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

<p>Careful planning of the use of water resources is critical in the semi-arid eastern Mediterranean region. The relevant areas are characterized by complex terrain and coastlines, and exhibit large spatial variability in seasonal precipitation. Global climate models provide only partial information on local&#8208;scale phenomenon, such as precipitation, primarily due to their coarse resolution. In this study, statistical downscaling algorithms, based on both synoptic scale past weather regimes and&#160; analogues and their associated observed precipitation at rain gauges, are operated for eighteen Israeli rain gauges in four hydrological basins with an altitude ranging between &#8208;200 and ~1000 m ASL. In order to project seasonal precipitation over Israel and its hydrologic basins, the algorithms are applied to six Coupled Model Inter&#8208;comparison Project Phase 5 (CMIP5) models for the end of the 21st century, according to the RCP4.5 and RCP8.5 scenarios. The downscaled models are able to capture quite well the seasonal precipitation distribution. All models display a significant reduction of seasonal precipitation for the 21st century of up to ~50% with variations depending on the scenario, algorithm and hydrological basin. The reduction is less acute when applying the weather regimes algorithm as it relies on past&#160; daily mean precipitation values per regime, while the analogues downscaling algorithm relies on the daily precipitation of the individual past analogues and therefore better captures the tails of the distribution. Moreover, the analogues downscaling algorithm projects a significant increase of outliers in the right tale of the distribution i.e. increase in extreme precipitation events. The reduction in seasonal precipitaton is due to both decrease in the frequency of the synoptic systems responsible for precipitation as well as reduction in the daily precipitation amounts at the stations. While the percentage of reduction is quite similar among stations (same reduction in the precipitating synoptic systems that affect the whole area), the reduced amounts are different as they are characterized by different seasonal precipitation amounts. In some cases reductions in precipitation can lead to transition of some areas to semi-arid and arid climates. The statistical downscaling methods applied in this study can be easily transferred to other regions where long&#8208;term datasets of observed precipitation are available.</p>

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

Journal of Climate ◽

10.1175/jcli-d-18-0669.1 ◽

2020 ◽

Vol 33 (1) ◽

pp. 397-404 ◽

Cited By ~ 1

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.

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Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

Climate Dynamics ◽

10.1007/s00382-021-05679-y ◽

2021 ◽

Author(s):

Nicola Cortesi ◽

Verónica Torralba ◽

Llorenó Lledó ◽

Andrea Manrique-Suñén ◽

Nube Gonzalez-Reviriego ◽

...

Keyword(s):

Annual Cycle ◽

Winter Season ◽

Skill Score ◽

Seasonal Forecast ◽

Forecast Skill ◽

Seasonal Forecasts ◽

Sea Level Pressure ◽

Weather Regimes ◽

Start Dates ◽

Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse9040367 ◽

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.

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Evaluation of CMIP5 models and ensemble climate projections using a Bayesian approach: a case study of the Upper Indus Basin, Pakistan

Environmental and Ecological Statistics ◽

10.1007/s10651-021-00490-8 ◽

2021 ◽

Author(s):

Firdos Khan ◽

Jürgen Pilz ◽

Shaukat Ali

Keyword(s):

Bayesian Approach ◽

Indus Basin ◽

Cmip5 Models ◽

Climate Projections ◽

Upper Indus Basin

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

Scientific Reports ◽

10.1038/s41598-021-84807-4 ◽

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.

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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

Progress in Earth and Planetary Science ◽

10.1186/s40645-020-00394-4 ◽

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.

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