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Register a new userAssessing the consistency between short-term global temperature trends in observations and climate model projections
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Assessing the consistency between short-term global temperature trends in observations and climate model projections is a challenging problem. While climate models capture many processes governing short-term climate fluctuations, they are not expected to simulate the specific timing of these somewhat random phenomena - the occurrence of which may impact the realized trend. Therefore, to assess model performance, we develop distributions of projected temperature trends from a collection of climate models running the IPCC A1B emissions scenario. We evaluate where observed trends of length 5 to 15 years fall within the distribution of model trends of the same length. We find that current trends lie near the lower limits of the model distributions, with cumulative probability-of-occurrence values typically between 5 percent and 20 percent, and probabilities below 5 percent not uncommon. Our results indicate cause for concern regarding the consistency between climate model projections and observed climate behavior under conditions of increasing anthropogenic greenhouse-gas emissions.
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Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn from multiple RCMs due to large uncertainties in regional-scale climate projections. Ensembles of multi-RCM projections driven under different perceivable socio-economic scenarios are required to capture the probable path of growth, and provide the behavior of future climate and impacts on various biophysical systems and economic sectors dependent on such systems. The Centre for Climate Change Research, Indian Institute of Tropical Meteorology (CCCR-IITM) has generated an ensemble of high resolution downscaled projections of regional climate and monsoon over South Asia until 2100 for the Intergovernmental Panel for Climate Change (IPCC)using a RCM (ICTP-RegCM4) at 50 km horizontal resolution, by driving the regional model with lateral and lower boundary conditions from multiple global atmosphere-ocean coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The future projections are based on three Representation Concentration Pathway (RCP) scenarios (viz., RCP2.6, RCP4.5, RCP8.5) of the IPCC.
When the climate system is forced, e.g. by emission of greenhouse gases, it responds on multiple time scales. As temperatures rise, feedback processes might intensify or weaken. Current methods to analyze feedback strength, however, do not take such state dependency into account; they only consider changes in (global mean) temperature and assume all feedbacks are linearly related to that. This makes (transient) changes in feedback strengths almost intangible and generally leads to underestimation of future warming. Here, we present a multivariate (and spatially explicit) framework that facilitates dissection of climate feedbacks over time scales. Using this framework, information on the composition of projected (transient) future climates and feedback strengths can be obtained. Moreover, it can be used to make projections for many emission scenarios through linear response theory. The new framework is illustrated using the Community Earth System Model version 2 (CESM2).
In order to investigate the scope of uncertainty in projections of GCMs for Tehran province, a multi-model projection composed of 15 models is employed. The projected changes in minimum temperature, maximum temperature, precipitation, and solar radiation under the A1B scenario for Tehran province are investigated for 2011-2030, 2046-2065, and 2080-2099. GCM projections for the study region are downscaled by the LARS-WG5 model. Uncertainty among the projections is evaluated from three perspectives: large-scale climate scenarios downscaled values, and mean decadal changes. 15 GCMs unanimously project an increasing trend in the temperature for the study region. Also, uncertainty in the projections for the summer months is greater than projection uncertainty for other months. The mean absolute surface temperature increase for the three periods is projected to be about 0.8{deg}C, 2.4{deg}C, and 3.8{deg}C in the summers, respectively. The uncertainty of the multi-model projections for precipitation in summer seasons, and the radiation in the springs and falls is higher than other seasons for the study region. Model projections indicate that for the three future periods and relative to their baseline period, springtime precipitation will decrease about 5%, 10%, and 20%, and springtime radiation will increase about 0.5%, 1.5%, and 3%, respectively. The projected mean decadal changes indicate an increase in temperature and radiation and a decrease in precipitation. Furthermore, the performance of the GCMs in simulating the baseline climate by the MOTP method does not indicate any distinct pattern among the GCMs for the study region.
Based on theoretical and experimental consideration of the first (the Twomey effect) and second indirect aerosol effects the quasianalytic description of physical connection between the galactic cosmic rays intensity and the Earths cloud cover is obtained. It is shown that the basic equation of the Earths climate energy-balance model is described by the bifurcation equation (with respect to the temperature of the Earths surface) in the form of assembly-type catastrophe with the two governing parameters defining the variations of insolation and Earths magnetic field (or the galactic cosmic rays intensity in the atmosphere), respectively. The principle of hierarchical climatic models construction, which consists in the structural invariance of balance equations of these models evolving on different time scales, is described.
We perform a comparison of WMAP 9-year (WMAP9) and Planck 2015 cosmic microwave background (CMB) temperature power spectra across multipoles $30leqellleq1200$. We generate simulations to estimate the correlation between the two datasets due to cosmic variance from observing the same sky. We find that their spectra are consistent within $1sigma$. While we do not implement the optimal $C^{-1}$ estimator on WMAP maps as in the WMAP9 analysis, we demonstrate that the change of pixel weighting only shifts our results at most at the $0.66sigma$ level. We also show that changing the fiducial power spectrum for simulations only impacts the comparison at around $0.1sigma$ level. We exclude $ell<30$ both because WMAP9 data were included in the Planck 2015 $ell<30$ analysis, and because the cosmic variance uncertainty on these scales is large enough that any remaining systematic difference between the experiments is extremely unlikely to affect cosmological constraints. The consistency shown in our analysis provides high confidence in both the WMAP9 temperature power spectrum and the overlapping multipole region of Planck 2015s, virtually independent of any assumed cosmological model. Our results indicate that cosmological model differences between Planck and WMAP do not arise from measurement differences, but from the high multipoles not measured by WMAP.
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