C. Prigent

Global inundation dynamics inferred from multiple satellite observations, 1993–2000

Wetlands and surface waters are recognized to play important roles in climate, hydrologic and biogeochemical cycles, and availability of water resources. Until now, quantitative, global time series of spatial and temporal dynamics of inundation have been unavailable. This study presents the first global estimate of monthly inundated areas for 1993-2000. The data set is derived from a multisatellite method employing passive microwave land surface emissivities calculated from SSM/I and ISCCP observations, ERS scatterometer responses, and AVHRR visible and near-infrared reflectances. The satellite data are used to calculate inundated fractions of equal area grid cells (0.25° x 0.25° at the equator), taking into account the contribution of vegetation to the passive microwave signal. Global inundated area varies from a maximum of 5.86 x 10 6 km 2 (average for 1993-2000) to a mean minimum of 2.12 x 10 6 km 2 . These values are considered consistent with existing independent, static inventories. The new multisatellite estimates also show good agreement with regional high-resolution SAR observations over the Amazon basin. The seasonal and interannual variations in inundation have been evaluated against rain rate estimates from the Global Precipitation Climatology Project (GPCP) and water levels in wetlands, lakes, and rivers measured with satellite altimeters. The inundation data base is now being used for hydrology modeling and methane studies in GCMs.

Global Intercomparison of 12 Land Surface Heat Flux Estimates

[1]xa0A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993–1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/Mosaic/Noah). An intercomparison of the global latent heat flux (Qle) annual means shows a spread of ∼20 W m−2 (all-product global average of ∼45 W m−2). A similar spread is observed for the sensible (Qh) and net radiative (Rn) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Qle and Qh absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Qle differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Qle and Rn were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations

The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for nonlinear cases; however, first guess estimates, which are used in variational assimilation methods to avoid problems of solution nonuniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first guess. Conceptual bridges are established between the neural network and variational assimilation methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from Special Sensor Microwave Imager observations. The retrieval, in parallel, of all these quantities improves the results for consistancy reasons. A database to train the neural network is calculated with a radiative transfer model and a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data, and from microwave emissivity atlases previously calculated. The results of the neural network inversion are very encouraging. The theoretical RMS error of the surface temperature retrieval over the globe is 1.3 K in clear-sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a theoretical RMS error of 3.8 kg m−2 in clear conditions and 4.9 kg m−2 in cloudy situations. The theoretical RMS error in cloud liquid water path is 0.08 kg m−2. The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.

Interannual variability of surface water extent at the global scale, 1993–2004

[1]xa0Land surface waters play a primary role in the global water cycle and climate. As a consequence, there is a widespread demand for accurate and long-term quantitative observations of their distribution over the whole globe. This study presents the first global data set that quantifies the monthly distribution of surface water extent at ∼25 km sampling intervals over 12 years (1993–2004). These estimates, generated from complementary multiple-satellite observations, including passive (Special Sensor Microwave Imager) and active (ERS scatterometer) microwaves along with visible and near-infrared imagery (advanced very high-resolution radiometer; AVHRR), were first developed over 1993–2000. The ERS encountered technical problems in 2001 and the processing scheme had to be adapted to extend the time series. Here we investigate and discuss the adjustments of the methodology, compare the various options, and show that the data set can be extended with good confidence beyond 2000, using ERS and AVHRR mean monthly climatologies. In addition to a large seasonal and interannual variability, the new results show a slight overall decrease in global inundated area between 1993 and 2004, representing an ∼5.7% reduction of the mean annual maximum in 12 years. The decrease is mainly observed in the tropics during the 1990s. Over inland water bodies and large river basins, we assess the variability of the surface water extent against related variables such as in situ river discharges, altimeter-derived and in situ river/floodplain water level heights, and precipitation estimates. This new 12 year data set of global surface water extent represents an unprecedented source of information for future hydrological or methane modeling.

Mineral dust aerosols in the NASA Goddard Institute for Space Sciences ModelE atmospheric general circulation model

[1]xa0We describe an updated model of the dust aerosol cycle embedded within the NASA Goddard Institute for Space Studies ‘ModelE’ atmospheric general circulation model (AGCM). The model dust distribution is compared to observations ranging from aerosol optical thickness and surface concentration to deposition and size distribution. The agreement with observations is improved compared to previous distributions computed by either an older version of the GISS AGCM or an offline tracer transport model. The largest improvement is in dust transport over the Atlantic due to increased emission over the Sahara. This increase comes from subgrid wind fluctuations associated with dry convective eddies driven by intense summertime heating. Representation of ‘preferred sources’ of soil dust particles is also fundamental to the improvement. The observations suggest that deposition is too efficient in the model, partly due to AGCM rainfall errors.

Changes in land surface water dynamics since the 1990s and relation to population pressure

[1] We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Citation: Prigent, C., F. Papa, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews (2012), Changes in land surface water dynamics since the 1990s and relation to population pressure, Geophys. Res. Lett., 39, L08403, doi:10.1029/2012GL051276.

A new river flooding scheme for global climate applications: Off-line evaluation over South America

[1]xa0Given their strong interaction with both climate and the carbon cycle, wetlands and surface waters need to be parameterized in global general circulation models. For this purpose, a new simple flooding river scheme is proposed and evaluated over South America. The flood dynamics is described through the coupling between the ISBA land surface model and the TRIP river routing model including a prognostic flood reservoir. This reservoir fills when the river height exceeds a critical value and vice versa. The reservoir interacts with the soil hydrology through infiltration and with the overlying atmosphere through precipitation interception and free water surface evaporation. The model is tested in off-line mode using the 10-year ISLSCP-2 atmospheric forcing. The evaluation is made against satellite-derived inundation estimates as well as in situ river discharge observations. Besides a basin-scale increase in annual surface evaporation, the results show improved monthly discharges over the Amazon and Parana rivers, as well as a reasonable agreement between the simulated flooded areas and satellite-derived inundation estimates.

Global off-line evaluation of the ISBA-TRIP flood model

This study presents an off-line global evaluation of the ISBA-TRIP hydrological model including a two-way flood scheme. The flood dynamics is indeed described through the daily coupling between the ISBA land surface model and the TRIP river routing model including a prognostic flood reservoir. This reservoir fills when the river height exceeds the critical river bankfull height and vice versa. The flood interacts with the soil hydrology through infiltration and with the overlying atmosphere through precipitation interception and free water surface evaporation. The model is evaluated over a relatively long period (1986–2006) at 1° resolution using the Princeton University 3-hourly atmospheric forcing. Four simulations are performed in order to assess the model sensitivity to the river bankfull height. The evaluation is made against satellite-derived global inundation estimates as well as in situ river discharge observations at 122 gauging stations. First, the results show a reasonable simulation of the global distribution of simulated floodplains when compared to satellite-derived estimates. At basin scale, the comparison reveals some discrepancies, both in terms of climatology and interannual variability, but the results remain acceptable for a simple large-scale model. In addition, the simulated river discharges are improved in term of efficiency scores for more than 50% of the 122 stations and deteriorated for 4% only. Two mechanisms mainly explain this positive impact: an increase in evapotranspiration that limits the annual discharge overestimation found when flooding is not taking into account and a smoothed river peak flow when the floodplain storage is significant. Finally, the sensitivity experiments suggest that the river bankfull depth is potentially tunable according to the river discharge scores to control the accuracy of the simulated flooded areas and its related increase in land surface evaporation. Such a tuning could be relevant at least for climate studies in which the spatio-temporal variations in precipitation are generally poorly represented.

The El Nino-Southern Oscillation and wetland methane interannual variability

Global measurements of atmospheric methane (CH4) concentrations continue to show large interannual variability whose origin is only partly understood. Here we quantify the influence of the El Nino-Southern Oscillation (ENSO) on wetland CH4 emissions, which are thought to be the dominant contributor to interannual variability of the CH4 sources. We use a simple wetland CH4 model that captures variability in wetland extent and soil carbon to model the spatial and temporal dynamics of wetland CH4 emissions from 1950-2005 and compare these results to an ENSO index. We are able to explain a large fraction of the global and tropical variability in wetland CH4 emissions through correlation with the ENSO index. We find that repeated El Nino events throughout the 1980s and 1990s were a contributing factor towards reducing CH4 emissions and stabilizing atmospheric CH4 concentrations. An increase in emissions from the boreal region would likely strengthen the feedback between ENSO and interannual variability in global wetland CH4 emissions. Our analysis emphasizes that climate variability has a significant impact on wetland CH4 emissions, which should be taken into account when considering future trends in CH4 sources. Citation: Hodson, E. L., B. Poulter, N. E. Zimmermann, C. Prigent, and J. O. Kaplan (2011), The El Nino-Southern Oscillation and wetland methane interannual variability, Geophys. Res. Lett., 38, L08810, doi:10.1029/2011GL046861.

An Evaluation of Microwave Land Surface Emissivities Over the Continental United States to Benefit GPM-Era Precipitation Algorithms

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earths surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.