scholarly journals Hemispheric asymmetries in recent changes of the stratospheric circulation

2022 ◽  
Author(s):  
Felix Ploeger ◽  
Hella Garny
Keyword(s):  
Hemispheric Asymmetry ◽  
Climate Models ◽  
Climate Model ◽  
Transport Model ◽  
Internal Variability ◽  
Residual Circulation ◽  
Hemispheric Asymmetries ◽  
Model Simulations ◽  
Transit Times ◽  
Climate Model Simulations

Abstract. Despite the expected opposite effects of ozone recovery, the stratospheric Brewer-Dobson circulation (BDC) has been found to weaken in the Northern hemisphere (NH) relative to the Southern hemisphere (SH) in recent decades, inducing substantial effects on chemical composition. We investigate hemispheric asymmetries in BDC changes since about 2000 in simulations with the transport model CLaMS driven with different reanalyses (ERA5, ERA-Interim, JRA-55, MERRA-2) and contrast those to a suite of free-running climate model simulations. We find that age of air increases robustly in the NH stratosphere relative to the SH in all reanalyses considered. Related nitrous oxide changes agree well between reanalysis-driven simulations and satellite measurements, providing observational evidence for the hemispheric asymmetry in BDC changes. Residual circulation metrics further show that the composition changes are caused by structural BDC changes related to an upward shift and strengthening of the deep BDC branch, resulting in longer transit times, and a downward shift and weakening shallow branch in the NH relative to the SH. All reanalyses agree on this mechanism. Although climate model simulations show that ozone recovery will lead to overall reduced circulation and age of air trends, the hemispherically asymmetric signal in circulation trends is small compared to internal variability. Therefore, the observed circulation trends over the recent past are not in contradiction to expectations from climate models. Furthermore, the hemispheric asymmetry in BDC trends imprints on the composition of the lower stratosphere and the signal might propagate into the troposphere, potentially affecting composition down to the surface.

scholarly journals Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

2021 ◽  
Vol 12 (2) ◽  
pp. 401-418
Author(s):  
Nicola Maher ◽  
Sebastian Milinski ◽  
Ralf Ludwig
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Internal Variability ◽  
Climate System ◽  
Single Type ◽  
Special Issue ◽  
Large Ensemble ◽  
Model Simulations ◽  
Different Types ◽  
Climate Model Simulations

Abstract. Single model initial-condition large ensembles (SMILEs) are valuable tools that can be used to investigate the climate system. SMILEs allow scientists to quantify and separate the internal variability of the climate system and its response to external forcing, with different types of SMILEs appropriate to answer different scientific questions. In this editorial we first provide an introduction to SMILEs and an overview of the studies in the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. These studies analyse a range of different types of SMILEs including global climate models (GCMs), regionally downscaled climate models (RCMs), a hydrological model with input from a RCM SMILE, a SMILE with prescribed sea surface temperature (SST) built for event attribution, a SMILE that assimilates observed data, and an initialised regional model. These studies provide novel methods, that can be used with SMILEs. The methods published in this issue include a snapshot empirical orthogonal function analysis used to investigate El Niño–Southern Oscillation teleconnections; the partitioning of future uncertainty into model differences, internal variability, and scenario choices; a weighting scheme for multi-model ensembles that can incorporate SMILEs; and a method to identify the required ensemble size for any given problem. Studies in this special issue also focus on RCM SMILEs, with projections of the North Atlantic Oscillation and its regional impacts assessed over Europe, and an RCM SMILE intercomparison. Finally a subset of studies investigate projected impacts of global warming, with increased water flows projected for future hydrometeorological events in southern Ontario; precipitation projections over central Europe are investigated and found to be inconsistent across models in the Alps, with a continuation of past tendencies in Mid-Europe; and equatorial Asia is found to have an increase in the probability of large fire and drought events under higher levels of warming. These studies demonstrate the utility of different types of SMILEs. In the second part of this editorial we provide a perspective on how three types of SMILEs could be combined to exploit the advantages of each. To do so we use a GCM SMILE and an RCM SMILE with all forcings, as well as a naturally forced GCM SMILE (nat-GCM) over the European domain. We utilise one of the key advantages of SMILEs, precisely separating the forced response and internal variability within an individual model to investigate a variety of simple questions. Broadly we show that the GCM can be used to investigate broad-scale patterns and can be directly compared to the nat-GCM to attribute forced changes to either anthropogenic emissions or volcanoes. The RCM provides high-resolution spatial information of both the forced change and the internal variability around this change at different warming levels. By combining all three ensembles we can gain information that would not be available using a single type of SMILE alone, providing a perspective on future research that could be undertaken using these tools.

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

scholarly journals The DeepMIP contribution to PMIP4: experimental design for model simulations of the EECO, PETM, and pre-PETM (version 1.0)

2017 ◽  
Vol 10 (2) ◽  
pp. 889-901 ◽  
Author(s):  
Daniel J. Lunt ◽  
Matthew Huber ◽  
Eleni Anagnostou ◽  
Michiel L. J. Baatsen ◽  
Rodrigo Caballero ◽  
...  
Keyword(s):  
Experimental Design ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Geological Data ◽  
Time Model ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations

Abstract. Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( >  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

scholarly journals The stratospheric Brewer–Dobson circulation inferred from age of air in the ERA5 reanalysis

2021 ◽  
Author(s):  
Felix Ploeger ◽  
Mohamadou Diallo ◽  
Edward Charlesworth ◽  
Paul Konopka ◽  
Bernard Legras ◽  
...  
Keyword(s):  
Diabatic Heating ◽  
Climate Models ◽  
Lower Stratosphere ◽  
Transport Model ◽  
Residual Circulation ◽  
Heating Rates ◽  
Weather Forecasts ◽  
Trend Pattern ◽  
Transit Times ◽  
Medium Range

Abstract. This paper investigates the global stratospheric Brewer–Dobson circulation (BDC) in the ERA5 meteorological reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). The analysis is based on simulations of stratospheric mean age of air, including the full age spectrum, with the Lagrangian transport model CLaMS, driven by winds and total diabatic heating rates from the reanalysis. ERA5-based results are compared to those of the preceding ERA–Interim reanalysis. Our results show a significantly slower BDC for ERA5 than for ERA–Interim, manifesting in weaker diabatic heating rates and larger age of air. In the tropical lower stratosphere, heating rates are 30–40 % weaker in ERA5, likely correcting a known bias in ERA–Interim. Above, ERA5 age of air appears slightly high-biased and the BDC slightly slow compared to tracer observations. The age trend in ERA5 over 1989–2018 is negative throughout the stratosphere, as climate models predict in response to global warming. However, the age decrease is not linear over the period but exhibits steplike changes which could be caused by muti-annual variability or changes in the assimilation system. Over the 2002–2012 period, ERA5 age shows a similar hemispheric dipole trend pattern as ERA–Interim, with age increasing in the NH and decreasing in the SH. Shifts in the age spectrum peak and residual circulation transit times indicate that reanalysis differences in age are likely caused by differences in the residual circulation. In particular, the shallow BDC branch accelerates similarly in both reanalyses while the deep branch accelerates in ERA5 and decelerates in ERA–Interim.

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

scholarly journals Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface

2019 ◽  
Vol 32 (13) ◽  
pp. 4089-4102 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden ◽  
Angeline G. Pendergrass
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract We analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

scholarly journals On the Relationship between Mean Monsoon Precipitation and Low Pressure Systems in Climate Model Simulations

2015 ◽  
Vol 28 (13) ◽  
pp. 5305-5324 ◽  
Author(s):  
V. Praveen ◽  
S. Sandeep ◽  
R. S. Ajayamohan
Keyword(s):  
Summer Monsoon ◽  
Climate Models ◽  
Climate Model ◽  
Low Pressure ◽  
Tracking Algorithm ◽  
Monsoon Precipitation ◽  
Model Simulations ◽  
Detection And Tracking ◽  
Climate Model Simulations ◽  
Low Pressure Systems

Abstract The north-northwest-propagating low pressure systems (LPS) are an important component of the Indian summer monsoon (ISM). The objective detection and tracking of LPS in reanalysis products and climate model simulations are challenging because of the weak structure of the LPS compared to tropical cyclones. Therefore, the skill of reanalyses and climate models in simulating the monsoon LPS is unknown. A robust method is presented here to objectively identify and track LPS, which mimics the conventional identification and tracking algorithm based on detecting closed isobars on surface pressure charts. The new LPS tracking technique allows a fair comparison between the observed and simulated LPS. The analysis based on the new tracking algorithm shows that the reanalyses from ERA-Interim and MERRA were able to reproduce the observed climatology and interannual variability of the monsoon LPS with a fair degree of accuracy. Further, the newly developed LPS detection and tracking algorithm is also applied to the climate model simulations of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CMIP5 models show considerable spread in terms of their skill in LPS simulation. About 60% of the observed total summer monsoon precipitation over east-central India is found to be associated with LPS activities, while in model simulations this ratio varies between 5% and 60%. Those models that simulate synoptic activity realistically are found to have better skill in simulating seasonal mean monsoon precipitation. The model-to-model variability in the simulated synoptic activity is found to be linked to the intermodel spread in zonal wind shear over the Indian region, which is further linked to inadequate representation of the tropical easterly jet in climate models. These findings elucidate the mechanisms behind the model simulation of ISM precipitation, synoptic activity, and their interdependence.

scholarly journals Increased variability in Greenland Ice Sheet runoff from satellite observations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Slater ◽  
Andrew Shepherd ◽  
Malcolm McMillan ◽  
Amber Leeson ◽  
Lin Gilbert ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Model Simulations ◽  
Climate Model Simulations

AbstractRunoff from the Greenland Ice Sheet has increased over recent decades affecting global sea level, regional ocean circulation, and coastal marine ecosystems, and it now accounts for most of the contemporary mass imbalance. Estimates of runoff are typically derived from regional climate models because satellite records have been limited to assessments of melting extent. Here, we use CryoSat-2 satellite altimetry to produce direct measurements of Greenland’s runoff variability, based on seasonal changes in the ice sheet’s surface elevation. Between 2011 and 2020, Greenland’s ablation zone thinned on average by 1.4 ± 0.4 m each summer and thickened by 0.9 ± 0.4 m each winter. By adjusting for the steady-state divergence of ice, we estimate that runoff was 357 ± 58 Gt/yr on average – in close agreement with regional climate model simulations (root mean square difference of 47 to 60 Gt/yr). As well as being 21 % higher between 2011 and 2020 than over the preceding three decades, runoff is now also 60 % more variable from year-to-year as a consequence of large-scale fluctuations in atmospheric circulation. Because this variability is not captured in global climate model simulations, our satellite record of runoff should help to refine them and improve confidence in their projections.

scholarly journals Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

2019 ◽  
Vol 12 (7) ◽  
pp. 3017-3043 ◽  
Author(s):  
Sihan Li ◽  
David E. Rupp ◽  
Linnia Hawkins ◽  
Philip W. Mote ◽  
Doug McNeall ◽  
...  
Keyword(s):  
Parameter Space ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Bias Reduction ◽  
Sensitivity Analyses ◽  
Model Framework ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global–regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.

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