scholarly journals The subtropical ridge in CMIP5 models, and implications for projections of rainfall in southeast Australia

2015 ◽  
Vol 65 (1) ◽  
pp. 90-106 ◽  
Author(s):  
M Grose ◽  
B Timbal ◽  
L Wilson ◽  
J Bathols ◽  
D Kent
Keyword(s):  
Cmip5 Models ◽  
Southeast Australia ◽  
Subtropical Ridge

scholarly journals The relationship between the subtropical ridge and Australian temperatures

2018 ◽  
Vol 68 (1) ◽  
pp. 201
Author(s):  
Acacia Pepler ◽  
Linden Ashcroft ◽  
Blair Trewin
Keyword(s):  
Strong Influence ◽  
Southeast Australia ◽  
Eastern Australia ◽  
Autumn And Winter ◽  
The Relationship ◽  
Subtropical Ridge ◽  
Australian Rainfall

The intensity and latitude of the subtropical ridge over eastern Australia is strongly associated with southeast Australian rainfall, particularly during the cool months of the year. We show that the subtropical ridge also exerts a strong influence on temperatures across much of Australia, with warmer daytime temperatures and more warm extremes across southern Australia when the subtropical ridge is stronger than average, which is largely independent of the relationship between the subtropical ridge and rainfall. A strong subtropical ridge is also linked to warmer than average minimum temperatures over southern Australia throughout much of the year, except from May to August when a strong ridge is associated with cooler mean minimum temperatures and an increased frequency of cool nights. This relationship, and the observed strengthening of the subtropical ridge during autumn and winter in recent decades, can partially explain the weaker warming trends in minimum temperatures in southeast Australia compared to elsewhere in the country over the period 1960-2016.

Influence of Global-Scale Variability on the Subtropical Ridge over Southeast Australia

2011 ◽  
Vol 24 (23) ◽  
pp. 6035-6053 ◽  
Author(s):  
Wenju Cai ◽  
Peter van Rensch ◽  
Tim Cowan
Keyword(s):  
Indian Ocean ◽  
Southern Oscillation ◽  
Climate Modes ◽  
Southeast Australia ◽  
The Indian Ocean ◽  
Wave Trains ◽  
The Impact ◽  
Austral Autumn ◽  
Subtropical Ridge

Abstract In recent decades, southeast Australia (SEA) has experienced a severe rainfall decline, with a maximum reduction in the austral autumn season. The cause(s) of this decline remain unclear. This study examines the interaction between remote large-scale climate modes and an atmospheric phenomenon known as the subtropical ridge (STR) at the local scale. A focus is placed on the utility of using the STR as a bridge for understanding how these remote climate drivers influence SEA rainfall through a response in local atmospheric conditions. Using observational data since 1979, it is found that a strong seasonality exists in the impact of the STR on SEA rainfall. In austral autumn, because SEA rainfall is poorly correlated with the STR intensity (STRI) and STR position (STRP) on an interannual basis, it follows that most of the autumn rainfall reduction cannot be explained by the STRI changes in this season. There is also no clear relationship between the autumn STR and known remote modes of variability. Reductions in SEA rainfall have occurred in the austral winter and spring seasons; however, neither is significant. During winter, although El Niño–Southern Oscillation (ENSO) has little impact on the STR, there is a significant influence from the Indian Ocean dipole (IOD) and the southern annular mode (SAM). The IOD impact is conducted through equivalent-barotropic Rossby wave trains stemming from the eastern Indian Ocean in response to the IOD-induced anomalous convection and divergence. These wave trains modify the intensity and position of the ridge over SEA. The impact from the SAM is similarly projected onto the STRI and STRP. The STR trend accounts for the entire observed decline in SEA winter rainfall, 80% of which is contributed by the upward trend of the IOD; the SAM exhibits virtually no trend over the 30-yr period in this season. In spring, SEA rainfall shows strong interannual variability and is well correlated with the STRI; the ridge itself is influenced by the IOD and ENSO but not by the SAM. The Indian Ocean is a major pathway for ENSO’s impact on SEA rainfall in this season, which is conducted by two wave trains emanating from the east and west poles of the IOD. These wave train patterns share an anomalously high surface pressure center south of Australia, which does not align with the STR over SEA. As such, only a small portion of the STRI variance is accounted for by fluctuations in ENSO and the IOD. Long-term changes in the STRI account for about 90% of the observed decline in SEA spring rainfall, all of which are due to a recent increased frequency in the number of positive IOD events (upward IOD trend); ENSO shows no long-term trend over the 30-yr period. In summary, variability and change in winter and spring rainfall across SEA can be understood through the impact of remote climate modes, such as ENSO, the IOD, and the SAM, on the STR. This approach, however, offers no utility for understanding what drives the long-term SEA autumn rainfall decline, the dynamics of which remain elusive.

scholarly journals Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

2020 ◽  
Vol 33 (1) ◽  
pp. 397-404 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Time Windows ◽  
Internal Variability ◽  
Historical Record ◽  
Cmip5 Models ◽  
Temperature Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Total Uncertainty ◽  
The Impact ◽  
The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

scholarly journals CMIP5-Based Projection of Decadal and Seasonal Sea Surface Temperature Variations in East China Shelf Seas

2021 ◽  
Vol 9 (4) ◽  
pp. 367
Author(s):  
Huiqiang Lu ◽  
Chuan Xie ◽  
Cuicui Zhang ◽  
Jingsheng Zhai
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
The Yellow Sea ◽  
East China ◽  
Cmip5 Models ◽  
Projection Data ◽  
Observation Data ◽  
Model Intercomparison ◽  
Shelf Seas

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C.

scholarly journals Hydrological droughts in the southern Andes (40–45°S) from an ensemble experiment using CMIP5 and CMIP6 models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rodrigo Aguayo ◽  
Jorge León-Muñoz ◽  
René Garreaud ◽  
Aldo Montecinos
Keyword(s):  
Climate Models ◽  
Physicochemical Characteristics ◽  
Climate Impact ◽  
Economic Consequences ◽  
Cmip5 Models ◽  
Southern Andes ◽  
Weap Model ◽  
Coastal System ◽  
Hydrological Droughts ◽  
Impacts Of Climate Change

AbstractThe decrease in freshwater input to the coastal system of the Southern Andes (40–45°S) during the last decades has altered the physicochemical characteristics of the coastal water column, causing significant environmental, social and economic consequences. Considering these impacts, the objectives were to analyze historical severe droughts and their climate drivers, and to evaluate the hydrological impacts of climate change in the intermediate future (2040–2070). Hydrological modelling was performed in the Puelo River basin (41°S) using the Water Evaluation and Planning (WEAP) model. The hydrological response and its uncertainty were compared using different combinations of CMIP projects (n = 2), climate models (n = 5), scenarios (n = 3) and univariate statistical downscaling methods (n = 3). The 90 scenarios projected increases in the duration, hydrological deficit and frequency of severe droughts of varying duration (1 to 6 months). The three downscaling methodologies converged to similar results, with no significant differences between them. In contrast, the hydroclimatic projections obtained with the CMIP6 and CMIP5 models found significant climatic (greater trends in summer and autumn) and hydrological (longer droughts) differences. It is recommended that future climate impact assessments adapt the new simulations as more CMIP6 models become available.

scholarly journals Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rui Ito ◽  
Tosiyuki Nakaegawa ◽  
Izuru Takayabu
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
General Circulation ◽  
Annual Precipitation ◽  
Cmip5 Models ◽  
Climate Projections ◽  
The Tibetan Plateau ◽  
Model Ensemble ◽  
Uncertainty Range ◽  
High Performing

AbstractEnsembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

scholarly journals Understanding the cold biases of CMIP5 models over China with weather regimes

2021 ◽  
Author(s):  
Wei Chen ◽  
Da-Bang Jiang ◽  
Xian-Mei Lang ◽  
Zhi-Ping Tian
Keyword(s):  
Cmip5 Models ◽  
Weather Regimes
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