Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

AbstractStudies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

Download Full-text

Documenting Climate Models and Their Simulations

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-11-00035.1 ◽

2013 ◽

Vol 94 (5) ◽

pp. 623-627 ◽

Cited By ~ 16

Author(s):

Eric Guilyardi ◽

V. Balaji ◽

Bryan Lawrence ◽

Sarah Callaghan ◽

Cecelia Deluca ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Climate Modeling ◽

Coupled Model ◽

Climate Change Impacts ◽

Model Output ◽

Initial Application ◽

Wide Range ◽

Community Initiative ◽

Climate Model Output

The results of climate models are of increasing and widespread importance. No longer is climate model output of sole interest to climate scientists and researchers in the climate change impacts and adaptation fields. Now nonspecialists such as government officials, policy makers, and the general public all have an increasing need to access climate model output and understand its implications. For this host of users, accurate and complete metadata (i.e., information about how and why the data were produced) is required to document the climate modeling results. Here we describe a pilot community initiative to collect and make available documentation of climate models and their simulations. In an initial application, a metadata repository is being established to provide information of this kind for a major internationally coordinated modeling activity known as CMIP5 (Coupled Model Intercomparison Project, Phase 5). It is expected that for a wide range of stakeholders, this and similar community-managed metadata repositories will spur development of analysis tools that facilitate discovery and exploitation of Earth system simulations.

Download Full-text

Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models

npj Climate and Atmospheric Science ◽

10.1038/s41612-020-00159-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Zhili Wang ◽

Lei Lin ◽

Yangyang Xu ◽

Huizheng Che ◽

Xiaoye Zhang ◽

...

Keyword(s):

Climate Change ◽

Radiative Forcing ◽

Impact Analysis ◽

Climate Model ◽

Regional Climate ◽

Eastern China ◽

Coupled Model ◽

Regional Climate Change ◽

Anthropogenic Aerosol ◽

Wide Range

AbstractAnthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.

Download Full-text

Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget

Bulletin of the American Meteorological Society ◽

10.1175/bams-88-3-375 ◽

2007 ◽

Vol 88 (3) ◽

pp. 375-384 ◽

Cited By ~ 39

Author(s):

E. S. Takle ◽

J. Roads ◽

B. Rockel ◽

W. J. Gutowski ◽

R. W. Arritt ◽

...

Keyword(s):

Energy Budget ◽

Climate Models ◽

Climate Model ◽

Water Cycle ◽

Regional Climate ◽

Climate Conditions ◽

Continental Scale ◽

Global Water Cycle ◽

Wide Range ◽

Global Water

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

Download Full-text

Influence of Ocean and Atmosphere Components on Simulated Climate Sensitivities

Journal of Climate ◽

10.1175/jcli-d-12-00121.1 ◽

2013 ◽

Vol 26 (1) ◽

pp. 231-245 ◽

Cited By ~ 27

Author(s):

Michael Winton ◽

Alistair Adcroft ◽

Stephen M. Griffies ◽

Robert W. Hallberg ◽

Larry W. Horowitz ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Model Simulation ◽

Coupled Model ◽

Ocean Model ◽

Historical Simulation ◽

Model Version ◽

Salinity Response ◽

The Impact

Abstract The influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities is examined by comparing three GFDL climate models used for phase 5 of the Coupled Model Intercomparison Project (CMIP5). The base model ESM2M is closely related to GFDL’s CMIP3 climate model version 2.1 (CM2.1), and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. The authors compare the impact of this “ocean swap” with an “atmosphere swap” that produces the GFDL Climate Model version 3 (CM3) by replacing the AM2 atmospheric component with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multidecadal response time scale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high-latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early twentieth century and an associated cooling, which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.

Download Full-text

How to combine climate models and observations in event attribution?

10.5194/egusphere-egu21-14636 ◽

2021 ◽

Author(s):

Yoann Robin ◽

Aurélien Ribes

Keyword(s):

Bayesian Method ◽

Climate Models ◽

Climate Model ◽

Coupled Model ◽

Model Data ◽

Human Influence ◽

Scale Parameters ◽

Event Attribution ◽

Intercomparison Project ◽

Climate Model Simulations

We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP) models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that both the probability and the intensity of that event have increased significantly in response to human influence. Remarkably, we find that the heat wave considered might not have been possible without climate change. Our results also suggest that combining model data with observations can improve the description of hot temperature distribution.

Download Full-text

Back to the Future: Using Long-Term Observational and Paleo-Proxy Reconstructions to Improve Model Projections of Antarctic Climate

Geosciences ◽

10.3390/geosciences9060255 ◽

2019 ◽

Vol 9 (6) ◽

pp. 255 ◽

Cited By ~ 6

Author(s):

Thomas J. Bracegirdle ◽

Florence Colleoni ◽

Nerilie J. Abram ◽

Nancy A. N. Bertler ◽

Daniel A. Dixon ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Ice Core ◽

Global Climate Models ◽

Climate Projections ◽

Past Performance ◽

Wide Range ◽

Proxy Reconstructions

Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.

Download Full-text

Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

Geoscientific Model Development ◽

10.5194/gmd-12-2727-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 2727-2765 ◽

Cited By ~ 58

Author(s):

Hiroaki Tatebe ◽

Tomoo Ogura ◽

Tomoko Nitta ◽

Yoshiki Komuro ◽

Koji Ogochi ◽

...

Keyword(s):

Climate Variability ◽

Climate Sensitivity ◽

Radiative Forcing ◽

Climate Model ◽

Coupled Model ◽

Climate Feedback ◽

Climate Projections ◽

Internal Climate Variability ◽

Wide Range ◽

Mean Climate

Abstract. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC), called MIROC6, was cooperatively developed by a Japanese modeling community. In the present paper, simulated mean climate, internal climate variability, and climate sensitivity in MIROC6 are evaluated and briefly summarized in comparison with the previous version of our climate model (MIROC5) and observations. The results show that the overall reproducibility of mean climate and internal climate variability in MIROC6 is better than that in MIROC5. The tropical climate systems (e.g., summertime precipitation in the western Pacific and the eastward-propagating Madden–Julian oscillation) and the midlatitude atmospheric circulation (e.g., the westerlies, the polar night jet, and troposphere–stratosphere interactions) are significantly improved in MIROC6. These improvements can be attributed to the newly implemented parameterization for shallow convective processes and to the inclusion of the stratosphere. While there are significant differences in climates and variabilities between the two models, the effective climate sensitivity of 2.6 K remains the same because the differences in radiative forcing and climate feedback tend to offset each other. With an aim towards contributing to the sixth phase of the Coupled Model Intercomparison Project, designated simulations tackling a wide range of climate science issues, as well as seasonal to decadal climate predictions and future climate projections, are currently ongoing using MIROC6.

Download Full-text

A simple object-oriented and open source model for scientific and policy analyses of the global carbon cycle – Hector v0.1

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-7075-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 7075-7119

Author(s):

C. A. Hartin ◽

P. Patel ◽

A. Schwarber ◽

R. P. Link ◽

B. P. Bond-Lamberty

Keyword(s):

Carbon Cycle ◽

Open Source ◽

Climate Models ◽

Modular Design ◽

Climate Model ◽

Global Climate ◽

Coupled Model ◽

Object Oriented ◽

Climate Mitigation ◽

Carbon Cycle Model

Abstract. Simple climate models play an integral role in policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v0.1, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global scale earth system processes. Hector has three main carbon pools: an atmosphere, land, and ocean. The model's terrestrial carbon cycle includes respiration and primary production, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector's actively solves the inorganic carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2] and surface temperatures. The model simulates all four Representative Concentration Pathways with high correlations (R>0.7) with current observations, MAGICC (a well-known simple climate model), and the Coupled Model Intercomparison Project version 5. Hector is freely available under an open source license, and its modular design will facilitate a broad range of research in various areas.

Download Full-text

Analysing Conceptual Climate Models with Monte Carlo Basin Bifurcation Analysis

10.5194/egusphere-egu2020-18301 ◽

2020 ◽

Author(s):

Maximilian Gelbrecht ◽

Jürgen Kurths ◽

Frank Hellmann

Keyword(s):

Monte Carlo ◽

Complex Systems ◽

Bifurcation Analysis ◽

Climate Models ◽

Climate Model ◽

Initial Conditions ◽

Balance Model ◽

High Dimensional ◽

Asymptotic State ◽

Wide Range

Many high-dimensional complex systems such as climate models exhibit an enormously complex landscape of possible asymptotic state. On most occasions these are challenging to analyse with traditional bifurcation analysis methods. Often, one is also more broadly interested in classes of asymptotic states. Here, we present a novel numerical approach prepared for analysing such high-dimensional multistable complex systems: Monte Carlo Basin Bifurcation Analysis (MCBB).&#160; Based on random sampling and clustering methods, we identify the type of dynamic regimes with the largest basins of attraction and track how the volume of these basins change with the system parameters. In order to due this suitable, easy to compute, statistics of trajectories with randomly generated initial conditions and parameters are clustered by an algorithm such as DBSCAN. Due to the modular and flexible nature of the method, it has a wide range of possible applications. While initially oscillator networks were one of the main applications of this methods, here we present an analysis of a simple conceptual climate model setup up by coupling an energy balance model to the Lorenz96 system. The method is available to use as a package for the Julia language.&#160;

Download Full-text

Did tropical precipitation improve in CMIP6 simulations?

10.5194/egusphere-egu2020-3579 ◽

2020 ◽

Author(s):

Stephanie Fiedler ◽

Traute Crueger ◽

Roberta D’Agostino ◽

Karsten Peters ◽

Tobias Becker ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Southern Oscillation ◽

Spatial Scales ◽

Precipitation Amount ◽

Coupled Model ◽

Internal Variability ◽

Atmospheric Composition ◽

Tropical Precipitation ◽

General Aspects

Climate models are known to have biases in tropical precipitation. We assessed to what extent simulations of tropical precipitation have improved in the new Coupled Model Intercomparison Project (CMIP) phase six, using state-of-the-art observational products and model results from the earlier CMIP phases three and five. We characterize tropical precipitation with different well-established metrics. Our assessment includes (1) general aspects of the mean climatology like precipitation associated with the Intertropical Convergence Zone and shallow cloud regimes in the tropics, (2) solar radiative effects including the summer monsoons and the time of occurrence of tropical precipitation in the course of the day, (3) modes of internal variability such as the Madden-Julian Oscillation and the El Ni&#241;o Southern Oscillation, and (4) changes in the course of the 20th century. The results point to improvements of CMIP6 models for some metrics, e.g., the occurrence of drizzle events and consecutive dry days. However, no improvements of CMIP6 models are identified for other aspects of tropical precipitation. These include the area and intensity of the global summer monsoon as well as the diurnal cycle of the tropical precipitation amount, frequency and intensity.All our metrics taken together, CMIP6 models show no systematic improvement of tropical precipitation across different temporal and spatial scales. The model biases in the spatial distribution of tropical precipitation are typically larger than the changes associated with anthropogenic warming. Given the pace of climate change as compared to the pace of climate model improvements, we suggest to use novel modeling approaches to understand the responseof tropical precipitation to changes in atmospheric composition.

Download Full-text