Engineering topics
Publications and source records attributed to Koster, R. D..
To develop a seasonal scale drought forecasting system to strengthen FEWS NET's progressive early warning efforts in Africa and the Middle East. This presentation provides an overview of the implementation, validation, and ongoing operational applications of this system.
New model structure for peatlands results in improved skill metrics (without any parameter calibration) Simulated surface soil moisture strongly affected by new model, but reliable soil moisture data lacking for validation.
Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.
The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.
The atmospheric absorbing aerosols such as dust, black carbon (BC), organic carbon (OC) are now well known warming factors in the atmosphere. However, when these aerosols deposit onto the snow surface, it causes darkening of snow and thereby absorbing more energy at the snow surface leading to the accelerated melting of snow. If this happens over Himalayan glacier surface, the glacier meltings are expected and may contribute the mass balance changes though the mass balance itself is more complicated issue. Glacier has mainly two parts: ablation and accumulation zones. Those are separated by the Equilibrium Line Altitude (ELA). Above and below ELA, snow accumulation and melting are dominant, respectively. The change of ELA will influence the glacier disappearance in future. In the Himalayan region, many glacier are debris covered glacier at the terminus (i.e., in the ablation zone). Debris is pieces of rock from local land and the debris covered parts are probably not affected by any deposition of the absorbing aerosols because the snow surface is already covered by debris (the debris covered parts have different mechanism of melting). Hence, the contribution of the snow darkening effect is considered to be most important "over non debris covered part" of the Himalayan glacier (i.e., over the snow or ice surface area). To discuss the whole glacier retreat, mass balance of each glacier is most important including the discussion on glacier flow, vertical compaction of glacier, melting amount, etc. The contribution of the snow darkening is mostly associated with "the snow/ice surface melting". Note that the surface melting itself is not always directly related to glacier retreats because sometimes melt water refreezes inside of the glacier. We should discuss glacier retreats in terms of not only the snow darkening but also other contributions to the mass balance.
Hydrologists have long speculated that soil moisture information can be used to increase skill in monthly to seasonal forecast systems. For this to be true, though, three conditions must be satisfied: (1) an imposed initial soil moisture anomaly in the forecast system must have some memory, so that it persists into the forecast period; (2) the modeled atmosphere must respond in a predictable way to the persisted anomaly; and (3) the forecast model must correctly represent both the soil moisture memory and the atmospheric response as they occur in nature. In this short paper, we review some recent work at NSIPP (NASA Seasonal-to-Interannual Prediction Project) that addresses all three conditions.
Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.
The ability to predict precipitation several months in advance would have a significant impact on water resource management. This talk provides an overview of a project aimed at developing this prediction capability. NASA's Seasonal-to-Interannual Prediction Project (NSIPP) will generate seasonal-to-interannual sea surface temperature predictions through detailed ocean circulation modeling and will then translate these SST forecasts into forecasts of continental precipitation through the application of an atmospheric general circulation model and a "SVAT"-type land surface model. As part of the process, ocean variables (e.g., height) and land variables (e.g., soil moisture) will be updated regularly via data assimilation. The overview will include a discussion of the variability inherent in such a modeling system and will provide some quantitative estimates of the absolute upper limits of seasonal-to-interannual precipitation predictability.
A new GCM-scale land surface modeling strategy that explicitly accounts for subgrid soil moisture variability and its effects on evaporation and runoff is now being explored. In a break from traditional modeling strategies, the continental surface is disaggregated into a mosaic of hydrological catchments, with boundaries that are not dictated by a regular grid but by topography. Within each catchment, the variability of soil moisture is deduced from TOP-MODEL equations with a special treatment of the unsaturated zone. This paper gives an overview of this new approach and presents the general framework for its off-line evaluation over North-America.
The accurate forecasting of el nino or la nina conditions in the tropical Pacific can potentially lead to valuable predictions of hydrological anomalies over land at seasonal to interannual timescales. Even with highly accurate earth system models, though, our ability to generate these continental forecasts will always be limited by the chaotic nature of the atmospheric circulation. The nature of this fundamental limitation is explored through the use of 16-member ensembles of multi-decade GCM simulations. In each simulation of the first ensemble, sea surface temperatures (SSTs) are given the same realistic interannual variations over a 45-year period, and land surface state is allowed to evolve with that of the atmosphere. Analysis of the results shows that the SSTs control the temporal organization of continental precipitation anomalies to a significant extent in the tropics and to a much smaller extent in midlatitudes. In each simulation of the second ensemble, we prescribe SSTs as before, but we also prescribe interannual variations in the low frequency component of evaporation efficiency over land. Thus, in the second ensemble, we effectively make the extreme assumption that surface boundary conditions across the globe are perfectly predictable, and we quantify the consistency with which the atmosphere (particularly precipitation) responds to these boundary conditions. The resulting "absolute upper limit" on the predictability of precipitation is found to be quite high in the tropics yet only moderate in many midlatitude regions.
Well-documented present-day distributions of stable water isotopes (HDO and others) show the existence, in middle and high latitudes, of a linear relationship between the mean annual isotope content of precipitation (SD and 51"0) and the mean annual temperature at the precipitation site. Paleoclimatologists have used this relationship, which is particularly well obeyed over Greenland and Antarctica, to infer paleotemperatures from ice core data. There is, however, growing evidence that spatial and temporal isotope/ surface temperature slopes differ, thus complicating the use of stable water isotopes as paleothermometers. In this paper we review empirical estimates of temporal slopes in polar regions and relevant information that can be inferred from isotope models: simple, Rayleigh-type distillation models and (particularly over Greenland) general circulation models (GCMS) fitted with isotope tracer diagnostics. Empirical estimates of temporal slopes appear consistently lower than present-day spatial slopes and are dependent on the timescale considered. This difference is most probably due to changes in the evaporative origins of moisture, changes in the seasonality of the precipitation, changes in the strength of the inversion layer, or some combination of these changes. Isotope models have not yet been used to evaluate the relative influences of these different factors. The apparent disagreement in the temporal and spatial slopes clearly makes calibrating the isotope paleothermometer difficult. Nevertheless, the use of a (calibrated) isotope paleothermometer appears justified; empirical estimates and most (though not all) GCM results support the practice of interpreting ice core isotope records in terms of local temperature changes.
Incorporating the full geochemical cycles of stable water isotopes (HDO and H2O-18) into an atmospheric general circulation model (GCM) allows an improved understanding of global delta-D and delta-O-18 distributions and might even allow an analysis of the GCM's hydrological cycle. A detailed sensitivity analysis using the NASA/Goddard Institute for Space Studies (GISS) model II GCM is presented that examines the nature of isotope modeling. The tests indicate that delta-D and delta-O-18 values in nonpolar regions are not strongly sensitive to details in the model precipitation parameterizations. This result, while implying that isotope modeling has limited potential use in the calibration of GCM convection schemes, also suggests that certain necessarily arbitrary aspects of these schemes are adequate for many isotope studies. Deuterium excess, a second-order variable, does show some sensitivity to precipitation parameterization and thus may be more useful for GCM calibration.
The cycles of the water isotopic species (HDO and H2O-18) have been incorporated into the NASA Goddard Institute for Space Studies atmospheric general circulation model (GCM). The results of a three-year simulation for present-day conditions are discussed, with special emphasis on the comparison between predicted and observed isotopic distributions for both the seasonal and annual time scales. The observed seasonal cycles are generally well simulated. For the annual scale the observed linear relationship between delta O-18 and the surface temperature at middle and high latitudes, as well as the absence of any correlation between these fields in tropical and equatorial regions, are properly obeyed by the GCM simulation. In the tropical and equatorial regions the delta O-18 patterns for both observations and the GCM are influenced by the amount of rainfall. There is excellent agreement between the simulated and observed delta D-delta O-18 relationship throughout the world.