An Evaluation of Surface Atmospheric Changes over the Arctic Ocean for 2000–09 Using Recent Reanalyses

Inter-comparison of extra-tropical cyclone activity in nine reanalysis datasets

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.

At the National Centers for Environmental Prediction (NCEP), a reanalysis of the atmosphere, ocean, sea ice and land over the period 1979–2009, referred to as the climate forecast system reanalysis (CFSR), was recently completed. The oceanic component of CFSR includes many advances: (a) the MOM4 ocean model with an interactive sea-ice, (b) the 6 h coupled model forecast as the first guess, (c) inclusion of the mean climatological river runoff, and (d) high spatial (0.5° × 0.5°) and temporal (hourly) model outputs. Since the CFSR will be used by many in initializing/validating ocean models and climate research, the primary motivation of the paper is to inform the user community about the saline features in the CFSR ocean component, and how the ocean reanalysis compares with in situ observations and previous reanalysis. The net ocean surface heat flux of the CFSR has smaller biases compared to the sum of the latent and sensible heat fluxes from the objectively analyzed air-sea fluxes (OAFlux) and the shortwave and longwave radiation fluxes from the International Satellite Cloud Climatology Project (ISCCP-FD) than the NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2) in both the tropics and extratropics. The ocean surface wind stress of the CFSR has smaller biases and higher correlation with the ERA40 produced by the European Centre for Medium-Range Weather Forecasts than the R1 and R2, particularly in the tropical Indian and Pacific Ocean. The CFSR also has smaller errors compared to the QuickSCAT climatology for September 1999 to October 2009 than the R1 and R2. However, the trade winds of the CFSR in the central equatorial Pacific are too strong prior to 1999, and become close to observations once the ATOVS radiance data are assimilated in late 1998. A sudden reduction of easterly wind bias is related to the sudden onset of a warm bias in the eastern equatorial Pacific temperature around 1998/1999. The sea surface height and top 300 m heat content (HC300) of the CFSR compare with observations better than the GODAS in the tropical Indian Ocean and extratropics, but much worse in the tropical Atlantic, probably due to discontinuity in the deep ocean temperature and salinity caused by the six data streams of the CFSR. In terms of climate variability, the CFSR provides a good simulation of tropical instability waves and oceanic Kelvin waves in the tropical Pacific, and the dominant modes of HC300 that are associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation and Atlantic Meridional Overturning Circulation.

Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM‐KWAJEX (Tropical Rainfall Measuring Mission‐Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55‐year Reanalysis (JRA‐55), Climate Forecast System Reanalysis (CFSR), and Modern‐Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid‐scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.

Solar radiation plays an important role in the cryospheric water cycle, especially in alpine regions. This study presents an evaluation of the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA2), ERA5, High Asia Refined analysis version 2 (HAR v2), JRA-55, and National Centers for Environmental Prediction/Climate Forecast System Reanalysis datasets at different time scales by comparing observed datasets from July 2010 to December 2015 at 4,550 m in the Laohugou Basin. In terms of shortwave radiation, ERA5 performs significantly better than the other reanalysis radiation datasets. For downward shortwave radiation, HAR v2 performs better than ERA5 on only two timescales, 3 months and half-year, with mean absolute errors (MAEs) of 13.28 and 7.96 w/m2. The upward shortwave radiation, ERA5, outperforms the other reanalysis datasets on all 12 timescales. For downward longwave radiation, ERA5 also performs significantly better, with only MERRA2 outperforming ERA5 on the daily scale and annual scale, with R2, bias, root mean square error, and MAE of 0.6, 0.95, −9.51 w/m2, −9.41 w/m2, 34.98 w/m2, 9.46 w/m2, and 27.52 w/m2, 9.41 w/m2, respectively. In the upward longwave radiation, HAR v2 performs better than the other reanalysis datasets on all timescales, except for ERA5, which has a better R2 of 0.92 on the annual scale. All the reanalysis datasets can show the variation trend of the four radiation parameters in different seasons and achieve a better performance in winter. Therefore, ERA5 is recommended for regions without shortwave radiation observations, and HAR v2 and ERA5 are recommended for longwave radiation simulations. Although there are obvious shortcomings in the reanalysis radiation datasets, they still provide important supplementary information for research in high-altitude areas, where the observed datasets are too sparse.

Alaska is experiencing effects of global climate change that are due, in large part, to the positive feedback mechanisms associated with polar amplification. The major risk factors include loss of sea ice and glaciers, thawing permafrost, increased wildfires, and ocean acidification. Reanalyses, integral to understanding mechanisms of Alaska’s past climate and to helping to calibrate modeling efforts, are based on the output of weather forecast models that assimilate observations. This study evaluates temperature and precipitation from five reanalyses at monthly and daily time scales for the period 1979–2009. Monthly data are evaluated spatially at grid points and for six climate zones in Alaska. In addition, daily maximum temperature, minimum temperature, and precipitation from reanalyses are compared with meteorological-station data at six locations. The reanalyses evaluated in this study include the NCEP–NCAR reanalysis (R1), North American Regional Reanalysis (NARR), Climate Forecast System Reanalysis (CFSR), ERA-Interim, and the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Maps of seasonal bias and standard deviation, constructed from monthly data, show how the reanalyses agree with observations spatially. Cross correlations between the monthly gridded and daily station time series are computed to provide a measure of confidence that data users can assume when selecting reanalysis data in a region without many surface observations. A review of natural hazards in Alaska indicates that MERRA is the top reanalysis for wildfire and interior-flooding applications. CFSR is the recommended reanalysis for North Slope coastal erosion issues and, along with ERA-Interim, for heavy precipitation in southeastern Alaska.

We compare and examine diurnal temperature tides including their migrating component (DW1) from the troposphere to the lower mesosphere, using data from Thermosphere‐Ionosphere‐Mesosphere‐Energetics and Dynamics/Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER) and from six different reanalysis data sets: (1) the Modern Era Retrospective analysis for Research and Applications (MERRA), (2) the European Centre for Medium‐range Weather Forecasts (ECMWF) reanalysis (ERA‐Interim) (3) the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), (4) the Japanese 25‐year reanalysis by Japanese Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI) (JRA25), (5) the NCEP/National Center for Atmospheric Research reanalysis (NCEP1), and (6) the NCEP and Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP‐II) reanalysis data (NCEP2). The horizontal and vertical structures of the diurnal tides in SABER and reanalyses reasonably agree, although the amplitudes are up to 30–50% smaller in the reanalyses than in the SABER in the upper stratosphere to lower mesosphere. Of all tidal components, the DW1 is dominant while a clear eastward propagating zonal wave number 3 component (DE3) is observed at midlatitudes of the Southern Hemisphere in winter. Among the six reanalyses, MERRA, ERA‐Interim and CFSR are better at reproducing realistic diurnal tides. It is found that the diurnal tides extracted from SABER data in the winter‐hemisphere stratosphere suffer from sampling issues that are caused by short‐term variations of the background temperature. In addition, the GSWM underestimates the amplitude in the midlatitude upper stratosphere by about 50%.

The National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) is initialized four times per day (0000, 0600, 1200, and 1800 UTC). NCEP upgraded their operational CFS to version 2 on March 30, 2011. This is the same model that was used to create the NCEP Climate Forecast System Reanalysis (CFSR), and the purpose of this dataset is to extend CFSR. The 6-hourly atmospheric, oceanic and land surface analyzed products and forecasts, available at 0.2, 0.5, 1.0, and 2.5 degree horizontal resolutions, are archived here beginning with January 1, 2011 as an extension of CFSR. The RDA is not archiving any of the CFS seasonal forecasts. For more information about CFS, please see http://cfs.ncep.noaa.gov/ [http://cfs.ncep.noaa.gov/].

The Los Alamos sea ice model (CICE) is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction. The prescribed atmospheric forcings to drive CICE are from the NCEP Climate Forecast System Reanalysis (CFSR). A built-in mixed layer ocean model in CICE is used. Initial conditions for the sea ice and the mixed layer ocean are also from CFSR in the control experiments. The simulated sea ice extent is generally in good agreement with observations in the warm season at all lead times up to 12 months both in the Arctic and Antarctic, suggesting that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the Arctic sea ice thickness forecast has a positive bias stemming from the initial conditions, and this bias often persists for more than a season, limiting the model’s seasonal forecast skill. When this bias is reduced by initializing ice thickness using the CryoSat-2 satellite observations while keeping all other initial fields unchanged in the CS2_IC experiments, both simulated ice edge and thickness improve. This confirms the important roles of sea ice thickness initialization in sea ice seasonal prediction seen in many studies.

The Los Alamos sea ice model (CICE) is being tested in standalone mode for its suitability for seasonal time scale prediction. The prescribed atmospheric forcings to drive the model are from the NCEP Climate Forecast System Reanalysis (CFSR). A built-in mixed layer ocean model in CICE is used. Initial conditions for the sea ice and the mixed layer ocean in the control experiments are also from CFSR. The simulated sea ice extent in the Arctic in control experiments is generally in good agreement with observations in the warm season at all lead times up to 12 months, suggesting that CICE is able to provide useful ice edge information for seasonal prediction. However, the ice thickness forecast has a positive bias stemming from the initial conditions and often persists for more than a season, limiting the model’s seasonal forecast skill. In addition, thicker ice has a lower melting rate in the warm season, both at the bottom and top, contributing to this positive bias. When this bias is removed by initializing the model using ice thickness data from satellite observations while keeping all other initial fields unchanged, both simulated ice edge and thickness improve. This indicates the important role of ice thickness initialization in sea ice seasonal prediction.

Event Abstract Back to Event Spatial and temporal seasonal trends in the major global upwelling areas during the last 30 years Rubén V. Rodríguez1*, Ramón G. Gesteira1, Francisco S. González2, Maite D. Rodríguez1 and Ines Á. Fernández1 1 Vigo university, Spain 2 Aveiro University, Portugal Winds at the ocean surface are a key element in the Earth system having a great impact on many economic activities and becoming really important to several oceanographic and atmospheric applications. Thus, wind above sea surface constitutes a critical parameter to analyze coastal phenomena as upwelling events. Upwelling regions are areas of high productivity compared to other areas of the ocean. In fact, over 20% of global fish catches occur in upwelling areas, although these areas occupy less than 1% of the surface of the world's oceans. Therefore, accurate surface wind datasets are needed to better analyze these processes. In this study wind data obtained from NCEP Climate Forecast System Reanalysis (CFSR) database was used to analyse the four most important upwelling systems (Benguela, Canarias, Peru and California). Wind data are available for the whole world at a spatial resolution of 0.3 x 0.3 degrees and a temporal resolution of 6h from January 1982 to December 2010. Alongshore wind stress was considered to analyse variations in upwelling intensity and trends were analysed on these upwelling regions. Comparison with previous studies showed the highly dependence of the results of the length of the time series, the selected areas, the season considered for the analysis and the resolution of the used database. Keywords: CFSR, wind stress, upwelling, East Pacific coast, East Atlantic coast, upwelling trends Conference: IMMR | International Meeting on Marine Research 2014, Peniche, Portugal, 10 Jul - 11 Jul, 2014. Presentation Type: Poster Presentation Topic: BIODIVERSITY, CONSERVATION AND COASTAL MANAGEMENT Citation: Rodríguez RV, Gesteira RG, González FS, Rodríguez MD and Fernández IÁ (2014). Spatial and temporal seasonal trends in the major global upwelling areas during the last 30 years. Front. Mar. Sci. Conference Abstract: IMMR | International Meeting on Marine Research 2014. doi: 10.3389/conf.fmars.2014.02.00099 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Apr 2014; Published Online: 18 Jul 2014. * Correspondence: Mr. Rubén V Rodríguez, Vigo university, Ourense, Spain, ruvarela@uvigo.es Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Rubén V Rodríguez Ramón G Gesteira Francisco S González Maite D Rodríguez Ines Á Fernández Google Rubén V Rodríguez Ramón G Gesteira Francisco S González Maite D Rodríguez Ines Á Fernández Google Scholar Rubén V Rodríguez Ramón G Gesteira Francisco S González Maite D Rodríguez Ines Á Fernández PubMed Rubén V Rodríguez Ramón G Gesteira Francisco S González Maite D Rodríguez Ines Á Fernández Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The atmospheric moisture budget from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) is evaluated in polar regions for the period 1979–2005 and compared with previous estimates, accumulation syntheses over polar ice sheets, and in situ Arctic precipitation observations. The system is based on a nonspectral background model and utilizes the incremental analysis update scheme. The annual moisture convergence from MERRA for the north polar cap is comparable to previous estimates using 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) and earlier reanalyses but it is more than 50% larger than MERRA precipitation minus evaporation (P − E) computed from physics output fields. This imbalance is comparable to earlier reanalyses for the Arctic. For the south polar cap, the imbalance is 20%. The MERRA physics output fields are also found to be overly sensitive to changes in the satellite observing system, particularly over data-sparse regions of the Southern Ocean. Comparisons between MERRA and prognostic fields from two contemporary reanalyses yield a spread of values from 6% of the mean over the Antarctic Ice Sheet to 61% over a domain of the Arctic Ocean. These issues highlight continued problems associated with the representation of cold-climate physical processes in global data assimilation models. The distribution of MERRA surface fluxes over the major polar ice sheets emphasizes larger values along the coastal escarpments, which agrees more closely with recent assessments of ice sheet accumulation using regional models. Differences between these results and earlier assessments illustrate a continued ambiguity in the surface moisture flux distribution over Greenland and Antarctica. The higher spatial and temporal resolution as well as the availability of all budget components, including analysis increments in MERRA, offer prospects for an improved representation of the high-latitude water cycle in reanalyses.

The Indian summer monsoon rainfall is dominated by oceanic sources of moisture. However, land surface processes also have a significant role in the generation of precipitation within the Indian subcontinent. Evapotranspiration over a region supplies moisture to the atmosphere, which may lead to precipitation in the same region. This is known as recycled precipitation. The role of evapotranspiration as an additional source of moisture to precipitation has been investigated in earlier studies at continental scales; however, the amount of monsoon precipitation generated from evapotranspiration has not been quantified at the daily scale for the Indian subcontinent. To examine the role of land surface hydrology in regional precipitation and to quantify recycled precipitation, the dynamic recycling model at a daily scale with NCEP Climate Forecast System Reanalysis (CFSR) data for the period of 1980–2010 is used. A high precipitation recycling ratio, that is, the ratio of recycled precipitation to total precipitation, is found at the end of the monsoon (September). As the monsoon progresses in India, enhanced soil moisture and vegetation cover lead to increased evapotranspiration and recycled precipitation. The recycling ratio is highest (around 25%) in northeastern India, which has high vegetation cover leading to high evapotranspiration. Recycled precipitation over central and northeastern India in September is responsible for delaying the withdrawal of the summer monsoon over these regions. A trend analysis of recycled precipitation shows a statistically significant decreasing trend in northeastern India.

<p>Arctic sea ice has reduced significantly over recent decades and is projected to reduce further over this century. This has made the Arctic more accessible and increased opportunities for the expansion of business and industrial activities.  As a result, the exposure and risk of humans and infrastructure to extreme storms will increase in the Arctic.</p><p>Our understanding of the current risk from storms comes from analysing the past, for example, by using storm tracking algorithms to detect storms in reanalysis datasets.  However, there are multiple reanalysis datasets available from different institutions and there are multiple storm tracking methods.  Previous studies have found that there can be differences between reanalysis datasets and between storm tracking methods in the climatology of storms, particularly in mid-latitude regions rather than the Arctic.  In this study, we aimed to improve the understanding of Arctic storms by assessing their characteristics in multiple global reanalyses, the ECMWF-Interim Reanalysis (ERA-Interim), the 55-Year Japanese Reanalysis (JRA-55), the NASA-Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and the NCEP-Climate Forecast System Reanalysis (NCEP-CFSR), using the same storm tracking method based on 850 hPa relative vorticity and mean sea level pressure.</p><p>The results from this study show that there are no significant trends in Arctic storm characteristics between 1980-2017, even though the Arctic has undergone rapid change.  Although some similar Arctic storm characteristics are found between the reanalysis datasets, there are generally higher differences between the reanalyses in winter (DJF) than in summer (JJA).  In addition, substantial differences can arise between using the same storm tracking method based on 850 hPa relative vorticity or mean sea level pressure, which adds to the uncertainty associated with current Arctic storm characteristics.</p>

A single-model 16-member ensemble is used to investigate how external model factors can affect model performance. Ensemble members are constructed with the land surface model (LSM) Joint UK Land Environment Simulator (JULES), with different choices of meteorological forcing [in situ, NCEP Climate Forecast System Reanalysis (CFSR)/CFSv2, or Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] and ancillary datasets (in situ or remotely sensed), and with four time step modes. Effects of temporal averaging are investigated by comparing the hourly, daily, monthly, and seasonal ensemble performance against snow depth and water equivalent, soil temperature and moisture, and latent and sensible heat fluxes from one forest site and one clearing in the boreal ecozone of Finnish Lapland. Results show that meteorological data are the largest source of uncertainty; differences in ancillary data have little effect on model results. Although generally informative and representative, aggregated performance metrics fail to identify “right results for the wrong reasons”; to do so, scrutinizing of time series and of interactions between variables is necessary. Temporal averaging over longer intervals improves metrics—with the notable exception of bias, which increases—by reducing the effects of internal data and model variability on model response. Model evaluation during shoulder seasons (fall minus spring) identifies weaknesses in the reanalyses datasets that conventional seasonal performance (winter minus summer) neglects. In view of the importance of snow on the range of results obtained with the same model, let alone identical simulations using different temporal averaging, it is recommended that systematic evaluation, quantification of errors, and uncertainties in snow-covered regions be incorporated in future efforts to standardize evaluation methods of LSMs.