Two Limits of Initial-Value Decadal Predictability in a CGCM

2010 ◽  
Vol 23 (23) ◽  
pp. 6292-6311 ◽  
Author(s):  
Grant Branstator ◽  
Haiyan Teng
Keyword(s):  
Initial Conditions ◽  
Heat Content ◽  
Forced Response ◽  
Climate System ◽  
Change Scenario ◽  
Model Errors ◽  
Initial Value ◽  
Climate System Model ◽  
Predictive Skill ◽  
Decadal Climate

Abstract When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results of this study are sensitive and insensitive to are also discussed.

The Value of Initialization on Decadal Timescales: State-Dependent Predictability in the CESM Decadal Prediction Large Ensemble

2020 ◽  
Vol 33 (17) ◽  
pp. 7353-7370
Author(s):  
H. M. Christensen ◽  
J. Berner ◽  
S. Yeager
Keyword(s):  
Time Scale ◽  
Initial Conditions ◽  
Heat Content ◽  
Atlantic Multidecadal Oscillation ◽  
Forced Response ◽  
Climate Change Signal ◽  
Seasonal Forecasts ◽  
The North ◽  
State Dependent ◽  
Start Dates

AbstractInformation in decadal climate prediction arises from a well-initialized ocean state and from the predicted response to an external forcing. The length of time over which the initial conditions benefit the decadal forecast depends on the start date of the forecast. We characterize this state-dependent predictability for decadal forecasts of upper ocean heat content in the Community Earth System Model. We find regionally dependent initial condition predictability, with extended predictability generally observed in the extratropics. We also detect state-dependent predictability, with the year of loss of information from the initialization varying between start dates. The decadal forecasts in the North Atlantic show substantial information from the initial conditions beyond the 10-yr forecast window, and a high degree of state-dependent predictability. We find some evidence for state-dependent predictability in the ensemble spread in this region, similar to that seen in weather and subseasonal-to-seasonal forecasts. For some start dates, an increase of information with lead time is observed, for which the initialized forecasts predict a growing phase of the Atlantic multidecadal oscillation. Finally we consider the information in the forecast from the initial conditions relative to the forced response, and quantify the crossover time scale after which the forcing provides more information. We demonstrate that the climate change signal projects onto different patterns than the signal from the initial conditions. This means that even after the crossover time scale has been reached in a basin-averaged sense, the benefits of initialization can be felt locally on longer time scales.

Initial-value predictability of Antarctic sea ice in the Community Climate System Model 3

2013 ◽  
Vol 40 (10) ◽  
pp. 2121-2124 ◽  
Author(s):  
Marika M. Holland ◽  
Edward Blanchard-Wrigglesworth ◽  
Jennifer Kay ◽  
Steven Vavrus
Keyword(s):  
Sea Ice ◽  
Climate System ◽  
System Model ◽  
Initial Value ◽  
Community Climate System Model ◽  
Climate System Model ◽  
Antarctic Sea Ice ◽  
Community Climate

scholarly journals The implications of initial model drift for decadal climate predictability using EC-Earth

2016 ◽  
Author(s):  
Andreas Sterl
Keyword(s):  
Climate Model ◽  
North Atlantic Ocean ◽  
Initial Conditions ◽  
Deep Convection ◽  
Heat Content ◽  
The North ◽  
Atmospheric Noise ◽  
Large Heat ◽  
Decadal Climate Predictability ◽  
Decadal Climate

Abstract. The large heat capacity of the ocean as compared to the atmosphere provides a memory in the climate system that might have the potential for skilful climate predictions a few years ahead. However, experiments so far have only found limited predictability after accounting for the deterministic forcing signal provided by increased greenhouse gas concentrations. One of the problems is the drift that occurs when the model moves away from the initial conditions towards its own climate. This drift is often larger than the decadal signal to be predicted. In this paper we describe the drift occurring in the North Atlantic Ocean in the EC-Earth climate model and relate it to the lack of decadal predictability in that region. While this drift may be resolution dependent and disappear in higher resolution models, we identify a second reason for the low predictability. A subsurface heat content anomaly can only influence de atmosphere if (deep) convection couples it to the surface, but the occurrence of deep convection events is random and probably mainly determined by unpredictable atmospheric noise.

scholarly journals Predictability of Seasonal Streamflow Forecasting Based on CSM: Case Studies of Top Three Largest Rivers in China

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 162
Author(s):  
Lyuliu Liu ◽  
Ying Wu ◽  
Peiqun Zhang ◽  
Jianqing Zhai ◽  
Li Zhang ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate Models ◽  
Climate System ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Relative Operating Characteristic ◽  
Climate System Model ◽  
Seasonal Streamflow

Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons.

The Detection and Attribution of Climate Change Using an Ensemble of Opportunity

2007 ◽  
Vol 20 (3) ◽  
pp. 504-516 ◽  
Author(s):  
DáithíA. Stone ◽  
Myles R. Allen ◽  
Frank Selten ◽  
Michael Kliphuis ◽  
Peter A. Stott
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
General Circulation ◽  
Initial Conditions ◽  
Climate System ◽  
Response Patterns ◽  
Sulfate Aerosols ◽  
Temporal Response ◽  
Climate System Model ◽  
Detection And Attribution

Abstract The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis. Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996–2005 decade relative to the 1940–49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources. The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques.

scholarly journals The CMIP6 Historical Simulation Datasets Produced by the Climate System Model CAMS-CSM

2020 ◽  
Author(s):  
Xinyao Rong ◽  
Jian Li ◽  
Haoming Chen ◽  
Jingzhi Su ◽  
Lijuan Hua ◽  
...  
Keyword(s):  
Air Temperature ◽  
Initial Conditions ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Climate System ◽  
System Model ◽  
Model Description ◽  
Climate System Model

AbstractThis paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences (CAMS) climate system model (CAMS-CSM), which are contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). The model description, experiment design and model outputs are presented. Three members’ historical experiments are conducted by CAMS-CSM, with two members starting from different initial conditions, and one excluding the stratospheric aerosol to identify the effect of volcanic eruptions. The outputs of the historical experiments are also validated using observational data. It is found that the model can reproduce the climatological mean states and seasonal cycle of the major climate system quantities, including the surface air temperature, precipitation, and the equatorial thermocline. The long-term trend of air temperature and precipitation is also reasonably captured by CAMS-CSM. There are still some biases in the model that need further improvement. This paper can help the users to better understand the performance and the datasets of CAMS-CSM.

Climate Change Projections for the Twenty-First Century and Climate Change Commitment in the CCSM3

2006 ◽  
Vol 19 (11) ◽  
pp. 2597-2616 ◽  
Author(s):  
Gerald A. Meehl ◽  
Warren M. Washington ◽  
Benjamin D. Santer ◽  
William D. Collins ◽  
Julie M. Arblaster ◽  
...  
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
Sea Ice ◽  
Sea Level Rise ◽  
Sea Level ◽  
Climate System ◽  
First Century ◽  
Change Scenario ◽  
Climate System Model ◽  
Twenty First Century

Abstract Climate change scenario simulations with the Community Climate System Model version 3 (CCSM3), a global coupled climate model, show that if concentrations of all greenhouse gases (GHGs) could have been stabilized at the year 2000, the climate system would already be committed to 0.4°C more warming by the end of the twenty-first century. Committed sea level rise by 2100 is about an order of magnitude more, percentage-wise, compared to sea level rise simulated in the twentieth century. This increase in the model is produced only by thermal expansion of seawater, and does not take into account melt from ice sheets and glaciers, which could at least double that number. Several tenths of a degree of additional warming occurs in the model for the next 200 yr in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) B1 and A1B scenarios after stabilization in the year 2100, but with twice as much sea level rise after 100 yr, and doubling yet again in the next 100 yr to 2300. At the end of the twenty-first century, the warming in the tropical Pacific for the A2, A1B, and B1 scenarios resembles an El Niño–like response, likely due to cloud feedbacks in the model as shown in an earlier version. Greatest warming occurs at high northern latitudes and over continents. The monsoon regimes intensify somewhat in the future warmer climate, with decreases of sea level pressure at high latitudes and increases in the subtropics and parts of the midlatitudes. There is a weak summer midlatitude soil moisture drying in this model as documented in previous models. Sea ice distributions in both hemispheres are somewhat overextensive, but with about the right ice thickness at the end of the twentieth century. Future decreases in sea ice with global warming are proportional to the temperature response from the forcing scenarios, with the high forcing scenario, A2, producing an ice-free Arctic in summer by the year 2100.

scholarly journals Decadal climate prediction (project GCEP)

2008 ◽  
Vol 367 (1890) ◽  
pp. 925-937 ◽  
Author(s):  
Keith Haines ◽  
Leon Hermanson ◽  
Chunlei Liu ◽  
Debbie Putt ◽  
Rowan Sutton ◽  
...  
Keyword(s):  
Climate Models ◽  
Heat Content ◽  
Academic Community ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Decadal Prediction ◽  
Predictive Skill ◽  
International Panel ◽  
Early Results ◽  
Decadal Climate

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

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