climate model projections

Please check your email for instructions on resetting your password. Hence in this study, we evaluate and apply an existing weighting method based both on model quality and independence (Knutti et al., 2017), to constrain projected warming in the CMIP6 simulations under the SSP scenarios using the GSAT trend. Users can choose from a number of climate variables summarized at the monthly, seasonal, or annual timescales and both create plots and acquire the resultant data. For example, we find best‐estimate observationally constrained 5–95% warming ranges of 2.72–4.77 K and 0.52–1.66 K for 2081–2100 under the SSP5‐8.5 and SSP1‐2.6 scenarios, respectively, with upper bounds substantially lower than the corresponding unconstrained ranges of 2.48–5.34 K and 0.47–1.87 K for 2081–2100. However, with the aerosol forcing and response having remained approximately constant since the 1970s (Forster et al., 2013; Nijsse et al., 2020), the lengthening observational record now affords us a period of more than four decades in which the observed climate response has been dominated by the effects of greenhouse gas increases and over which warming trends are closely correlated with Transient Climate Response (Daines et al., 2016; Jimenez‐de‐la‐Cuesta & Mauritsen, 2019; Nijsse et al., 2020) and future warming in scenarios in which radiative forcing is dominated by further greenhouse gas increases. The interface not only provides climate variables, such as precipitation,  but also variables pertinent to agricultural systems and energy, such as growing degree and heating/cooling degree days. In the CMIP6 archive, for each SSP, only single ensemble members are available for some models, while a large number of ensemble members are available for others. The performances of the unweighted approach and weighting scheme are compared with the simulation being used as pseudo‐observations using root‐mean‐square‐error (RMSE) and correlation (r) between pseudo observations and statistical model predictions (Figure 2). In this sensitivity analysis, the ensemble mean is used in the distance measure Di in order to reduce the influence of internal variability. We find there is no substantial difference between the weighting results in this case and the means of the single ensemble samples (Table S6, Figure S6). These data are from the "B1" emissions scenario of the Special Report on Emissions Scenarios (SRES) set by the Intergovernmental Panel on Climate Change (IPCC). The dynamic mapping interface provides a straightforward way for scientists and decision-makers to visualize climate information. The timeseries shows model simulations from 1950 to 2005 and simulations from 2006 to 2100 under two greenhouse gas emissions scenarios. Future scenario simulations in CMIP6 were coordinated by the ScenarioMIP project (O'Neill et al., 2016) and are driven by a new set of emissions and land use scenarios, known as Shared Socioeconomic Pathways (SSPs) (Riahi et al., 2017), produced using scenarios of future socioeconomic development to drive integrated assessment models. The numbers marked at the top of each bar in panel b represent number of member from each model. The weighted distribution of GSAT changes has a slightly lower mean than the unweighted model mean. Panel (e) shows the best estimates of the 5–95% ranges of weighted (green) and unweighted (gray) results for other projection periods. These links also give access to data from earlier IPCC Assessment Reports. The corner plot of Figure 1a shows the distribution of correlation values. This tool is useful for visualizing changes in a single variable evolving from year to year at a specific location. This tool provides a graphical summary of seasonal climate forecasts of temperature and precipitation for the next seven months for a selected location. In a sensitivity analysis, we also use gridded SAT climatologies over the period 1979–2014. As shown in Figures 2a and 2b, the weighted results using the GSAT trend show better performance than the unweighted results both for the historical period and future projections, as measured by both correlation and RMSE. (b) Comparison of simulated (colored bars) and observed (black dashed line) GSAT trends (units: K/y) over 1970–2014. This tool displays a dashboard of real-time climate information for any location in the contiguous US. The red histogram shows the PDF for correlation of historical GSAT trend and future warming in 2081–2100. Properties of Rocks, Computational The trend in global mean temperature over 1970–2014 is correlated well with projected future warming across the CMIP6 multimodel ensemble (Text S4; Figure 1a; the correlation coefficient is 0.80). Applying the method using the observed 1970–2014 warming trend results in only small changes in the mean and lower bound of CMIP6 projected warming but substantially reduces the upper bound of projected early‐, mid‐ and late‐21st century warming under all SSP scenarios. This tool provides maps and graphical summaries of future projections of climate-related metrics from global climate models and future emission scenarios for a selected tribal geography. Compared with unweighted projections, the weighting method results in robustly large and positive correlation coefficients between pseudo observations and mean predicted warming (Figure 2c). Maps of different flavors of drought can be compared side by side, for a specific location, and against the U.S. Drought Monitor assessment. Geophysics, Mathematical Multiple studies have argued for approaches other than using an unweighted ensemble of climate models to make projections, as not all models are equally skillful in reproducing observations (Brunner et al., 2019; Gillett, 2015; Knutti et al., 2017; Lorenz et al., 2018). Distributions of projected GSAT warming between 1995–2014 and 2081–2100 in each of four scenarios (panels a–d), both constrained by observations (green) and unconstrained (black), based on 5,000 samples each with one randomly selected ensemble member per model. Repeating these calculations using the RMSD of gridded SAT (Text S6), we find that this quantity is not as useful as the GSAT trend for constraining future warming with historical records. This tool provides a map summary of seasonal climate forecasts of temperature and precipitation for the next seven months. Since the imperfect model analyses demonstrate that the weighting method has better performance than unweighted averages and probabilistic validation demonstrates that the weighting method performs well on uncertainty estimation, the weights obtained from the observed and simulated GSAT trends over the historical period by equation 1 can be applied to climate change projections for which we do not have observational estimates. Therefore, we scaled the observed HadCRUT4 trend over this period by this ratio, in order to estimate the observed globally complete GSAT trend and then used this value when we derived weights based on the observations. Working off-campus? In the imperfect model test applied to mid‐century warming (end‐of‐century warming) under SSP5‐8.5 and considering means across 5,000 single‐member per model samples, we find that the method gives 26% (25%) narrower best estimate confidence limits than the unweighted ensemble, with a correlation coefficient of 0.40 (0.42) between the mean weighted projection and truth and good performance in terms of probabilistic validation. As a sensitivity analysis, we also conduct an analysis using all ensemble members, giving equal weights to each ensemble member from individual models and calculating weights based on the ensemble mean for each model and further weighting individual ensemble members by the inverse of the ensemble size (Text S2). In our model analysis we generally consider globally complete GSAT rather than using blended near‐surface air temperature over land and ice and SST over the ocean masked with observational coverage as in HadCRUT4 (GMST). The relationship between historical GSAT trend and future warming among models is robust. In section 2 we describe the data sets and the methods used. and Chemical Oceanography, Physical This tool displays a dashboard of projected future climate information for any location in the contiguous US. Then we evaluate the weighting method and weighting metric using a cross‐validated imperfect model test and probabilistic validation. In order to compare the performance of the weighting method compared with unweighted averages, we conduct a cross‐validated imperfect model analysis of the CMIP6 simulations. Geology and Geophysics, Physical Panel (a) and panel (b) show the distributions of RMSE decrease due to weighting (relative to unweighted) for historical GSAT trends (green shading) and projected GSAT change under SSP5‐8.5 (2041–2060 with red shading and 2081–2100 with black shading, respectively). This fact motivates investigating how the uncertainty range is affected by considering the entire set of simulations or only considering single ensemble members from each model. Objects, Solid Surface Finally, for SSP3‐7.0 and SSP5‐8.5, we find that the projection upper bounds show wide distributions across individual ensemble samples (Figures 3c and 3d); the widths of the distributions are reduced substantially by weighting. These maps show the average of a set of climate model experiments projecting changes in surface temperature for the period 2050-2059, relative to the period from 1971-1999. When climate change projections are made using the compound metric involving GSAT trend and RMSD of gridded SAT, we find the results are close to the projection using GSAT trend alone (Table S6). Any queries (other than missing content) should be directed to the corresponding author for the article.

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