scholarly journals The efficient global primitive equation climate model SPEEDO

2009 ◽  
Vol 2 (2) ◽  
pp. 1115-1155 ◽  
Author(s):  
C. A. Severijns ◽  
W. Hazeleger
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Future Climate ◽  
Wave Number ◽  
Future Climate Change ◽  
Change Scenario ◽  
Primitive Equation

Abstract. The efficient primitive-equation coupled atmosphere-ocean model SPEEDO is presented. The model includes an interactive sea-ice and land component. SPEEDO is a global earth system model of intermediate complexity. It has a horizontal resolution of T30 (triangular truncation at wave number 30) and 8 vertical layers in the atmosphere, and a horizontal resolution of 2 degrees and 20 levels in the ocean. The parameterizations in SPEEDO are developed in such a way that it is a fast model suitable for large ensembles or long runs on a workstation. The model has no flux correction. We compare the mean state and inter-annual variability of the model with observational fields of the atmosphere and ocean. In particular the atmospheric circulation, the mid-latitude patterns of variability and teleconnections from the tropics are well simulated. To show the model's capabilities, we performed a long control run and an ensemble experiment with enhanced greenhouse gasses. The long control run shows that the model is stable. CO2 doubling and future climate change scenario experiments show a climate sensitivity of 1.84 K W−1 m−2, which is within the range of state-of-the-art climate models. The spatial response patterns are comparable to state-of-the-art, higher resolution models. However, for very high greenhouse concentrations the parameterizations are not valid. We conclude that the model is suitable for past, current and future climate simulations and for exploring wide parameter ranges and mechanisms of variability. However, as with any model, users should be careful when using the model beyond the range of physical realism of the parameterizations and model setup.

scholarly journals The efficient global primitive equation climate model SPEEDO V2.0

2010 ◽  
Vol 3 (1) ◽  
pp. 105-122 ◽  
Author(s):  
C. A. Severijns ◽  
W. Hazeleger
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Future Climate ◽  
Wave Number ◽  
Future Climate Change ◽  
Change Scenario ◽  
Primitive Equation

Abstract. The efficient primitive-equation coupled atmosphere-ocean model SPEEDO V2.0 is presented. The model includes an interactive sea-ice and land component. SPEEDO is a global earth system model of intermediate complexity. It has a horizontal resolution of T30 (triangular truncation at wave number 30) and 8 vertical layers in the atmosphere, and a horizontal resolution of 2 degrees and 20 levels in the ocean. The parameterisations in SPEEDO are developed in such a way that it is a fast model suitable for large ensembles or long runs (of O(104) years) on a typical current workstation. The model has no flux correction. We compare the mean state and inter-annual variability of the model with observational fields of the atmosphere and ocean. In particular the atmospheric circulation, the mid-latitude patterns of variability and teleconnections from the tropics are well simulated. To show the capabilities of the model, we performed a long control run and an ensemble experiment with enhanced greenhouse gases. The long control run shows that the model is stable. CO2 doubling and future climate change scenario experiments show a climate sensitivity of 1.84 K W-1 m2, which is within the range of state-of-the-art climate models. The spatial response patterns are comparable to state-of-the-art, higher resolution models. However, for very high greenhouse gas concentrations the parameterisations are not valid. We conclude that the model is suitable for past, current and future climate simulations and for exploring wide parameter ranges and mechanisms of variability. However, as with any model, users should be careful when using the model beyond the range of physical realism of the parameterisations and model setup.

scholarly journals An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, Using CMIP5 Climate Models

2017 ◽  
Vol 30 (17) ◽  
pp. 6701-6722 ◽  
Author(s):  
Daniel Bannister ◽  
Michael Herzog ◽  
Hans-F. Graf ◽  
J. Scott Hosking ◽  
C. Alan Short
Keyword(s):  
Climate Change ◽  
Sichuan Basin ◽  
Climate Models ◽  
Distribution Functions ◽  
Future Climate ◽  
First Century ◽  
Future Climate Change ◽  
Change Scenario ◽  
The Sichuan Basin ◽  
Twenty First Century

The Sichuan basin is one of the most densely populated regions of China, making the area particularly vulnerable to the adverse impacts associated with future climate change. As such, climate models are important for understanding regional and local impacts of climate change and variability, like heat stress and drought. In this study, climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are validated over the Sichuan basin by evaluating how well each model can capture the phase, amplitude, and variability of the regionally observed mean, maximum, and minimum temperature between 1979 and 2005. The results reveal that the majority of the models do not capture the basic spatial pattern and observed means, trends, and probability distribution functions. In particular, mean and minimum temperatures are underestimated, especially during the winter, resulting in biases exceeding −3°C. Models that reasonably represent the complex basin topography are found to generally have lower biases overall. The five most skillful climate models with respect to the regional climate of the Sichuan basin are selected to explore twenty-first-century temperature projections for the region. Under the CMIP5 high-emission future climate change scenario, representative concentration pathway 8.5 (RCP8.5), the temperatures are projected to increase by approximately 4°C (with an average warming rate of +0.72°C decade−1), with the greatest warming located over the central plains of the Sichuan basin, by 2100. Moreover, the frequency of extreme months (where mean temperature exceeds 28°C) is shown to increase in the twenty-first century at a faster rate compared to the twentieth century.

Simulation of the mid-Pliocene Warm Period using HadGEM3-GC31-LL: Pliocene climate relative to the pre-industrial era, previous model versions, other climate models and proxy data

2021 ◽  
Author(s):  
Charles Williams ◽  
Daniel Lunt ◽  
Alistair Sellar ◽  
William Roberts ◽  
Robin Smith ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
High Sensitivity ◽  
Warm Period ◽  
Future Climate ◽  
Future Climate Change ◽  
Last Interglacial ◽  
Proxy Data ◽  
Global Mean

<p>To better understand the processes contributing to future climate change, palaeoclimate model simulations are an important tool because they allow testing of the models’ ability to simulate very different climates than that of today.  As part of CMIP6/PMIP4, the latest version of the UK’s physical climate model, HadGEM3-GC31-LL (hereafter, for brevity, HadGEM3), was recently used to simulate the mid-Holocene (~6 ka) and Last Interglacial (~127 ka) simulations and the results were compared to the preindustrial era, previous versions of the same model and proxy data (see Williams et al. 2020, Climate of the Past).  Here, we use the same model to go further back in time, presenting the results from the mid-Pliocene Warm Period (~3.3 to 3 ma, hereafter the “Pliocene” for brevity).  This period is of particular interest when it comes to projections of future climate change under various scenarios of CO<sub>2</sub> emissions, because it is the most recent time in Earth’s history when CO<sub>2</sub> levels were roughly equivalent to today.  In response, albeit due to slower mechanisms than today’s anthropogenic fossil fuel driven-change, during the Pliocene global mean temperatures were 2-3°C higher than today, more so at the poles.</p><p> </p><p>Here, we present results from the HadGEM3 Pliocene simulation.  The model is responding to the Pliocene boundary conditions in a manner consistent with current understanding and existing literature.  When compared to the preindustrial era, global mean temperatures are currently ~5°C higher, with the majority of warming coming from high latitudes due to polar amplification from a lack of sea ice.  Relative to other models within the Pliocene Modelling Intercomparison Project (PlioMIP), this is the 2<sup>nd</sup> warmest model, with the majority of others only showing up to a 4.5°C increase and many a lot less.  This is consistent with the relatively high sensitivity of HadGEM3, relative to other CMIP6-class models.  When compared to a previous generation of the same UK model, HadCM3, similar patterns of both surface temperature and precipitation changes are shown (relative to preindustrial).  Moreover, when the simulations are compared to proxy data, the results suggest that the HadGEM3 Pliocene simulation is closer to the reconstructions than its predecessor.</p>

scholarly journals Trends in projections of standardized precipitation indices in a future climate in Poland

2016 ◽  
Vol 20 (5) ◽  
pp. 1947-1969 ◽  
Author(s):  
Marzena Osuch ◽  
Renata J. Romanowicz ◽  
Deborah Lawrence ◽  
Wai K. Wong
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Standardized Precipitation Index ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Projections ◽  
Monthly Precipitation

Abstract. Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index (SPI). The time series of precipitation represent six different climate model runs under the selected emission scenario for the period 1971–2099. Monthly precipitation values were used to estimate the SPI for multiple timescales (1, 3, 6, 12, and 24 months) for a spatial resolution of 25 km for the whole country. Trends in the SPI were analysed using the Mann–Kendall test with Sen's slope estimator for each grid cell for each climate model projection and aggregation scale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the timescale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarize the mechanisms underlying the influence of bias correction on trends in precipitation and the SPI using a simple example of a linear bias correction procedure. In both cases, the bias correction by QM does not change the direction of changes but can change the slope of trend, and the influence of bias correction on SPI is much reduced. We also have noticed that the results for the same global climate model, driving different regional climate model, are characterized by a similar pattern of changes, although this behaviour is not seen at all timescales and seasons.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

scholarly journals Predicted Future Benefits for an Endemic Rodent in the Irano-Turanian Region

Climate ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Suzanna Meeussen ◽  
Anouschka Hof
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Species Distribution Modelling ◽  
Geographic Range ◽  
Future Climate ◽  
Future Climate Change ◽  
Distribution Ranges ◽  
Bioclimatic Variables ◽  
Suitable Area ◽  
The Impact

Climate change is expected to have an impact on the geographical distribution ranges of species. Endemic species and those with a restricted geographic range may be especially vulnerable. The Persian jird (Meriones persicus) is an endemic rodent inhabiting the mountainous areas of the Irano-Turanian region, where future desertification may form a threat to the species. In this study, the species distribution modelling algorithm MaxEnt was used to assess the impact of future climate change on the geographic distribution range of the Persian jird. Predictions were made under two Representative Concentration Pathways and five different climate models for the years 2050 and 2070. It was found that both bioclimatic variables and land use variables were important in determining potential suitability of the region for the species to occur. In most cases, the future predictions showed an expansion of the geographic range of the Persian jird which indicates that the species is not under immediate threat. There are however uncertainties with regards to its current range. Predictions may therefore be an over or underestimation of the total suitable area. Further research is thus needed to confirm the current geographic range of the Persian jird to be able to improve assessments of the impact of future climate change.

scholarly journals Evaluating the Uncertainty Induced by the Virtual Salt Flux Assumption in Climate Simulations and Future Projections

2010 ◽  
Vol 23 (1) ◽  
pp. 80-96 ◽  
Author(s):  
Jianjun Yin ◽  
Ronald J. Stouffer ◽  
Michael J. Spelman ◽  
Stephen M. Griffies
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Ocean Model ◽  
Salt Flux ◽  
Freshwater Flux ◽  
Unexpected Result ◽  
Geophysical Fluid Dynamics Laboratory ◽  
External Forcing ◽  
Future Evolution ◽  
Near Term

Abstract The unphysical virtual salt flux (VSF) formulation widely used in the ocean component of climate models has the potential to cause systematic and significant biases in modeling the climate system and projecting its future evolution. Here a freshwater flux (FWF) and a virtual salt flux version of the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1) are used to evaluate and quantify the uncertainties induced by the VSF formulation. Both unforced and forced runs with the two model versions are performed and compared in detail. It is found that the differences between the two versions are generally small or statistically insignificant in the unforced control runs and in the runs with a small external forcing. In response to a large external forcing, however, some biases in the VSF version become significant, especially the responses of regional salinity and global sea level. However, many fundamental aspects of the responses differ only quantitatively between the two versions. An unexpected result is the distinctly different ENSO responses. Under a strong external freshwater forcing, the great enhancement of the ENSO variability simulated by the FWF version does not occur in the VSF version and is caused by the overexpansion of the top model layer. In summary, the principle assumption behind using virtual salt flux is not seriously violated and the VSF model has the ability to simulate the current climate and project near-term climate evolution. For some special studies such as a large hosing experiment, however, both the VSF formulation and the use of the FWF in the geopotential coordinate ocean model could have some deficiencies and one should be cautious to avoid them.

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>

scholarly journals Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 262 ◽  
Author(s):  
Coraline Wyard ◽  
Sébastien Doutreloup ◽  
Alexandre Belleflamme ◽  
Martin Wild ◽  
Xavier Fettweis
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Radiative Flux ◽  
Added Value ◽  
Historical Trends ◽  
The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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