On the parallelisation of weather and climate models

The numerical models used for weather forecasting and climate studies need very large computing resources. The current research in the field indicates that for accurate forecasts, one needs to use models at very high resolution, sophisticated data assimilation techniques and physical parameterisation schemes and multi-model ensemble integrations. In fact the spatial resolution required for accurate forecasts may demand computing power which is prohibitively high considering the processing power of a single processor of any supercomputer. During the last two decades, the developments in computing technology show the emergence of parallel computers with a number of processors which are capable of supplying enormously large computing power as against a single computer. Today, a cluster of workstations or personal computers can be used in parallel to integrate a global climate model for a long time. However, there are bottlenecks to be overcome in order to achieve maximum efficiency. Inter-processor communication is the key issue in case of global weather and climate models. The present paper aims at discussing the status of parallelisation of weather and climate models at leading centres of operational forecasting and research, the inherent parallelism in weather and climate models, the problems encountered in inter-processing communication and various ways of achieving maximum parallel efficiency.

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How Uncertainty in Field Measurements of Ice Nucleating Particles Influences Modeled Cloud Forcing

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-17-0089.1 ◽

2018 ◽

Vol 75 (1) ◽

pp. 179-187 ◽

Cited By ~ 5

Author(s):

S. Garimella ◽

D. A. Rothenberg ◽

M. J. Wolf ◽

C. Wang ◽

D. J. Cziczo

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Field Measurements ◽

Measurement Variability ◽

Cloud Forcing ◽

Constant Bias ◽

Weather And Climate ◽

Bias Range

Field and laboratory measurements using continuous flow diffusion chambers (CFDCs) have been used to construct parameterizations of the number of ice nucleating particles (INPs) in mixed-phase and completely glaciated clouds in weather and climate models. Because of flow nonidealities, CFDC measurements are subject to systematic low biases. Here, the authors investigate the effects of this undercounting bias on simulated cloud forcing in a global climate model. The authors assess the influence of measurement variability by constructing a stochastic parameterization framework to endogenize measurement uncertainty. The authors find that simulated anthropogenic longwave ice-bearing cloud forcing in a global climate model can vary up to 0.8 W m−2 and can change sign from positive to negative within the experimentally constrained bias range. Considering the variability in the undercounting bias, in a range consistent with recent experiments, leads to a larger negative cloud forcing than that when the variability is ignored and only a constant bias is assumed.

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Empirical diagnosis and forecasting of extremes in a changing climate: a case study of Russia

10.5194/egusphere-egu2020-21251 ◽

2020 ◽

Author(s):

Vladyslav Evstigneev ◽

Lemeshko Natalya

Keyword(s):

Time Series ◽

Climate Models ◽

Climate Model ◽

Extreme Values ◽

Global Climate ◽

Global Climate Model ◽

Northern Caucasus ◽

Strong Impact ◽

Changing Climate ◽

Weather And Climate

Extreme events have a strong impact on economic and ecological systems, causing dramatic effects on agriculture, health and other socio-economic activities. Predicting these impacts is of great importance, that is why climate studies over the last decades have focused on weather and climate change extremes both in the future and in the past.Statistical analysis of observational data is still considered as the basic one in climatology. It allows one to study regional manifestations of the global processes in the climate system of different temporal scales of variability. The results of such a retrospective analysis are usually used for validation of global or regional climate models, for statistical forecasting of expected changes as well as for implementation of methods for dynamical and empirical-statistical downscaling of global climate model output to the regional scales. This issue becomes particularly relevant when studying extreme meteorological events in a changing climate.The goal of the present study is to develop an algorithm of empirical diagnosis and forecasting of extremes in a changing climate. The algorithm suggested here is based on (a) technique of nonlinear time series decomposition into empirical modes from noise to trend - EMD method (Huang et al., 1998), (b) modeling of extreme values distribution by GEV, (c) reproduction of correlation structure of climatic series with long "memory" using fractional-integrated autoregressive models - moving average (FARIMA), (d) generation of ensemble of "artificial" surrogate time series using stochastic iterative amplitude adjusted Fourier transform algorithm (Venema V. et al., 2006).Such an approach allows one not only to make a thorough statistical diagnosis of regional meteorological extremes in a non-stationary climate but also to make an empirical forecasting of the weather and climate anomalies into the near future. The algorithm was implemented and tested using daily data on air temperature and precipitation at meteorological stations of different climate regions: the upper Volga region, the territory of the Northern Caucasus and the Azov-Black sea coast region.This research was supported by the Russian Foundation for Basic Research (projects No. 18-05-01073 and 19-29-05243).

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The MJO-QBO Relationship in a GCM with Stratospheric Nudging

Journal of Climate ◽

10.1175/jcli-d-20-0636.1 ◽

2021 ◽

pp. 1-69

Author(s):

Zane Martin ◽

Clara Orbe ◽

Shuguang Wang ◽

Adam Sobel

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Global Climate Models ◽

Boreal Winter ◽

Quasi Biennial Oscillation ◽

Meridional Winds ◽

Goddard Institute ◽

Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

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A new ensemble-based statistical methodology to verify changes in weather and climate models

10.5194/egusphere-egu21-4255 ◽

2021 ◽

Author(s):

Christian Zeman ◽

Christoph Schär

Keyword(s):

Climate Models ◽

Climate Model ◽

Numerical Models ◽

Hypothesis Test ◽

Model Development ◽

Atmospheric Model ◽

Statistical Hypothesis ◽

Model Parameters ◽

Rounding Errors ◽

Weather And Climate

Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.

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Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

The Journal of Agricultural Science ◽

10.1017/s0021859616000654 ◽

2016 ◽

Vol 155 (3) ◽

pp. 407-420 ◽

Cited By ~ 7

Author(s):

R. S. SILVA ◽

L. KUMAR ◽

F. SHABANI ◽

M. C. PICANÇO

Keyword(s):

Climate Change ◽

Global Warming ◽

Open Field ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Current Model ◽

Global Climate Models ◽

The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

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Impact of heterogeneous chemistry on the distribution of Glyoxal and Methylglyoxal in the troposphere

10.5194/egusphere-egu21-10780 ◽

2021 ◽

Author(s):

Ramiro Checa-Garcia ◽

Didier Didier Hauglustaine ◽

Yves Balkanski ◽

Paola Formenti

Keyword(s):

Atmospheric Chemistry ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Experimental Simulation ◽

Heterogeneous Chemistry ◽

Closed Set ◽

Sensitivity Studies ◽

The Tropics

Glyoxal (GL) and methylglyoxal (MGL) are the smallest di-carbonyls present in the atmosphere. They hydrate easily, a process that is followed by an oligomerisation. As a consequence, it is considered that they participate actively in the formation of secondary organic aerosols (SOA) and therefore, they are being introduced in the current climate models with interactive chemistry to assess their importance on atmospheric chemistry. In our study we present the introduction of glyoxal in the INCA global model. A new closed set of gas-phase&#160; reactions is analysed first with a box model. Then the simulated global distribution of glyoxal by the global climate model is compared with satellite observations. We show that the oxidation of volatile organic compounds and acetylene, together with the photolysis of more complex di-carbonyls allows us to reproduce well glyoxal seasonal cycle in the tropics but it requires an additional sink in several northern hemispheric regions. Additional sensitivity studies are being conducted by introducing &#160;GL and MGL interactions with dust and SOA according to new uptake &#160;coefficients obtained by dedicated experiments in the CESAM instrument (Chamber of Experimental Simulation of Atmospheric Multiphases). The effects of these heterogeneous chemistry processes will be quantified in the light of the new chamber measurements &#160;and also evaluated in terms of optical properties of aged dust aerosol &#160;and the changes in direct radiative effects &#160;of the involved aerosol species.

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How detailed do cloud microphysics need to be in climate models?

10.5194/egusphere-egu21-7115 ◽

2021 ◽

Author(s):

Ulrike Proske ◽

Sylvaine Ferrachat ◽

David Neubauer ◽

Ulrike Lohmann

Keyword(s):

Radiative Forcing ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Computing Time ◽

Cloud Microphysics ◽

Model Response ◽

Microphysical Processes ◽

Improved Accuracy

Clouds are of major importance for the climate system, but the radiative forcing resulting from their interaction with aerosols remains uncertain. To improve the representation of clouds in climate models, the parameterisations of cloud microphysical processes (CMPs) have become increasingly detailed. However, more detailed climate models do not necessarily result in improved accuracy for estimates of radiative forcing (Knutti and Sedl&#225;&#269;ek, 2013; Carslaw et al., 2018). On the contrary, simpler formulations are cheaper, sufficient for some applications, and allow for an easier understanding of the respective process' effect in the model.This study aims to gain an understanding which CMP parameterisation complexity is sufficient through simplification. We gradually phase out processes such as riming or aggregation from the global climate model ECHAM-HAM, meaning that the processes are only allowed to exhibit a fraction of their effect on the model state. The shape of the model response as a function of the artificially scaled effect of a given process helps to understand the importance of this process for the model response and its potential for simplification. For example, if partially removing a process induces only minor alterations in the present day climate, this process presents as a good candidate for simplification. This may be then further investigated, for example in terms of computing time. The resulting sensitivities to CMP complexity are envisioned to guide CMP model simplifications as well as steer research towards those processes where a more accurate representation proves to be necessary.&#160; Carslaw, Kenneth, Lindsay Lee, Leighton Regayre, and Jill Johnson (Feb. 2018). &#8220;Climate Models Are Uncertain, but We Can Do Something About It&#8221;. In: Eos 99. doi: 10.1029/2018EO093757Knutti, Reto and Jan Sedl&#225;&#269;ek (Apr. 2013). &#8220;Robustness and Uncertainties in the New CMIP5 Climate Model Projections&#8221;. In: Nature Climate Change 3.4, pp. 369&#8211;373. doi: 10.1038/nclimate1716

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Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

Journal of Climate ◽

10.1175/jcli-d-18-0104.1 ◽

2018 ◽

Vol 32 (1) ◽

pp. 195-212 ◽

Cited By ~ 9

Author(s):

Sicheng He ◽

Jing Yang ◽

Qing Bao ◽

Lei Wang ◽

Bin Wang

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Yangtze River Basin ◽

Global Climate Model ◽

Global Climate Models ◽

Global Ocean ◽

East China ◽

Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

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Dust Aerosol Important for Snowball Earth Deglaciation

Journal of Climate ◽

10.1175/2010jcli3378.1 ◽

2010 ◽

Vol 23 (15) ◽

pp. 4121-4132 ◽

Cited By ~ 26

Author(s):

Dorian S. Abbot ◽

Itay Halevy

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Atmospheric Model ◽

Dust Aerosol ◽

Snowball Earth ◽

Dust Aerosols ◽

Co2 Levels ◽

The Impact

Abstract Most previous global climate model simulations could only produce the termination of Snowball Earth episodes at CO2 partial pressures of several tenths of a bar, which is roughly an order of magnitude higher than recent estimates of CO2 levels during and shortly after Snowball events. These simulations have neglected the impact of dust aerosols on radiative transfer, which is an assumption of potentially grave importance. In this paper it is argued, using the Dust Entrainment and Deposition (DEAD) box model driven by GCM results, that atmospheric dust aerosol concentrations may have been one to two orders of magnitude higher during a Snowball Earth event than today. It is furthermore asserted on the basis of calculations using NCAR’s Single Column Atmospheric Model (SCAM)—a radiative–convective model with sophisticated aerosol, cloud, and radiative parameterizations—that when the surface albedo is high, such increases in dust aerosol loading can produce several times more surface warming than an increase in the partial pressure of CO2 from 10−4 to 10−1 bar. Therefore the conclusion is reached that including dust aerosols in simulations may reconcile the CO2 levels required for Snowball termination in climate models with observations.

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Black Carbon Increases Frequency of Extreme ENSO Events

Journal of Climate ◽

10.1175/jcli-d-19-0549.1 ◽

2019 ◽

Vol 32 (23) ◽

pp. 8323-8333 ◽

Cited By ~ 3

Author(s):

Sijia Lou ◽

Yang Yang ◽

Hailong Wang ◽

Jian Lu ◽

Steven J. Smith ◽

...

Keyword(s):

Black Carbon ◽

Climate Models ◽

Climate Model ◽

Southern Oscillation ◽

Global Climate ◽

Global Climate Model ◽

Co2 Concentration ◽

Enso Events ◽

Aerosol Forcing ◽

Earth’S Climate

ABSTRACT El Niño–Southern Oscillation (ENSO) is the leading mode of Earth’s climate variability at interannual time scales with profound ecological and societal impacts, and it is projected to intensify in many climate models as the climate warms under the forcing of increasing CO2 concentration. Since the preindustrial era, black carbon (BC) emissions have substantially increased in the Northern Hemisphere. But how BC aerosol forcing may influence the occurrence of the extreme ENSO events has rarely been investigated. In this study, using simulations of a global climate model, we show that increases in BC emissions from both the midlatitudes and Arctic weaken latitudinal temperature gradients and northward heat transport, decrease tropical energy divergence, and increase sea surface temperature over the tropical oceans, with a surprising consequential increase in the frequency of extreme ENSO events. A corollary of this study is that reducing BC emissions might serve to mitigate the possible increasing frequency of extreme ENSO events under greenhouse warming, if the modeling result can be translated into the climate in reality.

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