Assessment and enhancement of MERRA land surface hydrology estimates

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979 present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies

Synthesis of Satellite Microwave Observations for Monitoring Global Land-Atmosphere CO2 Exchange

This dissertation describes the estimation, error quantification, and incorporation of land surface information from microwave satellite remote sensing for modeling global ecosystem land-atmosphere net CO2 exchange. Retrieval algorithms were developed for estimating soil moisture, surface water, surface temperature, and vegetation phenology from microwave imagery timeseries. Soil moisture retrievals were merged with model-based soil moisture estimates and incorporated into a light-use efficiency model for vegetation productivity coupled to a soil decomposition model. Results, including state and uncertainty estimates, were evaluated with a global eddy covariance flux tower network and other independent global model- and remote-sensing based products

Combining satellite observations with a virtual ground-based remote sensing network for monitoring atmospheric stability

Atmospheric stability plays an essential role in the evolution of weather events. While the upper troposphere is sampled by satellite sensors, and in-situ sensors measure the atmospheric state close to the surface, only sporadic information from radiosondes or aircraft observations is available in the planetary boundary layer. Ground-based remote sensing offers the possibility to continuously and automatically monitor the atmospheric state in the boundary layer. Microwave radiometers (MWR) provide temporally resolved temperature and humidity profiles in the boundary layer and accurate values of integrated water vapor and liquid water path, while the DIfferential Absorption Lidar (DIAL) measures humidity profiles with high vertical and temporal resolution up to 3000 m height. Both instruments have the potential to complement satellite observations by additional information from the lowest atmospheric layers, particularly under cloudy conditions. The main objective of this work is to investigate the potential of ground-based and satellite sensors, as well as their synergy, for monitoring atmospheric stability. The first part of the study represents a neural network retrieval of stability indices, integrated water vapor, and liquid water path from simulated satellite- and ground-based measurements based on the reanalysis COSMO-REA2. The satellite-based instruments considered in the study are the currently operational Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the future Infrared Sounder (IRS), both in geostationary orbit, and the Advanced Microwave Sounding Unit (AMSU-A) and Infrared Atmospheric Sounding Interferometer (IASI), both deployed on polar orbiting satellites. Compared to the retrieval based on satellite observations, the additional ground-based MWR/DIAL measurements provide valuable improvements not only in the presence of clouds, which represent a limiting factor for infrared SEVIRI, IRS, and IASI, but also under clear sky conditions. The root-mean-square error for Convective Available Potential Energy (CAPE), for instance, is reduced by 24% if IRS observations are complemented by ground-based MWR measurements. The second part represents an attempt to assess the representativeness of observations of a single ground-based MWR and the impact of a network of MWR if combined with future geostationary IRS measurements. For this purpose, the reanalysis fields (150*150 km) in the western part of Germany were used to simulate MWR and IRS observations and to develop a neural network retrieval of CAPE and Lifted Index (LI). Further analysis was performed in the space of retrieved parameters CAPE and LI. The impact of additional ground-based network observations was investigated in two ways. First, using spatial statistical interpolation method, the fields of CAPE/LI retrieved from IRS observations were merged with the CAPE/LI values from MWR network taking into account the corresponding error covariance matrices of both retrievals. Within this method, the contribution of a ground-based network consisting of a varying number of radiometers (from one to 25) was shown to be significant under cloudy conditions. The second approach mimics the assimilation of satellite and ground-based observations in the space of retrieved CAPE/LI fields. Assuming the persistence of atmospheric fields for a period of six hours, the CAPE/LI fields calculated from reanalysis were taken as a first guess in an assimilation step. Observations, represented by CAPE/LI fields obtained from satellite and ground-based measurements with +6 hours delay, were assimilated by spatial interpolation. Within this method, the added value of ground-based observations, if compared to satellite contribution, is highly dependent on the current weather situation, cloudiness, and the position of ground-based instruments. For CAPE, the synergy of ground-based MWR and satellite IRS observations is essential even under clear sky conditions, since both passive sensors can not capture atmospheric profiles, needed for calculation of CAPE, with sufficient accuracy. Whereas for LI, the assimilation of observations of 25 MWR distributed in the domain is equivalent to the assimilation of horizontally resolved IRS observations, indicating that in the presence of clouds, MWR observations could replace cloud-affected IRS measurements. Within both approaches, it could be shown that the contribution of ground-based observations is more pronounced under cloudy conditions and is most valuable for the first 25 sensors located in the domain

Assessment of MERRA-2 Land Surface Energy Flux Estimates

In the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here we assess aspects of the MERRA-2 land surface energy budget and 2 m air temperatures (T(sup 2m)). For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 W/sq m), the sensible heating (by 6 W/sq m), and the downwelling shortwave radiation (by 14 W/sq m), while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10-15 W/sq m each). These results differ only slightly from those for NASA's previous reanalysis, MERRA. Comparison to various gridded reference data sets over Boreal summer (June-July-August) suggests that MERRA-2 has particularly large positive biases (>20 W/sq m) where LH is energy-limited, and that these biases are associated with evaporative fraction biases rather than radiation biases. For time series of monthly means during Boreal summer, the globally averaged anomaly correlations (R(sub anom)) with reference data were improved from MERRA to MERRA-2, for LH (from 0.39 to 0.48 vs. GLEAM data) and the daily maximum T(sup 2m) (from 0.69 to 0.75 vs. CRU data). In regions where T(sup 2m) is particularly sensitive to the precipitation corrections (including the central US, the Sahel, and parts of south Asia), the changes in the T(sup 2m) R(sub anom) are relatively large, suggesting that the observed precipitation influenced the T(sup 2m) performance

Diagnosis of the atmospheric hydrological cycle and its variability in the present-day climate

This thesis investigates some important aspects of the atmospheric branch of the hydrological cycle in the modern day climate from an observational perspective. Data quality is evaluated, focusing on two state-of-the-art reanalysis products, ERA-I and JRA-55. Regional-scale discrepancies among reanalyses and observations, especially in their annual cycles, are found in the warm pool, Amazon, Gulf stream and Indian subcontinent regions. In the tropics, oceanic evaporation and its temporal variability are notably greater in JRA-55 than in ERA-I and satellite-based estimates, while both reanalyses overestimate precipitation. Higher tropical precipitation and evaporation, accompanied by a slightly lower level of total column water (TCW), might suggest a more intense hydrological cycle, but this can be an ill-defined concept especially when analysis increments mask “spin-down” errors in reanalysis models. Analysis increments arise to remove unphysical residuals in the atmospheric water budget, and these are explored via a cluster analysis to identify regimes with common behavior. Consistent for ERA-I and JRA-55, the regime with the largest negative residuals (greater moisture outputs than inputs) exceeding 50% of mean precipitation occurs during the dry season of some low latitude regions that feature strong seasonality, high evapotranspiration and high moisture divergence. Errors in the moisture divergence are likely responsible because they correlate strongly with the budget residual. Empirical Orthogonal Function (EOF) and Self Organizing Map (SOM) analyses are applied to identify the dominant inter-annual patterns of vertically-integrated moisture divergence variability. They reveal that the transition from strong La Niña through to extreme El Niño events is not a linear one and that the EOF orthogonality constraint results in the patterns being split between leading EOFs that are non-linearly related. The SOM analysis captures the range of responses to the El Niño Southern Oscillation (ENSO), indicating that the distinction between the moderate and extreme El Niños can be as great as the difference between La Niña and moderate El Niños, from a moisture divergence point of view. On diurnal time scales, horizontal moisture fluxes vary in response to thermodynamic and dynamic effects. TCW shows a global scale diurnal cycle that peaks around 1800 - 2100 local time with a peak-to-trough magnitude of 0.4mm. Semi-diurnal variations in surface winds and pressure, consistent with atmospheric tidal theory, create a westward propagating moisture convergence/divergence wave along the equator. Finally, the importance of Tropical Cyclones (TCs) as a source of freshwater for the North American continent is estimated using an ensemble of schemes designed to attribute onshore moisture fluxes to TCs. Averaged over the 2004–2012 hurricane seasons and integrated over the western, southern and eastern coasts of North America, the seven schemes attribute 7 to 18% (mean 14 %) of total net onshore flux to Atlantic TCs. A reduced contribution of 10% (range 9 to 11 %) was found for the 1980–2003 period, though only two schemes could be applied to this earlier period. Over the whole 1980–2012 period, a further 8% (range 6 to 9% from two schemes) was attributed to East Pacific TCs, resulting in a total TC contribution of 19% (range 17 to 22 %) to the ocean-to-land moisture transport onto the North American continent between May and November. The inter-annual variability does not appear to be strongly related to ENSO

Synthesis and Assessment Product

This Synthesis and Assessment Product is an important revision to the conclusions of earlier reports from the U.S. National Research Council and the Intergovernmental Panel on Climate Change. Previously reported discrepancies between the amount of warming near the surface and higher in the atmosphere have been used to challenge the reliability of climate models and the reality of human-induced global warming. Specifically, surface data showed substantial global-average warming, while early versions of satellite and radiosonde data showed little or no warming above the surface. This significant discrepancy no longer exists because errors in the satellite and radiosonde data have been identified and corrected. New data sets have also been developed that do not show such discrepancies. This Synthesis and Assessment Product is an important revision to the conclusions of earlier reports from the U.S. National Research Council and the Intergovernmental Panel on Climate Change. For recent decades, all current atmospheric data sets now show global-average warming that is similar to the surface warming. While these data are consistent with the results from climate models at the global scale, discrepancies in the tropics remain to be resolved. Nevertheless, the most recent observational and model evidence has increased confidence in our understanding of observed climatic changes and their causes

Satellite observations of stratospheric hydrogen fluoride and comparisons with SLIMCAT calculations

The vast majority of emissions of fluorine-containing molecules are anthropogenic in nature, e.g. chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), and hydrofluorocarbons (HFCs). Many of these fluorine-containing species deplete stratospheric ozone and are regulated by the Montreal Protocol. Once in the atmosphere they slowly degrade, ultimately leading to the formation of hydrogen fluoride (HF), the dominant reservoir of stratospheric fluorine due to its extreme stability. Monitoring the growth of stratospheric HF is therefore an important marker for the success of the Montreal Protocol. We report the comparison of global distributions and trends of HF measured in the Earth's atmosphere by the satellite remote-sensing instruments ACE-FTS (Atmospheric Chemistry Experiment Fourier transform spectrometer), which has been recording atmospheric spectra since 2004, and HALOE (HALogen Occultation Experiment), which recorded atmospheric spectra between 1991 and 2005, with the output of SLIMCAT, a state-of-the-art three-dimensional chemical transport model. In general the agreement between observation and model is good, although the ACE-FTS measurements are biased high by  ∼  10 % relative to HALOE. The observed global HF trends reveal a substantial slowing down in the rate of increase of HF since the 1990s: 4.97 ± 0.12 % year−1 (1991–1997; HALOE), 1.12 ± 0.08 % year−1 (1998–2005; HALOE), and 0.52 ± 0.03 % year−1 (2004–2012; ACE-FTS). In comparison, SLIMCAT calculates trends of 4.01, 1.10, and 0.48 % year−1, respectively, for the same periods; the agreement is very good for all but the earlier of the two HALOE periods. Furthermore, the observations reveal variations in the HF trends with latitude and altitude; for example, between 2004 and 2012 HF actually decreased in the Southern Hemisphere below  ∼  35 km. An additional SLIMCAT simulation with repeating meteorology for the year 2000 produces much cleaner trends in HF with minimal variations with latitude and altitude. Therefore, the variations with latitude and altitude in the observed HF trends are due to variability in stratospheric dynamics on the timescale of a few years. Overall, the agreement between observation and model points towards the ongoing success of the Montreal Protocol and the usefulness of HF as a metric for stratospheric fluorine

The Southern Ocean Observing System (SOOS)

[in “State of the Climate in 2014” : Special Supplement to the Bulletin of the American Meteorological Society Vol. 96, No. 7, July 2015