Investigating the recent apparent hiatus in surface temperature increases: 1. Construction of two 30‐member Earth System Model ensembles

Abstract. The importance of evaluating models through paleoclimate simulations is becoming more recognized in efforts to improve climate projection. To evaluate an integrated Earth System Model, MIROC-ESM, we performed simulations in time-slice experiments for the mid-Holocene (6000 yr before present, 6 ka) and preindustrial (1850 AD, 0 ka) periods under the protocol of the Coupled Model Intercomparison Project 5/Paleoclimate Modelling Intercomparison Project 3. We first give an overview of the simulated global climates by comparing with simulations using a previous version of the MIROC model (MIROC3), which is an atmosphere–ocean coupled general circulation model. We then comprehensively discuss various aspects of climate change with 6 ka forcing and how the differences in the models can affect the results. We also discuss the representation of the precipitation enhancement at 6 ka over northern Africa. The precipitation enhancement at 6 ka over northern Africa according to MIROC-ESM does not differ greatly from that obtained with MIROC3, which means that newly developed components such as dynamic vegetation and improvements in the atmospheric processes do not have significant impacts on the representation of the 6 ka monsoon change suggested by proxy records. Although there is no drastic difference between the African monsoon representations of the two models, there are small but significant differences in the precipitation enhancement over the Sahara in early summer, which can be related to the representation of the sea surface temperature rather than the vegetation coupling in MIROC-ESM. Because the oceanic parts of the two models are identical, the difference in the sea surface temperature change is ultimately attributed to the difference in the atmospheric and/or land modules, and possibly the difference in the representation of low-level clouds.

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The IITM Earth System Model: Transformation of a Seasonal Prediction Model to a Long-Term Climate Model

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-13-00276.1 ◽

2015 ◽

Vol 96 (8) ◽

pp. 1351-1367 ◽

Cited By ~ 22

Author(s):

P. Swapna ◽

M. K. Roxy ◽

K. Aparna ◽

K. Kulkarni ◽

A. G. Prajeesh ◽

...

Keyword(s):

Prediction Model ◽

Surface Temperature ◽

Mixed Layer ◽

Climate Model ◽

Seasonal Prediction ◽

Earth System Model ◽

System Model ◽

Cold Bias ◽

Earth System ◽

Global Mean

Abstract With the goal of building an Earth system model appropriate for detection, attribution, and projection of changes in the South Asian monsoon, a state-of-the-art seasonal prediction model, namely the Climate Forecast System version 2 (CFSv2) has been adapted to a climate model suitable for extended climate simulations at the Indian Institute of Tropical Meteorology (IITM), Pune, India. While the CFSv2 model has been skillful in predicting the Indian summer monsoon (ISM) on seasonal time scales, a century-long simulation with it shows biases in the ocean mixed layer, resulting in a 1.5°C cold bias in the global mean surface air temperature, a cold bias in the sea surface temperature (SST), and a cooler-than-observed troposphere. These biases limit the utility of CFSv2 to study climate change issues. To address biases, and to develop an Indian Earth System Model (IITM ESMv1), the ocean component in CFSv2 was replaced at IITM with an improved version, having better physics and interactive ocean biogeochemistry. A 100-yr simulation with the new coupled model (with biogeochemistry switched off) shows substantial improvements, particularly in global mean surface temperature, tropical SST, and mixed layer depth. The model demonstrates fidelity in capturing the dominant modes of climate variability such as the ENSO and Pacific decadal oscillation. The ENSO–ISM teleconnections and the seasonal leads and lags are also well simulated. The model, a successful result of Indo–U.S. collaboration, will contribute to the IPCC’s Sixth Assessment Report (AR6) simulations, a first for India.

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Community Earth System Model Simulations Reveal the Relative Importance of Afforestation and Forest Management to Surface Temperature in Eastern North America

Forests ◽

10.3390/f8120499 ◽

2017 ◽

Vol 8 (12) ◽

pp. 499 ◽

Cited By ~ 3

Author(s):

◽

Keyword(s):

Forest Management ◽

North America ◽

Surface Temperature ◽

Earth System Model ◽

System Model ◽

Eastern North America ◽

Relative Importance ◽

Earth System ◽

Model Simulations ◽

Community Earth System Model

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Reversibility of Historical and Future Climate Change with a Complex Earth System Model

10.21203/rs.3.rs-724162/v1 ◽

2021 ◽

Author(s):

Shili Yang ◽

Di Tian ◽

JieMing Chou ◽

Ting Wei ◽

Xian Zhu ◽

...

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Earth System Model ◽

Meridional Overturning Circulation ◽

System Model ◽

Convective Precipitation ◽

Future Climate Change ◽

Earth System ◽

Initial Values ◽

Precipitation Total

Abstract The reversibility of a wide range of components of the earth system was investigated by comparing forward and time-reversed historical and future simulations of a coupled earth system model known as the Beijing Normal University earth system model. Many characteristics of the climate system, including the surface temperature, ocean heat content (OHC), convective precipitation, total runoff, ground evaporation, soil moisture, sea ice extent and Atlantic Meridional Overturning Circulation, did not fully return to their initial values when the historical or future natural and anthropogenic forcing agents were reversed. The surface temperature and OHC declines lagged behind the decline in greenhouse gases (GHGs). Reverses in other variables occurred in direct response to the decline in GHGs. The sea level increased, even after all of the forces returned to the original values. Furthermore, most of the climate variables did not return to their original values because of thermal inertial. The end states of variables, other than those related to thermal storage, mainly depended on the original state of the natural and anthropogenic forces, and were unaffected by the future growth rate of the GHGs. The climate policy implication of this study is that climate change cannot be completely reversed even if all the external forces are returned to their initial values.

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A weighting scheme to incorporate large ensembles in multi-model ensemble projections

10.5194/esd-2019-69 ◽

2019 ◽

Cited By ~ 2

Author(s):

Anna Louise Merrifield ◽

Lukas Brunner ◽

Ruth Lorenz ◽

Reto Knutti

Keyword(s):

Internal Variability ◽

Ensemble Member ◽

Earth System Model ◽

System Model ◽

Initial Condition ◽

Earth System ◽

Model Ensemble ◽

Single Model ◽

Large Ensembles ◽

Model Ensembles

Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50–100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend. The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.

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Effects of strongly eddying oceans on multidecadal climate variability in the Community Earth System Model

10.5194/os-2020-85 ◽

2020 ◽

Author(s):

André Jüling ◽

Anna von der Heydt ◽

Henk A. Dijkstra

Keyword(s):

Climate Variability ◽

Surface Temperature ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Multidecadal Variability ◽

Global Mean Surface Temperature ◽

Ocean Flows ◽

Community Earth System Model ◽

Global Mean

Abstract. Climate variability on multidecadal time scales appears to be organized in pronounced patterns with clear expressions in sea surface temperature, such as the Atlantic Multidecadal Variability and the Pacific Decadal Oscillation. These patterns are now well studied both in observations and global climate models and are important in the attribution of climate change. Results from CMIP5 models have indicated large biases in these patterns with consequences for ocean heat storage variability and eventually the global mean surface temperature. In this paper, we use two multi-century Community Earth System Model simulations at coarse (1°) and fine (0.1°) ocean model horizontal grid spacing to study the effects of the representation of mesoscale ocean flows on major patterns of multidecadal variability. We find that resolving mesoscale ocean flows both improves the characteristics of the modes of variability with respect to observations and increases the amplitude of the heat content variability in the individual ocean basins. The effect on the global mean surface temperature is relatively minor.

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A dissection of the surface temperature biases in the Community Earth System Model

Climate Dynamics ◽

10.1007/s00382-013-2029-9 ◽

2013 ◽

Vol 43 (7-8) ◽

pp. 2043-2059 ◽

Cited By ~ 14

Author(s):

Tae-Won Park ◽

Yi Deng ◽

Ming Cai ◽

Jee-Hoon Jeong ◽

Renjun Zhou

Keyword(s):

Surface Temperature ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Community Earth System Model

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Sensitivities and Responses of Land Surface Temperature to Deforestation-Induced Biophysical Changes in Two Global Earth System Models

Journal of Climate ◽

10.1175/jcli-d-19-0725.1 ◽

2020 ◽

Vol 33 (19) ◽

pp. 8381-8399

Author(s):

Weilin Liao ◽

Xiaoping Liu ◽

Elizabeth Burakowski ◽

Dagang Wang ◽

Linying Wang ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Surface Resistance ◽

Aerodynamic Resistance ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Biophysical Changes ◽

Earth System Models

AbstractWhile the significance of quantifying the biophysical effects of deforestation is rarely disputed, the sensitivities of land surface temperature (LST) to deforestation-induced changes in different biophysical factors (e.g., albedo, aerodynamic resistance, and surface resistance) and the relative importance of those biophysical changes remain elusive. Based on the subgrid-scale outputs from two global Earth system models (ESMs, i.e., the Geophysical Fluid Dynamics Laboratory Earth System Model and the Community Earth System Model) and an improved attribution framework, the sensitivities and responses of LST to deforestation are examined. Both models show that changes in aerodynamic resistance are the most important factor responsible for LST changes, with other factors such as albedo and surface resistance playing secondary but important roles. However, the magnitude of the contributions from different biophysical factors to LST changes is quite different for the two ESMs. We find that the differences between the two models in terms of the sensitivities are smaller than those of the corresponding biophysical changes, indicating that the dissimilarity between the two models in terms of LST responses to deforestation is more related to the magnitude of biophysical changes. It is the first time that the attribution of subgrid surface temperature variability is comprehensively compared based on simulations with two commonly used global ESMs. This study yields new insights into the similarity and dissimilarity in terms of how the biophysical processes are represented in different ESMs and further improves our understanding of how deforestation impacts on the local surface climate.

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A consistent prescription of stratospheric aerosol for both radiation and chemistry in the Community Earth System Model (CESM1)

Geoscientific Model Development ◽

10.5194/gmd-9-2459-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2459-2470 ◽

Cited By ~ 11

Author(s):

Ryan Reynolds Neely III ◽

Andrew J. Conley ◽

Francis Vitt ◽

Jean-François Lamarque

Keyword(s):

Surface Temperature ◽

Temperature Response ◽

Stratospheric Aerosol ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Climate System Model ◽

Pinatubo Eruption ◽

Community Earth System Model ◽

Global Mean

Abstract. Here we describe an updated parameterization for prescribing stratospheric aerosol in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1). The need for a new parameterization is motivated by the poor response of the CESM1 (formerly referred to as the Community Climate System Model, version 4, CCSM4) simulations contributed to the Coupled Model Intercomparison Project 5 (CMIP5) to colossal volcanic perturbations to the stratospheric aerosol layer (such as the 1991 Pinatubo eruption or the 1883 Krakatau eruption) in comparison to observations. In particular, the scheme used in the CMIP5 simulations by CESM1 simulated a global mean surface temperature decrease that was inconsistent with the GISS Surface Temperature Analysis (GISTEMP), NOAA's National Climatic Data Center, and the Hadley Centre of the UK Met Office (HADCRUT4). The new parameterization takes advantage of recent improvements in historical stratospheric aerosol databases to allow for variations in both the mass loading and size of the prescribed aerosol. An ensemble of simulations utilizing the old and new schemes shows CESM1's improved response to the 1991 Pinatubo eruption. Most significantly, the new scheme more accurately simulates the temperature response of the stratosphere due to local aerosol heating. Results also indicate that the new scheme decreases the global mean temperature response to the 1991 Pinatubo eruption by half of the observed temperature change, and modelled climate variability precludes statements as to the significance of this change.

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A weighting scheme to incorporate large ensembles in multi-model ensemble projections

10.5194/egusphere-egu2020-4524 ◽

2020 ◽

Author(s):

Anna Merrifield ◽

Lukas Brunner ◽

Ruth Lorenz ◽

Reto Knutti

Keyword(s):

Internal Variability ◽

Ensemble Member ◽

Earth System Model ◽

System Model ◽

Initial Condition ◽

Earth System ◽

Model Ensemble ◽

Single Model ◽

Large Ensembles ◽

Model Ensembles

<p>Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50&#8211;100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of historical redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend.</p><p>The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.</p>

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