Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS

Characterizing the elements of Earth's radiative budget: Applying uncertainty quantification to the CESM

Surface longwave emissivity can be less than unity and vary significantly with frequency. However, most climate models still assume a blackbody surface in the longwave (LW) radiation scheme of their atmosphere models. This study incorporates realistic surface spectral emissivity into the atmospheric component of the Community Earth System Model (CESM), version 1.1.1, and evaluates its impact on simulated climate. By ensuring consistency of the broadband surface longwave flux across different components of the CESM, the top-of-the-atmosphere (TOA) energy balance in the modified model can be attained without retuning the model. Inclusion of surface spectral emissivity, however, leads to a decrease of net upward longwave flux at the surface and a comparable increase of latent heat flux. Global-mean surface temperature difference between the modified and standard CESM simulation is 0.20 K for the fully coupled run and 0.45 K for the slab-ocean run. Noticeable surface temperature differences between the modified and standard CESM simulations are seen over the Sahara Desert and polar regions. Accordingly, the climatological mean sea ice fraction in the modified CESM simulation can be less than that in the standard CESM simulation by as much as 0.1 in some regions. When spectral emissivities of sea ice and open ocean surfaces are considered, the broadband LW sea ice emissivity feedback is estimated to be −0.003 W m−2 K−1, assuming flat ice emissivity as sea ice emissivity, and 0.002 W m−2 K−1, assuming coarse snow emissivity as sea ice emissivity, which are two orders of magnitude smaller than the surface albedo feedback.

Extratropical cyclones play a dominant role in the Mediterranean. They are important for local water supplies, but they can also cause severe damage due to heavy winds, extreme precipitation and coastal floods. Over the last decades, a decrease in the number of extratropical cyclones in the Mediterranean has been observed. Climate models suggest that this decreasing trend will continue in the future under global warming, leading to fewer storms and dryer conditions over the region compared to the present. However, it is much less clear how extreme cyclones in the Mediterranean will respond to climate change. Our previous study, based on a simulation from the Community Earth System Model (CESM) covering the last 3500 years, indicates that extreme cyclones show a distinct centennial variability in frequency, cyclone-related precipitation and wind speed. In addition, we found a weak relation between atmospheric circulation modes and varying cyclone characteristics across different regions in the Mediterranean. However, the coarse horizontal resolution of CESM (2.0°×2.5°) is not very well suited to resolve the mesoscale cyclones that often occur in the Mediterranean. For this study, we downscaled the CESM simulation for the period 1821–2100 (RCP8.5 scenario from 2005 onwards) to a horizontal grid resolution of 20 km using the Weather Research and Forecasting (WRF) model. The WRF simulation can resolve the cyclone characteristics in the Mediterranean more accurately than CESM. Additionally, the WRF simulation is able to reproduce the complexity of cyclone-related wind speed and precipitation in a much more detailed way. Preliminary results show a strong decrease in cyclone frequency as a result of global warming. However, this trend is much less clear for extreme cyclones with respect to wind speed and precipitation. Using the long downscaled WRF simulation, we intend to identify characteristics in CESM that lead to extreme wind and precipitation in the downscaled simulation. Additionally, we will investigate the most extreme wind and precipitation events in the Mediterranean to understand what processes are better captured at smaller scales than in the global model.

<p>Sudden and strong positive sea surface temperatures (SST) anomalies in the far eastern Pacific that are tied to the rather narrow NINO1+2 (0-10°S, 90°W-80°W) region are known as coastal El Niño events. In contrast to the well-studied features of typical basin-scale ENSO events, the frequency, origin and relevant processes of coastal El Niños are largely unknown. Here, we analyze their characteristics and future behavior using observational data and simulations with the Community Earth System Model (CESM), which exhibits skill in simulating precipitation, wind and SST fields associated with coastal El Niños. We find that tropical Pacific basin-scale ocean dynamics – in sharp contrast to a typical El Niño event  – play no major role in the evolution of a coastal El Niño. On the other hand, we find that atmospheric circulation anomalies from the Southern Hemisphere lead the evolution of coastal El Niño events, by causing warm SST anomalies that then propagate into the NINO1+2 region. Once initiated, local thermodynamical feedback processes such as cloud radiation feedbacks are responsible for the growth and decay of the events. Greenhouse gas forcing leads to an increase in the frequency of coastal El Niño events and a shift of their peak month in CESM simulations, related to a shallowing of the thermocline and changes in Rossby wave forcing from the Southern Hemisphere. In conclusion, based on our results, we will demonstrate the potential for increased predictability of coastal El Niño events, whose intense coastal SST warming and associated extreme precipitation poses a serious threat for local communities via loss of life and severe economic damage.</p>

Improved understanding of the drivers of climate variability, particularly over the last millennium, and its influence on Antarctic ice melt have important implications for projecting ice sheet resilience in a changing climate. Here, we investigated the variability in Antarctic climate and sea ice extent during the last millennium (850–1850 CE) by comparing paleoenvironmental reconstructions with simulations from the Community Earth System Model Last Millennium Ensemble (CESM-LME). Atmospheric and oceanic response to external forcing in CESM-LME simulations typically take the form of an Antarctic dipole: cooling over most of Antarctica and warming east of the Antarctic Peninsula. This configuration is also observed in ice core records. Unforced variability and a dipole response to large volcanic eruptions contribute to weaker cooling in the Antarctic than the Arctic, consistent with the absence of a strong volcanic signal in Antarctic ice core records. The ensemble does not support a clear link between the dipole pattern and baseline shifts in the Southern Annular Mode and El Niño-Southern Oscillation proposed by some paleoclimate reconstructions. Our analysis provides a point of comparison for paleoclimate reconstructions and highlights the role of internal climate variability in driving modeled last millennium climate evolution in the Antarctic.

The far‐infrared (wavelengths longer than 17μm) has been shown to be extremely important for radiative processes in the earth's atmosphere. The strength of the water vapor continuum absorption in this spectral region has largely been predicted using observations at other wavelengths that have been extrapolated using semiempirical approaches such as the Clough‐Kneizys‐Davies (CKD) family of models. Recent field experiments using new far‐infrared instrumentation have supported a factor of 2 decrease in the modeled strength of the foreign continuum at 50μm and a factor of 1.5 increase in the self‐continuum at 24μm in the Clough‐Kneizys‐Davies continuum model (CKD v2.4); these changes are incorporated in the Mlawer‐Tobin‐CKD continuum model (MT_CKD v2.4). The water vapor continuum in the Community Earth System Model (CESM v1.0) was modified to use the newer model, and the impacts of this change were investigated by comparing output from the original and modified CESM for 20 year integrations with prescribed sea surface temperatures. The change results in an increase in the net upward longwave flux of order 0.5 W m−2between 300 and 400 mb, and a decrease in this flux of about the same magnitude for altitudes below 600 mb. The radiative impact results in a small but statistically significant change in the mean temperature and humidity fields, and also a slight decrease (order 0.5%) of high‐cloud amount. The change in the cloud amount modified the longwave cloud radiative forcing, which partially offset the radiative heating caused by the change in the water vapor continuum absorption model.

Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that coveredmore » topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?« less

With the development of computer science, more and more complex problems rely on the help of computers for solving. When facing the parameter optimization problem of complex models, traditional intelligent optimization algorithms often require multiple iterations on the target problem. It can bring unacceptable costs and resource costs in dealing with these complex problems. In order to solve the parameter optimization of complex problems, in this paper we propose a multi-level surrogate-assisted optimization algorithm (MLSAO). By constructing surrogate models at different levels, the algorithm effectively explores the parameter space, avoiding local optima and enhancing optimization efficiency. The method combines two optimization algorithms, differential evolution (DE) and Downhill simplex method. DE is focused on global level surrogate model optimization. Downhill simplex is concentrated on local level surrogate model update. Random forest and inverse distance weighting (IDW) are constructed for global and local level surrogate model respectively. These methods leverage their respective advantages at different stages of the algorithm. The MLSAO algorithm is evaluated against other state-of-the-art approaches using benchmark functions of varying dimensions. Comprehensive results from the comparisons showcase the superior performance of the MLSAO algorithm in addressing expensive optimization problems. Moreover, we implement the MLSAO algorithm for tuning precipitation parameters in the Community Earth System Model (CESM). The outcomes reveal its effective enhancement of CESM's simulation accuracy for precipitation in the North Indian Ocean and the North Pacific region. These experiments demonstrate that MLSAO can better address parameter optimization problems under complex conditions.

The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper‐ocean over the past three decades. State‐of‐the‐art ice‐ocean models, by contrast, tend to simulate a surface layer that is saltier and less stratified than observed. Here, we examine decadal changes to seasonal processes that may contribute to this wide‐reaching model bias using climate model simulations from the Community Earth System Model and below‐ice observations from the Arctic Ice Dynamics Joint Experiment in 1975 and Ice Tethered Profilers in 2006–2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be mainly attributed to unrealistically deep vertical mixing in the model, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement with broad implications for Arctic sea ice and ecosystem dynamics.

This study evaluates different hypotheses of the origin of the Little Ice Age, focusing on the long-term response of Arctic sea ice and oceanic circulation to solar and volcanic perturbations. The authors analyze the Last Millennium Ensemble of climate model simulations carried out with the Community Earth System Model at the National Center for Atmospheric Research. The authors examine the duration and strength of volcanic perturbations, and the effects of initial and boundary conditions, such as the phase of the Atlantic multidecadal oscillation. They evaluate the impacts of these factors on decadal-to-multicentennial perturbations of the cryospheric, oceanic, and atmospheric components of the climate system. The authors show that, at least in the Last Millennium Ensemble, volcanic eruptions are followed by a decadal-scale positive response of the Atlantic multidecadal overturning circulation, followed by a centennial-scale enhancement of the Northern Hemispheric sea ice extent. It is hypothesized that a few mechanisms, not just one, may have to play a role in consistently explaining such a simulated climate response at both decadal and centennial time scales. The authors argue that large volcanic forcing is necessary to explain the origin and duration of Little Ice Age–like perturbations in the Last Millennium Ensemble. Other forcings might play a role as well. In particular, prolonged fluctuations in solar irradiance associated with solar minima potentially amplify the enhancement of the magnitude of volcanically triggered anomalies of Arctic sea ice extent.

Simulation of tropical precipitation in global climate models remains a challenge, in which double Intertropical Convergence Zone (ITCZ) bias is a prominent manifest. Compared with the original cloud scheme, a new diagnostic statistical cloud macrophysics scheme alleviates the spurious southern ITCZ bias associated with warm sea surface temperature (SST) bias over the central Pacific in the National Center for Atmospheric Research (NCAR) Community Earth System Model, version 1 (CESM1). Owing to the reduced net surface shortwave radiation associated with the increased low cloud fraction and liquid water path over the southeastern Pacific Ocean, the new scheme reduced local SST and further suppressed convection and precipitation. Stronger Walker circulation and enhanced tropical easterlies lead to increased tropical ocean zonal currents, which further lead to reduced SST in the central Pacific due to increased oceanic cold advection. As a result, over the central Pacific, SST cooling suppresses precipitation and alleviates the double ITCZ bias. The study suggests that low clouds over subtropical eastern ocean regions can impact tropical circulation and precipitation via a strong coupling with SST and ocean dynamics.

While the primary origin of ocean diapycnal diffusivity is commonly attributed to stratified turbulence induced by breaking internal waves (IWs), verifying diffusivity values in ocean circulation models within specific geographical regions remains challenging due to limited microstructure measurements. Recent analyses of a downscaled global ocean simulation into higher-resolution regional setups northeast of Hawaii, reveal a notably enhanced fit between simulated IW spectra and in-situ profiler measurements like the Garrett-Munk spectrum [Nelson et al. (2020), Pan et al. (2020), Thakur et al. (2022)]. In this study, we utilize this dynamically downscaled ocean simulation to scrutinize the dynamics of IW-breaking and the wave-turbulence cascade in this region explicitly. Employing a modified version of the Kappa Profile Parameterization (KPP), we infer the horizontally-averaged vertical profile of diapycnal diffusivity. Comparing this inferred profile to the background profile used in low-resolution coupled climate models—such as the Community Earth System Model (CESM) by the US National Center for Atmospheric Research (NCAR)—is a central aspect of our investigation. Our exploration reveals that the wavefield in the high-resolution regional domain is dominated by a well-resolved spectrum of low-mode IWs, predictable through appropriate eigenvalue computations for stratified flow. Finally, we propose a novel tentative approach to enhance the KPP parameterization. This approach holds promise for refining our understanding of diapycnal diffusivity, offering valuable insights for improving ocean circulation models.  

An adequate characterization of internal modes of climate variability (MoV) is a prerequisite for both accurate seasonal predictions and climate change detection and attribution. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify intermodel contrasts. In this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of global coupled modes is found to improve across successive E3SM and CESM versions in conjunction with the fidelity of the base state climate while the patterns of extratropical modes are well simulated across the ensembles. Various persistent shortcomings for all MoV are however identified and discussed. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics. Significance Statement Modes of variability play a critical role in prediction of seasonal to decadal climate variability and detection of forced climate change, but historically many modes have been poorly simulated by coupled climate models. Using recently produced large ensembles, this work demonstrates the improved simulation of a broad range of internal modes in successive versions of the E3SM and CESM and discusses opportunities for further advances.

Model output generated using the National Center for Atmospheric Research (NCAR) Community Climate System Model and Community Earth System Model, showing negative feedback from low clouds has had a major role in stabilizing climate through Earth's history.

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.