scholarly journals Global Circulation Models (GCMs) Simulate the Current Temperature Only If the Shortwave Radiation Anomaly of the 2000s Has Been Omitted

2021 ◽  
pp. 45-52
Author(s):  
Antero Ollila
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Coupled Model ◽  
General Circulation Models ◽  
Scientific Basis ◽  
Shortwave Radiation ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Paris Climate Agreement

The research article of Gillett et al. was published in Nature Climate Change (NCC) in March 2021. The objective of the NCC study was to simulate human-induced forcings to warming by applying 13 CMIP6 (Coupled Model Intercomparison Project Phase 6) climate models. NCC did not accept the author’s remarks as a “Matters arising” article. The purpose of this article is to detail the original three remarks and one additional remark: 1) the discrepancy between the graphs and reported numerical values, 2) the forcings of aerosols and clouds, 3) the positive water feedback, and 4) the calculation basis of the Paris agreement. The most important finding is that General Circulation Models (GCMs) used in simulations omit the significant shortwave anomaly from 2001 to 2019, which causes a temperature error of 0.3°C according to climate change physics of Gillett et al. For the year 2019, this error is 0.8°C showing the magnitude of shortwave anomaly impact. The main reason for this error turns out to be the positive water feedback generally applied in climate models. The scientific basis of the Paris climate agreement is faulty for the same reason.

scholarly journals Potential Impact of Climate Change on Schistosomiasis: A Global Assessment Attempt

2018 ◽  
Vol 3 (4) ◽  
pp. 117 ◽  
Author(s):  
Guo-Jing Yang ◽  
Robert Bergquist
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Maximum Temperature ◽  
Intergovernmental Panel ◽  
Meteorological Model ◽  
Ambient Temperatures ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Parasite Stage ◽  
Near Future

Based on an ensemble of global circulation models (GCMs), four representative concentration pathways (RCPs) and several ongoing and planned Coupled Model Intercomparison Projects (CMIPs), the Intergovernmental Panel on Climate Change (IPCC) predicts that global, average temperatures will increase by at least 1.5 °C in the near future and more by the end of the century if greenhouse gases (GHGs) emissions are not genuinely tempered. While the RCPs are indicative of various amounts of GHGs in the atmosphere the CMIPs are designed to improve the workings of the GCMs. We chose RCP4.5 which represented a medium GHG emission increase and CMIP5, the most recently completed CMIP phase. Combining this meteorological model with a biological counterpart model accounted for replication and survival of the snail intermediate host as well as maturation of the parasite stage inside the snail at different ambient temperatures. The potential geographical distribution of the three main schistosome species: Schistosoma japonicum, S. mansoni and S. haematobium was investigated with reference to their different transmission capabilities at the monthly mean temperature, the maximum temperature of the warmest month(s) and the minimum temperature of the coldest month(s). The set of six maps representing the predicted situations in 2021–2050 and 2071–2100 for each species mainly showed increased transmission areas for all three species but they also left room for potential shrinkages in certain areas.

scholarly journals Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1

2019 ◽  
Vol 12 (8) ◽  
pp. 3725-3743 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Sea Ice ◽  
Northern Hemisphere ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Coupled Model ◽  
General Circulation Models

Abstract. We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most Northern Hemisphere basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemisphere phenomena, and, more generally, the utility of MPAS-A for studying climate change at spatial scales generally unachievable in GCMs.

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

Ensemble climate predictions using climate models and observational constraints

2007 ◽  
Vol 365 (1857) ◽  
pp. 2029-2052 ◽  
Author(s):  
Peter A Stott ◽  
Chris E Forest
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Changes ◽  
Climate Models ◽  
State Of The Art ◽  
General Circulation Models ◽  
Computationally Efficient ◽  
Temperature Changes ◽  
Large Ensembles ◽  
Past Climate Change

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean–atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

Can Isopycnal Mixing Control the Stability of the Thermohaline Circulation in Ocean Climate Models?

2006 ◽  
Vol 19 (21) ◽  
pp. 5637-5651 ◽  
Author(s):  
Willem P. Sijp ◽  
Michael Bates ◽  
Matthew H. England
Keyword(s):  
North Atlantic ◽  
General Circulation ◽  
Climate Models ◽  
Thermohaline Circulation ◽  
Vertical Component ◽  
Coupled Model ◽  
General Circulation Models ◽  
Tracer Transport ◽  
Horizontal Diffusion ◽  
The Stability

Abstract Convective overturning arising from static instability during winter is thought to play a crucial role in the formation of North Atlantic Deep Water (NADW). In ocean general circulation models (OGCMs), a strong reduction in convective penetration depth arises when horizontal diffusion (HD) is replaced by Gent and McWilliams (GM) mixing to model the effect of mesoscale eddies on tracer advection. In areas of sinking, the role of vertical tracer transport due to convection is largely replaced by the vertical component of isopycnal diffusion along sloping isopycnals. Here, the effect of this change in tracer transport physics on the stability of NADW formation under freshwater (FW) perturbations of the North Atlantic (NA) in a coupled model is examined. It is found that there is a significantly increased stability of NADW to FW input when GM is used in spite of GM experiments exhibiting consistently weaker NADW formation rates in unperturbed steady states. It is also found that there is a significant increase in NADW stability upon the introduction of isopycnal diffusion in the absence of GM. This indicates that isopycnal diffusion of tracer rather than isopycnal thickness diffusion is responsible for the increased NADW stability observed in the GM run. This result is robust with respect to the choice of isopycnal diffusion coefficient. Also, the NADW behavior in the isopycnal run, which includes a fixed background horizontal diffusivity, demonstrates that HD is not responsible in itself for reducing NADW stability when simple horizontal diffusion is used. Our results suggest that care should be taken when interpreting the results of coarse grid models with regard to NADW sensitivity to FW anomalies, regardless of the choice of mixing scheme.

Estimation of hydrological response to future climate change in a cold watershed

2018 ◽  
Vol 10 (1) ◽  
pp. 78-88 ◽  
Author(s):  
Jian Sha ◽  
Zhong-Liang Wang ◽  
Yue Zhao ◽  
Yan-Xue Xu ◽  
Xue Li
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Water System ◽  
Coupled Model ◽  
Weather Conditions ◽  
General Circulation Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Research Station ◽  
Hydrological Process

Abstract The vulnerability of the natural water system in cold areas to future climate change is of great concern. A coupled model approach was applied in the headwater watershed area of Yalu River in the northeastern part of China to estimate the response of hydrological processes to future climate change with moderate data. The stochastic Long Ashton Research Station Weather Generator was used to downscale the results of general circulation models to generate synthetic daily weather series in the 2050s and 2080s under various projected scenarios, which were applied as input data of the Generalized Watershed Loading Functions hydrological model for future hydrological process estimations. The results showed that future wetter and hotter weather conditions would have positive impacts on the watershed runoff yields but negative impacts on the watershed groundwater flow yields. The freezing period in winter would be shortened with earlier snowmelt peaks in spring. These would result in less snow cover in winter and shift the monthly allocations of streamflow with more yields in March but less in April and May, which should be of great concern for future local management. The proposed approach of the coupled model application is effective and can be used in other similar areas.

scholarly journals Nearest Neighbor–Genetic Algorithm for Downscaling of Climate Change Data from GCMs

2016 ◽  
Vol 55 (3) ◽  
pp. 773-789
Author(s):  
Soojun Kim ◽  
Jaewon Kwak ◽  
Hung Soo Kim ◽  
Younghun Jung ◽  
Gilho Kim
Keyword(s):  
Climate Change ◽  
Genetic Algorithm ◽  
General Circulation ◽  
Nearest Neighbor ◽  
Coupled Model ◽  
Temporal Correlation ◽  
General Circulation Models ◽  
Temperature Fields ◽  
Change Scenario ◽  
Limited Applicability

AbstractThe spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor–genetic algorithm (NN–GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN–GA. The NN–GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.

scholarly journals The Climatology of the Atmospheric Boundary Layer in Contemporary Global Climate Models

2018 ◽  
Vol 31 (22) ◽  
pp. 9151-9173 ◽  
Author(s):  
Richard Davy
Keyword(s):  
Boundary Layer ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Seasonal Cycles ◽  
Layer Depth

Here, we present the climatology of the planetary boundary layer depth in 18 contemporary general circulation models (GCMs) in simulations of the late-twentieth-century climate that were part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). We used a bulk Richardson methodology to establish the boundary layer depth from the 6-hourly synoptic-snapshot data available in the CMIP5 archives. We present an ensemble analysis of the climatological mean, diurnal cycle, and seasonal cycle of the boundary layer depth in these models and compare it to the climatologies from the ECMWF ERA-Interim reanalysis. Overall, we find that the CMIP5 models do a reasonably good job of reproducing the distribution of mean boundary layer depth, although the geographical patterns vary considerably between models. However, the models are biased toward weaker diurnal and seasonal cycles in the boundary layer depth and generally produce much deeper boundary layers at night and during the winter than are found in the reanalysis. These biases are likely to reduce the ability of these models to accurately represent other properties of the diurnal and seasonal cycles, and the sensitivity of these cycles to climate change.

scholarly journals Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

2018 ◽  
Vol 11 (1) ◽  
pp. 200-216 ◽  
Author(s):  
Reza Haji Hosseini ◽  
Saeed Golian ◽  
Jafar Yazdi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Fuzzy Inference ◽  
Global Climate ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Data Driven ◽  
Observation Data ◽  
Inference System

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.

Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

2018 ◽  
Vol 99 (10) ◽  
pp. 2093-2106 ◽  
Author(s):  
Ambarish V. Karmalkar
Keyword(s):  
Climate Change ◽  
North America ◽  
General Circulation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Internal Variability ◽  
General Circulation Models ◽  
Eastern United States ◽  
The Impact

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

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