis for Europe. Part 2: 2D analysis of surface temperature, precipitation and wind

Abstract: The set-up and performance of the regional reanalysis for Europe with the HIgh-Resolution Limited-Area Model (HIRLAM) to a 3D grid-mesh with 22 km resolution for the years 1989–2010 have been presented in Part 1. This part describes how the 3D dataset is further downscaled and used as input for an analysis of a number of surface-related parameters: 2 m temperature, minimum and maximum daily temperatures, 10 m wind, and daily precipitation. The analysis is done on a 2D grid-mesh with 5 km grid spacing using the MESoscale ANalysis system (MESAN) for temperature and precipitation and a dynamical adaptation method (DYNAD) for the 10 m wind. Results from MESAN and DYNAD are compared with observations and the HIRLAM 3D-Var reanalysis. A couple of cases with severe weather are studied to illustrate how such events are represented in the analyses. The comparisons show statistically significant added value in comparison to the HIRLAM reanalysis.

Major improvement of altimetry sea level estimations using pressure-derived corrections based on ERA-Interim atmospheric reanalysis

Abstract: . The new dynamic atmospheric correction (DAC) and dry tropospheric (DT) correction derived from the ERA-Interim meteorological reanalysis have been computed for the 1992–2013 altimeter period. Using these new corrections significantly improves sea level estimations for short temporal signals ( Concerning more recent missions (Jason-1, Jason-2, and Envisat), results are very similar between ERA-Interim and ECMWF-based corrections: on average for the global ocean, the operational DAC becomes slightly better than DAC_ERA only from the year 2006, likely due to the switch of the operational forcing to a higher spatial resolution. At regional scale, both DACs are similar in the deep ocean but DAC_ERA raises the residual crossovers' variance in some shallow water regions, indicating a slight degradation in the most recent years of the study. In the second decade of altimetry, unexpectedly DT_ERA still gives better results compared to the operational DT. Concerning climate signals, both DAC_ERA and DT_ERA have a low impact on global mean sea level rise (MSL) trends, but they can have a strong impact on long-term regional trends' estimation, up to several millimeters per year locally.

On the ambiguous nature of the 11 year solar cycle signal in upper stratospheric ozone

Abstract: Up to now our understanding of the 11year ozone solar cycle signal (SCS) in the upper stratosphere has been largely based on the Stratospheric Aerosol and Gas Experiment (SAGE) II (v6.2) data record, which indicated a large positive signal which could not be reproduced by models, calling into question our understanding of the chemistry of the upper stratosphere. Here we present an analysis of new v7.0 SAGE II data which shows a smaller upper stratosphere ozone SCS, due to a more realistic ozone-temperature anticorrelation. New simulations from a state-of-art 3-D chemical transport model show a small SCS in the upper stratosphere, which is in agreement with SAGE v7.0 data and the shorter Halogen Occultation Experiment and Microwave Limb Sounder records. However, despite these improvements in the SAGE II data, there are still large uncertainties in current observational and meteorological reanalysis data sets, so accurate quantification of the influence of solar flux variability on the climate system remains an open scientific question.

Design and validation of MEDRYS, a Mediterranean Sea reanalysis over the period 1992–2013

Abstract: The French research community in the Mediter-ranean Sea modeling and the French operational ocean forecasting center Mercator Ocean have gathered their skill and expertise in physical oceanography, ocean modeling, atmospheric forcings and data assimilation to carry out a MEDiterranean sea ReanalYsiS (MEDRYS) at high resolution for the period 1992–2013. The ocean model used is NEMOMED12, a Mediterranean configuration of NEMO with a 1/12 • (∼ 7 km) horizontal resolution and 75 vertical z levels with partial steps. At the surface, it is forced by a new atmospheric-forcing data set (ALDERA), coming from a dynamical downscaling of the ERA-Interim atmospheric reanalysis by the regional climate model ALADIN-Climate with a 12 km horizontal and 3 h temporal resolutions. This configuration is used to carry a 34-year hindcast simulation over the period 1979–2013 (NM12-FREE), which is the initial state of the reanalysis in October 1992. MEDRYS uses the existing Mercator Ocean data assimilation system SAM2 that is based on a reduced-order Kalman filter with a three-dimensional (3-D) multivariate modal decomposition of the forecast error. Altimeter data, satellite sea surface temperature (SST) and temperature and salinity vertical profiles are jointly assimilated. This paper describes the configuration we used to perform MEDRYS. We then validate the skills of the data assimilation system. It is shown that the data assimilation restores a good average temperature and salinity at intermediate layers compared to the hindcast. No particular biases are identified in the bottom layers. However, the re-analysis shows slight positive biases of 0.02 psu and 0.15 • C above 150 m depth. In the validation stage, it is also shown that the assimilation allows one to better reproduce water, heat and salt transports through the Strait of Gibraltar. Finally , the ability of the reanalysis to represent the sea surface high-frequency variability is shown.

Estimation of the Ocean Water Albedo From Remote Sensing and Meteorological Reanalysis Data

Abstract: Ocean water albedo (OWA) plays an important role in the global climate variation. Compared with the achievements in land surface albedo studies, the global distributions of ocean water and sea ice albedo are seldom addressed. This study designed an operational global OWA algorithm based on the three-component reflectance model of the ocean water: sun glint, whitecaps, and water-leaving reflectance. The related achievements in these three areas are reviewed and integrated into the operational algorithm. After the sensitive analysis, the algorithm is compared with previous studies and validated with ground observations at COVE site located 25 km east of Virginia Beach (36.91° N, 75.71° W), and the results indicate that the proposed algorithm is generally consistent with previous parameterization scheme. As an example, the global OWAs in summer and winter 2011 are generated using the remote sensing reflectance data sets via the Moderate Resolution Imaging Spectroradiometer and Modern-Era Retrospective analysis for Research and Applications meteorological reanalysis data set. The generated product includes instantaneous (e.g., local noon) and daily mean OWAs under both clear-sky and white-sky conditions. Upon the examples, the local noon clear-sky OWA shows a significant latitude variation due to the dominance of the solar angle, whereas the white-sky OWA is sensitive to wind speeds and optical constituents. The global distribution of the daily mean OWA exhibits a similar trend to the local noon OWA. However, the daily mean clear-sky OWA is significantly larger than the local noon OWA; this finding should be noted when using OWA products for energy balance research. Additionally, all forms of OWA products exhibit increase in coastal areas with high input of terrestrial matters.

Effects of all‐sky assimilation of GCOM‐W/AMSR2 radiances in the ECMWF numerical weather prediction system

Abstract: This paper assesses the impact of Advanced Microwave Scanning Radiometer-2 (AMSR2) radiances in the all-sky assimilation of the European Centre for Medium-Range Weather Forecasts (ECMWF). Individual impacts of three microwave imagers including AMSR2 were examined by using a baseline experiment that had no microwave imager data. The three microwave imagers brought similar improvements in humidity, temperature, and wind first guess (FG) fields in the troposphere. Improvements were found both in fits to analyses and to other observations. Moreover, significant improvements of wind and geopotential height fields in the troposphere were found in day 3 to day 6 forecasts. AMSR2 made larger improvements than other microwave imagers to geopotential height forecasts in the northern hemisphere. The use of AMSR2 radiance data in addition to the other existing microwave imagers gave mixed results. Consistent improvements in the FG fit to humidity observations were confirmed. However, the FG fit for several channels of the microwave temperature sounding instruments was degraded. The mean FG departure of AMSR2 showed biases that varied according to time of day and meteorological conditions. The causes of the biases were identified as insufficient representation of cloud liquid water path (LWP) in the forecast model under unstable conditions and insufficient amplitude of LWP diurnal variation in stratocumulus areas in the tropics. The assimilation of too much biased data might start to bring negative effects for the analyses and forecasts, which for some parameters could outweigh the improvements in the assimilation. However despite this AMSR2 brought significant improvements of the geopotential height field in the southern hemisphere lower troposphere for day 5 to day 7 forecasts.

Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model

Abstract: This paper studies the problem of the assimilation of precipitable water vapor (PWV), estimated by synthetic aperture radar interferometry, using the Weather Research and Forecast Data Assimilation model 3-D variational data assimilation system. The experiment is designed to assess the impact of the PWV assimilation on the hydrometers and the rainfall predictions during 12 h after the assimilation time. A methodology to obtain calibrated maps of PWV and estimated their precision is also presented. The forecasts are compared with GPS estimates of PWV and with rainfall observations from a meteorological radar. Results show that after data assimilation, there is a correction of the bias in the PWV prediction and an improvement in the prediction of the weak to moderate rainfall up to 9 h after the assimilation time.

Comparison between Total Cloud Cover in Four Reanalysis Products and Cloud Measured by Visual Observations at U.S. Weather Stations

Abstract: A homogeneity-adjusted dataset of total cloud cover from weather stations in the contiguous United States is compared with cloud cover in four state-of-the-art global reanalysis products: the Climate Forecast System Reanalysis from NCEP, the Modern-Era Retrospective Analysis for Research and Applications from NASA, ERA-Interim from ECMWF, and the Japanese 55-year Reanalysis Project from the Japan Meteorological Agency. The reanalysis products examined in this study generally show much lower cloud amount than visual weather station data, and this underestimation appears to be generally consistent with their overestimation of downward surface shortwave fluxes when compared with surface radiation data from the Surface Radiation Network. Nevertheless, the reanalysis products largely succeed in simulating the main aspects of interannual variability of cloudiness for large-scale means, as measured by correlations of 0.81–0.90 for U.S. mean time series. Trends in the reanalysis datasets for the U.S. mean...

Evaluation of landfalling atmospheric rivers along the U.S. West Coast in reanalysis data sets

Abstract: An intercomparison of landfalling atmospheric rivers (ARs) between four reanalysis data sets using one satellite-derived AR detection method as a metric to characterize landfalling atmospheric rivers (ARs) along the U.S. West Coast is performed over 15 cool seasons (October–March) during the period from water years 1998 to 2012. The four reanalysis data sets analyzed in this study are the Climate System Forecast Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ERA-Interim (ERA-I), and the Twentieth Century Reanalysis version 2 (20CR) data set. The Atmospheric River Detection Tool is used to identify AR features in the total vertically integrated water vapor (IWV) data of the reanalysis data, and validation of the reanalysis AR data are compared with AR data derived from satellite IWV observations. The AR landfall data from reanalysis were generally found to be in good agreement with satellite observations. Reanalysis data with less (CFSR) or no assimilation (20CR) of the satellite data used in this study had greater bias with AR characteristics such as IWV, width, and landfall location. The 20CR ensemble data were found to better characterize the AR landfall characteristics than the 20CR ensemble mean although all 20CR data underestimated AR landfalls particularly in the southern section of the U.S. West Coast. Overall AR landfall detections for the 15 year cool season period were within 5% of the satellite for the CFSR, MERRA, and ERA-I data.

Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989-2013

Abstract: A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data. Keywords: data assimilation, physical oceanography, Baltic Sea, reanalysis (Published: 11 March 2016) Citation: Tellus A 2016, 68, 24220, http://dx.doi.org/10.3402/tellusa.v68.24220

Inter-comparison of stratospheric mean-meridional circulation and eddy mixing among six reanalysis data sets

Abstract: . The stratospheric mean-meridional circulation (MMC) and eddy mixing are compared among six meteorological reanalysis data sets: NCEP-NCAR, NCEP-CFSR, ERA-40, ERA-Interim, JRA-25, and JRA-55 for the period 1979–2012. The reanalysis data sets produced using advanced systems (i.e., NCEP-CFSR, ERA-Interim, and JRA-55) generally reveal a weaker MMC in the Northern Hemisphere (NH) compared with those produced using older systems (i.e., NCEP/NCAR, ERA-40, and JRA-25). The mean mixing strength differs largely among the data products. In the NH lower stratosphere, the contribution of planetary-scale mixing is larger in the new data sets than in the old data sets, whereas that of small-scale mixing is weaker in the new data sets. Conventional data assimilation techniques introduce analysis increments without maintaining physical balance, which may have caused an overly strong MMC and spurious small-scale eddies in the old data sets. At the NH mid-latitudes, only ERA-Interim reveals a weakening MMC trend in the deep branch of the Brewer–Dobson circulation (BDC). The relative importance of the eddy mixing compared with the mean-meridional transport in the subtropical lower stratosphere shows increasing trends in ERA-Interim and JRA-55; this together with the weakened MMC in the deep branch may imply an increasing age-of-air (AoA) in the NH middle stratosphere in ERA-Interim. Overall, discrepancies between the different variables and trends therein as derived from the different reanalyses are still relatively large, suggesting that more investments in these products are needed in order to obtain a consolidated picture of observed changes in the BDC and the mechanisms that drive them.

Towards a probabilistic regional reanalysis system for Europe: evaluation of precipitation from experiments

Abstract: A new development in the field of reanalyses is the incorporation of uncertainty estimation capabilities. We have developed a probabilistic regional reanalysis system for the CORDEX-EUR11 domain that is based on the numerical weather prediction model COSMO at a 12-km grid spacing. The lateral boundary conditions of all ensemble members are provided by the global reanalysis ERA-Interim. In the basic implementation of the system, uncertainties due to observation errors are estimated. Atmospheric assimilation of conventional observations perturbed by means of random samples of observation error yields estimates of the reanalysis uncertainty conditioned to observation errors. The data assimilation employed is a new scheme based on observation nudging that we denote ensemble nudging. The lower boundary of the atmosphere is regularly updated by external snow depth, sea surface temperature and soil moisture analyses. One of the most important purposes of reanalyses is the estimation of so-called essential climate variables. For regional reanalyses, precipitation has been identified as one of the essential climate variables that are potentially better represented than in other climate data sets. For that reason, we assess the representation of precipitation in our system in a pilot study. Based on two experiments, each of which extends over one month, we conduct a preliminary comparison to the global reanalysis ERA-Interim, a dynamical downscaling of the latter and the high-resolution regional reanalysis COSMO-REA6. In a next step, we assess our reanalysis system’s probabilistic capabilities versus the ECMWF-EPS in terms of six-hourly precipitation sums. The added value of our probabilistic regional reanalysis system motivates the current production of a 5-year-long test reanalysis COSMO-EN-REA12 in the framework of the FP7-funded project Uncertainties in Ensembles of Regional Re-Analyses (UERRA). Keywords: climate dynamics, essential climate variables, ensemble data assimilation, CORDEX, uncertainty estimation, precipitation (Published: 21 November 2016) Citation: Tellus A 2016, 68, 32209, http://dx.doi.org/10.3402/tellusa.v68.32209

A First Evaluation of ERA-20CM over China

Abstract: As an important global data resource, reanalysis is widely applied for climate impact studies of the past several decades. For the first time, monthly mean temperature and monthly total precipitation derived from the newest generation reanalysis product—the ECMWF twentieth-century reanalysis dataset (ERA-20CM)—is quantitatively evaluated based on probability density functions and 702 meteorological stations during the period of 1960–2009 across China. This study attempts to investigate how well each member ensemble prediction of ERA-20CM performs for different regions. Generally, all ensemble predictions in ERA-20CM are able to recreate the real conditions on a comparable level. More than 90% of the observed probability for temperature and more than 80% of the probabilities for precipitation could be captured by ERA-20CM over China. However, the performance changes significantly from region to region because of different topographical features and climate characteristics. The Tibetan Plateau is th...

Carbon monoxide climatology derived from the trajectory mapping of globalMOZAIC-IAGOS data

Abstract: . A three-dimensional gridded climatology of carbon monoxide (CO) has been developed by trajectory mapping of global MOZAIC-IAGOS in situ measurements from commercial aircraft data. CO measurements made during aircraft ascent and descent, comprising nearly 41 200 profiles at 148 airports worldwide from December 2001 to December 2012, are used. Forward and backward trajectories are calculated from meteorological reanalysis data in order to map the CO measurements to other locations and so to fill in the spatial domain. This domain-filling technique employs 15 800 000 calculated trajectories to map otherwise sparse MOZAIC-IAGOS data into a quasi-global field. The resulting trajectory-mapped CO data set is archived monthly from 2001 to 2012 on a grid of 5° longitude × 5° latitude × 1 km altitude, from the surface to 14 km altitude. The mapping product has been carefully evaluated, firstly by comparing maps constructed using only forward trajectories and using only backward trajectories. The two methods show similar global CO distribution patterns. The magnitude of their differences is most commonly 10 % or less and found to be less than 30 % for almost all cases. Secondly, the method has been validated by comparing profiles for individual airports with those produced by the mapping method when data from that site are excluded. While there are larger differences below 2 km, the two methods agree very well between 2 and 10 km with the magnitude of biases within 20 %. Finally, the mapping product is compared with global MOZAIC-IAGOS cruise-level data, which were not included in the trajectory-mapped data set, and with independent data from the NOAA aircraft flask sampling program. The trajectory-mapped MOZAIC-IAGOS CO values show generally good agreement with both independent data sets. Maps are also compared with version 6 data from the Measurements Of Pollution In The Troposphere (MOPITT) satellite instrument. Both data sets clearly show major regional CO sources such as biomass burning in Central and southern Africa and anthropogenic emissions in eastern China. While the maps show similar features and patterns, and relative biases are small in the lowermost troposphere, we find differences of ∼ 20 % in CO volume mixing ratios between 500 and 300 hPa. These upper-tropospheric biases are not related to the mapping procedure, as almost identical differences are found with the original in situ MOZAIC-IAGOS data. The total CO trajectory-mapped MOZAIC-IAGOS column is also higher than the MOPITT CO total column by 12–16 %. The data set shows the seasonal CO cycle over different latitude bands and altitude ranges as well as long-term trends over different latitude bands. We observe a decline in CO over the northern hemispheric extratropics and the tropics consistent with that reported by previous studies using other data sources. We anticipate use of the trajectory-mapped MOZAIC-IAGOS CO data set as an a priori climatology for satellite retrieval and for air quality model validation and initialization.