scholarly journals On the Correspondence between Seasonal Forecast Biases and Long-Term Climate Biases in Sea Surface Temperature

2020 ◽  
Vol 34 (1) ◽  
pp. 427-446
Author(s):  
Hsi-Yen Ma ◽  
A. Cheska Siongco ◽  
Stephen A. Klein ◽  
Shaocheng Xie ◽  
Alicia R. Karspeck ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
Initial Conditions ◽  
Global Climate Models ◽  
Sea Surface ◽  
Substantial Part ◽  
Seasonal Forecasts ◽  
Climate Simulations

AbstractThe correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.

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 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.

Dominant Influence of ENSO-Like and Global Sea Surface Temperature Patterns on Changes in Prevailing Boreal Summer Tropical Cyclone Tracks over the Western North Pacific

2020 ◽  
Vol 33 (22) ◽  
pp. 9551-9565
Author(s):  
Haikun Zhao ◽  
Philp J. Klotzbach ◽  
Shaohua Chen
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
North Pacific ◽  
Western North Pacific ◽  
Sea Surface ◽  
Track Pattern ◽  
Global Sea Surface Temperature

AbstractA conventional empirical orthogonal function (EOF) analysis is performed on summertime (May–October) western North Pacific (WNP) tropical cyclone (TC) track density anomalies during 1970–2012. The first leading EOF mode is characterized by a consistent spatial distribution across the WNP basin, which is closely related to an El Niño–Southern Oscillation (ENSO)-like pattern that prevails on both interannual and interdecadal time scales. The second EOF mode is represented by a tripole pattern with consistent changes in westward and recurving tracks but with an opposite change for west-northwestward TC tracks. This second EOF pattern is dominated by consistent global sea surface temperature anomaly (SSTA) patterns on interannual and interdecadal time scales, along with a long-term increasing global temperature trend. Observed WNP TC tracks have three distinct interdecadal epochs (1970–86, 1987–97, and 1998–2012) based on EOF analyses. The interdecadal change is largely determined by the changing impact of ENSO-like and consistent global SSTA patterns. When global SSTAs are cool (warm) during 1970–86 (1998–2012), these SSTAs exert a dominant impact and generate a tripole track pattern that is similar to the positive (negative) second EOF mode. In contrast, a predominately El Niño–like SSTA pattern during 1987–97 contributed to increasing TC occurrences across most of the WNP during this 11-yr period. These findings are consistent with long-term trends in TC tracks, with a tripole track pattern observed as global SSTs increase. This study reveals the potential large-scale physical mechanisms driving the changes of WNP TC tracks in association with climate change.

scholarly journals Role of sea surface temperature responses in simulation of the climatic effect of mineral dust aerosol

2011 ◽  
Vol 11 (12) ◽  
pp. 6049-6062 ◽  
Author(s):  
X. Yue ◽  
H. Liao ◽  
H. J. Wang ◽  
S. L. Li ◽  
J. P. Tang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Radiative Forcing ◽  
Mineral Dust ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climatic Effect ◽  
Radiative Effects ◽  
Climate Simulations

Abstract. Mineral dust aerosol can be transported over the nearby oceans and influence the energy balance at the sea surface. The role of dust-induced sea surface temperature (SST) responses in simulations of the climatic effect of dust is examined by using a general circulation model with online simulation of mineral dust and a coupled mixed-layer ocean model. Both the longwave and shortwave radiative effects of mineral dust aerosol are considered in climate simulations. The SST responses are found to be very influential on simulated dust-induced climate change, especially when climate simulations consider the two-way dust-climate coupling to account for the feedbacks. With prescribed SSTs and dust concentrations, we obtain an increase of 0.02 K in the global and annual mean surface air temperature (SAT) in response to dust radiative effects. In contrast, when SSTs are allowed to respond to radiative forcing of dust in the presence of the dust cycle-climate interactions, we obtain a global and annual mean cooling of 0.09 K in SAT by dust. The extra cooling simulated with the SST responses can be attributed to the following two factors: (1) The negative net (shortwave plus longwave) radiative forcing of dust at the surface reduces SST, which decreases latent heat fluxes and upward transport of water vapor, resulting in less warming in the atmosphere; (2) The positive feedback between SST responses and dust cycle. The dust-induced reductions in SST lead to reductions in precipitation (or wet deposition of dust) and hence increase the global burden of small dust particles. These small particles have strong scattering effects, which enhance the dust cooling at the surface and further reduce SSTs.

scholarly journals Long term trends in the sea surface temperature of the Black Sea

Ocean Science ◽  
2010 ◽  
Vol 6 (2) ◽  
pp. 491-501 ◽  
Author(s):  
G. I. Shapiro ◽  
D. L. Aleynik ◽  
L. D. Mee
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Black Sea ◽  
Sea Surface ◽  
The Black Sea ◽  
The North ◽  
Sst Variability ◽  
Long Term Trends ◽  
Cooling Trend

Abstract. There is growing understanding that recent deterioration of the Black Sea ecosystem was partly due to changes in the marine physical environment. This study uses high resolution 0.25° climatology to analyze sea surface temperature variability over the 20th century in two contrasting regions of the sea. Results show that the deep Black Sea was cooling during the first three quarters of the century and was warming in the last 15–20 years; on aggregate there was a statistically significant cooling trend. The SST variability over the Western shelf was more volatile and it does not show statistically significant trends. The cooling of the deep Black Sea is at variance with the general trend in the North Atlantic and may be related to the decrease of westerly winds over the Black Sea, and a greater influence of the Siberian anticyclone. The timing of the changeover from cooling to warming coincides with the regime shift in the Black Sea ecosystem.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

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