State-dependency of temperature variability in transient simulations of the last Deglaciation from models of varying complexity

2021 ◽  
Author(s):  
Elisa Ziegler ◽  
Heather Andres ◽  
Beatrice Ellerhoff ◽  
Marie-Luise Kapsch ◽  
Steffen Kutterolf ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Power Spectra ◽  
Temperature Variability ◽  
Similar Degree ◽  
Last Deglaciation ◽  
State Dependency ◽  
Transient Simulations ◽  
The Last Deglaciation

<div> <p>Much about the response of temperature variability to a change in the climate's mean state, as the one projected for the current century, remains uncertain. These uncertainties include spatiotemporal patterns, the magnitude, and, in some cases, even the sign. For the last Deglaciation, - the last change in global mean temperature of a similar degree to that expected in projections - variability analyses of climate model simulations and temperature proxies produce conflicting results. </p> </div><div> <p>Here, we build a hierarchy of transient simulations covering the period since the Last Glacial Maximum about 26k years ago. We include a range of climate models, from conceptual to complex Earth System Models. The simulations cover a variety of temporal and spatial resolutions, parameterizations, and modeled processes. For annual to multi-millennial temporal as well as regional to global spatial scales, we compare variability patterns and power spectra and analyze how they relate to model properties and the background state of Earth's climate. This allows for the examination of regional temperature differences between low, middle, and high latitudes and at locations of available paleoclimate proxy records. For sets of sensitivity experiments, we investigate effects of changes to ice sheets, sea ice, and in volcanic, solar, greenhouse, and orbital forcing on modeled climate variability.  </p> </div><div> <p>Thus, our analysis provides insights into when and how models disagree with each other and with proxies, and what differences arise due to specific models, simulation setups, and boundary conditions. Based on these results, we can then gauge the degree of complexity which is required to reproduce past temperature variability and predict its changes in the future. </p> </div>

scholarly journals Transient climate simulations of the deglaciation 21–9 thousand years before present; PMIP4 Core experiment design and boundary conditions

2015 ◽  
Vol 8 (10) ◽  
pp. 9045-9102 ◽  
Author(s):  
R. F. Ivanovic ◽  
L. J. Gregoire ◽  
M. Kageyama ◽  
D. M. Roche ◽  
P. J. Valdes ◽  
...  
Keyword(s):  
Working Group ◽  
Ad Hoc ◽  
Climate Models ◽  
Ice Sheet ◽  
Before Present ◽  
Last Deglaciation ◽  
Transient Simulations ◽  
The Last Deglaciation ◽  
Set Up ◽  
Core Simulation

Abstract. The last deglaciation, which marked the transition between the last glacial and present interglacial periods, was punctuated by a series of rapid (centennial and decadal) climate changes. Numerical climate models are useful for investigating mechanisms that underpin the events, especially now that some of the complex models can be run for multiple millennia. We have set up a Paleoclimate Modelling Intercomparison Project (PMIP) working group to coordinate efforts to run transient simulations of the last deglaciation, and to facilitate the dissemination of expertise between modellers and those engaged with reconstructing the climate of the last 21 thousand years. Here, we present the design of a coordinated Core simulation over the period 21–9 thousand years before present (ka) with time varying orbital forcing, greenhouse gases, ice sheets, and other geographical changes. A choice of two ice sheet reconstructions is given, but no ice sheet or iceberg meltwater should be prescribed in the Core simulation. Additional focussed simulations will also be coordinated on an ad-hoc basis by the working group, for example to investigate the effect of ice sheet and iceberg meltwater, and the uncertainty in other forcings. Some of these focussed simulations will focus on shorter durations around specific events to allow the more computationally expensive models to take part.

scholarly journals Transient climate simulations of the deglaciation 21–9 thousand years before present (version 1) – PMIP4 Core experiment design and boundary conditions

2016 ◽  
Vol 9 (7) ◽  
pp. 2563-2587 ◽  
Author(s):  
Ruza F. Ivanovic ◽  
Lauren J. Gregoire ◽  
Masa Kageyama ◽  
Didier M. Roche ◽  
Paul J. Valdes ◽  
...  
Keyword(s):  
Working Group ◽  
Ad Hoc ◽  
Climate Models ◽  
Before Present ◽  
Last Deglaciation ◽  
Transient Simulations ◽  
Complex Models ◽  
The Last Deglaciation ◽  
Decadal Climate ◽  
Set Up

Abstract. The last deglaciation, which marked the transition between the last glacial and present interglacial periods, was punctuated by a series of rapid (centennial and decadal) climate changes. Numerical climate models are useful for investigating mechanisms that underpin the climate change events, especially now that some of the complex models can be run for multiple millennia. We have set up a Paleoclimate Modelling Intercomparison Project (PMIP) working group to coordinate efforts to run transient simulations of the last deglaciation, and to facilitate the dissemination of expertise between modellers and those engaged with reconstructing the climate of the last 21 000 years. Here, we present the design of a coordinated Core experiment over the period 21–9 thousand years before present (ka) with time-varying orbital forcing, greenhouse gases, ice sheets and other geographical changes. A choice of two ice sheet reconstructions is given, and we make recommendations for prescribing ice meltwater (or not) in the Core experiment. Additional focussed simulations will also be coordinated on an ad hoc basis by the working group, for example to investigate more thoroughly the effect of ice meltwater on climate system evolution, and to examine the uncertainty in other forcings. Some of these focussed simulations will target shorter durations around specific events in order to understand them in more detail and allow for the more computationally expensive models to take part.

scholarly journals The Driving Mechanisms on Southern Ocean Upwelling Change during the Last Deglaciation

Geosciences ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 266
Author(s):  
Wei Liu ◽  
Zhengyu Liu ◽  
Shouwei Li
Keyword(s):  
Southern Ocean ◽  
Climate Model ◽  
Model Simulation ◽  
Early Stage ◽  
Surface Wind ◽  
Ekman Pumping ◽  
Last Deglaciation ◽  
Buoyancy Forcing ◽  
Local Topography ◽  
The Last Deglaciation

We explore the change in Southern Ocean upwelling during the last deglaciation, based on proxy records and a transient climate model simulation. Our analyses suggest that, beyond a conventional mechanism of the Southern Hemisphere westerlies shift, Southern Ocean upwelling is strongly influenced by surface buoyancy forcing and the local topography. Over the Antarctic Circumpolar Current region, the zonal mean and local upwelled flows exhibited distinct evolution patterns during the last deglaciation, since they are driven by different mechanisms. The zonal mean upwelling is primarily driven by surface wind stress via zonal mean Ekman pumping, whereas local upwelling is driven by both wind and buoyancy forcing, and is tightly coupled to local topography. During the early stage of the last deglaciation, the vertical extension of the upwelled flows increased downstream of submarine ridges but decreased upstream, which led to enhanced and diminished local upwelling, downstream and upstream of the submarine ridges, respectively.

scholarly journals Different ocean states and transient characteristics in Last Glacial Maximum simulations and implications for deglaciation

2013 ◽  
Vol 9 (5) ◽  
pp. 2319-2333 ◽  
Author(s):  
X. Zhang ◽  
G. Lohmann ◽  
G. Knorr ◽  
X. Xu
Keyword(s):  
Last Glacial Maximum ◽  
Model Comparison ◽  
Climate Model ◽  
Deep Ocean ◽  
Glacial Maximum ◽  
Meridional Overturning Circulation ◽  
Last Deglaciation ◽  
New Paradigm ◽  
Last Glacial ◽  
The Last Deglaciation

Abstract. The last deglaciation is one of the best constrained global-scale climate changes documented by climate archives. Nevertheless, understanding of the underlying dynamics is still limited, especially with respect to abrupt climate shifts and associated changes in the Atlantic meridional overturning circulation (AMOC) during glacial and deglacial periods. A fundamental issue is how to obtain an appropriate climate state at the Last Glacial Maximum (LGM, 21 000 yr before present, 21 ka BP) that can be used as an initial condition for deglaciation. With the aid of a comprehensive climate model, we found that initial ocean states play an important role on the equilibrium timescale of the simulated glacial ocean. Independent of the initialization, the climatological surface characteristics are similar and quasi-stationary, even when trends in the deep ocean are still significant, which provides an explanation for the large spread of simulated LGM ocean states among the Paleoclimate Modeling Intercomparison Project phase 2 (PMIP2) models. Accordingly, we emphasize that caution must be taken when alleged quasi-stationary states, inferred on the basis of surface properties, are used as a reference for both model inter-comparison and data model comparison. The simulated ocean state with the most realistic AMOC is characterized by a pronounced vertical stratification, in line with reconstructions. Hosing experiments further suggest that the response of the glacial ocean is dependent on the ocean background state, i.e. only the state with robust stratification shows an overshoot behavior in the North Atlantic. We propose that the salinity stratification represents a key control on the AMOC pattern and its transient response to perturbations. Furthermore, additional experiments suggest that the stratified deep ocean formed prior to the LGM during a time of minimum obliquity (~ 27 ka BP). This indicates that changes in the glacial deep ocean already occur before the last deglaciation. In combination, these findings represent a new paradigm for the LGM and the last deglaciation, which challenges the conventional evaluation of glacial and deglacial AMOC changes based on an ocean state derived from 21 ka BP boundary conditions.

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.

Did tropical precipitation improve in CMIP6 simulations?

2020 ◽  
Author(s):  
Stephanie Fiedler ◽  
Traute Crueger ◽  
Roberta D’Agostino ◽  
Karsten Peters ◽  
Tobias Becker ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Internal Variability ◽  
Atmospheric Composition ◽  
Tropical Precipitation ◽  
General Aspects

<p>Climate models are known to have biases in tropical precipitation. We assessed to what extent simulations of tropical precipitation have improved in the new Coupled Model Intercomparison Project (CMIP) phase six, using state-of-the-art observational products and model results from the earlier CMIP phases three and five. We characterize tropical precipitation with different well-established metrics. Our assessment includes (1) general aspects of the mean climatology like precipitation associated with the Intertropical Convergence Zone and shallow cloud regimes in the tropics, (2) solar radiative effects including the summer monsoons and the time of occurrence of tropical precipitation in the course of the day, (3) modes of internal variability such as the Madden-Julian Oscillation and the El Niño Southern Oscillation, and (4) changes in the course of the 20th century. The results point to improvements of CMIP6 models for some metrics, e.g., the occurrence of drizzle events and consecutive dry days. However, no improvements of CMIP6 models are identified for other aspects of tropical precipitation. These include the area and intensity of the global summer monsoon as well as the diurnal cycle of the tropical precipitation amount, frequency and intensity.</p><p>All our metrics taken together, CMIP6 models show no systematic improvement of tropical precipitation across different temporal and spatial scales. The model biases in the spatial distribution of tropical precipitation are typically larger than the changes associated with anthropogenic warming. Given the pace of climate change as compared to the pace of climate model improvements, we suggest to use novel modeling approaches to understand the responseof tropical precipitation to changes in atmospheric composition.</p>

Stochastic comparison of climate model outputs to observed relative humidity fields

2020 ◽  
Author(s):  
Apostolos Koumakis ◽  
Panayiotis Dimitriadis ◽  
Theano Iliopoulou ◽  
Demetris Koutsoyiannis
Keyword(s):  
Relative Humidity ◽  
Climate Models ◽  
Climate Model ◽  
Historical Data ◽  
Spatial Scales ◽  
Stochastic Methods ◽  
Dependence Structure ◽  
Stochastic Comparison ◽  
Planning And Design ◽  
Temporal And Spatial

<p>Stochastic comparison of climate model outputs to observed relative humidity fields</p><p>We compare the stochastic behaviour of relative humidity outputs of climate models for the 20<sup>th</sup> century to the historical data (stations and reanalysis fields) at several temporal and spatial scales. In particular we examine the marginal distributions and the dependence structure with emphasis on the Hurst-Kolmogorov behaviour. The comparison aims to contribute to the quantification of reliability and predictive uncertainty of relative humidity climate model outputs over different scales in a framework of assessing their relevance for engineering planning and design.</p><p> </p><p>(Acknowledgement: This research is conducted within the frame of the course "Stochastic Methods" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.)</p>

scholarly journals Assessment of Bias Assumptions for Climate Models

2014 ◽  
Vol 27 (17) ◽  
pp. 6799-6818 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Lower Troposphere ◽  
Regional Climate Models ◽  
Added Value ◽  
Model Biases ◽  
Interaction Component

Abstract Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods. Such assumptions are inevitable because of the lack of future observations. This manuscript reviews different bias assumptions found in the literature and provides measures to assess their validity. The authors explicitly separate climate change from multidecadal variability to systematically analyze climate model biases in seasonal and regional surface temperature averages, using global and regional climate models (GCMs and RCMs) from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project over Europe. For centennial time scales, it is found that a linear bias extrapolation for GCMs is best supported by the analysis: that is, it is generally not correct to assume that model biases are independent of the climate state. Results also show that RCMs behave markedly differently when forced with different drivers. RCM and GCM biases are not additive, and there is a significant interaction component in the bias of the RCM–GCM model chain that depends on both the RCM and GCM considered. This result questions previous studies that deduce biases (and ultimately projections) in RCM–GCM combinations from reanalysis-driven simulations. The authors suggest that the aforementioned interaction component derives from the refined RCM representation of dynamical and physical processes in the lower troposphere, which may nonlinearly depend upon the larger-scale circulation stemming from the driving GCM. The authors’ analyses also show that RCMs provide added value and that the combined RCM–GCM approach yields, in general, smaller biases in seasonal surface temperature and interannual variability, particularly in summer and even for spatial scales that are, in principle, well resolved by the GCMs.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals <sup>10</sup>Be in last deglacial climate simulated by ECHAM5-HAM – Part 1: Climatological influences on <sup>10</sup>Be deposition

2013 ◽  
Vol 9 (4) ◽  
pp. 3681-3709 ◽  
Author(s):  
U. Heikkilä ◽  
S. J. Phipps ◽  
A. M. Smith
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Mass Conservation ◽  
High Latitudes ◽  
Last Deglaciation ◽  
Surface Cooling ◽  
Climate System Model ◽  
The Last Deglaciation ◽  
The Impact ◽  
Atmospheric Concentrations

Abstract. Reconstruction of solar irradiance has only been possible for the Holocene so far. During the last deglaciation two solar proxies (10Be and 14C) deviate strongly, both of them being influenced by climatic changes in a different way. This work addresses the climate influence on 10Be deposition by means of ECHAM5-HAM atmospheric aerosol-climate model simulations, forced by sea surface temperatures and sea ice extent created by the coupled climate system model CSIRO Mk3L. Three time slice simulations were performed during the last deglaciation: 10 000 BP ("10k"), 11 000 BP ("11k") and 12 000 BP ("12k"), each 30 yr long. The same 10Be production rate was used in each simulation to isolate the impact of climate on 10Be deposition. The changes are found to follow roughly the reduction in the greenhouse gas concentrations within the simulations. The 10k and 11k simulations produce a surface cooling which is symmetrically amplified in the 12k simulation. The precipitation rate is only slightly reduced at high latitudes, but there is a northward shift in the polar jet in the Northern Hemisphere and the stratospheric westerly winds are significantly weakened. These changes occur where the sea ice change is largest in the deglaciation simulations. This leads to a longer residence time of 10Be in the stratosphere by 30 (10k and 11k) to 80 (12k) days, heavily increasing the atmospheric concentrations. Furthermore the shift of westerlies in the troposphere leads to an increase of tropospheric 10Be concentrations, especially at high latitudes. The contribution of dry deposition generally increases, but decreases where sea ice changes are largest. In total, the 10Be deposition rate changes by no more than 20% at mid- to high latitudes, but by up to 50% in the tropics. We conclude that on "long" time scales (a year to a few years), climatic influences on 10Be deposition remain small even though atmospheric concentrations can vary significantly. Averaged over a longer period all 10Be produced has to be deposited by mass conservation. This dominates over any climatic influences on 10Be deposition. Snow concentrations, however, do not follow mass conservation and can potentially be impacted more by climate due to precipitation changes. Quantifying the impact of deglacial climate modulation on 10Be in terms of preserving the solar signal locally is analysed in an accompanying paper (Heikkilä et al., 2013).

Sign in / Sign up

Export Citation Format

Share Document