# Towards Ensemble-Based Kilometer-Scale Climate Simulations over the Third Pole Region

Andreas Prein, Nikolina Ban, Tinghai Ou, Jianping Tang, Koichi Sakaguchi, Emily Collier, Sanjay Jayanarayanan, Lu Li, Stefan Sobolowski, Xingchao Chen, et al.

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Andreas Prein, Nikolina Ban, Tinghai Ou, Jianping Tang, Koichi Sakaguchi, et al. Towards Ensemble-Based Kilometer-Scale Climate Simulations over the Third Pole Region. Climate Dynamics, 2022, 10.1007/s00382-022-06543-3. hal-03866891

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# Towards Ensemble-Based Kilometer-Scale Climate Simulations over the Third Pole Region

**Andreas F. Prein** (prein@ucar.edu)

National Center for Atmospheric Research https://orcid.org/0000-0001-6250-179X

**Nikolina Ban**

Universität Innsbruck: Universitat Innsbruck

**Tinghai Ou**

University of Gothenburg: Goteborgs Universitet

**Jianping Tang**

Nanjing University

**Koichi Sakaguchi**

PNNL: Pacific Northwest National Laboratory

**Emily Collier**

University of Innsbruck: Universitat Innsbruck

**Sanjay Jayanarayanan**

Ministry of Earth Sciences

**Lu Li**

NORCE Norwegian Research Centre AS

**Stefan Sobolowski**

NORCE Norwegian Research Centre AS

**Xingchao Chen**

The Pennsylvania State University

**Xu Zhou**

CAS Institute of Tibetan Plateau Research: Institute of Tibetan Plateau Research Chinese Academy of Sciences

**Hui-Wen Lai**

University of Gothenburg: Goteborgs Universitet

**Shiori Sugimoto**

Japan Agency for Marine-Earth Science and Technology: Kaiyo Kenkyu Kaihatsu Kiko

**Liwei Zou**

Institute of Atmospheric Physics Chinese Academy of Sciences

**Shabeh ul Hasson**

University of Hamburg: Universitat Hamburg

**Marie Ekstrom**

Cardiff University
**Praveen Kumar Pothapakula**

Goethe University Frankfurt: Goethe-Universitat Frankfurt am Main

**Romilly Stuart**

University of Bergen: Universitetet i Bergen

**Hans Christian Steen-Larsen**

University of Bergen: Universitetet i Bergen

**Ruby Leung**

PNNL: Pacific Northwest National Laboratory

**Danijel Belusic**

SMHI: Sveriges meteorologiska och hydrologiska institut

**Julia Kukulies**

University of Gothenburg: Goteborgs Universitet

**Julia Curio**

University of Gothenburg: Goteborgs Universitet

**Deliang Chen**

University of Gothenburg: Goteborgs Universitet

**Research Article**

**Keywords:** Kilometer-scale modeling, Third Pole Region, CORDEX, Ensembles

**Posted Date:** May 3rd, 2022

**DOI:** https://doi.org/10.21203/rs.3.rs-1570621/v1

**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License.

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Towards Ensemble-Based Kilometer-Scale
Climate Simulations over the Third Pole
Region

Andreas F Prein¹*†, Nikola Ban²†, Tinghai Ou⁷†, Jianping

Tang†, Koichi Sakaguchi³†, Emily Collier²†, Sanjay

Jayanarayanan⁴†, Lu Li⁵†, Stefan Sobolowski⁵†, Xingchao

Chen⁶†, Xu Zhou⁸†, Hui-Wen Lai⁷†, Shiori Sugimoto⁹†, Liwei

Zou¹²†, Shabeh ul Hasson¹³†, Marie Ekstrom¹⁴†, Praveen

Kumar Pothapakula¹⁶,¹⁷†, Romilly Stuart¹⁵,¹⁸†, Hans

Christian Steen-Larsen¹⁵†, Ruby Leung³†, Danijel

Belusic¹⁰†, Julia Kukulies⁷†, Julia Curio⁷† and Deliang Chen⁷†

¹* MMM Laboratory, National Center for Atmospheric Research, 3090 Center Green Dr., Boulder, 80301, CO, U.S.A..

²Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innrain 52f, Innsbruck, 6020, Tirol, Austria.

³Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, 99354, WA, USA.

⁴Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Dr. Homi Bhabha Road, Pune, 411008, Maharashtra, India.

⁵NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Nygrdsgaten 112, Bergen, 5008, Norway.

⁶Department of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, Walker Building, University Park, 16802, Pennsylvania, USA.

⁷Department of Earth Sciences, University of Gothenburg, Guldhedsgatan 5A, Gothenburg, 40530, Sweden.
2 *Convection-Permitting Third Pole Experiment*

⁸National Tibetan Plateau Data Center, State Key Laboratory of
Tibetan Plateau Earth System and Resource Environment,
Institute of Tibetan Plateau Research, Building 3, No.16 Lincui
Road, Beijing, 100101, China.

⁹Japan Agency for Marine-Earth Science and Technology,
3173-25 Showa-machi, Kanazawa-ku, Yokohama, 2360001, Japan.

¹⁰Swedish Meteorological and Hydrological Institute (SMHI),
Folkborgsgatan 17, Norrkping, 601 76, Sweden.

¹¹Nanjing University (NJU), 163 Xianlin Road, Qixia District,
Nanjing, 210023, Jiangsu Province, China.

¹²Institute of Atmospheric Physics, Chinese Academy of Sciences,
HuaYanLi No. 40, QiJiaHuoZi, ChaoYang District, BeiJing,
100049, China.

¹³HAREME Lab, Institute of Geography, Center for Earth
System Research and Sustainability (CEN), Universitt Hamburg,
Bundesstrasse 55, Hamburg, 20146, Hamburg, Germany.

¹⁴Cardiff University, Park Place, Cardiff, CF10 3AT, Wales, UK.

¹⁵University of Bergen, P.O.Box 7800, Bergen, 5020, Norway.

¹⁶Institute for Atmospheric and Environmental Sciences, Goethe
University, Altenhferallee 1, Frankfurt am Main, 60438, Hesse,
Germany.

¹⁷Now at: Karlsruhe Institute of Technology (KIT),
Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen,
76344, Baden-Württemberg, Germany.

¹⁸Laboratoire des Sciences du Climat et de l'Environnement,
IPSL, UMR 8212, CEA-CNRS-UVSQ-UPS, Gif sur Yvette,
France.

*Corresponding author(s). E-mail(s): prein@ucar.edu;
Contributing authors: nikolina.ban@uibk.ac.at; tinghai.ou@gu.se;
jptang@nju.edu.cn; Koichi.Sakaguchi@pnnl.gov;
emily.collier@uibk.ac.at; sanjay@tropmet.res.in;
luli@norceresearch.no; stso@norceresearch.no; xzc55@psu.edu;
xuzhou@itpcas.ac.cn; hui-wen.lai@gu.se; shiorisug@jamstec.go.jp;
zoulw@lasg.iap.ac.cn; fgmv053@uni-hamburg.de;
mce7212@gmail.com; praveen.pothapakula@kit.edu;
romilly.harris-stuart@lsce.ipsl.fr; ruby.leung@pnnl.gov;
danijel.belusic@smhi.se; julia.kukulies@gu.se;
j.curio@reading.ac.uk; deliang@gvc.gu.se;
Hans.Christian.Steen-Larsen@uib.no;
†These authors contributed equally to this work.

**Abstract**

The Tibetan Plateau and its surrounding mountains have an average elevation of 4,400 m and a glaciated area of ~100,000 km² giving it the name "Third Pole (TP) region". The TP is the headwater of many major rivers in Asia that provide fresh water to hundreds of millions of people. Climate change is altering the energy and water cycle of the TP at a record pace but the future of this region is highly uncertain due to major challenges in simulating weather and climate processes in this complex area. The Convection-Permitting Third Pole (CPTP) project is a Coordinated Regional Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) that aims to revolutionize our understanding of climate change impacts on the TP through ensemble-based, kilometer-scale climate modeling. Here we present the experimental design and first results from multi-model, multi-physics ensemble simulations of three case studies. The five participating modeling systems show high performance across a range of meteorological situations and are close to having "observational quality" in simulating precipitation and near-surface temperature. This is partly due to the large differences between observational datasets in this region, which are the leading source of uncertainty in model evaluations. However, a systematic cold bias above 2,000 m exists in most modeling systems. Model physics sensitivity tests performed with the Weather Research and Forecasting (WRF) model show that planetary boundary layer (PBL) physics and microphysics contribute equally to model uncertainties. Additionally, larger domains result in better model performance. We conclude by describing high-priority research needs and the next steps in the CPTP project.

**Keywords:** Kilometer-scale modeling, Third Pole Region, CORDEX, Ensembles

# 1 Introduction

One of the largest stores of ice and snow, outside of the Arctic and Antarctic is the Tibetan Plateau and surrounding mountain ranges, known as the Third Pole (TP) [1]. The TP plays a significant role in the atmospheric circulation of the Northern Hemisphere [2] and the global climate system more generally, and is highly sensitive to human-induced climate change [3]. Additionally, the TP is the headwater region of many major Asian rivers including
4 *Convection-Permitting Third Pole Experiment*

the Indus, Brahmaputra, Ganges, Mekong, Yellow, and the Yangtze providing
freshwater to hundreds of millions of people [4]. Because of the TP's complex
terrain and harsh environment, ground-based observations are scarce and satel-
lite observations are error-prone [5], which hampers efforts to study regionally
important physical processes, systems, and the effect of climate change on the
TP water cycle. Horizontal resolutions of prevailing global reanalysis datasets
are generally coarser than 30 km. This is not sufficient to accurately repro-
duce precipitation over steep topography or arising from convection and other
mesoscale systems that are characterized by small-scale variability [see e.g.,
6]. As a result, our understanding of the impacts of climate change on the TP
and downstream regions remains incomplete.

One of the main visions of CORDEX (Coordinated Regional Climate
Downscaling Experiment) is to advance and coordinate science and applica-
tion of regional climate downscaling through global collaborations [7]. These
regional climate downscaling techniques are used to provide higher-resolution
climate information at regional and local scales that conventional global cli-
mate models (GCMs) cannot represent. Standard dynamical downscaling in
CORDEX experiments is done at the grid spacing of 12-50 km, and they do
indeed add value at regional scales in comparison to GCMs [see e.g., 8]. How-
ever, decreasing the grid spacing of regional climate models (RCMs) from
50 km to 12 km does not always show a clear benefit [9–11].

To solve emerging scientific challenges<fnref n="1" /> that cannot be addressed within
the general CORDEX framework, a more targeted experimental setup, so-
called ?Flagship Pilot Studies (FPS)? were initiated. These are intended to
enable the CORDEX community to better address a number of the chal-
lenges in a coordinated way [12]. One such challenge is to better understand
regional and local processes and phenomena relevant to climate change that

<footnote n="1">¹A list can be found here: https://cordex.org/experiment-guidelines/flagship-pilot-studies/.</footnote>
cannot be investigated with standard CORDEX simulations and for which even higher-resolution simulations are needed [13]. Such phenomena include extreme precipitation producing storms such as mesoscale convective systems (MCSs) [14, 15], orographic precipitation [16], snowpack dynamics [17], or land-atmosphere coupling [18].

Over recent years, climate simulations with a grid spacing around 5 km and less are emerging over different regions of the world and underline the importance of explicit treatment of convection in climate models [19]. Such kilometer-scale models typically do not use deep convection parametrizations, which are large sources of uncertainty in climate simulations [20]. An increase in the resolution and explicit treatment of convection has led to major improvements in the simulation of precipitation [21–24], clouds [25], snow cover [26–30], and local wind systems like sea-breezes [31]. In addition to better performance, such simulations also show differences in the climate change signal when compared to coarser-resolution models [15, 32–34].

Recent studies employing kilometer-scale models over the TP region have shown its potential in resolving local processes in complex terrain such as valley winds and their interactions with large-scale forcing. Resolving such processes at a kilometer or sub-kilometer scale realistically reproduces the diurnal and seasonal cycle characteristics, lapse rate, the spatial distribution of climatic variables, precipitation frequency and intensity, and water vapor transport towards the TP [35–39].

Until recently, convection-permitting studies were typically performed with only one model and for one region. However, in the last years, multi-model high-resolution, ensemble simulations started to emerge. For instance, Kendon et al.[40] presented a twelve-member ensemble of km-scale projections (spanning three 20-year periods) providing an initial estimate of uncertainties at
6 Convection-Permitting Third Pole Experiment

158 km-scales and sampling the uncertainty in the model physics but not in the
159 km-scale model itself. One of the first multi-model km-scale was produced
160 within the CORDEX FPS on Convective Phenomena over Europe and the
161 Mediterranean [41]. Ban et al.[42] presented a set of 23 simulations conducted
162 with 6 different regional climate models over the greater Alpine region. The
163 results are encouraging, as they confirm previous findings of a large reduction
164 of biases in precipitation diurnal cycle and heavy precipitation. Furthermore,
165 they indicate that the uncertainties in climate simulations might decrease by
166 using km-scale models compared to coarser-resolution models [42, 43].

167 Motivated by the previous studies, the Convection-Permitting Third Pole
168 (CPTP) CORDEX-FPS, was initiated in 2019 to provide high-resolution cli-
169 mate information for the TP region. The main goal of CPTP is to enhance
170 our understanding of the current and future water cycle and its extremes over
171 the TP region. The first phase of the program is to assess the skill of km-
172 scale regional models concerning the simulation of convection, precipitation,
173 and snowfall over the TP region. The second phase will see decadal current
174 and future climate simulation runs within the next three years.
175

175 In this study, we present the results of the first phase of this project. We
176 conducted three reanalysis-driven case experiments that represent important
177 meteorological phenomena associated with the TP - an MCS, a heavy snowfall
178 event, and a Monsoon case - with a multi-model and multi-physics ensemble.
179 The main motivation for these case studies was to set up and test the models
180 in different weather situations over the TP. The simulations were performed
181 over a relatively large common region shown in Figure 1, with a grid spacing
182 of less than 5 km. Some of the models were run for the first time at such a
183 high spatial resolution over this region. This paper provides an overview of the
184 experimental design and the model performance in simulating precipitation
and near-surface air temperature. More detailed analyzes of the presented case study simulations are currently performed and will be published in separate manuscripts.

The objectives of this study are the following:

1. Evaluating various high-resolution models over the TP under various weather conditions and atmospheric processes.

2. Assessing major sources of model performance uncertainties, and

3. Identifying appropriate model setups and/or major biases that can inform the setup of climate runs over the TP.

# 2 Data and Methods

## 2.1 Description of Selected Cases

We selected three cases for our modeling tests that span a wide variety of atmospheric processes, seasons, and impacts.

### 2.1.1 MCS - July 2008

MCSs are important features of convective precipitation over the TP [e.g. 44, 45]. On July 18, 2008, a mesoscale Tibetan Plateau Vortex (TPV) formed in the western part of the TP, traveled eastward, moved off the TP, and then traveled north-eastward to the coast of the Yellow Sea [46, 47]. When the TPV reached the eastern TP, it triggered an MCS at the eastern edges of the TP and produced a substantial amount of convective precipitation in the Yangtze river basin [45]. A station in the Sichuan basin recorded a maximum 24-h accumulated precipitation of 288 mm during this event [46]. The core simulation period for this case is July 14, 2008, 0 UTC – July 24, 2008, 0 UTC.
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## 2.1.2 Monsoon Precipitation - August 2014

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August 2014 was one of the wettest Augusts in recent decades due to active Monsoon precipitation and embedded extreme precipitation episodes. One particularly intense extreme event led to massive flooding and landslides in western Nepal on Augusts 14–15 2014. During the event, a maximum precipitation amount of 528 mm in 24 h was observed at the Chisapani station [48]. The core simulation period for this case is July 27, 2014, 0 UTC – September 1, 2014, 0 UTC.

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## 2.1.3 Heavy Snow Event - October 2018

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A heavy snow event was recorded at an Institute of Tibetan Plateau Research station over the TP. The event occurred between October 4–8, 2018 around the Nam Co station (30°46.44N, 90°59.31E, 4,730 m a.s.l.). This was the largest snowfall event in October since recording began in 2005 at this station. On October 3, 2018, a gust of cold air passed over the group lakes area, causing the daily minimum temperature of the region to drop by about 4.5 °C within 24 hours. As a result, the daily minimum temperature reached the freezing point about one week earlier than in previous years. The snowfall to the east of Nam Co was more than 50 cm while it was only 5 cm in the west of Nam Co. The core simulation period for this case is October 1, 2018, 0 UTC – October 9, 2018, 0 UTC.

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## 2.2 Model Simulations

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The CPTP community agreed on a minimal modeling domain that all modeling experiments have to include (D1 from hereon). The minimum domain size is given not as corner points of a region but as central points to allow for a more efficient design of model domains concerning the employed map projections
(Fig. 1). D1 encapsulates the TP region and was selected to minimize the computational cost for running kilometer-scale models. It was recommended to use a coarser resolution parent domain that incorporates a larger region (domain D2) if modelers decided to run their kilometer-scale simulations on D1.

Case study simulations were performed with four modeling systems. An overview of the participating models and their main model settings is shown in Table 1. All simulations downscale the fifth generation ECMWF atmospheric reanalysis (ERA5 [49]) without the application of nudging.

The Advanced Research (AR) Weather Research and Forecasting (WRF) model [50, 51] version 4.2 was used by 9 modelers (see Table 2). WRF is a state-of-the-art mesoscale numerical weather prediction system that is widely used for modeling across weather and climate time scales. WRF uses fully-compressible, Eulerian non-hydrostatic equations that conserve dry air mass and scalar mass. It uses a staggered Arakawa C-grid with a terrain-following, mass-based, hybrid sigma-pressure vertical coordinate system with a vertically stretched grid. WRF features a large range of model physics options. We performed a reference simulation with physics settings that perform well in the U.S. [29] (see Table 1). The reference simulations are offline nested and initialized from 12 km grid spacing simulations (performed on D2) that started at least 18 months before the case occurrence. The initial and lateral boundary conditions from the 12 km simulations were shared to increase the comparability of WRF sensitivity experiments. Twenty-four, twenty-one, and twelve additional WRF simulations were performed, based on the shared initial and lateral boundary conditions, for the MCS, Snow, and Monsoon cases, respectively. Two of these simulations reproduced the reference run on a different high-performance computer (HPC) system to understand sensitivities
10 *Convection-Permitting Third Pole Experiment*

260 to HPC architectures. The other simulations subsequently perturbed the plan-
261 etary boundary layer (PBL) scheme, microphysics scheme, land surface model
262 (LSM), applied a scale aware cumulus scheme, or performed a time-lagged
263 ensemble to estimate the effect of natural variability on the MCS case. For an
264 overview of the simulation names and perturbed physics see Table 2. Two addi-
265 tional simulations are performed where one uses the reference physics set up
266 and runs the 4 km WRF simulation on D2 by directly downscaling ERA5 and
267 the second repeats the reference simulation over D1 but directly downscaling
268 ERA5 instead of using the 12 km run data.

269 The Model for Prediction Across Scale (MPAS) is a modeling framework
270 to numerically solve the equations of geophysical fluid dynamics [52, 53].
271 Currently, four MPAS models have been developed to simulate different com-
272 ponents of the climate system: atmosphere, ocean, sea ice, and land ice
273 (http://mpas-dev.github.io/). The MPAS-Atmosphere solves fully compress-
274 ible, non-hydrostatic equations of mass, momentum, and energy conservation
275 for the atmosphere [54]. MPAS shares many aspects of its numerical scheme
276 (e.g., time integration and numerical damping) [55] and physics parameteriza-
277 tions with the WRF model [56]. The distinct features of the MPAS model are
278 the global unstructured mesh on a sphere (Spherical Centroidal Voronoi Tes-
279 sellations (SCVT), [52, 57], the C-grid finite-volume scheme designed for the
280 SCVT mesh, and a new terrain-following vertical grid [58]. The SCVT mesh
281 can be configured as a global quasi-uniform resolution mesh and as a variable-
282 resolution (VR) mesh in which grid spacing smoothly transitions from the
283 refined to the coarse domain. The SCVT can also be set up as a regional grid,
284 making it possible to compare the global and limited-area modeling approaches
285 within one modeling framework [59], which is the approach taken here.
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For this study, we use the MPAS-Atmosphere version 7.0 and a global variable-resolution SCVT grid, on which the grid spacing smoothly changes from ~4 km over TP to ~32 km over the rest of the globe. A predefined convection-permitting physics parameterization suite, which includes a scale-aware parameterization scheme for convection, is used (Table 1). The global VR mesh does not need lateral boundary conditions, therefore only the daily-mean sea surface temperature and sea ice fraction are prescribed from ERA5, and input to the model once a day. For the MCS case, two additional global VR simulations are performed using time-lagged initial conditions (ERA5 state at 6 and 12 hours before the initial time, same as those of the WRF initial condition ensemble). A regional SCVT grid is also produced by extracting the high-resolution domain from the global 4–32 km grid. For this work we limit our analysis of the limited-area simulation to the MCS case, because the regional MPAS simulation over TP experiences stronger numerical instabilities than in the global VR simulation and an optimal configuration of the regional SCVT over TP is being tested at the time of writing. For the limited-area simulation, 6-hourly lateral boundary conditions are prescribed from ERA5, and a shorter model time step than the global VR is used (12 s vs. 24 s). All the other configurations are identical between the global and limited-area MPAS simulations.

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Another modeling group employed the COSMO (Consortium for Small Scale Modeling) model run in climate mode (COSMO-CLM, hereafter CCLM; [60, 61]). CCLM is a non-hydrostatic, fully compressible, limited-area model, which similarly to the WRF and MPAS model employs an Arakawa C-grid and a terrain-following vertical coordinate with a user-defined stretched grid. The simulations employed the accelerated version of CCLM, which has been refactored to exploit heterogeneous node architectures [62] and provides a
313 ~5×speed-up compared to the version that only runs on central processing
314 units. The model was configured with a 12-km parent domain and a 2.2-km
315 grid spacing child domain, the latter of which encompassed the extent of D2 (cf.
316 Fig.1). Initial and lateral boundary conditions for the 12-km domain were pro-
317 vided at 6-hourly intervals from the ERA5 reanalysis, while the 2.2-km domain
318 was run offline with lateral boundary conditions updated hourly from the
319 parent domain. CCLM has been used extensively for kilometer-scale climate
320 simulations over the European Alps [e.g., 23, 42] and was applied over High
321 Mountain Asia for the first time for these simulations using similar options for
322 the model configuration and physics. A few settings were changed to improve
323 model stability over the complex orography, including using a different upper
324 boundary condition and introducing cold-pool diffusion in the 2.2-km domain.

325 The Icosahedral Nonhydrostatic model (ICON) is a unified next-generation
326 global numerical weather prediction and climate modeling system [63], and was
327 used by two modeling groups (see Table 2) in this study. The ICON model was
328 co-developed by the Max Planck Institute for Meteorology and the German
329 Weather Service. The dynamical core of ICON is formulated on an icosahedral-
330 triangular Arakawa C grid. It has a height-based vertical coordinate system
331 with terrain following layers at the surface. For this study, one simulation used
332 the ICON model version 2.6.1, while the other 2.6.3 in its operational numeri-
333 cal weather prediction mode. The limited-area simulations were performed on
334 D1 for one simulation, and on the domain represented with light blue color in
335 Fig. 1 for the other simulation. The horizontal resolution for both the simula-
336 tions was about 3.3km. The deep convection parameterization was switched off
337 for both the simulation runs, while a shallow convection scheme is switched on
338 for one of the simulations. Other details of the ICON model settings are sum-
339 marized in Table 1. The ICON simulations were forced with initial and lateral
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boundary conditions directly from the ERA5 reanalysis at 3-hourly intervals for one simulation, and 1-hourly intervals for the other simulation. The ICON simulations were completed on the Chinese Earth System Science Numerical Simulator Facility ?EarthLab? and on Goethe-HLR HPC cluster at the Goethe University Frankfurt.

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Last but not least, the Regional Climate Model version 4 (RegCM4) is developed and maintained at the Abdus Salam International Centre for Theoretical Physics (ICTP) in Italy [64, 65] was also utilized. The standard version of RegCM4 with hydrostatic dynamics is a widely used system that has been applied to local and regional seasonal forecasting and climate change studies for all regions of the globe, including the TP region [e.g., 66, 67]. The CPTP case study experiments are performed with a development version 4.8.0 of RegCM4 model with non-hydrostatic dynamics [65]. The initial and 6-hourly lateral boundary conditions and sea surface temperature for the 4 km simulations on D2 are directly derived from ERA5. The model has a wide choice of physical parameterizations for processes such as non-convective clouds and resolved-scale precipitation, land surface models, planetary boundary layer turbulent diffusion, ocean surface fluxes etc. [64, 65]. The physics options adopted for the CPTP experiments are summarized in Table 1. The land surface processes are described using the Biosphere -Atmosphere Transfer Scheme (BATS; [68]). The resolved-scale cloud microphysics is treated by a simple subgrid explicit moisture scheme (SUBEX, [69]), which has only one prognostic equation for calculating cloud water content based on autoconversion of cloud water to rainwater, accretion of rainwater, and evaporation of raindrops. The cloud fractional cover is diagnostically calculated as a function of grid point average

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14 *Convection-Permitting Third Pole Experiment*

relative humidity. SUBEX does not treat cold cloud microphysics and the frac-
tion of ice is diagnosed as a function of temperature in the RegCM4 radiation
scheme from radiative transfer calculations [64].

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2.3 Observations

The TP region is an observationally sparse area with a harsh environment
that introduces large observational uncertainties. To account for these uncer-
tainties, we use an ensemble of in situ, remote-sensing, and reanalysis-based
precipitation and near-surface temperature datasets that provide data on daily
or higher resolution.

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For precipitation, we use the CHIRPS [70], GPM-IMERG [71], CPC [72],
and ERA5 [49] datasets. The Climate Hazards group Infrared Precipitation
with Stations (CHIRPS) dataset uses infrared satellite observations blended
with station data to create a quasi-global (50°S-50°N), 0.05° grid spacing, daily
precipitation estimate. NASA's Global Precipitation Measurement (GPM)
Integrated Multi-satellite Retrievals for GPM (IMERG) product intercali-
brates, merges, and interpolates all available satellite microwave precipitation
estimates, microwave-calibrated infrared satellite estimates, and precipitation
gauge observations on a global 0.1° grid every 30-minutes. NOAA Climate
Prediction Center (CPC) daily precipitation estimates combine over 30,000
gauges to create a global, land-only gridded product with 0.5° spacing. ERA5
uses data assimilation to combine a large variety of in-situ and remote sens-
ing observations with forecasts from an advanced global model to create an
estimate of the global atmospheric state. Data is provided hourly on a global
30 km grid.

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Gridded precipitation datasets that are based on or calibrated with gauge
data typically feature an underestimation of precipitation that stems from
two primary sources. First, gauges under-catch precipitation particularly in exposed and snow-dominated environments [73]. Second, precipitation gauges are typically located in valleys and not on mountain slopes. This results in a sampling bias of precipitation values since precipitation has a complex altitude dependence in the Himalayas region [74]. To account for these biases, we apply monthly bias correction estimates from Beck et al. (2020) [75]. These estimates are based on water budget calculations taking into account precipitation, runoff, and evaporation using 9,372 streamflow stations worldwide.

For near-surface temperature, we again use ERA5 and CPC data in addition to GLDAS [76] and Berkeley Earth [77] observations. The Global Land Data Assimilation System (GLDAS) merges ground-based and satellite observational data with advanced land surface modeling techniques. GLDAS provides 3-hourly data on a 0.25° global grid. The Berkeley Earth dataset combines station observations with an algorithm that is optimized for climate change assessments and provides daily data on a 1° global, land-only grid. All gridded observational datasets and model outputs are bi-linearly interpolated to the ERA5 grid for model evaluation.

In addition to these gridded datasets, we also use Hadley Center Integrated Surface Database (HadISD) sub-daily station observations for model validation [78]. Global sub-daily station records are collected from the stations that meet specific selection criteria and a suite of quality control tests are performed. Hourly temperature data and 6-hourly precipitation accumulations are compared to model output from the nearest native model grid cell if there are at least 30% valid observations during the evaluation period. Using inverse distance averaging of the four closest model grid cells leads to similar results.
**2.4 Methods**

The analysis of modeling uncertainties is performed similarly to Rowell (2006) [79]. Therefore, we classify different sources of uncertainty for which simulations are available in the CPTP case study ensemble. We investigate three general classes of uncertainty – model formulation uncertainty, observational uncertainty, and initial condition uncertainty – and six classes that are specific to the WRF model ensemble – PBL scheme, microphysics, LSM, convection scheme, domain size, and high-performance computing system sensitivity. Uncertainties are estimated by averaging over all categories, except the one under consideration, and calculating the standard deviation over the data points in the considered category. For instance, model formulation uncertainties are estimated by averaging over all available simulations from a particular model. This results in one data point per model, which is used in the calculation of the standard deviation.

The model formulation uncertainty stems from differences in the assumptions and equations used in the participating climate models. The observational uncertainties are estimated by comparing different gridded observational products with each other. The initial condition uncertainty addresses the chaotic nature of the simulated cases, which arises from constructing a time-lagged initial condition ensemble. This has only been done for the MCS case with WRF and MPAS, but we expect the results to be similar for other modeling systems and cases. PBL, microphysics, LSM, and convective scheme uncertainties are estimated from the WRF physics sensitivity studies. The domain size uncertainty is estimated by running WRF simulations on D1 and D2, while HPC system sensitivities are calculated from running the same WRF setup on different computer architectures.
The Rowell (2006)[79] method of estimating uncertainties is beneficial due to its simplicity and easily interpretable results. However, it might result in unreliable uncertainty estimates for categories that are sparsely populated (e.g., convection scheme uncertainties) and does not account for effects from interactions between categories (e.g., PBL scheme impacts on simulations on different domain sizes).

Even though most of the simulations are run with the same horizontal grid spacing of 4 km, the modeling systems may vary in their effective resolution and capability to resolve deep convective processes, i.e. due to differences in the numerical schemes [80, 81]. To get an indication of how well kinetic energy (KE) is retained at different spatial scales in the used models, we intercompare KE power spectra based on horizontal wind speeds at 500 hPa and 200 hPa. The python package `scipy.fttpack` (https://docs.scipy.org/doc/scipy/reference/generated/scipy.fttpack.dctn.html) is used to compute spectral coefficients from 3-hourly two-dimensional wind fields applying a discrete cosine transformation [82]. The power spectra were constructed by calculating the variances of these spectral coefficients for specific wavelength bins, following the steps in Denis et al. (2002) [82]). Note that all model output was first reduced to the domain D1 (Fig. 1) since the offset of the KE spectra is domain-sensitive.

## 3 Results

This section presents results from the analysis of kinetic energy (Section 3.1), precipitation (Section 3.2), near surface temperature (Section 3.3), and the analysis of uncertainty sources (Section 3.4).
## 3.1 Spectral analysis

Although the grid spacing of the five participating modeling systems is similar, their ability to simulate fine-scale atmospheric dynamics might be different due to different choices in how to solve the equations of motion and how diffusive the models are [81]. Fig 2 shows power spectra of horizontal kinetic energy at 500 hPa (a) and 200 hPa (b) for the participating modeling systems (UIBK-CCLM50n, r1i2p1_NCAR-WRF42, r1i1p1_PNNL-MPASLA7, IAP-ICON2.6, GUF-ICON2.6.3, IITM-RegCM480) and their driving reanalysis ERA5 based on hourly data from the 10-day long MCS case simulation (the spectra for the other cases look very similar – not shown). The KE spectra for the simulations converge well with ERA5 at higher wavelengths, where kinetic energy shows the largest variability, and spatial features are expected to be similar because they are mainly produced by the large-scale circulation. The km-scale simulations retain more energy at the small wavelengths compared to ERA5 and the 12 km simulations that served as parent simulations for nested runs with WRF and CCLM. This maintenance of kinetic energy can be attributed to the higher effective resolution in the km-scale simulations and to the fact that deep convection is not parametrized, as shown in Zeman et al. (2021) [81].

The gray lines in Fig. 2a-d indicate theoretical slopes derived from observations [83] that describe the energy loss at synoptic ($k^{-3}$) and mesoscales ($k^{-5/3}$) as a function of wavenumber $k$. These can be compared to the actual spectral slopes for the KE spectra in Fig. 2c-d. It is discernable that the simulated slopes at 500 hPa are shallower than the expected slope for mesoscales, while the slopes at 200 hPa show closer resemblance to the expected slopes for synoptic and mesoscales. For wavelengths around 1,000 km, most of the KE spectra exhibit slopes similar to $k^{-3}$. The slopes become shallower and more similar to $k^{-5/3}$ at around 400 km, which is a typical transition zone between
synoptic and mesoscale [81, e.g.]. The shallower slope at 500 hPa compared to 200 hPa is likely caused by the complex terrain that covers most of the domain (Fig. 1) and is strongly affecting the flow at 500 hPa. As pointed out by Skamarock et al. (2014) [50] and Blazica et al. (2013) [84], topography can have considerable effects on divergent kinetic energy, for example through the generation of gravity waves or other topographic flow interactions.

Comparing the 12 km simulations from CCLM and WRF with their respective higher resolution runs at 2.2 km and 4 km grid spacing, it can be seen that the KE spectra start to diverge at wavelengths of ~50 km at 500 hPa and ~70 km at 200 hPa. This abrupt loss in KE energy can be interpreted as the effective resolution of the models, which is also reflected in the energy drop at ~20 km in the 4 km WRF simulation and at 10 km in the 2.2 km CCLM simulation. For WRF and CCLM, this corresponds to an effective resolution of about 4–6Δx. The KE spectra for MPAS look similar to those of WRF, even though MPAS tails off slightly earlier for kinetic energy at 500 hPa. These results are consistent with Skamarock et al. (2014) [85], who demonstrated that global MPAS simulations produce an effective resolution of about 6Δx. The reason for RegCM4’s premature loss in energy at ~20 km is not clear since the model uses a relatively short time step, which should be beneficial for the maintenance of high-frequency waves [81]. While ICON’s spectra look similar to WRF at 500 hPa, it maintains a k⁻³ slope until very small wavelengths at 200 hPa. While the KE spectra for ICON2.6 and ICON2.6.3 look similar at 500 hPa, they diverge from wavelengths smaller 200 km at 200 hPa. The exact cause of these differences is unclear since the two simulations differ in model version, time step, domain size, and model physics (Table 1). The spectrum for ICON2.6.3 agrees more with WRF and MPAS for wavelengths > 200 km.
20 Convection-Permitting Third Pole Experiment

The different physics options within the WRF ensemble do not substan-
tially affect the KE spectra (not shown), but the small differences between
MPAS and WRF4km at 500 hPa for smaller wavelengths might be partly
caused by the use of a scale-aware convective parameterization scheme in
MPAS, while convection is explicitly simulated in the WRF simulations
(Table 1).

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3.2 Precipitation Analyses

Here we evaluate the accuracy of simulated precipitation against a range of
observational products. The goal is to provide an overview of the models’ skill
in capturing key features such as spatial precipitation patterns, precipitation
accumulations over time, and 6-hourly precipitation rates.

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Fig. 3 shows the accumulated precipitation during the MCS case between
July 16–21, 2008. Flood-producing rainfall was produced by the MCS in the
Chinese province of Sichuan and heavy rainfall was also reported in the south-
ern foothills of the Himalayas. All observational datasets (Fig. 3a–d) show a
precipitation maximum at ~115° east and ~30° north, which varies in spatial
extent and intensity between the datasets. Only the GUF-ICON2.6.3 simula-
tion (Fig. 3h) was able to capture the exact location of the MCS precipitation.
Most simulations have a precipitation maximum that is displaced towards the
south or miss the MCS precipitation completely. The RegCM4 simulation is
the only run that has a northward displacement of the precipitation center
(Fig. 3i). The precipitation along the southern foothills of the Himalayas is
accurately simulated in most models. Differences between the simulated event
average and the GPM-IMERGB precipitation can be seen in Supplementary
Fig. S1. The amount and spatial distribution of event average precipitation is
well captured by most models for the Monsoon case (Supplementary Fig. S2

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and S3) while most models show more precipitation during the Snow case in the eastern foothills and on the Tibetan Plateau (Supplementary Fig. S4 and S5).

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The domain-averaged accumulated precipitation for all three cases is shown in Fig. 4. For the Monsoon case (Fig. 4a), the observational datasets agree well when the precipitation correction of Beck et al. (2020) [75] is applied. The ERA5 precipitation, which does not suffer from undercatch and sampling biases, is much higher than the one from station-based datasets if the correction is not applied. The WRF ensemble has a large spread of accumulations that encompasses all observational datasets and all simulations from other modeling systems, except for those from the RegCM4 model. The source of this spread will be explored in a later section. The simulations with the MPAS and ICON model are similar to the non-bias corrected observational datasets, while precipitation in the CCLM and RegCM4 simulations is comparable to the bias-corrected version of these datasets and ERA5 precipitation.

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The results from the domain average accumulated precipitation during the MCS case (Fig. 4b) are similar to the Monsoon case. Again, the WRF ensemble spread is large and encompasses the observational datasets and other model simulations. The simulated precipitation is overall centered on the observed precipitation, which is in part due to compensating errors in the spatial representation of precipitation (e.g., see Fig. 3). The largest differences between observational and modeled precipitation can be found in the Snow case (Fig. 4c). Here, the models tend to simulate much higher precipitation accumulations and the WRF ensemble does not bracket the other models, nor the observational datasets, even after bias correction.
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To assess the models’ performance in simulating precipitation patterns we show the event-averaged precipitation pattern correlation coefficients, standard deviation, and centered root mean square error in Taylor diagrams [86] (Fig. 5). Modeled correlation coefficients are between 0.63–0.82 (except for r1i1p3_Cardiff-WRF42) for the Monsoon case (Fig. 5a), 0.3–0.65 for the MCS case (Fig. 5b), and 0.22–0.5 for the Snow case (Fig. 5c). The differences between the cases are not only due to the models’ ability to capture precipitation under different atmospheric settings but are also a result of uncertainties in observational datasets. Most models have larger spatial variability (i.e., standard deviations) than the observational datasets, particularly during the Snow case. This is in part due to the higher effective resolution of the kilometer-scale simulations compared to the observational datasets. The best performing simulations according to this metric are the GUF-ICON2.6.3 simulations, PNNL-MPAS7 runs, the IAP-CAS-ICON2.6 simulation, and the WRF simulations that directly downscale ERA5 data over the larger domain D2 (r1i2p1_NCAR-WRF42). The r1i1p3_Cardiff-WRF42 run is an outlier concerning the low correlation coefficients in all cases suggesting that the combination of the MYNN3 PBL scheme and the WDM7 microphysics results in subpar model performance. Interestingly, using the schemes separately such as in r1i1p2_Cardiff-WRF42 and r1i1p3_UGOT-WRF42 does not deteriorate the simulations. The IITM-RegCM480 simulations are outliers due to their high spatial variability during the Monsoon and MCS case.

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The simulations can capture the gauge-based 6-hourly precipitation probability density functions well (Fig. 6) and outperform the ERA5 precipitation, which overestimates weak precipitation frequencies and underestimates high precipitation frequencies. The WRF ensemble spread encompasses most of the other model solutions. The IMERG precipitation accumulations agree
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well with those from the stations except for the Snow case, which has large observational uncertainties.

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The correlogram in Fig. 7 provides insights into the similarity of case average spatial precipitation accumulation patterns. The upper left corner of the correlograms shows the correlation coefficients comparing the observational datasets. Overall, correlation coefficients are high indicating good agreement between the observed spatial precipitation patterns. The lowest correlation coefficients can be found for the Snow case confirming the previously discussed observational uncertainties. Bias correcting the observational datasets (indicated by a _BC at the end of the dataset name in Fig. 7) has no systematic impact on the correlation coefficients.

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Comparing the observational datasets to the WRF model ensemble (lower left rectangle in the correlogram) and the other model simulations (middle left rectangle) again shows that the r1i1p3_Cardiff-WRF42 simulations have lower correlation coefficients than the other models, independent of the observational dataset and the case considered. The highest correlations are seen in the GUF-ICON2.6.3, PNNL-MPAS7, IAP-CAS-ICON2.6, and the WRF large domain simulations (r1i2p1_NCAR-WRF42, r1i3p1_NCAR-WRF42, r1i4p1_NCAR-WRF42) independent of the observational dataset.

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The correlation coefficients between the WRF ensemble and the other model simulations (middle horizontal block) are comparable to the coefficients obtained when comparing the models to the observations. Precipitation patterns from most WRF simulations (rightmost block) are highly correlated indicating that there is little sensitivity to changing the model physics. Notable exceptions are the r1i1p3_Cardiff-WRF42 simulation, which uses the MYNN3 PBL and WDM7 PBL scheme, and the large domain WRF simulations

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that directly downscale ERA5 (r1i2p1_NCAR-WRF42, r1i3p1_NCAR-WRF42, r1i4p1_NCAR-WRF42).

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**3.3 Two Meter Above Ground Air Temperature Analysis**

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This section presents a similar analysis as the previous section but for T2M. Fig. 8 shows the July 16–23, 2008 average T2M for the MCS case from the observational datasets (Fig. 8a–d) and the model ensemble (Fig. 8e–al). As expected, T2M strongly depends on topography with very warm temperatures in India and south-eastern China and substantially colder temperatures over the TP. The observational datasets agree reasonably well concerning the large-scale pattern of T2M but large uncertainties exist on regional scales (see Supplementary Fig. S6). The simulations are generally able to capture the spatial pattern of T2M. The most noticeable difference is found in the UIBK-CCLM50n run, which has a warmer T2M over the plateau than other simulations agreeing with the CPC observations. T2M differences for the Monsoon and Snow case are similar to the MCS case with UIBK-CCLM50n generally simulating the warmest temperatures over the plateau and WRF simulating the coldest (particularly during the Snow case; see Supplementary Fig. S7–S10).

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The domain D1 average daily T2M time series are shown in Fig. 9. The temporal evolution of temperature is well captured by most models but many modeling systems, except for CCLM and RegCM, simulate lower T2M than found in the observational datasets, which is similar to the ERA5 forcing data.

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Hourly T2M probability density functions from station observations show a left-skewed (i.e., cold temperature) distribution which is caused by stations on the Tibetan Plateau. This is most noticeable during the Snow case, which features a cold air outbreak over the plateau. Most simulations can capture
the observed PDFs well except for the r1i1p1_PNNL-MPAS7 Monsoon and
the r1i1p2_Cardiff-WRF42 Snow simulations. The differences in the MPAS run
are likely due to the global nature of this simulation, which allows it to drift
from the observed weather during the month-long simulation. Additionally,
the IITM-RegCM480 runs also exhibit noticeable differences in the left tail of
their PDF.

Case average T2M spatial correlation coefficients are larger than 0.95 com-
paring the observational and model datasets to GLDAS (Fig. 11). Interestingly,
the Snow case has the highest correlation coefficients between the observa-
tional and modeled datasets, indicating that previously shown differences in
Fig. 9c are primarily caused by systematic (domain-wide) T2M biases that do
not affect pattern correlation coefficients substantially. The high pattern cor-
relation coefficients are primarily caused by the dominant role of topography
on T2M, which can be well captured with CPM modeling systems.

The vertical distribution of T2M differences compared to station data is
shown in Fig. 12. Most of the stations above 2,000 m are on the Tibetan
Plateau. Temperatures are better simulated at elevations below 2,000 m, and
most models have a cold bias at higher elevations. Exceptions are the CCLM
simulations and the r1i1p1_PNNL-MPAS7 Monsoon case run. The largest cold
biases above 2,000 m are found in the RegCM4 simulations, particularly during
the Snow case.

3.4 Uncertainty Sources

In this section, we estimate the sources of model performance uncertainties
in simulating case-average precipitation and T2M patterns and amounts (see
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method section for more details). We use the GPM-IMERG and GLDAS obser-
vations as ground truth. However, using different datasets as references does
not change the main conclusions of this section.

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For instance, we derive the pattern correlation coefficient for the case
average precipitation between the observational datasets compared to GPM-
IMERG and calculate the standard deviation of the resulting correlation
coefficients. This gives us an estimate of observational uncertainties (Fig. 13a).
This uncertainty estimate can be directly compared to other sources of
uncertainty as introduced in the methods section.

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Differences in gridded observational datasets are the largest source of
uncertainties for all cases and statistics except for absolute differences in pre-
cipitation where differences in the model formulation are larger (Fig. 13). This
highlights the challenges of constraining observational fields in the TP region.
The model formulation standard deviations are of similar magnitude for all
cases except for a larger spread in correlation coefficients during the Snow case
(Fig. 13a) and a larger spread in absolute precipitation differences during the
MCS case (Fig. 13b). The ICON simulations show the overall highest skill in
simulating the precipitation statistics. MPAS runs come in as a close second,
which is remarkable since the global MPAS simulations are not constrained by
ERA5 lateral boundary conditions and have a much larger degree of freedom to
deviate from observed weather patterns compared to the regional models. Dif-
ferences in T2M correlation coefficients are much smaller than for precipitation
and the RegCM4 simulations perform best when it comes to capturing T2M
statistics (Fig. 13c). The ensemble average WRF statistics are sub-optimal,
however, WRF can be configured to perform equally well as other modeling
systems as can be seen when looking at the physics sensitivity results.

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Varying initial conditions by creating a time-lag ensemble causes very small uncertainties in the MCS case using the WRF and MPAS model. This indicates that differences between e.g., modeling systems or WRF physics schemes are robust and are not heavily affected by chaotic variability for the MCS case. We expect similar results for the Snow case, which has a comparable runtime to the MCS case, but internal variability might be larger during the Monsoon case due to the longer integration.

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The large number of WRF simulations allows us to estimate sources of uncertainties due to different model physics. Three institutes ran WRF simulations using the reference settings (see Table 1) from which we can assess uncertainties from running WRF on different high-performance computing (HPC) systems. These uncertainties are typically an order of magnitude smaller than the leading sources of uncertainty and do not affect our results substantially. Also, sensitivities to the use of a scale-aware deep convection scheme and different land surface models are small. However, land surface model sensitivities are likely underestimated in these short case study simulations since land surface processes can have a long memory, and uncertainties might grow in climate-length simulations.

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The leading sources of WRF modeling uncertainties are the formulation of the microphysics, PBL schemes, and regional domain size. Which uncertainty source dominates is case- and metric-dependent. Small sensitivities are found for simulating spatial T2M patterns (Fig. 13c), except for varying the microphysics during the Snow case where the WDM7 and WDM6 schemes result in lower correlation coefficients compared to the other tested schemes. Similarly, WDM7 and WDM6 result in larger T2M absolute biases (Fig. 13d). For precipitation metrics, microphysics uncertainties are largest for the Monsoon and MCS case and smallest for the Snow case. Again, the usage of the WDM6 and
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particularly the WDM7 scheme results in lower model performance concerning
the simulation of precipitation patterns. Simulations that use the Thomp-
son and Ylin microphysics result in the overall best performance in capturing
precipitation. Concerning PBL schemes, using the MYNN3 scheme results in
sub-optimal performance for simulating T2M absolute differences, spatial pat-
terns, and precipitation pattern correlations in all simulated cases. Using the
YSU or Bourgeault PBL schemes provides the overall best performance con-
cerning T2M metrics while schemes other than MYNN3 perform similarly well
for precipitation metrics. Performing WRF simulations on the large domain
(D2) always results in better T2M and precipitation simulations compared to
simulations on D1. T2M improvements are particularly large during the Mon-
soon case, while absolute precipitation differences are not improved during this
case.

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4 Summary, Discussion, and Conclusions

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In this paper, we present the first results of the Convection-Permitting Third
Pole (CPTP) CORDEX Flagship Pilot Study, which is an internationally coor-
dinated effort to improve our understanding of climate change impacts on the
water cycle and its extremes over the TP region. We outline the experimental
design and show results from multi-model, multi-physics case study simulations
that focus on a flood-producing MCS case in 2008, a heavy rain-producing
Monsoon period in 2014, and a heavy snow event in October 2018. The pri-
mary goal of these experiments is to better understand the performance of
state-of-the-art, non-hydrostatic regional modeling systems in simulating pre-
cipitation and T2M during different weather situations over the TP region and
to find suitable model settings that can be used for climate-length runs in the
future. The following points summarize our main findings:
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* The performance of the five participating modeling systems is comparable but varies from case to case and among metrics. Overall, ICON and MPAS are performing best in simulating precipitation while RegCM4 performs best in simulating T2M.

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* While there is no obvious connection between the KE spectra of a model and its skill in simulating precipitation and T2M, the spectra show important differences related to the model formulation and indicate that models like e.g. WRF, CCLM, and ICON are characterized by higher variability at smaller scales as compared to e.g. RegCM4.

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* The large number of WRF simulations allows us to investigate sensitivities to model physics. The WRF multi-physics ensemble typically encompasses the spread of the other modeling systems particularly for simulating precipitation, similar to previously published results over Europe [87].

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* Differences in observational datasets are the dominant source of uncertainty in the model evaluation and are typically larger than model formulation, and model physics uncertainties. Model biases and observational uncertainties are particularly large for the Snow case, which might be partly related to precipitation undercatch issues [73]. This highlights a major challenge for evaluating model simulations in this region and observationally constraining the water and energy cycle over the TP. There is an urgent need to improve our in-situ and remotely sensed observational capabilities over the TP, which is one of the main goals of the Third Pole Environment (TPE) [1] and Asian Precipitation Experiment (AsiaPEX) [88] projects.

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* WRF modeling experiments show that sensitivities due to the domain size (comparing simulations on D1 and D2) are of a similar order of magnitude as uncertainties stemming from the microphysics and PBL scheme. Generally, running the WRF kilometer-scale simulations on the large model domain
(D2) improves the model results for all cases and metrics except for the
Monsoon case absolute precipitation differences.

* WRF PBL scheme uncertainties have similar magnitudes for all cases and are comparable to uncertainties stemming from the microphysics. While the performance of a PBL scheme is case- and metric-dependent, we see that the MYNN3 scheme results in sub-optimal performance compared with the YSU, UW CAM, and MYNN2.5 schemes (except for absolute precipitation differences). However, the poorer performance of MYNN3 is largely due to its interactions with the WDM7 microphysics scheme while using it in combination with the Thompson scheme results in much better performance.

* Using the WDM6 and WDM7 microphysics schemes consistently results in lower than average performance independent of the case, variable, or metric in agreement to previous research over the Himalaya [89]. The other microphysics schemes perform equally well and have varying and non-systematic strengths and weaknesses depending on the case.

* There are systematic model deficiencies that most models share such as cold bias above 2,000 m a.m.s.l. (except for CCLM) and a displacement of the heavy precipitation center towards the south (towards the north in case of RegCM) for the MCS case. Model performance in simulating precipitation is generally lowest during the Snow case, partly related to the large observational uncertainties. Future modeling efforts will aim to better understand the sources of these differences and to improve the overall model performance.

In-depth analyses that focus on single cases are ongoing that will provide
additional insights into model performance [e.g., 90]. The results from this
study will inform the modeling setup of climate time-scale simulations in the
CPTP project. The CPTP WRF modeling community has already adapted
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their modeling strategy and will perform future simulations on the larger model
domain (D2) due to the systematically better model performance when run-
ning simulations on D2 that is shown here. Currently, we are running ensemble
simulations of the 2020 water year (Oct. 2019 to Sep. 2020) to better under-
stand the seasonality of model biases and the potential for bias accumulations
over time. We selected this recent period due to the availability of high-quality
observational datasets over the TP from the TPE project [1]. These year-long
simulations will be the basis for decadal runs of historical and future climate
periods that will be performed by CPTP modelers in the next three years.
Those runs will provide unprecedented insights into the changing energy and
water cycle over the TP.

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One key finding of this paper is the urgent need to better constrain water
cycle processes over the TP region. This is essential not only for model valida-
tion but also to monitor climate and environmental changes in this vulnerable
area. High-quality in-situ and remote sensing observations are essential for
this task but are extremely challenging to obtain due to the remoteness and
extreme environments of the TP region. Fusing observational datasets with
high-resolution modeling systems by creating e.g., a kilometer-scale regional
reanalysis, such as has been done in other mountain regions [91], could allevi-
ate some of the observational challenges and modeling biases and allow us to
gain novel insights into the weather and climate of the TP.
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Topography and computational domains used in the CPTP project. The minimum domain sizes for domain D1 and D2 are shown according to their top-center (TC), right-center (RC), bottom-center (BC), and left-center (LC) points.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2** Power spectra of horizontal kinetic energy [m² s⁻²] at 500 hPa (a) and 200 hPa (b) and corresponding spectral slopes (c, d) as a function of wavenumber [k rad⁻¹] and wavelength [km]. The power spectra are computed using the extent of the domain D1. Spectral slopes are high-pass filtered using a moving average to reduce noise. The gray lines mark the theoretical slopes for synoptic (k⁻³) and meso-scale (k⁻⁵/³) energy loss.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Average daily precipitation from the MCS case during July 16–23, 2008. The top row (a–d) shows various gridded observational datasets and rows below show the model simulated precipitation. The black rectangle shows the approximate area of heavy precipitation that was caused by the MCS event.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 4** Domain average accumulated precipitation during the Monsoon (a), MCS (b), and Snow (c) case experiment. Black lines show various observational datasets, gray lines show bias corrected observational datasets following [75], and colored lines show simulated precipitation. The gray contour shows the WRF ensemble spread. Only the WRF simulation with the lowest absolute difference compared to IMERG and with the lowest and largest precipitation accumulation are shown.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5** Taylor diagrams showing the case average precipitation pattern correlation, spatial standard deviation, and centred root mean square error comparing observational (black symbols) and modeled precipitation (colored numbers) with IMERG observations.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 6** Probability density function (PDF) of 6-hourly precipitation accumulations comparing HadISD and ADP station observations (thick black line) with ERA5 (dotted black line), IMERG (black dashed line), and modeled data (colored lines). The map on top of each panel shows the station locations, station altitude (colors), and completeness of record (circle size; only stations with more than 30% of valid data are considered). Gray contours show the WRF ensemble spread. Additionally, the WRF simulations with the lowest absolute PDF difference and the largest PDF difference are shown. The bin size to calculate PDFs is 1 mm 6h⁻¹ and we use a ±2 mm 6h⁻¹ window around each bin to reduce noise in the PDF calculation.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 7** Correlogram showing pattern correlation coefficients of case study average accumulated precipitation between various observational datasets and the model simulations.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 8** Average daily mean 2 m above surface air temperature for the MCS case during July 16–23, 2008. The top row (a–d) shows various gridded observational datasets and rows below show the model simulated T2M.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 9** Domain average daily mean T2M during the Monsoon (a), MCS (b), and Snow (c) case experiment. Black lines show various observational datasets and colored lines show simulated precipitation. The gray contour shows the WRF ensemble spread. Only the WRF simulation with the lowest absolute difference compared to GLDAS and with the lowest and largest T2M values are shown.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 10** Probability density function (PDF) of hourly T2M observations from the HadISD station dataset (thick black line) with ERA5 (dotted black line), IMERG (black dashed line), and modeled data (colored lines). The map on top of each panel shows the station locations, station altitude (colors), and completeness of record (circle size; only stations with more than 30% of valid data are considered). Gray contours show the WRF ensemble spread. Additionally, the WRF simulation with the lowest absolute PDF difference and the largest PDF difference are shown. The bin size to calculate PDFs is 1 °C and we use a ±2 °C window around each bin to reduce noise in the PDF calculation.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 11** Taylor diagrams showing the case average T2M pattern correlation, spatial standard deviation, and centred root mean square error comparing observational (black symbols) and modeled precipitation (colored numbers) compared to GLDAS observations.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 12** Differences in T2M between model simulations and hourly HadISD station observations dependent on the height above sea level. The spatial station distribution is the same as shown in the maps in Fig. 10. Differences are calculated in bins with 100 m size and we use a ±250 m window around each bin to reduce noise. The WRF ensemble spread is shown in gray contours and only the WRF simulations with the lowest and largest absolute difference to the station data are shown. The r1i1p1_IITM-RegCM480 results are off the chart to the left and not visible in these plots. The number of stations in each bin is shown as a dotted black line on the secondary x-axis. We only show values for bins with more than five stations.</figcaption></figure>
Convection-Permitting Third Pole Experiment

<figure><img src="image_14.png" /><figcaption>**Fig. 13** Pattern correlation coefficients for event average precipitation (a) and T2M (c) and domain average absolute precipitating (b) and T2M differences (d). IMERG and GLDAS are used as reference for precipitation and T2M analyses respectively. Nine groups of points are shown for each case. The spread of these points provide an uncertainty estimate from a specified source: "Model" is the RCM formulation uncertainty, "Observation" represents the uncertainty between different observational products, "Initial Condition" is associated with the spread within two time-lagged initial condition ensembles (only available for the MCS case), "PBL" uncertainties associated with different planetary boundary layer schemes in WRF, "Microphysics" formulation uncertainty in WRF, "LSM" show land surface model uncertainties in WRF, "Convective scheme" shows the impact of using a scale aware cumulus scheme or no convection scheme in WRF, "Domain size" shows sensitivities to the RCM domain size in WRF, and HPC system shows the spread in identical WRF simulations that were run on different computing systems. The standard deviation (SD) of each point cloud is shown at the bottom of each plot. The number of members in each averaged group is shown in the legend.</figcaption></figure>
**Supplementary information.** This article contains supplementary information consisting of 10 Figures.

**Acknowledgments.** UIBK acknowledges PRACE for awarding them access to Piz Daint at the Swiss National Supercomputing Center (CSCS, Switzerland). They also acknowledge the Federal Office for Meteorology and Climatology MeteoSwiss, the Swiss National Supercomputing Centre (CSCS), and ETH Zürich for their contributions to the development of the GPU-accelerated version of COSMO. In addition, they acknowledge DKRZ for providing COSMO-ready ERA5 boundary data. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory. The MPAS simulations were performed using computational resources provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC02-05CH11231. K.S. and R.L. were supported by the U.S. Department of Energy Office of Science Biological and Environmental Research as part of the Global and Regional Model Analysis program area. S.S acknowledges the Data Analyzer in JAMSTEC for the WRF simulations and is supported by JSPS KAKENHI Grant Number JP20K04095. The computations by the University of Gothenburg were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre in Sweden (NSC) partially funded by the Swedish Research Council through grant agreement no. 2018-05973. XC acknowledges the high-performance computing support from the Texas Advanced Computing Center (TACC) and San Diego Supercomputer
<table><caption>Table 1 Main model settings of reference experiments.</caption><thead><tr><th>Model</th><th>Reference</th><th>LBC frequ.</th><th>Δx</th><th>Nesting</th><th>Buffer [cells]</th><th>levels</th><th>Radiation</th><th>Shallow Convection</th><th>Microphysics</th><th>Land-surface</th><th>PBL</th><th>Soil initialization</th><th>Aerosols</th><th>Lake model</th><th>Δt</th></tr></thead><tbody><tr><td>WRF 4.2</td><td>[50, 51]</td><td>6-hour</td><td>4 km</td><td>4 km is nested in 12 km parent run</td><td>4</td><td>38</td><td>RRTMG scheme for LW and SW [92]</td><td>none</td><td>Thompson et al. (2008) [93]</td><td>Noah-MP [94]</td><td>Yonsei University (YSU) [95, 96]</td><td>Initialized with 1 1/2 year 12 km spinup simulation</td><td>none</td><td>Lake surface temperature is calculated from monthly mean 2m air temperature</td><td>15 s</td></tr><tr><td>MPAS-Atm. 7.0</td><td>[54]</td><td>Global VR mesh; Daily for SST/sea ice no</td><td>4 km over TP, 32 km over the rest of the globe</td><td>N.A.</td><td>N.A.</td><td>55</td><td>RRTMG scheme for LW and SW [97]</td><td>Scale-aware Grell-Freitas [98]</td><td>Thompson [99]</td><td>Noah [100]</td><td>MYNN level 2.5 [101]</td><td>from ERA5</td><td>none</td><td>none</td><td>24 s</td></tr><tr><td>MPAS-Atm. 7.0</td><td>[59]</td><td>6-hour for lateral BC, and daily for SST/sea ice</td><td>4 km</td><td>ERA5</td><td>5</td><td>55</td><td>RRTMG scheme for LW and SW [97]</td><td>Scale-aware Grell-Freitas [98]</td><td>Thompson [99]</td><td>Noah [100]</td><td>MYNN level 2.5 [101]</td><td>from ERA5</td><td>none</td><td>none</td><td>12 s</td></tr><tr><td>RegCM 4.8.0</td><td></td><td>6-hour</td><td>4 km</td><td>4 km simulation is forced with hourly ERA5</td><td>12</td><td>41</td><td>Modified CCM3 (Kiehl et al. 1996)</td><td>none</td><td>SUBgrid EXPLICIT MOISTURE SCHEME (SUBEX) [69]</td><td>Biosphere-Atmosphere Transfer Scheme version 1e [68]</td><td>Holtslag and Boville (1993)</td><td>from ERA5</td><td>none</td><td>none</td><td>10 s</td></tr><tr><td>COSMO-CLM v.5.0.n1</td><td>[60-62]</td><td>6-hour</td><td>2.2 km</td><td>2.2 km simulation is nested in a 12 km parent domain</td><td>25</td><td>60</td><td>Ritter and Geleyn (1992) [102] for short- and long-wave radiation</td><td>Tiedtke scheme for shallow convection [103]</td><td>One-moment scheme [104, 105]</td><td>TERRA (multi-layer version)</td><td>1-D TKE based diagnostic (level 2.5) closure</td><td>Soil and snow fields in the 2.2 km simulations are initialized from a one-year spinup simulation with the 12-km domain</td><td>AeroCon Global AOD data</td><td>Flake [106]</td><td>15 s</td></tr><tr><td>ICON 2.6.1</td><td>[63, 107]</td><td>3-hour</td><td>3.3 km</td><td>ERA5</td><td>8</td><td>75</td><td>RRTM scheme [108]</td><td>none</td><td>one-moment cloud microphysical scheme with graupel [61]</td><td>Tiled TERRA [109, 110]</td><td>Prognostic TKE [111]</td><td>from ERA5</td><td>none</td><td>Flake [106]</td><td>30 s</td></tr><tr><td>ICON 2.6.3</td><td>[112]</td><td>1-hour</td><td>3.3 km</td><td>ERA5</td><td>10</td><td>50</td><td>RRTM scheme [108]</td><td>Tiedtke-Bechtold [103, 113]</td><td>one-moment cloud microphysical scheme with graupel [61]</td><td>Tiled TERRA [109, 110]</td><td>Orr et al. (2010) [114]</td><td>from ERA5</td><td>Tegen JGR 1997 (NASA/GISS) [115]</td><td>Flake [106]</td><td>20 s</td></tr></tbody></table>
<table><caption>**Table 2** Overview of WRF physics experiments. Gray cells indicate that the correspondent physics option has been changed compared to the WRF reference setup (Table 1).</caption><thead><tr><th>Name</th><th>MYNN2.5 [101, 116, 117]</th><th>MYJ [18, 119]</th><th>MYNN3 [101, 116, 117]</th><th>BouLac [120]</th><th>QNSE, [121]</th><th>UW CAM [122]</th><th>Shin-Hong [123]</th><th>GMB [124]</th><th>Noah LSM<fnref n="1" /> [125]</th><th>WDM7 [126]</th><th>WDM6 [127]</th><th>Morrison [128]</th><th>Ylin [129]</th><th>CAM [122]</th><th>MSKF<fnref n="2" /> [130, 131]</th><th>Domain D2</th><th>D1 direct ERA5</th><th>initialization -6h</th><th>initialization -12h</th></tr></thead><tbody><tr><td>rli1p1_NCAR</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli2p1_NCAR</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli3p1_NCAR</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli4p1_NCAR</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli5p1_NCAR</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_UGOT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p2_UGOT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p3_UGOT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_JAMSTEC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_UHH</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_Cardiff</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p2_Cardiff</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p3_Cardiff</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_ITP</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_PSU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_NORCE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p1_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p2_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p3_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p4_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p5_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p6_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p7_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p9_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>rli1p10_NJU</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Center (SDSC). S.J. acknowledges the support provided for the RegCM4 simulations by the IITM Pratyush high performance computing facility. This is a contribution no 10 to CORDEX-FPS-CPTP.

<footnote n="1">¹Land surface model (LSM).</footnote>

<footnote n="2">²Multi-scale Kain-Fritsch deep convection scheme.</footnote>
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* Funding: UHH is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2037 'CLICCS - Climate, Climatic Change, and Society' - Project Number: 390683824, contribution to the Center for Earth System Research and Sustainability (CEN) of Universitt Hamburg. PKP is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - TRR 301 - Project-ID 428312742. SS and LL gratefully acknowledge the support of a Bjerknes Fast Track Initiative from the Norwegian Education Directorate and HPC support through NOTUR/NorStore projects nn9820k/nn9001k. NCAR is sponsored by the National Science Foundation under Cooperative Agreement 1852977. PNNL is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.

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* Competing Interests: None

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* Author Contributions: All authors contributed to the study conception and design. The first draft of the manuscript was written by Andreas F. Prein and Nikolina Ban and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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* Data Availability: The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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**Supplementary Files**

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* SupplementCPTPCaseStudyOberview.pdf