Meteorological Reanalysis Datasets Research Articles

Impact of the MJO on the boreal winter extratropical circulation

AbstractThe effect of the Madden‐Julian Oscillation (MJO) on the Northern Hemisphere wintertime stratospheric polar vortex is evaluated using a meteorological reanalysis data set and a modern atmospheric general circulation model. The MJO influences the tropospheric North Pacific; and in particular, it modulates the heat flux that is in phase with the climatological planetary waves in both the troposphere and stratosphere. The phase of the MJO in which convection is propagating into the tropical central Pacific leads to a weakened vortex, while suppressed MJO convection in this region is associated with a stronger vortex. Subsequently, the MJO modulates the phase of the tropospheric North Atlantic Oscillation (also known as the Arctic Oscillation or the Northern Annular Mode). While the responses in the model and in the reanalysis data differ in some respects, they both indicate that the MJO can remotely impact the extratropical tropospheric circulation via the stratosphere.

Climate-driven uncertainties in modeling terrestrial energy and water fluxes: a site-level to global-scale analysis

We used a land surface model constrained using data from flux tower sites, to analyze the biases in ecosystem energy and water fluxes arising due to the use of meteorological reanalysis datasets. Following site-level model calibration encompassing major vegetation types from the tropics to the northern high-latitudes, we repeated the site and global simulations using two reanalysis datasets: the NCEP/NCAR and the CRUNCEP. In comparison with the model simulations using observed meteorology from sites, the reanalysis-driven simulations produced several systematic biases in net radiation (Rn ), latent heat (LE), and sensible heat (H) fluxes. These include: (i) persistently positive tropical/subtropical biases in Rn using the NCEP/NCAR, and gradually transitioning to negative Rn biases in the higher latitudes; (ii) large positive H biases in the tropics/subtropics using the NCEP/NCAR; (iii) negative LE biases using the NCEP/NCAR above 40°N; (iv) high tropical LE using the CRUNCEP in comparison with observationally derived global estimates; and (v) flux-partitioning biases from canopy and ground components. Across vegetation types, we investigated the role of the meteorological drivers (shortwave and longwave radiation, atmospheric humidity, temperature, precipitation) and their seasonal biases in controlling these reanalysis-driven uncertainties. At the global scale, our site-level analysis explains several model-data differences in the LE and H fluxes when compared with observationally derived global estimates of these fluxes. Using our results, we discuss the implications of site-level model calibration on subsequent regional/global applications to study energy and hydrological processes. The flux-partitioning biases presented in this study have potential implications on the couplings among terrestrial carbon, energy, and water fluxes, and for the calibration of land-atmosphere parameterizations that are dependent on LE/H partitioning.

Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45kgCm(-2) yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman etal., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.

Biases in Reanalysis Snowfall Found by Comparing the JULES Land Surface Model to GlobSnow

Abstract Snow exerts a strong influence on weather and climate. Accurate representation of snow processes within models is needed to ensure accurate predictions. Snow processes are known to be a weakness of land surface models (LSMs), and studies suggest that more complex snow physics is needed to avoid early melt. In this study the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow) snow water equivalent and NASA’s “MOD10C1” snow cover products are used to assess the accuracy of snow processes within the Joint U.K. Land Environment Simulator (JULES). JULES is run “offline” from a general circulation model and so is driven by meteorological reanalysis datasets: “Princeton,” Water and Global Change–Global Precipitation Climatology Centre (WATCH–GPCC), and WATCH–Climatic Research Unit (CRU). This reveals that when the model achieves the correct peak accumulation, snow does not melt early. However, generally snow does melt early because peak accumulation is too low. Examination of the meteorological reanalysis data shows that not enough snow falls to achieve observed peak accumulations. Thus, the earlier studies’ conclusions may be as a result of weaknesses in the driving data, rather than in model snow processes. These reanalysis products “bias correct” precipitation using observed gauge data with an undercatch correction, overriding the benefit of any other datasets used in their creation. This paper argues that using gauge data to bias-correct reanalysis data is not appropriate for snow-affected regions during winter and can lead to confusion when evaluating model processes.

Implications of the North Atlantic Oscillation for a UK–Norway Renewable power system

UK wind-power capacity is increasing and new transmission links are proposed with Norway, where hydropower dominates the electricity mix. Weather affects both these renewable resources and the demand for electricity. The dominant large-scale pattern of Euro-Atlantic atmospheric variability is the North Atlantic Oscillation (NAO), associated with positive correlations in wind, temperature and precipitation over northern Europe. The NAO's effect on wind-power and demand in the UK and Norway is examined, focussing on March when Norwegian hydropower reserves are low and the combined power system might be most susceptible to atmospheric variations. The NCEP/NCAR meteorological reanalysis dataset (1948–2010) is used to drive simple models for demand and wind-power, and ‘demand-net-wind’ (DNW) is estimated for positive, neutral and negative NAO states. Cold, calm conditions in NAO− cause increased demand and decreased wind-power compared to other NAO states. Under a 2020 wind-power capacity scenario, the increase in DNW in NAO− relative to NAO neutral is equivalent to nearly 25% of the present-day average rate of March Norwegian hydropower usage. As the NAO varies on long timescales (months to decades), and there is potentially some skill in monthly predictions, we argue that it is important to understand its impact on European power systems.

Are the teleconnections of Central Pacific and Eastern Pacific El Niño distinct in boreal wintertime?

A meteorological reanalysis dataset and experiments of the Goddard Earth Observing System Chemistry-Climate Model, Version 2 (GEOS V2 CCM) are used to study the boreal winter season teleconnections in the Pacific-North America region and in the stratosphere generated by Central Pacific and Eastern Pacific El Nino. In the reanalysis data, the sign of the North Pacific and stratospheric response to Central Pacific El Nino is sensitive to the composite size, the specific Central Pacific El Nino index used, and the month or seasonal average that is examined, highlighting the limitations of the short observational record. Long model integrations suggest that the response to the two types of El Nino are similar in both the extratropical troposphere and stratosphere. Namely, both Central Pacific and Eastern Pacific El Nino lead to a deepened North Pacific low and a weakened polar vortex, and the effects are stronger in late winter than in early winter. However, the long experiments do indicate some differences between the two types of El Nino events regarding the latitude of the North Pacific trough, the early winter polar stratospheric response, surface temperature and precipitation over North America, and globally averaged surface temperature. These differences are generally consistent with, though smaller than, those noted in previous studies.

Why might stratospheric sudden warmings occur with similar frequency in El Niño and La Niña winters?

The effect of El Niño‐Southern Oscillation (ENSO) on the frequency and character of Northern Hemisphere major mid‐winter stratospheric sudden warmings (SSWs) is evaluated using a meteorological reanalysis data set and comprehensive chemistry‐climate models. There is an apparent inconsistency between the impact of opposite phases of ENSO on the seasonal mean vortex and on SSWs: El Niño leads to an anomalously warm, and La Niña leads to an anomalously cool, seasonal mean polar stratospheric state, but both phases of ENSO lead to an increased SSW frequency. A resolution to this apparent paradox is here proposed: the region in the North Pacific most strongly associated with precursors of SSWs is not strongly influenced by El Niño and La Niña teleconnections. In the observational record, both La Niña and El Niño lead to similar anomalies in the region associated with precursors of SSWs and, consistent with this, there is a similar SSW frequency in La Niña and El Niño winters. A similar correspondence between the penetration of ENSO teleconnections into the SSW precursor region and SSW frequency is found in the comprehensive chemistry‐climate models. The inability of some of the models to capture the observed relationship between La Niña and SSW frequency appears related to whether the modeled ENSO teleconnections result in extreme anomalies in the region most closely associated with SSWs. Finally, it is confirmed that the seasonal mean polar vortex response to ENSO is only weakly related to the relative frequency of SSWs during El Niño and La Niña.

Observed connection between stratospheric sudden warmings and the Madden‐Julian Oscillation

The effect of the Madden‐Julian Oscillation (MJO) on the Northern Hemisphere wintertime stratospheric polar vortex and major, mid‐winter stratospheric sudden warmings (SSWs) is evaluated using a meteorological reanalysis dataset. The MJO influences the region in the tropospheric North Pacific sector that is most strongly associated with a SSW. Consistent with this, SSWs in the reanalysis record have tended to follow certain MJO phases. The magnitude of the influence of the MJO on the vortex is comparable to that associated with the Quasi‐Biennial Oscillation and El Niño. The MJO could be used to improve intra‐seasonal projections of the Northern Hemisphere high latitude circulation, and in particular of the tropospheric Northern Annular Mode, at lags exceeding one month.

MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

Abstract. A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather prediction model. The land cover map of Estonia needed for the model was derived using DMCii (Disaster Monitoring Constellation International Imaging) SLIM-6-22 (Surrey Linear Imager - 6 channel - 22 m resolution) images and ancillary information. The EST_PP model was run for the period from years 2003 to 2011. The results of GPP and NPP simulation are compared with the available global MODIS (Moderate Resolution Imaging Spectroradiometer) GPP/NPP product and with the Estonian statistical data on yearly volume increment in forests and on yield of agricultural crops. The NPP simulation results on coniferous and deciduous forests are compared with the data from tree ring analyses from different counties. These comparisons show us that the simulated country average yearly NPP values for Estonian forests agree reasonably well with the indirect estimates from other sources, taking into account the rather high uncertainty of the model predictions, uncertainty of forest inventory-based estimates and limited representativity of existing tree ring data. However, problems arise with the ability of present versions of EST_PP and MODIS NPP models to adequately simulate the regional differences of productivity and of variability of productivity in different years. The model needs some modification and the basic LUE principles to be tested in Estonia. Nevertheless, the MODIS NPP and EST_PP models offer additional possibilities to map yearly productivity and carbon sequestration by Estonian vegetation. There is a perspective to add the model-simulated NPP values into the national inventory datasets.

The Mechanical Energies of the Global Atmosphere in El Niño and La Niña Years

Abstract Two meteorological reanalysis datasets are analyzed to determine the mechanical energies of the global atmosphere in the El Niño and La Niña years. The general consistency of the mean energy components between the two datasets reveals ~1%–3% increase and ~2%–3% decrease in the mean energies in the El Niño years and La Niña years, respectively. These analyses further reveal that the tropospheric temperature responds to the sea surface temperature anomaly with a time lag of two months, which leads to the varying mean atmospheric energies in the El Niño and La Niña years.

Evaluation of two precipitation data sets for the Rhine River using streamflow simulations

Abstract. This paper presents an extended version of a widely used precipitation data set and evaluates it along with a recently released precipitation data set, using streamflow simulations. First, the existing precipitation data set issued by the Commission for the Hydrology of the Rhine basin (CHR), originally covering the period 1961–1995, was extended until 2008 using a number of additional precipitation data sets. Next, the extended version of the CHR, together with E-OBS Version 4 (ECA & D gridded data set) were evaluated for their performance in the Rhine basin for extreme events. Finally, the two aforementioned precipitation data sets and a meteorological reanalysis data set were used to force a hydrological model, evaluating the influence of different precipitation forcings on the annual mean and extreme discharges compared to observational discharges for the period from 1990 until 2008. The extended version of CHR showed good agreement in terms of mean annual cycle, extreme discharge (both high and low flows), and spatial distribution of correlations with observed discharge. E-OBS performed well with respect to extreme discharge. However, its performance of the mean annual cycle in winter was rather poor and remarkably well in the summer. Also, CHR08 outperformed E-OBS in terms of temporal correlations in most of the analyzed sub-catchment means. The length extension for the CHR and the even longer length of E-OBS permit the assessment of extreme discharge and precipitation values with lower uncertainty for longer return periods. This assessment classifies both of the presented precipitation data sets as possible reference data sets for future studies in hydrological applications.

Improvements to a MODIS global terrestrial evapotranspiration algorithm

MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519–536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km 2 land surface ET dataset for the 109.03 Million km 2 global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8 × 10 3 km 3, agrees very well with other reported estimates of 65.5 × 10 3 km 3 over the terrestrial land surface, which is much higher than 45.8 × 10 3 km 3 estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39 mm day −1 to 0.33 mm day −1 driven by tower meteorological data, and from 0.40 mm day −1 to 0.31 mm day −1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10–30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.

Wind field differences between three meteorological reanalysis data sets detected by evaluating atmospheric excitation of Earth rotation

We compared atmospheric excitations of the varying Earth rotation calculated from three different meteorological reanalysis data sets (NCEP/NCAR, NCEP‐DOE and ERA‐40) over 23 years, from 1979 to 2001. Atmospheric excitations are evaluated by atmospheric angular momentum (AAM) functions. We found that differences in AAM functions are primarily caused by differences in the wind data, especially the northward wind data, between the three reanalysis data sets. Differences between the NCEP‐DOE and ERA‐40 AAM functions are larger than those between the NCEP/NCAR and NCEP‐DOE ones. The northward wind differences that have a large effect on the equatorial AAM functions originate from the upper troposphere at the tropics and midlatitudes in the southern hemisphere, especially in three regions: east Pacific off Latin America, Africa and the Indian Ocean. The eastward wind differences are seen at midlatitudes in the southern hemisphere. These differences are not confined to a single frequency. The difference spectra show a reddish frequency dependence with some peaks in seasonal bands and tidal diurnal bands. Since the eastward and northward winds over the tropics can most efficiently change the Earth's spin rate and polar motion, respectively, the wind accuracy over the tropics directly determines the accuracy in evaluations of the atmospheric excitations.

Polar Mesoscale Cyclones in the Northeast Atlantic: Comparing Climatologies from ERA-40 and Satellite Imagery

Abstract Polar mesoscale cyclones over the subarctic are thought to be an important component of the coupled atmosphere–ocean climate system. However, the relatively small scale of these features presents some concern as to their representation in the meteorological reanalysis datasets that are commonly used to drive ocean models. Here polar mesocyclones are detected in the 40-Year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis dataset (ERA-40) in mean sea level pressure and 500-hPa geopotential height, using an automated cyclone detection algorithm. The results are compared to polar mesocyclones detected in satellite imagery over the northeast Atlantic, for the period October 1993–September 1995. Similar trends in monthly cyclone numbers and a similar spatial distribution are found. However, there is a bias in the size of cyclones detected in the reanalysis. Up to 80% of cyclones larger than 500 km are detected in MSL pressure, but this hit rate decreases, approximately linearly, to ∼40% for 250-km-scale cyclones and to ∼20% for 100-km-scale cyclones. Consequently a substantial component of the associated air–sea fluxes may be missing from the reanalysis, presenting a serious shortcoming when using such reanalysis data for ocean modeling simulations. Eight maxima in cyclone density are apparent in the mean sea level pressure, clustered around synoptic observing stations in the northeast Atlantic. They are likely spurious, and a result of unidentified shortcomings in the ERA-40 data assimilation procedure.

Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's satellites, Terra and Aqua, dramatically improves our ability to accurately and continuously monitor the terrestrial biosphere. MODIS information is used to estimate global terrestrial primary production weekly and annually in near‐real time at a 1‐km resolution. MODIS terrestrial primary production requires daily gridded assimilation meteorological data as inputs, and the accuracy of the existing meteorological reanalysis data sets show marked differences both spatially and temporally. This study compares surface meteorological data sets from three well‐documented global reanalyses, NASA Data Assimilation Office (DAO), European Centre for Medium‐Range Weather Forecasts (ECMWF) (ERA‐40) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1, with observed weather station data and other gridded data interpolated from the observations, to evaluate the sensitivity of MODIS global terrestrial gross and net primary production (GPP and NPP) to the uncertainties of meteorological inputs both in the United States and the global vegetated areas. NCEP tends to overestimate surface solar radiation, and underestimate both temperature and vapor pressure deficit (VPD). ECMWF has the highest accuracy but its radiation is lower in tropical regions, and the accuracy of DAO lies between NCEP and ECMWF. Biases in temperature are mainly responsible for large VPD biases in reanalyses. MODIS NPP contains more uncertainties than GPP. Global total MODIS GPP and NPP driven by DAO, ECMWF, and NCEP show notable differences (>20 Pg C/yr) with the highest estimates from NCEP and the lowest from ECMWF. Again, the DAO results lie somewhere between NCEP and ECMWF estimates. Spatially, the larger discrepancies among reanalyses and their derived MODIS GPP and NPP occur in the tropics. These results reveal that the biases in meteorological reanalyses can introduce substantial error into GPP and NPP estimations, and emphasize the need to minimize these biases to improve the quality of MODIS GPP and NPP products.

Temporal variability of dust mobilization and concentration in source regions

The time‐varying properties of dust mobilization and concentration variability in source areas are studied in model simulations to understand the processes that control entrainment of dust into the atmosphere. Two different meteorological reanalysis data sets (National Centers for Environmental Prediction/National Center for Atmospheric Research and NASA Data Assimilation Office) and two different source parameterizations are used for the analysis, which is done for six different main dust source regions. The results show that ∼35–70% variance of dust mobilization is associated with diurnal variability of dust mobilization in the major dust sources regions, and this is similar using different meteorological analyses and source parameterizations. Synoptic‐scale variability is responsible for 6–50% of the dust mobilization, with more variation between different meteorology and parameterizations. The variability of dust concentrations in source regions is very sensitive to source meteorology and parameterization, with the synoptic variability responsible for 30–50% of the variability in some configurations or 6–40% in other configurations. Diurnal variability in concentrations varies from 20–50 to 25–80% depending on the model configuration. Analyses suggest that dust mobilization and dust concentration are more influenced by synoptic variability in Australia and east Asia than in North Africa and Arabian dust source regions, consistent with observations of cold fronts and cyclone movements systems driving dust mobilization and transport in Australia and east Asia source regions. Differences in variability between different meteorological data sets and source parameterizations suggest that possible dust feedbacks onto the dust cycle may be sensitive to source parameterization and model.