scholarly journals On the role of the Antarctic Slope Front on the occurrence of the Weddell Sea polynya under climate change

2021 ◽  
pp. 1-56
Author(s):  
Joseph W. Lockwood ◽  
Carolina O. Dufour ◽  
Stephen M. Griffies ◽  
Michael Winton
Keyword(s):  
Climate Change ◽  
Climate Warming ◽  
Climate Models ◽  
Climate Model ◽  
Open Ocean ◽  
Weddell Sea ◽  
Lateral Spread ◽  
Cmip5 Models ◽  
Change Scenario ◽  
The Antarctic

AbstractThis study investigates the occurrence of the Weddell Sea Polynya under an idealized climate change scenario by evaluating simulations from climate models of different ocean resolutions. The GFDL-CM2.6 climate model, with roughly 3.8 km horizontal ocean grid spacing in the high latitudes, forms aWeddell Sea Polynya at similar time and duration under idealized climate change forcing as under pre-industrial forcing. In contrast, all convective models forming the fifth phase of the Coupled Model Intercomparison Project (CMIP5) show either a cessation or a slowdown of Weddell Sea Polynya events under climate warming. The representation of the Antarctic Slope Current and related Antarctic Slope Front is found to be key in explaining the differences between the two categories of models, with these features being more realistic in CM2.6 than in CMIP5. In CM2.6, the freshwater input driven by sea ice melt and enhanced runoff found under climate warming largely remains on the shelf region since the slope front restricts the lateral spread of the freshwater. In contrast, for most CMIP5 models, open ocean stratification is enhanced by freshening since the absence of a slope front allows coastal freshwater anomalies to spread into the open ocean. This enhanced freshening contributes to the slow down the occurrence ofWeddell Sea Polynyas. Hence, an improved representation of Weddell Sea shelf processes in current climate models is desirable to increase our ability to predict the fate of the Weddell Sea Polynyas under climate change.

On the role of the Antarctic Slope Front on the occurrence of theWeddell Sea polynya under climate change

2021 ◽  
Author(s):  
Joseph Lockwood ◽  
Carolina Dufour ◽  
Stephen Griffies ◽  
Michael Winton
Keyword(s):  
Climate Change ◽  
Climate Warming ◽  
Climate Models ◽  
Climate Model ◽  
Open Ocean ◽  
Weddell Sea ◽  
Lateral Spread ◽  
Cmip5 Models ◽  
Change Scenario ◽  
The Antarctic

<p>This study investigates the occurrence of the Weddell Sea Polynya under an idealized</p><p>climate change scenario by evaluating simulations from climate models of different</p><p>ocean resolutions. The GFDL-CM2.6 climate model, with roughly 3.8 km</p><p>horizontal ocean grid spacing in the high latitudes, forms a Weddell Sea Polynya at</p><p>similar time and duration under idealized climate change forcing as under pre-industrial</p><p>forcing. In contrast, all convective models forming the fifth phase of the Coupled Model</p><p>Intercomparison Project (CMIP5) show either a cessation or a slowdown of Weddell</p><p>Sea Polynya events under climate warming. The representation of the Antarctic Slope</p><p>Current and related Antarctic Slope Front is found to be key in explaining the</p><p>differences between the two categories of models, with these features being more</p><p>realistic in CM2.6 than in CMIP5. In CM2.6, the freshwater input driven by sea ice melt</p><p>and enhanced runoff found under climate warming largely remains on the shelf region</p><p>since the slope front restricts the lateral spread of the freshwater. In contrast, for most</p><p>CMIP5 models, open ocean stratification is enhanced by freshening since the absence</p><p>of a slope front allows coastal freshwater anomalies to spread into the open ocean.</p><p>This enhanced freshening contributes to the slow down the occurrence of Weddell Sea</p><p>Polynyas. Hence, an improved representation of Weddell Sea shelf processes in</p><p>current climate models is desirable to increase our ability to predict the fate of the</p><p>Weddell Sea Polynyas under climate change.</p>

scholarly journals The response of the Northern Hemisphere storm tracks and jetstreams to climate change in the CMIP3, CMIP5, and CMIP6 climate models

2020 ◽  
Author(s):  
Ben Harvey ◽  
Peter Cook ◽  
Len Shaffrey ◽  
Reinhard Schiemann
Keyword(s):  
Climate Change ◽  
Spatial Pattern ◽  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Storm Track ◽  
Coupled Model ◽  
Storm Tracks ◽  
Cmip5 Models ◽  
Jet Streams

<p>Understanding and predicting how extratropical cyclones might respond to climate change is essential for assessing future weather risks and informing climate change adaptation strategies. Climate model simulations provide a vital component of this assessment, with the caveat that their representation of the present-day climate is adequate. In this study the representation of the NH storm tracks and jet streams and their responses to climate change are evaluated across the three major phases of the Coupled Model Intercomparison Project: CMIP3 (2007), CMIP5 (2012), and CMIP6 (2019). The aim is to quantity how present-day biases in the NH storm tracks and jet streams have evolved with model developments, and to further our understanding of their responses to climate change.</p><p>The spatial pattern of the present-day biases in CMIP3, CMIP5, and CMIP6 are similar. However, the magnitude of the biases in the CMIP6 models is substantially lower in the DJF North Atlantic storm track and jet stream than in the CMIP3 and CMIP5 models. In summer, the biases in the JJA North Atlantic and North Pacific storm tracks are also much reduced in the CMIP6 models. Despite this, the spatial pattern of the climate change response in the NH storm tracks and jet streams are similar across the CMIP3, CMIP5, and CMIP6 ensembles. The SSP2-4.5 scenario responses in the CMIP6 models are substantially larger than in the corresponding RCP4.5 CMIP5 models, consistent with the larger climate sensitivities of the CMIP6 models compared to CMIP5.</p>

Kriging Approach to Quantile Delta Mapping (QDM) for Spatial Downscaling of Climate Change Scenario

2020 ◽  
Author(s):  
Yong-Tak Kim ◽  
Carlos H R Lima ◽  
Hyun-Han Kwon
Keyword(s):  
Climate Change ◽  
Spatial Variability ◽  
Bias Correction ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Change Scenario ◽  
Proposed Model ◽  
Spatial Downscaling

<p>Rainfall simulation by climate model is generally provided at coarse grids and bias correction is routinely needed for the hydrological applications. This study aims to explore an alternative approach to downscale daily rainfall simulated by the regional climate model (RCM) at any desired grid resolution along with bias correction using a Kriging model, which better represents spatial dependencies of distribution parameters across the watershed. The Kringing model also aims to reproduce the spatial variability observed in the ground rainfall gauge. The proposed model is validated through the entire weather stations in South Korea and climate change scenarios simulated by the five different RCMs informed by two GCMs. The results confirmed that the proposed spatial downscaling model could reproduce the observed rainfall statistics and spatial variability of rainfall. The proposed model further applied to the climate change scenario. A discussion of the potential uses of the mode is offered.</p><p>KEYWORDS: Climate Change Scenario, Global Climate Models, Regional Climate Models, Statistical Downscaling, Spatial-Temporal Bias</p><p> </p><p>Acknowledgement</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01215)</p>

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

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 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.

scholarly journals Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Joseph Leedale ◽  
Adrian M. Tompkins ◽  
Cyril Caminade ◽  
Anne E. Jones ◽  
Grigory Nikulin ◽  
...  
Keyword(s):  
Climate Change ◽  
Malaria Transmission ◽  
Climate Models ◽  
Climate Model ◽  
Eastern Africa ◽  
Future Climate Change ◽  
Climate Projections ◽  
Model Ensemble ◽  
East African Community ◽  
Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

scholarly journals Evaluating quantile-based bias adjustment methods for climate change scenarios

2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Climate Change Signal ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Statistical Bias ◽  
Bias Adjustment ◽  
Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

scholarly journals Uncertainty in water resources availability in the Okavango River Basin as a result of climate change

2010 ◽  
Vol 7 (4) ◽  
pp. 5737-5768 ◽  
Author(s):  
D. A. Hughes ◽  
D. G. Kingston ◽  
M. C. Todd
Keyword(s):  
Climate Change ◽  
Water Resource Management ◽  
Climate Models ◽  
Climate Model ◽  
Management Policy ◽  
Mean Flow ◽  
Considerable Uncertainty ◽  
Okavango River ◽  
Projected Changes ◽  
Change In Mean

Abstract. This paper assesses the hydrological response to scenarios of climate change in the Okavango River catchment in Southern Africa. Climate scenarios are constructed representing different changes in global mean temperature from an ensemble of 7 climate models assessed in the IPCC AR4. The results show a substantial change in mean flow associated with a global warming of 2 °C. However, there is considerable uncertainty in the sign and magnitude of the projected changes between different climate models, implying that the ensemble mean is not an appropriate generalised indicator of impact. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation. There is also a clear need to evaluate the physical mechanisms associated with the model projected changes in this region. The implications for water resource management policy are considered.

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

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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