scholarly journals A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
Fiona Johnson ◽  
Ashish Sharma
Keyword(s):  
General Circulation Model ◽  
Time Scales ◽  
Bias Correction ◽  
General Circulation ◽  
Circulation Model ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Model Precipitation

scholarly journals A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling

2016 ◽  
Vol 29 (10) ◽  
pp. 3519-3539 ◽  
Author(s):  
Rajeshwar Mehrotra ◽  
Ashish Sharma
Keyword(s):  
Water Resources ◽  
Time Scales ◽  
Bias Correction ◽  
Time Scale ◽  
General Circulation ◽  
Circulation Model ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Multiple Variables ◽  
Multivariate Quantile

Abstract A novel multivariate quantile-matching nesting bias correction approach is developed to remove systematic biases in general circulation model (GCM) outputs over multiple time scales. This is a significant advancement over typical quantile-matching alternatives available for bias correction, as they implicitly assume that correction of individual variable attributes will lead to correction of dependence biases between multiple variables. Furthermore, existing approaches perform bias correction at a given time scale (e.g., daily), whereas applications often require biases to be addressed at more than one time scale (such as annual in the case of most water resources planning projects). The proposed approach addresses all these issues, and additionally attempts to correct for lag-1 dependence (and cross-dependence) attributes across multiple time scales. The approach is called multivariate recursive quantile nesting bias correction (MRQNBC). The fidelity of the approach is demonstrated by applying it to a vector of CSIRO Mk3 GCM atmospheric variables and comparing the results with the commonly used quantile-matching approach. Following this, the implications of the approach in hydrology- and water resources–related applications are demonstrated by feeding the bias-corrected data to a rainfall downscaling model and comparing the downscaled rainfall attributes for current and future climate. The proposed approach is shown to represent the variability and persistence related attributes better and can thus be expected to have important consequences for the simulation of occurrence and intensity of extreme events such as floods and droughts in downscaled simulations, of importance in various climate impact assessment applications.

scholarly journals On the bias correction of general circulation model output for Indian summer monsoon

2012 ◽  
Vol 20 (3) ◽  
pp. 349-356 ◽  
Author(s):  
Nachiketa Acharya ◽  
Surajit Chattopadhyay ◽  
U. C. Mohanty ◽  
S. K. Dash ◽  
L. N. Sahoo
Keyword(s):  
General Circulation Model ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Bias Correction ◽  
General Circulation ◽  
Circulation Model ◽  
Model Output ◽  
General Circulation Model Output

scholarly journals Impact of changes in river nutrient fluxes on the global marine silicon cycle: a model comparison

2009 ◽  
Vol 6 (2) ◽  
pp. 4463-4492
Author(s):  
C. Y. Bernard ◽  
G. G. Laruelle ◽  
C. P. Slomp ◽  
C. Heinze
Keyword(s):  
General Circulation Model ◽  
Time Scales ◽  
General Circulation ◽  
Circulation Model ◽  
Box Model ◽  
Global Ocean ◽  
Export Production ◽  
River Input ◽  
Silicon Cycle ◽  
Ocean Response

Abstract. The availability of dissolved silica in the ocean provides a major control on the growth of siliceous phytoplankton. Diatoms in particular account for a large proportion of oceanic primary production. The original source of the silica is rock weathering, followed by transport of dissolved and biogenic silica to the coastal zone. This model study aims at assessing the sensitivity of the global marine silicon cycle to variations in the river input of silica and other nutrients on timescales ranging from several centuries to millennia. We compare the performance of a box model for the marine Si cycle to that of a global biogeochemical ocean general circulation model (HAMOCC2 and 5). Results indicate that the average global ocean response to changes in river input of Si is surprisingly similar in the models on time scales up to 150 kyrs. While the trends in export production and opal burial are the same, the box model shows a delayed response to the imposed perturbations compared to the general circulation model. Results of both models confirm the important role of the continental margins as a sink for silica at the global scale. While general circulation models are indispensable when assessing the spatial variation in opal export production and biogenic Si burial in the ocean, this study demonstrates that box models provide a good alternative when studying the average global ocean response to perturbations of the oceanic silica cycle (especially on longer time scales).

scholarly journals Assessment of the influence of bias correction on meteorological drought projections for Poland

2015 ◽  
Vol 12 (10) ◽  
pp. 10331-10377 ◽  
Author(s):  
M. Osuch ◽  
R. J. Romanowicz ◽  
D. Lawrence ◽  
W. K. Wong
Keyword(s):  
Time Scales ◽  
Bias Correction ◽  
Climate Model ◽  
Precipitation Index ◽  
Future Climate Change ◽  
Multiple Time Scales ◽  
Climate Projections ◽  
Multiple Time ◽  
Monthly Precipitation ◽  
Standard Precipitation Index

Abstract. Possible future climate change effects on drought severity in Poland are estimated for six ENSEMBLE climate projections using the Standard Precipitation Index (SPI). The time series of precipitation represent six different RCM/GCM run under the A1B SRES scenario for the period 1971–2099. Monthly precipitation values were used to estimate the Standard Precipitation Index (SPI) for multiple time scales (1, 3, 6, 12 and 24 months) for a spatial resolution of 25 km × 25 km for the whole country. Trends in SPI were analysed using a Mann–Kendall test with Sen's slope estimator for each 25 km × 25 km grid cell for each RCM/GCM projection and timescale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded E-OBS precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the time scale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarise the mechanisms underlying the influence of bias correction on trends using a simple example of a linear bias correction procedure. In the case of precipitation the bias correction by QM does not change the direction of changes but can change the slope of trend. We also have noticed that the results for the same GCM, with differing RCMs, are characterized by similar pattern of changes, although this behaviour is not seen at all time scales and seasons.

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