The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models

Abstract. Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

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Using PRIMAVERA high-resolution global climate models for European windstorm risk assessment in present and future climates for the (re)insurance industry

10.5194/egusphere-egu2020-7676 ◽

2020 ◽

Author(s):

Julia Lockwood ◽

Erika Palin ◽

Galina Guentchev ◽

Malcolm Roberts

Keyword(s):

High Resolution ◽

Climate Models ◽

Climate Model ◽

Insurance Industry ◽

Global Climate ◽

Storm Track ◽

Global Climate Models ◽

Climate Modelling ◽

Business And Society ◽

High Resolution Models

PRIMAVERA is a European Union Horizon2020 project about creating a new generation of advanced and well-evaluated high-resolution global climate models, for the bene&#64257;t of governments, business and society in general. The project has been engaging with several sectors, including &#64257;nance, transport, and energy, to understand the extent to which any improved process understanding arising from high-resolution global climate modelling can &#8211; in turn &#8211; help with using climate model output to address user needs.In this talk we will outline our work for the &#64257;nance and (re)insurance industries.&#160; Following consultation with members of the industry, we are using PRIMAVERA climate models to generate a European windstorm event set for use in catastrophe modelling and risk analysis.&#160; The event set is generated from five different climate models, each run at a selection of resolutions ranging from 18-140km, covering the period 1950-2050, giving approximately 1700 years of climate model data in total.&#160; High-resolution climate models tend to have reduced biases in storm track position (which is too zonal in low-resolution climate models) and windstorm intensity.&#160; We will compare the properties of the windstorm footprints and associated risk across the different models and resolutions, to assess whether the high-resolution models lead to improved estimation of European windstorm risk.&#160; We will also compare windstorm risk in present and future climates, to see if a consistent picture emerges between models.&#160; Finally we will address the question of whether the event sets from each PRIMAVERA model can be combined to form a multi-model event set ensemble covering thousands of years of windstorm data.

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The importance of model resolution on simulated precipitation in Europe – from global to regional model

10.5194/wcd-2020-31 ◽

2020 ◽

Cited By ~ 1

Author(s):

Gustav Strandberg ◽

Petter Lind

Keyword(s):

High Resolution ◽

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Horizontal Resolution ◽

Model Resolution ◽

Simulated Precipitation ◽

The Difference ◽

Model Ensembles ◽

High Resolution Models

Abstract. Precipitation, and especially extreme precipitation, is a key climate variable as it effects large parts of society. It is difficult to simulate in a climate model because of its large variability in time and space. This study investigates the importance of model resolution on the simulated precipitation in Europe for a wide range of climate model ensembles: from global climate models (GCM) at horizontal resolution of around 300 km to regional climate models (RCM) at horizontal resolution of 12.5 km. The aim is to investigate the differences between models and model ensembles, but also to evaluate their performance compared to gridded observations from E-OBS. Model resolution has a clear effect on precipitation. Generally, extreme precipitation is more intense and more frequent in high-resolution models compared to low-resolution models. Models of low resolution tend to underestimate intense precipitation. This is improved in high-resolution simulations, but there is a risk that high resolution models overestimate precipitation. This effect is seen in all ensembles, and GCMs and RCMs of similar resolution give similar results. The number of precipitation days, which is more governed by large-scale atmospheric flow, is not dependent on model resolution, while the number of days with heavy precipitation is. The difference between different models is often larger than between the low- and high-resolution versions of the same model, which makes it difficult to quantify the improvement. In this sense the quality of an ensemble is depending more on the models it consists of rather than the average resolution of the ensemble. Furthermore, the difference in simulated precipitation between an RCM and the driving GCM depend more on the choice of RCM and less on the down-scaling itself; as different RCMs driven by the same GCM may give different results. The results presented here are in line with previous similar studies but this is the first time an analysis like this is done across such relatively large model ensembles of different resolutions, and with a method studying all parts of the precipitation distribution.

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Representing ozone extremes in European megacities: the importance of resolution in a global chemistry climate model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-27423-2013 ◽

2013 ◽

Vol 13 (10) ◽

pp. 27423-27458

Author(s):

Z. S. Stock ◽

M. R. Russo ◽

J. A. Pyle

Keyword(s):

Atmospheric Chemistry ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Surface Ozone ◽

Horizontal Resolution ◽

Global Climate Models ◽

Ozone Concentrations ◽

Coarse Resolution ◽

The Impact

Abstract. The continuing growth of the world's urban population has led to an increasing number of cities with more than 10 million inhabitants. The higher emissions of pollutants, coupled to higher population density, makes predictions of air quality in these megacities of particular importance from both a science and a policy perspective. Global climate models are typically run at coarse resolution to enable both the efficient running of long time integrations, and the ability to run multiple future climate scenarios. However, when considering surface ozone concentrations at the local scale, coarse resolution can lead to inaccuracies arising from the highly non-linear ozone chemistry and the sensitivity of ozone to the distribution of its precursors on smaller scales. In this study, we use UM-UKCA, a global atmospheric chemistry model, coupled to the UK Met Office Unified Model, to investigate the impact of model resolution on tropospheric ozone, ranging from global to local scales. We focus on the model's ability to represent the probability of high ozone concentrations in the summer and low ozone concentrations, associated with polluted megacity environments, in the winter, and how this varies with horizontal resolution. We perform time-slice integrations with two model configurations at typical climate resolution (CR, ~150 km) and at a higher resolution (HR, ~40 km). The CR configuration leads to overestimation of ozone concentrations on both regional and local scales, while it gives broadly similar results to the HR configuration on the global scale. The HR configuration is found to produce a more realistic diurnal cycle of ozone concentrations and to give a better representation of the probability density function of ozone values in urban areas such as the megacities of London and Paris. We discuss the possible causes for the observed difference in model behaviour between CR and HR configurations and estimate the relative contribution of chemical and meteorological factors at the different scales.

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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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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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Assessing Future Spatio-Temporal Changes in Crop Suitability and Planting Season over West Africa: Using the Concept of Crop-Climate Departure

Climate ◽

10.3390/cli7090102 ◽

2019 ◽

Vol 7 (9) ◽

pp. 102 ◽

Cited By ~ 5

Author(s):

Temitope S. Egbebiyi ◽

Chris Lennard ◽

Olivier Crespo ◽

Phillip Mukwenha ◽

Shakirudeen Lawal ◽

...

Keyword(s):

West Africa ◽

Linear Correlation ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Models ◽

Crop Suitability ◽

Suitable Area ◽

The Impact ◽

Planting Season

The changing climate is posing significant threats to agriculture, the most vulnerable sector, and the main source of livelihood in West Africa. This study assesses the impact of the climate-departure on the crop suitability and planting month over West Africa. We used 10 CMIP5 Global climate models bias-corrected simulations downscaled by the CORDEX regional climate model, RCA4 to drive the crop suitability model, Ecocrop. We applied the concept of the crop-climate departure (CCD) to evaluate future changes in the crop suitability and planting month for five crop types, cereals, legumes, fruits, root and tuber and horticulture over the historical and future months. Our result shows a reduction (negative linear correlation) and an expansion (positive linear correlation) in the suitable area and crop suitability index value in the Guinea-Savanna and Sahel (southern Sahel) zone, respectively. The horticulture crop was the most negatively affected with a decrease in the suitable area while cereals and legumes benefited from the expansion in suitable areas into the Sahel zone. In general, CCD would likely lead to a delay in the planting season by 2–4 months except for the orange and early planting dates by about 2–3 months for cassava. No projected changes in the planting month are observed for the plantain and pineapple which are annual crops. The study is relevant for a short and long-term adaptation option and planning for future changes in the crop suitability and planting month to improve food security in the region.

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The moisture budget of tropical cyclones: large scale environmental constraints and sensitivity to model horizontal resolution

10.5194/egusphere-egu2020-19270 ◽

2020 ◽

Author(s):

Benoit Vanniere ◽

Malcolm Roberts ◽

Pier Luigi Vidale ◽

Kevin Hodges ◽

Marie-Estelle Demory

Keyword(s):

Tropical Cyclones ◽

Large Scale ◽

Climate Models ◽

Potential Temperature ◽

Large Change ◽

Moisture Flux ◽

Horizontal Resolution ◽

Moisture Budget ◽

High Resolution Models ◽

The Impact

Previous studies have shown that, the number, intensity and structure of simulated tropical cyclones (TC) in climate models get closer to the observations as the horizontal resolution is increased. However, the sensitivity of tropical cyclone precipitation and moisture budget to changes in resolution has received less attention. In this study, we use the five-model ensemble from project PRIMAVERA/HighResMIP to investigate the systematic changes associated with the water budget of tropical cyclones in a range of horizontal resolutions from 1&#186; to 0.25&#186;. Our results show that despite a large change in the distribution of TC intensity with resolution, the distribution of precipitation per TC does not change significantly. This result is explained by the large scale balance which characterises the moisture budget of TCs, i.e. radii of ~15&#186; a scale that low and high resolution models represent equally well. The wind profile is found to converge between low and high resolutions for radii > 5&#186;, resulting in a moisture flux convergence into the TC with similar magnitude at low and high resolutions. In contrast to precipitation per TC, the larger TC intensity at higher resolution is explained by the larger surface latent heat flux near the center of the storm, which leads to an increase in equivalent potential temperature and warmer core anomalies, despite representing a negligible contribution to the moisture budget. We discuss the complication arising from the choice of the tracking algorithm when assessing the impact of model resolution and the implications of such a constraint on the TC moisture budget in the context of climate change.

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Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2008.0033 ◽

2008 ◽

Vol 366 (1875) ◽

pp. 2559-2577 ◽

Cited By ~ 58

Author(s):

J Berner ◽

F.J Doblas-Reyes ◽

T.N Palmer ◽

G Shutts ◽

A Weisheimer

Keyword(s):

Cellular Automaton ◽

Systematic Error ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Seasonal Prediction ◽

Global Climate Models ◽

Seasonal Forecasts ◽

The Impact

The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean–atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.

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Testing robustness of co-existing climate states

10.5194/egusphere-egu21-9902 ◽

2021 ◽

Author(s):

Charline Ragon ◽

Valerio Lembo ◽

Valerio Lucarini ◽

Christian Vérard ◽

Jérôme Kasparian ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Water Mass ◽

Hydrological Cycle ◽

Initial Conditions ◽

Frictional Heating ◽

Horizontal Resolution ◽

Climate Dynamics ◽

Mechanical Work ◽

The Impact

The climate can be regarded as a stationary non-equilibrium statistical system (Gallavotti 2006): a continuous and spatially inhomogeneous input of solar energy enters at the top-of-atmosphere and compensates the action of non-conservative forces, mainly occurring at small scales, to give rise to a statistically steady state (or attractor) for the whole climate. Depending on the initial conditions and the range of forcing, all other parameters being the same, some climate models have the property to settle down on different attractors. Multi-stability reflects how energy, water mass and entropy can be re-distributed in multiple ways among the climate components, such as the atmosphere, the ocean or the ice, through a different balance between nonlinear mechanisms. Starting from a configuration where competing climate attractors occur under the same forcing, we have explored their robustness performing two kinds of numerical experiment. First, we have investigated the impact of frictional heating on the overall energy balance and we have shown that such contribution, generally neglected in the atmospheric component of climate models, has crucial consequences on conservation properties: it improves the energy imbalance at top-of-atmosphere, typically non negligible in coarse simulations (Wild et al. 2020), strengthens the hydrological cycle, mitigates the mechanical work associated to atmospheric circulation intensity and reduces the heat transport peaks in the ocean. Second, we have compared two bulk formulas for the cloud albedo, one where it is constant everywhere and the other where it increases with latitude, as implemented in the new version of the atmospheric module SPEEDY in order to improve comparisons with observational data (Kucharski 2013). We have checked that this new parameterization does not affect energy and water-mass imbalances, while reduces global temperature and water-mass transport on the attractor, giving rise to a larger conversion of heat into mechanical work in the atmosphere.In order to perform such studies, we have run the climate model MITgcm on coupled aquaplanets at 2.8 horizontal resolution until steady states are reached (Brunetti el al. 2019) and we have applied the Thermodynamic Diagnostic Tool (TheDiaTo, Lembo et al. 2019). &#160;References: Brunetti, Kasparian, V&#233;rard, Climate Dynamics 53, 6293 (2019)Gallavotti, Math. Phys. 3, 530 (2006)Kucharski et al., Bulletin of the American Meteorological Society 94, 25 (2013)Lembo, Lunkeit, Lucarini, Geoscientific Model Development 12, 3805 (2019)Wild, Climate Dynamics 55, 553 (2020)

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High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-091.1 ◽

2013 ◽

Vol 14 (4) ◽

pp. 1175-1193 ◽

Cited By ~ 36

Author(s):

Irena Ott ◽

Doris Duethmann ◽

Joachim Liebert ◽

Peter Berg ◽

Hendrik Feldmann ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Impact Analysis ◽

Climate Models ◽

Global Climate Models ◽

Climate Change Signal ◽

Climate Modelling ◽

Community Land Model ◽

The Impact ◽

River Catchments

Abstract The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

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