scholarly journals High-Resolution Future Projections of Temperature and Precipitation in the Canary Islands

2015 ◽  
Vol 28 (19) ◽  
pp. 7846-7856 ◽  
Author(s):  
Francisco J. Expósito ◽  
Albano González ◽  
Juan C. Pérez ◽  
Juan P. Díaz ◽  
David Taima
Keyword(s):  
Soil Moisture ◽  
Canary Islands ◽  
Climate Models ◽  
Global Climate ◽  
Statistical Significance ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Reanalysis Dataset ◽  
Temperature And Precipitation

Abstract The complex orography of the Canary Islands favors the creation of microclimates, which cannot be studied using global climate models or regional models with moderate resolution. In this work, WRF is used to perform a dynamic climate regionalization in the archipelago, using the pseudo–global warming technique to compute the initial and boundary conditions from a reanalysis dataset and from results of 14 global climate models. The simulations were performed for three decades, one at present (1995–2004) and two in the future (2045–54 and 2090–99), and for two different greenhouse gas scenarios (RCP4.5 and RCP8.5), defined in phase 5 of the Coupled Model Intercomparison Project. The obtained results, at a 5-km horizontal resolution, show a clear dependence of temperature increase with height and a positive change in diurnal temperature range, which is mainly due to a reduction in soil moisture and a slight decrease in cloud cover. This negative change in soil moisture is mainly a consequence of a decrease in precipitation, although the evaluation of simulated reduction in precipitation does not show statistical significance in most of the Canary Islands for the analyzed periods and scenarios.

scholarly journals Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Suchada Kamworapan ◽  
Chinnawat Surussavadee
Keyword(s):  
Southeast Asia ◽  
Climate Models ◽  
Performance Metrics ◽  
Global Climate ◽  
Total Error ◽  
Coupled Model ◽  
Global Climate Models ◽  
Ensemble Average ◽  
Temperature And Precipitation ◽  
Intercomparison Project

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

scholarly journals Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

2022 ◽  
Author(s):  
Mohammad Naser Sediqi ◽  
Vempi Satriya Adi Hendrawan ◽  
Daisuke Komori
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Climatic Regions ◽  
Temperature And Precipitation ◽  
Intercomparison Project ◽  
Precipitation And Temperature

Abstract The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals A new world natural vegetation map for global change studies

2008 ◽  
Vol 80 (2) ◽  
pp. 397-408 ◽  
Author(s):  
David M. Lapola ◽  
Marcos D. Oyama ◽  
Carlos A. Nobre ◽  
Gilvan Sampaio
Keyword(s):  
Large Scale ◽  
New World ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Vegetation Classification ◽  
Natural Vegetation ◽  
Global Climate Models ◽  
Vegetation Map ◽  
Vegetation Maps

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

2021 ◽  
Author(s):  
SOURABH SHRIVASTAVA ◽  
RAM AVTAR ◽  
PRASANTA KUMAR BAL
Keyword(s):  
Machine Learning ◽  
North American ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Summer Monsoon Rainfall ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Model Ensemble ◽  
The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

scholarly journals Historical Spatial and Temporal Climate Trends in Southern Ontario, Canada

2017 ◽  
Vol 56 (10) ◽  
pp. 2767-2787 ◽  
Author(s):  
Hussein Wazneh ◽  
M. Altaf Arain ◽  
Paulin Coulibaly
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Indices ◽  
Total Precipitation ◽  
Southern Ontario ◽  
Temperature And Precipitation ◽  
Annual Total ◽  
Precipitation Indices ◽  
Temperature Indices

AbstractSpatial and temporal trends in historical temperature and precipitation extreme events were evaluated for southern Ontario, Canada. A number of climate indices were computed using observed and regional and global climate datasets for the area of study over the 1951–2013 period. A decrease in the frequency of cold temperature extremes and an increase in the frequency of warm temperature extremes was observed in the region. Overall, the numbers of extremely cold days decreased and hot nights increased. Nighttime warming was greater than daytime warming. The annual total precipitation and the frequency of extreme precipitation also increased. Spatially, for the precipitation indices, no significant trends were observed for annual total precipitation and extremely wet days in the southwest and the central part of Ontario. For temperature indices, cool days and warm night have significant trends in more than 90% of the study area. In general, the spatial variability of precipitation indices is much higher than that of temperature indices. In terms of comparisons between observed and simulated data, results showed large differences for both temperature and precipitation indices. For this region, the regional climate model was able to reproduce historical observed trends in climate indices very well as compared with global climate models. The statistical bias-correction method generally improved the ability of the global climate models to accurately simulate observed trends in climate indices.

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

2020 ◽  
Author(s):  
Anja Katzenberger ◽  
Jacob Schewe ◽  
Julia Pongratz ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Models ◽  
Monsoon Rainfall ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Global Mean Temperature ◽  
Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

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