How many modes does it take to describe climate change?: The lessons from an experiment

2021 ◽  
Author(s):  
Berengere Dubrulle
Keyword(s):  
Climate Change ◽  
Co2 Emissions ◽  
Degrees Of Freedom ◽  
Climate Models ◽  
Present Climate ◽  
Finite Resolution ◽  
Small Scales ◽  
Impossible Task ◽  
Laboratory Analog

<p>According to everyone's experience, predicting the weather reliably for more than 8 days seems an impossible task for our best weather agencies. At the same time, politicians and citizens are asking scientists for decades of climate predictions to help them make decisions, especially on CO2 emissions. To what extent is this request scientifically admissible?</p><p> </p><p>In this lecture I will investigate this question, focusing on the topic of predictions of bifurcations of the atmospheric or oceanic circulations. In such case, the issue is whether present climate models, that have necessarily a finite resolution and a smaller number of degrees of freedom than the actual terrestrial systems, are able to reproduce spontaneous or forced bifurcations. For this, I will use recent results obtained by my group in a laboratory analog of such systems, so called von Karman flow, in which spontaneous bifurcations of the circulation take place. I will detail the analogy, and investigate the nature of bifurcations, the number of degrees of freedom that characterizes it and discuss what is the effect of reducing the number of degrees of freedom in such system.</p><p>I will also discuss the role of fluctuations and their origin, and stress the importance of describing very small scales to capture fluctuations of correct intensity and scale.</p>

scholarly journals Lewis fry Richardson Medal Lecture – How many modes are neededto predict climate bifurcations?: Lessons from an experiment

2021 ◽  
Author(s):  
Bérengère Dubrulle ◽  
François Daviaud ◽  
Davide Faranda ◽  
Louis Marié ◽  
Brice Saint-Michel
Keyword(s):  
Degrees Of Freedom ◽  
Climate Models ◽  
Environmental Policies ◽  
Climate Projections ◽  
Finite Resolution ◽  
The Pacific ◽  
The Pacific Ocean ◽  
Lewis Fry Richardson ◽  
Do So

Abstract. According to everyone’s experience, predicting the weather reliably over more than 8 days seems an impossible taskfor our best weather agencies. At the same time, politicians and citizens are asking scientists for climate projections severaldecades into the future to guide economic and environmental policies, especially regarding the maximum admissible emissions of CO2. To what extent is this request scientifically admissible? In this lecture we will investigate this question, focusing on the topic of predictions of transitions between metastable statesof the atmospheric or oceanic circulations. Two relevant exemples are the switching between zonal and blocked atmosphericcirculation at midlatitudes and the alternance of El Niño and La Niña phases in the Pacific ocean. The main issue is whetherpresent climate models, that necessarily have a finite resolution and a smaller number of degrees of freedom than the actualterrestrial system, are able to reproduce such spontaneous or forced transitions. To do so, we will draw an analogy betweenclimate observations and results obtained in our group on a laboratory-scale, turbulent, von Kármán flow, in which spontaneoustransitions between different states of the circulation take place. We will detail the analogy, and investigate the nature of thetransitions, the number of degrees of freedom that characterizes the latter and discuss the effect of reducing the number ofdegrees of freedom in such systems. We will also discuss the role of fluctuations and their origin, and stress the importance ofdescribing very small scales to capture fluctuations of correct intensity and scale.

Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts

2015 ◽  
Vol 16 (2) ◽  
pp. 762-780 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Martyn P. Clark ◽  
Naoki Mizukami ◽  
Andrew J. Newman ◽  
Michael Barlage ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Change Scenario ◽  
Hydrologic Models ◽  
Climate System Model

Abstract The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

Large contribution from anthropogenic warming to an emerging North American megadrought

Science ◽  
2020 ◽  
Vol 368 (6488) ◽  
pp. 314-318 ◽  
Author(s):  
A. Park Williams ◽  
Edward R. Cook ◽  
Jason E. Smerdon ◽  
Benjamin I. Cook ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
North America ◽  
Relative Humidity ◽  
Climate Models ◽  
Hydrological Modeling ◽  
Drought Severity ◽  
Large Contribution ◽  
Moderate Drought

Severe and persistent 21st-century drought in southwestern North America (SWNA) motivates comparisons to medieval megadroughts and questions about the role of anthropogenic climate change. We use hydrological modeling and new 1200-year tree-ring reconstructions of summer soil moisture to demonstrate that the 2000–2018 SWNA drought was the second driest 19-year period since 800 CE, exceeded only by a late-1500s megadrought. The megadrought-like trajectory of 2000–2018 soil moisture was driven by natural variability superimposed on drying due to anthropogenic warming. Anthropogenic trends in temperature, relative humidity, and precipitation estimated from 31 climate models account for 46% (model interquartiles of 34 to 103%) of the 2000–2018 drought severity, pushing an otherwise moderate drought onto a trajectory comparable to the worst SWNA megadroughts since 800 CE.

scholarly journals Climate projections of a multi-variate heat stress index: the role of downscaling and bias correction

2019 ◽  
Author(s):  
Ana Casanueva ◽  
Sven Kotlarski ◽  
Sixto Herrera ◽  
Andreas M. Fischer ◽  
Tord Kjellstrom ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Bias Correction ◽  
Climate Models ◽  
Dew Point ◽  
Added Value ◽  
Stress Index ◽  
Corrected Data ◽  
Heat Stress Index

Abstract. Along with the higher demand of bias-corrected data for climate impact studies, the number of available data sets has largely increased in the recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on Global Climate Models (GCMs). Complementary, the CORDEX initiative has incorporated experiments based on regionally-downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distil the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses. In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCM and EURO-CORDEX Regional Climate Models (RCMs), at approximately 150 km, 50 km and 12 km horizontal resolution, respectively) in order to assess the role of downscaling and bias correction in a multi-variate framework. The analysis is carried out for the wet bulb globe temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dew point temperature, which in this work are individually bias-corrected prior to the index calculation. Our results show that the added value of RCMs with respect to the driving GCM is limited after bias correction. The two bias correction methods are able to adjust the central part of the WBGT distribution, but some added value of QM is found in WBGT percentiles and in the intervariable relationships. The evaluation in present climate of such multivariate indices should be performed with caution since biases in the individual variables might compensate, thus leading to better performance for the wrong reason. Climate change projections of WBGT reveal a larger increase of summer mean heat stress for the GCM than for the RCMs, related to the well-known reduced summer warming of the EURO-CORDEX RCMs. These differences are lowered after QM, since this bias correction method modifies the change signals and brings the results for GCM and RCMs closer to each other. We also highlight the need of large ensembles of simulations to assess the feasibility of the derived projections.

Climate change in the tropical Atlantic

2020 ◽  
Author(s):  
Noel Keenlyside ◽  
Lander Crespo ◽  
Shunya Koseki ◽  
Lea Svendsen ◽  
Ingo Richter
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Coupled Model ◽  
Historical Period ◽  
Tropical Atlantic ◽  
Gulf Of Guinea ◽  
Coupled Models ◽  
Model Biases ◽  
Warming Pattern

<p>The tropical Atlantic SST have warmed by about 1 degree over the historical period, with greatest warming in the east, along the African coast and in the Gulf of Guinea. Experiments performed from the Coupled Model Intercomparison Projects (CMIP) indicate that models fail to reproduce this warming pattern, instead showing a rather uniform warming. Future projections with these models also tend to show rather uniform warming. In constrast. results from anomaly coupled models indicate that model biases impact the ability of climate models to simulate warming patterns in the tropical Atlantic. Here we investigate the role of model biases on climate change in the tropical Atlantic in the CMIP experiments. In addition, we have analyzed impacts of global warming on tropical Atlantic climate variability, and we assess the sensitive of the results are to model biases.</p>

Inference Towards the Best Explanation: Reflections on the Issue of Climate Change

2015 ◽  
Vol 61 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Robert D. Holt
Keyword(s):  
Climate Change ◽  
Life Histories ◽  
Climate Models ◽  
Natural World ◽  
The Earth ◽  
Qualitative Evidence ◽  
The World ◽  
Main Factor ◽  
Impacts Of Climate Change

Science is an organized enterprise of inquiry which tries to tie together multiple strands of evidence to craft coherent explanations for disparate patterns in the natural world. Philosophers call this enterprise “inference towards the best explanation”. Such inferences at times depend upon detailed quantitative models, but at times one can rely upon the confluence of multiple strands of qualitative evidence. Humans are having unquestionable influences upon many aspects of the earth system at present, on land, in freshwater systems, and indeed the ocean, including devastating impacts on biodiversity. There are many patterns in the world at present – shrinking glaciers, shifting seasonal patterns in species’ life histories, and altered spatial distributions – which point to the signal of climate change, independent of the details of quantitative climate models. Yet, there are many other factors at play, often confounding clear assessment of the specific role of climate change in observed changes in the world. A deeper synoptic understanding of the drivers and impacts of climate change would be incredibly valuable and is urgently needed, even if in the end (though this seems increasingly unlikely) anthropogenic drivers were not the main factor underlying observed climate change.

scholarly journals Numerical Bifurcation Methods applied to Climate Models: Analysis beyond Simulation

2019 ◽  
Author(s):  
Henk A. Dijkstra
Keyword(s):  
Degrees Of Freedom ◽  
Climate Models ◽  
State Of The Art ◽  
Climate System ◽  
Special Issue ◽  
Personal View ◽  
Spatially Extended ◽  
System Variability ◽  
Numerical Bifurcation

Abstract. In this special issue contribution, I provide a personal view on the role of bifurcation analysis of climate models in the development of a theory of climate system variability. The state-of-the-art of the methodology is shortly outlined and the main part of the paper deals with examples of what has been done and what has been learned. In addressing these issues, I will discuss the role of a hierarchy of climate models, concentrate on results for spatially extended (stochastic) models (having many degrees of freedom) and evaluate the importance of these results for a theory of climate system variability.

scholarly journals Attribution of the role of climate change in the forest fires in Sweden 2018

2021 ◽  
Vol 21 (7) ◽  
pp. 2169-2179
Author(s):  
Folmer Krikken ◽  
Flavio Lehner ◽  
Karsten Haustein ◽  
Igor Drobyshev ◽  
Geert Jan van Oldenborgh
Keyword(s):  
Climate Change ◽  
Confidence Interval ◽  
Forest Fires ◽  
Climate Models ◽  
Reanalysis Data ◽  
Fire Weather ◽  
Northern Sweden ◽  
Southern Sweden ◽  
Weather Risk

Abstract. In this study, we analyse the role of climate change in the forest fires that raged through large parts of Sweden in the summer of 2018 from a meteorological perspective. This is done by studying the Canadian Fire Weather Index (FWI) based on sub-daily data, both in reanalysis data sets (ERA-Interim, ERA5, the Japanese 55 year Reanalysis, JRA-55, and Modern-Era Retrospective analysis for Research and Applications version 2, MERRA-2) and three large-ensemble climate models (EC-Earth, weather@home, W@H, and Community Earth System Model, CESM) simulations. The FWI, based on reanalysis, correlates well with the observed burnt area in summer (r=0.6 to 0.8). We find that the maximum FWI in July 2018 had return times of ∼24 years (90 % CI, confidence interval, > 10 years) for southern and northern Sweden. Furthermore, we find a negative trend of the FWI for southern Sweden over the 1979 to 2017 time period in the reanalyses, yielding a non-significant reduced probability of such an event. However, the short observational record, large uncertainty between the reanalysis products and large natural variability of the FWI give a large confidence interval around this number that easily includes no change, so we cannot draw robust conclusions from reanalysis data. The three large-ensembles with climate models point to a roughly 1.1 (0.9 to 1.4) times increased probability (non-significant) for such events in the current climate relative to preindustrial climate. For a future climate (2 ∘C warming), we find a roughly 2 (1.5 to 3) times increased probability for such events relative to the preindustrial climate. The increased fire weather risk is mainly attributed to the increase in temperature. The other main factor, i.e. precipitation during summer months, is projected to increase for northern Sweden and decrease for southern Sweden. We, however, do not find a clear change in prolonged dry periods in summer months that could explain the increased fire weather risk in the climate models. In summary, we find a (non-significant) reduced probability of such events based on reanalyses, a small (non-significant) increased probability due to global warming up to now and a more robust (significant) increase in the risk for such events in the future based on the climate models.

scholarly journals Climate projections of a multivariate heat stress index: the role of downscaling and bias correction

2019 ◽  
Vol 12 (8) ◽  
pp. 3419-3438 ◽  
Author(s):  
Ana Casanueva ◽  
Sven Kotlarski ◽  
Sixto Herrera ◽  
Andreas M. Fischer ◽  
Tord Kjellstrom ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Bias Correction ◽  
Climate Models ◽  
Dew Point ◽  
Added Value ◽  
Stress Index ◽  
Corrected Data ◽  
Heat Stress Index

Abstract. Along with the higher demand for bias-corrected data for climate impact studies, the number of available data sets has largely increased in recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on global climate models (GCMs). In a complementary way, the CORDEX initiative has incorporated experiments based on regionally downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distil the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses. In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCM and EURO-CORDEX regional climate models (RCMs), at approximately 150, 50 and 12 km horizontal resolution) in order to assess the role of downscaling and bias correction in a multivariate framework. The analysis is carried out for the wet-bulb globe temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dew-point temperature, which in this work are individually bias corrected prior to the index calculation. Our results show that the added value of RCMs with respect to the driving GCM is limited after bias correction. The two bias correction methods are able to adjust the central part of the WBGT distribution, but some added value of QM is found in WBGT percentiles and in the inter-variable relationships. The evaluation in present climate of such multivariate indices should be performed with caution since biases in the individual variables might compensate, thus leading to better performance for the wrong reason. Climate change projections of WBGT reveal a larger increase in summer mean heat stress for the GCM than for the RCMs, related to the well-known reduced summer warming of the EURO-CORDEX RCMs. These differences are lowered after QM, since this bias correction method modifies the change signals and brings the results for the GCM and RCMs closer to each other. We also highlight the need for large ensembles of simulations to assess the feasibility of the derived projections.

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