Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-to-year variation of tree growth over northeastern France

Abstract

, et al.. Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-toyear variation of tree growth over northeastern France.. Agricultural and Forest Meteorology, Elsevier Masson, 2017, 232, pp.247-264. 10.1016/j.agrformet.2016.08.021. hal-01360339 Agricultural and Forest Meteorology 232 (2017 Contents lists available at ScienceDirect Agricultural and Forest Meteorology j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g r f o r m e t Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-to-year variation of tree growth over northeastern France [1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008]. Previous results show a strong deficiency of the WRF model for simulating precipitation, especially when convective processes are involved. In order to reduce such biases, a Quantile Mapping (QM) method is applied to WRF-simulated precipitation using the mesoscale atmospheric analyses system SAFRAN («Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie») that provides precipitation data at an 8 km resolution. Raw and post-corrected model outputs are next used to compute the soil water balance of 30 Douglas-fir and 57 common Beech stands across Burgundy, for which radial growth data are available. Results show that the QM method succeeds at reducing the model's wet biases in spring and summer. Significant improvements are also noted for rainfall seasonality and interannual variability, as well as its spatial distribution. Based on both raw and post-corrected rainfall time series, a Soil Water Deficit Index (SWDI) is next computed as the sum of the daily deviations between the relative extractible water and a critical value of 40% below which the low soil water content induce stomatal regulation. Post-correcting WRF precipitation does not significantly improve the simulation of the SWDI upon the raw (uncorrected) model outputs. Two characteristic years were diagnosed to explain this unexpected lack of improvement. Although the QM method allows producing realistic precipitation amounts, it does not correct the timing errors produced by the climate model, which is yet a major issue to obtain reliable estimators of local-scale bioclimatic conditions for impact studies. A realistic temporality of simulated precipitation is thus required before using any systematic post-correction method for appropriate climate impact assessment over temperate forests.

Seasonal Forecasting of Onset of Summer Rains over South Africa

“…The average dates of onset derived The computation of the spatial distribution of the anomaly correlation coefficients between anomalies of the forecast onset dates and the verifying datasets reveals no significant correlation between the two, thereby indicating the skill of the models to be less in forecasting the dates of onset over the region. The bias correction of the forecast precipitation also does not improve the correlation coefficients as the bias correction techniques do not correct the timing errors produced by the models (Boulard et al 2017). These results indicate that the global model SINTEX-F2 and the regional model WRF are to be improved to better represent the atmospheric processes responsible for rains over South Africa, which would lead to an improvement in the spatial distribution of precipitation over South Africa and hence the forecast of onset dates of rains over South Africa during the summer season.…”

Ecological analysis of large floristic and plant-sociological datasets – opportunities and limitations

“…Partly this was compensated by the study by bioclimatic variables (Hijmans et al 2005;Karger et al 2016). Other approaches successfully integrated effects of climatic variation in models (Boulard et al 2017). Climatic gradients are steep especially in the mountainous Mediterranean study areas and thus global climate datasets can underestimate topographic, regional and local effects (Nadeau et al 2017).…”