The Experts below are selected from a list of 320637 Experts worldwide ranked by ideXlab platform
Yvan J. Orsolini - One of the best experts on this subject based on the ideXlab platform.
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arctic summer storm track in cmip3 5 Climate Models
Climate Dynamics, 2015Co-Authors: Kazuaki Nishii, Hisashi Nakamura, Yvan J. OrsoliniAbstract:Model performance and future projection of Arctic summertime storm-track activity and associated background states are assessed on the basis of Coupled Model Intercomparison Project Phase 3 (CMIP3)/5 (CMIP5) Climate Models. Despite some improvement in the CMIP5 Models relative to the CMIP3 Models, most of the Climate Models underestimate summertime storm-track activity over the Arctic Ocean compared to six reanalysis data sets as measured locally as the variance of subweekly fluctuations of sea level pressure. Its large inter-model spread (i.e., model-to-model differences) is correlated with that of the intensity of the Beaufort Sea High and the lower-tropospheric westerlies in the Arctic region. Most of the CMIP3/5 Models project the enhancement of storm-track activity over the Arctic Ocean off the eastern Siberian and Alaskan coasts, the region called the Arctic Ocean Cyclone Maximum, in association with the strengthening of the westerlies in the warmed Climate. A model with stronger enhancement of the storm-track activity tends to accompany stronger land-sea contrast in surface air temperature across the Siberian coast, which reflects greater surface warming over the continent and slower warming over the Arctic Ocean. Other processes, however, may also be likely to contribute to the future changes of the storm-track activity, which gives uncertainty in the projection by multiple Climate Models. Our analysis suggests that further clarification of those processes that influence storm-track activity over the Arctic is necessary for more reliable future projections of the Arctic Climate.
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Arctic summer storm track in CMIP3/5 Climate Models
Climate Dynamics, 2014Co-Authors: Kazuaki Nishii, Hisashi Nakamura, Yvan J. OrsoliniAbstract:Model performance and future projection of Arctic summertime storm-track activity and associated background states are assessed on the basis of Coupled Model Intercomparison Project Phase 3 (CMIP3)/5 (CMIP5) Climate Models. Despite some improvement in the CMIP5 Models relative to the CMIP3 Models, most of the Climate Models underestimate summertime storm-track activity over the Arctic Ocean compared to six reanalysis data sets as measured locally as the variance of subweekly fluctuations of sea level pressure. Its large inter-model spread (i.e., model-to-model differences) is correlated with that of the intensity of the Beaufort Sea High and the lower-tropospheric westerlies in the Arctic region. Most of the CMIP3/5 Models project the enhancement of storm-track activity over the Arctic Ocean off the eastern Siberian and Alaskan coasts, the region called the Arctic Ocean Cyclone Maximum, in association with the strengthening of the westerlies in the warmed Climate. A model with stronger enhancement of the storm-track activity tends to accompany stronger land-sea contrast in surface air temperature across the Siberian coast, which reflects greater surface warming over the continent and slower warming over the Arctic Ocean. Other processes, however, may also be likely to contribute to the future changes of the storm-track activity, which gives uncertainty in the projection by multiple Climate Models. Our analysis suggests that further clarification of those processes that influence storm-track activity over the Arctic is necessary for more reliable future projections of the Arctic Climate.
Robert K. Niven - One of the best experts on this subject based on the ideXlab platform.
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Maximum-entropy weighting of multiple earth Climate Models
Climate Dynamics, 2012Co-Authors: Robert K. NivenAbstract:A maximum entropy-based framework is presented for the synthesis of projections from multiple Earth Climate Models. This identifies the most representative (most probable) model from a set of Climate Models—as defined by specified constraints—eliminating the need to calculate the entire set. Two approaches are developed, based on individual Climate Models or ensembles of Models, subject to a single cost (energy) constraint or competing cost-benefit constraints. A finite-time limit on the minimum cost of modifying a model synthesis framework, at finite rates of change, is also reported.
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Maximum-entropy weighting of multiple earth Climate Models
Climate Dynamics, 2011Co-Authors: Robert K. NivenAbstract:A maximum entropy-based framework is presented for the synthesis of projections from multiple Earth Climate Models. This identifies the most representative (most probable) model from a set of Climate Models -- as defined by specified constraints -- eliminating the need to calculate the entire set. Two approaches are developed, based on individual Climate Models or ensembles of Models, subject to a single cost (energy) constraint or competing cost-benefit constraints. A finite-time limit on the minimum cost of modifying a model synthesis framework, at finite rates of change, is also reported.Comment: Inspired by discussions at the Mathematical and Statistical Approaches to Climate Modelling and Prediction workshop, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, 11 Aug. to 22 Dec. 2010. Accepted for publication in Climate Dynamics, 8 August 201
Christoph Schär - One of the best experts on this subject based on the ideXlab platform.
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Kilometer-scale Climate Models: Prospects and challenges
Bulletin of the American Meteorological Society, 2020Co-Authors: Christoph Schär, Oliver Fuhrer, Andrea Arteaga, Nikolina Ban, Christophe Charpilloz, Salvatore Di Girolamo, Laureline Hentgen, Torsten Hoefler, Xavier Lapillonne, David LeutwylerAbstract:AbstractCurrently major efforts are underway toward refining the horizontal resolution (or grid spacing) of Climate Models to about 1 km, using both global and regional Climate Models (GCMs and RCM...
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Projected changes in surface solar radiation in CMIP5 global Climate Models and in EURO-CORDEX regional Climate Models for Europe
Climate Dynamics, 2017Co-Authors: Blanka Bartók, Christoph Schär, Martin Wild, Doris Folini, Daniel Lüthi, Sven Kotlarski, Robert Vautard, Sonia Jerez, Zoltán ImecsAbstract:The objective of the present work is to compare the projections of surface solar radiation (SSR) simulated by four regional Climate Models (CCLM, RCA4, WRF, ALADIN) with the respective fields of their ten driving CMIP5 global Climate Models. First the annual and seasonal SSR changes are examined in the regional and in the global Climate Models based on the RCP8.5 emission scenarios. The results show significant discrepancies between the projected SSR, the multi-model mean of RCMs indicates a decrease in SSR of −0.60 W/m2 per decade over Europe, while the multi-model mean of the associated GCMs used to drive the RCMs gives an increase in SSR of +0.39 W/m2 per decade for the period of 2006–2100 over Europe. At seasonal scale the largest differences appear in spring and summer. The different signs of SSR projected changes can be interpreted as the consequence of the different behavior of cloud cover in global and regional Climate Models. Cloudiness shows a significant decline in GCMs with −0.24% per decade which explains the extra income in SSR, while in case of the regional Models no significant changes in cloudiness can be detected. The reduction of SSR in RCMs can be attributed to increasing atmospheric absorption in line with the increase of water vapor content. Both global and regional Models overestimate SSR in absolute terms as compared to surface observations, in line with an underestimation of cloud cover. Regional Models further have difficulties to adequately reproduce the observed trends in SSR over the past decades.
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Objective calibration of regional Climate Models
Journal of Geophysical Research: Atmospheres, 2012Co-Authors: Omar Bellprat, Sven Kotlarski, Daniel Lüthi, Christoph SchärAbstract:[1] Climate Models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art Climate Models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional Climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional Climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20–50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional Climate model. Objective calibration of parameterizations with regional Models could also serve as a strategy toward improving parameterization packages of global Climate Models.
Toshiaki Shinoda - One of the best experts on this subject based on the ideXlab platform.
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Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models
Journal of Climate, 2014Co-Authors: Jialin Lin, Taotao Qian, Toshiaki ShinodaAbstract:AbstractThis study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global Climate Models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global Climate Models still have significant difficulty in simulating the SEP stratocumulus clouds and associated cloud feedback. Comparing with observations, the Models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave CRF and net CRF. Only two of the eight Models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the Models are associated...
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stratocumulus clouds in southeastern pacific simulated by eight cmip5 cfmip global Climate Models
Journal of Climate, 2014Co-Authors: Jialin Lin, Taotao Qian, Toshiaki ShinodaAbstract:AbstractThis study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global Climate Models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global Climate Models still have significant difficulty in simulating the SEP stratocumulus clouds and associated cloud feedback. Comparing with observations, the Models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave CRF and net CRF. Only two of the eight Models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the Models are associated...
Kazuaki Nishii - One of the best experts on this subject based on the ideXlab platform.
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arctic summer storm track in cmip3 5 Climate Models
Climate Dynamics, 2015Co-Authors: Kazuaki Nishii, Hisashi Nakamura, Yvan J. OrsoliniAbstract:Model performance and future projection of Arctic summertime storm-track activity and associated background states are assessed on the basis of Coupled Model Intercomparison Project Phase 3 (CMIP3)/5 (CMIP5) Climate Models. Despite some improvement in the CMIP5 Models relative to the CMIP3 Models, most of the Climate Models underestimate summertime storm-track activity over the Arctic Ocean compared to six reanalysis data sets as measured locally as the variance of subweekly fluctuations of sea level pressure. Its large inter-model spread (i.e., model-to-model differences) is correlated with that of the intensity of the Beaufort Sea High and the lower-tropospheric westerlies in the Arctic region. Most of the CMIP3/5 Models project the enhancement of storm-track activity over the Arctic Ocean off the eastern Siberian and Alaskan coasts, the region called the Arctic Ocean Cyclone Maximum, in association with the strengthening of the westerlies in the warmed Climate. A model with stronger enhancement of the storm-track activity tends to accompany stronger land-sea contrast in surface air temperature across the Siberian coast, which reflects greater surface warming over the continent and slower warming over the Arctic Ocean. Other processes, however, may also be likely to contribute to the future changes of the storm-track activity, which gives uncertainty in the projection by multiple Climate Models. Our analysis suggests that further clarification of those processes that influence storm-track activity over the Arctic is necessary for more reliable future projections of the Arctic Climate.
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Arctic summer storm track in CMIP3/5 Climate Models
Climate Dynamics, 2014Co-Authors: Kazuaki Nishii, Hisashi Nakamura, Yvan J. OrsoliniAbstract:Model performance and future projection of Arctic summertime storm-track activity and associated background states are assessed on the basis of Coupled Model Intercomparison Project Phase 3 (CMIP3)/5 (CMIP5) Climate Models. Despite some improvement in the CMIP5 Models relative to the CMIP3 Models, most of the Climate Models underestimate summertime storm-track activity over the Arctic Ocean compared to six reanalysis data sets as measured locally as the variance of subweekly fluctuations of sea level pressure. Its large inter-model spread (i.e., model-to-model differences) is correlated with that of the intensity of the Beaufort Sea High and the lower-tropospheric westerlies in the Arctic region. Most of the CMIP3/5 Models project the enhancement of storm-track activity over the Arctic Ocean off the eastern Siberian and Alaskan coasts, the region called the Arctic Ocean Cyclone Maximum, in association with the strengthening of the westerlies in the warmed Climate. A model with stronger enhancement of the storm-track activity tends to accompany stronger land-sea contrast in surface air temperature across the Siberian coast, which reflects greater surface warming over the continent and slower warming over the Arctic Ocean. Other processes, however, may also be likely to contribute to the future changes of the storm-track activity, which gives uncertainty in the projection by multiple Climate Models. Our analysis suggests that further clarification of those processes that influence storm-track activity over the Arctic is necessary for more reliable future projections of the Arctic Climate.
