Decadal North Atlantic Surface Temperature Prediction Skill in CMIP6

2020 ◽  
Author(s):  
Leonard Borchert ◽  
Matthew Menary ◽  
Didier Swingedouw ◽  
Giovanni Sgubin ◽  
Leon Hermanson ◽  
...  
Keyword(s):  
Surface Temperature ◽  
North Atlantic ◽  
Large Scale ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Climate Science ◽  
Prediction Skill ◽  
Oceanic Response ◽  
Decadal Variations ◽  
The Individual

<p>Due to its wide-ranging impacts, predicting decadal variations of sea surface temperature (SST) in the subpolar North Atlantic remains a key goal of climate science. Here, we compare the representation of observed subpolar SST variations since 1960 in initialized and uninitialized historical simulations from the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5/6). CMIP6 simulations demonstrate improved skill in this region with 88% (initialized vs. 77% non-initialized) observed variance explained post-1980 compared to 42% (8%) in CMIP5. During this time, we find particularly high agreement between observations and historical simulations in CMIP6, indicating a more prominent role for forcing in driving observed subpolar SST changes than previously thought. Analysis of single-forcing experiments suggests much of this post-1980 agreement is due to natural forcings, explaining ~55% of the observed variance, consistent with a conceptual model of the large-scale oceanic response to volcanic forcing.<br />SPG SST skill differs between individual model ensemble means in CMIP6 hindcasts. Prediction skill for summer surface air temperature over Europe appears to be seasonally and regionally connected to the individual models’ skill at predicting SPG SST, illustrating the societal value of understanding SPG SST prediction skill.</p>

Skillful prediction of the winter North Atlantic Oscillation

2020 ◽  
Author(s):  
Ke Fan
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Coupled Model ◽  
Sea Surface ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Coupled Models

<p>The winter North Atlantic oscillation (NAO), is a crucial part of our understanding of Eurasian and Atlantic climate variability and predictability. However, both the statistical forecast model and the coupled model showed the limited forecasting skill for the winter NAO. In this study, we developed effective prediction schemes based on the interannual increment prediction method and verified their performance based on the climate hindcasts of the coupled ocean–atmosphere climate models（DEMETER, ENSEMBLES，CFSV2）. This approach utilizes the year-to-year increment of a variable (i.e. a difference in a variable between the current year and the previous year, e.g. DY of a variable) as the predictand rather than the anomaly of the variable. The results demonstrate that the new schemes can generally improve prediction skill of the winter NAO compared to the raw coupled model’s output（DEMETER, ENSEMBLES，CFSV2）. Also, the new schemes show higher skill in prediction of abnormal NAO cases than the climatological prediction. Scheme-I uses just the NAO in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme-II is a hybrid prediction model that contains two predictors: the NAO derived from the coupled models, and the observed preceding autumn Atlantic sea surface temperature in the form of year-to-year increments. Scheme-II shows an even better prediction skill of the winter NAO than Scheme-I. Besides, a new statistical forecast scheme was also developed using observed North Atlantic sea surface temperature and Eurasian snow cover in the preceding autumn to predict the upcoming winter NAO. The statistical prediction model showed high predictive skill in reproducing the interannual and interdecadal variability of NAO in boreal winter.</p>

Troposphere–stratosphere response to large-scale North Atlantic Ocean variability in an atmosphere/ocean coupled model

2015 ◽  
Vol 46 (5-6) ◽  
pp. 1397-1415 ◽  
Author(s):  
N.-E. Omrani ◽  
Jürgen Bader ◽  
N. S. Keenlyside ◽  
Elisa Manzini
Keyword(s):  
Atlantic Ocean ◽  
North Atlantic ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Coupled Model ◽  
Ocean Variability

scholarly journals Climate feedbacks induced by the North Atlantic freshwater forcing in a coupled model of intermediate complexity

2010 ◽  
Vol 25 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Flávio Barbosa Justino ◽  
Jeferson Prietsch Machado
Keyword(s):  
Heat Transport ◽  
North Atlantic ◽  
Thermohaline Circulation ◽  
Coupled Model ◽  
The North ◽  
Poleward Heat Transport ◽  
Freshwater Forcing ◽  
Oceanic Response ◽  
The North Atlantic ◽  
The Tropics

Based on coupled model simulations (ECBilt-Clio), we investigate the atmospheric and oceanic response to sustained freshwater input into the North Atlantic under the glacial maximum background state. The results demonstrate that a weakening of the thermohaline circulation triggered by weaker density flux leads to rapid changes in global sea-ice volume and reduced poleward heat transport in the Northern Hemisphere (NH). In the Southern Hemisphere (SH), however, the oceanic heat transport increases substantially. This in turn leads to strong cooling over the North Atlantic whereas the SH extratropical region warms up. The suppression of the NADW also drastically changes the atmospheric circulation. The associated northward wind anomalies over the North Atlantic increase the warm air advection from the tropics and induce the transport of tropical saltier water to mid-latitudes. This negative atmospheric-oceanic feedback should play an important role to resume the NADW, after the freshwater forcing ends up.

scholarly journals Improved simulation of 19th- and 20th-century North Atlantic hurricane frequency after correcting historical sea surface temperatures

2021 ◽  
Vol 7 (26) ◽  
pp. eabg6931
Author(s):  
Duo Chan ◽  
Gabriel A. Vecchi ◽  
Wenchang Yang ◽  
Peter Huybers
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
20Th Century ◽  
North Atlantic ◽  
Sea Surface ◽  
Model Data ◽  
Atmospheric Models ◽  
Decadal Variations ◽  
Hurricane Prediction ◽  
19Th And 20Th Century

Confidence in dynamical and statistical hurricane prediction is rooted in the skillful reproduction of hurricane frequency using sea surface temperature (SST) patterns, but an ensemble of high-resolution atmospheric simulation extending to the 1880s indicates model-data disagreements that exceed those expected from documented uncertainties. We apply recently developed corrections for biases in historical SSTs that lead to revisions in tropical to subtropical SST gradients by ±0.1°C. Revised atmospheric simulations have 20% adjustments in the decadal variations of hurricane frequency and become more consistent with observations. The improved simulation skill from revised SST estimates not only supports the utility of high-resolution atmospheric models for hurricane projections but also highlights the need for accurate estimates of past and future patterns of SST changes.

scholarly journals Relationship between Holocene climate variations over southern Greenland and eastern Baffin Island and synoptic circulation pattern

2009 ◽  
Vol 5 (3) ◽  
pp. 347-359 ◽  
Author(s):  
B. Fréchette ◽  
A. de Vernal
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Surface Air Temperature ◽  
Sea Surface ◽  
Baffin Island ◽  
North Atlantic Current ◽  
Climate Trends ◽  
Temperature Reconstructions ◽  
Southwest Greenland

Abstract. Lake pollen records from southwest Greenland and eastern Baffin Island show strong regionalism in climate trends of the last 7000 cal years. July surface air temperature reconstructions from pollen indicate larger amplitude cooling in southwest Greenland (>3.0°C) than in eastern Baffin Island (<1.0°C). This west-east gradient in climate change is consistent with August sea-surface temperature reconstructions from dinocyst records that indicate decreasing temperature and/or strength of the North Atlantic Current to the east during the Holocene while the eastern Canadian margins under the Labrador Current influence display slight warming. Complementary to air and sea-surface temperature records, the lake pollen data led to reconstruct increased cloudiness in southern Greenland, which points to increasing cyclonic activity since 7000 cal years BP west of Greenland. Together, the terrestrial and marine records of the northwest North Atlantic therefore suggest a shift from a dominant NAO+ during the early-mid Holocene to dominant NAO- in the late Holocene.

scholarly journals Decadal Variations of North Atlantic Sea Surface Temperature in Observations and CMIP3 Simulations*

2010 ◽  
Vol 23 (17) ◽  
pp. 4619-4636 ◽  
Author(s):  
Nathan Jamison ◽  
Sergey Kravtsov
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
North Atlantic ◽  
Climate Models ◽  
Decadal Variability ◽  
Singular Spectrum Analysis ◽  
Global Climate Models ◽  
Sea Surface ◽  
Decadal Variations

Abstract This study evaluates the ability of the global climate models that compose phase 3 of the Coupled Model Intercomparison Project (CMIP3) to simulate intrinsic decadal variations detected in the observed North Atlantic sea surface temperature (SST) record via multichannel singular spectrum analysis (M-SSA). M-SSA identifies statistically significant signals in the observed SSTs, with time scales of 5–10, 10–15, and 15–30 yr; all of these signals have distinctive spatiotemporal characteristics and are consistent with previous studies. Many of the CMIP3 twentieth-century simulations are characterized by quasi-oscillatory behavior within one or more of the three observationally motivated frequency bands specified above; however, only a fraction of these models also capture the spatial patterns of the observed signals. The models best reproduce the observed quasi-regular SST variations in the high-frequency, 5–10-yr band, while the observed signals in the intermediate, 10–15-yr band have turned out to be most difficult to capture. A handful of models capture the patterns and, sometimes, the spectral character of the observed variability in the two or three bands simultaneously. These results imply that the decadal prediction skill of the models considered—to be estimated within the CMIP5 framework—would be stratified according to the models’ performance in capturing the time scales and patterns of the observed decadal SST variations. They also warrant further research into the dynamical causes of the observed and simulated decadal variability, as well as into apparent differences in the representation of these variations by individual CMIP3 models.

scholarly journals Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhongfeng Xu ◽  
Ying Han ◽  
Chi-Yung Tam ◽  
Zong-Liang Yang ◽  
Congbin Fu
Keyword(s):  
Large Scale ◽  
Dynamical Downscaling ◽  
Linear Trend ◽  
Coupled Model ◽  
Future Climate ◽  
Atmospheric Environment ◽  
Important Approach ◽  
Corrected Data ◽  
Mean Climate ◽  
The Individual

AbstractDynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

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