Bei N., F. Q. Zhang, 2007: Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei-Yu front of China. Quart. J. Roy. Meteor. Soc., 133, 83- 99.10.1002/qj.20bc887c700b97cdc07fa5328bd55acff7http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.20%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1002/qj.20/abstractAbstract Summertime heavy precipitation associated with the quasi-stationary Mei-Yu front often causes severe flooding along the Yangtze river basin in China. This study explores the mesoscale predictability of one such event near Wuhan, the capital city of Hubei Province. The 20-21 July rainfall event contributed to making the 1998 flood season the worst in this region since 1954. Various sensitivity experiments were performed to examine the impact of both realistic and idealized initial condition uncertainties of different scales and amplitudes on the prediction of the mesoscale precipitation systems along the Mei-Yu front. While it is found that mesoscale model simulations initialized with global analyses at a 36-h lead time can depict the evolution of the synoptic environment reasonably well, there are large variations between different experiments in the prediction of the mesoscale details and heavy precipitation of this event. It was also found that larger-scale, larger-amplitude initial uncertainties generally led to larger forecast divergence than did uncertainties of smaller scales and small amplitudes. However, the forecast errors induced by perturbations of the same amplitude but at different scales are very similar if the initial error is sufficiently small. Error growth is strongly nonlinear and small-amplitude initial errors, which are far smaller than those of current observational networks, may grow rapidly and quickly saturate at smaller scales. They subsequently grow upscale, leading to significant forecast uncertainties at increasingly larger scales. In agreement with previous studies, moist convection is found to be the key to the rapid error growth leading to limited mesoscale predictability. These findings further suggest that, while there is significant scope for improving forecast skill by improving forecast models and initial conditions, mesoscale predictability of such a heavy precipitation event is inherently limited. Copyright 漏 2007 Royal Meteorological Society
Deser C., R. Knutti, S. Solomon, and A. S. Phillips, 2012: Communication of the role of natural variability in future North American climate. Nature Clim.Change, 2, 775- 779.f785f7ac1d5f96d0a400b9985e307685http%3A%2F%2Faobpla.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1038%2Fnclimate1562%26link_type%3DDOIhttp://aobpla.oxfordjournals.org/external-ref?access_num=10.1038/nclimate1562&link_type=DOI
Hawkins E., R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 1095- 1107.0cf71ab3bc8fb65adc4353277848af87http%3A%2F%2Ficesjms.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2F2009BAMS2607.1%26link_type%3DDOIhttp://icesjms.oxfordjournals.org/external-ref?access_num=10.1175/2009BAMS2607.1&link_type=DOI
IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,Solomon et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.10.3103/S1068373907090014a022c41c87e278017903e5ba2d886f42http%3A%2F%2Flink.springer.com%2F10.3103%2FS1068373907090014http://link.springer.com/10.3103/S1068373907090014Basic results of IPCC Working Group II, derived in the process of the work at the Fourth Assessment Report, are considered in brief. The results are given in conformity with the Summary for Policymakers adopted at the Plenary Meeting of Working Group II in Brussels on April 6, 2007. The authors’ comments on some results connected with the key vulnerable elements of natural and socioeconomic systems are given in the conclusion.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,Stocker et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
Ji F., Z. H. Wu, J. P. Huang, and E. P. Chassignet, 2014: Evolution of land surface air temperature trend. Nature Clim.Change, 4( 6), 462- 466.271e8bdd-ddfd-4d78-8eb4-aceb23915fb93670ba88f234441f79ce8fad164e8941http%3A%2F%2Fwww.nature.com%2Fnclimate%2Fjournal%2Fv4%2Fn6%2Ffig_tab%2Fnclimate2223_F1.htmlrefpaperuri:(b361fdf786e031aad7105d3b1c89472c)/s?wd=paperuri%3A%28b361fdf786e031aad7105d3b1c89472c%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.nature.com%2Fnclimate%2Fjournal%2Fv4%2Fn6%2Ffig_tab%2Fnclimate2223_F1.html&ie=utf-8&sc_us=2629784202899817286
Jones C., F. Giorgi, and G. Assar, 2011: The coordinated regional downscaling experiment: CORDEX, an international downscaling link to CMIP5. CLIVAR Exchanges, 16, 34- 39.
Li W., C. E. Forest, and J. Barsugli, 2012: Comparing two methods to estimate the sensitivity of regional climate simulations to tropical SST anomalies. J. Geophys. Res., 117, D20103.10.1029/2011JD0171861ec0452a166da144f2a1cddeda6b3990http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011JD017186%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2011JD017186/citedbyWe perform ensemble simulations using NCAR CAM3.1 T42 forced by perturbed SST fields to estimate the sensitivity of regional climate change at seasonal scales to tropical SST anomalies. We compare the sensitivity and linear reconstruction of regional climate change to tropical SST anomalies from the patch method and the random perturbation method (RPM). The patch method adds one SST anomaly patch at a certain location of the tropical ocean to the prescribed SST field at one time. The RPM method randomly perturbs the climatological SST field with spatially coherent anomalies and estimates the anomalous response with respect to the climatological equilibrium state. The two methods provide generally consistent sensitivity information and similar reconstruction of the regional response over the global scale and tropical regions. If only the dominant sensitivity information is desired, the RPM method is about twelve times more computationally efficient than the patch method due mainly to the larger area-integrated amplitude of the SST forcing used.
Mearns L. O., I. Bogardi, F. Giorgi, I. Matyasovszky, and M. Palecki, 1999: Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling. J. Geophys. Res., 104, 6603- 6621.10.1029/1998JD200042ccef01b2c3db618591dd9262717e186bhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F1998JD200042%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/1998JD200042/citedbyWe compare regional climate change scenarios (temperature and precipitation) over eastern Nebraska produced by a semiempirical statistical downscaling (SDS) technique and regional climate model (RegCM2) experiments, both using large scale information from the same coarse resolution general circulation model (GCM) control and 2 脳 COsimulations. The SDS method is based on the circulation pattern classification technique in combination with stochastic generation of daily time series of temperature and precipitation. It uses daily values of 700 mbar geopotential heights as the large-scale circulation variable. The regional climate model is driven by initial and lateral boundary conditions from the GCM. The RegCM2 exhibited greater spatial variability than the SDS method for change in both temperature and precipitation. The SDS method produced a seasonal cycle of temperature change with a much larger amplitude than that of the RegCM2 or the GCM. Daily variability of temperature mainly decreased for both downscaling methods and the GCM. Changes in mean daily precipitation varied between SDS and RegCM2. The RegCM2 simulated both increases and decreases in the probability of precipitation, while the SDS method produced only increases. We explore possible dynamical and physical reasons for the differences in the scenarios produced by the two methods and the GCM.
Meehl, G. A., Coauthors, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 1383- 1394.10.1175/BAMS-88-9-13836fd4a310-68ce-467d-adad-d113a3a4c59ceacef3f9380c1d61727f2dc0818855cdhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F230891547_The_WCRP_CMIP3_multimodel_datasetrefpaperuri:(67cd38da4affc22d172b55ae2cc47580)http://www.researchgate.net/publication/230891547_The_WCRP_CMIP3_multimodel_datasetThe article discusses the use of global coupled climate models for conducting climatic research. It focuses on the rapid climate changes facing the Earth, and the experiments that are being conducted by a World Climate Research Programme (WCRP) committee. It is stated that open access to climate simulation data has helped students and scientists from around the globe to analyze model data on weather changes. It is stated that significant contributions have recently been made not only to the Intergovernmental Panel on Climate Change (IPCC), but also to the human knowledge of climate variability and forecasting.
Murphy J. M., D. M. H. Sexton, D. N. Barnett, G. S. Jones, M. J. Webb, M. Collins, and D. A. Stainforth, 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430, 768- 772.10.1038/nature0277115306806bef9f08a83b0cd0f83dae0b56b30f2b5http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F15306806%2Fhttp://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM15306806Comprehensive global climate models are the only tools that account for the complex set of processes which will determine future climate change at both a global and regional level. Planners are typically faced with a wide range of predicted changes from different models of unknown relative quality, owing to large but unquantified uncertainties in the modelling process. Here we report a systematic attempt to determine the range of climate changes consistent with these uncertainties, based on a 53-member ensemble of model versions constructed by varying model parameters. We estimate a probability density function for the sensitivity of climate to a doubling of atmospheric carbon dioxide levels, and obtain a 5-95 per cent probability range of 2.4-5.4 degrees C. Our probability density function is constrained by objective estimates of the relative reliability of different model versions, the choice of model parameters that are varied and their uncertainty ranges, specified on the basis of expert advice. Our ensemble produces a range of regional changes much wider than indicated by traditional methods based on scaling the response patterns of an individual simulation.
Separovic L., R. de Ela, and R. Laprise, 2008: Reproducible and irreproducible components in ensemble simulations with a Regional Climate Model. Mon. Wea. Rev. 136, 4942- 4961.10.1175/2008MWR2393.1323690241cea784785237eb84172eb70http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136.4942Shttp://adsabs.harvard.edu/abs/2008MWRv..136.4942SAbstract High-resolution limited-area models (LAMs) have been widely employed to downscale coarse-resolution climate simulations or objective analyses. The growing evidence that LAM climate statistics can be sensitive to initial conditions suggests that a deterministic verification of LAM solutions in terms of finescale atmospheric features might be misguided. In this study a 20-member ensemble of LAM integrations with perturbed initial conditions, driven by NCEP-揘CAR reanalyses, is conducted for a summer season over a midlatitude domain. Ensemble simulations allow for the separation of the downscaled information in two parts: a unique, reproducible component associated with lateral-boundary and surface forcing, and an irreproducible component associated with internal variability. The partition in the reproducible and irreproducible components and their seasonal statistics is examined as a function of horizontal length scale, geographical position within the domain, height, and weather episodes during the season. The scale analysis of time-dependent model variables shows that, at scales smaller than a few hundred kilometers, the irreproducible component dominates, on average, the model solution, implying that the downscaled information at these scales is mainly in stochastic form. The constraint exerted by the surface forcing on the internal variability is weak. For seasonal averages, the reproducible component dominates at all scales, although for precipitation the reproducible and irreproducible components are of the same order of magnitude at scales smaller than 150 km. These results imply a need for a probabilistic approach to LAM climate simulations and their verification, especially for shorter integration times, from months to seasons.
Taylor K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485- 498.0a93ff62-7ac1-4eaa-951b-da834bb5d6acd378bae55de68ca8b37ba4ba57a3c0b9http%3A%2F%2Fwww.nrcresearchpress.com%2Fservlet%2Flinkout%3Fsuffix%3Drefg32%2Fref32%26dbid%3D16%26doi%3D10.1139%252Fcjfr-2015-0218%26key%3D10.1175%252FBAMS-D-11-00094.1refpaperuri:(102c64f576f0dc49ca552e6df691421b)/s?wd=paperuri%3A%28102c64f576f0dc49ca552e6df691421b%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.nrcresearchpress.com%2Fservlet%2Flinkout%3Fsuffix%3Drefg32%2Fref32%26dbid%3D16%26doi%3D10.1139%252Fcjfr-2015-0218%26key%3D10.1175%252FBAMS-D-11-00094.1&ie=utf-8&sc_us=12075330750330000437
Tebaldi C., R. L. Smith, D. Nychka, and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles. J. Climate., 18, 1524- 1540.10.1175/JCLI3363.1f4ae71745a477b203b2b066ca7f91746http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2005JCli...18.1524Thttp://adsabs.harvard.edu/abs/2005JCli...18.1524TA Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere-ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where model results agree (or outlying projections are discounted) to multimodal curves where models that cannot be discounted on the basis of bias give diverging projections. Besides the basic statistical model, the authors consider including correlation between present and future temperature responses, and test alternative forms of probability distributions for the model error terms. It is suggested that a probabilistic approach, particularly in the form of a Bayesian model, is a useful platform from which to synthesize the information from an ensemble of simulations. The probability distributions of temperature change reveal features such as multimodality and long tails that could not otherwise be easily discerned. Furthermore, the Bayesian model can serve as an interdisciplinary tool through which climate modelers, climatologists, and statisticians can work more closely. For example, climate modelers, through their expert judgment, could contribute to the formulations of prior distributions in the statistical model.
Timm O., H. F. Diaz, 2009: Synoptic-statistical approach to regional downscaling of IPCC twenty-first-century climate projections: Seasonal rainfall over the Hawaiian Islands. J.Climate, 22, 4261- 4280.6fa6cfef-5e0a-4cce-b9ec-39311471fd860b1339589fa438a63c2c77d3a71ff3b0http%3A%2F%2Fconphys.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1175%2F2009JCLI2833.1%26link_type%3DDOIrefpaperuri:(29136ba164f0b20078532020d32c2eca)http://conphys.oxfordjournals.org/external-ref?access_num=10.1175/2009JCLI2833.1&link_type=DOI
von Storch, H., E. Zorita, U. Cubasch, 1993: Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime. J.Climate, 6, 1161- 1171.8451bfbf9b1fd005e5c57e612ee39f2ahttp%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Di0004-8038-125-1-1-ref104%26dbid%3D16%26doi%3D10.1525%252Fauk.2008.125.1.1%26key%3D10.1175%252F1520-0442%281993%290062.0.CO%253B2/s?wd=paperuri%3A%2876497ba658487f46f240ed5f13a0291f%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Di0004-8038-125-1-1-ref104%26dbid%3D16%26doi%3D10.1525%252Fauk.2008.125.1.1%26key%3D10.1175%252F1520-0442%281993%290062.0.CO%253B2&ie=utf-8&sc_us=13656971595309439261
Watterson I. G., P. H. Whetton, 2011: Distributions of decadal means of temperature and precipitation change under global warming. J. Geophys. Res., 116, D07101.10.1029/2010jd014502472f6a059600f60dde4e1fc5bf768b07http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2010JD014502%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2010JD014502/fullThere remains uncertainty in the projected climate change over the 21st century, in part because of the range of responses forced by rising greenhouse gas concentrations among global climate models. This paper applies a method of estimating distributions and "probability density functions" (PDFs) for forced change, based on the pattern scaling technique and previously used for Australia, to generate changes in temperature and precipitation at locations over the globe, from simulations of 23 CMIP3 models. Changes for 2030 and 2100, under the A1B scenario for concentrations, for both seasonal and annual cases are presented. The PDFs for temperature have a standard deviation that averages 31% of the mean change, and they tend to be positively skewed. The standard deviation for precipitation averages 15% of the base climate mean, leading to five and 95 percentile estimates that are of opposite sign for most of the globe. A further source of uncertainty of change for a particular period of time, such as a decadal average, is the unforced or internal variability of climate. A joint probability distribution approach is used to produce PDFs for decadal means by adding in an estimate of internal variability. In the decade centered on 2030, this broadens the PDFs substantially. The results are related to time series of observations and projections over 1900-2100 for the agricultural regions of Iowa and the Murray-Darling Basin. For most land areas, warming becomes clearly discernable, allowing for both uncertainties, in the next few decades. Data files of the key results are provided.
Watterson I. G., J. Bathols, and C. Heady, 2014: What influences the skill of climate models over the continents? Bull. Amer. Meteor. Soc., 95, 689- 700.10.1175/BAMS-D-12-00136.1a650ef0ab28c3c9d3d136674022b7c38http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014BAMS...95..689Whttp://adsabs.harvard.edu/abs/2014BAMS...95..689WClimate modeling groups from four continents have submitted simulations as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). With climate impact assessment in mind, we test the accuracy of the seasonal averages of temperature, precipitation, and mean sea level pressure, compared to two observational datasets. Nondimensional skill scores have been generated for the global land and six continental domains. For most cases the 25 models analyzed perform well, particularly the models from Europe. Overall, this CMIP5 ensemble shows improved skill over the earlier (ca. 2005) CMIP3 ensemble of 24 models. This improvement is seen for each variable and continent, and in each case it is largely consistent with the increased resolution on average of CMIP5, given the correlation between scores and grid length found across the combined ensemble. From this apparent influence on skill, the smaller average score for the 13 Earth system models in CMIP5 is consistent with their mostly lower resolution. There is some variation in the ranking of models by skill score for the global, versus continental, measures of skill, and this prompts consideration of the potential influence of a regional focus that model developers might have. While some models rank considerably better in their 'home' continent than globally, most have similar ranks in the two domains. Averaging over each ensemble, the home rank is better by only one or two ranks, indicating that the location of development is only a minor influence.
Whetton P., I. Macadam, J. Bathols, and J. O'Grady, 2007: Assessment of the use of current climate patterns to evaluate regional enhanced greenhouse response patterns of climate models. Geophys. Res. Lett., 34, L14701.10.1029/2007GL0300255eb02e1b7fede8275db84cccd7b6624ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL030025%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2007GL030025/fullOutput of multiple global climate models is often considered in the generation of regional climate change projections. The reliability of the regional responses of models to enhanced greenhouse forcing is often assessed by comparing their current climate simulation against observations. The rationale for this assessment is that a model should be able to reproduce key aspects of the present climate if it is to be used to provide guidance for future changes in climate. However, the best way to assess the current climate of a model is unresolved. One can assess regional average or grid point model biases for the variable and season for which projections are to be prepared. However, a model that performs well for a target variable, season and location, may perform poorly for another variable, season or location, in which case model processes would be suspect. Other approaches consider spatial patterns and multiple variables, but this then raises the issue of how large an area the patterns should cover and what set of variables should be considered. We demonstrate how such a pattern-based approach can be evaluated by investigating the relationship between inter-model similarity in patterns of current regional climate and inter-model similarity in patterns of regional enhanced greenhouse response using data from the CMIP3 multi-model database. By making the assumption that the real world behaves like a typical climate model, we can use these results to assess whether the testing of the current climate patterns of the models against observations can be used to discriminate amongst enhanced greenhouse results of the models. Correlations of moderate magnitude are common in our results, indicating the value of testing the current regional climate simulation of models. Notably, relationships vary significantly regionally (e.g. are weakest in the tropics), cross variables (e.g. current climate mean sea level pressure is related to temperature and precipitation change in some extra-tropical regions) and can be insensitive to whether the current climate is assessed in the region concerned or globally. The presentation will also consider how the approach may be applied to performance-weighting of models in the generation of regional climate projections and in the assessment of the independence of output from different models.
Zhou T.-J., R. C. Yu, 2006: Twentieth-century surface air temperature over china and the globe simulated by coupled climate models. J.Climate, 19( 22), 5843- 5858.10.1175/JCLI3952.11c8eac2a2c6b55e7b168a892761ec656http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006JCli...19.5843Zhttp://adsabs.harvard.edu/abs/2006JCli...19.5843ZNot Available