A brief discussion of the history, strengths and limitations of conceptual climate models for pre-Quaternary time

1993 ◽  
Vol 341 (1297) ◽  
pp. 263-266 ◽  

Although it has been recognized at least since the time of Darwin and Agassiz that climate has varied significantly over geologic time, the study of global palaeoclimate did not come into its own until the theory of continental drift became ascendant. Initial studies in the early 1960s used climate models to test the reconstructions of continental positions. These studies, many collected in a pair of symposium volumes edited by A. E. M. Nairn, used a zonal model of climate or simple modifications thereof to predict how certain palaeoclimatic indicators - principally evaporites, coals, carbonates, red beds, and eolian sandstones - should be distributed on the continents through time if the continental reconstructions were correct. Even at that early stage in the development of continental reconstructions, past patterns of sedimentation were more clearly explained than had previously been the case. Continental reconstructions eventually began to stabilize, at least with respect to the major plates, in the late 1970s. Most of the information for positioning the continents came from paleomagnetic and structural data, but some elements of continental reconstructions relied heavily on climatic data - and the zonal climate model - for positioning. Nevertheless, it was at this time that studies of global palaeoclimate, independent of the concerns about the positions of the continents, could begin in earnest. A primary need was independence of the continental reconstructions from palaeoclimatic data, an ideal even now fully realized only for the late Mesozoic and Cenozoic. The term ‘conceptual climate model’ was coined by J . E. Kutzbach in reference to models published in the early 1980s. Like numerical models, conceptual climate models are based on the fundamentals of atmospheric circulation as determined from studies of the modern climate system, without explicitly treating atmospheric dynamics. They are reproducible and useful for developing an understanding of major changes in climate patterns driven by the changing positions of the continents. Despite their simplicity and non-explicit treatment of atmospheric dynamics, conceptual climate models have proved to be surprisingly robust in that the patterns predicted by explicitly dynamical models are similar for any given geologic period.

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>


2021 ◽  
Author(s):  
Joshua Dorrington

<p>Weather over the Euro-Atlantic region during winter is highly variable, with rich and chaotic internal atmospheric dynamics. In particular, the non-linear breaking of Rossby waves irreversibly mixes potential vorticity contours and so triggers shifts in the latitude of the eddy driven jet and establishes persistent anticyclonic blocking events. The concept of atmospheric regimes captures the tendency for blocks – and for the jet – to persist in a small number of preferred locations. Regimes then provide a non-linear basis through which model deficiencies, interdecadal variability and forced trends in the Euro-Atlantic circulation can be studied.</p><p>A drawback of past regime approaches is that they were unable to easily capture both the dynamics of the jet and of blocking anticyclones simultaneously. In this work we apply a recently developed regime framework, which is able to capture both these important aspects while reducing sampling variability, to the CMIP6 climate model ensemble. We analyse both the historical variability and biases of blocking and jet structure in this latest generation of climate models, and make new estimates of the anthropogenic forced trend over the coming century.</p><p> </p>


2015 ◽  
Vol 56 (70) ◽  
pp. 175-183 ◽  
Author(s):  
Andrew Zammit-Mangion ◽  
Jonathan L. Bamber ◽  
Nana W. Schoen ◽  
Jonathan C. Rougier

AbstractCombinations of various numerical models and datasets with diverse observation characteristics have been used to assess the mass evolution of ice sheets. As a consequence, a wide range of estimates have been produced using markedly different methodologies, data, approximation methods and model assumptions. Current attempts to reconcile these estimates using simple combination methods are unsatisfactory, as common sources of errors across different methodologies may not be accurately quantified (e.g. systematic biases in models). Here we provide a general approach which deals with this issue by considering all data sources simultaneously, and, crucially, by reducing the dependence on numerical models. The methodology is based on exploiting the different space–time characteristics of the relevant ice-sheet processes, and using statistical smoothing methods to establish the causes of the observed change. In omitting direct dependence on numerical models, the methodology provides a novel means for assessing glacio-isostatic adjustment and climate models alike, using remote-sensing datasets. This is particularly advantageous in Antarctica, where in situ measurements are difficult to obtain. We illustrate the methodology by using it to infer Antarctica’s mass trend from 2003 to 2009 and produce surface mass-balance anomaly estimates to validate the RACMO2.1 regional climate model.


MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 191-200
Author(s):  
S. K. DASH

The numerical models used for weather forecasting and climate studies need very large computing resources. The current research in the field indicates that for accurate forecasts, one needs to use models at very high resolution, sophisticated data assimilation techniques and physical parameterisation schemes and multi-model ensemble integrations. In fact the spatial resolution required for accurate forecasts may demand computing power which is prohibitively high considering the processing power of a single processor of any supercomputer. During the last two decades, the developments in computing technology show the emergence of parallel computers with a number of processors which are capable of supplying enormously large computing power as against a single computer. Today, a cluster of workstations or personal computers can be used in parallel to integrate a global climate model for a long time. However, there are bottlenecks to be overcome in order to achieve maximum efficiency. Inter-processor communication is the key issue in case of global weather and climate models. The present paper aims at discussing the status of  parallelisation of weather and climate models at leading centres of  operational forecasting and research, the inherent parallelism in weather and climate models, the problems encountered in inter-processing communication and various ways of achieving maximum parallel efficiency.


2021 ◽  
Author(s):  
Floortje van den Heuvel ◽  
Thomas Lachlan-Cope ◽  
Jonathan Witherstone ◽  
Dean Hurren ◽  
Anna Jones

<p><span><span>Our limited understanding of clouds is a major source of uncertainty in climate sensitivity and climate model projections. The Southern Ocean is </span></span><span><span>the largest</span></span><span> </span><span><span>region</span></span><span><span> on Earth where climate models present large biases </span></span><span><span>in</span></span><span><span> short and long wave radiation fluxes which in turn affect the representation of sea surface temperatures, sea ice and ultimately large scale circulation in the S</span></span><span><span>outhern Hemisphere</span></span><span><span>. Evidence suggests that the poor representation of mixed phase clouds at the micro- and macro scales is responsible for the model biases in this region. The Southern Ocean Clouds (SOC) project </span></span><span><span>will be</span></span><span><span> a multi-scale, multi-platform approach with the aim of improving understanding of aerosol and cloud microphysics in this region, and their representation in numerical models. </span></span></p><p><span><span>Although this years’ first SOC measurement season has suffered greatly from travel restrictions, we have installed an Optical Particle Counter (OPC) on a ship (The James Clark Ross – JCR) and recorded aerosol measurements as it was travelling through the Atlantic sector of the Southern Ocean towards the Antarctic Peninsula, and while the ship was moored at South Georgia and Port Stanley. Over the course of one month, the OPC recorded particle sizes between 0.35 and 40 micrometers every six seconds. This study will present the data from this first, rather short Antarctic SOC season. It will present the analyses of the obtained OPC data alongside satellite observations and model reanalyses in the same region.</span></span></p>


Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1074
Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi

Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach.


2021 ◽  
Author(s):  
Davide Faranda ◽  
Gabriele Messori ◽  
Pascal Yiou ◽  
Soulivanh Thao ◽  
Flavio Pons ◽  
...  

<p>Although the lifecycle of hurricanes is well understood, it is a struggle to represent their dynamics in numerical models, under both present and future climates. We consider the atmospheric circulation as a chaotic dynamical system, and show that the formation of a hurricane corresponds to a reduction of the phase space of the atmospheric dynamics to a low-dimensional state. This behavior is typical of Bose-Einstein condensates. These are states of the matter where all particles have the same dynamical properties. For hurricanes, this corresponds to a "rotational mode" around the eye of the cyclone, with all air parcels effectively behaving as spins oriented in a single direction. This finding paves the way for new parametrisations when simulating hurricanes in numerical climate models.</p>


2020 ◽  
Author(s):  
Shizhu Wang ◽  
Qiang Wang ◽  
Qi Shu ◽  
Patrick Scholz ◽  
Gerrit Lohmann ◽  
...  

<p>Numerical models have been widely utilized to simulate the ocean and climate system. Parameterizations of some important processes, however, including the vertical mixing induced by surface waves, are still missing in many ocean models. In this work we incorporate the vertical mixing induced by non-breaking surface waves derived from a wave model into the multi-resolution Finite Element Sea ice-Ocean Model (FESOM), and compare its effect with that of shortwave penetration, another key process to vertically redistribute the heat in the upper ocean. Numerical experiments reveal that both processes ameliorate the simulation of upper-ocean temperature in mid and low latitudes mainly on the summer hemisphere. The regions where nonbreaking wave generates stronger improvement are where large temperature bias exists. The non-breaking surface waves plays a more significant role in decreasing the mean cold biases at 50 m (by 1.0 °C, in comparison to 0.5 °C achieved by applying shortwave penetration). We conclude that the incorporation of mixing induced by non-breaking surface waves into FESOM is practically very helpful, and suggest that it needs to be considered in other ocean climate models as well.</p>


Author(s):  
Weijia Qian ◽  
Howard H. Chang

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.


2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.


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