Transport matrices from standard ocean-model output and quantifying circulation response to climate change

2019 ◽  
Vol 135 ◽  
pp. 1-13 ◽  
Author(s):  
Matthew A. Chamberlain ◽  
Richard J. Matear ◽  
Mark Holzer ◽  
Daohua Bi ◽  
Simon J. Marsland
Keyword(s):  
Climate Change ◽  
Ocean Model ◽  
Model Output

scholarly journals Dynamic Downscaling of the Impact of Climate Change on the Ocean Circulation in the Galápagos Archipelago

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyun Liu ◽  
Lian Xie ◽  
John M. Morrison ◽  
Daniel Kamykowski
Keyword(s):  
Climate Change ◽  
Ocean Circulation ◽  
Global Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Ocean Model ◽  
Reanalysis Dataset ◽  
Equatorial Undercurrent ◽  
Galapagos Archipelago ◽  
The Impact

The regional impact of global climate change on the ocean circulation around the Galápagos Archipelago is studied using the Hybrid Coordinate Ocean Model (HYCOM) configured for a four-level nested domain system. The modeling system is validated and calibrated using daily atmospheric forcing derived from the NCEP/NCAR reanalysis dataset from 1951 to 2007. The potential impact of future anthropogenic global warming (AGW) in the Galápagos region is examined using the calibrated HYCOM with forcing derived from the IPCC-AR4 climate model. Results show that although the oceanic variability in the entire Galápagos region is significantly affected by global climate change, the degree of such effects is inhomogeneous across the region. The upwelling region to the west of the Isabella Island shows relatively slower warming trends compared to the eastern Galápagos region. Diagnostic analysis suggests that the variability in the western Galápagos upwelling region is affected mainly by equatorial undercurrent (EUC) and Panama currents, while the central/east Galápagos is predominantly affected by both Peru and EUC currents. The inhomogeneous responses in different regions of the Galápagos Archipelago to future AGW can be explained by the incoherent changes of the various current systems in the Galápagos region as a result of global climate change.

scholarly journals Detection of Fronts as a Metric for Numerical Model Accuracy

2019 ◽  
Vol 36 (8) ◽  
pp. 1547-1561
Author(s):  
Elizabeth M. Douglass ◽  
Andrea C. Mask
Keyword(s):  
Numerical Model ◽  
Region Of Interest ◽  
Ocean Model ◽  
Minimum Size ◽  
Model Output ◽  
Oceanic Fronts ◽  
Hybrid Coordinate Ocean Model ◽  
Quantitative Metrics ◽  
Coastal Ocean Model ◽  
Along Track

AbstractAs numerical modeling advances, quantitative metrics are necessary to determine whether the model output accurately represents the observed ocean. Here, a metric is developed based on whether a model places oceanic fronts in the proper location. Fronts are observed and assessed directly from along-track satellite altimetry. Numerical model output is then interpolated to the locations of the along-track data, and fronts are detected in the model output. Scores are determined from the percentage of observed fronts correctly simulated in the model and from the percentage of modeled fronts confirmed by observations. These scores depend on certain parameters such as the minimum size of a front, which will be shown to be geographically dependent. An analysis of two models, the Hybrid Coordinate Ocean Model (HYCOM) and the Navy Coastal Ocean Model (NCOM), is presented as an example of how this metric might be applied and interpreted. In this example, scores are found to be relatively stable in time, but strongly dependent on the mesoscale variability in the region of interest. In all cases, the metric indicates that there are more observed fronts not found in the models than there are modeled fronts missing from observations. In addition to the score itself, the analysis demonstrates that modeled fronts have smaller amplitude and are less steep than observed fronts.

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

scholarly journals Nemo-Nordic 1.0: A NEMO based ocean model for Baltic & North Seas, research and operational applications

2018 ◽  
Author(s):  
Robinson Hordoir ◽  
Lars Axell ◽  
Anders Höglund ◽  
Christian Dieterich ◽  
Filippa Fransner ◽  
...  
Keyword(s):  
Climate Change ◽  
Ocean Model ◽  
Anthropogenic Pressure ◽  
Short Term ◽  
To Come ◽  
Ocean Forecasting ◽  
The Baltic ◽  
Industrialised Countries ◽  
High Degree

Abstract. We present Nemo-Nordic, a Baltic & North Sea model based on the NEMO ocean engine. Surrounded by highly industrialised countries, the Baltic and North seas, and their assets associated with shipping, fishing and tourism; are vulnerable to anthropogenic pressure and climate change. Ocean models providing reliable forecasts, and enabling climatic studies, are important tools for the shipping infrastructure and to get a better understanding of effects of climate change on the marine ecosystems. Nemo-Nordic is intended to come as a tool for both short term and long term simulations, and to be used for ocean forecasting as well as process and climatic studies. Here, the scientific and technical choices within Nemo-Nordic are introduced, and the reasons behind the design of the model and its domain, and the inclusions of the two seas, are explained. The model's ability to represent barotropic and baroclinic dynamics, as well as the vertical structure of the water column, is presented. Biases are shown and discussed. The short term capabilities of the model are presented, and especially its capabilities to represent sea level on an hourly timescale with a high degree of accuracy. We also show that the model can represent longer time scale, with a focus on the Major Baltic Inflows and the variability of deep water salinity in the Baltic Sea.

scholarly journals An Overview of CMIP5 and the Experiment Design

2012 ◽  
Vol 93 (4) ◽  
pp. 485-498 ◽  
Author(s):  
Karl E. Taylor ◽  
Ronald J. Stouffer ◽  
Gerald A. Meehl
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Experiment Design ◽  
Global Climate Models ◽  
Model Output ◽  
Earth System ◽  
Intergovernmental Panel ◽  
System Models ◽  
Earth System Models

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.

Comparisons of atmosphere–ocean simulations of greenhouse gas-induced climate change for pre-industrial and hypothetical ‘no-anthropogenic’ radiative forcing, relative to present day

The Holocene ◽  
2011 ◽  
Vol 21 (5) ◽  
pp. 793-801 ◽  
Author(s):  
J.E. Kutzbach ◽  
S.J. Vavrus ◽  
W.F. Ruddiman ◽  
G. Philippon-Berthier
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Greenhouse Gas ◽  
Ocean Circulation ◽  
Radiative Forcing ◽  
Deep Ocean ◽  
Lower Surface ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Slab Ocean

We compare climate simulations for Present-Day (PD), Pre-Industrial (PI) time, and a hypothetical (inferred) state termed No-Anthropogenic (NA) based upon the low greenhouse gas (GHG) levels of the late stages of previous interglacials that are comparable in time (orbital configuration) to the present interglacial. We use a fully coupled dynamical atmosphere–ocean model, the CCSM3. We find a consistent trend toward colder climate (lower surface temperature, more snow and sea-ice cover, lower ocean temperature, and modified ocean circulation) as the net change in GHG radiative forcing trends more negative from PD to PI to NA. The climatic response of these variables becomes larger relative to the changed GHG forcing for each step toward a colder climate state (PD to PI to NA). This amplification is significantly enhanced using the dynamical atmosphere–ocean model compared with our previous results with an atmosphere–slab ocean model, a result that conforms to earlier idealized GHG forcing experiments. However, in our case this amplification is not an idealized result, but instead helps frame important questions concerning aspects of Holocene climate change. This enhanced amplification effect leads to an increase in our estimate of the climate’s response to inferred early anthropogenic CO2 increases (NA to PI) relative to the response to industrial-era CO2 increases (PI to PD). Although observations of the climate for the hypothetical NA (inferred from observations of previous interglacials) and for PI have significant uncertainties, our new results using CCSM3 are in better agreement with these observations than our previous results from an atmospheric model coupled to a static slab ocean. The results support more strongly inferences by Ruddiman concerning indirect effects of ocean solubility/sea-ice/deep ocean ventilation feedbacks that may have contributed to a further increase in late-Holocene atmospheric CO2 beyond that caused by early anthropogenic emissions alone.

scholarly journals How Much Should Climate Model Output Be Smoothed in Space?

2011 ◽  
Vol 24 (3) ◽  
pp. 867-880 ◽  
Author(s):  
Jouni Räisänen ◽  
Jussi S. Ylhäisi
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Future Climate Change ◽  
Small Scale ◽  
Model Output ◽  
Climate Change Projections ◽  
Local Conditions ◽  
Climate Model Output ◽  
Mean Climate

Abstract The general decrease in the quality of climate model output with decreasing scale suggests a need for spatial smoothing to suppress the most unreliable small-scale features. However, even if correctly simulated, a large-scale average retained by the smoothing may not be representative of the local conditions, which are of primary interest in many impact studies. Here, the authors study this trade-off using simulations of temperature and precipitation by 24 climate models within the Third Coupled Model Intercomparison Project, to find the scale of smoothing at which the mean-square difference between smoothed model output and gridbox-scale reality is minimized. This is done for present-day time mean climate, recent temperature trends, and projections of future climate change, using cross validation between the models for the latter. The optimal scale depends strongly on the number of models used, being much smaller for multimodel means than for individual model simulations. It also depends on the variable considered and, in the case of climate change projections, the time horizon. For multimodel-mean climate change projections for the late twenty-first century, only very slight smoothing appears to be beneficial, and the resulting potential improvement is negligible for practical purposes. The use of smoothing as a means to improve the sampling for probabilistic climate change projections is also briefly explored.

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