scholarly journals Dependence of Precipitation Scaling Patterns on Emission Scenarios for Representative Concentration Pathways

2013 ◽  
Vol 26 (22) ◽  
pp. 8868-8879 ◽  
Author(s):  
Yasuhiro Ishizaki ◽  
Hideo Shiogama ◽  
Seita Emori ◽  
Tokuta Yokohata ◽  
Toru Nozawa ◽  
...  

Abstract Pattern scaling is an efficient way to generate projections of regional climate change for various emission scenarios. This approach assumes that the spatial pattern of changes per degree of global warming (scaling pattern) is the same among emission scenarios. The hypothesis was tested for the scaling pattern of precipitation by focusing on the scenario dependence of aerosol scaling patterns. The scenario dependence of aerosol scaling patterns induced the scenario dependence of the surface shortwave radiation scaling pattern. The scenario dependence of the surface shortwave radiation scaling pattern over the ocean tended to induce the scenario dependence of evaporation scaling patterns. The scenario dependence of evaporation scaling patterns led to the scenario dependence of precipitation scaling patterns locally and downwind. Contrariwise, when the scenario dependence of aerosol scaling patterns occurred over land, the scenario dependence of surface shortwave radiation scaling patterns induced the scenario dependence of the scaling patterns of evaporation, surface longwave radiation, and sensible heat. Consequently, the scenario dependence of evaporation scaling patterns was smaller over land, and the scenario dependence of precipitation scaling patterns tended to be insignificant. Moreover, the scenario dependence of the southern annular mode and polar amplification caused some of the scenario dependence of precipitation scaling patterns. In this study, only one global climate mode was analyzed. In addition, sensitivity experiments that remove aerosol emissions from some regions or some kinds of aerosols are ideal to separate the impacts of aerosols. Thus, an analysis of the dependencies of precipitation scaling pattern among global climate models and the sensitivity experiments are required in future work.

2009 ◽  
Vol 106 (21) ◽  
pp. 8441-8446 ◽  
Author(s):  
D. W. Pierce ◽  
T. P. Barnett ◽  
B. D. Santer ◽  
P. J. Gleckler

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1516 ◽  
Author(s):  
Zhijie Ta ◽  
Yang Yu ◽  
Lingxiao Sun ◽  
Xi Chen ◽  
Guijin Mu ◽  
...  

The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.


2019 ◽  
Vol 32 (10) ◽  
pp. 3051-3067 ◽  
Author(s):  
Aiko Voigt ◽  
Nicole Albern ◽  
Georgios Papavasileiou

Abstract Previous work showed that the poleward expansion of the annual-mean zonal-mean atmospheric circulation in response to global warming is strongly modulated by changes in clouds and their radiative heating of the surface and atmosphere. Here, a hierarchy and an ensemble of global climate models are used to study the circulation impact of changes in atmospheric cloud-radiative heating in the absence of changes in sea surface temperature (SST), which is referred to as the atmospheric pathway of the cloud-radiative impact. For the MPI-ESM model, the atmospheric pathway is responsible for about half of the total cloud-radiative impact, and in fact half of the total circulation response. Changes in atmospheric cloud-radiative heating are substantial in both the lower and upper troposphere. However, because SST is prescribed the atmospheric pathway is dominated by changes in upper-tropospheric cloud-radiative heating, which in large part results from the upward shift of high-level clouds. The poleward circulation expansion via the atmospheric pathway and changes in upper-tropospheric cloud-radiative heating are qualitatively robust across three global models, yet their magnitudes vary by a factor of 3. A substantial part of these magnitude differences are related to the upper-tropospheric radiative heating by high-level clouds in the present-day climate. A comparison with observations highlights the model deficits in representing the radiative heating by high-level clouds and indicates that reducing these deficits can contribute to improved predictions of regional climate change.


Climate ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ge Peng ◽  
Jessica L. Matthews ◽  
Muyin Wang ◽  
Russell Vose ◽  
Liqiang Sun

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.


2019 ◽  
Vol 41 (4) ◽  
pp. 374-387 ◽  
Author(s):  
Nguyen Thi Tuyet ◽  
Ngo Duc Thanh ◽  
Phan Van Tan

The study examined the performance of six regional climate experiments conducted under the framework of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA) project and their ensemble product (ENS) in simulating temperature at 2 m (T2m) and rainfall (R) in seven climatic sub-regions of Vietnam. The six experiments were named following the names of their driving Global Climate Models (GCMs), i.e., CNRM, CSIRO, ECEA, GFDL, HADG and MPI. The observation data for the period 1986–2005 from 66 stations in Vietnam were used to compare with the model outputs. Results showed that cold biases were prominent among the experiments and ENS well reproduced the seasonal cycle of temperature in the Northeast, Red River Delta, North Central and Central Highlands regions. For rainfall, all the experiments showed wet biases and CSIRO exhibited the best. A scoring system was elaborated to objectively rank the performance of the experiments and the ENS experiment was reported to be the best.


2015 ◽  
Vol 28 (15) ◽  
pp. 6249-6266 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär

Abstract A Bayesian hierarchical model for heterogeneous multimodel ensembles of global and regional climate models is presented. By applying the methodology herein to regional and seasonal temperature averages from the ENSEMBLES project, probabilistic projections of future climate are derived. Intermodel correlations that are particularly strong between regional climate models and their driving global climate models are explicitly accounted for. Instead of working with time slices, a data archive is investigated in a transient setting. This enables a coherent treatment of internal variability on multidecadal time scales. Results are presented for four European regions to highlight the feasibility of the approach. In particular, the methodology is able to objectively identify patterns of variability changes, in ways that previously required subjective expert knowledge. Furthermore, this study underlines that assumptions about bias changes have an effect on the projected warming. It is also shown that validating the out-of-sample predictive performance is possible on short-term prediction horizons and that the hierarchical model herein is competitive. Additionally, the findings indicate that instead of running a large suite of regional climate models all forced by the same driver, priority should be given to a rich diversity of global climate models that force a number of regional climate models in the experimental design of future multimodel ensembles.


Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3299
Author(s):  
Christina M. Botai ◽  
Joel O. Botai ◽  
Nosipho N. Zwane ◽  
Patrick Hayombe ◽  
Eric K. Wamiti ◽  
...  

This research study evaluated the projected future climate and anticipated impacts on water-linked sectors on the transboundary Limpopo River Basin (LRB) with a focus on South Africa. Streamflow was simulated from two CORDEX-Africa regional climate models (RCMs) forced by the 5th phase of the Coupled Model Inter-Comparison Project (CMIP5) Global Climate Models (GCMs), namely, the CanESM2m and IPSL-CM5A-MR climate models. Three climate projection time intervals were considered spanning from 2006 to 2099 and delineated as follows: current climatology (2006–2035), near future (2036–2065) and end of century future projection (2070–2099). Statistical metrics derived from the projected streamflow were used to assess the impacts of the changing climate on water-linked sectors. These metrics included streamflow trends, low and high flow quantile probabilities, the Standardized Streamflow Index (SSI) trends and the proportion (%) of dry and wet years, as well as drought monitoring indicators. Based on the Mann-Kendall (MK) trend test, the LRB is projected to experience reduced streamflow in both the near and the distant future. The basin is projected to experience frequent dry and wet conditions that can translate to drought and flash floods, respectively. In particular, a high proportion of dry and a few incidences of wet years are expected in the basin in the future. In general, the findings of this research study will inform and enhance climate change adaptation and mitigation policy decisions and implementation thereof, to sustain the livelihoods of vulnerable communities.


2020 ◽  
Author(s):  
Lianyi Guo

<p>Four bias-correction methods, i.e. Gamma Cumulative Distribution Function (GamCDF), Quantile-Quantile Adjustment (QQadj), Equidistant CDF Matching (EDCDF) and Transform CDF (CDF-t), were applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four methods, which helps understanding their nature and essence in identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias-correction algorithm based on a comprehensive evaluation of different rainfall indices. Future precipitation projections corresponds to the global warming levels of 1.5°C and 2°C under RCP8.5 were obtained using the bias correction methods. The multi-algorithm and multi-model ensemble characteristics allow to explore the spreading of results, considered as a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias-correction methods is smaller than that among dynamical downscaling simulations. The four bias-correction methods with CDF-t at the top all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility.</p>


2014 ◽  
Vol 15 (2) ◽  
pp. 830-843 ◽  
Author(s):  
D. D’Onofrio ◽  
E. Palazzi ◽  
J. von Hardenberg ◽  
A. Provenzale ◽  
S. Calmanti

Abstract Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.


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