ITS1.8/AS5.5 | Downscaling: methods, applications and added value

ITS1.8/AS5.5

EDI
Downscaling: methods, applications and added value
Co-organized by ESSI4
Convener: Marlis Hofer | Co-conveners: Jonathan Eden, Tanja ZerennerECSECS, Cornelia KleinECSECS
Orals
| Fri, 28 Apr, 14:00–15:45 (CEST), 16:15–18:00 (CEST)
 
Room 1.14
Posters on site
| Attendance Fri, 28 Apr, 10:45–12:30 (CEST)
 
Hall X5
Posters virtual
| Attendance Fri, 28 Apr, 10:45–12:30 (CEST)
 
vHall AS
Orals |
Fri, 14:00
Fri, 10:45
Fri, 10:45
Downscaling aims to process and refine global climate model output to provide information at spatial and temporal scales suitable for impact studies. In response to the current challenges posed by climate change and variability, downscaling techniques continue to play an important role in the development of user-driven climate information and new climate services and products. In fact, the "user's dilemma" is no longer that there is a lack of downscaled data, but rather how to select amongst the available datasets and to assess their credibility. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.

Furthermore, epistemologists have started to revisit current practices of climate model validation. This new thread of discussion encourages to clarify the issue of added value of downscaling, i.e. the value gained through adding another level of complexity to the uncertainty cascade. For example, the ‘adequacy-for-purpose view’ may offer a more holistic approach to the evaluation of downscaling models (and atmospheric models, in general) as it considers, for example, user perspectives next to a model’s representational accuracy.

In our session, we aim to bring together scientists from the various geoscientific disciplines interrelated through downscaling: atmospheric modeling, climate change impact modeling, machine learning and verification research. We also invite philosophers of climate science to enrich our discussion about novel challenges faced by the evaluation of increasingly complex simulation models.

Contributions to this session may address, but are not limited to:

- newly available downscaling products,
- applications relying on downscaled data,
- downscaling method development, including the potential for machine learning,
- bias correction and statistical postprocessing,
- challenges in the data management of kilometer-scale simulations,
- verification, uncertainty quantification and the added value of downscaling,
- downscaling approaches in light of computational epistemology.

Orals: Fri, 28 Apr | Room 1.14

Chairpersons: Cornelia Klein, Jonathan Eden
14:00–14:05
Downscaling model evaluation and user perspectives
14:05–14:25
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EGU23-3343
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ITS1.8/AS5.5
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solicited
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Highlight
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Virtual presentation
Robert Wilby and Christian Dawson

Statistical and dynamical downscaling techniques are widely applied in the development of local climate change scenarios. This talk traces the conceptual development of downscaling as a decision-support tool for climate risk assessment, resilience and adaptation planning. Four epochs are identified: (1) early exploration of local changes in key climate variables, such as temperature and precipitation extremes; (2) application of downscaled scenarios to climate impacts modelling (such as for agriculture yield or water resource assessments); (3) advent of ensemble-based methods and more sophisticated handling of uncertainty in the downscaling-impacts workflow; and (4) use of downscaled scenarios to stress-test adaptation options under plausible ranges of climate and non-climatic conditions. Each phase is illustrated by and reflected in the development of the Statistical DownScaling Model – Decision Centric (SDSM-DC) over more than two decades. Questions around fitness for purpose and appropriate uses of the tool are explored. The talk concludes by considering: where next for downscaling?

How to cite: Wilby, R. and Dawson, C.: Conceptual development and use of downscaled climate model information, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3343, https://doi.org/10.5194/egusphere-egu23-3343, 2023.

14:25–14:35
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EGU23-14254
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ITS1.8/AS5.5
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ECS
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On-site presentation
Fiona Spuler, Jakob Wessel, Chiara Cagnazzo, and Edward Comyn-Platt

Statistical bias adjustment is now common practice when using climate models for impact studies, prior to or in conjunction with downscaling methods. Examples of widely used methodologies include CDFt (Vrac et al. 2016), ISIMIP3BASD (Lange 2019) or equidistant CDF matching (Li et al. 2010). Though common practice, recent papers (Maraun et al. 2017) have found fundamental issues with statistical bias adjustment. When multivariate aspects are not evaluated, improper use of bias adjustment is not detected. Fundamental misspecifications of the climate model, such as the displacement of large-scale circulation, cannot be corrected. Furthermore, results are sensitive to internal climate variability over the reference period (Bonnet et al 2022). If applied, bias adjustment methods should therefore be evaluated carefully in multivariate aspects and targeted to the use-case at hand.

However, good practice in the evaluation and application of bias adjustment methods is inhibited by what we frame as practical issues. If at all, published bias adjustment methods are often published as individual software packages across different programming languages (mostly R and Python) that do not allow users to adapt aspects of the method, such as the fit distribution, to their use-case. Existing open-source software packages, such as ISIMIP3BASD or CDFt, often do not offer an evaluation framework that covers multivariate (spatial, temporal, multi-variable) aspects necessary to detect misuse of methods, or user-specific impact metrics. Several of these issues apply to downscaling similarly.

To address some of these practical issues, we developed the open-source software package ibicus in collaboration with ECWMF (available on PyPi, extensive documentation https://ibicus.readthedocs.io/en/latest/index.html, published under Apache 2.0 licence). The package implements eight peer-reviewed bias adjustment methods in a common framework. It also includes an extensive evaluation framework covering multivariate aspects as well as the ETCCDI climate indices. The package thereby contributes to enhanced flexibility and ease-of-use of better evaluation practises in bias adjustment.

Our contribution presents three case studies using ibicus, highlighting a number of pitfalls in the usage of bias adjustment for climate impact modelling, and shows possible ways to address these issues. We investigate extreme indices of precipitation and compound extreme temperature-precipitation indices, modification of the climate change trend, and dry spell length as an example of a temporal index, over northern Spain and Turkey.

We evaluate how bias adjustment adds to the ‘cascade of uncertainty’ and how this can be made transparent in the different use-cases. We also demonstrate how some of the fundamental issues that can arise when applying bias adjustment can be detected and how evaluation of spatial and temporal aspects such as dry spell length can be made specific to the use-case at hand to detect improper use of bias adjustment. Lastly, we demonstrate how the ‘best’ bias adjustment method may depend on the metric of interest, and therefore a user-centric design of comparison and evaluation methods is necessary.

How to cite: Spuler, F., Wessel, J., Cagnazzo, C., and Comyn-Platt, E.: Case studies in bias adjustment: addressing potential pitfalls through model comparison and evaluation using a new open-source python package, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14254, https://doi.org/10.5194/egusphere-egu23-14254, 2023.

14:35–14:45
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EGU23-1296
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ITS1.8/AS5.5
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On-site presentation
Alfonso Hernanz, Carlos Correa, Marta Domínguez, Esteban Rodríguez-Guisado, and Ernesto Rodríguez-Camino

Two main approaches to downscale global climate projections are possible: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a Statistical Downscaling Model (SDM) based on Machine Learning and six Regional Climate Models (RCMs) from EURO-CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation. The study expands at a continental scale over Europe, with a spatial resolution of 0.11o and daily data. Both the SDM and the RCMs are driven by the ERA-Interim reanalysis, and observations are taken from the gridded dataset E-OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, in order to analyze the intervariable consistency, a multivariable index (Fire Weather Index) derived from the fundamental variables has been included. The SDM has reached better scores than the RCMs for all the evaluated aspects with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long-term trends or the sensitivity of different methods to being driven by Global Climate Models instead of by a reanalysis, is needed.

How to cite: Hernanz, A., Correa, C., Domínguez, M., Rodríguez-Guisado, E., and Rodríguez-Camino, E.: Can Statistical Downscaling based on Machine Learning compete with Regional Climate Models? A comparison for temperature, precipitation, wind, humidity and radiation over Europe under present conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1296, https://doi.org/10.5194/egusphere-egu23-1296, 2023.

Statistically postprocessing GCM output prior to the application of RCMs
14:45–14:55
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EGU23-11595
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ITS1.8/AS5.5
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ECS
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On-site presentation
Shuchang Liu, Christian Zeman, and Christoph Schär

The long-existing double-ITCZ problem in GCMs affects not only the models' ability in simulating the current climate, but also implies limitations regarding the assessment of climate sensitivity and global climate change. Using a regional climate model (RCM) with explicit convection at a horizontal grid spacing of 12 km in a large computational domain covering the tropical and sub-tropical Atlantic, we develop a bias-correction downscaling methodology to remove the biases of a driving GCM. The methodology is related to the pseudo-global warming (PGW) approach. Normally this method is used to impress the climate-change signal to a reanalysis-driven RCM simulation, but it can also be used to modulate the lateral-boundary conditions of a GCM, such as to remove the large-scale biases. We show that the double ITCZ problem persists with classical dynamical downscaling (i.e. when driving the RCM directly by the GCM output), but with our bias-corrected downscaling the double ITCZ problem can be removed. Detailed analysis reveals that the main cause of the double ITCZ problem can be attributed to the GCMs' SST bias. Compared to the GCMs' AMIP simulations, RCMs with higher resolution allow explicit deep convection and enable a better simulation of tropical convection and clouds. By improving the corresponding radiative forcing, vertical motion is better simulated. Subsidence stronger to the south of the ITCZ pushes the ITCZ more north in the boreal spring, which is consistent with the observation of the ITCZ. The developed methodology provides an opportunity for better constraining climate sensitivity by removing double-ITCZ biases.

How to cite: Liu, S., Zeman, C., and Schär, C.: Understanding the double-ITCZ problem over the Atlantic with bias-corrected downscaling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11595, https://doi.org/10.5194/egusphere-egu23-11595, 2023.

14:55–15:05
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EGU23-4495
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ITS1.8/AS5.5
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On-site presentation
Muralidhar Adakudlu, Elena Xoplaki, and Niklas Luther

Regional climate models, due to their systematic biases, are not usable for impact assessment and policy-relevant applications. It is common to post-process the regional model outputs with appropriate bias correction methodologies to provide reliable climate change information. We apply a distribution-based, trend-preserving quantile mapping procedure to bias correct the projections of daily precipitation and temperature from an ensemble of 5 RCMs driven by 5 GCMs, each at a resolution of 0.11°, chosen from the EURO-CORDEX initiative. The gridded observations from the German Weather Service, DWD-HYRAS, has been used as a reference for the bias correction. The impact of the bias correction is found to be more pronounced on precipitation than on temperature, as the precipitation biases are larger. The models are wetter and underestimate (overestimate) the daily maximum (minimum) temperature. The correction method eliminates large parts of these biases and maps the distributions of both the variables well with that of observations. The bias adjustment also leads to the narrowing down of the uncertainties in the projected changes of both the variables. The decomposition of total variance into model uncertainty and internal variability suggests that the bias correction acts mostly on the former component. The internal variability component does not seem, however, to undergo considerable changes following the bias correction. Due to the reduction of the uncertainty, we find a slight improvement in the signal-to-noise ratio in the projections. 

How to cite: Adakudlu, M., Xoplaki, E., and Luther, N.: Implications of statistical bias adjustment for uncertainties in regional model projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4495, https://doi.org/10.5194/egusphere-egu23-4495, 2023.

15:05–15:15
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EGU23-3741
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ITS1.8/AS5.5
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ECS
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On-site presentation
Meng-Zhuo Zhang, Ying Han, Zhongfeng Xu, and Weidong Guo

Dynamical downscaling is a widely-used approach to generate regional projections of future climate extremes at a finer scale. Previous studies indicated that the global climate model (GCM) bias correction method prior to dynamical downscaling can improve the simulation of the climate extreme to a certain extent. Recently, a new bias correction method termed MVT was developed. Note that this method did not correct the GCM biases of the climate extreme event explicitly. In this study, we evaluate the MVT method in terms of various climate extreme events through three dynamical downscaling simulations over Asia-western North Pacific with 25 km grid spacing throughout 1980–2014, and further investigate to what extent and how this bias correction method can improve the simulation of downscaled climate extreme events. The dynamical downscaling simulations driven by the original GCM dataset derived from the MPI-ESM1-2-HR (hereafter WRF_GCM), the bias-corrected GCM (hereafter WRF_GCMbc) are validated against that driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset, respectively. The results suggest that compared with the WRF_GCM, the WRF_GCMbc shows more than 26% decrease in root mean square errors of the precipitation and temperature extreme indices, and even 61% out of seasonal extreme indices show more than 50% reduction. Such improvements in the WRF_GCMbc are primarily caused by the correct simulation of the large-scale circulation due to the GCM bias correction. The large-scale circulation in turn improves the simulation of the precipitation and cloud by the water vapor transport and further improves the simulation of the 2m temperature by the radiation process and the surface energy balance, which contribute to the better simulation of the precipitation and temperature extreme indices.

How to cite: Zhang, M.-Z., Han, Y., Xu, Z., and Guo, W.: Validation of MVT bias correction in dynamical downscaling simulations for climate extreme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3741, https://doi.org/10.5194/egusphere-egu23-3741, 2023.

Downscaling method further development: new tools and revisions
15:15–15:25
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EGU23-7470
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ITS1.8/AS5.5
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On-site presentation
Sebastian G. Mutz and Daniel Boateng

The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has lead to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (pyESD; github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualisation, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of codes. The package is highly modular and flexible, and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed or even glacial retreat. We demonstrate the effectiveness of the new PP-ESD framework by generating station-based downscaling products of precipitation and temperature for complex mountainous terrain in Southwest Germany.

How to cite: Mutz, S. G. and Boateng, D.: pyESD: An open-source Python framework for empirical-statistical downscaling of climate information, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7470, https://doi.org/10.5194/egusphere-egu23-7470, 2023.

15:25–15:35
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EGU23-11124
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ITS1.8/AS5.5
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On-site presentation
Peter Berg, Thomas Bosshard, Lars Bärring, Johan Södling, Renate Wilcke, Wei Yang, and Klaus Zimmermann

Bias adjustment of climate models is today normally performed with quantile mapping methods that account for the whole distribution of the parameter. The bulk of the distribution is well described as long as sufficient data records are used (Berg et al., 2012), however, the extreme tails will always suffer from large uncertainties. These uncertainties stem from both the climate model and the reference data set, which prevents a robust and detailed identification of bias in the extreme tail. Empirical quantile mapping methods are therefore prone to overfitting, and may introduce substantial bias when applied outside the calibration period. Commonly, a constant adjustment is applied for values outside the range of the calibration period, but there is room for improvements of the extrapolation method.

While working with a climate service for Sweden, a clear offset was identified between data adjusted within and outside the calibration period for an extreme indicator of daily maximum precipitation. This study explores different extrapolation methods for the extreme tail of the distribution in the spline-based empirical quantile mapping method of the MIdAS bias adjustment method (Berg et al., 2022). By limiting the bias adjustment to the first 95% of the distribution, and thereafter applying a constant or a linear fit to the remaining 5% of data in the tail, the offset is strongly reduced and the adjusted extremes become more robust and plausible.

Berg, P., Feldmann, H., & Panitz, H. J. (2012). Bias correction of high resolution regional climate model data. Journal of Hydrology448, 80-92.

Berg, P., Bosshard, T., Yang, W., & Zimmermann, K. (2022). MIdASv0. 2.1–MultI-scale bias AdjuStment. Geoscientific Model Development15(15), 6165-6180.

How to cite: Berg, P., Bosshard, T., Bärring, L., Södling, J., Wilcke, R., Yang, W., and Zimmermann, K.: Reducing negative impacts of bias adjustment on the distribution tail and extreme climate indicators in MIdAS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11124, https://doi.org/10.5194/egusphere-egu23-11124, 2023.

15:35–15:45
Coffee break
Chairpersons: Cornelia Klein, Jonathan Eden
16:15–16:20
Downscaling method further development: new tools and revisions (continued)
16:20–16:30
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EGU23-14253
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ITS1.8/AS5.5
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ECS
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Highlight
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Virtual presentation
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Henry Addison, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison, and Peter Watson

High resolution rainfall projections are useful for planning for climate change [1] but are expensive to produce using physical simulations. We make novel use of a state-of-the-art generative machine learning (ML) method, diffusion models [2], to more cheaply generate high resolution (8.8km) daily mean rainfall samples over England and Wales conditioned on low resolution (60km) climate model variables. The downscaling model is trained on output from the Met Office UK convection-permitting model (CPM) [3]. We then apply it to predict high-resolution rainfall based on either coarsened CPM output or output from the Met Office HadGEM3 general circulation model (GCM). The downscaling model is stochastic and able to produce samples of high-resolution rainfall that have realistic spatial structure, which previous methods struggle to achieve. It is also easy to train and should better estimate the probability of extreme events compared to previous generative ML approaches.

The downscaling model samples match well the rainfall distribution of CPM simulation output. We use as our conditioning variables We obtained further improvements by also including high-resolution, location-specific parameters that are learnt during the ML training phase. We will discuss the challenges of applying the model trained on coarsened CPM variables to GCM variables and present results about the method’s ability to reproduce the spatial and temporal behaviour of rainfall and extreme events that are better represented in the CPM than the GCM due to the CPM’s ability to model atmospheric convection.

References

[1] Kendon, E. J. et al. (2021). Update to the UKCP Local (2.2km) projections. Science report, Met Office Hadley Centre, Exeter, UK. [Online]. Available: https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf

[2] Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.

[3] Met Office Hadley Centre. (2019). UKCP18 Local Projections at 2.2km Resolution for 1980-2080, Centre for Environmental Data Analysis. [Online]. Available: https://catalogue.ceda.ac.uk/uuid/d5822183143c4011a2bb304ee7c0baf7

How to cite: Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P.: Downscaling with a machine learning-based emulator of a local-scale UK climate model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14253, https://doi.org/10.5194/egusphere-egu23-14253, 2023.

16:30–16:40
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EGU23-164
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ITS1.8/AS5.5
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ECS
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Highlight
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On-site presentation
Sandro Oswald, Stefan Schneider, Maja Zuvela-Aloise, Claudia Hahn, and Clemens Wastl

Extreme temperatures, especially long-lasting heat and cold waves in urban areas, lead to thermal stress of the population and increase the number of weather-related health risks and deaths. The observed climate trend and the associated increase of extreme weather events are expected to continue in the future. Thus, the evaluation of urban thermal stress and the associated health effects becomes an important issue for urban planning and risk management. For Austrian cities, an information system for temperature warnings already exists (Weather warnings, ZAMG), which is based on the information of regional weather forecast models. However, this information does not have the required spatial resolution needed to resolve urban structure and thus to account for the urban heat island effect or cold stress situations in winter.

The aim of this project is to provide the basis for the improvement of extreme weather/thermal (dis)comfort warning systems in Austrian major cities by using high-resolution weather predictions (100 m). Therefore, the soil model SURFEX (developed by Météo France) coupled with the AROME numerical weather forecast model is applied to selected cities in Austria and used to determine the best model configuration to compute short-term forecasts (+60 hours). This method provides not a full dynamical model, but a way of pyhsical downscaling with height corrections and a high-resolution surface model.

In this project, land use parameterization will be updated and improved based on Pan-European High Resolution Layers (e.g. Urban Atlas) of the Copernicus Land Monitoring service in ECOCLIMAP (predefined land use classes for SURFEX). The model output will be verified with in-situ operational and crowd-sourced observations. Furthermore, the results will be compared to the micro-scale urban climate model MUKLIMO_3 from the German Weather Service (100 m) and various thermal infrared (TIR with 150 to 250 m) datasets. The novel modeling approach for simulating thermal stress in urban areas serves as the basis for improving the operational prediction system of extreme temperatures, for optimizing the future extreme weather warning system at the ZAMG, and for decision-making for the involved cities and their stakeholders.

How to cite: Oswald, S., Schneider, S., Zuvela-Aloise, M., Hahn, C., and Wastl, C.: Improvement and verification of urban extreme temperature predictions with satellite and ground observations in Austria (VERITAS-AT), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-164, https://doi.org/10.5194/egusphere-egu23-164, 2023.

16:40–16:50
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EGU23-2260
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ITS1.8/AS5.5
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ECS
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On-site presentation
Brian Böker, Patrick Laux, Patrick Olschewski, and Harald Kunstmann

The reliable prediction of flash flood relevant heavy precipitation events under climate change conditions remains a challenging task for the downscaling community. Therefore, a huge variety of downscaling approaches have been proposed and successfully applied, however, there is still potential for improvements. The conducted study aims to investigate potential improvements by circulation pattern (CP) trends conservation and their utilization for CP conditional statistical downscaling of daily summer precipitation in the (pre-)alpine region of Bavaria. The CPs have been created taking only atmospheric variables into consideration and the link to precipitation is established via CP conditional cumulative distribution functions (CDF) of the observed precipitation at selected measurement sites across the region. The derived CDFs allow for the sampling of CP conditional precipitation values at the station scale which are subsequently bias corrected by quantile mapping (QM) and parametric transfer functions (PTFs) as tested methods. The predicted precipitation values have been evaluated against obervations using different performance measures such as Kling-Gupta Efficiency (KGE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). In order to properly account for extreme events the evaluation has been conducted for the complete precipitation distribution and for the distribution above the 95th percentile seperately. The results show that the described CP conditional downscaling approach is capable of yielding more accurate daily precipitation values especially in the extremes compartment in which an average gain in prediction skill of + 0.24 and a maximum gain of + 0.6 in terms of KGE has been observed. This shows that the conservation of trends and atmospheric information through CPs and their utilization for downscaling can lead to improved precipitation downscaling results.

How to cite: Böker, B., Laux, P., Olschewski, P., and Kunstmann, H.: Accurate heavy precipitation prediction in an (pre-)alpine area: The benefit of trend conservation in circulation pattern conditional statistical downscaling., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2260, https://doi.org/10.5194/egusphere-egu23-2260, 2023.

16:50–17:00
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EGU23-10595
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ITS1.8/AS5.5
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On-site presentation
Nick Brown, Stephan Gruber, and Bin Cao

The lack of long-term and consistent meteorological observations limits the application of land-surface simulators (e.g., of phenomena in hydrology, the cryosphere, ecology) at remote locations. For example, most permafrost areas are remote and lacking consistent meteorological time series, models that describe permafrost change over time cannot be driven for comparison with observations or for impact studies. Reanalysis-derived time series are valuable because they are available with global coverage, for a long time period, and for a broad set of physically consistent variables. Multiple reanalyses can be used to provide estimates of uncertainty. Practically, however, this data is difficult to use for several reasons: grid-scale reanalyses must be downscaled and interpolated horizontally (and vertically within the atmospheric column for mountains regions) to the site‑scale, differences in variables, units, and delivery between reanalyses must be reconciled, and large volumes of data need to be handled. Globsim is an open-source python library (available via GitHub) that was developed to handle these challenges and to facilitate a simulation workflow that takes advantage of the multiple reanalysis products available today. It outputs sub-daily meteorological time series that resemble meteorological stations for any location on the planet. Since the release of the first version of Globsim, we have improved usability, refactored code for maintainability and speed, and fixed a number of bugs. We also added support for ERA5 ensemble data, and added more sophisticated heuristic downscaling algorithms, including TOPOscale for elevation-adjusted radiative fluxes. We use Globsim as a core tool in a multi-model permafrost simulation workflow and, as a future step, we intend to use it as part of a debiasing routine to make predictions of permafrost using climate scenarios. We expect this tool to be broadly applicable to climate change impact modelers and other scientists using climate driven simulations working in (remote) locations that lack meteorological data of sufficient quality and duration for their application.

How to cite: Brown, N., Gruber, S., and Cao, B.: Globsim v.3 – Improvements to an open-source software library for utilizing atmospheric reanalyses in point-scale land surface simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10595, https://doi.org/10.5194/egusphere-egu23-10595, 2023.

17:00–17:10
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EGU23-17077
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ITS1.8/AS5.5
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ECS
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On-site presentation
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Mikel N. Legasa, Soulivanh Thao, Mathieu Vrac, Ana Casanueva, and Rodrigo Manzanas
Under the perfect prognosis approach, statistical downscaling (SD, Gutiérrez et al., 2019) methods aim to learn the relationships between large-scale variables from reanalysis and local observational records. Typically, these statistical relationships, which can be learnt employing many different statistical and machine learning models, are subsequently applied to downscale future global climate model (GCM) simulations, obtaining local projections for the region and variables of interest. 
A posteriori random forests (APRFs) were introduced in a recent paper (Legasa et al., 2021) for precipitation downscaling, but can be potentially used to estimate any probabilitydistribution. While performing similarly to other state-of-the-art machine learning methodologies like convolutional neural networks in terms of predictive performance (as measured in terms of correlation of the downscaled series with the observed series), APRFs produce less biased simulations, as measured by several distributional indicators.Furthermore, climate change signals projected by APRFs are consistent with those given by the raw GCM outputs, thus proving suitable for downscaling local climate change scenarios (Legasa et al. 2023, in review). Moreover, they also automatically select the most adequate large-scale variables and geographical domain of interest, a time-consuming task and potential source of uncertainty (Manzanas et al. 2020) when downscaling climate change projections.
In this work we show how the APRF methodology can be easily extended to more complex and multivariate distributions. One of the proposed extensions is temporal APRFs, which explicitly model the transition in time for a variable and location of interest (e.g. the rainfall probability conditioned to the dry/wet state of the previous day), thus improving the temporal consistency of the downscaled series in terms of several temporal (e.g. spells) indicators. Other possible extensions within the APRF framework include predicting the joint probability distribution of several geographical locations, thus improving the spatial consistency of the downscaled series; and modeling the multivariate joint distribution of different meteorological variables (e.g. precipitation, humidity and temperature).
 
References
Gutiérrez, J.M., Maraun, D., Widmann, M. et al. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment. Int. J. Climatol. 2019; 39: 3750– 3785. doi: https://doi.org/10.1002/joc.5462
Legasa, M. N., Manzanas, R., Calviño, A., & Gutiérrez, J. M. (2022). A posteriori random forests for stochastic downscaling of precipitation by predicting probability distributions. Water Resources Research, 58 (4), e2021WR030272. doi: https://doi.org/10.1029/2021WR030272
Legasa, M. N., Thao, S., Vrac, M., & Manzanas, R. (2023). Assessing Three Perfect Prognosis Methods for Statistical Downscaling of Climate Change Precipitation Scenarios. Submitted to Geophysical Research Letters.
Manzanas, R., Fiwa, L., Vanya, C. et al. Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi. Climatic Change 162, 1437-1453 (2020). doi: https://doi.org/10.1007/s10584-020-02867-3

How to cite: Legasa, M. N., Thao, S., Vrac, M., Casanueva, A., and Manzanas, R.: Extending A Posteriori Random Forests for Multivariate Statistical Downscaling of Climate Change Projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17077, https://doi.org/10.5194/egusphere-egu23-17077, 2023.

Perfect model studies
17:10–17:20
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EGU23-6529
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ITS1.8/AS5.5
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ECS
|
On-site presentation
Remy Bonnet, Mathieu Vrac, Olivier Boucher, and Xia Jin

Climate simulations often need to be adjusted before carrying out climate impact studies at regional scale in order to reduce the biases often present in climate models. To do that, bias adjustment methods are usually applied to climate output simulations and are calibrated over a reference period. This period ideally includes good observational coverage and is often defined as the 2 or 3 more recent decades. However, on these timescales, the climate state may be influenced by the low-frequency internal climate variability. There is therefore a risk of introducing a bias to the climate projections by bias-adjusting simulations with low-frequency variability in a different phase to that of the observations. We proposed here a new pseudo-reality framework using an ensemble of simulations performed with the IPSL-CM6A-LR climate model in order to assess the impact of the low-frequency internal climate variability of the North Atlantic sea surface temperatures on bias-adjusted projections of mean and extreme surface temperature over Europe. We show that adjusting a simulation in a similar phase of the Atlantic Multidecadal Variability to that of the pseudo-observations reduces the pseudo-biases in temperature projections. Therefore, for models and regions where low frequency internal variability matters, it is recommended to sample relevant climate simulations to be bias adjusted in a model ensemble or alternatively to use a very long reference period when possible.

How to cite: Bonnet, R., Vrac, M., Boucher, O., and Jin, X.: Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6529, https://doi.org/10.5194/egusphere-egu23-6529, 2023.

17:20–17:30
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EGU23-12899
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ITS1.8/AS5.5
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ECS
|
On-site presentation
Renata Tatsch Eidt, Giorgia Verri, Vladimir Santos da Costa, Murat Gunduz, and Antonio Navarra

In this study, the predictability of the coastal ocean is assessed in a downscaling exercise for the Adriatic Sea using NEMO 3.6 over a 19 years’ time window (2001-2019). Inspired by the perfect model approach (Denis et al. 2002, De Elia et al. 2002) using a dynamical downscaling setup, a high resolution (2 km) experiment (Big Brother – BB) for the entire Adriatic Sea is used as the “true” reference for a smaller domain, downscaling experiment (Little Brother – LB) in the Northern Adriatic subbasin. The LB experiment has the same horizontal resolution as the BB (2 km) and is downscaled from a low resolution parent model (6 km), in a ratio of 1/3 resolution jump. The 2 km horizontal resolution fits the purpose of reaching an eddy-permitting grid spacing in the Adriatic basin (Masina and Pinardi, 1994; Cushman-Roisin et al. 2002).

Power spectral density analysis is used to evaluate the kinetic energy variance on the frequency domain among the experiments and compare them with the BB experiment. Overall, the LB is more energetic than the parent model, and the timing of the peaks of energy coincides with the ones of the BB. The energy on the 1 year signal is higher in the LB than the BB. The LB can recover a significant amount of energy for all peaks, with special attention to the 6 months period, which is poorly captured by the parent model. The 4 months signal is equally represented in BB and LB, while there is an underestimation of the 6 months signal of LB with respect to BB. Energy in the LB does not deviate from BB more than ~20% in the low frequencies and ~10% in the high frequencies, while the parent model presents in a whole lower energy than the BB, with higher differences on the low frequencies.

The Northern Adriatic circulation is largely influenced by the surface buoyancy flux and the wind forcing (Cessi et al., 2014), which play a significant role in the energy budget and the anti-estuarine overturning circulation of the Adriatic basin. Differences between LB and parent model results may be associated with the energy cascade due to interactions of internal dynamic processes which are differently represented at different resolutions. Differences between LB and BB results are the effect of the downscaling method and the horizontal resolution ratio between the parent model and the nested LB.

Moreover, the analysis of the wavenumber spectra allows a clear overview of the energy distribution in the space domain among the experiments and the representation of small-scale features in the LB. Small scale features less than twice the grid spacing (~12 km) are absent in the low-resolution parent model outputs. Therefore, the comparison with the true reference, BB, reveals the energy spectrum of the parent model solves only the larger scales, while the downscaling LB can recover the smaller scales absent in the initial and lateral boundary conditions.

How to cite: Tatsch Eidt, R., Verri, G., Santos da Costa, V., Gunduz, M., and Navarra, A.: A downscaling exercise for the Adriatic Sea in a perfect model approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12899, https://doi.org/10.5194/egusphere-egu23-12899, 2023.

Seasonal forecasts and downscaling for renewable energy research
17:30–17:40
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EGU23-9644
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ITS1.8/AS5.5
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ECS
|
On-site presentation
Qing Lin, Fatemeh Heidari, Edgar Fabián Espitia Sarmiento, Muralidhar Adakudlu, Andrea Toreti, and Elena Xoplaki

Copernicus Climate Change Service (C3S) integrates multiple seasonal forecast models of climate variables with multiple ensemble realizations. Assessing the risks of natural hazards with high impacts on human and natural systems and providing actionable services at the local scale require high-resolution predictions. We implement the AI-based approach proposed by Heidari et al. (2023) to address such needs and reach a kilometer scale. While downscaling seasonal forecasts, it is crucial to transfer the full range of the uncertainties given by the ensembles.

This study assesses how uncertainty is transferred by an AI-based downscaling approach. Quantile-based metrics are here used to measure the ensemble variability between seasonal forecasts and their downscaled products. On the other side, quantile-based metrics can also give an alternative description of the ensemble variabilities, which could replace the raw ensemble members in the downscaling process. In this study, the AI-downscaling system is tested by inputting (a) raw ensemble members and (b) quantile-based metrics. Transferred uncertainty and downscaling accuracy are then evaluated to develop and implement an optimal downscaling approach with hazard-dependent inputs being selected at  regional and local scales.

 

Heidari F., Lin Q., Espitia Sarmiento E.F., Toreti A., and Xoplaki E. (2023): A deep learning technique to realistically bias correct and downscale seasonal forecast ensembles of climate variables towards the development of an AI-based early warning system, EGU 2023 abstract

How to cite: Lin, Q., Heidari, F., Espitia Sarmiento, E. F., Adakudlu, M., Toreti, A., and Xoplaki, E.: Analysis of Ensemble Uncertainty Transfer in AI-Based Downscaling of C3S Seasonal Forecast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9644, https://doi.org/10.5194/egusphere-egu23-9644, 2023.

17:40–17:50
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EGU23-12395
|
ITS1.8/AS5.5
|
Virtual presentation
Gloria Rea, Daniele Galuzzo, and Marco Formenton

Enel, as most of the Energy Players, has an important exposure on weather risk due to the indirect effect of the power demand and to the direct effects on renewable production. A large component of such risk comes from the hydroelectric production, this is especially true in Southern America where, in some countries, it can represent up to 70% of the total production. We present a practical development of an operational chain to extract information from the seasonal forecasts produced by SEAS5. It works on some catchments in Colombia and Peru with the aim to provide an ensemble forecast of monthly precipitations at a high resolution from the fields at low resolution provided by Copernicus. To produce the high-resolution fields of precipitations we developed a procedure based on Lorenz et al. (2021); for our scope, the biases of the SEAS5 forecasts are corrected following a reference climatology obtained from the SEAS5 hindcasts that is calibrated over the cumulative distribution function calculated be mean of historical measurements of the IDEAM weather stations. The method and preliminary results as well as the validation will be shown in this work.

How to cite: Rea, G., Galuzzo, D., and Formenton, M.: Development of an operational seasonal forecast in Colombia and Peru by mean of statistical downscaling of the SEAS5-Copernicus data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12395, https://doi.org/10.5194/egusphere-egu23-12395, 2023.

17:50–18:00

Posters on site: Fri, 28 Apr, 10:45–12:30 | Hall X5

Chairpersons: Cornelia Klein, Jonathan Eden
Downscaling model evaluation and intercomparisons
X5.118
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EGU23-353
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ITS1.8/AS5.5
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ECS
|
María Ofelia Molina, Joao Careto, Claudia Gutiérrez, Enrique Sánchez, and Pedro Soares

In the recent past, the increase in computational resources allowed researchers to run simulations at increasingly horizontal and time resolutions. One such project is the World Climate Research Program’s Coordinated Regional Downscaling Experiments Flagship Pilot Studies (FPS) on convective phenomena. This FPS encompasses a set of simulations driven by the ERA-Interim reanalysis for the period from 2000-2009 (hindcast) and by the Coupled Model Intercomparison Project Phase 5 Global models for the 1996-2005 period (historical). Most models feature a horizontal resolution of 2.2 to 3 km, nested in an intermediate resolution of 12-25 km. An extended Alpine domain is considered for the simulations, due to the complexity of the mountain system together with heavy precipitation events, a large observational network and the high population density of the area. This initiative aims to build first-of-its-kind ensemble climate experiments of convective-permitting models to investigate convective processes over Europe and the Mediterranean.

 

In this study, the Distribution Added Value metric is used to determine the improvement of the representation of all available FPS hindcast and historical simulations for the daily mean wind speed. The analysis is performed on normalized empirical probability distributions and considers station observation data as a reference. The use of a normalized metric allows for spatial comparison among the different altitudes and seasons. This approach permits a direct assessment of the added value between the higher resolution convection-permitting regional climate model simulations against their global driving simulations and respective coarser resolution Regional Model counterparts. Although the complexity of such simulations, those not always reveal an added value. In general, results show that models add value to their reanalysis or forcing global model, but the nature and magnitude of the improvement on the representation of wind speed vary depending on the model, the spatial distribution and the season.

 

How to cite: Molina, M. O., Careto, J., Gutiérrez, C., Sánchez, E., and Soares, P.: The added value of regional climate simulations at kilometre-scale resolution to describe daily wind speed: the CORDEX FPS-Convection multi-model ensemble runs over the Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-353, https://doi.org/10.5194/egusphere-egu23-353, 2023.

X5.119
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EGU23-13876
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ITS1.8/AS5.5
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ECS
Daniele Peano, Lorenzo Sangelantoni, and Carmen Alvarez-Castro

Climate change impacts assessment crucially relies on climate information at high temporal and spatial resolutions, not available from global climate models (GCMs) involved in the coupled model intercomparison project (CMIP). At the same time, dynamically downscaled regional climate model simulations do not provide global-scale coverage and in several cases are computationally too expensive.

For this reason, downscaling techniques are commonly applied to bridge the resolution gap between GCM simulations and impact studies. The most common methodology is the statistical downscaling approach. However, statistical downscaling fast computation comes at a price, it does not account for physical and dynamic processes potentially inflates temporal variability of the original simulations’ resolution. Given this limitation, the analogs technique may represent a valuable alternative since it considers both large and local scales dynamics balanced by a reasonable increase in computational costs.

The present study explores differences, added value, and limitations characterizing state-of-the-art bias adjustment/statistical downscaling based on a stochastic quantile mapping approach and the analogs technique. In particular, the comparison applies to the data computed in the inter-sectoral impact model intercomparison project (ISIMIP) and data obtained by applying the analogs method based on the same ISIMIP reference dataset. The two approaches are compared and evaluated in terms of the historical period observed statistics reproduction for a few climate variables over European regions.

This study is performed in the framework of GoNEXUS and NEXOGENESIS European projects.

How to cite: Peano, D., Sangelantoni, L., and Alvarez-Castro, C.: Evaluating state-of-art statistical downscaling and analogs approaches on historical climate statistics over European regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13876, https://doi.org/10.5194/egusphere-egu23-13876, 2023.

X5.120
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EGU23-6618
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ITS1.8/AS5.5
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ECS
Intercomparison of machine learning algorithms for downscaling of coarse resolution temperature over complex terrain like the Alps
(withdrawn)
Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, and Dino Zardi
New downscaling tools and datasets
X5.121
|
EGU23-8829
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ITS1.8/AS5.5
|
ECS
Elena Leonarduzzi and Reed M Maxwell

Knowing soil moisture conditions accurately is extremely important for natural hazards prediction, agriculture, and other water resources management practices. Remote sensing products have been used more and more in these contexts. Their main advantage is the spatial coverage, which allows one to obtain continental or even global products. Nevertheless, there are limitations associated with them, such as reduced penetrating depth, impact of cloudiness and snow/ice, and low spatial and temporal resolutions. To compensate for the low spatial resolution, downscaling techniques have been developed that combine different remote sensing products and/or other data considered to affect soil moisture redistribution. The main limitation in their development, is the lack of data to validate the techniques and the final product. Oftentimes in situ measurements are used for the calibration/training and for the testing/verification. These are very sparse, i.e., only available at few locations, and hard to compare directly, as both the satellite products and the downscaled estimates are volumetric and not point estimates.

Here, we create a soil moisture downscaling playground by generating soil moisture estimates with a physics-based hydrological model (ParFlow-CLM) at different resolutions, from a few kilometers to 100 meters. Having continuous gridded estimates of high- and low- resolution soil moisture with a reliable physics-based model, allows us to test and compare different downscaling techniques as well as the impact on the scaling of individual inputs/parameters. As an initial experiment, we model the East Taylor catchment (Colorado, USA) at 100m and 1000m resolution, by only changing the topography (i.e., all other inputs are resolved at 1000m), which is not only the best-known input even at high resolutions, but also the most impactful in soil moisture redistribution. The best performing downscaling technique will allow us, in an operational setup, to run the physics-based model at a coarser resolution but still have a high-resolution product in a computationally inexpensive manner. Beyond our application, the high- and low- resolution simulations generated in this work can be used for the validation of any downscaling technique also applicable with remote sensing products.

How to cite: Leonarduzzi, E. and Maxwell, R. M.: A soil moisture downscaling playground of multiple resolution physics-based simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8829, https://doi.org/10.5194/egusphere-egu23-8829, 2023.

X5.122
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EGU23-2735
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ITS1.8/AS5.5
Martin Dubrovský, Radan Huth, Petr Stepanek, Ondrej Lhotka, Jiri Miksovsky, and Jan Meitner

While much effort has been devoted to analyzing long-term changes of temperature and precipitation in mean values and extremes, studies on changes in variability have been rather scarce. Trends in variability are, however, important, among others because their interaction with trends in mean values determines the degree with which extremes would change. The knowledge of long-term changes in temporal variability is essential for assessments of climate change impacts on various sectors, including hydrology (floods and droughts), agriculture, health, and energy demand and production.

SPAGETTA is a stochastic spatial daily weather generator (WG), which uses first-order multivariate (dimension = number of variables X number of gridpoints) autoregressive model to represent the spatial and temporal variability of surface weather variables (including precipitation and temperature). We consider the generator to be a suitable tool for assessing changes in the spatial and temporal variability of the weather series because of following reasons: (A) The inter-gridpoint lag-0 and lag-1(day) correlations included in a set of WG parameters may serve as representatives for spatial and temporal variability of input weather variables. (B) Statistical significance of changes in the lag-0 and lag-1 correlations derived from the input series may be easily assessed by comparing the changes with a variability of the lag-0 and lag-1 correlations related to the stochasticity in input weather series (the variability is assessed across a set of multiple realisations of the synthetic series). (C) Separate effects of changes in various statistical characteristics on any climatic characteristic may be easily assessed. Specifically, having analysed changes in the means, variability and inter-gridpoint correlations (e.g. based on RCM simulations of the future climate), we may modify only a selected (possibly only a single one) WG parameter(s) before producing the synthetic series and analysing effect of climate change on the climatic characteristics.

In the first part of the contribution, we employ SPAGETTA generator to analyse changes in interdiurnal variability of precipitation and temperature in 8 European regions (defined in Dubrovsky et al 2020, Theor Appl Climatol) using (a) gridded observational (last N years vs. first N years in available E-OBS times series) and (b) RCM-simulated surface weather series (2070-2099 vs 1971-2000; outputs from 19 RCMs available from the CORDEX database are analysed). In doing this, we assess the statistical significance of the detected changes. In the second part, we assess separate effects of changes in the means, variability and lag-0 & lag-1 correlations of temperature and precipitation (the changes based on a set of 19 RCM simulations are used to modify the corresponding WG parameters) on a set of climatic indices - including a set of compound precipitation-temperature characteristics representing spells of days with spatially significant extent of significantly non-normal weather (e.g. hot-dry spells).

How to cite: Dubrovský, M., Huth, R., Stepanek, P., Lhotka, O., Miksovsky, J., and Meitner, J.: Spatial and Temporal Variability of Precipitation and Temperature: Analysis of Recent Changes and Future Development with Use of the Weather Generator and RCM-Based Climate Change Scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2735, https://doi.org/10.5194/egusphere-egu23-2735, 2023.

X5.123
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EGU23-3479
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ITS1.8/AS5.5
Stéphane Goyette and Jérôme Kasparian

An atmospheric single-column model (SCM) developed in the framework of the Canadian Regional Climate Model, CRCM, driven by NCEP-NCAR reanalyses is applied to study the non-linear interactions between the surface and the planetary boundary layer (Goyette et al., 2020). The approach to solve the model equations and the technique described may be implemented in any RCM system environment as a model option. The working hypothesis underlying this SCM formulation is that a substantial portion of the variability simulated in the column can be reproduced by processes operating in the vertical dimension and a lesser portion comes from processes operating in the horizontal dimension. This SCM offers interesting prospects as the horizontal and vertical resolution of the RCM is ever increasing. Due to its low computational cost, multiple simulations may be carried out in a short period of time. In this paper, a range of possible results obtained by changing the lower boundary from open water surface to land, and by varying model parameters are mainly shown for central Mediterranean but also for other applications. Results show that the model responded in a highly nonlinear but coherent manner in the lowest levels with changes in air temperature, moisture and windspeed profiles. The latter are consistent with those of the surface vertical sensible, latent heat and momentum fluxes. For example in the central Mediterranean, during a simulated year, air temperature is increased during all the seasons. Specific humidity is increased during the autumn and winter seasons but decreased by during the spring and summer seasons thus showing the contrasting influence of the land surface. The potential for further developments, as well as some guidance as to how to handle mixed land/open water coupling in RCMs, is also provided.

GOYETTE, Stéphane, FONSECA, Cédric, TRUSCELLO, Léonard. Assessment of nonlinear effects of a deep subgrid lake with an atmospheric single‐column model. In: International Journal of Climatology, 2020. doi: 10.1002/joc.6890

How to cite: Goyette, S. and Kasparian, J.: Numerical investigation with a coupled single-column surface-atmosphere model and an application to central Mediterranean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3479, https://doi.org/10.5194/egusphere-egu23-3479, 2023.

X5.124
|
EGU23-8234
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ITS1.8/AS5.5
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ECS
|
Highlight
|
Imfeld Noemi and Brönnimann Stefan

Numerous historical sources report on hazardous past climate and weather events that had considerable impacts on society. Studying changes in their occurrence or mechanisms behind such events is however hampered by a lack of spatial weather information. For Switzerland, we created a daily high-resolution (1x1 km2) reconstruction of temperature and precipitation fields for the years 1763 to 1960 using an analog resampling method based on observational data. The resampled fields are further post-processed by assimilating temperature observations and quantile mapping the precipitation fields. Together with the present-day meteorological fields, this forms a more than 250-year long gridded data set.

We use this data set to evaluate changes in spring weather impacts over the last 250 years. The spring season receives fewer attention since it has no extreme events in absolute terms. However, it is relevant since weather conditions in spring can delay vegetation onset and growth, and can create substantial vegetation damages due to for example late frost and snow events. We evaluate therefore the long-term changes of spring fresh snow days, late frost days, frost days, and warm days, and compare it to changes of spring onset and reconstructed phenological stages.

How to cite: Noemi, I. and Stefan, B.: Weather reconstruction and application for Switzerland: Long-term changes of spring weather impacts since 1763, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8234, https://doi.org/10.5194/egusphere-egu23-8234, 2023.

X5.125
|
EGU23-3922
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ITS1.8/AS5.5
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ECS
Weipeng Lu and Qihao Weng

The gridded population, crucial for resource allocation and emergency support, is mainly downscaled from the census data with administrative divisions. A common dasymetric mapping approach is building a regression model between aggregated geospatial properties and population potential at the administrative level and then applying this model directly to the grid level. The aggregation of geospatial properties often relies on statistical methods like averaging. However, the difference in scale between the two levels can lead to the heterogeneity of geospatial properties, which causes a gap between the training domain and the target domain and makes these methods fail to preserve the physical meaning of geographic properties. To address this issue, we propose a deep learning-based approach, in which a sophisticated loss function involving tripartite elements, gridded geospatial properties, gridded population potential, and administrative population potential, is designed. In this way, scale heterogeneity both in aggregation and domains can be avoided. In this study, a 30-meter resolution population density map of Hong Kong is produced through the proposed approach. The validation result shows that compared with both the machine learning-based or the artificial neural network-based one, the proposed approach gets a lower RMSE and potentially provides a more accurate reference for detailed urban management.

How to cite: Lu, W. and Weng, Q.: Scale Heterogeneity Avoided Dasymetric Mapping for the Gridded Population, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3922, https://doi.org/10.5194/egusphere-egu23-3922, 2023.

Downscaling model applications
X5.126
|
EGU23-6168
|
ITS1.8/AS5.5
|
ECS
Caroline Legrand, Bruno Wilhelm, and Benoît Hingray

Floods are highly destructive natural hazards causing widespread impacts on socio-ecosystems. This hazard could be further amplified with the ongoing climate change, which will likely alter magnitude and frequency of floods. Estimating how flood-rich periods could change in the future is however challenging. The classical approach is to estimate future changes in floods from hydrological simulations forced by time series scenarios of weather variables for different future climate scenarios. The development of relevant weather scenarios for this is often critical. To be adapted to the critical space and time scales of the considered basins, weather scenarios are thus typically produced from climate models with downscaling models, either dynamical or statistical.

In this study, we assessed the ability of two typical simulations chains to reproduce over the last century (1902-2009) and from large-scale atmospheric information only observed temporal variations of river discharges and flood events of the Upper Rhône River (10,900 km²). The modeling chains are made up of (i) the atmospheric reanalysis ERA-20C, (ii) either the statistical downscaling model SCAMP (Raynaud et al., 2020) or the dynamical downscaling model MAR (Gallée and Schayes, 1994), and (iii) the glacio-hydrological model GSM-SOCONT (Schaefli et al., 2005).

The daily Mean Areal Temperature (MAT) and Precipitation (MAP) time series were compared to the observed ones over the period 1961-2009. The meteorological results highlight the need for a bias-correction for both downscaling models. To avoid irrelevant simulations of the snowpack dynamics, especially for high elevations, the bias-correction was needed not only for the precipitation and temperature scenarios but also for the lapse scenarios of the dynamical downscaling chain. Simulated discharges are globally in very good agreement with the reference ones in the bias-corrected simulations. Whatever the river basin considered, the multi-scale observed variations of discharges are well reproduced (daily, seasonal and interannual). The reconstruction power of the chains is lower for low frequency hydrological situations, namely low flow sequences and annual discharge maxima. Flood events tend to be underestimated by each simulation chain.

Flood activity was also estimated from the discharge time series using the Peak Over Threshold (POT) method. The results over the last century are very promising, and encourage us to continue towards simulations over the last millennium. Outputs from the PMIP4 experiments (CESM1 Last Millennium Ensemble) will be statistically downscaled with the SCAMP model (for reasons of computation costs) and used as forcings in the GSM-SOCONT model.

References: 
- Raynaud et al. (2020) HESS doi.org/10.5194/hess-24-4339-2020 
- Gallée and Schayes, 1994 MWR doi:10.1175/1520-0493(1994)122<0671:DOATDM>2.0.CO;2
- Schaefli et al. (2005) HESS doi.org/10.5194/hess-9-95-2005

How to cite: Legrand, C., Wilhelm, B., and Hingray, B.: Simulating river discharges variations and flood events from large-scale atmospheric information with statistical and dynamical downscaling models: Example of the Upper Rhône River, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6168, https://doi.org/10.5194/egusphere-egu23-6168, 2023.

X5.127
|
EGU23-5321
|
ITS1.8/AS5.5
Mengshi Cui

In this study, a multi-model ensemble of regional climate and air quality coupling model system was established to evaluate current climate and air pollution in China during 2010-2014. Meteorological initial and boundary conditions were obtained from the multi earth system models used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) with a dynamical downscaling method and the National Centers for Environmental Prediction Final Analysis (NCEP-FNL) reanalysis data. These downscaling data under the historical scenario and FNL data were applied to driven the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) to simulate current climate and air quality. A comprehensive evaluation of the current five years was conducted against the ground-level meteorological and chemical observations. The performances for the 2 m temperature were very well and consistently overestimated the wind speed at 10 m by 0.8~1.2 m/s. PM2.5 and ozone concentrations were underestimated by the downscaling data driven simulations compared with the FNL data. The model performance was relatively well and can be used to study the impacts of climate change on China's future air quality and pollution events in the context of carbon neutrality and clean air, which may shed light on policy formulation for medium and long-term air quality management and climate change alleviation.

How to cite: Cui, M.: Multi-model downscaling simulations of regional climate and air quality in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5321, https://doi.org/10.5194/egusphere-egu23-5321, 2023.

X5.128
|
EGU23-617
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ITS1.8/AS5.5
|
ECS
Glauber Willian de Souza Ferreira, Michelle Simões Reboita, and João Gabriel Martins Ribeiro

Global Climate Models (GCMs) are fundamental for simulating future climate conditions. However, such tools have limitations like their coarse resolution, systematic biases, and considerable uncertainties and spread among the projections generated by different models. Thus, raw outputs from GCMs are insufficient for regional-scale studies, which can be solved using downscaling techniques. These methods are particularly relevant for South America (SA), given the continent's climate regimes and topographic complexity. Moreover, critical socio-economic activities developed in SA, such as rainfed agriculture and hydroelectric power generation, are highly dependent on climate conditions and susceptible to extreme events, which can lead to intense droughts or floods depending on the region. Given the background, this study aims to analyze the performance of the statistical downscaling technique Quantile Delta Mapping (QDM) applied to precipitation projections simulated by an ensemble composed of eight GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for SA. In this manner, we evaluate both the original precipitation projections from the GCMs, and after applying the QDM statistical downscaling technique. Daily precipitation data from the Climate Prediction Center (CPC), with a horizontal resolution of 0.5°, and from the Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEPV2), with a horizontal resolution of 0.1°, are used as a reference, so the final resolution of the GCMs (and the ensemble) projections after the QDM technique application is the same from the different validation databases. Preliminary results with CPC indicate a satisfactory performance of the technique on precipitation simulations over SA.

 

The authors thank the CAPES, the R&D Program regulated by ANEEL, and the companies Engie Brasil Energia and Energética Estreito for their financial support.

How to cite: de Souza Ferreira, G. W., Simões Reboita, M., and Martins Ribeiro, J. G.: Applying statistical downscaling to CMIP6 projections of precipitation for South America: Analysis of pre and post-processed simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-617, https://doi.org/10.5194/egusphere-egu23-617, 2023.

Posters virtual: Fri, 28 Apr, 10:45–12:30 | vHall AS

Chairpersons: Cornelia Klein, Jonathan Eden
vAS.20
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EGU23-15537
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ITS1.8/AS5.5
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ECS
Andrea Menapace, Pranav Dhawan, Daniele Dalla Torre, Michele Larcher, and Maurizio Righetti

Global and regional climate models are constantly improving the quality of their outputs with increasingly fine spatial and temporal resolutions. These products, which comprise, for instance, reanalysis, reforecast and forecast, can be used for several applications, such as boundary conditions for climate simulations, initial conditions for local weather forecasting, and reference datasets for environmental and energy uses. Nevertheless, many authors have pointed out that such climate models are not suitable for direct use in local applications due to the presence of biases between the model results and the metered data. At this aim, several statistical methodologies have been proposed to correct and downscale the climate models outputs and make it available also for local purposes. Therefore, the purpose of this contribution is to analyse the current state-of-the-art statistical bias correction methods on different time aggregation to assess the capabilities of these methods from monthly to hourly temporal scale.

This study is carried out on the Trentino- Alto Adige, which is an alpine region in north Italy equipped with several measuring weather stations, around 300. The temperature and precipitation observations have been then used to produce a reference dataset through the geostatistical interpolation method called kriging. Instead, ERA5-Land, the reanalysis of ECMWF, has been adopted for the bias correction analysis. Several methods have been tested comprising of univariate and multivariate method including: linear scaling, variance scaling, local intensity scaling, local power transformation, quantile mapping, quantile delta mapping, and multivariate bias correction methods such as MBCn, MBCp, and MBCr. The time scale investigated are monthly, daily and hourly aggregations.

The results show a general decreasing of the performance of all the bias correction methods with the increase in the time-frequency of the weather variables. In particular, the mean absolute error of the corrected daily temperature is 50% larger than the monthly one, and the same 50% increase in error is found between daily and hourly corrected data. The increase in error with decreasing temporal resolution is even more pronounced for the precipitation variable, which is known to be discontinuous with respect to temperature. Multivariate bias correction methods seem to have difficulty maintaining dependencies between variables in the case of high-frequency data.

Although the results on the hourly data are not so scarce, it is evident that more depth analysis of temporal high-resolution climate data is needed, including sub-hourly data in the future, and therefore become crucial to develop new methodologies capable of correcting sub-daily bias. In conclusion, with this work, the authors seek to support research in the direction of providing high-frequency weather data for local applications, which are crucial, for example, in hydrological simulations for the assessment of hydrogeological risks and the management of renewable energy in the electricity market.

How to cite: Menapace, A., Dhawan, P., Dalla Torre, D., Larcher, M., and Righetti, M.: Analysis of the statistical bias correction of ERA5-Land on different time aggregations in Trentino-Alto Adige, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15537, https://doi.org/10.5194/egusphere-egu23-15537, 2023.