Building an advanced climate model: Program plan for the CHAMMP (Computer Hardware, Advanced Mathematics, and Model Physics) Climate Modeling Program

ABSTRACT. The eastern region of Santa Catarina State (Brazil) has an important history of natural disasters due to extreme rainfall events. Floods and landslides are enhancedby local features such as orography and urbanization: the replacement of natural surface coverage causing more surface runoff and, hence, ﬂooding. Thus, studies of this type of events – which directly inﬂuence life in the towns – take on increasing importance. This work makes a quantitative analysis of occurrences of extreme rainfall events in the eastern and northern regions of Santa Catarina State in the last 60 years, through individual analysis, considering the history of ﬂoods ineach selected town, as well as an estimate through to the end of century following regional climate modeling. A positive linear trend, in most of the towns studied, was observed in the results, indicating greater frequency of these events in recent decades, and the HadRM3P climate model shows a heterogeneous increase of events for all towns in the period from 2071 to 2100.Keywords: ﬂoods, climate modeling, linear trend. RESUMO. A região leste do Estado de Santa Catarina tem um importante histórico de desastres naturais ocasionados por eventos extremos de precipitação. Inundações e deslizamentos de terra são potencializados pelo relevo acidentado e pela urbanização das cidades da região: a vegetação nativa vem sendo removida acarretando um maior escoamento superﬁcial e, consequentemente, em inundações. Desta forma, torna-se de suma importância os estudos acerca deste tipo de evento que inﬂuencia diretamente a sociedade em geral. Neste trabalho é realizada uma análise quantitativa do número de eventos severos de precipitação ocorridos nas regiões leste e norte de Santa Catarina dos últimos 60 anos, por meio de uma análise pontual, considerandoo histórico de inundações de cada cidade selecionada, além de uma projeção para o ﬁm do século de acordo com modelagem climática regional. Na análise dos resultados observou-se uma tendência linear positiva na maioria das cidades, indicando uma maior frequência deste tipo de evento nas últimas décadas, e o modelo climático HadRM3P mostra um aumento heterogêneo no número de eventos para todas as cidades no período de 2071 a 2100.Palavras-chave: inundações, modelagem climática, tendência linear.

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Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0

Geoscientific Model Development ◽

10.5194/gmd-14-4865-2021 ◽

2021 ◽

Vol 14 (8) ◽

pp. 4865-4890

Author(s):

Peter Uhe ◽

Daniel Mitchell ◽

Paul D. Bates ◽

Nans Addor ◽

Jeff Neal ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

River Flow ◽

Climate Model ◽

Climate Modeling ◽

Flood Hazard ◽

Spatial Scales ◽

Computational Cost ◽

Data Sets ◽

Flood Inundation

Abstract. Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.

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Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

Geoscientific Model Development ◽

10.5194/gmd-13-2355-2020 ◽

2020 ◽

Vol 13 (5) ◽

pp. 2355-2377

Author(s):

Vijay S. Mahadevan ◽

Iulian Grindeanu ◽

Robert Jacob ◽

Jason Sarich

Keyword(s):

Large Scale ◽

Climate Models ◽

Climate Model ◽

Climate Modeling ◽

System Modeling ◽

Spectral Element ◽

Scientific Workflow ◽

Model Coupling ◽

Earth System ◽

Modeling Framework

Abstract. One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) (Hill et al., 2004) and TempestRemap (Ullrich et al., 2013) offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) (Larson et al., 2001) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) (Tautges et al., 2004; Mahadevan et al., 2015) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

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Estimating Surface Melt on the Larsen Ice Shelf Using a Deep Neural Network: Opportunities and Challenges

10.5194/egusphere-egu21-908 ◽

2021 ◽

Author(s):

Zhongyang Hu ◽

Peter Kuipers Munneke ◽

Stef Lhermitte ◽

Maaike Izeboud ◽

Michiel van den Broeke

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Neural Network ◽

Climate Model ◽

Climate Modeling ◽

Coefficient Of Determination ◽

Ice Shelf ◽

Heterogeneous Terrain ◽

Surface Melt ◽

Larsen Ice Shelf

Presently, surface melt over Antarctica is estimated using climate modeling or remote sensing. However, accurately estimating surface melt remains challenging. Both climate modeling and remote sensing have limitations, particularly in the most crucial areas with intense surface melt.&#160; The motivation of our study is to investigate the opportunities and challenges in improving the accuracy of surface melt estimation using a deep neural network. The trained deep neural network uses meteorological observations from automatic weather stations (AWS) and surface albedo observations from satellite imagery to improve surface melt simulations from the regional atmospheric climate model version 2.3p2 (RACMO2). Based on observations from three AWS at the Larsen B and C Ice Shelves, cross-validation shows a high accuracy (root mean square error = 0.898 mm.w.e.d&#8722;1, mean absolute error = 0.429 mm.w.e.d&#8722;1, and coefficient of determination = 0.958). The deep neural network also outperforms conventional machine learning models (e.g., random forest regression, XGBoost) and a shallow neural network. To compute surface melt for the entire Larsen Ice Shelf, the deep neural network is applied to RACMO2 simulations. The resulting, corrected surface melt shows a better correlation with the AWS observations in AWS 14 and 17, but not in AWS 18. Also, the spatial pattern of the surface melt is improved compared to the original RACMO2 simulation. A possible explanation for the mismatch at AWS 18 is its complex geophysical setting. Even though our study shows an opportunity to improve surface melt simulations using a deep neural network, further study is needed to refine the method, especially for complicated, heterogeneous terrain.

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The cloud–aerosol–radiation (CAR) ensemble modeling system

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-8335-2013 ◽

2013 ◽

Vol 13 (16) ◽

pp. 8335-8364 ◽

Cited By ~ 13

Author(s):

X.-Z. Liang ◽

F. Zhang

Keyword(s):

Radiative Forcing ◽

General Circulation ◽

Climate Model ◽

Climate Modeling ◽

General Circulation Models ◽

Climate Impacts ◽

Sensitivity Analyses ◽

General Description ◽

Ensemble Modeling ◽

Uncertainty Range

Abstract. A cloud–aerosol–radiation (CAR) ensemble modeling system has been developed to incorporate the largest choices of alternate parameterizations for cloud properties (cover, water, radius, optics, geometry), aerosol properties (type, profile, optics), radiation transfers (solar, infrared), and their interactions. These schemes form the most comprehensive collection currently available in the literature, including those used by the world's leading general circulation models (GCMs). CAR provides a unique framework to determine (via intercomparison across all schemes), reduce (via optimized ensemble simulations), and attribute specific key factors for (via physical process sensitivity analyses) the model discrepancies and uncertainties in representing greenhouse gas, aerosol, and cloud radiative forcing effects. This study presents a general description of the CAR system and illustrates its capabilities for climate modeling applications, especially in the context of estimating climate sensitivity and uncertainty range caused by cloud–aerosol–radiation interactions. For demonstration purposes, the evaluation is based on several CAR standalone and coupled climate model experiments, each comparing a limited subset of the full system ensemble with up to 896 members. It is shown that the quantification of radiative forcings and climate impacts strongly depends on the choices of the cloud, aerosol, and radiation schemes. The prevailing schemes used in current GCMs are likely insufficient in variety and physically biased in a significant way. There exists large room for improvement by optimally combining radiation transfer with cloud property schemes.

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Advances in representing interactive methane in ModelE2-YIBs (version 1.1)

10.5194/gmd-2018-85 ◽

2018 ◽

Author(s):

Kandice L. Harper ◽

Yiqi Zheng ◽

Nadine Unger

Keyword(s):

Climate Model ◽

Climate Modeling ◽

Optimization Procedure ◽

Relative Difference ◽

Methane Emissions ◽

Optimization Process ◽

Sink Strength ◽

Mixing Ratio ◽

Atmospheric Methane ◽

Tight Coupling

Abstract. Methane (CH4) is both a greenhouse gas and a precursor of tropospheric ozone, making it an important focus of chemistry–climate interactions. Methane has both anthropogenic and natural emission sources, and reaction with the atmosphere's principal oxidizing agent, the hydroxyl radical (OH), is the dominant tropospheric loss process of methane. The tight coupling between methane and OH abundances drives indirect linkages between methane and other short-lived air pollutants and prompts the use of interactive methane chemistry in global chemistry–climate modeling. In this study, an updated contemporary inventory of natural methane emissions and the soil sink is developed using an optimization procedure that applies published emissions data to the NASA GISS ModelE2-Yale Interactive terrestrial Biosphere (ModelE2-YIBs) global chemistry–climate model. Methane observations from the global surface air-sampling network of the Earth System Research Laboratory (ESRL) of the U.S. National Oceanic and Atmospheric Administration (NOAA) are used to guide refinement of the natural methane inventory. The optimization process indicates global annual wetland methane emissions of 140 Tg CH4 y−1. The updated inventory includes total global annual methane emissions from natural sources of 181 Tg CH4 y−1 and a global annual methane soil sink of 60 Tg CH4 y−1. An interactive-methane simulation is run using ModelE2-YIBs, applying dynamic methane emissions and the updated natural methane emissions inventory that results from the optimization process. The simulated methane chemical lifetime of 10.4 ± 0.1 years corresponds well to observed lifetimes. The simulated year 2005 global-mean surface methane concentration is 1.1 % higher than the observed value from the NOAA ESRL measurements. Comparison of the simulated atmospheric methane distribution with the NOAA ESRL surface observations at 50 measurement locations finds that the simulated annual methane mixing ratio is within 1 % (i.e., +1 % to −1 %) of the observed value at 76 % of locations. Considering the 50 stations, the mean relative difference between the simulated and observed annual methane mixing ratio is a model overestimate of only 0.5 %. Comparison of simulated annual column-averaged methane concentrations with SCIAMACHY satellite retrievals provides an independent post-optimization evaluation of modeled methane. The comparison finds a slight model underestimate in 95 % of grid cells, suggesting that the applied methane source in the model is slightly underestimated or the model's methane sink strength is slightly too strong outside of the surface layer. Overall, the strong agreement between simulated and observed methane lifetimes and concentrations indicates that the ModelE2-YIBs chemistry–climate model is able to capture the principal processes that control atmospheric methane.

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The “faint young Sun paradox”: further exploration of the role of dynamical heat-flux feed backs in maintaining global climate stability

Journal of Glaciology ◽

10.1017/s0022143000017780 ◽

1995 ◽

Vol 41 (137) ◽

pp. 87-90 ◽

Cited By ~ 5

Author(s):

Gyula I. Molnar ◽

William J. Gutowski

Keyword(s):

Heat Flux ◽

Climate Model ◽

Climate Modeling ◽

Global Climate ◽

Early Earth ◽

Meridional Heat Transport ◽

Climate Stability ◽

The Earth ◽

Physically Based

AbstractThe climate-modeling problems associated with global change underline the importance of understanding paleoclimates. The available evidence, which suggests that the Earth has never been fully glaciated, poses an especially serious problem for the early Earth when the Sun was about 20–30% fainter than today. In conventional explanations of this “faint young Sun paradox”, presumed very high levels of atmospheric greenhouse gases are required to prevent runaway glaciation of the Earth. Here we explore other possible explanations of this paradox. As an extension of our previous work on this subject, we illustrate how-dynamical beat-flux feed backs may have prevented the early Earth from freezing. Our simulations are carried out using a two-dimensional, seasonal-climate model with physically based parameterizations for atmospheric meridional-heat transport and sea ice. It ís found that dynamical heat-flux feed backs alone may have protected the Archean Earth against a runaway glaciation to a considerable degree.

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Estimating the Uncertainty in a Regional Climate Model Related to Initial and Lateral Boundary Conditions

Journal of Climate ◽

10.1175/jcli-3293.1 ◽

2005 ◽

Vol 18 (7) ◽

pp. 917-933 ◽

Cited By ~ 72

Author(s):

Wanli Wu ◽

Amanda H. Lynch ◽

Aaron Rivers

Keyword(s):

Boundary Conditions ◽

Climate Model ◽

Initial Conditions ◽

Climate Modeling ◽

Regional Climate ◽

Regional Climate Modeling ◽

Regional Model ◽

Lateral Boundary ◽

Lateral Boundary Conditions ◽

The Impact

Abstract There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected.

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Boreal Summer Synoptic-Scale Waves over the Western North Pacific in Multimodel Simulations

Journal of Climate ◽

10.1175/jcli-d-15-0696.1 ◽

2016 ◽

Vol 29 (12) ◽

pp. 4487-4508 ◽

Cited By ~ 13

Author(s):

Haikun Zhao ◽

Xianan Jiang ◽

Liguang Wu

Keyword(s):

Spatial Pattern ◽

North Pacific ◽

General Circulation ◽

Western North Pacific ◽

Climate Model ◽

Climate Modeling ◽

Active Role ◽

General Circulation Models ◽

Boreal Summer ◽

Synoptic Scale

During boreal summer, vigorous synoptic-scale wave (SSW) activity, often evident as southeast–northwest-oriented wave trains, prevails over the western North Pacific (WNP). In spite of their active role for regional weather and climate, modeling studies on SSWs are rather limited. In this study, a comprehensive survey on climate model capability in representing the WNP SSWs is conducted by analyzing simulations from 27 recent general circulation models (GCMs). Results suggest that it is challenging for GCMs to realistically represent the observed SSWs. Only 2 models out of the 27 GCMs generally well simulate both the intensity and spatial pattern of the observed SSW mode. Plausible key processes for realistic simulations of SSW activity are further explored. It is illustrated that GCM skill in representing the spatial pattern of the SSW is highly correlated to its skill in simulating the summer mean patterns of the low-level convergence associated with the WNP monsoon trough and conversion from eddy available potential energy (EAPE) to eddy kinetic energy (EKE). Meanwhile, simulated SSW intensity is found to be significantly correlated to the amplitude of 850-hPa vorticity, divergence, and conversion from EAPE to EKE over the WNP. The observed modulations of SSW activity by the Madden–Julian oscillation are able to be captured in several model simulations.

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