TY - JOUR
T1 - High resolution decadal precipitation predictions over the continental United States for impacts assessment
AU - Salvi, Kaustubh
AU - Villarini, Gabriele
AU - Vecchi, Gabriel A.
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/10
Y1 - 2017/10
N2 - Unprecedented alterations in precipitation characteristics over the last century and especially in the last two decades have posed serious socio-economic problems to society in terms of hydro-meteorological extremes, in particular flooding and droughts. The origin of these alterations has its roots in changing climatic conditions; however, its threatening implications can only be dealt with through meticulous planning that is based on realistic and skillful decadal precipitation predictions (DPPs). Skillful DPPs represent a very challenging prospect because of the complexities associated with precipitation predictions. Because of the limited skill and coarse spatial resolution, the DPPs provided by General Circulation Models (GCMs) fail to be directly applicable for impact assessment. Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches. For both the approaches, statistical relationships established over the calibration period (1961–1990) are applied to the retrospective and near future decadal predictions by GCMs to obtain DPPs at ∼4 km resolution. The skill is quantified across different metrics that evaluate potential skill, biases, long-term statistical properties, and uncertainty. Both the statistical approaches show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DPPs from nine GCMs with 4-km spatial resolution, which can be used as a key input for impacts assessments.
AB - Unprecedented alterations in precipitation characteristics over the last century and especially in the last two decades have posed serious socio-economic problems to society in terms of hydro-meteorological extremes, in particular flooding and droughts. The origin of these alterations has its roots in changing climatic conditions; however, its threatening implications can only be dealt with through meticulous planning that is based on realistic and skillful decadal precipitation predictions (DPPs). Skillful DPPs represent a very challenging prospect because of the complexities associated with precipitation predictions. Because of the limited skill and coarse spatial resolution, the DPPs provided by General Circulation Models (GCMs) fail to be directly applicable for impact assessment. Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches. For both the approaches, statistical relationships established over the calibration period (1961–1990) are applied to the retrospective and near future decadal predictions by GCMs to obtain DPPs at ∼4 km resolution. The skill is quantified across different metrics that evaluate potential skill, biases, long-term statistical properties, and uncertainty. Both the statistical approaches show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DPPs from nine GCMs with 4-km spatial resolution, which can be used as a key input for impacts assessments.
KW - Continental United States
KW - Decadal predictions
KW - Precipitation
KW - Statistical downscaling
UR - http://www.scopus.com/inward/record.url?scp=85028718121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028718121&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2017.07.043
DO - 10.1016/j.jhydrol.2017.07.043
M3 - Article
AN - SCOPUS:85028718121
SN - 0022-1694
VL - 553
SP - 559
EP - 573
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -