scholarly journals ­Comparison of Statistical Downscaling of Summer Daily Precipitation Through a Certain Perfect Prognosis and Bias Corrections - A Case Study Across China

2021 ◽  
Author(s):  
Yonghe Liu ◽  
Xiyue Wang ◽  
Mingshi Wang ◽  
Hailin Wang
Keyword(s):  
Statistical Downscaling ◽  
Linear Models ◽  
Daily Precipitation ◽  
Coupled Model ◽  
Summer Precipitation ◽  
Multiple Model ◽  
Future Projections ◽  
Global Circulation Models ◽  
The Future ◽  
Bias Corrections

Abstract Fewer perfect prognosis (PP) based statistical downscaling were applied to future projections produced by global circulation models (GCM), when compared with the method of model output statistics (MOS). This study is a trial to use a multiple variable based PP downscaling for summer daily precipitation at many sites in China and to compare with the MOS. For the PP method (denoted as ‘OGB-PP’), predictors for each site are screened from surface-level variables in ERA-Interim reanalysis by an optimal grid-box method, then the biases in predictors are corrected and fitted to generalized linear models to downscale daily precipitation. The historical and the future simulations under the medium emission scenario (often represented as ‘RCP4.5’), produced by three GCMs (CanESM2, HadGEM2-ES and GFDL-ESM2G) in the coupled model intercomparison project phase five (CMIP5) were used as the downscaling bases. The bias correction based MOS downscaling (denoted as ‘BC-MOS’) were used to compare with the OGB-PP. The OGB-PP generally produced the climatological mean of summer precipitation across China, based on both ERAI and CMIP5 historical simulations. The downscaled spatial patterns of long-term changes are diverse, depending on the different GCMs, different predictor-bias corrections, and the choices on selecting PP and MOS. The annual variations downscaled by OGB-PP have small differences among the choices of different predictor-bias corrections, but have large difference to that downscaled by BC-MOS. The future changes downscaled from each GCM are sensitive to the bias corrections on predictors. The overall change patterns in some OGB-PP results on future projections produced similar trends as those projected by other multiple-model downscaling in CMIP5, while the result of the BC-MOS on the same GCMs did not, implying that PP methods may be promising. OGB-PP produced more significant increasing/decreasing trends and larger spatial variability of trends than the BC-MOS methods did. The reason maybe that in OGB-PP the independent precipitation modeling mechanism and the freely selected grid-box predictors can give rise to more diverse outputs over different sites than that from BC-MOS, which can contribute additional local variability.

Gridded Statistical Downscaling Based on Interpolation of Parameters and Predictor Locations for Summer Daily Precipitation in North China

2019 ◽  
Vol 58 (10) ◽  
pp. 2295-2311
Author(s):  
Yonghe Liu ◽  
Jinming Feng ◽  
Zongliang Yang ◽  
Yonghong Hu ◽  
Jianlin Li
Keyword(s):  
Distance Function ◽  
Statistical Downscaling ◽  
Spatial Dependence ◽  
Large Scale ◽  
North China ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Summer Precipitation ◽  
Single Station ◽  
Dense Grid

AbstractFew statistical downscaling applications have provided gridded products that can provide downscaled values for a no-gauge area as is done by dynamical downscaling. In this study, a gridded statistical downscaling scheme is presented to downscale summer precipitation to a dense grid that covers North China. The main innovation of this scheme is interpolating the parameters of single-station models to this dense grid and assigning optimal predictor values according to an interpolated predictand–predictor distance function. This method can produce spatial dependence (spatial autocorrelation) and transmit the spatial heterogeneity of predictor values from the large-scale predictors to the downscaled outputs. Such gridded output at no-gauge stations shows performances comparable to that at the gauged stations. The area mean precipitation of the downscaled results is comparable to other products. The main value of the downscaling scheme is that it can obtain reasonable outputs for no-gauge stations.

scholarly journals Projections of Heat Waves Events in the Intra-Americas Region Using Multimodel Ensemble

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Moises Angeles-Malaspina ◽  
Jorge E. González-Cruz ◽  
Nazario Ramírez-Beltran
Keyword(s):  
Heat Waves ◽  
Regional Climate ◽  
Maximum Amplitude ◽  
Amplitude Distribution ◽  
Heat Index ◽  
Multimodel Ensemble ◽  
Current Time ◽  
Future Projections ◽  
Global Circulation Models ◽  
The Future

Significant accelerated warming of the Sea Surface Temperature of 0.15°C per decade (1982–2012) was recently detected, which motivated the research for the present consequences and future projections on the heat index and heat waves in the intra-Americas region. Present records every six hours are retrieved from NCEP reanalysis (1948–2015) to calculate heat waves changes. Heat index intensification has been detected in the region since 1998 and driven by surface pressure changes, sinking air enhancement, and warm/weaker cold advection. This regional warmer atmosphere leads to heat waves intensification with changes in both frequency and maximum amplitude distribution. Future projections using a multimodel ensemble mean for five global circulation models were used to project heat waves in the future under two scenarios: RCP4.5 and RCP8.5. Massive heat waves events were projected at the end of the 21st century, particularly in the RCP8.5 scenario. Consequently, the regional climate change in the current time and in the future will require special attention to mitigate the more intense and frequent heat waves impacts on human health, countries’ economies, and energy demands in the IAR.

scholarly journals Using paleo-climate comparisons to constrain future projections in CMIP5

2013 ◽  
Vol 9 (1) ◽  
pp. 775-835 ◽  
Author(s):  
G. A. Schmidt ◽  
J. D. Annan ◽  
P. J. Bartlein ◽  
B. I. Cook ◽  
E. Guilyardi ◽  
...  
Keyword(s):  
Best Practice ◽  
Climate Model ◽  
Coupled Model ◽  
Sea Ice Extent ◽  
Future Projections ◽  
Climate Simulations ◽  
The Future ◽  
Intercomparison Project ◽  
Model Variability ◽  
The Last Glacial

Abstract. We present a description of the theoretical framework and "best practice" for using the paleo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to constrain future projections of climate using the same models. The constraints arise from measures of skill in hindcasting paleo-climate changes from the present over 3 periods: the Last Glacial Maximum (LGM) (21 thousand years before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (850–1850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of paleo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitation/temperature or sea ice extent to indicate that models that produce the best agreement with paleoclimate information give demonstrably different future results than the rest of the models. We also find that some comparisons, for instance associated with model variability, are strongly dependent on uncertain forcing timeseries, or show time dependent behaviour, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the paleo-climate simulations to help inform the future projections and urge all the modeling groups to complete this subset of the CMIP5 runs.

scholarly journals Mean States and Future Projections of Precipitation Over the Monsoon Transitional Zone in China in CMIP5 and CMIP6 Models

2021 ◽  
Author(s):  
Jinling Piao ◽  
Wen Chen ◽  
Shangfeng Chen ◽  
Hainan Gong ◽  
Lin Wang
Keyword(s):  
Dominant Role ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Summer Precipitation ◽  
Transitional Zone ◽  
Cmip5 Models ◽  
Future Projections ◽  
Moisture Advection ◽  
Model Ensembles ◽  
High Consistency

Abstract The mean states and future projections of precipitation over the monsoon transitional zone (MTZ) in China are examined based on the historical and climate change projection simulations from phase 5 and phase 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Ensemble means of CMIP6 models exhibit a clear improvement in capturing the annual mean and seasonal cycle of the precipitation over the MTZ, both in its spatial pattern and magnitude, compared to the counterparts of CMIP5 models. In addition, both CMIP5&6 models project a significant increase in the annual total precipitation amount and annual precipitation range, but with slightly stronger changes in CMIP6. For the climatological mean precipitation amount, the two versions’ model ensembles show high consistency in the substantial role played by local evaporation in the supply of moisture in both the present-day and future-projection scenarios, with little contribution from the horizontal and vertical advection of moisture. The precipitation amount is projected to increase in all seasons, but with the strongest signals in summer. An analysis of the moisture budget indicates that the increase in summer precipitation is mainly due to evaporation and vertical moisture advection changes in both CMIP5&6 models. However, the change in vertical moisture advection in CMIP5 is primarily attributable to the thermodynamic effects associated with the humidity changes. By contrast, the dynamic effects induced by the atmospheric circulation changes play a dominant role for CMIP6, which is likely related to the stronger warming gradient between the mid–high latitudes and the tropics.

scholarly journals Relationship between the Future Projections of Sahel Rainfall and the Simulation Biases of Present South Asian and Western North Pacific Rainfall in Summer

2019 ◽  
Vol 32 (4) ◽  
pp. 1327-1343 ◽  
Author(s):  
Yuhan Yan ◽  
Riyu Lu ◽  
Chaofan Li
Keyword(s):  
South Asia ◽  
North Pacific ◽  
Western North Pacific ◽  
Climate Models ◽  
Coupled Model ◽  
Rainfall Simulation ◽  
Future Projections ◽  
The Future ◽  
Sahel Rainfall ◽  
Rainfall Projection

Confident model projections of regional climate, in particular precipitation, could be very useful for designing climate change adaptation, particularly for vulnerable regions such as the Sahel. However, there is an extremely large uncertainty in the future Sahel rainfall projections made by current climate models. In this study, we find a close relationship between the future Sahel rainfall projections and present rainfall simulation biases in South Asia and the western North Pacific in summer, using the historical simulations and future projections of phase 5 of the Coupled Model Intercomparison Project (CMIP5). This future–present relationship can be used to calibrate Sahel rainfall projections since historical simulation biases can be much more reliably estimated than future change. The accordingly calibrated results show a substantial increase in both precipitation and precipitation minus evaporation in the future Sahel, in comparison with the multimodel ensemble (MME) result. This relationship between the historical rainfall bias and future Sahel rainfall projection is suggested to lie with the different schemes of convective parameterization among models: some schemes tend to result in both overestimated (underestimated) historical rainfall in South Asia (the western North Pacific) and enhanced future Sahel rainfall projection, while other schemes result in the opposite.

scholarly journals A Framework for Evaluating Model Credibility for Warm-Season Precipitation in Northeastern North America: A Case Study of CMIP5 Simulations and Projections

2014 ◽  
Vol 27 (2) ◽  
pp. 493-510 ◽  
Author(s):  
Jeanne M. Thibeault ◽  
Anji Seth
Keyword(s):  
Moisture Transport ◽  
Geopotential Height ◽  
Coupled Model ◽  
Warm Season ◽  
Summer Precipitation ◽  
Cmip5 Models ◽  
Western Atlantic ◽  
Future Projections ◽  
Northeast Region

Abstract Future projections of northeastern North American warm-season precipitation [June–August (JJA)] indicate substantial uncertainty. Atmospheric processes important to the northeast-region JJA precipitation are identified and a first evaluation of the ability of five phase 5 of the Coupled Model Intercomparison Project (CMIP5) models to simulate these processes is performed. In this case study, the authors develop a set of process-based analyses forming a framework for evaluating model credibility in the northeast region. This framework includes evaluation of models’ ability to simulate observed spatial patterns and amounts of mean precipitation; dynamical atmospheric circulation features, moisture transport, and moisture divergence important to interannual precipitation variability; long-term trends; and SST patterns important to northeast-region summer precipitation.Wet summers in the northeast region are associated with 1) negative 500-hPa geopotential height anomalies centered near the Great Lakes; 2) positive 500-hPa geopotential height anomalies over the western Atlantic east of the Mid-Atlantic states; 3) northeastward moisture flow and increased moisture convergence along the Eastern Seaboard; 4) increased moisture divergence off the U.S. Southeast coast; and 5) positive sea level pressure (SLP) anomalies in the western Atlantic, possibly related to cold tropical Atlantic SSTs and southwest ridging of the North Atlantic anticyclone. Models are generally able to simulate these features but vary compared to observations. Models capture regional moisture transport and convergence anomalies associated with wet summers reasonably well, despite errors in simulating the climatology. Identifying sources of intermodel differences in future projections is important, determining processes relevant for model credibility. In particular, changes in moisture divergence control the sign of northeast-region summer precipitation changes, making it a critical component of process-level analyses for the region.

scholarly journals Using palaeo-climate comparisons to constrain future projections in CMIP5

2014 ◽  
Vol 10 (1) ◽  
pp. 221-250 ◽  
Author(s):  
G. A. Schmidt ◽  
J. D. Annan ◽  
P. J. Bartlein ◽  
B. I. Cook ◽  
E. Guilyardi ◽  
...  
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Climate Projections ◽  
Sea Ice Extent ◽  
Future Projections ◽  
Climate Simulations ◽  
The Future ◽  
Intercomparison Project ◽  
Selection Of ◽  
The Last Glacial

Abstract. We present a selection of methodologies for using the palaeo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to attempt to constrain future climate projections using the same models. The constraints arise from measures of skill in hindcasting palaeo-climate changes from the present over three periods: the Last Glacial Maximum (LGM) (21 000 yr before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (850–1850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of palaeo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitation/temperature or sea ice extent to indicate that models that produce the best agreement with palaeo-climate information give demonstrably different future results than the rest of the models. We also explore cases where comparisons are strongly dependent on uncertain forcing time series or show important non-stationarity, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the palaeo-climate simulations to help inform the future projections and urge all the modelling groups to complete this subset of the CMIP5 runs.

scholarly journals Simulation of Extreme Precipitation in Four Climate Regions in China by General Circulation Models (GCMs): Performance and Projections

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1509
Author(s):  
Mengru Zhang ◽  
Xiaoli Yang ◽  
Liliang Ren ◽  
Ming Pan ◽  
Shanhu Jiang ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
General Circulation ◽  
Global Climate ◽  
Summer Precipitation ◽  
General Circulation Models ◽  
Cumulative Distribution ◽  
Extreme Precipitation Events ◽  
Increasing Trend ◽  
The Future ◽  
Semi Arid

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

scholarly journals Configuration and intercomparison of deep learning neural models for statistical downscaling

2020 ◽  
Vol 13 (4) ◽  
pp. 2109-2124 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Regional Climate ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).

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