Abstract
Abstract. This work presents a new bias-correction method for precipitation over complex terrain that explicitly considers orographic characteristics. This consideration offers a good alternative to the standard empirical quantile mapping (EQM) method during colder climate states in which the orography strongly deviates from the present-day state, e.g. during glacial conditions such as the Last Glacial Maximum (LGM). Such a method is needed in the event that absolute precipitation fields are used, e.g. as input for glacier modelling or to assess potential human occupation and according migration routes in past climate states. The new bias correction and its performance are presented for Switzerland using regional climate model simulations at 2 km resolution driven by global climate model outputs obtained under perpetual 1990 and LGM conditions. Comparing the present-day regional climate model simulation with observations, we find a strong seasonality and, especially during colder months, a height dependence of the bias in precipitation. Thus, we suggest a three-step correction method consisting of (i) a separation into different orographic characteristics, (ii) correction of very low intensity precipitation, and (iii) the application of an EQM, which is applied to each month separately. We find that separating the orography into 400 m height intervals provides the overall most reasonable correction of the biases in precipitation. The new method is able to fully correct the seasonal precipitation bias induced by the global climate model. At the same time, some regional biases remain, in particular positive biases over high elevated areas in winter and negative biases in deep valleys and Ticino in winter and summer. A rigorous temporal and spatial cross-validation with independent data exhibits robust results. The new bias-correction method certainly leaves some drawbacks under present-day conditions. However, the application to the LGM demonstrates that it is a more appropriate correction compared to the standard EQM under highly different climate conditions as the latter imprints present-day orographic features into the LGM climate.
Highlights
The hydrological cycle is an important component in the Earth’s climate system because of its capability to transport and redistribute mass and energy around the world
Even though regional climate models can solve atmospheric equations on a much finer scale than global models, the simulated precipitation patterns still show large biases for present-day climate when comparing them to observations. This has, for example, been illustrated by the Coordinated Regional Downscaling Experiment (CORDEX) simulations analysed by Casanueva et al (2016) and Rajczak and Schär (2017). Are these biases produced by initial and boundary conditions provided by global climate models (GCMs), but they are related to regions characterised by complex topography and to processes that correspond to a finer scale, such as cloud microphysical processes
We present a new bias-correction method for precipitation over complex topography, which takes orographic characteristics into account
Summary
The hydrological cycle is an important component in the Earth’s climate system because of its capability to transport and redistribute mass and energy around the world. Are these biases produced by initial and boundary conditions provided by global climate models (GCMs), but they are related to regions characterised by complex topography and to processes that correspond to a finer scale, such as cloud microphysical processes These processes need to be parameterised as they cannot be explicitly resolved because of the RCM resolution used in CORDEX (Boer, 1993; Zhang and McFarlane, 1995; Fu, 1996; Haslinger et al, 2013; Yang et al, 2013; Warrach-Sagi et al, 2013; Maraun and Widmann, 2015; Hui et al, 2016). Our work aims at presenting a new biascorrection method that fills this gap by using orographic features as variables for the correction Such a correction avoids the explicit usage of current atmospheric circulation and provides a new alternative to the standard EQM for areas with complex topography during highly different climate states, i.e. glacial times.
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