Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach
Hydrology and Earth System Sciences Copernicus Publications 20:7 (2016) 2737-2743
Abstract:
Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model. Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty.Detection and attribution of human influence on regional precipitation
Nature Climate Change Springer Nature 6:7 (2016) 669-675
Human influence on climate in the 2014 southern England winter floods and their impacts
Nature Climate Change Springer Science and Business Media LLC 6:6 (2016) 627-634
Benchmarking Northern Hemisphere midlatitude atmospheric synoptic variability in centennial reanalysis and numerical simulations
GEOPHYSICAL RESEARCH LETTERS 43:10 (2016) 5442-5449
Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability
JOURNAL OF CLIMATE 29:10 (2016) 3831-3840