CFSv2-Based Seasonal Hydroclimatic Forecasts over the Conterminous United States

Abstract

There is a long history of debate on the usefulness of climate model-based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R 2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%-70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R 2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R 2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

“…While the GCMs have demonstrated advanced configurations and realistic representations of the climate systems, the use of GCMs' predictions is still restricted by their coarse resolution and inherent systematic biases. To overcome these limitations, the GCMs' predictions at seasonal timescales are usually downscaled and bias-corrected before being used in hydrological applications (e.g., Wood et al, 2002;Luo and Wood, 2008;Yuan et al, 2013;Tian et al, 2014). The Climate Forecast System version 2 (CFSv2) is a recently developed GCM by the National Centers for Environmental Prediction (NCEP) (Saha et al, 2014).…”

“…The CFSv2 model has run retrospectively to produce forecasts (hereafter reforecasts or hindcasts) every 5 days from 1982 to 2009. Despite the availability of those CFSv2 daily hindcasts, temporal downscaling of the seasonal predictions is still routinely done from monthly to daily without using the daily forecast information (e.g., Yuan et al, 2013), with the assumption that the accuracy of daily information is limited at the seasonal timescale. At the sub-seasonal timescale, the usefulness of these daily or sub-daily precipitation or temperature forecasts compared to the monthly disaggregated forecasts has not been assessed.…”

Hydrological monitoring and seasonal forecasting: Progress and perspectives

“…This may be because the precipitation amount and variation are both low in the dry regions, leading to the weak influence of precipitation to the hydrological estimates (e.g., runoff and soil moisture) (Mo and Lettenmaier, 2014). In addition, the influence of ICs to seasonal hydrological predictability has an obvious interannual variability, e.g., with more important role in neutral years than in El Niño-Southern Oscillation (ENSO)-dominant years (Yuan et al, 2013b;Sinha and Sankarasubramanian, 2013). Through an assessment during the hydrological extremes, the role of ICs differs on the phase of hydrological extremes.…”

“…The CGCM climate outputs, after appropriate downscaling procedures, can be directly used to force the hydrological model for seasonal prediction of hydrological fields. Previous studies have assessed the CGCM-based seasonal forecast skill for a multi-decadal hindcast period (e.g., Wood et al, 2005;Luo and Wood, 2008;Mo et al, 2012;Yuan et al, 2013b;Bastola et al, 2013;Mo and Lettenmaier, 2014), and broadly indicate that CGCM-based hydrological forecast skill has marginal improvement relative to ESP beyond 1 month. This suggests much more efforts are needed to improve the CGCMs' predictive skill, especially for those variables relevant to hydrology.…”

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

“…Dynamical forecasting primarily relies on computed drought indicators, such as the standardized precipitation index (SPI; McKee and Kleist, 1993), based on forecast precipitation retrieved from seasonal climate forecast systems (Dutra et al, 2013(Dutra et al, , 2014Mo and Lyon, 2015;Yoon et al, 2012). Although dynamically predicted precipitation is useful information for drought situations, especially for short-term fore-casting 1 month ahead, it also contains high levels of uncertainty and limited skill with respect to long lead times (Wood et al, 2015;Yoon et al, 2012;Yuan et al, 2013). In contrast, statistical drought prediction is an additional source of prospective drought information (Behrangi et al, 2015;Hao et al, 2014).…”