scholarly journals Effect of coupled global climate models sea surface temperature biases on simulated climate of the western United States

2018 ◽  
Vol 38 (14) ◽  
pp. 5386-5404 ◽  
Author(s):  
John F. Mejia ◽  
Darko Koračin ◽  
Eric M. Wilcox
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Sea Surface ◽  
Western United States ◽  
Simulated Climate

Joint projections of North Pacific sea surface temperature from different global climate models

2012 ◽  
Vol 23 (5) ◽  
pp. 451-465 ◽  
Author(s):  
Francisco Beltrán ◽  
Bruno Sansó ◽  
Ricardo T. Lemos ◽  
Roy Mendelssohn
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Pacific ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Sea Surface

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.

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

scholarly journals A prognostic pollen emissions model for climate models (PECM1.0)

2017 ◽  
Author(s):  
Matthew C. Wozniak ◽  
Allison Steiner
Keyword(s):  
United States ◽  
Land Cover ◽  
Climate Models ◽  
Climate Model ◽  
Vegetation Type ◽  
Global Climate ◽  
The United States ◽  
Global Climate Models ◽  
Pollen Counts ◽  
Surface Pollen

Abstract. We develop a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in a regional climate model (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model: (1) using a taxa-specific land cover database, phenology and emission potential, and (2) a PFT-based land cover, phenology and emission potential. The resulting surface concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model, however we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

scholarly journals The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction

2016 ◽  
Vol 12 (7) ◽  
pp. 1519-1538 ◽  
Author(s):  
Harry Dowsett ◽  
Aisling Dolan ◽  
David Rowley ◽  
Robert Moucha ◽  
Alessandro M. Forte ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Sea Surface ◽  
Paleoenvironmental Reconstruction ◽  
Dynamic Topography ◽  
Intercomparison Project

Abstract. The mid-Piacenzian is known as a period of relative warmth when compared to the present day. A comprehensive understanding of conditions during the Piacenzian serves as both a conceptual model and a source for boundary conditions as well as means of verification of global climate model experiments. In this paper we present the PRISM4 reconstruction, a paleoenvironmental reconstruction of the mid-Piacenzian ( ∼  3 Ma) containing data for paleogeography, land and sea ice, sea-surface temperature, vegetation, soils, and lakes. Our retrodicted paleogeography takes into account glacial isostatic adjustments and changes in dynamic topography. Soils and lakes, both significant as land surface features, are introduced to the PRISM reconstruction for the first time. Sea-surface temperature and vegetation reconstructions are unchanged but now have confidence assessments. The PRISM4 reconstruction is being used as boundary condition data for the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) experiments.

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 Effect of empirical correction of sea-surface temperature biases on the CRCM5-simulated climate and projected climate changes over North America

2019 ◽  
Vol 53 (1-2) ◽  
pp. 453-476
Author(s):  
Leticia Hernández-Díaz ◽  
Oumarou Nikiéma ◽  
René Laprise ◽  
Katja Winger ◽  
Samuel Dandoy
Keyword(s):  
North America ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Changes ◽  
Sea Surface ◽  
Empirical Correction ◽  
Simulated Climate

Progress in Monthly Climate Forecasting with a Physical Model

1981 ◽  
Vol 62 (12) ◽  
pp. 1666-1675 ◽  
Author(s):  
Julian Adem ◽  
William L. Donn
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Physical Model ◽  
Long Range ◽  
Surface Albedo ◽  
Sea Surface ◽  
Climate Forecasting ◽  
Temperature And Precipitation ◽  
Ice Conditions

A long-range forecasting technique, based on a physical model that emphasizes thermodynamics, is applied to the prediction of anomalies of temperature and precipitation for the Northern Hemisphere. Monthly forecasts are initialized with the sea surface temperature, 700 mb temperature and surface albedo, including variable snow-ice conditions. Application to the hot spell and drought in the summer of 1980 for the contiguous United States shows very encouraging skill when verified for the standard 100-station NOAA grid.

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