GFDL's CM2 Global Coupled Climate Models. Part IV: Idealized Climate Response

2006 ◽  
Vol 19 (5) ◽  
pp. 723-740 ◽  
Author(s):  
R. J. Stouffer ◽  
A. J. Broccoli ◽  
T. L. Delworth ◽  
K. W. Dixon ◽  
R. Gudgel ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Response ◽  
Atmospheric Co2 Concentration ◽  
Group I

Abstract The climate response to idealized changes in the atmospheric CO2 concentration by the new GFDL climate model (CM2) is documented. This new model is very different from earlier GFDL models in its parameterizations of subgrid-scale physical processes, numerical algorithms, and resolution. The model was constructed to be useful for both seasonal-to-interannual predictions and climate change research. Unlike previous versions of the global coupled GFDL climate models, CM2 does not use flux adjustments to maintain a stable control climate. Results from two model versions, Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), are presented. Two atmosphere–mixed layer ocean or slab models, Slab Model versions 2.0 (SM2.0) and 2.1 (SM2.1), are constructed corresponding to CM2.0 and CM2.1. Using the SM2 models to estimate the climate sensitivity, it is found that the equilibrium globally averaged surface air temperature increases 2.9 (SM2.0) and 3.4 K (SM2.1) for a doubling of the atmospheric CO2 concentration. When forced by a 1% per year CO2 increase, the surface air temperature difference around the time of CO2 doubling [transient climate response (TCR)] is about 1.6 K for both coupled model versions (CM2.0 and CM2.1). The simulated warming is near the median of the responses documented for the climate models used in the 2001 Intergovernmental Panel on Climate Change (IPCC) Working Group I Third Assessment Report (TAR). The thermohaline circulation (THC) weakened in response to increasing atmospheric CO2. By the time of CO2 doubling, the weakening in CM2.1 is larger than that found in CM2.0: 7 and 4 Sv (1 Sv ≡ 106 m3 s−1), respectively. However, the THC in the control integration of CM2.1 is stronger than in CM2.0, so that the percentage change in the THC between the two versions is more similar. The average THC change for the models presented in the TAR is about 3 or 4 Sv; however, the range across the model results is very large, varying from a slight increase (+2 Sv) to a large decrease (−10 Sv).

scholarly journals Limitations of the 1 % experiment as the benchmark idealized experiment for carbon cycle intercomparison in C<sup>4</sup>MIP

2018 ◽  
Author(s):  
Andrew H. MacDougall
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Smooth Transition ◽  
Atmospheric Co2 Concentration ◽  
Benchmark Experiment

Abstract. Idealized climate change simulations are used as benchmark experiments to facilitate the comparison of ensembles of climate models. In the Fifth Assessment Report of the IPCC the 1 % per yearly compounded change in atmospheric CO2 concentration experiment was used to compare Earth System Models with full representations of the global carbon cycle (C4MIP). However this ``1 % experiment'' was never intended for such a purpose and implies a rise in atmospheric CO2 concentration at double the rate of the instrumental record. Here we examine this choice by using an intermediate complexity climate model to compare the 1 % experiment to an idealized CO2 pathway derived from a logistic function. The comparison shows that the logistic experiment has three key differences from the 1 % experiment. (1) The Logistic experiment exhibits a transition of the land biosphere from a carbon sink to a carbon source, a feature absent from the 1 % experiment. (2) The ocean uptake of carbon comes to dominate the carbon cycle as emissions decelerate, a feature that cannot be captured by the 1 % experiment as emissions always accelerate in that experiment. (3) The permafrost carbon feedback to climate change in the 1 % experiment is less than half the strength of the feedback seen in the logistic experiment. The logistic experiment also allows smooth transition to zero or negative emission states, allowing these states to be examined without sharp discontinuities in CO2 emissions. The protocol for the CMIP6 iteration of C4MIP again sets the 1 % experiment as the benchmark experiment for model intercomparison, however clever use of the Tier 2 experiments may alleviate some of the limitations outlined here. Given the limitations of the 1 % experiment as the benchmark experiment for carbon cycle intercomparisons, adding a logistic or similar idealized experiment to the protocol of the CMIP7 iteration of C4MIP is recommended.

scholarly journals Investigating the evolution of major Northern Hemisphere ice sheets during the last glacial-interglacial cycle

2009 ◽  
Vol 5 (3) ◽  
pp. 329-345 ◽  
Author(s):  
S. Bonelli ◽  
S. Charbit ◽  
M. Kageyama ◽  
M.-N. Woillez ◽  
G. Ramstein ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Climate Model ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Glacial Cycle ◽  
Last Glacial ◽  
Atmospheric Co2 Concentration ◽  
The Last Glacial

Abstract. A 2.5-dimensional climate model of intermediate complexity, CLIMBER-2, fully coupled with the GREMLINS 3-D thermo-mechanical ice sheet model is used to simulate the evolution of major Northern Hemisphere ice sheets during the last glacial-interglacial cycle and to investigate the ice sheets responses to both insolation and atmospheric CO2 concentration. This model reproduces the main phases of advance and retreat of Northern Hemisphere ice sheets during the last glacial cycle, although the amplitude of these variations is less pronounced than those based on sea level reconstructions. At the last glacial maximum, the simulated ice volume is 52.5×1015 m3 and the spatial distribution of both the American and Eurasian ice complexes is in reasonable agreement with observations, with the exception of the marine parts of these former ice sheets. A set of sensitivity studies has also been performed to assess the sensitivity of the Northern Hemisphere ice sheets to both insolation and atmospheric CO2. Our results suggest that the decrease of summer insolation is the main factor responsible for the early build up of the North American ice sheet around 120 kyr BP, in agreement with benthic foraminifera δ18O signals. In contrast, low insolation and low atmospheric CO2 concentration are both necessary to trigger a long-lasting glaciation over Eurasia.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

&lt;p&gt;Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important&amp;#160;as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.&lt;/p&gt;&lt;p&gt;Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.&lt;/p&gt;&lt;p&gt;To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.&lt;/p&gt;&lt;p&gt;The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.&lt;/p&gt;

scholarly journals Quantifying Carbon Cycle Feedbacks

2009 ◽  
Vol 22 (19) ◽  
pp. 5232-5250 ◽  
Author(s):  
J. M. Gregory ◽  
C. D. Jones ◽  
P. Cadule ◽  
P. Friedlingstein
Keyword(s):  
Carbon Cycle ◽  
Co2 Emissions ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Feedback ◽  
Climate Response ◽  
Carbon Cycle Model ◽  
Atmospheric Co2 Concentration ◽  
Intercomparison Project ◽  
Earth System Models

Abstract Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time.

scholarly journals The CO<sub>2</sub> inhibition of terrestrial isoprene emission significantly affects future ozone projections

2008 ◽  
Vol 8 (6) ◽  
pp. 19891-19916 ◽  
Author(s):  
P. J. Young ◽  
A. Arneth ◽  
G. Schurgers ◽  
G. Zeng ◽  
J. A. Pyle
Keyword(s):  
Climate Model ◽  
Carbon Assimilation ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Isoprene Emission ◽  
Atmospheric Co2 Concentration ◽  
Source Regions ◽  
Late 21St Century ◽  
Response To Temperature ◽  
The Impact

Abstract. Simulations of future tropospheric composition often include substantial increases in biogenic isoprene emissions arising from the Arrhenius-like leaf emission response and warmer surface temperatures, and from enhanced vegetation productivity in response to temperature and atmospheric CO2 concentration. However, a number of recent laboratory and field data have suggested a direct inhibition of leaf isoprene production by increasing atmospheric CO2 concentration, notwithstanding isoprene being produced from precursor molecules that include some of the primary products of carbon assimilation. The cellular mechanism that underlies the decoupling of leaf photosynthesis and isoprene production still awaits a full explanation but accounting for this observation in a dynamic vegetation model that contains a semi-mechanistic treatment of isoprene emissions has been shown to change future global isoprene emission estimates notably. Here we use these estimates in conjunction with a chemistry-climate model to compare the effects of isoprene simulations without and with a direct CO2-inhibition on late 21st century O3 and OH levels. The impact on surface O3 was significant. Including the CO2-inhibition of isoprene resulted in opposing responses in polluted (O3 decreases of up to 10 ppbv) vs. less polluted (O3 increases of up to 10 ppbv) source regions, due to isoprene nitrate and peroxy acetyl nitrate (PAN) chemistry. OH concentration increased with relatively lower future isoprene emissions, decreasing methane lifetime by ~7 months. Our simulations underline the large uncertainties in future chemistry and climate studies due to biogenic emission patterns and emphasize the problems of using globally averaged climate metrics to quantify the atmospheric impact of reactive, heterogeneously distributed substances.

scholarly journals The Effects of Gridding Algorithms on the Statistical Moments and Their Trends of Daily Surface Air Temperature*

2015 ◽  
Vol 28 (23) ◽  
pp. 9188-9205 ◽  
Author(s):  
Nicholas R. Cavanaugh ◽  
Samuel S. P. Shen
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Models ◽  
Decadal Variability ◽  
Surface Air Temperature ◽  
Higher Order ◽  
Statistical Moments ◽  
Weighted Sums ◽  
Higher Order Moments ◽  
Station Data

Abstract This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network–Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT’s higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations.

scholarly journals Long-Term Climate Change Commitment and Reversibility: An EMIC Intercomparison

2013 ◽  
Vol 26 (16) ◽  
pp. 5782-5809 ◽  
Author(s):  
Kirsten Zickfeld ◽  
Michael Eby ◽  
Andrew J. Weaver ◽  
Kaitlin Alexander ◽  
Elisabeth Crespin ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Co2 Emissions ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Meridional Overturning Circulation ◽  
Atmospheric Co2 ◽  
Change Commitment

Abstract This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs for RCPs 4.5–8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination of CO2 emissions in all EMICs. Restoration of atmospheric CO2 from RCP to preindustrial levels over 100–1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to preindustrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2.

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