scholarly journals S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales

2021 ◽  
Vol 14 (10) ◽  
pp. 6177-6195
Author(s):  
Paul R. Halloran ◽  
Jennifer K. McWhorter ◽  
Beatriz Arellano Nava ◽  
Robert Marsh ◽  
William Skirving
Keyword(s):  
Climate Change ◽  
Computational Efficiency ◽  
Dynamical Downscaling ◽  
Climate Projections ◽  
Grid Cells ◽  
Computationally Efficient ◽  
Global Models ◽  
Shelf Seas ◽  
Shelf Sea ◽  
Set Up

Abstract. The marine impacts of climate change on our societies will be largely felt through coastal waters and shelf seas. These impacts involve sectors as diverse as tourism, fisheries and energy production. Projections of future marine climate change come from global models. Modelling at the global scale is required to capture the feedbacks and large-scale transport of physical properties such as heat, which occur within the climate system, but global models currently cannot provide detail in the shelf seas. Version 2 of the regional implementation of the Shelf Sea Physics and Primary Production (S2P3-R v2.0) model bridges the gap between global projections and local shelf-sea impacts. S2P3-R v2.0 is a highly simplified coastal shelf model, computationally efficient enough to be run across the shelf seas of the whole globe. Despite the simplified nature of the model, it can display regional skill comparable to state-of-the-art models, and at the scale of the global (excluding high latitudes) shelf seas it can explain >50 % of the interannual sea surface temperature (SST) variability in ∼60 % of grid cells and >80 % of interannual variability in ∼20 % of grid cells. The model can be run at any resolution for which the input data can be supplied, without expert technical knowledge, and using a modest off-the-shelf computer. The accessibility of S2P3-R v2.0 places it within reach of an array of coastal managers and policy makers, allowing it to be run routinely once set up and evaluated for a region under expert guidance. The computational efficiency and relative scientific simplicity of the tool make it ideally suited to educational applications. S2P3-R v2.0 is set up to be driven directly with output from reanalysis products or daily atmospheric output from climate models such as those which contribute to the sixth phase of the Climate Model Intercomparison Project, making it a valuable tool for semi-dynamical downscaling of climate projections. The updates introduced into version 2.0 of this model are primarily focused around the ability to geographical relocate the model, model usability and speed but also scientific improvements. The value of this model comes from its computational efficiency, which necessitates simplicity. This simplicity leads to several limitations, which are discussed in the context of evaluation at regional and global scales.

scholarly journals S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales

2021 ◽  
Author(s):  
Paul R. Halloran ◽  
Jennifer K. McWhorter ◽  
Beatriz Arellano Nava ◽  
Robert Marsh ◽  
William Skirving
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Climate Projections ◽  
Grid Cells ◽  
Computationally Efficient ◽  
Global Models ◽  
Shelf Seas ◽  
Shelf Sea

Abstract. The marine impacts of climate change on our societies will be largely felt through coastal waters and shelf seas. These impacts involve sectors as diverse as tourism, fisheries and energy production. Projections of future marine climate change come from global models. Modelling at the global scale is required to capture the feedbacks and large-scale transport of physical properties such as heat, which occur within the climate system, but global models currently cannot provide detail in the shelf-seas. Version 2 of the regional implementation of the Shelf Sea Physics and Primary Production (S2P3-R v2.0) model bridges the gap between global projections and local shelf-sea impacts. S2P3-R v2.0 is a highly simplified coastal shelf model, computationally efficient enough to be run across the shelf seas of the whole globe. Despite the simplified nature of the model, it can display regional skill comparable to state-of-the-art models, and at the scale of the global (excluding high-latitudes) shelf-seas can explain > 50 % of the interannual SST variability in ~60 % of grid cells, and > 80 % of interannual variability in ~20 % of grid cells. The model can be run at any resolution for which the input data can be supplied, without expert technical knowledge, and using a modest off-the-shelf computer. The accessibility of S2P3-R v2.0 places it within reach of an array of coastal managers and policy makers. S2P3-R v2.0 is set up to be driven directly with output from reanalysis products or daily atmospheric output from climate models such as those which contribute to the 6th phase of the Climate Model Intercomparison Project, making it a valuable tool for semi-dynamical downscaling of climate projections. The updates introduced into version 2.0 of this model are primarily focused around the ability to geographical relocate the model, model usability and speed, but also scientific improvements. The value of this model comes from its computational efficiency, which necessitates simplicity. This simplicity leads to several limitations, which are discussed in the context of evaluation at regional and global scales.

Climate change effects on UK shelf sea’s connectivity and hydrographic properties

2020 ◽  
Author(s):  
Claudia Gabriela Mayorga Adame ◽  
James Harle ◽  
Jason Holt ◽  
Artioli Yuri ◽  
Sarah Wakelin
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Climate Impacts ◽  
Change Models ◽  
The North ◽  
The Future ◽  
Shelf Seas ◽  
Shelf Sea ◽  
The Uk

<p>Climate change is expected to cause important changes in ocean physics, which will in turn have important effects on the marine ecosystems. The ReCICLE project (<strong>Resolving Climate Impacts on shelf and CoastaL seas Ecosystems</strong>) aims to identify and quantify the envelope of response to climate change of lower trophic level shelf-sea ecosystems and their functional interactions, in order to assess the vulnerability of ecosystem goods and services in the UK shelf seas. The central tool for this work is an ensemble of coupled hydrodynamic-biogeochemical ecosystem models NEMO-ERSEM Atlantic Margin Model configuration at 7 km horizontal resolution (AMM7), forced by different CIMP5 global climate change models to generate downscaled scenarios for future decades.</p><p>Changes in connectivity patterns are expected to affect coastal populations of marine organisms in shelf seas. Holt et al 2018 (GRL https://doi.org/10.1029/2018GL078878) showed the potential for radical reorganization of the North Sea circulation in earlier simulations. To assess this particular issue particle tracking experiments are carried out during two 10 year time slices, in the recent past (2000-2010) and in the future (2040-2050) in ensemble members of the ReCICLE AMM7 regional downscaling showing contrasting circulation patterns. Surface particles were uniformly seeded in the UK shelf seas every month and tracked for 30 days. The resulting particle trajectories are analysed with cluster analysis technics aiming to determine if persistent oceanographic boundaries re-arrange in the future climate scenarios. The ecological effects of circulation and water masses changes in the future ocean are discussed from a Lagrangian perspective.</p><p> </p>

scholarly journals Selecting Ensemble Members to Provide Regional Climate Change Information

2012 ◽  
Vol 25 (20) ◽  
pp. 7100-7121 ◽  
Author(s):  
Carol F. McSweeney ◽  
Richard G. Jones ◽  
Ben B. B. Booth
Keyword(s):  
Climate Change ◽  
Summer Monsoon ◽  
Dynamical Downscaling ◽  
Asian Summer Monsoon ◽  
Regional Climate ◽  
Full Range ◽  
Regional Climate Change ◽  
Climate Projections ◽  
Similar Data ◽  
Asian Summer

Abstract Climate model ensembles, such as the Coupled Model Intercomparison Project, phase 3 (CMIP3), are used to characterize broadscale ranges of projected regional climate change and their impacts. The 17-member Hadley Centre perturbed physics GCM ensemble [Quantifying Uncertainty in Model Predictions (“QUMP”)] extends this capability by including data enabling dynamical downscaling of these ranges, and similar data are now being made available from the CMIP phase 5 (CMIP5) GCMs. These raise new opportunities to provide and apply high-resolution regional climate projections. This study highlights the importance of employing a well-considered sampling strategy from available ensembles to provide scientifically credible information on regional climate change while minimizing the computational complexity of ensemble downscaling. A subset of the QUMP ensemble is selected for a downscaling program in Vietnam using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate modeling system. Multiannual mean fields from each GCM are assessed with a focus on the Asian summer monsoon, given its importance to proposed applications of the projections. First, the study examines whether any model should be eliminated because significant deficiencies in its simulation may render its future climate projections unrealistic. No evidence is found to eliminate any of the 17 GCMs on these grounds. Second, the range of their future projections is explored and five models that best represent the full range of future climates are identified. The subset characterizes the range of both global and regional responses, and patterns of rainfall response, the wettest and driest projections for Vietnam, and different projected Asian summer monsoon changes. How these ranges of responses compare with those in the CMIP3 ensemble are also assessed, finding differences in both the signal and the spread of results in Southeast Asia.

scholarly journals Downscaling GCM data for climate change impact assessments on rainfall: a practical application for the Brahmani-Baitarani river basin

2016 ◽  
Author(s):  
R. J. Dahm ◽  
U. K. Singh ◽  
M. Lal ◽  
M. Marchand ◽  
F. C. Sperna Weiland ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Bias Correction ◽  
Dynamical Downscaling ◽  
Meteorological Data ◽  
Local Level ◽  
Climate Projections ◽  
Mountainous Regions ◽  
Wide Range ◽  
Rainfall Extremes

Abstract. The delta of the Brahmani-Baitarani river basin, located in the eastern part of India, frequently experiences severe floods. For flood risk analysis and water system design, insights in the possible future changes in extreme rainfall events caused by climate change are of major importance. There is a wide range of statistical and dynamical downscaling and bias-correction methods available to generate local climate projections that also consider changes in rainfall extremes. Yet the applicability of these methods highly depends on availability of meteorological observations at local level. In the developing countries data and model availability may be limited, either due to the lack of actual existence of these data or because political data sensitivity hampers open sharing. We here present the climate change analysis we performed for the Brahmani-Baitarani river basin focusing on changes in four selected indices for rainfall extremes using data from three performance-based selected GCMs that are part of the 5th Coupled Model Intercomparison Project (CMIP5). We apply and compare two widely used and easy to implement bias correction approaches. These methods were selected as best suited due to the absence of reliable long historic meteorological data. We present the main changes – likely increases in monsoon rainfall especially in the Mountainous regions and a likely increase of the number of heavy rain days. In addition, we discuss the gap between state-of-the-art downscaling techniques and the actual options one is faced with in local scale climate change assessments.

scholarly journals Assessment of future rainfall for the Brahmani-Baitarani river basin – practical implications of limited data availability

2018 ◽  
Vol 10 (4) ◽  
pp. 782-798
Author(s):  
R. J. Dahm ◽  
F. C. Sperna Weiland ◽  
U. K. Singh ◽  
M. Lal ◽  
M. Marchand ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Bias Correction ◽  
Dynamical Downscaling ◽  
Meteorological Data ◽  
Data Availability ◽  
Climate Projections ◽  
Change Analysis ◽  
Mountainous Regions ◽  
Wide Range

Abstract Severe floods are common in the Brahmani-Baitarani river basin in India. Insights into the implications of climate change on rainfall extremes and resulting floods are of major importance to improve flood risk analysis and water system design. A wide range of statistical and dynamical downscaling and bias-correction methods for the generation of local climate projections exists. Yet, the applicability of these methods highly depends on availability of meteorological data. In developing countries, data availability is often limited, either because data do not exist or because of restrictions on use. We here present a climate change analysis for the Brahmani-Baitarani river basin focusing on changes in rainfall using data from three GCMs from the Fifth Coupled Model Intercomparison Project (CMIP5) that were selected based on their performance. We apply and compare two widely used and easy to implement bias-correction methods. These were selected because reliable open historical meteorological datasets required for advanced methods were not available. The results indicate likely increases in monsoon rainfall especially in the mountainous regions and likely increases in the number of heavy rain days. We conclude with a discussion on the gap between state-of-the-art downscaling techniques and the actual options in regional climate change assessments.

scholarly journals Capturing convection essential for projections of climate change in African dust emission

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Luis Garcia-Carreras ◽  
John H. Marsham ◽  
Rachel A. Stratton ◽  
Simon Tucker
Keyword(s):  
Climate Change ◽  
Climate Changes ◽  
Climate Models ◽  
Dust Emission ◽  
Cold Pool ◽  
Climate Projections ◽  
Moist Convection ◽  
Global Models ◽  
Pan African ◽  
African Climate

AbstractThe summertime Sahara and Sahel are the world’s largest source of airborne mineral dust. Cold-pool outflows from moist convection (‘haboobs’) are a dominant source of summertime uplift but are essentially missing in global models, raising major questions on the reliability of climate projections of dust and dust impacts. Here we use convection-permitting simulations of pan-African climate change, which explicitly capture haboobs, to investigate whether this key limitation of global models affects projections. We show that explicit convection is key to capturing the observed summertime maximum of dust-generating winds, which is missed with parameterised convection. Despite this, future climate changes in dust-generating winds are more sensitive to the effects of explicit convection on the wider meteorology than they are to the haboobs themselves, with model differences in the change in dust-generating winds reaching 60% of current values. The results therefore show the importance of improving convection in climate models for dust projections.

scholarly journals Dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

2021 ◽  
Author(s):  
Yuan Qiu ◽  
Jinming Feng ◽  
Zhongwei Yan ◽  
Jun Wang
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Climate Changes ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
High Elevation ◽  
Global Climate Models ◽  
Climate Projections

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

scholarly journals High resolution climate change projections for the Pyrenees region

2020 ◽  
Vol 17 ◽  
pp. 191-208
Author(s):  
María P. Amblar-Francés ◽  
Petra Ramos-Calzado ◽  
Jorge Sanchis-Lladó ◽  
Alfonso Hernanz-Lázaro ◽  
María C. Peral-García ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
21St Century ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Climate Conditions ◽  
The Impact

Abstract. The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

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