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 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 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 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.

scholarly journals Objectively Determined Resolution-Dependent Threshold Criteria for the Detection of Tropical Cyclones in Climate Models and Reanalyses

2007 ◽  
Vol 20 (10) ◽  
pp. 2307-2314 ◽  
Author(s):  
K. J. E. Walsh ◽  
M. Fiorino ◽  
C. W. Landsea ◽  
K. L. McInnes
Keyword(s):  
Tropical Cyclones ◽  
Wind Profile ◽  
Climate Models ◽  
Climate Model ◽  
Two Dimensional ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
Wind Profiles ◽  
Threshold Criteria ◽  
Threshold Wind Speed

Abstract Objectively derived resolution-dependent criteria are defined for the detection of tropical cyclones in model simulations and observationally based analyses. These criteria are derived from the wind profiles of observed tropical cyclones, averaged at various resolutions. Both an analytical wind profile model and two-dimensional observed wind analyses are used. The results show that the threshold wind speed of an observed tropical cyclone varies roughly linearly with resolution. The criteria derived here are compared to the numerous different criteria previously employed in climate model simulations. The resulting method provides a simple means of comparing climate model simulations and reanalyses.

Reconciling the BDC response in climate models to the volcanic forcings with reanalyses

2020 ◽  
Author(s):  
Mohamadou Diallo ◽  
Hella Garny ◽  
Roland Eichinger ◽  
Valentina Aquila ◽  
Manfred Ern ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Model Simulations ◽  
Current Climate ◽  
Climate Simulations ◽  
Pinatubo Eruption ◽  
Speed Up ◽  
Stratospheric Circulation ◽  
Volcanic Aerosols ◽  
Climate Model Simulations

<p>The stratospheric Brewer--Dobson circulation (BDC) is an important element of climate system as it determines the concentration of radiatively active trace gases like water vapor, ozone and aerosol above the tropopause. Climate models predict that increasing greenhouse gas levels speed up the stratospheric circulation. BDC changes is substantially modulated by different modes of climate variability (QBO, ENSO, solar cycle), including the volcanic aerosols. However, such variability is often not reliably included or represented in current climate model simulations, challenging the evaluation of models’ behavior against observations and constituting a major uncertainty in current climate simulations. </p><p>Here, we investigate the main differences between the reanalysis and the CCMI/CMIP6 climate models’ response to stratospheric volcanic forcings regarding the depth/strength of the stratospheric BDC, with a focus on potential changes in the deep and shallow circulation branches. We also discuss the key reasons of the discrepancies (incl. uncertainties associated with volcanological forcing datasets and missing direct aerosol heating in the reanalysis) in the BDC response between reanalysis-driven and climate model simulations in the lower, mid and upper stratosphere. Finally, we assess the dynamical mechanisms involved in the volcanically-induced BDC changes to understand the opposite regime between lower, middle and upper stratosphere after the Mt Pinatubo eruption.</p>

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 The representation of location by regional climate models in complex terrain

2015 ◽  
Vol 12 (3) ◽  
pp. 3011-3028 ◽  
Author(s):  
D. Maraun ◽  
M. Widmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Signal ◽  
Model Errors ◽  
Model Simulations ◽  
Local Grid ◽  
Climate Model Simulations

Abstract. To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. But based on regional climate model simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of moutain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong airmasses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.

The influence of the tropical troposphere on the QBO in model simulations

2020 ◽  
Author(s):  
Federico Serva ◽  
Chiara Cagnazzo ◽  
Bo Christiansen ◽  
Shuting Yang
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Horizontal Resolution ◽  
Quasi Biennial Oscillation ◽  
Model Simulations ◽  
Enso Events ◽  
Phase Alignment ◽  
Climate Model Simulations ◽  
The Tropics

<pre>The equatorial Quasi-Biennial Oscillation (QBO) and the El Ni<strong>ñ</strong>o-Southern Oscillation (ENSO) are major patterns of climate variability. Mutual interactions between these two modes have been highlighted in the observational record, but this has not been much tested using climate model simulations. In observations, the amplitude is larger during cold and smaller during warm events, while the descent rate is slower during cold and faster during warm events. Here we discuss how climate models represent this relationship, in terms of the changes of the QBO properties. The results for a multi-model ensemble, with atmosphere-only and ocean-atmosphere simulations, are discussed. The phase alignment of the QBO after the 1997/98 warm ENSO, already documented in previous works, is also present in a large ensemble of uncoupled experiments. It is found that a high horizontal resolution is needed to realistically reproduce the observed modulation of the QBO descent rate under strong ENSO events. The QBO amplitude response is instead weak at any horizontal resolution. Robust changes emerging in the projections and aspects of the modelled stratosphere-troposphere coupling in the tropics are presented. </pre>

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