scholarly journals Moving beyond the Total Sea Ice Extent in Gauging Model Biases

2016 ◽  
Vol 29 (24) ◽  
pp. 8965-8987 ◽  
Author(s):  
Detelina P. Ivanova ◽  
Peter J. Gleckler ◽  
Karl E. Taylor ◽  
Paul J. Durack ◽  
Kate D. Marvel
Keyword(s):  
Sea Ice ◽  
Error Compensation ◽  
Climate Modeling ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Model Errors ◽  
Sea Ice Extent ◽  
Model Biases ◽  
The Impact

Abstract Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.

scholarly journals How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?

2012 ◽  
Vol 6 (5) ◽  
pp. 3539-3573 ◽  
Author(s):  
V. Zunz ◽  
H. Goosse ◽  
F. Massonnet
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Positive Trend ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The One ◽  
The Impact

Abstract. Observations over the last 30 yr have shown that the sea ice extent in the Southern Ocean has slightly increased since 1979. Mechanisms responsible for this positive trend have not been well established yet and climate models are generally unable to simulate correctly this expansion. In this study, we focus on two related hypotheses that could explain the misrepresentation of the positive trend in sea ice extent by climate models: an unrealistic internal variability and an inadequate initialization of the system. For that purpose, we analyze the evolution of sea ice around the Antarctic simulated by 24 different general circulation models involved in the 5th Coupled Model Intercomparison Project (CMIP5). On the one hand, historical simulations, driven by external forcing and initialized without observations, are examined. They provide information about the mean state, the variability and the trend in sea ice extent simulated by each model. On the other hand, decadal prediction experiments, driven by external forcing and initialized with some observed fields, allow us to assess the impact of the representation of the observed initial state on the quality of model predictions. Our analyses show that CMIP5 models respond to the forcing, including the one induced by stratospheric ozone depletion, by reducing the sea ice cover in the Southern Ocean. Some simulations display an increase in sea ice extent. However, models strongly overestimate the variability of sea ice extent and the initialization methods currently used in models do not improve systematically the simulated trends in sea ice extent. On the basis of those results, a critical role of the internal variability in the observed increase in the sea ice extent in the Southern Ocean could not be ruled out but current models results appear inadequate to test more precisely this hypothesis.

scholarly journals An Initial Assessment of Antarctic Sea Ice Extent in the CMIP5 Models

2013 ◽  
Vol 26 (5) ◽  
pp. 1473-1484 ◽  
Author(s):  
John Turner ◽  
Thomas J. Bracegirdle ◽  
Tony Phillips ◽  
Gareth J. Marshall ◽  
J. Scott Hosking
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Initial Assessment ◽  
Cmip5 Models ◽  
Full Time ◽  
Model Intercomparison ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Intercomparison Project ◽  
The Mean

Abstract This paper examines the annual cycle and trends in Antarctic sea ice extent (SIE) for 18 models used in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that were run with historical forcing for the 1850s to 2005. Many of the models have an annual SIE cycle that differs markedly from that observed over the last 30 years. The majority of models have too small of an SIE at the minimum in February, while several of the models have less than two-thirds of the observed SIE at the September maximum. In contrast to the satellite data, which exhibit a slight increase in SIE, the mean SIE of the models over 1979–2005 shows a decrease in each month, with the greatest multimodel mean percentage monthly decline of 13.6% decade−1 in February and the greatest absolute loss of ice of −0.40 × 106 km2 decade−1 in September. The models have very large differences in SIE over 1860–2005. Most of the control runs have statistically significant trends in SIE over their full time span, and all of the models have a negative trend in SIE since the mid-nineteenth century. The negative SIE trends in most of the model runs over 1979–2005 are a continuation of an earlier decline, suggesting that the processes responsible for the observed increase over the last 30 years are not being simulated correctly.

scholarly journals Attributing Causes of 2015 Record Minimum Sea-Ice Extent in the Sea of Okhotsk

2017 ◽  
Vol 30 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Seungmok Paik ◽  
Seung-Ki Min ◽  
Yeon-Hee Kim ◽  
Baek-Min Kim ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The Sea Of Okhotsk ◽  
The North

In 2015, the sea ice extent (SIE) over the Sea of Okhotsk (Okhotsk SIE) hit a record low since 1979 during February–March, the period when the sea ice extent generally reaches its annual maximum. To quantify the role of anthropogenic influences on the changes observed in Okhotsk SIE, this study employed a fraction of attributable risk (FAR) analysis to compare the probability of occurrence of extreme Okhotsk SIE events and long-term SIE trends using phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations performed with and without anthropogenic forcing. It was found that because of anthropogenic influence, both the probability of extreme low Okhotsk SIEs that exceed the 2015 event and the observed long-term trends during 1979–2015 have increased by more than 4 times (FAR = 0.76 to 1). In addition, it is suggested that a strong negative phase of the North Pacific Oscillation (NPO) during midwinter (January–February) 2015 also contributed to the 2015 extreme SIE event. An analysis based on multiple linear regression was conducted to quantify relative contributions of the external forcing (anthropogenic plus natural) and the NPO (internal variability) to the observed SIE changes. About 56.0% and 24.7% of the 2015 SIE anomaly was estimated to be attributable to the external forcing and the strong negative NPO influence, respectively. The external forcing was also found to explain about 86.1% of the observed long-term SIE trend. Further, projections from the CMIP5 models indicate that a sea ice–free condition may occur in the Sea of Okhotsk by the late twenty-first century in some models.

scholarly journals Assessment of sea ice simulations in the CMIP5 Models

2014 ◽  
Vol 8 (3) ◽  
pp. 3413-3435
Author(s):  
Q. Shu ◽  
Z. Song ◽  
F. Qiao
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Linear Trend ◽  
Coupled Model ◽  
Ocean Modeling ◽  
The Arctic ◽  
Cmip5 Models ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Almost All

Abstract. The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations and Global Ice–Ocean Modeling and Assimilation System (GIOMAS) data in this study. Forty-nine models, almost all of the CMIP5 climate models and Earth System Models, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.56 × 105 km2 decade−1; only 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative (−3.36 × 105 km2 decade−1). For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS data, the SIV values in both Antarctic and Arctic are too small, especially in spring and winter. The GIOMAS SIV in September is 16.7 × 103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0 × 103 km3, almost 22% less. The Arctic SIV of CMIP5 in April is 26.8 × 103 km3, which is also less than the GIOMAS SIV (29.3 × 103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin although the SIE is fairly well simulated.

scholarly journals CAS-FGOALS Datasets for the Two Interglacial Epochs of the Holocene and the Last Interglacial in PMIP4

2020 ◽  
Vol 37 (10) ◽  
pp. 1034-1044
Author(s):  
Weipeng Zheng ◽  
Yongqiang Yu ◽  
Yihua Luan ◽  
Shuwen Zhao ◽  
Bian He ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Model Performance ◽  
Coupled Model ◽  
Global Ocean ◽  
Last Interglacial ◽  
Proxy Data ◽  
Intercomparison Project ◽  
The Impact

Abstract Two versions of the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System model (CAS-FGOALS), version f3-L and g3, are used to simulate the two interglacial epochs of the mid-Holocene and the Last Interglacial in phase 4 of the Paleoclimate Modelling Intercomparison Project (PMIP4), which aims to study the impact of changes in orbital parameters on the Earth’s climate. Following the PMIP4 experimental protocols, four simulations for the mid-Holocene and two simulations for the Last Interglacial have been completed, and all the data, including monthly and daily outputs for the atmospheric, oceanic, land and sea-ice components, have been released on the Earth System Grid Federation (ESGF) node. These datasets contribute to PMIP4 and CMIP6 (phase 6 of the Coupled Model Intercomparison Project) by providing the variables necessary for the two interglacial periods. In this paper, the basic information of the CAS-FGOALS models and the protocols for the two interglacials are briefly described, and the datasets are validated using proxy records. Results suggest that the CAS-FGOALS models capture the large-scale changes in the climate system in response to changes in solar insolation during the interglacial epochs, including warming in mid-to-high latitudes, changes in the hydrological cycle, the seasonal variation in the extent of sea ice, and the damping of interannual variabilities in the tropical Pacific. Meanwhile, disagreements within and between the models and the proxy data are also presented. These datasets will help the modeling and the proxy data communities with a better understanding of model performance and biases in paleoclimate simulations.

scholarly journals The Antarctic Sea Ice Response to the Ozone Hole in Climate Models

2014 ◽  
Vol 27 (3) ◽  
pp. 1336-1342 ◽  
Author(s):  
Michael Sigmond ◽  
John C. Fyfe
Keyword(s):  
Sea Ice ◽  
Southern Hemisphere ◽  
Ozone Depletion ◽  
Climate Models ◽  
Stratospheric Ozone ◽  
Coupled Model ◽  
Ozone Hole ◽  
Cmip5 Models ◽  
Time Varying ◽  
Sea Ice Extent

Abstract It has been suggested that the increase of Southern Hemisphere sea ice extent since the 1970s can be explained by ozone depletion in the Southern Hemisphere stratosphere. In a previous study, the authors have shown that in a coupled atmosphere–ocean–sea ice model the ozone hole does not lead to an increase but to a decrease in sea ice extent. Here, the robustness of this result is established through the analysis of models from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). Comparison of the mean sea ice trends in CMIP3 models with and without time-varying stratospheric ozone suggests that ozone depletion is associated with decreased sea ice extent, and ozone recovery acts to mitigate the future sea ice decrease associated with increasing greenhouse gases. All available historical simulations with CMIP5 models that were designed to isolate the effect of time-varying ozone concentrations show decreased sea ice extent in response to historical ozone trends. In most models, the historical sea ice extent trends are mainly driven by historical greenhouse gas forcing, with ozone forcing playing a secondary role.

scholarly journals Sea-ice extent provides a limited metric of model performance

2013 ◽  
Vol 7 (3) ◽  
pp. 3095-3131 ◽  
Author(s):  
D. Notz
Keyword(s):  
Sea Ice ◽  
Model Performance ◽  
Internal Variability ◽  
Ice Cover ◽  
Cmip5 Models ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Observational Uncertainty ◽  
Satellite Retrievals

Abstract. We examine the common practice of using sea-ice extent as the primary metric to evaluate modeled sea-ice coverage. Based on this analysis, we recommend a possible best practice for model evaluation. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different compared to biases in the geophysically more meaningful sea-ice area. These differences come about by a different frequency distribution of high-concentration sea-ice: while in summer about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover with large areas of high concentration sea ice, the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. The different behaviour of the CMIP5 models can be explained by their different distribution of excess heat between lateral melt and sea-ice thinning. Differences in grid geometry and round-off errors during interpolation only have a minor impact on the different biases in sea-ice extent and sea-ice area. Because of regional cancellation of biases in the integrative measures sea-ice extent and sea-ice area, these measures show little correlation with the more meaningful mean absolute bias in sea-ice concentration. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: much of the differences between modeled and observed trends can simply be explained by internal variability. Only for the absolute value of sea-ice area, differences between observations and models are so large that they cannot be explained by either observational uncertainty nor internal variability.

scholarly journals Systematic Errors in South Asian Monsoon Simulation: Importance of Equatorial Indian Ocean Processes

2017 ◽  
Vol 30 (20) ◽  
pp. 8159-8178 ◽  
Author(s):  
H. Annamalai ◽  
Bunmei Taguchi ◽  
Julian P. McCreary ◽  
Motoki Nagura ◽  
Toru Miyama
Keyword(s):  
Indian Ocean ◽  
South Asia ◽  
South Asian ◽  
Coupled Model ◽  
Equatorial Indian Ocean ◽  
Coupled Processes ◽  
Cmip5 Models ◽  
Model Errors ◽  
Bjerknes Feedback ◽  
Sst Bias

Abstract Forecasting monsoon rainfall using dynamical climate models has met with little success, partly due to models’ inability to represent the monsoon climatological state accurately. In this article the nature and dynamical causes of their biases are investigated. The approach is to analyze errors in multimodel-mean climatological fields determined from CMIP5, and to carry out sensitivity experiments using a coupled model [the Coupled Model for the Earth Simulator (CFES)] that does represent the monsoon realistically. Precipitation errors in the CMIP5 models persist throughout the annual cycle, with positive (negative) errors occurring over the near-equatorial western Indian Ocean (South Asia). Model errors indicate that an easterly wind stress bias Δτ along the equator begins during April–May and peaks during November; the severity of the Δτ is that the Wyrtki jets, eastward-flowing equatorial currents during the intermonsoon seasons (April–May and October–November), are almost eliminated. An erroneous east–west SST gradient (warm west and cold east) develops in June. The structure of the model errors indicates that they arise from Bjerknes feedback in the equatorial Indian Ocean (EIO). Vertically integrated moisture and moist static energy budgets confirm that warm SST bias in the western EIO anchors moist processes that cause the positive precipitation bias there. In CFES sensitivity experiments in which Δτ or warm SST bias over the western EIO is artificially introduced, errors in the EIO are similar to those in the CMIP5 models; moreover, precipitation over South Asia is reduced. An overall implication of these results is that South Asian rainfall errors in CMIP5 models are linked to errors of coupled processes in the western EIO, and in coupled models correct representation of EIO coupled processes (Bjerknes feedback) is a necessary condition for realistic monsoon simulation.

scholarly journals Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

2016 ◽  
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Column ◽  
Regional Analysis ◽  
Soil Layer ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

scholarly journals Brief communication: lack of agreement in remote sensing detection of cyclonic drift caused by Atlantic weather in Antarctic sea ice

2021 ◽  
Author(s):  
Wayne de Jager ◽  
Marcello Vichi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Alternative Methods ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
The Impact

Abstract. Sea-ice extent variability, a measure based on satellite-derived sea ice concentration measurements, has traditionally been used as an essential climate variable to evaluate the impact of climate change on polar regions. However, concentration- based measurements of ice variability do not allow to discriminate the relative contributions made by thermodynamic and dynamic processes, prompting the need to use sea-ice drift products and develop alternative methods to quantify changes in sea ice dynamics that would indicate trends in Antarctic ice characteristics. Here, we present a new method to automate the detection of rotational drift features in Antarctic sea ice at daily timescales using currently available remote sensing ice motion products from EUMETSAT OSI SAF. Results show that there is a large discrepancy in the detection of cyclonic drift features between products, both in terms of intensity and year-to-year distributions, thus diminishing the confidence at which ice drift variability can be further analysed. Product comparisons showed that there was good agreement in detecting anticyclonic drift, and cyclonic drift features were measured to be 1.5–2.2 times more intense than anticyclonic features. The most intense features were detected by the merged product, suggesting that the processing chain used for this product could be injecting additional rotational momentum into the resultant drift vectors. We conclude that it is therefore necessary to better understand why the products lack agreement before further trend analysis of these drift features and their climatic significance can be assessed.

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