Influence of Spatially Variable Instrument Networks on Climatic Averages

Copyright 1991 by the American Geophysical Union.Instrument networks for measuring surface air temperature (T) and precipitation (P) have varied considerably over the last century. Inadequate observing‐station locations have produced incomplete, uneven, and biased samples of the spatial variability in climate and, in turn, terrestrial and global scale averages of T and P have been biased. New high‐resolution climatologies [Legates and Willmott, 1990a; 1990b] are intensively sampled and integrated to illustrate the effects of these nontrivial sampling biases. Since station networks may not represent spatial climatic variability adequately, their ability to represent climate through time is suspect

Uncertainties in Precipitation and Their Impacts on Runoff Estimates

Water balance calculations are becoming increasingly important for earth-system studies. Precipitation is one of the most critical input variables for such calculations because it is the immediate source of water for the land surface hydrological budget. Numerous precipitation datasets have been developed in the last two decades, but these datasets often show marked differences in their spatial and temporal distribution of this key hydrological variable. This paper compares six monthly precipitation datasets—Climate Research Unit of University of East Anglia (CRU), Willmott–Matsuura (WM), Global Precipitation Climate Center (GPCC), Global Precipitation Climatology Project (GPCP), Tropical Rainfall Measuring Mission (TRMM), and NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (NCEP-2)—to assess the uncertainties in these datasets and their impact on the terrestrial water balance. The six datasets tested in the present paper were climatologically averaged and compared by calculating various statistics of the differences. The climatologically averaged monthly precipitation estimates were applied as inputs to a water balance model to estimate runoff and the uncertainties in runoff arising directly from the precipitation estimates. The results of this study highlight the need for accurate precipitation inputs for water balance calculations. These results also demonstrate the need to improve precipitation estimates in arid and semiarid regions, where slight changes in precipitation can result in dramatic changes in the runoff response due to the nonlinearity of the runoff-generation processes

Analyzing the discharge regime of a large tropical river through remote sensing, ground-based climatic data, and modeling

This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Niño-;Southern Oscillation (ENSO) event on water balances within the drainage basin

Statistics for the Evaluation and Comparison of Models

Copyright 1985 by the American Geophysical Union.Procedures that may be used to evaluate the operational performance of a wide spectrum of geophysical models are introduced. Primarily using a complementary set of difference measures, both model accuracy and precision can be meaningfully estimated, regardless of whether the model predictions are manifesteda s scalars,d irections,o r vectors.I t is additionally suggestedth at the reliability of the accuracy and precision measures can be determined from bootstrap estimates of confidence and significance. Recommendedp roceduresa re illustrated with a comparativee valuation of two models that estimate wind velocity over the South Atlantic Bight

Decomposition of the mean absolute error (MAE) into systematic and unsystematic components.

When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River

An Integrated System for Water Budget Closure Over the Pan-Tropical Land Mass: A TRMM Validation Effort

A concerted effort was undertaken this past year to analyze the National Centers for Environmental Prediction (NCEP) and Department of Energy (DOE) Reanalysis II tropical hydrologic cycle. The topics included in this progress report are: (1) analysis of the surface hydrologic cycle; (2) long term CO2 sensitivity simulations with NCAR's CCM3 and global recycling rates; (3) tropical atmospheric hydrologic cycle; (4) GCIP (Global Continental Scale International Project) WEBS (Water and Energy Budget Synthesis); (5) the impact of precipitation uncertainties on global water balance calculations

Climate and other models may be more accurate than reported

Almost all areas of the sciences use models to study and predict physical phenomena, but predictions and conclusions are only as good as the models on which they are based. The statistical assessment of errors in model prediction and model estimation is of fundamental importance. Recent reports of the Intergovernmental Panel on Climate Change (IPCC), for example, present and interpret several commonly used estimates of average error to evaluate and compare the accuracies of global climate model simulations [Flato et al., 2013]