scholarly journals Impact of Model Resolution on the Simulation of Precipitation Extremes over China

2021 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Neng Luo ◽  
Yan Guo
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Yellow River ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Precipitation Extremes ◽  
The Tibetan Plateau ◽  
Model Resolution ◽  
High Resolution Models ◽  
The Impact

Climate models tend to overestimate light precipitation and underestimate heavy precipitation due to low model resolution. This work investigated the impact of model resolution on simulating the precipitation extremes over China during 1995–2014, based on five models from Coupled Model Intercomparison Project 6 (CMIP6), each having low- and high-resolution versions. Six extreme indices were employed: simple daily intensity index (SDII), wet days (WD), total precipitation (PRCPTOT), extreme precipitation amount (R95p), heavy precipitation days (R20mm), and consecutive dry days (CDD). Models with high resolution demonstrated better performance in reproducing the pattern of climatological precipitation extremes over China, especially in the western Sichuan Basin along the eastern side of the Tibetan Plateau (D1), South China (D2), and the Yangtze-Yellow River basins (D3). Decreased biases of precipitation exist in all high-resolution models over D1, with the largest decease in root mean square error (RMSE) being 48.4% in CNRM-CM6. The improvement could be attributed to fewer weak precipitation events (0 mm/day–10 mm/day) in high-resolution models in comparison with their counterparts with low resolutions. In addition, high-resolution models also show smaller biases over D2, which is associated with better capturing of the distribution of daily precipitation frequency and improvement of the simulation of the vertical distribution of moisture content.

scholarly journals The importance of model resolution on simulated precipitation in Europe – from global to regional model

2020 ◽  
Author(s):  
Gustav Strandberg ◽  
Petter Lind
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Model Resolution ◽  
Simulated Precipitation ◽  
The Difference ◽  
Model Ensembles ◽  
High Resolution Models

Abstract. Precipitation, and especially extreme precipitation, is a key climate variable as it effects large parts of society. It is difficult to simulate in a climate model because of its large variability in time and space. This study investigates the importance of model resolution on the simulated precipitation in Europe for a wide range of climate model ensembles: from global climate models (GCM) at horizontal resolution of around 300 km to regional climate models (RCM) at horizontal resolution of 12.5 km. The aim is to investigate the differences between models and model ensembles, but also to evaluate their performance compared to gridded observations from E-OBS. Model resolution has a clear effect on precipitation. Generally, extreme precipitation is more intense and more frequent in high-resolution models compared to low-resolution models. Models of low resolution tend to underestimate intense precipitation. This is improved in high-resolution simulations, but there is a risk that high resolution models overestimate precipitation. This effect is seen in all ensembles, and GCMs and RCMs of similar resolution give similar results. The number of precipitation days, which is more governed by large-scale atmospheric flow, is not dependent on model resolution, while the number of days with heavy precipitation is. The difference between different models is often larger than between the low- and high-resolution versions of the same model, which makes it difficult to quantify the improvement. In this sense the quality of an ensemble is depending more on the models it consists of rather than the average resolution of the ensemble. Furthermore, the difference in simulated precipitation between an RCM and the driving GCM depend more on the choice of RCM and less on the down-scaling itself; as different RCMs driven by the same GCM may give different results. The results presented here are in line with previous similar studies but this is the first time an analysis like this is done across such relatively large model ensembles of different resolutions, and with a method studying all parts of the precipitation distribution.

scholarly journals Reconstructing Flood Events in Mediterranean Coastal Areas Using Different Reanalyses and High-Resolution Meteorological Models

2020 ◽  
Vol 21 (8) ◽  
pp. 1865-1887
Author(s):  
A. Senatore ◽  
S. Davolio ◽  
L. Furnari ◽  
G. Mendicino
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Hydrological Model ◽  
Numerical Models ◽  
Heavy Precipitation ◽  
High Impact ◽  
Added Value ◽  
Distributed Hydrological Model ◽  
The Impact ◽  
Small Catchments

AbstractReliable reanalysis products can be exploited to drive mesoscale numerical models and generate high-resolution reconstructions of high-impact weather events. Within this framework, regional weather and climate models may greatly benefit from the recent release of the ERA5 product, an improvement to the ERA-Interim dataset. In this study, two different convection-permitting models driven by these two reanalysis datasets are used to reproduce three heavy precipitation events affecting a Mediterranean region. Moreover, different sea surface temperature (SST) initializations are tested to assess how higher-resolution SST fields improve the simulation of high-impact events characterized by strong air–sea interactions. Finally, the coupling with a distributed hydrological model allows evaluating the impact at the ground, specifically assessing the possible added value of the ERA5 dataset for the high-resolution simulation of extreme hydrometeorological events over the Calabria region (southern Italy). Results, based on the comparison against multiple-source precipitation observations, show no clear systematic benefit to using the ERA5 dataset; moreover, intense convective activity can introduce uncertainties masking the signal provided by the boundary conditions of the different reanalyses. The effect of the high-resolution SST fields is even more difficult to detect. The uncertainties propagate and amplify along the modeling chain, where the spatial resolution increases up to the hydrological model. Nevertheless, even in very small catchments, some of the experiments provide reasonably accurate results, suggesting that an ensemble approach could be suitable to cope with uncertainties affecting the overall meteo-hydrological chain, especially for small catchments.

scholarly journals TRU-NET: a deep learning approach to high resolution prediction of rainfall

2021 ◽  
Author(s):  
Rilwan A. Adewoyin ◽  
Peter Dueben ◽  
Peter Watson ◽  
Yulan He ◽  
Ritabrata Dutta
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Spatial Resolution ◽  
Climate Models ◽  
Heavy Precipitation ◽  
Learning Approach ◽  
Multi Scale ◽  
Weather Model ◽  
The Impact ◽  
Precipitation Events

AbstractClimate models (CM) are used to evaluate the impact of climate change on the risk of floods and heavy precipitation events. However, these numerical simulators produce outputs with low spatial resolution that exhibit difficulties representing precipitation events accurately. This is mainly due to computational limitations on the spatial resolution used when simulating multi-scale weather dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using input data from a reanalysis product, that is comparable to a climate model’s output, but can be directly related to precipitation observations at a given time and location. Further, our input excludes local precipitation, but includes model fields (weather variables) that are more predictable and generalizable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We also propose a non-stochastic variant of the conditional-continuous (CC) loss function to capture the zero-skewed patterns of rainfall. Experiments show that our models, trained with our CC loss, consistently attain lower RMSE and MAE scores than a DL model prevalent in precipitation downscaling and outperform a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various data formulation strategies, for the training and test sets, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions.

scholarly journals Convective processes in high resolution models: Impact of the lead time of the simulation

2018 ◽  
Vol 1 (35) ◽  
Author(s):  
Alvaro Lavin-Gullon ◽  
Jesus Fernandez ◽  
Rita M. Cardoso ◽  
Klaus Goergen ◽  
Sebastian Knist ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Internal Variability ◽  
Heavy Precipitation ◽  
Convective Processes ◽  
High Resolution Models ◽  
Precipitation Events

Most heavy precipitation events occurring in the world are associated with convective processes. As these phenomena produce severe economic and societal impacts, it is crucial to get to know their behaviour and their evolution in a future climate. For this reason, the international project CORDEX (Coordinated Regional climate Downscaling Experiment) proposed the Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean, focused on the study of convection in Europe. In this initiative, multi-model and multi-physics results and uncertainties of regional climate models (RCMs) are explored by means of ensembles of simulations. In this work, we additionally explore the role of internal variability to explain the differences found in the results by different model configurations.

scholarly journals Impact of Higher Resolution on Precipitation over China in CMIP6 HighResMIP Models

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 762
Author(s):  
Xiaoge Xin ◽  
Tongwen Wu ◽  
Weihua Jie ◽  
Jie Zhang
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Southwest China ◽  
Asian Summer Monsoon ◽  
Skill Score ◽  
Horizontal Resolution ◽  
The Tibetan Plateau ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
The Impact

Climate models participated in the High Resolution Model Intercomparison Project (HighResMIP) of Coupled Model Intercomparison Project 6 (CMIP6) are evaluated to reveal the impact of enhanced resolution in simulating the climatological distribution of precipitation over China. The multi-model mean (MME) of five models with 30–50 km horizontal resolution in the atmosphere (MME-50) had a better performance in reproducing the observed spatial patterns of precipitation over Northwest China and Southwest China than the MME of their lower-resolution (70–140 km) models (MME-100). Such an improvement is mainly attributed to the topographical rainfall reproduced by the higher-resolution models, which have the superiority of reproducing the local vertical circulation over the complex terrain. The MME-50 also improves the skill score of the 850-hPa southwesterly of the Indian-Burma trough relative to the MME-100, which may contribute to better simulation skill of precipitation over Southwest China. The MME-50 (0.92) has a close skill score to the MME-100 (0.91) in the simulation of East Asian summer monsoon, which explains why the MME-50 cannot improve the simulation skill of the precipitation over Southeast China and Northern China. The skill score of the precipitation over the Tibetan Plateau (TP) simulated by the MME-50 is even lower than the MME-100, revealing that simulating the climate over the high plateau is still a challenge for the high-resolution models.

scholarly journals The importance of horizontal model resolution on simulated precipitation in Europe – from global to regional models

2021 ◽  
Vol 2 (1) ◽  
pp. 181-204
Author(s):  
Gustav Strandberg ◽  
Petter Lind
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Heavy Precipitation ◽  
Model Resolution ◽  
Grid Spacing ◽  
Low Resolution ◽  
Simulated Precipitation ◽  
Precipitation Indices ◽  
The Difference ◽  
Model Ensembles

Abstract. Precipitation is a key climate variable that affects large parts of society, especially in situations with excess amounts. Climate change projections show an intensified hydrological cycle through changes in intensity, frequency, and duration of precipitation events. Still, due to the complexity of precipitation processes and their large variability in time and space, climate models struggle to represent precipitation accurately. This study investigates the simulated precipitation in Europe in available climate model ensembles that cover a range of horizontal model resolutions. The ensembles used are global climate models (GCMs) from CMIP5 and CMIP6 (∼100–300 km horizontal grid spacing at mid-latitudes), GCMs from the PRIMAVERA project at sparse (∼80–160 km) and dense (∼25–50 km) grid spacing, and CORDEX regional climate models (RCMs) at sparse (∼50 km) and dense (∼12.5 km) grid spacing. The aim is to seasonally and regionally over Europe investigate the differences between models and model ensembles in the representation of the precipitation distribution in its entirety and through analysis of selected standard precipitation indices. In addition, the model ensemble performances are compared to gridded observations from E-OBS. The impact of model resolution on simulated precipitation is evident. Overall, in all seasons and regions the largest differences between resolutions are seen for moderate and high precipitation rates, where the largest precipitation rates are seen in the RCMs with the highest resolution (i.e. CORDEX 12.5 km) and the smallest rates in the CMIP GCMs. However, when compared to E-OBS, the high-resolution models most often overestimate high-intensity precipitation amounts, especially the CORDEX 12.5 km resolution models. An additional comparison to a regional data set of high quality lends, on the other hand, more confidence to the high-resolution model results. The effect of resolution is larger for precipitation indices describing heavy precipitation (e.g. maximum 1 d precipitation) than for indices describing the large-scale atmospheric circulation (e.g. the number of precipitation days), especially in regions with complex topography and in summer when precipitation is predominantly caused by convective processes. Importantly, the systematic differences between low resolution and high resolution also remain when all data are regridded to common grids of 0.5∘×0.5∘ and 2∘×2∘ prior to analysis. This shows that the differences are effects of model physics and better resolved surface properties and not due to the different grids on which the analysis is performed. PRIMAVERA high resolution and CORDEX low resolution give similar results as they are of similar resolution. Within the PRIMAVERA and CORDEX ensembles, there are clear differences between the low- and high-resolution simulations. Once reaching ∼50 km the difference between different models is often larger than between the low- and high-resolution versions of the same model. For indices describing precipitation days and heavy precipitation, the difference between two models can be twice as large as the difference between two resolutions, in both the PRIMAVERA and CORDEX ensembles. Even though increasing resolution improves the simulated precipitation in comparison to observations, the inter-model variability is still large, particularly in summer when smaller-scale processes and interactions are more prevalent and model formulations (such as convective parameterisations) become more important.

scholarly journals The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models

2020 ◽  
Vol 6 (2) ◽  
pp. 115-139
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner
Keyword(s):  
High Resolution ◽  
Observational Data ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Boreal Winter ◽  
Weather Stations ◽  
High Resolution Models ◽  
The Impact

Abstract. Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

scholarly journals Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

2016 ◽  
Vol 20 (9) ◽  
pp. 3843-3857 ◽  
Author(s):  
Hossein Tabari ◽  
Rozemien De Troch ◽  
Olivier Giot ◽  
Rafiq Hamdi ◽  
Piet Termonia ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Analysis ◽  
Climate Models ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Extremes ◽  
Added Value ◽  
Intensity Duration Frequency ◽  
High Resolution Models

Abstract. This study explores whether climate models with higher spatial resolutions provide higher accuracy for precipitation simulations and/or different climate change signals. The outputs from two convection-permitting climate models (ALARO and CCLM) with a spatial resolution of 3–4 km are compared with those from the coarse-scale driving models or reanalysis data for simulating/projecting daily and sub-daily precipitation quantiles. Validation of historical design precipitation statistics derived from intensity–duration–frequency (IDF) curves shows a better match of the convection-permitting model results with the observations-based IDF statistics compared to the driving GCMs and reanalysis data. This is the case for simulation of local sub-daily precipitation extremes during the summer season, while the convection-permitting models do not appear to bring added value to simulation of daily precipitation extremes. Results moreover indicate that one has to be careful in assuming spatial-scale independency of climate change signals for the delta change downscaling method, as high-resolution models may show larger changes in extreme precipitation. These larger changes appear to be dependent on the timescale, since such intensification is not observed for daily timescales for both the ALARO and CCLM models.

Climate change in the High Mountain Asia simulated with CMIP6 models

2021 ◽  
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Climate Models ◽  
Global Climate Models ◽  
Cold Bias ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Model Biases ◽  
Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 °C (± 3.2 °C), associated with an over-extended relative snow cover extent of 53 % (± 62 %), and a relative excess of precipitation of 139 % (± 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 °C (± 0.5 °C), 3.4 °C (± 0.7 °C), 5.2 °C (± 1.2 °C), and 6.6 °C (± 1.5 °C); a relative decrease in the snow cover extent of 10 % (± 4.1 %), 19 % (± 5 %), 29 % (± 8 %), and 35 % (± 9 %); and an increase in total precipitation of 9 % (± 5 %), 13 % (± 7 %), 19 % (± 11 %), and 27 % (± 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

scholarly journals Observed Changes of Rain-Season Precipitation in China from 1960 to 2018

2021 ◽  
Vol 18 (19) ◽  
pp. 10031
Author(s):  
Yanyu Zhang ◽  
Shuying Zang ◽  
Xiangjin Shen ◽  
Gaohua Fan
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Regional Climate ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Central China ◽  
The Tibetan Plateau ◽  
Natural Ecosystems ◽  
Daily Precipitation Data ◽  
Rain Season

Precipitation during the main rain season is important for natural ecosystems and human activities. In this study, according to daily precipitation data from 515 weather stations in China, we analyzed the spatiotemporal variation of rain-season (May–September) precipitation in China from 1960 to 2018. The results showed that rain-season precipitation decreased over China from 1960 to 2018. Rain-season heavy (25 ≤ p < 50 mm/day) and very heavy (p ≥ 50 mm/day) precipitation showed increasing trends, while rain-season moderate (10 ≤ p < 25 mm/day) and light (0.1 ≤ p < 10 mm/day) precipitation showed decreasing trends from 1960 to 2018. The temporal changes of precipitation indicated that rain-season light and moderate precipitation displayed downward trends in China from 1980 to 2010 and rain-season heavy and very heavy precipitation showed fluctuant variation from 1960 to 2018. Changes of rain-season precipitation showed clear regional differences. Northwest China and the Tibetan Plateau showed the largest positive trends of precipitation amount and days. In contrast, negative trends were found for almost all precipitation grades in North China Plain, Northeast China, and North Central China. Changes toward drier conditions in these regions probably had a severe impact on agricultural production. In East China, Southeast China and Southwest China, heavy and very heavy precipitation had increased while light and moderate precipitation had decreased. This result implied an increasing risk of flood and mudslides in these regions. The advance in understanding of precipitation change in China will contribute to exactly predict the regional climate change under the background of global climate change.

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