scholarly journals Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget

2017 ◽  
Vol 114 (50) ◽  
pp. 13126-13131 ◽  
Author(s):  
Paulo Ceppi ◽  
Jonathan M. Gregory
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Sea Surface ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Climate Feedback ◽  
Temperature Pattern ◽  
Climate Feedbacks

Climate feedbacks generally become smaller in magnitude over time under CO2 forcing in coupled climate models, leading to an increase in the effective climate sensitivity, the estimated global-mean surface warming in steady state for doubled CO2. Here, we show that the evolution of climate feedbacks in models is consistent with the effect of a change in tropospheric stability, as has recently been hypothesized, and the latter is itself driven by the evolution of the pattern of sea-surface temperature response. The change in climate feedback is mainly associated with a decrease in marine tropical low cloud (a more positive shortwave cloud feedback) and with a less negative lapse-rate feedback, as expected from a decrease in stability. Smaller changes in surface albedo and humidity feedbacks also contribute to the overall change in feedback, but are unexplained by stability. The spatial pattern of feedback changes closely matches the pattern of stability changes, with the largest increase in feedback occurring in the tropical East Pacific. Relationships qualitatively similar to those in the models among sea-surface temperature pattern, stability, and radiative budget are also found in observations on interannual time scales. Our results suggest that constraining the future evolution of sea-surface temperature patterns and tropospheric stability will be necessary for constraining climate sensitivity.

scholarly journals CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Weiying Peng ◽  
Quanliang Chen ◽  
Shijie Zhou ◽  
Ping Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Sea Surface ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Initial Field ◽  
Offshore Area ◽  
Model Based

AbstractSeasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

scholarly journals On the Interpretation of the North Atlantic Averaged Sea Surface Temperature

2020 ◽  
Vol 33 (14) ◽  
pp. 6025-6045
Author(s):  
Jing Sun ◽  
Mojib Latif ◽  
Wonsun Park ◽  
Taewook Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Climate Models ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
The North ◽  
Sst Variability ◽  
The North Atlantic ◽  
Different Time Scales

AbstractThe North Atlantic (NA) basin-averaged sea surface temperature (NASST) is often used as an index to study climate variability in the NA sector. However, there is still some debate on what drives it. Based on observations and climate models, an analysis of the different influences on the NASST index and its low-pass filtered version, the Atlantic multidecadal oscillation (AMO) index, is provided. In particular, the relationships of the two indices with some of its mechanistic drivers including the Atlantic meridional overturning circulation (AMOC) are investigated. In observations, the NASST index accounts for significant SST variability over the tropical and subpolar NA. The NASST index is shown to lump together SST variability originating from different mechanisms operating on different time scales. The AMO index emphasizes the subpolar SST variability. In the climate models, the SST-anomaly pattern associated with the NASST index is similar. The AMO index, however, only represents pronounced SST variability over the extratropical NA, and this variability is significantly linked to the AMOC. There is a sensitivity of this linkage to the cold NA SST bias observed in many climate models. Models suffering from a large cold bias exhibit a relatively weak linkage between the AMOC and AMO and vice versa. Finally, the basin-averaged SST in its unfiltered form, which has been used to question a strong influence of ocean dynamics on NA SST variability, mixes together multiple types of variability occurring on different time scales and therefore underemphasizes the role of ocean dynamics in the multidecadal variability of NA SSTs.

scholarly journals Do Climate Models Capture the Tropical Influences on North Pacific Sea Surface Temperature Variability?

2011 ◽  
Vol 24 (23) ◽  
pp. 6203-6209 ◽  
Author(s):  
Fabian Lienert ◽  
John C. Fyfe ◽  
William J. Merryfield
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Pacific ◽  
Climate Models ◽  
Southern Oscillation ◽  
Temperature Variability ◽  
Sea Surface ◽  
Model Errors ◽  
The North ◽  
The North Pacific

Abstract This study evaluates the ability of global climate models to reproduce observed tropical influences on North Pacific Ocean sea surface temperature variability. In an ensemble of climate models, the study finds that the simulated North Pacific response to El Niño–Southern Oscillation (ENSO) forcing is systematically delayed relative to the observed response because of winter and spring mixed layers in the North Pacific that are too deep and air–sea feedbacks that are too weak. Model biases in mixed layer depth and air–sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by about 30%. The study also shows that simulated North Pacific variability has more power at lower frequencies than is observed because of model errors originating in the tropics and extratropics. Implications of these results for predictions on seasonal, decadal, and longer time scales are discussed.

scholarly journals The role of local sea surface temperature pattern changes in shaping climate change in the North Atlantic sector

2018 ◽  
Vol 52 (1-2) ◽  
pp. 417-438 ◽  
Author(s):  
Ralf Hand ◽  
Noel S. Keenlyside ◽  
Nour-Eddine Omrani ◽  
Jürgen Bader ◽  
Richard J. Greatbatch
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Sea Surface ◽  
Temperature Pattern ◽  
Atlantic Sector ◽  
The North ◽  
The North Atlantic

South Atlantic sea surface temperature anomalies and air-sea interactions: stochastic models

1994 ◽  
Vol 12 (9) ◽  
pp. 903-909
Author(s):  
S. G. Dobrovolski
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
South Atlantic ◽  
Entropy Method ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Leading Role ◽  
Sea Surface Temperature Anomalies ◽  
Dynamic Stochastic

Abstract. Data on the South Atlantic monthly sea surface temperature anomalies (SSTA) are analysed using the maximum-entropy method. It is shown that the Markov first-order process can describe, to a first approximation, SSTA series. The region of maximum SSTA values coincides with the zone of maximum residual white noise values (sub-Antarctic hydrological front). The theory of dynamic-stochastic climate models is applied to estimate the variability of South Atlantic SSTA and air-sea interactions. The Adem model is used as a deterministic block of the dynamic-stochastic model. Experiments show satisfactorily the SSTA intensification in the sub-Antarctic front zone, with appropriate standard deviations, and demonstrate the leading role of the abnormal drift currents in these processes.

scholarly journals Time-Varying Climate Sensitivity from Regional Feedbacks

2013 ◽  
Vol 26 (13) ◽  
pp. 4518-4534 ◽  
Author(s):  
Kyle C. Armour ◽  
Cecilia M. Bitz ◽  
Gerard H. Roe
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Time Variation ◽  
Radiative Forcing ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Feedback ◽  
Climate Feedbacks ◽  
Geographic Pattern ◽  
Surface Warming

Abstract The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.

scholarly journals How Well Do We Understand and Evaluate Climate Change Feedback Processes?

2006 ◽  
Vol 19 (15) ◽  
pp. 3445-3482 ◽  
Author(s):  
Sandrine Bony ◽  
Robert Colman ◽  
Vladimir M. Kattsov ◽  
Richard P. Allan ◽  
Christopher S. Bretherton ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
General Circulation ◽  
External Perturbation ◽  
General Circulation Models ◽  
Lapse Rate ◽  
Climate Feedback ◽  
Climate Feedbacks ◽  
Intergovernmental Panel ◽  
Global Estimates

Abstract Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

scholarly journals Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach

2011 ◽  
Vol 24 (7) ◽  
pp. 1869-1877 ◽  
Author(s):  
Shahadat Chowdhury ◽  
Ashish Sharma
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Nearest Neighbor ◽  
Sea Surface ◽  
Base Case ◽  
Grid Cells ◽  
Combination Approach ◽  
Global Grid ◽  
Global Sea Surface Temperature

Abstract This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958–2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.

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