Abstract. Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to an underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of default and Arctic SVS2-Crocus were driven with in-situ meteorological data and evaluated using measurements of snowpack properties (SWE, depth, density and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada over 32-years (1991–2023). Results show that both default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (RMSE for both ensembles: 55 kg m-2) and snow depth (default RMSE: 0.22 m; Arctic RMSE: 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m-3 to 103 kg m-3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE: 67 kg m-3; Arctic RMSE: 65 kg m-3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than default SVS2-Crocus when simulating snow density profiles. The top performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications.
We present here the first climatology of extreme sub-daily rainfall for a large part of western Europe, developed using a newly developed set of sub-daily rainfall indices, derived from a recently created global sub-daily rainfall (GSDR) dataset. The indices describe sub-daily (3-hourly) rainfall extremes in terms of their intensity, frequency and timing. Analysis of the frequency index (number of 3hr periods with >20 mm rain (R3hr20mm)) and a suite of intensity indices including the annual maxima (Rx3hr) and values of the 99.9th percentile at each gauge (R99.9p3hr) indicate a peak in the frequency and intensity of short-duration rainfall extremes in summer across most of western Europe, while areas around the north-west Mediterranean experience the highest intensities and frequencies of 3-hr rainfall in autumn. The index of contribution from 3-h annual maxima to the daily total (Rx3hrP) indicates that these events are often produced by very short-duration or very peaked storms, with a tendency towards a late afternoon or evening peak in the time of occurrence. There are also clear differences in the spatiotemporal occurrence of the sub-daily extremes when compared to extreme daily rainfall indices, which could have repercussions for flood management. Additional analysis of the sub-daily rainfall indices within the context of Köppen-Geiger climate zones indicates Mediterranean-type climate zones experience more intense and more frequent sub-daily extremes, while the intensity of rainfall within cooler climate zones is lower and the most intense events are restricted to summer. The climate zones are found to be a relatively good indicator of the extreme rainfall characteristics that can be expected in a region. Being able to compare sub-daily rainfall characteristics across a wide region in this manner greatly enhances our ability to investigate future variability and change in sub-daily extremes and will aid in high-resolution climate model validation.
The baseline climate inputs for this study are based primarily on downscaling and bias correction (for temperature) of the High Asia Refined Analysis (HAR, Maussion et al., 2014) dynamical downscaling product, as described in Section 3.2.Given the uncertainties in climate input fields in this data-sparse context, simulations were also performed using two alternative input derivation strategies (Section 3.2.3).These strategies are summarised in Table S1 below.The strategies are not independent, as their main purpose is to indicate whether the conclusions reached on snowpack process representations, the focus of this study, are unduly affected by the downscaling and bias correction approaches described in Section 3.2.2.Precipitation is kept consistent between strategies, as the HAR represents by far the best available source of distributed precipitation fields (Pritchard et al., 2019).The focus is thus on climate variables used in surface energy balance calculations.The implications of using these alternative input strategies are discussed in Section 5 and Section S5.
The "Karakoram Vortex" (KV), hereafter also referred to as the "Western Tibetan Vortex" (WTV), has recently been recognized as a large-scale atmospheric circulation system related to warmer (cooler) near-surface and mid-lower troposphere temperatures above the Karakoram in the western Tibetan Plateau (TP). It is characterized by a deep, anti-cyclonic (cyclonic) wind anomaly associated with higher (lower) geopotential height in the troposphere, during winter and summer seasons. In this study, we further investigate the seasonality and basic features of the WTV in all four seasons, and explore its year-to-year variability and influence on regional climate. We find the WTV accounts for the majority of year-to-year circulation variability over the WTP as it can explain over 50% ( $${R^2} \geqslant 0.5$$ ) variance of the WTP circulation on multiple levels throughout the troposphere, which declines towards the eastern side of the TP in most seasons. The WTV is not only more (less) active but also has a bigger (smaller) domain area, with a deeper (shallower) structure, in winter and spring (summer and autumn). We find that the WTV is sensitive to both the location and intensity of the Subtropical Westerly Jet (SWJ), but the relationship is highly dependent on the climatological mean location of SWJ axes relative to the TP in different seasons. We also show that the WTV significantly modulates surface and stratospheric air temperatures, north–south precipitation patterns and total column ozone surrounding the western TP. As such, the WTV has important implications for the understanding of atmospheric, hydrological and glaciological variability over the TP.
Sub-daily rainfall observations are vital to help us understand, model and adapt to changing climate extremes. However, gauge records often have quality issues, for example due to equipment malfunctions and recording errors. This paper presents a new, open-source quality control algorithm (GSDR-QC) to identify these issues in hourly rainfall data, along with an application of the algorithm to the Global Sub-Daily Rainfall (GSDR) observational dataset. The algorithm is based on 25 quality checks, which are combined into a simple rule base to remove suspicious data. The quality checks and rule base are adaptable to help incorporate local or regional information. Comparison with manually quality-controlled gauge data shows that the procedure results in an overall improvement to the quality of the GSDR dataset. A UK case study further demonstrates the performance of the GSDR-QC procedure, while showing how region-specific data and understanding can be incorporated into the quality control process.
Abstract. Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation, and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation, and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of the default and Arctic SVS2-Crocus were driven with in situ meteorological data and evaluated using measurements of snowpack properties (snow water equivalent, SWE; depth; density; and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada, over 32 years (1991–2023). Results show that both the default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (root-mean-square error, RMSE, for both ensembles – 55 kg m−2) and snow depth (default RMSE – 0.22 m; Arctic RMSE – 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m−3 to 103 kg m−3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE – 67 kg m−3; Arctic RMSE – 65 kg m−3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than the default SVS2-Crocus when simulating snow density profiles. The top-performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and to prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications.
Abstract Precipitation indices based on daily gauge observations are well established, openly available and widely used to detect and understand climate change. However, in many areas of climate science and risk management, it has become increasingly important to understand precipitation characteristics, variability and extremes at shorter (sub-daily) durations. Yet, no unified dataset of sub-daily indices has previously been available, due in large part to the lesser availability of suitable observations. Following extensive efforts in data collection and quality control, this study presents a new global dataset of sub-daily precipitation indices calculated from a unique database of 18,591 gauge time series. Developed together with prospective users, the indices describe sub-daily precipitation variability and extremes in terms of intensity, duration and frequency properties. The indices are published for each gauge where possible, alongside a gridded data product based on all gauges. The dataset will be useful in many fields concerned with variability and extremes in the climate system, as well as in climate model evaluation and management of floods and other risks.
Random field simulations are increasingly useful for exploring variability and uncertainty in spatiotemporal precipitation patterns. Mountain regions present unique challenges due to gauge sparsity, topography, and the potential impacts of precipitation phase. To address these challenges, this paper presents open-source Python code (RM-mountain) for conditional spatial precipitation random field simulation in mountain regions, combining a modified Random Mixing Whittaker-Shannon Python (RMWSPy) approach with the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. Using precipitation observations from three gauges as conditioning constraints, three key modifications are implemented: 1) enhanced representation of spatial covariance using time series data to compensate for gauge sparsity; 2) elevation dependence; and 3) evaluation of seasonal effects such as snow melt. Ensemble simulations for each modification demonstrate improvements in the quality of spatial precipitation fields and resulting simulated streamflow (NSE increased from 0.76 to 0.84, bias reduced from -20.94 to -8.29) in a small mountain catchment in Alberta, Canada.
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